Search Results (81)

Integrating futuristic humanoids like Tesla Optimus into supply chain operations represents groundbreaking automation and workforce efficiency innovation. This study investigates the potential of humanoids to address critical supply chain challenges, such as labor shortages, rising operational costs, and the demand for sustainable practices. Considering its ability to handle worker-intensive, hazardous, and repetitive duties, humanoids could offer an alternative to business challenges like inefficient operations, health and safety concerns, and worker shortages. Intelligent robotics plays an essential role in improving productivity, supporting sustainability, and transforming workforce dynamics as supply chains become increasingly complex. The study examines the effects of humanoids on workforce reallocation, manufacturing sustainability, and supply chain productivity. The current research reviews the usefulness, advantages, and downsides of integrating humanoids into supply chains. This study uses a mixed-method approach, incorporating case studies, qualitative productivity data, and expert interviews. According to Tesla, Optimus could significantly enhance supply chain efficiency by reducing error rates, streamlining workflows, and enabling 24/7 operations. It could also help meet sustainability goals by lowering waste and energy consumption. The study limits Tesla’s experience, modern technologies, and inadequate information from various industrial and geographical contexts. However, this study will be eye-opening for industries requiring such humanoid robots for their operations. Additional studies need to deal with factors like high implementation expenses, potential job displacement, and flexibility in changing supply chain demands. While focused on Tesla, this study provides insights that can inform broader applications of humanoid robotics in supply chains across industries. This study presents an in-depth review of humanoid involvement in developing future supply chain models. It also offers helpful knowledge that will assist industries in considering adopting comparable robotic integration as a strategic decision. Full article

The emergence of smart technologies and the wide adoption of the Internet of Things (IoT) have revolutionized various sectors, yet they have also introduced significant security challenges due to the extensive attack surface they present. In recent years, many efforts have been made to minimize the attack surface. However, most IoT devices are resource-constrained with limited processing power, memory storage, and energy sources. Such devices lack the sufficient means for running existing resource-hungry security solutions, which in turn makes it challenging to secure IoT networks from sophisticated attacks. Feature Selection (FS) approaches in Machine Learning enabled Intrusion Detection Systems (IDS) have gained considerable attention in recent years for having the potential to detect sophisticated cyber-attacks while adhering to the resource limitations issues in IoT networks. Apropos of that, several researchers proposed FS-enabled IDS for IoT networks with a focus on lightweight security solutions. This work presents a comprehensive study discussing FS-enabled lightweight IDS tailored for resource-constrained IoT devices, with a special focus on the emerging Ensemble Feature Selection (EFS) techniques, portraying a new direction for the research community to inspect. The research aims to pave the way for the effective design of futuristic FS/EFS-enabled lightweight IDS for IoT networks, addressing the critical need for robust security measures in the face of resource limitations. Full article

The careful arrangement of nanomaterials (NMs) holds promise for revolutionizing various fields, from electronics and biosensing to medicine and optics. This review delves into the intricacies of nano-assembly (NA) techniques, focusing on oriented-assembly methodologies and stimuli-dependent approaches. The introduction provides a comprehensive overview of the significance and potential applications of NA, setting the stage for review. The oriented-assembly section elucidates methodologies for the precise alignment and organization of NMs, crucial for achieving desired functionalities. The subsequent section delves into stimuli-dependent techniques, categorizing them into chemical and physical stimuli-based approaches. Chemical stimuli-based self-assembly methods, including solvent, acid–base, biomolecule, metal ion, and gas-induced assembly, are discussed in detail by presenting examples. Additionally, physical stimuli such as light, magnetic fields, electric fields, and temperature are examined for their role in driving self-assembly processes. Looking ahead, the review outlines futuristic scopes and perspectives in NA, highlighting emerging trends and potential breakthroughs. Finally, concluding remarks summarize key findings and underscore the significance of NA in shaping future technologies. This comprehensive review serves as a valuable resource for researchers and practitioners, offering insights into the diverse methodologies and potential applications of NA in interdisciplinary research fields. Full article

