Search Results (611)

Background: Peritumoral brain edema (PTBE) is the most frequent complication for intracranial meningiomas following radiotherapy, yet no clinically validated biologically effective dose (BED) threshold capable of reliably predicting PTBE has currently been established. Although conventional radiobiological models typically assume an α/β ratio of 2–4 for benign meningiomas, whether these values accurately reflect the dose–response characteristics underlying radiation-induced PTBE remains unclear. Methods: We analyzed 67 intact meningiomas in the convexity, parasagittal, or falcine regions treated with primary linear accelerator (LINAC)-based radiotherapy. The BED values were recalculated using α/β ratios ranging from 2 to 20, and receiver operating characteristic (ROC) analyses were performed to identify the optimal BED thresholds for predicting PTBE. The most informative α/β ratio was defined as the value yielding the highest Youden’s J statistic. Results: The ROC analyses showed that an assumed α/β ratio of 14 provided the highest discriminative accuracy for predicting PTBE in the overall cohort and markedly superior performance in patients younger than 70 years (area under the curve (AUC) 0.945; Youden’s J = 0.871). The optimal BED threshold for predicting PTBE was approximately 41 Gy (α/β = 14), corresponding to ~18 Gy in a single fraction and ~5.8 Gy per fraction in a five-fraction regimen. Conclusions: The BED values calculated using α/β ratios near 14 provide the most reliable estimate of PTBE risk following primary LINAC-based radiotherapy for convexity, parasagittal, and falcine meningiomas. Maintaining prescription doses below this threshold may help reduce the likelihood of PTBE in this patient population. Full article

Physical-layer security offers low probability of interception (LPI) in wireless communication systems. While prior methods such as the directional beamforming and secrecy coding schemes require knowledge of the eavesdropper (Eve)’s channel, passive eavesdropping limits their practicality. Artificial additive noise and artificial fast fading (AFF) schemes address the issue by degrading detection ability of a potential Eve without knowing its channel information. In particular, AFF achieves LPI by effectively shortening the coherence time of Eve’s channel using a random precoder while keeping the legitimate receiver (Bob)’s channel deterministic. In this paper, we propose a novel AFF design for spatial multiplexing multi-input multi-output (MIMO) amplify-and-forward (AF) relay systems. First, we formulate an optimization problem to achieve minimum mean squared error (MMSE) of Bob’s signals while guaranteeing LPI conditions from Eve, which is generally non-convex. To tackle the non-convexity of the problem, we apply a convex set approximation technique and thereby derive a simple closed-form design. Finally, we evaluated the performance of both Bob and Eve via computer simulations to demonstrate the effectiveness of our proposed design. Full article

This paper studies a novel movable antenna (MA)-aided Cell-Free Massive MIMO system to leverage the corresponding spatial degrees of freedom (DoFs) for improving the performance of distributed wireless networks. We aim to maximize the ergodic sum capacity by jointly optimizing the MA positions and the transmit covariance matrix based on statistical channel state information (CSI). To address the non-convex stochastic optimization problem, we propose a novel Constrained Stochastic Successive Convex Approximation (CSSCA) framework, enhanced with a robust slack-variable mechanism to handle non-convex antenna spacing constraints and ensure iterative feasibility. Numerical results show that the considered MA-enhanced system can significantly improve the ergodic capacity compared to fixed-antenna cell-free systems and that the proposed algorithm exhibits robust convergence behavior. Full article

