Deployment Of Such Technologies Research Articles

A new sub-class linear discriminant for miniature spectrometer based food analysis

The well-known and extensively studied Linear Discriminant Analysis (LDA) can have its performance lowered in scenarios where data is not homoscedastic or not Gaussian. That is, the classical assumptions when LDA models are built are not applicable, and consequently LDA projections would not be able to extract the needed features to explain the intrinsic structure of data and for classes to be separated. As with many real word data sets, data obtained using miniature spectrometers can suffer from such drawbacks which would limit the deployment of such technology needed for food analysis. The solution presented in the paper is to divide classes into subclasses and to use means of sub classes, classes, and data in the suggested between classes scatter metric. Further, samples belonging to the same subclass are used to build a measure of within subclass scatterness. Such a solution solves the shortcoming of the classical LDA. The obtained results when using the proposed solution on food data and on general machine learning datasets show that the work in this paper compares well to and is very competitive with similar sub-class LDA algorithms in the literature. An extension to a Hilbert space is also presented; and the kernel version of the presented solution can be fused with its linear counter parts to yield improved classification rates.

Leveraging ChatGPT in Law Enforcement

Abstract: The advent of powerful tools like ChatGPT has opened up exciting new possibilities for augmenting law enforcement capabilities, elevating its efficiency and expanding its capacity. The AI-driven capabilities of ChatGPT can be harnessed to usher in novel applications in diverse areas such as language translation, customer service, content generation, and even law enforcement. In the realm of law enforcement, ChatGPT's potential is boundless, ranging from the generation of detailed reports and the transcription and translation of vital documents to the profiling of criminals and the automation of legal documents. However, the deployment of such technology in law enforcement raises critical issues of privacy, safeguarding fundamental rights, and the perpetuation of societal biases. Furthermore, the deployment of emerging technologies like ChatGPT could also open up new avenues for committing cybercrimes. Therefore, it is crucial to address these issues and establish regulatory frameworks that govern the use of such advanced technologies. In this paper, we provide legal and technical recommendations to regulate the use of ChatGPT and mitigate the potential risks associated with its usage, including the perpetration of various cybercrimes.

Human-First, Please: Assessing Citizen Views and Industrial Ambition for Emotional AI in Recommender Systems

This paper qualitatively explores the views of diverse members of the British public on applications of biometric emotional AI technologies patented by two globally dominant consumer-facing recommender systems, Amazon and Spotify. Examining Amazon and Spotify patents for biometric profiling of users’ emotions, disposition, and behaviour to offer them tailored services, ads, and products from their wider platforms, this paper points to industrial ambition regarding emotional AI. Little is known about ordinary people’s views on deployment of such technology, and given the complex, abstract, and future-facing nature of such technologies, ascertaining informed lay views is hard. We address this through our innovative, qualitative study of diverse British-based adults (n=46) that presents to them near-horizon use cases in an interactive fictional narrative that deploys design fiction principles and ContraVision techniques. We find the themes of “usefulness,” “resignation,” “uneasy terms of engagement,” and “human-first,” adding rich and nuanced insights to prior survey work on users’ views towards biometric-based emotional AI technologies. In contributing to a richer understanding of whether emotional AI technologies should be deployed in consumer-facing recommender systems, and if so, on what terms, we find that well-established policy-friendly criticisms apply to global emotional AI recommender systems. We conclude, however, that problems of alienation and need for a human-first approach to emerging AI technology are the most significant criticisms.

Optimal sensor placement methodology of triaxial accelerometers using combined metaheuristic algorithms for structural health monitoring applications

Structural health monitoring applications present a valuable opportunity for cities to improve their infrastructures environmental life cycle, reduce operational maintenance cost and enhance their resiliency through early damage detection capabilities. The dilemma with structural health monitoring resides in balancing between the number of sensors to be installed and the amount of data that needs to be transferred, stored, and managed. These technical complications directly impact the cost efficiency of such solutions. Thus, limiting the wide range deployment of such technology. Henceforth, Optimal Sensor Placement (OSP) methodologies offers the possibility to optimize the sensor configuration that will ensure a proper and cost-efficient monitoring system to obtain the maximum modal information related to any infrastructure type.In this study, a novel “Modal Assurance Criterion (MAC)”-based methodology with the aim of the optimal sensor placement is presented using a 410 m high rise structure. The Modal Kinetic Energy (MKE) and a MAC-based objective function are combined in the suggested methodology. The optimization problem is solved using a new hybrid metaheuristic algorithm that combines Teaching–Learning-Based Optimization (TLBO), Artificial Bee Colonies (ABC), and Stochastic Paint Optimizer (SPO). With no user-defined parameters, the combination of algorithms exhibits up to 86% better fitness, avoids local optimums due to higher accuracy, and shows promising results for cost calculation optimization because it requires 50% to 70% fewer iterations compared to six evolutionary algorithms that are usually used.

