Direct and Indirect Detection of Neutralino Dark Matter and Collider Signatures in an SO(10)SO(10) Model with Two Intermediate Scales

We investigate the detectability of neutralino Dark Matter via direct and indirect searches as well as collider signatures of an $SO(10)$ model with two intermediate scales. We compare the direct Dark Matter detection cross section and the muon flux due to neutralino annihilation in the Sun that we obtain in this model with mSUGRA predictions and with the sensitivity of current and future experiments. In both cases, we find that the detectability improves as the model deviates more from mSUGRA. In order to study collider signatures, we choose two benchmark points that represent the main phenomenological features of the model: a lower value of $|\mu|$ and reduced third generation sfermion masses due to extra Yukawa coupling contributions in the Renormalization Group Equations, and increased first and second generation slepton masses due to new gaugino loop contributions. We show that measurements at the LHC can distinguish this model from mSUGRA in both cases, by counting events containing leptonically decaying $Z^0$ bosons, heavy neutral Higgs bosons, or like--sign lepton pairs.Comment: 21 pages, 16 figure

Within-Home versus Between-Home Variability of House Dust Endotoxin in a Birth Cohort

Endotoxin exposure has been proposed as an environmental determinant of allergen responses in children. To better understand the implications of using a single measurement of house dust endotoxin to characterize exposure in the first year of life, we evaluated room-specific within-home and between-home variability in dust endotoxin obtained from 470 households in Boston, Massachusetts. Homes were sampled up to two times over 5–11 months. We analyzed 1,287 dust samples from the kitchen, family room, and baby’s bedroom for endotoxin. We fit a mixed-effects model to estimate mean levels and the variation of endotoxin between homes, between rooms, and between sampling times. Endotoxin ranged from 2 to 1,945 units per milligram of dust. Levels were highest during summer and lowest in the winter. Mean endotoxin levels varied significantly from room to room. Cross-sectionally, endotoxin was moderately correlated between family room and bedroom floor (r = 0.30), between family room and kitchen (r = 0.32), and between kitchen and bedroom (r = 0.42). Adjusting for season, the correlation of endotoxin levels within homes over time was 0.65 for both the bedroom and kitchen and 0.54 for the family room. The temporal within-home variance of endotoxin was lowest for bedroom floor samples and highest for kitchen samples. Between-home variance was lowest in the family room and highest for kitchen samples. Adjusting for season, within-home variation was less than between-home variation for all three rooms. These results suggest that room-to-room and home-to-home differences in endotoxin influence the total variability more than factors affecting endotoxin levels within a room over time

An effective communication and computation model based on a hybridgraph-deeplearning approach for SIoT.

Social Edge Service (SES) is an emerging mechanism in the Social Internet of Things (SIoT) orchestration for effective user-centric reliable communication and computation. The services are affected by active and/or passive attacks such as replay attacks, message tampering because of sharing the same spectrum, as well as inadequate trust measurement methods among intelligent devices (roadside units, mobile edge devices, servers) during computing and content-sharing. These issues lead to computation and communication overhead of servers and computation nodes. To address this issue, we propose the HybridgrAph-Deep-learning (HAD) approach in two stages for secure communication and computation. First, the Adaptive Trust Weight (ATW) model with relation-based feedback fusion analysis to estimate the fitness-priority of every node based on directed graph theory to detect malicious nodes and reduce computation and communication overhead. Second, a Quotient User-centric Coeval-Learning (QUCL) mechanism to formulate secure channel selection, and Nash equilibrium method for optimizing the communication to share data over edge devices. The simulation results confirm that our proposed approach has achieved effective communication and computation performance, and enhanced Social Edge Services (SES) reliability than state-of-the-art approaches

Stochastic Systems: Modeling, Optimization, and Applications

The special issue of Mathematical Problems in Engineering deals with the issues of modeling, optimization, and applications associated with stochastic systems. This special issue provides a forum for researchers and practitioners to publish quality research work on modeling, optimization approaches, and their applications in the context of theory analysis and engineering developments. The accepted papers in this special issue include stochastic stability, stabilization and control optimization, stochastic optimization, particle swarm optimization, modeling and identification methods, signal processing, and robust filtering. The issue includes thirty-nine papers out of which six consider the stability and stabilization problems of stochastic systems. Twelve papers cover the problems of the controller design and relevant optimization algorithms

A quantum-inspired sensor consolidation measurement approach for cyber-physical systems.

Cyber-Physical System (CPS) devices interconnect to grab data over a common platform from industrial applications. Maintaining immense data and making instant decision analysis by selecting a feasible node to meet latency constraints is challenging. To address this issue, we design a quantum-inspired online node consolidation (QONC) algorithm based on a time-sensitive measurement reinforcement system for making decisions to evaluate the feasible node, ensuring reliable service and deploying the node at the appropriate position for accurate data computation and communication. We design the Angular-based node position analysis method to localize the node through rotation and t-gate to mitigate latency and enhance system performance. We formalize the estimation and selection of the feasible node based on quantum formalization node parameters (node contiguity, node optimal knack rate, node heterogeneity, probability of fusion variance error ratio). We design a fitness function to assess the probability of node fitness before selection. The simulation results convince us that our approach achieves an effective measurement rate of performance index by reducing the average error ratio from 0.17-0.22, increasing the average coverage ratio from 29% to 42%, and the qualitative execution frequency of services. Moreover, the proposed model achieves a 74.3% offloading reduction accuracy and a 70.2% service reliability rate compared to state-of-the-art approaches. Our system is scalable and efficient under numerous simulation frameworks

ASXC2 approach: a service-X cost optimization strategy based on edge orchestration for IIoT.

