Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of the impact of climate change on peanut yield in Senegal

This paper treats the impacts of climate change on peanut yield in Ziguinchor (Southwest of Senegal) during the near future (2021 to 2050) and the far future (2071 to 2100). The Decision Support System for Agrotechnology Transfer (DSSAT) crop model was run using daily weather data (maximum and minimum surface temperature, rainfall and solar radiation) of four (4) regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX). Two climate change scenarios (RCP4.5 and RCP8.5) are used to assess the climate change impact on peanut yield. First of all, the DSSAT crop model is calibrated and validated for peanut using relevant observed data: peanut yield and meteorological data. Compared to the reference period (1976 to 2005), the RCMs exhibit some disparities in the projected rainfall during the near and the far future. The ensemble mean of the models (Ens/RCMs) predict a strong decrease of rainfall under the RCP4.5 scenario and a slight decrease under the RCP8.5 scenario during both periods. A gradual increase in mean temperature is predicted by all models. However, this increase is stronger for the RCP8.5 scenario. Analysis of the yield change during the near future shows a decrease for all RCMs except RACMO22T model under the two considered scenarios. During the far future, all RCMs predict a decrease of the peanut yield. Moreover, this decrease is stronger for the RCP8.5 scenario. These results indicated that the peanut crop could be negatively affected by the climate change and adaptation strategies are needed to protect this sector. Key words: Climate change, Regional Climate Models, Coordinated Regional Climate Downscaling Experiment (CORDEX), Decision Support System for Agrotechnology Transfer (DSSAT), peanut.
Implementation and Analysis of an Algorithm on Positive Integer Addition for Quantum Computing

In this project, an positive integer addition algorithm for quantum computing is analyzed which does not makes use of carry bits. The paper shows a general parallelization method. Using this method, the circuit runs in O(n) time instead of O(n^2). The algorithm is executed with different input sizes and error rates such that usable values are found.
Numerical Study of Mixing of a Supercritical Jet in a Supercritical Environment

Sullivan, Neil MSAE, Embry-Riddle Aeronautical University, July 2017. Mixing of a Supercritical Jet in a Supercritical Environment. A numerical simulation campaign is conducted to better elucidate flow physics and modeling requirements of a supercritical (SC) nitrogen jet injected into a tank of quiescent SC nitrogen. The goals of this work are twofold: to inform the design of injectors and combustion chambers for use in the direct-fired supercritical CO2 (s-CO2) power generation cycle and cryogenic liquid propellant rockets, and to investigate the extent to which meaningful flow characterization can be achieved with computationally expedient methods, using commercial software. Reynolds-Averaged Navier-Stokes (RANS) and Large Eddy Simulation (LES) approaches are used in STAR-CCM+ versions 10.06. 010 and 12.02. 011. Jet disintegration is evaluated with velocity, density and temperature profiles, potential core penetration and identification of turbulent length scales. These data are compared with experimental data and evaluated against other modeling approaches. Mixing behavior is expected to mimic that of a single-phase jet, and be diffusion-driven, as there will be no droplet formation in the supercritical phase. Challenges are encountered in high computational requirements inherent to unsteady LES. Challenges are also encountered in simulation stability and convergence given large flow gradients near jet exit, large fluid property gradients near the critical point, and the small length scale of energetic flow features unique to this high-pressure thermodynamic regime. Simulation results over-predict core penetration compared to experiment and previous numerical efforts and show an overall slower transition to ambient conditions. It is shown however that commercial code can correctly synthesize the overall flow physics and trends of the
Effects of organizational learning on performance: the moderating roles of trust in leaders and organizational justice

