Miracle-dz/UniFAR-FTUs huggingface dataset:
Distribution Route Optimization for Electric Vehicles in Urban Cold Chain Logistics for Fresh Products under Time-Varying Traffic Conditions

Electric vehicles (EVs) have been widely used in urban cold chain logistic distribution and transportation of fresh products. In this paper, an electric vehicle routing problem (EVRP) model under time-varying traffic conditions is designed for planning the itinerary for fresh products in the urban cold chain. The object of the EVRP model is to minimize the total cost of logistic distribution that includes economic cost and fresh value loss cost. To reflect the real situation, the EVRP model considers several influencing factors, including time-varying road network traffic, road type, client’s time-window requirement, freshness of fresh products, and en route queuing for charging. Furthermore, to address the EVRP, an improved adaptive ant colony algorithm is designed. Simulation test results show that the proposed method can allow EVs to effectively avoid traffic congestion during the distribution process, reduce the total distribution cost, and improve the performance of the cold chain logistic distribution process for fresh products.
Changes in inner contents of ‘Kyoho’ grape berry during the growth and ripening period

The grape berry morphologically consists of epidermis, an outer wall, an inner wall and placenta. The inner contents such as soluble solids, organic acids and moisture distribution of grape berries are dramatically changed during the véraison between the growth and ripening period. However, we know little about the changes in the inner contents of the outer wall, inner wall and placenta. Our purpose of the study is to clarify the tissue specifi city of the total soluble solids content, sugar composition, fl esh fi rmness and moisture distribution of ‘Kyoho’ grape berry during growth and ripening period. The moisture distribution of the grape berries was analyzed with magnetic resonance imaging (MRI). The total soluble solids contents of the outer wall were higher than those of the inner wall during the investigation period. In this study, fructose, glucose and sucrose were detected in the berries. The concentrations of these sugars in the outer wall were higher than those in the inner wall; however, the components of these sugars were not different between the outer wall and inner wall. Even within the same berry, the fl esh fi rmness and the moisture distribution were different from one part of the tissue to another. These results indicate that the growth rate of grape berry varies considerably among the different parts of the berry.
Profiles of Online Students and the Impact of Their University Experience

In recent decades, there has been a steady growth in the population who enter higher education in both brick-and-mortar and, in particular, online universities. This has led to an increase in heterogeneous student profiles in a relatively short period of time. The purpose of this paper was to explore the student profiles at a university that gives all its courses online, namely the Universitat Oberta de Catalunya (UOC), and analyse students’ perceptions of their university experience. With this goal in mind, we constructed a student typology based on their social conditions and backgrounds using multiple correspondence analysis. Subsequently, an analysis of variance (Kruskall-Wallis test) was run to detect whether there were any differences in students’ perceptions of the impact of their university experience (N = 1850). Although the prevailing profile of students in the online university continues to reflect students with responsibilities outside of the university (e.g., work and/or family), new profiles have been observed, made up of younger students without any work or family responsibilities. In turn, younger students’ distinct perceptions of their university experience has been observed, depending on student profiles, with older students having more intrinsic perceptions, focused on learning and the acquisition of theoretical knowledge.
Testing Darwin’s hypothesis about the most wonderful plant in the world: The Venus flytrap’s marginal spikes are a ‘horrid prison’ for moderate-sized insect prey

Botanical carnivory is a novel feeding strategy associated with numerous physiological and morphological adaptations. However, the benefits of these novel carnivorous traits are rarely tested. Here, we used field observations and lab experiments to test the prey capture function of the marginal spikes on snap traps of the Venus flytrap (Dionaea muscipula). Our field and laboratory results suggested surprisingly inefficient capture success: fewer than 1 in 4 prey encounters led to prey capture. Removing the marginal spikes decreased the rate of prey capture success for moderate-sized cricket prey by 90%, but this effect disappeared for larger prey. The nonlinear benefit of spikes suggests that they provide a better cage for capturing more abundant insects of moderate and small sizes, but may also provide a foothold for rare large prey to escape. Our observations support Darwin’s hypothesis that the marginal spikes form a ‘horrid prison’ that increases prey capture success for moderate-sized prey, but the decreasing benefit for larger prey is unexpected and previously undocumented. Thus, we find surprising complexity in the adaptive landscape for one of the most wonderful evolutionary innovations among all plants. These findings further enrich our understanding of the evolution and diversification of novel trap morphology in carnivorous plants.
TRANSFECTION STUDIES SUGGEST A ROLE FOR PKC-z IN GLUCOSE TRANSPORT*

