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Integration of Industry 4.0 with Personalized Medicine: A shift of the paradigm in healthcare
No full textThe convergence of personalized medicine and Industry 4.0 technologies is revolutionizing healthcare by enabling precise, efficient, and patient- centered interventions. This chapter investigates the integration of these two transformative domains, emphasizing their combined potential to enhance patient care, therapeutic outcomes, and healthcare delivery systems. Personalized medicine principles, including genetic profiling, biomarker identification, targeted therapies, predictive modeling, therapeutic monitoring, patient stratification, and data integration, are explored in depth, demonstrating their alignment with Industry 4.0 innovations. Technologies such as artificial intelligence, big data analytics, and the Internet of Things (IoT) are reshaping diagnostics, treatment customization, and real- time monitoring. Despite promising advancements, challenges persist, including data security, ethical concerns, and integration complexities
Development of a Basilar Membrane-Inspired Mechanical Spectrum Analyzer Using Metastructures for Enhanced Frequency Selectivity
Get PDFThis study introduces a mechanical spectrum analyzer (MSA) inspired by the tonotopic organization of the basilar membrane (BM), designed to achieve two critical features. First, it replicates the traveling-wave behavior of the BM, characterized by energy dissipation without reflections at the boundaries. Second, it enables the physical encoding of the wave energy into distinct spectral components. Moving beyond the conventional focus on metamaterial design, this research investigates wave propagation behavior and energy dissipation within metastructures, with particular attention to how individual unit cells absorb energy. To achieve these objectives, a metastructural design methodology is employed. Experimental characterization of metastructure samples with varying numbers of unit cells is performed, with reflection and absorption coefficients used to quantify energy absorption and assess bandgap quality. Simulations of a basilar membrane-inspired structure incorporating multiple sets of dynamic vibration resonators (DVRs) demonstrate frequency selectivity akin to the natural BM. The design features four types of DVRs, resulting in stepped bandgaps and enabling the MSA to function as a frequency filter. The findings reveal that the proposed MSA effectively achieves frequency-selective wave propagation and broad bandgap performance. The quantitative analysis of energy dissipation, complemented by qualitative demonstrations of wave behavior, highlights the potential of this metastructural approach to enhance frequency selectivity and improve sound processing. These results lay the groundwork for future exploration of 2D metastructures and applications such as energy harvesting and advanced wave filtering
Rational Design of Catalytically Active Sites in Metal-Free Carbon Materials for Electrocatalytic CO2 Reduction: A Review
No full textElectrocatalytic conversion of carbon dioxide (CO2) into value-added chemicals is a promising avenue for reducing greenhouse gas emissions while storing renewable energy in its chemical form. Metal-free carbon-based materials have attracted growing interest as electrocatalysts for CO2 reduction due to their abundance, low cost, stability, and tunable electronic structures. However, pristine carbon materials, such as graphene, lack catalytic activity due to the weak physisorption of CO2, necessitating the creation of active sites to boost electrocatalytic performance. In this review, we highlight recent advances in tailoring carbon frameworks through two key strategies to enhance the performance of electrocatalytic CO2 reduction. The first strategy, heteroatom doping─including nitrogen, phosphorus, boron, and fluorine─creates localized electronic states that direct reaction pathways toward specific products. The second strategy involves engineering defects, such as vacancies or pentagonal/octagonal ring structures, which significantly boost local electron density and adsorbate binding affinity, thereby lowering CO2 activation barriers. By creating these active sites, the electrocatalytic performance of carbon materials can be enhanced by over 2 orders of magnitude compared with inert carbon. Additionally, mechanistic insights from both experimental and computational studies are discussed, illustrating how electronic reconfiguration, spin density, and local coordination environments govern catalytic activity and selectivity. Finally, we outline challenges and future research directions for achieving sustainable CO2 electroreduction
Effect of in-cylinder heat transfer and surface temperatures on knock in an SI engine
