VibeOps: Intelligent Spacecraft Operations Co-Pilot

Although current athlete performance monitoring system designs undertaken within sports engineering and technology research have a strong focus on achieving functionality requirements, considerations on usability evaluation and testing during the design process are limited. Conventional design research highlights the importance of factoring usability at the heart of performance monitoring system designs like human-computer interfaces. Consequently, based on a user-centred approach, this research aimed to design a digital dashboard to optimise a scouting information flow within a professional basketball club and improve usability issues in the design by executing formative usability assessment approaches. Thematic analysis conducted on the data obtained from an initial semi-structured interview with the Head Coach of the club clarified dashboard functionality and design requirements, importance of the scouting process and information on potential system users. Thereafter, guided by the Build-Measure-Learn cycle in The Lean Startup framework, Minimum Viable Products (MVPs) of the dashboard were designed and 25 usability issues in the initial MVP were identified from heuristic evaluations conducted with three usability experts. Following system redesign, task-based think aloud usability testing sessions conducted with five coaches (i.e. users) at the club unravelled further nine usability issues in the dashboard, which were resolved by systematically pivoting to new MVPs during the iterative design process. Overall, this paper outlines a systematic approach to conducting formative usability assessment of sports technologies and highlights the importance of resolving specific usability issues present in technological systems like dashboards designed by practitioners embedded in applied sport environments.

Failure localization in microservice environments is increasingly challenging. While large language models (LLMs) have shown promise in software engineering tasks, existing approaches struggle to effectively integrate multi-modal telemetry data (e.g., log, metric and trace) and provide interpretable results. This paper presents LocaleXpert, a novel failure localization system that combines specialized LLM-based agents with traditional AIOps methods to diagnose issues in microservice environments. LocaleXpert introduces three key innovations: (1) a modular pipeline that transforms metrics, logs, and traces into natural language descriptions that LLMs can effectively process, (2) specialized expert agents that analyze each data type and collaborate to identify root causes, and (3) an interpretation mechanism that produces clear, actionable explanations of its reasoning process. Evaluation results show that LocaleXpert significantly outperforming baseline approaches in both accuracy and interpretability. The system has been successfully deployed in Microsoft's AIOpsLab benchmark, demonstrating its effectiveness.

The purpose of this DOE SBIR award was to develop new scanning probe microscopy tools for the elucidation of electrical and electrochemical properties at the nanoscale. High performance probes are important for energy materials research. In both solar cells and batteries, the essential physics and chemistry occur at the nanoscale and nanoscale characterization tools are required for their elucidation. Two products were developed: an electrically shielded probe and an electrically conductive probe with low leakage currents. Our processes were based on post processing of existing probes and fabrication methods were tested, leading to a laser micromachining based approach that led to the first minimal viable product. We invented a method to make a reliable electrical contact that attaches a flexible printed circuit board to the chip. enable leak-free operation crucial when used in conductive liquids and electrolytes. This proprietary solution improves chip handling, reduces contamination, and maintains effective electrical contact. The probes fabricated in this Phase IIa are a minimal viable product which attracted attention from various tip manufacturers and instrument manufacturers alike.

This conceptual study tackles the digital-adoption gap in Jakarta's kos-kosan boarding-house rooms, where landlords managing 5–20 units still juggle paper ledgers despite near-universal smartphone and WhatsApp use. Guided by the Technology Acceptance Model (TAM), Lean Start-up logic, and the Business Model Canvas (BMC), this research develops an integrated theoretical framework that explains behavioral hesitancy, sequences low-risk Minimum Viable Product (MVP) iterations, and pinpoints the "missing-middle" niche for a boarding house management platform. The proposed framework positions TAM to surface core drivers (perceived usefulness and ease of use) recast for WhatsApp-first workflows. Lean Start-up methodology will then map these insights into quick, feedback-rich MVP cycles tuned to resource-constrained settings. BMC will situate the validated feature set in a defendable market position underserved by premium PropTech and ultra-basic bots. This conceptual foundation establishes the theoretical groundwork for future empirical phases, which will combine stakeholder interviews and platform benchmarking to diagnose pain points, quantify adoption triggers, and refine the MVP and business model through qualitative fieldwork and pilot deployments. By linking behavior, experimentation, and strategy, this study lays a theory-driven pathway toward inclusive digital transformation in informal housing in emerging economies, with empirical validation planned for subsequent research phases.

