Broadcom (Israel)

Fibrodysplasia ossificans progressiva (FOP) is an ultra-rare, autosomal dominant disorder characterized by episodic yet cumulative heterotopic ossification (HO) of connective tissues. Flare-ups, i.e., sites of local soft tissue inflammation, are associated with swelling, joint stiffness, and pain are commonly associated with FOP. This study aimed to investigate the content validity of the Flares Diary by gaining an in-depth understanding of the experience of FOP flare-ups in people with FOP and by debriefing the Flares Diary to assess the relevance, comprehensiveness, and understandability of the instrument. Adults with FOP who participated in the Phase 2 LUMINA-1 trial (NCT03188666) of garetosmab, or who were recruited by email via the International Fibrodysplasia Ossificans Progressiva Association, were enrolled in this study. Trained qualitative researchers conducted in-depth semi-structured interviews with all participants comprising concept elicitation and cognitive debriefing of the Flares Diary, with participants encouraged to describe flare-up symptoms and impacts in their own words. All interviews were conducted between March 3, 2022, and March7, 2023. A conceptual model was developed based on participant-reported symptoms and impacts of flare-ups. Thematically analyzed interview transcripts from 20 adults with FOP identified concepts of localized and systemic symptoms, as well as impacts on participants' daily lives associated with flare-ups. Minimal issues were reported with understanding the Flares Diary, and participants found that the items were comprehensive and matched their own experience of flare-ups. The results confirm the adequacy of the Flares Diary and that the symptoms assessed are important to people with FOP. Graphical abstract available for this article. ClinicalTrials.gov identifier, NCT03188666.

Traditional virtual machine replication based on full copy cloning results in heavy resource duplication aswell as overheads in time that discourage dynamic scaling properties found in current cloud settings.Architectural improvements based on memory deduplication and copy-on-write functionality radically alter provisioning processes

The as‐cast Al–Ce–Mg alloy exhibits decent room‐temperature strength and medium‐temperature thermal stability, attributed to the load‐transfer effect of coarsening‐resistant Al 11 Ce 3 eutectic phases and Mg‐induced solid‐solution strengthening, but its strengthening potential is limited by inherently coarse cast microstructure. To address this, this study refines Al 11 Ce 3 via simple hot extrusion and further enhances strength by incorporating Sc/Zr—elements forming L1 2 ‐structured Al 3 (Sc,Zr) coherent nanophases—with extrusion fragmenting coarse Al 11 Ce 3 , inducing a heterogeneous fine/coarse‐grain lamellar structure and retaining the Al 3 (Sc,Zr)‐driven refinement of primary α‐Al and eutectic lamellae postprocessing. Performance wise, as‐cast Al–Ce–Mg(‐Sc–Zr) has 150 MPa strength; extruded Al–Ce–Mg reaches 246 MPa (room temp) and 112 MPa (250 °C); aged‐extruded Al–Ce–Mg–Sc–Zr excels with 400 MPa (room temp), 215 MPa (250 °C), and ductile fracture. Quantitatively, extrusion boosts Al–Ce–Mg's room‐temperature strength by ≈66%, while Sc/Zr microalloying adds 60% more and nearly doubles high‐temperature strength, with the alloy integrating grain‐refinement, solid‐solution, Al 11 Ce 3 particle, and precipitation strengthening—supported by hot extrusion for low‐cost, large‐scale industrial production.

