2014 marked the first emergence of avian influenza A(H5N8) in Jeonbuk Province, South Korea, which then quickly spread worldwide. In the midst of the 2020-21 H5N8 outbreak, it spread to domestic poultry and wild waterfowl shorebirds, leading to the first human infection in Astrakhan Oblast, Russia. Despite being clinically asymptomatic and without direct human-to-human transmission, the World Health Organisation stressed the need for continued risk assessment given the nature of Influenza to reassort and generate novel strains. Given its promiscuity and spread to humans, the urgency to understand the mechanisms of possible species jumping to avert disastrous pandemics is increasing. Addressing the epidemiology of H5N8 and its mechanisms of species jumping and its implications, mutational and reassortment libraries can potentially be built, allowing them to be tested on various models complemented with deep-sequencing and automation. With the knowledge on mutational patterns, cellular pathways, drug resistance mechanisms and effects of host proteins can allow better preparedness against H5N8 and other influenza A viruses.

The average working person can spend between 35-60 hours a week in the workplace, making it an influential place for mental well-being while also being a place for socio-economic contribution. Workplace incivility can diminish positive mental health outcomes and negatively impact work engagement through increased social anxiety. To investigate this, 118 working adults aged between 19 to 67 years old in Singapore were recruited for a survey comprising of demographics questions, the Workplace Incivility Scale, Brief DSM-5 Social Anxiety Disorder Severity Scale, Utrecht Work Engagement Scale-9 over the period of November 2022 to April 2023. Correlational, regression and mediation analysis showed workplace incivility scale scores to significantly pre-dict social anxiety after controlling for covariates, supporting our hypothesis that employees exposed to work-place incivility would have higher levels of social anxiety that mediated work engagement after controlling for age and gender. The findings here show workplace incivility to be a possible intervention target for social anx-iety to reduce negative impacts on work engagement in order to improve employee experience and retention for organizations.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

The target of an antibody plays a significant role in the success of antibody-based therapeutics and diagnostics, and to an extent, that of vaccine development. This importance is focussed on the target binding site – epitope, where epitope selection as a part of design thinking beyond traditional antigen selection using whole cell or whole protein immunisation can positively impact success. With purified recombinant protein production and peptide synthesis to display limited/selected epitopes, intrinsic factors that can affect the functioning of resulting antibodies can be more easily selected for. Many of these factors stem from the location of the epitope that can affect accessibility of the antibody to the epitope at a cellular or molecular level, direct inhibition of target antigen activity, conservation of function despite escape mutations, and even non-competitive inhibition sites. Through the incorporation of novel computational methods for predicting antigen changes to model-informed drug discovery and development, superior vaccines and antibody-based therapeutics or diagnostics can now be more easily designed to mitigate failures. With detailed examples, this review highlights the new opportunities, factors and methods of predicting antigenic changes for consideration in sagacious epitope selection.

The negative impact of the modern-day lifestyle on the environment is aggravated during the COVID-19 pandemic through the increased use of single-use plastics from food takeaways to medical supplies. Similarly, the closure of food outlets and disrupted supply chains have also resulted in significant food wastage. As the pandemic rages on, the aggravation of increased waste becomes an increasingly urgent problem that threatens the biodiversity, ecosystems, and human health worldwide through pollution. While there are existing methods to deal with the organic and plastic waste, many of the solutions also cause additional problems. Increasingly proposed as a natural solution to man-made unnatural problems, there are insect solutions for dealing with the artificial and organic waste products towards a circular economy, making the use of natural insect solutions commercially sustainable. This review discusses the findings and how some of these insects, particularly the Hermetia illucens, Tenebrio molitor, and Zophobas morio, can play an increasing important role in food and plastics, with a focus on the latter.

HIV treatment strategies against viral enzymes are continuously hampered by viral drug resistance. Recent findings show that viral substrate Gag contributes to HIV-1 Protease Inhibitor (PI) resistance, leading to demands for new strategies in HIV treatment where Gag is recognized as a drug target. To successfully target Gag, there is a need of in-depth understanding of the Gag polyprotein and the effects of Gag mutations. Here, we propose new strategies in designing novel Gag inhibitors against existing and novel emerging Gag mutations via a structural understanding of the Gag-Protease relationship in PI resistance. In this review, we discuss the role of both novel and previously reported mutations, revealing insights to how they aid in PI resistance, and how new Gag inhibitors can be designed.

The high mutation rate of human immunodeficiency virus type 1 (HIV-1) plays a major role in treatment resistance from the development of vaccines to long-lasting drugs. In addressing the crux of the issue, various attempts to estimate the mutation rate of HIV-1 resulted in a large range of 10-5 - 10-3 errors/bp/cycle due to the use of different types of investigation methods. In this review, we discuss the different assay methods, their findings on the mutation rates of HIV-1 and how the location of these mutations can be further analyzed for their potential allosteric effects to reveal potentially new inhibitors with different pharmacodynamics that can be used to circumvent fast occurring HIV drug resistance. Given that HIV is one of the fastest mutating viruses, it is a good model for comprehensive study of its mutations that can give rise to much horizontal understanding towards overall viral drug resistance as well as emerging viral diseases.

Therapeutics antibodies have increasingly shifted the paradigm of disease treatments, from small molecules to biologics, especially in cancer therapy. Despite the increasing number of antibody candidates, much remains unknown about the antibody and how its various regions interact. In fact, the constant region can govern effects that might be useful in reducing the unwanted consequences resulted from systemic circulation. For this reason, apart from the commonly used IgG isotypes, IgA antibodies are promising therapeutics drugs, given its localized mucosal effects. While the antibody Fc effector cell activity has been well explored, recent research has shown evidences that the constant region of the antibody can also influence antigen binding, challenging the conventional idea of region-specific antibody functions. To further investigate this, we analyzed the IgA antibody constant and its allosteric effects onto the antigen binding regions, using recombinant Pertuzumab IgA1 and IgA2 variants. We found mutations in the C-region to reduce Her2 binding, and our computational structural analysis showed that such allosteric communications were highly dependent on the antibody hinge, providing the evidence to consider antibodies as a whole protein rather than a sum of functional regions.

Langya Henipavirus (LayV) is a new zoonotic Henipavirus reported in 2022. Patients infected by LayV show common cold symptoms, such as fever, fatigue, and cough. Non-fatal cases associated with this virus have been reported in Henan and Shandong provinces in China. Should human-to-human transmission occur, it has the potential to be a pandemic like SARS-CoV-2. LayV is an enveloped negative-sense RNA virus. Its polymerase (encoded by the L gene) is considered a conserved target shared within Henipavirus viruses, allowing for qPCR detection, in which primers play a pivotal role. For the development of a diagnostic kit, we designed three primer sets targeting the L gene and evaluated their detection of plasmids carrying part of the L gene. Primer sets 1 and 2 were found to be suitable for further sensitivity, specificity, and efficiency testing. Both primer sets were able to amplify PCR products in the presence of human saliva, and both the melt curve analysis and gel electrophoresis showed distinct peaks and bands, supporting the specificity of the qPCR primers. Based on standard curve analysis, both primers detected as low as approximately 1000 copies of the L gene. Given the specificity and robustness to work in the presence of saliva, the primers can be used for pandemic-preparedness in LayV detection.

The COVID-19 pandemic measures have negatively impacted education worldwide via travel restrictions, student visas, and physical attendanc,e among many other fac-tors. As one of the first places to impose strict pandemic measures and one of the last places to lift the measures, Mainland China provided a unique venue to investigate the effect of public health pandemic control policies on tertiary education. Considering the large portion of international students in medical schools and the higher susceptibility to the pandemic measures due to the natural synergy with teaching hospitals, medical schools can be an early forefront indicator of the impact of the pandemic on tertiary education. We surveyed 513 participants international medical students from Wen-zhou Medical University’s school of international studies in 2019 and 2022 about their satisfaction and demographic and cultural adaptation parameters. We found that the pandemic restrictions did not generally impact student satisfaction. Further analyzing the associated parameters, we found teaching satisfaction to be independent of age (P=.159), years in China (P=.896) and Chinese Level (P=.577) in the medical university. Teaching satisfaction is associated with the region of origin (P <0.001), acceptance of local social norms (P <0.001), satisfaction with the core values of Chinese culture (P <0.001), acclimatization to life in China (P <0.001), can easily make Chinese friends (P <0.001) among other factors. The findings of this study provide an understanding of the effects of pandemic measures on tertiary education satisfaction and other factors that can aid in international student satisfaction.

With roots beyond behavioural economics to psychology, nudges can be applied for influencing healthy behaviours such as food choice and portions to decrease obesity for better public health outcomes. However, the effectiveness of the type of nudges are contentious with conflicting literature. In this pilot study, we conducted a 23-day study surveying the food choices that included portion, locus of control, demographic data, and psychological measures of personality, perceived stress, narcissism, regulatory focus, food choice motive and dietary restraint, with the participants given four intervention conditions of 12 instant messaging sent every two days through WhatsApp. The messages were either factual (control), focused on consequences, through social comparison, or persuasive. Running over the COVID19 pandemic, 17 participants completed the full surveys showing significant effects between the experimental conditions with the psychological parameters except for diet confidence and extraversion and conscientiousness, as well as cognitive restraint. We found BMI and waistline measurements to be suitable measurements, with promising results from the fear and social comparison nudges for food-related behaviours and exercise. Our pilot findings have implications to the use of nudges upon which future studies investigating psychological factors can build on.

Tissue-like phantoms are widely used as a model for mimicking the optical properties of live tissue. This paper presents the results of a diffusion reflection method as well as fluorescence lifetime imaging microscopy measurements of fluorescein-conjugated gold nanorods in solution as well as inserted in solid tissue-imitating phantoms. A lack of consistency between the fluorescence lifetime results of the solutions and the phantoms raises a question about the ability of tissue-like phantoms to maintain the optical properties of inserted contrast agents.

The “Crisis of Reproducibility” has received considerable attention both within the scientific community and without. While factors associated with scientific culture and practical practice are most often invoked, I propose that the Crisis of Reproducibility is ultimately a failure of generalization with a fundamental scientific basis in the methods used for biomedical research. The Denominator Problem describes how limitations intrinsic to the two primary approaches of biomedical research, clinical studies and pre-clinical experimental biology, lead to an inability to effectively characterize the full extent of biological heterogeneity, which compromises the task of generalizing acquired knowledge. Drawing on the example of the unifying role of theory in the physical sciences, I propose that multi-scale mathematical and dynamic computational models, when mapped to the modular structure of biological systems, can serve a unifying role as formal representations of what is conserved and similar from one biological context to another. This ability to explicitly describe the generation of heterogeneity from similarity addresses the Denominator Problem and provides a scientific response to the Crisis of Reproducibility.

