Novel gain-tuning for sliding mode control of second-order mechanical systems: theory and experiments.

The sliding mode control is well-known as a useful control technique that can be applied in several real-world applications. However, a straightforward and efficient process of selecting the sliding mode control gains remains a challenging but interesting topic. This paper investigates a novel gain tuning method for the sliding mode control of second-order mechanical systems. Firstly, we obtain relations between the gains and the natural and damping ratio of the closed-loop system. Secondly, the time constant of the system's actuators and the system response performance criteria, including settling time and delay time, are taken into consideration to determine appropriate ranges of the gains. These gain ranges allow control designers to select the controller gains in a time-saving manner and ensure that the desired system performance is met and the actuators work properly. Finally, the proposed method is applied to the gain tuning process of a sliding mode altitude controller for an actual quadcopter unmanned aerial vehicle. Simulation and experimental results demonstrate the applicability and effectiveness of this method.

Electrophilic reactivity of the Busulfan metabolite, EdAG, towards cellular thiols and inhibition of human thioredoxin-1.

Busulfan is an alkylating agent used in chemotherapy conditioning regimens prior to hematopoietic stem cell transplantation (HSCT). However, its administration is associated with a great risk of adverse toxicities, which have been historically attributed to busulfan's mechanism of non-specific DNA alkylation. A phase II generated metabolite of busulfan, EdAG (γ-glutamyldehydroalanylglycine), is a dehydroalanine analog of glutathione (GSH) with an electrophilic moiety, suggesting it may bind to proteins and disrupt biological function. However, EdAG's reactions with common cellular thiols such as glutathione (GSH) and l-cysteine are understudied, along with possible inhibition of glutathionylation-dependent enzymes (with active site cysteine residues). We established a physiologically-relevant in vitro model to readily measure thiol loss over time. Using this model, we compared the apparent rates of thiol depletion in the presence of EdAG or arecoline, a toxic constituent of the areca (betel) nut and known GSH depletor. Simulated kinetic modeling revealed that the mean (±SE) alpha (α) second order rate constants describing GSH and l-cysteine depletion in the presence of EdAG were 0.00522 (0.00845) µM-1∙min-1 and 0.0207 (0.00721) µM-1∙min-1, respectively; in the presence of arecoline, the apparent rates of depletion were 0.0619 (0.009) µM-1∙min-1 and 0.2834 (0.0637) µM-1∙min-1 for GSH and l-cysteine, respectively. Under these experimental conditions, we conclude that EdAG was a weaker electrophile than arecoline. Arecoline and EdAG both depleted apparent l-cysteine concentrations to a much greater extent than GSH, approximately 4.58-fold and 3.97-fold change greater, respectively. EdAG modestly inhibited (∼20%) the human thioredoxin-1 (hTrx-1) catalyzed reduction of insulin with a mean IC50 of 93 µM [95% CI: 78.6-110 µM). In summary, EdAG's ability to spontaneously react with endogenous thiols and inhibit hTrx-1 are potentially biochemically relevant in humans. These findings continue to support the growing concept that EdAG, an underrecognized phase II metabolite of busulfan, plays a role in untoward cellular toxicities during busulfan pharmacotherapy.

Real-Time Task Assignment Approach Leveraging Reinforcement Learning with Evolution Strategies for Long-Term Latency Minimization in Fog Computing.

The emerging fog computing technology is characterized by an ultralow latency response, which benefits a massive number of time-sensitive services and applications in the Internet of things (IoT) era. To this end, the fog computing infrastructure must minimize latencies for both service delivery and execution phases. While the transmission latency significantly depends on external factors (e.g., channel bandwidth, communication resources, and interferences), the computation latency can be considered as an internal issue that the fog computing infrastructure could actively self-handle. From this view point, we propose a reinforcement learning approach that utilizes the evolution strategies for real-time task assignment among fog servers to minimize the total computation latency during a long-term period. Experimental results demonstrate that the proposed approach reduces the latency by approximately 16.1% compared to the existing methods. Additionally, the proposed learning algorithm has low computational complexity and an effectively parallel operation; therefore, it is especially appropriate to be implemented in modern heterogeneous computing platforms.

Adaptive MCS selection and resource planning for energy-efficient communication in LTE-M based IoT sensing platform.