We presently studied (a) insulin effects on protein kinase C (PKC) and (b) effects of transfection-induced, stable expression of PKC isoforms on glucose transport in 3T3/L1 cells. In both fibroblasts and adipocytes, insulin provoked increases in membrane PKC enzyme activity and membrane levels of PKC-a and PKC-b. However, insulin-induced increases in PKC enzyme activity were apparent in both non-down-regulated adipocytes and adipocytes that were down-regulated by overnight treatment with 5 mM phorbol ester, which largely depletes PKC-a, PKC-b, and PKC-e, but not PKC-z. Moreover, insulin provoked increases in the enzyme activity of immunoprecipitable PKC-z. In transfection studies, stable overexpression of wild-type or constitutively active forms of PKC-a, PKC-b1, and PKC-b2 failed to influence basal or insulin-stimulated glucose transport (2deoxyglucose uptake) in fibroblasts and adipocytes, despite inhibiting insulin effects on glycogen synthesis. In contrast, stable overexpression of wild-type PKC-z increased, and a dominant-negative mutant form of PKC-z decreased, basal and insulin-stimulated glucose transport in fibroblasts and adipocytes. These findings suggested that: (a) insulin activates PKC-z, as well as PKC-a and b; and (b) PKC-z is required for, and may contribute to, insulin effects on glucose transport in 3T3/L1 cells.
The Influences of the Physical Parameters on the Performance of a Methanol Steam Reformer

Methanol is regarded as an important feedstock for hydrogen production due to its high energy density and superior transportability. In this work, an experimental platform was constructed to evaluate the performance of a small-scale methanol steam reformer. The objective was aimed at the influences of various physical parameters on the methanol steam reforming (MSR) process. The hydrogen, carbon monoxide and carbon dioxide production rate, as well as methanol conversion, were experimentally analyzed with respect to different levels of the space velocity of the feedstock, the porosity and temperature of the catalyst bed, and the steam-to-carbon (S/C) ratio. It revealed the methanol conversion was sensitive to the temperature of the catalyst bed and the catalyst loading. The maximum methanol conversion does not necessarily correspond to maximum hydrogen production rate since a portion of the generated hydrogen was extracted during the water-gas shift process. The hydrogen production rate can reach about 2.7 times of the methanol feed rate. In addition, the by-product concentration of CO was significantly influenced by temperature and S/C ratio. On the other hand, a packed-bed dispersion model of methanol conversion is established to analyze the influences of the aforementioned physical parameters. Accordingly, dimensionless parameter groups can be discussed as well. It is anticipated to provide clues to systematic design of a MSR reactor.
Transformer-Based Long-Context End-to-End Speech Recognition

This paper presents an approach to long-context end-to-end automatic speech recognition (ASR) using Transformers, aiming at improving ASR accuracy for long audio recordings such as lecture and conversational speeches. Most end-to-end ASR systems are basically designed to recognize independent utterances, but contextual information (e.g., speaker or topic) over multiple utterances is known to be useful for ASR. There are some prior studies on RNN-based models that utilize such contextual information, but very few on Transformers, which are becoming more popular in end-to-end ASR. In this paper, we propose a Transformer-based architecture that accepts multiple consecutive utterances at the same time and predicts an output sequence for the last utterance. This is repeated in a sliding window fashion with one-utterance shifts to recognize the entire recording. Based on this framework, we also investigate how to design the context window and train the model eﬀectively in monologue (one speaker) and dialogue (two speakers) scenarios. We demonstrate the eﬀectiveness of our approach using monologue benchmarks on CSJ and TED-LIUM3 and dialogue benchmarks on SWITCHBOARD and HKUST, showing signiﬁcant error reduction from single-utterance ASR baselines with or without speaker i-vectors Abstract This paper presents an approach to long-context end-to-end automatic speech recognition (ASR) using Transformers, aiming at improving ASR accuracy for long audio recordings such as lecture and conversational speeches. Most end-to-end ASR systems are basically designed to recognize independent utterances, but contextual information (e.g., speaker or topic) over multiple utterances is known to be useful for ASR. There are some prior studies on RNN-based models that utilize such contextual information, but very few on Transformers, which are becoming more popular in end-to-end ASR. In this paper, we propose a Transformer-based architecture that accepts multiple consecutive utterances at the same time and predicts an output sequence for the last utterance. This is repeated in a sliding-window fashion with one-utterance shifts to recognize the entire recording. Based on this framework, we also investigate how to design the context window and train the model effectively in monologue (one speaker) and dialogue (two speakers) scenar-ios. We demonstrate the effectiveness of our approach using monologue benchmarks on CSJ and TED-LIUM3 and dialogue benchmarks on SWITCHBOARD and HKUST, showing signif-icant error reduction from single-utterance ASR baselines with or without speaker i-vectors.
THE PARTICIPATORY APPROACH FOR WATERFRONT DEVELOPMENT IN MEDITERRANEAN COUNTRIES. A CASE STUDY OF TRIPOLI, LEBANON WATERFRONT

