Periostin Promotes the Proliferation, Differentiation and Mineralization of Osteoblasts from Ovariectomized Rats.

Perimenopausal period causes a significant amount of bone loss, which results in primary osteoporosis (OP). The Periostin (Postn) may play important roles in the pathogenesis of OP after ovariectomized (OVX) rats. To identify the roles of Postn in the bone marrow mesenchymal stem cell derived osteoblasts (BMSC-OB) in OVX rats, we investigated the expression of Wnt/ß-catenin signaling pathways in BMSC-OB and the effects of Postn on bone formation by development of BMSC-OB cultures. Twenty-four female Sprague-Dawley rats at 6 months were randomized into 3 groups: sham-operated (SHAM) group, OVX group and OVX+Postn group. The rats were killed after 3 months, and their bilateral femora and tibiae were collected for BMSC-OB culture, Micro-CT Analysis, Bone Histomorphometric Measurement, Transmission Electron Microscopy and Immunohistochemistry Staining. The dose/time-dependent effects of Postn on the proliferation, differentiation and mineralization of BMSC-OB and the expression of osteoblastic markers were measured in in vitro experiments. We found increased Postn increased bone mass, promoted bone formation of trabeculae, Wnt signaling and the osteogenic activity in osteoblasts in sublesional femur. Postn have the function to enhance cell proliferation, differentiation and mineralization at a proper concentration and incubation time. Interestingly, in BMSC-OB from OVX rats treated with the different dose of Postn, the osteoblastic markers expression and Wnt/ß-catenin signaling pathways were significantly promoted. The direct effect of Postn may lead to inhibit excessive bone resorption and increase bone formation through the Wnt/ß-catenin signaling pathways after OVX. Postn may play a very important role in the pathogenesis of OP after OVX.

Alleviation of iron deficiency in pear by ammonium nitrate and nitric oxide.

BACKGROUND: Iron is essential for the growth and development of trace elements in plants, and iron deficiency can lead to leaf chlorosis. Ammonium and nitrate are the major forms of nitrogen present in soils. Ammonium nitrate alleviates the chlorosis of leaves caused by iron deficiency, but the mechanism is not clear in pear. RESULTS: Ammonium nitrate induced the increase of nitric oxide (NO) under iron deficiency. We further analyzed the effect of NO by exogenous NO treatment. The results showed that ammonium nitrate and NO increased the activity of ferric chelate reductase. NO induced the expression of multiple IRT genes and promoted the transmembrane transport of irons. Ammonium nitrate and NO promoted the activity of nitrogen assimilation-related enzymes and the nitrogen absorption capacity, and they also increased glutamine synthetase activity. Finally, ammonium nitrate and NO increased chlorophyll synthesis, with subsequent increase in the photosynthetic capacity of plants and accumulation of biomass. CONCLUSION: Ammonium nitrate indirectly alleviates the symptoms of plant yellowing by promoting the increase of NO, which increases the response of iron transporters. Both substances increase the nitrogen accumulation in plants. This study demonstrates a new option for minimizing Fe deficiency by regulating the balance between nutrients.

Molecular-Level Zn-Ion Transfer Pump Specifically Functioning on (002) Facets Enables Durable Zn Anodes.

The modification of metallic Zn anode contributes to solving the cycling issue of Zn-ion batteries (ZIBs) by restraining the dendrite growth and side reactions. In this regard, modulating (002) Zn is an effective way to prolong the lifespan of ZIBs with a parallel arrangement of Zn deposition. Herein, the authors propose to add trace amounts of Zn(BF4 )2 additive in 3 M ZnSO4 to promote in-plane Zn deposition by forming a BF4 - -[Zn(H2 O)6 ]2+ -[Zn(BF4 )3 ]- transfer process and specifically functioning on (002) facets. In this way, the optimized electrolyte highly boosts the cycling stability of Zn anodes with a long lifespan at 34.2% Zn utilization (500 h/10 mA cm-2 ) and 51.3% Zn utilization (360 h/10 mA cm-2 ; 834 h/1 mA cm-2 ). Moreover, the electroplated Zn on Cu substrate exhibits a competitive cumulative plating capacity (CPC) of 2.87 Ah cm-2 under harsh conditions. The assembled Zn|(NH4 )2 V6 O16 ·3H2 O full cells with a high cathode loading of 29.12 mg cm-2 also realizes almost no capacity degradation even after 2000 cycles at 2 A g-1 . With this cost-effective strategy, it is promising to push the development of aqueous ZIBs as well as provide inspiration for metal anode optimization in other energy storage systems.

