Time-dependent stochastic shortest path(s) algorithms for a scheduled transportation network

Following on from our work concerning travellers’ preferences in public transportation networks (Wu and Hartley, 2004), we introduce the concept of stochasticity to our algorithms. Stochasticity greatly increases the complexity of the route finding problem, so greater algorithmic efficiency becomes imperative. Public transportation networks (buses, trains) have two important features: edges can only be traversed at certain points in time and the weights of these edges change in a day and have an uncertainty associated with them. These features determine that a public transportation network is a stochastic and time-dependent network. Finding multiple shortest paths in a both stochastic and time-dependent network is currently regarded as the most difficult task in the route finding problems (Loui, 1983). This paper discusses the use of k-shortest-paths (KSP) algorithms to find optimal route(s) through a network in which the edge weights are defined by probability distributions. A comprehensive review of shortest path(s) algorithms with probabilistic graphs was conducted

Understanding the structure and dynamics of cationic surfactants from studies of pure solid phases

A homologous series of n-alkyl trimethylammonium bromide surfactants, H(CH2)nN+(CH3)3 Br, from C10TAB to C18TAB have been studied systematically in the bulk over a wide range of temperatures. Common features in the structures are identified, with packing dominated by the co-ordination of the cationic head groups with bromide anions and interdigitation of the hydrocarbon chains. This arrangement provides an explanation for the thin adsorbed bilayers that have been observed at various hydrophilic surfaces from aqueous solutions in previous studies. The molecular volumes and arrangement are comparable with structures of a number of different self-assembled amphiphiles. For these surfactants with bromide counter-ions, formation of crystal hydrates was not observed. The alkyl chains are highly mobile and at high temperatures a plastic phase is found for all materials with a transition enthalpy that is similar to the melting enthalpy of many long alkyl chains. Other unexpected phase transitions depend more markedly on the hydrocarbon chain length and evidently depend on delicate balances of the various contributions to the free energy

Human motion tracking based on complementary Kalman filter

Miniaturized Inertial Measurement Unit (IMU) has been widely used in many motion capturing applications. In order to overcome stability and noise problems of IMU, a lot of efforts have been made to develop appropriate data fusion method to obtain reliable orientation estimation from IMU data. This article presents a method which models the errors of orientation, gyroscope bias and magnetic disturbance, and compensate the errors of state variables with complementary Kalman filter in a body motion capture system. Experimental results have shown that the proposed method significantly reduces the accumulative orientation estimation errors

Beat-to-beat ambulatory blood pressure estimation based on random forest

Ambulatory blood pressure is critical in predicting some major cardiovascular events; therefore, cuff-less and noninvasive beat-to-beat ambulatory blood pressure measure-ment is of great significance. Machine-learning methods have shown the potential to derive the relationship between physio-logical signal features and ABP. In this paper, we apply random forest method to systematically explorer the inherent connections between photoplethysmography signal, electrocardiogram signal and ambulatory blood pressure. To archive this goal, 18 features were extracted from PPG and ECG signals. Several models with most significant features as inputs and beat-to-beat ABP as outputs were trained and tested on data from the Multi-Parameter Intelligent Monitoring in Intensive Care II database. Results indicate that compared with the common pulse transit time method, the RF method gives a better performance for one-hour continuous estimation of diastolic blood pressure and systolic blood pressure under both the Association for the Advancement of Medical Instrumentation and British Hyper-tension Society standard

Effects of diet and/or exercise in enhancing spinal cord sensorimotor learning.

Given that the spinal cord is capable of learning sensorimotor tasks and that dietary interventions can influence learning involving supraspinal centers, we asked whether the presence of omega-3 fatty acid docosahexaenoic acid (DHA) and the curry spice curcumin (Cur) by themselves or in combination with voluntary exercise could affect spinal cord learning in adult spinal mice. Using an instrumental learning paradigm to assess spinal learning we observed that mice fed a diet containing DHA/Cur performed better in the spinal learning paradigm than mice fed a diet deficient in DHA/Cur. The enhanced performance was accompanied by increases in the mRNA levels of molecular markers of learning, i.e., BDNF, CREB, CaMKII, and syntaxin 3. Concurrent exposure to exercise was complementary to the dietary treatment effects on spinal learning. The diet containing DHA/Cur resulted in higher levels of DHA and lower levels of omega-6 fatty acid arachidonic acid (AA) in the spinal cord than the diet deficient in DHA/Cur. The level of spinal learning was inversely related to the ratio of AA:DHA. These results emphasize the capacity of select dietary factors and exercise to foster spinal cord learning. Given the non-invasiveness and safety of the modulation of diet and exercise, these interventions should be considered in light of their potential to enhance relearning of sensorimotor tasks during rehabilitative training paradigms after a spinal cord injury

