From controlling engine performance to enabling life-saving advanced driver-assistance systems (ADAS), the modern vehicle relies on a network of electronic systems that demand exceptional precision and reliability. At the heart of these systems are ball grid arrays (BGAs)—the tiny solder joints that provide the electrical and mechanical connections essential for smooth operation (see Figure 1). While they may be invisible to the casual observer, BGAs are integral to automotive technology.

Yet, as vehicles become more intelligent, interconnected, and electrified, the demands on BGAs grow. Extreme environments, continuous operation, and evolving functionality challenge their reliability at every turn. How can the automotive industry ensure these vital components remain robust under such relentless stress? This article dives into the challenges BGAs face and the innovations redefining their reliability

Electric vehicle (EV) batteries play a crucial role in sustainable transportation, with reliability being pivotal to their performance, longevity, and environmental impact. This study explores battery reliability from micro (individual user), meso (industry), and macro (societal) perspectives, emphasizing interconnected factors and challenges across the lifecycle. A novel lifecycle framework is proposed, introducing the concept of “Zero-Life” reliability to expand traditional evaluation methods. By integrating the reliability ecosystem with a dynamic system approach, this research offers comprehensive insights into the optimization of EV battery systems. Furthermore, an expansive Social–Industrial Large Knowledge Model (S-ILKM) is presented, bridging micro- and macro-level insights to enhance reliability across lifecycle stages. The findings provide a systematic pathway to advance EV battery reliability, aligning with global sustainability objectives and fostering innovation in sustainable mobility.

Fault detection in 3D printers is crucial for safety and quality assurance, emphasizing proactive prediction over reactive rectification based on manufacturing factors. Presently, most detection techniques rely on shallow models with limited representational capabilities, necessitating manual feature extraction from the captured signals. This manual process is not only cumbersome and potentially costly but often requires intricate domain-specific knowledge. Additionally, these handcrafted features might not optimally distinguish between normal and faulty samples, potentially reducing prediction accuracy. In this study, we introduce an end-to-end approach using the Deep Support Vector Data Description model for fault detection in 3D printers. This design inherently facilitates automatic feature learning, where the features are synergistically optimized for fault detection. Our experiments leverage magnetic field signals for fault detection in 3D printers, using 1D convolutional layers to discern temporal signal patterns and wide kernels in the initial layer to mitigate high-frequency noise. Furthermore, our model can be easily adapted to integrate multi-channel signals for enhanced accuracy. Evaluations on real-world data from a delta 3D printer underscore the superiority of our method compared to existing alternatives.

In an era of rapidly advancing lighting technology and evolving public library roles, this study introduces a groundbreaking strategy for human-centric and integrative lighting asset management. Embracing both visual and non-visual effects, “integrative lighting” aims to enhance users’ physiological and psychological well-being. Despite technological progress, notably with LEDs, current asset management often lags, relying on reactionary measures rather than proactive strategies. As public libraries transform into dynamic learning hubs, the significance of indoor lighting, impacting both physical health and holistic well-being, cannot be understated. Yet, many existing solutions are based on controlled lab tests, bypassing the diverse real-world needs of public libraries. Aiming to explore and develop human-centric and integrative lighting asset management strategies to optimize lighting environments in public libraries, this research offers a cohesive approach encompassing context identification, a management framework, and a maturity assessment model. Additionally, this study highlights the synergy between the role of the lighting asset manager, ISO 55000 principles, and these foundational strategies. This holistic approach not only reinvents lighting in public libraries but also aligns it with the broader Sustainable Development Goals (SDGs), advocating for light as a conduit of comprehensive human betterment. The current study is primarily qualitative in nature. While this study is based on public libraries in Nordic countries, the implications and findings can be of interest and value to a broader international audience.

Traditional reliability evaluation of lighting sources often assesses only 50% of a lamp's volume, which can lead to performance disparities and misapplications due to their limited reflection of real-world scenarios. To address the limitations, it is essential to adopt advanced asset management approaches that enhance awareness and provide a more comprehensive evaluation framework. This paper delves into the nuances of human-centric and integrative lighting asset management in Swedish public libraries, employing a qualitative field study to ascertain the alignment of current practices with these advanced lighting principles. Expanding library services to 20 high-latitude locations (>55° N) in Sweden, our research employed field observations, stakeholder interviews, and questionnaires, coupled with a thorough gap analysis, to understand the current landscape and stakeholder perceptions. Our findings reveal a dichotomy between the existing conditions of library lighting and the stakeholders' experiences and expectations. Despite the intention to create conducive environments, there is a clear disconnect, with overt problems and covert challenges affecting user satisfaction and efficacy of lighting management. Managers, staff, and users reported varied concerns, including eye strain and discomfort, indicative of substantial room for improvement. The study advocates for a paradigm shift in not only lighting asset management but also reliability evaluation of lighting sources, moving toward continuous improvement, and enhanced awareness and training on human-centric and integrative lighting principles.

Traditional asset management of lighting systems typically focuses on functionality, cost, and lifespan. In contrast, a human-centric approach prioritizes social sustainability and user well-being by ensuring lighting assets “provide the right light at the right time” for diverse activities. Light-emitting diode (LED) bulbs, known for energy efficiency and longevity, have become a preferred choice, yet public libraries often struggle to manage these assets sustainably, remaining in a reactive “fix/replace when it breaks” stage. Current predictive methods, such as artificial intelligence and machine learning, rely on laboratory data that often overlook real-world contexts, leading to performance gaps. This paper presents a context-driven, human-centric methodology for LED prognosis and maintenance strategies in public libraries, employing limited degradation data from LED testing. Advanced analytical techniques, including Markov Chain Monte Carlo (MCMC) and Deviance Information Criterion (DIC), support a shift from function-based to performance-based reliability assessment. By incorporating Mean Time of Exposure (MTOE) and Critical Integrated Levels (CILs), the approach defines optimal maintenance inspection intervals. This research enhances sustainable LED lighting management in public libraries, offering a framework adaptable to broader applications and aligned with human-centric goals.