Optimal Beamforming for Hybrid Satellite Terrestrial Networks with Nonlinear PA and Imperfect CSIT

In hybrid satellite-terrestrial networks (HSTNs), spectrum sharing is crucial to alleviate the "spectrum scarcity" problem. Therein, the transmit beams should be carefully designed to mitigate the inter-satellite-terrestrial interference. Different from previous studies, this work considers the impact of both nonlinear power amplifier (PA) and large-scale channel state information at the transmitter (CSIT) on beamforming. These phenomena are usually inevitable in a practical HSTN. Based on the Saleh model of PA nonlinearity and the large-scale multi-beam satellite channel parameters, we formulate a beamforming optimization problem to maximize the achievable rate of the satellite system while ensuring that the inter-satellite-terrestrial interference is below a given threshold. The optimal amplitude and phase of desired beams are derived in a decoupled manner. Simulation results demonstrate the superiority of the proposed beamforming scheme.Comment: 5 pages, 5 figures, journa

Aerial small cells using coordinated multiple UAVs : an energy efficiency optimization perspective

Recently, unmanned aerial vehicle (UAV) communications have attracted great research interest. Due to the limited on-board energy, the optimization of energy efficiency (EE) is critical for UAV communications. In this paper, we propose an EE maximization scheme for UAV swarm-enabled small cell networks using large-scale channel state information at the transmitter (CSIT). The proposed scheme provides an agile coordination strategy for the UAVs in a swarm under energy constraints. We first formulate the EE maximization problem, where the objective function is defined as the ratio of the ergodic total data size to the total energy consumption. After that, an accurate approximation is derived to remove the intractable expectation operator in the objective function. As the newly formulated problem is non-convex, we decompose it into two subproblems to optimize the transmit power and the hovering time in an iterative way. Further by leveraging the max-min and linear optimization tools, both subproblems are efficiently solved. Simulation results demonstrate the superiority of our EE maximization scheme

Nova+^+: Generative Language Models for Binaries

Generative large language models (LLMs) pre-trained on code have shown impressive effectiveness in code generation, program repair, and document analysis. However, existing generative LLMs focus on source code and are not specialized for binaries. There are three main challenges for LLMs to model and learn binary code: hex-decimal values, complex global dependencies, and compiler optimization levels. To bring the benefit of LLMs to the binary domain, we develop Nova and Nova$^+$, which are LLMs pre-trained on binary corpora. Nova is pre-trained with the standard language modeling task, showing significantly better capability on five benchmarks for three downstream tasks: binary code similarity detection (BCSD), binary code translation (BCT), and binary code recovery (BCR), over GPT-3.5 and other existing techniques. We build Nova$^+$ to further boost Nova using two new pre-training tasks, i.e., optimization generation and optimization level prediction, which are designed to learn binary optimization and align equivalent binaries. Nova$^+$ shows overall the best performance for all three downstream tasks on five benchmarks, demonstrating the contributions of the new pre-training tasks

Construction of an interferon regulatory factors-related risk model for predicting prognosis, immune microenvironment and immunotherapy in clear cell renal cell carcinoma

BackgroundInterferon regulatory factors (IRFs) played complex and essential roles in progression, prognosis, and immune microenvironment in clear cell renal cell carcinoma (ccRCC). The purpose of this study was to construct a novel IRFs-related risk model to predict prognosis, tumor microenvironment (TME) and immunotherapy response in ccRCC.MethodsMulti-omics analysis of IRFs in ccRCC was performed based on bulk RNA sequencing and single cell RNA sequencing data. According to the expression profiles of IRFs, the ccRCC samples were clustered by non-negative matrix factorization (NMF) algorithm. Then, least absolute shrinkage and selection operator (LASSO) and Cox regression analyses were applied to construct a risk model to predict prognosis, immune cells infiltration, immunotherapy response and targeted drug sensitivity in ccRCC. Furthermore, a nomogram comprising the risk model and clinical characteristics was established.ResultsTwo molecular subtypes with different prognosis, clinical characteristics and infiltration levels of immune cells were identified in ccRCC. The IRFs-related risk model was developed as an independent prognostic indicator in the TCGA-KIRC cohort and validated in the E-MTAB-1980 cohort. The overall survival of patients in the low-risk group was better than that in the high-risk group. The risk model was superior to clinical characteristics and the ClearCode34 model in predicting the prognosis. In addition, a nomogram was developed to improve the clinical utility of the risk model. Moreover, the high-risk group had higher infiltration levels of CD8+ T cell, macrophages, T follicular helper cells and T helper (Th1) cells and activity score of type I IFN response but lower infiltration levels of mast cells and activity score of type II IFN response. Cancer immunity cycle showed that the immune activity score of most steps was remarkably higher in the high-risk group. TIDE scores indicated that patients in the low-risk group were more likely responsive to immunotherapy. Patients in different risk groups showed diverse drug sensitivity to axitinib, sorafenib, gefitinib, erlotinib, dasatinib and rapamycin.ConclusionsIn brief, a robust and effective risk model was developed to predict prognosis, TME characteristics and responses to immunotherapy and targeted drugs in ccRCC, which might provide new insights into personalized and precise therapeutic strategies

