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Abstract Viral nervous necrosis (VNN) poses a significant threat to the aquaculture industry, causing substantial losses and economic burdens. The disease, attributed to nervous necrosis viruses within the Betanodavirus genus, is particularly pervasive in the Mediterranean region, affecting various fish species across all production stages with mortality rates reaching 100%. Developing effective preventive measures against VNN is imperative. In this study, we employed rigorous immunoinformatics techniques to design a novel multi-epitope vaccine targeting VNN. Five RNA-directed RNA polymerases, crucial to the lifecycle of Betanodavirus, were selected as vaccine targets. The antigenicity and favorable physicochemical properties of these proteins were confirmed, and epitope mapping identified cytotoxic T lymphocyte, helper T lymphocyte, and linear B lymphocyte epitopes essential for eliciting a robust immune response. The selected epitopes, characterized by high antigenicity, non-allergenicity, and non-toxicity, were further enhanced by adding PADRE sequences and hBD adjuvants to increase immunogenicity. Two vaccine constructs were developed by linking epitopes using appropriate linkers, demonstrating high antigenicity, solubility, and stability. Molecular dynamics simulations revealed stable interactions between the vaccine constructs and Toll-like receptors (TLRs), essential for pathogen recognition and immune response activation in fish. Notably, vaccine construct V2 exhibited superior stability and binding affinity with TLR8, suggesting its potential as a promising candidate for VNN prevention. Overall, our study presents a comprehensive approach to VNN vaccine design utilizing immunoinformatics, offering safe, immunogenic, and effective solutions across multiple Betanodavirus species. Further experimental validation in model animals is recommended to fully assess the vaccine’s efficacy. This research contributes to improved vaccine development against diverse fish pathogens by addressing emerging challenges and individualized immunization requirements in aquaculture.

This paper offers a comprehensive overview of recent advancements in digital twin technology applied to additive manufacturing (AM), focusing on recent research trends, methodologies, and the integration of machine learning. By identifying emerging developments and addressing challenges, it serves as a roadmap for future research. Specifically, it examines various AM types, evolving trends, and methodologies within digital twin frameworks, highlighting the role of machine learning in enhancing AM processes. Ultimately, the paper aims to underscore the significance of digital twin technology in advancing smart manufacturing practices. A total of 133 papers were identified for analysis through IEEExplore, ScienceDirect, Web of Science, and Google Scholar and web resource. Approximately 74% of the papers are journals and 21% are conferences and proceedings. Moreover, 78% of the journal papers were Q1 journals. The paper identifies the potential benefits of digital twins at different levels, the existing problems associated with implementing digital twin in additive manufacturing, recent advancements, the existing approaches, and the framework. This review provides a comprehensive overview of the current landscape of research in digital twin technology for additive manufacturing, utilizing the latest resources to identify cutting-edge developments and methodologies. Through an exploration of potential benefits and implementation challenges, the review offers valuable insights to researchers and practitioners in the field. Additionally, it contributes to the discourse by offering a nuanced discussion on future research directions, paving the way for further advancements.

Additive manufacturing of PLA parts using wax-based filament as a support structure was investigated by implementing the 2-level 3-factors full factorial design of experiments (DoE). The aim of the experiments was to identify significant FDM factors that affect the surface roughness of the parent part in the PLA/Wax interfacial region. In addition, the important factors influencing bonding strength between PLA and wax were identified using the same full factorial DoE. The printing parameters chosen for the analysis are printing temperature, nozzle speed, and the pattern type at the interfacial area. The findings of the study show that the wax-based filament can be effectively used as support material with the condition that the PLA printing temperature is at its lowest value and the nozzle speed is close to 40 mm/s. With the optimal printing condition, the surface roughness value (Ra) can reach as low as 18 μm value and the bonding strength of 0.27 MPa at the interfacial area. The results of the study have revealed the capability of FDM technology to join materials having considerably dissimilar properties.

Antimicrobial resistance (AMR) is a growing public health concern globally, with the threat of a post-antibiotic era, where common infections can become fatal, a very plausible reality. Despite ongoing efforts to control AMR, both mortality and expenses have increased. To combat this threat, a thorough understanding of the mechanisms and the driver behind this issue needs to be known. The key mechanisms of resistance are modification or destruction of antimicrobials, reduction of access to the target, and alteration of the target. These mechanisms may be present in the microorganisms naturally or may have been acquired from other microorganisms. As AMR jeopardizes the successful prevention and treatment of many infectious diseases, this article looks at the causes of AMR, along with the possible mechanisms of resistance development, and suggested control strategies to deal with the problem conclusively. [ J Adv Biotechnol Exp Ther 2023; 6(2.000): 468-482]

