Parallel Control Research Articles

A Resampling Approach for Causal Inference on Novel Two-Point Time-Series with Application to Identify Risk Factors for Type-2 Diabetes and Cardiovascular Disease

AbstractTwo-point time-series data, characterized by baseline and follow-up observations, are frequently encountered in health research. We study a novel two-point time-series structure without a control group, which is driven by an observational routine clinical dataset collected to monitor key risk markers of type-2 diabetes (T2D) and cardiovascular disease (CVD). We propose a resampling approach called “I-Rand” for independently sampling one of the two-time points for each individual and making inferences on the estimated causal effects based on matching methods. The proposed method is illustrated with data from a service-based dietary intervention to promote a low-carbohydrate diet (LCD), designed to impact risk of T2D and CVD. Baseline data contain a pre-intervention health record of study participants, and health data after LCD intervention are recorded at the follow-up visit, providing a two-point time-series pattern without a parallel control group. Using this approach we find that obesity is a significant risk factor of T2D and CVD, and an LCD approach can significantly mitigate the risks of T2D and CVD. We provide code that implements our method.

Hybrid energy storage power allocation strategy based on parameter-optimized VMD algorithm for marine micro gas turbine power system

The power system onboard ships is typically a low-inertia, small-capacity isolated grid that is highly susceptible to system disturbances and instability, especially when connected to high power pulse loads. To mitigate power fluctuations and ensure stable operation, a hybrid energy storage system (HESS), which comprises the battery system and flywheel energy storage devices, has emerged as a promising solution. This paper explores the control strategy and coordinated operation of marine gas turbine power generation systems operating under pulse load conditions and presents a novel hybrid energy storage power allocation strategy based on variational mode decomposition (VMD). Specifically, we propose to implement parameter optimization of VMD using an artificial hummingbird algorithm (AHA), which enables effective primary allocation of hybrid energy storage power. To achieve secondary power allocation, we design two fuzzy controllers to optimize the state of charge (SOC) of battery system and speed of flywheel device. To evaluate the feasibility and effectiveness of the proposed control strategy, we establish a theoretical model of a shipboard micro gas turbine (MGT) direct current (DC) power system, which includes an MGT, generator, HESS, and pulse power load. Our simulation results demonstrate that the artificial hummingbird algorithm outperforms the particle swarm algorithm concerning search speed and calculation accuracy for parameter optimization of variational mode decomposition. Moreover, the proposed parameter-optimized VMD method can adaptively decompose pulse load power fluctuations and achieve optimal allocation of the flywheel and battery power in the HESS. Finally, the parallel fuzzy controller design effectively optimizes the operation ranges of the flywheel device and battery system, prevents over-charging and over-discharging of energy storage equipment, ensures that the SOC of energy storage equipment remains within a fixed interval, and reduces frequent and high-power charging and discharging of batteries.

Optimal Power Quality Enhancement using a Radial Distribution System with an Improved Unified Power Quality Conditioner

Massive electric power distribution over long distances with consequential Power Quality (PQ) challenges such as voltage sags and power losses are some of the significant attributes of a Radial Distribution Network (RDN). Deployment of Power Angle Regulated (PAR) based Unified Power Quality Conditioner (UPQC) in a distribution network is also securing attraction because of the latest recorded achievements and improvements in Voltage Source Inverter (VSI) built power electronic systems. However, optimal allocation of this kind of device to mitigate PQ problems remains a challenge for achieving set objectives. Consequently, this study considers the best possible allocation of PAR and Improved-UPQC know as I-UPQC in the distribution network to enhance power network performance. The identification of optimal location is achieved through the application of hybridization of the Genetic Algorithm and Improved Particle Swarm Optimization (GA & IPSO). The deterministic approach is based on the weight factor of various objective functions. The allocation is attained with a selection of reactive power control between inverter connected in parallel and series and control angle variables of the device through its dynamic involvement of total system loss derivatives. Performances of the I-UPQC based distribution system during diverse power transfers are observed. Convergence characteristic of deterministic approach at different disturbance percentages is analyzed and presented. Imaginary power circulation enhanced the voltage-associated challenges at the range of 0.949 to 0.9977. Hence, power dissipation minimized to 1.15 percent compared to the initial 3.35 percent, according to results of I-UPQC allocation in RDN utilizing mathematical and optimization technique. Additionally, the network losses, voltage dip, and minimum bus voltage profile all fall within the regulatory standards of less than 2%, 5%, and 5%, correspondingly. Also, the performance of the compensated network for both ordinary and optimized scenarios indicated the fitness of the projected method in accomplishing an operational optimization of RDN, specifically for voltage profile (VP) improvement and I-UPQC's series and shunt inverter share imaginary power.