Batangas State University is a premier university in the CALABARZON Region in the Philippines. Last June 2015–May 2016, the highest number of enrollees was recorded at 45,860, but transition to the 13-year basic education (K-12), resulted in enrollment drop to 37,966. Fifteen per cent of the population are engineering majors. True to its motto of Leading Innovations, Transforming Lives, BatStateU established Center for Technopreneurship and Innovation (CTI) last Fiscal Year 2014–2015. Technopreneurship course was piloted in the Bachelor of Science in Electronics Engineering, Computer Engineering, Computer Science and Information Technology. The researchers aim to determine how technopreneurship contributes in honing the entrepreneurial and innovative skills of students. The researchers conducted survey assessment and evaluation among students of the course with a demo day as a culminating activity wherein participants pitch their Minimum Viable Products (MVP). After four semesters, 1260 students and 20 faculty facilitators were provided with hands-on skills in technopreneurship. From a traditional mindset, students were equipped with skills and courage to be employers of start-ups. Technopreneurship is an Outcomes-Based Education Assessment tool for the students’ ability to communicate effectively; ability to function on multidisciplinary teams; and ability to design and implement MVPs

Validated learning plays a critical role in the lean startup approach to entrepreneurship education. We argue that, compared to validated learning from business generalists, student entrepreneurs who seek feedback from technical specialists develop superior minimum viable products (MVPs). We further suggest that the different effects of feedback from business generalists and technical specialists on the quality of MVPs are mediated by student entrepreneurs’ failure analysis. Using a between-subjects, randomized field experiment with a longitudinal design, we find our hypotheses supported. The results indicate that validated learning from technical specialists, in addition to business generalists, should be incorporated into the lean startup pedagogy to guide student entrepreneurs for better MVPs.

Biopharma Is Going Digital … Bit by Bit

Lots of software companies rely on web technologies to test market hypotheses in order to develop viable businesses. They often need to quickly build web services that are at the core of their Minimum Viable Products (MVPs). MVPs must be reliable whereas they are based on specifications and hypotheses that are likely to change. Web services need to be well documented, to make it easy to develop applications that consume them. Model Driven Engineering approaches have been proposed and used to develop and evolve web services on one hand, and document them on the other hand. However, these approaches lack the ability to be suitable for both (i) rapid prototyping, (ii) model verification, (iii) compatibility with common programming languages and (iv) alignment between documentation and implementation. Here we propose a meta-model to express web services, the related tool to verify models consistency and an integration of this approach into the OpenAPI Specification. We adopt a shallow verification process to allow rapid prototyping by developers who are not formal methods experts, while still offering design-time guarantees that improve product quality and development efficiency. Web services are defined using parametric components which enable to express and formally verify web service patterns and to safely reuse them in other contexts. We built a tool to check consistency of extended OpenAPI 3.0 models and associated components implementations in order to generate corresponding web services. This allows us to give flexibility and verification support to developers, even in the context of an incremental development, as illustrated by a case study.

The Development of Customer Development Stages in the Tourism Directory Application Service using Design Thinking is a development process based on the stages of a survey of user needs in defining problems and formulating solutions to these problems. This research aims to gain knowledge and insight related to user needs for a tourist directory application service, identify service features that are by the needs of tourist directory application service users, and obtain a Minimum Viable Product (MVP) on the tourist directory application service owned by the current author (https://gatewee.com). The research conducted is in the form of surveys and interviews with users through the stages of Customer Development and uses a user-based problem-solving approach through Design Thinking. Methods This research is qualitative and quantitative research that describes the user-based development strategy of the Tourism Directory Application Service. The results of the research are User Persona, identification of features, focus on feature development, prototypes, Usability Test results, MVP, targeting strategies & business models and business development plans.