This study focuses on the development of a large-dimensional 3D printer and its challenges in general. The major fused deposition modeling (FDM) printers focus on printing small-scale parts due to their challenges in printing large-scale objects using thermoplastic polymer filaments. A novel large-dimensional multi-extrusion FDM printer is developed at the workshop and printed several large-dimensional objects to emphasize its prospects in developing large-scale products. The printer has a print bed with a dimension of 900 mm × 1100 mm × 770 mm with respect to the length–width–height (L–W–H), respectively. There are many challenges to successfully printing large-dimensional objects using FDM technology. The experimental design elaborates on the challenges experienced during printing various large-dimensional objects. In addition, the paper focuses on the qualitative analysis of the optimal process parameters in section 4.5. Based on the experimental results, the key challenges are found to be uneven bed temperature, bending of print bed due to thermal effect, surface unsticks due to lack of adhesive force, surrounding temperature, and irregular filament feed. Experimental results also validate the key design specifications and their impact on enhancing large-scale 3D printing. The developed printer is capable of printing large-scale objects with five different thermoplastic materials using five individual extruders simultaneously. It adds a new dimension of flexible automation in additive manufacturing (AM).

Perovskites are presently being considered as a feasible choice for hole-transport materials in photovoltaic devices. One material that stands out among the options is Cs2SiX6, where X indicates Cl, Br, and I. This material is of special interest due to its potential as a lead-free alternative, offering a variety of halide options. Moreover, several experimental inquiries have exhibited favorable outcomes; nevertheless, their theoretical or computational framework is limited and insufficient. As a result, the present investigation has opted for the crystal Cs2SiX6 and synthesized Cs2SiX6, Cs2SiCl6, Cs2SiBr6 and Cs2SiI6 to examine their electronic structures and optical properties using the DFT functional. The electronic structure of Cs2SnBr6, was calculated using GGA with PBE functional, yielding a band gap of 2.434 eV. It should be noted that the experimental value of Cs2SnBr6 was 2.450 eV. Furthermore, the band gaps of Cs2SiCl6, Cs2SiBr6, and Cs2SiI6 were calculated by GGA with PBE to be 1.540 eV, 1.291 eV, and 0.261 eV, respectively. In this study, various pseudopotential techniques are employed to investigate the electrical structures. Five different densities functional theory (DFT) functionals are utilized to determine the most accurate functional. Additionally, the study focuses on the LDA, a unique junction photovoltaic material. In addition, the six optical properties, specifically absorption, reflection, refractive index, conductivity, dielectric function, and loss function, are calculated to provide additional understanding of material qualities in the presence of visual evidence. The utilization of the Density of States (DOS) and the Partial Density of States (PDOS) was employed in order to calculate the electronic structure and bonding properties. For a material to effectively operate as a single-junction photovoltaic, it is imperative that its band gap remains within the specified range of 0.261–1.540 eV. The materials being discussed are conserved within this designated range and employed for their intended purposes. In summary, our research yields strong evidence suggesting that the aforementioned materials lack carcinogenic qualities and demonstrate only moderate degrees of toxicity. In sharp contrast, it has been observed that perovskites containing lead exhibit considerably higher levels of toxicity.

This research paper inquires about different attributes of Computer-aided processes in garments production. This perspective Research was done by our courageous team from 2021 to 2022. It reveals adequate information on the Garments industry's Intension and criteria for choosing CAD/CAM software. For the sake of this Research, we visited more than 600 industries to gather raw data; Every Industry tried to attend this Research from a different region of Bangladesh willingly. After collecting all raw data from the garments industry. The data of the Garments industry was coordinated by Excel program. Consequently, the data was analyzed and implemented statistically to identify the Garments Industry attribute for satisfaction with CAD/CAM software. This process also detects many challenges and defines and advises a proper solution to the problems that the Garments industry is facing in the current situation. This research paper demonstrates adequate information about the Garment's criteria and demand in purchasing garments Computer-aided software.

Additive manufacturing is a promising manufacturing process with diverse applications, but ensuring the quality and reliability of the manufactured products are key challenges. The digital twin has emerged as a technology solution to address these challenge, allowing real-time monitoring and control of the manufacturing process. This paper proposes a digital twin system framework for additive manufacturing that integrates machine learning models, employing Unity, OctoPrint, and Raspberry Pi for real-time control and monitoring. Particularly, the system utilizes machine learning models for defect detection, achieving an Average Precision (AP) score of 92%, with specific performance metrics of 91% for defected objects and 94% for non-defected objects, demonstrating high efficiency. The Unity client user interface is also developed for control and visualization, facilitating easy additive manufacturing process monitoring. This research article presents a detailed description of the proposed digital twin framework and its workflow for implementation, the machine learning models, and the Unity client user interface. It also demonstrates the effectiveness of the integrated system through case studies and experimental results. The main findings show that the proposed digital twin system met its functional requirements and effectively detects defects and provides real-time control and monitoring of the additive manufacturing process. This paper contributes to the growing field of digital twin technology and additive manufacturing, providing a promising solution for enhancing the quality and reliability in the field of additive manufacturing.

