Sonochemical Synthesis and Structural Characterization and DFT Calculations of a Novel Nano Flower Pb(II) Coordination Compound [Pb(phen)2(4-abs)2]n

Nano-structures of a new coordination polymer of divalent Pb with the ligands 1,10-phenanthroline (phen) and 4-aminobenzene sulfonic acid (4-abs), [Pb(phen)₂(4-abs)₂]_n (1), were synthesized by a sonochemical method that produced a coordination polymer of nanometer size. The new nano-structure was characterized by scanning electron microscopy, X-ray powder diffraction and IR spectroscopy and elemental analysis. Compound 1 was structurally characterized by single crystal X-ray diffraction and showed that the coordination number of the Pb(II) ions is six; i.e. (PbN₄O₂). Compound 1 also has a stereochemically active lone pair of electrons and a hemi-directed coordination sphere. The chains interact with each other through π–π stacking to create a 3D framework. The structure of 1 was optimized by density functional theory calculations. The calculated structural parameters and IR spectra of 1 are consistent with the crystal structure.     

Human action recognition using Pose-based discriminant embedding

Manifold learning is an efficient approach for recognizing human actions. Most of the previous embedding methods are learned based on the distances between frames as data points. Thus they may be efficient in the frame recognition framework, but they will not guarantee to give optimum results when sequences are to be classified as in the case of action recognition in which temporal constraints convey important information. In the sequence recognition framework, sequences are compared based on the distances defined between sets of points. Among them Spatio-temporal Correlation Distance (SCD) is an efficient measure for comparing ordered sequences. In this paper we propose a novel embedding which is optimum in the sequence recognition framework based on SCD as the distance measure. Specifically, the proposed embedding minimizes the sum of the distances between intra-class sequences while seeking to maximize the sum of distances between inter-class points. Action sequences are represented by key poses chosen equidistantly from one action period. The action period is computed by a modified correlation-based method. Action recognition is achieved by comparing the projected sequences in the low-dimensional subspace using SCD or Hausdorff distance in a nearest neighbor framework. Several experiments are carried out on three popular datasets. The method is shown not only to classify the actions efficiently obtaining results comparable to the state of the art on all datasets, but also to be robust to additive noise and tolerant to occlusion, deformation and change in view point. Moreover, the method outperforms other classical dimension reduction techniques and performs faster by choosing less number of postures.     

Germ cells from mouse and human embryonic stem cells

Mammalian gametes are derived from a founder population of primordial germ cells (PGCs) that are determined early in embryogenesis and set aside for unique development. Understanding the mechanisms of PGC determination and differentiation is important for elucidating causes of infertility and how endocrine disrupting chemicals may potentially increase susceptibility to congenital reproductive abnormalities and conditions such as testicular cancer in adulthood (testicular dysgenesis syndrome). Primordial germ cells are closely related to embryonic stem cells (ESCs) and embryonic germ (EG) cells and comparisons between these cell types are providing new information about pluripotency and epigenetic processes. Murine ESCs can differentiate to PGCs, gametes and even blastocysts - recently live mouse pups were born from sperm generated from mESCs. Although investigations are still preliminary, human embryonic stem cells (hESCs) apparently display a similar developmental capacity to generate PGCs and immature gametes. Exactly how such gamete-like cells are generated during stem cell culture remains unclear especially as in vitro conditions are ill-defined. The findings are discussed in relation to the mechanisms of human PGC and gamete development and the biotechnology of hESCs and hEG cells.     

Modifications to the Lax–Wendroff scheme for hyperbolic systems with source terms

For wave phenomena in one spatial dimension, governed by hyperbolic partial differential equations with source terms, the standard Lax–Wendroff scheme leads to oscillations at discontinuous wavefronts even if the Courant–Friedrich–Lewy (CFL) number is set equal to unity. We modify the Lax–Wendroff scheme for hyperbolic systems with source terms based on characteristic analysis to preserve the wave profile correctly when the CFL number is set equal to 1. The new scheme can be used as easily as the original Lax–Wendroff scheme since the calculation of the characteristics is not introduced in the new scheme. Thus, additional computations of characteristics are not necessary. We also extend our method for higher spatial dimensions and illustrate our approach by numerical examples. Copyright © 1999 John Wiley & Sons, Ltd.     

