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Domus is designed to be a fast, efficient and incredibly spacious sailing trimaran with volume similar to a 60 metre motor yacht.

Circa 2017

Electrocaloric effect turns exotic plastics into silent, efficient cooling devices.

Ken OtwellI thought the claim WAS fraud by Twitter? Twitter fraudulently under-reporting the bot numbers.

Mike Lorreymisrepresenting real user numbers is, actually, fraud, so he gets out of the billion dollar fee.

Shubham Ghosh Roy shared a post.

Continue reading “Here’s What Makes Japan’s Futuristic City So Smart” »

Outbreak and rapid spread of coronavirus disease (COVID-19) caused by coronavirus acute respiratory syndrome (SARS-CoV-2) caused severe acute respiratory syndrome (SARS-CoV-2) that started in Wuhan, and has become a global problem because of the high rate of human-to-human transmission and severe respiratory infections. Because of high prevalence of SARS-CoV-2, which threatens many people worldwide, rapid diagnosis and simple treatment are needed. Genome editing is a nucleic acid-based approach to altering the genome by artificially changes in genetic information and induce irreversible changes in the function of target gene. Clustered, regularly interspaced short palindromic repeats (CRISPR/Cas) could be a practical and straightforward approach to this disease. CRISPR/Cas system contains Cas protein, which is controlled by a small RNA molecule to create a double-stranded DNA gap. Evidence suggested that CRISPR/Cas was also usable for diagnosis and treatment of SARS-CoV-2 infection. In this review study, we discoursed on application of CRISPR technology in detection and treatment of SARS-CoV-2 infection. Another aspect of this study was to introduce potential future problems in use of CRISPR/Cas technology.

Coronavirus disease (COVID-19) was spread in December 2019 and was recognized as a zoonotic disease (Drosten et al., 2017; Andersen et al., 2020). Severe acute respiratory syndrome (SARS) virus was detected in sputum samples in 2003, and advanced stages in fecal samples may have been transmitted to humans by an intermediate host such as bats and civets (Wang and Eaton, 2007; Graham and Baric, 2010). Severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) can be transmitted from an unknown carrier to a healthy person who could infect many people. SARS-CoV-2 resulted in pneumonia in Wuhan, China, with various symptoms reported. The disease has developed into a pandemic (Wu C. et al., 2020; Wu D. et al., 2020; Guan et al., 2020). Appropriate methods could treat and control the disease. CRISPR/Cas9 was first recognized as a microbial immune system through which these organisms acquire immunity to invading viruses and plasmids (Garneau et al., 2010).

New estimates from the World Health Organization (WHO) show that the full death toll associated directly or indirectly with the COVID-19 pandemic (described as “excess mortality”) between 1 January 2020 and 31 December 2021 was approximately 14.9 million (range 13.3 million to 16.6 million). “These sobering data not only point to the impact of the pandemic but also to the need for all countries to invest in more resilient health systems that can sustain essential health services during crises, including stronger health information systems,” said Dr Tedros Adhanom Ghebreyesus, WHO Director-General. “WHO is committed to working with all countries to strengthen their health information systems to generate better data for better decisions and better outcomes.” Excess mortality is calculated as the difference between the number of deaths that have occurred and the number that would be expected in the absence of the pandemic based on data from earlier years. Excess mortality includes deaths associated with COVID-19 directly (due to the disease) or indirectly (due to the pandemic’s impact on health systems and society). Deaths linked indirectly to COVID-19 are attributable to other health conditions for which people were unable to access prevention and treatment because health systems were overburdened by the pandemic. The estimated number of excess deaths can be influenced also by deaths averted during the pandemic due to lower risks of certain events, like motor-vehicle accidents or occupational injuries. Most of the excess deaths (84%) are concentrated in South-East Asia, Europe, and the Americas. Some 68% of excess deaths are concentrated in just 10 countries globally. Middle-income countries account for 81% of the 14.9 million excess deaths (53% in lower-middle-income countries and 28% in upper-middle-income countries) over the 24-month period, with high-income and low-income countries each accounting for 15% and 4%, respectively. The estimates for a 24-month period (2020 and 2021) include a breakdown of excess mortality by age and sex. They confirm that the global death toll was higher for men than for women (57% male, 43% female) and higher among older adults. The absolute count of the excess deaths is affected by the population size. The number of excess deaths per 100,000 gives a more objective picture of the pandemic than reported COVID-19 mortality data.“Measurement of excess mortality is an essential component to understand the impact of the pandemic. Shifts in mortality trends provide decision-makers information to guide policies to reduce mortality and effectively prevent future crises. Because of limited investments in data systems in many countries, the true extent of excess mortality often remains hidden,” said Dr Samira Asma, Assistant Director-General for Data, Analytics and Delivery at WHO. “These new estimates use the best available data and have been produced using a robust methodology and a completely transparent approach.”“Data is the foundation of our work every day to promote health, keep the world safe, and serve the vulnerable. We know where the data gaps are, and we must collectively intensify our support to countries, so that every country has the capability to track outbreaks in real-time, ensure delivery of essential health services, and safeguard population health,” said Dr Ibrahima Socé Fall, Assistant Director-General for Emergency Response. The production of these estimates is a result of a global collaboration supported by the work of the Technical Advisory Group for COVID-19 Mortality Assessment and country consultations. This group, convened jointly by the WHO and the United Nations Department of Economic and Social Affairs (UN DESA), consists of many of the world’s leading experts, who developed an innovative methodology to generate comparable mortality estimates even where data are incomplete or unavailable. This methodology has been invaluable as many countries still lack capacity for reliable mortality surveillance and therefore do not collect and generate the data needed to calculate excess mortality. Using the publicly available methodology, countries can use their own data to generate or update their own estimates. “The United Nations system is working together to deliver an authoritative assessment of the global toll of lives lost from the pandemic. This work is an important part of UN DESA’s ongoing collaboration with WHO and other partners to improve global mortality estimates,” said Mr Liu Zhenmin, United Nations Under-Secretary-General for Economic and Social Affairs. Mr Stefan Schweinfest, Director of the Statistics Division of UN DESA, added: “Data deficiencies make it difficult to assess the true scope of a crisis, with serious consequences for people’s lives. The pandemic has been a stark reminder of the need for better coordination of data systems within countries and for increased international support for building better systems, including for the registration of deaths and other vital events.” Note for editors: The methods were developed by the Technical Advisory Group for COVID-19 Mortality Assessment, co-chaired by Professor Debbie Bradshaw and Dr. Kevin McCormack with extensive support from Professor Jon Wakefield at the University of Washington. The methods rely on a statistical model derived using information from countries with adequate data; the model is used to generate estimates for countries with little or no data available. The methods and estimates will continue to be updated as additional data become available and in consultation with countries.

