Search Results (1,163)

In the existing literature, targeting rules are typically determined separately for monetary and fiscal policy. This article proposes a framework for determining targeting rules that account for the policy mix of both monetary and fiscal policy. The aim of this study is to compare selected optimization methods used to derive targeting rules as solutions to a constrained minimization problem. The constraints are defined by a model that incorporates a monetary and fiscal policy mix. The optimization methods applied include the linear–quadratic regulator, Bellman dynamic programming, and Euler’s calculus of variations. The resulting targeting rules are solutions to a discrete-time optimization problem with a finite horizon and without discounting. In this article, we define targeting rules that take into account the monetary and fiscal policy mix. The derived rules allow for the calculation of optimal values for the interest rate and the balance-to-GDP ratio, which ensure price stability, a stable debt-to-GDP ratio, and the desired GDP growth dynamics. It can be noted that all the optimization methods used yield the same optimal vector of decision variables, and the specific method applied does not affect the form of the targeting rules. Full article

Modular process plants represent a promising strategy to address the increasing need for flexibility and accelerated market deployment in the production of fine and specialty chemicals. However, these modular systems are inherently susceptible to wear and fault development, while condition monitoring methods tailored to such systems remain scarce. This study presents a proof of concept for a targeted fault diagnosis approach of the modular hydraulic systems of such modular process plants and reports on its experimental validation. The methodology comprises two stages: First, model-based symptoms are calculated independently for each module and subsequently utilized within a centralized diagnostic system. This rule-based diagnosis incorporates generalized module interactions, quantified fault degrees, and the plant topology. Importantly, uncertainties arising from measurement equipment, model fidelity, and parameter variability are incorporated and systematically propagated throughout the diagnosis. The validation was conducted on a modular test rig specifically designed to simulate a range of single-fault scenarios across more than 1200 stationary operating points. The results underscore the robustness of the proposed approach: the correct fault was consistently identified, with the estimated fault magnitudes closely aligning with the actual values, exhibiting an average discrepancy of 0.029 for internal leakage of a positive displacement pump. The overall discrepancy for the experimental validation of all fault types was 0.12. Notably, no false alarms were observed, and the displayed uncertainty was considered plausible, though there remains potential for refinement. In summary, this study demonstrates the successful application of model-based symptoms for a rule-based diagnosis, representing a significant advancement toward reliable fault detection in modular hydraulic systems. Full article

With the continuous scaling-down of integrated circuit feature sizes, inverse lithography technology (ILT), as the most groundbreaking resolution enhancement technique (RET), has become crucial in advanced semiconductor manufacturing. By directly optimizing mask patterns through inverse computation rather than rule-based local corrections, ILT can more accurately approximate target design patterns while extending the process window. However, current mainstream ILT approaches—whether machine learning-based or gradient descent-based—all face the challenge of balancing mask optimization quality and computational time. Moreover, ILT often faces a trade-off between imaging fidelity and manufacturability; fidelity-prioritized optimization leads to explosive growth in mask complexity, whereas manufacturability constraints require compromising fidelity. To address these challenges, we propose an iterative deep learning-based ILT framework incorporating a lightweight model, ghost and adaptive attention U-net (EAAUnet) to accelerate runtime and reduce computational overhead while progressively improving mask quality through multiple iterations based on the pre-trained network model. Compared to recent state-of-the-art (SOTA) ILT solutions, our approach achieves up to a 39% improvement in mask quality metrics. Additionally, we introduce a mask constraint scheme to regulate complex SRAF (sub-resolution assist feature) patterns on the mask, effectively reducing manufacturing complexity. Full article