Parallel adaptive event-triggered asynchronous control for two-time-scale fuzzy semi-Markov jump systems under deception attacks

In this brief, the parallel adaptive event-triggered (PAET) asynchronous control issue for a category of two-time-scale fuzzy semi-Markov jump systems (TTS-FSMJSs) is investigated. To save limited network resources, a novel PAET strategy is designed to process the data from slow and fast channels of TTS-FSMJSs separately. Meanwhile, the mode-dependent exponential decay adaptive law effectively avoids Zeno behavior. In addition, in order to reduce the influence of the attack signal in the communication network, an attack strategy with norm-bounded uncertainty is constructed. Based on the proposed PAET strategy, a composite asynchronous controller with time-varying mode emission probabilities (EPs) is designed. When the singular perturbation parameter is not greater than the given upper bound, the system is stable. The sufficient conditions for the closed-loop system is exponentially stable with H∞ performance are ensured by utilizing the integral inequalities. Ultimately, the validity of the proposed approach is illustrated through the DC motor model.

Effect of Combined Intervention of Yoga and Physiotherapy as Compared to Only Physiotherapy on Lower Limb Muscle Strength, Balance and Range of Motion among Elders with Compromised Stair Climbing in Puducherry - A Parallel Arm Non-Randomized Control Trial.

The activity of daily living (ADL) related to stair climbing is one of the most compromised ADLs among the elders. It requires good muscle strength, balance, and range of motion (ROM) in the lower limb. We aimed to investigate the effects of Yoga complemented with physiotherapy (Intervention group) compared to only physiotherapy (control group) on lower limb muscle strength, balance, and ROMs among elders with ADL limitation in stair climbing. Sixty-five community-dwelling elders with compromised stair climbing were enrolled. Elders who consented to yoga along with physiotherapy were enrolled in the intervention arm. Data were collected and analyzed following 12 weeks of intervention. Endline assessment showed that the intervention group had more improvement in all test scores and ROMs as compared to the control group, although there were improvements in both arms as compared to baseline. Considering the beneficial effects of both interventions, yoga can be added to gain additional advantages.Trial Registration:Indian Clinical Trials Registry number CTRI/2021/08/035825.

High-Efficiency Multioutput LLC Resonant Converter With Multiwinding Transformer and Cost-Effective Analog Control Circuit

A new multi-output LLC resonant converter with a multi-winding transformer is proposed. In the proposed converter, the main output is controlled by the pulse frequency modulation of the primary switches, and the additional outputs are regulated by the pulse width modulation of the secondary synchronous rectifiers (SRs). Synchronization between the primary switches and the SRs can be easily achieved by sensing the voltage across the secondary side of the transformer at the main output with simple and cost-effective analog circuitry. The proposed converter features high efficiency because of the reduced transformer core loss and conduction losses in the primary switches. It also achieves high power density because of the small size of the transformer, resonant capacitor, and primary switches. The validity of the proposed converter is verified through experiments on a prototype for a 55-inch TV power supply converting 390 VDC input to 13 V/6.3 A and 65-83 V/ 2.25 A outputs.

