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Abstract This article proposes a novel online reinforcement learning‐based linear quadratic regulator for the three‐level neutral‐point clamped DC/AC voltage source inverter. The proposed controller employs online updated fixed‐weight recurrent neural network (NN) and policy iteration to dynamically adjust the optimal control gains based on real‐time measurements without any knowledge of the system model or offline pre‐training. Moreover, it produces a constant switching frequency with low current harmonics. Compared to the existing control methods, it provides superior control performance, guaranteed control stability, and simplified NN design. Experimental results are presented to verify the effectiveness of the proposed control method.

Abstract This study thoroughly investigates the NoisyNet Deep Deterministic Policy Gradient (DDPG) for frequency regulation. Compared with the conventional DDPG method, the suggested method can provide several benefits. First, the parameter noise will explore different strategies more thoroughly and can potentially discover better policies that it might miss if only action noise were used, which helps the actor achieve an optimal control strategy, resulting in enhanced dynamic response. Second, by employing the delayed policy update policy work with the proposed framework, the training process exhibits faster convergence, enabling rapid adaptation to changing disturbances. To substantiate its efficacy, the scheme is subjected to simulation tests on both an IEEE three‐area power system, an IEEE 39 bus power system, and an IEEE 68 bus system. A comprehensive performance comparison was performed against other DDPG‐based methods to validate and evaluate the performance of the proposed LFC scheme.

Abstract Ancillary service provision and peak shaving (PS) play essential roles in the current day‐to‐day power system operation, which is challenged by the increasing renewable generation penetration. Providing these critical services using classical approaches such as peak load generators has been limited due to high operational costs and environmental impacts. The use of battery energy storage systems (BESS) is another popular method that is limited by high initial investment costs and high degradation rates. In this work, a novel approach to utilize industrial loads and BESS to provide multiple power system services in different stages is proposed. Industrial loads such as aluminium crushers are known for their intensive electricity consumption. Nevertheless, when applied in frequency regulation (FR), they perform poorly due to their discrete nature in operation. This drawback and the aforementioned BESS shortcomings are addressed by combining on‐site BESS with plant machinery to provide FR services and recover BESS related costs. Later, depending on the optimal capacity distribution, BESS usage is extended into the energy arbitrage market to provide PS services. This approach resulted in higher earnings for participating customers and network operators, as well as in less CO2 emissions, and minimal BESS degradations. An Australian case study of the South West Interconnected System, along with Worsley Alumina refinery data of Western Australia has been used to showcase the model performances.

Objective Intertrochanteric fracture of the femur is a common fracture in older people. Due to the poor systemic condition and prognosis of elderly patients, it is prone to more complications. We introduce the bone‐setting concept in the design of the robots, which are used for intertrochanteric fracture of the femur reduction. The purpose of this study is to compare the effect of bone‐setting robots and conventional reduction in the treatment of intertrochanteric fracture of the femur (IFF). Methods From June 2021 to January 2023, 60 patients with IFF who were treated surgically were assigned to bone‐setting robots group and conventional reduction methods group in this retrospective study. The reduction time, operation time, total time, intraoperative blood loss, incision length, fluoroscopy time, and the follow‐up time were reviewed. The visual analogue scale (VAS) and Harris scores were used for functional assessment. For continuous variables, independent t‐tests were applied; for categorical data, the chi‐square test was applied. The significance level as p 0.05). The reduction time, operation time, total time, intraoperative blood loss, and fluoroscopy time were less than those in the bone‐setting robots reduction group compared to the conventional reduction group. In the bone‐setting robots reduction group, the preoperative VAS score was 6.2 ± 1.3, the Harris score was 35.3 ± 3.1, 1 week after surgery VAS score was 3.3 ± 1.2, the Harris score was 57.3 ± 3.7, and at the last follow‐up VAS score was 2.4 ± 0.8, and the Harris score was 88.7 ± 3.4. While in the conventional reduction group, the preoperative VAS score was 6.3 ± 1.3, the Harris score was 35.9 ± 2.9, 1 week after surgery VAS score was 4.8 ± 1.4, the Harris score was 46.8 ± 2.8, and at the last follow‐up VAS score was 2.6 ± 0.8, and the Harris score was 87.3 ± 3.3. There were no significant differences between the two groups at the preoperative and 6‐month postoperative follow‐ups in VAS score and Harris score (p > 0.05, p > 0.05, respectively). But the difference was statistically significant at the one‐week postoperative follow‐up in VAS and Harris scores (p