This study investigated the interaction between cannabidiol (CBD) derivatives and the GPR55 receptor using a bioinformatics-driven molecular docking approach. GPR55, implicated in central nervous system (CNS) pathologies, represents a promising target for novel therapeutics. Drug-likeness evaluation via SwissADME confirmed that all selected derivatives complied with Lipinski′s Rule of Five, exhibiting favorable physicochemical properties with molecular weights below 500 Da and acceptable logP values. Molecular docking simulations, performed using AutoDock Vina through PyRx, revealed strong binding affinities, with docking scores ranging from −9.2 to −7.2 kcal/mol, indicating thermodynamically feasible interactions. Visualization and interaction analysis identified a conserved binding pocket involving key residues, including TYR101, PHE102, TYR106, ILE156, PHE169, MET172, TRP177, PRO184, LEU185, LEU270 and MET274. Ligand clustering in this region further supports the presence of a structurally defined binding site. Molecular dynamics simulations of GPR55 in complex with the three top-scoring ligands (3″-HOCBD, THC, and CBL) revealed that all ligands remained stably bound within the cavity over 100 ns, with ligand-specific rearrangements. Predicted oral bioavailability was moderate (0.55), consistent with the need for optimized formulations to enhance systemic absorption. These findings suggest that CBD derivatives may act as potential modulators of GPR55, offering a basis for the development of novel CNS-targeted therapeutics. Full article

Artificial intelligence (AI) is transforming cardiac electrophysiology across the entire care pathway, from arrhythmia detection on 12-lead electrocardiograms (ECGs) and wearables to the guidance of catheter ablation procedures, through to outcome prediction and therapeutic personalization. End-to-end deep learning (DL) models have achieved cardiologist-level performance in rhythm classification and prognostic estimation on standard ECGs, with a reported arrhythmia classification accuracy of ≥95% and an atrial fibrillation detection sensitivity/specificity of ≥96%. The application of AI to wearable devices enables population-scale screening and digital triage pathways. In the electrophysiology (EP) laboratory, AI standardizes the interpretation of intracardiac electrograms (EGMs) and supports target selection, and machine learning (ML)-guided strategies have improved ablation outcomes. In patients with cardiac implantable electronic devices (CIEDs), remote monitoring feeds multiparametric models capable of anticipating heart-failure decompensation and arrhythmic risk. This review outlines the principal modeling paradigms of supervised learning (regression models, support vector machines, neural networks, and random forests) and unsupervised learning (clustering, dimensionality reduction, association rule learning) and examines emerging technologies in electrophysiology (digital twins, physics-informed neural networks, DL for imaging, graph neural networks, and on-device AI). However, major challenges remain for clinical translation, including an external validation rate below 30% and workflow integration below 20%, which represent core obstacles to real-world adoption. A joint clinical engineering roadmap is essential to translate prototypes into reliable, bedside tools. Full article

This paper examines democratic backsliding through the lens of gender justice, focusing on recent political developments in Israel. Since early 2023, the ruling coalition has advanced a judicial overhaul designed to reduce judicial independence and consolidate executive control. These changes should be understood in tandem with a wave of suggested legislation targeting gender equality, women’s rights, and protections against discrimination in public life, education, and civil services. A qualitative analysis of governmental legislative initiatives reveals a troubling pattern: efforts to erode judicial independence are closely followed by laws that institutionalize gender segregation and undermine gender justice. This sequence reflects a deliberate strategy—first dismantling the legal safeguards, then attacking the rights they once protected. In response, women have played a leading role in Israel’s pro-democracy protest movement, using highly visible, gendered forms of resistance to signal that gender justice is a core democratic concern. The paper concludes that democratic backsliding in Israel is gendered in both its structure and its consequences, and any assessment of its impact must account for its disproportionate harm to women and marginalized communities. Full article

On 10 August 2025, a powerful earthquake (M_w = 6.1) occurred in Balıkesir, located within the Aegean Graben System, one of Türkiye’s major tectonic elements, and was felt across a very wide region. This study presents a comprehensive assessment of the seismotectonic characteristics, recorded ground motions, and observed structural performance during this earthquake, focusing specifically on implications for regional seismic hazard assessment. Peak ground acceleration values obtained from local accelerometer stations were compared with predicted peak ground accelerations. The study also conducted comparisons for Balıkesir districts using the two most recent earthquake hazard maps used in Türkiye. Comparative hazard analyses revealed whether existing seismic hazard maps adequately represent Balıkesir. The findings highlight the need for region-specific hazard model updates, improved implementation of earthquake-resistant design rules, and targeted retrofit strategies to mitigate future earthquake risk. The methodology adopted in this study involved comparative hazard analysis using the last two seismic hazard maps, evaluation of PGA’s across 20 districts of Balıkesir Province, and a field-based survey of structural damage. This integrative approach ensured that both seismological and engineering perspectives were comprehensively addressed. Full article

