Probability Of User Research Articles

Energy Scheduling for Networked Microgrids With Co-Generation and Energy Storage

This paper proposes an online algorithm for energy storage management in networked microgrids (MGs) with co-generation based on the concept of quality-of-service in electricity (QoSE). The concept of networked MG with distributed renewable energy supply and co-generation makes power supply smarter for electricity/heat using, which has advantages of increasing power supply efficiency and reliability by coordinately scheduling the power supply in a networked way. The demands include quality usage of electricity load and heat. The networked MG central controller aims to minimize the operation cost and guarantee the outage probability of quality usage, i.e., QoSE, by scheduling electricity among renewable energy sources, energy storage systems, co-generation, and external utility market. We formulate the problem as a stochastic programming problem with QoSE and battery capacity constraints. By introducing the QoSE virtual queues and energy storage virtual queues, we transform the original problem into a problem that is applicable to employ the Lyapunov optimization technique. The proposed algorithm is an online algorithm with low complexity for practical implementation, and also provides several deterministic performance bounds. We perform extensive simulations to demonstrate the effectiveness of the proposed algorithm, which exhibits significant efficiency on operation cost reduction compared with an alternative benchmark solution.

The Impact of Post-Mastectomy Chest-Wall Radiation on Overall Survival for Intermediate-Risk Breast Cancer Patients: A National Cancer Data Base Analysis

The recommendation of post-mastectomy radiation therapy (PMRT) to breast cancer women with intermediate risk disease still requires a great deal of clinical judgment without a single, validated formula. We aimed to assess the impact of PMRT on overall survival (OS) among patients with breast cancer considered intermediate risk for local recurrence by querying the National Cancer Data Base (NCDB) 2004-2014 for breast cancer. We replicated the SUPREMO phase III clinical trial by including patients (age >=18) with pT1-2N1 (or pT2N0 with either histologic Grade = 3 or presence of LVI) who underwent total mastectomy and did not receive neoadjuvant therapy. PMRT had a total radiation dose 40-70 Gy and was given within 6 months after surgery. OS was defined as months from the date of surgery. Logistic regression was applied to assess the pattern of PMRT utilization. Cox proportional hazard model was used for the association with OS. Propensity score (PS) overlap weighting was implemented to balance all observed baseline characteristics and to eliminate the selection bias. The effect of PMRT in the subgroups was estimated through the multivariable model (MVA) with interactions. We obtained 35,244 eligible subjects with a median follow up of 65.5 months. The median age was 57, 82.6% were white, 55.6% had 1 positive lymph node (PLN), 43.7% of tumors were pT1, the median tumor size was 2.2 cm, 38.4% had Grade III-IV, 96.2% had a negative surgical margin, and 68.3% received adjuvant chemotherapy. 4841 (13.7%) received PMRT while 30,403 (86.3%) received no radiation (NR). There was a clearly increasing trend of PMRT utilization over the years. In MVA by logistic regression, the factors associated with a higher probability of PMRT usage include PLN =2-3, present of LVI, pT2, and younger age. The 10-year survival rate was 72.1% (PMRT) vs. 68.7% (NR) by KM method. In MVA with OS by controlling for baseline demographic and disease characteristics, the hazard ratio (HR) for PMRT vs. NR was 0.81 (95%CI: 0.74-0.89), and it was 0.84 (95%CI: 0.74-0.95) by the PS weighting approach. In the subgroups, the HR was 0.75 (95%CI: 0.64-0.88) among patients with PLN = 3, 0.69 (95%CI: 0.59-0.81) among women not receiving adjuvant chemotherapy, 0.68 (95%CI: 0.60-0.77) among women with age > 60, and 0.72 (95%CI: 0.58-0.90) among patients whose tumors exhibited LVI. In this large retrospective study based on NCDB, on average, PMRT provided significant long-term survival benefit among intermediate risk breast cancer patients, with a larger survival benefits noted amongst patients with 3 PLN, age > 60, or the presence of LVI. More informative guidance for the utilization of PMRT may require additional information such as disease specific survival or recurrence (which is unavailable in NCDB), and we are looking forward to the results from the SUPREMO trial.