The mitogen-activated protein kinase (MAPK) pathway is an important intracellular signaling cascade that plays a key role in various cellular processes. Understanding the regulatory mechanisms of this pathway is essential for developing effective interventions and targeted therapies for related diseases. Recent advances in single-cell proteomic technologies have provided unprecedented opportunities to investigate the heterogeneity and noise within complex, multi-signaling networks across diverse cells and cell types. Mathematical modeling has become a powerful interdisciplinary tool that bridges mathematics and experimental biology, providing valuable insights into these intricate cellular processes. In addition, statistical methods have been developed to infer pathway topologies and estimate unknown parameters within dynamic models. This review presents a comprehensive analysis of how mathematical modeling of the MAPK pathway deepens our understanding of its regulatory mechanisms, enhances the prediction of system behavior, and informs experimental research, with a particular focus on recent advances in modeling and inference using single-cell proteomic data.

The identification of influencing factors (IFs) of land surface temperature (LST) is crucial for developing effective strategies to mitigate global warming and conducting other relevant studies. However, most previous studies ignored the potential impact of interactions between IFs, which might lead to biased conclusions. Generalized additivity models (GAMs) can provide more explanatory results compared to traditional machine learning models. Therefore, this study employs GAMs to investigate the impact of IFs and their interactions on LST, aiming to accurately detect significant factors that drive the changes in LST. The results of this case study conducted in Nanjing, China, showed that the GAMs incorporating the interactions between factors could improve the fitness of LST and enhance the explanatory power of the model. The autumn model exhibited the most significant improvement in performance, with an increase of 0.19 in adjusted-R2 and a 17.9% increase in deviance explained. In the seasonal model without interaction, vegetation, impervious surface, water body, precipitation, sunshine hours, and relative humidity showed significant effects on LST. However, when considering the interaction, the previously observed significant influence of the water body in spring and impervious surface in summer on LST became insignificant. In addition, under the interaction of precipitation, relative humidity, and sunshine hours, as well as the cooling effect of NDVI, there was no statistically significant upward trend in the seasonal mean LST during 2000–2020. Our study suggests that taking into account the interactions between IFs can identify the driving factors that affect LST more accurately.

The difficulties associated with polyetheretherketone (PEEK) implants and soft tissue integration for craniomaxillofacial bone repair have led to a series of complications that limit the clinical benefits. In this study, 3D printed multi-stage microporous PEEK implants coated with bFGF via polydopamine were fabricated to enhance PEEK implant-soft tissue integration. Multistage microporous PEEK scaffolds prepared by sulfonation of concentrated sulfuric acid were coated with polydopamine, and then used as templates for electrophoretic deposition of bFGF bioactive factors. Achieving polydopamine and bFGF sustained release, the composite PEEK scaffolds possessed good mechanical properties, hydrophilicity, protein adhesion properties. The in vitro results indicated that bFGF/polydopamine-loaded PEEK exhibited good biocompatibility to rabbit embryonic fibroblasts (REF) by promoting cell proliferation, adhesion, and migration. Ribonucleic acid sequencing (RNA-seq) revealed that bFGF/polydopamine-loaded PEEK implants significantly upregulated the expression of genes and proteins associated with soft tissue integration and activated Wnt/β-catenin signaling in biological processes, but related expression of genes and proteins was significantly downregulated when the Wnt/β-catenin signaling was inhibited. Furthermore, in vivo bFGF/polydopamine-loaded PEEK implants exhibited excellent performance in improving the growth and adhesion of the surrounding soft tissue. In summary, bFGF/polydopamine-loaded PEEK implants possess soft tissue integration properties by activating the Wnt/β-catenin signaling, which have a potential translational clinical application in the future.

Studying the firing dynamics and phase synchronization behavior of heterogeneous coupled networks helps us understand the mechanism of human brain activity. In this study, we propose a novel small heterogeneous coupled network in which the 2D Hopfield neural network (HNN) and the 2D Hindmarsh–Rose (HR) neuron are coupled through a locally active memristor. The simulation results show that the network exhibits complex dynamic behavior and is different from the usual phase synchronization. More specifically, the membrane potential of the 2D HR neuron exhibits five stable firing modes as the coupling parameter k1 changes. In addition, it is found that in the local region of k1, the number of spikes in bursting firing increases with the increase in k1. More interestingly, the network gradually changes from synchronous to asynchronous during the increase in the coupling parameter k1 but suddenly becomes synchronous around the coupling parameter k1 = 1.96. As far as we know, this abnormal synchronization behavior is different from the existing findings. This research is inspired by the fact that the episodic synchronous abnormal firing of excitatory neurons in the hippocampus of the brain can lead to diseases such as epilepsy. This helps us further understand the mechanism of brain activity and build bionic systems. Finally, we design the simulation circuit of the network and implement it on an STM32 microcontroller.