The construction industry remains one of the most hazardous sectors despite its economic importance, with high-altitude fall accidents being the most prevalent and deadly type of incident. This paper aimed to study and analyze the accident data of the past accident cases in China and find out the key causes and rules of the accidents. This research analyzed 1223 Chinese accident reports (2014–2023) using Latent Dirichlet Allocation topic modeling to identify causal factors, followed by Apriori algorithm correlation analysis to reveal accident causation patterns. This study comprehensively uses topic model, association rules and visualization methods to systematically analyze the causes of high-altitude fall accidents. The research identified 24 distinct accident cause topics across personnel, equipment, management, and environmental dimensions. Key findings revealed that incorrect use of labor protective equipment, inadequate safety inspections, and failure to implement safety management protocols were persistent issues throughout the study period. Notably, the post COVID-19 pandemic introduced new safety challenges, with the intensity of topics related to “subject of responsibility for safety production has not been implemented” showing significant post-pandemic increases. These findings highlight the evolving nature of construction safety challenges and the need for targeted interventions to address persistent and emerging risks. Full article

The China Railway Express (China-Europe container railway freight transport) is pivotal to Eurasian freight, yet its transcontinental railway faces escalating cascading risks. We develop a coupled map lattice (CML) model representing the physical infrastructure layer and the operational traffic layer concurrently to quantify and mitigate cascading failures. Twenty critical stations are identified by integrating TOPSIS entropy weighting with grey relational analysis in dual-layer networks. The enhanced CML embeds node-degree, edge-betweenness, and freight-flow coupling coefficients, and introduces two adaptive cargo-redistribution rules—distance-based and load-based for real-time rerouting. Extensive simulations reveal that network resilience peaks when the coupling coefficient equals 0.4. Under targeted attacks, cascading failures propagate within three to four iterations and reduce network efficiency by more than 50%, indicating the vital function of higher importance nodes. Distance-based redistribution outperforms load-based redistribution after node failures, whereas the opposite occurs after edge failures. These findings attract our attention that redundant border corridors and intelligent monitoring should be deployed, while redistribution rules and multi-tier emergency response systems should be employed according to different scenarios. The proposed methodology provides a dual-layer analytical framework for addressing cascading risks of transcontinental networks, offering actionable guidance for intelligent transportation management of international intermodal freight networks. Full article

The Bacillus anthracis pXO1 plasmid, encoding ~143 proteins, presents a compact model for exploring protein function and evolutionary patterns using protein language models. Due to the organism’s slow evolutionary rate, its limited amino acid variation enhances detection of physiologically relevant patterns in plasmid protein composition. In this study, we applied embedding-based analyses and machine learning methods to characterize pXO1 protein modules across diverse B. anthracis lineages. We generated protein sequence embeddings, constructed phylogenies, and compared plasmid content with whole genome variation. While whole genome and plasmid-based phylogenies diverge, the composition of proteins encoded along the pXO1 plasmid revealed lineage specific structure. Association rule mining combined with decision tree classification produced plasmid-encoded targets for assessing anthrax sublineage, which yielded functionally redundant protein modules that reflected geographic and phylogenetic patterns. A conserved DNA replication module exhibited both shared and B. anthracis lineage specific features. These results show that pXO1 plasmid protein modules contain biologically meaningful and evolutionarily informative signatures, exemplifying their value in phylogeographic characterizations of bacterial pathogens. This framework can be extended to study additional virulence plasmids across Bacillus and other environmental pathogens using scalable protein language model tools. Full article

