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The goal of this paper is to fabricate innovative diaphragm headphones using graphene oxide paper (GOP) and GOP/epoxy nanocomposites (GOPC). Initially, graphene oxide suspension is fabricated, and the vacuum filtration method is adopted to make GOP. Then, vacuum bag molding is used to fabricate GOPC from GOP. Hot pressing and associated molds are adopted to fabricate line-indented (GOPC-L) or curve-indented patterns (GOPC-C) on the GOPC. The performances of one kind of GOP and three kinds of GOPC diaphragm headphones are analyzed based on their sound pressure level (SPL) curves achieved by the Soundcheck measurement system. There are four important processing parameters that will influence the performance of the diaphragm, including material type GOP versus GOPC, indented pattern type, sonication time on suspension, and graphene weight fraction in suspension. Compliances of various diaphragms are measured by the Klippel LPM laser measurement system. The results indicate that effects of sonication time and graphene weight fraction on SPL of GOP and GOPC headphones are in reverse, and this is associated with their difference on compliance (modulus), mass, damping ratio, and microstructure uniformity. Either GOPC-L or GOPC-C seems to improve the microstructure of the GOPC, and leads to better SPL performance. The correlation between the previous four factors and SPLs of four kinds of diaphragm headphones is proposed by using scanning electron microscope (SEM) to examine the microstructure of these diaphragms.

Mycobacteria form some of the most notorious and difficult-to-treat bacterial pathogens. As a group, they are intrinsically resistant to many commonly used antibiotics, such as tetracyclines and beta-lactams. In addition to intrinsic resistances, acquired multidrug resistance has also been observed and documented in Mycobacterium tuberculosis (MTB), Mycobacterium leprae and non-tuberculous mycobacteria (NTM). To combat multidrug resistant infections by these pathogens, innovative antimicrobials and treatment regimens are required. In this regard, linezolid, an oxazolidinone introduced for clinical use just two decades ago, was added to the therapeutic armamentarium for drug-resistant mycobacteria. It exhibits antibacterial activity by binding to the 50S ribosomal subunit and inhibiting protein synthesis. Unfortunately, linezolid resistance has now been documented in MTB and NTM, in many parts of the world. Most linezolid-resistant mycobacterial strains show mutations in the ribosome or related genes, such as in the rplC, rrl and tsnR genes. Non-ribosomal mechanisms appear to be rare. One such mechanism was associated with a mutation in fadD32, which encodes a protein that plays an important role in mycolic acid synthesis. Mycobacterial efflux proteins have also been implicated in linezolid resistance. This review summarises current knowledge of genetic determinants of linezolid resistance in mycobacteria, with the aim of contributing information that could facilitate the discovery of new therapeutic approaches to overcome, delay or avoid further developments of drug resistance among these important pathogens.

Background: Oral cancers have limited diagnostic tools to aid clinical management. Current evidence indicates that alterations in hemidesmosomes, the adhesion complexes primarily involved in epithelial attachment to the basement membrane, are correlated to cancer phenotype for multiple cancers. This systematic review aimed to assess the experimental evidence for hemidesmosomal alterations, specifically in relation to oral potentially malignant disorders and oral squamous cell carcinomas. Methods: We conducted a systemic review to summarise the available literature on hemidesmosomal components and their role in oral pre-cancer and cancer. Relevant studies were retrieved from a comprehensive search of Scopus, Ovid MEDLINE, Ovid Embase and Web of Science. Results: 26 articles met the inclusion criteria, of which 19 were in vitro studies, 4 in vivo studies, 1 in vitro and in vivo study, and 2 in vitro and cohort studies. Among them, 15 studies discussed individual alpha-6 and/or beta-4 subunits, 12 studies discussed the alpha-6 beta-4 heterodimers, 6 studies discussed the entire hemidesmosome complex, 5 studies discussed bullous pemphigoid-180, 3 studies discussed plectin, 3 studies discussed bullous pemphigoid antigen-1 and 1 study discussed tetraspanin. Conclusion: Heterogeneity in cell type, experimental models, and methods were observed. Alterations in hemidesmosomal components were shown to contribute to oral pre-cancer and cancer. We conclude that there is sufficient evidence for hemidesmosomes and their components to be potential biomarkers for evaluating oral carcinogenesis.

