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The rapid growth of Long Range (LoRa) devices has led to network congestion, reducing spectrum and energy efficiency. To address this problem, we propose an energy-efficient reinforcement learning method for distributed LoRa networks, enabling each device to independently select appropriate transmission parameters, i.e., channel, transmission power (TP), and bandwidth (BW) based on acknowledgment (ACK) feedback and energy consumption. Our method employs the Upper Confidence Bound (UCB)1-tuned algorithm and incorporates energy metrics into the reward function, achieving lower power consumption and high transmission success rates. Designed to be lightweight for resource-constrained IoT devices, it was implemented on real LoRa hardware and tested in dense network scenarios. Experimental results show that the proposed method outperforms fixed allocation, adaptive data rate low-complexity (ADR-Lite), and ϵ-greedy methods in both transmission success rate and energy efficiency.

It is essential to accurately diagnose bearing faults to avoid property losses or casualties in the industry caused by motor failures. Recently, the methods of fault diagnosis for bearings using deep learning methods have improved the safety of motor operations in a reliable and intelligent way. However, most of the work is mainly suitable for situations where there is sufficient monitoring data of the bearings. In industrial systems, only a small amount of monitoring data can be collected by the bearing sensors due to the harsh monitoring conditions and the short time of the signals of some special motor bearings. To solve the issue above, this paper introduces a transfer learning strategy by focusing on the multi-local model bearing fault based on small sample fusion. The algorithm mainly includes the following steps: (1) constructing a parallel Bi-LSTM sub-network to extract features from bearing vibration and current signals of industrial motor bearings, serially fusing the extracted vibration and current signal features for fault classification, and using them as a source domain fault diagnosis model; (2) measuring the distribution difference between the source domain bearing data and the target bearing data using the maximum mean difference algorithm; (3) based on the distribution differences between the source domain and the target domain, transferring the network parameters of the source domain fault diagnosis model, fine-tuning the network structure of the source domain fault diagnosis model, and obtaining the target domain fault diagnosis model. A performance evaluation reveals that a higher fault diagnosis accuracy under small sample fusion can be maintained by the proposed method compared to other methods. In addition, the early training time of the fault diagnosis model can be reduced, and its generalization ability can be improved to a great extent. Specifically, the fault diagnosis accuracy can be improved to higher than 80% while the training time can be reduced to 15.3% by using the proposed method.

In recent years, the industrial motor bearing fault diagnosis method based on deep learning and multi-source information fusion has made some research progress, and research results show that the uncertainty of noise interference and signal measurement error has been improved to a certain extent. However, the multi-source heterogeneous information of industrial motor bearings not only improves the stability and fault tolerance of the bearing fault diagnosis model but also brings conflicts in information fusion. If the conflicts caused by multi-source information cannot be reasonably resolved, it will be difficult to make further judgments on the bearing faults of industrial motors. Therefore, solving the multi-source information conflict effectively while fully using the complementarity of bearing multi-source heterogeneous information is an urgent problem to be solved in developing industrial motor-bearing fault diagnosis technology. This paper proposes an industrial motor bearing fault diagnosis algorithm based on multi-local model decision conflict resolution (MLMF-CR) to fully integrate multi-source heterogeneous information and reasonably resolve multi-source information conflicts. After the initial characteristic signal selection and cleaning of the vibration and current signals of industrial motor bearings, the algorithm deeply excavates the characteristic information of the bearing signals in each fault state through the local fault diagnosis model based on the bidirectional long short-term memory network (Bi-LSTM) and forms a local diagnosis. After the decision is made, evidence theory is used for fusion. In addition, the high conflict situation that may occur in the process of decision-making fusion is also considered. To this end, the trust degree distribution is introduced to reduce information conflict. Specifically, according to the difference in the sensitivity and reliability of bearing faults under different operating environments or specific conditions, the degree of difference in faults is refined into balanced sensitivity and unbalanced sensitivity. When the fault sensitivity is balanced, the trust of different information sources is quantified by support and uncertainty. When the sensitivity is unbalanced, gray relational analysis is used to assign trust degrees to different information sources. The algorithm can effectively resolve the high degree of conflict in the decision-making fusion process while considering the complementarity of multi-source heterogeneous information. Experiments evaluate the effectiveness of the proposed method.

Mechanotransduction in endothelial cells is critical to maintain vascular homeostasis and can contribute to disease development, yet the molecules responsible for sensing flow remain largely unknown. Here, we demonstrate that the discoidin domain receptor 1 (DDR1) tyrosine kinase is a direct mechanosensor and is essential for connecting the force imposed by shear to the endothelial responses. We identify the flow-induced activation of endothelial DDR1 to be atherogenic. Shear force likely causes conformational changes of DDR1 ectodomain by unfolding its DS-like domain to expose the buried cysteine-287, whose exposure facilitates force-induced receptor oligomerization and phase separation. Upon shearing, DDR1 forms liquid-like biomolecular condensates and co-condenses with YWHAE, leading to nuclear translocation of YAP. Our findings establish a previously uncharacterized role of DDR1 in directly sensing flow, propose a conceptual framework for understanding upstream regulation of the YAP signaling, and offer a mechanism by which endothelial activation of DDR1 promotes atherosclerosis. Mechanotransduction in endothelial cells is critical to maintain vascular homeostasis. Here, the authors show that the discoidin domain receptor 1 tyrosine kinase is a mechanosensor is essential for connecting the force imposed by shear to endothelial responses.

