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Eri silk fiber has superior thermal insulation behavior, better softness than cotton fiber. However, Eri silk’s use in the commercial arena has not yet taken off. The purpose of the study is to explore the comfort properties of the fabric, which enhances the commercial acceptance of Eri silk clothing.

In this investigation, three different single knit Eri silk structures were produced with different loop lengths and yarn counts to analyze the influence of process variables on low-stress mechanical properties. To execute the research work, Eri silk spun yarn of three different linear densities (15 tex, 20 tex, 25 tex) were chosen. Three different knitted structures were produced, such as single jersey, popcorn and cellular blister, and two different loop lengths were also selected.

The cellular blister structure has shown appreciable low-stress properties next highest position was attained by the popcorn structure. Yarn fineness and loop length were significant with most of the low-stress properties.

The findings of this research will contribute to a greater awareness of Eri silk knitted fabric and its process parameters in relation to its commercial utility.

This study was conducted to explore the influence of knit structure, loop length and yarn count on the low-stress properties of Eri silk-based thermal clothing.

In the field of research on the application of digital printing to textile materials, there are still many research issues that arise from the very demanding interaction of digital printing technology and the complex, heterogeneous surface system of textile materials. This is precisely why the area of pre-treatment of textile materials is in need of research, and the purpose of this research was to establish the level of influence of physical and chemical activation of the textile surface with plasma and the possibility of improving the quality of the print and colour reproduction.

The paper deals with the possibility of applying argon and oxygen cold low-pressure plasma in the processing of cellulose knitted fabrics, with the aim of improving the quality of the print and colour reproduction in digital pigment inkjet printing. The selected raw material samples were 100% raw cotton and lyocell. After plasma treatment, the samples were printed by digital ink jet printing with water-based pigment printing ink. An analysis of the micromorphological structure of untreated and plasma-treated samples before and after printing was carried out, and a comparative analysis of the colour of the printed elements was carried out depending on the pre-treatment.

The conducted research showed a positive influence of plasma pre-treatment on the coverage of the fibre surface with pigments, the uniformity of pigment distribution along the fibre surface and the uniformity of the distribution of the polymeric binder layer. This has a positive effect on colour reproduction. Also, certain improvements in colourfastness to washing were obtained.

Considering the complexity of the topic, although exhaustive, this research is not sufficient in itself, but opens up new questions and gives ideas for further research that must be carried out in this area.

Also, this kind of research contributes to the possibility of adopting the idea of industrial plasma transformation, as an ecologically sustainable functionalisation of textiles, which has not yet been established.

This research is certainly a contribution to the establishment of acceptable textile pre-treatment methods in the field of digital printing, as one of the key quality factors in digital textile printing (DTP). Considering the still large number of obstacles and unanswered questions encountered in the field of digital printing on textiles, this kind of research is a strong contribution to the understanding of the fundamental mechanisms of the complex interaction between printing ink and textile.

This paper aims to demonstrate that, in line with the emerging trend of multifunctional yarn development, cotton yarn can effectively harness renewable solar energy to achieve photothermal conversion and thermochromism. This innovation not only maintains the comfort associated with natural fiber cotton yarn but also enhances its ultraviolet (UV) light resistance.

In this work, 4% zirconium carbide (ZrC) and thermochromic powder were adhered to cotton yarn through polyurethane (PU) by sizing coating method. After sizing, the two cotton yarns are twisted by ring spinning to obtain composite yarns with photothermal conversion and thermochromic functions.

The yarn obtained by cotton/6%PU/8% thermochromic dye single yarn and cotton/6%PU/4% ZrC single yarn composite is the best match. After 5 min of infrared light, the temperature of the composite yarn rose to the maximum, increasing by 36.1°C. The ΔE* value before and after irradiation of infrared lamp is 26.565, which proves that the thermochromic function is good. The yarn dryness unevenness was significantly reduced by 27.2%. The composite yarn has a UPF value of up to 89.22, and its performance characteristics remain stable after 100 minutes of washing.

The composite yarn’s photothermal conversion and thermochromism functions are mutually reinforcing. Using sunlight can simultaneously achieve heating and discoloration effects without consuming additional energy. The cotton yarn used in this application is versatile, and suitable for a wide range of uses including clothing, temperature visualization detection and other scenarios.

This study aims to identify how Brazilian followers of an X/Twitter profile engage in theory-building processes leading up to the January 8, 2023 riots in Brasília, the Brazilian capital. This paper seeks to understand how cognitive and sociocultural processes weave together to weaponize collective knowledge construction that, in isolation, could be seen as virtuous but, in specific contexts, might lead to radicalization.

This study uses qualitative content analysis of comments on ambiguous X/Twitter posts published by a conspiratorial profile associated with former President Jair Bolsonaro. Content was published in the three weeks that preceded the coup d’état attempt by Bolsonaro supporters on January 8, 2023.

Findings point to users’ resorting to intuitive knowledge to support sensemaking processes in their search for subliminal meanings in tweets. That includes, for example, attempts to crack binary code-encrypted messages. This study also identified practices of cross-media sourcing, where users collect evidence from alternative social media channels to interpret messages containing verbal and visual information. Finally, this study found that religious symbols are often instrumentalized and become a lens through which followers organize information to integrate with their existing knowledge and assumptions.

With this work, the authors build on existing scholarship on epistemologies used by conspiratorial and radicalized groups as they engage in systematic sensemaking and often refer to religion to interpret messages that motivate extreme political position-taking. This study addresses a similar phenomenon as it unfolds in an understudied geographical context (Brazil) and seeks to demonstrate how individuals engage in collective sensemaking practices. The authors hope that their findings inform educators as they explore the affordances of social media to foster positive collective learning experiences in reasoning supported by social media.

