Miracle-dz/UniFAR-FTUs huggingface dataset:
THE IMPACT OF ELECTRONIC MONEY BALANCE TOP UP FEATURE ON SMARTPHONE IN TRANSACTIONS ON PUBLIC SATISFACTION USING TAM

The purpose of this research was to identify and analyze public interest in the transaction process using Near Field Communication (NFC) in people who often use public transportation in performing their daily activities. This research focused on the NFC feature that was used for transactions using electronic money. The design method used in this research is descriptive associative which questions the relationship between two or more variables because the model used in this research is the Technology Acceptance Model which refers to the explanation of the main factors from user behavior towards user acceptance of NFC technology. The analysis was conducted on people regarding their daily activities in performing transactions using electronic money. The results achieved are data that can be seen from the people’s interests and needs towards smartphones with NFC features gathered from the responses given by the people. The conclusion is that the data from this research can help existing smartphone producers to implement NFC features on affordable smartphones for the public.
in-Supply of palmitic, stearic, and oleic acid changes rumen fiber digestibility and microbial composition

The concept that fat supplementation impairs total-tract fiber digestibility in ruminants has been widely accepted over the past decades. Nevertheless, the recent interest in the dietary fatty acid profile to dairy cows enlightened the possible beneficial impact of specific fatty acids (e.g., palmitic, stearic, and oleic acids) on total-tract fiber digestibility. Since palmitic, stearic, and oleic acids are the main fatty acids present in ruminal bacterial cells, we hypothesize that the dietary supply of these fatty acids will favor their incorporation into the bacterial cell membranes, which will support the growth and enrichment of fiber-digesting bacteria in the rumen. Our objective in this experiment was to investigate how dietary supply of palmitic, stearic, and oleic acid affect fiber digestion, bacterial membrane fatty acid profile, microbial growth, and composition of the rumen bacterial community. Diets were randomly assigned to 8 single-flow continuous culture fermenters arranged in a replicated 4 × 4 Latin square with four 11-day experimental periods. Treatments were: 1) a control basal diet without supplemental fatty acids (CON); 2) the control diet plus palmitic acid (PA); 3) the control diet plus stearic acid (SA); and 4) the control diet plus oleic acid (OA). All fatty acid treatments were included in the diet at 1.5% of the diet (dry matter [DM] basis). The basal diet contained 50% orchardgrass hay and 50% concentrate (DM basis) and was supplied at a rate of 60 g of DM/day in two equal daily offers (0800 and 1600 h). Data were analyzed using a mixed model considering treatments as fixed effect and period and fermenter as random effects. Our results indicate that PA increased in vitro fiber digestibility by 6 percentage units compared with the CON, whilst SA had no effect and OA decreased fiber digestibility by 8 percentage units. Oleic acid decreased protein expression of the enzymes acetyl CoA carboxylase compared with CON and PA, while fatty acid synthase was reduced by PA, SA, and OA. We observed that PA, but not SA or OA, altered the bacterial community composition by enhancing bacterial groups responsible for fiber digestion. Although the dietary fatty acids did not affect the total lipid content and the phospholipid fraction in the bacterial cell, PA increased the flow of anteiso C13:0 and anteiso C15:0 in the phospholipidic membrane compared to the other treatments. In addition, OA increased the flow of C18:1 cis-9 and decreased C18:2 cis-9, cis-12 in the bacterial phospholipidic membranes compared to the other treatments. Palmitic acid tended to increase bacterial growth compared to other treatments, whereas SA and OA did not affect bacterial growth compared with CON. To our knowledge, this is the first research providing evidence that palmitic acid supports ruminal fiber digestion through shifts in bacterial fatty acid metabolism that result in changes in growth and abundance of fiber-degrading bacteria in the microbial community.
Natural Volatility, Welfare and Taxation Natural Volatility, Welfare and Taxation