The main impediment to practical application is the toxicity of lead ions in halide perovskite absorbing materials. Computing tools based on density functional theory (DFT) were used to predict the intrinsic properties of potential for double perovskites to be effective and suitable for optoelectronic applications, replacing the conventional lead halide perovskites with environmentally friendly elements. The Generalized Gradient Approximation (GGA) with Perdew-Burke-Ernzerhof (PBE) functional was used to screen homovalent alternatives for B and X-site ions in vacancy-ordered double perovskite Cs2BX6 (B=Pt, Ni, X= Cl, Br) for solar cell applications. Using the GGA with PBE functional, the band gap was calculated to be 1.411 eV, 0.482 eV, and 0.378 eV for the Cs2PtBr6, Cs2NiCl6, and Cs2NiBr6, respectively. The experimental band gap value of mother crystal's (Cs2PtBr6) was at 1.42 eV. Next, the DOS, PDOS and optical properties were computed using GGA with PBE functional. Then, the local density approximation (LDA) with Ceperley and Alder with Perdew and Zunger (CA-PZ) was executed to compare the GGA with PBE for electronic band structure. In addition, the OTFG ultra soft, OTFG norm conserving, ultra soft and norm conserving methods of pseudopotential were used for both GGA with PBE and LDA with CA-PZ to make and ensure the right or accurate DFT functional for those crystals. At last, the optical properties and their toxicity have been evaluated for their rational design of potential double perovskite materials with improved optoelectronic properties.

Lasia spinosa (L.) Thw. (L. spinosa) is widely used as a folk remedy for different physical ailments, and its neurological effects have yet to be assessed. Phytochemicals status of L. spinosa was evaluated by GC-MS analysis. Membrane stabilization test, elevated plus maze (EPM) tests and hole board tests (HBT), tail suspension tests (TST) and thiopental sodium-induced sleeping tests (TISTT) were used to assess anti-inflammatory, anxiolytic and anti-depressant activity. Fourteen compounds have been recorded from GC-MS analysis. The LSCTF showed 68.66 ± 2.46% hemolysis protections (p

Warpage is one of the prominent issues in Fused Filament Fabrication. The cause of this is the rapid cooling of the polymer during extrusion. The residual thermal stresses accumulated within the print part result in a shape distortion and subsequent detachment of the object from the print bed. In this study, both experimental and numerical approaches were used to identify the stresses due to thermal shrinking that occurs in soft polymers such as wax. A temperature sweep test was performed using a rotational rheometer to measure the magnitude of axial forces that are generated due to the thermal shrinking of a thin layer of 3D printable wax. The thermal stresses responsible for warpage were computed analytically and using the FEA. It was found that due to thermal processes, the stress magnitude can reach a value of 1.17 MPa. This value is enough to cause the plastic deformation in the wax part having a thin elongated shape. In addition, Taguchi’s robust design has identified two major FFF parameters that impact the warpage in amorphous soft polymers. They are the printing speed and the print bed temperature. To achieve a low level of warpage, it is important to make sure that the layer deposition occurs at medium speeds and the print bed temperature is moderately high according to the findings of this study.

Electronics band structures, the total density of state, the partial density of state and optical properties were investigated by DFT method for A2InAgCl6 (A= Cs, Na, K, and Rb). Moreover, the band gap of Na2InAgCl6, K2InAgCl6, Rb2InAgCl6 and Cs2InAgCl6 were calculated at 1.039 eV, 1.041 eV, 1.039 eV and 1.089 eV by GGA with PBE, 0.784 eV, 0.769 eV, 0.775 eV and 0.770 eV by LDA with CA-PZ and 1.310 eV, 1.152 eV, 1.180 eV and 1.169 eV by GGA with RPBE functionals. The density of states (DOS) and partial density of states (PDOS) were evaluated. Among of used functionals, GGA with RPBE functional has considered the appropriate and acceptable method for calculation of band gap where the 1.310 eV of band gap was reported for Cs2InAgCl6, which is close to experimental value at 1.370 eV. The crystal, Na2InAgCl6, has considered as the best perovskites cell among other four due to low band gap, and the main cause is revealed that it has attached the lightest Na atom to convey the low band gap as lower surface or atomic size atom.