Tourism forecasting has garnered considerable interest. However, integrating tourism forecasting with volatility is significantly less typical. This study investigates the performance of both the single models and their combinations for forecasting the volatility of tourism demand. The seasonal autoregressive integrated moving average (SARIMA) model is used to construct the mean equation, and three single models, namely the generalized autoregressive conditional heteroscedasticity (GARCH) family models, the error-trend-seasonal exponential smoothing (ETS-ES) model, and the innovative smooth transition exponential smoothing (STES) model, are employed to estimate the volatility of monthly tourist arrivals into Malaysia. This study also assesses the accuracy of forecasts using simple average (SA), minimum variance (MV), and novel smooth transition (ST). STES performs the best of the single models for forecasting the out-of-sample of tourism demand volatility, followed closely by ETS-ES. In contrast, the ST combining method surpasses SA and MV. Interestingly, forecast combining methods do not always outperform the best single model, but they consistently outperform the worst single model. The MCS and DM tests confirm the aforementioned findings. This article merits consideration for future forecasting research on tourism demand volatility.

Despite the fact that the concept of forecasting has emerged in the realm of tourism, studies delving into this sector have yet to provide a comprehensive overview of the evolution of tourism forecasting visualization. This research presents an analysis of the current state-of-the-art tourism demand forecasting (TDF) and combined tourism demand forecasting (CTDF) systems. Based on the Web of Science Core Collection database, this study built a framework for bibliometric analysis from these fields in three distinct phases (1980–2021). Furthermore, the VOSviewer analysis software was employed to yield a clearer picture of the current status and developments in tourism forecasting research. Descriptive analysis and comprehensive knowledge network mappings using approaches such as co-citation analysis and cooperation networking were employed to identify trending research topics, the most important countries/regions, institutions, publications, and articles, and the most influential researchers. The results yielded demonstrate that scientific output pertaining to TDF exceeds the output pertaining to CTDF. However, there has been a substantial and exponential increase in both situations over recent years. In addition, the results indicated that tourism forecasting research has become increasingly diversified, with numerous combined methods presented. Furthermore, the most influential papers and writers were evaluated based on their citations, publications, network position, and relevance. The contemporary themes were also analyzed, and obstacles to the expansion of the literature were identified. This is the first study on two topics to demonstrate the ways in which bibliometric visualization can assist researchers in gaining perspectives in the tourism forecasting field by effectively communicating key findings, facilitating data exploration, and providing valuable data for future research.

Long-term trends in sunshine duration in Chinese cities have been closely linked to factors caused by air pollution. To understand this impact on sunshine duration (SD), surface solar radiation from 1981 to 2020, annual PM2.5 concentration from 2012 to 2020 and air pollution index (API) data from 2013 to 2020 collected in ten representative cities in China were investigated, and the long-term relationship of SD with diffuse fraction (DF), aerosol option depth (AOD), annual PM2.5 concentration and API were analyzed. The results indicated that trends in SD varied across cities. SD decreased in seven of the ten selected cities’ stations in the past 40 years, and the annual mean SD decreased from −0.03 h d−1 per decade to −0.36 h d−1 per decade—particularly in the Beijing North China Plain, Shanghai and Wuhan stations in the Yangtze River delta, where the trend coefficients were lower than −0.5. Conversely, increases in varying degrees of SD were found in Kunming (0.38 h d−1 per decade), Guangzhou and Shenyang in Southwest, South and Northeast China, respectively—with the biggest trend coefficient of 0.54 in Kunming. In addition to the SD variation, the DF in the ten city stations increased continuously from 1981 to 2010 and then declined after 2010, which is closely related to decreases in the annual PM2.5 concentration after 2012. The correlation coefficients between DF and SD ranged from −0.04 to −0.62, validating their negative relationship and the slight increasing trend in SD in recent ten years. The annual averages for SD and the DF plateaued in the 2010s due to the stringent pollution controls established by the Chinese government after 2010. Furthermore, the correlation coefficients between SD and the API ranged from −0.12 to −0.58, demonstrating a negative relationship between SD and the API.