Under the “Dual Carbon” strategy, high-penetration integration of distributed generators (DG) into distribution networks has triggered bidirectional power flow and reactive power-voltage violations. This phenomenon undermines the accuracy guarantee of conventional relaxation models (represented by second-order cone programming, SOCP), causing solutions to deviate from the AC power flow feasible region. Notably, ensuring solution feasibility becomes particularly crucial in engineering practice. To address this problem, this paper proposes a collaborative optimization framework integrating convex inner approximation (CIA) theory and a solution recovery algorithm. First, a system relaxation model is constructed using CIA, which strictly enforces ACPF constraints while preserving the computational efficiency of convex optimization. Second, aiming at the conservatism drawback introduced by the CIA method, an admissible region correction strategy based on Stochastic Gradient Descent is designed to narrow the dual gap of the solution. Furthermore, a multi-objective optimization framework is established, incorporating voltage security, operational economy, and renewable energy accommodation rate. Finally, simulations on the IEEE 33/69/118-bus systems demonstrate that the proposed method outperforms the traditional SOCP approach in the 24 h sequential optimization, reducing voltage deviation by 22.6%, power loss by 24.7%, and solution time by 45.4%. Compared with the CIA method, it improves the DG utilization rate by 30.5%. The proposed method exhibits superior generality compared to conventional approaches. Within the upper limit range of network penetration (approximately 60%), it addresses the issue of conservative power output of DG, thereby effectively promoting the utilization of renewable energy. Full article

The resummation of Stieltjes series remains a key challenge in mathematical physics, especially when Padé approximants fail, as in the case of superfactorially divergent series. Weniger’s $\delta$-transformation, which incorporates a priori structural information on Stieltjes series, offers a superior framework with respect to Padé. In the present work, the following fundamental question is addressed: Is the $\delta$-transformation, once it is applied to a typical Stieltjes series, capable of correctly simulating the branch cut structure of the corresponding Stieltjes function? Here, it is proved that the intrinsic log-convexity of the Stieltjes moment sequence (guaranteed via the positivity of Hankel’s determinants) allows the necessary condition for $\delta$ to have all real poles to be satisfied. The same condition, however, is not sufficient to guarantee this. In attempting to bridge such a gap, we propose a mechanism rooted in the iterative action of a specific linear differential operator acting on a class of suitable auxiliary log-concave polynomials. To this end, we show that the denominator of the $\delta$-approximants can always be recast as a high-order derivative of a log-concave polynomial. Then, on invoking the Gauss–Lucas theorem, a consistent geometrical justification of the $\delta$ pole positioning is proposed. Through such an approach, the pole alignment along the negative real axis can be viewed as the result of the progressive restriction of the convex hull under differentiation. Since a fully rigorous proof of this conjecture remains an open challenge, in order to substantiate it, a comprehensive numerical investigation across an extensive catalog of Stieltjes series is proposed. Our results provide systematic evidence of the potential $\delta$-transformation ability to mimic the singularity structure of several target functions, including those involving superfactorial divergences. Full article

The integration of reconfigurable intelligent surfaces (RISs) with flying ad hoc networks (FANETs) offers new opportunities to enhance performance in aerial communications. This paper proposes a novel FANET architecture in which each unmanned aerial vehicle (UAV) or drone is equipped with an RIS comprising M passive elements, enabling dynamic manipulation of the wireless propagation environment. We address the joint power allocation and RIS configuration problem to maximize the sum spectral efficiency, subject to constraints on maximum transmit power and unit-modulus phase shifts. The formulated optimization problem is non-convex due to coupled variables and interference. We develop an alternating optimization-based joint power and phase shift (AO-JPPS) algorithm that decomposes the problem into two subproblems: power allocation via successive convex approximation and phase optimization via Riemannian manifold optimization. A key contribution is addressing the RIS coupling effect, where the configuration of each RIS simultaneously influences multiple communication links. Complexity analysis reveals polynomial-time scalability, while derived performance bounds provide theoretical insights. Numerical simulations demonstrate that our approach achieves significant spectral efficiency gains over conventional FANETs, establishing the effectiveness of RIS-assisted drone networks for future wireless applications. Full article

