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Moiré lattices consist of two superimposed identical periodic structures with a relative rotation angle. Moiré lattices have several applications in everyday life, including artistic design, the textile industry, architecture, image processing, metrology and interferometry. For scientific studies, they have been produced using coupled graphene–hexagonal boron nitride monolayers 1 , 2 , graphene–graphene layers 3 , 4 and graphene quasicrystals on a silicon carbide surface 5 . The recent surge of interest in moiré lattices arises from the possibility of exploring many salient physical phenomena in such systems; examples include commensurable–incommensurable transitions and topological defects 2 , the emergence of insulating states owing to band flattening 3 , 6 , unconventional superconductivity 4 controlled by the rotation angle 7 , 8 , the quantum Hall effect 9 , the realization of non-Abelian gauge potentials 10 and the appearance of quasicrystals at special rotation angles 11 . A fundamental question that remains unexplored concerns the evolution of waves in the potentials defined by moiré lattices. Here we experimentally create two-dimensional photonic moiré lattices, which—unlike their material counterparts—have readily controllable parameters and symmetry, allowing us to explore transitions between structures with fundamentally different geometries (periodic, general aperiodic and quasicrystal). We observe localization of light in deterministic linear lattices that is based on flat-band physics 6 , in contrast to previous schemes based on light diffusion in optical quasicrystals 12 , where disorder is required 13 for the onset of Anderson localization 14 (that is, wave localization in random media). Using commensurable and incommensurable moiré patterns, we experimentally demonstrate the two-dimensional localization–delocalization transition of light. Moiré lattices may feature an almost arbitrary geometry that is consistent with the crystallographic symmetry groups of the sublattices, and therefore afford a powerful tool for controlling the properties of light patterns and exploring the physics of periodic–aperiodic phase transitions and two-dimensional wavepacket phenomena relevant to several areas of science, including optics, acoustics, condensed matter and atomic physics. A superposition of tunable photonic lattices is used to create optical moiré patterns and demonstrate the resulting localization of light waves through a mechanism based on flat-band physics.

The circular economy model has recently gained a lot of attention worldwide from scientists, business people and authorities. The importance of the transition towards a more circular economy has also been noticed in the European Union. The new regulations provide the enabling framework for the circular economy to flourish. At the same time, although there is no standardized approach to creating a circular economy, while defining appropriate policies, care must be taken that they are suitable for particular industries. The limits of the present linear economy model (take-make-waste) are extremely apparent when examining the textile and clothing industry. The transition to a circular economy requires significant changes in both production and consumption models. This article uses a literature review and industry examples to identify and evaluate challenges faced by the clothing and textile industry in adapting to the circular economy model.

A total of seven yeast strains from 18 xylanolytic and/or xylose-fermenting yeast species isolated from the wood-feeding termite Reticulitermes chinenesis could efficiently decolorize various azo dyes under high-salt conditions. Of these strains, a novel and unique azo-degrading and halotolerant yeast, Sterigmatomyces halophilus SSA1575, has been investigated in this study. This strain could significantly decolorize four combinations of a mixture of dyes. It showed a high capability for decolorizing Reactive Black 5 (RB5) even at 1,500 mg L −1 . The strain SSA1575 still showed a high capability for decolorizing a 50 mg L −1 RB5 with a salt mixing at a NaCl concentration of up to 80 g L −1 . It also exhibited significant ability to decolorize repeated additions of dye aliquots, with a reduction in time of up to 18 h. Most of the tested carbon and nitrogen sources could significantly enhance a RB5 decolorization. However, this process was inhibited by the addition of sucrose and sodium nitrate. NADH-dichlorophenol indophenol (NADH-DCIP) reductase and lignin peroxidase were determined as the key reductase and oxidase of S. halophilus SSA1575. Finally, strain SSA1575, can effectively detoxify RB5 into non-toxic products. Overall, S. halophilus SSA1575, might be a promising halotolerant yeast valued for the treatment of various textile effluents with high salinity.

