Explore the vast range of titles available.

Digital twin is often viewed as a technology that can assist engineers and researchers make data-driven system and network-level decisions. Across the scientific literature, digital twins have been consistently theorized as a strong solution to facilitate proactive discovery of system failures, system and network efficiency improvement, system and network operation optimization, among others. With their strong affinity to the industrial metaverse concept, digital twins have the potential to offer high-value propositions that are unique to the energy sector stakeholders to realize the true potential of physical and digital convergence and pertinent sustainability goals. Although the technology has been known for a long time in theory, its practical real-world applications have been so far limited, nevertheless with tremendous growth projections. In the energy sector, there have been theoretical and lab-level experimental analysis of digital twins but few of those experiments resulted in real-world deployments. There may be many contributing factors to any friction associated with real-world scalable deployment in the energy sector such as cost, regulatory, and compliance requirements, and measurable and comparable methods to evaluate performance and return on investment. Those factors can be potentially addressed if the digital twin applications are built on the foundations of a scalable and interoperable framework that can drive a digital twin application across the project lifecycle: from ideation to theoretical deep dive to proof of concept to large-scale experiment to real-world deployment at scale. This paper is an attempt to define a digital twin open architecture framework that comprises a digital twin technology stack (D-Arc) coupled with information flow, sequence, and object diagrams. Those artifacts can be used by energy sector engineers and researchers to use any digital twin platform to drive research and engineering. This paper also provides critical details related to cybersecurity aspects, data management processes, and relevant energy sector use cases.

A collection of applications of polymer matrix syntactic foams is presented in this article. Syntactic foams are lightweight porous composites that found their early applications in marine structures due to their naturally buoyant behavior and low moisture absorption. Their light weight has been beneficial in weight sensitive aerospace structures. Syntactic foams have pushed the performance boundaries for composites and have enabled the development of vehicles for traveling to the deepest parts of the ocean and to other planets. The high volume fraction of porosity in syntactic foams also enabled their applications in thermal insulation of pipelines in oil and gas industry. The possibility of tailoring the mechanical and thermal properties of syntactic foams through a combination of material selection, hollow particle volume fraction, and hollow particle wall thickness has helped in rapidly growing these applications. The low coefficient of thermal expansion and dimensional stability at high temperatures are now leading their use in electronic packaging, composite tooling, and thermoforming plug assists. Methods have been developed to tailor the mechanical and thermal properties of syntactic foams independent of each other over a wide range, which is a significant advantage over other traditional particulate and fibrous composites.

Additive manufacturing (AM) is now widely used for research and industrial production. The benchmark data for mechanical properties of additively manufactured specimens is very useful for many communities. This data article presents a tensile testing dataset of ASTM D638 size specimens without and with embedded internal geometrical features printed using polylactic acid (PLA) in a Fused Filament Fabrication (FFF) additive manufacturing process. The added features can mimic defects of various shapes and sizes. This work is a supplement to the published research article Assisted defect detection by in-process monitoring of additive manufacturing using optical imaging and infrared thermography (Additive Manufacturing, 2023, 103483). The printed specimens were tensile tested. Stress-strain graphs were developed and used to calculate the mechanical properties such as ultimate tensile strength (UTS) and strain at UTS. The mechanical properties, the correlations between mechanical properties and size, shape and location of geometrical features (defects), and the trends in mechanical properties can be useful in benchmarking the results of other researchers.

Multidrug-resistant tuberculosis (MDR-TB)/rifampicin-resistant TB (RR-TB) is human-made problem and emerging due to poor management of TB and is a threat to control of TB. Early suspicion and diagnosis are important. Culture and drug susceptibility testing are gold standards, but newer molecular methods help in rapid diagnosis. Once diagnosed, prompt treatment should be started, preferably under direct observation. Treatment can be standardized or individualized. Conventional regimen takes up to 24 months but recently shorter regimen of up to 12 months was introduced in specific subset of MDR-TB/RR-TB patients. Management of MDR-TB/RR-TB is complicated, costlier, and challenging and is a concern for human health worldwide. It must be emphasized that optimal treatment of MDR-TB/RR-TB alone is not sufficient. Efforts must be made to ensure effective use of first- and second-line anti-TB drugs.

