Miracle-dz/UniFAR-FTUs huggingface dataset:
Utilization comparison of small-cell accommodation with PON-based mobile fronthaul

The network between the baseband units (BBUs) and the remote radio heads (RRHs) in a centralized radio access network (C-RAN) architecture is called the mobile fronthaul (MFH). A passive optical network (PON) is expected to reduce the deployment cost of MFH by sharing the optical fibers. Using system-level simulation results as a basis, we report that MFH downlink utilization can be improved with PON, regardless of the functional splitting point between BBUs and RRHs. We then clarify the application range of PON-based architectures, which represents the number of small cells that a PON system can accommodate with a certain amount of user equipment and certain data rates.
Productivity and Export Market Participation: Evidence from Colombia

We study the relationship between total factor productivity (TFP) and exporting decisions for Colombian manufacturing firms during 2005-2013. We find that productivity increases a firm's probability of being an exporter, and that exporters have higher productivity, with a premium as high as 85 percent. These findings are robust to several TFP measures. Moreover, we find that not all exporters are equal: firms that export continuously, that export a greater number of products, and/or that export to a larger number of destinations tend to be more productive. We do not find, however, any relationship between productivity and the type of destination or exported product. Finally, we find evidence that future exporters have an ex ante productivity advantage, and (weaker) evidence of TFP increasing after a firm becomes an exporter.
PL-SIM : A Generic Simulation Model for Studying Strategic SPI in the Automotive Industry

Triggered by the seminal work of Watts Humphrey [5], much effort has been invested for assessing and improving software process capability and technology during the last decade. Although initial research has been done (e.g., [4]), precise knowledge about the effects that specific SPI measures have on the performance of a specific software development organisation is still a difficult issue that needs further investigation and experimentation. Within DaimlerChrysler (embedded) software development plays an important role due to the increasing amount of software in cars. In order to constantly improve the development of software, various activities have been initiated like experience-based process improvement [10] or the goal question metrics method [11]. During the last decade, several authors have stressed the potential of simulation as an analysis and decisionsupport tool for software managers (e.g., [2][7][8][9]). It is tempting to adopt simulation modelling to help managers explore their SPI strategies by analysing the (potential) effectiveness of key SPI measures on organisational performance. The specific purpose of the simulation model presented in this paper is to support DaimlerChrysler Research and Technology in conducting a study that aims at exploring the level of process leadership (PL) achievable by one of DaimlerChrysler's car divisions
Leaders, Followers, and Laggards: Adoption of the Us Conference of Mayors Climate Protection Agreement in California

Limited quantitative research has been devoted to voluntary climate actions at the local level. It is unclear why some cities act as leaders in the fight against climate change, some act as followers, while others remain laggards. This study tests hypotheses about local climate proactiveness to mitigate climate change. Applying a survival analysis to California cities' adoption of the US Conference of Mayors Climate Protection Agreement, I examine the association between cities' adoption of the agreement, a range of cities' characteristics, and the behavior of neighboring jurisdictions. The results suggest important roles of the overall capacity of a local jurisdiction, its staff capacity with relevant expertise, average household income level, and voter preferences. Study results also suggest the importance of local traffic conditions and the positive peer effect.
Theta and Gamma Coordination of Hippocampal Networks during Waking and Rapid Eye Movement Sleep

Rapid eye movement (REM) sleep has been considered a paradoxical state because, despite the high behavioral threshold to arousing perturbations, gross physiological patterns in the forebrain resemble those of waking states. To understand how intrahippocampal networks interact during REM sleep, we used 96 site silicon probes to record from different hippocampal subregions and compared the patterns of activity during waking exploration and REM sleep. Dentate/CA3 theta and gamma synchrony was significantly higher during REM sleep compared with active waking. In contrast, gamma power in CA1 and CA3–CA1 gamma coherence showed significant decreases in REM sleep. Changes in unit firing rhythmicity and unit-field coherence specified the local generation of these patterns. Although these patterns of hippocampal network coordination characterized the more common tonic periods of REM sleep (∼95% of total REM), we also detected large phasic bursts of local field potential power in the dentate molecular layer that were accompanied by transient increases in the firing of dentate and CA1 neurons. In contrast to tonic REM periods, phasic REM epochs were characterized by higher theta and gamma synchrony among the dentate, CA3, and CA1 regions. These data suggest enhanced dentate processing, but limited CA3–CA1 coordination during tonic REM sleep. In contrast, phasic bursts of activity during REM sleep may provide windows of opportunity to synchronize the hippocampal trisynaptic loop and increase output to cortical targets. We hypothesize that tonic REM sleep may support off-line mnemonic processing, whereas phasic bursts of activity during REM may promote memory consolidation.
Localized policy-based target tracking using wireless sensor networks