Background: In adult spinal deformity (ASD) surgery, appropriate rod length determination is crucial, as excessive cranial rod length can lead to skin problems, especially in thin elderly patients if proximal junctional kyphosis (PJK) develops. In adolescent idiopathic scoliosis (AIS), correction is primarily performed in the coronal plane, and rod length changes are relatively predictable. Moreover, PJK is uncommon in AIS, making excess rod length rarely a clinical concern. In contrast, ASD correction involves more complex three-dimensional realignment, including restoration of lumbar lordosis (LL), which makes it challenging to predict postoperative changes in rod trace length (RTL). Furthermore, because PJK occurs more frequently in ASD surgery, appropriate rod length selection becomes clinically important. This study aimed to quantitatively evaluate changes in RTL before and after posterior correction. Method: Thirty patients with ASD who underwent staged lateral lumbar interbody fusion (LLIF) followed by posterior corrective fusion from T9 to the pelvis were retrospectively analyzed. RTL before posterior correction (Pre-RTL) was estimated from the planned screw insertional point on axial CT after LLIF, and postoperative RTL (Post-RTL) was measured from screw head centers on post-operative CT. LL and Cobb angle were assessed before and after posterior correction. Correlations between RTL change and alignment change were evaluated. Results: Postoperative RTL was shortened in all patients, with an average reduction of approximately 16–17 mm. RTL shortening demonstrated significant correlations with LL correction (R = 0.51, p = 0.003) and Cobb angle correction (R = 0.70, p = 0.00001). Greater shortening of RTL was observed on the convex side in patients with preoperative Cobb angle ≥ 10° (p = 0.04). Conclusions: Greater coronal deformity, particularly on the convex side, was associated with increased RTL shortening. These findings suggest that routine preparation of excessively long rods may be unnecessary. Consideration of anticipated RTL shortening may help avoid excessive cranial rod length and potentially reduce the risk of skin complications associated with PJK, particularly in thin elderly patients. Full article

Covert communication assisted by unmanned aerial vehicles (UAVs) can achieve a low detection probability in complex environments through auxiliary strategies, including dynamic trajectory planning and power management, etc. This paper proposes a dual-UAV scheme, where one UAV transmits covert information while the other one generates stochastic jamming to disrupt the eavesdropper and reduce the probability of detection. We propose a dual-mode jamming scheme which can efficiently enhance the average covert rate (ACR). A joint optimization of the dual UAVs’ flight speeds, accelerations, transmit power, and trajectories is conducted to achieve the maximum ACR. Given the high complexity and non-convexity, we develop a dedicated algorithm to solve it. To be specific, the optimization is decomposed into three sub-problems, and we transform them into tractable convex forms using successive convex approximation (SCA). Numerical results verify the efficacy of dual-mode jamming in boosting ACR and confirm the effectiveness of this algorithm in enhancing CC performance. Full article

This work presents a straightforward procedure for constructing the solution to the steady-state energy-transfer process in a system of two convex, opaque, gray bodies, with the aim of determining the temperature distribution within these bodies when separated by a vacuum. The methodology proposed in this work combines a sequence of elements that are functions obtained from the solution of uncomplicated, well-known linear, uncoupled heat transfer problems, thereby enabling solutions to be obtained using tools found in basic engineering textbooks. Specifically, these well-known problems resemble classical conduction-convection heat transfer problems, in which the boundary condition is described by the noteworthy Newton’s law of cooling. The limit of sequences of elements that are solutions to straightforward linear problems corresponds to the original, complex, coupled nonlinear problem. The convergence of these sequences is mathematically proven. The phenomenon (considered in this work) encompasses those involving black bodies. Since each element of the sequence arises from a well-known linear problem, numerical approximations can be used to obtain it, yielding a simple and powerful tool for simulations. Some presented results highlight the importance of considering thermal interaction between the two bodies, even in the absence of physical contact. In particular, the alterations in the temperature distributions of two separate gray bodies are explicitly shown to result from their thermal interaction. Full article

