Adaptive Resource Allocation Algorithms For Data And Energy Integrated Networks Supporting Internet of Things

According to the forecast, there are around 2.1 billion IoT devices connected to the network by 2022. The rapidly increased IoT devices bring enormous pressure to the energy management work as most of them are battery-powered gadgets. What’s more, in some specific scenarios, the IoT nodes are fitted in some extreme environment. For example, a large-scale IoT pressure sensor system is deployed underneath the floor to detect people moving across the floor. A density-viscosity sensor is deployed inside the fermenting vat to discriminate variations in density and viscosity for monitoring the wine fermentation. A strain distribution wireless sensor for detecting the crack formation of the bridge is deployed underneath the bridge and attached near the welded part of the steel. It is difficult for people to have an access to the extreme environment. Hence, the energy management work, namely, replacing batteries for the rapidly increased IoT sensors in the extreme environment brings more challenges. In order to reduce the frequency of changing batteries, the thesis proposes a self-management Data and Energy Integrated Network (DEIN) system, which designs a stable and controllable ambient RF resource to charge the battery-less IoT wireless devices. It embraces an adaptive energy management mechanism for automatically maintaining the energy level of the battery-less IoT wireless devices, which always keeps the devices within a workable voltage range that is from 2.9 to 4.0 volts. Based on the DEIN system, RF energy transmission is achieved by transmitting the designed packets with enhanced transmission power. However, it partly occupies the bandwidth which was only used for wireless information transmission. Hence, a scheduling cycle mechanism is proposed in the thesis for organizing the RF energy and wireless information transmission in separate time slots. In addition, a bandwidth allocation algorithm is proposed to minimize the bandwidth for RF energy transmission in order to maximize the throughput of wireless information. To harvest the RF energy, the RF-to-DC energy conversion is essential at the receiver side. According to the existing technologies, the hardware design of the RF-to-DC energy converter is normally realized by the voltage rectifier which is structured by multiple Schottky diodes and capacitors. Research proves that a maximum of 84% RF-to-DC conversion efficiency is obtained by comparing a variety of different wireless band for transmitting RF energy. Furthermore, there is energy loss in the air during transmitting the RF energy to the receiver. Moreover, the circuital loss happens when the harvested energy is utilized by electronic components. Hence, how to improve the efficiency of RF energy utilization is considered in the thesis. According to the scenario proposed in the thesis, the harvested energy is mainly consumed for uplink transmission. a resource allocation algorithm is proposed to minimize the system’s energy consumption per bit of uplink data. It works out the optimal transmission power for RF energy as well as the bandwidth allocated for RF energy and wireless information transmission. Referring to the existing RF energy transmission and harvesting application on the market, the Powercast uses the supercapacitor to preserve the harvested RF energy. Due to the lack of self-control energy management mechanism for the embedded sensor, the harvested energy is consumed quickly, and the system has to keep transmitting RF energy. Existing jobs have proposed energy-saving methods for IoT wireless devices such as how to put them in sleep mode and how to reduce transmission power. However,they are not adaptive, and that would be an issue for a practical application. In the thesis, an energy-saving algorithm is designed to adaptively manage the transmission power of the device for uplink data transmission. The algorithm balances the trade-off between the transmission power and the packet loss rate. It finds the optimal transmission power to minimize the average energy cost for uplink data transmission, which saves the harvested energy to reduce the frequency of RF energy transmission to free more bandwidth for wireless information

STUDIES TO IMPROVE EXHAUST SYSTEM ACOUSTIC PERFORMANCE BY DETERMINATION AND ASSESSMENT OF THE SOURCE CHARACTERISTICS AND IMPEDANCE OPTIMIZATION

It is shown that the relationship between an impedance change and the dynamic response of a linear system is in the form of the Moebius transformation. The Moebius transformation is a conformal complex transformation that maps straight lines and circles in one complex plane into straight lines and circles in another complex plane. The center and radius of the mapped circle can be predicted provided that all the complex coefficients are known. This feature enables rapid determination of the optimal impedance change to achieve desired performance. This dissertation is primarily focused on the application of the Moebius transformation to enhance vibro-acoustic performance of exhaust systems and expedite the assessment due to modifications. It is shown that an optimal acoustic impedance change can be made to improve both structural and acoustic performance, without increasing the overall dimension and mass of the exhaust system. Application examples include mufflers and enclosures. In addition, it is demonstrated that the approach can be used to assess vibration isolators. In many instances, the source properties (source strength and source impedance) will also greatly influence exhaust system performance through sound reflections and resonances. Thus it is of interest to acoustically characterize the sources and assess the sensitivity of performance towards source impedance. In this dissertation, the experimental characterization of source properties is demonstrated for a diesel engine. Moreover, the same approach can be utilized to characterize other sources like refrigeration systems. It is also shown that the range of variation of performance can be effectively determined given the range of source impedance using the Moebius transformation. This optimization approach is first applied on conventional single-inlet single-outlet exhaust systems and is later applied to multi-inlet multi-outlet (MIMO) systems as well, with proper adjustment. The analytic model for MIMO systems is explained in details and validated experimentally. The sensitivity of MIMO system performance due to source properties is also investigated using the Moebius transformation