Geopolymers are high-performance, cost-effective materials made from industrial waste that ideally fit the needs of 3D printing technology used in construction. The novelty of the present work lies in the investigation of methods to mix geopolymer concrete from fly ash (FA) class F, ground granulated blast furnace slag (GGBS), and raw calcined kaolin clay (RCKC) to determine the mixing procedure which provides the best mechanical strength and structural integrity. The experimental results show that aluminosilicates with different reaction parameters when mixed one after another provide the optimal results while the geopolymer concrete possesses the highest compressive strength and the denser structure. The results demonstrated that the reactivity of GGBS, FA, and RCKC increased for different depolymerization speeds of the selected aluminosilicates. This research will provide results on how to improve the mixing order for geopolymer synthesis for 3D printing demands. The highest compressive strength and denser structure of geopolymer concrete is achieved when each type of aluminosilicate is mixed with an alkaline medium separately.

Billions of people are using smartphones and computers with poor posture. A careless attitude towards spinal posture could be dangerous for long-term spinal health, leading eventually to curvature of the spine. Ignoring this fact and its treatment at the early stage will significantly deteriorate spinal health and force surgical intervention. Instead of developing an automated posture-correcting system, the existing research mostly focused on a posture-monitoring system to inform the users via a human interface, e.g., Bluetooth-based devices. Therefore, this paper proposes a novel posture-correction method to automatically prevent spinal disease by facilitating proper posture habits. Specifically, we develop a fluid-driven wearable posture corrector, whose skeleton can be fabricated simply using a 3D printer, to estimate angular posture deviation using sensors and provide appropriate assistance to correct the posture habit of the user. Mounted sensors provide the degree of postural bending, and a controller regulates the appropriate signals to provide a friendly pulling force as a reminder to the user through a fluid-driven actuator. The skeleton with a fluid-driven tool is designed to mimic the motion of the spinal posture by activating the actuator, which injects (or releases) the fluid into (or from) the skeleton frame and regulates forces to reduce the angular deviation of the skeleton. The 3D-printed skeleton with a flexible rubber tube has been experimentally evaluated to ensure proper actuating mechanism through the adjustment of air pressure. It is found that, by applying air pressure in the range of 0 to 101.4 kPa, the skeleton is pulled back approximately 1 N to 7 N forces, minimizing the angle up to 12.44∘ with respect to the initial steady stage, which leads to a maximum posture correction of 32.55% angle (θ) of poor posture. From the above experiments, we ensure the functionality of the proposed posture corrector in producing backward forces to correct the posture automatically.

This research paper aims to improve the quality of 3D printed parts made of the wax filament by implementing the Taguchi orthogonal array process optimization method. The manufactured parts can be used as cost-effective investment casting patterns. With the Taguchi method, it was concluded that the nozzle temperature has the most effect on the dimensional accuracy of printed parts. In addition, thermal, mechanical, and rheological characterization were performed on the wax filament, revealing several important findings. For instance, the rheological studies identified the low viscosity of melted wax at printing temperatures. This resulted in the rough surface of the printed parts. To improve the surface roughness, a post-processing procedure was implemented using a white spirit as a surface smoothing agent.

Carbon Fibre Reinforced Polymers (CFRPs) are commonly used materials in manufacturing components and products in the automotive, aerospace, and wind energy industries generating thousands of tons of waste, thus creating a threat to the environment if not recycled. Therefore, it is important for both academia and industry to investigate various ways of recycling this material. However, there is an urgent need for a reliable cost predication system to assist in making informed decisions, planning sustainable treatment, and developing pricing strategies for different waste treatment scenarios. This research paper presents the development of a fuzzy logic-based system to perform cost estimation of recycling processes of the CFRP. The developed system has taken into consideration uncertainties such as the characteristics of End of Life (EoL) material including its size and weight, its origin and diversity of existing recycling methods, and quantity of recycling waste. Cost drivers were divided into categories such as dismantling, transportation, operation, and capital cost. The system was developed by creating 243 fuzzy rules and three levels of fuzzy sets. Moreover, an interactive user-friendly interface was developed to enable the user to use the system easily and efficiently. Finally, case study results were examined to compare the whole life recycling cost of four different recycling technologies in various scenarios of waste treatment. The developed fuzzy logic-based system has the capability in evaluating the cost structure of CFRP recycling techniques and take into consideration uncertainty factors. Hence, a major contribution of the developed system is its provision of the heuristic rules that aid the decision-making process for selecting a cost-effective recycling method. The visualisation facility of the developed system is also a useful tool in enabling potential users to forecast the cost of the CFRP recycling techniques upfront.