Generative Artificial Intelligence (AI), characterized by its ability to generate diverse forms of content including text, images, video and audio, has revolutionized many fields, including medical education. Generative AI leverages machine learning to create diverse content, enabling personalized learning, enhancing resource accessibility, and facilitating interactive case studies. This narrative review explores the integration of generative artificial intelligence (AI) into orthopedic education and training, highlighting its potential, current challenges, and future trajectory. A review of recent literature was conducted to evaluate the current applications, identify potential benefits, and outline limitations of integrating generative AI in orthopedic education. Key findings indicate that generative AI holds substantial promise in enhancing orthopedic training through its various applications such as providing real-time explanations, adaptive learning materials tailored to individual student’s specific needs, and immersive virtual simulations. However, despite its potential, the integration of generative AI into orthopedic education faces significant issues such as accuracy, bias, inconsistent outputs, ethical and regulatory concerns and the critical need for human oversight. Although generative AI models such as ChatGPT and others have shown impressive capabilities, their current performance on orthopedic exams remains suboptimal, highlighting the need for further development to match the complexity of clinical reasoning and knowledge application. Future research should focus on addressing these challenges through ongoing research, optimizing generative AI models for medical content, exploring best practices for ethical AI usage, curriculum integration and evaluating the long-term impact of these technologies on learning outcomes. By expanding AI’s knowledge base, refining its ability to interpret clinical images, and ensuring reliable, unbiased outputs, generative AI holds the potential to revolutionize orthopedic education. This work aims to provides a framework for incorporating generative AI into orthopedic curricula to create a more effective, engaging, and adaptive learning environment for future orthopedic practitioners.

Application of pneumatic separators in coal beneficiation is increasing rapidly over the last decade primarily due to their low capital and operating costs, and waste handling problems associated with traditional wet processing methods. Large amount of shale/rock that is extracted in coal production can be removed prior to transportation at the mine face by using this methodology. Due to the limited washing facilities in India, most of the thermal power plants burn raw coal from run-of-mine （ROM） to generate electricity. This practice causes poor utilization efficiency, high operating and maintenance costs, and high emission rates for the power plants. One potential method that can be utilized is the air-fluidized inclined vibrating deck technology. The technology was demonstrated on a pilot-scale at different coal washeries in India at a feed rate of 5-ton per hour. The pilot-scale evaluation showed that 20 %-25 % high-ash incombustible material can be eliminated from ROM feed with only minor losses in energy content （〈10 %） from respective ROM coal. Furthermore, a feasibility analysis showed significant economic gains in terms of transportation cost, improving power-plant efficiency, and reducing emissions rates by using the technology.

Genome-wide CRISPR sgRNA libraries have emerged as transformative tools to systematically probe gene function. While these libraries have been iterated over time to be more efficient, their large size limits their use in some applications. Here, we benchmarked publicly available genome-wide single-targeting sgRNA libraries and evaluated dual targeting as a strategy for pooled CRISPR loss-of-function screens. We leveraged this data to design two minimal genome-wide human CRISPR-Cas9 libraries that are 50% smaller than other libraries and that preserve specificity and sensitivity, thus enabling broader deployment at scale.

Abstract Intussusception in adults represents 1% of bowel obstructions and up to 0.02% of all hospital admissions. Amongst these, colo-colic intussusception of the descending colon forms the rarest of causes due to the fixed nature of the descending colon. Most of adult intussusceptions follow a lead point and are commonly due to colonic malignancy which may get missed on pre-operative evaluation. Surgery is usually warranted as these patients are usually symptomatic and at risk of vascular compromise, leading to perforations and obscure malignancies. We present a case of laparoscopic limited hemicolectomy and primary anastomosis in a middle-aged male who presented with colo-colic intussusception, which appeared to be following a malignant mass on imaging and lipoma on colonoscopic biopsy done twice. Keeping in mind the possibility of a malignant lead point, no attempt was made to reduce the intussusception and a vessel first approach with 5 cm margin on either side was performed.

Disease associated with SARS-CoV-2 also termed as Corovirus disease 2019 or COVID-19, has become a potential threat to public health by spreading across more than 200 countries worldwide within a short span of time. Tuberculosis (TB) is already existing as unprecedented pandemic worldwide over several years. Both diseases have many overlapping features but there are striking differences too. There is usually chronicity of symptoms in TB as compared to acute or rapid progression in COVID-19. Little evidence exists regarding TB and COVID-19 coinfection. It is anticipated that person with TB either in active, previously treated or latent forms are more at risk of poor outcomes with COVID-19. The relationship between the two diseases is still unclear at present, and more studies are needed to eble alyses of interactions and determints of outcomes in patients affected by both the diseases. Most of the countries across the world imposed tionwide lockdown to promote social distancing, which is one important preventive measure to mitigate the spread of COVID-19 pandemic. However, it becomes quite challenging to ensure smooth functioning of programmatic services, leading to disruption of routine TB care, leading to transmission of infection. Health authorities should frame polices that can support TB patients by providing diagnostic, magement and prevention services without any interruption during this era of ongoing COVID-19 pandemic. Effort should be made to control both the diseases simultaneously and avoid unfavourable outcome in near future.