The origins of segregation behaviors of solute atoms and their effect on the strength of α-Al//θ'-Al2Cu interfaces in Al-Cu alloys.

In many alloy systems, the segregation and strengthening of the solute atoms are caused by mechanical and chemical contributions. To uncover the origins of segregation behaviors and strengthening behaviors of the solute atoms Cd, Si, Sc and Zr at (001)α-Al//(001)θ' and (010)α-Al//(010)θ' interfaces, first-principles calculations were conducted. Results show that the chemical contribution primarily dominates the oscillatory segregation behaviors of Cd, Si, Sc and Zr on the Al matrix side. The oscillatory segregation behaviors of Cd, Si, Sc and Zr on the θ' side are mainly governed by both chemical and mechanical contributions. The segregation tendency of Cd at the (001)α-Al//(001)θ' interface (or (010)α-Al//(010)θ' interface) throughout the platelets is small (or strong) because the charge accumulation between Cd and the host atoms is weak (or significant). The segregation trend of Sc (or Zr) on the Al matrix side at the (001)α-Al//(001)θ' and (010)α-Al//(010)θ' interfaces is strong, which is attributed to significant charge accumulation between Sc (or Zr) and the host atoms. Si exhibits a favorable segregation tendency on the θ' side at both the (001)α-Al//(001)θ' and (010)α-Al//(010)θ' interfaces, which is ascribed to significant charge accumulation between Si and the host atoms. With the increase of Si, Sc and Zr coverage, the segregation tendencies of Si, Sc and Zr enhance. The segregation tendency of Cd decreases with the increase of Cd coverage. The first-principles tensile test for the interface was conducted. The work of dislocation emission was computed. Results show that the strengthening effects of solute atoms on the interface are primarily dominated by the chemical contribution. Sc (or Zr) segregation leads to an increase in the strength of the interface, which is majorly attributed to a strong electronic interaction between Sc (or Zr) and the host atoms. Cd segregation causes a weakening effect on the interface because of the weak electronic interaction between Cd and the host atoms. The ductility of the (001)α-Al//(001)θ' interface with the Sc (or Zr) is more significant than that with the Cd (or Si). This work provides a strategy for improving the mechanical properties of the Al-Cu alloys.

Chemokine (C-C Motif) Ligand 2/Chemokine Receptor 2 (CCR2) Axis Blockade to Delay Chondrocyte Hypertrophy as a Therapeutic Strategy for Osteoarthritis.

BACKGROUND Chondrocytes play a vital role in the later stages of osteoarthritis (OA). The roles of chemokine (C-C motif) ligand 2 (CCL2) and its receptor, chemokine receptor 2 (CCR2), are as yet poorly elucidated in chondrocyte hypertrophy (CH). Here, we aimed to regulate the CCL2/CCR2 axis and explore its effect on progression of CH. MATERIAL AND METHODS Chondrocytes isolated from patients with OA were used in the present study. In vitro experiments were conducted to test hypertrophic gene and CCL2/CCR2 expression in chondrocyte degeneration caused by interleukin (IL)-17A or CCL2 protein stimulation. In addition, inhibition of CCL2 and CCR2 was used to assess the role of CCL2 and CCR2 blockade in CH. Relative gene expression was determined with real-time polymerase chain reaction, western blot, or immunofluorescence. Hypertrophic changes were assessed with cell area measurement. Moreover, the viability of chondrocytes was analyzed using an MTT assay and flow cytometry was used to assess cell apoptosis. RESULTS CCL2 and CCR2 were upregulated in IL-17A-treated chondrocytes. The exogenic CCL2 stimulation also promoted CH and increased the expression of Type 10 collagen, RUNX2, and IHH, which could be reversed via suppression of CCR2. Inhibition of CCL2 and CCR2 expression was sufficient to: 1) protect Type 2 collagen synthesis; 2) alleviate IL-17A-induced overexpression of Type 10 collagen, RUNX2, and IHH; and 3) improve chondrocyte proliferation and apoptosis. CONCLUSIONS Blockading the CCL2/CCR2 axis plays a role in delaying the development of CH.