The deployment of fifth-generation (5G) wireless networks has already laid the ground-work for futuristic smart cities but along with this, it has also triggered the rapid growth of a wide range of applications, for example, the Internet of Everything (IoE), online gaming, extended/virtual reality (XR/VR), telemedicine, cloud computing, and others, which require ultra-low latency, ubiquitous coverage, higher data rates, extreme device density, ultra-high capacity, energy efficiency, and better reliability. Moreover, the predicted explosive surge in mobile traffic until 2030 along with envisioned potential use-cases/scenarios in a smart city context will far exceed the capabilities for which 5G was designed. Therefore, there is a need to harness the 6th Generation (6G) capabilities, which will not only meet the stringent requirements of smart megacities but can also open up a new range of potential applications. Other crucial concerns that need to be addressed are related to network security, data privacy, interoperability, the digital divide, and other integration issues. In this article, we examine current and emerging trends for the implementation of 6G in the smart city arena. Firstly, we give an inclusive and comprehensive review of potential 6th Generation (6G) mobile communication technologies that can find potential use in smart cities. The discussion of each technology also covers its potential benefits, challenges and future research direction. Secondly, we also explore promising smart city applications that will use these 6G technologies, such as, smart grids, smart healthcare, smart waste management, etc. In the conclusion part, we have also highlighted challenges and suggestions for possible future research directions. So, in a single paper, we have attempted to provide a wider perspective on 6G-enabled smart cities by including both the potential 6G technologies and their smart city applications. This paper will help readers gain a holistic view to ascertain the benefits, opportunities and applications that 6G technology can bring to meet the diverse, massive and futuristic requirements of smart cities. Full article

The population of Pakistan is increasing, with approximately 2% growth. Over the years, the country’s healthcare system has struggled to meet the needs of the population. Nevertheless, because of shortages compared to population distribution, people are facing primary healthcare challenges, specifically in rural environments. Because of the absence of standard health services, the quality of the health sector deteriorated over time. Therefore, this study aims to compute the shortage of health facilities in Badin, Pakistan, per local health standards. The information related to available health institutes was obtained from the office of the Director-General Health Office with the help of a questionnaire. The current population was determined, and the same was projected up to the year 2035 with the help of a compound interest model. The linear model was executed and found to be significant, with the values of R = 0.996, R² = 0.991, and Sig. F-change = 0.000. The Badin sub-region needed 201 basic health units, 37 rural health centers, and 746 dispensaries. The public health institutes were found unavailable as per demographic standards. This research set a platform for local authorities to take certain actions in framing essential policies to curtail the shortage of health institutions. This study is significant, as it confers existing and futuristic health institute demands. This research can serve as a model for remote sub-regions to address primary healthcare issues, including the fight against diseases and viruses. This research may also contribute to sustainable goal number 3, i.e., Good Health and Well-being. Full article

This study, conducted at Complutense Intermedia-Lab, employs a dual approach to explore university students’ use of Generative AI (GenAI), combining a survey with foresight methodologies (Sci-fi prototyping). The initial survey gathers baseline data on students’ experiences, attitudes, and concerns regarding GenAI, providing a comprehensive understanding of current practices among university students in Spain. This empirical foundation informs subsequent Sci-fi prototyping sessions, where students creatively envision future scenarios, fostering futurist thinking and deeper engagement. By integrating principles of Responsible Research and Innovation (RRI), this approach facilitates a nuanced exploration of GenAI’s potential impacts on education. The incorporation of both quantitative data collection and qualitative foresight methods in this study serves to navigate challenges and level opportunities of promoting the ethical and inclusive incorporation of GenAI in Higher Education, ensuring that future innovations align with societal values and needs. Full article

