Miracle-dz/UniFAR-FTUs huggingface dataset:
Araştirma Makalesi / Research Article ANALYTIC HIERARCHY PROCESS FOR SUPPLIER SELECTION PROBLEM IN SUPPLY CHAIN MANAGEMENT: CASE STUDY OF A TEXTILE MANUFACTURER FIRM

Supply Chain Management (SCM) offers some business solutions to companies in various sectors by increasing customer service while minimizing costs. Supply chains for suppliers, manufacturers, distribution centers, resellers, customers or any other members differs from each other depending on their business functions, goals, types of organizations etc. Thus, the definition of supply chain varies from one company to another. But in general, SCM can be defined as a complex business relations network that contains synchronized efforts among these business entities to plan, control, coordinate and distribute raw materials, parts, subassemblies and finished goods from suppliers to ultimate customers. It can be strongly claimed that, the most important objective of SCM is to ensure production and delivery of products at the right diversity, with the right amounts, at the right time and to the right locations. In order to achieve this aim, some strategic decisions have to be made such as finding number of plants (manufacturing facilities, distribution centers, cross-docks etc.) to be opened, defining location of these plants, choosing suppliers to use (called Supplier Selection Problem), determining transportation times and modes, and creating supply chain strategies. In this study, supplier selection problem in SCM is considered with the help of Analytic Hierarchy Process. A case study in a textile manufacturer is presented.
A Feasibility Study of Bandwidth Smoothing on the World-Wide Web Using Machine Learning ; CU-CS-879-99

The bandwidth usage due to HTTP traffic often varies considerably over the course of a day, requiring high network performance during peak periods while leaving network resources unused during off-peak periods. We show that using these extra network resources to prefetch web content during off-peak periods can significantly reduce peak bandwidth usage without compromising cache consistency. With large HTTP traffic variations it is therefore feasible to apply “bandwidth smoothing” to reduce the cost and the required capacity of a network infrastructure. In addition to reducing the peak network demand, bandwidth smoothing improves cache hit rates. We calculate the potential reduction in bandwidth for a given bandwidth usage profile, and show that a simple hueristic has poor prefetch accuracy. We then apply machine learning techniques to automatically develop prefetch strategies that have high accuracy. Our results are based on web proxy traces generated at a large corporate Internet exchange point and data collected from recent scans of popular web sites.
Some considerations on the cryogenic calibration technique for microwave and millimeter wave ground-based radiometry

This manuscript describes the cryogenic calibration technique applied to the MP3000A radiometer, which employs a liquid nitrogen (LN2) cooled target as a temperature primary calibration standard (black body). This instrument has recently been deployed in different parts of Brazil as part of the CHUVA Project field campaigns. Taking into account that a reliable calibration is critical for data retrieval and analysis, a system calibration was performed at 22 - 30 GHz (K band) and 51 - 59 GHz (V band) frequency ranges emphasizing the systematic evaluation of the technique. In order to obtain good results for a number of atmospheric parameters, the brightness temperature observation errors raised by the radiometer should be below 0.5 K. The results show that the errors and deviations in the brightness temperatures measurements associated to the LN2 calibration are considered satisfactory. However, further studies must be carried out in order to better understand all the effects involved in the retrieval of atmospheric parameters and also to outline system precision enhancements.
POD-1/Tcf21 overexpression reduces endogenous SF-1 and StAR expression in rat adrenal cells