Improved Local Search with Momentum for Bayesian Networks Structure Learning.

Bayesian Networks structure learning (BNSL) is a troublesome problem that aims to search for an optimal structure. An exact search tends to sacrifice a significant amount of time and memory to promote accuracy, while the local search can tackle complex networks with thousands of variables but commonly gets stuck in a local optimum. In this paper, two novel and practical operators and a derived operator are proposed to perturb structures and maintain the acyclicity. Then, we design a framework, incorporating an influential perturbation factor integrated by three proposed operators, to escape current local optimal and improve the dilemma that outcomes trap in local optimal. The experimental results illustrate that our algorithm can output competitive results compared with the state-of-the-art constraint-based method in most cases. Meanwhile, our algorithm reaches an equivalent or better solution found by the state-of-the-art exact search and hybrid methods.

Topology-Dependent Brownian Gyromotion of a Single Skyrmion.

Noninteracting particles exhibiting Brownian motion have been observed in many occasions of sciences, such as molecules suspended in liquids, optically trapped microbeads, and spin textures in magnetic materials. In particular, a detailed examination of Brownian motion of spin textures is important for designing thermally stable spintronic devices, which motivates the present study. In this Letter, through using temporally and spatially resolved polar magneto-optic Kerr effect microscopy, we have experimentally observed the thermal fluctuation-induced random walk of a single isolated Néel-type magnetic skyrmion in an interfacially asymmetric Ta/CoFeB/TaO_{x} multilayer. An intriguing topology-dependent Brownian gyromotion behavior of skyrmions has been identified. The onset of Brownian gyromotion of a single skyrmion induced by thermal effects, including a nonlinear temperature-dependent diffusion coefficient and topology-dependent gyromotion are further formulated based on the stochastic Thiele equation. The experimental and numerical demonstration of topology-dependent Brownian gyromotion of skyrmions can be useful for understanding the nonequilibrium magnetization dynamics and implementing spintronic devices.

Beneficial consequences of Lupeol on middle cerebral artery-induced cerebral ischemia in the rat involves Nrf2 and P38 MAPK modulation.

Lupeol has been reported to exhibit anti-inflammatory and anti-tumor activities in many diseases, but its potential effects in cerebral ischemia injury have not been studied to date. In this work we present evidence for a beneficial effect of lupeol in a rat model of middle cerebral artery occlusion (MCAO) followed by reperfusion (MCAO/R) injury and provide some histological and biochemical evidence for its mechanism of action. A cerebral MCAO rat model was established by vascular occlusion for 2 h, followed by 24 h reperfusion period. The infarct volume, neurological deficits, and brain water content were compared with animals treated during reperfusion with different concentrations of lupeol. Macroscopic parameters, cell viability, pro-inflammatory factors generation, as well as oxidative stress parameters and associated apoptotic signaling cascades were evaluated. Treatment with lupeol significantly reduced the cerebral infarct volume and water content and recovered neuro behavioral functions in affected rats. Lupeol treatment down-regulated the expression of oxidative stress and inflammation factors. In addition, lupeol activated Nrf2, suppressed caspase-3 activity, reduced BAX/Bcl-2 ratio and inhibited phosphorylation of p38 MAPK. The data suggest that lupeol may exert protective effects against cerebral ischemia by suppressing oxidative stress and reduction of inflammation factors possible via activation of nuclear transcription factors and inhibition of cell death pathways.

Microglia TREM2: A Potential Role in the Mechanism of Action of Electroacupuncture in an Alzheimer's Disease Animal Model.

Alzheimer's disease (AD) is one of the most serious public health concerns facing the world. Its characteristic feature is neuroinflammation due to microglial activation. Electroacupuncture is one of the therapies employed to improve the condition of patients with AD, although its mechanism of action is still to be determined. Triggering receptor expressed on myeloid cells 2 (TREM2) is a microglia-specific receptor that is involved in regulating neuroinflammation in AD. In this study, we applied senescence-accelerated mouse-prone 8 mice as the AD animal model, used the Morris water maze, and applied hematoxylin and eosin staining, immunofluorescence double staining, and Western blotting, to explore the effects and potential mechanisms of action of electroacupuncture. In summary, this study suggested that electroacupuncture treatment could improve the learning and memory abilities (p < 0.05) and protect neurons. These effects result from acupuncture could upregulate TREM2 expression in the hippocampus (p < 0.01), which was essential for the anti-inflammatory effects in the AD animal model. However, further studies are needed to conclusively demonstrate the mechanism of action of electroacupuncture in AD.

