The escalating complexity of modern digital systems has imposed significant challenges on integrated circuit (IC) design, necessitating tools that can simplify the IC design flow. The advent of Large Language Models (LLMs) has been seen as a promising development, with the potential to automate the generation of Hardware Description Language (HDL) code, thereby streamlining digital IC design. However, the practical application of LLMs in this area faces substantial hurdles. Notably, current LLMs often generate HDL code with small but critical syntax errors and struggle to accurately convey the high-level semantics of circuit designs. These issues significantly undermine the utility of LLMs for IC design, leading to misinterpretations and inefficiencies. In response to these challenges, this paper presents targeted strategies to harness the capabilities of LLMs for digital ASIC design. We outline approaches that improve the reliability and accuracy of HDL code generation by LLMs. As a practical demonstration of these strategies, we detail the development of a simple three-phase Pulse Width Modulation (PWM) generator. This project, part of the "Efabless AI-Generated Open-Source Chip Design Challenge," successfully passed the Design Rule Check (DRC) and was fabricated, showcasing the potential of LLMs to enhance digital ASIC design. This work underscores the feasibility and benefits of integrating LLMs into the IC design process, offering a novel approach to overcoming the complexities of modern digital systems.

Collaborative logistics has been widely recognised as an effective avenue to reduce carbon emissions by enhanced truck utilisation and reduced travel distance. However, stakeholders' participation in collaborations is hindered by information-sharing barriers and the absence of integrated systems. We, thus, in this paper addresses these barriers by investigating an integrated platform that foster collaboration through the integration of agents with digital twins. Specifically, we employ a multi-agent system approach to integrate stakeholders and physical mobile assets in collaborative logistics, representing them as agents. We introduce a loosely-coupled system architecture that facilitates the connection between physical and digital systems, enabling the integration of agents with digital twins. Using this architecture, we implement the platform (or testbed). The resulting testbed, comprising a physical environment and a digital replica, is a digital twin that integrates distributed entities involved in collaborative logistics. The effectiveness of the testbed is demonstrated through a carrier collaboration scenario. This paper is among the earliest few efforts to investigate the integration of agents and digital twin concepts and goes beyond the conceptual discussion of existing studies to the technical implementation of such integration. Transportation is the largest contributor to greenhouse gas (GHG) emissions [1]. Among all transportation modes, trucks are the second-largest source of emissions after cars and taxis. However, they are currently utilised inefficiently, operating at around 60% of their weight capacity, and approximately 30% of the distance they travel carries no freight [2]. Collaborative logistics has been widely recognised as an effective pathway to enhance truck utilisation [3] [4] [5]. This approach involves carriers collaborating through coalition to collectively fulfil delivery requests, achieving reduced total cost and travel distance through economies of scale. Two key barriers, among others [5], contribute to this challenge: 1) Lack of Trusted Platforms: Concerns business secrecy may deter carriers from sharing data with centralised platforms, despite the environmental and economic benefits. These barriers hinder stakeholders' participation in collaboration.

Smart Digital twins (SDTs) are being increasingly used to virtually replicate and predict the behaviors of complex physical systems through continual data assimilation enabling the optimization of the performance of these systems by controlling the actions of systems. Recently, deep learning (DL) models have significantly enhanced the capabilities of SDTs, particularly for tasks such as predictive maintenance, anomaly detection, and optimization. In many domains, including medicine, engineering, and education, SDTs use image data (image-based SDTs) to observe and learn system behaviors and control their behaviors. This paper focuses on various approaches and associated challenges in developing image-based SDTs by continually assimilating image data from physical systems. The paper also discusses the challenges involved in designing and implementing DL models for SDTs, including data acquisition, processing, and interpretation. In addition, insights into the future directions and opportunities for developing new image-based DL approaches to develop robust SDTs are provided. This includes the potential for using generative models for data augmentation, developing multi-modal DL models, and exploring the integration of DL with other technologies, including 5G, edge computing, and IoT. In this paper, we describe the image-based SDTs, which enable broader adoption of the digital twin DT paradigms across a broad spectrum of areas and the development of new methods to improve the abilities of SDTs in replicating, predicting, and optimizing the behavior of complex systems.

