The low battery autonomy of Unnamed Aerial Vehicles (UAVs or drones) can make smart farming (precision agriculture), disaster recovery, and the fighting against dengue vector applications difficult. This article considers two approaches, first enumerating the characteristics observed in these three IoT application types and then modeling an UAV's battery recharge coordination using the Agent-Based Simulation (ABS) approach. In this way, we propose that each drone inside the swarm does not communicate concerning this recharge coordination decision, reducing energy usage and permitting remote usage. A total of 6000 simulations were run to evaluate how two proposed policies, the BaseLine (BL) and ChargerThershold (CT) coordination recharging policy, behave in 30 situations regarding how each simulation sets conclude the simulation runs and how much time they work until recharging results. CT policy shows more reliable results in extreme system usage. This work conclusion presents the potential of these three IoT applications to achieve their perpetual service without communication between drones and ground stations. This work can be a baseline for future policies and simulation parameter enhancements.

The Covid-19 coronavirus crisis is accelerating the pace of digital transformation among companies of all shapes and sizes, and the public sector is no exception, as decision-makers rally to find digital solutions to meet fast-changing requirements despite underlying legacy challenges. While the immediate focus is to limit the human, social and economic loss, operating in the "new normal" will mean extra pressure on IT in the months to come. Public sector bodies need to use digital channels to inform and serve citizens, while at the same time, many functions have gone all-digital during the coronavirus outbreak, increasing demand for efficient back-end systems. The pandemic has exposed the need to improve technology efficiency for the continuity of government. Computer Weekly spoke to specialists operating in the public sector to gain an overall view of the key trends and hurdles facing buyers as the crisis unfolds.

Solutions Review's annual Vendors to Know in Data Management Solutions provides the details on the most critical solution providers in the space. The editors at Solutions Review continually research the most prominent and influential data management vendors to assist buyers in search of the tools befitting the needs of their organization. Choosing the right vendor and solution can be a complicated process; it requires constant market research and often comes down to more than just the solution and its technical capabilities. To make your search a little easier, we listed the vendors to know in data management software. Note: Companies are listed in alphabetical order.

Humans have long wondered if artificial intelligence (AI) would replace them in their work. But right now, business transactions still need a human touch. Several years ago, I was working with a European financial company that was renovating its disaster recovery and failover technologies in its data center. The goal was to automate many of the alerts in systems so that IT would have early warnings before any system failed. The technology worked so well that the CIO was faced with a choice: Does he totally automate failover, with the automation taking over system failover functions if and when the automation determined it was needed? Or does he leave the "last mile" of failover--that point when the alerts tell you a mission-critical system is going to fail and you have to personally make the decision to go into failover and recovery--to himself, where it is he who pushes the button?

ICHEC, the national high-performance computing authority of Ireland, recently participated in the xView2 disaster recovery challenge run by the US Defense Innovation Unit and other Humanitarian Assistance and Disaster Recovery (HADR) organisations. Models developed during the challenge including those developed at ICHEC are currently being tested by agencies responding to the ongoing bushfires in Australia. XView2 Challenge is based on using high resolution imagery to see the details of specific damage conditions in overhead imagery of a disaster area. The challenge involved building AI models to locate and classify the severity of damage to buildings using pairs of pre and post disaster satellite images. Models like these allow those responding to disasters to rapidly assess the damage left in their wake, enabling more effective response efforts and potentially saving lives.

The Titanic challenge hosted by Kaggle is a competition in which the goal is to predict the survival or the death of a given passenger based on a set of variables describing him such as his age, his sex, or his passenger class on the boat. This post is the opportunity to share my solution with you. To make this tutorial more "academic" so that anyone could benefit, I will first start with an exploratory data analysis (EDA) then I'll follow with feature engineering and finally present the predictive model I set up. Throughout this jupyter notebook, I will be using Python at each level of the pipeline. Two datasets are available: a training set and a test set.

With data emerging as one of the most valuable assets for any business, organizations can't afford to lose even a single megabyte of the information they collect. Unfortunately, even in the most robust technical environments, there's always a risk that something could go wrong. While companies may not be able to prevent disasters from happening altogether, it is possible to take steps that will protect your organization and productivity in case the worst comes to pass. That's what disaster recovery and business continuity plans are for. A business continuity plan ensures that no matter what happens in your company, from a flood in your data center to a security breach, you're ready to get your venture back up and running in no time.

Hurricanes, earthquakes, wildfires, floods, volcanic eruptions and tornadoes are constant threats to society. While they have always happened, humans are now on the scene. Human lives and property are constantly threatened. Such risks are further amplified along coastlines, fault lines, volcanic regions, or flood zones. Craig Fugate is the former FEMA Administrator under President Obama and one of the most effective emergency managers in the world (period).

Scale out read-heavy workloads on Azure Database for PostgreSQL with read replicas, which enable continuous, asynchronous replication of data from one Azure Database for PostgreSQL master server to up to five Azure Database for PostgreSQL read replica servers in the same region. Replica servers are read-only except for writes replicated from data changes on the master. Stopping replication to a replica server causes it to become a standalone server that accepts reads and writes. Replicas are new servers that can be managed in similar ways as normal standalone Azure Database for PostgreSQL servers. For each read replica, you are billed for the provisioned compute in vCores and provisioned storage in GB/month.

As data becomes the only real competitive advantage feeding increased operational efficiencies, better customer intimacy and constantly improving customer experience, it is imperative that enterprises shift their disaster recovery efforts from just focusing on availability and reliability of services to ensure that their data assets are recoverable and re-integratable into various data powered scenarios backing their business. Modern enterprises require data in many shapes and forms across the board for powering planning, ideating, experimenting and designing/developing new products and services. These business-critical scenarios are often dependent on data that has been transformed, processed and made suitable to meet the requirements. As the "distance" between raw data and transformed data that drives products and services increases due to increasingly complex techniques of transformation, disaster recovery needs to include the not just the time to bring up the copy of lost data online but the time it takes to retransform the data. AI techniques such as Machine Learning, NLP, Anomaly Detection etc. produce "models" that can be leveraged to drive predictions, classifications and categorization.