During gonad and adrenal development, the POD-1/capsulin/TCF21transcription factor negatively regulates SF-1/NR5A1expression, with higher SF-1 levels being associated with increased adrenal cell proliferation and tumorigenesis. In adrenocortical tumor cells, POD-1 binds to the SF-1 E-box promoter region, decreasing SF-1 expression. However, the modulation of SF-1 expression by POD-1 has not previously been described in normal adrenal cells. Here, we analyzed the basal expression of Pod-1 and Sf-1 in primary cultures of glomerulosa (G) and fasciculata/reticularis (F/R) cells isolated from male Sprague-Dawley rats, and investigated whether POD-1 overexpression modulates the expression of endogenous Sf-1 and its target genes in these cells. POD-1 overexpression, following the transfection of pCMVMycPod-1, significantly decreased the endogenous levels of Sf-1 mRNA and protein in F/R cells, but not in G cells, and also decreased the expression of the SF-1 target StAR in F/R cells. In G cells overexpressing POD-1, no modulation of the expression of SF-1 targets, StAR and CYP11B2, was observed. Our data showing that G and F/R cells respond differently to ectopic POD-1 expression emphasize the functional differences between the outer and inner zones of the adrenal cortex, and support the hypothesis that SF-1 is regulated by POD-1/Tcf21 in normal adrenocortical cells lacking the alterations in cellular physiology found in tumor cells.
Drug Use among Street Children in Tehran, Iran: A Qualitative Study

Introduction and objective Globally, children who work and live on the streets are at higher risk of undesired behavioral health outcomes, including increased drug use and abuse. Considering the rapid growth of this population in Iran and the lack of program planning that is partly due to a scarcity of research-based information, this study was conducted in 2013 to investigate drug use among street children in Tehran. 1Method With a qualitative design, we conducted a Rapid Assessment and Response Survey of street children in Tehran, 2012–2013. Data were also obtained from 10 focus group discussions with street children using semi-structured questionnaires and 27 in-depth interviews with key informants in governmental, non-governmental, and international organizations. Results The variation in age at first use, type of drugs, and pattern of drug use were found based on ethnicity. Consuming of Alcohol was reported to be more among an ethnic group. Drug use problems were commonly described among families of street children. Children whose parents had drug-use problems described using drugs earlier than other children. Informants reported that families with drug-related problems used children for procurement of drugs. Children themselves described using drugs to cope with stress, and to reduce physical and psychological stressors and problems, such as fatigue, sadness, and pressure, resulting from frequent failures in life. Conclusion These results suggest that intervention and prevention programs dealing with drug use of street children in Iran should include family and peers when addressing drug use by street children.
An Efficient Method of License Plate Location

This paper presents a novel approach of license plate location. The proposed algorithm involves the following three steps. First, the vertical edges of the vehicle image are extracted by Sobel operator. Second, HSV color space and integral image are employed to locate candidates in yellow license plates and non-yellow license plates. Finally, connected component analysis is to locate the region of license plate accurately. Experimental results on a large volume of natural-scene vehicle plate image sets, which are extracted from low-quality video sequences, demonstrate that our technique achieves a verification rate of around 95% on yellow license plates and 99% on non-yellow ones. The total time of processing one yellow image is less than 0.1s and the non-yellow one is less than 0.05s, meeting the requirements of real-time application.
Finite-state Scriptural Translation

We use robust and fast Finite-State Machines (FSMs) to solve scriptural translation problems. We describe a phonetico-morphotactic pivot UIT (universal intermediate transcription), based on the common phonetic repository of Indo-Pak languages. It is also extendable to other language groups. We describe a finite-state scriptural translation model based on finite-state transducers and UIT. We report its performance on Hindi, Urdu, Punjabi and Seraiki corpora. For evaluation, we design two classification scales based on the word and sentence accuracies for translation system classifications. We also show that subjective evaluations are vital for real life usage of a translation system in addition to objective evaluations.
Frequency-Dependent Recruitment of Fast Amino Acid and Slow Neuropeptide Neurotransmitter Release Controls Gonadotropin-Releasing Hormone Neuron Excitability