Underwater image often exhibit detail blurring and color distortion due to light scattering, impurities, and other influences, obscuring essential textures and details. This presents a challenge for existing super-resolution techniques in identifying and extracting effective features, making high-quality reconstruction difficult. This research aims to innovate underwater image super-resolution technology to tackle this challenge. Initially, an underwater image degradation model was created by integrating random subsampling, Gaussian blur, mixed noise, and suspended particle simulation to generate a highly realistic synthetic dataset, thereby training the network to adapt to various degradation factors. Subsequently, to enhance the network's capability to extract key features, improvements were made based on the symmetrically structured Blind Super-Resolution Generative Adversarial Network (BSRGAN) model architecture. An attention mechanism based on energy functions was introduced within the generator to assess the importance of each pixel, and a weighted fusion strategy of adversarial loss, reconstruction loss, and perceptual loss was utilized to improve the quality of image reconstruction. Experimental results demonstrate that the proposed method achieved significant improvements in Peak Signal-to-Noise Ratio (PSNR) and Underwater Image Quality Measure (UIQM) by 0.85 dB and 0.19, respectively, significantly enhancing the visual perception quality and indicating its feasibility in super-resolution applications.

A variable annuity is a popular life insurance product that comes with financial guarantees. Using Monte Carlo simulation to value a large variable annuity portfolio is extremely time-consuming. Metamodeling approaches have been proposed in the literature to speed up the valuation process. In metamodeling, a metamodel is first fitted to a small number of variable annuity contracts and then used to predict the values of all other contracts. However, metamodels that have been investigated in the literature are sophisticated predictive models. In this paper, we investigate the use of linear regression models with interaction effects for the valuation of large variable annuity portfolios. Our numerical results show that linear regression models with interactions are able to produce accurate predictions and can be useful additions to the toolbox of metamodels that insurance companies can use to speed up the valuation of large VA portfolios.

The basis of the work of electroencephalography (EEG) is the registration of electrical impulses from the brain or some of its individual areas using a special sensor/electrode. This method is used for the treatment and diagnosis of various diseases. The use of wet electrodes in this case does not seem viable, for several well-known reasons. As a result of this, a detailed analysis of modern EEG sensors developed over the past few years is carried out, which will allow researchers to choose this type of sensor more carefully and, as a result, conduct their research more competently. Due to the absence of any standards in the production and testing of dry EEG sensors, the main moment of this manuscript is a detailed description of the necessary steps for testing a dry electrode, which will allow researchers to maximize the potential of the sensor in the various type of research.

Dynamic hedging has been adopted by many insurance companies to mitigate the financial risks associated with variable annuity guarantees. In order to simulate the performance of dynamic hedging for variable annuity products, insurance companies rely on nested stochastic projections, which is highly computationally intensive and often prohibitive for large variable annuity portfolios. Metamodeling techniques have recently been proposed to address the computational issues. However, it is difficult for researchers to obtain real datasets from insurance companies to test metamodeling techniques and publish the results in academic journals. In this paper, we create synthetic datasets that can be used for the purpose of addressing the computational issues associated with the nested stochastic valuation of large variable annuity portfolios. The runtime used to create these synthetic datasets would be about 3 years if a single CPU were used. These datasets are readily available to researchers and practitioners so that they can focus on testing metamodeling techniques.

GaN ultraviolet photodetector with metal-semiconductor-metal structure is achieved by growing on a periodic trapezoid column-shape patterned sapphire substrate using metalorganic chemical vapor deposition. Under 5-V reverse bias, the photodetector fabricated on such patterned sapphire substrate exhibits a lower dark current, a higher photocurrent, and a 476 % enhancement in the maximum responsivity as compare with those of the photodetector fabricated on conventional flat sapphire substrate. It is also found that the much larger UV-to-visible rejection ratio and the fact that responsivity drops in a smaller cut-off region are observed from photodetector fabricated by using a periodic trapezoid column-shape patterned sapphire substrate. These phenomena may all be attributed to the reduction of threading dislocation density and the improved quality of GaN film, as well as the internal reflection and/or scattering effect on the interface between GaN film and the periodic trapezoid column-shape pattern of the substrate.

Visual data on the geographic distribution of carbon storage helps policy maker to formulate countermeasures on global warming. However, Taiwan, as an island showing diversity in climate and topography, had lacked valid visual data in distribution of forest carbon storage between the last to forest surveys (1993-2015). This study established a model to achieve an estimation that capable to demonstrate the distribution of forest carbon storage. This model uses land use, stand morphology, carbon conversion coefficient databases accordingly for 51 types of major forest in Taiwan. An estimation on 2006 was conducted and shows an overall carbon storage of 165.65 Mt C, with forest carbon storage per unit area of 71.56 t C ha–1, where natural forests and plantations respectively contributed of 114.15 Mt C (68.9%) and 51.50 Mt C (31.1%). By assuming no change in land use type, the carbon sequestration from 2006 to 2007 by the 51 forest types was estimated to be 5.21 Mt C yr–1 using historical tree growth and mortality rates. The result reflects the reality of the land use status and event of coverage shifting with time by combining to the two forest surveys in Taiwan.

This paper is a response to the EPR paper titled: "Can quantum-mechanical description of physical reality be considered complete?", published in Physical Review in 1935. A quantum-mechanical (QM) measurement function describes a distribution of local results, while each empirical measurement process produces one result as exact as allowed by a measuring instrument calibrated to a non-local unit standard. Repeating these empirical measurements produces a Gaussian distribution of measurement results. The QM and empirical measurement result distributions can be compared. To precisely compare a QM measurement function describing a distribution of eigenvectors to a distribution of repetitive empirical measurement results, it is necessary to determine, by calibration, the precision of the eigenvectors to the same standard as the empirical results, because each eigenvector evidences uncertainty relative to a standard. When the calibration process is recognized as formal as well as empirical, QM measurement function results and metrology measurement process results are unified.

This paper is a response to the EPR paper titled: "Can quantum-mechanical description of physical reality be considered complete?", published in Physical Review in 1935. A quantum-mechanical (QM) measurement function describes a distribution of local results and each empirical measurement process produces one result as exact as allowed by a measuring instrument calibrated to a non-local unit standard. Repeating these empirical measurements produces a bell shaped distribution of measurement results. Each of these distributions can be compared to the other. To precisely compare a QM measurement function describing a distribution of eigenvectors to a distribution of repetitive empirical measurement results, it is necessary to determine, by calibration, the precision of the eigenvectors to the same standard as the empirical results, because each eigenvector evidences uncertainty relative to a standard. When the calibration process is recognized as formal as well as empirical, QM measurement function results and metrology measurement process results are unified.

Personal protective equipment (P PE) rules must be respected and followed to the letter of healthcare professionals in Cameroon to avoid the transmission of infectious diseases. This research proposes solutions to the problems of non-adherence and non-compliance among healthcare workers in Cameroon towards PPE rules. Inadequate access to suitable PPE, discomfort when wearing PPE during poor quality, and a lack of training on correct PPE use are just a few of these obstacles amongst HCWs in Cameroon. Hospitals and healthcare facilities should prioritize offering thorough instruction on PPE use and pinpoint the need to adhere consistently to PPE rules to remove these obstacles. They should also ensure that all healthcare personnel have access to and a sufficient supply of the proper PPE. Through the organization of impromptu visits by surveillance teams to identify areas of support and training, there is a need for assistance from government bodies and public health groups in creating awareness of the security value of PPE use among healthcare professionals. There is a lack of PPE in health care settings in rural areas in Cameroon, thus a need for an adequate supply. Then can we put appropriate PPE policies and practices for HCWs in Cameroon, as they will keep to it.Healthcare professionals in Cameroon must comprehend that following PPE standards protects not just their selves from diseases but also their patients, coworkers, and communities. We are accountable for ensuring everyone's safety and well-being, and adhering to PPE regulations is an essential first step.

The kiwiberry is a trendy variety that shows a high demand for consumption but presents commercialization challenges due to its short life. The purpose of this study is to compare the effects of edible alginate coatings in prolonging the shelf life of this fruit. Two different forms were tested (coarse emulsions(C) and nanoemulsions(N)) and the effects of two different coating application techniques (spraying(S) and dipping(D)) were tested. Additionally, the use of Vis-NIR for quality monitorization was assessed as well. Fruits were treated with ten different treatments, using the following base formulations:Alginate(AL) 2%(w/v) enriched with eugenol(Eug) and citral(Cit) at minimum inhibitory concentrations (0.10% and 0.15%) and double MIC. Samples were taken from cold storage (0° C), at 2 and 4 weeks. For fruit quality evaluation, colour analysis, firmness, soluble solids content, dry matter and weight loss were performed. During storage, the colour and SSC parameters increased. Firmness decreased over time.Weight loss was the parameter with higher significant differences, increasing over time and more pronounced in treatments with higher essential oils components. This study suggests that AL-S, AL-D, AL-Cit 0.15+Eug0.1-N-S, AL-Cit0.15+Eug0.1-N-D and AL-Cit0.15+Eug0.1-C-D can preserve kiwiberries quality. Moreover, we verify that the Vis-NIR spectral analysis could be beneficial for performing coatings discrimination.

It is concerning that Cameroon's urban centers are experiencing an alarming rise in air pollution as a result of urbanization, population growth, industrial activity, and transportation emissions. We examined the causes, health impacts, research methodology, case studies, and government measures to address the urgent issue of air pollution in Cameroon's urban regions. We have emphasized how Cameroon's air pollution levels have significantly increased as a result of causes like population expansion, urbanization, industrial emissions, and automobile exhaust A thorough discussion of the negative health impacts of extended exposure to air pollution is included, with special attention to the sensitivity of the old and young. These effects include respiratory disorders, cardiovascular ailments, and early mortality. We emphasized the pressing need for all-encompassing strategies to address air pollution, such as increased public awareness campaigns, stronger emissions regulations, the development of renewable energy sources, and better waste management. The techniques employed in studies on air pollution, like as epidemiological research and air quality monitoring, are also explained to provide light on the unique health dangers that Cameroonian urban dwellers must contend with. We emphasized in our conclusion the significance of joint efforts by legislators, medical professionals, and the general people to reduce air pollution and protect public health in Cameroon in order to create a cleaner and healthier future. In order to protect public health in the future, it is imperative that Cameroon's air pollution be addressed.

