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The purpose of this paper is to model a nacre-like composite material, consisting of tablets and polyurea tablet/tablet interfaces, B₄C. This composite material is being considered in the construction of the so-called backing-plate, a layer within a multi-functional/multi-layer armor system.

Considering the basic functions of the backing-plate (i.e. to provide structural support for the ceramic-strike-face and to stop a high-velocity projectile and the accompanying fragments) in such an armor system, the composite-material architecture is optimized with respect to simultaneously achieving high flexural stiffness and high ballistic-penetration resistance. Flexural stiffness and penetration resistance, for a given architecture of the nacre-like composite material, are assessed using a series of transient non-linear dynamics finite-element analyses. The suitability of the optimized composite material for use in backing-plate applications is then evaluated by comparing its performance against that of the rolled homogeneous armor (RHA), a common choice for the backing-plate material.

The results obtained established: a trade-off between the requirements for a high flexural stiffness and a high ballistic-penetration resistance in the nacre-like composite material; and overall superiority of the subject composite material over the RHA when used in the construction of the backing-plate within multi-functional/multi-layer armor systems.

This study extends the authors previous research on nacre-mimetic armor to optimize the architecture of the armor with respect to its flexural stiffness and ballistic-penetration resistance, so that these properties could be increased over the levels attained in the current choice (RHA) for the backing layer of multi-functional/multi-layer armor.

Photographic films and glass plates are widely used as phototools for PCB production. In this two‐part paper photographic and physical characteristics of the products are discussed as well as their differences and specific features. Terms such as contrast, density, speed, processing, etc. are explained and dimensional stability is discussed in depth. Suggestions are made for proper handling of the photographic materials.

Polyester multifilament is used to produce the face and back layer of warp knitted spacer fabric (WKSF) and these two layers are connected by polyester monofilament as a middle layer. This fabric has unique and extraordinary characteristics, and different possibilities of fabric structure and the middle layer thickness are tried to find out the moisture management properties. The paper aims to discuss these issues.

This study investigates the influence of fabric thickness and structure on moisture management properties.

Polyester monofilament quickly up takes the water molecule from the water reservoir and transfers it by capillary action. The gravitational force and the availability space between the two outer surface layers restrict the movement of water molecules, although the pressure develops to push the molecules from the water reservoir. As a result, all the spacer fabric samples attain the equilibrium state very quickly. WKSF and the hexagonal net structure prove to be better in vertical wicking.

The liquid movement is quick in the front side of the spacer fabric, and the rate of wicking is higher in open structure than in the closed structure. It confirms that the hexagonal net structure produces high pore size on fabric and it reaches maximum wicking values. Fabric thickness does not have much influence on the vertical wicking properties of all fabric samples, and the rate of liquid movement produces a similar trend. In in-plane wicking, the polyester monofilament in the middle layer of spacer fabric plays a major role rather than the outer surface layers of fabric.

Double-jacquard technique is referred as the most advanced technology for forming patterns on both layers of a 3D fabric knitted on a double-needle bar warp-knitting machine. In order to realize the computer-aided design and simulation of jacquard patterns, the purpose of this paper is to propose a mathematic model for representation of jacquard structures and an improved mass-spring model to improve the simulation of structural deformation behavior.

Primarily, it analyzes the jacquard patterning method and displacing principle to design jacquard structures on each layer and linking structures of two layers. Based on that, a loop geometry defined by six key points and segmental lines is built to transfer the jacquard bitmap and lapping movements into a fabric of loops and therefore realizing patterns visualization. Afterwards, an improved mass-spring model is built to simulate structural deformation, in which the fabric is simplified as a mesh of uniformly distributed mass particles. Each loop is treated as a massless particle while underlaps are referred as structural springs connecting loops particles. Elastic forces of these springs on each loop particle is calculated according to the Hook’s law and Newton’s second law, and then based on the explicit Euler’s equations, motion state of each particle is solved including the velocity and the shift.

