TE Connectivity (Switzerland)

Cardiovascular disease remains a significant global health burden. Bempedoic acid is a novel, oral adenosine triphosphate-citrate lyase inhibitor that lowers low-density lipoprotein cholesterol (LDL-C). While randomized controlled trials (RCTs) have established its efficacy and safety, evidence from real-world clinical practice is needed. A systematic literature review (SLR) was conducted to identify studies reporting real-world use of bempedoic acid as monotherapy, in fixed dose combination with ezetimibe, or in combination with any other lipid lowering therapy (LLT). MEDLINE, EMBASE, and Cochrane Library were searched from inception to February 19, 2025. Two reviewers independently screened studies, with discrepancies resolved by a third. Data extraction and validation were conducted, and the risk of bias assessed using the Newcastle-Ottawa Scale. Of 269 records, 28 reports on 22 unique studies met the eligibility criteria. Most studies evaluated LDL-C changes from baseline after initiation of bempedoic acid; one pragmatic randomized trial was identified. Bempedoic acid achieved LDL-C reductions of 15% to 39% when added to maximally tolerated statins, 22% to 38% in statin intolerant cohorts, and 18% to 42% in mixed statin intolerant cohorts. Safety data was limited; treatment discontinuation ranged from 9% to 36%, with higher rates in cohorts with greater statin-intolerance. This SLR shows bempedoic acid, with or without other LLTs, achieves a minimum of approximately 20% LDL-C reductions, consistent with RCTs. Data suggests it may be effective as an adjunct to statins and as an oral alternative for statin-intolerant patients.

We now stand on the cusp of a similar scenario in nanodevice fabrication. As we move into the far sub–50-nm scale, traditional fabrication methods start to falter. There is a transition needed to solve this scaling problem, and the proposed solution lies in machine learning. Over the past few years, there have been several successful and significant attempts to train a learning machine to generate patterns for nanodevices. These strategies overcome traditional limitations, such as grid-based design processes or the time-intensive nature of design, by simulating fabrication or material properties. This opens up new design spaces that save significant time and money. Although this prospect itself is an exciting demonstration of the power of machine learning, our goal is twofold. On the one hand, we wish to overview the current landscape of machine learning for nanodevice fabrication, spread over various applications and techniques. On the other, this chapter pushes the conversation beyond the current state of affairs and explores possible future directions for machine learning–based nanodevice fabrication. We have organized this essay where we start with an illustration of machine learning for different stages of the nanofabrication process. In that process, we also summarize the different methods used in several related experimental studies, which helped researchers achieve rapid prototyping of a number of nanofabrication strategies, as well as rapid quantitative discovery of new and accurate metric space estimators. We then summarize the recent related reviews, which include both commercial hardware and experimental studies. Finally, we discuss various open problems, as well as the future directions in this research space within a broader context.

Ultra-wideband (UWB) radar has become increasingly popular for short-range sensing applications due to the commercial availability, energy efficiency, and integration potential within existing wireless ecosystems. The widespread availability of commercial UWB chips, originally designed for secure ranging and localisation, has made radar sensing more accessible. These chips are low-cost, energy-efficient, and already integrated into many consumer devices, making them an attractive platform for developing radar-based sensing solutions. The high rate pulse repetition frequency (HRP) UWB PHY uses short pulses across a wide frequency range and allows for precise time-of-flight measurements and fine spatial resolution. This makes it well-suited for detecting small movements and resolving targets in cluttered environments. Sensing applications range from indoor localisation and gesture recognition to monitoring of vital signs. However, evaluating the sensing performance of UWB radar systems in diverse scenarios remains challenging. The complexity of real-world environments and the variability of target behaviours make it difficult to test UWB devices consistently. This paper presents a method for simulating UWB radar targets and assessing sensing performance under controlled conditions. By emulating target reflections and clutter characteristics, the proposed approach enables repeatable and scalable testing of UWB radar systems, facilitating the development and benchmarking of robust sensing solutions.

Reducing our society's energy demand is critical to address the sustainability challenge. While the Internet currently accounts for 1–-1.5% of global electricity consumption and continues to grow, the energy demands of one of its core components---routers---remain poorly understood. The available power data is limited and not fine-grained enough, offering little actionable insight into strategies for effectively reducing the Internet's energy consumption.

The recent emergence of Large Language Models (LLMs) has progressed toward feasible use-cases of conversational agents which expand into domain-specific advanced uses in healthcare, financial services, metal fabrication, and legal services. Nevertheless, to integrate LLMs in production of such niche areas, they need a powerful operational framework, named the LLMOps, that will secure scalability, compliance, security, and performance optimization. The current paper proposes an architecture of domain-specific LLM APIs, orienting at the design modules, domain-constrained prompt engineering, knowledge-based inference architecture, and continuous fine-tuning pipelines. The framework suggested will include API orchestration layers, domain ontologies, compliance-aware data governance, along with latency, throughput, and explainability monitors, to control the demands of latency, throughput and explainability. In addition, the architecture uses hybrid deployment options that combine on-premises, cloud, and edge-based forms of LLM realizations to support the various requirements in the industry. The experimental testing of several spheres indicates a higher precision in responding as well as a diminution of the prevalence of hallucinate moments and compliance with the sector-based regulations. The results establish LLMOps-enabled APIs as an important foundation to the next generation of industry grade language interfaces, a port of call between the general-purpose LLM capabilities and enterprise-specific tasks.

The increasing demand for high voltage systems across various sectors, including battery storage, electric vertical take-off and landing (eVTOL) aircraft, and electric vehicles (EVs), has led to a growing need for efficient HVDC contactors. Traditional trial-and-error design methods face significant challenges in addressing arcing phenomena, resulting in prolonged development cycles and suboptimal performance. This paper presents an advanced simulation-based design methodology that integrates Maxwell for electromagnetic analysis and Fluent for computational fluid dynamics, coupled with sophisticated Design of Experiments (DoE) techniques. By employing multi-physics simulations and optimization strategies, this approach enables a comprehensive understanding of arcing behavior, facilitates rapid design iterations, and optimizes contactor performance. The proposed methodology significantly reduces development time, enhances reliability, and increases the chances of achieving first time-right in design, thereby addressing the critical needs of the evolving high-voltage landscape in electrification technologies.