MobiFuzzyTrust: An efficient fuzzy trust inference mechanism in mobile social networks

PublishedJournal Article© 2014 IEEE. Mobile social networks (MSNs) facilitate connections between mobile users and allow them to find other potential users who have similar interests through mobile devices, communicate with them, and benefit from their information. As MSNs are distributed public virtual social spaces, the available information may not be trustworthy to all. Therefore, mobile users are often at risk since they may not have any prior knowledge about others who are socially connected. To address this problem, trust inference plays a critical role for establishing social links between mobile users in MSNs. Taking into account the nonsemantical representation of trust between users of the existing trust models in social networks, this paper proposes a new fuzzy inference mechanism, namely MobiFuzzyTrust, for inferring trust semantically from one mobile user to another that may not be directly connected in the trust graph of MSNs. First, a mobile context including an intersection of prestige of users, location, time, and social context is constructed. Second, a mobile context aware trust model is devised to evaluate the trust value between two mobile users efficiently. Finally, the fuzzy linguistic technique is used to express the trust between two mobile users and enhance the human's understanding of trust. Real-world mobile dataset is adopted to evaluate the performance of the MobiFuzzyTrust inference mechanism. The experimental results demonstrate that MobiFuzzyTrust can efficiently infer trust with a high precision.This work was partly supported by the National Nature Science Foundation of China under grant 61201219 and the EU FP7 CLIMBER project under Grant Agreement No. PIRSES-GA-2012-318939

A tensor-based approach for big data representation and dimensionality reduction

PublishedJournal Article© 2013 IEEE. Variety and veracity are two distinct characteristics of large-scale and heterogeneous data. It has been a great challenge to efficiently represent and process big data with a unified scheme. In this paper, a unified tensor model is proposed to represent the unstructured, semistructured, and structured data. With tensor extension operator, various types of data are represented as subtensors and then are merged to a unified tensor. In order to extract the core tensor which is small but contains valuable information, an incremental high order singular value decomposition (IHOSVD) method is presented. By recursively applying the incremental matrix decomposition algorithm, IHOSVD is able to update the orthogonal bases and compute the new core tensor. Analyzes in terms of time complexity, memory usage, and approximation accuracy of the proposed method are provided in this paper. A case study illustrates that approximate data reconstructed from the core set containing 18% elements can guarantee 93% accuracy in general. Theoretical analyzes and experimental results demonstrate that the proposed unified tensor model and IHOSVD method are efficient for big data representation and dimensionality reduction

An optimized computational model for multi-community-cloud social collaboration

PublishedCommunity Cloud Computing is an emerging and promising computing model for a specific community with common concerns, such as security, compliance and jurisdiction. It utilizes the spare resources of networked computers to provide the facilities so that the community gains services from the cloud. The effective collaboration among the community clouds offers a powerful computing capacity for complex tasks containing the subtasks that need data exchange. Selecting the best group of community clouds that are the most economy-efficient, communication-efficient, secured, and trusted to accomplish a complex task is very challenging. To address this problem, we first formulate a computational model for multi-community-cloud collaboration, namely MG3. The proposed model is then optimized from four aspects: minimizing the sum of access cost and monetary cost, maximizing the security-level agreement and trust among the community clouds. Furthermore, an efficient and comprehensive selection algorithm is devised to extract the best group of community clouds in MG3. Finally, the extensive simulation experiments and performance analysis of the proposed algorithm are conducted. The results demonstrate that the proposed algorithm outperforms the minimal set coverings based algorithm and the random algorithm. Moreover, the proposed comprehensive community clouds selection algorithm can guarantee good global performance in terms of access cost, monetary cost, security level and trust between user and community clouds

Altered auditory processes pattern predicts cognitive decline in older adults: different modalities with aging

BackgroundCohort studies have shown that older adults with hearing impairment as assessed by self-report or behavioral measures are at higher risk of developing dementia many years later. A fine-grained examination of auditory processing holds promise for more effective screening of older adults at risk of cognitive decline. The auditory mismatch negativity (MMN) measure enables one to gain insights into the neurobiological substrate of central auditory processing. We hypothesized that older adults showing compromised indexes of MMN at baseline would exhibit cognitive decline at the one-year follow-up.MethodsWe performed cognitive evaluations with the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS; Form A and Form B) in 108 community-dwelling older adults and acquired EEG via the classic passive auditory oddball paradigm at baseline and 12-month follow-up.ResultsThe results showed that young-old adults with future cognitive decline showed a decrease in MMN peak amplitude, accompanied by a forward-shifting latency, whereas in older adults it showed a delay in MMN latency, and unchanged MMN peak amplitude at midline electrodes (Fz, FCz and Cz). Furthermore, the peak amplitude of the MMN decreases with age in older adults aged 70–80 years rather than 60–70 years or > 80 years.ConclusionThe altered MMN model exists in different aging stages and it’s a promising electrophysiological predictor of cognitive decline in older adults. In addition, further research is needed to determine the neural mechanisms and potential implications of the accelerated decline in MMN in older adults

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.Comment: 27 pages, 18 figures. International Journal of Digital Earth (2020

