Accurate 3D deformation control of deformable soft tissues is of paramount importance in robotic-assisted surgeries. Selecting optimal grasping points is a fundamental challenge, as the deformation behavior is highly dependent on the applied forces and their locations. This paper presents an efficient grasping point selection algorithm using optimization-based inverse finite element method for tissue manipulation tasks. We propose a method for the automatic identification of optimal grasping points that minimize feature or shape errors during deformation tasks. Specifically, we formulate the grasping task as a quadratic programming problem while considering the complex mechanical coupling within the tissue structure. Our method effectively accommodates both discrete key points and point clouds as input, and can simultaneously determine multiple optimal grasping points in one optimization process. We validate the proposed method in simulation on a tissue and liver model, demonstrating its feasibility and efficiency in various scenarios. Real-world experiments are conducted on a silicone liver phantom to further validate the effectiveness of our proposed method.

This research proposes an Internet of Things (IoT)-enabled adaptive robotic navigation framework tailored for smart campuses and urban mobility systems. It aims to overcome critical limitations in existing systems that rely on static data, lack real-time adaptability, and perform poorly in dynamic or adverse environments. The proposed system uniquely integrates heterogeneous real-time data sources including traffic, obstacle, and weather captured from IoT sensors into a unified decision-making architecture. It combines a graph neural network for dynamic environmental modeling, a convolutional neural network for obstacle mapping, and a multilayer perceptron for weather-aware path assessment. A proximal policy optimization-based reinforcement learning (RL) controller then computes continuous control actions. A novel multi-objective reward function adaptively adjusts priorities between travel time, energy efficiency, collision risk, and terrain stability based on the current IoT context, enabling fine-grained, scenario-aware optimization. The system is deployed on resource-constrained edge hardware (Jetson Nano), proving its feasibility for real-time embedded applications. Simulations across diverse scenarios including urban traffic congestion, dynamic obstacle handling, and adverse weather demonstrate 95% navigation accuracy, 98% obstacle detection precision, and near-optimal route selection. The framework sustains real-time operation with 10 Hz decision throughput and sub-300 ms latency, outperforming traditional static and rule-based systems while sustaining over 92% performance consistency under adverse weather. This work introduces a first-of-its-kind modular framework that fuses IoT sensory data, adaptive RL control, and edge deployment for robust, efficient navigation. It establishes a scalable baseline for real-world autonomous mobility in smart city ecosystems.

The natural variability of atmospheric 14C has been significantly altered by anthropogenic activities linked to technological advancements and energy consumption over the past two and a half centuries. The Suess effect, a consequence of the combustion of old carbon (fossil fuels) since the mid-18th century and the bomb peak from the mid-20th century’s thermonuclear tests, has obscured the natural 14C signal in the atmosphere. This study presents a 14C analysis of leaves, flowers, and grass collected from various locations worldwide. Over the last 10 years, more than 150 samples have been collected and used as materials for experiments conducted by students in physics lab classes (Department of Physics, ETH Zurich) or as part of school projects. Short-lived vegetal fragments are ideal material for teaching radiocarbon dating and demonstrating our research. The collection of data presented here underscores the sensitivity of radiocarbon analysis for detecting fossil carbon components. Trees from urban sites worldwide demonstrate a dilution of the atmospheric 14C concentration of 2–3%. Trees growing close to busy roads and traffic show a dilution of up to 10%. Moreover, the data show a fading trend of the bomb peak observed from 2015 to the present, as well as the direct impact of fossil CO2 on the 14C concentration of the living biota around us.

Levels of cancellativity in commutative monoids M, determined by stable-rank values in $\mathbb {Z}_{> 0} \cup \{\infty \}$ for elements of M, are investigated. The behavior of the stable ranks of multiples $ka$, for $k \in \mathbb {Z}_{> 0}$ and $a \in M$, is determined. In the case of a refinement monoid M, the possible stable-rank values in archimedean components of M are pinned down. Finally, stable rank in monoids built from isomorphism or other equivalence classes of modules over a ring is discussed.

The recovery concept has been widely integrated into mental health policy, services, and research. However, its applicability across diverse contexts and populations remains a subject of ongoing critique. This paper discusses key issues pertaining to the suitability of implementing the recovery approach in child and youth mental health services, including adult-centric foundations of the recovery concept; limited multi-stakeholder participatory research; privileging individualistic ideologies; differences in developmental stages; differences in illness trajectory, mental health experiences, and service provider interactions; and different systems of care and social environments. The paper concludes with recommendations for future research.

Quantitative research has established a strong association between ethnopolitical exclusion and civil war onset, but direct investigation of the proposed causal pathway has been limited. This article applies large-N qualitative analysis (LNQA) to 15 post–Cold War cases to trace how exclusion may generate grievances, mobilization, and conflict escalation. In nine cases, grievance-based mobilization preceded civil war, and escalation followed governments’ reliance on indiscriminate repression or on inconsistent mixes of rejection and accommodation. In six cases, however, conflict itself produced exclusion, revealing recursive dynamics rather than a one-way sequence. These findings refine grievance theory by showing that escalation is shaped by patterns of state response and that exclusion may also emerge as a result of violence. More broadly, the study demonstrates how systematic qualitative analysis across multiple cases can trace mechanisms, address concerns about endogeneity and measurement validity, and still support cautious generalization.