Ultimately Bounded Filtering for Time-Delayed Nonlinear Stochastic Systems with Uniform Quantizations under Random Access Protocol.

This paper investigates the ultimately bounded filtering problem for a kind of time-delay nonlinear stochastic systems with random access protocol (RAP) and uniform quantization effects (UQEs). In order to reduce the occurrence of data conflicts, the RAP is employed to regulate the information transmissions over the shared communication channel. The scheduling behavior of the RAP is characterized by a Markov chain with known transition probabilities. On the other hand, the measurement outputs are quantized by the uniform quantizer before being transmitted via the communication channel. The objective of this paper is to devise a nonlinear filter such that, in the simultaneous presence of RAP and UQEs, the filtering error dynamics is exponentially ultimately bounded in mean square (EUBMS). By resorting to the stochastic analysis technique and the Lyapunov stability theory, sufficient conditions are obtained under which the desired nonlinear filter exists, and then the filter design algorithm is presented. At last, two simulation examples are given to validate the proposed filtering strategy.

Thioglycolic acid-capped ZnSe quantum dots as nanoprobe for cobalt(II) and iron(III) via measurement of grey level, UV-vis spectra and dynamic light scattering.

Thioglycolic acid-functionalized ZnSe quantum dots (QDs) as a colorimetric nanoprobe were prepared and applied to the determination of cobalt(II) and iron(III). Test strips were obtained by a dipping-drying process. On exposure to Co(II), they undergo a color change from white to brown, and on exposure to Fe(III) from white to pink. The limits of detection (LOD) are 2.6 mg L-1 for Co(II) and 2.2 mg L-1 for Fe(III). Test strips introduce a low-cost, portable, rapid and convenient tool for determination of Co(II) and Fe(III). In addition, two other analytical methods have been studied for detection of Co(II) and Fe(III) at low concentration. The first is UV-vis spectrometry which has a LOD as low as 0.14 mg L-1 for Co(II) (at 412 nm) and 0.12 mg L-1 for Fe(III) (at 400 nm). The second is dynamic light scattering (DLS) with a LOD of 3.0 µg L-1 for Co(II) and 2.5 µg L-1 for Fe(III). Graphical abstract Thioglycolic acid-functionalized ZnSe quantum dots (TGA-ZnSe QDs) show high sensitivity and low detection limits for Co2+ and Fe3+.

Mechanistic Insight into DNA-Guided Control of Nanoparticle Morphologies.

Although shapes and surface characteristics of nanoparticles are known to play important roles in defining their properties, it remains challenging to fine-tune the morphologies systematically and predictably. Recently, we have shown that DNA molecules can serve as programmable ligands to fine-tune the morphologies of nanomaterials. Despite this discovery, the mechanism of how the morphology can be controlled and the roles of the DNA molecules in contributing to such control are not understood. We herein report mechanistic investigation of DNA-mediated morphological evolution of gold nanoprism seeds into nonagon, hexagon, and six-pointed stars, some of which display rough surfaces, in the presence of homo-oligomeric T30, G20, C30, and A30. The growth, elucidated through various analytical methods including UV-vis, SEM, TEM, zeta potential, fluorescence, and cyclic voltammetry, is found to occur in two stages: control of shape, followed by control of thickness. A careful analysis of diffraction patterns of the nanoprism seeds as well as the resulting intermediate shapes by TEM allowed us to deduce the exact sequence of shape evolution. Through systematic comparison of the nanoparticle growth process, the DNA molecules were found to play important roles by influencing diffusion of the Au precursor to the seed and modulating the growth through differences in DNA desorption, density, and mobility on the seed surface. These insights into the mechanism of DNA-guided control of nanomaterial morphologies provide deeper understanding of the interactions between the DNA and nanomaterials and will allow better control of the shapes and surface properties of many nanomaterials.

DNA-Encoded Tuning of Geometric and Plasmonic Properties of Nanoparticles Growing from Gold Nanorod Seeds.