Given its huge economic, nutritional, and social value, extra virgin olive oil (EVOO) is an essential food. This flagship product of the countries bordering the Mediterranean basin is one of the most frauded products worldwide. Although traditional chemical analyses have demonstrated to be reliable tools for olive drupes and EVOO quality assessment, they present several drawbacks; the urgent need for fast and non-destructive techniques thus motivated this review. Given the lack of comprehensive reviews in the literature, our first aim was to summarize the current knowledge regarding applying spectroscopies, imaging analyses, and other non-destructive techniques to olives and EVOO. The second aim was to highlight the most innovative and futuristic applications and outline the future research prospects within this strategic production chain. With respect to olive drupes, the most interesting results were obtained using RGB imaging and NIR spectroscopy, particularly using portable NIR devices and specific digital cameras for in-field or in-mill monitoring. Nevertheless, it is important to highlight that RGB imaging and NIR spectroscopy need to be integrated with flesh hardness measurements, given the higher reliability of this parameter compared to olive skin color. Finally, with respect to EVOO, although several useful applications of visible imagining, UV–Visible, NIR, and Mid-Infrared spectroscopies have been found, the online monitoring of EVOO quality using NIR spectroscopy strikes us as being the most interesting technique for improving the EVOO production chain in the near future. Full article

This concept paper explores possible collective identities for a future wildland fire workforce. Taking inspiration from the work of futurists who foresee an end to the dominant fire exclusion/suppression paradigm, and assuming that an emerging fire restoration/resilience paradigm shift replaces it, this paper engages in speculative explorations of the process and product of this paradigm shift with respect to the future collective identities of a workforce conducting ecological fire management. Social constructionist assumptions from symbolic interactionist sociological theory, Gramscian political theory’s concept of hegemony, and new social movement theory’s concept of collective identity all provide the intellectual foundations for the discussion. This concept paper argues that in order to actualize a paradigm shift, more than advances in scientific research or reforms of government policies will be required—the wildland fire community will need to become (or join) a social movement engaged in collective actions. An imaginary social movement, the “Greenfire revolution,” is invented to help illustrate how the selected theories and concepts might apply in the social construction of ecological fire management and the collective identities of its future workforce. Full article

In industry 4.0, where the automation and digitalization of entities and processes are fundamental, artificial intelligence (AI) is increasingly becoming a pivotal tool offering innovative solutions in various domains. In this context, nutrition, a critical aspect of public health, is no exception to the fields influenced by the integration of AI technology. This study aims to comprehensively investigate the current landscape of AI in nutrition, providing a deep understanding of the potential of AI, machine learning (ML), and deep learning (DL) in nutrition sciences and highlighting eventual challenges and futuristic directions. A hybrid approach from the systematic literature review (SLR) guidelines and the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines was adopted to systematically analyze the scientific literature from a search of major databases on artificial intelligence in nutrition sciences. A rigorous study selection was conducted using the most appropriate eligibility criteria, followed by a methodological quality assessment ensuring the robustness of the included studies. This review identifies several AI applications in nutrition, spanning smart and personalized nutrition, dietary assessment, food recognition and tracking, predictive modeling for disease prevention, and disease diagnosis and monitoring. The selected studies demonstrated the versatility of machine learning and deep learning techniques in handling complex relationships within nutritional datasets. This study provides a comprehensive overview of the current state of AI applications in nutrition sciences and identifies challenges and opportunities. With the rapid advancement in AI, its integration into nutrition holds significant promise to enhance individual nutritional outcomes and optimize dietary recommendations. Researchers, policymakers, and healthcare professionals can utilize this research to design future projects and support evidence-based decision-making in AI for nutrition and dietary guidance. Full article

Advanced visual display technologies typically supplement the out-of-window view with separate displays (e.g., analog speedometer or artificial horizon) or with overlays (e.g., projected speedometer or map). Studies on head-up displays suggest that altering the out-of-window view itself is superior to supplemental displays, as sensor-based information not normally visible to the driver can be included. Such novel synthetic displays have been researched for cockpit implementation but less so for driving. We discuss such view-altering synthetic displays in general, and camera–monitor systems (CMS) designed to replace rear-view mirrors as a special instance of a novel synthetic display in the automotive domain. In a standard CMS, a camera feed is presented on a monitor, but could also be integrated into the windshield of the car. More importantly, the camera feed can undergo alterations, augmentations, or condensations before being displayed. The implications of these technologies are discussed, along with findings from an experiment examining the impact of information reduction on a time-to-contact (TTC) estimation task. In this experiment, observers judged the TTC of approaching cars based on the synthetic display of a futuristic CMS. Promisingly, TTC estimations were unaffected by information reduction. The study also emphasizes the significance of the visual reference frame. Full article

