A Novel Cooperative AI-Based Fall Risk Prediction Model for Older Adults.

Older adults make up about 12% of the public sector, primary care, and hospital use and represent a large proportion of the users of healthcare services. Older people are also more vulnerable to serious injury from unexpected falls due to tripping, slipping, or illness. This underscores the immediate necessity of stable and cost-effective e-health technologies in maintaining independent living.

A polygeneric immunogen composed of 22 venoms from sub-Saharan African snakes to expand the neutralization scope of the EchiTAb-plus-ICP antivenom.

Recent research suggests that a polygeneric immunogen made from the venoms of the most medically important viperid and elapid snakes in sub-Saharan Africa could elicit a broader antibody response in horses compared to the current EchiTAb-plus-ICP antivenom, especially against neurotoxic elapid venoms. To test this, 25 horses that have been regularly immunized to produce this antivenom were reimmunized with an immunogen containing 22 venoms from various snake species from the genera , , , and both spitting and non-spitting . The plasma collected from these horses was processed using the caprylic acid method to produce an industrial-scale freeze-dried antivenom.

Survey on Intrusion Detection Systems Based on Machine Learning Techniques for the Protection of Critical Infrastructure.

Industrial control systems (ICSs), supervisory control and data acquisition (SCADA) systems, and distributed control systems (DCSs) are fundamental components of critical infrastructure (CI). CI supports the operation of transportation and health systems, electric and thermal plants, and water treatment facilities, among others. These infrastructures are not insulated anymore, and their connection to fourth industrial revolution technologies has expanded the attack surface.

LPWAN Key Exchange: A Centralised Lightweight Approach.

The Internet of Things (IoT) is one of the fastest emerging technologies in the industry. It includes diverse applications with different requirements to provide services to users. Secure, low-powered, and long-range transmissions are some of the most vital requirements in developing IoT applications.

A Dynamic Algorithm for Interference Management in D2D-Enabled Heterogeneous Cellular Networks: Modeling and Analysis.

To supporting a wider and diverse range of applications, device-to-device (D2D) communication is a key enabler in heterogeneous cellular networks (HetCNets). It plays an important role in fulfilling the performance and quality of service (QoS) requirements for 5G networks and beyond. D2D-enabled cellular networks enable user equipment (UE) to communicate directly, without any or with a partial association with base stations (eNBs).

Early Detection of Prediabetes and T2DM Using Wearable Sensors and Internet-of-Things-Based Monitoring Applications.

Background: Prediabetes and type 2 diabetes mellitus (T2DM) are one of the major long-term health conditions affecting global healthcare delivery. One of the few effective approaches is to actively manage diabetes via a healthy and active lifestyle.

Objectives: This research is focused on early detection of prediabetes and T2DM using wearable technology and Internet-of-Things-based monitoring applications.

[Puerperal insanity:a comparative reading of Argentina and Colombia, 1880-1950].

Our goal is to understand the appearance and spread of forms of puerperal insanity in Argentina and Colombia in the late nineteenth and early twentieth century, as well as their decline or disappearance around the 1940s. This is a historical and hermeneutical study, which uses the concepts of "field of visibility" and "ecological niche" for a transitory disease. There was no correlation between pregnancy, childbirth and puerperium and the state of delirium that led to commitment, which was attributed to predisposing factors; furthermore, forms of puerperal insanity were nosographically distinct due to their unique etiopathogeneses.

ISeeU: Visually interpretable deep learning for mortality prediction inside the ICU.

To improve the performance of Intensive Care Units (ICUs), the field of bio-statistics has developed scores which try to predict the likelihood of negative outcomes. These help evaluate the effectiveness of treatments and clinical practice, and also help to identify patients with unexpected outcomes. However, they have been shown by several studies to offer sub-optimal performance.

Clinical Decision Support for Early Detection of Prediabetes and Type 2 Diabetes Mellitus Using Wearable Technology.

Worldwide spending on long-term and chronic care conditions is increasing to a point that requires immediate interventions and advancements to reduce the burden of the healthcare cost. This research is focused on early detection of prediabetes and type 2 diabetes mellitus (T2DM) using wearable technology. An artificial intelligence model was developed based on adaptive-neuro fuzzy interference to detect prediabetes and T2DM via individualized monitoring.