Can Information Technology Promote Academic Performance of School-aged Children? An Empirical Study Based on CEPS

It is inconclusive whether the information technology can promote academic performance of school-aged children. Based on the China Education Panel Survey data provided by the NSRC, we used the method of quasi-experimental of propensity score matching to discuss the net effect of information technology on academic achievements. We found that: i) A significant difference existed in the academic performance between urban and rural school-aged children. The urban students were superior to those from the rural areas, whereas no positive correlation was found between the educational level and family economic condition; ii) Family human capital stock and family size (single child families) were positively correlated with internet usage, but the academic pressure reduced the probability of family Internet usage; iii) PSM revealed the negative impact of information technology on students’ achievements when stratified into urban versus rural, and poverty versus non-poverty. The improvement of Chinese and English was realized indirectly by changing reading habits, while mathematics was directly by taking effective learning time. Our findings implied that a three-party linkage among the government, the school, and the family is required to emphasize the instrumental rationality of the information technology, promote the formation of the Internet learning preference, and support the development of the information technology hardware.

Design of a conflict-detection air traffic control tool for the implementation of continuous climb operations: A case study at Palma TMA

Continuous climb operation is an operational concept that allows airlines to perform an optimal departing trajectory avoiding air traffic control segregation requirements. This concept implies the design and integration of air traffic flows for the sake of safety performance. This paper designs a new conflict-detection air traffic control tool based on the blocking-area concept, characterises the conflict probability between air traffic flows and assesses the impact of continuous climb operation integration in a terminal manoeuvring area. In this paper, a conflict is set out by the infringement of vertical and longitudinal separation minima and coincides with the probability of air traffic control tool usage. Moreover, this research discusses two different approaches for the conflict-detection air traffic control tool: a static approach considering nominal continuous climb operations and landing trajectories, and a dynamic approach that assesses 105 continuous climb operations and landing trajectories. Finally, the air traffic control tool is implemented using Palma TMA data and proves that out of 11 intersections (between departing and landing routes), solely 4 generate vertical separation infringements. The conflict probability between continuous climb operations and arrivals is less than 10−5. Except for one intersection, that is roughly 10−2, similar to current air traffic control intervention designed levels. Therefore, results conclude the viability of the conflict-detection air traffic control tool and continuous climb operations integration.

Can the English stand the bottle like the Dutch? Effects of relational categories on object perception

ABSTRACT Does language influence how we perceive the world? This study examines how linguistic encoding of relational information by means of verbs implicitly affects visual processing, by measuring perceptual judgements behaviourally, and visual perception and attention in EEG. Verbal systems can vary cross-linguistically: Dutch uses posture verbs to describe inanimate object configurations (the bottle stands/lies on the table). In English, however, such use of posture verbs is rare (the bottle is on the table). Using this test case, we ask (1) whether previously attested language-perception interactions extend to more complex domains, and (2) whether differences in linguistic usage probabilities affect perception. We report three nonverbal experiments in which Dutch and English participants performed a picture-matching task. Prime and target pictures contained object configurations (e.g., a bottle on a table); in the critical condition, prime and target showed a mismatch in object position (standing/lying). In both language groups, we found similar responses, suggesting that probabilistic differences in linguistic encoding of relational information do not affect perception.

Bandwidth Allocation Based on Personality Traits on Smartphone Usage and Channel Condition

How to allocate resources in the era of Big Data in telecommunications becomes a new issue. Smartphone data could be a function of personality, as the smartphone supports interpersonal interaction, and the data collected from the smartphone usage often contains rich customer opinion and behavioral information. A bandwidth allocation method based on smartphone users' personality traits and channel condition is studied in a unified mathematical framework in this paper. Personalizing bandwidth allocation could be done by analyzing smartphone users' personality traits, resulting in business intelligence, a smarter and more efficient usage of the limited bandwidth, while taking channel fading conditions into account. Using the diagnostic inference, the service provider could calculate the user's probability of having each personality trait stand on its data usage. One step further, its bandwidth usage of the following period can be predicted using predictive inference. For our proposed bandwidth allocation scheme, both the outage capacity and outage probability are studied in fading channel. Therefore, the service providers shall better allocate the limited bandwidth, provide more personal service to each user, and adjust the bandwidth allocation further on account of the real channel condition.

Aversion to a sure loss: turning investors into gamblers

In situations where there is a lack of relevant information for proper decisionmaking, investors tend to be ambiguity-averse, opting for the probabilities with predictable outcomes. In cases where investors are faced with options without known probabilities, they tend to choose an option that will guarantee the most negligible loss. When faced with a diminutive loss, the investors will try to avoid these losses, even if that means exposing more resources to potential losses. Average down investment strategy can help to overcome losses faster while risking large amounts of resources, thus, showing investors’ propensity for loss-aversion. However, stock trading under ambiguity is not rational behavior, and is closer to gambling than investing, even when faced with losses. The choice of average down as investment strategy can explain the effect that dominates during investment decision-making process when losses occur due to ambiguity. This paper presents the results of a research conducted in order to determine if investors will use average down strategy when there is a lack of information on the market, and the size of the loss when they will be willing to use it. Obtained results show strong probability of average down usage among the subject group, thus proving aversion to a sure loss. The relation between risk aversion and aversion to a sure loss is tested. The results show that there is no influence of risk aversion on choice to use average down investment strategy. The results and possible applications of the research are thoroughly discussed.

