Joint Optimization of Signal Design and Resource Allocation in Wireless D2D Edge Computing

In this paper, we study the distributed computational capabilities of device-to-device (D2D) networks. A key characteristic of D2D networks is that their topologies are reconfigurable to cope with network demands. For distributed computing, resource management is challenging due to limited network and communication resources, leading to inter-channel interference. To overcome this, recent research has addressed the problems of wireless scheduling, subchannel allocation, power allocation, and multiple-input multiple-output (MIMO) signal design, but has not considered them jointly. In this paper, unlike previous mobile edge computing (MEC) approaches, we propose a joint optimization of wireless MIMO signal design and network resource allocation to maximize energy efficiency. Given that the resulting problem is a non-convex mixed integer program (MIP) which is prohibitive to solve at scale, we decompose its solution into two parts: (i) a resource allocation subproblem, which optimizes the link selection and subchannel allocations, and (ii) MIMO signal design subproblem, which optimizes the transmit beamformer, transmit power, and receive combiner. Simulation results using wireless edge topologies show that our method yields substantial improvements in energy efficiency compared with cases of no offloading and partially optimized methods and that the efficiency scales well with the size of the network.Comment: 10 pages, 7 figures, Accepted by INFOCOM 202

Dissociation of ssDNA - Single-Walled Carbon Nanotube Hybrids by Watson-Crick Base Pairing

The unwrapping event of ssDNA from the SWNT during the Watson-Crick base paring is investigated through electrical and optical methods, and binding energy calculations. While the ssDNA-metallic SWNT hybrid shows the p-type semiconducting property, the hybridization product recovered metallic properties. The gel electrophoresis directly verifies the result of wrapping and unwrapping events which was also reflected to the Raman shifts. Our molecular dynamics simulations and binding energy calculations provide atomistic description for the pathway to this phenomenon. This nano-physical phenomenon will open up a new approach for nano-bio sensing of specific sequences with the advantages of efficient particle-based recognition, no labeling, and direct electrical detection which can be easily realized into a microfluidic chip format.Comment: 4 pages, 4 figure

Clinical features and long-term prognosis of acute fibrinous and organizing pneumonia histologically confirmed by surgical lung biopsy

Abstract Background Acute fibrinous and organizing pneumonia (AFOP) is a rare interstitial pneumonia characterized by intra-alveolar fibrin deposition and organizing pneumonia. The clinical manifestations and long-term prognosis of AFOP are unclear. Our objective was to investigate the clinical features and prognosis of AFOP. Methods We identified patients diagnosed with AFOP by surgical lung biopsy between January 2011 and May 2018 at Seoul National University Bundang Hospital. We retrospectively reviewed clinical and radiologic findings, treatment, and outcomes of AFOP. Results Fifteen patients with histologically confirmed lung biopsies were included. The median follow-up duration was 2.4 (range, 0.1–82) months. The median age was 55 (range, 33–75) years, and four patients were immunocompromised. Fever was the most common clinical presentation (86.7%). Patchy ground-glass opacities and/or consolidations were the most predominant findings on chest computed tomography images. Nine patients (60%) received mechanical ventilator care, and eight patients (53.3%) died. The non-survivors tended to have slightly higher body mass index (BMI) and a long interval between symptom onset and diagnosis than the survivors, but these findings were not statistically significant. Among seven survivors, five patients were discharged without dyspnea and oxygen supplement. Conclusions The clinical course of AFOP was variable. Although AFOP was fatal, most of the patients who recovered from AFOP maintained normal life without supplemental oxygen therapy and respiratory symptoms

Two nights of recovery sleep restores hippocampal connectivity but not episodic memory after total sleep deprivation

Sleep deprivation significantly impairs a range of cognitive and brain function, particularly episodic memory and the underlying hippocampal function. However, it remains controversial whether one or two nights of recovery sleep following sleep deprivation fully restores brain and cognitive function. In this study, we used functional magnetic resonance imaging (fMRI) and examined the effects of two consecutive nights (20-hour time-in-bed) of recovery sleep on resting-state hippocampal connectivity and episodic memory deficits following one night of total sleep deprivation (TSD) in 39 healthy adults in a controlled in-laboratory protocol. TSD significantly reduced memory performance in a scene recognition task, impaired hippocampal connectivity to multiple prefrontal and default mode network regions, and disrupted the relationships between memory performance and hippocampal connectivity. Following TSD, two nights of recovery sleep restored hippocampal connectivity to baseline levels, but did not fully restore memory performance nor its associations with hippocampal connectivity. These findings suggest that more than two nights of recovery sleep are needed to fully restore memory function and hippocampal-memory associations after one night of total sleep loss