Recently, the application of memristors in chaotic systems has been extensively studied. Unfortunately, there is limited literature on the introduction of discrete memristors into chaotic maps, especially into non-classical multidimensional maps. For this reason, this paper establishes a new three-dimensional parabolic chaotic map model; in order to improve the complexity and randomness of the map, it is coupled with a square-charge-controlled discrete memristor to design a new four-dimensional memristive hyperchaotic map. Firstly, the stability of the two maps is discussed. And their dynamical properties are compared using Lyapunov exponential spectra and bifurcation diagrams. Then, the phase diagram and iteration sequence of the 4D memristive hyperchaotic map are obtained. Meanwhile, we investigate the hyperchaotic states, the transient chaos, state transfer and attractor coexistence phenomena of the four-dimensional memristive map. In particular, the special state transfer phenomenon of switching from a periodic attractor to a quasi-periodic attractor and the special coexistence phenomenon of a quasi-periodic attractor coexisting with a quasi-periodic attractor around fixed points are found, which have not been observed in other systems. Finally, the phase-track diagrams and iterative sequence diagrams of the four-dimensional memristive map are verified on a digital experimental platform, revealing its potential for practical applications.

Objective To compare the effects of repairing and not repairing the lateral ulnar collateral ligament (LUCL) when surgically treating elbow varus posteromedial rotatory instability (PMRI). Methods In this retrospective study spanning June 2014 to February 2019, 24 patients with elbow PMRI who were treated surgically were assigned to group RL (Repair LUCL) or group NL (Non‐repair LUCL) depending on whether the LUCL was repaired. Hospitalization time, operation time, intraoperative blood loss, and related complications were reviewed. The elbow range of motion (ROM), the visual analog scale (VAS), the Mayo elbow performance score (MEPS), and the disabilities of the arm, shoulder, and hand (DASH) score were used for functional assessment. Results Among the 24 patients with PMRI, 15 were assigned to group RL and nine were assigned to group NL. The mean blood loss (184.66 ± 20.3 vs 207.33 ± 19.447, P

Abstract Obesity is a serious health issue in the world and is related to a higher risk of suffering metabolic diseases. Understanding the molecular basis of obesity is critical to identify new targets to treat obesity and obesity‐associated metabolic diseases. N6‐methyladenosine (m6A) modification is the most common form of ribonucleic acid modification, which has attracted increasing interest of researchers in recent years, as it is reported that m6A has vital functions in diseases and everyday life activities. Recent studies showed that m6A modification was decreased in obese adipose tissue, and appeared to play a regulatory role in many obesity‐associated biological processes, including adipogenesis, lipid metabolism and insulin resistance. In this review, we discussed the emerging advances in m6A modification in obesity to provide a novel therapeutic strategy for fighting against obesity.

Osteoarthritis (OA) is one of the progressing chronic joint associated with by many complex factors such as age, obesity, and trauma. Knee osteoarthritis (KOA) is the most common type of OA. KOA is characterized by articular cartilage destruction and degeneration, synovial inflammation, and abnormal subchondral bone changes. To date, no practical clinical approach has been able to modify the pathological progression of KOA. Drug therapy is limited to pain control and may lead to serious side effects when taken for a long time. Therefore, searching for safer and more reliable treatments has become necessary. Interestingly, more and more research has focused on natural products, and monomeric compounds derived from natural products have received much attention as drug candidates for KOA treatment. Resveratrol (RES), a natural phenolic compound, has various pharmacological and biological activities, including anti-cancer, anti-apoptotic, and anti-decay. Recently, studies on the effects of RES on maintaining the normal homeostasis of chondrocytes in KOA have received increasing attention, which seems to be attributed to the multi-targeted effects of RES on chondrocyte function. This review summarizes preclinical trials, clinical trials, and emerging tissue engineering studies of RES for KOA and discusses the specific mechanisms by which RES alleviates KOA. A better understanding of the pharmacological role of RES in KOA could provide clinical implications for intervention in the development of KOA.

Mathematical models of tumor-immune interactions provide an analytic framework for studying tumor-immune dynamics. In this paper, we present a mathematical model to describe tumor-immune cell interactions, focusing on the role of the natural killer (NK) cells and CD8+ cytotoxic T lymphocytes (CTLs) in immune surveillance. According to the experimental and clinical results, we determine part of the model parameters to reduce the model parameter space. Then we analyze the local geometric properties of the equilibria of model and carry out numerical simulations to verify the conditions for the stability properties of equilibrium points. Numerical results suggest that the host immune system alone is not fully effective against progression of tumor cells, and CTLs play a crucial role in immune surveillance.

