This study uses A-share listed companies on the Shanghai and Shenzhen stock exchanges from 2011 to 2024 as a sample to investigate the impact of corporate ESG performance on equity financing costs. The results indicate that improved ESG performance significantly reduces the cost of equity financing, and this conclusion remains robust after variable replacements and the exclusion of samples from the COVID-19 period. Commercial credit and earnings per share serve as transmission mechanisms between ESG performance and equity financing costs, while the role of stock liquidity follows a specific logic. Heterogeneity analysis shows that the effect is more pronounced in non-polluting firms, firms outside the eastern region, non-asset-intensive firms, and firms whose executives have a financial background.

ABSTRACT This article offers a postsecular reading of Wormen en engelen (Worms and Angels, 2017) by Maarten van der Graaff, a novel that critically engages with the Dutch literary tradition of representing Protestant upbringing. While authors such as Jan Wolkers and Maarten ’t Hart framed religion within narratives of secular emancipation, Wormen en engelen questions the validity of such stories and shows a renewed interest in Christian religion. First, the novel ascribes a specific spatiotemporal logic to classical secularization narratives, based on a spatial dichotomy and a teleological time frame. An analysis of the novel’s spatiotemporal organization reveals how the narrative can at first still be read as playfully engaging with these conventions, but ultimately transcends them. Second, the novel introduces new metaphors for understanding religion and secularity. These metaphors frame religious institutions and experiences through phenomena the narrator elsewhere associates with secularity, such as the post-Fordist economic order and technologically mediated, embodied experiences. By foregrounding these dynamics, Wormen en engelen oscillates between a renewed appreciation of religion and the awareness that religion is equally influenced by secularity, rather than existing in opposition to it.

Learning-based policies have demonstrated success in many robotic applications, but often lack explainability. We propose a neuro-symbolic explanation framework that generates a weighted signal temporal logic (wSTL) specification which describes a robot policy in a human-interpretable form. Existing methods typically produce explanations that are verbose and inconsistent, which hinders explainability, and are loose, which limits meaningful insights. We address these issues by introducing a simplification process consisting of predicate filtering, regularization, and iterative pruning. We also introduce three explainability metrics—conciseness, consistency, and strictness—to assess explanation quality beyond conventional classification accuracy. Our method—<sc xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TLNet</small>—is validated in three simulated robotic environments, where it outperforms baselines in generating concise, consistent, and strict wSTL explanations without sacrificing accuracy. This work bridges policy learning and explainability through formal methods, contributing to more transparent decision-making in robotics.

Autonomous vehicles must obey the rules of the road to safely participate in road traffic. To enforce these rules during motion planning, they are often formalized in temporal logic. Such formalizations need to be very general to cover all possible traffic situations, resulting in large and complex logic formulas. During motion planning, however, we are usually confronted with a concrete scenario in which parts of the formulas may be irrelevant. Since specification-compliant motion planning under complex specifications is computationally challenging, we aim to simplify the traffic rules by removing these irrelevant parts. To this end, we first present a general algorithm that augments linear temporal logic formulas with scenario-specific knowledge. Then, we provide a method for extracting knowledge from traffic scenarios to augment traffic rules. We can formally guarantee that the augmented specification is equivalent to the original formula in the given scenario. Therefore, subsequent motion planning modules that handle temporal logic specifications need only consider the augmented formulas. We benchmark our approach in recorded real-world scenarios to demonstrate that it can significantly accelerate specification-compliant motion planning.

