The AI gambit — leveraging artificial intelligence to combat climate change: opportunities, challenges, and recommendations

In this article we analyse the role that artificial intelligence (AI) could play, and is playing, to combat global climate change. We identify two crucial opportunities that AI offers in this domain: it can help improve and expand current understanding of climate change and it contribute to combating the climate crisis effectively. However, the development of AI also raises two sets of problems when considering climate change: the possible exacerbation of social and ethical challenges already associated with AI, and the contribution to climate change of the greenhouse gases emitted by training data and computation-intensive AI systems. We assess the carbon footprint of AI research, and the factors that influence AI’s greenhouse gas (GHG) emissions in this domain. We find that the carbon footprint of AI research may be significant and highlight the need for more evidence concerning the trade-off between the GHG emissions generated by AI research and the energy and resource efficiency gains that AI can offer. In light of our analysis, we argue that leveraging the opportunities offered by AI for global climate change whilst limiting its risks is a gambit which requires responsive, evidence-based and effective governance to become a winning strategy. We conclude by identifying the European Union as being especially well-placed to play a leading role in this policy response and provide 13 recommendations that are designed to identify and harness the opportunities of AI for combating climate change, while reducing its impact on the environment

A History of Materials and Technologies Development

The purpose of the book is to provide the students with the text that presents an introductory knowledge about the development of materials and technologies and includes the most commonly available information on human development. The idea of the publication has been generated referring to the materials taken from the organic and non-organic evolution of nature. The suggested texts might be found a purposeful tool for the University students proceeding with studying engineering due to the fact that all subjects in this particular field more or less have to cover the history and development of the studied object. It is expected that studying different materials and technologies will help the students with a better understanding of driving forces, positive and negative consequences of technological development, etc

New Mexico Daily Lobo, Volume 078, No 96, 2/18/1975

New Mexico Daily Lobo, Volume 078, No 96, 2/18/1975https://digitalrepository.unm.edu/daily_lobo_1975/1022/thumbnail.jp

Atti del XXIII Convegno Nazionale di Agrometeorologia Agricoltura 4.0 e cambiamento climatico: il ruolo dell’Agrometeorologia

Con il termine Agricoltura 4.0 si indica la più recente evoluzione tecnica e tecnologica che interessa il settore dell’agricoltura. Grazie all'impiego delle attuali tecnologie come Remote Sensing, Internet of Things, Intelligenza Artificiale, Big Data, Cloud Computing ecc., è possibile oggi migliorare significativamente l'efficienza delle attività agricole, e la loro resilienza ai fattori di stress. La quantità di dati di interesse agronomico che possono essere analizzati e processati grazie agli strumenti dell’Agricoltura 4.0 è davvero enorme: dati meteorologici, pedologici, stato fisiologico e fitosanitario delle colture possono essere accuratamente monitorati su ampia scala, consentendo di fornire agli agricoltori un sistema di supporto decisionale così dal mettersi al riparo il più possibile dalle intrinseche incertezze dell’attività di impresa nel settore agricolo, in un frangente storico in cui tali incertezze sono esacerbate dagli effetti del Cambiamento Climatico, il quale rappresenta sicuramente la principale sfida che oggi gli enti di ricerca in ambito agronomico, i consorzi e gli imprenditori agricoli sono chiamati ad affrontare. Il rapporto tra Agrometeorologia e Agricoltura 4.0 è quindi strettissimo, e di vitale importanza per l’evoluzione del settore. Tale evoluzione, nello specifico, è orientata verso gli obiettivi fondamentali di adattamento al Cambiamento Climatico e di mantenimento di rese alte e soddisfacenti con un impiego di risorse razionale e sostenibile. L’Agrometeorologia ha un ruolo chiave nel raggiungimento di questi ambiziosi obiettivi: non si può infatti parlare di agricoltura di precisione senza una attenta e professionale gestione delle risorse idriche e la modellistica agrometeorologica svolge un ruolo chiave anche nella difesa delle colture dagli stress di origine biotica e abiotica (malattie fungine, danni da caldo, danni da gelo ecc.). È inoltre essenziale che tutte queste applicazioni raggiungano efficacemente gli utilizzatori finali, ossia gli imprenditori agricoli, e non possono quindi esimersi da una analisi di accessibilità, fruibilità, interoperabilità e scalabilità dei protocolli e dei servizi messi a disposizione. In questo contesto l’AIAM propone, per il 23° convegno Nazionale, l’analisi di questi temi, conducendo una disamina del ruolo attuale dell’Agrometeorologia e dell’Agricoltura 4.0 nella gestione delle risorse naturali, nella difesa delle piante e nell’implementazione di sistemi e strumenti per l’elaborazione e la divulgazione delle informazioni, al fine di creare un servizio per gli agricoltori