One of the major challenges in single-cell data analysis is the determination of cellular developmental trajectories using single-cell data. Although substantial studies have been conducted in recent years, more effective methods are still strongly needed to infer the developmental processes accurately. This work devises a new method, named DTFLOW, for determining the pseudo-temporal trajectories with multiple branches. DTFLOW consists of two major steps: a new method called Bhattacharyya kernel feature decomposition (BKFD) to reduce the data dimensions, and a novel approach named Reverse Searching on k-nearest neighbor graph (RSKG) to identify the multi-branching processes of cellular differentiation. In BKFD, we first establish a stationary distribution for each cell to represent the transition of cellular developmental states based on the random walk with restart algorithm, and then propose a new distance metric for calculating pseudotime of single cells by introducing the Bhattacharyya kernel matrix. The effectiveness of DTFLOW is rigorously examined by using four single-cell datasets. We compare the efficiency of DTFLOW with the published state-of-the-art methods. Simulation results suggest that DTFLOW has superior accuracy and strong robustness properties for constructing pseudotime trajectories. The Python source code of DTFLOW can be freely accessed at https://github.com/statway/DTFLOW.

Aiming at the influence of wind power and load uncertainty on the transient stability of a power system under low carbon mode, this paper first proposes a collaborative preventive and emergency control model of transient stability by distribution preserving graph representation learning (DPG). Second, the uncertainty set of wind power output and load demand is studied, and the mathematical form of the two-stage robust transient stability collaborative control model is proposed. Then, the latest artificial intelligence technology is embedded into the global optimization algorithm of the model so as to further improve the solving efficiency of the algorithm. Finally, based on the developed improved two-stage robust optimization framework, an effective collaborative control method for transient stability is developed. The transient stability prediction and control system developed in this project is not only conducive to large-scale wind power grid connection but also expected to make academic contributions to development of power system transient stability and practical simulation verification.

Studies have shown that sexual transmission, both heterosexually and homosexually, is one of the main ways of HBV infection. Based on this fact, we propose a mathematical model to study the sexual transmission of HBV among adults by classifying adults into men and women and considering both same-sex and opposite-sex transmissions of HBV in adults. Firstly, we calculate the basic reproduction number $ R_{0} $ and the disease-free equilibrium point $ E_{0} $ . Secondly, by analysing the sensitivity of $ R_{0} $ in terms of model parameters, we find that the infection rate among people who have same-sex partners, the frequency of homosexual contact and the immunity rate of adults play important roles in the transmission of HBV. Moreover, we use our model to fit the reported data in China and forecast the trend of hepatitis B. Our results demonstrate that popularizing the basic knowledge of HBV among residents, advocating healthy and reasonable sexual life style, reducing the number of adult carriers, and increasing the immunization rate of adults are effective measures to prevent and control hepatitis B.

Designing energy efficient, flexible opto-electronic systems integrated with textiles remains a challenge. Here, the authors propose a solution-based blow-spinning technique for 3D flexible and stretchable metal oxide nanofiber networks for multimodal and monolithically integrated wearable electronics.

This paper proposes a new comprehensive integrated planning strategy for the resilience enhancement of power system, including determining the transmission expansion and sectionalizing-based optimal black start (BS) resources allocation of battery energy storage system (BESS) during extreme events. The planning model comprises of two stages, namely the normal stage and resilience stage. The first stage is to make the collaborative planning decisions, i.e., constructing transmission lines and installation of BESS as BS sources. The second stage minimizes the power system generation cost and load shedding cost under disaster conditions. The planning algorithm is developed to describe the uncertainty of transmission lines outage status, which can be affected by the normal stage expansion states. In algorithm ways, the model was solved with a duality-based column and constraint generation (D-CCG) method. Taking IEEE 30-bus and 39-bus system for examples and GUROBI/CPLEX was utilized to solve proposed model. Numerical cases show the effectiveness of the model and the superiority of the solution strategy.

