We propose a clustering procedure to group K populations into subgroups with the same dependence structure. The method is adapted to paired population and can be used with panel data. It relies on the differences between orthogonal projection coefficients of the K density copulas estimated from the K populations. Each cluster is then constituted by populations having significantly similar dependence structures. A recent test statistic from Ngounou-Bakam and Pommeret (2022) is used to construct automatically such clusters. The procedure is data driven and depends on the asymptotic level of the test. We illustrate our clustering algorithm via numerical studies and through two real datasets: a panel of financial datasets and insurance dataset of losses and allocated loss adjustment expense.

Hazardous Lighting Market size was valued at $1.8 billion in 2020 and it is estimated to grow at a CAGR of 2.29% during 2021-2026. The growth is mainly attributed to the increasing investment on various industries, high penetration of internet of things (IoT), increasing demand for efficient advanced lighting solutions across industries and rapid industrialization in emerging economies. Furthermore, the constant innovation in advanced technologies such as artificial intelligence (AI), machine learning (ML), radio-frequency identification (RFID) along with other wireless technologies, which are being used for producing advanced connected hazardous lighting system; and awareness regarding energy conservation boost the growth of hazardous lighting market. Furthermore, government's initiatives for greener strategies to support sustainable development across the world, is one of the major driving factors of hazardous lighting industry. Hence, the above mentioned factors will drive the adoption rate of various hazardous lighting solutions such as industrial LED lighting, fluorescent lighting, high-intensity discharge lamps and others, during the forecast period 2021-2026.

SensOre (ASX:S3N) aims to become the top performing global minerals targeting company through deployment of big data, artificial intelligence (AI)/machine learning technologies and geoscience expertise. Richard Taylor, CEO, SensOre Ltd said"The Gecko North Project demonstrates the combination of AI target generation and conventional exploration techniques coming together to fast-track target development. It demonstrates our novel approach to project generation bringing together international funding for battery minerals with world class exploration expertise with our partners in LithGold. Importantly, this approach gives SensOre shareholders the opportunity to benefit from the technology with exposure to any major discovery." Kevin Schultz, Executive Chairman, LithGold Minerals said"The agreement allows LithGold to partner with an exciting mining technology group and to focus on our portfolio of projects while retaining the precious metal rights over the Gecko North Project which first attracted us to the area. We look forward to seeing the lithium potential of the area developed further."

Viscosity in the metallurgical and glass industry plays a fundamental role in its production processes, also in the area of geophysics. As its experimental measurement is financially expensive, also in terms of time, several mathematical models were built to provide viscosity results as a function of several variables, such as chemical composition and temperature, in linear and nonlinear models. A database was built in order to produce a nonlinear model by artificial neural networks by variation of hyperparameters to provide reliable predictions of viscosity in relation to chemical systems and temperatures. The model produced named Viskositas demonstrated better statistical evaluations of mean absolute error, standard deviation and coefficient of determination in relation to the test database when compared to different models from literature and 1 commercial model, offering predictions with lower errors, less variability and less generation of outliers.

The foliar disease is due by Bacteria, Fungi, and Viruses. These diseases can attack leaves and cause spots, complete death and defoliation of leaves, affecting the plant's health. The data have, consists of four types of images healthy leaf, apple rust which is caused by a fungus called Gymnosporangium juniperi-virginianae, apple scab, which is caused by the ascomycete fungus Venturia inaequalis, and the last one is leaf which contains two are more diseases. Nowadays the yield of crops is up to mark in terms of quality and quantity due to many reasons quality of soil, pollution, fertilizers etc. which results in loss of income and quality of the field. Farmers are not aware of the diseases and their causes and solution.

Software has been developed for knowledge discovery, prediction and management for over 30 years. However, there are still unresolved pain points when using existing project development and artifact management methodologies. Historically, there has been a lack of applicable methodologies. Further, methodologies that have been applied, such as Agile, have several limitations including scientific unfalsifiability that reduce their applicability. Evident, a development methodology rooted in the philosophy of logical reasoning and EKB, a knowledge base topology, are proposed. Many pain points in data mining, machine learning and general knowledge management are alleviated conceptually. Evident can be extended potentially to accelerate philosophical exploration, science discovery, education as well as knowledge sharing & retention across the globe. EKB offers one solution of storing information as knowledge, a granular level above data. Related topics in computer history, software engineering, database, sensing hardware, philosophy, and project & organization & military managements are also discussed.

We present SpeechMatrix, a large-scale multilingual corpus of speech-to-speech translations mined from real speech of European Parliament recordings. It contains speech alignments in 136 language pairs with a total of 418 thousand hours of speech. To evaluate the quality of this parallel speech, we train bilingual speech-to-speech translation models on mined data only and establish extensive baseline results on EuroParl-ST, VoxPopuli and FLEURS test sets. Enabled by the multilinguality of SpeechMatrix, we also explore multilingual speech-to-speech translation, a topic which was addressed by few other works. We also demonstrate that model pre-training and sparse scaling using Mixture-of-Experts bring large gains to translation performance. The mined data and models are freely available.

Drones have shown to be useful aerial vehicles for unmanned transport missions such as food and medical supply delivery. This can be leveraged to deliver life-saving nutrition and medicine for people in emergency situations. However, commercial drones can generally only carry 10 % - 30 % of their own mass as payload, which limits the amount of food delivery in a single flight. One novel solution to noticeably increase the food-carrying ratio of a drone, is recreating some structures of a drone, such as the wings, with edible materials. We thus propose a drone, which is no longer only a food transporting aircraft, but itself is partially edible, increasing its food-carrying mass ratio to 50 %, owing to its edible wings. Furthermore, should the edible drone be left behind in the environment after performing its task in an emergency situation, it will be more biodegradable than its non-edible counterpart, leaving less waste in the environment. Here we describe the choice of materials and scalable design of edible wings, and validate the method in a flight-capable prototype that can provide 300 kcal and carry a payload of 80 g of water.

The term "artificial intelligence" has generated pages of dystopian copy surrounding the displacement of jobs and the dehumanization of the workplace, but in farm fields, AI and machine learning are proving to be an efficient ally of growers for combating weeds and keeping expenses in check. In the 1980s, researchers were elated when they developed sprayers capable of on-the-go determination between bare ground and growing plants -- a breakthrough that paved the way for what is now widely known as GreenSeeker technology. Crude sensors that differentiated the color of soil vs. the color of green plant material led to precision spectral radiance technology that provides the backbone of today's remote sensing used in precise fertilizer placement. As digital memory became increasingly miniaturized, it was possible to photograph and catalog various weeds in computer files used by applicators to further differentiate weeds from growing crops as they travel across fields -- the entry of AI into agriculture, a debut that will forever change farm practices. John Deere's first See & Spray system introduced in 2021 allowed growers to reduce their non-residual pre-emergence herbicide use by more than 75% by targeting and spraying only weeds on fallow ground.

Among the components of the World Economic Forum's Great Resetare a drastically reduced population and the replacement of human labor with robots and artificial intelligence (AI). The question immediately comes to mind: can robots and AI really make all the stuff for the elites after they have gotten rid of the people? Because a plan has been formulated and described does not mean that it is possible to realize. The plan may contradict laws of logic or reality, or assume the existence of resources that do not exist. Podcaster and journalist James Delingpole, speaking to investigative journalist Whitney Webb on October 23, 2021, discussed this topic with his guest. One of the main pillars of that is automation and artificial intelligence.