The impact of general anesthesia on the outcomes of preterm infants with gestational age less than 32 weeks delivered via cesarean section

Background:Recent advancements in China’s perinatal and neonatal intensive care have significantly reduced neonatal mortality, yet preterm births before 32 weeks remain the primary cause of neonatal fatalities and contribute to long-term disabilities. The prognosis of very preterm infants (VPIs) is significantly affected by factors including the intrauterine environment, delivery method and neonatal intensive care. Cesarean section which often used for preterm births has implications that are not fully understood, particularly concerning the type of anesthesia used. This study examines the impact of general anesthesia (GA) during cesarean delivery on VPI outcomes, aiming to identify strategies for mitigating GA-associated risks.Methods:This cohort study analyzed 1,029 VPIs born via cesarean section under 32 weeks’ gestation at our single-center from 1 January 2018, to 31 December 2022. Detailed medical records, encompassing perioperative information, maternal data and neonatal outcomes were meticulously examined. The primary aim of this investigation was to compare maternal characteristics and neonatal outcomes between VPIs delivered under GA and neuraxial anesthesia (NA). A significance level of p < 0.05 was established.Results:Of the 1,029 VPIs analyzed, 87.95% (n = 905) were delivered via NA and 12.05% (n = 124) via GA. Mothers with hypertensive pregnancy diseases and emergency operations were more inclined to choose GA. VPIs delivered under GA showed a lower Apgar score at one and 5 minutes (p < 0.01), increased need for tracheal intubation resuscitation (32.2% vs. 12.2%, p < 0.01) and a greater incidence of severe neurological injury (SNI) (14.5% vs. 5%, p < 0.01). Multivariable analysis revealed GA was significantly associated with lower Apgar scores at one (OR 6.321, 95% CI 3.729–10.714; p < 0.01) and 5 minutes (OR 4.535, 95% CI 2.975–6.913; p < 0.01), higher risk of tracheal intubation resuscitation (OR = 3.133, 95% CI = 1.939–5.061; p < 0.01) and SNI (OR = 3.019, 95% CI = 1.615–5.643; p < 0.01). Furthermore, for VPIs delivered under GA, a prolonged interval from skin incision to fetus delivery was associated with a lower 5-min Apgar score (p < 0.01).Conclusion:This study revealed the significant impact of GA on adverse outcomes among VPIs. In cases when GA is required, proactive measures should be instituted for the care of VPIs such as expediting the interval from skin incision to fetal delivery

Investigation of the Mathematical Relationship between the Aortic Valve and Aortic Root: Implications for Precise Guidance in Aortic Valve Repair