Energy-Efficient Software

The energy consumption of ICT is growing at an unprecedented pace. The main drivers for this growth are the widespread diffusion of mobile devices and the proliferation of datacenters, the most power-hungry IT facilities. In addition, it is predicted that the demand for ICT technologies and services will increase in the coming years. Finding solutions to decrease ICT energy footprint is and will be a top priority for researchers and professionals in the field. As a matter of fact, hardware technology has substantially improved throughout the years: modern ICT devices are definitely more energy efficient than their predecessors, in terms of performance per watt. However, as recent studies show, these improvements are not effectively reducing the growth rate of ICT energy consumption. This suggests that these devices are not used in an energy-efficient way. Hence, we have to look at software. Modern software applications are not designed and implemented with energy efficiency in mind. As hardware became more and more powerful (and cheaper), software developers were not concerned anymore with optimizing resource usage. Rather, they focused on providing additional features, adding layers of abstraction and complexity to their products. This ultimately resulted in bloated, slow software applications that waste hardware resources -- and consequently, energy. In this dissertation, the relationship between software behavior and hardware energy consumption is explored in detail. For this purpose, the abstraction levels of software are traversed upwards, from source code to architectural components. Empirical research methods and evidence-based software engineering approaches serve as a basis. First of all, this dissertation shows the relevance of software over energy consumption. Secondly, it gives examples of best practices and tactics that can be adopted to improve software energy efficiency, or design energy-efficient software from scratch. Finally, this knowledge is synthesized in a conceptual framework that gives the reader an overview of possible strategies for software energy efficiency, along with examples and suggestions for future research

The use of carbon footprinting studies to determine the greenhouse gas emissions associated with the provision of aspects of renal healthcare within the National Health Service

Climate change presents a major threat to global health and will further exacerbate the health inequalities that exist internationally. However, the provision of healthcare results in considerable greenhouse gas (GHG) emissions and is therefore contributing to climate change itself. Meanwhile, the integration of strategies to address climate change into global health efforts will realise health co-benefits. Meeting the challenging carbon reduction targets set within the NHS will require an improved understanding of the GHG emissions association with different forms of healthcare. This thesis explores the environmental impact of the provision of renal medicine services within the United Kingdom, placing a particular emphasis upon GHG emissions. The approach required, and the opportunities that exist, to reduce the environmental impact of renal medicine services are first explored through a review of the existing literature and a survey of the current practices of renal services in England, Scotland and Wales. A study, adhering to the principles of PAS2050, of the GHG emissions attributable to an individual renal service is then reported. This is the first assessment of the carbon footprint of an individual specialty service to include both direct and indirect GHG emissions. Consideration is given to how the results might inform carbon reduction strategies. Indicative carbon burdens for outpatient appointments and inpatient admissions are derived in order to facilitate future modelling of the emissions attributable to different clinical pathways of care. A second study, in which the GHG emissions attributable to different forms of an individual treatment (haemodialysis) are determined, is then presented. Finally, four case studies of good environmental practice within renal medicine, identified from the earlier literature search and survey, are presented in the context of the results of these studies

From wearable towards epidermal computing : soft wearable devices for rich interaction on the skin