No full textA single-cylinder, four-stroke, spark-ignition research engine instrumented with heat flux probes in the cylinder head, cylinder liner and piston was tested at two engine speeds and two loads to analyze the effect of in-cylinder heat transfer and surface temperatures on engine knock. During the intake stroke and part of the compression stroke the metal temperature is higher than the charge temperature. Heat transfer from the metal to the air-fuel mixture increases the temperature of the charge resulting in higher end gas temperatures, which can exacerbate knock. In the initial phase of testing, the heat transfer and surface temperatures were varied by changing oil temperature, coolant temperature, and coolant flow rate. In the second phase of testing, individual cycles from the same operating condition were binned according to their knock levels and correlations with the heat transfer before the occurrence of knock were analyzed. The results showed that in-cylinder heat transfer is not a dominant factor impacting engine knock, but an increase in surface temperatures does increase knock intensity. The results also confirmed that knock has a significant impact on the heat transfer that occurs from the gas to the metal after knock onset
Graddiv-conforming spectral element method for fourth-order div problems
No full textThis paper introduces a novel numerical method to solve fourth-order div problems using graddiv-conforming spectral elements on cuboidal meshes. We start by determining the continuity requirements for graddiv-conforming spectral elements, followed by constructing these elements using generalized Jacobi polynomials and the Piola transformation. The resulting basis functions exhibit a hierarchical structure, making them easily extendable to higher orders. We apply these graddiv-conforming spectral elements to solve the fourth-order div problem and present numerical examples to verify both the efficiency and effectiveness of the method
Implementation of Associative Learning Using Cognitive-Inspired Robotic System
No full textDeep learning, relying on large datasets, has made significant advancements through annotated data for training. However, this reliance restricts its feasibility in fields like planetary robotics. In contrast, animals learn through interaction with their environment, forming associations between events and objects, a process known as associative learning. By emulating this biological learning method, we can potentially overcome the challenges of data scarcity in deep learning. Most current implementations of associative memory are limited to small-scale simulations and offline environments. This study, however, takes a bold step forward, exploring the application of associative learning in a real-world setting using a cognitive-inspired robotic system and neuromorphic hardware, specifically Intel’s Loihi chip. This chip, designed to emulate the structure and function of the human brain, is ideally suited for our research. It can replicate fear conditioning without the need for pretraining or labeled datasets. In our practical demonstration, the cognitive-inspired robot learns to associate a light stimulus with a vibration stimulus, as evidenced by its movement responses. Synaptic weights are adjusted using Hebbian learning principles during this associative learning process. Integrating Intel’s Loihi chip into our system allows visual signal processing through specialized neural assemblies, significantly enhancing the robot’s learning capabilities
Spatially Continuous Mapping of Pre-fire Fuel Characteristics with Imaging Spectroscopy and Lidar for Fire Emissions Modeling
Get PDFFuels are a large source of uncertainty in fire emissions estimates due to variability in the physical and chemical properties of fuels and how they are represented. These uncertainties can be addressed using imaging spectroscopy and lidar data, that provide observations of the chemical and physical traits and spatial distribution of vegetation. Combined with ground fuel measurements, these data provide information on fuel distribution and quantity important for mapping and modeling fire effects. In this study, we present a methodology to develop models and continuous maps of pre-fire fuel characteristics for use in fire emissions modeling. We first addressed any spatial gaps over fire areas for Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) chemical trait data using Random Forests regression and for derived fractional cover. We used the AVIRIS fractional cover and chemical traits or AVIRIS estimates alongside lidar, multispectral, and topographic variables to build fuel characteristic models informed by ground measurements with partial least squares regression. We derived maps of predictive uncertainty alongside a suite of uncertainty statistics for each fuel characteristic that inform the use of fuels data within fire effects models. We used two study sites: the Williams Flats wildfire in eastern Washington state, USA and three prescribed crown fires in Utah, USA. The results show similar error between calibration and validation sets and NRMSE of around 20% or lower for a majority of the fuel models. We present fuel characteristic and uncertainty maps for all fires. This study shows that the use of imaging spectroscopy and lidar data have the potential to represent fuel heterogeneity and continuously map fuel characteristics for fire effects modeling