Modelling of sediment transport and bed deformation in rivers with continuous bends

Peer reviewedPostprin

Breast-feeding patterns in rural China : a population based study

Background: Breast-feeding is considered to contribute infant’s health and development, protecting children from a variety of acute and chronic disorders as well as potential benefits for mothers. Breastfeeding has also potential to protect against childhood obesity; however, the evidence on the risk factors of breastfeeding remains unclear especially in the context of rural settings. Objective: The objective of this study is to analyze the patterns and determinants of breastfeeding in three rural provinces of China. Methods: A survey of new mothers in five rural counties among three province-level administrative divisions (Anhui, Shanxi, and Chongqing) in China was conducted in 2009. Data were collected by an interview after the interventions, including the demographic and pregnancy related characteristics of the mothers. Exclusive breastfeeding was measured by asking mothers the duration of the breastfeed only (in months) and categorized into two (0 to5 months and 6 to 11 months). Logistic regression was used to model the relationship of measured characteristics to the duration of exclusive breastfeeding. Odds ratios (ORs) and their 95% confidence intervals (CIs) were reported as the measure of the associations. Results: The rate for exclusive breastfeeding over 6 months is relatively low. Only 3.9% of the babies received exclusive breastfeeding for more than 6 months in Chongqing. The key demographic determinants vary significantly among provinces. In Anhui, maternal age was found to be positively associated with exclusive breastfeeding duration, and in Shaanxi family income was found negatively associated with exclusive breastfeeding duration. Mothers with higher education level in this study were less likely to practice exclusive breastfeeding. Conclusion: Exclusive breastfeeding over 6 months is relatively low in rural China, however the pattern largely varied by province. The key demographic determinants of exclusive breastfeeding include maternal age, education, occupation, as well as paternal occupation and family income

MALPRESENTATION AT DELIVERY AND ITS ASSOCIATION BETWEEN CHILD AUTISM SPECTRUM DISORDER AND COGNITIVE IMPAIRMENT

The prevalence of autism spectrum disorder (ASD) has increased, and ASD causes substantial burden for individuals and their families. The prevalence of cognitive impairment also increased in children. ASD and cognitive function are neurodevelopmental disorders with multiple factors involved; however, specific risk factors remain unclear. Previous studies have shown associations between cesarean delivery and neurodevelopmental disorders; and limited studies focused on malpresentation at delivery, a common indication for cesarean delivery, and its association with ASD or cognitive function. The studies are limited by inconclusive results, by using outcome measurements with limited validity, or by not accounting for the gestational age-dependency prevalence of malpresentation. To address these limitations, this study utilized data from the Study to Explore Early Development, a case-control study conducted in the United States. In Aim 1, we identified malpresentation and evaluated the association between malpresentation at delivery and ASD. In Aim 2, we evaluated the association between malpresentation and cognitive function in ASD and children from the general sample separately. In our study, we included 1371 children with ASD and 1576 population controls for Aim 1; and 1368 children with ASD and 1576 children from the general sample for Aim 2. We assessed whether the observed associations were modified by maternal pre-pregnancy body mass index (BMI) and gestational age. In Aim 1, we found an association between malpresentation and ASD (ORa=1.36, 95% CI: 1.06, 1.74), after adjustment for maternal age, poverty level, maternal hypertensive disorder, and maternal smoking. The association was similar for other malpresentations and breech. We did not find the association was modified by gestational age or pre-pregnancy BMI. We did not have evidence that malpresentation was associated with below average cognitive function, either in the ASD or the children from the general sample. Our findings suggest that malpresentation is associated with ASD, but may not be associated with cognitive function. Future well-powered studies should investigate the role of gestational age or pre-pregnancy BMI in these associations. These results can help identify children at higher risk of ASD for whom developmental screening at younger ages may allow for early identification and potentially earlier intervention.Doctor of Philosoph