Cyclical components are analytically computed in a theoretical model of stochas-tic endogenous fluctuations and growth. Volatility is shown to depend on the speed of convergence of the cyclical component, the expected length of a cycle and on the altitude of the slump. Taxes affect these channels and can therefore explain crosscountry differences and breaks over time in volatility. With ex-ogenous sources of fluctuations, a special case of our model, decentralized factor allocation is efficient. With endogenous fluctuations and growth, decentralized factor allocation is inefficient and (time-invariant) taxes can (destabilize ze the economy. No unambiguous link exists between volatility and welfare. Weil for discussions and seminar participants at various places for comments. This research is part of a programme supported by the Belgian government (Poles d'Attraction inter-universitaires PAI P5/21).
Robotics and Autonomous Systems

The problem of finding stable grasps has been widely studied in robotics. However, in many applications the resulting grasps should not only be stable but also applicable for a particular task. Task-specific grasps are closely linked to object categories so that objects in a same category can be often used to perform the same task. This paper presents a probabilistic approach for task-specific stable grasping of objects with shape variations inside the category. An optimal grasp is found as a grasp that is maximally likely to be task compatible and stable taking into account shape uncertainty in a probabilistic context. The method requires only partial models of new objects for grasp generation and only few models and example grasps are used during the training stage. The experiments show that the approach can use multiple models to generalizetonewobjectsinthatitoutperformsgraspingbasedontheclosestmodel.Themethodisshown togeneratestablegraspsfornewobjectsbelongingtothesameclassaswellasforsimilarinshapeobjects of different categories.
Edinburgh Research Explorer Losing One's Grip: A Bivariate Growth Curve Model of Grip Strength and Nonverbal Reasoning From Age 79 to 87 Years in the Lothian Birth Cohort 1921

The LBC1921 data were collected by grants from the Biotechnology and Biological Sciences Research Council (wave 1; Ref. 15/S18386), a Royal Society-Wolfson Research Merit Award (wave 2), and the Chief Scientist Office of the Scottish Government’s Health Directorates (wave 3; Ref. CZB/4/505). The research was conducted in the Centre for Cognitive Ageing and Cognitive Epidemiology, part of the Lifelong Health and Wellbeing Initiative (G0700704/84698) with joint funding from the Biotechnology and Biological Sciences Research Council (BBSRC), Engineering and Physical Sciences Research Council (EPSRC), Economic and Social Research Council (ESRC), and Medical Research Council (MRC).
APACHE HADOOP PERFORMANCE EVALUATION WITH RESOURCES MONITORING TOOLS, AND PARAMETERS OPTIMIZATION: IOT EMERGING DEMAND

Recently, IoT has revealed a key value in the smart cities. Our living comfortability level has been improved. Such technology requires extensive data processing especially when it is a real time driven data. Apache Hadoop framework is a necessary and efficient model that can be incorporated with the IoT technology. Hadoop, the open-source framework, is typically used for off-line batch processing on large-scale clusters. It has a wide range of applications in the big data industry due to its capability in processing massive data in distributed and parallel environments. However, several aspects should be carefully evaluated before deploying Hadoop-based solutions. The authors thoroughly investigate the Apache Hadoop framework with the focus on factors that directly affect its performance. The work discusses and evaluates two crucial dimensions of Hadoop systems; monitoring tools and their impact on the performance of the Apache Hadoop based clusters, and the most influential parameters and the optimization techniques of Apache Hadoop based systems. Results showed that monitoring tools play a major role in Hadoop-based solutions planning and maintenance. According to the used experimental settings, the Cacti monitoring tool consumes around 45% of the memory usage, however memory usage in Ganglia is more efficient than Cacti tool (i.e., on average around 2.5%). For CPU utilization, both monitoring tools are efficient and the monitoring tool usage amount is almost negligible. The results also showed that there is a shortlist of critical parameters that significantly affect the overall performance. Based on the results, the authors conclude the paper by future directions and possible improvements that need further explorations and experiments.
Occupancy Modeling of Forest Carnivores in Missouri