Human skin provides a large, always available, and easy to access real-estate for interaction. Recent advances in new materials, electronics, and human-computer interaction have led to the emergence of electronic devices that reside directly on the user's skin. These conformal devices, referred to as Epidermal Devices, have mechanical properties compatible with human skin: they are very thin, often thinner than human hair; they elastically deform when the body is moving, and stretch with the user's skin. Firstly, this thesis provides a conceptual understanding of Epidermal Devices in the HCI literature. We compare and contrast them with other technical approaches that enable novel on-skin interactions. Then, through a multi-disciplinary analysis of Epidermal Devices, we identify the design goals and challenges that need to be addressed for advancing this emerging research area in HCI. Following this, our fundamental empirical research investigated how epidermal devices of different rigidity levels affect passive and active tactile perception. Generally, a correlation was found between the device rigidity and tactile sensitivity thresholds as well as roughness discrimination ability. Based on these findings, we derive design recommendations for realizing epidermal devices. Secondly, this thesis contributes novel Epidermal Devices that enable rich on-body interaction. SkinMarks contributes to the fabrication and design of novel Epidermal Devices that are highly skin-conformal and enable touch, squeeze, and bend sensing with co-located visual output. These devices can be deployed on highly challenging body locations, enabling novel interaction techniques and expanding the design space of on-body interaction. Multi-Touch Skin enables high-resolution multi-touch input on the body. We present the first non-rectangular and high-resolution multi-touch sensor overlays for use on skin and introduce a design tool that generates such sensors in custom shapes and sizes. Empirical results from two technical evaluations confirm that the sensor achieves a high signal-to-noise ratio on the body under various grounding conditions and has a high spatial accuracy even when subjected to strong deformations. Thirdly, Epidermal Devices are in contact with the skin, they offer opportunities for sensing rich physiological signals from the body. To leverage this unique property, this thesis presents rapid fabrication and computational design techniques for realizing Multi-Modal Epidermal Devices that can measure multiple physiological signals from the human body. Devices fabricated through these techniques can measure ECG (Electrocardiogram), EMG (Electromyogram), and EDA (Electro-Dermal Activity). We also contribute a computational design and optimization method based on underlying human anatomical models to create optimized device designs that provide an optimal trade-off between physiological signal acquisition capability and device size. The graphical tool allows for easily specifying design preferences and to visually analyze the generated designs in real-time, enabling designer-in-the-loop optimization. Experimental results show high quantitative agreement between the prediction of the optimizer and experimentally collected physiological data. Finally, taking a multi-disciplinary perspective, we outline the roadmap for future research in this area by highlighting the next important steps, opportunities, and challenges. Taken together, this thesis contributes towards a holistic understanding of Epidermal Devices}: it provides an empirical and conceptual understanding as well as technical insights through contributions in DIY (Do-It-Yourself), rapid fabrication, and computational design techniques.Die menschliche Haut bietet eine große, stets verfügbare und leicht zugängliche Fläche für Interaktion. Jüngste Fortschritte in den Bereichen Materialwissenschaft, Elektronik und Mensch-Computer-Interaktion (Human-Computer-Interaction, HCI) [so that you can later use the Englisch abbreviation] haben zur Entwicklung elektronischer Geräte geführt, die sich direkt auf der Haut des Benutzers befinden. Diese sogenannten Epidermisgeräte haben mechanische Eigenschaften, die mit der menschlichen Haut kompatibel sind: Sie sind sehr dünn, oft dünner als ein menschliches Haar; sie verformen sich elastisch, wenn sich der Körper bewegt, und dehnen sich mit der Haut des Benutzers. Diese Thesis bietet, erstens, ein konzeptionelles Verständnis von Epidermisgeräten in der HCI-Literatur. Wir vergleichen sie mit anderen technischen Ansätzen, die neuartige Interaktionen auf der Haut ermöglichen. Dann identifizieren wir durch eine multidisziplinäre Analyse von Epidermisgeräten die Designziele und Herausforderungen, die angegangen werden müssen, um diesen aufstrebenden Forschungsbereich voranzubringen. Im Anschluss daran untersuchten wir in unserer empirischen Grundlagenforschung, wie epidermale Geräte unterschiedlicher Steifigkeit die passive und aktive taktile Wahrnehmung beeinflussen. Im Allgemeinen wurde eine Korrelation zwischen der Steifigkeit des Geräts und den taktilen Empfindlichkeitsschwellen sowie der Fähigkeit zur Rauheitsunterscheidung festgestellt. Basierend auf diesen Ergebnissen leiten wir Designempfehlungen für die Realisierung epidermaler Geräte ab. Zweitens trägt diese Thesis zu neuartigen Epidermisgeräten bei, die eine reichhaltige Interaktion am Körper ermöglichen. SkinMarks trägt zur Herstellung und zum Design neuartiger Epidermisgeräte bei, die hochgradig an die Haut angepasst sind und Berührungs-, Quetsch- und Biegesensoren mit gleichzeitiger visueller Ausgabe ermöglichen. Diese Geräte können an sehr schwierigen Körperstellen eingesetzt werden, ermöglichen neuartige Interaktionstechniken und erweitern den Designraum für die Interaktion am Körper. Multi-Touch Skin ermöglicht hochauflösende Multi-Touch-Eingaben am Körper. Wir präsentieren die ersten nicht-rechteckigen und hochauflösenden Multi-Touch-Sensor-Overlays zur Verwendung auf der Haut und stellen ein Design-Tool vor, das solche Sensoren in benutzerdefinierten Formen und Größen erzeugt. Empirische Ergebnisse aus zwei technischen Evaluierungen bestätigen, dass der Sensor auf dem Körper unter verschiedenen Bedingungen ein hohes Signal-Rausch-Verhältnis erreicht und eine hohe räumliche Auflösung aufweist, selbst wenn er starken Verformungen ausgesetzt ist. Drittens, da Epidermisgeräte in Kontakt mit der Haut stehen, bieten sie die Möglichkeit, reichhaltige physiologische Signale des Körpers zu erfassen. Um diese einzigartige Eigenschaft zu nutzen, werden in dieser Arbeit Techniken zur schnellen Herstellung und zum computergestützten Design von multimodalen Epidermisgeräten vorgestellt, die mehrere physiologische Signale des menschlichen Körpers messen können. Die mit diesen Techniken hergestellten Geräte können EKG (Elektrokardiogramm), EMG (Elektromyogramm) und EDA (elektrodermale Aktivität) messen. Darüber hinaus stellen wir eine computergestützte Design- und Optimierungsmethode vor, die auf den zugrunde liegenden anatomischen Modellen des Menschen basiert, um optimierte Gerätedesigns zu erstellen. Diese Designs bieten einen optimalen Kompromiss zwischen der Fähigkeit zur Erfassung physiologischer Signale und der Größe des Geräts. Das grafische Tool ermöglicht die einfache Festlegung von Designpräferenzen und die visuelle Analyse der generierten Designs in Echtzeit, was eine Optimierung durch den Designer im laufenden Betrieb ermöglicht. Experimentelle Ergebnisse zeigen eine hohe quantitative Übereinstimmung zwischen den Vorhersagen des Optimierers und den experimentell erfassten physiologischen Daten. Schließlich skizzieren wir aus einer multidisziplinären Perspektive einen Fahrplan für zukünftige Forschung in diesem Bereich, indem wir die nächsten wichtigen Schritte, Möglichkeiten und Herausforderungen hervorheben. Insgesamt trägt diese Arbeit zu einem ganzheitlichen Verständnis von Epidermisgeräten bei: Sie liefert ein empirisches und konzeptionelles Verständnis sowie technische Einblicke durch Beiträge zu DIY (Do-It-Yourself), schneller Fertigung und computergestützten Entwurfstechniken

