Search Results (38)

Supply chain resilience (SRES) is essential for firms aiming to alleviate the impact of interruptions and maintain operational continuity and sustainability in performance. In the context of the FMCG industries of Pakistan and Saudi Arabia, characterized by intricate and vulnerable supply chains, there exists an urgent necessity for solutions that bolster resilience. This study examines the influence of critical factors resilient suppliers (RS), transportation capacity (TC), flexibility (FLEX), network complexity (NC), and supply chain dynamism (SPD) on SRES. A quantitative methodology was utilized, examining survey data from 611 participants with Partial Least Squares Structural Equation Modeling (PLS-SEM) and Necessary Condition Analysis (NCA). The results indicate that RS and TC are essential for directly improving SRES, whereas FLEX and SPD facilitate increased adaptability and reactivity. The NCA emphasizes the need to control NC to avert bottlenecks that may impede resilience. This study indicates that SRES emerges from the dynamic interplay of several elements, rather than from separate enhancements. Although NC exerts a negligible direct influence, adeptly managing complexity is crucial for reducing disruptions and inefficiencies. The results underscore that fortifying RS, TC, and FLEX in unison improves resilience and adaptation to market volatility and disturbances. This study provides various theoretical and managerial insights. A systems theory approach highlights the interdependence of supply chain components, whereas the Theory of Constraints (TOC) posits that excessive NC can hinder resilience. Management should concentrate on maximizing RS and TC until declining returns are evident, thereafter redirecting efforts towards improving FLEX and minimizing NC. Furthermore, optimizing processes and facilitating swift decision-making are essential for maintaining resilience. Full article

Background/Objectives: Our lab has previously reported that Grifola frondosa (maitake mushroom) GF5000 has antidiabetic potential owing to its ability to improve insulin resistance. This study aimed to gain insight into the system-level hypoglycemic mechanisms of GF5000 using transcriptomics, proteomics, and network pharmacology. This study provides new insights into the hypoglycemic mechanisms of GF5000, identifying key molecular targets involved in mitigating insulin resistance in T2DM. Methods: Liver protein and gene expression in normal control (NC), diabetic control (DC), and GF5000-treated (GF5000) rats were analyzed via iTRAQ and RNA-seq. The relationships between differentially expressed genes (DEGs), differentially expressed proteins (DEPs), and type 2 diabetes (T2DM) disease targets were studied using Metascape and the Cytoscape GeneMANIA plug-in. Results: One hundred and fifty-two DEGs and sixty-two DEPs were identified; twenty DEGs/DEPs exhibited the same trend in mRNA and protein expression levels when comparing the GF5000 vs. DC groups. The Metascape analysis revealed that the T2DM disease targets included four DEGs—Gck, Scd, Abcb4, and Cyp3a9—and two DEPs—glucokinase and acetyl-CoA carboxylase 2. A Cytoscape–GeneMANIA analysis of thirteen DEGs/DEPs related to T2DM showed that Apoa1/Apolipoprotein A-I, Gckr/glucokinase regulatory protein, and Gck/glucokinase had the highest connectivity and centrality in the topological network. The qPCR results confirmed that GF5000 increased the mRNA expression of GCK in GCK-knockdown HepG2 cells. Conclusions: These results provide theoretical evidence for the use of GF5000 as a potential active nutritional ingredient for the prevention and treatment of T2DM. Our findings suggest that GF5000 targets multiple pathways implicated in T2DM, offering a multi-faceted approach to disease management and prevention. Full article