My research used infrared remote cameras, trackplate boxes, and scat transects to survey for carnivores at 53 sites throughout the southern forested region of Missouri. Cameras and track-plates both detected Eastern spotted skunk (Spilogale putorius), striped skunk (Mephitis mephitis), raccoon (Procyon lotor) and opossum (Didelphis virginiana). Coyote (Canis latrans), bobcat (Lynx rufus) and gray fox (Urocyon cinereoargenteus) were only documented with remote cameras. Combining methods yielded a more accurate picture of the community composition. Occupancy by eastern spotted skunk was best predicted by habitat and competitor presence. Striped skunk occupancy models indicated features related to settled areas and competitor presence were the most important predictors. The predictive models for raccoon and opossum lacked much resolution, predicting very high occupancy values throughout the region. Probability of actual occupancy (PAO) for coyotes was best predicted by measures of human disturbance and prey distribution. Bobcats were primarily influenced by coyote presence and prey availability. Gray foxes were the most heterogeneous in their regional predicted distribution, and were best predicted by measures of coyote and bobcat presence as well as by landscape elements related to humans and settled areas.
Oocyte Arrested at Metaphase II Stage Were Derived From Human Pluripotent Stem Cells in Vitro

Xiaoli Yu (  yuxiaoli1221@126.com ) Ningxia Medical University Ning Wang Northwest A&F University: Northwest Agriculture and Forestry University Yingxin Zhang Ningxia Medical University Xiang Wang Ningxia Medical University Yikai Qiu Ningxia Medical University Hongyan Wang General Hospital of Ningxia Medical University Guoping Wang Yinchuan Maternal and Child Health Hospitial Xiuying Pei Ningxia Medical University Yahui Ren Henan University of Urban Construction Chunfang Ha General Hospital of Ningxia Medical University Ping Chen General Hospital of Ningxia Medical University Li Wang General Hospital of Ningxia Medical University Huayan Wang Northwest A&F University: Northwest Agriculture and Forestry University
DEVELOPING AN INTELLIGENT SYSTEM FOR PREDICTING BANKRUPTCY

Bankruptcy prediction is a very important metric for making economic decisions. It is a very critical element in credit risk analysis. Machine learning based classifiers have been heavily utilized in predicting bankruptcy. In terms of machine learning, predicting bankruptcy with imbalanced dataset is a very big challenge. Despite the current existing models for bankruptcy prediction, finding a model that achieves a high-performance measurement with imbalanced datasets is still an interesting point of research. The reason behind this challenge is the fact that imbalanced dataset leads to misclassification results. This paper aims to develop a robust machine learning based model for predicting bankruptcy with solving imbalanced dataset problem. Four different re-sampling strategies were applied to solve the imbalanced class distribution problem based on three popular datasets. The used datasets were downloaded from the University of California, Irvine (UCI) machine learning repository. The developed model based on different single and ensemble machine learning classifiers. The overall experimental results showed that the best performance of the developed model with the Polish dataset was 97% for accuracy and 95.4% for AUC (Area Under the Curve). Moreover, the best performance with the Australian dataset was 88.4% for accuracy and 92% for AUC. And the best performance with the German dataset was 81.5% for accuracy and 83.4% for AUC ratio.
Edinburgh Research Explorer The Edinburgh International Accents of English Corpus: Towards the Democratization of English ASR

English is the most widely spoken language in the world, used daily by millions of people as a first or second language in many different contexts. As a result, there are many varieties of English. Although the great many advances in English automatic speech recognition (ASR) over the past decades, results are usually reported based on test datasets which fail to represent the diversity of English as spoken today around the globe. We present the first release of The Edinburgh International Accents of English Corpus ( EdAcc ). This dataset attempts to better represent the wide diversity of English, encompassing almost 40 hours of dyadic video call conversations be-tween friends. Unlike other datasets, EdAcc includes a wide range of first and second-language varieties of English and a linguistic background profile of each speaker. Results on latest public, and commercial models show that EdAcc highlights shortcomings of current English ASR models. The best performing model, trained on 680 thousand hours of transcribed data, obtains an average of 19.7% word error rate (WER) – in contrast to the 2.7% WER obtained when evaluated on US English clean read speech. Across all models, we observe a drop in performance on Jamaican, Indonesian, Nigerian, and Kenyan English speakers. Recordings, linguistic backgrounds, data statement, and evaluation scripts are released on our website under CC-BY-SA 1 license https://ramonsanabria.github. io/edacc/. We hope that this work will encourage future research on a wider range of English varieties to create more accessible speech technologies.
Reconstructing peak discharges of historic floods of the river Ahr, Germany