As an important part of IoTization trends, wireless sensing technologies have been involved in many fields of human life. In cellular network evolution, the long term evolution advanced (LTE-A) networks including machine-type communication (MTC) features (named LTE-M) provide a promising infrastructure for a proliferation of Internet of things (IoT) sensing platform. However, LTE-M may not be optimally exploited for directly supporting such low-data-rate devices in terms of energy efficiency since it depends on core technologies of LTE that are originally designed for high-data-rate services. Focusing on this circumstance, we propose a novel adaptive modulation and coding selection (AMCS) algorithm to address the energy consumption problem in the LTE-M based IoT-sensing platform. The proposed algorithm determines the optimal pair of MCS and the number of primary resource blocks (#PRBs), at which the transport block size is sufficient to packetize the sensing data within the minimum transmit power. In addition, a quantity-oriented resource planning (QORP) technique that utilizes these optimal MCS levels as main criteria for spectrum allocation has been proposed for better adapting to the sensing node requirements. The simulation results reveal that the proposed approach significantly reduces the energy consumption of IoT sensing nodes and #PRBs up to 23.09% and 25.98%, respectively.

Adaptive Suspicious Prevention for Defending DoS Attacks in SDN-Based Convergent Networks.

The convergent communication network will play an important role as a single platform to unify heterogeneous networks and integrate emerging technologies and existing legacy networks. Although there have been proposed many feasible solutions, they could not become convergent frameworks since they mainly focused on converting functions between various protocols and interfaces in edge networks, and handling functions for multiple services in core networks, e.g., the Multi-protocol Label Switching (MPLS) technique. Software-defined networking (SDN), on the other hand, is expected to be the ideal future for the convergent network since it can provide a controllable, dynamic, and cost-effective network. However, SDN has an original structural vulnerability behind a lot of advantages, which is the centralized control plane. As the brains of the network, a controller manages the whole network, which is attractive to attackers. In this context, we proposes a novel solution called adaptive suspicious prevention (ASP) mechanism to protect the controller from the Denial of Service (DoS) attacks that could incapacitate an SDN. The ASP is integrated with OpenFlow protocol to detect and prevent DoS attacks effectively. Our comprehensive experimental results show that the ASP enhances the resilience of an SDN network against DoS attacks by up to 38%.

Gyro Drift Correction for An Indirect Kalman Filter Based Sensor Fusion Driver.

Sensor fusion techniques have made a significant contribution to the success of the recently emerging mobile applications era because a variety of mobile applications operate based on multi-sensing information from the surrounding environment, such as navigation systems, fitness trackers, interactive virtual reality games, etc. For these applications, the accuracy of sensing information plays an important role to improve the user experience (UX) quality, especially with gyroscopes and accelerometers. Therefore, in this paper, we proposed a novel mechanism to resolve the gyro drift problem, which negatively affects the accuracy of orientation computations in the indirect Kalman filter based sensor fusion. Our mechanism focuses on addressing the issues of external feedback loops and non-gyro error elements contained in the state vectors of an indirect Kalman filter. Moreover, the mechanism is implemented in the device-driver layer, providing lower process latency and transparency capabilities for the upper applications. These advances are relevant to millions of legacy applications since utilizing our mechanism does not require the existing applications to be re-programmed. The experimental results show that the root mean square errors (RMSE) before and after applying our mechanism are significantly reduced from 6.3 × 10(-1) to 5.3 × 10(-7), respectively.

A multicenter evaluation of adjuvant therapy in women with optimally resected stage IIIC endometrial cancer.

OBJECTIVE: To determine if there is an advantage to combination chemotherapy and radiation for optimally resected stage IIIC endometrial cancer (EC). METHODS: A multicenter retrospective analysis of patients with EC from 1991 to 2008 was conducted. Inclusion criteria were lymph node assessment and optimally resected disease. Recurrence-free (RFS) and overall survival (OS) were analyzed using Kaplan-Meier method and Cox proportional hazards model. RESULTS: 265 patients with optimally resected stage IIIC EC were identified. Postoperative therapies included radiotherapy in 17% (n=45), chemotherapy in 17% (n=46), and both chemotherapy and radiation in 61% (n=161). Three-year RFS was 56% for chemotherapy alone, compared to 73% for radiation alone, and 73% for combination therapy (p=0.12). Those receiving chemotherapy alone had the worst 3-year OS (78%) compared to either radiotherapy alone (95%) or combination therapy (90%) (p=0.005). After adjustment for stage and grade those treated with chemotherapy alone were at a 2.2 fold increased risk of recurrence (95% CI, 1.2 to 4.2; p=0.02) and 4.0 fold increased risk of death (95% CI, 1.6 to 10.0; p=0.004) compared to those treated with chemotherapy and radiation. In contrast there was no significant difference in RFS [HR=1.0 (95% CI, 0.5 to 2.0; p=0.92)] or OS [HR=1.1 (95% CI, 0.3 to 3.6; p=0.91)] for those treated with radiation alone compared to those treated with chemotherapy and radiation. CONCLUSION: Adjuvant therapy with either radiation alone or chemotherapy and radiation was associated with improved outcomes for patients with optimally resected stage IIIC EC compared to those treated with chemotherapy only.