Purpose This article examines how digital maturity, social and environmental practices and collaborative openness drive social and environmental innovation under two different organisational logics: social economy enterprises (SEE) and non-social enterprises (NSEE). The aim is to identify which mechanisms are shared and which diverge between SEE and NSEE, as well as the conditions under which external collaboration enhances these mechanisms. In doing so, we provide comparative evidence to guide managerial decision-making and support policies aligned with sustainable transition in European contexts. Design/methodology/approach We conduct a cross-sectional comparative study using microdata from Flash Eurobarometer 486 (EU-27). We constructed 1:1 matched subsamples (propensity score) for SEE and NSEE, balancing size, age, turnover, country and sector (n˜578 per group). We estimate a PLS-SEM model linking digital maturity and social and environmental practices with socio-environmental innovation, incorporating interaction with access to partners and plausible mediations. We report explanatory power and predictive validity and perform sensitivity analyses using alternative model specifications and item inclusion/exclusion). Findings The results show that digital maturity and, especially, environmental practices are consistently associated with social and environmental innovation in both groups, with the effect of digitalisation being more intense among social economy enterprises (SEE). Social practices also contribute to innovation, although with varying intensity depending on the type of enterprise. Regarding external collaboration, access to partners has no direct effect, but plays a significant moderating role by strengthening the relationship between social practices and innovation exclusively in SEE. Originality/value Using comparable matched subsamples, we demonstrate that the same levers – digital maturity and sustainable practices – generate different returns in SEE and NSEE. We integrate direct and indirect effects, as well as moderation by access to partners, into a single analytical framework, showing that openness adds value only when supported by internal social commitments, particularly in SEE. We therefore reframe social and environmental innovation as a result of contingent combinations of internal practices and external collaboration, as opposed to universal recipes. From a practical perspective, these findings provide managers and policymakers with guidance on how to sequence digital, social, and environmental investments and to design partnership and support policies aligned with the specific organisational logic of SEE and NSEE.

Research on appearance-based discrimination is growing, yet limited attention has been paid to how hairstyle influences perceptions of employability, particularly outside Western contexts. This study investigates how visible hairstyles function as markers of aesthetic labour and shape perceptions of employability in the Nigerian labour market, drawing on a mixed-methods approach that combines an experimental face perception survey with follow-up interviews of hiring managers. While the survey shows that applicants with visibly unconventional hairstyles are rated less favourably, especially men and those applying for customer-facing roles, the interviews reveal deeper cultural meanings attached to grooming standards. Hiring managers described hairstyles not only as markers of organisational fit but as visual cues of discipline, moral character, and social respectability. These interpretations were shaped by intersecting influences, including cultural norms, religious values and institutional branding concerns. The study contributes to research on aesthetic labour and intersectionality by demonstrating how professionalism is constructed through culturally specific logics of appearance, with implications for workplace inclusion and HR policy in postcolonial contexts.

The improvement of the governance system is an important adaptation factor for medium-sized powers to join the "Belt and Road Initiative" cooperation. The sound governance system, rule system and implementation capacity are highly consistent with the basic concept of win-win cooperation advocated by "Belt and Road Initiative". It will help to reduce institutional costs and various risks and challenges in international cooperation, and improve the efficiency of cooperation. Chile is a typical representative of a medium-sized power with a relatively perfect governance system in Latin America. Chile is an important partner for China to carry out "Belt and Road Initiative" Latin American infrastructure construction cooperation with stable political situation, complete legal system and clear strategic planning. its railway modernization is the main platform for "facility connectivity" between China and Chile. It centrally reflects the special logic and wisdom of the medium-sized powers participating in the cooperation of "Belt and Road Initiative" with complete system. The author takes the largest single contract of Chilean National Railway Company as the case study object, taking the governance mechanism as the starting point, on the basis of medium power theory and embedded autonomy theory, this paper discusses the governance of the project from three aspects: system docking, interest coordination and risk prevention and control. And contact the literature and cases of friendly relations between the two countries, transnational knowledge transfer, medium-power governance, unification of railway standards, bilateral trade, digital Silk Road, industrial linkage development, foreign investment strategy, foreign governance and so on. This paper reveals the inherent law of Belt and Road Initiative cooperation among medium-sized powers with complete system based on multi-dimensional governance system, and summarizes the replicable experience and enlightenment, so as to provide theoretical reference and practical plan for deepening multi-field cooperation between China and similar countries.

As social networks continue to expand, smart advertising increasingly depends on machine learning to deliver personalized and effective advertisements. Federated Learning (FL) is a distributed learning paradigm that supports privacy-preserving advertising by training models locally while avoiding direct sharing of raw user data. However, ensuring the correctness, reliability, and operational robustness of FL-driven smart advertising systems remains a significant challenge, particularly in distributed and user-facing environments. In this study, we investigate the use of model checking as a formal verification technique for validating key properties of an FL-based smart advertising workflow in social networks. We combine a structured finite-state modeling approach with Linear Temporal Logic (LTL) specifications and model-checking tools to assess correctness, availability, and baseline privacy requirements. Using controlled simulation-based configurations, we show that, for a setup with 100 users and 20 edge servers, the system delivers advertisements to all users and the global model successfully processes 200 out of 200 requests. We further analyze verification overhead through detection-time measurements, observing an increase in average detection time from 10.05 s to 11.98 s as the number of users rises from 20 to 100. These results indicate that the proposed framework can provide practical assurance for FL-enabled smart advertising workflows, support more reliable deployment in distributed intelligent systems, and improve trustworthiness in real advertising applications.