Wireless Sensor Networks (WSN)-based surveillance applications necessitate tracking a target's trajectory with a high degree of precision. Further, target tracking schemes should consider energy consumption in these resource-constrained networks. In this work, we propose an energy-efficient target tracking algorithm, which minimizes the number of nodes in the network that should be activated for tracking the movement of the target. We model the movement of a target based on the Gauss Markov Mobility Model [Camp et al. 2002]. On detecting a target, the cluster head which detects it activates an optimal number of nodes within its cluster, so that these nodes start sensing the target. A Markov Decision Process (MDP)-based framework is designed to adaptively determine the optimal policy for selecting the nodes localized with each cluster. As the distance between the node and the target decreases, the Received Signal Strength (RSS) increases, thereby increasing the precision of the readings of sensing the target at each node. Simulations show that our proposed algorithm is energy-efficient. Also, the accuracy of the tracked trajectory varies between 50% to 1% over time.
Estimation of Travel Time Distribution and Travel Time Derivatives

With the complexity of transportation systems, generating optimal routing decisions is a critical issue. This thesis focuses on how routing decisions can be made by considering the distribution of travel time and the associated risk. More specifically, the routing decision process is modeled in a way that explicitly considers the dependence between travel time among different links and the risk associated with the volatility of travel time. Furthermore, the computation of this volatility allows for the development of the travel time derivative, which is the financial derivative based on travel time. It serves as a value pricing (also known as congestion pricing) scheme that is based on not only the level of congestion but also its uncertainties. In addition to the introduction (Chapter 1), the literature review (Chapter 2), and the conclusion (Chapter 6), the thesis consists of two major parts: In the first part (Chapters 3 and 4), the travel time distribution for transportation links and paths, conditioned on the latest observations, is estimated to enable routing decisions based on risk. Chapter 3 sets up the basic decision framework by modeling the dependent structure between the travel time distributions for nearby links using the copula method. In Chapter 4, the framework is generalized to estimate the travel time distribution for a given path using Gaussian copula mixture models (GCMM). To explore more information from fundamental traffic conditions, a scenario-based GCMM is studied: A distribution of the path scenario (systematic factor) is defined first; the dependent structure between constructing links in the path is modeled as a Gaussian copula for each scenario, and the scenario-wise path travel time distribution can be obtained based on this copula. The final estimates are calculated by integrating the scenario-wise path travel time distributions over the distribution of the path scenario. In a discrete setting, it is a weighted sum of these conditional travel time distributions. The property of the scenario-based GCMM is studied when the number of scenarios changes. Furthermore, general GCMM are introduced for better finite scenario performance, and extended expectation-maximum algorithms are designed to estimate the model parameters, which introduces an innovative copula-based machine learning method. iii In the second part (Chapter 5), travel time derivatives are introduced as financial derivatives based on road travel time a non-tradable underlying asset and a more flexible alternative for value pricing. The chapter addresses (a) the necessity of introducing such derivatives (that is, the demand for hedging), (b) the market and design of the product, and (c) the pricing schemes. The pricing schemes are designed based on the travel time data from loop detectors, which are modeled as mean reverting processes driven by diffusion processes. The no-arbitrage principle is used to generate the price.
Mining Spatial Trends by a Colony of Cooperative Ant Agents

Large amounts of spatially referenced data has been aggregated in various application domains such as geographic information systems (GIS), environmental studies, banking and retailing, which motivates the highly demanding field of spatial data mining. So far many optimization problems have been better solved inspired by the foraging behavior of ant colonies. In this paper we propose a novel algorithm for the discovery of spatial trends as one of the most valuable and comprehensive patterns potentially found in a spatial database. Our algorithm applies the emergent intelligent behavior of ant colonies to handle the huge search space encountered in the discovery of this knowledge. We apply an effective greedy heuristic combined with the trail intensity being laid by ants using a spatial path. The experimental results on a real banking spatial database show that our method has higher efficiency in performance of the discovery process and in the quality of trend patterns discovered compared to other existing approaches using non-intelligent heuristics.
Heparanase Activates Antithrombin through the Binding to Its Heparin Binding Site