Effective production control requires aligning strategic planning with real-time execution under dynamic and stochastic conditions. This study proposes a scalable dual-agent Deep Reinforcement Learning (DRL) framework for the joint optimisation of Work-In-Process (WIP) control and job sequencing in flow-shop environments. A strategic DQN agent regulates global WIP to meet throughput targets, while a tactical DQN agent adaptively selects dispatching rules at the machine level on an event-driven basis. Parameter sharing in the tactical agent ensures inherent scalability, overcoming the combinatorial complexity of multi-machine scheduling. The agents coordinate indirectly via a shared simulation environment, learning to balance global stability with local responsiveness. The framework is validated through a discrete-event simulation integrating agent-based modelling, demonstrating consistent performance across multiple production scales (5–15 machines) and process time variabilities. Results show that the approach matches or surpasses analytical benchmarks and outperforms static rule-based strategies, highlighting its robustness, adaptability, and potential as a foundation for future Hierarchical Reinforcement Learning applications in manufacturing. Full article

In this paper, we present a unique method to determine the level of bot contamination of web-based user agents. It is common practice for bots and robotic agents to masquerade as human-like to avoid content and performance limitations. This paper continues our previous work, using over 600 million web log entries collected from over 4000 domains to derive and generalize how the prominence of specific web browser versions progresses over time, assuming genuine human agency. Here, we introduce a parametric model capable of reproducing this progression in a tunable way. This simulation allows us to tag human-generated traffic in our data accurately. Along with the highest confidence self-tagged bot traffic, we train a Transformer-based classifier that can determine the bot contamination—a botness metric of user-agents without prior labels. Unlike traditional syntactic or rule-based filters, our model learns temporal patterns of raw and heuristic-derived features, capturing nuanced shifts in request volume, response ratios, content targeting, and entropy-based indicators over time. This rolling window-based pre-classification of traffic allows content providers to bin streams according to their bot infusion levels and direct them to several specifically tuned filtering pipelines, given the current load levels and available free resources. We also show that aggregated traffic data from multiple sources can enhance our model’s accuracy and can be further tailored to regional characteristics using localized metadata from standard web server logs. Our ability to adjust the heuristics to geographical or use case specifics makes our method robust and flexible. Our evaluation highlights that 65% of unclassified traffic is bot-based, underscoring the urgency of robust detection systems. We also propose practical methods for independent or third-party verification and further classification by abusiveness. Full article

Background: Advanced heart failure (HF) carries high morbidity and mortality, and deterioration on the heart transplantation (HT) waiting list remains a major challenge. Intermittent outpatient levosimendan has been proposed as a bridge strategy, but the optimal regimen and its impact on peri-transplant outcomes remain uncertain. Within a personalized-medicine framework, we targeted a low-output/INTERMACS 3 phenotype and operationalized an adaptable, protocolized levosimendan pathway focused on perfusion/congestion stabilization to preserve transplant candidacy. Methods: We conducted a single-center, retrospective cohort study of 25 consecutive adults actively listed for HT between 2019 and 2024, treated with a standardized outpatient program of a 14-day interval of 6 h intravenous levosimendan infusions (target 0.2 μg/kg/min infusions) continued until transplant. Personalization in this program was operationalized through (i) phenotype-based eligibility (low CI and elevated filling pressures despite GDMT), (ii) predefined titration and safety rules for blood pressure, arrhythmias, and renal function, and (iii) individualized continuation until transplant with nurse-supervised monitoring and review of patient trajectories. Baseline characteristics, treatment exposure and safety, changes in hospitalizations and biomarkers, and peri-transplant outcomes were analyzed. Results: Patients were predominantly male (68%), with a mean age of 47.9 ± 17.5 years and severe LV dysfunction (LVEF 30.6 ± 9.8%). Median treatment duration was 131 days (IQR 60–241). No infusions required discontinuation for hypotension or arrhythmia, and no adverse events were directly attributed to levosimendan. Two patients (8%) died on the waiting list, both unrelated to therapy. During treatment, HF hospitalizations decreased significantly compared with the previous 6 months (48% vs. 20%, p = 0.033), renal function remained stable, and NT-proBNP trended downward. Of the 23 patients transplanted, two (9%) underwent urgent HT during decompensation. Post-transplant, vasoplegia occurred in 26% (n = 6 of 23), and 30-day mortality was 9% (n = 2 of 23). Conclusions: By defining the target phenotype, therapeutic goals, and adaptation rules, this study shows how a standardized but flexible outpatient levosimendan regimen can function as a personalized bridge strategy for low-output advanced HF. The approach was associated with fewer hospitalizations, stable renal function, and acceptable peri-transplant outcomes, and merits confirmation in multicenter cohorts with attention to patient heterogeneity and treatment effect refinement. Full article