PurposeThe purpose of this paper is to identify how organizational learning processes influence perceived organizational performance and examine the moderating roles of organizational justice and trust in managers therein.Design/methodology/approachThis study develops a theoretical model to exhibit how knowledge acquisition and transfer activities influence perceived organizational performance. Data were collected from 515 respondents and analyzed by PROCESS macro for SPSS.FindingsThis study found that feedback learning flows are strongly mediated between learning stocks and organizational performance. It also found that organizational justice moderates the effect of learning stocks on organizational performance through feed-forward learning flows, while trust in manager moderates the effect of learning stocks on organizational performance through feedback learning flows.Research limitations/implicationsThis study has a limitation in which it uses self-report data to measure all constructs. The objective measure may be necessary for future study.Practical implicationsThe implications of this study are twofold. First, it finds that the higher organizational justice, the better the transfer of knowledge from the bottom up. When firms need to explore new knowledge, fairness in procedure and the distribution system is critical. Second, the higher the trust in management, the better the transfer of knowledge from the top down. The role of managers is instrumental in persuading employees and disseminating knowledge.Originality/valueCombining functionalist and critical perspectives and developing a theoretical model, this study contributes to the understanding of how trust and justice facilitate learning activities within organizations.
TRay: active THz imaging autonomous biometric secured access control and tracking gate system

Recent security concerns worldwide have made it necessary to put a large number of people through detailed security inspections in critical infrastructure and border entrances. It is desired to implement inspection processes, which will enhance security while providing improvements in several aspects, such as answering the high throughput at crowded places, standardizing control processes, minimizing operator errors and increasing the feel of security, without making individuals uncomfortable as possible. To answer these requirements, Active Terahertz Imaging Autonomous Biometric Security Access Control and Tracking Gate System, or TRay, integrates various security technologies. TRay offers epassport control, biometric identity check (fingerprint, face etc.) and THz imaging for concealed object detection. During the inspection process, the person using the gate is guided through e-passport control and biometric identity check steps by following a set of instructions, without requiring the assistance of an operator. The person will also be scanned for concealed objects by a THz imaging system. Locations of possible threat objects and contraband, made of metal and, in addition, ceramic, plastic, liquids etc. are identified using object detection algorithms. The system is able to generate a warning in case of a detected object, for further inspection of the location of the body part by security personnel. THz imaging system operates at a standoff distance, which allows scanning to be performed at any point during the inspection process, eliminating the need to use confined spaces for body scanning. Less human intervention and misunderstandings, decreased waiting time at passport/identification checkpoints, improved public satisfaction, standardized control duration, optimized use of space, easy tracking of passenger transitions, generating alarms in case of undesired transitions, 24/7 operation, forged document usage prevention and decreased workload on the security personnel are further advantages of the system, which is unique in terms of the features it combines.
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson Sampling

State-of-the-art approaches to partially observable planning like POMCP are based on stochastic tree search. While these approaches are computationally efficient, they may still construct search trees of considerable size, which could limit the performance due to restricted memory resources. In this paper, we propose Partially Observable Stacked Thompson Sampling (POSTS), a memory bounded approach to openloop planning in large POMDPs, which optimizes a fixed size stack of Thompson Sampling bandits. We empirically evaluate POSTS in four large benchmark problems and compare its performance with different tree-based approaches. We show that POSTS achieves competitive performance compared to tree-based open-loop planning and offers a performancememory tradeoff, making it suitable for partially observable planning with highly restricted computational and memory resources.
Design and Biological Evaluation of Manganese‐ and Ruthenium‐Based Hybrid CO‐RMs (HYCOs)

Interest in the therapeutic effects of carbon monoxide (CO), a product of heme degradation catalyzed by the enzyme heme oxygenase‐1 (HO‐1), has led to the development of CO‐releasing molecules (CO‐RMs) for the controlled delivery of this gas in vivo. We recently proposed conjugating a cobalt‐based CO‐RM with various activators of nuclear factor erythroid 2‐related factor 2 (Nrf2), the transcription factor that regulates HO‐1 expression, in order to exploit the beneficial effects of exogenous and endogenous CO. In this study, we describe the preparation of hybrid molecules (termed HYCOs) conjugating a fumaric acid derivative as an Nrf2 activator to a Mn‐ or a Ru‐based CO‐RM known to be pharmacologically active. With the exception of an acyl‐manganese complex, these hybrids were obtained by associating the two bioactive entities by means of a linker of variable structure. X‐ray diffraction analyses and preliminary biological investigations are also presented.
Improvements and applications of the elements of prototype-based clustering

