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Our study explores the effects of immigration on the employment of native middle-skilled workers, focusing on how this effect varies with the specificity of their occupational skill bundles.

Exploiting the 2002 opening of the Swiss labor market to EU workers and using register data on the location and occupation of these workers, our findings provide novel results on the labor market effects of immigration.

We show that the inflow of EU workers led to an increase in the employment of native middle-skilled workers with highly specific occupational skills. This finding could be attributed to immigrant workers reducing existing skill gaps, enhancing the quality of job-worker matches, and alleviating firms' capacity restrictions. This allowed firms to create new jobs, thereby providing increased employment options for middle-skilled workers with highly specialized skills.

Previous literature has predominantly highlighted the disadvantages of specific occupational skills compared to general skills in the context of labor market shocks. However, our findings reveal that workers with specific occupational skills can benefit from an immigration-driven labour market shock. These results suggest that policy conclusions regarding the role of specific occupational skills should be more nuanced.

Intense interest has been shown in creating new and effective biocide agents as a result of changes in bacterial isolates, bacterial susceptibility to antibiotics, an increase in patients with burns and wounds and the difficulty of treating infections and antimicrobial resistance. Woven, nonwoven and knitted materials are used to make dressings; however, nonwoven dressings are becoming more popular because of their softness and high absorption capacity. Additionally, textiles have excellent geometrical, physical and mechanical features including three-dimensional structure availability, air, vapor and liquid permeability, strength, extensibility, flexibility and diversity of fiber length, fineness and cross-sectional shapes. It is necessary to treat every burn according to international protocol and along with it has to focus on particular problems of patients and the best possible results.

The objective of this paper is to conduct a thorough examination of research pertaining to the utilization of textiles, as well as alternative materials and innovative techniques, in the context of burn wound dressings. Through a critical analysis of the findings, this study intends to provide valuable insights that can inform and guide future research endeavors in this field.

In the past years, there have been several dressings such as xeroform petrolatum gauze, silver-impregnated dressings, biological dressings, hydrocolloid dressings, polyurethane film dressings, silicon-coated nylon dressings, dressings for biosynthetic skin substitutes, hydrogel dressings, newly developed dressings, scaffold bandages, Sorbalgon wound dressing, negative pressure therapy, enzymatic debridement and high-pressure water irrigation developed for the fast healing of burn wounds.

This research conducts a thorough analysis of the role of textiles in modern burn wound dressings.

This study aims to deal with both the development and mechanical investigations of unsaturated polyester matrix (UPR) composites containing recycled polyethylene terephthalate (PET) fibers as new fillers.

UPR/PET fibers composites have been developed as mats by incorporating 5, 8, 13 and 18 parts per hundred of rubber (phr) of 6-, 10- and 15-mm length PET fibers from the recycling of postconsumer bottles. The mechanical and physical properties of the composites were investigated as a function of fiber content and length. A significant increase in stress at break and in ultimate stress (sr) were observed for composites reinforced with 5 and 8 phr of 15-mm length PET fibers. The Izod impact strength of UPR/mat PET fiber composites as a function of fiber rate and length showed that the 5 and 8 phr composites for the 15-mm length PET fiber have the optimal mechanical properties 13.55 and 10.50 Kj/m², respectively. The morphological study showed that the strong adhesion resulting from the affinity of the PET fiber for the UPR matrix. The ductile fracture of materials reinforced with 5 and 8 phr is confirmed by the fiber deformation and fracture surface roughness.

This study concluded that the PET fiber enhances the properties of composites, a good correlation was observed between the results of the mechanical tests and the structural analysis revealing that for the lower concentrations, the PET fibers are well dispersed into the resin, but entanglements are evidenced when the fiber content increases.

It can be shown from scanning electron microscopy micrographs that the fabrication technique produced composites with good interfacial adhesion between PET fibers and UPR matrix.

Gas-insulated switchgear (GIS) stands as a pivotal component in power systems, susceptible to partial discharge occurrences. Nevertheless, manual inspection proves labor-intensive, exhibits a low defect detection rate. Conventional inspection robots face limitations, unable to perform live line measurements or adapt effectively to diverse environmental conditions. This paper aims to introduce a novel solution: the GIS ultrasonic partial discharge detection robot (GBOT), designed to assume the role of substation personnel in inspection tasks.

GBOT is a mobile manipulator system divided into three subsystems: autonomous location and navigation, vision-guided and force-controlled manipulator and data detection and analysis. These subsystems collaborate, incorporating simultaneous localization and mapping, path planning, target recognition and signal processing, admittance control. This paper also introduces a path planning method designed to adapt to the substation environment. In addition, a flexible end effector is designed for full contact between the probe and the device.

The robot fulfills the requirements for substation GIS inspections. It can conduct efficient and low-cost path planning with narrow passages in the constructed substation map, realizes a sufficiently stable detection contact and perform high defect detection rate.

The robot mitigates the labor intensity of grid maintenance personnel, enhances inspection efficiency and safety and advances the intelligence and digitization of power equipment maintenance and monitoring. This research also provides valuable insights for the broader application of mobile manipulators in diverse fields.