Association between physical activity and metabolic syndrome in middle-aged Japanese: a cross-sectional study

<p>Abstract</p> <p>Background</p> <p>Although many studies have reported an association between self-reported physical activity and metabolic syndrome (MetS), there is limited information on the optimal level of physical activity required to prevent MetS. This study aimed to determine the association between objectively measured physical activity and MetS in middle-aged Japanese individuals. We also determined the optimal cutoff value for physical activity required to decrease the risk of developing MetS.</p> <p>Methods</p> <p>A total of 179 men and 304 women, aged between 30 and 64 years, participated in this study. Participants were divided into two groups using the Japanese criteria for MetS as those with MetS or pre-MetS, and those without MetS. Participants were considered to be physically active if they achieved a physical activity level of 23 metabolic equivalents (METs) h/week, measured using a triaxial accelerometer. The association between physical activity and MetS was analyzed using logistic regression with the following covariates: sex, age, sedentary time, low intensity activity, calorie intake, smoking, menopause and body mass index. We also evaluated the factors that determined the association between the prevalence of MetS and pre-MetS and the physical activity cutoff value using classification and regression tree (CART) analysis.</p> <p>Results</p> <p>The odds ratio for MetS and pre-MetS was 2.20 for physically inactive participants (< 23 METs h/week), compared with physically active participants (≥ 23 METs h/week). The corresponding odds ratios for men and women were 2.27 (<it>P </it>< 0.01) and 1.95 (not significant), respectively. CART analyses revealed that moderate-vigorous physical activity of > 26.5 METs h/week was sufficient to decrease the prevalence of MetS and pre-MetS in middle-aged Japanese men and women.</p> <p>Conclusions</p> <p>The results of this cross-sectional study indicate that the Exercise and Physical Activity Reference for Health Promotion 2006 is inversely associated with the prevalence of MetS in men. Our results also suggest that moderate physical activity of > 26.5 METs h/week may decrease the risk of developing MetS and pre-MetS in middle-aged Japanese individuals.</p

Proof-of-concept of a Pneumatic Ankle Foot Orthosis Powered by a Custom Compressor for Drop Foot Correction

Pneumatic transmission has several advantages in developing powered ankle foot orthosis (AFO) systems, such as the flexibility in placing pneumatic components for mass distribution and providing high back-drivability via simple valve control. However, pneumatic systems are generally tethered to large stationary air compressors that restrict them for being used as daily assistive devices. In this study, we improved a previously developed wearable (untethered) custom compressor that can be worn (1.5 kg) at the waist of the body and can generate adequate amount of pressurized air (maximum pressure of 1050 kPa and a flow rate of 15.1 mL/sec at 550 kPa) to power a unilateral active AFO used to assist the dorsiflexion (DF) motion of drop-foot patients. The finalized system can provide a maximum assistive torque of 10 Nm and induces an average 0.03 +/- 0.06 Nm resistive torque when free movement is provided. The system was tested for two unilateral drop-foot patients. The proposed system showed an average improvement of 13.6 degrees of peak dorsiflexion angle during the swing phase of the gait cycle.N

Deep Reinforcement Learning-Based Adaptive IRS Control with Limited Feedback Codebooks

Intelligent reflecting surfaces (IRS) consist of configurable meta-atoms, which can alter the wireless propagation environment through design of their reflection coefficients. We consider adaptive IRS control in the practical setting where (i) the IRS reflection coefficients are attained by adjusting tunable elements embedded in the meta-atoms, (ii) the IRS reflection coefficients are affected by the incident angles of the incoming signals, (iii) the IRS is deployed in multi-path, time-varying channels, and (iv) the feedback link from the base station (BS) to the IRS has a low data rate. Conventional optimization-based IRS control protocols, which rely on channel estimation and conveying the optimized variables to the IRS, are not practical in this setting due to the difficulty of channel estimation and the low data rate of the feedback channel. To address these challenges, we develop a novel adaptive codebook-based limited feedback protocol to control the IRS. We propose two solutions for adaptive IRS codebook design: (i) random adjacency (RA), which utilizes correlations across the channel realizations, and (ii) deep neural network policy-based IRS control (DPIC), which is based on a deep reinforcement learning. Numerical evaluations show that the data rate and average data rate over one coherence time are improved substantially by the proposed schemes.Comment: Accepted for publication in IEEE International Conference on Communications (ICC), 2022. arXiv admin note: substantial text overlap with arXiv:2112.0187