Achilles tendon injury has become a common sports injury clinically, and its treatment and rehabilitation are essential, while the regenerative capacity of the Achilles tendon in adult mammals is limited. Therefore, it is necessary to promote the repair and remodelling of the Achilles tendon through efficient interventions. Biodegradable polymer materials are one of the most popular in the treatment and repair of soft tissues, ligaments, muscles, and organs injured by organisms to enhance the function of their wounded sites. Thus, it plays a specific role in “compensation” and is widely used in clinical medicine and rehabilitation. This review summarized the progress of poly (ε-caprolactone), polylactic acid, poly (lactic-co-glycolic acid), poly (trimethylene carbonate) (PTMC), and polydioxanone (PDS) in repairing Achilles tendon injury, indicating that the biodegradable polymers have succeeded in improving and treating Achilles tendon injuries. However, some problems such as lack of good affinity with cells and uncontrollable degradation of the biodegradable polymers should be overcome in repairing Achilles tendon injury. Therefore, the development of modified biodegradable polymers to make them an ideal repair material that meets the requirements is vital in improving Achilles tendon injuries. With the continuous development and close cooperation of life sciences and material sciences, excellent materials for repairing Achilles tendon injuries will undoubtedly be produced. The treatment of Achilles tendon injuries will be more straightforward, which will be a boon for many athletes.

Osteoarthritis (OA) is a common degenerative joint disease and is a leading cause of disability and reduced quality of life worldwide. There are currently no clinical treatments that can stop or slow down OA. Drugs have pain-relieving effects, but they do not slow down the course of OA and their long-term use can lead to serious side effects. Therefore, safe and clinically appropriate long-term treatments for OA are urgently needed. Autophagy is an intracellular protective mechanism, and targeting autophagy-related pathways has been found to prevent and treat various diseases. Attenuation of the autophagic pathway has now been found to disrupt cartilage homeostasis and plays an important role in the development of OA. Therefore, modulation of autophagic signaling pathways mediating cartilage homeostasis has been considered as a potential therapeutic option for OA. Phytochemicals are active ingredients from plants that have recently been found to reduce inflammatory factor levels in cartilage as well as attenuate chondrocyte apoptosis by modulating autophagy-related signaling pathways, which are not only widely available but also have the potential to alleviate the symptoms of OA. We reviewed preclinical studies and clinical studies of phytochemicals mediating autophagy to regulate cartilage homeostasis for the treatment of OA. The results suggest that phytochemicals derived from plant extracts can target relevant autophagic pathways as complementary and alternative agents for the treatment of OA if subjected to rigorous clinical trials and pharmacological tests.

The rapid progress in biological experimental technologies has generated a huge amount of experimental data to investigate complex regulatory mechanisms. Various mathematical models have been proposed to simulate the dynamic properties of molecular processes using the experimental data. However, it is still difficult to estimate unknown parameters in mathematical models for the dynamics in different cells due to the high demand for computing power. In this work, we propose a population statistical inference algorithm to improve the computing efficiency. In the first step, this algorithm clusters single cells into a number of groups based on the distances between each pair of cells. In each cluster, we then infer the parameters of the mathematical model for the first cell. We propose an adaptive approach that uses the inferred parameter values of the first cell to formulate the prior distribution and acceptance criteria of the following cells. Three regulatory network models were used to examine the efficiency and effectiveness of the designed algorithm. The computational results show that the new method reduces the computational time significantly and provides an effective algorithm to infer the parameters of regulatory networks in a large number of cells.

The aim of this paper is to elucidate the risk of lung carcinogenesis from cigarette smoking among current and former smokers. To achieve this goal, we have set up a stochastic three-stage model to fit the data of Surveillance, Epidemiology, and End Results (SEER) program besides the data set of smoking derived from the Nurses' Health Study cohort of females (NHS) and the Health Professionals Follow up Study cohort of men (HPFS). The calculations are performed by considering both mutation and clonal expansion rates as parameters in each compartment. For current smokers, our findings show that cigarette smoking has more significant impact on the mutation rates of cells than the clonal expansion rates of premalignant cells among men and women. In particular, for male patients, cigarette smoking affects the mutation in normal cells and the transformation from premalignant cells to malignant ones in the optimal model. In addition, cigarette smoking induces only the initial mutation rates in normal cells among American women. For current and former smokers, cigarette smoking stimulates only the clonal expansion rate of the first premalignant cells in both sexes. However, we find that the impact of cigarette smoking is minimal in former smokers who have stopped smoking for over ten years among men and women in US.

In this paper we study an SLIR epidemic model with nonmonotonic incidence rate, which describes the psychological effect of certain serious diseases on the community when the number of infectives is getting larger. By carrying out a global analysis of the model and studying the stability of the disease-free equilibrium and the endemic equilibrium, we show that either the number of infective individuals tends to zero or the disease persists as time evolves. For the stochastic model, we prove the existence, uniqueness and positivity of the solution of the model. Then, we investigate the stability of the model and we prove that the infective tends asymptotically to zero exponentially almost surely as $R_0 < 1$. We also proved that the SLIR model has the ergodic property as the fluctuation is small, where the positive solution converges weakly to the unique stationary distribution.