Spring load restriction methods: A comprehensive review
No full textIn cold regions, the seasonal freeze–thaw cycles constitute a significant challenge for pavement, leading to structural impairments and diminished long-term performance. During winter, the frozen water and ice formations increase pavement stiffness and bearing capacity. However, during the spring thaw, the liquid water above the frozen layer can be trapped by the impermeable frozen soil. This leads to a reduction in soil shear strength and pavement bearing capacity, resulting in deformations and damage to the roads. To mitigate these costs, Spring/Seasonal Load Restrictions (SLRs) policies have been implemented to limit axle loads and protect roads during the thaw-weakening. The success of SLR policies depends on an accurate estimation of the start date and duration of the reduced bearing capacity period. SLRs should also strike a balance between minimizing pavement damage and allowing traffic to flow freely as possible. This paper presents a comprehensive review of the existing SLR practices associated with their underlying mechanisms and different categories. SLR practices in Northern America are also summarized to evaluate the industry standards. In-depth discussions are added at the end based on this review to highlight the knowledge gaps and drawbacks of the current state of the practice
Artificial Intelligence and Internet of Things Integration in Pharmaceutical Manufacturing: A Smart Synergy
Get PDFBackground: The integration of artificial intelligence (AI) with the internet of things (IoTs) represents a significant advancement in pharmaceutical manufacturing and effectively bridges the gap between digital and physical worlds. With AI algorithms integrated into IoTs sensors, there is an improvement in the production process and quality control for better overall efficiency. This integration facilitates enabling machine learning and deep learning for real-time analysis, predictive maintenance, and automation—continuously monitoring key manufacturing parameters. Objective: This paper reviews the current applications and potential impacts of integrating AI and the IoTs in concert with key enabling technologies like cloud computing and data analytics, within the pharmaceutical sector. Results: Applications discussed herein focus on industrial predictive analytics and quality, underpinned by case studies showing improvements in product quality and reductions in downtime. Yet, many challenges remain, including data integration and the ethical implications of AI-driven decisions, and most of all, regulatory compliance. This review also discusses recent trends, such as AI in drug discovery and blockchain for data traceability, with the intent to outline the future of autonomous pharmaceutical manufacturing. Conclusions: In the end, this review points to basic frameworks and applications that illustrate ways to overcome existing barriers to production with increased efficiency, personalization, and sustainability
Estimating Snow Coverage Percentage on Solar Panels Using Drone Imagery and Machine Learning for Enhanced Energy Efficiency
Get PDFSnow accumulation on solar panels presents a significant challenge to energy generation in snowy regions, reducing the efficiency of solar photovoltaic (PV) systems and impacting economic viability. While prior studies have explored snow detection using fixed-camera setups, these methods suffer from scalability limitations, stationary viewpoints, and the need for reference images. This study introduces an automated deep-learning framework that leverages drone-captured imagery to detect and quantify snow coverage on solar panels, aiming to enhance power forecasting and optimize snow removal strategies in winter conditions. We developed and evaluated two approaches using YOLO-based models: Approach 1, a high-precision method utilizing a two-class detection model, and Approach 2, a real-time single-class detection model optimized for fast inference. While Approach 1 demonstrated superior accuracy, achieving an overall precision of 89% and recall of 82%, it is computationally expensive, making it more suitable for strategic decision making. Approach 2, with a precision of 93% and a recall of 75%, provides a lightweight and efficient alternative for real-time monitoring but is sensitive to lighting variations. The proposed framework calculates snow coverage percentages (SCP) to support snow removal planning, minimize downtime, and optimize power generation. Compared to fixed-camera-based snow detection models, our approach leverages drone imagery to improve detection precision while offering greater scalability to be adopted for large solar farms. Qualitative and quantitative analysis of both approaches is presented in this paper, highlighting their strengths and weaknesses in different environmental conditions