Based on the above method, a simulator for double-layer jacquard fabrics is developed via Visual C++ language to visualize the patterned fabrics with pitting effects. With a jacquard shoe fabric as an example, this simulation model is proved to be practical and efficient by comparing the simulation result and real fabric.

Because of limited researches, 3D simulation modeling of this double-layer jacquard fabric will be studied in the further research.

The implement of this simulation method will offer the industries a time-saving and cost-saving approach for new fabrics development.

This approach can be used as a reference for simulating other knitted fabrics with jacquard patterns, such as jacquard garment fabrics and home textile fabrics.

The main challenge in preparing body armor is achieving a high protection level by using lightweight materials with minimum cost.

In this study, a three-hybrid multilayered armor system is prepared for protection against a ballistic impact wave. These armor systems consist of glass or ceramic tile as a front layer followed by three intermediate layers made of woven fiber reinforced polymer composites and a back layer made of either aluminum or polypropylene.

All armor systems were successful in impeding the projectile from perforating, that is materials selection played an important role in stopping the ballistic impact wave. Almost an identical ballistic behavior was recorded between the experimental and numerical simulation by using ANSYS AUTODYN which means that the simulation could be used in advance to reduce the time required for practical experiments and the cost of using materials in experimental tests will be lessened. The effect of projectile geometry also had been studied, and it showed a noticeable role in changing ballistic behavior.

The originality of this research is in using carbon and glass fiber which are woven together in addition to adding polypropylene layers in armor preparation.

The purpose of this paper is to study the effect of individual layers of cadmium telluride (CdTe) solar cell to improve the efficiency of the photovoltaic cell.

To improve the performances of CdTe thin-film solar cells, the thickness of CdTe and cadmium sulfide (CdS) have been modified separately. High-efficiency ultra-thin CdTe solar cell with ZnTe layer as back surface field (BSF) was achieved. The CdTe solar cell is under AM1.5 g illumination using a one-dimensional (1-D) model, i.e. personal computer one dimensional (PC1D).

The highest conversion efficiency of about 15.3 per cent was achieved for ultrathin CdTe solar cell with a ZnTe BSF layer. The results of simulation were compared with experimental and analytical results by other researchers.

In this paper, according to the authors’ knowledge ZnO:Al/CdS/CdTe/ZnTe is simulated by PC1D model for the first time and is compared with experimental result (ZnO:Al/CdS/CdTe). The results show a suitable performance.

The paper aims to use the Trilobal® polyester (Y cross-section) for producing fabrics suitable for fencing suits and evaluating their various properties.

Double weave structure was chosen to produce the samples by using six different face structures and two back structures divided into two groups according to the back structures. They were evaluated by their physical and mechanical properties such as tensile strength, puncture resistance, air permeability and humidity properties in horizontal and vertical wicking, drying rate and water vapor transmission.

Fencing sport recently is one of the most growing sports in the world, which necessitates special requirements and properties of fencing suit, either mechanical properties, which allow the easily and freely movement for the athlete, or the comfort properties that save the player’s effort and energy for a long time to improve his performance.

ANOVA test analysis showed highly significant results in some properties comparing back and face structures of the double weave fabric high correlation coefficient were found between packing density factor of produced fabric and the weft material types. The final results showed the produced sample that weaved with plain 1/1 for back structure and warp rib 2/2 for face structure achieved the best results, followed by the produced sample weaved with plain 1/1 for back structure and weft rib 2/2 for face structure compared with the other produced samples.

Because the dynamic characteristics of the stock market are nonlinear, it is unclear whether stock prices can be predicted. This paper aims to explore the predictability of the stock price index from a long-memory perspective. The authors propose hybrid models to predict the next-day closing price index and explore the policy effects behind stock prices. The paper aims to discuss the aforementioned ideas.