This paper proposes a simplified four-level (S4L) inverter based dynamic voltage restorer (DVR). In the proposed configuration, the dual buck stage can output full, two thirds, and one third of dc link voltage. Including the zero voltage provided by the two-level inverter, the output of the proposed inverter can be four levels. With the same switching frequency, the S4L inverter based DVR achieves better performance than the existing two-level and three-level inverters based DVRs. Moreover, compared with the existing four-level inverter based DVRs, it requires less active switches/diodes and only requires a single DC power source. The experimental results are provided for the validation of the proposed system.

In this paper, we have set up a mathematical model on the basic life cycle of clonorchiasis to fit the data of human clonorchiasis infection ratios of Guangzhou City of Guangdong Province in China from 2006-2012. By this model, we have proved that the condition of the basic reproductive number $R_0>1$ or $R_0 < 1$ corresponds the globally asymptotically stable of the endemic equilibrium or the disease-free equilibrium, respectively. The basic reproductive number is estimated as $1.41$ with those optimal parameters. Some efficient strategies to control clonorchiasis are provided by numerical analysis of the mathematical model.

The use of the semiconductor heterojunction channel layer has been explored as a method for improving the performance of metal oxide thin-film transistors (TFTs). The excellent electrical performance and stability of heterojunction TFTs is easy for vacuum-based techniques, but difficult for the solution process. Here, we fabricated In2O3/In2O3:Gd (In2O3/InGdO) heterojunction TFTs using a solution process and compared the electrical properties with single-layer In2O3 TFTs and In2O3:Gd (InGdO) TFTs. The In2O3/InGdO TFT consisted of a highly conductive In2O3 film as the primary transmission layer and a subconductive InGdO film as the buffer layer, and exhibited excellent electrical performance. Furthermore, by altering the Gd dopant concentration, we obtained an optimal In2O3/InGdO TFT with a higher saturation mobility (µ) of 4.34 cm2V−1s−1, a near-zero threshold voltage (Vth), a small off-state current (Ioff) of 1.24×10−9 A, a large on/off current ratio (Ion/Ioff) of 3.18×105, a small subthreshold swing (SS), and an appropriate positive bias stability (PBS). Finally, an aging test was performed after three months, indicating that In2O3/InGdO TFTs enable long-term air stability while retaining a high-mobility optimal switching property. This study suggests that the role of a high-performance In2O3/InGdO heterojunction channel layer fabricated by the solution process in the TFT is underlined, which further explores a broad pathway for the development of high-performance, low-cost, and large-area oxide electronics.

The rolling bearing is a crucial component of the rotating machine, and it is particularly vital to ensure its normal operation. In addition, the selection of different category features will add uncertainty and bias to the classification results. In order to decrease the interference of these factors to fault diagnosis, a new method that automatically learns the features of the data combined with Markov transition field (MTF) and convolutional neural network (CNN) is proposed in this paper, namely MTF-CNN. The MTF contributes to convert the original time series into corresponding figures, and the CNN is used to extract the deep feature information in the figure to complete the fault diagnosis. The effectiveness of the proposed method is verified by two public data sets. The experimental results show that MTF-CNN can classify different types of faults, and the highest accuracy rate can reach 100%. Likewise, the classification accuracy of this method is higher than some existing methods.

One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the inferred networks, and choose the edges with the largest weights as the putative regulations between genes or proteins. The developed method is rigorously assessed by the six benchmark networks in DREAM challenges, the mitogen-activated protein (MAP) kinase pathway, and a cancer-specific gene regulatory network. The inferred networks are compared with those obtained by using two up-to-date inference methods. The accuracy of the inferred networks shows that the developed method is effective for discovering molecular regulatory systems.

Cancer diseases lead to the second-highest death rate all over the world. For treating tumors, one of the most common schemes is chemotherapy, which can decrease the tumor size and control the progression of cancer diseases. To better understand the mechanisms of chemotherapy, we developed a mathematical model of tumor growth under chemotherapy. This model includes both immune system response and drug therapy. We characterize the symmetrical properties and dynamics of this differential equation model by finding the equilibrium points and exploring the stability and symmetry properties in a range of model parameters. Sensitivity analyses suggest that the chemotherapy drug-induced tumor mortality rate and the drug decay rate contribute significantly to the determination of treatment outcomes. Numerical simulations highlight the importance of CTL activation in tumor chemotherapy.