Long-Range (LoRa) devices have been deployed in many Internet of Things (IoT) applications due to their ability to communicate over long distances with low power consumption. The scalability and communication performance of the LoRa systems are highly dependent on the spreading factor (SF) and channel allocations. In particular, it is important to set the SF appropriately according to the distance between the LoRa device and the gateway since the signal reception sensitivity and bit rate depend on the used SF, which are in a trade-off relationship. In addition, considering the surge in the number of LoRa devices recently, the scalability of LoRa systems is also greatly affected by the channels that the LoRa devices use for communications. It was demonstrated that the lightweight decentralized learning-based joint channel and SF-selection methods can make appropriate decisions with low computational complexity and power consumption in our previous study. However, the effect of the location situation of the LoRa devices on the communication performance in a practical larger-scale LoRa system has not been studied. Hence, to clarify the effect of the location situation of the LoRa devices on the communication performance in LoRa systems, in this paper, we implemented and evaluated the learning-based joint channel and SF-selection methods in a practical LoRa system. In the learning-based methods, the channel and SF are decided only based on the ACKnowledge information. The learning methods evaluated in this paper were the Tug of War dynamics, Upper Confidence Bound 1, and ϵ-greedy algorithms. Moreover, to consider the relevance of the channel and SF, we propose a combinational multi-armed bandit-based joint channel and SF-selection method. Compared with the independent methods, the combinations of the channel and SF are set as arms. Conversely, the SF and channel are set as independent arms in the independent methods that are evaluated in our previous work. From the experimental results, we can see the following points. First, the combinatorial methods can achieve a higher frame success rate and fairness than the independent methods. In addition, the FSR can be improved by joint channel and SF selection compared to SF selection only. Moreover, the channel and SF selection dependents on the location situation to a great extent.

Fibrosis, a pathological alteration of the repair response, involves continuous organ damage, scar formation, and eventual functional failure in various chronic inflammatory disorders. Unfortunately, clinical practice offers limited treatment strategies, leading to high mortality rates in chronic diseases. As part of investigations into gaseous mediators, or gasotransmitters, including nitric oxide (NO), carbon monoxide (CO), and hydrogen sulfide (H2S), numerous studies have confirmed their beneficial roles in attenuating fibrosis. Their therapeutic mechanisms, which involve inhibiting oxidative stress, inflammation, apoptosis, and proliferation, have been increasingly elucidated. Additionally, novel gasotransmitters like hydrogen (H2) and sulfur dioxide (SO2) have emerged as promising options for fibrosis treatment. In this review, we primarily demonstrate and summarize the protective and therapeutic effects of gaseous mediators in the process of fibrosis, with a focus on elucidating the underlying molecular mechanisms involved in combating fibrosis.

The laser chaos decision maker has been demonstrated to enable ultra-high-speed solutions of multiarmed bandit problems or decision-making in the GHz order. However, the underlying mechanisms are not well understood. In this paper, we analyze the chaotic dynamics inherent in experimentally observed laser chaos time series via surrogate data and further accelerate the decision-making performance via parameter optimization. We first evaluate the negative autocorrelation in a chaotic time series and its impact on decision-making detail. Then, we analyze the decision-making ability using three different surrogate chaos time series to examine the underlying mechanism. We clarify that the negative autocorrelation of laser chaos improves decision-making and that the amplitude distribution of the original laser chaos time series is not optimal. Hence, we introduce a new parameter for adjusting the amplitude distribution of the laser chaos to enhance the decision-making performance. This study provides a new insight into exploiting the supremacy of chaotic dynamics in artificially constructed intelligent systems.

Abstract—STAT3 has neuroprotective effect via non-canonical activation and mitochondrial translocation, but its effect on ropivacaine-induced neurotoxicity remains unclear. Our previous study revealed that apoptosis was an important mechanism of ropivacaine-induced neurotoxicity; this study is to illustrate the relationship between STAT3 with ropivacaine-induced apoptosis. Those results showed that ropivacaine treatment decreased cell viability, induced cell cycle arrest in the G0/G1 phase, apoptosis, oxidative stress, and mitochondrial dysfunction in PC12 cells. Moreover, ropivacaine decreased the phosphorylated levels of STAT3 at Ser727 and downregulated the expression of STAT3 upstream gene IL-6. The mitochondrial translocation of STAT3 was also hindered by ropivacaine. To further illustrate the connection of STAT3 protein structure with ropivacaine, the autodock-vina was used to examine the interaction between STAT3 and ropivacaine, and the results showed that ropivacaine could bind to STAT3’s proline site and other sites. In addition, the activator and inhibitor of mitoSTAT3 translocation were used to demonstrate it was involved in ropivacaine-induced apoptosis; the results showed that enhancing the mitochondrial STAT3 translocation could prevent ropivacaine-induced apoptosis. Finally, the expression of p-STAT3 and the levels of apoptosis in the spinal cord were also detected; the results were consistent with the cell experiment; ropivacaine decreased the expression of p-STAT3 protein and increased the levels of apoptosis in the spinal cord. We demonstrated that ropivacaine induced apoptosis by inhibiting the phosphorylation of STAT3 at Ser727 and the mitochondrial STAT3 translocation. This effect was reversed by the activation of the mitochondrial STAT3 translocation.

In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.