Synergistic use of ultra-high-performance fiber-reinforced concrete (UHPFRC) and carbon fiber-reinforced polymer (CFRP) for improving the impact resistance of concrete-filled steel tubes

Concrete-filled steel tubes (CFSTs) have received growing attention, owing to their rapid construction, reduced labor requirement, and reasonable material cost. While in service, the CFSTs can be subjected to unexpected impact loads, originating from vehicle and vessel collision, as well as water- and wind-borne debris impact. Such extreme loading events often cause a partial or complete failure of conventional CFSTs, risking the safety and performance of the entire structural systems that rely on them. To address this issue, the current study explores how two advanced composite materials, including ultra-high-performance fiber-reinforced concrete (UHPFRC) and carbon fiber-reinforced polymer (CFRP), can be utilized to provide superior mechanical properties and minimize the vulnerability of CFSTs to impact loads. The composite materials under consideration are appropriate for both new and existing structures, in which normal-strength concrete can be replaced with UHPFRC, while CFRP sheets can further strengthen the CFSTs. For obtaining in-depth insights, a computational framework validated with the experimental tests was developed in the current study. Using a set of representative impact scenarios, various response measures, such as internal forces and deflections, as well as the energy absorbed by the CFSTs, were recorded during impact simulations. The investigations were then further extended to capture the influence of the main design parameters related to concrete, CFRP, and steel tube. From the conducted investigations, an energy absorption index was introduced, as a measure to evaluate the performance of CFSTs under impact loads.     

Optimal design of double-layer barrel vaults using genetic and pattern search algorithms and optimized neural network as surrogate model

This paper presents a combined method based on optimized neural networks and optimization algorithms to solve structural optimization problems. The main idea is to utilize an optimized artificial neural network (OANN) as a surrogate model to reduce the number of computations for structural analysis. First, the OANN is trained appropriately. Subsequently, the main optimization problem is solved using the OANN and a population-based algorithm. The algorithms considered in this step are the arithmetic optimization algorithm (AOA) and genetic algorithm (GA). Finally, the abovementioned problem is solved using the optimal point obtained from the previous step and the pattern search (PS) algorithm. To evaluate the performance of the proposed method, two numerical examples are considered. In the first example, the performance of two algorithms, OANN + AOA + PS and OANN + GA + PS, is investigated. Using the GA reduces the elapsed time by approximately 50% compared with using the AOA. Results show that both the OANN + GA + PS and OANN + AOA + PS algorithms perform well in solving structural optimization problems and achieve the same optimal design. However, the OANN + GA + PS algorithm requires significantly fewer function evaluations to achieve the same accuracy as the OANN + AOA + PS algorithm.     

Diversity achieving full-duplex DF relaying with joint relay-antenna selection under Nakagami-m fading environment

One of the main imperfections degrading the performance of full-duplex (FD) relaying systems is the outage floor. In this work, a power scaling method is proposed, which overcomes this effect even when there does not exist a direct channel between source and destination nodes. The system is composed of $K$ decode-and-forward (DF) FD relays over the Nakagami-m fading environment. To promote system performance, joint antenna and relay selection methods are employed in the FD relaying systems. Each FD relay is equipped with multiple antennas for receiving and the other for transmitting the information. The transmitting and receiving antennas are chosen based on the instantaneous channel variations, and one relay is selected to improve the signal-to-interference and noise ratio (SINR) of the FD relaying system. The performance of the proposed design is investigated. Moreover, the closed-form equations of the ergodic capacity and outage probability are attained. The analytical results are confirmed by different simulations. Results indicate that the proposed design achieves an additional spatial diversity gain because of using the antenna selection at the relay nodes. Moreover, by power scaling (PS) method, the system performance is effectively improved compared to the conventional FD relaying structures.