In search of ethical pandemic technology.

The duration of the pandemic over the last two years has witnessed the steering of multiple technological interventions by governments. These interventions - ranging from contact tracing applications to vaccine certificates - have been developed in the specific context of the pandemic, and were meant to address its unique requirements. This family of technological interventions may be termed "pandemic technology" - having diverse uses such as preventing the transmission of Covid-19, and aiding the relaxation of pandemic-induced restrictions. We propose a four-rung ethical paradigm for the deployment of such technology. We call it the STEP model and its four pillars consist of (i) sunset clauses, (ii) trust, (iii) equity, and (iv) privacy preservation.

Do environmental attitudes affect public acceptance of key enabling technologies? Assessing the influence of environmental awareness and trust on public perceptions about nanotechnology

Key Enabling Technologies (KETs) play a crucial role in modern societies due to their substantial contribution to cleaner production, sustainable development, and societal well-being across industrial sectors. However, to prompt the successful deployment of such technologies in diverse societal and market contexts, public acceptance constitutes a crucial requirement. As public knowledge of KETs is often limited, “cognitive shortcuts” – such as attitudes and personal beliefs – play a crucial role in shaping public opinion about enabling technologies. Drawing on the case of nanotechnology, the paper advances that environmental attitudes contribute to shape layman's perceptions about novel enabling technologies. Based on data collected from a survey of Italian citizens, the study investigates the influence of environmental awareness on public perceptions of benefits and risks of nanotechnology. By means of hierarchical regression equations, the study also tests the moderation of public trust in the environmental commitment of societal actors deemed responsible for nanotechnology development – i.e. high-tech companies, governmental agencies and research universities. The results show that environmental awareness positively relates with both perceived benefits and risks of nanotechnology, while the different effects of trust are contingent upon the actor in which trust is put. The contribution of the study point to the role of public trust in shaping perceptions of novel technology: the study's implications for practice thus inform the development of information strategies aimed at supporting the acceptance of KETs among the larger public.

Engaging with different transport user groups for a smooth: Transition to connected and automated mobility

Connected and automated vehicles could benefit society by increasing transport safety, environmental sustainability and inclusiveness. Engaging with citizens to raise awareness and discuss about the opportunities and threats they bring is paramount for a successful deployment of such technologies. This paper presents the results of several activities with women and teenager participants: focus group discussions, demonstrations of an automated vehicle prototype and feelings’ capture associated to automated vehicles, engaging with them, aiming at acquiring their knowledge, expectations and concerns for a smooth transition to connected and automated vehicles. The work conducted shows that technological innovations could result in societal benefits, once a technology is understood and accepted at societal level where the role played by knowledge sharing and dissemination are key.

The case for global regulation of carbon capture and storage and artificial intelligence for climate change

This article investigates the trends in the global law-making process as it relates to carbon capture and storage (CCS) and associated technology. It detects regulatory deficiencies and makes a case favouring robust global regulation of such technology in realizing climate aspirations. CCS is recognized as an indispensable ally in the worldwide enterprise that seeks to limit carbon dioxide emissions. This article probes the relatively new and developing area of international regulation of the process of CCS and related technology, in particular Artificial Intelligence (AI), a dimension that promises noteworthy results on the climate change front. It presents a case for the broader deployment of the CCS technology – including more advanced AI options – to enhance in a more radical fashion the prospect of meeting global climate goals and thus stabilising global temperature (that is, achieving net zero CO2 emissions globally in the early 2050s), identifies the need for new and revised sets of rules to govern the development, design and deployment of such technology, and argues in favour of the applicability of existing general sustainable development principles.