The rapid growth of fintech applications has brought revolutionary changes to financial services but also heightened challenges regarding security, scalability, and regulatory compliance. Traditional monolithic architectures and reactive security controls aren’t designed to handle higher transaction volumes, latency requirements, and increasingly sophisticated cyber threats. In response, this dissertation presents an innovative cloud-native framework for building highly secure, scalable relational web & mobile applications in fintech. The cloud-native approach utilizes a microservices architecture combined with integrated DevSecOps practices, AI-enabled fraud detection, and predictive autoscaling to achieve increased performance and security. Objective evaluations against four state-of-the-art fintech frameworks demonstrate massive improvements, with 0.5 vulnerabilities per KLOC (72% reduction), 120 ms p95 latency (65% lower), an SLA adherence of 99.96%, and an F1-score of 0.945 for fraud detection (5–9% improvement). Additionally, overall compliance with PCI-DSS and GDPR was 99.2%, and usability tests using the SUS could achieve a score of 88, compared with existing solutions achieving 10–15 points lower. Taken together, the results support the conclusion that the proposed framework is a strong, high performance, solution that can maintain the high speed and strict requirements of fintech ecosystems while delivering a better user experience.

The MDS-UPDRS was not specifically developed for early-stage Parkinson’s disease (PD). This study investigated a sub-score of MDS-UPDRS Part III to assess bradykinesia and rigidity, which would be more targeted and clinically meaningful to assess disease progression in early-stage PD. A cross-sectional Rasch model was applied to the Parkinson’s Progression Markers Initiative (PPMI) untreated PD cohort data (N = 423) to define an adequate item set. Additionally, we evaluated how the daily life items from MDS-UPDRS Part II relate to bradykinesia and rigidity severity. A sub-score of 15 MDS-UPDRS Part III items focusing on limb-related bradykinesia and rigidity demonstrated good measurement properties in early-stage PD. Items adequately targeted to the study sample, and a meaningful hierarchy supported the validity of the scale. The scale was invariant to symptomatic treatment intake. Onset of clinical signs was represented as a milestone in the emergence of impairment in patients’ daily functioning.

Cracks in concrete structures, caused by aging, adjacent construction, and seismic activity, pose critical risks to structural integrity, durability, and serviceability. Traditional monitoring methods based solely on absolute thresholds are inadequate for detecting progressive crack growth at early stages. This study proposes a big data-driven anomaly detection model that combines absolute threshold evaluation with periodic trend analysis to enable both real-time monitoring and early anomaly identification. By incorporating relative comparisons, the model captures subtle variations within allowable limits, thereby enhancing sensitivity to incipient defects. Validation was conducted using approximately 2700 simulated datasets with an increase–hold–increase pattern and 470,000 real-world crack measurements. The model successfully detected four major anomalies, including abrupt shifts and cumulative deviations, and time series visualizations identified the exact onset of abnormal behavior. Through periodic fluctuation analysis and the Isolation Forest algorithm, the model effectively classified risk trends and supported proactive crack management. Rather than defining fixed labels or thresholds for the detected results, this study focused on verifying whether the analysis of detected crack data accurately reflected actual trends. To support interpretability and potential applicability, the detection outcomes were presented using quantitative descriptors such as anomaly count, anomaly score, and persistence. The proposed framework addresses the limitations of conventional digital monitoring by enabling early intervention below predefined thresholds. This data-driven approach contributes to structural health management by facilitating timely detection of potential risks and strengthening preventive maintenance strategies.

<div class="section abstract"><div class="htmlview paragraph">The early stages of product planning and concepting in advanced engineering domains are often hampered by high uncertainty, fragmented decision-making, and unstructured data. Traditional planning methodologies routinely lead to misalignment, inefficient risk assessments, and suboptimal product strategies. To address these challenges, we propose an AI-agentic decision intelligence (DI) framework that leverages Large Language Models (LLMs) to enhance decision-making in product planning and concept development. The proposed framework uses the transformative natural language processing capabilities and comprehensive knowledge of LLMs to capture and refine stakeholder intent, improve stakeholder engagement, and optimize workflow orchestration. Implementation of the framework is facilitated by state-of-the-art and rapidly evolving open-source tools, ensuring scalability and readiness for corporate environments. By enhancing decision confidence, adaptability, and automation, the framework provides a valuable platform for both defense and commercial product development environments.</div></div>

