UAV-Aided MIMO communications for 5G Internet of Things

The unmanned aerial vehicle (UAV) is a promising enabler of the Internet of Things (IoT) vision, due to its agile maneuverability. In this paper, we explore the potential gain of UAV-aided data collection in a generalized IoT scenario. Particularly, a composite channel model including both large-scale and small-scale fading is used to depict typical propagation environments. Moreover, rigorous energy constraints are considered to characterize IoT devices as practically as possible. A multi-antenna UAV is employed, which can communicate with a cluster of single-antenna IoT devices to form a virtual MIMO link. We formulate a whole-trajectory-oriented optimization problem, where the transmission duration and the transmit power of all devices are jointly designed to maximize the data collection efficiency for the whole flight. Different from previous studies, only the slowly-varying large-scale channel state information (CSI) is assumed available, to coincide with the fact that practically it is quite difficult to predictively acquire the random small-scale channel fading prior to the UAV flight. We propose an iterative scheme to overcome the non-convexity of the formulated problem. The presented scheme can provide a significant performance gain over traditional schemes and converges quickly

Effect of Cd isoelectronic substitution on thermoelectric properties of Zn0.995Na0.005Sb

AbstractZnSb as a kind of material with abundant resource and low cost has a low thermal conductivity and a high Seebeck coefficient, giving the potential of high thermoelectric properties. In this paper, Cd isoelectronic substitution was adopted to further improve the thermoelectric performance by reducing the lattice thermal conductivity of ZnSb. The results show that Cd substitution reduces the lattice thermal conductivity and increases the electrical conductivity. A high ZT value of 1.22 is achieved at 350 °C for Zn0.915Na0.005Cd0.08Sb

Aging and cancer hallmarks as therapeutic targets

The prevalence of age-related diseases has been progressively increasing worldwide. The pathogenesis of these disorders, including cancer, is closely associated with an age-dependent decline in cellular functions. There are multiple layers of crosstalk between aging processes and oncogenesis. Therefore, a better understanding of the interplay between aging and cancer will facilitate the development of novel therapeutic intervention strategies for malignancies at advanced ages. Recent review articles have concisely summarized and updated aging and cancer hallmark features and adequately discussed the interplays at both the cellular and molecular levels. Because aging and cancer share several key mechanisms that define the hallmarks, targeting shared features, such as cellular senescence, may be beneficial for cancer prevention and treatments. Notably, senolysis, an innovative therapeutic intervention for selectively removing senescent cells, holds great promise for developing new therapeutic approaches for cancers and other age-related diseases, such as viral infections and cardiovascular diseases. Herein we briefly summarize the recently updated knowledge on aging and cancer hallmarks, as well as the advances in senolysis for age-related conditions

An intrinsic connection between COVID-19 and aging

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) caused a rapidly spreading outbreak of coronavirus disease 2019 (the COVID-19 pandemic). COVID-19 has severely affected healthcare systems worldwide, as well as the global economy, and has significantly increased morbidity and mortality rates. The majority of COVID-19-related deaths occurred in older individuals, primarily among those with concomitant diseases, including metabolic, respiratory, and cardiovascular diseases. Aging hallmarks, such as cellular senescence, chronic inflammation, and genomic instability, partially explain the increased disease severity at the molecular level with advancing age. Other multifactorial considerations, including healthcare facilities, socioeconomic status, and dissemination of epidemic information, may help control morbidity in the elderly population. While the World Health Organization declared an end to the emergency status of COVID-19 in May 2023, physical and emotional impairments may persist after recovery from the virus. Precautions should therefore be taken to prevent future pandemics, and suitable emphasis should be placed on addressing persistent COVID-19 and preventing future pandemics

Facile synthesis of a nickel sulfide (NiS) hierarchical flower for the electrochemical oxidation of H2O2 and the methanol oxidation reaction (MOR).