Hematopoiesis is a highly complex developmental process that produces various types of blood cells. This process is regulated by different genetic networks that control the proliferation, differentiation, and maturation of hematopoietic stem cells (HSCs). Although substantial progress has been made for understanding hematopoiesis, the detailed regulatory mechanisms for the fate determination of HSCs are still unraveled. In this study, we propose a novel approach to infer the detailed regulatory mechanisms. This work is designed to develop a mathematical framework that is able to realize nonlinear gene expression dynamics accurately. In particular, we intended to investigate the effect of possible protein heterodimers and/or synergistic effect in genetic regulation. This approach includes the Extended Forward Search Algorithm to infer network structure (top-down approach) and a non-linear mathematical model to infer dynamical property (bottom-up approach). Based on the published experimental data, we study two regulatory networks of 11 genes for regulating the erythrocyte differentiation pathway and the neutrophil differentiation pathway. The proposed algorithm is first applied to predict the network topologies among 11 genes and 55 non-linear terms which may be for heterodimers and/or synergistic effect. Then, the unknown model parameters are estimated by fitting simulations to the expression data of two different differentiation pathways. In addition, the edge deletion test is conducted to remove possible insignificant regulations from the inferred networks. Furthermore, the robustness property of the mathematical model is employed as an additional criterion to choose better network reconstruction results. Our simulation results successfully realized experimental data for two different differentiation pathways, which suggests that the proposed approach is an effective method to infer the topological structure and dynamic property of genetic regulations.

Abstract The development of p-type metal oxide thin-film transistors (TFTs) is far behind the n-type counterparts. Here, p-type CuAlO2 thin films were deposited by spin coating and annealed in nitrogen atmosphere at different temperature. The effect of post-annealing temperature on the microstructure, chemical compositions, morphology, and optical properties of the thin films was investigated systematically. The phase conversion from a mixture of CuAl2O4 and CuO to nanocrystalline CuAlO2 was achieved when annealing temperature was higher than 900 °C, as well as the transmittance, optical energy band gap, grain size, and surface roughness of the films increase with the increase of annealing temperature. Next, bottom-gate p-type TFTs with CuAlO2 channel layer were fabricated on SiO2/Si substrate. It was found that the TFT performance was strongly dependent on the physical properties and the chemical composition of channel layer. The optimized nanocrystalline CuAlO2 TFT exhibits a threshold voltage of − 1.3 V, a mobility of ~ 0.1 cm2 V−1 s−1, and a current on/off ratio of ~ 103. This report on solution-processed p-type CuAlO2 TFTs represents a significant progress towards low-cost complementary metal oxide semiconductor logic circuits.

Recent advances in experimental biology studies have produced large amount of molecular activity data. In particular, individual patient data provide non-time series information for the molecular activities in disease conditions. The challenge is how to design effective algorithms to infer regulatory networks using the individual patient datasets and consequently address the issue of network symmetry. This work is aimed at developing an efficient pipeline to reverse-engineer regulatory networks based on the individual patient proteomic data. The first step uses the SCOUT algorithm to infer the pseudo-time trajectory of individual patients. Then the path-consistent method with part mutual information is used to construct a static network that contains the potential protein interactions. To address the issue of network symmetry in terms of undirected symmetric network, a dynamic model of ordinary differential equations is used to further remove false interactions to derive asymmetric networks. In this work a dataset from triple-negative breast cancer patients is used to develop a protein-protein interaction network with 15 proteins.

In general, media coverage would not be implemented unless the number of infected cases reaches some critical number. To reflect this feature, we incorporate the media effect and a critical number of infected cases into the disease transmission rate and consider an susceptible-infected-susceptible epidemic model with logistic growth. Our model analysis shows that early media alert and strong media effects are preferable to decrease the numbers of infected cases at endemic equilibria. Furthermore, we noticed that the model may have up to three endemic equilibria and bi-stability can occur in a threshold interval for the critical number. Note that the interval depends on parameters for the focal disease and the media effect. It is possible to roughly estimate the interval for re-emerging diseases in a given region. Therefore, the result could be useful to health policymakers. Global stability is also obtained when the model admits a unique endemic equilibrium.

In this paper, we study the geometric properties of a class of nonlinear polynomial vector fields in $\mathbb{R}^3$. By virtue of their induced vector fields, their global topological structures are discussed and we get that there are at least 82 types of invariant regions with different topological classification without considering the closed orbit. Finally, we give a sufficient condition of the existence of a closed orbit of the vector field.