Hämäläinen, Joonas Improvements and Applications of the Elements of Prototype-Based Clustering Jyväskylä: University of Jyväskylä, 2018, 58 p.(+included articles) (JYU Dissertations ISSN 2489-9003; 43) ISBN 978-951-39-7621-7 (PDF) Finnish summary Diss. Clustering or cluster analysis is an essential part of data mining, machine learning, and pattern recognition. The most popularly applied clustering methods are partitioning-based or prototype-based methods. Prototype-based clustering methods usually have easy implementability and good scalability. These methods, such as K-means clustering, have been used for different applications in various fields. On the other hand, prototype-based clustering methods are typically sensitive to initialization, and the selection of the number of clusters for knowledge discovery purposes is not straightforward. In the era of big data, in high-velocity, ever-growing datasets, which can also be erroneous, outlier intensive and sparse, research has arisen focused on the development of efficient prototype-based clustering methods for more challenging datasets. This collection of articles primarily focuses on developing prototype-based clustering for more scalable, efficient and reliable data processing. To achieve these goals, improvements and modifications have been made to prototype-based clustering in six included articles. Additionally an application of the prototype-based clustering to supervised learning in regression problems is also covered. In general, these efforts advance the knowledge discovery process towards more reliable data processing and big data.
PREPARATION AND CHARACTERIZATION OF CELLULOSE NANOFIBERS ISOLATED FROM LETTUCE PEEL

Abstract: Cellulose nanofibers (CNFs) were isolated from lettuce peel using specific chemical treatments, i.e., NaOH alkali solution treatment, NaClO2 bleaching and sulfuric acid hydrolysis. Both cellulose and cellulose nanofibers were characterized by Fourier transform infrared spectroscopy (FTIR) and X-ray diffraction (XRD). Morphological features and particle size distribution of cellulose and nanostructures were explored using transmission electron microscopy (TEM), scanning electron microscope (SEM) and the Malvern laser particle size analyzer. Results showed that particle diameters of the majority of morphological nanofibrillated cellulose were in the range of 10 to 20 nm, and length between 190 to 460 nm. The crystallinity index reached 41.57%, which increased by 117.3% compared with the raw material. The crystalline type of lettuce peel nanofibers belongs to cellulose type I. Moreover，thermal degradation temperature of CNFs is 271.7 °C which is 47.3 °C higher than that of the untreated peel. The CNFs obtained from these treatments can be used as additional biocomposites in reinforced polymer manufacturing processes or the food industry.
The effect of synthetic essential amino acids supplementation to low crude protein diets on growth performance and serum metabolites in broiler chickens

Objective: To assess the influence of reduction of crude protein (CP) by 5% and 10% with supplementation of crystalline synthetic essential amino acids (EAAs) on performance and carcass traits of broilers. Design: Randomized controlled study Animals: A total of 320 one-day-old broiler chicks, (Cobb 500) reared up to 35 days of age were randomly allocated into eight experimental groups with four replicates (10 each). Procedures: The treatments as follow; the first group of chicks were fed on control protein to meet the CP requirement and total AAs according to Cobb 500 recommendation; the second group were fed idle EAAs (Id EAAs) to meet the recommended CP and the digestible EAAs. The third, fourth, and fifth groups were fed on diet 5% reduction of CP with EAAs supplementation exceed the digestible EAAs requirement of Cobb by 110, 120, and 130% (LCP3+110EAAs, LCP4+120EAAs, LCP5+130EAAs). However, the sixth, seventh, eighth groups of chicks were fed on diet 10% reduction of CP with AAs supplementation exceed the digestible EAAs requirement of Cobb by 110, 120, and 130% (LCP6+110EAAs, LCP7+120EAAs, LCP8+130EAAs). Results: Broilers of group four, five, six, seven and eight showed a significant decrease final body weight (BW), body weight gain (BWG) and feed intake at 21 and 35 days of age. However, the same variables were significantly enhanced in broilers of group one, two, and three (p <0.05). Additionally, there was a significant decrease of relative weight of abdominal fat pad of broilers in groups three, four, five compared to other treatment groups. Serum uric acid and cholesterol were linearly decreased with reduction of CP and supplementation with synthetic essential amino acids. Conclusion and clinical relevance: The present results indicate that reduction of CP by 5% or 10% with crystalline EAAs supplementation by 110, 120 and 130% more than the digestible EAAs of broiler recommendation did not support the growth performance.
Effect of preharvest chitosan application on bioactive compounds of and sunflower sprouts during storage.