The Sunyaev-Zeldovich effect (SZe) is a global distortion of Cosmic Microwave Bckground (CMB) spectrum as result of its interaction with a hot electron plasma in the intracluster medium for large gravitational virialized structures such as galaxy clusters. Furthermore, this hot gas of electrons emits X-Rays due to its fall in the gravitational potential well of the cluster. The analysis of SZe and X-Ray data, provide a method for calculating distances to galaxy clusters at any redshift (Angular diameter distance (d_A) and gas mass fraction (f_gas)). On the other side, many of these galaxy clusters produce a Strong Gravitational Lens effect (SGL), which has become an useful astrophysical tool for cosmology. We use these cosmological tests, in addition to the more traditional ones (SNIa, CMB, BAO), to constraint alternative models of dark energy (ωCDM, CPL, IDE, EDE) and study the history of expansion through the cosmographic parameters (H(z), q(z), j(z)). Using Akaike and Bayesian Information Criterion (AIC, BIC) we find that the ωCDM and ΛCDM models are the most favored by the observational data. In addition, we found that at low redshift appears an peculiar behavior of slowdown of aceleration, which occurs only on dynamical dark energy models using only galaxy clusters (d_A,clusters + f_gas).

The aim of this study was to synthesize and characterize a novel Methacrylate- functionalized Calcium Phosphate (MCP) used as a bioactive compound for innovative dental composites. The characterization was accomplished by Attenuated Total Reflectance Fourier-Transform Infrared Spectroscopy (ATR-FTIR), X-Ray Diffraction Analysis (XRDA), Scanning Electron Microscopy (SEM), and EnergyDispersive Spectroscopy (EDS). The incorporation of MCP as a bioactive filler in esthetic dental composite formulations and the ability of MCP containing dental composites to promote precipitation of hydroxyapatite (HAp) on the surfaces of those dental composites was explored. The translucency parameter, depth of cure, degree of conversion, ion release profile, and other physical properties of composites were studied with respect to the amount of MCP added to the composites. Composites containing 3 Wt.%, 6 Wt.%, and 20 Wt.% MCP were evaluated at 7, 14, and 21 days. The progress of surface precipitation of hydroxyapatite on MCP-containing dental composites was studied by systematically increasing the MCP content in the composite and the time of specimen storage in Dulbecco’s phosphate-buffered solution with calcium and magnesium. It was found that there was a direct correlation between the percentage of MCP in a composite formulation, the amount of time the specimen was stored in PBS, and the deposition of hydroxyapatite on the composite’s surface

Change orders have received considerable attention from researchers thus far, but none have considered pricing strategies of change orders through the interaction between general contractors and subcontractors. Previous studies found that contractors’ opportunistic bidding considering beyond-contractual reward (BCR) in the execution stage can be reduced by improving the construction management system and strengthening the supervision of contractors’ performance. However, the BCR remains in ecology of construction engineering. This study proposes an integrated evolutionary game theory-system dynamics model (ET-SD model) and simulates the pricing strategy of change orders between general contractors and subcontractors to explore the root cause of BCR phenomenon. Sensitivity analysis on the evolutionary dynamics of payoff is explored. Results reveal that change orders with BCR maintain Nash equilibrium and evolutionary stable strategy (ESS) unless changing the payoff structure between general and subcontractors’ pricing strategies. This study presents important managerial insights from the evolutionary game perspectives, nature of change orders, and payoff of the alternative.

This paper investigated reliability modeling for systems subject to dependent competing risks considering that the variation of failure threshold is not considered in most studies on competing failure reliability. Firstly, the variation of degradation quantity under shocks was analyzed, and the variation of threshold was considered on this basis. Secondly, the cumulative degradation under the influence of the random shock process was analyzed. The attractive property of copula is symmetry. Then, A linear Wiener process model is applied to model performance degradation failure, and a multi-performance-degradation correlated-competition model based on a Copula function is constructed, which considers the correlated competition between multiperformance degradation failures. Lastly, the micromotor system is used to analyze the applicability of the proposed model for bivariate instances. demonstrating the rationality and effectiveness of the proposed model.

Biochemical compositions of black carpenter ants (Camponotus pennsylvanicus) were analyzed using standard methods. The proximate composition (%) were as follows: crude protein (22.50), crude fibre (1.46), carbohydrate by difference (30.86) and energy (35.4Kcal). The predominant mineral was potassium and nickel was the least. The antinutritional properties (mgkg^-1) ranged as follows: Oxalate (1.35), phytate (62.79), and tannins (0.72). The results suggested that the ant may be a good source of nutrition.

GaN-based micro-size light-emitting diodes (µLEDs) have a number of appealing and distinctive benefits for display, visible-light communication (VLC), and other novel applications. The smaller size of LEDs affords them the benefits of enhanced current expansion, less self-heating effects, and higher current density bearing capacity. Low external quantum efficiency (EQE) resulting from non-radiative recombination and quantum confined stark effect (QCSE) is the serious barrier for applications of µLEDs. In this work, the reasons for the poor EQE of µLEDs are reviewed, as well as the optimization techniques for improving the EQE of µLEDs.

We study the brain segmentation by dividing the brain into multiple tissues. Given possible brain segmentation by deep, machine learning can be efficiently exploited to expedite the segmentation process in the clinical practice. To accomplish segmentation process, a MRI and tissues transfer using generative adversarial networks is proposed. Given the better result, we propose the transfer model using GAN. For the case of the brain tissues, white matter (WM), gray matter (GM) and cerebrospinal fluid (CSF) are segmented. Empirical results show that this proposed model significantly improved segmentation results compared to the stat-of-the-art results. Furthermore, a dice coefficient (DC) metric is used to evaluate the model performance.

Background and Objectives. Prevalence of GERD is increased among individuals with obesity and RYGB is considered as the most effective procedure to control GERD and obesity. However, some patients continue to have GERD after RYGB. The aim of the study was to investigate the prevalence and the risk factors of GERD after RYGB. Material and Methods. This prospective study included 180 patients operated with RYGB and followed for on average of 12.2 (0.6) years. 126 (70%) patients agreed to participate in online consultations and provided data on weight, GERD symptoms, filled GERD-HRQL, TFEQ-18 and GSRS questionnaires. Results. The average age before surgery was 42.7 (10.5) years and BMI was 45.2 (6.4) kg/m2. 128 (71.1%) were females and preoperative GERD was diagnosed in 74 (41.1%) patients. At 12 years follow-up the mean %EBMIL and %TWL were 60.37 and 25.73, respectively. The median %WR was 18.0 (39.0). The GERD was present in 30 (23.8%) patients, of whom 12 (40%) continued to have GERD symptoms and 18 (60%) developed de novo GERD. The GERD-HRQL score significantly decreased from 3.0 (8.0) at baseline to 2.0 (5.0) (p=0.028) at 12 years. GSRS Diarrhea and Indigestion median scores increased significantly, from 1.33 (0.67) to 1.5 (2.42) (p 3.2.

Many corporations have now revised their value statements to showcase their commitment to sustainable development and green business due to the pressing issues of climate and environmental damage. Despite the importance of value statements that can direct companies to achieve sustainable goals, what kind of values can be perceived as organizational orientations to sustainability remains unknown. This study explores how the general public views the inclusion of sustainable components in value statements from the listed companies in Hong Kong by using Q-sort methodology (n = 30). Analyzing the final sample of 40 value statements extracted from 27 listed companies, we identified three factors related to sustainability, namely, Empowering business process management, Empowering customer-centric excellence, and Empowering sustainable progress. This study provides an additional component to the traditional triple bottom line by suggesting that internal operation of a company is an emerging idea to understand corporate sustainability.

This review article focuses on the role of Artificial Intelligence (AI) in transforming healthcare in Africa, specifically in combatting infectious diseases, neglected tropical diseases (NTDs), and antimicrobial resistance. We provide a comprehensive overview of the significance of AI in the healthcare industry, highlighting its urgency and importance in addressing these specific health challenges in Africa. We begin by discussing the role of AI in infectious disease surveillance and outbreak detection. We explore how AI technology can be employed for real-time tracking and prediction of outbreaks, providing examples of successful AI applications in infectious disease surveillance within the African context.Next, we examine the potential of AI-enabled diagnosis and treatment for faster and more accurate diagnoses of infectious diseases and NTDs. We highlight specific examples of AI applications in diagnosing and treating these diseases in Africa, showcasing the potential of AI to improve clinical outcomes and save lives.Furthermore, we focus on how AI-driven drug discovery and development can expedite the search for new treatments for infectious diseases and combat antimicrobial resistance. We present examples of AI applications in drug discovery within the African context, illustrating the potential for AI to revolutionize the development of effective therapeutics.In addition, we delve into how AI-powered public health interventions can enhance the design and implementation of targeted interventions. We explore how AI can optimize resource allocation and facilitate data-driven decision-making processes, providing examples of AI applications in public health in Africa.Finally, we address the challenges and limitations of implementing AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. We discuss potential barriers and ethical concerns surrounding AI applications in healthcare, aiming to encourage informed and responsible utilization of AI technologies.Overall, this review emphasizes the importance and potential of AI in combatting infectious diseases, NTDs, and antimicrobial resistance in Africa. It positions AI as a catalyst for revolutionizing healthcare in the region, leading to more effective disease surveillance, diagnosis, treatment, drug discovery, and public health interventions.

: Soil free living nematodes community composition was compared for five agro-management systems in a basaltic soil. The sites differed in their long-term management system and were designated as organic farming (OM), conventional (MI), natural pasture (NP), foliage farming (FF) and open field (OF). The effect of five diverse land use types in vineyard growth in basaltic soils on soil free living nematodes, density, diversity, and functionality in the plant rhizosphere was examined. We found 22 families, 42 genera of which only 14 genera were present in all soil samples, three bacteria feeders, 4 fungi feeders, 3 omnivores-predators and 4 plant parasites, in the soil collected from the five sampling sites. The organic and conventional managements were found to influence soil free living nematode community and its trophic composition. The higher abundance of bacteria feeders included the Cephalobidae and Panagrolaimidae families. The fungi feeders included the Anguinidae and Aphelenchidae families. Ominvores-predators included the Aprocelaimidae and Qudsianematidae, and the plant parasites included the Anguinidae (genus Anguina), Nothotylenchidae and Tylenchidae families. The changes in plant parasite nematodes were one of the main pests in the below-ground rhizosphere biome. Promoting the bacterial community could be a beneficial and promising way for a considerable increase in organic management.