Probabilistic computing with voltage-controlled dynamics in magnetic tunnel junctions

Probabilistic (p-) computing is a physics-based approach to addressing computational problems which are difficult to solve by conventional von Neumann computers. A key requirement for p-computing is the realization of fast, compact, and energy-efficient probabilistic bits. Stochastic magnetic tunnel junctions (MTJs) with low energy barriers, where the relative dwell time in each state is controlled by current, have been proposed as a candidate to implement p-bits. This approach presents challenges due to the need for precise control of a small energy barrier across large numbers of MTJs, and due to the need for an analog control signal. Here we demonstrate an alternative p-bit design based on perpendicular MTJs that uses the voltage-controlled magnetic anisotropy (VCMA) effect to create the random state of a p-bit on demand. The MTJs are stable (i.e. have large energy barriers) in the absence of voltage, and VCMA-induced dynamics are used to generate random numbers in less than 10 ns/bit. We then show a compact method of implementing p-bits by using VC-MTJs without a bias current. As a demonstration of the feasibility of the proposed p-bits and high quality of the generated random numbers, we solve up to 40 bit integer factorization problems using experimental bit-streams generated by VC-MTJs. Our proposal can impact the development of p-computers, both by supporting a fully spintronic implementation of a p-bit, and alternatively, by enabling true random number generation at low cost for ultralow-power and compact p-computers implemented in complementary metal-oxide semiconductor chips.

Εfficacy and safety of vortioxetine (Lu AA21004) in the treatment of adult patients with major depressive disorder: A systematic review and a meta‑analysis of randomized controlled trials.

Vortioxetine is a novel drug for the treatment of major depressive disorder (MDD). It has been reported that vortioxetine exhibits positive effect on the acute stage of MDD, while it can effectively prevent the recurrence of MDD during the maintenance period. Currently, the results of systematic reviews on vortioxetine are insufficient since several efficacy measures, such as the 24-Items Hamilton Rating Scale for Depression (HADRS-24) total score and other safety factors have not been evaluated. Therefore, the present study aimed to evaluate the efficacy and safety of different doses of vortioxetine on the treatment of adult patients with MDD via assessing more efficacy and safety indicators. The clinical, double-blind, parallel and randomized controlled trials (RCTs) on the effect of vortioxetine on MDD were retrieved from PubMed\Medline, EBSCO, Embase, Cochrane Library, OVID, Web of Science and clinical trial registration websites from database inception to November 2022. A total of two investigators independently screened the included references and independently evaluated their quality. The meta-analysis was performed using Revman 5.0 software. The present systematic review was registered in PROSPERO (registration no. CRD42018106343). In the present study 11 RCTs were included, with a total of 4,908 adult patients with MDD. More specifically, 1,158 patients were included in the 5-mg vortioxetine group, 736 in the 10-mg group, 298 in the 15-mg group, 864 in the 20-mg group and 1,852 in the placebo group. All 11 studies were randomized, double-blinded and parallel control trials, and all publications were evaluated as high quality. The meta-analysis results showed that patients in the 5-, 10- and 20-mg vortioxetine groups exhibited significantly higher Montgomery-Asberg Depression Rating Scale (MADRS) response (≥50%) and remission (≤10%) rates compared with the placebo group (P<0.05). The pooled analysis also revealed a statistically significant change in the total score of HADRS-24, MADRS, Sheehan Disability Scale (SDS), Clinical Global Impression Scale-Improvement (CGI-I) and HADRS-24 response rate in the 10- and 20-mg vortioxetine groups compared with the placebo group (P<0.05). However, no statistically significant changes in the total score of HADRS-24, MADRS, SDS, CGI-I and HADRS-24 response rate were obtained in the 5-mg group compared with the placebo group (P>0.05). Furthermore, the most common adverse events were nausea, hyperhidrosis, insomnia and vomiting, the incidence of which was increased with higher doses of vortioxetine. Overall, the results suggested that vortioxetine administration at doses of 5-20 mg was significantly effective and safe compared with placebo in the treatment of MDD. However, 5 mg vortioxetine displayed no difference in the HADRS-24, MADRS, SDS and CGI-I total scores, and HADRS-24 response rate. Furthermore, patient treatment with increasing vortioxetine doses was associated with good tolerance and high safety. Nevertheless, more multi-center, high-quality and long-term RCTs are still needed to support the aforementioned findings.

Evaluation of Four Multispecies Probiotic Cocktails in a Human Colonic Fermentation Model.