The robot is a mobile manipulator system used in GIS detection, offering a viable alternative to grid personnel for equipment inspections. Comparing with the previous robotic systems, this system can work in live electrical detection, demonstrating robust environmental adaptability and superior efficiency.

In this study, we aim to investigate the connectedness spillovers among major cryptocurrency markets. Moreover, we also explore to identify factors driving this connectedness, particularly focusing on the sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies under Twitter-based economic uncertainties and US economic policy uncertainty. Finally, we investigate the extent to which cryptocurrency markets serve as a safe haven, hedge, and diversifier from news-based uncertainties.

This study employs the connectedness approach following the combination of Ando et al. (2022) QVAR and Baruník and Krehlík's (2018) frequency connectedness methodologies into the framework proposed by Diebold and Yilmaz (2012, 2014). The data covered from November 10, 2017, to April 21, 2023, and the factors driving cryptocurrency connectedness spillovers are identified and examined. The sentimentality of total, short-term, and long-term return connectedness spillovers among cryptocurrencies, concerning Twitter-based economic uncertainties and US economic policy uncertainty, are analyzed. We apply the Wavelet quantile correlation (WQC) method developed by Kumar and Padakandla (2022) to explore the effects of Twitter-based economic uncertainties and US economic policy uncertainty on Cryptocurrency market connectedness risk spillovers. Besides, we check and present the robustness of WQC findings with the multivariate stochastic volatility method.

Our findings indicate that Ethereum and Bitcoin are net shock transmitters at the center of the connectedness return network. Ethereum and Bitcoin hold the highest market capitalization and value in the cryptocurrency market, respectively. This suggests that return shocks originating from these two cryptocurrencies have the most significant impact on other cryptocurrencies. Tether and Monero are the net receivers of return shocks, while Cardano and XRP exhibit weak shock-transmitting characteristics through returns. In terms of return spillovers, Ethereum is the most effective, followed by Bitcoin and Stellar. Further analysis reveals that Twitter economic policy uncertainty and US economic policy uncertainty are effective drivers of short-term and total directional spillovers. These uncertainty indices exhibit positive coefficient signs in short-term and total directional spillovers, which turn predominantly negative in different magnitudes and frequency ranges in the long term. In addition, we also document that as the Total Connectedness Index (TCI) value increases, market risk also rises. Also, our empirical findings provide significant evidence of Twitter-based economic uncertainties and US economic policy uncertainty that affect short-term market risks. Hence, we state that risk-connectedness spillovers in cryptocurrency markets enclose permanent or temporary shock variations. Besides, findings of the low value of long-term spillovers suggest that risk shocks in cryptocurrency markets are not permanent, indicating long-term changes require careful monitoring and control over market dynamics.

In this study, we find evidence that Twitter's news-based uncertainty and US economic policy uncertainty have a significant effect on short-term market risk spillovers. Furthermore, we observe that high cryptocurrency market risk spillovers coincide with periods of events such as the US-China trade tensions in January 2018, the Brexit process in February 2019, and the COVID-19 outbreak in November 2019. Next, we observe a decline in cryptocurrency market risk spillovers after March 2020. The reason for this mitigation of market risk spillover may be that the Fed's quantitative easing signals have initiated a relaxation process in the markets. Because the Fed's signal to fight inflation in March 2022 also coincides with the period when risk spillover increased in crypto markets. Based on this, we present evidence that the FED's communication mechanism with the markets can potentially affect both short- and long-term expectations. In this context, we can say that our hypothesis that uncertainty about the news causes short-term risks to increase has been confirmed. Our findings may have investment policy implications for portfolio managers and investors generally in terms of reducing financial risks.

Our paper contributes to the literature by examining the interconnectedness among major cryptocurrencies and the drivers behind them, particularly focusing on the role of news-based economic uncertainties. More broadly, we calculate the utilization of advanced methodologies and the incorporation of real-time economic uncertainty data to enhance the originality and value of the research, which provides insights into the dynamics of cryptocurrency markets.

This study aims to investigate the effects of three significant events – the 1994 CFA currency depreciation, the 2008 Global Financial Crisis (GFC), and instances of political coups – on the relationships between FDI inflow, economic growth, and governance within the Central African Economic and Monetary Community (CEMAC) countries. It seeks to evaluate how these events influence the linkages between FDI, economic growth, and governance, to aid the understanding of responses to external shocks and internal political disruptions.

The study employs a panel Vector Autoregression (VAR) analysis using data from 1990 to 2019 by exploring the dynamic relationships among FDI inflow, economic growth, and aggregate governance indicators within the CEMAC sub-region. The analysis was conducted utilizing the EViews software package, facilitating robust examination through the introduction of the Bayesian VAR to facilitate the interpretation of parameters and the data.

