Independent Broadcasting Associates

Machine learning (ML) is revolutionizing industries by providing innovative solutions that drive sustainable economic growth and market efficiency. The integration of ML techniques in various sectors, including finance, healthcare, manufacturing, and energy, offers significant potential for enhancing productivity, optimizing resource allocation, and fostering innovation. This review explores the transformative impact of ML on economic growth and market efficiency, emphasizing its role in predictive analytics, decision-making, and automation. In finance, ML algorithms are employed to predict market trends, manage risks, and detect fraudulent activities. By analyzing vast amounts of data in real-time, ML models enable financial institutions to make informed decisions, reduce operational costs, and improve customer experiences. In healthcare, ML applications range from diagnosing diseases and predicting patient outcomes to optimizing treatment plans and managing healthcare resources, thereby improving patient care and reducing costs. The manufacturing sector benefits from ML through predictive maintenance, quality control, and supply chain optimization. ML models analyze data from sensors and production lines to predict equipment failures, ensuring timely maintenance and minimizing downtime. This leads to increased productivity and reduced waste, contributing to economic sustainability. Energy management is another critical area where ML drives efficiency. ML algorithms optimize energy consumption, forecast renewable energy production, and enhance grid management. By integrating ML into energy systems, organizations can reduce their carbon footprint and operational costs while promoting sustainable energy practices. Moreover, ML enhances market efficiency by enabling businesses to understand consumer behavior and preferences. Retailers use ML to personalize marketing strategies, optimize pricing, and manage inventory effectively. This not only boosts sales but also improves customer satisfaction and loyalty. Despite the significant benefits, the adoption of ML presents challenges such as data privacy concerns, algorithmic bias, and the need for skilled professionals. Addressing these challenges requires robust regulatory frameworks, ethical guidelines, and investment in education and training. In conclusion, harnessing ML techniques is pivotal for driving sustainable economic growth and market efficiency. By leveraging ML's capabilities in predictive analytics, decision-making, and automation, industries can achieve higher productivity, optimized resource utilization, and innovative solutions that contribute to long-term economic sustainability and competitiveness. As ML continues to evolve, its potential to transform various sectors and promote sustainable development remains immense.

Background: In the face of rising healthcare expenditures nationally, current literature suggests that primary care-based longitudinal payment models may improve care quality and lower cardiovascular care costs. Despite innovation in this space, there has been no study that critically examines the efficacy of virtual-first cardiology care as an adjunct to existing primary care models. Methods: We performed a retrospective review of patients enrolled in Heartbeat Health’s Longitudinal Care program which provides virtual-first cardiology care to patients in partnership with their primary care team. The program included telemedicine visits with specialist cardiology teams, medication prescription, testing, and remote patient monitoring when indicated. We reviewed all new diagnoses, changes in blood pressure, initiation of guideline directed medical therapy (GDMT), and patient satisfaction. Results: The final cohort included 230 patients (average age = 71.7 yrs). Meaningful increases in new diagnoses were evident (Figure). Regarding blood pressure control, 30.8% saw improvement during the program and another 20.6% were at their goal by the time of graduation. Aspirin therapy (either primary or secondary prevention) increased from 28.3% to 35.7% in all-comers, while primary stroke prevention in patients with atrial fibrillation increased from 50% to 62.5%. Additionally, 46.5% (n=107) of patients were placed on GDMT for at least one of the conditions listed in Figure. Average patient Net Promoter Score was 87. Conclusions: Virtual-first cardiology care is a promising opportunity for value-based care organizations. Among other benefits, increased use of preventative therapy, enhanced GDMT, and newly found and managed diagnoses hold the potential to close gaps in care and reduce long-term costs.

The Permian Basin of west Texas spans two major subbasins — the Midland Basin and the Delaware Basin. Both basins contain Wolfcampian- to Leonardian-age turbidites that form thick sections of prolific unconventional reservoirs. For the past several years, the most active drilling targets in the United States have been the Wolfberry turbidite interval of the Midland Basin and the Wolfbone turbidite interval in the Delaware Basin. We have used two new technologies to examine the internal architecture and fabric of a thick interval of these unconventional drilling targets with seismic reflection seismology. First, we used seismic interpretation software that uses unsupervised machine learning (ML), so that a higher level of detail could be extracted from seismic images. Second, we complemented our P-P imaging of Wolfberry turbidites with a new seismic imaging option, that being SV-P (or converted-P) imaging. Because vertical vibrators, particularly arrays of vertical vibrators, produce downgoing P and downgoing SV illuminating wavefields, SV-P reflections can usually be extracted from the same vertical-geophone responses as are P-P reflections. The combination of these two images essentially doubles the amount of information that can be extracted from data generated by P sources and recorded with vertical geophones. SV-P imaging with P sources has been ignored by reflection seismologists for decades, so we felt an obligation to illustrate the value of this ignored seismic mode. These two new tools — SV-P imaging and interpreting P-P and SV-P images via unsupervised ML — expanded our insights into the internal architecture and fabric of Wolfberry turbidites. Our work provides interpreters a much-needed example of applying unsupervised ML technology in a joint interpretation of P- and S-wave data.