We present the results of a twofold experimental and computational study of (0001) GaN/AlN multilayers forming pseudomorphic superlattices. High-Resolution Transmission Electron Microscopy (HRTEM) shows that heterostructures with GaN Quantum Wells (QW) four c-lattice parameters thick are misfit-dislocation free. Accurate structural data are extracted from HRTEM images via a new methodology optimizing the residual elastic energy stored in the samples. Total energy calculations are performed with several models analogous to the experimental QWs with increasing thicknesses of GaN, whereas this of the AlN barrier is kept fixed at n=8 c-lattice parameters. With vanishing external stresses, minimum energy configurations of the studied systems correspond to different strain states. Linear elasticity accurately yields the corresponding lattice parameters, suppressing the need for on-purpose total energy calculations. Theoretically justified parabolic fits of the excess interfacial energy yield the values of interfacial stress and elastic stiffness as functions of the GaN QW thickness. Total species-projected densities of states and gap values extracted therefrom allow deciphering the effect of the evolving strain on the electronic structure of the superlattice. It is found that the gap energy decreases linearly with increasing the strain of the QW. These results are briefly discussed in the light shed by previous works from the literature.

Regulation (EU) No 305/2011 lays down harmonized conditions for marketing construction products in the European Union. One of its consequences has been the introduction of the product standard EN 50575 and standard EN 130501-6, concerning power, control, and communication cables permanently installed in buildings to prevent the risk of a fire and its consequences. EN 13501-6 provides the reaction to fire classifications for cables, the test methods to be performed, the requirements to meet a specific reaction to fire, and additional classifications for smoke production, flaming droplets, and acidity. EN 60754-2 is the technical standard used to assess acidity, and it defines three classes: a1, a2, and a3 (the less performant), based on pH and conductivity measurements. Due to the release of hydrogen chloride during the combustion, acidity is the weak point of PVC cables, which are not yet capable of achieving the a1 or a2 classes required in specific locations according to a fire risk assessment. EN 13501-6 does not include EN 60754-1. Both EN 60754-1 and EN 60754-2 are common standards for determining halogen gas content, acidity, and conductivity, respectively. They involve the same type of tube furnace but with different heating regimes, final temperatures, and detection methods. EN 60754-2 must be performed at temperatures between 935 – 965 °C. The paper demonstrates that, when EN 60754-2 is carried out with the thermal profile of EN 60754-1 or at 500 °C in isothermal conditions, the evolution of hydrogen chloride changes significantly if potent acid scavengers are utilized in the PVC compounds. The reason lies behind the kinetic of hydrogen chloride release during the combustion of PVC compounds: the higher the temperature or faster the heat release, the quicker hydrogen chloride evolution and the lower the probability for the acid scavenger to trap it. That further highlights the "fragility" of EN 60754-2 as a tool for assessing the risks associated with the release of hydrogen chloride in case of fire

Implementation of novel blood-based biomarkers is desired to reduce diagnostic delay and burden for myositis patients. In this retrospective study, the potential of C-X-C motif chemokine ligand 10 (CXCL10) and growth differentiation factor 15 (GDF15) was explored in an established patient cohort diagnosed with immune-mediated necrotizing myopathy (IMNM; n=21), sporadic inclusion body myositis (IBM; n=18), polymyositis (PM; n=3), dermatomyositis (DM; n=2), and anti-synthetase syndrome (ASS; n=1), comparing with healthy controls (n=10) and patients with a hereditary neuromuscular disorder (n=14). CXCL10 and GDF15 were quantified in sera with enzyme-linked immunosorbent assays and immunolocalized in skeletal muscle tissue. In myositis patients, serum CXCL10 levels were significantly increased 9.6--fold compared to healthy and 4.2-fold compared to disease controls. Mean levels in IBM (929±658 pg/ml) were significantly higher than in IMNM (425±324 pg/ml). With the threshold set to 180pg/ml of CXCL10, myositis patients could be differentiated from healthy and disease controls with a sensitivity of 0.80 and a specificity of 0.71. Incorporating a threshold of 300 pg/ml for GDF15 reduced false negatives to two IMNM patients only. Subsets of muscle-infiltrating immune cells expressed CXCL10, and serum levels correlated with muscle inflammation grade. We propose adding circulating CXCL10 and GDF15 to the blood-based diagnostic toolkit for myositis as a valuable patient-friendly approach.

This paper is motivated by spatio-temporal pattern in the occurrence of Leishmaniasis in Afghanistan and the relatively high number of zero counts. We hold the view that correlations that arise from spatial and temporal sources are inherently distinct. Our method decouples these two sources of correlations, there are at least two advantages in taking this approach. First, it circumvents the need to inverting a large correlation matrix, which is a commonly encountered problem in spatio-temporal analyses. Second, it simplifies the modelling of complex relationships such as anisotropy, which would have been extremely difficult or impossible if spatio-temporal correlations were simultaneously considered. We identify three challenges in the modelling of a spatio-temporal process: (1) accommodation of covariances that arise from spatial and temporal sources; (2) choosing the correct covariance structure and (3) extending to situations where a covariance is not the natural measure of association. Moreover, because the data covers a period that overlaps with the US invasion of Afghanistan, the high number of zero counts may be the result of no disease incidence or lapse of data collection. To resolve this issue, a model truncated at zero built on a foundation of the generalized estimating equations was proposed.

60\% of the global population is expected to be in cities by 2050. At the same time the climate is changing at an accelerated rate. In this short paper we discuss the conflation of these two phenomena and how it may change the cities of the future. We formulate this as an optimization problem that could help answer important questions such as where are they likely to be located? How are they to be powered? What would be their demand for mobility and how might this be met? How over or under ground might these cities be? This paper first looks at a historical paradigm of cities in the absence of the climate crisis and then proposes the new paradigm to look at cities of the future faced by the challenge of the climate change crisis.

The healthcare situation in Africa is characterized by limited resources and a high burden of disease, presenting significant challenges to the healthcare systems in the region. The integration of Artificial Intelligence (AI) technologies in African healthcare holds immense potential for addressing these challenges and improving health outcomes. This paper aims to provide a comprehensive understanding of the current healthcare landscape in Africa and the urgent need for AI-driven solutions.In addition to discussing the potential applications of AI in African healthcare, such as disease diagnosis, treatment planning, and medical imaging analysis, this manuscript seeks to shed light on the specific challenges hindering the implementation of AI in the region. By providing detailed examples and case studies, we aim to offer insights into the obstacles that need to be overcome and potential solutions that can be pursued.By leveraging AI technologies, Africa has the opportunity to revolutionize its health systems, bridging the gap in access to quality healthcare across the continent. The objective of this review article is to not only highlight the potential benefits that AI can bring to African healthcare but also to emphasize the urgent need for action and collaboration in implementing and scaling up AI innovations in the region. By doing so, African countries can pave the way for improved health outcomes and ensure that their populations receive the healthcare services they deserve.

Artificial intelligence (AI) has emerged as a powerful tool for addressing the challenges posed by climate change and their impact on public health. By leveraging its capacity to analyze and predict climatic patterns, AI offers opportunities to enhance resource management and develop effective strategies for climate change mitigation. Moreover, AI can contribute to generating sustainable solutions that address the complex and interconnected nature of climate change. For example, AI can enable the optimization of energy consumption and facilitate the integration of renewable energy sources into existing systems. It can also support the development of climate models that provide timely and accurate predictions, enabling policymakers to implement proactive measures for disaster preparedness and response. Furthermore, AI-powered disease surveillance and mitigation techniques can improve public health outcomes by identifying patterns and trends in the spread of diseases in relation to climatic factors.However, the widespread adoption of AI-based solutions is not without challenges. Ethical concerns surrounding privacy and data ownership must be addressed, as the use of AI requires access to large datasets, raising potential privacy risks. Technical constraints, such as limited computational power and the need for sophisticated algorithms, also pose obstacles to the implementation of AI in climate change mitigation strategies. Furthermore, issues related to the accessibility and affordability of AI technologies must be resolved to ensure equitable distribution and maximize its potential impact on public health.To fully harness the power of AI in addressing climate change and improving public health outcomes, it is crucial to promote innovation, multidisciplinary collaboration, and open data science. Innovation can drive the development of new AI algorithms and technologies specifically tailored to address climate change challenges. Multidisciplinary approaches that bring together experts from diverse fields, including climate science, public health, and computer science, can foster a holistic understanding of complex systems and enable the design of comprehensive solutions. Finally, open data science practices, such as sharing data and algorithms, can facilitate collaboration and accelerate progress in mitigating climate change and its public health impacts.In conclusion, AI offers promising opportunities for effectively addressing climate change and its impact on public health. However, to fully realize its potential, it is essential to tackle ethical and privacy concerns, overcome technical constraints, and ensure accessibility and affordability. By promoting innovation, multidisciplinary collaboration, and open data science, we can unlock the transformative power of AI in mitigating climate change and improving public health outcomes.

Bactrocera dorsalis Hendel is a highly invasive horticultural pest of major economic importance worldwide. In Burkina Faso, it is one of the main insect pests constraining the mango production and export. Understanding the biology and the genetic dynamics of this insect pest provide cru-cial information for the development of effective control measures. The aim of this study was to understand the distribution, diversity and genetic structure of B. dorsalis in Burkina Faso. Male flies were collected transversally in Burkina Faso and analyzed by PCR using 10 microsatellite markers. The results showed an abundance of B. dorsalis varying from 87 to 2986 flies per trap per day in the different sampling sites. Genetic diversity was high in all sites with an average Shan-non's Information Index (I) of 0.72 per site. Gene flow was high between study populations and ranged from 10.62 to 27.53 number of migrants. Bayesian admixture analysis showed no evi-dence of structure while Discriminant Analysis of Principal Components identified three weakly separated clusters in the population of B. dorsalis in Burkina Faso. The results of this study could be used to optimize the effectiveness of current control interventions and to guide the imple-mentation of new, innovative and sustainable strategies.

Multimodality requires integration of heterogeneous transportation data to construct a broad view of the transportation network. Many new transportation services are emerging with being isolated from previously existing networks. This lead them to publish their data sources to the web -- according to Linked Data Principles -- in order to gain visibility. Our interest is to use these data to construct an extended transportation network that links these new services to existing ones. The main problems we tackle in this article fall in the categories of automatic schema matching and data interlinking. We propose an approach that uses web services as mediators to help in automatically detect geospatial properties and map them between two different schemas. On the other hand, we propose a new interlinking approach that enables user to define rich semantic links between datasets in a flexible and customizable way.

