This work describes experiments on thermal dynamics of pure H2O excited by hydrodynamic cavitation, which has been reported to facilitate the spin conversion of para- and ortho-isomers at water interfaces. Previous measurements by NMR and capillary methods of excited samples demonstrated changes of proton density by 12-15%, the surface tension up to 15.7%, which can be attributed to a non-equilibrium para-/ortho- ratio. Beside these changes, we also expect a variation of heat capacity. Experiments use a differential calorimetric approach with two devices: one with an active thermostat for diathermic measurements, another is fully passive for long-term measurements. Samples after excitation are degassed at -0.09MPa and thermally equalized in a water bath. Conducted attempts demonstrated changes in the heat capacity of experimental samples by 4.17%--5.72% measured in the transient dynamics within 60 min after excitation, which decreases to 2.08% in the steady-state dynamics 90-120 min after excitation. Additionally, we observed occurrence of thermal fluctuations at the level of 10^-3 C relative temperature on 20-40 min mesoscale dynamics and a long-term increase of such fluctuations in experimental samples. Obtained results are reproducible in both devices and are supported by previously published outcomes on four-photon scattering spectra in the range from -1.5 to 1.5 cm^-1 and electrochemical reactivity in CO2 and H2O2 pathways. Based on these results, we propose a hypothesis about ongoing spin conversion process on mesoscopic scales under weak influx of energy caused by thermal, EM or geomagnetic factors; this enables explaining electrochemical and thermal anomalies observed in long-term measurements.

Nothing is more comforting than being greeted by your favorite drink just as you walk through the door of the corner café. While a thoughtful barista knows you take a macchiato every Wednesday morning at 8:15, it's much more difficult in a digital space for your preferred brands to personalize your experience. Talking Data, China's largest third-party mobile data platform, understands that everyday choices and behaviors paint a picture of who we are and what we value. Currently, Talking Data is seeking to leverage behavioral data from more than 70% of the 500 million mobile devices active daily in China to help its clients better understand and interact with their audiences. So, the business problem is to predict the demographic characteristics of the users using their app usage,geographical location and device properties.

DataVisor, the leading fraud detection company with solutions powered by transformational AI technology, announced the availability of dEdge, an anti-fraud solution that detects malicious devices in real-time, empowering organizations to uncover known and unknown attacks early, and take action with confidence. "Most consumer-facing organizations today provide their customers opportunities to interact with the business through an online channel. Even traditional industries like banking enable customers to bank through mobile applications. To validate the authenticity of this interaction, data needs to be collected and analyzed at the source" With growing adoption of mobile devices and the emergence of the always-on economy, by many measures, when organizations realize that they have been subject to a cyber-attack, it is already too late. Modern fraud detection and prevention require a transformational approach, one that represents a shift back to an earlier point along the timeline of a fraud attack.

In this walkthrough I show how an end-to-end anomaly detection system can be implemented for IoT use cases. The scenario is intentionally kept simple for illustration purposes and to allow generalizing to different scenarios in industry. The solution is built on Microsoft's Azure stack and includes multiple cloud services that allow handling data streaming, data processing, model training/predicting, and data storage. The main component here is Batch AI, a cloud service that enables users to submit parallel jobs to a cluster of high performing virtual machines. The business problem addressed in this walkthrough is: monitoring sensor measurements of multiple devices and predicting potential anomalies that might lead to failures across these devices. A manufacturing plant has sensors attached to its machines.