Reconstruction of Level Cross Sampled Signals Using Sparse Signals & Backtracking Iterative Hard Thresholding

Industry 4.0 applications involve more number of sensors or Internet of Things (IoT) devices to support automation in the industry. It involves more number of computations to analyze the sensor data collected from several critical parts of the processing units. Sparse signal processing is one which has numerous applications in area od communication and signal processing. This paper presents a new approach to reduce the computations with the help of level cross sampling (LCS) and a backtracking based iterative hard thresholding (BIHT) algorithm for reconstruction. The process involve, an information signal is converted to a random sparse signal using non-uniform sampling at the transmitter side and then it can be reconstructed back using BIHT algorithm at receiver side. Simulation results exhibit the superior performance of the proposed BIHT reconstruction in comparison with the literatur

Reconstruction of Level Cross Sampled Signals using Sparse Signals & Backtracking Iterative Hard Thresholding

245-248Industry 4.0 applications involve more number of sensors or Internet of Things (IoT) devices to support automation in the industry. It involves more number of computations to analyze the sensor data collected from several critical parts of the processing units. Sparse signal processing is one which has numerous applications in area of communication and signal processing. This paper presents a novel approach to reduce the computations with the help of level cross sampling (LCS) and a backtracking based iterative hard thresholding (BIHT) algorithm for reconstruction. The process involves, an information signal is converted to a random sparse signal using non-uniform sampling at the transmitter side and then it can be reconstructed back using BIHT algorithm at receiver side. Simulation results exhibit the superior performance of the proposed BIHT reconstruction in comparison with the literature

Securing the Skies: Cybersecurity Strategies for Smart City Cloud using Various Algorithams

As smart cities continue to evolve, their reliance on cloud computing technologies becomes increasingly apparent, enabling the seamless integration of data-driven services and urban functionalities. However, this transformation also raises concerns about the security of the vast and interconnected cloud infrastructures that underpin these cities' operations. This paper explores the critical intersection of cloud computing and cybersecurity within the context of smart cities. This research is dealing with challenges posed by the rapid expansion of smart city initiatives and their reliance on cloud-based solutions. It investigates the vulnerabilities that emerge from this technological convergence, emphasizing the potential risks to data privacy, urban services, and citizen well-being. The abstract presents a comprehensive overview of the evolving threat landscape that smart cities face in the realm of cloud computing. To address these challenges, the abstract highlights the importance of proactive cybersecurity strategies tailored specifically to the unique needs of smart cities. It underscores the significance of adopting a multi-layered approach that encompasses robust encryption protocols, intrusion detection systems, threat intelligence sharing, and collaborative efforts among stakeholders. Drawing insights from existing research and real-world case studies, the abstract showcases innovative solutions that leverage advanced technologies like artificial intelligence and blockchain to fortify the security posture of smart city cloud infrastructures. It explores the role of data governance, user authentication, and anomaly detection in creating a resilient cybersecurity framework that safeguards critical urban systems

Interventional Strategies to Delay Aging-Related Dysfunctions of the Musculoskeletal System

Aging affects bones, cartilage, muscles, and other connective tissue in the musculoskeletal system, leading to numerous age-related pathologies including osteoporosis, osteoarthritis, and sarcopenia. Understanding healthy aging may therefore open new therapeutic targets, thereby leading to the development of novel approaches to prevent several age-related orthopaedic diseases. It is well recognized that aging-related stem cell depletion and dysfunction leads to reduced regenerative capacity in various musculoskeletal tissues. However, more recent evidence suggests that dysregulated autophagy and cellular senescence might be fundamental mechanisms associated with aging-related musculoskeletal decline. The mammalian/mechanical target of Rapamycin (mTOR) is known to be an essential negative regulator of autophagy, and its inhibition has been demonstrated to promote longevity in numerous species. Besides, several reports demonstrate that selective elimination of senescent cells and their cognate Senescence-Associated Secretory Phenotype (SASP) can mitigate musculoskeletal tissue decline. Therefore, senolytic drugs/agents that can specifically target senescent cells, may offer a novel therapeutic strategy to treat a litany of age-related orthopaedic conditions. This chapter focuses on osteoarthritis and osteoporosis, very common debilitating orthopaedic conditions, and reviews current concepts highlighting new therapeutic strategies, including the mTOR inhibitors, senolytic agents, and mesenchymal stem cell (MSC)-based therapies

Expression analysis of human adipose-derived stem cells during in vitro differentiation to an adipocyte lineage

Background: Adipose tissue-derived stromal stem cells (ASCs) represent a promising regenerative resource for soft tissue reconstruction. Although autologous grafting of whole fat has long been practiced, a major clinical limitation of this technique is inconsistent long-term graft retention. To understand the changes in cell function during the transition of ASCs into fully mature fat cells, we compared the transcriptome profiles of cultured undifferentiated human primary ASCs under conditions leading to acquisition of a mature adipocyte phenotype. Methods: Microarray analysis was performed on total RNA extracted from separate ACS isolates of six human adult females before and after 7 days (7 days: early stage) and 21 days (21 days: late stage) of adipocyte differentiation in vitro. Differential gene expression profiles were determined using Partek Genomics Suite Version 6.4 for analysis of variance (ANOVA) based on time in culture. We also performed unsupervised hierarchical clustering to test for gene expression patterns among the three cell populations. Ingenuity Pathway Analysis was used to determine biologically significant networks and canonical pathways relevant to adipogenesis. Results: Cells at each stage showed remarkable intra-group consistency of expression profiles while abundant differences were detected across stages and groups. More than 14,000 transcripts were significantly altered during differentiation while ~6000 transcripts were affected between 7 days and 21 days cultures. Setting a cutoff of +/-two-fold change, 1350 transcripts were elevated while 2929 genes were significantly decreased by 7 days. Comparison of early and late stage cultures revealed increased expression of 1107 transcripts while 606 genes showed significantly reduced expression. In addition to confirming differential expression of known markers of adipogenesis (e.g., FABP4, ADIPOQ, PLIN4), multiple genes and signaling pathways not previously known to be involved in regulating adipogenesis were identified (e.g. POSTN, PPP1R1A, FGF11) as potential novel mediators of adipogenesis. Quantitative RT-PCR validated the microarray results. Conclusions: ASC maturation into an adipocyte phenotype proceeds from a gene expression program that involves thousands of genes. This is the first study to compare mRNA expression profiles during early and late stage adipogenesis using cultured human primary ASCs from multiple patients

