Shandong University of Political Science and Law

The butterfly genus Acytolepis (Lepidoptera: Lycaenidae: Polyommatinae) is widely distributed in the Indomalayan and Australasian realms. However, no complete mitochondrial genome has been reported for this genus, leaving its mitogenomic characteristics and evolutionary patterns unclear. In this study, we sequenced and characterized the first complete mitogenome of Acytolepis puspa, the type species of the genus. The circular double-stranded mitogenome is 15,511 bp in length and comprises 13 protein-coding genes (PCGs), two ribosomal RNA genes (rRNAs), 22 transfer RNA genes (tRNAs), and one A + T-rich control region, exhibiting a typical gene content and organization conserved in Lepidoptera. Comparative analyses of Polyommatinae mitogenomes revealed pronounced heterogeneity in evolutionary rates among PCGs, with ND6 and ND3 showing relatively high nucleotide diversity, whereas COX1 was the most conserved gene. Selection pressure analyses indicated that all PCGs are evolving under purifying selection, with ATP8 and ND6 exhibiting relatively relaxed selective constraints compared to other genes. Phylogenetic analyses based on concatenated mitochondrial PCGs using both Maximum likelihood (ML) and Bayesian inference (BI) methods produced identical and well-supported topologies, recovering A. puspa as the sister taxon to Celastrina species within Polyommatinae. Overall, this study provides the first mitogenomic resource for Acytolepis, enriches mitochondrial molecular markers for lycaenid butterflies, and contributes new insights into mitogenome evolution and phylogenetic relationships within Polyommatinae.

Improving total factor carbon productivity (TFCP) is the core pathway to China’s low-carbon economic transformation and achieving the “dual carbon” goals. Based on panel data of 30 Chinese provincial-level regions from 2010 to 2023, this paper measures regional TFCP via an undesirable-output super-efficiency SBM model and empirically analyzes the impacts and spatial spillover characteristics of industrial intelligence and the digital economy on TFCP using a Spatial Durbin Model (SDM). Results show China’s TFCP rose overall but exhibited a widening regional gap of “higher in the east, lower in the west”, with significant positive spatial autocorrelation in regional TFCP. The digital economy exerts a significantly positive direct effect and strong positive spatial spillover effect on TFCP, forming a “local driving + spatial radiation” promotion pattern. Industrial intelligence has an insignificantly negative direct effect on local TFCP, yet its positive spatial spillover effect is significant at the 1% level, leading to a significantly positive total effect that reflects its obvious spatial externality, with low-carbon dividends more prominent in regional coordination. Both factors show notable regional heterogeneity: industrial intelligence has a significantly negative direct effect in the east, significantly positive in the central region and insignificant in the west, with positive indirect effects in the east and west; the digital economy presents “local-spillover dual drive” in the east, “local-dominated drive” in the central region and “spillover-dominated drive” in the west. Among control variables, coal-based energy consumption structure and secondary industry-dominated industrial structure significantly inhibit regional TFCP with strong negative spatial spillovers; green finance has an insignificant positive effect, while FDI shows an insignificantly positive direct effect and significantly negative indirect effect due to the “pollution haven” effect. The work clarifies the spatial effects and regional heterogeneity of industrial intelligence and the digital economy on TFCP, providing empirical evidence and policy references for formulating differentiated regional coordination policies, leveraging the two as a “dual engine” to boost China’s regional TFCP and advance high-quality green and low-carbon economic development.

The integration of artificial intelligence into criminal sentencing decisions represents one of the most consequential applications of algorithmic systems in contemporary governance. While AI-assisted risk assessment tools promise enhanced consistency and predictive accuracy, their deployment in judicial contexts raises profound concerns regarding transparency, due process, and fundamental rights. This paper proposes a comprehensive framework for AI-assisted sentencing modeling that embeds explainability as a foundational constraint rather than an afterthought. Drawing upon the landmark State v. Loomis decision, empirical analyses of the COMPAS algorithm, and emerging regulatory frameworks including the European Union Artificial Intelligence Act, we examine the tension between predictive performance and interpretive transparency. Our framework integrates a three-layer explanation architecture: inherent interpretability through generalized additive models (GA2Ms) providing transparent global structure, exact local feature attribution derived directly from the additive model decomposition without approximation, and counterfactual reasoning that identifies minimal input changes altering risk classifications. We demonstrate through rigorous experimental validation on the ProPublica COMPAS dataset (n = 6172) that explainability-constrained models achieve comparable predictive validity to opaque alternatives (AUC 0.71 versus 0.70–0.72 for black-box methods) while satisfying constitutional due process requirements and emerging regulatory mandates under the EU Artificial Intelligence Act. The impossibility theorems governing algorithmic fairness are examined in light of their implications for sentencing equity, and we propose that transparent model architectures enable targeted interventions unavailable when decision logic remains concealed. The paper concludes with policy guidance for jurisdictions seeking to implement AI-assisted sentencing systems that balance public safety objectives with procedural fairness and individual rights.

