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The aim of this study was to investigate the psychological distress and symptoms among disaster rescue workers who involved in the rescue effort in the aftermath of Sichuan earthquake, 2008. The study was based on literature review on previous conducted researches, after the establishment of literature selecting principle, a number of researches was considered to be qualified and thus a thorough scanning and analysis carried out. The findings was analysed and categorized into different themes. The findings of this paper suggested the psychological stress from the disaster scene was multi-dimensional, not only the difficult rescue work stresses the rescue crew, but also the stressors raised from the physical environment as well as individuals' own life statues. The symptoms experienced from sampled rescue workers was summarized into 4 categories: psychosomatic symptoms, conceptual symptoms, emotional symptoms and behavioural symptoms. The implication of this paper were seen to be needed to heighten public's arousal of these potential secondary victims of disaster, and to assist the establish of psychological early stage recognization and intervention for the rescue workers. This study has certain limitations, that is well discussed in the conclusion chapter, and some unanswered questions were suggested for future studies.
The main objective of this thesis is to evaluate the risk of clogging in the leachate recirculation system and to study the methods to reduce the risk. Furthermore, the aim is to study the feasibility of biological anoxic-aerobic processes to pre-treatment of landfill leachate at low-temperature. Importance is placed on the effect of activated sludge process and biofilm process. The experimental part is conducted by using laboratory-scale facilities and leachates from Ämmässuo landfill. The removal of both NH4-N and NOx-N are the major parameters of the effects of the treatment processes. Finally, it is expected to discover an economical and efficient solution on the leachate pre-treatment for leachate recirculation system in Ämmässuo. This solution can supply the leachate recirculation system with safe recirculated leachate and help it to achieve the optimal results. Moreover, the experimental work can also provide the experiences for the future leachate treatment in Ämmässuo, which can reduce landfill leachate impacts on environment and the costs to treat the landfill leachate. Even the leachate pre-treatment for leachate recirculation system and the future leachate treatment in Ämmässuo can be made an overall plan together, because the only difference between these are the requirements for their effluents.
Online co-creation allows companies to leverage external sources of knowledge to sustain product or service innovation. Users’ knowledge is regarded as such a potential source. Understanding user behaviors and innovation types is vital to improving a company’s sustainable innovation. Many prior studies mainly categorized online community members into core and peripheral members based on their posting frequencies. However, little research has gone beyond that categorization and examined whether there may be different types of core members who may contribute to product or service innovation differently, especially in the context of co-creation. The objectives of this study are three-fold: (1) to identify core members of a company-hosted online co-creation community automatically by considering several dimensions of individual members, including posting behavior, the generated content, and social network features; (2) to categorize and compare the contributions of different types of core members in the community, aiming to identify community members who may play leadership roles in sustainable innovation; and (3) to investigate the influence of those different types of core members on other community members. The data collected from a company-hosted online co-creation community in China were analyzed. Through analysis, we developed a novel innovation-oriented topology of core community members consisting of eight types. Based on Practice Theory, we also explored how those different types of core community members may influence other members’ behavior. Finally, based on the findings, we propose strategies and guidelines for practitioners to keep different types of community members actively engaged in online co-creation and to manage sustainable innovation practice better.
The spray cooling enhancement method has consistently been the focus area for research as a highly effective cooling method that can alter the properties of spray media by allowing the addition of different types of additives. In this study, an open spray cooling system was established for experimental purposes. Firstly, the effects of nozzles on the spray cooling characteristics were investigated through four kinds of nozzle experiments. Al2O3-H2O, TiO2-H2O, ZrO2-H2O, and SiO2-H2O nanofluids were chosen as cooling substances based on the optimal nozzles, and the effects of the type and concentration of nanoparticles on cooling performance were studied. Based on the performance of the nanoparticles, sodium dodecyl benzenesulfonate(SDBS) was selected as the surfactant for Al2O3 and TiO2 nanoparticles, while cetyltrimethyl ammonium bromide(CTAB) was selected as the surfactant for ZrO2 and SiO2 nanoparticles. The effects of surfactants with different concentrations on the heat transfer performance of nanofluids were studied. The results showed that when the mass fraction of SiO2 nanoparticles was 0.2% and CTAB was 0.005%, an optimal cooling effect was achieved; which was 5.9% higher than that of water and 1.7% higher than that obtained without CTAB.
