BMC Software (United States)

The article examines how generative artificial intelligence is reshaping Web UI development, primarily by reconfiguring the infrastructure layer, interface component libraries, and design systems, thereby transforming not the speed of layout implementation but the economics of reusable artefacts. The aim of the study is to provide a conceptual explanation of why, under the mass adoption of LLM-based assistants and intelligent interface methods, legacy monetization models based on selling ready-made themes, templates, screen collections, and even paid access to components begin to lose stability. The relevance of the work stems from the fact that contemporary web development functions as a dependency network of package ecosystems, where component libraries act as channels for scaling practices, while AI drastically reduces the cost of obtaining a first working version and reproducing standard solutions, simultaneously increasing the variability of results and the risk of divergence between teams. The novelty of the article lies in shifting the focus from the technological effect of generation to the shift in the unit of value: it is shown that in conditions of commoditization of form, market power moves toward what cannot be generated in a single pass, toward the manageability of the design system, verifiable contracts (tokens, standards, rules of composition), design–code integrations, automated quality control, and guarantees of compatibility and predictable updates. The main conclusion is that the winners are not vendors of yet another button, but platforms that sell trust, disciplined change, and reproducible UI evolution. The article is intended for developers, design leads, product managers, and creators of design systems who are choosing strategies for sustainable monetization in the era of generative AI.

Kubernetes-based multi-cloud architecture is becoming an integral part of modern enterprises, which makes ensuring its security a top priority. The main threats in such a system are related to configuration management, network security, access control and compliance with regulatory requirements. For effective security management, it is proposed to use CNAPP platforms that integrate several protection mechanisms, including cloud security Management (CSPM) and Kubernetes Security Management (KSPM). Automation of monitoring processes and authentication in Kubernetes, including centralized identity and access management, play an important role. The use of encryption technologies, software-defined networks (SDN) and regular audits contribute to reducing risks. The implementation of advanced security strategies and their constant adaptation to new threats ensures the sustainability of the cyber environment of multi-cloud deployments and the maintenance of data integrity.

Objective: To compare the clinical outcome in terms of efficacy and safety of Ivabradine as adjunctive therapy in patients with acute heart failure. Methods: A prospective clinical trial was conducted involving patients admitted with acute heart failure, who were randomized to receive either standard care alone or standard care with adjunctive Ivabradine therapy. Efficacy endpoints included improvement in symptoms, left ventricular ejection fraction (LVEF), and reduction in heart rate. Safety endpoints encompassed adverse events, including bradycardia and hypotension. Data were collected at baseline, during treatment, and at follow-up visits. Results: The mean age (67.5 vs. 68.2 years), Gender distribution, mean ejection fraction (35.7 ± 5.2 vs 36.1 ± 4.8), NYHA class, and prevalent comorbidities were comparable between both groups. The mortality rate (10% vs. 15%), and hospital readmissions (20% vs. 30%, p=0.12) showed no significant difference between both groups. The improvement in NYHA class (65% vs. 50%, p=0.04) was significantly (P-value > 0.05) higher in the treatment group. Treatment group demonstrated a significantly greater increase in ejection fraction (mean change 4.8% vs. 2.3%, p<0.001). Clinical outcomes, heart rate reduction, 6-minute walk distance, reduction in NT-proBNP, all-cause mortality and rate of heart failure hospitalizations were found significantly (p-value < 0.05) better in treatment group. Conclusion: This prospective clinical trial suggests that Ivabradine as adjunctive therapy in acute heart failure is efficacious in improving symptoms and reducing heart rate, with a favorable safety profile. These findings support the consideration of Ivabradine as an adjunctive treatment option in acute heart failure management. Keywords: Ivabradine, acute heart failure, adjunctive therapy, clinical trial, efficacy, safety.

The Australian government has recently legislated a 43% emissions reduction target for 2030 to drive net zero ambitions for 2050. The government plans for carbon capture and storage (CCS) to play a significant role in achieving this through greenhouse gas storage licence rounds. Rapid screening of Australia’s carbon storage opportunities is necessary to position future acreage release and ultimately identify and mature carbon storage sites. An industry collaboration between Geoscience Australia and SLB has conducted a basin-scale carbon storage assessment project for the Barrow and Dampier sub-basins. The project has built and modernised a subsurface database fit for CCS screening. The dataset was used as the foundation to create a carbon storage opportunity portfolio and storage resource assessment for the area. The project has three major streams: (1) CCS-focussed reprocessing, depth imaging and merging of 29 legacy seismic surveys covering 26,150 km2 – focussed on detailed image resolution for containment analysis. (2) A basin-wide audit of 1620 wells and databasing of available log data, 129 wells were carried through for petrophysical and rock physics analysis relevant to CCS. (3) A portfolio of carbon storage opportunities and associated capacity assessment for the Barrow–Dampier including saline aquifers and depleted fields ranked on storage capacity, reservoir and seal properties and integrity. The results of this study will be released via the National Offshore Petroleum Information Management System as a public resource to expedite CCS implementation for the region.