Bacteriotherapy represents an attractive approach for both prophylaxis and treatment of human diseases. However, combining probiotic bacteria in "cocktails" is underexplored, despite its potential as an alternative multi-target therapy. Herein, three-strain probiotic mixtures containing different combinations of Bacillus (Bc.) coagulans [ATB-BCS-042], Levilactobacillus (Lv.) brevis [THT 0303101], Lacticaseibacillus (Lc.) paracasei [THT 031901], Bacillus subtilis subsp. natto [ATB-BSN-049], Enterococcus faecium [ATB-EFM-030], and Bifidobacterium (Bf.) animalis subsp. lactis [THT 010802] were prepared. Four cocktails (PA: Bc. coagulans + Lv. brevis + Lc. paracasei, PB: Bc. subtilis subsp. natto + Lv. brevis + Lc. paracasei, PC: E. faecium + Lv. brevis + Lc. paracasei, PD: Bc. coagulans + Lv. brevis + Bf. animalis subsp. lactis) were tested using a short-term (72h) simulation of the human colonic microbiota in a final dose of 6 × 109CFU. All these probiotic mixtures significantly increased butyrate production compared to the parallel control experiment. PA and PB promoted a bifidogenic effect and facilitated lactobacilli colonization. Furthermore, reporter gene assays using the AhR_HT29-Lucia cell line revealed that fermentation supernatants from PA and PB notably induced AhR transactivity. Subsequent examination of the metabolic outputs of PA and PB in intestinal epithelial models using cell culture inserts suggested no significant impact on the transepithelial electricalresistance (TEER). Assessment of the expression of proinflammatory and anti-inflammatory cytokines, as well as AhR-related target genes in the Caco-2 cell monolayers indicated that PB's metabolic output upregulated most of the measured endpoints. Thisin vitro investigation evaluated the potential impact of four multispecies probiotic mixtures in the human colonic microbiota and identified a promising formulation comprising a combination of Bc. subtilis subsp. natto, Lv. brevis, and Lc. paracasei as a promising formulation for further study.

Dual-site beta tACS over rIFG and M1 enhances response inhibition: A parallel multiple control and replication study

Response inhibition is a core component of cognitive control. Past electrophysiology and neuroimaging studies have identified beta oscillations and inhibitory control cortical regions correlated with response inhibition, including the right inferior frontal gyrus (rIFG) and primary motor cortex (M1). Hence, increasing beta activity in multiple brain regions is a potential way to enhance response inhibition. Here, a novel dual-site transcranial alternating current stimulation (tACS) method was used to modulate beta activity over the rIFG-M1 network in a sample of 115 (excluding 2 participants) with multiple control groups and a replicated experimental design. In Experiment 1, 70 healthy participants were randomly assigned to three dual-site beta-tACS groups, including in-phase, anti-phase or sham stimulation. During and after stimulation, participants were required to complete the stop-signal task, and electroencephalography (EEG) was collected before and after stimulation. The Barratt Impulsiveness Scale was completed before the experiment to evaluate participants' impulsiveness. In addition, we conducted an active control experiment with a sample size of 20 to exclude the potential effects of the dual-site tACS “return” electrode. To validate the behavioural findings of Experiment 1, 25 healthy participants took part in Experiment 2 and were randomized into two groups, including in-phase and sham stimulation groups. We found that compared to the sham group, in-phase but not anti-phase beta-tACS significantly improved both response inhibition performance and beta synchronization of the inhibitory control network in Experiment 1. Furthermore, the increased beta synchronization was correlated with enhanced response inhibition. In an independent sample of Experiment 2, the enhanced response inhibition performance observed in the in-phase group was replicated. After combining the data from the above two experiments, the time dynamics analysis revealed that the in-phase beta-tACS effect occurred in the post-stimulation period but not the stimulation period. The state-dependence analysis showed that individuals with poorer baseline response inhibition or higher attentional impulsiveness had greater improvement in response inhibition for the in-phase group. These findings strongly support that response inhibition in healthy adults can be improved by in-phase dual-site beta-tACS of the rIFG-M1 network, and provide a new potential treatment targets of synchronized cortical network activity for patients with clinically deficient response inhibition.

Effectiveness and safety of opioids on breathlessness and exercise endurance in patients with chronic obstructive pulmonary disease: A systematic review and meta-analysis of randomised controlled trials.