The authors found a long memory in the stock price index series using modified R/S and GPH tests, and propose an improved bi-directional gated recurrent units (BiGRU) hybrid network framework to predict the next-day stock price index. The proposed framework integrates (1) A de-noising module—Singular Spectrum Analysis (SSA) algorithm, (2) a predictive module—BiGRU model, and (3) an optimization module—Grid Search Cross-validation (GSCV) algorithm.

Three critical findings are long memory, fit effectiveness and model optimization. There is long memory (predictability) in the stock price index series. The proposed framework yields predictions of optimum fit. Data de-noising and parameter optimization can improve the model fit.

The empirical data are obtained from the financial data of listed companies in the Wind Financial Terminal. The model can accurately predict stock price index series, guide investors to make reasonable investment decisions, and provide a basis for establishing individual industry stock investment strategies.

If the index series in the stock market exhibits long-memory characteristics, the policy implication is that fractal markets, even in the nonlinear case, allow for a corresponding distribution pattern in the value of portfolio assets. The risk of stock price volatility in various sectors has expanded due to the effects of the COVID-19 pandemic and the R-U conflict on the stock market. Predicting future trends by forecasting stock prices is critical for minimizing financial risk. The ability to mitigate the epidemic’s impact and stop losses promptly is relevant to market regulators, companies and other relevant stakeholders.

Although long memory exists, the stock price index series can be predicted. However, price fluctuations are unstable and chaotic, and traditional mathematical and statistical methods cannot provide precise predictions. The network framework proposed in this paper has robust horizontal connections between units, strong memory capability and stronger generalization ability than traditional network structures. The authors demonstrate significant performance improvements of SSA-BiGRU-GSCV over comparison models on Chinese stocks.

The purpose of this paper is to optimize the simultaneous flight performance of a hexarotor unmanned aerial vehicle (UAV) by using simultaneous perturbation stochastic approximation (i.e. SPSA), deep neural network and proportional integral derivative (i.e. PID) according to varying arm length (i.e. morphing).

In this paper, proper PID gain coefficients and morphing ratio were obtained using the stochastic optimization method, also known as SPSA to maximize flight efficiency. Because it is difficult to establish an analytical connection between the morphing ratio and hexarotor moments of inertia, the deep neural network was used to obtain the moments of inertia according to the morphing ratio. By using SPSA and deep neural network, the best performance indexes were obtained and both longitudinal and lateral flight simulations were performed with the obtained data.

With SPSA, the best PID coefficients and morphing ratio are obtained for both longitudinal and lateral flight. Because the hexarotor solid body model changes according to the morphing ratio, the moment of inertia values used in the simulations also change. According to the morphing ratio, the moment of inertia values was obtained with the deep neural network over a created data set.

It takes a long time to obtain the morphing ratio suitable for the hexarotor model and the PID gain coefficients suitable for this morphing ratio. However, this situation can be overcome with the proposed SPSA. In addition, it takes a long time to obtain the appropriate moments of inertia according to the morphing ratio. However, in this case, it was overcome using the deep neural network.

Determining the morphing ratio and PID gain coefficients using the optimization method, as well as determining the moments of inertia using the deep neural network, is very useful as it can increase the efficiency of hexarotor flight and flight efficiently with different arm lengths. With the proposed method, the hexarotor design performance criteria (i.e. rise time, settling time and overshoot) values were significantly improved compared to similar studies.

Determining the hexarotor flight parameters using SPSA and deep neural network provides advantages in terms of time, cost and applicability.

The hexarotor flight efficiency is improved with the proposed SPSA and deep neural network approaches. In addition, the desired flight parameters can be obtained more quickly and reliably with the proposed approaches. The design performance criteria were also improved, enabling the hexarotor UAV to follow the given trajectory in the best way and providing convenience for end users. SPSA was preferred because it converged faster than other methods. While other methods perform 2n operations per iteration, SPSA only performs two operations. To obtain the moment of inertia, many physical parameter values of the UAV are required in the existing methods. In the proposed method, by creating a date set, only arm length and moment of inertia were estimated without the need to obtain physical parameters with the deep neural network structure.