What's old is new again. At least, it is if we are talking about analog computing. The moment you hear the phrase "analog computing," you might be forgiven for thinking we are talking about the hipsters of the technology world. The people who prefer vinyl over Spotify. The ones that want to bring back typewriters to replace word processors, or the folks who prize handwritten notes over those generated by ChatGPT.

This report covers results from the 15th "Future of the Internet" canvassing that Pew Research Center and Elon University's Imagining the Internet Center have conducted together to gather expert views about important digital issues. This is a nonscientific canvassing based on a nonrandom sample; this broad array of opinions about the potential influence of current trends may lead between 2022 and 2035 represents only the points of view of the individuals who responded to the queries. Pew Research Center and Elon's Imagining the Internet Center sampled from a database of experts to canvass from a wide range of fields, inviting entrepreneurs, professionals and policy people based in government bodies, nonprofits and foundations, technology businesses and think tanks, as well as interested academics and technology innovators. The predictions reported here came in response to a set of questions in an online canvassing conducted between June 29 and Aug. 8, 2022. In all, 540 technology innovators and developers, business and policy leaders, researchers and activists responded in some way to the question covered in this report. More on the methodology underlying this canvassing and the participants can be found in the section titled "About this canvassing of experts." Advances in the internet, artificial intelligence (AI) and online applications have allowed humans to vastly expand their capabilities and increase their capacity to tackle complex problems. These advances have given people the ability to instantly access and share knowledge and amplified their personal and collective power to understand and shape their surroundings. Today there is general agreement that smart machines, bots and systems powered mostly by machine learning and artificial intelligence will quickly increase in speed and sophistication between now and 2035.

How AI helps various businesses across industries to find the right solutions to address their challenges more adequately? Artificial Intelligence refers to the simulation of human intelligence processes by machines, especially computer systems. It is engineering that makes machines capable of doing a similar task of using computers to understand human intelligence. There are four types of AI which are as follows. It improves production rates along with productivity and allows businesses to use raw materials more efficiently.

While a robot doctor at the bedside is not on the horizon, data-driven digital health is transforming how we receive care - and society is still playing catch-up on the ramifications. In 2012, Professor Enrico Coiera, Founding Director of the Centre for Health Informatics (CHI) at the Australian Institute for Health Innovation, published a paper titled The Dangerous Decade. In it, he warned that more information and communication technology (ICT) would be deployed into healthcare in the 10 years to 2022 than in the health system's entire history to date. "Systems will be larger in scope, more complex, and move from regional to national and supranational scale," he wrote. "Yet we are at roughly the same place the aviation industry was in the 1950s with respect to system safety."

It seems to be in every other headline. There's no doubt AI has the potential to transform medical device manufacturing. Rather than focus on a complete transformation, manufacturers can see benefits by focusing on simply enhancing it. AI on a manufacturing line doesn't have to mean everything is automated and there are no humans involved. On the contrary, humans continue to be essential in manufacturing, even with AI.

As CIOs and other executives look for ways to expand sustainability initiatives, there's a growing awareness that initiatives can't stop at the four walls of the data center or office building. Today's structures can contain hundreds of thousands of components that consume energy and add to an organization's carbon footprint. In fact, buildings consume one-third of all energy globally and produce one-quarter of all greenhouse gas emissions (GHGs), according to The World Resources Institute. What's more, business and IT leaders are often narrowly focused on improving sustainability in data centers and procuring greener computing systems. Yet they overlook critical ways that technology can shrink a carbon footprint. "There is a growing awareness that buildings and workspaces are a crucial part of sustainability initiatives," states Bryon Carlock, National Real Estate Practice Leader for consulting firm PwC.

How AI enhances security for IoT environments. Elon Musk's prediction that AI will outsmart humans in less than 5 years is a bold statement, predicting that machines will possess super-human qualities which help boost organizations' profits and goals. For many, these ideas belong in sci-fi fantasies rather than as a future fixture of working practices. In the broadest sense, there are no signs that AI comes close to human consciousness or sentience. When we talk about the power of AI, it's more helpful to consider the specific use cases and sectors where it will, and is having, a transformative effect – and there is one area in particular where AI has been seen to mimic the capabilities of complex human thought processes: cyber security.