Opioids are recommended to treat advanced refractory dyspnoea despite optimal therapy by the American Thoracic Society clinical practice guidelines, while newly published randomised controlled trials of opioids in chronic obstructive pulmonary disease yield conflicting results. This study aimed to evaluate the effectiveness and safety of opioids for patients with chronic obstructive pulmonary disease. Systematic review and meta-analysis (PROSPERO CRD42021272556). Databases of PubMed, EMBASE and CENTRAL were searched from inception to 2022 for eligible randomised controlled trials. Twenty-four studies including 975 patients, were included. In cross-over studies, opioids improved breathlessness (standardised mean difference, -0.43; 95% CI, -0.55 to -0.30; I2 = 18%) and exercise endurance (standardised mean difference, 0.22; 95% CI, 0.02-0.41; I2 = 70%). However, opioids failed to improve dyspnoea (standardised mean difference, -0.02; 95% CI, -0.22 to 0.19; I2 = 39%) and exercise endurance (standardised mean difference, 0.00; 95% CI, -0.27 to 0.27; I2 = 0%) in parallel control studies that administered sustained-release opioids for more than 1 week. The opioids used in most crossover studies were short-acting and rarely associated with serious adverse effects. Only minor side effects such as dizziness, nausea, constipation and vomiting were identified for short-acting opioids. Sustained-release opioids did not improve dyspnoea and exercise endurance. Short-acting opioids appeared to be safe, have potential to lessen dyspnoea and improve exercise endurance, supporting benefit in managing episodes of breathlessness and providing prophylactic treatment for exertional dyspnoea.

Dichotomy value iteration with parallel learning design towards discrete-time zero-sum games

In this paper, a novel parallel learning framework is developed to solve zero-sum games for discrete-time nonlinear systems. Briefly, the purpose of this study is to determine a tentative function according to the prior knowledge of the value iteration (VI) algorithm. The learning process of the parallel controllers can be guided by the tentative function. That is to say, the neighborhood of the optimal cost function can be compressed within a small range via two typical exploration policies. Based on the parallel learning framework, a novel dichotomy VI algorithm is established to accelerate the learning speed. It is shown that the parallel controllers will converge to the optimal policy from contrary initial policies. Finally, two typical systems are used to demonstrate the learning performance of the constructed dichotomy VI algorithm.

Hamiltonian Neural Network 6-DoF Rigid-Body Dynamic Modeling Based on Energy Variation Estimation

This study introduces a novel deep modeling approach that utilizes Hamiltonian neural networks to address the challenges of modeling the six degrees of freedom rigid-body dynamics induced by control inputs in various domains such as aerospace, robotics, and automotive engineering. The proposed method is based on the principles of Hamiltonian dynamics and employs an inductive bias in the form of a constructed bias for both conservative and varying energies, effectively tackling the modeling issues arising from time-varying energy in controlled rigid-body dynamics. This constructed bias captures the information regarding the changes in the rigid body’s energy. The presented method not only achieves highly accurate modeling but also preserves the inherent bidirectional time-sliding inference in Hamiltonian-based modeling approaches. Experimental results demonstrate that our method outperforms existing techniques in the time-varying six degrees of freedom dynamic modeling of aircraft and missile guidance, enabling high-precision modeling and feedback correction. The findings of our research hold significant potential for the kinematic modeling of time-varying energy systems, parallel system state prediction and control, inverse motion inference, and autonomous decision-making in military applications.

Parallel Impedance-Reshaping Control Strategy for Suppressing Wide Range High-Frequency Resonance of Shunt Compensated DFIG System

The shunt compensated grid (SCG) connecting with doubly-fed induction generator (DFIG) system easily suffers from high-frequency resonance (HFR). Existing studies reshape the DFIG based on a pre-calculated frequency to suppress the HFR. When facing voltage disturbance, shunt capacitors are added to the grid which greatly changes the resonance frequency, probably resulting functional failure of HFR suppression. In this paper, a parallel impedance-reshaping control (PIRC) strategy for the rotor side converter (RSC) and grid-side converter (GSC) control is proposed to suppress the HFR of the DFIG-SCG system when the shunt capacitor varies in a wide range. A high-order coefficient is combined with the original impedance of the RSC-induction generator (RSC-IG) side, and a damping controller is designed to reshape the impedance of the DFIG system. Based on the parallel structure of the equivalent impedance of the GSC side and the RSC-IG side, the impact of the shunt capacitor to the controller can be eliminated, thus providing effective HFR suppression in a wide frequency range. Case studies demonstrate the advantage of the PIRC in HFR suppression and harmonic suppression.