\n The rising popularity of generative models together with the growing need for flexible and exact inferences have motivated the machine learning community to look for expressive yet tractable probabilistic models. Probabilistic circuits (PCs) are a family of tractable probabilistic models capable of answering a wide range of queries exactly and in polynomial time. Their operational syntax in the form of a computational graph and their principled probabilistic semantics allow their parameters to be estimated by the highly scalable and efficient optimization techniques used in deep learning. Importantly, tractability is tightly linked to constraints on their underlying graph: by enforcing certain structural assumptions, queries like marginals, maximum a posteriori or entropy become linear time computable while still retaining great expressivity. While inference is usually straightforward, learning PCs that both obey the needed structural restrictions and exploit their expressive power has proven a challenge. Current state-of-the-art structure learning algorithms for PCs can be roughly divided into three main categories. Most learning algorithms seek to generate a usually tree-shaped circuit from recursive decompositions on data, often through clustering and costly statistical (in)dependence tests, which can become prohibitive in higher dimensional data. Alternatively, other approaches involve constructing an intricate network by growing an initial circuit through structural preserving iterative methods. Besides depending on a sufficiently expressive initial structure, these can possibly take several minutes per iteration and many iterations until visible improvement. Lastly, other approaches involve randomly generating a probabilistic circuit by some criterion. Although usually less performant compared to other methods, random PCs are orders of magnitude more time efficient. With this in mind, this dissertation aims to propose fast and scalable random structure learning algorithms for PCs from two different standpoints: from a logical point of view, we efficiently construct a highly structured binary PC that takes certain knowledge in the form of logical constraints and scalably translate them into a probabilistic circuit; from the viewpoint of data guided structure search, we propose hierarchically building PCs from random hyperplanes. We empirically show that either approach is competitive against state-of-the-art methods of the same class, and that their performance can be further boosted by simple ensemble strategies.\n

Elite sporting performance is the result of a multitude of different factors including, but not limited to, physical conditioning as well as psychological, nutritional, immunological, sleep and fatigue status. In an attempt to model an athlete’s performance a Two-Systems approach was first suggested by Banister et. al. (1975) and later refined by Busso et. al. (1994). In this model, an input of ‘load’ contribution to 2 unconstrainted variables known as fitness and fatigue. Current work within the BMC Racing Team has moved away from this model incorporating additional variables into the model and by monitoring an athlete’s fatigue rather than estimating it from load measurements. Information pertaining to sleep, nutrition, stress, muscle soreness, self-perception and mood state are recorded via a daily questionnaire. Load is calculated using a new methodology known as TLC (Total Load Cycling). Strategies allowing prescription of load and life-style management is now being optimised based on the relative contribution of each of these variables to each individual rider’s performance.

A 65-year-old man with metastatic melanoma was started on immunotherapy, switched to alternative targeted therapy and developed disseminated intravascular coagulation. This case underscores the complexity of interpreting and managing side effects of novel therapies in cancer care, identification of progression versus pseudoprogression and challenges in sequencing treatments in metastatic melanoma.

Objective: to evaluate the effect of ethanolic extract Fragaria Vesca on serum glucose levels in diet induced obese rats.\n\nMaterial and methods: Male Wister albino rats weighing 200- 250 gm, were divided into 3 groups of 6 animals each. The animals of all the groups except normal group were given a lipid diet consisting of cholesterol (1%), cholic acid (0.5%), casein (20%), choline (0.25%), d-l-methionin1(0.4%), coconut oil (25%), multi vitamin mix (3.5%) and sucrose (48.4%) with standard pellet diet for 30 days [20]. Growth rate was monitored during the treatment.\n\nResults: There was significantly decrease in blood glucose in standard group compared to HFD model (P< 0.05). But there was no significant change among other groups\n\nConclusion: There was no significant change in the blood glucose level in all the groups except the standard group, but there was reduction in body weight.

Digital Data is everywhere - from massive amounts of single sourced to huge conglomerations of smaller multi-sourced data. Organizations have long been collecting repositories of data, yet continue to struggle to create valuable information. In this paper, we propose a Data to Decisions Framework (D2D) to outline the course of action needed to procure the information required by organizations. D2D not only relies on data collection and process analysis, but upon human interaction as well. The study addresses the following problem: Data is obtained and stored from the perspective of the design of the application - not from the customer's point of view. The paper considers anomaly detection and cluster analysis to assist in the process of identifying and classifying Information Technology System Outages with the objective of providing the appropriate view for analysis. The paper also reveals how transparency and collaboration with key stakeholders then influence the process rules and properties to drive selection of target measures and definitions. The human touch, combined with rich data sets, is unique to the D2D Framework and is the driver in bringing real and desired results across the organization.