We engineered a dynamic hydrogen-bonded “molecular straitjacket” in the zero-dimensional antimony chloride (H2cpp)(Hfa)SbCl6 via a mixed-cation strategy. In-situ variable-temperature single-crystal diffraction directly reveals that this network contracts within a specific thermal window, forcing a structural rigidification of the luminescent octahedron. This simultaneously suppresses non-radiative loss (leading to emission enhancement, ATQ) and attenuates exciton-lattice coupling (causing a spectral blueshift). This work provides atomistic insights and a design blueprint for rationally engineering stimuli-responsive luminescent materials through dynamic supramolecular interfaces .

With the explosion of computation-intensive and latency-sensitive vehicular applications, multi-tier edge computing systems consisting of vehicular edge computing (VEC) server, server vehicles (SeVs) and task vehicles (TaVs) have been proposed to provide task offloading services for resource-limited TaVs. Since normally both edge servers (VEC server and SeVs) and TaVs are selfish and care about only their own benefits, reasonable pricing strategies and task offloading decisions should be carefully designed to motivate them to help TaVs. To this end, we propose a multi-leader multi-follower Stackelberg game-based joint task offloading and pricing strategy for multi-tier VEC systems by assuming edge servers (ESs) to be leaders and TaVs to be the followers. With the game, ESs maximize their own utility by optimizing the pricing strategies of their computing resources. According to the computing resources’ prices set by ESs, the TaVs as the followers make task offloading decisions to maximize their individual utility by jointly designing user association, bandwidth purchasing volume and computing resources purchasing volume. In solving the optimization problem, we prove there exists a unique Stackelberg equilibrium (SE) solution. The task offloading problem of the followers is decomposed into a resource purchasing sub-problem and a user association sub-problem, which are first solved by a joint optimization of bandwidth and computing (JOBC) algorithm and a Gale-Shapley-based matching game bilateral selection (GSMB) algorithm, respectively. Then, based on the followers’ task offloading decisions, the pricing problem is solved by a bisection search method. Finally, a joint optimization task offloading and pricing strategy (JOTPS) algorithm is established to obtain the SE. Simulation results show that the proposed scheme can maximize the individual utility of ESs and TaVs compared to those existing benchmark schemes.

Integrating session frequency, temporal, and spatial data is critical for recommendation systems. This paper proposes a novel GNN-based session recommendation algorithm using an improved gated graph convolutional network to capture item relationships and evolving user interests. Firstly, a multidimensional information-aware session graph is constructed incorporating frequency, temporal, and spatial data. Secondly, item features from GNN layers are concatenated with multidimensional features, with weights calculated via a fully connected layer and Softmax. Residual connections add original item features to preliminary fused features for enhanced representations. The model uses cross-entropy loss and Adam optimizer for training, recommending top-K items with highest probabilities. Experimental comparisons with GCE-GNN, SEDGNN, and other algorithms on Diginetica, Yoochoose, and Nowlaying datasets validate the superiority of the proposed FTLAGNN model.

This study investigates feminist advertising issues in the context of rising female consciousness, conducting a detailed analysis of their characteristics from a semiotic perspective. Through case studies of commercial advertisements from brands like Dove and Nike, we propose strategic recommendations based on research findings. Grounded in symbolic consumption theory, the study explores how commercial advertisements reconstruct feminist symbolism through approaches such as "authentic beauty" and "sports empowerment." The findings reveal that while these advertisements ostensibly promote diversity and inclusion, they essentially reduce feminism to consumable symbols. They subtly influence female consumers 'aesthetic perceptions, fostering new aesthetic norms and consumption expectations. This process traps women in commercial appropriation and value shifts, diluting consumer agency. The research provides valuable insights for understanding gender representation and symbolic forms in commercial advertising, offering references for optimizing advertisements design. This study provides theoretical references and strategic suggestions for how advertising practices can truly respond to feminist demands. Future research can further focus on consumers' interpretation and resistance to symbolic feminist advertisements, as well as the differences in acceptance effects caused by symbolic differences in cross-cultural contexts.

This paper presents an intelligent design framework for mid-infrared broadband high-power supercontinuum generation through physics-informed deep learning. We propose a hybrid architecture combining memory-augmented recurrent neural networks with self-attention mechanisms and nonlinear Schrödinger equation constraints. The network processes spectral evolution across propagation distances while enforcing energy conservation and dispersion-nonlinearity balance. Experimental validation demonstrates accurate prediction of supercontinuum spanning 1.0 to 4.0 micrometers under varying pump conditions with peak powers from 10 to 2000 Watts and pulse widths from 50 to 500 femtoseconds. The physics-constrained loss function reduces prediction error by $73 \%$ compared to purely data-driven baselines, enabling intelligent fiber design optimization with 40fold computational speedup over traditional split-step Fourier methods.