Abstract The Hoek–Brown constant mi is a key input parameter in the Hoek–Brown failure criterion developed for estimating rock mass properties. The Hoek–Brown constant mi values are traditionally estimated from results of triaxial compression tests, but these tests are time-consuming and expensive. In the absence of laboratory test data, guideline chart and empirical regression models have been proposed in the literature to estimate mi values, and they give a general trend of mi. Instead of only using either the guideline chart or regression models, information from both sources can be systematically integrated to improve estimates of mi. In this study, a Bayesian approach is developed for probabilistic characterization of mi, using information from guideline chart, regression model and site-specific uniaxial compression strength (UCS) test values. The probabilistic characterization of mi provides a large number of mi samples for conventional statistical analysis of mi, including its full probability distribution. The proposed approach is illustrated and validated using real UCS and triaxial compression test data from a granite site at Forsmark, Sweden. To evaluate the reliability of the proposed method, mi values estimated from the proposed method are compared with those predicted from a separate analysis which uses triaxial compression tests data. In addition, a sensitivity study is performed to explore the effect of site-specific input on the evolution of mi. The approach provides reasonable statistics and probability distribution of mi at a specific site, and the mi samples can be directly used in rock engineering design and analysis, especially in Hoek–Brown failure criterion to predict rock failure.
Spontaneous corrosion and uncontrolled dendrite accumulation of Zn rapidly degrades zinc–metal battery performance. Artificial interfaces have been widely fabricated on Zn metal anodes, yet most interfaces are detrimental to ion transfer and adapt poorly to spatial changes during Zn plating/stripping. Herein, a hybrid interface, consisting of a thermoplastic polyurethane (TPU) fiber matrix and Zn-alginate (ZA) filler, is designed, which serves as a physical barrier between anode and electrolyte to inhibit side reactions. Encouragingly, ZA regulates Zn2+ transport and endows uniform Zn deposition by inducing plating/stripping underneath the hybrid interface. At the same time, the TPU frame acts as a super-elastic constraint to further suppress rampant dendrite evolution and accommodate a large amount of deposited Zn. Consequently, the interface-protected Zn anode delivers high cycling stability (1200 h at 5 mA cm–2/5 mA h cm–2; 500 h at 10 mA cm–2/10 mA h cm–2), realizing an exceptional cumulative capacity of over 6000 mA h cm–2. This enhancement is well maintained in the full cell when coupled with a vanadium-based cathode. The unique matrix-filler architecture and mechanistic insights unraveled in this study are expected to provide a general principle in designing functional interfaces for metal anodes.
The wide-spread overuse and misuse of antibiotics has led to major risks to human health, which demands breakthrough technologies for elimination of antibiotics from water streams. Membrane-based water purification has drawn substantial interest for this purpose. However, high permeance and high antibiotic-removal efficiency remain extremely challenging. In this work, the use of polyphenol-based nanoengineering to functionalize conventional microporous membranes capable of ultrafast removal of ten different antibiotics in an in-line flowthrough purification system is explored. The high adsorption kinetics of these nanocoatings enable a record-high permeance (9,774 L m−2 h−1 bar−1) with exceptional removal rate and efficiency, at a relatively low energy cost (0.09 kWh m−3), even in a real-world wastewater treatment. Molecular dynamics simulations provide detailed insights into the role of polyphenol-based nanocoatings and their multiple molecular interactions with antibiotics. This work provides a promising and sustainable platform for engineering the next-generation adsorption-based membranes for clean water production.
