Miracle-dz/UniFAR-FTUs huggingface dataset:
Tier‐aware joint subchannel and power allocation in uplink OFDMA heterogeneous networks

We study the problem of tier‐aware subchannel and power allocation in the uplink of a two‐tier orthogonal frequency‐division multiple access heterogeneous network. We formulate the joint subchannel and power allocation problem in the macro‐tier as an optimization problem that is aware of the existence of the femto‐tier and aims to maximize the sum of tolerable interference caused by femto‐tier on its allocated subchannel(s) subject to the minimum data rate requirements of the macrocell user equipments (MUEs). The resource allocation problem for the macro‐tier is an NP‐hard mixed‐integer nonlinear programming (MINLP) problem. To address it, we reformulate and transform it to a tractable mixed‐integer linear programming (MILP) problem, which is optimally addressed with polynomial‐time complexity. We formulate the joint subchannel and power allocation problem in the femto‐tier as an optimization problem that is aware of the existence of the macro‐tier and aims to maximize the sum rate of the femtocell user equipments subject to the maximum tolerable interference caused to the MUEs. To address this MINLP problem, we transform it to a MILP problem through a linear approximation, which is solved optimally by IBM CPLEX solver. In addition, we develop a distributed and efficient algorithm that addresses this optimization problem suboptimally with a lower computational complexity. Numerical results show that our proposed algorithms outperform the existing algorithms in terms of network sum rate.
Modeling Student Teachers’ Information-Seeking Behaviors While Learning With Network-Based Tutors

Information seeking and acquisition is fundamental to learning from hypermedia, but student teachers often fail to regulate their own learning. A total of 68 students learned with either a static or a dynamic version of nBrowser, a network-based tutor that adapted recommendations of online resources to support information seeking and acquisition. Results indicated that the student teachers’ ratings for the usefulness of online resources can be predicted with 69% accuracy and 77% accuracy on the entire data set using the fully specified model. The learner model is discussed in designing a recommender system that supports information-seeking behaviors in the context of network-based tutoring systems.
Image acquisition and processing of a laser warning system based on planar array CCD

A grating diffraction laser warning system is a type of electro-optic countermeasure equipment based on grating diffraction,it can detect the parameter information of the incident laser in real-time.To get the wavelength and the azimuth of the incident laser,with the grating diffraction principle,the image signal was acquired by a area planar array CCD,the image processing was done by digital signal processor,and the image processing algorithms were exploited.The theory was analyzed and experimental results were verified,the image acquisition and processing of the grating diffraction laser warning system based on area planar array CCD was designed,the detection of the incident laser was realized.The experimental results show that the system can detect and mark the incident laser effectively,the system is stable and real-time.
An Algorithm of General Fuzzy Inference with the Reductive Property

Zadeh’s CRI method is applied in fuzzy reasoning wildly, but it is not reductive. First examples are given to show that Wang Guojun’s Triple I Method and other similarity-based fuzzy reasoning methods do not have the reductive property for multi-rules fuzzy inference. Then an algorithm are proposed of general fuzzy inference with the reductive property.
A Meta Analysis of Factors Affecting Perceived Usefulness and Perceived Ease of Use in The Adoption of E-Learning Systems

The full potential of e-learning, a trend that is of growing importance lately, will not be reaped unless the users fully utilize the system, triggering extensive research to be conducted in order to provide valuable insight on a myriad of variables influencing user acceptance in e-learning systems. The main purpose of the study is to determine the factors that affect the intention of users to use e-learning and to get results which can guide system developers and researchers. In accordance with this purpose, 203 studies investigating the e-learning acceptance of the users through the Technology Acceptance Model (TAM) were found in the literature. In those studies, variables which are suggested to determine Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) and results of related hypotheses are analyzed. Finally, a model is proposed. In this model, the most widely accepted hypotheses, affecting PU and PEOU according to the literature are included in the original TAM. As a result; it determines Self Efficacy-PEOU, Subjective Norm-PU, Self Efficacy-PU, Interaction-PU, Enjoyment-PEOU, Anxiety-PEOU, Enjoyment-PU, Compatibility-PU, Subjective Norm-PEOU and Interaction-PEOU as variables that have statistical significance in users’ PU and PEOU, respectively. Moreover, the study examines the relationship between the User Satisfaction and original TAM variables, and proposes the Acceptance and Satisfaction Model for E-Learning (ASME) as a model to best explain the dependent variables described above.
Location impact on source and linguistic features for information credibility of social media