The rise in frequency and complexity of malware attacks are viewed as a major threat to modern digital infrastructure, which means that traditional signature-based detection methods are becoming less effective. As cyber threats continue to evolve, there is a growing need for intelligent systems to accurately and proactively identify and prevent malware infections. This study presents a new hybrid context-aware malware detection framework(HCAMDF) based on artificial intelligence (AI), which combines static file analysis, dynamic behavioural analysis, and contextual metadata to provide more accurate and timely detection. HCADMF has a multi-layer architecture, which consists of lightweight static classifiers such as Long Short Term Memory (LSTM) for real-time behavioral analysis, and an ensemble risk scoring through the integration of multiple layers of prediction. Experimental evaluations of the new/methodology with benchmark datasets, EMBER and CICMalMem2022, showed that the new approach provides superior performances with an accuracy of 97.3%, only a 1.5% false positive rate and minimal detection delay compared to several existing machine learning(ML) and deep learning(DL) established methods in the same fields. The results show strong evidence that hybrid AI can detect both existing and novel malware variants, and lay the foundation on intelligent security systems that can enable real-time detection and adapt to a rapidly evolving threat landscape.

Introduction: Congenital hypertrophy of the retinal pigment epithelium (CHRPE) is a benign proliferation of the melanin-producing retinal pigment epithelium (RPE). Although often a benign and incidental finding, multifocal CHRPE may mimic lesions associated with familial adenomatous polyposis (FAP). Case Description: We describe an 8-year-old girl presenting with optic disc pallor and widespread multifocal bear track CHRPE observed bilaterally on dilated fundoscopy. Fundus autofluorescence (FAF) imaging showed uniform areas of hypoautofluorescence corresponding to the bear track lesions. Spectral domain optical coherence tomography (SD-OCT) demonstrated normal lamination without atrophy. The full-field electroretinogram (ffERG) was within normal limits. Whole-genome sequencing (WGS) revealed a likely pathogenic heterozygous variant in the STAG2 gene (c.3222dup, p.Ser1075IlefsTer12). Conclusions: We present a rare case of bilateral, panretinal bear track CHRPE in a child with a likely pathogenic variant in STAG2. Using multimodal imaging, we contrast bear track lesions of the retina with FAP-associated CHRPE. We also present possible ophthalmic manifestations in carriers of pathogenic STAG2 variants.

Emergency communication systems face unprecedented challenges during crises, where traditional infrastructure often fails to meet the escalating demands of coordinated response operations. This article presents the transformative integration of artificial intelligence technologies into emergency communication networks for emergency planners, city planners, and community leaders, addressing critical limitations through advanced machine learning algorithms, predictive analytics, and adaptive resource allocation mechanisms. The convergence of edge computing, Internet of Things architectures, and intelligent networking protocols creates sophisticated communication systems capable of real-time data processing and dynamic traffic management during emergencies. AI-enabled networks demonstrate substantial improvements across multiple domains, including disaster response, law enforcement coordination, healthcare emergency management, and autonomous vehicle integration through ensemble learning methods, convolutional neural networks, and reinforcement learning algorithms with documented performance improvements of 15-25% over traditional systems. The technological foundation encompasses mobile edge computing for latency reduction, distributed machine learning frameworks for pattern recognition, and advanced optimization strategies utilizing deep reinforcement learning for resource allocation. Implementation considerations include deployment costs ranging from $2-8 million per city, depending on population size, phased rollout timelines of 18-36 months, and comprehensive stakeholder coordination requirements. However, deployment requires careful consideration of data privacy protection through advanced encryption protocols, cybersecurity vulnerabilities, including adversarial attacks on AI models, and infrastructure resilience challenges during multi-hazard scenarios. Privacy-preserving techniques, including differential privacy and federated learning architectures, address regulatory compliance requirements while maintaining operational effectiveness. The article examines comprehensive risk mitigation strategies encompassing fault-tolerant design principles, redundant system architectures, and robust security frameworks essential for reliable deployment in critical emergency response scenarios.