In the current industrial landscape, a significant number of sectors are grappling with the challenges posed by unstructured data, which incurs financial losses amounting to millions annually. If harnessed effectively, this data has the potential to substantially boost operational efficiency. Traditional methods for extracting information have their limitations; however, solutions powered by artificial intelligence (AI) could provide a more fitting alternative. There is an evident gap in scholarly research concerning a comprehensive evaluation of AI-driven techniques for the extraction of information from unstructured content. This systematic literature review aims to identify, assess, and deliberate on prospective research directions within the field of unstructured document information extraction. It has been observed that prevailing extraction methods primarily depend on static patterns or rules, often proving inadequate when faced with complex document structures typically encountered in real-world scenarios, such as medical records. Datasets currently available to the public suffer from low quality and are tailored for specific tasks only. This underscores an urgent need for developing new datasets that accurately reflect complex issues encountered in practical settings. The review reveals that AI-based techniques show promise in autonomously extracting information from diverse unstructured documents, encompassing both printed and handwritten text. Challenges arise, however, when dealing with varied document layouts. Proposing a framework through hybrid AI-based approaches, this review envisions processing a high-quality dataset for automatic information extraction from unstructured documents. Additionally, it emphasizes the importance of collaborative efforts between organizations and researchers to address the diverse challenges associated with unstructured data analysis.

The need for power-efficient devices, such as smart sensor nodes, mobile devices, and portable digital gadgets, is markedly increasing and these devices are becoming commonly used in daily life. These devices continue to demand an energy-efficient cache memory designed on Static Random-Access Memory (SRAM) with enhanced speed, performance, and stability to perform on-chip data processing and faster computations. This paper presents an energy-efficient and variability-resilient 11T (E²VR11T) SRAM cell, which is designed with a novel Data-Aware Read–Write Assist (DARWA) technique. The E²VR11T cell comprises 11 transistors and operates with single-ended read and dynamic differential write circuits. The simulated results in a 45 nm CMOS technology exhibit 71.63% and 58.77% lower read energy than ST9T and LP10T and lower write energies of 28.25% and 51.79% against S8T and LP10T cells, respectively. The leakage power is reduced by 56.32% and 40.90% compared to ST9T and LP10T cells. The read static noise margin (RSNM) is improved by 1.94× and 0.18×, while the write noise margin (WNM) is improved by 19.57% and 8.70% against C6T and S8T cells. The variability investigation using the Monte Carlo simulation on 5000 samples highly validates the robustness and variability resilience of the proposed cell. The improved overall performance of the proposed E²VR11T cell makes it suitable for low-power applications.

Innovations in food manufacturing support the agenda for sustainable development goal 9 (SDG9) on industry, innovation and infrastructure. Pursuant to this goal, this study aims to develop an automated multi-spit lamb rotisserie machine that potentially improves the lamb-roasting productivity for small and medium enterprises (SMEs). The conceptualisation involved patents, scholarly literature and product reviews of lamb-roasting devices. The design and analysis are performed using Autodesk Inventor 2019. A scaled-down prototype is developed and tested with (1) roasting output, (2) roasting time and (3) temperature stability tests. The data for test (1) are analysed by comparing the means between control and experimental groups. The data for tests (2) and (3) are analysed using the t-test and Mann–Whitney U test, respectively. Significant differences are observed in tests (1) and (2), with outcomes being in favour of the proposed invention. The prototype cooks 92.27% faster with 700% more meat than a regular lamb roaster. It also cooks at a stable temperature. The cost analysis indicated that this invention could be sold at USD 278 if mass-produced. The design is structurally simple, inexpensive and easy to manufacture, allowing SMEs that rely on traditional spit-based machines to enhance their ability in producing roast lamb.