Background: The study was aimed at investigating the mathematical relationship between the aortic valve and aortic root through CTA imaging-based reconstruction. Methods: We selected 121 healthy participants and analyzed the measurements of aortic root dimensions, including the sinotubular junction (SJT), ventriculo-arterial junction (VAJ), maximum sinus diameter (SD), sinus height (SH), effective height (eH) and coaptation height (cH). We also reconstructed 3-D aortic valve cusps using CTA imaging to calculate the aortic cusp surface areas. Data were collected to analyze the ratios and the correlation between aortic valve and aortic root dimensions. Results: Among healthy participants, the STJ was approximately 10% larger than the VAJ, and the SD was 1.375 times larger than the VAJ. The average eH and cH were 8.94 mm and 3.62 mm, respectively. The aortic cusp surface areas were larger in men than women. Regardless of sex, the non-coronary cusp was found to be largest, and was followed by the right coronary cusp and the left coronary cusp. Although the aortic root dimensions were also significantly larger in in men than women, the STJ to VAJ, SD to VAJ, and SH to VAJ ratios did not significantly differ by sex. The mathematical relationship between the aortic cusp surface areas and VAJ orifice area was calculated as aortic cusp surface areas Conclusions: The aortic root has specific geometric ratios. The mathematical relationship between the aortic valve and aortic root might be used to guide aortic valve repair

Differences in lower extremity kinematics and kinetics during a side-cutting task in patients with and without chronic ankle instability

BackgroundPatients with chronic ankle instability (CAI) have demonstrated altered hip and knee movement strategies during walking and running, but these movement modalities do not involve changes in speed and direction, making it difficult to simulate the conditions of real sports, whereas side-cutting task can provide CAI patients with a more realistic athletic challenge. However, there is limited literature examining the kinematic and kinetic differences in the hip, knee, and ankle joints of CAI patients during the side-cutting task.ObjectiveTo assess differences in lower extremity joint kinematics and kinetics during the side-cutting task in individuals with and without CAI.DesignCross-sectional study.Participants48 males, 24 in each of the CAI group and healthy control group; 40 females, 20 in each of the CAI group and healthy control group.MethodsLower extremity three-dimensional kinematic and kinetics data were evaluated by using a three-dimensional motion analysis system during the initial contact (IC) and toe off (TO) while side-cutting.ResultsCompared with healthy controls, male patients with CAI exhibited greater hip flexion and external rotation angles, knee internal rotation angles, smaller knee flexion angles and ankle inversion angles, greater hip external rotation moments, and greater knee abduction moments; female patients with CAI exhibited smaller hip and knee flexion angles, greater hip external rotation angles, larger ankle inversion angles and internal rotation angles, smaller hip external rotation moments, and greater knee abduction moments.ConclusionOur findings indicate that patients with CAI exhibit altered lower limb joint kinematics and kinetics during side-cutting task, with significant sex-specific differences. These movement pattern changes involve proximal joint compensation to stabilize the unstable distal ankle joint; however, these compensatory changes are not always favorable. The greater hip external rotation moment and greater knee internal rotation angle demonstrated by male CAI patients, the smaller hip flexion angle and greater ankle internal rotation angle demonstrated by female CAI patients, and the smaller knee flexion angle and greater knee abduction moment common to both sexes may impair the lower limb's ability to effectively absorb and dissipate ground reaction forces, potentially elevating the risk of lower extremity injuries

Target driven visual navigation for a mobile robot using deep reinforcement learning

Target-driven visual navigation remains a critical challenge for autonomous mobile robots (AMRs) operating in dynamic, unstructured environments. Traditional approaches relying on pre-built maps or GPS-based localization often fail in GPS-denied indoor spaces or scenarios requiring adaptation to unseen layouts. This dissertation presents a novel deep reinforcement learning (DRL) framework that enables AMRs to navigate toward alphanumeric targets using egocentric visual inputs, eliminating dependency on prior environmental knowledge. The proposed framework integrates three key innovations: (1) zero-shot object detection for robust localization of numeric targets without class-specific training, (2) Transformer-based Optical Character Recognition (TrOCR) for discriminative feature extraction, and (3) Principal Component Analysis (PCA) to enhance numeric differentiation by reducing redundant visual information. Leveraging procedural environment generation via ProcTHOR, we create diverse corridor configurations with varying lighting, textures, and obstacle layouts to ensure generalization. The navigation policy is optimized through Proximal Policy Optimization (PPO), combining sparse rewards for target proximity with penalties for inefficient movements. Experimental evaluations demonstrate that the proposed model achieves a 70% success rate in target navigation tasks, marking a significant breakthrough compared to baseline models that lack alphanumeric image recognition capabilities. This performance gap highlights the critical role of integrating visual-textual understanding for navigating alphanumeric targets.Master's degre