Google's second-generation Nest Hub smart display now comes with radar-based sleep tracking as it attempts to keep Amazon's Alexa at bay. The new Nest Hub costs £89.99 on launch, which makes it cheaper than its predecessor and slightly undercuts competitors of a similar size. The second-generation unit has the same design as the original but is ever-so-slightly taller. The 7in LCD screen looks great and is crisp enough for viewing at arm's length or further, making it perfect for use as a digital photo frame. The body is now made of recycled plastic and the screen is covered in an edgeless glass, which makes it easier to wipe clean.
Artificial intelligence (AI) offers government utilities a transformative opportunity to improve public service, update outdated processes and centralize data. In particular, water utilities can use AI to make timely repairs and adjustments in a way that poses fewer inconveniences for citizens. One way to facilitate such AI modernization within water utilities is through public-private partnerships. Cities like Tucson, Ariz., and Newark, N.J., are leading by example. Tucson teamed up with VODA.ai in August 2020 to bring AI technology to its water infrastructure systems. Prior to using AI technology as a tool, the city relied on pipe-break history and human judgment to drive maintenance projects. Newark Water and Sewer also adopted AI to make more informed decisions and to take a more proactive approach to improve water infrastructure, according to Tiffany Stewart, assistant director of the utility. The department is using two artificial intelligence systems for unique purposes.
AZoCleantech speaks with Peter Hammond from the UK Centre for Ecology and Hydrology. Peter was previously a researcher using the AI to spot subtle signs in children's facial features that could indicate different genetic conditions. The methods and software developed for this previous research are now being used on sewage treatment data without any alteration. The research was motivated by a personal interest in the deterioration of the River Windrush, a small tributary of the River Thames that runs next to my house. It has coincided with a surge of interest in the UK on the pollution of rivers and coastal waters by sewage pollution.
Artificial Intelligence has seeped into every sector for good, making lives easier and better. One such crucial sector which is benefitting from the benefits of AI is the water industry. Water being the most essential natural resource for human life is also available in a small amount. There is only 2.5% of the earth's water which is freshwater. And, out of the total, only 0.5% is available freshwater, hence ringing an alarm for the need to conserve and manage this natural resource better. The water crisis in India is as real as it can get.
History repeats itself, but it doesn't have to. I was inspired to launch my startup, Varuna, when Austin Water released its first-ever boil water warning in 2018 -- a moment eerily similar to the massive winter storm in Texas just a few weeks ago. Because the water utility companies didn't have enough real-time data to measure water quality in individual neighborhoods, they took the blanket approach of asking all of the city's 950,000 residents to boil any water ingested through drinking or cooking. After several days of substantially reducing water usage -- and seeing more than 625,000 plastic bottles of water handed out across the city -- I set out to find a solution. A systems engineer by trade and a problem-solver by nature, I repurposed our dishwasher's sensor to create my first water-quality measurement device.
Sensor Technology and Artificial Intelligence for Water Management seem quite fascinating. Likewise, water conservation for sustainable development has been and still the most apprehended process. A better conservation and management process would necessarily help the economy grow secure and stable for the future. From the elementary levels of education, we learn about the usage of water efficiently for better health and stability. Conservation and management have always been a moral trait and governing this with maximum advocacy was always the challenge.
Norwegian waste management frontrunner Bjorstaddalen has opened the country's first robotic sorting facility for C&D and C&I waste in the municipality of Skien in Norway. The fully automated robotic sorting station supplied by ZenRobotics features robotic arms that will perform up to 6000 picks per hour. The robotic sorting station is set up as a standalone waste sorting process connected to Bjorstaddalen's existing material recycling facility that has a total capacity of 150 000 tons per hour. By investing in AI and robot technologies, Bjorstaddalen aims to become a leader in material recycling in Norway. The robotic sorting station will substantially increase material recovery, reducing waste incineration and making a major leap toward the circular economy.
Nearly 1,000 "dark discharges" of untreated sewage from two water company treatment plants in England have been detected by scientists using artificial intelligence to map spills. The use of machine learning to shine a light on the scale of pollution from untreated effluent being spilled into rivers could be a crucial tool in efforts to improve the quality of rivers, a paper says. Prof Peter Hammond, visiting scientist at the UK Centre for Ecology and Hydrology, who co-authored the paper published in the journal Clean Water, used artificial intelligence to analyse data from two unidentified water companies' waste treatment works from 2009 to 2018. The AI identified 926 "dark discharges" – or previously unknown spills – from the storm overflows at the two treatment plants. Discharges of untreated sewage from storm overflows, or CSOs, are permitted only in exceptional circumstances, such as extreme rainfall, the European commission has ruled.