Water-Sediment Regimes and River Health

Source: ICHE Conference Archive - https://mdi-de.baw.de/icheArchiv

Measuring service quality of online banking in China

Objectives of study: The objective of this thesis is to develop a multiple item scale for measuring service quality of online banking in Bozhou City, Anhui Province, China. Briefly speaking, the first theoretical objective of this study is to discuss concept e-service quality as well as related e-service quality models, especially E-S-QUAL/E-RecS-QUAL (Parasuraman et al. 2005).The second objective is to define and establish one suitable multiple e-service item scale for China with the help of E-S-QUAL/E-RecS-QUAL model and other related models. The objective of empirical part is to get the refined measurement scale for online banking service through data collection and analysis in Bozhou City, Anhui Province, China. Academic background and methodology: Service quality plays an important role as competitive weapon and a significant differentiator for many service organizations (Parasuraman & Zeithaml, 1988), involving e-banking industry. According to Wang et. al (2003), good e-service quality offering is the key issue to survive in the intensively competitive banking market, especially maintain customer satisfaction. As a result of this phenomenon, a good understanding of service attributes that customers use to evaluate online banking service quality is needed for banks so that the performance of e-service is able to be monitored and immediate adjustments and improve can be done as soon as possible. In this study, an overview of service quality (including e-service quality) and related literature is discussed, especially in the context of online banking industry that is taken as the case subject. Moreover, the study adopts E-S-QUAL/E-RecS-QUAL scale (Parasuraman et al. 2005) to estabilish a suitable multiple e-service item scale for measuring online banking in China. The target group for this study is limited to young and middle aged people between 19-39, e.g. university students etc. Survey sending and gathering is chosen as data collection for this thesis. Findings and conclusions: Through the process of data collection and factor analysis in the empirical part, the refined scale for measuring online banking in China was identified, involving 3 dimensions and 14 items: customer service, privacy and preferential and reliable treatment. This finding indicated that the dimensions and items from E-S-QUAL/E-RecS-QUAL needed to be reorganized and reinterpreted for measuring online banking in Bozhou City of China

Contrastive Learning for Time Series on Dynamic Graphs

There have been several recent efforts towards developing representations for multivariate time-series in an unsupervised learning framework. Such representations can prove beneficial in tasks such as activity recognition, health monitoring, and anomaly detection. In this paper, we consider a setting where we observe time-series at each node in a dynamic graph. We propose a framework called GraphTNC for unsupervised learning of joint representations of the graph and the time-series. Our approach employs a contrastive learning strategy. Based on an assumption that the time-series and graph evolution dynamics are piecewise smooth, we identify local windows of time where the signals exhibit approximate stationarity. We then train an encoding that allows the distribution of signals within a neighborhood to be distinguished from the distribution of non-neighboring signals. We first demonstrate the performance of our proposed framework using synthetic data, and subsequently we show that it can prove beneficial for the classification task with real-world datasets

Gammaproteobacteria, a core taxon in the guts of soil fauna, are potential responders to environmental concentrations of soil pollutants

Background: The ubiquitous gut microbiotas acquired from the environment contribute to host health. The gut microbiotas of soil invertebrates are gradually assembled from the microecological region of the soil ecosystem which they inhabit, but little is known about their characteristics when the hosts are under environmental stress. The rapid development of high-throughput DNA sequencing in the last decade has provided unprecedented insights and opportunities to characterize the gut microbiotas of soil invertebrates. Here, we characterized the core, transient, and rare bacterial taxa in the guts of soil invertebrates using the core index (CI) and developed a new theory of global microbial diversity of soil ecological microregions. Results: We found that the Gammaproteobacteria could respond indiscriminately to the exposure to environmental concentrations of soil pollutants and were closely associated with the physiology and function of the host. Meanwhile, machine-learning models based on metadata calculated that Gammaproteobacteria were the core bacteria with the highest colonization potential in the gut, and further identified that they were the best indicator taxon of the response to environmental concentrations of soil pollution. Gammaproteobacteria also closely correlated with the abundance of antibiotic resistance genes. Conclusions: Our results determined that Gammaproteobacteria were an indicator taxon in the guts of the soil invertebrates that responded to environmental concentrations of soil pollutants, thus providing an effective theoretical basis for subsequent assessments of soil ecological risk. The results of the physiological and biochemical analyses of the host and the microbial-community functions, and the antibiotic resistance of Gammaproteobacteria, provide new insights for evaluating global soil ecological health