The effects of preharvest chitosan treatments on antioxidant activities and bioactive compounds of sunflower sprouts during cold storage were investigated. The sprouts were watered with 0, 0.1, 0.5 and 1.0 % (w/v) chitosan solutions prior harvest 24 h. After harvest, the sprouts were stored at 4 ± 1 °C for 9 d. The investigated parameters were visual appearance, antioxidant activities such as ferric reducing antioxidant potential (FRAP), DPPH radical scavenging (DFRS) activity and bioactive compounds including total phenols, flavonoids and ascorbic acid concentrations. The visual appearance of the sprouts of all treatments was maintained during storage at 4 ± 1 °C for 9 d. Preharvest chitosan treatment enhanced antioxidant and DFRS activities, total phenols, flavonoids and ascorbic acid contents of the sprouts, especially at 0.1 % chitosan. During the storage, 1.0 % chitosan treatment induced FRAP, DFRS activity and all bioactive compounds rather than others treatments. These suggest that preharvest chitosan treatment is an effective alternative improving nutritional quality of the sunflower sprouts during cold storage.
Location Inference for Non-Geotagged Tweets in User Timelines

Social media like Twitter have become globally popular in the past decade. Thanks to the high penetration of smartphones, social media users are increasingly going mobile. This trend has contributed to foster various location based services deployed on social media, the success of which heavily depends on the availability and accuracy of users’ location information. However, only a very small fraction of tweets in Twitter are geo-tagged. Therefore, it is necessary to infer locations for tweets in order to attain the purpose of those location based services. In this paper, we tackle this problem by scrutinizing Twitter user timelines in a novel fashion. First of all, we split each user's tweet timeline temporally into a number of clusters, each tending to imply a distinct location. Subsequently, we adapt two machine learning models to our setting and design classifiers that classify each tweet cluster into one of the pre-defined location classes at the city level. The Bayes based model focuses on the information gain of words with location implications in the user-generated contents. The convolutional LSTM model treats user-generated contents and their associated locations as sequences and employs bidirectional LSTM and convolution operation to make location inferences. The two models are evaluated on a large set of real Twitter data. The experimental results suggest that our models are effective at inferring locations for non-geotagged tweets and the models outperform the state-of-the-art and alternative approaches significantly in terms of inference accuracy.
COMPARISON OF DIFFERENT PYTHON DEVELOPMENT LIBRARIES TO SOLVE FLUID FLOW PRO-BLEMS

RESUMO – With the advance of programming languages, the availability of better hardware, and the emergence of new libraries for numerical development, it has been possible to use high level abstractions to prototype and solve complex problems in many ﬁelds of knowledge. In the context of numerical simulation using the Finite Volume Method, those new tools can be used, so that the developer is able to make experiments with less eﬀort. This work compares several libraries from the Python programming language (Pure Python implementation, Cython, NumPy, Numba, Scipy, petsc4py
Coal recovery from coal washing plant tailings with Knelson concentrator

ABSTRACT Due to the increasing technology, the energy requirement is also increasing, this situation causes rapid depletion of our underground resources. In such a case, the efficiency of coal washing plants should be increased. In addition, coal waste should be enriched and brought to the economy. In this study, the enrichment of slime coal waste at the Koyunağılı coal washing plant had been examined. These wastes had 61.7% ash and 1740 kcal/kg LHV (Lower Heating Value) on dry basis. Experimental design methods were used in the study. The aim of the first phase of the study was to obtain coarse-size coal with 0.5 mm sieving. Coal was obtained with an ash content of 31.20% and ash of 3,980 kcal/kg LHV. The second aim was to enrich of −0,5 mm particle size with the Knelson Concentrator and a fine coal containing 2210 kcal/kg LHV with 55.89% ash content was obtained with 67.81% combustion recovery. In addition, 81.61% combustible recovery was obtained when two products were evaluated together. Thus, as a result of two-step enrichment, 45.61% ash and 2947 kcal/kg of calorific value of fine powder coal were obtained.
Investigation of CCN1 role(s) in Mantle Cell Lymphoma (MCL)