Modelling graded sediment transport and bed evolution in a tidal harbour

This paper presents the development of a sediment transport model to predict the bed evolution processes in estuarine and coastal waters. The model is based on an existing hydrodynamic and sediment transport model, with significant refinements being made to enhance its capability for simulating the transport of graded sediments under nonequilibrium conditions. A multifraction sediment transport model has been developed to replace the existing single-fraction model. The sediment mixture is divided into several fractions according to the grain size. For each fraction, the particle size is considered to be uniform, and its transport form, either as suspended load or bed load, is determined by the magnitude of a suspension index. A bed evolution model is developed to simulate the processes of bed level change and sediment grain size sorting. The model is applied to a laboratory model harbour, for which measurements of tidal currents and bed level inside the harbour are available. The water level, velocity distributions, and bed level changes predicted by the numerical model are compared with the laboratory data. Comparisons are also made between predictions made by the fractional model and the single-size model. The effect of bed sediment size change on the erosion process has been investigated

Predicting near-field dam-break flow and impact force using a 3D model

A three-dimensional (3D) numerical model based on the unsteady Reynolds equations was used to simulate near-field dam-break flows and estimate the impact force on obstacles. The model employs a projection method to solve the governing equations and the method of volume of fluid (VOF) to capture the water surface movement. The model is first applied to simulate two physical model experiments of dam-break flows. Model-predicted pressure, water depth and velocity distributions are compared with laboratory measurements. For the second case, the 3D-VOF model predictions are also compared with predictions made by a two-dimensional model. The 3D-VOF model is then used to calculate the impact force of dam-break flow on a steady obstacle. A physical model experiment is set up to assist the numerical model study. The model-predicted impact force on the obstacle and the critical condition for it to move are compared with the measurements from the experiment

Dietary Habits and Depression in Community-Dwelling Chinese Older Adults: Cross-Sectional Analysis of the Moderating Role of Physical Exercise

Background: Healthy diets and physical exercise, two modifiable lifestyle factors, are protective against depression in older adults. This study aimed to investigate whether physical exercise may influence the associations of dietary habits with depression in Chinese community-dwelling older adults. Methods: In the 2018 wave of the Chinese Longitudinal Healthy Longevity Survey, 12,708 community-dwelling older adults aged ≥65 years were included for analyses. Older adults’ dietary habits (including daily intake of food components such as fruits, vegetables, animal oil, and so on) and physical exercise were assessed. Depression was evaluated via the 10 item Center for Epidemiologic Studies Depression (CES-D-10) scale. The influences of physical exercise on the associations of dietary habits with depression were estimated using logistic regression models adjusted for confounders. Results: Older adults who took physical exercise had a significantly decreased probability of depression (adjusted OR = 0.73, p p-values: from p < 0.001). When stratified by physical exercise, older adults who ate fruits or vegetables had consistent decreased risk of depression, no matter whether they took physical exercise or not (adjusted ORs = 0.52–0.70), while the intake of eggs, nut products, and vitamins were inversely associated, and animal oil was consistently positively associated with depression only in older adults who did not take physical exercise (adjusted ORs = 0.79, 0.68, 0.63, and 1.67, respectively). Conclusions: Physical exercise may conceal the potential protective effects of some healthy dietary habits in terms of depression and counteract the detrimental effects of the unhealthy habits. Some dietary habits may be considered as alternative protective measures for depression in community-dwelling older adults when physical exercise cannot be performed

Meta-Analysis of the Effects of Organic Chromium Supplementation on the Growth Performance and Carcass Quality of Weaned and Growing-Finishing Pigs

Many factors influence the effects of exogenous organic chromium (EO-Cr) on the growth performance and carcass qualities of weaned and growing-finishing pigs, such as pig growth stages, types of EO-Cr, period of supplementation, and farm management. However, it is challenging to comprehensively consider all factors in one study. To solve this problem, we searched all relative literature published from 1 January 2000 to 1 January 2023, to systematically analyze and review the effects of EO-Cr on pig growth performance and carcass qualities via meta-analysis. Thirty-five papers were filtered and analyzed, which involved 4366 pigs. The results showed that, for weaned piglets, EO-Cr diets significantly increased the average daily gain (ADG, p p = 0.022) but reduced the feed–gain ratio (p = 0.004). In addition, for growing-finishing pigs, EO-Cr supplementation significantly increased the ADG (p p = 0.020), and loin muscle area (p p = 0.071), feed–gain ratio (p = 0.692), dressing percent (p = 0.989), or back fat thickness (p = 0.142). Moreover, the effect of EO-Cr was greater in weaned piglets than in growing-finishing pigs. In terms of the dose effect of the supplement, chromium nicotinate is the most suitable EO-Cr type for weaned piglets with an optimal dosage range of 0.125–0.150 mg/kg. On the other hand, chromium picolinate is the most suitable EO-Cr type for growing-finishing pigs with an optimal dosage range of 0.250–0.300 mg/kg. In conclusion, EO-Cr supplementation is beneficial for enhancing the growth performance and carcass qualities of both weaned and growing-finishing pigs