PurposeSocial media platforms provide a source of information about events. However, this information may not be credible, and the distance between an information source and the event may impact on that credibility. Therefore, the purpose of this paper is to address an understanding of the relationship between sources, physical distance from that event and the impact on credibility in social media.Design/methodology/approachIn this paper, the authors focus on the impact of location on the distribution of content sources (informativeness and source) for different events, and identify the semantic features of the sources and the content of different credibility levels.FindingsThe study found that source location impacts on the number of sources across different events. Location also impacts on the proportion of semantic features in social media content.Research limitations/implicationsThis study illustrated the influence of location on credibility in social media. The study provided an overview of the relationship between content types including semantic features, the source and event locations. However, the authors will include the findings of this study to build the credibility model in the future research.Practical implicationsThe results of this study provide a new understanding of reasons behind the overestimation problem in current credibility models when applied to different domains: such models need to be trained on data from the same place of event, as that can make the model more stable.Originality/valueThis study investigates several events – including crisis, politics and entertainment – with steady methodology. This gives new insights about the distribution of sources, credibility and other information types within and outside the country of an event. Also, this study used the power of location to find alternative approaches to assess credibility in social media.
Fault-Tolerant Oriented Hierarchical Control and Configuration of Modular Multilevel Converter for Shipboard MVdc System

Medium-voltage dc (MVdc) distribution system is considered as the promising power architecture of future shipboard power system. Fault-tolerance ability is of great importance for power system on ships. In this paper, zonal dc–dc conversion system based on modular multilevel converter (MMC) and three-phase bridge rectifier are proposed with its hierarchical fault-tolerant scheme. The topology configurations of the front-end MMC with multiwinding medium frequency transformer under normal and postfault circumstances are presented. The reconfiguration scheme and fault-tolerant control strategy are also proposed to ride-through submodule (SM) level fault, phase level fault and bus level fault. Based on the hierarchical fault-tolerant scheme, redundant configuration of the MMC SM is further analyzed with the hierarchical reliability modeling. The effectiveness of the proposed control strategy is verified by the experimental results.
Coupling Computer Models through Linking Their Statistical Emulators

Direct coupling of computer models is often difficult for computational and logistical reasons. We propose coupling computer models by linking independently developed Gaussian process emulators (GaSPs) of these models. Linked emulators are developed that are closed form, namely normally distributed with closed form predictive mean and variance functions. These are compared with a more direct emulation strategy, namely running the coupled computer models and directly emulating the system; perhaps surprisingly, this direct emulator was inferior in all illustrations. Pedagogical examples are given as well as an application to coupling of real computer models.
ECC Cache: A Lightweight Error Detection for Phase-Change Memory Stuck-At Faults

DRAM scaling has been slowed down. Emerging non-volatile memories (e.g., Phase-Change Memory) promises higher density, better scalability, and persistence. However, endurance is a fundamental issue that hinders the broad adoption of PCM-after repeated writes, a PCM cell can get stuck at a value and be no longer programmable. The prevalence of this stuck-at fault issue requires error detection and correction mechanisms for PCM. Existing solutions such as verify-after-write adds additional latency to PCM writes, which degrades overall system performance. Other solutions like in-memory error-correcting code (ECC) requires a high storage overhead and introduces more reliability issue because ECC bits tend to wear out faster than the protected data bits. In this paper, a novel stuck-at faults detection technique is proposed to improve performance and reliability simultaneously. Since stuck-at faults can only be detected after new writes, ECC does not need to be stored permanently and can be deleted immediately after a one-time detection, which helps to reduce ECC storage overhead. Therefore, this work proposes to use a small on-chip ECC cache to store the temporary ECC entries, which does not suffer from endurance issue. To maximize the utilization of the limited cache space, this work optimizes ECC entry insertion and deletion mechanisms and exploits memory bank-level parallelism to minimize performance impact. For the evaluated workloads, the proposed ECC cache achieves an average of 9.4% of the performance improvement over the baseline with a verify-after-write detection.
The efficiency improvement of port state control based on ship accident Bayesian networks

Ship accident has always been the focus in shipping and it is concerned by port state authorities. This study tries to investigate the impact of various factors on ship accident along with the port state’s detected deficiency items. Very importantly, it manages to identify the structural connections between the checked deficiency items. The data used in this study are mainly from Lloyd’s register of shipping, International Maritime Organization and Tokyo Memorandum of Understanding, with a total of 64,847 observations obtained. The Bayesian network model is employed and the Greedy thick thinning and Bayesian search algorithms are used to learn the structural networks. In addition to the impacts of the deficiency items and the ship inherent attributes on ship accidents, this study identifies some key deficiency items for port states. It also analyzes the intense connections between the key deficiency items with others. This helps simplify the port state’s inspection procedure and improve operational efficiency.
Encouraging Energy Efficiency: Product Labels Activate Temporal Tradeoffs