An Adaptive, Data-Driven Personalized Advisor for Increasing Physical Activity.

In recent years, there has been growing interest in the use of fitness trackers and smartphone applications for promoting physical activity. Many of these applications use accelerometers to estimate the level of activity that users engage in and provide visual reports of a user's step counts. When provided, most recommendations are limited to popular general health advice. In our study, we develop an approach for providing data-driven and personalized recommendations for intraday activity planning. We generate an hour-by-hour activity plan that is based on the user's probability of adhering to the plan. The user's probability of adherence to the plan is personalized, based on his/her past activity patterns and current activity target. Using this approach, we can tailor notifications (e.g., reminders, encouragement) to each user. We can also dynamically update the user's activity plan at mid-day, if his/her actual activity deviates sufficiently from the original plan. In this paper, we describe an implementation of our approach and report our technical findings with respect to identifying typical activity patterns from historical data, predicting whether an activity target will be achieved, and adapting an activity plan based on a user's actual performance throughout the day.

Receptivity to eHealth Services in the Hungarian Population of Mureş County, Romania

Abstract One of the most dynamically evolving sectors of our days is eHealth. More and more applications, software, devices, etc. are launched that make healthcare segments accessible not only for professionals but for laics, too. This study examines to what extent the adult Hungarian population of Mureş County is ready to become eHealth participative, i.e. an active agent of its own healthcare attendance, in order to make use of the advantages offered by modern technologies that provide information and help understand our diseases, their prevention, and health maintenance. The probability of eHealth usage is approximated by the frequency of Internet usage. Social determinants of info-communication tool usage are assessed, controlling for covariates. Age has the strongest impact upon the frequency of Internet usage. The younger is the respondent, the more likely he/she is to be a frequent user, and, apart from this, only the educational level determines Internet use, higher education implying more frequent usage.

Minimization of Residential Energy Cost Considering Energy Storage System and EV With Driving Usage Probabilities

Electric vehicle (EV) can be applied to discharge power back to the grid, which is called vehicle-to-grid (V2G) technology. There is a constant debate on whether V2G is an economically viable option due to the high battery degradation cost. In this work, the cost benefit of EV customers participating in V2G has been studied using different feed-in tariffs (FITs). A model is developed for minimization of energy cost for residential users, which includes an EV, a separate energy storage system (ESS), and renewable energy supply. Key factors such as the EV driving usage, the degradation cost of EV and ESS batteries are considered. The EV driving usage is established through a designed survey, from which the probability of vehicle parking and plug in at home, the probabilities of EV under driving and parking outside can be calculated. Comprehensive case studies have been undertaken to investigate the optimization strategies under various scenarios. Two types of electricity tariffs, time-of-use (TOU) and fixed tariffs, are considered. It is revealed that certain threshold levels of FITs are expected to allow users benefit from V2G. Compared with nonoptimized operation, the cost saving with the optimized strategy is evident in the case studies.

Revisiting form typicality of nouns and verbs

Abstract Research has shown that, in English, the mapping between a word’s form and its syntactic category is not entirely arbitrary. Though formal differences between lexical categories are subtle, adults are sensitive to them and access this knowledge when retrieving or manipulating grammatical category information. Studies of form typicality have so far exclusively investigated unambiguous (or disambiguated) wordforms. We test the prediction that form typicality also affects visual processing of ambiguous wordforms, with formal features correlating, not with a form’s designation as a particular category, but with a form’s probability of being used as a particular category. Our results indicate that “form discrepancy”, a measure of how well a form’s category usage matches up with its form (i.e. typically nouny forms associated with high probability of usage as a noun), is a significant predictor of lexical decision response time. These data are in line with models in which category is not specified for roots in the lexicon but rather assigned within syntactic or semantic context, and show that distributional information about grammatical category usage is automatically accessed in visual word processing.