Mantle Cell Lymphoma (MCL) is a comparatively rare non-Hodgkin`s lymphoma which is characterised by the overexpression of cyclin D1. Many patients present with or progress to advanced stage disease within three years. Disease progression involves down regulation of cyclin D1. Moreover, MCL is broadly considered as an incurable disease with median survival of patients being 3-4 years. CCN1, a matricellular protein involved in stem cell signalling within the haematopoietic microenvironment is highly expressed in early stage MCL cells and down-regulated in advanced stage disease. We have used the human MCL cell lines REC1<G519<JVM2 as a model for disease aggression. We have investigated the role(s) of CCN1, cyclin D1 and cyclin dependent kinase inhibitors in MCL progression. CCN1 dysregulation is identified in MCL progression whereby the magnitude of CCN1 expression in human MCL cells is REC1˃G519˃JVM2 cells by RQ-PCR, depicting a decrease in CCN1 expression with disease progression. Further investigation of CCN1 protein expression by western blotting showed that whilst expression of full–length CCN1 (42kDa) barely altered through the cell lines, expression of the truncated form (20kDa) was high in REC1 cells (OD:1.0) reduced in G519 cells (OD:0.5) and barely detected in JVM2 cells depicting decreased with disease progression. We have then demonstrated that cyclin D1 and cyclin dependent kinase inhibitors (p21and p27) are also involved in disease progression using the above MCL cell line model. Cyclin D1 was highly expressed in REC1 cells (OD: 1.0), reduced to one fifth in G519 cells (OD: 0.2) and not detected by western blotting in JVM2 cells. p27 followed a similar profile of expression as cyclin D1. Conversely, p21 was absent in 4 the REC1 cells and showed increasing expression in G519 and JVM2 cells. Subcellular localization detected p21/ p27 primarily within the cytoplasm and absent from the nucleus, consistent with altered roles in treatment resistance. Mitochondrial detection of p21 in the JVM2 cell line supports an additional anti-apoptotic role. CCN1 likely plays a key role in B cell development; dysregulation of CCN1 may support MCL progression with p21 and p27 forming molecular signatures associated with progressive disease. REC1 cells and JVM2 cells were genetically modified using lentivirus to identify the potential pathways associated with CCN1. CCN1 knockdown was performed in REC1 cells (REC1 KD) and CCN1 overexpression in JVM2 cells (CCN1 OE). Proteomics analysis of JVM2 OE and REC1 KD revealed interesting results showing regulation of 44 proteins. 19 proteins regulated by CCN1 that simultaneously downregulated in the CCN1 KD model and up-regulated in CCN1 OE model. 25 proteins modulated by CCN1 that simultaneously up-regulated in CCN1 KD model and down-regulated in CCN1 OE
MULTIDIMENSIONAL ENGAGEMENT IN LEARNING—AN INTEGRATED INSTRUCTIONAL DESIGN APPROACH

learning. This important problem in daily instruction has produced numerous research activities focusing on conditions and consequences of student engagement. However, these activities have led to a complex aspects of student engagement based on a review of literature. Findings from the review have been used to formulate 15 instructional strategies as part of a hierarchically organized instructional design approach for incorporating multidimensional engagement into elements of learning (i.e., learning materials for lessons or instructional units). Strategies for cognitive engagement concern engagement levels such as knowledge, comprehension, convergent thinking, evaluation, and synthesis. Strategies for motivational social-emotional engagement are related to self-assertion, entertainment, belongingness, adaptiveness, and security. Finally, in order to support research and implementation, measurement instruments on multidimensional levels of engagement and related instructional strategies are proposed.