The anteroventral periventricular nucleus (AVPV) is thought to play a key role in regulating the excitability of gonadotropin-releasing hormone (GnRH) neurons that control fertility. Using an angled, parahorizontal brain slice preparation we have undertaken a series of electrophysiological experiments to examine how the AVPV controls GnRH neurons in adult male and female mice. More than half (59%) of GnRH neurons located in the rostral preoptic area were found to receive monosynaptic inputs from the AVPV in a sex-dependent manner. AVPV stimulation frequencies <1 Hz generated short-latency action potentials in GnRH neurons with GABA and glutamate mediating >90% of the evoked fast synaptic currents. The AVPV GABA input was dominant and found to excite or inhibit GnRH neurons in a cell-dependent manner. Increasing the AVPV stimulation frequency to 5–10 Hz resulted in the appearance of additional poststimulus inhibitory as well as delayed excitatory responses in GnRH neurons that were independent of ionotropic amino acid receptors. The inhibition observed immediately following the end of the stimulation period was mediated partly by GABAB receptors, while the delayed activation was mediated by the neuropeptide kisspeptin. The latter response was essentially absent in Gpr54 knock-out mice and abolished by a Gpr54 antagonist. Together, these studies show that AVPV neurons provide direct amino acid and neuropeptidergic inputs to GnRH neurons. Low-frequency activation generates predominant GABA/glutamate release with higher frequency activation recruiting release of kisspeptin. This frequency-dependent release of amino acid and neuropeptide neurotransmitters greatly expands the range of AVPV control of GnRH neuron excitability.
Input-Specific GABAergic Signaling to Newborn Neurons in Adult Dentate Gyrus

Adult neurogenesis is the multistage process of generating neurons from adult neural stem cells. Accumulating evidence indicates that GABAergic depolarization is an important regulator of this process. Here, we examined GABAergic signaling to newly generated granule cells (GCs) of the adult mouse dentate gyrus. We show that the first synaptic currents in newborn GCs are generated by activation of GABAA receptors by GABA with a spatiotemporal profile suggestive of transmitter spillover. However, the GABAergic response is not attributable to spillover from surrounding perisomatic synapses. Rather, our results suggest that slow synaptic responses in newborn GCs are generated by dedicated inputs that produce a relatively low concentration of GABA at postsynaptic receptors, similar to slow IPSCs in mature GCs. This form of synaptic signaling drives robust phasic depolarization of newborn GCs when the interneuron network is synchronously active, revealing a potential mechanism that translates hippocampal activity into regulation of adult neurogenesis via synaptic release of GABA.
ANN based detection of Breast Cancer in mammograph images

Breast cancer is one of the most familiar cancer which is found in women worldwide and about 25% of women in the world are affected by this cancer. The aim of this present work is to determine Breast cancer in mammography images using artificial neural network. Microcalcification are first enhanced based on their illumination and non regularity. Then by the use of an iterative threshold selection method, microcalcification is located. This includes rebuilding the shapes of micro calcification and removing the isolated pixels which is done by employing the mathematical technique. In this paper the significant Breast Cancer Feature are extracted and determined. Using these significant features, the Breast cancer mammography images are analyzed.
Planning for Data Mining Tool ( PDM ) Extended Abstract

We present a tool, PDM (Planning for Data Mining), based on Automated Planning that helps users (non necessarily experts on data mining) to perform DM (Data Mining) tasks. The starting point is a definition of the DM task to be carried out and the output is a set of plans that are executed in a DM tool to obtain a set of models and statistics. Plans are data-mining knowledge flows, i.e. sequences of DM actions that should be executed over the initial datasets to obtain the final models. However, the number of feasible plans that solve the same DM task is huge making necessary to rank them by some criterion. In a first approach, the ranking is performed following some expert estimations on the desired mining-results of the DM actions. Afterwards, these estimations are improved using machine learning techniques. In order to define the DM task, we use emerging standards, such as PMML (Predictive Model Markup Language). PMML is the leading standard for statistical and DM models and supported by over 20 vendors and organizations. With PMML, it is straightforward to develop a model on one system using one application and deploy the model on another system using another application. The PMML file is automatically translated into a planning problem described in PDDL2.1. So, any state-of-the art planner can be used to generate a plan (or plans), i.e. the sequence of DM actions that should be executed over the initial dataset to obtain the final model. Each plan or knowledge flow is executed by a machine learning engine. In our case, we employ one of the most used DM tools, WEKA (Witten and Frank 2005). In WEKA, knowledge flows are described as files with a specific format, KFML, and datasets are described as ARFF (AttributeRelation File Format) files. The results of the DM process can be evaluated, and new plans may be requested to the planning system.
Green Information Technology Audit and Digitalization in Small Medium Enterprise (SME): Factors That Influence Intentions to Use Hotel Websites