Abstract Public waterfronts are considered an important touristic target in coastal cities. The development of these spaces is affected by many social, economic and political issues. Public participation has been an important tool for communities to influence the making of public spaces. This research aims to develop a participatory approach for waterfront development in Mediterranean countries by studying people behavior and activities on public waterfronts and investigating the socio-economic factors that influence the usage of these areas. It is a bottom-up approach that analyzes how activities emerged from people needs. This research is a mixture of qualitative and quantitative approaches that undertakes a case study with its activities and is based on socio-economic studies, behavioral and physical analysis. The used tools and techniques are: documentation, direct observations, lengthy-interviews and questionnaires with stakeholders (users and visitors). This paper is a preliminary study and part of an ongoing PhD research where the outcome will be a participatory matrix approach dedicated to decision makers to facilitate the effective participation of stakeholders in development of the waterfront. Also, it should improves the waterfront economic status, enhances the development and allows the new plans to be implemented in order to make a valuable public space open to the all citizens and tourists
Recent methods for numerical simulation of turbulent combustion in gas turbines

Numerical simulations for gas turbines rely mostly on Reynolds Averaged Navier Stokes simulations (RANS) but also on more recent techniques such as Large Eddy Simulations which provide insights into unsteady combustion phenomena such as ignition, flashback, quenching or combustion instabilities. RANS techniques for combustion constitute now classical tools in the CFD community and will not be described here. These tools have also reached certain limits in gas turbines flows. In such configurations, strongly swirling flows constitute difficult cases for RANS: the existence of very strong hydrodynamic unstable modes such as Precessing Vortex Cores (PVC) requires intrinsically unsteady approaches. Moreover, acoustics and coupled acoustics / combustion flows are important in gas turbines and can change the nature of the flow drastically. For these interactions, RANS is not adequate and must be replaced by LES and by acoustic codes which can predict the acoustic field in the turbine. This paper describes recent tools developed for LES in gas turbines and shows examples of applications in two configurations: a large scale burner (typical of high power industrial gas turbine) installed on a square combustion chamber in Karlsruhe and a prototype of small aeronautic gas turbine burner developed within the European Preccinsta project. Both non-reacting and reacting flow cases are discussed. LES results are compared to experimental data in terms of axial and azimuthal velocities (mean and RMS), averaged temperature and existence of natural instabilities such as PVC (precessing vortex core). For these complex geometries, results demonstrate the capacity of the LES to predict the mean flow, with and without combustion as well as its main unstable modes: it is shown for example that the PVC mode is very strong for the cold flow but is damped with combustion in both configurations. This paper also shows the necessity of complementing LES results with acoustic analysis to understand and predict the various combustion modes observed in a combustor.
Biochemical Studies on IGF and IGF-Binding Proteins Interactions & Structural Investigations on the SH3 Domain of Crk-associated Tyrosine Kinase Substrate p130cas (CAS)

In my work, I have undertaken the thorough biochemical and biostructural studies of insulin-like growth factor binding proteins (IGFBPs), and the SH3 domain of Crk-associated substrate p130cas (CAS). Based on NMR and crystal structures of mini-IGFBP-5, mutants were designed investigate binding interactions between IGF and IGFBP-5. Kinetic analysis using surface plasmon resonance (SPR) and isothermal titration calorimetry (ITC) showed the degree to which IGFBP-5 has evolved to optimize binding strength, as all mutants showed varying degrees of lowered binding affinities. The thesis demonstrates also the usefulness of in vitro protein expression system (RTS) with the description of successful expression of soluble and active IGFBP-4 full length. A high resolution X-ray structure of the recombinant human CAS SH3 domain has been determined providing a framework for the study of CAS SH3 domain protein-protein interactions, including applications of drug design.
Inference of large modified Poisson-type graphical models: Application to RNA-seq data in childhood atopic asthma studies

Recent advances in next-generation sequencing technology have yielded huge amounts of transcriptomic data. The discreteness and the high dimensions of RNA-seq data have posed great challenges in biological network analysis. Although estimation theories for high-dimensional modiﬁed Poisson-type graphical models have been proposed for the network analysis of count-valued data, the statistical inference of these models is still largely unknown. We herein propose a two-step procedure in both edge-wise and global statistical inference of these modiﬁed Poisson-type graphical models using a cutting-edge generalized low-dimensional projection approach for bias correction. Extensive simulations and a real example with ground truth illustrate asymptotic normality of edge-wise inference and more accurate inferential results in multiple testing compared to the sole estimation and the inferential method under normal assumption. Furthermore, the application of our method to novel RNA-seq data of childhood atopic asthma in Puerto Ricans demonstrates more biologically meaningful results compared to the sole estimation and the inferential methods based on Gaussian and nonparanormal graphical models.
Methodology for thermal optimization of motor spindles