Parkinson’s disease (PD) is characterized by motor deficits and a wide variety of non-motor symptoms. The age of onset, rate of disease progression and the precise profile of motor and non-motor symptoms display considerable individual variation. Neuropathologically, the loss of substantia nigra dopaminergic neurons is a key feature of PD. The vast majority of PD patients exhibit alpha-synuclein aggregates in several brain regions, but there is also great variability in the neuropathology between individuals. While the dopamine replacement therapies can reduce motor symptoms, current therapies do not modify the disease progression. Numerous clinical trials using a wide variety of approaches have failed to achieve disease modification. It has been suggested that the heterogeneity of PD is a major contributing factor to the failure of disease modification trials, and that it is unlikely that a single treatment will be effective in all patients. Precision medicine, using drugs designed to target the pathophysiology in a manner that is specific to each individual with PD, has been suggested as a way forward. PD patients can be stratified according to whether they carry one of the risk variants associated with elevated PD risk. In this review we assess current clinical trials targeting two enzymes, leucine-rich repeat kinase 2 (LRRK2) and glucocerebrosidase (GBA), which are encoded by two most common PD risk genes. Because the details of the pathogenic processes coupled to the different LRRK2and GBA risk variants are not fully understood, we ask if these precision medicine-based intervention strategies will prove “precise“ or “personalized“ enough to modify the disease process in PD patients. We also consider at what phases of the disease that such strategies might be effective, in light of the genes being primarily associated with the risk of developing disease in the first place, and less clearly linked to the rate of disease progression. Finally, we critically evaluate the notion that therapies targeting LRRK2 and GBA might be relevant to a wider segment of PD patients, beyond those that actually carry risk variants of these genes.

Motivated by Autonomous vehicle idea and driving safety issues, driver assistance system such as Active braking, Cruise Control, Lane departure warning lane keeping and etc. has become a very active research area. However, this paper presents the performance and robustness analysis of a model predictive control and proportional integral derivative control for lane keeping maneuvers of an autonomous vehicle using computer vision simulation studies. A simulation study was carried out where a vehicle model based on single tracked bicycle model was developed in MATLAB/SIMULINK environment together with a vision dynamic system. Both PID controller and MPC were simulated to maintain the desired reference trajectory of the vehicle by controlling steering angle. Further performance and robustness analysis were carried out and the simulation results show that the proposed control system for the PID control achieved its objective even though it was less robust in maintaining its performance under various conditions like vehicle load change, different longitudinal speed and different cornering stiffness. While in the case of MPC the optimizer made sure that the predicted future trajectory of the vehicle output tracks the desired reference trajectory and was more robust in maintaining its performance under same conditions as in PID.

Clothing consumers mostly purchase, use, and dispose of clothing products in a linear business model. This approach pollutes the environment and causes economic and social crises in the ecosystems. This study evaluates the amount of generated postconsumer clothing waste (PCCW) and proposes recycling as a sustainable waste management approach. Data were collected to understand disposal behavior of consumers with 474 respondents selected from households in Bahir Dar city. The findings show that all the determinant factors on the PCCW generation level except religion were significant. The most frequently disposed type of clothing waste is jeans-trouser (51%). The CEBM was used to develop alternative products from a jeans-based clothing waste. The jean's fiber and fabric-reinforced composite materials were developed using unsaturated polyester resin as a sustainable way of PCCW management strategy. The shredded form fiber-reinforced composite was found to have a smoother surface than that of the chopped or cut fabric-reinforced composite. The product was characterized by physical and mechanical properties. The study can be further extended to the scope of the entire consumers in Ethiopia and alternative recyclable products can be studied.

Porous tantalum (Ta) is a promising biomaterial and has been applied in orthopedics and dentistry for nearly two decades. The high porosity and interconnected pore structure of porous Ta promise fine bone ingrowth and new bone formation within the inner space, which further guarantee rapid osteointegration and bone-implant stability in long term. Porous Ta has high wettability and surface energy that can facilitate adherence, proliferation and mineralization of osteoblasts. Meanwhile, low elastic modulus and high friction coefficient of porous Ta can effectively avoid stress shield effect, minimize marginal bone loss and ensure primary stability. Accordingly, the satisfactory clinical application of porous Ta based implants or prostheses are mainly derived from its excellent biological and mechanical properties. With the advent of additive manufacturing, personalized porous Ta based implants or prostheses have shown their clinical value in the treatment of individual patient who need specially designed implant or prosthesis. In addition, many modification methods have been introduced to enhance the bioactivity and antibacterial property of porous Ta with promising in vitro and in vivo research results. In any case, choosing suitable patients is of great importance to guarantee surgical success after porous Ta insertion.

As sustainability gains significance within Higher Education Institutions (HEIs) worldwide, the University of British Columbia (UBC) stands as one of the global champions of sustainability. In 2019, Times Higher Education ranked UBC as number one in the world for taking urgent action to combat climate change and its impacts and ranked one in Canada for making cities inclusive, safe, resilient, and sustainable. Over the years, UBC students have been instrumental to sustainability on the UBC campus by advocating for divestment, climate justice, and other sustainability commitments and projects in the university. Hence, this qualitative study examines students’ engagement with or their perception of the university’s sustainability programs and image. The study found that students acknowledged and commended the university’s sustainability efforts in teaching, research, providing sustainability-related opportunities for students, and in sustainability operations. However, students also addressed hesitation on the part of university administration in championing climate justice and bolder climate action. The conclusion is that continued support and engagement with students are critical for UBC to achieve its climate action plans and sustainability goals in general. The study contributes to the ongoing discourse on the influential role of young people and the youth climate movement in catalyzing ambitious global climate action at all levels.

Jellyfish search (JS) algorithm impersonates the foraging behavior of jellyfish in the ocean. It is a new developed meta-heuristic algorithm that solves complex and real world optimization problems. The global explore capability and robustness of JS are strong, but JS still has great development space in solving complex optimization problems with high dimensions and multiple local optima. Therefore, an enhanced jellyfish search (EJS) algorithm is developed in this study, and three improvements are made: (i) By adding sine and cosine learning factors, the jellyfish can learn from both random individual and best individual during Type B motion in swarm to enhance the optimization capability and convergence speed; (ii) Adding local escape operator can skip local optimal trap and boost the exploitation ability of JS; (iii) Opposition-based learning operator and quasi-opposition learning operator can increase and strengthen the population distribution more diversified, and better individuals are selected from present and new opposition-solution to participates in the next iteration, which can boost the solution’s quality, meanwhile convergence speed is fasted and its precision is increased. In addition, the performance contrast of the developed EJS and some previous outstanding and advanced methods are evaluated on CEC2017, CEC2019 test suite and six real engineering example of case. It is demonstrated that EJS algorithm escaped the trap of local optimum, enhanced the solution’s quality and the calculation speed. What’s more, the practical engineering applications of EJS algorithm also verify its superiority and effectiveness in solving both constrained and unconstrained optimization problems, and it stretched one’s mind for solving such optimization problems.

The main characteristic of polycystic kidney disease is the development of multiple fluid filled renal cysts. The discovery of mislocated Na,K-ATPase in the apical membrane of cyst lining epithelia alluded to a reversal of polarity as a possible explanation to the fluid secretion. The topic of apical Na,K-ATPase in cysts remains controversial. We investigated the localization of the Na,K-ATPase and assessed the apical-basolateral polarization of cyst lining epithelia by means of immunohistochemistry in kidney tissue from six polycystic kidney disease patients undergoing nephrectomy. The Na,K-ATPase α1 subunit was conventionally situated in the basolateral membrane of all immunoreactive cysts. Proteins of the Crumbs and partitioning defective (Par) complexes were localized to the apical membrane domain in cyst epithelial cells. The apical t-SNARE protein Syntaxin-3 also immunolocalized to the apical domain of cyst lining epithelial cells. Proteins of the basolateral Scribble complex immunolocalized to the basolateral domain of cysts. Furthermore, we confirmed that cysts can originate from virtually any tubular segment with preserved polarity. In conclusion, we find no evidence of a reversal in apical-basolateral polarity in cyst lining epithelia in polycystic kidney disease.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

In this paper we propose the generation of synthetic small and more sophisticated molecule structures that optimize the binding affinity to a target (ASYNT-GAN). To achieve this we leverage on three important achievements in A.I.: Attention, Deep Learning on Graphs and Generative Adversarial Networks. Similar to text generation based on parts of text we are able to generate a molecule architecture based on an existing target. By adopting this approach, we propose a novel way of searching for existing compounds that are suitable candidates. Similar to question and answer Natural Language solutions we are able to find drugs with highest relevance to a target. We are able to identify substructures of the molecular structure that are the most suitable for binding. In addition, we are proposing a novel way of generating the molecule in 3D space in such a way that the binding is optimized. We show that we are able to generate compound structures and protein structures that are optimised for binding to a target.

We demonstrate for the first time efficient mechanical to electrical energy conversion using InGaN/GaN nanowires (NWs). Using an atomic force microscope equipped with a modified Resiscope module, we analyse the piezoelectric energy generation of GaN NWs and demonstrate an important enhancement when integrating in their volume a thick In-rich InGaN insertion. The piezoelectric response of InGaN/GaN NWs can be tuned as a function of the InGaN insertion thickness and position in the NW volume. The energy harvesting is favoured by the presence of a PtSi/GaN Schottky diode which allows to efficiently collect the piezo-charges generated by InGaN/GaN NWs. Average output voltages up to 330 ± 70 mV and a maximum value of 470 mV per NW has been measured for nanostructures integrating 70 nm-thick InGaN insertion capped with a thin GaN top layer. This latter value establishes an increase of about 35% of the piezo-conversion capacity in comparison with binary p-doped GaN NWs. By considering these output signals, we estimate a maximum power density generated by one layer of dense InGaN/GaN-based NW of about 3.3 W/cm². These results settle the new state-of-the-art for piezo-generation from GaN-based NWs and offer a promising perspective for extending the performances of the piezoelectric sources.

Ethiopian Airlines is a leading airline in the world. Its supply chain management is one of the most critical aspects in achieving its sustainability objectives. The present study aims to assess the sustainability practices and strategies of Ethiopian Airways in its supply chain, identifying areas for improvement and recommending strategies for enhancing sustainability. The study concentrates on pertinent sustainability concerns within the aviation sector while examining antecedent research on the sustainability of Ethiopian Airlines’ supply chain management strategies. The findings of the study reveal that there are several areas in need of improvement, including the implementation of waste reduction measures, the establishment of a recycling program, and the reduction of single-use plastic usage. It is suggested that promoting responsible tourism embodying sustainable practices is imperative, particularly within the airline context.