This article presents an approach to deductive program synthesis using Gentzen’s sequent calculus within the framework of logic programming. By leveraging sequent calculus as a formal system for structured logical inference, our method automates the derivation of provably correct programs from specifications expressed in negation-free first-order predicate logic. We formalize the syntax and semantics of sequent calculus, implementing its core inference rules (introduction and elimination rules) as predicates in logic programming to enable scalable synthesis. Practical examples demonstrate the transformation of logical specifications into executable programs. The approach ensures formal correctness through a constructive semantics inspired by Kleene's realizability, with synthesized programs operating in a subrecursive language to guarantee termination. We evaluate the method's strengths, including its reliability for safety-critical systems, and its limitations, such as computational complexity for unbounded constructions. Compared to AI-driven synthesis, our approach prioritizes formal guarantees, complementing modern trends like relational programming. Future research directions include optimizing computational efficiency and extending applicability to complex real-world problems.

To assess the feasibility of a Static Automated Perimetry in children test (cSAP)designed to return clinically useful information about a visual field when stopped after any number of presentations while testing the visual fields of children. The cSAP test was engineered with specific location selection, test logic and data presentation as the core of the perimetric procedure. Some strategies were added to engage children in the test such as fixation markers that changed shape and colour, splitting the test into 'levels' and a small visual and audio reward at the end of each level. The method was run dichoptically on the Topcon Tempo Perimeter using the Open Perimetry Interface. This is a report of a validation study on 10 adults with visual field loss, looking at the differences between the final estimated field and the fields reported after each presentation, as well as comparing to a simulated baseline procedure with the same test logic but random location selection. Additionally, the experience of using the method to test 11 children aged 4-9 years is also reported, with six being retested 3 months later. Stopping cSAP after any number of presentations always gave a better estimate of the final field than the baseline method in the adult eyes, although four of the 10 differences were small. The youngest child, 4 years of age, had difficulty focussing on the test to get a useful field, but generally all other children engaged in the task and returned a sensible visual field result. There was an obvious learning effect across the re-tested younger children. cSAP is a feasible method for testing children or others who may not complete a standard visual field test, with design advantages over conventional SAP tests for this purpose.

While vision-language models (VLMs) excel at tasks involving single images or short videos, they still struggle with Long Video Question Answering (LVQA) due to its demand for complex multi-step temporal reasoning. Vanilla approaches, which simply sample frames uniformly and feed them to a VLM along with the question, incur significant token overhead. This forces aggressive downsampling of long videos, causing models to miss fine-grained visual structure, subtle event transitions, and key temporal cues. Recent works attempt to overcome these limitations through heuristic approaches; however, they lack explicit mechanisms for encoding temporal relationships and fail to provide any formal guarantees that the sampled context actually encodes the compositional or causal logic required by the question. To address these foundational gaps, we introduce NeuS-QA, a training-free, plug-and-play neuro-symbolic pipeline for LVQA. NeuS-QA first translates a natural language question into a logic specification that models the temporal relationship between frame-level events. Next, we construct a video automaton to model the video's frame-by-frame event progression, and finally employ model checking to compare the automaton against the specification to identify all video segments that satisfy the question's logical requirements. Only these logic-verified segments are submitted to the VLM, thus improving interpretability, reducing hallucinations, and enabling compositional reasoning without modifying or fine-tuning the model. Experiments on the LongVideoBench and CinePile benchmarks show that NeuS-QA significantly improves performance by over 10%, particularly on questions involving event ordering, causality, and multi-step reasoning.

Over the past several decades, immigration authorities in Germany have framed ‘unclarified identity’ as a problem, calling for increasingly complicated methodologies to make migrant bodies legible to state deportation efforts. These efforts include technical devices and information infrastructures, such as fingerprint scanners or databases, but also specific administrative logics, such as the in-between status of the Duldung Light ‘for persons without a clarified identity’, a suspended deportation status that pressures individuals to attend embassy hearings or reveal their identity documents by imposing sanctions such as a ban from employment and a severe reduction of social benefits. This article traces the emergence of the Duldung Light and explores the political function of connected forms of state sanctioning, some aimed at controlling larger movements by discouraging migration, others aimed at pressuring individuals to perform nationality. The findings additionally point to at times contradictory logics within state efforts to produce and manage deportability.