Spiking neural networks (SNNs) have emerged as a promising model for energy-efficient, event-driven processing of asynchronous event streams from Dynamic Vision Sensors (DVSs), a class of neuromorphic image sensors with microsecond-level latency and high dynamic range. Nevertheless, challenges persist in optimising training and effectively handling spatio-temporal complexity, which limits their potential for real-time applications on embedded sensing systems such as object tracking and recognition. Targeting this neuromorphic sensing pipeline, this paper proposes the Dynamic Tracking with Event Attention Spiking Network (DTEASN), a novel framework designed to address these challenges by employing a pure SNN architecture, bypassing conventional convolutional neural network (CNN) operations, and reducing GPU resource dependency, while tailoring the processing to DVS signal characteristics (asynchrony, sparsity, and polarity). The model incorporates two innovative, self-developed components: an event-driven multi-scale attention mechanism and a spatio-temporal event convolver, both of which significantly enhance spatio-temporal feature extraction from raw DVS events. An Event-Weighted Spiking Loss (EW-SLoss) is introduced to optimise the learning process by prioritising informative events and improving robustness to sensor noise. Additionally, a lightweight event tracking mechanism and a custom synaptic connection rule are proposed to further improve model efficiency for low-power, edge deployment. The efficacy of DTEASN is demonstrated through empirical results on event-based (DVS) object recognition and tracking benchmarks, where it outperforms conventional methods in accuracy, latency, event throughput (events/s) and spike rate (spikes/s), memory footprint, spike-efficiency (energy proxy), and overall computational efficiency under typical DVS settings. By virtue of its event-aligned, sparse computation, the framework is amenable to highly parallel neuromorphic hardware, supporting on- or near-sensor inference for embedded applications. Full article

Background: Emergency laparotomy (EL) carries high morbidity and mortality relative to elective abdominal surgery. While Enhanced Recovery After Surgery (ERAS) principles improve outcomes in elective care, their translation to emergencies is inconsistent. The emergency department (ED) provides a window for rapid risk stratification and pre-optimization, provided that interventions do not delay definitive surgery. Methods: We conducted a PRISMA-ScR–conformant scoping review to map ED-initiated, ERAS-aligned strategies for EL. PubMed, Scopus, and Cochrane were searched in February 2025. Eligible sources comprised ERAS guidelines, systematic reviews, cohort studies, consensus statements, and programmatic reports. Evidence was charted across five a priori domains: (i) ERAS standards, (ii) comparative effectiveness, (iii) ED-feasible pre-optimization, (iv) risk stratification (Emergency Surgery Score [ESS], frailty, sarcopenia), and (v) oncological emergencies. Results: Thirty-four sources met inclusion. ERAS guidelines codify rapid assessment, multimodal intraoperative care, and early postoperative rehabilitation under a strict no-delay rule. Meta-analysis and cohort data suggest ERAS-aligned pathways reduce complications and length of stay, though heterogeneity persists. ED-feasible measures include multimodal analgesia, goal-directed fluids, early safe nutrition, respiratory preparation, and anemia/micronutrient optimization (IV iron, vitamin B12, folate, vitamin D). Sarcopenia, frailty, and ESS consistently predicted adverse outcomes, supporting targeted bundle activation. Evidence from oncological emergencies indicates feasibility under no-delay governance. Conclusions: A minimal, ED-initiated, ERAS-aligned bundle is feasible, guideline-concordant, and may shorten hospitalization and reduce complications in EL. We propose a practical framework that links rapid risk stratification, opportunistic pre-optimization, and explicit continuity into intra- and postoperative care; future studies should test fidelity, costs, and outcome impact in pragmatic emergency pathways. Full article