Heparanase is an endoglycosidase that participates in morphogenesis, tissue repair, heparan sulphates turnover and immune response processes. It is over-expressed in tumor cells favoring the metastasis as it penetrates the endothelial layer that lines blood vessels and facilitates the metastasis by degradation of heparan sulphate proteoglycans of the extracellular matrix. Heparanase may also affect the hemostatic system in a non-enzymatic manner, up-regulating the expression of tissue factor, which is the initiator of blood coagulation, and dissociating tissue factor pathway inhibitor on the cell surface membrane of endothelial and tumor cells, thus resulting in a procoagulant state. Trying to check the effect of heparanase on heparin, a highly sulphated glycosaminoglycan, when it activates antithrombin, our results demonstrated that heparanase, but not proheparanase, interacted directly with antithrombin in a non-covalent manner. This interaction resulted in the activation of antithrombin, which is the most important endogenous anticoagulant. This activation mainly accelerated FXa inhibition, supporting an allosteric activation effect. Heparanase bound to the heparin binding site of antithrombin as the activation of Pro41Leu, Arg47Cys, Lys114Ala and Lys125Alaantithrombin mutants was impaired when it was compared to wild type antithrombin. Intrinsic fluorescence analysis showed that heparanase induced an activating conformational change in antithrombin similar to that induced by heparin and with a KD of 18.81 pM. In conclusion, under physiological pH and low levels of tissue factor, heparanase may exert a non-enzymatic function interacting and activating the inhibitory function of antithrombin.
Impact of Bug Reporter's Reputation on Bug-Fix Times

Software Analytics is gaining momentum as a result of involved empirical research in enhancing quality and productivity of software engineering activities. There have been rigorous research efforts in the areas of bug prediction and testing effort prediction by making use of historical data. The problem of predicting bug fix times is an interesting problem with lots of advantages to industry but there have been limited efforts to solve this problem. We introduce a new feature, the score of bug reporter, to predict bug fix time. The prediction accuracies of proposed model built with the new feature along with the other prominent features are found to be better than existing models.
What's the point? Frame-wise Pointing Gesture Recognition with Latent-Dynamic Conditional Random Fields

We use Latent-Dynamic Conditional Random Fields to perform skeleton-based pointing gesture classification at each time instance of a video sequence, where we achieve a frame-wise pointing accuracy of roughly 83%. Subsequently, we determine continuous time sequences of arbitrary length that form individual pointing gestures and this way reliably detect pointing gestures at a false positive detection rate of 0.63%.
Using MALDI-TOF Mass Spectrometry to Identify Drug Resistant Staphylococcal Isolates from Nonhospital Environments in Brunei Darussalam

Drug resistant bacteria have been a growing threat to the community and hospitals due to the misuse of antibiotics by humans, industrialization, and lack of novel antimicrobials currently available. Little is known about the prevalence of drug resistant bacteria in nonhealthcare environments in Brunei Darussalam and about how antibiotic resistant genes are transferred within these environments. Human contact points from different types of environments in Brunei Darussalam, varying from urban to jungle settings, were swabbed and cultured onto selective media to isolate staphylococci bacteria before performing antimicrobial susceptibility testing on the isolates. The identity of the isolates was determined using MALDI-TOF mass spectrometry (MS). Staphylococci isolates resistant to oxacillin were further tested for their minimum inhibitory concentration (MIC). PCR analysis of the mecA gene, a gene that confers resistance to oxacillin, is done to determine the level of resistance to oxacillin. Ten different staphylococcal species were identified by MALDI-TOF-MS analysis. Out of the 36 staphylococci isolates, 24 were resistant to multiple antibiotics including two isolates which were oxacillin resistant. Some staphylococci isolates had similar antibiotic resistance profiles to other staphylococci isolates of different species in the same location. This work provides the first-ever evidence of drug resistant staphylococci in the nonhospital environment in Brunei Darussalam.
A review and comparative analysis of the Russian Federation Government Quality Award

Purpose – The paper aims to present a review and comparison of the Russian Federation Government Quality Award (RFGQA) with the three major business excellence models, Malcolm Baldrige National Quality Award (MBNQA), European Foundation for Quality Management (EFQM) Award and Deming Prize. Design/methodology/approach – The paper briefly reviews the RFGQA through a desk-top research method. Then, it adapts the comparative approach used in a similar study by Vokurka et al. (2000). Thus, the comparative analysis consisted in contrasting two characteristics of the RFGQA with those of the MBNQA, EFQM Award and Deming Prize, namely, award descriptors (i.e. objectives and criteria) and emphasis placed on excellence criteria (i.e. weighting). The study also includes a mapping assessment to explore up to what extent the RFGQA addresses the criteria of the major models. Findings – Although the RFGQA was designed based on the concept and structure of the EFQM model, the results of the study indicate that there are s.
2 Phases of development from Spaghetti Code to Component Based Software Development

The Component Based Software Development (CBSD) is the new answer in the field of Software Engineering to the challenge of creating large software systems.Systems developed this way are more stabil, they have better quality, they are cheaper and meet better the requirements. This paper discusses the role and importance of Component Based Software Development in the evolution of Software Methodology, and it analyzes the special aspects of Software Engineering training in Higher Education.
Ray based modelling of indoor channels for capacity evaluation