With the rapid advancement of the Internet of Things (IoT), there is a global surge in network traffic. Software-Defined Networks (SDNs) provide a holistic network perspective, facilitating software-based traffic analysis, and are more suitable to handle dynamic loads than a traditional network. The standard SDN architecture control plane has been designed for a single controller or multiple distributed controllers; however, a logically centralized single controller faces severe bottleneck issues. Most proposed solutions in the literature are based on the static deployment of multiple controllers without the consideration of flow fluctuations and traffic bursts, which ultimately leads to a lack of load balancing among controllers in real time, resulting in increased network latency. Moreover, some methods addressing dynamic controller mapping in multi-controller SDNs consider load fluctuation and latency but face controller placement problems. Earlier, we proposed priority scheduling and congestion control algorithm (eSDN) and dynamic mapping of controllers for dynamic SDN (dSDN) to address this issue. However, the future growth of IoT is unpredictable and potentially exponential; to accommodate this futuristic trend, we need an intelligent solution to handle the complexity of growing heterogeneous devices and minimize network latency. Therefore, this paper continues our previous research and proposes temporal deep Q learning in the dSDN controller. A Temporal Deep Q learning Network (tDQN) serves as a self-learning reinforcement-based model. The agent in the tDQN learns to improve decision-making for switch-controller mapping through a reward–punish scheme, maximizing the goal of reducing network latency during the iterative learning process. Our approach—tDQN—effectively addresses dynamic flow mapping and latency optimization without increasing the number of optimally placed controllers. A multi-objective optimization problem for flow fluctuation is formulated to divert the traffic to the best-suited controller dynamically. Extensive simulation results with varied network scenarios and traffic show that the tDQN outperforms traditional networks, eSDNs, and dSDNs in terms of throughput, delay, jitter, packet delivery ratio, and packet loss. Full article

The emerging yet promising paradigm of the Internet of Vehicles (IoV) has recently gained considerable attention from researchers from academia and industry. As an indispensable constituent of the futuristic smart cities, the underlying essence of the IoV is to facilitate vehicles to exchange safety-critical information with the other vehicles in their neighborhood, vulnerable pedestrians, supporting infrastructure, and the backbone network via vehicle-to-everything communication in a bid to enhance the road safety by mitigating the unwarranted road accidents via ensuring safer navigation together with guaranteeing the intelligent traffic flows. This requires that the safety-critical messages exchanged within an IoV network and the vehicles that disseminate the same are highly reliable (i.e., trustworthy); otherwise, the entire IoV network could be jeopardized. A state-of-the-art trust-based mechanism is, therefore, highly imperative for identifying and removing malicious vehicles from an IoV network. Accordingly, in this paper, a machine learning-based trust management mechanism, MESMERIC, has been proposed that takes into account the notions of direct trust (encompassing the trust attributes of interaction success rate, similarity, familiarity, and reward and punishment), indirect trust (involving confidence of a particular trustor on the neighboring nodes of a trustee, and the direct trust between the said neighboring nodes and the trustee), and context (comprising vehicle types and operating scenarios) in order to not only ascertain the trust of vehicles in an IoV network but to segregate the trustworthy vehicles from the untrustworthy ones by means of an optimal decision boundary. A comprehensive evaluation of the envisaged trust management mechanism has been carried out which demonstrates that it outperforms other state-of-the-art trust management mechanisms. Full article