With the advent of big data era, data centers (DCs) related energy use accounts for approximately 3% of the global electric power consumption. As the augmentation of data-processing performance and thermal density in DCs, its energy use will undoubtedly continuously burst. Meanwhile, the potential safety hazards, caused by IT equipment local overheating, threaten the safe operation and restrict the further development of DCs. Thus, efficient cooling approaches should be applied in DCs to ensure its safe operation and optimize its thermal environment and cooling efficiency. Phase change cooling (PCC) technology is regarded as one of the effective and widely-used cooling methods, which have been applied in DCs for several years. In this paper, the up-to-date PCC technologies are reviewed and summarized, as well as the latest progress in DC cooling field. Four main PCC technologies are discussed in this paper, namely independent heat pipe cooling, integrated heat pipe cooling, two-phase immersion cooling, and cold storage systems. Finally, the shortcomings of the current researches on the PCC methods are summarized, while some suggestions for future researches are provided to promote the application of PCC technologies and achieve safe operations and energy savings in data centers.
Unmanned aerial vehicles (UAVs) have been widely applied in various fields, including but not limited to military, industry, and agriculture. But UAVs also confront severe security threats, which slows down their wide application. Current research mainly focuses on the security of a single UAV system, while paying little attention to UAV-related communications, especially in the physical and network layers. Thus, it becomes necessary to summarize potential security threats and corresponding countermeasures. To this end, we study mainstream attacks on UAV communications in order to propose security requirements. Then we present a comprehensive review on existing security countermeasures for enhancing UAV communication security in both the physical and network layers. We conclude with open issues and future prospects of UAV security.
Population aging is a global problem. When people getting older, the physical function is declining. In current China, physical disabled people over 65 account for half of the whole disabled population. More and more elderly people are facing disability. However, the proportion of treatment and recovery is quite low because nurses lack systematic and effective methods to care and help patient and their families. This research aimed to find out health promotion methods to improve the living quality for physically disabled elderly people. The purpose was helping nurses to provide the knowledge and skills to physical disabled elderly people, helping their families to promote physical disabled elderly people's health and improve their living quality. The research was implemented as a literature review. The data was searched using the following two databases: CINAHL (EBSCO) and PubMed. Overall, six articles were chosen to be reviewed. Content analysis was applied in the analysis of the data. Two main categories were summarized: support for the elderly and their family caregivers, exercise and physical support. Result showed that nurses can help physically disabled elderly people to improve their quality of life through these ways. Nurses can choose the methods depend on the environment and patients’ condition.
Population aging is a global problem. When people getting older, the physical function is declining. In current China, physical disabled people over 65 account for half of the whole disabled population. More and more elderly people are facing disability. However, the proportion of treatment and recovery is quite low because nurses lack systematic and effective methods to care and help patient and their families. This research aimed to find out health promotion methods to improve the living quality for physically disabled elderly people. The purpose was helping nurses to provide the knowledge and skills to physical disabled elderly people, helping their families to promote physical disabled elderly people's health and improve their living quality. The research was implemented as a literature review. The data was searched using the following two databases: CINAHL (EBSCO) and PubMed. Overall, six articles were chosen to be reviewed. Content analysis was applied in the analysis of the data. Two main categories were summarized: support for the elderly and their family caregivers, exercise and physical support. Result showed that nurses can help physically disabled elderly people to improve their quality of life through these ways. Nurses can choose the methods depend on the environment and patients’ condition.
Default options are an increasingly common tool used by organizations, managers, and policymakers to guide individuals’ behavior. We wondered whether the known preference for default options could constitute a nudge to achieve more equitable or more efficient results. Combining with event-related potentials, we found that both the default option and distributive justice contributed significantly to decision-making. The N200s and P300s were extracted using the tensor decomposition, which showed superiority in terms of capturing multi-domain features. The results demonstrated that greater brain activity associated with conflict monitoring was elicited in the trade-off between equity and efficiency when the default could not represent a socially desirable action. Besides, participants attached more motivational/affective significance to equitable defaults than inequitable but maybe efficient default options. Further, individuals with larger neural response differences between equitable and inequitable defaults appeared to be more inequity aversion in behavior. These findings offer a novel perspective on the role of default effects on distributive justice, while contributing to both organizational policy and practice by using the default to improve social welfare.