A tutorial-style introduction to recent research on using logical specifications to encode RL tasks illustrates theoretical limitations and practical solutions.

This paper presents new theorems solving differential equations of nth-order, where the possibility of calculating solutions nearly in parallel is considered. These theorems are based on an engineering methodology that forwards the concept of solutions architecting according to an engineering approach, where the process of developing expressions and sub expressions of solutions is based on requirements engineering, analysis, design, and then developing the complete algebraic formulas of solutions to be scalable and projectable on any orders or degrees of equations. The new engineering methodology in this paper is initially developed to solve nth degree polynomial equations in general forms while using specific new unified formulas composed of radical expressions, which allow calculating the roots nearly in parallel. Then, this paper forwards this engineering methodology by using the roots of nth degree polynomial equations in general forms to solve differential equations of nth order. This methodology presents specific logic, statements, conditions, mathematical expressions, and new unified formulas that allow calculating the solutions of nth degree polynomials and nth-order differential equations. In addition, this paper presents the results of applying this engineered methodology to differential and polynomial equations of fourth degree, fifth degree, and sixth degree. This methodology is built on precise details that provide step-by-step logic and formulas to calculate the solutions, which allow concretizing multiple theorems, formulating the algebraic expressions of all solutions for different orders and degrees of equations, where the possibility of calculating the values of these solutions nearly in parallel while relying on distributed structures of terms.

We study control synthesis under Signal Temporal Logic (STL) specifications for driving scenarios where strict rule satisfaction is not always feasible and human experts exhibit context-dependent flexibility. We represent such behavior using robustness slackness—learned rule-wise lower bounds on STL robustness—and introduce sub-goals that encode intermediate intent in the state/output space (e.g., lane-level waypoints). Prior learning-based MPC–STL methods typically infer slackness with VAE priors and plug it into MPC, but these priors can underrepresent multimodal and rare yet valid expert behaviors and do not explicitly model intermediate intent. We propose a diffusion-guided MPC–STL framework that jointly learns slackness and sub-goals from demonstrations and integrates both into STL-constrained MPC. A conditional diffusion model generates pairs of (rule-wise slackness, sub-goal) conditioned on features from the ego vehicle, surrounding traffic, and road context. At run time, a few denoising steps produce samples for the current situation; slackness values define soft STL margins, while sub-goals shape the MPC objective via a terminal (optionally stage) cost, enabling context-dependent trade-offs between rule relaxation and task completion. In closed-loop simulations on held-out highD track-driving scenarios, our method improves task success and yields more realistic lane-changing behavior compared to imitation-learning baselines and MPC–STL variants using CVAE slackness or strict rule enforcement, while remaining computationally tractable for receding-horizon MPC in our experimental setting.

&lt;div&gt;In the recent years, the use of conventional passenger vehicles has been increasingly discouraged, from European-level policies to local municipal regulations, due to the urgent need to reduce greenhouse gas emissions and urban pollution. In response to these challenges, the PRIN2020 project HySUM (&lt;i&gt;Hybrid SUstainable Mobility platform&lt;/i&gt;) explores innovative hybrid powertrain solutions for light and heavy quadricycles to achieve near-zero pollutant emissions, focusing on internal combustion engine hybrid electric vehicles and fuel cell hybrid electric vehicles. Taking all these aspects into consideration, this article proposes an integrated solution for cooling/HVAC circuits, to improve energy efficiency and occupants’ comfort, while focusing on proper battery operation, with a recuperator heat exchanger used to recover the available heat at the powertrain output, in order to reduce the HVAC heater energy consumption. The complexity of the circuit requires a specific control logic to be implemented to simultaneously ensure cabin comfort, effective thermal management of the battery, and minimize energy consumption. The study is applied to the HySUM fuel cell/battery hybrid L-class electric vehicle. A thermal and electrical model for predicting the heat generation and the state of charge of the battery under dynamic load profiles is employed to better understand the potential of the thermal integration of the battery cooling with the HVAC system. The simulation results are encouraging and demonstrate the effectiveness of the proposed thermal load management. Significant energy savings are achieved through the use of the recuperator during driving, while battery thermal management is accomplished without the need for a dedicated circuit, by utilizing conditioned air from the HVAC/cabin system. Unlike traditional lightweight electrified vehicles, which often lack efficient HVAC systems, this solution enhances energy efficiency and guarantees reliable component operation in varying environmental conditions.&lt;/div&gt;