Artificial intelligence (AI) is increasingly being used to improve the quality control of point-of-care diagnostics. This is caused by a number of factors, including the following: 1. AI can accelerate and improve testing accuracy. In comparison to humans, AI technology can review data more quickly and precisely, reducing errors and improving overall quality assurance. 2. When it comes to improving POC test findings on healthcare issues such as infectious diseases or medical crises such as heart attacks, AI can be used for sophisticated predictive analytics and modeling that aid in better decision-making. 3. Artificial intelligence facilitates process automation, increasing productivity and lowering labor costs in labor-intensive tasks such as testing and analyzing samples collected at point-of-care facilities. 4. The use of AI enables organizations implementing these solutions to gain insights from large volumes of raw diagnostic data generated faster and more accurately, allowing them to build solid frameworks around preventive care initiatives and significantly influence public health outcomes.5. Artificial intelligence (AI) has been demonstrated to be a useful tool for real-time monitoring systems that identify any problems with test results early so that they can be corrected before affected patients receive inaccurate diagnoses or treatment plans based on false information provided by diagnostic tests performed at points of care such as clinics or hospitals.Using these technologies would allow healthcare organizations to spend less on labor while still receiving exact diagnoses and rapid treatment delivery at a fraction of the cost that manual approaches required earlier.

In the year in which the world "stopped" due to the pandemic, many people didn't have that option and had to keep moving as the only alternative to survive. One of the main destinations in South America, during the quarantine measures established around the world, was Brazil, especially because of the crisis Venezuela went through in the second half of the 2010s. Throughout the research, we observed measures that were applied by the Brazilian government on the grounds of public health issues or cases of health risks related to COVID-19. An analysis of the first measures published by the Brazilian government reveals an effort to securitize borders, which at the same time restricted the government's responsibility to fulfil its role of protecting migrants in need.

We present an open-source Star Tracker (ST) for Attitude Determination. Our implementation makes use of open source software of common usage in astronomy running on a Raspberry Pi 2 platform. The developed platform is open and available for parties interested in further development. Evaluation of the system showed that the ST is suitable for Cubesats of 2U or larger. The ST platform is capable of solving the Lost-In-Space (LIS) problem. Currently, the average accuracy reached of the algorithm is close to 5 seconds of arc with an average processing time of 75 seconds. However, a average accuracy of about 3 minutes of arc can be reached with 35 seconds in average processing time. We also describe the evaluation procedure, the found conclusions of this procedure, in particular the section of the algorithm where most of the processing time is spent and other possible source of errors not included in this study.

In the quest to develop a more effective and representative electoral system that offers a higher global satisfaction while also resisting strategic voting, this paper introduces the Bipartisan/Range method. This electoral system operates in two separate rounds: a first round where voters rank candidates, and if no Condorcet winner emerges, a second round where voters score candidates within a small head set known as the bipartisan set. The proposed method takes inspiration from randomized Condorcet voting system, probably the most strategy-resistant Condorcet methods, but eliminates the non-determinism while improving efficiency in the case of paradoxes. By introducing an unpredictable second round spaced in time and prohibiting predictive surveys, voters face difficulties in anticipating paradoxes, and insincere votes carry inherent risks. Furthermore, the Bipartisan set's limited size mitigates the range voting problem of exaggerated votes. Thus, the method has the potential to be both efficient in reflecting voters' preferences and resistant to strategic voting, exceeding expectations from Arrow's theorem. The method's versatility allows for extensions to multiple candidates, enhancing its applicability across various electoral contexts. Additionally, we have developed BipartiVox, an online platform specifically designed to conduct elections using the Bipartisan/Range method, making it accessible to a wider audience. In conclusion, the Bipartisan/Range method represents a substantial contribution to the field of voting systems, providing a practical, strategy-resistant, and efficient alternative that aligns closely with genuine voter preferences.

Sleep disorders have become a public health problem for many reasons. The aim of this systematic review is to describe social, economic, and environmental determinants of sleep disturbances in Australia and New Zealand. A total of 22 studies, 14 from Australia and 8 from New Zealand involving children, adolescents and adults are included in the review. In summary, we found that sleep health disparities among children and adolescents in both countries are strongly associated to parental SES. Overall, lower family SES such as low parental income, low parental education, low SES area index are associated with higher sleep disturbances among children and adolescents. In adults, results revealed that SES determinants such as low income, lower education, unemployment, and night work were associated with sleep disturbances. Specifically, unemployment was associated with poor sleep quality, insomnia, and increased likelihood of suboptimal sleep efficiency. Low income and lower education were associated with high risk of OSA, longer sleep duration, insomnia, higher odds of suboptimal sleep duration and sleep onset latency.

The rise of the Internet of Things (IoT) has opened up exciting possibilities for new applications. One such novel application is the modernization of maritime communications. Effective maritime communication is vital for ensuring the safety of crew members, vessels, and cargo. The maritime industry is responsible for the transportation of a significant portion of global trade, and as such, the efficient and secure transfer of information is essential to maintain the flow of goods and services. With the increasing complexity of maritime operations, technological advancements such as unmanned aerial vehicles (UAVs), autonomous underwater vehicles (AUVs), and the Internet of Ships (IoS) have been introduced to enhance communication and operational efficiency. However, these technologies also bring new challenges in terms of security and network management. Compromised IT systems, with escalated privileges, can potentially enable easy and ready access to operational technology (OT) systems and networks with the same privileges, with an increased risk of zero-day attacks. In this paper, we first provide a review of the current state and modalities of maritime communications. We then review the current adoption of Software-Defined Radios (SDRs) and Software-Defined Networks (SDNs) in the maritime industry and evaluate their impact, as IoT enablers, on the future of maritime industry operations, safety, and security.

Anticipating and planning for the urgent response to large-scale disasters is critical to increase the probability of survival in these events. These incidents present various challenges that complicate the response, such as unfavorable weather conditions, difficulties in accessing affected areas, and the geographical spread of the victims. Furthermore, local socioeconomic factors such as insufficient prevention education, limited disaster assets, and inadequate coordination between public and private emergency services may complicate these situations. In large-scale emergencies, multiple hotspots are usually observed, which requires efforts to coordinate the strategic allocation of human and material resources in different geographical areas. Therefore, the precise management of these resources based on the specific needs of each area becomes fundamental. To address these complexities, this paper proposes a methodology that models these scenarios as an optimization problem, focusing on the location and allocation of resources in mass casualty incidents. The proposed case study is Mexico City in a post-earthquake scenario, using voluntary geographic information, open government data, and historical data from the September 19, 2017 earthquake. Additionally, it is assumed that the resources that require optimal location and allocation are ambulances, which focus on medical issues that affect the survival of victims. The designed solution involves the use of a metaheuristic optimization technique, along with a parameter tuning technique, to find configurations that perform at different instances of the problem, i.e., different hypothetical scenarios that can be used as a reference for future possible situations. Finally, the objective is to present the different solutions graphically, accompanied by relevant information to facilitate the decision-making process of the authorities responsible for the practical implementation of these solutions.

For automobile insurance, it has long been implied that when a policyholder made at least one claim in the prior year, the subsequent premium is likely to increase. When this happens, the policyholder may seek to switch to another insurance company to possibly avoid paying for a higher premium. In such situations, insurers may be faced with the challenges of policyholder retention by keeping premiums low in the face of competition. In this paper, we seek to find empirical evidence of possible association between policyholder switching after a claim and the associated change in premium. In accomplishing this goal, we employ the method of association rule learning, a data mining technique that has its origins in marketing for analyzing and understanding consumer purchase behavior. We apply this unique technique in two stages. In the first stage, we identify policyholder and vehicle characteristics that affect the size of the claim and resulting change in premium regardless of policy switch. In the second stage, together with policyholder and vehicle characteristics, we identify the association among the size of the claim, the level of premium increase and policy switch. This empirical process is often challenging to insurers because they are unable to observe the new premium for those policyholders who switched. However, we used a 9-year claims data for the entire Singapore automobile insurance market that allowed us to track information before and after the switch. Our results provide evidence of a strong association among the size of the claim, the level of premium increase and policy switch. We attribute this to the possible inefficiency of the insurance market because of the lack of sharing and exchange of claims history among the companies.

Transition between the unfolded and native states of the ordered globular proteins is accompanied by accumulation of several intermediates, such as pre-molten globule, wet molten globule, and dry molten globule. Structurally equivalent conformations can serve as native functional states of intrinsically disordered proteins. This overview captures the characteristics and importance of these molten globules in both structured and intrinsically disordered proteins. It also discusses examples of engineered molten globules. The formation of these intermediates under the conditions of macromolecular crowding and their interactions with nanomaterials are also reviewed.

Acute myeloid leukemia (AML) is the second common hematologic malignancy in children. The incidence of childhood AML is much lower than acute lymphoblastic leukemia (ALL), which makes the childhood AML a rare disease in children. The role of genetic abnormalities in AML classification, management and prognosis prediction is much more important than before. The WHO classification of myeloid neoplasms and the European LeukemiaNet (ELN) classification were both revised in 2022. The application of the new information in childhood AML will be upcoming in the next few years. The frequency of each genetic abnormality in adult and childhood AML is different, therefore, in this review we emphasize in well-known genetic subtypes in childhood AML, including Core-binding factor AML (CBF AML), KMT2Ar (KMT2A/11q23 rearrangement) AML, normal karyotypes AML with somatic mutations, unbalanced cytogenetic abnormalities AML, NUP98 11p15/NUP09 rearrangement AML and acute promyelocytic leukemia (APL). Current risk group classification, management algorithm in childhood AML and novel treatment modalities, such as targeted therapy, immune therapy, chimeric antigen receptor (CAR) T-cell therapy are reviewed. Finally, the indications of hematopoietic stem cell transplantation (HSCT) in AML are discussed.

This study empirically analyzes how R&D expenditures affect firm value using a regression model for listed firms in Shandong Province, China. The dependent variable is measured by To-bin Q, which measures firm value, and the experimental variable among the independent varia-bles is R&D expenditure. In addition, financial leverage and firm size, which have been found to have a significant impact on firm value in previous studies, were selected as control variables. The study sample consisted of 554 data points analyzed over a nine-year period from 2010 to 2018. Without controlling for variables that affect firm value, the correlation analysis shows that R&D expenditures of listed firms in Shandong Province, China have a significant positive impact on firm value at the 1% level. For a more precise analysis, we ran a multivariate regression analysis controlling for other variables that affect firm value and found that R&D expenditure has a significant positive impact on firm value at the 1% level. These findings suggest that man-agers of listed companies in Shandong Province, China, need to actively engage in R&D activi-ties to increase firm value. Companies in Shandong Province, China, are currently experiencing very strong growth, and this study, which shows that R&D is the key to increasing the value of these companies, can help companies in Shandong formulate their business strategies.