Canonical polyadic decomposition (CPD) of multi-subject complex-valued fMRI data can be used to provide spatially and temporally shared components among groups with both magnitude and phase information. However, the CPD model is not well formulated due to the large subject variability in the spatial and temporal modalities, as well as the high noise level in complex-valued fMRI data. Considering that the shift-invariant CPD can model temporal variability across subjects, we propose to further impose a phase sparsity constraint on the shared spatial maps to denoise the complex-valued components and to model the inter-subject spatial variability as well. More precisely, subject-specific time delays are first estimated for the complex-valued shared time courses in the framework of real-valued shift-invariant CPD. Source phase sparsity is then imposed on the complex-valued shared spatial maps. A smoothed $\ell _{\mathbf {{0}}}$ norm is specifically used to reduce voxels with large phase values after phase de-ambiguity based on the small phase characteristic of BOLD-related voxels. The results from both the simulated and experimental fMRI data demonstrate improvements of the proposed method over three complex-valued algorithms, namely, tensor-based spatial ICA, shift-invariant CPD and CPD without spatiotemporal constraints. When comparing with a real-valued algorithm combining shift-invariant CPD and ICA, the proposed method detects 178.7% more contiguous task-related activations.
Abstract The uniaxial compressive strength (UCS) and Youngs’ modulus (E) of rock are important parameters required during design and stability analysis of mining and geotechnical structures. There is correlation between UCS and E of rock, and the proper estimation of such correlation is important for reliable mining engineering analysis. However, limited quantity of UCS and E data pairs often available for most mining project sites makes it difficult to estimate reliable correlation between UCS and E. This study addresses the difficulty by developing Bayesian approach for characterizing the site-specific joint probability distribution of UCS and E that is data-driven, without the use of an empirical model. A major novelty of the proposed approach over previous studies is that it does not require selection or integration of a regression model as input to characterize the correlation between UCS and E. The likelihood function in the proposed approach is directly constructed from only limited UCS and E data pairs and their prior information as inputs. The Bayesian approach is incorporated into Markov Chain Monte Carlo (MCMC) simulation to generate samples pairs of UCS and E, which are then analysed for marginal statistics, marginal probability distribution, joint probability distribution and correlation. Real data of UCS and E obtained from uniaxial compression tests on migmatites at the Sanandaj-Sirjan zone in Iran is used to demonstrate the approach. The marginal statistics, distributions and correlation coefficient from the proposed approach is consistent with those of the measured data from the adopted site. This indicates that the approach is effective in characterizing the correlation between UCS and E, and can be used when there is need for such characterization at a site with limited data. Simulated data are also used in the approach and the results show that the quality and quantity of information available as inputs play an important role in the efficiency of the characterization by the approach. The hallmark of the proposed approach is that it is data-driven, and practitioners do not need to determine and select an appropriate site-specific regression model to evaluate the correlation between UCS and E of rock.
Nowadays, wireless data connections (2G, 3G and WiFi) have been the main- stream technologies for accessing Internet on modern mobile devices. However, users aware that heavy use of data transmission for web access via wireless interfaces leads battery life drain badly. In order to extend battery life time and improve user experience, we present the solution for offering "energy-efficiency web access on mobile devices". A new compression strategy named selective-compression is introduced as an improvement of traditional HTTP compression in this thesis. The selective-compression strategy can properly handle binaries of web contents. And its mechanism relies on client/remote proxy pair structure. From analysis of the experiment results, we make conclusion that the selective-compression strategy can bring nice benefits for energy saving and delay deduction on mobile devices while accessing web pages that include massive binaries. Furthermore, we give the suggestion to web developers and web service providers about how to create energy-efficient web pages.