The current research presents the "10-year energy cost" label as an effective nudge to increase energy efficient choices by retail consumers. Its efficacy is demonstrated in both lab and field studies (Studies 1a and 1b) and compared to other energy labels (Studies 4 and 5). The current work proposes that it is effective partly because many consumers have a latent "long-term dollar cost-reduction" goal. Normally, when consumers purchase energy using products, they do not think about long-term costs. However, the "10-year energy cost" label activates this latent goal in the presence of an appropriate behavioral path to achieving the goal, increasing the proportion of energy efficient choices (Studies 3 and 4). Thus, this nudge is somewhat selective, having its greatest impact on consumers who self-report actively pursuing long-term cost reduction goals (Study 5).
Analysis Of Mppt Techniques Applied To The Dcm Multiphase Boost Converter For The Mitigation Of Partial Shading In Pv Arrays

This work presents the study and implementation of a multi-input dc-dc interleaved boost converter used in solar photovoltaic systems. Initially, a comparative analysis between two popular techniques used to track the maximum power point is performed to demonstrate whether controlling the PV array voltage or current has significant impact on the solar energy conversion into electricity. Due to their popularity, perturb and observe and incremental conductance are chosen and evaluated through simulation tests. Considering that the results obtained with both techniques controlling either the voltage or the current of the modules are similar, incremental conductance is implemented in and dc-dc interleaved boost converter operating in discontinuous current mode. Thus, it is possible to mitigate the partial shading effects on photovoltaic arrays, also reducing size of passive elements and providing fast transient response. Experimental results on a PV system using a digital signal processor are presented to validate the proposed approach. 1
Model-based Parameter Estimation Method for Terahertz Signals

Parameter estimation hasn’t been investigated in terahertz non-destructive testing (NDT) field due to the lack of the prior distribution knowledge of terahertz signals. In this study, a statistical model of terahertz signal is proposed to model the terahertz echo signal. The critical issue mainly focuses on how to obtain the optimal parameter estimation from the complex terahertz echo signal. Therefore, we provide two estimators: the maximum likelihood estimation (MLE) based on expectation maximization algorithm (EM), and the lasso estimation based on the sparse representation. Simulation and experiments have been implemented to analyze the fitting performance of the proposed model. The results validate the effectiveness and applicability of the statistical model, and indicate that the Lasso estimator outperforms the EM estimator for the parameter estimation of terahertz signal, which provides a new statistical distribution model and parameter estimation method for the terahertz signal in terahertz NDT.
A small-gain method for integrated guidance and control in approach and landing phase

The guidance and control systems of reentry vehicles are usually designed separately and then integrated, but the scheme can be argued that synergistic relationships between the two subsystems are not fully exploited. In order to improve the performance of reusable launch vehicles (RLVs), this paper proposes an integrated guidance and control (IGC) law for approach and landing of a RLV. According to the idea of reference-trajectory guidance, the angle of attack and bank angle commands are designed using sliding mode control method to make the reference-trajectory tacking error converge into a small neighborhood of zero. An IGC law is developed utilizing generalized small-gain theorem to enforce the commands, and theoretical analysis shows that the law can guarantee the stability of the overall system. The Monte Carlo simulation confirms the effectiveness of the proposed design approach.
Self-Adaptive Middleware for Model-Based Network Adaptations

Adding self-adaptive capabilities to systems is one way to reduce administrative effort and to cope with changing contexts at runtime. Trends in pervasive computing such as IoT increase the number of devices and the amount of context information which strengthens the need for self-adaptive software. Existing frameworks for building self-adaptive software can help to reduce development effort. However, modeling the reconfiguration behavior of a system including its planned execution context is still a challenging task. It should easily be possible for developers without knowledge about building adaptation logics to specify adaptation possibilities. Further, it would be beneficial for these interconnected devices to support network adaptations as concepts such as SDN can help to change networks at runtime. The goal of our research is to enhance distributed and pervasive systems by developing a middleware for defining network adaptations using a model-based approach. Utilizing such a middleware could help to always achieve the best network performance in distributed settings at runtime.
Regularized fuzzy neural networks based on nullneurons for problems of classification of patterns

This paper presents a learning algorithm for fuzzy neural networks based on nullneurons able to generate accurate and transparent models. The learning algorithm is based on ideas from Extreme Learning Machine, to achieve a low time complexity, and regularization method, resulting in sparse and accurate models. Experiments considering pattern classification are detailed. Results suggest the proposed approach as a promising alternative for pattern recognition with a good accuracy and some level of interpretability.
Danmaku Related to Video Content Facilitates Learning

Massive open online courses are one of the most prominent trends in higher education in recent years. Instructional videos play a significant role in the massive open online courses platform. This study tested the impact of sending danmaku related to video content versus sending danmaku not related to video content versus not sending danmaku in instructional video. We assessed students’ achievement, learning satisfaction, social presence, and cognitive load. Adopting a quasi-experimental design, we collected data from 137 participants. Result revealed that the danmaku related to video content improved social presence, learning satisfaction, and learning achievement but created more cognitive load.
Current Thermal Hydraulic Activities on Sodium-cooled Fast Reactors in Japan