The inspirational paper by Ash, Dowler, and Varsi in 1978, proposing to utilize in situ resources on Mars (ISRU) rather than bringing them from Earth, originated the field of Mars ISRU that has been the subject of research ever since. In this paper, we reviewed significant research reported on Mars ISRU since 1978 and reported briefly on accomplishments. We found that prior to 2014, progress on small tasks was sporadic and intermittent, always at low Technology Readiness Level (TRL). In 2014, the National Aeronautics and Space Administration (NASA) took a bold, imaginative, unprecedented step to fund a major project in Mars ISRU: the so-called “MOXIE” (Mars Oxygen In Situ Experiment), in which an oxygen production plant based on solid oxide electrolysis (SOEC) was developed, and finally demonstrated on Mars in 2022 and 2023. While MOXIE leaves behind it a wealth of accomplishments, there remains the need to close remaining gaps with additional laboratory and field work. Solid-oxide electrochemical cell (SOEC) technology has become a major area of worldwide investment for terrestrial energy and CO₂ control. There is a very strong overlap between this terrestrial technology and Mars ISRU. NASA has already leveraged the terrestrial development work via MOXIE. NASA can leverage further advances with a comparatively small investment beyond 2023. Because NASA is engaged in a major program to return humans to the Moon, NASA’s focus is on lunar ISRU. Unfortunately, the mission impact and return on investment for lunar ISRU does not compare to that for Mars ISRU. NASA’s concept for Mars ISRU is futuristic, involving autonomous mining, transporting, and processing large amounts of Mars regolith. This might well occur long after initial human landings which could better profit in the near-term from MOXIE technology. By continuing further development of SOEC technology beyond MOXIE, while leveraging large investments in terrestrial applications, NASA can develop the Mars ISRU appropriate to nearer term human missions at modest investment. The goal of this paper is to place the relatively mature MOXIE technology advance and solid oxide electrolysis in general in perspective to the historical evolution of low TRL Mars ISRU technology. Full article

In recent times, there has been an increased demand for electric vehicles. In this context, the energy management of the electric motor, which are an important constituent of electric vehicles, plays a pivotal role. A lot of research has been conducted on the optimization of heat flow through electric motors, thus reducing the wastage of energy via heat. Futuristic power sources may increasingly rely on cutting-edge innovations like energy harvesting and self-powered induction motors. In this context, effective thermal management techniques are discussed in this paper. Importance was given to the potential energy losses, hotspots, the influence of overheating on the motor efficiency, different cooling strategies, certain experimental approaches, and power control techniques. Two types of thermal analysis computation methods, namely the lumped-parameter circuit method (LPCM) and the finite element method (FEM), are discussed. Also, this paper reviews different cooling strategies. The experimental research showed that the efficiency was greater by 11% with the copper rotor compared to the aluminum rotor. Each rotor type was reviewed based on the temperature rise and efficiency at higher temperatures. The water-cooling method reduced the working temperatures by 39.49% at the end windings, 41.67% at the side windings, and by a huge margin of 56.95% at the yoke of the induction motor compared to the air-cooling method; hence, the water-cooling method is better. Lastly, modern cooling strategies are proposed to provide an effective thermal management solution for squirrel-cage induction motors. Full article

Point-of-care (POC) diagnostic platforms are globally employed in modern smart technologies to detect events or changes in the analyte concentration and provide qualitative and quantitative information in biosensing. Surface plasmon-coupled emission (SPCE) technology has emerged as an effective POC diagnostic tool for developing robust biosensing frameworks. The simplicity, robustness and relevance of the technology has attracted researchers in physical, chemical and biological milieu on account of its unique attributes such as high specificity, sensitivity, low background noise, highly polarized, sharply directional, excellent spectral resolution capabilities. In the past decade, numerous nano-fabrication methods have been developed for augmenting the performance of the conventional SPCE technology. Among them the utility of plasmonic gold nanoparticles (AuNPs) has enabled the demonstration of plethora of reliable biosensing platforms. Here, we review the nano-engineering and biosensing applications of AuNPs based on the shape, hollow morphology, metal–dielectric, nano-assembly and heterometallic nanohybrids under optical as well as biosensing competencies. The current review emphasizes the recent past and evaluates the latest advancements in the field to comprehend the futuristic scope and perspectives of exploiting Au nano-antennas for plasmonic hotspot generation in SPCE technology. Full article