Blockchain and Reinforcement Neural Network for Trusted Cloud-Enabled IoT Network

The rapid integration of Internet of Things (IoT) services and applications across various sectors is primarily driven by their ability to process real-time data and create intelligent environments through artificial intelligence for service consumers. However, the security and privacy of data have emerged as significant threats to consumers within IoT networks. Issues such as node tampering, phishing attacks, malicious code injection, malware threats, and the potential for Denial of Service (DoS) attacks pose serious risks to the safety and confidentiality of information. To solve this problem, we propose an integrated autonomous IoT network within a cloud architecture, employing Blockchain technology to heighten network security. The primary goal of this approach is to establish a Heterogeneous Autonomous Network (HAN), wherein data is processed and transmitted through cloud architecture. This network is integrated with a Reinforced Neural Network (RNN) called ClouD_RNN, specifically designed to classify the data perceived and collected by sensors. Further, the collected data is continuously monitored by an autonomous network and classified for fault detection and malicious activity. In addition, network security is enhanced by the Blockchain Adaptive Windowing Meta Optimization Protocol (BAWMOP). Extensive experimental results validate that our proposed approach significantly outperforms state-of-the-art approaches in terms of throughput, accuracy, end-to-end delay, data delivery ratio, network security, and energy efficiency

Ultrahigh power and energy density in partially ordered lithium-ion cathode materials

The rapid market growth of rechargeable batteries requires electrode materials that combine high power and energy and are made from earth-abundant elements. Here we show that combining a partial spinel-like cation order and substantial lithium excess enables both dense and fast energy storage. Cation overstoichiometry and the resulting partial order is used to eliminate the phase transitions typical of ordered spinels and enable a larger practical capacity, while lithium excess is synergistically used with fluorine substitution to create a high lithium mobility. With this strategy, we achieved specific energies greater than 1,100 Wh kg–1 and discharge rates up to 20 A g–1. Remarkably, the cathode materials thus obtained from inexpensive manganese present a rare case wherein an excellent rate capability coexists with a reversible oxygen redox activity. Our work shows the potential for designing cathode materials in the vast space between fully ordered and disordered compounds

High-level expression of the HIV entry inhibitor griffithsin from the plastid genome and retention of biological activity in dried tobacco leaves

The global HIV epidemic continues to grow, with 1.8 million new infections occurring per year. In the absence of a cure and an AIDS vaccine, there is a pressing need to prevent new infections in order to curb the disease. Topical microbicides that block viral entry into human cells can potentially prevent HIV infection. The antiviral lectin griffithsin has been identified as a highly potent inhibitor of HIV entry into human cells. Here we have explored the possibility to use transplastomic plants as an inexpensive production platform for griffithsin. We show that griffithsin accumulates in stably transformed tobacco chloroplasts to up to 5% of the total soluble protein of the plant. Griffithsin can be easily purified from leaf material and shows similarly high virus neutralization activity as griffithsin protein recombinantly expressed in bacteria. We also show that dried tobacco provides a storable source material for griffithsin purification, thus enabling quick scale-up of production on demand

Design, analysis, tools and applications for programmable high-speed and power-aware 4G processors

Data rate traffic and communication capacity demand have been increased continuously. Therefore, a highly advanced 4G wireless system is required to meet a high demand for modern mobile terminals. For getting a further improvement for 4G communication systems, new paradigms of design, analysis tools and applications for 4G communication processors are necessary. In this paper, some of these new paradigms are discussed. Furthermore, a single-step discrete cosine transform truncation (DCTT) method is proposed for the modeling-simulation in signal integrity verification for high-speed communication processors. ©2011 IEEE.published_or_final_versio