Infosys (India)

Background and Objectives: Intraoperative hypotension (IOH) is of great importance in preventing diseases such as postoperative myocardial infarction, acute kidney injury, and mortality. This study aimed to develop and validate machine learning and deep learning models that predict IOH using both biosignals and personalized clinical information for each patient. Materials and Methods: In this retrospective observational study, we used the VitalDB open dataset, which included intraoperative biosignals and clinical information from 6388 patients who underwent non-cardiac surgery between June 2016 and August 2017 at Seoul National University Hospital, Seoul, South Korea. The predictive performances of models trained with four waveforms (arterial blood pressure, electrocardiography, photoplethysmography, and capnography) and clinical information were evaluated and compared at time points at 5 min before the hypotensive event. To predict hypotensive events during surgery, we developed two predictive models: machine learning and deep learning. In total, 2611 patients were enrolled in this retrospective study. Machine and deep learning algorithms were developed and validated using raw waveforms and clinical information as inputs. Results: Gradient boosting machine showed predicted IOH with an AUROC and accuracy of 0.94 (0.93–0.95) and 0.88 (0.86–0.89). A hybrid CNN-RNN model also showed similar performance with an AUROC and accuracy of 0.94 (0.93–0.95) and 0.88 (0.87–0.89). Conclusions: This study developed and validated machine and deep learning models to predict IOH using waveform data and covariate values. In the future, we anticipate that the results of our study will contribute to predicting IOH in real time in the operating room and reducing the occurrence of IOH.

We study an adjacency-list-only evolutionary algorithm (EA) for Vertex Cover on Random Hyperbolic Graphs (HRG) and Geometric Inhomogeneous Random Graphs (GIRG) with exponent τ ∈ (2, 3). The EA uses an exact uncovered-edge sampler (edges selected with

A multivalent vaccine targeting high-consequence infectious diseases in Sub-Saharan Africa (SSA), which are linked to high mortality, morbidity and overlapping clinical manifestations, would significantly improve health security and economic stability in this region. Trivalent vector vaccines were devised to deliver digitally optimized versions of Orthoebolavirus, Orthomarburgvirus glycoproteins (GPs) and a Lassa mammarenavirus (LASV) nucleoprotein (NP) by a single Modified Vaccinia Ankara (MVA) known to protect against mpox virus (MPXV) along with a matched DNA vaccine. Three immunizations in mice and Hartley guinea pigs with MVA only or a DNA prime followed by two MVA administrations induced comparable levels of binding antibodies and LASV-specific T-cells, respectively. While DNA priming mitigated MVA-specific antibody responses, GP- and NP-specific antibodies developed already after a single MVA vaccination. Although a post-outbreak Ebola virus vaccine is available, outbreaks by other filoviruses, annual LASV epidemics and increased incidence of MPXV infections support the rationale for an MVA-based trivalent haemorrhagic fever vaccine for endemic and high-risk human populations in SSA.

Abstract We prove an axiomatic meta-theorem that yields O(log log n)-round randomized algorithms for maximal independent set (MIS) and maximal matching on broad classes of scale-free networks. Our framework unifies two settings: (i) geometry-driven rank-1 models (e.g., GIRG and soft-HRG) via a single high-probability “uniformity” event E that regularizes occupancies, cross-edges, and hub counts across all shells without referring to degree- or priority-defined sets; and (ii) power-law-bounded (PLB) degree families captured by PLB-U (upper tail), PLB-L (lower tail), and PLB-N (size-biased neighbor tail). The algorithms process vertices in degree bands. Hub–hub sparsity (from PLB-U via a weight pivot) gives bounded degeneracy within each band; R = O(1) independent micro-iterations of randomized greedy per phase stabilize progress by concentration. We expose a κ-fraction of edges per vertex using a per-edge public hash, and use PLB-N to sum the decaying band-mass effects across bands. Let γ = τ − 2 ∈ (0, 1) and choose any constant κ ∈ (0, 1/4). Then the per-phase survival probability for degree about B^J is exp(−Θ(B^{J(γ − κ(1 − γ))})). Setting κ_eff = (γ − κ(1 − γ))/2 > 0 yields a double-exponential degree cascade Δ_{i+1} ≤ Δ_i^{1 − κ_eff} with high probability. After O((1/κ_eff) log log n) phases, the maximum degree drops to polylog(n); any standard finisher with round complexity O(log Δ) + poly(log log n) then completes in O(log log n) rounds. Under geometry, the residual also finishes in O(log log n) rounds (and in O(1) on typical threshold-HRG instances). In CONGEST, messages are O(log n) bits per edge per round.

We prove that in spatial scale-free networks where geometry suppresses long edges sufficiently strongly, the Dominating Set problem is solvable in polynomial time with high probability. For Euclidean GIRGs in d ≥ 2 with degree-tail exponent τ ∈ (2, 3)

Microsoft Office 365 has fundamentally transformed enterprise IT by delivering integrated cloud-based productivity solutions that reshape how organizations operate, collaborate, and secure their digital workplaces. The transition from traditional on-premises software to cloud-based services represents a paradigm shift in enterprise computing, offering enhanced flexibility, collaboration capabilities, and cost optimization potential. This article examines the multifaceted impact of Office 365 on digital workplace transformation, Site Reliability Engineering practices, security and compliance frameworks, and strategic IT management considerations. Through analysis of implementation patterns, governance frameworks, and emerging best practices, the article provides insights into effectively leveraging Office 365 capabilities while addressing the challenges of cloud migration, integration, and ongoing management. The discussion encompasses critical dimensions including identity management, data protection, regulatory compliance, cost optimization, and the evolving role of IT departments in cloud-centric environments.

Recent advances in computer vision are paving the way for smarter artificial intelligence at the edge. These advances are rapidly enhancing the capacity of IoT devices to analyze images beyond basic recognition tasks. Object detection techniques enable cameras to identify not only the presence of objects but also their precise locations, while image segmentation can differentiate between the object and its background [1-3]. Furthermore, generative models possess the ability to create images based on learned examples. Object detection involves locating objects within an image and classifying them. Originally implemented through traditional image processing techniques, state-of-the-art methods now rely on deep-learning-based techniques [2,4,5]. Image segmentation divides an image into superpixels—meaningful regions or objects. By focusing on the primary objects, it simplifies the background. Semantic segmentation classifies each pixel into a category, while instance segmentation classifies pixels belonging to different instances of the same class. Generative models are capable of creating images that resemble those from the training dataset. To fully grasp the importance of these techniques, it is essential to understand the challenges of AI at the edge and the principles behind IoT.