Let $X_H$ be the number of copies of a fixed graph H in G(n,p). In 2016, Gilmer and Kopparty conjectured that a local central limit theorem should hold for $X_H$ as long as H is connected, $p\gg n^{-1/m(H)}$ and $n^2(1-p)\gg 1$, where m(H) denotes the m-density of H. Recently, Sah and Sawhney showed that the Gilmer–Kopparty conjecture holds for constant p. In this paper, we show that the Gilmer–Kopparty conjecture holds for triangle counts in the sparse range. More precisely, if $p \in (4n^{-1/2}, 1/2)$, then

\[ {} {} {}\sup_{x\in \mathcal{L}}\left| \dfrac{1}{\sqrt{2\pi}}e^{-x^2/2}-\sigma\cdot \mathbb{P}(X^* = x)\right|=n^{-1/2+o(1)}p^{1/2}, {} {}\]

$\sigma^2 = \mathbb{V}\text{ar}(X_{K_3})$

$X^{*}=(X_{K_3}-\mathbb{E}(X_{K_3}))/\sigma$

$\mathcal{L}$

$X^*$

$n^{-1}\ll p \lt c$

$c\in (0,1)$

$\ell_1$

$m_2$

In this paper, we investigate the ideal structure of uniform Roe algebras for general metric spaces beyond the scope of Yu’s Property A. Inspired by the ideal of ghost operators coming from expander graphs and in contrast to the notion of geometric ideal, we introduce a notion of ghostly ideal in a uniform Roe algebra, whose elements are locally invisible in certain directions at infinity. We show that the geometric ideal and the ghostly ideal are, respectively, the smallest and the largest element in the lattice of ideals with a common invariant open subset of the unit space of the coarse groupoid by Skandalis–Tu–Yu, and hence the study of ideal structure can be reduced to classifying ideals between the geometric and the ghostly ones. We also provide a criterion to ensure that the geometric and the ghostly ideals have the same $K$-theory, which helps to recover counterexamples to the coarse Baum–Connes conjectures. Moreover, we introduce a notion of partial Property A for a metric space to characterize the situation in which the geometric ideal coincides with the ghostly ideal. As an application, we provide a concrete description for the maximal ideals in a uniform Roe algebra in terms of the minimal points in the Stone–Čech boundary of the space.

In this article, a 1 × 2 bandwidth (BW) and frequency-reconfigurable dielectric resonator-based multiple input multiple output (MIMO) antenna array is presented for 5G sub-6 GHz (3.3–6.0 GHz)/Wi-Fi 6E (5.925–6.425 GHz)/Wi-Fi 5G (5.15–5.85 GHz) applications. Additional dual-ring-open loop resonator structures with varied dimensions are introduced within antenna’s feeding network to achieve BW and frequency reconfigurability. RF PIN and varactor diodes (VDs) are integrated with proposed structure to enable switching between various modes and continuous tuning of frequency and BW, respectively. Further, Taguchi neural network (TNN) has been incorporated to predict percentage bandwidth of proposed antenna, getting a maximum deviation of only 0.6% from actual value. The proposed structure operated from 4.98 to 6.5 GHz, achieving wide continuous frequency tuning of 20.36% in passband and 6.1% reconfiguration for notch band. It also demonstrates continuous BW tunability from 16.69% to 34.44% with measured BWs of 19.58%, 34.44%, and 16.69% at 0, 3, and 8 V reverse bias voltages of VDs, respectively. MIMO antenna array structure also shows enhanced gain performance with a peak gain of 11.03 dBi and an overall gain above 7 dBi in the whole operating band.

In this article, we clarify the relation between the squeezing function and the Fridman invariant corresponding to a general domain $\Omega $ (not necessarily convex), where $\Omega $ is defined by

$$ \begin{align*}\Omega = \bigg\lbrace z \in \mathbb{C}^{r_{1}}\times\mathbb{C}^{r_{2}}\times\cdots\times\mathbb{C}^{r_{s}} : \sum\limits_{i\in I_{k}} ||z_{i}||^{m_{i}} < 1, 1\leq k \leq p \bigg\rbrace,\end{align*} $$

with $I_{k}\cap I_{l} = \emptyset $ if $k\neq l$, $I_{1}\cup I_{2} \cup \cdots \cup I_{p} = \lbrace 1, 2, \ldots , s\rbrace $, $n = r_{1} + r_{2} + \cdots + r_{s}$ and $m_{i}> 0$ for all i. Furthermore, we give an example of a domain whose squeezing function corresponding to $\Omega $ is not plurisubharmonic.

The bishops of the Russian Orthodox Church, like military and civil servants in late imperial Russia, underwent significant “aging,” with the median age rising substantially as a result of greater life expectancy. In contrast to existing scholarship, which advances a political explanation for staffing in the episcopate (above all, the high rate of turnover and transfers), this study seeks to show that demography and the service structure were the key factors. Rather than rely on the politicized memoir literature, this analysis is based on the diary of the presiding member of the Synod, which focuses on the rationale and problems in staffing the episcopate. Significantly, the diocesan bishops were not only overaged but overtasked, finding it ever more difficult to perform traditional, let alone, additional roles. All this provides a new perspective on the Church’s capacity to address the growing social and confessional challenges in late imperial Russia.