In the presented paper, peak discharges of historic floods in urbanized areas of different cities on the River Ahr in western Germany are reconstructed based on documentary sources from pre instrumental and the early instrumental period (1804–1937). Maximum water levels of five floods are denoted by flood-marks, with one of these events additionally documented by old photographs. The main challenge is the reconstruction of historic floodplain conditions and modifications influencing the cross-section area and the hydraulic roughness. In order to overcome this problem, a simple approach to estimate peak discharges of historic floods has been applied to the River Ahr. This approach includes a procedure for reconstructing the hydraulic parameters of the river channel and overflooded areas, coupled with an approach for the verification of estimated peak discharge reliability. Five reconstructed discharge maxima are presented. One of these events is well documented by numerous photographs, taken at different times of day, which allow the reconstruction of a hydrograph peak. The validation of the technique by comparison with recent gauged floods reveals results of adequate accuracy. The results show that reconstructed historic floods were partially larger than any gauged flood of the River Ahr.
of synaptogenesis and spinogenesis by

In the mammalian forebrain, the majority of excitatory synapses occur on dendritic spines. Changes in the number of these structures is important for brain development, plasticity and the reﬁnement of neuronal circuits. The formation of excitatory synapses involves the coordinated formation of dendritic spines and targeting of multi-protein complexes to nascent connections. Recent studies have demonstrated that the estrogen 17 β -estradiol (E2) can rapidly increase the number of dendritic spines, an effect consistent with the ability of E2 to rapidly inﬂuence cognitive function. However, the molecular composition of E2-induced spines and whether these protrusions form synaptic connections has not been fully elucidated. Moreover, which estrogen receptor(s) (ER) mediate these spine-morphogenic responses are not clear. Here, we report that acute E2 treatment results in the recruitment of postsynaptic density protein 95 (PSD-95) to novel dendritic spines. In addition neuroligin 1 (Nlg-1) and the NMDA receptor subunit GluN1 are recruited to nascent synapses in cortical neurons. The presence of these synaptic proteins at nascent synapses suggests that the machinery to allow pre-and post-synapses to form connections are present in E2-induced spines. We further demonstrate that E2 treatment results in the rapid and transient activation of extracellular signal-regulated kinase 1/2 (ERK1/2), Akt and the mammalian target of rapamycin (mTOR) signaling pathways. However, only ERK1/2 and Akt are required for E2-mediated spinogenesis. Using synthetic receptor modulators, we further demonstrate that activation of the estrogen receptor beta (ER β ) but not alpha (ER α ) mimics rapid E2-induced spinogenesis and synaptogenesis. Taken together these ﬁndings suggest that in primary cortical neurons, E2 signaling via ER β , but not through ER α , is capable of remodeling neuronal circuits by increasing the number of excitatory synapses.
Validation of the AATSR Meteo Product Sea-Surface Temperature Against In Situ Observations And Analyses