In the fast-paced and precision-driven environment of well construction operations, efficient decision-making is paramount. This paper introduces an advanced artificial intelligence (AI) model leveraging Large Language Models (LLM) to classify log records and complex WITSML data in real time. By integrating this AI model into drilling control rooms, decision-making efficiency has been enhanced by over 50 times, significantly reducing the time required for search and background analysis from hours to minutes. The implementation of LLMs such as GPT-3 and GPT-4 has revolutionized the process by enabling zero-shot inference, allowing for the accurate classification of complex texts without extensive labeled training datasets. This breakthrough has facilitated the rapid processing and analysis of structured and unstructured data, which is crucial for real-time response to critical events in drilling operations. A collaborative effort between drilling experts and digital developers resulted in a functional proof of concept within two weeks, expanding to a minimum viable product (MVP) in under 90 days. The AI model now categorizes technical texts into 42 distinct categories, surpassing initial goals and demonstrating substantial improvements in both accuracy and processing speed. This paper concludes by highlighting the significant operational efficiencies and data consistency achieved through the AI model. It also discusses potential future enhancements, including the integration of diversified data sources and the development of predictive and prescriptive models, positioning this AI-driven approach as a cornerstone for future advancements in well construction control rooms.

Taking an idea off the bench and into the market, often called technology transfer, encompasses a variety of activities by which inventions flow from basic research to commercial entities and then to public use. This article describes two useful tools to help improve the translation of ideas to market. The first is the Business Model Canvas, or Lean Canvas for Business (LCB), an existing tool that helps entrepreneurs identify a minimum viable product (MVP) and channels and methods to take it to market. The second is a new tool (in the early pilot test stages), the Lean Canvas for Invention (LCI), that can help academic researchers identify the most valuable research questions (MVRqs) that may yield inventions that could later move through a Business Model Canvas to become a viable product.

The paper presents Anatomy3DExplorer, a customized ChatGPT designed as a natural language dialogue interface for exploring 3D models of anatomical structures. It illustrates the significant potential of large language models (LLMs) as user-friendly interfaces for database access. Furthermore, it showcases the seamless integration of LLMs and database APIs, within the GPTS framework, offering a promising and straightforward approach.

Within a large global transformation program of a financial services company, a complex and challenging local wide-ranging project in a country in Latin America (abbreviated: LatAm) had to be started, which strived for a revolutionary change in data supply by creating a robust data warehouse on the basis of new platform technology in a fragmented and diverse IT landscape. The endeavor turned out to be a real “Travelling Organization,” comparable with expeditions into very unknown areas in the nineteenth century, where high degrees of flexibility, pragmatism, resilience, and improvisation skills are required and often the journey had to be done as “flying a plane with VFR (Visual Flight Rules).”At the end, the project succeeded to a certain extent though it did not exploit its full potential. The ultimate key target of having a specific data warehouse, an MVP (Minimum Viable Product), in place was realized, but it turned out to require further optimization steps. However, all previous local projects had failed, so at least a first step could be achieved. The reasons for the partial success and failure are described in detail—and give a good guidance for a mandatory setting check before starting transformational projects in a complex context of other diverse, more or less interlinked, transformational activities.

Breakthrough technologies can be considered as exponentially disruptive to organizations across industries within the last few decades of the 21st century, as they have significantly altered the way their business units or customers operate. Artificial Intelligence related cognitive technologies are some of the latest disruptive solutions currently being adopted by organizations. Organizational leaders may feel both the pressure and excitement of adopting such nascent technology quickly and at scale. However, due to organizational knowledge gaps of nascent solutions, transformative large-scale initiatives have a higher risk of negative impact on failure to implement. On the other hand, an iterative approach allows for the implementation to occur in smaller amounts and leaves room for incorporating feedback and lessons learned in future iterations, thus mitigating the risks involved with the undertaking. This article breaks down the nascent field of advanced cognitive technologies into three main categories based on their business use cases: process automation, cognitive insights, and cognitive engagement. It then explores implementing this technology in each of its three categories through the lens of a popular iterative product lifecycle management approach (i.e., the Minimum Viable Product) to reduce the risk of failure or other negative impacts on an organization adopting cognitive solutions.

In the competitive landscape of startups, achieving product-market fit is essential for survival and growth. This article introduces an expanded framework called “Product-Content-Market Fit” (PCMF), which integrates content strategy into the Minimum Viable Product (MVP) development process. Through case studies and existing literature, this study illustrates how strategic content creation aligns with customer needs and market demands, enhancing the effectiveness of MVPs. The research emphasizes the importance of a content strategy in the early stages of product development and its impact on market engagement and product validation.