A Simplistic and Cost-Effective Design for Real-World Development of an Ambient Assisted Living System for Fall Detection and Indoor Localization: Proof-of-Concept

Falls, highly common in the constantly increasing global aging population, can have a variety of negative effects on their health, well-being, and quality of life, including restricting their capabilities to conduct activities of daily living (ADLs), which are crucial for one’s sustenance. Timely assistance during falls is highly necessary, which involves tracking the indoor location of the elderly during their diverse navigational patterns associated with different activities to detect the precise location of a fall. With the decreasing caregiver population on a global scale, it is important that the future of intelligent living environments can detect falls during ADLs while being able to track the indoor location of the elderly in the real world. Prior works in these fields have several limitations, such as the lack of functionalities to detect falls and indoor locations in a simultaneous manner, high cost of implementation, complicated design, the requirement of multiple hardware components for deployment, and the necessity to develop new hardware for implementation, which make the wide-scale deployment of such technologies challenging. To address these challenges, this work proposes a cost-effective and simplistic design paradigm for an ambient assisted living system that can capture multimodal components of user behaviors during ADLs that are necessary for performing fall detection and indoor localization in a simultaneous manner in the real-world. Proof-of-concept results from real-world experiments are presented to uphold the effective working of the system. The findings from two comparative studies with prior works in this field are also presented to uphold the novelty of this work. The first comparative study shows how the proposed system outperforms prior works in the areas of indoor localization and fall detection in terms of the effectiveness of its software design and hardware design. The second comparative study shows that the cost of the development of this system is the lowest as compared to prior works in these fields, which involved real-world development of the underlining systems, thereby upholding its cost-effective nature.

Field investigation of novel self-sensing asphalt pavement for weigh-in-motion sensing

The integration of weigh-in-motion (WIM) sensors within highways or bridge structural health monitoring systems is becoming increasingly popular to ensure structural integrity and users safety. Compared to standard technologies, smart self-sensing materials and systems present a simpler sensing setup, a longer service life, and increased durability against environmental effects. Field deployment of such technologies requires characterization and design optimization for realistic scales. This paper presents a field investigation of the vehicle load-sensing capabilities of a newly developed low-cost, eco-friendly and high durability smart composite paving material. The novel contributions of the work include the design and installation of a full-scale sensing pavement section and of the sensing hardware and software using tailored low-cost electronics and a learning algorithm for vehicle load estimation. The outcomes of the research demonstrate the effectiveness of the proposed system for traffic monitoring of infrastructures and WIM sensing by estimating the gross weight of passing trucks within a 20% error during an autonomous sensing period of two months.

Monte Carlo simulations for water adsorption in porous materials: Best practices and new insights

AbstractTechnologies based on water adsorption such as water harvesting from air have tremendous potential in mitigating important global crises such as water scarcity. An important challenge to the deployment of such technologies is finding optimal adsorbent materials. Given the large materials space of available adsorbents, large‐scale computational screening can be extremely helpful for this task. This work explores the methods and details associated with such screening procedures and recommends best practices. We also shed light on the limitations of traditionally used and inexpensive to compute prescreening approaches involving geometric and energetic features to predict water adsorption behavior of porous materials. Such approaches can provide general trends to predict adsorption behavior but may lead to the overlook of potentially important structures due to the complex nature of water adsorption. This study offers insights for future water adsorption simulations to facilitate the development of optimal water adsorbents.

High Performance of Anion Exchange Blend Membranes Based on Novel Phosphonium Cation Polymers for All-Vanadium Redox Flow Battery Applications.

The deployment of alkaline anion exchange membranes (AEMs) in flow battery applications has the advantage of a low cationic species crossover rate. However, the alkaline stability conjugated to the low conductivity of hydroxide ions of anion exchange membranes (AEMs) still represents a major drawback for the large deployment of such technology. In this study, three types of tetraarylpolyphosphonium (pTAP)-based copolymers (namely, CP1, CP2, and CP3) are synthesized and blended with chitosan and polyvinylidene fluoride (PVDF) for the fabrication of AEMs. Chitosan, a green biopolymer, was employed as a blend to enhance the water uptake of the base ionomer matrix. It is proposed that the abundancy of hydroxyl groups in chitosan improves considerably the ionic conductivity, water transport, and ion selectivity of the membrane, together with facilitating the dispersion of the chitosan in the pTAP copolymer matrix. The purpose of blending PVDF is instead to provide stable mechanical strength to the composite blend. The chemical, mechanical, and thermal stabilities of the three fabricated composite-blend membranes (i.e., CM1, CM2, and CM3) were characterized. All the membranes exhibited a high water retaining capacity of up to 36.26% (recorded for CM2) along with a hydroxyl ion conductivity of 17.39 mS cm-1. Due to the strong interactions between pTAP copolymers, chitosan, and PVDF polymers (confirmed also by Fourier transform infrared spectroscopy), the studied anion exchange membranes are able to retain up to 97% of the original OH conductivity after 1 M KOH treatment at room temperature for 100 h. The three membranes, namely, CM1, CM2, and CM3, have vanadium ion permeabilities measured at 20 °C of 1.775 × 10-8, 1.718 × 10-8, and 1.648 × 10-8 cm2/s, respectively, which are lower than that for the commercially available Nafion. The good stability and remarkable cell performance of the composite-blend membranes reported here make them definitely excellent candidates for the future generation of vanadium redox flow batteries.