According to Chung and law (2003); Jeong et al (2003); Jeong and Lambert (2001) and Kim et al. (2003), information satisfaction is the most important requirement of online customers' purchases decision making. This need remains largely unmet despite the growing importance of e-commerce within the hospitality industry. According to Kim et al. (2005), the changing trend in the business activities is largely attributable to the fast and improved developments in information and telecommunications. As a result, Chung and Law, (2003) noted that the Internet is also helping to drive down overhead costs for the hospitality industry and cost of information for the customers, as the traditional method of communication is slowly being phased out. Similarly Kim et al (2005) argue that the Internet gives the customers more advantages by allowing them to obtain valuable information such as prices and hotel facilities without the need of getting into contact with any sales agents. In addition, the Internet provides the customers with numerous supplies allowing customers to access a pool of products and services information from which they can make choices and compare prices.
Assessment of Communication in Children With Autism Spectrum Disorder in South India: Influence of Environment

Assessment of communication skills in children with autism spectrum disorder (ASD) is challenging in an unfamiliar clinical environment due to their limited verbal output and inadequate motivation to communicate. To analyze whether the communication sample recorded at clinic represents the child’s competence and performance, this study compared caregiver–child interaction in 24 to 48 months old children with ASD (n = 10, M = 38.2 months) at clinic and home. The 30-minute caregiver–child interaction at clinic and home was video recorded and analyzed for frequency of pragmatic acts (initiations and responses) and duration of joint engagement. Results indicated that children initiated and responded more at home than at clinic, whereas caregivers initiated and responded more at clinic. The study provides evidence that communication profile of children with ASD in multiple environments needs to be considered for obtaining representative and reliable communication sample for child-centered assessment and intervention.
Building occupancy maps with a mixture of Gaussian processes

This paper proposes a new method for occupancy map building using a mixture of Gaussian processes. We consider occupancy maps as a binary classification problem of positions being occupied or not, and apply Gaussian processes. Particularly, since the computational complexity of Gaussian processes grows as O(n3), where n is the number of data points, we divide the training data into small subsets and apply a mixture of Gaussian processes. The procedure of our map building method consists of three steps. First, we cluster acquired data by grouping laser hit points on the same line into the same cluster. Then, we build local occupancy maps by using Gaussian processes with clustered data. Finally, local occupancy maps are merged into one by using a mixture of Gaussian processes. Simulation results will be compared with previous researches and provided demonstrating the benefits of the approach.
Personalised trails: How machines can learn to adopt their behaviour to suit individual learners.

This chapter discusses how the trails concept can be applied in providing personalised learning, with a focus on the use of trails in adaptive e-learning systems which adjust their behaviour in order to better cater for individual learners' needs. The first half of the chapter looks at some of the technological and theoretical issues posed by personalised trails: We present a generic architecture for personalisation which can potentially be deployed on a variety of hardware configurations, and then look at learner profiling, a key aspect of personalisation. Learner profiles store the information about learners that is used to provide personalisation, so the structure and content of the learner profile used by a system has implications for the kinds of personalisation and support for trails that can be provided. In this context we see how Semantic Web technologies can potentially be used to integrate disparate user profile specifications. In the second half of the chapter we look at the practicalities and practice of personalising trails in the context of e-learning. After a brief general discussion of some techniques that can be used to provide personalisation we use examples of real systems to demonstrate the potential for providing personalised trail support that currently exists. As well as systems that provide trails for learners our examples include adaptive hypertext techniques which provide a kind of trail support through adaptive presentation; systems that provide adaptive feedback to learners and which put learners in direct control of the adaptivity; social navigation systems and mobile computing systems. We conclude with some thoughts about the future of this burgeoning research area.
An XMM-Newton view of the extended "filament" near the cluster of galaxies Abell 85