Focusing on key issues on thermal hydraulics of sodium-cooled fast reactors (SFRs), namely, natural circulation decay heat removal, gas entrainment, sodium combustion, and sodium-water chemical reaction, this paper reviews the progress of evaluation methods on them. These issues are identified as safety concerns of SFRs in the s afety design criteria (SDC) and safety design guidelines (SDG) for Generation-IV SFRs. To meet the SDC and SDG, design studies and related researches are being carried out in an SFR development project in Japan. The goal of the thermal hydraulic studies is to assure higher level of safety and reliability along with economic competitiveness with concurrent light water reactors.
EMOTIONAL INTELLIGENCE AND RESILIENCY AMONG ADOLESCENTS

The aim of the present study was to explore the relationship between emotional intelligence and resilience and to examine the gender differences in resilience. Purposive and convenience sampling was employed for data collection, and the final sample size for the study was 109 (males= 56 and females= 53). The data was collected using Resiliency Scale for Children and Adolescents (RSCA) (Prince-Embury, 2006) and Schutte Emotional Intelligence Scale (SEIS) (Schutte et al., 1998). Independent samples t-test was used to compare the means of two groups on resiliency (males and females). Pearson Correlation was used to find the relationship between resiliency and emotional intelligence. The Cronbach alpha reliability was found to be 0.82 and 0.89 for resiliency scale (RSCA) and emotional intelligence scale (SEIS) respectively. Findings indicate that the scales were highly reliable for use on the selected population. There was no significant difference in the resilience of male and female adolescents (t= 0.31, p<.75). There was a moderate positive correlation (0.41) between emotional intelligence and resilience for the combined sample of male and female adolescents. The limitations and implications of the study were discussed.
Analysts ’ Consensus Forecasts and Mispricing

We find that analysts’ earnings forecasting performance is an analyst characteristic persistent over time and across firms. By dividing all analysts into the high and low quality (HQ and LQ) categories based on their forecast accuracy, we are able to generate a forecast measure superior to the consensus. The average forecast of the HQ analysts is more accurate than the consensus, but the market is unaware of this and reacts only to the surprise based on the consensus estimate. We find that the superiority of the HQ analysts comes from their better understanding of market-wide information. Recommendation changes of the HQ analysts predict future industry and market returns, while the consensus recommendation changes do not. Further, the VIX and the PEAD are higher following periods of greater uncertainty among the HQ analysts. Overall, the market inefficiently overweighs the information content of the consensus and does not utilize price-relevant information in the forecasts and recommendations of the HQ analysts. * Michaely is from Cornell University and IDC (rm34@cornell.edu). Rubin is from Simon Fraser University and IDC (arubin@sfu.ca). Segal is from IDC and Singapore Management University (dsegal@idc.ac.il). Vedrashko is from Simon Fraser University (awv@sfu.ca).
ANAEROBIC EXERCISE RESPONSE ON RECOVERY HEART RATE OFFOOTBALL PLAYERS

The aim of the present study was to examine the recovery heart rate response immediately after anaerobic exercise. To achieve the purpose of the study, 15 female football players who attended university coaching camp at Annamalai University, Chidambaram were selected. The age of the subjects ranged between 18 to 25 years as per university record. To assess the variability in recovery heart rate the heart rate was assessed at five different timesfor 30 seconds at 1:00 to 1:30, 2:00 to 2:30, 3:00 to 3:30, 4:00 to 4:30and 5:00 to 5:30 minutes after immediately cessation of 400 meters run. The collected data was analysed using OneWay Repeated Measure ANOVA for comparisons of mean values among five different times of recovery heart rate. When F is significant Scheffe’s test was applied as post hoc test to determine the paired mean difference if any, among different times of the recovery heart rate. The value of 0.05 was set for statistical significance. The results of study showed that there was significant difference in recovery heart rateamong first recovery period,second recovery period, third recovery period, fourth recovery period, fifth recovery period as an acute response to anaerobic exercise.
CEO Political Ideology and Financial Reporting Quality

The study investigates whether CEO political ideology is associated with financial reporting quality. Our evidence indicates that Republican CEOs exhibit lower discretionary accruals and lower likelihoods of beating earnings benchmarks, earnings misstatements and internal control weaknesses, which suggests that Republican CEOs promote higher financial reporting quality, relative to their Democratic and independent counterparts. Additional analyses reveal that firms with Republican CEOs pay lower audit fees, which is consistent with such firms’ perceived lower risk. In sum, our results support Francis et al.’s (2016) risk acceptance hypothesis and imply that the political ideology of CEOs, as captured by their political donations, may have significant influence on their firms’ financial reporting quality.