3D food printing (3DFP) is emerging as a vital innovation in the food industry’s pursuit of sustainability. 3DFP has evolved to significantly impact food production, offering the capability to create customized, nutritionally balanced foods. Central Asia has a higher than global average level of meat consumption per capita, which might be influenced by its historical and cultural background of nomadism. This dietary trend might potentially result in negative impacts on both the environment and human health outcomes, as it leads to increased greenhouse gas emissions and increased risk of chronic diseases. Reducing meat consumption holds the potential to address these sustainability and health issues. A possible strategy to reduce meat consumption and promote plant-based foods is 3D Food Printing (3DFP), which can rely on plant-protein sources from the region to create appealing and tasty alternatives for these populations. This review summarizes recent studies on plant protein-rich materials for 3DFP as a substitute to meet the growing global demand for meat as well as the 3DFP printing parameters associated with the different plant-based proteins currently used (e.g., lentils, soybeans, peas, and buckwheat). The findings revealed that buckwheat, a dietary staple in Central Asia, can be a promising choice for 3DFP technology due to its widespread consumption in the region, gluten-free nature, and highly nutritious profile.

In this study, the feasibility of commercially pure (CP)-Ti bipolar plates for fuel cells were assessed by designing, manufacturing, and evaluating thin plates fabricated through the laser powder bed fusion (L-PBF) technique. The width, height, and thickness of thin CP-Ti plates were carefully considered in its design to ensure comprehensive evaluation. The maximum displacement was measured through blue light scanning in accordance with the building direction. The finite element model and experimental results showed that the building layer per volume has a linear relationship with the maximum displacement and maximum residual tensile stress along the building direction. Thin plates with a high aspect ratio (198 × 53 × 1.5 mm) had the lowest maximum displacement (0.205 mm) when building in the height direction and had a high correlation coefficient with the finite element model (0.936). Proper aspect ratio design and building strategy enable highly accurate manufacturing of CP-Ti thin plates for fuel cell systems.

Sentiment analysis is an extensively explored domain within natural language processing (NLP); nevertheless, a significant emphasis has been placed on languages possessing ample resources, such as English. This paper delves into the transformative capabilities of Optical Character Recognition (OCR), Natural Language Processing (NLP), and Speech Recognition, emphasizing their potential impact on low-resource languages, particularly focusing on Kazakh. OCR, a pivotal AI technique, extracts characters from images, revolutionizing the digitization of business records and streamlining mundane tasks. NLP, another powerful AI tool, analyzes and generates human-like text, while Speech Recognition converts spoken words into text. According to findings the OCR model demonstrated effectiveness, achieving an accuracy rate of 85% with losses ranging from 1.5 to 2, while TTS (text-to-speech) method mean opinion score (MOS) results with 95% confidence intervals. In addition, AI Meta research center assessed over 40,000 diverse translations, including Kazakh language directions using the human-translated benchmark, they integrated human evaluation with a unique toxicity benchmark across all 200 languages to ensure translation safety. Achieving a substantial 44% improvement over the prior state-of-the-art, the model by Meta AI establishes a crucial foundation for realizing a universal translation system. However, there remains ample room for exploration and refinement in the integration of these AI technologies, particularly in expanding the scope of evaluation metrics and addressing potential challenges in low-resource language settings.

Additive manufacturing of composites offers advantages over metals since composites are lightweight, fatigue and corrosion-resistant, and show high strength and stiffness. This work investigates the tensile and flexural performance of continuous carbon-fiber reinforced (CCF) composites with different guide angles and number of layers. The cost and printing time analyses were also conducted. Tensile specimens with a contour-only specimen and one CCF layer with a 0° guide angle exhibited nearly comparable strength values. Increasing the number of CCF layers enhances the tensile properties. For the identical cost and reinforcement amount, 0°/0° provides a higher tensile strength and elastic modulus compared with 15°/−15°. The same phenomenon was observed for 15°/0°/−15° and 0°/0°/0°. The samples with one and two reinforcement layers had similar stiffness and maximum load values for flexural tests. For the samples with four layers, there was a considerable improvement in stiffness but a minor decrease in the maximum load.

Mechanical metamaterials are architected materials that exhibit unusual mechanical properties arising from their internal geometry. This paper reviews recent developments in the application of machine learning for the design and analysis of these structures. It categorizes common architectures, including strut-based lattices and triply periodic minimal surfaces, and details the end-to-end design workflow, from dataset preparation and preprocessing to the iterative, simulation-based validation approach. The review compares a range of model architectures. These include foundational models like deep neural networks, fully connected and convolutional neural networks, graph neural networks, and generative models such as GANs and diffusion models. Applications in mechanical property prediction and inverse design are highlighted with examples using finite element simulations and generative design models. A structured design workflow and a comparative summary of recent studies are presented to guide future research and application. This review aims to support the development of ML frameworks for next-generation metamaterial design.