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Opto-electrochemical variation with gel polymer electrolytes in transparent electrochemical capacitors for ionotronics
Get PDFAdvanced flexible ionotronic devices have found excellent applications in the next generation of electronic skin (e-skin) development for smart wearables, robotics, and prosthesis. In this work, we developed transparent ionotronic-based flexible electrochemical capacitors using gel electrolytes and indium tin oxide (ITO) based transparent flexible electrodes. Different gel electrolytes were prepared using various salts, including NaCl, KCl, and LiCl in a 1:1 ratio with a polyvinyl alcohol (PVA) solution and compared its electrochemical performances. The interaction between gel electrolytes and ITO electrodes was investigated through the development of transparent electrochemical capacitors (TEC). The stable and consistent supply of ions was provided by the gel, which is essential for the charge storage and discharge within the TEC. The total charge contribution of the developed TECs is found from the diffusion-controlled mechanism and is measured to be 4.59 mC cm−2 for a LiCl/PVA-based gel. The prepared TEC with LiCl/PVA gel electrolyte exhibited a specific capacitance of 6.61 mF cm−2 at 10 μA cm−2. The prepared electrolyte shows a transparency of 99% at 550 nm and the fabricated TEC using LiCl/PVA gel exhibited a direct bandgap of 5.34 eV. The primary benefits of such ionotronic-based TEC development point to its potential future applications in the manufacturing of transparent batteries, electrochromic energy storage devices, ionotronic-based sensors, and photoelectrochemical energy storage devices
Perceived barriers to implementing building information modeling in Iranian Small and Medium-Sized Enterprises (SMEs): a Delphi survey of construction experts
Get PDFBuilding information modeling (BIM) is a disruptive information technology tool in the construction sector. Although this technology had a significant impact on the manufacturing industries, it, like any other technology, encountered several challenges when applied to the construction sector. Conversely, small and medium-sized enterprises (SMEs) in developing economies often face significant impediments when using innovative technologies. Thus, this paper seeks to determine and investigate the perceived barriers to applying BIM in construction SMEs based in Iran. Three rounds of Delphi surveys were carried out with 15 BIM experts engaged in construction SMEs to identify the key barriers to BIM implementation in SMEs. An empirical survey questionnaire comprising these identified barriers was subsequently designed and disseminated to the invited experts. Altogether, 56 valid survey responses were received and analyzed. The study’s findings revealed SMEs management’s hesitancy to adopt BIM, stakeholders’ reluctance to change their established methods, and a lack of technical understanding as the critical impediments to BIM adoption in construction SMEs. Also, the study identified four barrier dimensions – technology, legal, management, and financial. These BIM implementation barrier dimensions can be employed to better allocate resources and financing for BIM deployment and construction innovation in SMEs. The study will assist major project stakeholders and SMEs make better-informed BIM adoption decisions, particularly in developing nations like Iran
Safety Assessment RP1047 Quillaja saponaria and Yucca schidigera (Magni-Phi®)
No full textAn application was submitted to the Food Standards Agency in April 2021 from Phibro Animal Health Corporation (“the applicant”) for the new authorisation of an additive (Magni-Phi®), a natural preparation of powdered dry Quillaja saponaria and dry Yucca schidigera, under the category “zootechnical additives” and functional group “digestibility enhancer and other (performance enhancer)”. The additive is proposed to be used in all avian species (excluding laying and breeding birds), with a proposed inclusion rate of 250 mg/kg of complete feed.The Advisory Committee on Animal Feedingstuffs (ACAF) was asked to review the dossier and the supplementary information submitted by the Applicant, and to advise the Food Standards Agency and Food Standards Scotland (FSA/FSS) in evaluating the dossier.The Advisory Committee on Animal Feedingstuffs (ACAF) initially evaluated the identity and characterisation of the additive. Upon receiving further information relating to the identity of the product, the manufacturing process, management of potential contaminants and homogeneity, the Committee concluded that the additive was correctly identified and characterised.The FSA/FSS concluded, based on the ACAF’s advice, that the additive can be considered safe for the target species, consumers, and the environment. With regard to user safety, the additive should be considered potentially harmful by inhalation, and as a potential eye irritant and skin sensitiser. The additive is not a skin irritant.The additive can be considered efficacious in broiler chickens when included in complete feed at a minimum dose of 250 mg/kg. This efficacy data can be extrapolated to other poultry for fattening and ornamental birds.The views of ACAF have been taken into account in this safety assessment which represents the opinion of the FSA/FSS
The Tip of the Iceberg: Exploring the Landscape of Policing in a Digitalized World
Get PDFIn the ever-evolving landscape of law enforcement, the advent of the digital age has ushered in transformative shifts in police practices. These shifts have sparked scholarly attention and fuelled an increased volume of research dedicated to unravelling the complex interplay between technology and policing. The surge in interest at the intersection of policing and technology has led to a diverse and intricate array of studies. The extensive and diverse nature of research on policing and technology makes it challenging to obtain a comprehensive overview of the current research landscape. In this special issue we explore the multifaceted realm of policing and technology and it becomes evident that the studies presented here merely scratch the surface, revealing just the tip of the iceberg in our exploration of this intricate intersection