Many disease-causing bacteria use a molecular syringe to inject dozens of their proteins, called effectors, into intestinal cells, blocking key immune responses. Ruano-Gallego et al. used the mouse pathogen Citrobacter rodentium to model effector function in vivo. They found that effectors work together as a network, allowing the microbe great flexibility in maintaining pathogenicity. An artificial intelligence platform correctly predicted colonization outcomes of alternative networks from the in vivo data. However, the host was able to bypass the obstacles erected by different effector networks and activate complementary immune responses that cleared the pathogen and induced protective immunity. Science , this issue p. [eabc9531] ### INTRODUCTION Infections with many Gram-negative pathogens, including Escherichia coli , Salmonella , Shigella , and Yersinia , rely on the injection of effectors via type III secretion systems (T3SSs). The effectors hijack cellular processes through multiple mechanisms, including molecular mimicry and diverse enzymatic activities. Although in vitro analyses have shown that individual effectors can exhibit complementary, interdependent, or antagonistic relationships, most in vivo studies have focused on the contribution of single effectors to pathogenesis. Citrobacter rodentium is a natural mouse pathogen that shares infection strategies and virulence factors with the human pathogens enteropathogenic and enterohemorrhagic E. coli (EPEC and EHEC). The ability of these pathogens to colonize the gastrointestinal tract is mediated by the injection of effectors via a T3SS. Although C. rodentium infects 31 effectors, the prototype EPEC strain E2348/69 translocates 21 effectors. ### RATIONALE The aim of this study was to test the hypotheses that, rather than operating individually, the T3SS effectors form robust intracellular networks that can sustain large contractions and that expanded effector repertoires play a role in distinct disease phenotypes and host adaption. ### RESULTS We tested the effector-network paradigm by infecting mice with >100 C. rodentium effector mutant combinations. First, using machine learning prediction algorithms, we discovered additional effectors, NleN and NleO. We then sequentially deleted effector genes from two distinct starting points to reach sustainable endpoints, which resulted in strains missing 19 unrelated effectors (CR14) or 10 effectors involved in the modulation of innate immune responses in intestinal epithelial cells (IECs) (CRi9). Moreover, we deleted Map and EspF, which target the mitochondria and disrupt tight junctions. Unexpectedly, all strains colonized the colon and activated conserved metabolic and antimicrobial processes in the IECs while eliciting distinct cytokine and immune cell infiltration responses. In particular, although infection with C. rodentium Δ map /Δ espF failed to induce secretion of interleukin-22 (IL-22), CR14 and CRi9 triggered heightened secretion of IL-6 and granulocyte-macrophage colony-stimulating factor (GM-CSF) and of IL-22, interferon-γ (IFN-γ), and IL-17 from colonic explants, respectively. Nonetheless, infection with CR14 or CRi9 induced protective immunity against secondary infections. Although Tir, EspZ, and NleA are essential, other effectors exhibit context-dependent essentiality in vivo. Moreover, C. rodentium expressing the effector repertoire of EPEC E2348/69 failed to efficiently colonize mice. We used curated functional information and our in vivo data to train a machine learning model that predicted values for colonization efficiency of previously uncharacterized mutant combinations. Notably, a mutant with a low predicted value, lacking only nleF , nleG8 , nleG1 , nleB , and espL , failed to colonize. ### CONCLUSION Our analysis revealed that T3SS effectors form robust networks, which can sustain substantial contractions while maintaining virulence, and that the composition of the effector network contributes to host adaptation. Alternative effector networks within a single pathogen triggered markedly different immune responses yet induced protective immunity. CR14 did not tolerate any further contraction, which suggests that this network reached its robustness limit with only 12 effectors. As the robustness limits of other effector networks depend on the contraction starting point and the order of the deletions, machine learning models could transform our ability to predict alternative network functions. Together, this study demonstrates the robustness of T3SS effector networks and the ability of IECs to withstand drastic perturbations while maintaining antibacterial functions. ![Figure] T3SS effectors form robust intracellular networks. T3SS effector networks can sustain substantial contractions while maintaining virulence. Using C. rodentium as a model showed that although triggering the conserved infection signatures in IECs, distinct networks induce divergent immune responses and affect host adaption. Because the robustness limit depends on the contraction sequence, machine learning models could transform our ability to predict the virulence potential of alternative networks. Infections with many Gram-negative pathogens, including Escherichia coli , Salmonella , Shigella , and Yersinia , rely on type III secretion system (T3SS) effectors. We hypothesized that while hijacking processes within mammalian cells, the effectors operate as a robust network that can tolerate substantial contractions. This was tested in vivo using the mouse pathogen Citrobacter rodentium (encoding 31 effectors). Sequential gene deletions showed that effector essentiality for infection was context dependent and that the network could tolerate 60% contraction while maintaining pathogenicity. Despite inducing very different colonic cytokine profiles (e.g., interleukin-22, interleukin-17, interferon-γ, or granulocyte-macrophage colony-stimulating factor), different networks induced protective immunity. Using data from >100 distinct mutant combinations, we built and trained a machine learning model able to predict colonization outcomes, which were confirmed experimentally. Furthermore, reproducing the human-restricted enteropathogenic E. coli effector repertoire in C. rodentium was not sufficient for efficient colonization, which implicates effector networks in host adaptation. These results unveil the extreme robustness of both T3SS effector networks and host responses. : /lookup/doi/10.1126/science.abc9531 : pending:yes
Municipal solid waste management is a major challenge for nowadays urban societies, because it accounts for a large proportion of public budget and, when mishandled, it can lead to environmental and social problems. This work focuses on the problem of locating waste bins in an urban area, which is considered to have a strong influence in the overall efficiency of the reverse logistic chain. This article contributes with an exact multiobjective approach to solve the waste bin location in which the optimization criteria that are considered are: the accessibility to the system (as quality of service measure), the investment cost, and the required frequency of waste removal from the bins (as a proxy of the posterior routing costs). In this approach, different methods to obtain the objectives ideal and nadir values over the Pareto front are proposed and compared. Then, a family of heuristic methods based on the PageRank algorithm is proposed which aims to optimize the accessibility to the system, the amount of collected waste and the installation cost. The experimental evaluation was performed on real-world scenarios of the cities of Montevideo, Uruguay, and Bah\'ia Blanca, Argentina. The obtained results show the competitiveness of the proposed approaches for constructing a set of candidate solutions that considers the different trade-offs between the optimization criteria.