High efficient detoxification of mustard gas surrogate based on nanofibrous fabric

In recent years, people pay more attention to the protection against chemical warfare agents, due to the increase in the probability of usage of these chemical warfare agents in wars or terrorist attacks. In this work, MgO nanoparticles were in-situ growth on the surface of poly(m-phenylene Isophthalamide) (PMIA) forming a flexible and breathable fabric for the detoxification of mustard gas surrogate. The as-prepared nanofibrous membrane possesses a “flower-like” structure of which endows not only increase the specific surface area of the composite but also prevent the agglomeration of the MgO nanoparticles. The detoxification ability of the PMIA@MgO nanofibrous fabric was demonstrated by gas chromatography-mass spectrometer (GC–MS). It is found that after 20 h of reaction time, 70.56% of the mustard gas surrogate have been decomposed.

Link-Layer Cooperation Based on Distributed TDMA MAC for Vehicular Networks

Cooperative medium access control (MAC) protocols have been proposed for improving communication reliability and throughput in wireless networks. In our previous work, a cooperative MAC scheme called Cooperative ADHOC MAC (CAH-MAC) has been proposed to increase the network throughput under a static networking scenario for vehicular communications. In this paper, we study the effects of relative mobility among nodes and channel fading on the performance of CAH-MAC. In a dynamic networking environment, system performance degrades due to cooperation collisions. To tackle this challenge, we present an enhanced CAH-MAC (eCAH-MAC) scheme, which avoids cooperation collisions and efficiently utilizes cooperation opportunities without disrupting the time-slot reservation operations. Through mathematical analysis and computer simulations, we show that eCAH-MAC increases the effectiveness of node cooperation by increasing utilization of an unreserved time slot. Furthermore, we perform extensive simulations for realistic networking scenarios to investigate the probability of successful cooperative relay transmission and usage of unreserved time slots in eCAH-MAC, in comparison with existing approaches.

Every Byte Counts

Quick responses to user actions are instrumental to the success of mobile applications. To ensure such responsiveness, applications often prefetch data objects before the user requests them. This way, applications can avoid the need to retrieve data through slow network connections during user interactions. However, prefetches may also harm. They increase launch delays and might cause substantial amounts of data to be downloaded through energy-hungry, cellular connections. In this paper, we propose EBC, a novel algorithm to schedule application prefetches and overcome their drawbacks. EBC computes application usage probabilities and traffic volume estimates to determine when and for which applications prefetches should be triggered. Thereby, it applies different strategies depending on whether a cellular or Wi-Fi connection is available. We evaluate the performance of EBC on two publicly available, large-scale data sets: LiveLab and Device Analyzer. Our results show that EBC can lower launch delays and ensure freshness of application content. At the same time, it reduces the amount of data downloaded through cellular connections. On the Device Analyzer data set, for instance, EBC achieves a 10% reduction in cellular traffic and a 36% better average freshness with respect to its closest competitor.

Hyperspectral Anomaly Detection via a Sparsity Score Estimation Framework

Anomaly detection has become an important topic in hyperspectral imagery (HSI) analysis over the last 20 years. HSIs usually possess complexly cluttered spectral signals due to the complicated conditions of the land-cover distribution. This in turn makes it difficult to obtain an accurate background estimation to distinguish the anomaly targets. The sparse learning technique provides a way to obtain an implicit background representation with the learned dictionary and corresponding sparse codes. In this paper, we explore the background/anomaly information content for each atom of the learned dictionary, from an analysis based on the frequency of the dictionary atoms for HSI reconstruction. From this perspective, we propose a novel sparsity score estimation framework for hyperspectral anomaly detection. First, an overcomplete dictionary and the corresponding sparse code matrix are obtained from the HSI. The frequency of each dictionary atom for reconstruction, which is also called the atom usage probability, is then estimated from the sparse code matrix. Finally, the estimated frequencies are transformed to the sparsity score for each pixel, which can be seen as the degree of “anomalousness.” In the proposed detection framework, two strategies are proposed to enhance the diversity between the background and anomaly information in the learned dictionary: 1) dictionary-based background feature transformation and 2) dictionary iterative reweighting. A series of real-world HSI data sets is utilized to evaluate the performance of the proposed framework. The experimental results show that the proposed framework achieves a superior performance compared to some of the state-of-the-art anomaly detection methods.

Off-label use of antimicrobials in neonates in a tertiary children's hospital.