The results indicate that, contrary to initial hypotheses, growth and governance do not emerge as determinants for attracting FDI within the CEMAC sub-region. However, governance stands out as a crucial determining factor for economic growth. Furthermore, the study suggests that the 1994 CFA currency depreciation, the 2008 GFC, and instances of political coups did not significantly impact FDI, growth, and governance within these countries. Despite the potential vulnerability of the CEMAC countries to external shocks, the effects of these events on the dynamics of FDI, economic growth, and governance were not apparent. Notably, political instability, as evidenced by coups, emerges as a significant factor shaping the interactions between FDI, growth, and governance in CEMAC countries.

These findings have significant implications for policymakers and stakeholders in the CEMAC countries. Understanding that governance has a central role in driving economic growth places great importance of prioritizing governance reforms to foster sustainable development. Moreover, the identification of political instability as a key determinant affecting the relationships between FDI, growth, and governance emphasizes the need for political stability and effective governance structures to attract and sustain FDI inflows as well as foster economic growth.

This study contributes to the existing literature by offering insights into the linkages between FDI, economic growth, governance, and external shocks within the CEMAC sub-region. By examining the specific impacts of the 1994 CFA currency depreciation, the 2008 GFC, and political coups on these dynamics, the study provides original perspectives on the resilience of CEMAC countries to external and internal disruptions.

In the digital age, the spread of online behavior and real-world information leads to social contagion. This study aims to investigate the contagion phenomenon of online physician choice and then discuss its potential influence on the sub-specialization process in the healthcare service industry. In specific, this study aims to propose the basic mechanism of infection and immunity as follows – exposure to antigen may lead to an immune response, and the success of the immune response may depend on the provision of appropriate immune signaling.

Data collected from haodf.com including 4 disease types and 247 physicians from 2008 to 2015 were used to test the proposed hypotheses. Panel vector autoregression method was utilized to analyze the panel data.

The obtained result shows that social contagion of physician choice over disease type is salient on e-consultation platforms, indicating that physicians associated with/on haodf.com are concentrating on an even narrower type of disease. Disclosing more simple signals (physician history orders) results in more disease concentration for that physician in the future. In contrast, disclosing more detailed signals (physician-contributed knowledge or physician reviews) leads to less disease concentration.

This finding implies that physician-contributed knowledge and physician reviews may act as immune signal which will tend to trigger a success immune response. This study not only suggests managers should be careful about the double-edged sword effect of online physician choice contagion but also provides the useful approaches to promote or restrain such a contagion in a flexible way.

This study aims to design and implement a multimaterial system for printing multifunctional specimens suitable for various sectors, with a particular focus on biomedical applications such as addressing mandibular bone loss.

To enhance both the mechanical and biological properties of scaffolds, an automatic multimaterial setup using vat photopolymerization was developed. This setup features a linear system with two resin vats and one ultrasonic cleaning tank, facilitating the integration of diverse materials and structures to optimize scaffold composition. Such versatility allows for the simultaneous achievement of various characteristics in scaffold design.

The printed multimaterial scaffolds, featuring 20 Wt.% hydroxylapatite (HA) on the interior and poly-L-lactic acid (PLLA) with 1 Wt.% graphene oxide (GO) on the exterior, exhibited favorable mechanical and biological properties at the optimum postcuring and heat-treatment time. Using an edited triply periodic minimal surface (TPMS) lattice structure further enhanced these properties. Various multimaterial specimens were successfully printed and evaluated, showcasing the capability of the setup to ensure functionality, cleanliness and adequate interface bonding. Additionally, a novel Gyroid TPMS scaffold with a nominal porosity of 50% was developed and experimentally validated.

This study demonstrates the successful fabrication of multimaterial components with minimal contaminations and suitable mechanical and biological properties. By combining PLLA-HA and PLLA-GO, this innovative technique holds significant promise for enhancing the effectiveness of regenerative procedures, particularly in the realm of dentistry.

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.

Nanotechnology (NT) advancements in personal protective textiles (PPT) or personal protective equipment (PPE) have alleviated spread and transmission of this highly contagious viral disease, and enabled enhancement of PPE, thereby fortifying antiviral behavior.

Review of a series of state of the art research papers on the subject matter.

This paper expounds on novel nanotechnological advancements in polymeric textile composites, emerging applications and fight against COVID-19 pandemic.

As a panacea to “public droplet prevention,” textiles have proven to be potentially effective as environmental droplet barriers (EDBs).

PPT in form of healthcare materials including surgical face masks (SFMs), gloves, goggles, respirators, gowns, uniforms, scrub-suits and other apparels play critical role in hindering the spreading of COVID-19 and other “oral-respiratory droplet contamination” both within and outside hospitals.

When used as double-layers, textiles display effectiveness as SFMs or surgical-fabrics, which reduces droplet transmission to <10 cm, within circumference of ∼0.3%.

NT advancements in textiles through nanoparticles, and sensor integration within textile materials have enhanced versatile sensory capabilities, robotics, flame retardancy, self-cleaning, electrical conductivity, flexibility and comfort, thereby availing it for health, medical, sporting, advanced engineering, pharmaceuticals, aerospace, military, automobile, food and agricultural applications, and more. Therefore, this paper expounds on recently emerging trends in nanotechnological influence in textiles for engineering and fight against COVID-19 pandemic.