Application of Metal-Organic Frameworks (MOFs) for Capturing CO2: Advancement and Challenges

The development of suitable solutions for capturing CO2 is one of the leading technical scenarios, among others, to mitigate greenhouse effect. Among all splitting techniques that could potentially address this important challenge, membrane and swing adsorption technologies are recognized to be of great promise for a variety of CO2 containing gas streams in industry. Nevertheless, the deployment of such technologies requires advanced materials with excellent/suitable thermodynamic and kinetics features that are further married to an optimally engineered design. The ultimate purpose is to achieve the desired high CO2 capturing capacity and efficiency. The targeted materials should possess adequate affinity toward CO2, in addition to high CO2 uptake and excellent chemical stability toward impurities such as SOx and NOx. Metal-Organic Frameworks (MOFs), solid-sate materials consisting of metal ions or clusters coordinated to organic ligands, showed technically interesting capabilities for gas splitting in general and CO2 capture in particular. This review presents an overview about the perspective of applying MOFs for capturing CO2 from different sources. The authors offer multidisciplinary discussion about the different aspects that would be key elements in progressing or cutting the path of research, development and innovation to final deployment of MOF as adsorbent for capturing CO2. An overview about the main MOFs with reported studies on CO2 capture from different sources will be proposed. Some general direction on how to design MOFs in order to address the trade-off of capacity vs. selectivity, which is highly desirable for large-scale CO2 emitting industries, will also, be given.

An Australian Perspective on Local Government Investment in Electric Vehicle Charging Infrastructure

Electric vehicle (EV) adoption is growing worldwide with increasing market pull from consumers and market push from manufacturers of vehicles and charging equipment, as well as others in the supply chain. Governments have begun developing policies to support EV uptake and local governments, in particular, are examining what role they should play. In Australia, a large country with low population density, EV uptake has been slower in comparison to other similar economies. This paper discusses the status of EV charging infrastructure deployment in Australia with regards to local governments, by considering the extent to which they are relied upon for the deployment of such technology and what motivates them to act. It also covers the work undertaken by the authors with one local government in developing an EV charging infrastructure business model that will help the local community adopt and benefit from EVs. An applied use of the business canvas methodology adapted to suit local government interests is presented to assess the risks and benefits that different business models offer. The paper offers insights into the strategic and pragmatic responsibilities local governments balance in seeking to expand the EV charging infrastructure in their jurisdiction.

Enclaves in the clouds

Trusted Execution Environments ('TEEs') or 'secure enclaves' aim at enabling more secure computation and data management. There is much enthusiasm for this technology, not least as we see increasing legal and regulatory attention on issues of security, privacy, and data management and use. With cloud providing the infrastructure for underpinning applications, data processing and analytics/ML, access to enclaves and enclave-backed technologies are increasingly being offered by service (cloud) providers - with the technology described as enabling confidential computing . This paper provides a high-level overview of the common security properties provided by enclaves, and considers how such technology, in being offered by cloud providers, relates to organizational legal and regulatory concerns. Focusing on data protection regulations, we explore the aspects of TEEs that might assist compliance, and who stands to benefit from the deployment of such technology in a service provision context.

The politics of low-carbon innovation: Implementing the European Union’s strategic energy technology plan

The EU Strategic Energy Technology Plan (SET-Plan) was adopted in 2008 to steer the funding of low-carbon technology research and innovation in Europe and thus accelerate the development and deployment of such technologies. Examining how and why the Plan attained its objectives, we first find that research and innovation funding came to steer the development of the SET-Plan rather than the converse. Second, we find three main explanations, drawing on theories of EU integration and policymaking: 1) differing research and innovation priorities among state, non-state (industry and research community), and institutional actors within the EU, making the Plan contested and leading to the mobilization of actors whose priorities had not been included in the Plan; 2) weak EU-level authority to govern the Plan; 3) diverging alignment between the Plan, constituting EU’s low-carbon technology push policy, and EU market pull policies, such as carbon pricing. These observations have relevance to other international efforts, such as Mission Innovation linked to the Paris Agreement.