INTRODUCTION: Achalasia is a pathology with an incidence of 1 in 100,000 inhabitants per year. There are very limited data on achalasia in the obese population, especially in those undergoing bariatric surgery. The approach of choice for cases of achalasia is fundoplication to correct the reflux; however, lacking a fundus due to a previous gastrectomy, an alternative that offers optimal results should be chosen. Here we present the surgical approach in a case of esophageal achalasia and a history of vertical sleeve gastrectomy, where we performed simultaneous Heller's cardiomyotomy and laparoscopic Roux-en-Y gastric bypass, as well as the results obtained. CASE PRESENTATION: 44-year-old woman with no chronic degenerative diseases, with a previous record of vertical sleeve gastrectomy 5 years ago, who started 1 year and 5 months earlier with dysphagia to liquids, then to solids, in addition to weight loss of 10 kilograms in 4 months. The BMI before vertical sleeve gastrectomy was 32 kg/m2, her BMI at the time of admission was 20 kg/m2; she also presented regurgitation and generalized weakness. After analyzing the surgical options, it was decided to perform a Heller cardiomyotomy and a Roux-en-Y gastric bypass. DISCUSSION AND CONCLUSIONS: The procedure turned out to be safe and successful in treating achalasia symptomatology, in addition to complete resolution of the reflux symptom.

(1) In recent ten years, with the fast development of digital and engineering manufacturing technology, additive manufacturing has already been more and more widely used in the field of dentistry, from the first personalized surgical guides to the latest personalized restoration crowns and root implants. (2) Especially, the bioprinting of teeth and tissue is of great potential to realize organ regeneration and finally improve the life quality. (3) In this review paper, we firstly presented the workflow of additive manufacturing technology. Then we summarized main applications and recent research progresses of additive manufacturing in dentistry. (4) Lastly, we sketched out some challenges and future directions of additive manufacturing technology in dentistry.

In today's context of rising sea levels and subsiding land topography, coastal flooding is a major concern for coastal areas, both nationally, regionally, and internationally. A detailed understanding of these two phenomena is a prerequisite for effective coastal flood management. The main objective of the present study is to contribute to the understanding of the impact of subsidence and sea-level rise on coastal flooding in Saint-Louis, and in particular the Langue de Barbarie, by producing a vulnerability map of submerged surfaces. To this end, the Synthetic Aperture Radar (SAR) interferometry technique is used to process a time series of Sentinel-1 images and estimate land subsidence in the Saint-Louis and Langue de Barbarie area. Interferometric SAR (InSAR) measurements are merged with RCP 2.6 sea-level rise scenarios to identify predicted flooded areas and provide a flood vulnerability map. At the Langue de Barbarie, topographic subsidence is estimated at between -6.4 and -0.4mm/year. In this study, we show that local land subsidence may increase vulnerability to flooding caused by sea-level rise based on 2100 projections. This may represent an increase in the flood zone in the study area. Based on the RCP 2.6 scenario and the subsidence rates obtained, a significant part of the St. Louis coastline and city would be subject to coastal and fluvial flooding for the years 2040 and 2100. For these dates, 29 to and 36 Km2 of the studied area would be occupied by water. Given this situation, research in Saint-Louis needs to focus on adaptation scenarios to coastal risks, to protect coastal communities and safeguard the city's historical heritage.

With the development of deep learning, the Super-Resolution (SR) reconstruction of microscopic images has improved significantly. However, the scarcity of microscopic images for training, the underutilization of hierarchical features in original Low Resolution (LR) images, and the high-frequency noise unrelated with the image structure generated during reconstruction process are still challenges in the Single Image Super-Resolution (SISR) field. Faced with these issues, we first collected sufficient microscopic images through Motic, a company engaged in the design and production of optical and digital microscopes, to establish a dataset. Secondly, we proposed a Residual Dense Attention Generative Adversarial Network (RDAGAN). The network comprises a generator, an image discriminator, and a feature discriminator. The generator includes a Residual Dense Block (RDB) and a Convolutional Block Attention Module (CBAM), focusing on extracting the hierarchical features of the original LR image. Simultaneously, the added feature discriminator enables the network to generate high-frequency features pertinent to the image’s structure. Finally, we conducted experimental analysis and compared our model with six classic models. Compared with the best model, our model improved PSNR and SSIM by about 1.5dB and 0.2, respectively.

Augment reality (AR) is crucial for immersive human-computer interaction (HCI) and vision of artificial intelligence (AI). Labeled data drove object recognition in AR. However, manual annotating data is expensive and labor-intensive, and furthermore, scanty labeled data limits the application of AR. Aiming at solving the problem of insufficient training data in AR object recognition, an automated vision data synthesis method called BAGAN is proposed in this paper based on the 3D modeling and GAN algorithm. Our approach has been validated to have better performance than other methods through image recognition task on natural image database ObjectNet3D. This study can shorten the algorithm development time of AR and expand the application scope of AR, which is of great significance to immersive interactive systems.

The development of biofuels and biodiesel industry specifically, has resulted in the generation of large amounts of glycerol as a surplus. The conversion of glycerol, into chemicals with indus-trial interest, through anaerobic fermentation using E. coli as a biocatalyst is an attractive alter-native to improve economic performance in the biodiesel industry, for example through the bi-otransformation of residual glycerol into ethanol. For 2019, the size of the global ethanol market was valued at $ 89.1 billion US dollar. Biodiesel can be produced from several materials like rapeseed, palm, soya, corn, palm kernel, frying oil or olive-pomace oil (OPO). The last, can be extracted from alperujo, byproduct of olive oil process. However, olive-pomace oil converting into biodiesel is not enough established to achieve a global olive-oil/biodiesel derived process with minimized waste generation. Traditionally biodiesel production process produces an equivalent of 10% by mass of glycerol as a waste or by-product. This work explores the conver-sion of residual glycerol into chemicals like ethanol and hydrogen with a focus on projecting raw material potential availability from OPO, contributing to the promotion of the use of biofu-els and biodegradable compounds.

Currently, the textile industry is a poorly automated sector, due in part to problems in the handling of deformable leather and textile parts during production operations. In this work, several problems in the handling process of leather and textile parts are addressed, introducing methods to increase the automation of the process. A pneumatic actuator designed to pick up textile or leather parts avoiding their deformation during transport has been developed. This actuator maximizes the number of gripping points to improve handling, making it more stable and efficient. Additionally, a vision system has been implemented in the part picking task which, in conjunction with the CAD information of the part, sends the modified gripping position of the part to the robot. This allows customized handling of each textile or leather part. Finally, validation tests have been carried out on this development, both in simulations and in laboratory conditions, demonstrating its viability and direct applicability in the production line.

Background & Motivation: Stroke outcomes following revascularization therapy (intravenous thrombolysis, IVT, and/or mechanical thrombectomy, MT) depend critically on time from stroke onset to treatment. Different service configurations may prioritise time to IVT or time to MT. In order to evaluate alternative acute stroke care configurations, it is necessary to estimate clinical outcomes given differing times to IVT and MT. Method: Model using an algorithm coded in Python. This is available at https://github.com/MichaelAllen1966/stroke_outcome_algorithm. Results: We demonstrate how the code may be used to estimate clinical outcomes given varying times to IVT and MT. Conclusion: Python code has been developed and shared to enable estimation of clinical outcome given times to IVT and MT. Here we share pseudocode and links to full Python code.

Palm fatty acid distillate (PFAD) is a by-product from the refining of crude palm oil. It comprises mainly of free fatty acids, having around 45% of palmitic and 33% oleic acids as the major components. Ultra-violet (UV) curable urethane acrylate (UA) oligomers could be synthesized from PFAD by the following procedure. A hydroxyl terminated macromer was first prepared by reacting PFAD with a mixture of isophthalic acid, phthalic anhydride, neopentagylcol (NPG) and pentaerythritol. The macromer is then reacted with 2-hydroxylethylacrylate (2HEA) and toluene diisocynate (TDI) to generate a resin containing acrylate side chains for UV curable application. A series of UA resins were prepared by using 15, 25, 45, 55 and 70% of PFAD respectively. The UA resin has Mw in the range of 3200 to 27,000. They could be cured by UV irradiation at intensity of 225mW/cm². Glass transition temperature (T_g) of the cured film was measured by differential scanning calorimeter (DSC), and hardness of the film was determined by pendulum hardness tester according to ASTM4366. The resins were used in wood coating application. All of the cured films showed good adhesion, hardness and chemical resistant for resins using up to 55% PFAD; however the resin at 70% did not cure properly.

Emergence of zoonotic-human pathogens is proven to be a lethal threat to public health, and RNA virus including influenza viruses, severe acute respiratory syndrome coronavirus, middle east respiratory syndrome coronavirus, Wuhan coronavirus (COVID-19), plays a pivotal role. As those viruses as airborne microorganisms spread mainly by tiny airborne particles, it is important to de-active those airborne particles before their entry into human bodies. In this study, we investigated the effect of far infrared (FIR) radiation on inhibition of airborne microorganisms. The result confirmed that double stand DNA from airborne microorganisms were stable under mild FIR radiation. However, single strand RNA from them was found to be sensitive to FIR radiation, indicating that RNA virus in airborne particles is instable under FIR radiation. Based on this observation, two models on usage of FIR radiation to prevent RNA virus transmission and cure RNA virus infection were proposed, implying that FIR radiation might be a cheap, convenient, and efficient method in clinic to treat RNA virus.

(1) Background: Patella and achilles tendon injuries have become increasingly prevalent due to an increasingly active population. These injuries can cause significant dysfunction which is especially true for elite athletes. Standard B-Mode ultrasound has been useful in the diagnosis of these injuries, however, only to a limited extent. Shear wave elastography (SWE) can aid in evaluation by objectively measuring tissue stiffness. Prior studies have proven shear wave values can be used to evaluate tendon tissue noting differences in healthy versus pathologic tendons. One main limitation remains a lack of normative data for specific tendon tissues which we aim to address in this study; (2) Methods: A cross-sectional study of healthy volunteers aged 18-65 without lower extremity injury was evaluated using A Samsung RS85 Prestige ultrasound machine with a 14L-2 MHz transducer. Shear wave values were collected in a standardized manner for the patellar tendon at one location, as well as the achilles tendon at the midportion and insertional areas; (3) Results: Data for 54 healthy adult patellar and achilles tendons was collected bilaterally. The mean SWE average of the patellar tendon (P1-P3) for all subjects was 96.3 +/- 10.9 kPA. A higher BMI was correlated with a decrease in patella tendon kPA and the mean PT1 was higher for males compared to females (adjusted mean 96.4 kPA for males and 88.5 kPA for females, mean difference = 7.9 kPA; P = 0.009). The mean SWE average of the insertional Achilles (AT1-AT3) for all subjects was 101.7 +/-16.2 kPA and for the midportion achilles (MTP1-MTP5) was 145.6 +/- 18.8 kPA. There was a decrease in kPA for insertional Achilles measurements in those with a higher BMI, but this did not reach statistical significance (p=.12); (4) Conclusion: This study provides normative SWE values for the patella and achilles tendon using the Samsung RS85. These values can be used for comparison to pathological tissue by means of the same examination protocol to better understand tendon degeneration. Further studies are warranted as to how SWE can be applied to the diagnosis of tendinopathy, partial and full-thickness tears, and response to treatment.