Various types of numerical control (NC) machine tools can be standardly operated and controlled based on NC data that can be easily generated using widespread CAD/CAM systems. On the other hand, the operation environments of industrial robots still depend on conventional teaching and playback systems provided by the makers, so it seems that they have not been standardized and unified like NC machine tools yet. Additionally, robotic functional extensions, e.g., the easy implementation of a machine learning model, such as a convolutional neural network (CNN), a visual feedback controller, cooperative control for multiple robots, and so on, has not been sufficiently realized yet. In this paper, a hyper cutter location source (HCLS)-data-based robotic interface is proposed to cope with the issues. Due to the HCLS-data-based robot interface, the robotic control sequence can be visually and unifiedly described as NC codes. In addition, a VGG19-based CNN model for defect detection, whose classification accuracy is over 99% and average time for forward calculation is 70 ms, can be systematically incorporated into a robotic control application that handles multiple robots. The effectiveness and validity of the proposed system are demonstrated through a cooperative pick and place task using three small-sized industrial robot MG400s and a peg-in-hole task while checking undesirable defects in workpieces with a CNN model without using any programmable logic controller (PLC). The specifications of the PC used for the experiments are CPU: Intel(R) Core(TM) i9-10850K CPU 3.60 GHz, GPU: NVIDIA GeForce RTX 3090, Main memory: 64 GB. Full article

In contemporary networked control systems (NCSs), ensuring robust and adaptive security measures against dynamic threats like jamming attacks is crucial. These attacks can disrupt the control signals, leading to degraded performance or even catastrophic failures. This paper introduces a novel approach to enhance NCS security by applying stochastic game theory to model and resolve interactions between a defender and a jammer. We develop a two-player zero-sum game where the defender employs mixed strategies to minimize the expected cost of maintaining system stability and control effectiveness in the face of potential jamming. Our model discretizes the state space and employs backward induction to dynamically update the value functions associated with various system states, reflecting the ongoing adjustment of strategies in response to the adversary’s actions. Utilizing linear programming in MATLAB, we optimize the defender’s mixed strategies to systematically mitigate the impact of jamming. The results from extensive simulations demonstrate the efficacy of our proposed strategies in attack scenarios, indicating a substantial enhancement in the resilience and performance of NCSs against jamming attacks. Specifically, the proposed method improved network state stability by $75\%$, reducing the fluctuation range by over $50\%$ compared with systems without defense mechanisms. This study not only advances the theoretical framework for security in NCSs but also provides practical insights for the design of resilient control systems under uncertainty. Full article

In this work, we consider the design of power-constrained networked control systems (NCSs) and a differential entropy-based fault-detection mechanism. For the NCS design of the control loop, we consider faults in the plant gain and unstable plant pole locations, either due to natural causes or malicious intent. Since the power-constrained approach utilized in the NCS design is a stationary approach, we then discuss the finite-time approximation of the power constraints for the relevant control loop signals. The network under study is formed by two additive white Gaussian noise (AWGN) channels located on the direct and feedback paths of the closed control loop. The finite-time approximation of the controller output signal allows us to estimate its differential entropy, which is used in our proposed fault-detection mechanism. After fault detection, we propose a fault-identification mechanism that is capable of correctly discriminating faults. Finally, we discuss the extension of the contributions developed here to future research directions, such as fault recovery and control resilience. Full article

Redundancies in modern systems, including multiple channels, processes, and storages, are often exploited to ensure robust operation. Similarly, a Networked Control System (NCS) may utilize multiple channels to facilitate reliable information transfer in case of channel failure. To enhance the performance of Linear Quadratic Gaussian (LQG) control in environments with multiple channels, delays, and packet errors, we propose channel-switching algorithms. Leveraging the encoder and decoder structure for channel modeling, we derive the decoder estimation error covariance matrix, characterizing LQG control performance with respect to delay. Based on this insight, we develop two threshold-based channel-switching algorithms, proven to ensure finite total decoder estimation error variance under certain conditions. Specific conditions are also identified where the proposed algorithms offer improved probabilistic stability. Numerical simulations confirm the superior performance of the proposed algorithms compared to conventional methods across diverse channel environments. Notably, the proposed algorithms demonstrate near-optimal performance in a practical operational scenario involving multiple channels, specifically 5G cellular link and Starlink. Full article

The network control system (NCS) of the parallel DC/DC converter is always affected by the signal transmission delay, and the ideal output performance is lost. In this paper, a typical parallel buck converter is taken as the research object. Firstly, a sliding mode controller (SMC) in the discrete domain is designed to enhance the robustness of the system. On this basis, the effects of different delays on the stability of the converter’s NCS are analyzed, and the actual effects of long/short delays on the converter’s NCS are obtained. To further solve the problem of damage to transmitted signals of the NCS by long delay, the SM controller designed in this paper is improved by incorporating a multi-step prediction method. This enhancement enables effective prediction and compensation of the delay signals lost by the NCS, ensuring the output performance of the parallel buck converter. Finally, the superiority of the proposed method is verified by designing simulations and experiments. Full article