In multi-robot systems, the successful execution of tasks typically depends on predefined instructions. However, existing approaches encounter substantial challenges in dynamic environments, particularly in autonomously reasoning, generating task instructions, and allocating tasks. These challenges are further exacerbated by the need to address complex temporal, spatial, and heterogeneous task constraints. To address these limitations, inspired by the success of the Large Language Models (LLMs) in natural language understanding and logical inference, this paper proposes an environment-driven and LLM-guided task inference and allocation framework with dual-system temporal logics. The framework consists of three key modules: the Environment Module, which employs environment LTL to continuous monitor and verify environmental resource constraints; the Inference Module, leveraging LLMs for autonomous generation and verification of robotic tasks in response to resource changes; and the Robot Module, which explores the feasible task allocation for the multi-robot system. When the resources in the environment do not satisfy the specification, the Inference Module is used to analyze and infer the feasible actions to be executed by the multi-robot system, so as to alleviate the environmental resource problem. Experimental results demonstrate the scalability, efficiency, and autonomy of our framework across varying task environments and robot configurations.

Navigation algorithms are responsible for decision-making in autonomous vehicles, generating safe trajectories based on post-processed sensor data to prevent collisions. However, failures in sensors or software modules can cause deviations from nominal behavior, potentially leading to unsafe situations. To comply with the ISO 26262 functional safety standard, road vehicles must perform a transition to a safe state when a failure is detected, until the corresponding safety goal is reached. This paper presents a novel control framework for navigation in autonomous vehicles, integrating data-driven reachability analysis and zonotopic predictive control to ensure functional safety (FuSa) under nominal conditions and potential system-level anomalies. The proposed framework explicitly accounts for interactions between vehicles, surrounding traffic participants, and infrastructure by incorporating traffic and infrastructure constraints into the control formulation. The proposed approach uses signal temporal logic specifications to formally define safe states and safety goals, guiding the zonotopic predictive control in executing safe maneuvers when they are required. The controller also enforces constraints on obstacle avoidance and infrastructure conditions, all represented as intervals or zonotopes for a unified formulation. The complete control framework is validated using data extracted from a real-world autonomous Renault Mégane (SAE Level 3), demonstrating its effectiveness in executing safe recovery maneuvers while ensuring compliance with the functional safety standard in real-world scenarios.

Signal Temporal Logic (STL) is widely used for specifying complex time-dependent behaviors in cyber-physical systems (CPSs), particularly in safety-critical domains. However, fault diagnosis (FD) and fault tolerant control (FTC) under nested STL (NSTL) specifications remain challenging, especially for nonlinear systems. This paper proposes a collaborative design (CoD) framework that jointly integrates FD and FTC under NSTL constraints to enhance detection accuracy and ensure robust system performance. First, a fault detection observer is developed by constructing fault tolerant feasible sets that can predict whether ongoing system trajectories satisfy NSTL specifications. To address the feasibility issue in real-time control synthesis, we introduce the concept of fault tolerant control with recursive feasibility (FTCRF), enabling the controller to maintain constraint satisfaction and system stability even under faults. A model predictive control scheme guided by control barrier functions (CBFs) ensures safe trajectory tracking within specified bounds. Simulation studies on single integrator and unicycle models demonstrate the effectiveness of the proposed method in accurately detecting faults and maintaining task satisfaction under NSTL constraints.

This is an empirical case of how data work performed by health personnel is rendered invisible because of the institutional logic of top-down control inscribed in the information system, which exerts a coercive pressure. It is important to study the effect of institutional logics on data work because such logics shape data work and the data produced. We use ethnographic methods to study how a specific institutional logic—which we refer to as a logic of top-down control—inscribed in information systems shapes data work related to training, data entry, and data representation in two large-scale health information systems in India. We discuss the tensions between the institutional logic as it is envisioned by change agents at the top of the hierarchy and how the data work is carried out, and how this tension ultimately shapes how the information systems are implemented and used.