Systematically controlling the morphology of nanoparticles, especially those growing from gold nanorod (AuNR) seeds, are underexplored; however, the AuNR and its related morphologies have shown promises in many applications. Herein we report the use of programmable DNA sequences to control AuNR overgrowth, resulting in gold nanoparticles varying from nanodumbbell to nanooctahedron, as well as shapes in between, with high yield and reproducibility. Kinetic studies revealed two representative pathways for the shape control evolving into distinct nanostructures. Furthermore, the geometric and plasmonic properties of the gold nanoparticles could be precisely controlled by adjusting the base compositions of DNA sequences or by introducing phosphorothioate modifications in the DNA. As a result, the surface plasmon resonance (SPR) peaks of the nanoparticles can be fine-tuned in a wide range, from visible to second near-infrared (NIR-II) region beyond 1000 nm.

Multi-view graph pooling with coarsened graph disentanglement.

Multi-view graph pooling utilizes information from multiple perspectives to generate a coarsened graph, exhibiting superior performance in graph-level tasks. However, existing methods mainly focus on the types of multi-view information to improve graph pooling operations, lacking explicit control over the pooling process and theoretical analysis of the relationships between views. In this paper, we rethink the current paradigm of multi-view graph pooling from an information theory perspective, subsequently introducing GDMGP, an innovative method for multi-view graph pooling derived from the principles of graph disentanglement. This approach effectively simplifies the original graph into a more structured, disentangled coarsened graph, enhancing the clarity and utility of the graph representation. Our approach begins with the design of a novel view mapper that dynamically integrates the node and topology information of the original graph. This integration enhances its information sufficiency. Next, we introduce a view fusion mechanism based on conditional entropy to accurately regulate the task-relevant information in the views, aiming to minimize information loss in the pooling process. Finally, to further enhance the expressiveness of the coarsened graph, we disentangle the fused view into task-relevant and task-irrelevant subgraphs through mutual information minimization, retaining the task-relevant subgraph for downstream tasks. We theoretically demonstrate that the performance of the coarsened graph generated by our GDMGP is superior to that of any single input view. The effectiveness of GDMGP is further validated by experimental results on seven public datasets.

Choice Function-Based Hyper-Heuristics for Causal Discovery under Linear Structural Equation Models.

Causal discovery is central to human cognition, and learning directed acyclic graphs (DAGs) is its foundation. Recently, many nature-inspired meta-heuristic optimization algorithms have been proposed to serve as the basis for DAG learning. However, a single meta-heuristic algorithm requires specific domain knowledge and empirical parameter tuning and cannot guarantee good performance in all cases. Hyper-heuristics provide an alternative methodology to meta-heuristics, enabling multiple heuristic algorithms to be combined and optimized to achieve better generalization ability. In this paper, we propose a multi-population choice function hyper-heuristic to discover the causal relationships encoded in a DAG. This algorithm provides a reasonable solution for combining structural priors or possible expert knowledge with swarm intelligence. Under a linear structural equation model (SEM), we first identify the partial v-structures through partial correlation analysis as the structural priors of the next nature-inspired swarm intelligence approach. Then, through partial correlation analysis, we can limit the search space. Experimental results demonstrate the effectiveness of the proposed methods compared to the earlier state-of-the-art methods on six standard networks.

A novel fixed-time error-monitoring neural network for solving dynamic quaternion-valued Sylvester equations.

This paper addresses the dynamic quaternion-valued Sylvester equation (DQSE) using the quaternion real representation and the neural network method. To transform the Sylvester equation in the quaternion field into an equivalent equation in the real field, three different real representation modes for the quaternion are adopted by considering the non-commutativity of quaternion multiplication. Based on the equivalent Sylvester equation in the real field, a novel recurrent neural network model with an integral design formula is proposed to solve the DQSE. The proposed model, referred to as the fixed-time error-monitoring neural network (FTEMNN), achieves fixed-time convergence through the action of a state-of-the-art nonlinear activation function. The fixed-time convergence of the FTEMNN model is theoretically analyzed. Two examples are presented to verify the performance of the FTEMNN model with a specific focus on fixed-time convergence. Furthermore, the chattering phenomenon of the FTEMNN model is discussed, and a saturation function scheme is designed. Finally, the practical value of the FTEMNN model is demonstrated through its application to image fusion denoising.

State Estimation for Markovian Jump Neural Networks Under Probabilistic Bit Flips: Allocating Constrained Bit Rates.