Most computation-intensive industry applications and servers encounter service-reliability challenges due to the limited resource capability of the edge. Achieving quality data fusion and accurate service reliability with optimized service-x execution cost is challenging. While existing systems have taken into account factors such as device service execution, residual resource ratio, and channel condition; the service execution time, cost, and utility ratios of requested services from devices and servers also have a significant impact on service execution cost. To enhance service quality and reliability, we design a 2-step adaptive service-X cost consolidation (ASXC 2) approach. This approach is based on the node-centric Lyapunov method and distributed Markov mechanism, aiming to optimize the service execution error rate during offloading. The node-centric Lyapunov method incorporates cost and utility functions and node-centric features to estimate the service cost before offloading. Additionally, the Markov mechanism-inspired service latency prediction model design assists in mitigating the ratio of offload-service execution errors by establishing a mobility-correlation matrix between devices and servers. In addition, the non-linear programming multi-tenancy heuristic method design help to predict the service preferences for improving the resource utilisation ratio. The simulations show the effectiveness of our approach. The model performance is enhanced with 0.13% service offloading efficiency, 0.82% rate of service completion when transmitting data size is 400 kb, and 0.058% average service offloading efficiency with 40 CPU Megacycles when the vehicle moves 60 Km/h speed around the server communication range. Our model simulations indicate that our approach is highly effective and suitable for lightweight, complex environments

Robust model predictive control for normbounded uncertain systems using new parameter dependent terminal weighting matrix,

Abstract A new robust model predictive control (MPC) technique is proposed for norm-bounded uncertain systems with input constraints. In order to improve feasibility and system performance, we propose an LMI condition for the cost monotonicity by using a new parameter dependent terminal weighting matrix. We formulate the problem as a minimization of the upper bound of infinite horizon cost function subject to the LMI condition for the cost monotonicity. A numerical example shows the effectiveness of the proposed method

Efficient LiDAR-trajectory affinity model for autonomous vehicle orchestration.

Computation and memory resource management strategies are the backbone of continuous object tracking in intelligent vehicle orchestration. Multi-object tracking generates enormous measurements of targets and extended object positions using light detection and ranging (Lidar) sensors. Designing an adequate object-tracking system is a global challenge because of dynamic object detection and data association uncertainties during scene understanding. In this regard, we develop an intelligent multi-objective tracking (IMOT) system with a novel measurement model, called the box data association inflate (BDAI) model, to assess each target's object state and trajectory without noise by using the Bayesian approach. The box object filter method filters ambiguous detection responses during data association. The theoretical proof of the box object filter is derived based on binomial expansion. Prognosticating a lower-dimension object than the original point object reduces the computational complexity of vehicle orchestration. Two datasets (NuScenes dataset and our lab dataset) are considered during the simulations, and our approach measures the kinematic states adequately with reduced computation complexity compared to state-of-the-art methods. The simulation outcomes show that our proposed method is effective and works well to detect and track objects. The NuScenes dataset contains 28130 samples for training, 6019 examples for validation and 6008 samples for testing. IMOT achieves 58.09% tracking accuracy and 71% mAP with 5 ms pre-processing time. The Jetson Xavier NX consumes 49.63% GPU and 9.37% average power and exhibits 25.32 ms latency compared to other approaches. Our system trains a single pair frame in 169.71 ms with affinity estimation time of 12.19 ms, track association time of 0.19 ms and mATE of 0.245 compared to state-of-the-art approaches

Elevated intracellular cAMP exacerbates vulnerability to oxidative stress in optic nerve head astrocytes.

Glaucoma is characterized by a progressive loss of retinal ganglion cells and their axons, but the underlying biological basis for the accompanying neurodegeneration is not known. Accumulating evidence indicates that structural and functional abnormalities of astrocytes within the optic nerve head (ONH) have a role. However, whether the activation of cyclic adenosine 3',5'-monophosphate (cAMP) signaling pathway is associated with astrocyte dysfunction in the ONH remains unknown. We report here that the cAMP/protein kinase A (PKA) pathway is critical to ONH astrocyte dysfunction, leading to caspase-3 activation and cell death via the AKT/Bim/Bax signaling pathway. Furthermore, elevated intracellular cAMP exacerbates vulnerability to oxidative stress in ONH astrocytes, and this may contribute to axonal damage in glaucomatous neurodegeneration. Inhibition of intracellular cAMP/PKA signaling activation protects ONH astrocytes by increasing AKT phosphorylation against oxidative stress. These results strongly indicate that activation of cAMP/PKA pathway has an important role in astrocyte dysfunction, and suggest that modulating cAMP/PKA pathway has therapeutic potential for glaucomatous ONH degeneration