Sign language was designed to allow hearing-impaired people to interact with others. Nonetheless, knowledge of sign language is uncommon in society, which leads to a communication barrier with the hearing-impaired community. Many studies of sign language recognition utilizing computer vision (CV) have been conducted worldwide to reduce such barriers. However, this approach is restricted by the visual angle and highly affected by environmental factors. In addition, CV usually involves the use of machine learning, which requires collaboration of a team of experts and utilization of high-cost hardware utilities; this increases the application cost in real-world situations. Thus, this study aims to design and implement a smart wearable American Sign Language (ASL) interpretation system using deep learning, which applies sensor fusion that “fuses” six inertial measurement units (IMUs). The IMUs are attached to all fingertips and the back of the hand to recognize sign language gestures; thus, the proposed method is not restricted by the field of view. The study reveals that this model achieves an average recognition rate of 99.81% for dynamic ASL gestures. Moreover, the proposed ASL recognition system can be further integrated with ICT and IoT technology to provide a feasible solution to assist hearing-impaired people in communicating with others and improve their quality of life.

Sign language is intentionally designed to allow deaf and dumb communities to convey messages and to connect with society. Unfortunately, learning and practicing sign language is not common among society; hence, this study developed a sign language recognition prototype using the Leap Motion Controller (LMC). Many existing studies have proposed methods for incomplete sign language recognition, whereas this study aimed for full American Sign Language (ASL) recognition, which consists of 26 letters and 10 digits. Most of the ASL letters are static (no movement), but certain ASL letters are dynamic (they require certain movements). Thus, this study also aimed to extract features from finger and hand motions to differentiate between the static and dynamic gestures. The experimental results revealed that the sign language recognition rates for the 26 letters using a support vector machine (SVM) and a deep neural network (DNN) are 80.30% and 93.81%, respectively. Meanwhile, the recognition rates for a combination of 26 letters and 10 digits are slightly lower, approximately 72.79% for the SVM and 88.79% for the DNN. As a result, the sign language recognition system has great potential for reducing the gap between deaf and dumb communities and others. The proposed prototype could also serve as an interpreter for the deaf and dumb in everyday life in service sectors, such as at the bank or post office.

Concrete mix design and the determination of concrete performance are not merely engineering studies, but also mathematical and statistical endeavors. The study of concrete mechanical properties involves a myriad of factors, including, but not limited to, the amount of each constituent material and its proportion, the type and dosage of chemical additives, and the inclusion of different waste materials. The number of factors and combinations make it difficult, or outright impossible, to formulate an expression of concrete performance through sheer experimentation. Hence, design of experiment has become a part of studies, involving concrete with material addition or replacement. This paper reviewed common design of experimental methods, implemented by past studies, which looked into the analysis of concrete performance. Several analysis methods were employed to optimize data collection and data analysis, such as analysis of variance (ANOVA), regression, Taguchi method, Response Surface Methodology, and Artificial Neural Network. It can be concluded that the use of statistical analysis is helpful for concrete material research, and all the reviewed designs of experimental methods are helpful in simplifying the work and saving time, while providing accurate prediction of concrete mechanical performance.

Image analysis techniques are gaining popularity in the studies of civil engineering materials. However, the current established image analysis methods often require advanced machinery and strict image acquisition procedures which may be challenging in actual construction practices. In this study, we develop a simplified image analysis technique that uses images with only a digital camera and does not have a strict image acquisition regime. Mortar with 10%, 20%, 30%, and 40% pozzolanic material as cement replacement are prepared for the study. The properties of mortar are evaluated with flow table test, compressive strength test, water absorption test, and surface porosity based on the proposed image analysis technique. The experimental results show that mortar specimens with 20% processed spent bleaching earth (PSBE) achieve the highest 28-day compressive strength and lowest water absorption. The quantified image analysis results show accurate representation of mortar quality with 20% PSBE mortar having the lowest porosity. The regression analysis found strong correlations between all experimental data and the compressive strength. Hence, the developed technique is verified to be feasible as supplementary mortar properties for the study of mortar with pozzolanic material.