VLC-Assisted Safety Message Dissemination in Roadside Infrastructure-Less IoV Systems: Modeling and Analysis
Get PDFInternet of Vehicles (IoV) is an emerging paradigm with significant potential to improve traffic efficiency and driving safety. Here, we focus on the design of a novel visible light communication (VLC)-assisted scheme to enable driving safety-related Internet of Vehicles (IoV) services that require ultrareliable and low-latency communications (URLLC). Specifically, the Vehicle-to-Vehicle (V2V) communication mode is adopted to satisfy the ultralow latency requirement of URLLC in roadside infrastructure-less IoV systems. In the outdoor V2V- VLC scenarios, the quality of the received optical signal is degraded by path loss, atmospheric turbulence and additive noise. In addition, the short-packet feature of URLLC introduces inevitable data decoding errors and imperfect channel state information (CSI). With this background, we aim to investigate the reliability performance of URLLC in outdoor V2V- VLC systems, which is described by the average packet loss probability under given user-plane transmission latency. First, we consider the ideal case of a perfect CSI at the receiver, and derive an analytical expression of average packet loss probability. Further, a closed-form approximation is provided to simplify the numerical calculation. Next, we extend the theoretical analysis to a practical V2V- VLC system with imperfect CSI at the receiver. Through numerical results, we validate the accuracy of our designed theoretical framework and propose ideas to enable driving safety-related IoV services in outdoor V2V- VLC systems
Rethinking ‘traditional masculinity’
No full textDoes the concept of ‘traditional masculinity’ risk marginalising migrant men and those living in ‘non-Western’ parts of the world
Evaluation of Frameworks That Combine Evolution and Learning to Design Robots in Complex Morphological Spaces
Get PDFJointly optimising both the body and brain of a robot is known to be a challenging task, especially when attempting to evolve designs in simulation that will subsequently be built in the real world. To address this, it is increasingly common to combine evolution with a learning algorithm that can either improve the inherited controllers of new offspring to fine tune them to the new body design or learn them from scratch. In this paper an approach is proposed in which a robot is specified indirectly by two compositional pattern producing networks (CPPN) encoded in a single genome, one which encodes the brain and the other the body. The body part of the genome is evolved using an evolutionary algorithm (EA), with an individual learning algorithm (also an EA) applied to the inherited controller to improve it. The goal of this paper is to determine how to utilise the results of learning process most effectively to improve task performance of the robot. Specifically, three variants are investigated: (1) evolution of the body+controller only; (2) a learning algorithm is applied to the inherited controller with the learned fitness assigned to the genome; (3) learning is applied and the genome is updated with the learned controller, as well as being assigned the learned fitness. Experiments are performed in three different scenarios chosen to favour different bodies and locomotion patterns. It is shown that better performance can be obtained using learning but only if the learned controller is inherited by the offspring
Can Generative AI Models Extract Deeper Sentiments as Compared to Traditional Deep Learning Algorithms?
No full textRecent advances in the context of deep learning have led to the development of generative artificial intelligence (AI) models which have shown remarkable performance in complex language understanding tasks. This study proposes an evaluation of traditional deep learning algorithms and generative AI models for sentiment analysis. Experimental results show that RoBERTa outperforms all models, including ChatGPT and Bard, suggesting that generative AI models are not yet able to capture the nuances and subtleties of sentiment in text. We provide valuable insights into the strengths and weaknesses of different models for sentiment analysis and offer guidance for researchers and practitioners in selecting suitable models for their tasks
Pre-Processing-based Fast Design of Multiple EM Structures with One Deep Neural Network
No full textDeep learning plays a vital role in the design of electromagnetic (EM) structures. However, in current research, a single neural network typically supports only one structure design and requires a complex framework to accommodate multiple structure designs. This paper proposes using one neural-assist design for facilitating multiple EM structures. We employ two filling methods to control the vector length, an identification method to ensure accurate prediction results, and a random auxiliary vectors method to increase the data volume and reduce loss. Subsequently, we design a forward neural network (FNN) and an inverse neural network (INN) using the proposed method. The developed neural network is used to complete the dual-passband frequency selective surface (DP-FSS), space-time-coding digital metasurface element (STCDME), single/dual absorbing metasurface (SDAM), and dual-stopband frequency selective surface (DS-FSS) designs. The mean absolute error (MAE) loss values for the FNN/INN predictions and actual results for these four structures are 0.019/0.116, 0.035/0.602, 5.14/0.146, and 0.018/0.07, respectively. Finally, we design four structures with the well-training network, fabricate DP-FSS and DS-FSS, and measure them in an anechoic chamber. The measurement and simulation results are in good agreement. The proposed method significantly reduces the complexity of multiple EM structure designs, decreases the need for multiple neural networks, and simplifies the design framework, thereby contributing to the development of AI-assisted EM structures
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