The synthesis of a novel hierarchical flower-like NiS via a solvothermal method for the electrochemcial oxidation of H2O2 on a carbon paste electrode with high catalytic activity for the (MOR) in an alkaline medium has been reported. Novel nickel sulfide (NiS) hierarchical flower-like structures were characterized by X-ray diffraction, scanning electron microscope, and transmission electron microscopy. A carbon paste electrode was modified with the as-prepared hierarchical flower-like NiS, resulting in a high electrocatalytic activity toward the oxidation of H2O2. The NiS-modified electrode was used for H2O2 sensing, which was achieved over a wide linear range from 0.5 μMto1.37mM(I/μA =-0.19025 + 0.06094 C/mM) with a low limit of detection (LOD) of 0.3 μM and a limit of quantitation (LOQ) of 0.8 μM. The hierarchical flower-like NiS also exhibited a high electrocatalytic activity for the methanol oxidation reaction (MOR) in an alkaline medium with a high tolerance toward the catalyst-poisoning species generated during the MOR. The MOR proceeded via the direct electrooxidation of methanol on the oxidized NiS surface layer because the oxidation peak potential of the MOR was more positive than that of the oxidation of NiS

Anti-hypertensive effect of a novel angiotensin II receptor neprilysin inhibitor (ARNi) -S086 in DSS rat model

IntroductionAngiotensin receptor-neprilysin inhibitor (ARNi), comprised of an angiotensin receptor blocker (ARB) and a neprilysin inhibitor (NEPi), has established itself as a safe and effective intervention for hypertension. S086 is a novel ARNi cocrystal developed by Salubris for the treatment of heart failure and hypertension.MethodsDahl Salt Sensitive (DSS) hypertensive rat model and telemetry system were employed in this study to investigate the anti-hypertensive efficacy of S086 and compare it with the first ARNi-LCZ696.Results and discussionThe study showed that oral administration of S086 dose-dependently lowered blood pressure (P < 0.001). The middle dosage of S086 (23 mg/kg) exhibited efficacy comparable to LCZ696 (68 mg/kg), while also demonstrating superiority at specific time points (P < 0.05). Notably, water consumption slightly decreased post-treatment compared to the vehicle group. Furthermore, there were significant increases in natriuresis and diuresis observed on the first day of treatment with 23 mg/kg and 68 mg/kg S086 (P < 0.001). However, over the course of treatment, the effects in all treatment groups gradually diminished. This study demonstrates the anti-hypertensive efficacy of S086 in DSS hypertensive rat model, offering promising avenues for the clinical development of S086 as a hypertension treatment

Asymmetrical Hierarchical Networks with Attentive Interactions for Interpretable Review-Based Recommendation

Recently, recommender systems have been able to emit substantially improved recommendations by leveraging user-provided reviews. Existing methods typically merge all reviews of a given user or item into a long document, and then process user and item documents in the same manner. In practice, however, these two sets of reviews are notably different: users' reviews reflect a variety of items that they have bought and are hence very heterogeneous in their topics, while an item's reviews pertain only to that single item and are thus topically homogeneous. In this work, we develop a novel neural network model that properly accounts for this important difference by means of asymmetric attentive modules. The user module learns to attend to only those signals that are relevant with respect to the target item, whereas the item module learns to extract the most salient contents with regard to properties of the item. Our multi-hierarchical paradigm accounts for the fact that neither are all reviews equally useful, nor are all sentences within each review equally pertinent. Extensive experimental results on a variety of real datasets demonstrate the effectiveness of our method

Dynamic Gaussian Mixture based Deep Generative Model For Robust Forecasting on Sparse Multivariate Time Series

Forecasting on sparse multivariate time series (MTS) aims to model the predictors of future values of time series given their incomplete past, which is important for many emerging applications. However, most existing methods process MTS's individually, and do not leverage the dynamic distributions underlying the MTS's, leading to sub-optimal results when the sparsity is high. To address this challenge, we propose a novel generative model, which tracks the transition of latent clusters, instead of isolated feature representations, to achieve robust modeling. It is characterized by a newly designed dynamic Gaussian mixture distribution, which captures the dynamics of clustering structures, and is used for emitting timeseries. The generative model is parameterized by neural networks. A structured inference network is also designed for enabling inductive analysis. A gating mechanism is further introduced to dynamically tune the Gaussian mixture distributions. Extensive experimental results on a variety of real-life datasets demonstrate the effectiveness of our method.Comment: This paper is accepted by AAAI 202