AI and Blockchain-assisted diagnostics in resource-limited setting

Diseases, including communicable and noncommunicable diseases, have been one of the major causes of human morbidity and mortality since the beginning of our history. Although many diseases have become treatable or preventable, thanks to interventions including pharmaceutical and technological advances, many people die each year in developing countries and remote rural areas due to limited (or even no) access to medical facilities and expertise. An accurate, rapid, and reliable diagnostic test is vital to improved disease treatment and prevention. However, running diagnostic tests usually requires complex, expensive instruments, professionally trained operators, and a stable power supply. Unfortunately, these resources are generally limited or unavailable in many low-resource settings. Although there are countless limitations in running diagnostic tests in low-resource settings, various endeavours have been made to overcome the existing obstacles. One of the most important advances has been the development of point-of-care or point-of-need tests. These diagnostic assays can be delivered in convenient formats and have successfully reduced the cost of running diagnostics, so playing an essential role in disease management and lifesaving in low-income countries. One key aspect of diagnosis may be the interpretation of the test, which can either be done by an expert in the field or by communicating that data to a remote expert or a “smart” system to interpret the data. Accurately interpreting the test outcome can help the patients receive appropriate treatment timely. However, issues presented in data management during such communication, such as tampered and counterfeited test results and unsecured data sharing between end users (patients) and professionals (doctors, healthcare workers, researchers, etc.). Also, problems like unreliable electricity supply and internet connection were found during the field study conducted by our group previously, and those issues can also delay the diagnosis of the disease. In this PhD study, an AI-assisted platform for DNA-based malaria diagnostic tests was developed and tested in the field. This platform allows users to run a test with a low-cost portable heater and record the test information with an Android phone. It can be used to run LAMP-based malaria tests with a portable heater and read the test results automatically with 97.8% accuracy. And it only takes around 20 milliseconds to classify one image on an inexpensive (~£100) Android phone. When the internet connection is available, the test information can be safely kept in a Blockchain network for future use to inform treatment or surveillance activities. Expertise developed in the deep neural network was also used to train algorithms for the diagnosis of retinopathies, involving developing methods for retina vessel segmentation and classification, which explores the possibility of applying AI to diagnostics in low-resource settings. In such settings, accessing medical expertise can be challenging. It has been found that using only a convolutional neural network is not sufficient in identifying arteries and veins. Models were trained for performing vessel segmentation and classification tasks; for segmenting vessels from the background achieved over 95% accuracy and over 0.8 mean average over the union score (MIoU) on the DRIVE dataset, while for A/V classification tasks, the MIoU decreased to less than 0.7. However, combining it with the traditional approach has the potential to achieve good performance. In addition, research was conducted on the utilisation of digital technologies to assist other researchers and engage with the public. To assist researchers in determining the minimum required sample size, a web-based calculator was developed during the COVID-19 pandemic. Furthermore, a website was created containing 360-degree images to help individuals comprehend the challenges of diagnostics and healthcare in developing regions and to raise awareness about how infectious diseases spread