Artificial tongues and leaves

The objective with synthetic multifunctional nanoarchitecture is to create large suprastructures with interesting functions. For this purpose, lipid bilayer membranes or conducting surfaces have been used as platforms and rigid-rod molecules as shape-persistent scaffolds. Examples for functions obtained by this approach include pores that can act as multicomponent sensors in complex matrices or rigid-rod π-stack architecture for artificial photosynthesis and photovoltaic

Portable automated radio-frequency scanner for non-destructive testing of carbon-fibre-reinforced polymer composites

A portable automated scanner for non-destructive testing of carbon-fibre-reinforced polymer (CFRP) composites has been developed. Measurement head has been equipped with an array of newly developed radio-frequency (RF) inductive sensors mounted on a flexible arm, which allows the measurement of curved CFRP samples. The scanner is also equipped with vacuum sucks providing mechanical stability. RF sensors operate in a frequency range spanning from 10 up to 300 MHz, where the largest sensitivity to defects buried below the front CFRP surface is expected. Unlike to ultrasonic testing, which will be used for reference, the proposed technique does not require additional couplants. Moreover, negligible cost and high repeatability of inductive sensors allows developing large scanning arrays, thus, substantially speeding up the measurements of large surfaces. The objective will be to present the results of an extensive measurement campaign undertaken for both planar and curved large CFRP samples, pointing out major achievements and potential challenges that still have to be addressed

Use of deep learning to develop continuous-risk models for adverse event prediction from electronic health records

Early prediction of patient outcomes is important for targeting preventive care. This protocol describes a practical workflow for developing deep-learning risk models that can predict various clinical and operational outcomes from structured electronic health record (EHR) data. The protocol comprises five main stages: formal problem definition, data pre-processing, architecture selection, calibration and uncertainty, and generalizability evaluation. We have applied the workflow to four endpoints (acute kidney injury, mortality, length of stay and 30-day hospital readmission). The workflow can enable continuous (e.g., triggered every 6 h) and static (e.g., triggered at 24 h after admission) predictions. We also provide an open-source codebase that illustrates some key principles in EHR modeling. This protocol can be used by interdisciplinary teams with programming and clinical expertise to build deep-learning prediction models with alternate data sources and prediction tasks

Ordered and Oriented Supramolecular n/p-Heterojunction Surface Architectures: Completion of the Primary Color Collection

In this study, we describe synthesis, characterization, and zipper assembly of yellow p-oligophenyl naphthalenediimide (POP-NDI) donor−acceptor hybrids. Moreover, we disclose, for the first time, results from the functional comparison of zipper and layer-by-layer (LBL) assembly as well as quartz crystal microbalance (QCM), atomic force microscopy (AFM), and molecular modeling data on zipper assembly. Compared to the previously reported blue and red NDIs, yellow NDIs are more π-acidic, easier to reduce, and harder to oxidize. The optoelectronic matching achieved in yellow POP-NDIs is reflected in quantitative and long-lived photoinduced charge separation, comparable to their red and much better than their blue counterparts. The direct comparison of zipper and LBL assemblies reveals that yellow zippers generate more photocurrent than blue zippers as well as LBL photosystems. Continuing linear growth found in QCM measurements demonstrates that photocurrent saturation at the critical assembly thickness occurs because more charges start to recombine before reaching the electrodes and not because of discontinued assembly. The found characteristics, such as significant critical thickness, strong photocurrents, large fill factors, and, according to AFM images, smooth surfaces, are important for optoelectronic performance and support the existence of highly ordered architectures

Exploring spatial-frequency-sequential relationships for motor imagery classification with recurrent neural network

Abstract Background Conventional methods of motor imagery brain computer interfaces (MI-BCIs) suffer from the limited number of samples and simplified features, so as to produce poor performances with spatial-frequency features and shallow classifiers. Methods Alternatively, this paper applies a deep recurrent neural network (RNN) with a sliding window cropping strategy (SWCS) to signal classification of MI-BCIs. The spatial-frequency features are first extracted by the filter bank common spatial pattern (FB-CSP) algorithm, and such features are cropped by the SWCS into time slices. By extracting spatial-frequency-sequential relationships, the cropped time slices are then fed into RNN for classification. In order to overcome the memory distractions, the commonly used gated recurrent unit (GRU) and long-short term memory (LSTM) unit are applied to the RNN architecture, and experimental results are used to determine which unit is more suitable for processing EEG signals. Results Experimental results on common BCI benchmark datasets show that the spatial-frequency-sequential relationships outperform all other competing spatial-frequency methods. In particular, the proposed GRU-RNN architecture achieves the lowest misclassification rates on all BCI benchmark datasets. Conclusion By introducing spatial-frequency-sequential relationships with cropping time slice samples, the proposed method gives a novel way to construct and model high accuracy and robustness MI-BCIs based on limited trials of EEG signals