Mudstone badlands are critical hotspots of erosion and sediment yield, and their rapid morphological changes serve as an ideal site for studying erosion processes. This study used high-resolution Unmanned Aerial Vehicle (UAV) photogrammetry to monitor erosion patterns on a mudstone badland platform in southwestern Taiwan over a 22-month period. Five UAV surveys conducted between 2017 and 2018 were processed using Structure-from-Motion photogrammetry to generate time-series digital surface models (DSMs). Topographic changes were quantified using DSMs of Difference (DoD). The results reveal intense surface lowering, with a mean erosion depth of 34.2 cm, equivalent to an average erosion rate of 18.7 cm yr⁻¹. Erosion is governed by a synergistic regime in which diffuse rain splash acts as the dominant background process, accounting for approximately 53% of total erosion, while concentrated flow drives localized gully incision. Morphometric analysis shows that erosion depth increases nonlinearly with slope, consistent with threshold hillslope behavior, but exhibits little dependence on the contributing area. Plan and profile curvature further influence the spatial distribution of erosion, with enhanced erosion on both strongly concave and convex surfaces relative to near-linear slopes. The gully network also exhibits rapid channel adjustment, including downstream meander migration and associated lateral bank erosion. These findings highlight the complex interactions among hillslope processes, gully dynamics, and base-level controls that govern badland landscape evolution and have important implications for erosion modeling and watershed management in high-intensity rainfall environments. Full article

With the aid of non-orthogonal multiple access (NOMA), this paper investigates simultaneous two-way communications for cooperative cognitive radio networks, where a group of secondary access points (APs) scattered over a primary cell not only serve their own users but also help the primary cell-edge users′ transmissions cooperatively. As a reward for the cooperation, these APs are granted full access to the primary frequency spectrum. To coordinate the two-way transmissions of the primary and secondary networks, we propose a spectrum-efficient cooperative scheme that only involves two transmission phases, and particularly, the two variable-length transmission phases endow the system with the capability of adapting to possible DL and UL traffic asymmetry. For the system design, we formulate a power minimization problem subject to the bidirectional transmission rate constraints of both networks. The formulated problem is shown to be nonlinear and nonconvex, and for the numerically efficient solution, we propose an iterative algorithm facilitated by the successive convex approximation technique. Simulation results show that the proposed design algorithm has fast convergence speed and is superior to the hybrid orthogonal multiple access and NOMA schemes. Full article

The broadcast nature of wireless channels introduces significant security vulnerabilities in information transmission, particularly when the eavesdropper is close to the legitimate destination. In such scenarios, the eavesdropping channel often exhibits high spatial correlation with, or even superior quality to, the legitimate channel. This makes it challenging for traditional power optimization methods to effectively suppress the eavesdropping rate. To address this challenge, this paper proposes an optimization method for the secrecy capacity of unmanned aerial vehicle (UAV) relaying based on the dynamic adjustment of the power allocation factor. By injecting artificial noise (AN) during signal forwarding and combining it with real-time channel state information, the power allocation factor can be dynamically adjusted to achieve precise jamming of the eavesdropping link. We consider a four-node communication model consisting of a source, a UAV, a legitimate destination, and a passive eavesdropper, and formulate a joint optimization problem to maximize the secrecy rate. Due to the non-convexity of the original problem, we introduce relaxation variables and apply successive convex approximation (SCA) to reformulate it into an equivalent convex optimization problem. An analytical solution for the power allocation factor is derived using the water-filling (WF) algorithm. Furthermore, an alternating iterative optimization algorithm with AN assistance is proposed to achieve global optimization of the system parameters. Simulation results demonstrate that, compared to traditional power optimization schemes, the proposed algorithm substantially suppresses the eavesdropping channel capacity while enhancing transmission efficiency, thereby significantly improving both secrecy performance and overall communication reliability. Full article