An image based ray-tracing approach is utilized to predict the capacity of a multiple input multiple output (MIMO) indoor wireless communication system. The results are compared to measured capacity and path loss values. The sensitivity of the spectrum efficiency estimation to physical propagation parameters such as electric permeability and conductivity of the transmission and reflection walls is demonstrated.
Improved layered space time architecture over quasi-static fading channels with unequal power allocation and multistage decoding

The Bell labs layered space-time (BLAST) architecture pioneered by Foschini was found to achieve high spectral efficiency with moderate complexity. However, the redundancy in error correcting codes in BLAST receiver is not used in detection improvement, as detection and decoding are carried out separately. In this paper, we investigate a new approach for improved performance of BLAST based on multi- stage decoding (MSD) and unequal transmit power allocation among layers, for transmission over flat quasi-static Rayleigh fading channels. The use of MSD exploits the inherent redundancy in the employed channel codes to improve detection in BLAST without the need for complex iterative decoding approaches. In addition, we investigate unequal transmit power allocation among layers for transmission over flat quasi-static Rayleigh fading channels. We first derive a theorem for power allocation that maximizes outage capacity. We then find the unequal power allocation required to guarantee equal outage capacities among layers in BLAST combined with MSD detection. The proposed power allocation simplifies implementation and improves error performance. Simulation results show that the proposed architecture significantly outperforms existing BLAST schemes in terms of error performance for transmission over flat quasi-static Rayleigh fading channels.
Resource-Based Regions, the Dutch Disease and City Development

This paper examines the relationship between resource development and industrialization. When transport costs are high, regions with more valuable natural resources offer higher welfare than other regions. However, when transport costs decrease, firms begin to move out of the region, resulting in the Dutch disease, initially in terms of industry shares, but eventually in terms of welfare too when transportation is sufficiently free. If resource goods are also used as manufacturing inputs as well as final goods, they can substitute for labor when wages rise, which tends to alleviate the Dutch disease by keeping production costs down. The model thus provides helpful insight for cities trying to develop efficiently their limited resources.
The effect of search mode on dimension weighting

In a visual feature search task, reaction times to a singleton target are known to be shorter when participants have advance knowledge of the defining-features of targets. The present study examined whether the prior-knowledge effect is influenced by search modes (feature vs. singleton). In addition, using a variant of the flanker task, the present study assessed whether prior-knowledge affected efficiency of attentional focusing to a target. When participants performed a target discrimination task (i.e., compound search task), using a singleton detection mode, no prior-knowledge effect was found (Experiments 1 and 3). However, when the same task was performed using a feature search mode, prior-knowledge facilitated performance (Experiment 2). This suggests that the dimension weighting of a target-defining feature is modulated by the search mode. Also flanker response congruency was affected by prior-knowledge suggesting that the dimension weighting correlated with attentional focusing to targets. On the other hand, inter-trial dimensional priming was not affected by the search mode. Implications for mechanisms of feature-based top-down control of attention in visual feature search are discussed.
Development of network management system for network services provider

Network management system (NMS) is important both in ensuring the correct operation of network devices and in maintaining the services that run on them. However, the relentless growth of DSL's users, meaning that IP traffic volumes nearly double every two years, renders real-time monitoring and analysis of every customer's service parameter is a very challenging problem. The most important problem is the bottleneck in centralized NMS which most of services provider use as SNMP-based system. This paper presents a new model which will be able to ease the problem from bandwidth consumption of SNMP-based by two main methods. Firstly, to ease the problem from bandwidth consumption of SNMP-based, this model will use the probabilistic data structure to decrease the number of packets of monitoring network device's parameter. Secondly, to ease the problem from the relentless growth of the number of the network's devices, this model is designed to support distributed network devices' operations in a distributed NMS fashion. Meaning that, the number of NMS's devices can be increased with the number of network's devices if it is necessary. By using both of two methods, customer expectations with bandwidth requirements and reliability requirements can be improved with this model.
On the Simulation of BER Performances for Transform Schemes Used in OFDM

Due to many special characteristics of OFDM it has been widely applied in various applications. Besides exponential transforms some other transforms like DCT and Wavelet can also be used in OFDM. Since last decade DCT is going to be a good alternative as it is a real transform hence less complex. In very recent scenario telecommunication industry has been attracted towards the wavelets. Wavelet applications are finding a way in which their performances match the current system for example in communication system. In this paper Computer simulation using MATLAB 7.0 has been used to analyze the performance of FFT, DCT and WPT OFDM in terms of bit error rate. All the simulations have been done using AWGN and Rayleigh channel. Along with it mapping schemes used in these simulations are BPSK, QPSK and Pi/4 DQPSK.