Tucker decomposition can provide an intuitive summary to understand brain function by decomposing multi-subject fMRI data into a core tensor and multiple factor matrices, and was mostly used to extract functional connectivity patterns across time/subjects using orthogonality constraints. However, these algorithms are unsuitable for extracting common spatial and temporal patterns across subjects due to distinct characteristics such as high-level noise. Motivated by a successful application of Tucker decomposition to image denoising and the intrinsic sparsity of spatial activations in fMRI, we propose a low-rank Tucker-2 model with spatial sparsity constraint to analyze multi-subject fMRI data. More precisely, we propose to impose a sparsity constraint on spatial maps by using an ℓp norm (0
We study a single-wall carbon nanotube (SWNT) Polyvinyl alcohol (PVA) composite as a saturable absorber (SA) for pulse generation in Yb-doped fiber lasers. The saturable absorption and optical limiting (OL) characteristics of the SWNT device are investigated. By combing these two nonlinear effects, we find out for the first time, to the best of our knowledge, that mode-locking can be obtained in the dissipative soliton regime at low pumping followed by Q-switching at high pumping, which is quite different from conventional pulse dynamic evolutions. The Qswitched state operating at higher pump powers is due to the OL effect. The inverted operating fiber laser can be applied in various potential applications such as versatile material processing, optical communication and radar system etc.
Spatial source phase, the phase information of spatial maps extracted from functional magnetic resonance imaging (fMRI) data by data‐driven methods such as independent component analysis (ICA), has rarely been studied. While the observed phase has been shown to convey unique brain information, the role of spatial source phase in representing the intrinsic activity of the brain is yet not clear. This study explores the spatial source phase for identifying spatial differences between patients with schizophrenia (SZs) and healthy controls (HCs) using complex‐valued resting‐state fMRI data from 82 individuals. ICA is first applied to preprocess fMRI data, and post‐ICA phase de‐ambiguity and denoising are then performed. The ability of spatial source phase to characterize spatial differences is examined by the homogeneity of variance test (voxel‐wise F‐test) with false discovery rate correction. Resampling techniques are performed to ensure that the observations are significant and reliable. We focus on two components of interest widely used in analyzing SZs, including the default mode network (DMN) and auditory cortex. Results show that the spatial source phase exhibits more significant variance changes and higher sensitivity to the spatial differences between SZs and HCs in the anterior areas of DMN and the left auditory cortex, compared to the magnitude of spatial activations. Our findings show that the spatial source phase can potentially serve as a new brain imaging biomarker and provide a novel perspective on differences in SZs compared to HCs, consistent with but extending previous work showing increased variability in patient data.
As a critical enabler of the next generation networks, network slicing can dynamically and flexibly create virtual networks leveraging necessary network resources for services with different quality of service (QoS) requirements. To satisfy the strict QoS requirements of some vertical industries, e.g., industry automation, the Deterministic Network (DetNet) concept has been recently proposed to investigate deterministic service provisioning with bounds on service latency, loss, and jitter. A critical issue for providing deterministic services is to determine the right amount of resources for a network slice to ensure the QoS requirements. In this paper, using the stochastic network calculus (SNC) method, we first obtain the amount of network resource needed for a network slice to ensure an end-to-end latency requirement under specific traffic demands. We then study the network slice deployment problem in an IP-over-WDM (wavelength division multiplexing) metro aggregation networks and propose a heuristic with three different objectives: minimizing traffic hops, minimizing lightpaths and minimizing wavelengths, which can help network service provider to optimize network deployment under different considerations. The simulation results show the comparison of the resource utilization under different strategies.
Mobile crowd sensing (MCS) acts as a key component of Internet of Things (IoT), which has attracted much attention. In an MCS system, participants play an important role, since all the data are collected and provided by them. It is challenging but essential to recruit credible participants and motive them to contribute high-quality data. In this article, we propose a learning-based credible participant recruitment strategy (LC-PRS), which aims to maximize the platform and participants' profits at the same time via MCS participation. Specifically, the LC-PRS consists of two mechanisms, that a learning-based reward allocation mechanism (L-RAM) first calculates the maximum offered reward for different locations based on the number of participants in each location. Under a budget constraint, the proposed L-RAM prefers to collect sensing data from locations in which relatively few data have so far been collected. Furthermore, for each location, we develop a credible participant recruitment mechanism (C-PRM), which employs semi-Markov model and game theory to predict the quality of data provided by each participant and to recruit participants based on the predictions and the maximum offered reward calculated by L-RAM. We formally show LC-PRS has the desirable properties of computational efficiency, selection efficiency, individual rationality, and truthfulness. We evaluate the proposed scheme via simulation using three real data sets. Extensive simulation results well justify the effectiveness of the proposed approach in comparison with the other two methods.