With the penetration of grid-connected renewable energy generation, microgrids are facing stability and power quality problems caused by renewable intermittency. To alleviate such problems, demand side management (DSM) of responsive loads, such as building air-conditioning system (BACS), has been proposed and studied. In recent years, numerous control approaches have been published for proper management of single BACS. The majority of these approaches focus on either the control of BACS for attenuating power fluctuations in the grid or the operating cost minimization on behalf of the residents. These two control objectives are paramount for BACS control in microgrids and can be conflicting. As such, they should be considered together in control design. As individual buildings may have different owners/residents, it is natural to control different BACSs in an autonomous and self-interested manner to minimize the operational costs for the owners/residents. Unfortunately, such “selfish” operation can result in abrupt and large power fluctuations at the point of common coupling (PCC) of the microgrid due to lack of coordination. Consequently, the original objective of mitigating power fluctuations generated by renewable intermittency cannot be achieved. To minimize the operating costs of individual BACSs and simultaneously ensure desirable overall power flow at PCC, this paper proposes a novel distributed control framework based on the dissipativity theory. The proposed method achieves the objective of renewable intermittency mitigation through proper coordination of distributed BACS controllers and is scalable and computationally efficient. Simulation studies are carried out to illustrate the efficacy of the proposed control framework.

The penetration of solar photovoltaic (PV) systems in residential areas contributes to the generation and usage of renewable energy. Despite its advantages, the PV system also creates problems caused by the intermittency of renewable energy. As suggested by researchers, such problems deteriorate the applicability of the PV system and have to be resolved by employing a battery energy storage system (BESS). With concern for the high investment cost, the choice of a cost-effective BESS with proper sizing is necessary. To this end, this paper proposes the employment of a vanadium redox flow battery (VRB), which possesses a long cycle life and high energy efficiency, for residential users with PV systems. It further proposes methods of computing the capital and maintenance cost of VRB systems and evaluating battery efficiency based on VRB electrochemical characteristics. Furthermore, by considering the cost and efficiency of VRB, the prevalent time-of-use electricity price, the solar feed-in tariff, the solar power profile and the user load pattern, an optimal sizing algorithm for VRB systems is proposed. Simulation studies are carried out to show the effectiveness of the proposed methods.

Embryonic stem cells are conventionally differentiated by modulating specific growth factors in the cell culture media. Recently the effect of cellular mechanical microenvironment in inducing phenotype specific differentiation has attracted considerable attention. We have shown the possibility of inducing endoderm differentiation by culturing the stem cells on fibrin substrates of specific stiffness. Here, we analyze the regulatory network involved in such mechanically induced endoderm differentiation under two different experimental configurations of 2-dimensional and 3-dimensional culture, respectively. Mouse embryonic stem cells are differentiated on an array of substrates of varying mechanical properties and analyzed for relevant endoderm markers. The experimental data set is further analyzed for identification of co-regulated transcription factors across different substrate conditions using the technique of bi-clustering. Overlapped bi-clusters are identified following an optimization formulation, which is solved using an evolutionary algorithm. While typically such analysis is performed at the mean value of expression data across experimental repeats, the variability of stem cell systems reduces the confidence on such analysis of mean data. Bootstrapping technique is thus integrated with the bi-clustering algorithm to determine sets of robust bi-clusters, which is found to differ significantly from corresponding bi-clusters at the mean data value. Analysis of robust bi-clusters reveals an overall similar network interaction as has been reported for chemically induced endoderm or endodermal organs but with differences in patterning between 2-dimensional and 3-dimensional culture. Such analysis sheds light on the pathway of stem cell differentiation indicating the prospect of the two culture configurations for further maturation.

Repeated acoustic events are ubiquitous temporal features of natural sounds. To reveal the neural representation of the sound repetition rate, a number of electrophysiological studies have been conducted on various mammals and it has been proposed that both the spike-time and firing rate of primary auditory cortex (A1) neurons encode the repetition rate. However, previous studies rarely examined how the experimental animals perceive the difference in the sound repetition rate, and a caveat to these experiments is that they compared physiological data obtained from animals with psychophysical data obtained from humans. In this study, for the first time, we directly investigated acoustic perception and the underlying neural mechanisms in the same experimental animal by examining spike activities in the A1 of free-moving cats while performing a Go/No-go task to discriminate the click-trains at different repetition rates (12.5-200 Hz). As reported by previous studies on passively listening animals, A1 neurons showed both synchronized and non-synchronized responses to the click-trains. We further found that the neural performance estimated from the precise temporal information of synchronized units was good enough to distinguish all 16.7-200 Hz from the 12.5 Hz repetition rate; however, the cats showed declining behavioral performance with the decrease of the target repetition rate, indicating an increase of difficulty in discriminating two slower click-trains. Such behavioral performance was well explained by the firing rate of some synchronized and non-synchronized units. Trial-by-trial analysis indicated that A1 activity was not affected by the cat's judgment of behavioral response. Our results suggest that the main function of A1 is to effectively represent temporal signals using both spike timing and firing rate, while the cats may read out the rate-coding information to perform the task in this experiment.