We have observed with XMM-Newton the extended 4 Mpc filament detected by the ROSAT PSPC in the neighbourhood of the cluster of galaxies Abell 85. We confirm that there is an extended feature, aligned at the same position angle as the major axis of the central cD, the bright cluster galaxies, and nearby groups and clusters. We find that the X-ray emission from the filament is best described by thermal emission with a temperature of ∼2 keV, which is significantly lower than the ambient cluster medium, but is significantly higher than anticipated for a gas in a weakly bound extended filament. It is not clear whether this is a filament of diffuse emission, a chain of several groups of galaxies, or stripped gas from the infalling south blob.
Quantifying the efficiency of river regulation

Abstract. Dam-affected hydrologic time series give rise to uncertainties when they are used for calibrating large-scale hydrologic models or for analysing runoff records. It is therefore necessary to identify and to quantify the impact of impoundments on runoff time series. Two different approaches were employed. The first, classic approach compares the volume of the dams that are located upstream from a station with the annual discharge. The catchment areas of the stations are calculated and then related to geo-referenced dam attributes. The paper introduces a data set of geo-referenced dams linked with 677 gauging stations in Europe. Second, the intensity of the impoundment impact on runoff times series can be quantified more exactly and directly when long-term runoff records are available. Dams cause a change in the variability of flow regimes. This effect can be measured using the model of linear single storage. The dam-caused storage change Δ S can be assessed through the volume of the emptying process between two flow regimes. As an example, the storage change Δ S is calculated for regulated long-term series of the Lulealven in northern Sweden.
Effects of Aerobic Exercise Intensity on Abdominal and Thigh Adipose Tissue and Skeletal Muscle Attenuation in Overweight Women with Type 2 Diabetes Mellitus

Background We investigated the effects of exercise intensity on abdominal and mid-thigh adipose tissue, attenuation of skeletal muscle, and insulin sensitivity in overweight women with type 2 diabetes mellitus (T2DM). Methods Twenty-eight patients were randomly assigned to control (CG, n=12), moderate intensity exercise (MEG, n=8), or vigorous intensity exercise (VEG, n=8) group. Subjects in both exercise groups completed a 12-week exercise program (MEG, 3.6 to 5.2 METs; VEG, ≥5.2 METs) that was monitored by accelerometers. We assessed body mass index (BMI), total fat area (TFA), visceral fat area (VFA), subcutaneous fat area (SFA), mid-thigh intramuscular adipose tissue (TIMAT), total skeletal muscle (TTM), low density skeletal muscle (TLDM), and normal density skeletal muscle (TNDM) using computed tomography, and measured insulin sensitivity with an insulin tolerance test (KITT), before and after the intervention. Results At baseline, the mean age was 53.8±7.9 years, duration of diabetes was 3.8±2.3 years, and BMI was 26.6±2.6 kg/m2. After 12 weeks, the percent change (%C) in BMI, TIMAT, and TLDM were not different among three groups. However, %C in TFA and VFA were significantly reduced in MEG compared to CG (P=0.026 and P=0.008, respectively). %C SFA was significantly reduced in VEG compared to CG (P=0.038) and %C TTM, TNDM, and KITT were significantly increased in VEG compared to the CG (P=0.044, P=0.007, and P=0.016, respectively). Conclusion Although there was no difference in the change in BMI among groups, TFA and VFA were more reduced in MEG, and only VEG increased TTM, TNDM, and insulin sensitivity compared to CG.