To address the presence of network-induced delays in networked control systems (NCSs), a dual event-triggered mechanism (DETM) is used to investigate the problem of reducing network delays and controller co-design. Firstly, the DETM of the sensor–controller (SC) and the controller–actuator (CA) is adopted. By determining whether the sampled data meet the event-triggered threshold conditions for network transmission, we effectively reduce the sampled data transmitted over the network, which can reduce a network delay by reducing occupation of the network resources. Secondly, a dual event-triggered NCS model with a network-induced delay is developed, and a Lyapunov function including a DETM and network-induced delay is chosen. The functional upper limit of the Lyapunov function is estimated by combining the Wirtinger’s-based integral inequality with the reciprocally convex approach. This results in a stability criterion for systems with low conservativeness and a controller co-design method for a DETM. Finally, the availability of this method was verified through a numerical example and case study. Full article

The integration of communication channels with the feedback loop in a networked control system (NCS) is attractive for many applications. A major challenge in the NCS is to reduce transmissions over the network between the sensors, the controller, and the actuators to avoid network congestion. An efficient approach to achieving this goal is the event-triggered implementation where the control actions are only updated when necessary from stability/performance perspectives. In particular, periodic event-triggered control (PETC) has garnered recent attention because of its practical implementation advantages. This paper focuses on the design of stabilizing PETC for linear time-invariant systems. It is assumed that the plant state is partially known; the feedback signal is sent to the controller at discrete-time instants via a digital channel; and an event-triggered controller is synthesized, solely based on the available plant measurement. The constructed event-triggering law is novel and only verified at periodic time instants; it is more adapted to practical implementations. The proposed approach ensures a global asymptotic stability property for the closed-loop system under mild conditions. The overall model is developed as a hybrid dynamical system to truly describe the mixed continuous-time and discrete-time dynamics. The stability is studied using appropriate Lyapunov functions. The efficiency of the technique is illustrated in the dynamic model of the tunnel diode system. Full article

Non-coding RNAs (ncRNAs) are indispensable for adjusting gene expression and genetic programming throughout development and for health as well as cardiovascular diseases. Cardiac arrhythmia is a frequent cardiovascular disease that has a complex pathology. Recent studies have shown that ncRNAs are also associated with cardiac arrhythmias. Many non-coding RNAs and/or genomes have been reported as genetic background for cardiac arrhythmias. In general, arrhythmias may be affected by several functional and structural changes in the myocardium of the heart. Therefore, ncRNAs might be indispensable regulators of gene expression in cardiomyocytes, which could play a dynamic role in regulating the stability of cardiac conduction and/or in the remodeling process. Although it remains almost unclear how ncRNAs regulate the expression of molecules for controlling cardiac conduction and/or the remodeling process, the gut microbiota and immune system within the intricate networks might be involved in the regulatory mechanisms. This study would discuss them and provide a research basis for ncRNA modulation, which might support the development of emerging innovative therapies against cardiac arrhythmias. Full article

Resting-state functional connectivity has been widely used for the past few years to forecast Alzheimer’s disease (AD). However, the conventional correlation calculation does not consider different frequency band features that may hold the brain atrophies’ original functional connectivity relationships. Previous works focuses on low-order neurodynamics and precisely manipulates the mono-band frequency span of resting-state functional magnetic imaging (rs-fMRI). They specifically use the mono-band frequency span of rs-fMRI, leaving out the high-order neurodynamics. By creating a high-order neuro-dynamic functional network employing several levels of rs-fMRI time-series data, such as slow4, slow5, and full-band ranges of (0.027 to 0.08 Hz), (0.01 to 0.027 Hz), and (0.01 to 0.08 Hz), we suggest an automated AD diagnosis system to address these challenges. It combines multiple customized deep learning models to provide unbiased evaluation, and a tenfold cross-validation is observed We have determined that to differentiate AD disorders from NC, the entire band ranges and slow4 and slow5, referred to as higher and lower frequency band approaches, are applied. The first method uses the SVM and KNN to deal with AD diseases. The second method uses the customized Alexnet and Inception blocks with rs-fMRI datasets from the ADNI organizations. We also tested the other machine learning and deep learning approaches by modifying various parameters and attained good accuracy levels. Our proposed model achieves good performance using three bands without any external feature selection. The results show that our system performance of accuracy (96.61%)/AUC (0.9663) is achieved in differentiating the AD subjects from normal controls. Furthermore, the good accuracies in classifying multiple stages of AD show the potentiality of our method for the clinical value of AD prediction. Full article

MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method. Full article

Introduction. Glaucoma and non-arteritic anterior ischemic optic neuropathy (NAION) are optic neuropathies that can both lead to irreversible blindness. Several studies have compared optical coherence tomography angiography (OCTA) findings in glaucoma and NAION in the presence of similar functional and structural damages with contradictory results. The goal of this study was to use a deep learning system to differentiate OCTA in glaucoma and NAION. Material and methods. Sixty eyes with glaucoma (including primary open angle glaucoma, angle-closure glaucoma, normal tension glaucoma, pigmentary glaucoma, pseudoexfoliative glaucoma and juvenile glaucoma), thirty eyes with atrophic NAION and forty control eyes (NC) were included. All patients underwent OCTA imaging and automatic segmentation was used to analyze the macular superficial capillary plexus (SCP) and the radial peripapillary capillary (RPC) plexus. We used the classic convolutional neural network (CNN) architecture of ResNet50. Attribution maps were obtained using the “Integrated Gradients” method. Results. The best performances were obtained with the SCP + RPC model achieving a mean area under the receiver operating characteristics curve (ROC AUC) of 0.94 (95% CI 0.92–0.96) for glaucoma, 0.90 (95% CI 0.86–0.94) for NAION and 0.96 (95% CI 0.96–0.97) for NC. Conclusion. This study shows that deep learning architecture can classify NAION, glaucoma and normal OCTA images with a good diagnostic performance and may outperform the specialist assessment. Full article

Plant–pathogen interactions induce a signal transmission series that stimulates the plant’s host defense system against pathogens and this, in turn, leads to disease resistance responses. Plant innate immunity mainly includes two lines of the defense system, called pathogen-associated molecular pattern-triggered immunity (PTI) and effector-triggered immunity (ETI). There is extensive signal exchange and recognition in the process of triggering the plant immune signaling network. Plant messenger signaling molecules, such as calcium ions, reactive oxygen species, and nitric oxide, and plant hormone signaling molecules, such as salicylic acid, jasmonic acid, and ethylene, play key roles in inducing plant defense responses. In addition, heterotrimeric G proteins, the mitogen-activated protein kinase cascade, and non-coding RNAs (ncRNAs) play important roles in regulating disease resistance and the defense signal transduction network. This paper summarizes the status and progress in plant disease resistance and disease resistance signal transduction pathway research in recent years; discusses the complexities of, and interactions among, defense signal pathways; and forecasts future research prospects to provide new ideas for the prevention and control of plant diseases. Full article

Inflammation is a biological response of the immune system to various insults, such as pathogens, toxic compounds, damaged cells, and radiation. The complex network of pro- and anti-inflammatory factors and their direction towards inflammation often leads to the development and progression of various inflammation-associated diseases. The role of small non-coding RNAs (small ncRNAs) in inflammation has gained much attention in the past two decades for their regulation of inflammatory gene expression at multiple levels and their potential to serve as biomarkers and therapeutic targets in various diseases. One group of small ncRNAs, microRNAs (miRNAs), has become a key regulator in various inflammatory disease conditions. Their fine-tuning of target gene regulation often turns out to be an important factor in controlling aberrant inflammatory reactions in the system. This review summarizes the biogenesis of miRNA and the mechanisms of miRNA-mediated gene regulation. The review also briefly discusses various pro- and anti-inflammatory miRNAs, their targets and functions, and provides a detailed discussion on the role of miR-10a in inflammation. Full article