Next Generation Industrial IoT Digitalization for Traceability in Metal Manufacturing Industry: A Case Study of Industry 4.0

This paper investigates digital traceability technologies taking careful consideration of the company’s needs to improve the traceability of products at the production of GPV Group as well as the efficiency and added value in their production cycles. GPV is primarily an electronics manufacturing service company (EMS) that manufactures electronic circuit boards, in addition to big metal products at their mechanics manufacturing sites. The company aims to embrace the next generation IoT technologies such as digital traceability in their internal supply chain at manufacturing sites in order to stay compatible with the Industry 4.0 requirements. In this paper, the capabilities of suitable digital traceability technologies are screened together with the actual GPV needs to determine if deployment of such technologies would benefit GPV shop floor operations and can solve the issues they face due to a lack of traceability. The traceability term refers to tracking the geolocation of products throughout the manufacturing steps and how that functionality can foster further optimization of the manufacturing processes. The paper focuses on comparing different IoT technologies and analyze their positive and negative attributes to identify a suitable technological solution for product traceability in the metal manufacturing industry. Finally, the paper proposes a suitable implementation road map for GPV, which can also be adopted from other metal manufacturing industries to deploy Industry 4.0 traceability at shop floor level.

Improving Computational Assessment of Porous Materials for Water Adsorption Applications via Flat Histogram Methods

Water adsorption in porous materials has recently drawn considerable attention for its tremendous potential in environmental applications such as water harvesting from air. A key step to the deployment of such technologies is the selection of optimal adsorbent materials as material performance directly impacts their efficiency. Owing to the vast materials space, computational studies may play a critically important role in facilitating the selection of the best material. The current state-of-the-art method to predict adsorption behavior of porous materials, the grand canonical Monte Carlo (GCMC) simulations, however, can converge very slowly due to its inefficient sampling of the phase space accessible to the system and may yield unreliable results. To this end, we have demonstrated a method from a class of techniques known as flat histogram methods, which can sample the accessible states of the system much more efficiently. Further, a so-called C-map method is proposed herein to efficiently determine the applicability of a material in water adsorption applications. These methods can offer unprecedented insights into the behaviors of the GCMC-predicted isotherms and, more importantly, are promising for large-scale computational screening of porous materials for water adsorption.

Blending virtual reality with traditional approaches to encourage engagement with core chemistry concepts relevant to an undergraduate pharmacy curriculum

Students studying pharmacy often lack motivation to engage with chemical principles. In particular, they often find fundamental chemical concepts such as the visualisation of three-dimensional structures challenging. This programme description reports the design and implementation of a teaching session where stand-alone virtual reality (VR) technology was offered alongside traditional methods of instruction in an attempt to encourage students to engage with fundamental chemistry concepts relating to the three-dimensional structures of drug molecules. By using VR technology in combination with paper-based in-class exercises and supporting material delivered via a virtual learning environment, the authors demonstrate that virtual reality could be used to both reinforce learning and enhance engagement with a topic which students often find challenging. A blended approach employing different methods of delivery presents the advantage that it allows the user control over the level of interaction with each individual element. As such, VR becomes a valuable instructional tool rather then the focus of the session or a distraction. To evaluate the sessions, student feedback was collected using a ‘ballot-box’ system where students could provide anonymous free response comments. Feedback from the session was overwhelmingly positive and it was noted that the optional use of VR technology kept students engaged in tackling paper-based exercises in an area of curriculum which learners often find difficult. It is noted that a blended approach can help to overcome several of the barriers in the use of VR in a classroom setting. In addition, it highlights that effective deployment of such technology is both viable and beneficial on a classroom scale. In the future, the authors hope to apply these methods to other parts of the curriculum

Integrated adsorption and absorption process for post-combustion CO2 capture

This study explored the feasibility of integrating an adsorption and solvent scrubbing process for post-combustion CO2 capture from a coal-fired power plant. This integrated process has two stages: the first is a vacuum swing adsorption (VSA) process using activated carbon as the adsorbent, and the second stage is a solvent scrubber/stripper system using monoethanolamine (30 wt-%) as the solvent. The results showed that the adsorption process could enrich CO2 in the flue gas from 12 to 50 mol-% with aCO2 recovery of > 90%, and the concentrated CO2 stream fed to the solvent scrubber had a significantly lower volumetric flowrate. The increased CO2 concentration and reduced feed flow to the absorption section resulted in significant reduction in the diameter of the solvent absorber, bringing the size of the absorber from uneconomically large to readily achievable domain. In addition, the VSA process could also remove most of the oxygen initially existed in the feed gas, alleviating the downstream corrosion and degradation problems in the absorption section. The findings in this work will reduce the technical risks associated with the state-of-the art solvent absorption technology for CO2 capture and thus accelerate the deployment of such technologies to reduce carbon emissions.