RESUME The AATSR Meteo product became available in near-real time as BUFR formatted files updated on an ESA ftp server from 19 August 2002. Since that time, data have been downloaded in near-real time and a series of routine monitoring and validation activities have been performed at the Met Office, UK. The Meteo product SST was compared to point measurements of SST from buoys, to in situ observations averaged on a 5° grid, and to a monthly 1° climate SST analysis field. Daily, weekly and monthly comparisons for August–November show that the Meteo product SST raises no serious concerns: the AATSR instrument and pre-launch SST retrieval algorithms seem to be performing adequately. The global monthly mean differences between Meteo product SST and the climate SST analysis were -0.07 K (σ = 0.79 K) and -0.08 K (σ = 0.77 K) for September and October, 2002, respectively. While these statistics show that the AATSR is providing a measure of SST close to expected values, it has been difficult to confirm the absolute accuracy of the product with the available data. There has been no evidence of an AATSR–in situ bias arising from the skin effect, even in night time data, suggesting that the Meteo product dual-view 3-channel SST may be ~0.1–0.2 K too warm.
Study of markovian queueing model with F-policy, impatience customers, feedback and re-service, server vacation and breakdown

A system with two heterogeneous servers with impatient customers and ﬁnite capacity is considered. The admission control of customers is based on F-policy. This paper is based on M/M/2 queues, and presents the study of two heterogeneous servers where one server is the primary server and other is secondary. Customers are primarily served by the main server, who also takes customer’s feedback into account. If customers are unsatisﬁed and opt for further service, the second server will serve them. The main server considered is unreliable, that is, it may breakdown at any time. The main server will go for vacation whenever there is no customer to serve. To analyze the performance of the model, we calculated the measures of performance and did the cost analysis. Impact of F-policy on system’s estimated proﬁt is shown. At last, conclusions are drawn.
Multi Agents usage in Green Supply Chain: Literature review

Because of increasing environmental awareness, companies are asked to implement new concepts, which aim to reduce the environmental impact of their activities. The Green Supply Chain Management (GSCM) is one of the key concepts adopted by many organizations to include the environmental thinking into the Supply Chain Management. However, the GSCM implementation is full of constraints in terms of the coordination and collaboration between all stakeholders. Therefore, many GSCM models and frameworks, like the multi agent systems (MAS), are suggested to facilitate this transition and reduce the complexity level along the Supply Chain. The objective of this paper is to study the different GSCM aspects as well as the usage of the multi agent systems for GSCM modelling.
AN EFFICIENT NONLINEAR ACCESS POLICY BASED ON QUADRATIC RESIDUE FOR CIPHERTEXT POLICY ATTRIBUTE BASED ENCRYPTION

Ciphertext Policy Attribute Based Encryption (CP-ABE) is an efficient encryption scheme as data owner is making decision about the attributes that can access his data and adding that attributes to access structure while encrypting that message. Most existing CP-ABE scheme are based traditional access structure such as linear secret sharing scheme which incur large ciphertext size and linearly increases according to the number of attributes. And those schemes have more computational overhead for calculating share for each attribute and when recalculating secret in data user side. In this paper, we propose a different secret sharing scheme that can be used in access policy for CP-ABE which will reduce the size of ciphertext and there by communication overhead. Furthermore, the proposed scheme reduced computational overhead of secret sharing scheme and improved overall efficiency of the scheme.
Proximity-dependent labeling identi�es dendritic cells that prime the antitumor CD4+ T cell response

Dendritic cells (DCs) are uniquely capable of transporting tumoral antigens to tumor-draining lymph nodes (tdLNs), where they initiate antitumor immunity and mediate checkpoint blockade immunotherapy. Despite recent advances, the full phenotype of the DCs involved in these processes has been di�cult to establish. Using LIPSTIC (Labeling Immune Partnerships by SorTagging Intercellular Contacts)-based single-cell transcriptomics, we identify individual DCs capable of presenting antigen to CD4 + T cells in the tdLN. These represent a small fraction of all DCs present in the tdLN and display a distinctive activated phenotype that includes production of cytokine IL-27, required for e�cient T cell priming and tumor rejection. Tumor progression results in loss of effective priming of naïve CD4 + T cells, downstream of transcriptional changes in DCs that are manifested already when they arrive at the tdLN. Collectively, our data reveal temporal shift in DC activation status over the course of the antitumor immune response.