Off-label (OL) use of drugs for hospitalized children is very common. OL use occurs especially in the youngest patients, neonates. This study focused on the OL use of antimicrobials in neonates. To our knowledge, only few studies have focused on the prevalence of OL use of antimicrobials in neonates. We investigated the OL use of antimicrobials in neonates in a tertiary children's hospital. First, we investigated what were the most consumed OL antimicrobials in defined daily doses according to hospital's registry data from neonatal intensive care unit (NICU) during 2009-2014. Second, we conducted a targeted retrospective study of premature neonates (400-2000g) with blood culture-positive infections and receiving antimicrobial therapy between 2005 and 2014 (N=282). The data were obtained from the electronic patient records and from the hospital's electronic infection registry. Statistical analysis was conducted by using a univariate logistic regression model fitted for OL usage. In NICU, 35% (7/20) of antimicrobials used were OL. Eighteen percent (51/282) of premature neonates with blood culture-positive infections received at least one antimicrobial OL. The most commonly used OL antimicrobials in neonates were meropenem 88% (45/51), rifampicin 18% (9/51), and ciprofloxacin 8% (4/51). The odds for OL use were significantly higher the smaller the neonate birth weight was. An increase in birth weight was found to statistically significantly decrease the probability of OL usage (odds ratio=0.85 for 100g increase in birth weight, p value <0.001). More studies in neonates on especially dosing and pharmacokinetics of antimicrobials are urgently needed.

Statistical Characteristics of Usage Behavior of Air Conditioners in The University Students’ Dormitories

With the improvement of students’ living standard in Chinese universities, more and more air conditioners have been installed in the students’ dormitories in recent years, and energy use of dormitories presents the rigid growth. Therefore, it is significant for university energy conservation and power planning to understand the characteristics of usage behavior of air conditioners in dormitories. Aiming at this, by taking a comprehensive Chinese University as an example, questionnaire surveys were carried out in the summer of 2016, to understand students’ air-conditioning usage behavior, and 474 questionnaires were collected. Real-time monitoring of the operation of air conditioners and outdoor and indoor temperature were conducted in four rooms of student dormitories from July to October in 2016. The usage characteristics of air conditioners are analyzed. Variance analysis is used to verify the significance of gender, discipline and educational level on the usage of air conditioners. The relationship between the usage probability of air conditioners and the outdoor temperature is further established by Logistic Regression. The results provide data support for energy consumption prediction and power planning of university campuses.

A high reliability and low latency routing algorithm in cognitive wireless mesh networks

A channel utility value computing (CUVC) algorithm is proposed. The channel utility value contains channel usage probability and channel stability. A high reliability and low latency wireless link weight computing algorithm (RL2W) is proposed. On this basis, a high reliability and low latency routing and spectrum allocation algorithm based on dynamic programming in cognitive wireless mesh networks (HRL2A) is proposed. High reliability and low latency route is the objective of HRL2A. Firstly, HRL2A computes the channel utility value using CUVC for constructing the high reliability route. Secondly, HRL2A uses the algorithm RL2W computing wireless link weight. Thirdly, the route of high reliability and low latency is constructed based on dynamic programming, and the wireless link channel is allocated. Simulation results show that HRL2A algorithm can achieve expectation goal. The construction route not only has higher reliability, but also has lower latency. The throughput has been increased.

A high reliability and low latency routing algorithm in cognitive wireless mesh networks

A channel utility value computing CUVC algorithm is proposed. The channel utility value contains channel usage probability and channel stability. A high reliability and low latency wireless link weight computing algorithm RL2W is proposed. On this basis, a high reliability and low latency routing and spectrum allocation algorithm based on dynamic programming in cognitive wireless mesh networks HRL2A is proposed. High reliability and low latency route is the objective of HRL2A. Firstly, HRL2A computes the channel utility value using CUVC for constructing the high reliability route. Secondly, HRL2A uses the algorithm RL2W computing wireless link weight. Thirdly, the route of high reliability and low latency is constructed based on dynamic programming, and the wireless link channel is allocated. Simulation results show that HRL2A algorithm can achieve expectation goal. The construction route not only has higher reliability, but also has lower latency. The throughput has been increased.

ДИНАМИКА ЦИФРОВОГО НЕРАВЕНСТВА В РОССИИ

The aim of the article is to fill in the gap in the studies of digital divide in Russia. Digital divide is a complex phenomenon describing unequal opportunities in access to Internet resources that cannot be explained only by affordability for Internet access or only by user’s personal choice. That is why the article considers a variety of socio-demographic factors affecting the Internet usage. Empirical section of the research is based on repeated cross-sectional data from the «Courier» database and investigates how digital divide is evolving as time goes by (period from July 2011 to November 2013). Binary logistic regression is used as the method of analysis. Statistical models show that common factors of digital inequality such as age, income, education, type of residence are related to both Internet usage in general and the use of Internet as an information source. The findings suggest that gender does not affect the probability of the Internet usage; however, men are more likely to use Internet than women. The dynamic component proves that the digital divide in the Internet use is decreasing but the digital divide in the information-oriented Internet usage is increasing.