Voltage Detection-Based Selective Harmonic Current Compensation Strategies for Photovoltaic Inverters

Voltage and current detection-based harmonic current compensation (VDB-HCC and CDB-HCC, respectively) strategies allow the use of photovoltaic (PV) inverters to enhance the grid power quality. CDB-HCC strategies require converter hardware retrofit by inserting an extra current sensor to measure load or downstream grid currents. On the other hand, VDB-HCC strategies are straightforward solutions employing only embedded measurements used for protection, control, and synchronization purposes. This paper provides a comprehensive comparison of three VDB-HCC strategies: VDB-HCC based on a single current loop (SCL); VDB-HCC based on dual parallel current control loops (DCL); and VDB-HCC based on parallel voltage and current control loops (VCL). The comparison includes the requirement of harmonic detection algorithms, PV array maximum power point tracking (MPPT) performance, stability over grid impedance variations and inverter power rating, steady-state harmonic compensation, enabling transient response, and computational burden. Experimental results using a 1.5-kW commercial PV inverter are conducted. Based on distributed HCC applications by PV systems, VCL has shown the best performance in terms of voltage distortion reduction under weak grids.

Reinforcement learning for energy-efficient control of parallel and identical machines

Nowadays, the growing interest in industry for enhancing the sustainability of manufacturing processes is becoming a major trend. Energy consumption can be lowered by controlling machine states with energy-efficient control policies that switch off/on the device. Recent studies have shown that Reinforcement Learning algorithms can effectively control manufacturing systems without the requirement of prior knowledge about system parameters. This is a significant factor since full information on system dynamics is difficult to obtain in real-world applications. This work proposes a new Reinforcement Learning-based algorithm to apply energy-efficient control strategies to a single workstation consisting of identical parallel machines. The model goal is to achieve the optimum trade-off between system productivity and energy demand without relying on full knowledge of the system dynamics. Numerical experiments confirm effectiveness, applicability, and generality of the proposed approach, even when applied to a real-world industrial system from the automotive sector.

AKT inhibition generates potent polyfunctional clinical grade AUTO1 CAR T-cells, enhancing function and survival

BackgroundAUTO1 is a fast off-rate CD19-targeting chimeric antigen receptor (CAR), which has been successfully tested in adult lymphoblastic leukemia. Tscm/Tcm-enriched CAR-T populations confer the best expansion and persistence, but Tscm/Tcm...

Research on Parallel Control Strategy of Grid-forming of Power Conversion System

A high proportion of power electronic equipment, distributed energy, and other new energy access to the grid system, low inertia and low damping to the power grid system, these kinds of power grid characteristics in power electric wire aging remote mountains or island areas, therefore, need energy storage system under the microgrid can provide inertial support for load or grid, improve the system damping characteristics. On this basis, the grid-forming control is proposed, so that the power conversion system can work under the off-grid, and can provide inertia and damping to maintain the system stability. In addition, large-scale energy storage systems often require multiple machines operating in parallel. In this paper, the topology of NPC for a power conversion system is studied and analyzed in off-grid mode.

The Influence of KE and EW Dipeptides in the Composition of the Thymalin Drug on Gene Expression and Protein Synthesis Involved in the Pathogenesis of COVID-19.