Efficiently processing broadband signals using convolutional neural networks (CNNs) could enhance the performance of machine learning tools for a wide range of real-time applications, including image recognition, remote sensing and environmental monitoring. However, past studies suggest that performing broadband convolutional processing computations directly in sensors is challenging, particularly when using conventional complementary metal-oxide-semiconductor (CMOS) technology, which underpins the functioning of most existing transistors.

Researchers at Huazhong University of Science and Technology and Nanjing University have recently investigated the possibility of achieving the convolutional processing of broadband signals using an alternative platform, namely van der Waals heterostructures. Their paper, published in Nature Electronics, could ultimately inform the development of better performing image recognition algorithms.

“Our paper was inspired by some our previous research works,” Tianyou Zhai, Xing Zhou and Feng Miao, three of the researchers who carried out the study, told TechXplore. “In studies published in Advanced Materials and Advanced Functional Materials, we realized type-III and type-II band-alignments in different heterostructures. Furthermore, we published a paper in Science Advances, where we realized a reconfigurable neural network vision sensor based on WSe₂.”

Researchers have developed an algorithm that can identify the basic needs of users from the text and images they share on social networks. The experts hope this tool will help psychologists to diagnose possible mental health problems. The study suggests that Spanish-speaking users are more likely to mention relationship problems when feeling depressed than English speakers.

We spend a substantial amount of our time sharing images, videos or thoughts on social networks such as Instagram, Facebook and Twitter. Now, a group of researchers from the Universitat Oberta de Catalunya (UOC) has developed an algorithm that aims to help psychologists diagnose possible mental health problems through the content people post on these platforms.

According to William Glasser’s Choice Theory, there are five basic needs that are central to all human behavior: Survival, Power, Freedom, Belonging and Fun. These needs even have an influence on the images we choose to upload to our Instagram page. “How we present ourselves on social media can provide useful information about behaviors, personalities, perspectives, motives and needs,” explained Mohammad Mahdi Dehshibi, who led this study within the AI for Human Well-being (AIWELL) group, which belongs to the Faculty of Computer Science, Multimedia and Telecommunications at the UOC.

Modern life can be full of baffling encounters with artificial intelligence—think misunderstandings with customer service chatbots or algorithmically misplaced hair metal in your Spotify playlist. These AI systems can’t effectively work with people because they have no idea that humans can behave in seemingly irrational ways, says Mustafa Mert Çelikok. He’s a Ph.D. student studying human-AI interaction, with the idea of taking the strengths and weaknesses of both sides and blending them into a superior decision-maker.

In the AI world, one example of such a hybrid is a “centaur.” It’s not a mythological horse–human, but a human-AI team. Centaurs appeared in chess in the late 1990s, when artificial intelligence systems became advanced enough to beat human champions. In place of a “human versus machine” matchup, centaur or cyborg chess involves one or more computer chess programs and human players on each side.

“This is the Formula 1 of chess,” says Çelikok. “Grandmasters have been defeated. Super AIs have been defeated. And grandmasters playing with powerful AIs have also lost.” As it turns out, novice players paired with AIs are the most successful. “Novices don’t have strong opinions” and can form effective decision-making partnerships with their AI teammates, while “grandmasters think they know better than AIs and override them when they disagree—that’s their downfall,” observes Çelikok.