We conduct a randomized experiment that generates exogenous variation in the access to foreign markets for rug producers in Egypt. Combined with detailed survey data, we causally identify the impact of exporting on firm performance. Treatment firms report 16–26% higher profits and exhibit large improvements in quality alongside reductions in output per hour relative to control firms. These findings do not simply reflect firms being offered higher margins to manufacture high-quality products that take longer to produce. Instead, we find evidence of learning-by-exporting whereby exporting improves technical efficiency. First, treatment firms have higher productivity and quality after controlling for rug specifications. Second, when asked to produce an identical domestic rug using the same inputs and same capital equipment, treatment firms produce higher quality rugs despite no difference in production time. Third, treatment firms exhibit learning curves over time. Finally, we document knowledge transfers with quality increasing most along the specific dimensions that the knowledge pertained to.

Will new technologies cause industries to shed jobs, requiring novel policies to address mass unemployment? Sometimes productivity-enhancing technology increases industry employment instead. In manufacturing, jobs grew along with productivity for a century or more; only later did productivity gains bring declining employment. What changed? The elasticity of demand. Using data over two centuries for US textile, steel and auto industries, this paper shows that automation initially spurred job growth because demand was highly elastic. But demand later became satiated, leading to job losses. A simple model explains why this pattern might be common, suggesting that today’s technologies may cause some industries to decline and others to grow. Automation might not cause mass unemployment, but it may well require workers to make disruptive transitions to new industries, requiring new skills and occupations.

In recent years, plasma treatment technology has attracted more attention in the textile industry, as it seems to be a promising economically and ecologically sound alternative to conventional wet-chemical processing techniques. Plasma surface treatment is a relatively simple process that is clean, solvent-free, time saving, and environmentally friendly. Moreover, plasma treatments offer the possibility to obtain typical textile finishes without changing the key textile properties. The efficiency of plasma treatment depends on several factors including the nature of the substrate and the treatment operating conditions. However, the application of plasma technology to different kinds of textile materials has not been fully exploited. This paper presents a review of the current literature on the surface modification of textiles by low-temperature plasma (LTP) technology. Its main objectives are to (i) investigate the influence of LTP treatment on the surface properties of natural and man made textile materials, (ii) outline the contribution of LTP treatment towards sustainable development, and (iii) examine the hurdles that LTP has to overcome in the textile industry.

We estimate the effects of electricity shortages on Indian manufacturers, instrumenting with supply shifts from hydroelectric power availability. We estimate that India y s average reported level of shortages reduces the average plant's revenues and producer surplus by 5 to 10 percent, but average productivity losses are significantly smaller because most inputs can be stored during outages. Shortages distort the plant size distribution, as there are significant economies of scale in generator costs and shortages more severely affect plants without generators. Simulations show that offering interruptible retail electricity contracts could substantially reduce the impacts of shortages.

A long-standing question is whether differences in management practices across firms can explain differences in productivity, especially in developing countries where these spreads appear particularly large. To investigate this, we ran a management field experiment on large Indian textile firms. We provided free consulting on management practices to randomly chosen treatment plants and compared their performance to a set of control plants. We find that adopting these management practices raised productivity by 17% in the first year through improved quality and efficiency and reduced inventory, and within three years led to the opening of more production plants. Why had the firms not adopted these profitable practices previously? Our results suggest that informational barriers were the primary factor explaining this lack of adoption. Also, because reallocation across firms appeared to be constrained by limits on managerial time, competition had not forced badly managed firms to exit.

There are different applications of computer vision and digital image processing in various applied domains and automated production process. In textile industry, fabric defect detection is considered as a challenging task as the quality and the price of any textile product are dependent on the efficiency and effectiveness of the automatic defect detection. Previously, manual human efforts are applied in textile industry to detect the defects in the fabric production process. Lack of concentration, human fatigue, and time consumption are the main drawbacks associated with the manual fabric defect detection process. Applications based on computer vision and digital image processing can address the abovementioned limitations and drawbacks. Since the last two decades, various computer vision-based applications are proposed in various research articles to address these limitations. In this review article, we aim to present a detailed study about various computer vision-based approaches with application in textile industry to detect fabric defects. The proposed study presents a detailed overview of histogram-based approaches, color-based approaches, image segmentation-based approaches, frequency domain operations, texture-based defect detection, sparse feature-based operation, image morphology operations, and recent trends of deep learning. The performance evaluation criteria for automatic fabric defect detection is also presented and discussed. The drawbacks and limitations associated with the existing published research are discussed in detail, and possible future research directions are also mentioned. This research study provides comprehensive details about computer vision and digital image processing applications to detect different types of fabric defects.