In this article, the state estimation problem is studied for Markovian jump neural networks (MJNNs) within a digital network framework. The wireless communication channel with limited bandwidth is characterized by a constrained bit rate, and the occurrence of bit flips during wireless transmission is mathematically modeled. A transmission mechanism, which includes coding-decoding under bit-rate constraints and considers probabilistic bit flips, is introduced, providing a thorough characterization of the digital transmission process. A mode-dependent remote estimator is designed, which is capable of effectively capturing the internal state of the neural network. Furthermore, a sufficient condition is proposed to ensure the estimation error to remain bounded under challenging network conditions. Within this theoretical framework, the relationship between the neural network's estimation performance and the bit rate is explored. Finally, a simulation example is provided to validate the theoretical findings.

Distributed Proportional-Integral Fuzzy State Estimation Over Sensor Networks Under Energy-Constrained Denial-of-Service Attacks.

This article deals with the distributed proportional-integral state estimation problem for nonlinear systems over sensor networks (SNs), where a number of spatially distributed sensor nodes are utilized to collect the system information. The signal transmissions among different sensor nodes are realized via their individual channels subject to energy-constrained Denial-of-Service (EC-DoS) cyber-attacks launched by the adversaries whose aim is to block the nodewise communications. Such EC-DoS attacks are characterized by a sequence of attack starting time-instants and a sequence of attack durations. Based on the measurement outputs of each node, a novel distributed fuzzy proportional-integral estimator is proposed that reflects the topological information of the SNs. The estimation error dynamics is shown to be regulated by a switching system under certain assumptions on the frequency and the duration of the EC-DoS attacks. Then, by resorting to the average dwell-time method, a unified framework is established to analyze the dynamical behaviors of the resultant estimation error system, and sufficient conditions are obtained to guarantee the stability as well as the weighted H∞ performance of the estimation error dynamics. Finally, a numerical example is given to verify the effectiveness of the proposed estimation scheme.

First-and Second-Order Sliding Mode Control Design for Networked 2-D Systems Under Round-Robin Protocol.

This article investigates the sliding mode control (SMC) problem for a class of uncertain 2-D systems described by the Roesser models with a bounded disturbance. In order to reduce the communication usage between the controller and the actuators, it is supposed that only one actuator node can gain the access to the network at each sampling time along horizontal or vertical direction, where a proper 2-D round-robin protocol is designed to periodically regulate the access token and a set of zero-order holders (ZOHs) is employed to keep the other actuator nodes unchanged until the next renewed signal arrives. Based on a novel 2-D common sliding function, a token-dependent 2-D SMC scheme with first-order sliding mode is appropriately constructed to cope with the impacts from the periodic scheduling signal and the ZOHs. Furthermore, a novel super-twisting-like 2-D SMC scheme with second-order sliding mode is designed to improve the robustness against the bounded disturbance. By resorting to token-dependent Lyapunov-like function, sufficient conditions are obtained to guarantee the ultimate boundedness of the horizontal and vertical states as well as the 2-D common sliding function. For acquiring the optimized gain matrices, two searching algorithms are formulated to solve two optimization problems arising from finding optimized control performance. Finally, two comparative examples are exploited to demonstrate the effectiveness and the advantageous of the proposed first-and second-order 2-D SMC design schemes under round-robin scheduling mechanism.

Efficient Predefined-Time Adaptive Neural Networks for Computing Time-Varying Tensor Moore-Penrose Inverse.

This article proposes predefined-time adaptive neural network (PTANN) and event-triggered PTANN (ET-PTANN) models to efficiently compute the time-varying tensor Moore-Penrose (MP) inverse. The PTANN model incorporates a novel adaptive parameter and activation function, enabling it to achieve strongly predefined-time convergence. Unlike traditional time-varying parameters that increase over time, the adaptive parameter is proportional to the error norm, thereby better allocating computational resources and improving efficiency. To further enhance efficiency, the ET-PTANN model combines an event trigger with the evolution formula, resulting in the adjustment of step size and reduction of computation frequency compared to the PTANN model. By conducting mathematical derivations, the article derives the upper bound of convergence time for the proposed neural network models and determines the minimum execution interval for the event trigger. A simulation example demonstrates that the PTANN and ET-PTANN models outperform other related neural network models in terms of computational efficiency and convergence rate. Finally, the practicality of the PTANN and ET-PTANN models is demonstrated through their application for mobile sound source localization.