Background and Clinical Significance: Focal hemorrhagic severity associated with posterior convexity pial brain arteriovenous malformation (AVM) cases can be exacerbated by hemodynamic stress focusing on focal areas of architectural weakness and by superficial venous outflow being restricted by non-redundant superficial venous drainage. This clinical case report exemplifies how bedside neurologic localization and angioarchitectural characteristics can inform the selection of microsurgical approaches for the treatment of ruptured AVMs that are directed at reducing hemorrhage recurrence risk through corridors based on rupture location. Case Presentation: An otherwise healthy young adult male (modified Rankin scale [mRS] pre-morbid = 0) initially presented with a thunderclap headache, emesis, photophobia, decreased level of consciousness (admitted Glasgow Coma Score [GCS] = 11; E3V3M5), and subsequent deficits including left-sided pyramidal weakness, visual field loss, and visuo-spatial neglect. A non-contrast computed tomogram (CT) confirmed an intraparenchymal hemorrhage (ICH) located within the right hemisphere’s posterior lobe. Angiographic evaluation of this AVM with catheter injection and three-dimensional reconstruction revealed a compact right occipital posterior convexity pial AVM (nidus 8 × 3 mm) supplied by distal cortical branches of the right middle cerebral artery (MCA); all blood draining from the nidus was directed to a single cortical vein which then drained into the superior sagittal sinus; there were two additional intranidal saccular aneurysms (approximately 3 × 2 mm and 3 × 3 mm). Because of the acute worsening secondary to ICH and because all venous drainage was superficial-only, a single-stage approach was selected given the urgency: decompressive evacuation of the hematoma via a corridor to the site of the AVM, followed by microsurgical removal of the AVM. The removal of the AVM was accomplished in a feeder-first, vein-last sequence, and en-passage arteries and parasagittal bridging veins were preserved throughout the procedure. Additionally, the two intranidal aneurysms identified as potential weak points during progressive devascularization of the AVM were specifically treated during the removal procedure. Following the successful removal of the AVM, the patient experienced a rapid recovery and returned to a nearly premorbid state of functioning, excepting a persistent small area of quadrantanopia. Conclusions: Rupture of posterior convexity AVMs may result in increased hemorrhagic severity due to localized architectural weaknesses in addition to the overall size of the AVM nidus. By correlating neurological findings, the topography of the hemorrhage, and angioarchitectural features early after rupture, emergency decisions regarding management can be better informed. The application of a hematoma-corridor, feeder-first/vein-last microsurgical approach for the treatment of such AVMs can achieve definitive curative results while minimizing damage to posterior cortical regions. Full article

This paper considers how a system designer can program a team of autonomous agents to coordinate with one another such that each agent selects (or covers) an individual resource with the goal that all agents collectively cover the maximum number of resources. Specifically, we study how agents can formulate strategies without information about other agents’ actions so that system-level performance remains robust in the presence of communication failures. First, we use an algorithmic approach to study the scenario in which all agents lose the ability to communicate with one another, have a symmetric set of resources to choose from, and select actions independently according to a probability distribution over the resources. We show that the distribution that maximizes the expected system-level objective under this approach can be computed by solving a convex optimization problem, and we introduce a novel polynomial-time heuristic based on subset selection. Further, both of the methods are guaranteed to be within $1-1/e$ of the system’s optimal in expectation. Second, we use a learning-based approach to study how a system designer can employ neural networks to approximate optimal agent strategies in the presence of communication failures. The neural network, trained on system-level optimal outcomes obtained through brute-force enumeration, generates utility functions that enable agents to make decisions in a distributed manner. Empirical results indicate the neural network often outperforms greedy and randomized baseline algorithms. Collectively, these findings provide a broad study of optimal agent behavior and its impact on system-level performance when the information available to agents is extremely limited. Full article

To address the problem of real-time coordinated control in gear selection and power distribution for hybrid energy storage systems, an adaptive real-time optimal control strategy is proposed in this study. Firstly, a vehicle dynamics model with a hybrid energy storage system and a two-speed mechanical automatic transmission (2AMT) is constructed. Next, a nonlinear optimization problem aiming to minimize battery energy consumption is established. To address the challenge of high computational complexity, polynomial fitting and variable substitution are employed to transform the original nonlinear problem into a convex optimization framework. This transformation enables the control variables to be directly obtained through efficient matrix operations with a global optimal analytical solution, thereby significantly improving computational efficiency. The real-time adaptive control strategy achieves forward-looking coordinated optimization of power distribution and gear selection. The simulation results show that the proposed method can achieve an effect similar to that of dynamic programming (DP) in terms of energy consumption but gains much higher computational efficiency. Compared with the rule-based strategy, the battery energy consumption is reduced by approximately 10%. The method demonstrates advantages both in terms of economy and real-time performance. Full article