Calcium imaging is a widely used approach in neuroscience to analyze neuronal activity patterns in neural networks to correlate animal behavior with neural activity. Confocal microscopy has been brought up as an option for synaptic imaging in the past (as compared to 2-photon microscopy), but one of its main drawbacks is the lack of options for processing, particularly in the context of synaptic imaging. Most existing pipelines, such as CaImAn, EZCalcium, etc. are designed for one/two photon microscopy and detect/analyze neuronal regions of interest (ROIs). Compared to neuronal calcium imaging, quantitative tools to analyze synaptic calcium signals in an automated manner are lacking due to the nanoscopic organization of synapses. To address this technical challenge, we developed a novel pipeline that analyzes synaptic ROIs to map the probability of synaptic transmission at the resolution of individual active zones: PACISCA. The pipeline has four main components: synapse annotation/image motion correction, flash identification, fluorescence tracing, and probability mapping. Our work provides an efficient computational framework to analyze synaptic calcium imaging signals from neuroscience experiments studying synaptic functions. Improving the reliability of our analytic tool on larger datasets will be the next steps of this work.

ObjectiveProlonged and unaddressed hypoxia can lead to poor patient outcomes. Proning has become a standard treatment in the management of patients with ARDS who have difficulty achieving adequate oxygen saturation. The purpose of this study was to describe the use of early proning of awake, non‐intubated patients in the emergency department (ED) during the COVID‐19 pandemic.MethodsThis pilot study was carried out in a single urban ED in New York City. We included patients suspected of having COVID‐19 with hypoxia on arrival. A standard pulse oximeter was used to measure SpO2. SpO2 measurements were recorded at triage and after 5 minutes of proning. Supplemental oxygenation methods included non‐rebreather mask (NRB) and nasal cannula. We also characterized post‐proning failure rates of intubation within the first 24 hours of arrival to the ED.ResultsFifty patients were included. Overall, the median SpO2 at triage was 80% (IQR 69 to 85). After application of supplemental oxygen was given to patients on room air it was 84% (IQR 75 to 90). After 5 minutes of proning was added SpO2 improved to 94% (IQR 90 to 95). Comparison of the pre‐ to post‐median by the Wilcoxon Rank‐sum test yielded P = 0.001. Thirteen patients (24%) failed to improve or maintain their oxygen saturations and required endotracheal intubation within 24 hours of arrival to the ED.ConclusionAwake early self‐proning in the emergency department demonstrated improved oxygen saturation in our COVID‐19 positive patients. Further studies are needed to support causality and determine the effect of proning on disease severity and mortality.

The Employment Appeal Tribunal’s (EAT) decision in Cavendish Munro Professional Risk Management Ltd v Geduld [2010] ICR 325 (‘Geduld’) was a problematic development. That was in large part because it encouraged a focus on whether there was a ‘disclosure of information’ without sufficient emphasis on the key issue of whether what was disclosed was reasonably believed to be a relevant failure. Linked to this has been an emphasis on (1) whether there was a disclosure of ‘facts’ and (2) whether what was disclosed amounted to a disclosure of information rather than making of an allegation, statement of a position or some other such concept. Subsequent decisions have illustrated the difficulty and fine judgments that may sometimes be involved in drawing these distinctions. There is however no need for a narrow approach given the requirement for a reasonable belief that the information disclosed tends to show a relevant failure. The most recent contribution to this line of cases, in Kilraine v Wandsworth LBC [2018] IRLR 846 (‘Kilraine’), has focussed on the need for specificity in what was disclosed. But despite emphasising the requirement for an evaluation of whether the information tends to show a relevant failure, it still provides support for an unnecessarily narrow and uncertain view of what constitutes ‘information’. The real danger overall is that whistleblowing legislation may fail to serve one of its primary purposes in providing protection to those who raise the alarm. In addition, the reasoning in Kilraine leaves an unsatisfactory difference of approach to the question as to whether there can be ex post facto justification for whether a belief was reasonably held, depending on whether the reasonable belief relates to the belief that the disclosure was made in the public interest or to whether information tends to show a relevant failure.

Since 1938, ASTM Committee C16 has been developing standards and disseminating information generated from research on the hygrothermal (i.e., heat-air-moisture) response of building envelope materials and systems. During the last eight decades, the building construction industry has witnessed many social, economical, and technical challenges. These challenges at times appeared to be overwhelming, but innovations and adaptations have been key factors for the advancement and state of the building construction industry today. This advancement has been possible because of the synergistic collaborations between academics, researchers, and industry managers. ASTM successfully provided a platform to stimulate and nurture these interactions over the years.