Thymalin is an immunomodulatory drug containing a polypeptide extract of thymus that has demonstrated efficacy in the therapy of acute respiratory distress syndrome and chronic obstructive pulmonary disease, as well as in complex therapy related to severe COVID-19 in middle-aged and elderly patients.. KE and EW dipeptides are active substances of Thymalin. There is evidence that KE stimulates cellular immunity and nonspecific resistance in organisms, exerting an activating effect on macrophages, blood lymphocytes, thymocytes, and neutrophils, while EW reduces angiotensin-induced vasoconstriction and preserves endothelium-dependent vascular relaxation by inhibiting ACE2, the target protein of SARS-CoV-2. However, the mechanism of the immunomodulatory action of Thymalin, KE, and EW during COVID-19 remains unclear. To identify the potential mechanism of action underlying the immunomodulatory activity of Thymalin and its active components, EW and KE dipeptides, we assessed inflammatory response in the context of COVID-19. Interactions between EW and KE dipeptides and double-stranded DNA (dsDNA) were investigated by molecular modeling and docking using ICM-Pro. Analysis of the possible effect of EW and KE dipeptides on gene expression and protein synthesis involved in the pathogenesis of COVID-19 was conducted through the use of bioinformatics methods, including a search for promoter sequences in the Eukaryotic Promoter Database, the determination of genes associated with the development of COVID-19 using the PathCards database of human biological pathways (pathway unification database), identification of the relationship between proteins through cluster analysis in the STRING database ('Search Tool for Retrieval of Interacting Genes/Proteins'), and assessment of the functional enrichment of protein-protein interaction (PPI) using the terms of gene ontology (GO) and the Markov cluster algorithm (MCL). After that, in vitro studying of a lipopolysaccharide (LPS)-induced model of inflammation using human peripheral blood mononuclear cells was performed. ELISA was applied to assess the level of cytokines (IL-1β, IL-6, TNFα) in the supernatant of cells with or without the impact of EW and KE peptides. Blood samples were obtained from four donors; for each cytokine, ELISA was performed 2-4 times, with two parallel experimental or control samples for each experiment (experiments to assess the effects of peptides on LPS-stimulated cells were repeated four times, while additional experiments with unstimulated cells were performed two times). Using molecular docking, GGAG was found to be the best dsDNA sequence in the classical B-form for binding the EW dipeptide, while GCGC is the preferred dsDNA sequence in the curved nucleosomal form for the KE dipeptide. Cluster analysis revealed that potential target genes for the EW and KE peptides encode the AKT1 and AKT2 proteins involved in the development of the cytokine storm. The specific targets for the EW peptide are the ACE2 and CYSLTR1 genes, and specific target for the KE peptide is the CHUK gene. Protein products of the ACE2, CYSLTR1, and CHUK genes are functionally associated with IL-1β, IL-6, TNF-α, IL-4, and IL-10 cytokines. An in vitro model of an inflammatory reaction demonstrated that Thymalin and EW and KE dipeptides reduced the synthesis of IL-1β, IL-6, and TNF-α cytokines in human peripheral blood mononuclear cells by 1.4-6.0 times. The immunomodulatory effect of Thymalin under the inflammatory response conditions in COVID-19 is based on the potential ability of its active components, EW and KE dipeptides, to regulate protein synthesis involved in the development of the cytokine storm.

Robust tool condition monitoring in Ti6Al4V milling based on specific force coefficients and growing self-organizing maps

Tool condition monitoring (TCM) is a mean to optimize production systems trying to use cutting tool life at its best. Nevertheless, nowadays available TCM algorithms typically lack robustness in order to be consistently applied in industrial scenarios. In this paper, an unsupervised artificial intelligence technique, based on Growing Self-Organizing Maps (GSOM), is presented in synergy with real-time specific force coefficients (SFC) estimation through the regression of instantaneous cutting forces. The conceived approach allows robustly mapping the SFC, exploiting process parameters and similarity to manage the variability of their estimation due to unmodelled phenomena, like machine dynamics and tool run-out. The devised approach allowed detecting the tool end-of-life in cutting tests with variable lubrication, machine tool and cutting speed, through the adoption of a self-starting control chart running on real-time clustered data. The solution was validated through the comparison of the GSOM framework with respect to the optimized self-starting control chart applied without GSOM clustering. The GSOM reached a root mean squared percentage error (RMSPE) of 13.2% with respect to 56.1% obtained with the analogous control chart in a full-set optimization scenario. When optimised on tests for a unique machine tool and tested on another machine tool, GSOM scored an RMSPE of 34.5%, whereas the optimized control chart scored 64.5%.