Abstract
Purpose: To evaluate the outcomes and durability of drug -eluting stents (DESs) for the treatment of hemodialysis access outflow stenosis. Material and Methods: A single -center retrospective analysis was conducted of all patients with hemodialysis vascular access outflow stenosis treated with a paclitaxel-coated DES (Eluvia; Boston Scientific, Marlborough, Massachusetts) between January 2020 and July 2022. A total of 34 DESs were implanted to treat outflow stenosis in 32 patients. Primary target lesion patency after stent deployment was the main outcome. Comparison between the time interval free from target lesion reintervention (TLR) after previous plain balloon angioplasty (PBA) and that after stent deployment for the same target lesion was considered a secondary outcome. Results: The primary patency at 6, 12, and 18 months was 63.1%, 47.6%, and 41.7%, respectively. The secondary patency rate was 100% at 18 months. The median time interval free from TLR increased from 4.1 to 11.9 months (P < .001). No adverse events were observed during the median follow-up period of 387 days. Conclusions: The patency rates after use of DES for hemodialysis access outflow stenosis were comparable with results for drug -coated balloons and stent grafts, addressing recoil and minimizing the risk of jailing by a covered stent.

Abstract
There are still important theoretical and empirical gaps in understanding the role of best practices (BPs), such as quality management, lean and new product development, in generating firm's performance advantage and overcoming trade-offs across distinct performance dimensions. We examine these issues through the perspective of performance frontiers, integrating in novel ways the resource-based theory with the emergent practice-based view. Hypotheses on relationships between BPs, performance advantage, and trade-offs are developed and tested with stationary and longitudinal (recall) data from a global survey of manufacturing firms. We use data envelopment analysis, which overcomes limitations of mainstream methods based on central tendency. Our findings support the view that BPs may serve as a source of enduring competitive advantage, based on their ability to lead to a heterogeneous range of dominant and difficult-to-imitate competitive positions. The study provides new insights on contemporary debates about the role of BPs in generating performance advantage and how practitioners can sustain internal support and extract benefits from them.

Abstract
Frequency stability in inverter-based renewable energy sources (RES)-dominated, low-inertia, power systems is a timely challenge. This paper employs a systematic approach, utilizing an artificial neural network (ANN) and dynamic simulation, to infer two key frequency stability indicators: nadir and rate of change of frequency (RoCoF). By reformulating the ANN mathematical model, these indicators are then integrated as mixed-integer non-linear constraints into a classical AC security-constrained optimal power flow (AC SCOPF), resulting in the proposed AC-F-SCOPF problem. The results of the proposed AC-F-SCOPF on the IEEE 39-bus system show that the problem identifies accurately the synchronous condensers which must run to ensure the frequency stability.

Abstract
Background: In many urban areas, 80% to 90% of pollutant emissions are generated by road traffic, particularly from heavy vehicles. With the anticipated surge in e-commerce logistics, the need for effective urban mobility control measures has become urgent, focusing on traffic restrictions and efficient enforcement tools. This work introduces Log-ON, a multi-stakeholder information system designed to facilitate the implementation and management of sustainable traffic restrictions. Methods: The proposed system was developed through extensive literature reviews, expert consultations, and feedback from logistics fleet managers. User-centered mock-ups were created for various stakeholders, including the public, regulatory authorities, logistics operators, and enforcement agencies, ensuring that the system effectively addresses a diverse set of needs. Results: By taking into account a wide range of influencing factors, Log-ON functions as a decision-support tool designed to optimize access restrictions for vehicles, particularly heavy vehicles, in urban environments. Conclusions: Log-ON's adoption promises significant improvements in urban mobility by reducing traffic-related pollution and fostering healthier, cleaner cities. However, traffic restrictions could increase delivery costs, potentially disrupting logistics operations. To address this, the development of new business models for last-mile delivery is essential, ensuring that sustainable traffic management strategies align with the economic challenges faced by logistics providers.

Abstract
Recently introduced Generalized Pre-trained Transformers (GPT) and conversional chatbots such as ChatGPT are causing deep society transformations. The incorporation of these Artificial Intelligence technologies can be beneficial in multiple science and development areas including Control Engineering. The evaluation of GPTs within Control Engineering Education and PID control is addressed in this work. Different types of interactions with GPTs are evaluated and the use of a personalized GPT for PID tuning explored. Copyright (C) 2024 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Abstract
The performance of time-predictable systems can be improved in multi-core processors using parallel programming models (e.g., OpenMP). However, schedulability analysis of parallel applications is a big challenge due to their sophisticated structure. The common drawbacks of current task-to-thread mapping approaches in OpenMP are that they (i) utilize a global queue in the mapping process, which may increase contention, (ii) do not apply heuristic techniques, which may reduce the predictability and performance of the system, and (iii) use basic analytical techniques, which may cause notable pessimism in the temporal conditions. Accordingly, this paper proposes a task-to-thread mapping method in multi-core processors based on the OpenMP framework. The mapping process is carried out through two phases: allocation and dispatching. Each thread has an allocation queue in order to minimize contention, and the allocation and dispatching processes are performed using several heuristic algorithms to enhance predictability. In the allocation phase, each task-part from the OpenMP DAG is allocated to one of the allocation queues, which includes both sibling and child task-parts. A suitable thread (i.e., allocation queue) is selected using one of the suggested heuristic allocation algorithms. In the dispatching phase, when a thread is idle, a task-part is selected from its allocation queue using one of the suggested heuristic dispatching algorithms and then dispatched to and executed by the thread. The performance of the proposed method is evaluated under different conditions (e.g., varying the number of tasks and the number of threads) in terms of application response time and overhead of the mapping process. The simulation results show that the proposed method surpasses the other methods, especially in the scenario that includes overhead of the mapping. In addition, a prototype implementation of the main heuristics is evaluated using two kernels from real-world applications, showing that the methods work better than LLVM's default scheduler in most of the configurations.