The originality of this study is twofold. First, this study uses an analytical lens that draws on the learning sciences and cognitive science for inquiry of radicalization happening around social media. The authors understand that social media lend themselves particularly interesting to the analysis, as they are settings where notions of mastery blur, and individuals engage in conversations on complex, controversial topics. With that engagement, they demonstrate willingness to reason collectively. Second, this study investigates how those phenomena unfold in an understudied context, responding to calls for more diversity in research in the learning sciences as well as in media studies.

Stiffness adjusting ability is essential for soft robotic arms to perform complex tasks. A soft state enables dexterous operation and safe interaction, while a rigid state enables large force output or heavy weight carrying. However, making a compact integration of soft actuators with powerful stiffness adjusting mechanisms is challenging. This study aims to develop a piston-like particle jamming mechanism for enhanced stiffness adjustment of a soft robotic arm.

The arm has two pairs of differential tendons for spatial bending, and a jamming core consists of four jamming units with particles sealed inside braided tubes for stiffness adjustment. The jamming core is pushed and pulled smoothly along the tendons by a piston, which is then driven by a motor and a ball screw mechanism.

The tip displacement of the arm under 150 N jamming force and no more than 0.3 kg load is minimal. The maximum stiffening ratio measured in the experiment under 150 N jamming force is up to 6–25 depends on the bending direction and added load of the arm, which is superior to most of the vacuum powered jamming method.

The proposed robotic arm makes an innovative compact integration of tendon-driven robotic arm and motor-driven piston-like particle jamming mechanism. The jamming force is much larger compared to conventional vacuum-powered systems and results in a superior stiffening ability.

The investigation concentrated on studying a distinct category of tubular heat exchanger that uses swirling airflow over tube bundle maintained at constant heat flux. Swirl flow is achieved using a novel perforated baffle plate with rectangular openings and multiple adjustable opposite-oriented saw-tooth flow deflectors. These deflectors were strategically placed at the inlet of the heat exchanger to create a swirling flow downstream.

The custom-built axial flow heat exchanger consists of three baffle plates arranged longitudinally supporting tube bundle maintained at constant heat flux. The baffle plate equipped with saw-tooth flow deflector of various geometry represented by space height ratio(e/h). Next, ambient air was then directed over the tube bundle at varying Reynolds number and the effect of baffle spacing (PR), Space height ratio (e/h) and inclination angle(a) of deflectors on performance of heat exchanger was experimentally analyzed.

The heat transfer augmentation of heat exchanger for given operating condition is strongly dependent on geometry, inclination angle of deflector and baffle spacing.

An average improvement of 1.42 times in thermal enhancement factor was observed with inclination angle of 30°, space height ratio of 0.4 and a pitch ratio of 1.2 when compared to a heat exchanger without a baffle plate under similar operating conditions.

This study aims to introduce a novel machine learning feature vector (MLFV) method to bring machine learning to overcome the time-consuming computational fluid dynamics (CFD) simulations for rapidly predicting turbulent flow characteristics with acceptable accuracy.

In this method, CFD snapshots are encoded in a tensor as the input training data. Then, the MLFV learns the relationship between data with a rod filter, which is named feature vector, to learn features by defining functions on it. To demonstrate the accuracy of the MLFV, this method is used to predict the velocity, temperature and turbulent kinetic energy fields of turbulent flow passing over an innovative nature-inspired Dolphin turbulator based on only ten CFD data.

The results indicate that MLFV and CFD contours alongside scatter plots have a good agreement between predicted and solved data with R² ≃ 1. Also, the error percentage contours and histograms reveal the high precisions of predictions with MAPE = 7.90E-02, 1.45E-02, 7.32E-02 and NRMSE = 1.30E-04, 1.61E-03, 4.54E-05 for prediction velocity, temperature, turbulent kinetic energy fields at Re = 20,000, respectively.

The method can have state-of-the-art applications in a wide range of CFD simulations with the ability to train based on small data, which is practical and logical regarding the number of required tests.

The paper introduces a novel, innovative and super-fast method named MLFV to address the time-consuming challenges associated with the traditional CFD approach to predict the physics of turbulent heat and fluid flow in real time with the superiority of training based on small data with acceptable accuracy.

Nowadays, residential buildings have become increasingly important due to the growing communities. The purpose of this study is to investigate the behavior of a steel structural framing system that incorporates lightweight load-bearing walls and slabs, and to compare the weight of materials used in cold-formed and hot-finished steel structural systems for affordable housing.

Four types of models consisting of 243 members were simulated. Model 1 is a cold-formed steel structural framing system, while Model 2 is a hot-finished steel structural framing system. Both Models 1 and 2 use lightweight wall panels and lightweight composite slabs. Models 3 and 4 are made with brick walls and precast reinforced concrete systems, respectively. These structures use different wall and slab materials, namely, brick walls and precast reinforced concrete. The analysis includes bending behavior, buckling resistance, shear resistance and torsional rotation analysis.

This study found that using thinner steel sections can increase the deflection value. Meanwhile, increasing member length and the ratio of slenderness will decrease buckling resistance. As the applied load increases, buckling deformation also increases. Furthermore, decreasing shear area causes a reduction in shear resistance. Thicker sections and the use of lightweight materials can decrease the torsional rotation value.

The weight comparison of the steel structures shows that Model 1, which is a cold-formed steel structure with lightweight wall panels and lightweight composite slabs, is the most suitable model due to its lightweight and affordability for housing. This model can also be used as a reference for the optimal design of modular structural framing using cold-formed steel materials in the field of civil engineering and as a promotional tool.