Heat losses in motor spindles lead to thermal loads and thus to undesired effects on the spindle performance. To minimize these effects, the development of spindles must ensure a thermally advantageous design. Nowadays, a variety of software tools exist to support the design process. In particular, simulation models based on the Finite Element Method (FEM) have become widely established. Some commercially available simulation software offer specific tools for time-efficient optimization of multi-physical tasks. This article describes a methodology for an iterative optimization of the thermal behavior of a newly developed motor spindle applying these tools. An initial spindle is modelled, parameterized and thermally advantageous modification potential is identified by means of a parameter correlation analysis. The spindle is thermally optimized by appropriate adjustments to the design. The validity of the simulation model is evaluated by comparing the simulation results with experimental data of prototype analysis. The experimental findings are used to improve the simulation model by applying a parameter optimization. Lastly, improvements that could be achieved in the scope of an exemplary design iteration applying this methodology are shown.
Specimen Copy: Not for sale

Citrullus lanatus (Thunb.) Mansf. (Watermelon) is an important crop plant in Kenya. Being monoecious, watermelon is entirely dependent upon pollination services usually by insects for production. Although the centre of origin for this plant is thought to be tropical Africa, essentially not much has been studied of its pollination requirements in this region. The current study investigated the identity of the wild pollinators of watermelon, their behaviour and relative pollination effi ciencies at Yatta, a farm near Thika (Eastern Province). The main pollinator for this crop was found to be the honey bee, Apis mellifera but three wild species of Lasioglossum were found as important pollinators. These wild bees have a signifi cantly higher (P< 0.0001) pollen deposition on stigmas of watermelon than honeybees. One of the Lasioglossum (Ctenonomia) sp. 4 deposited on average three times as much pollen as the honeybee. At about the time of stigmatic receptivity, the number of visits by this species to female fl owers increases until it equals visits to male fl owers irrespective of number of fl owers per plot. This behavioural pattern coupled with the high pollen deposition potential makes Lasioglossum (Ctenonomia) sp. 4 a superior candidate as an alternatively managed pollinator for watermelon. Knowing that visitation occurs mostly in the morning, and that fl owers last only for one day, spraying can be done in the later hours of the day when the pollinators have virtually stopped foraging on the fl owers. In view of the reported pollinator decline globally, the wild pollinator species reported in this study warrant further investigation on their nesting biology and potential for domestication.
Disorder, Social Capital, and Norm Violation

Adding to the debate about the “broken windows” thesis we discuss an explanation of minor norm violation based on the assumption that individuals infer expected sanctioning probabilities from contextual cues. We modify the classical framework of rational crime by signals of disorder, local social control, and their interaction. Testing our implications we present results from three field experiments showing that violations of norms, which prevent physical as well as social disorder, foster further violations of the same and of different norms. Varying the net gains from deviance it shows that disorder effects are limited to low cost situations. Moreover, we provide suggestive evidence that disorder effects are significantly stronger in neighborhoods with high social capital.
A WIRELESS MULTI-ACCESS SECURITY SYSTEM USING REAL-TIME FACE RECOGNITION TECHNIQUE

The security systems have been widely used in many applications, where the trend nowadays is to build such systems that are characterized by accuracy, high speed and low complexity. This work proposes an access control system with multi-entrance using the process of identifying human faces, where the system comprises two access nodes and a control unit. The features of detected faces at each node are wirelessly sent to the control unit for verification. The proposed face recognition method investigates the aim of data reduction and fast classification. The facial features are extracted using a multi-level discrete wavelet transform, which in turn achieves the first reduction in the manipulated data. The second stage of data reduction is implemented by calculating the mean of training images for each individual to construct one training vector instead of using multi-vector array. For identification, the similarities between the probe and global images are computed using Euclidean distance. The simulation results of the proposed face recognition method reach the best accuracy rates, whereas the real-time recognition achieves an accuracy of about 91.46 %.
Anomaly Detection of Uncertain Data Using Novel Filter Based Classification

Rapid growth of high speed network leads the risk of credit-card attacks on the complex networks are also increases accordingly. Anomaly discovery from the database is a process of filtering uncertain features, so that it can be used wide variety of applications. Anomaly detection on the complex data must take a long time due to the large number of features. A large number of anomaly prediction models have been implemented in the literature, unfortunately some anomaly detection models over data mining cannot cover all the normal or abnormal features. Traditional approaches mainly focus on detecting relevant patterns from the trained data in order to estimate the test data instances. In this proposed work, the robust partition based classifier is implemented to find the topmost anomalies using attribute relationships. This model efficiently detects the anomaly features along with uncertain features with high true positive rate.