The International Maritime Organization (IMO) has recently revised its strategy for shipping de-carbonization, deepening the ambition to reduce annual greenhouse gas emissions until 2050. The accomplishment of this strategy requires the large-scale deployment of alternative maritime fuels, whose diversity and technical characteristics impose transition challenges. While several studies address the production of these fuels, a notable gap lies in the analysis of the required adaptations in vessels and ports for their usage. This study aims to fill this gap through a comprehensive re-view of material compatibility, storage in ports/vessels, and bunkering technology. Firstly, we an-alyze key aspects of port/vessel adaptation: physical and chemical properties; energy conversion for propulsion; fuel feeding and storage; bunkering procedures. Then, we perform a maturity as-sessment, placing each studied fuel on the technological readiness scale, revealing the most prom-ising options regarding infrastructure adaptability. Finally, we develop a case study for Brazil, whose economy is grounded on maritime exports. Findings indicate that multi-product ports may have potential to serve as multi-fuel hubs, while the remaining ports are inclined to specific fuels. In terms of vessel categories, we find that oil tankers, chemical ships and gas carriers are the most ready for conversion in the short-term

ICT and digital technology adoption, sustainable development, community empowerment and local wisdom and destinations uniqueness are keywords that will be further explored in the de-velopment of tourism villages in Kenderan as the most importance of tourism destinations de-velopment from the village level. Therefor this research is aimed at developing suitable model for post smart or wise tourism village through proposing Culture and Tradition Festival and the application of a circular economy. Circular economy development in tourism villages focuses on the application of sustainable production principles in the tourism value chain, namely in creating tourism experiences together with tourists (co-creation of experience) and integrated in a tourism system that supports environmental sustainability. This research is applied research conducted with a sequential mixed method research by conducting qualitative research through expert judgment and quantitative research by circulating questionnaire to tourists visiting Kenderan village, while the collected data is analyzed by Structural Equation Modelling to see the rela-tionship between variables. The results of this research show that creative event as the locality aspects of tourist village, digital competence, and sustainability practice are the building blocks of post smart tourism village, while digital competence and sustainable practice have significant mediating effect of locality on post smart tourism village. To make tourism village possesses sustainable competitive advantages, the village must explore and promote its locality aspects to differentiate its tourist village by identifying the valuable, rare, inimitable and non-substitutable tourism resources. The implication of this research is that to develop smart tourism destination wiser and distinctive, tourist village should explore their local values and resources, embed them into its tourism basic components and core resources, and co create, distribute, deliver and promote it to global market through technology adoption and global sustainability value.

The absence of highly effective delivery systems is a major challenge for gene therapy. Our work was aimed at the development of novel cationic liposomes possessing high transfection efficiency. For this purpose, a novel disulfide polycationic amphiphile 2S4 was synthesized. Cationic liposomes based on 2S4 and a helper lipid DOPE were formed by the thin film hydration method and exhibited effective pDNA delivery into the HEK293 cells, with a maximal transfection activity superior to that of the commercial agent Lipofectamine^® 2000. Our results suggest that the polycationic amphiphile 2S4 is a promising candidate for in vitro nucleic acid delivery.

In this study, the causal-effect between carbon dioxide emissions and forestry production and trade was investigated in Ghana by employing a data spanning from 1961 to 2014 by using the VECM and ARDL model. Evidence of the long-run equilibrium relationship in the VECM shows that, a 1% increase in veneer sheet production reduces carbon dioxide emissions by 1.47% in the long-run. There was evidence of a bidirectional causality between carbon dioxide emissions and veneer sheet production, carbon dioxide emissions and wood charcoal production, and a unidirectional causality running from carbon dioxide emissions to wood fuel production and plywood production to carbon dioxide emissions. Evidence from the long-run equilibrium relationship in the ARDL model shows that; a 1% increase in plywood production will increase carbon dioxide emissions by 0.17% in the long-run, a 1% increase in sawnwood production will increase carbon dioxide emissions by 0.17% in the long-run, a 1% increase in wood charcoal production will increase carbon dioxide emissions by 0.36% in the long-run and a 1% increase in wood fuel production will increase carbon dioxide emissions by 0.37% in the long-run.

In this study, an attempt was made to investigate the Kenya case of multivariate causality of carbon dioxide emissions by employing a time series data spanning from 1961-2011 using the ARDL method of cointegration analysis. The long-run elasticities show that, a 1% increase in financial development increases carbon dioxide emissions by 0.28%, a 1% increase in GDP per capita increases carbon dioxide emissions by 1.32% and a 1% increase in urbanization decreases carbon dioxide emissions by 1.14%. There was a unidirectional causality running from financial development, food production index, GDP per capita, industrialization and urbanization to carbon dioxide emissions. The innovation accounting shows that 20% of future shocks in carbon dioxide emissions are due to fluctuations in financial development, 9% of future shocks in financial development are due to fluctuations urbanization and 22% of future shocks in food production index are due to fluctuations in carbon dioxide emissions.

This study utilizes repeated geoacoustic mapping to quantify the morphodynamic response of the nearshore to storm-induced changes. The aim of this study was to quantitatively map the nearshore zone of Assateague Island National Seashore (ASIS) to determine what changes in bottom sediments, benthic fauna and fish habitat are attributable to storm events including hurricane Sandy and the passage of hurricane Joaquin. Specifically, (1) the entire domain of the National Parks Service offshore area was mapped with side-scan sonar and multibeam bathymetry at a resolution comparable to that of the existing pre-storm survey, (2) a subset of the benthic stations were resampled that represented all sediment strata previously identified, and (3) newly obtained data were compared to that from the pre-storm survey to determined changes that could be attributed to specific storms such as Sandy and Joaquin. Capturing event specific dynamics requires rapid response surveys in close temporal association of the before and after period. The time-lapse between the pre-storm surveys for Sandy and our study meant that only a time and storm integrated signature for that storm could be obtained whereas with hurricane Joaquin we could identify impacts to the habitat type and geomorphology more directly related to that particular storm. This storm impacts study provides for the National Park Service direct documentation of storm-related changes in sediments and marine habitats on multiple scales: from large scale, side-scan sonar maps and interpretation of acoustic bottom types, to characterize as fully as possible habitats from 1 to 10 m up to many kilometer scales, as well as from point benthic samples within each sediment stratum and these results can help guide management of the island resources.

People counting applications have been used in diverse applications. The ability and accuracy of thermal imaging over conventional image cameras has led to the implementation of thermal cameras in people counting applications. This paper present a thermal people counting smart glass windows. The people counting application would be remotely monitored from a single centralized PC station as it’s connected to a multiplex of mass monitoring of 20 thermal camera, all embedded into different glass windows. The thermal cameras would then be able to detect body temperatures of all individuals who pass through any of the camera range and also count the numbers of people who passed through the camera range. The data gotten can then be further utilized in various ways, example is in the control of air conditioning and lightening.

Facial recognition systems are widely used systems around the globe. Their applications in real world scenarios like law enforcement, surveillance, ID card identification, information security, access control and many more has made them very important in today’s world. These systems have shown promising results and excellent performance; however, these systems need tremendous number of facial images for training before they are implemented in the real-world scenarios. The problem arises when we face lack of training images due to which the performance of such systems decreases exponentially. The problem becomes even worse when there is only one image per subject for training our facial recognition system, the probe image might contain variation like illumination, expression and disguise which are hard to be accurately recognized. Generative models have achieved great success for handling the lack of training data by generating synthetic data that resembles the original data. We have created a new method by fine tuning the basic Conditional Generative Adversarial Network (CGAN) to generate synthetic facial images provided only single image per subject. These synthetic facial images contain six variations that are smile, anger, stare, illumination and disguise. We increased the size of the dataset and then applied a convolutional neural network for facial recognition, which improved the accuracy of our system from 0.76 to 0.99.

Active magnetic bearings have higher efficiency than other bearings because there is no physical contact. However, this benefit is mitigated by the addition of electrical power consumption. It is therefore important for magnetic bearings to have efficient power electronics. Gallium nitride-based transistors are a relatively new form of transistor which have shown to be more efficient than MOSFETs. There is an insufficient body of literature in the area of application of these transistors for magnetic levitation. This work presents a simple 1 degree-of-freedom voltage controlled levitation experiment in which levitation is achieved with both a gallium nitride transistor and a MOSFET.
Voltage losses and current consumption are measured when using each transistor during levitation. In particular, transients during open-to-closed and closed-to-open states are measured for PWM pulsing. It is found that the
gallium nitride transistor is superior in both switching time and efficiency in situ for magnetic levitation.

The effect of growth temperature and precursor flows on the doping level and surface morphology of Ge-doped GaN layers was researched. The results show that germanium is more readily incorporated at low temperature, high growth rate and high V/III ratio, thus revealing a similar behavior to what was previously observed for indium. V-pit formation can be blocked at high temperature but also at low V/III ratio, the latter of which however causing step bunching.

In recent years, with the increasing research and development of the LED industry which contains GaN, it is expected that there will be a large amount of related wastes in the future. Especially the gallium has extremely high value of economic, therefore, it is necessary to establish the recycling system of the GaN waste. However, GaN is a direct-gap semiconductor and with high energy gap, high hardness, and high melting point make it difficult to recycle. Therefore, this study will analyze the physical characteristics of LED wastes containing GaN and carry out various leaching method to leach the valuable metals from the waste optimally. Different acids are used to find out the best reagent for leaching the gallium. Different experimental parameters are discussed such as the effect of the different acid agents , concentration, pressure, solid-liquid mass ratio, temperature, and time which influence the leaching efficiency of the gallium. In this study, various leaching methods which effect the leaching efficiency of the gallium are compared and the advantages and disadvantages are discussed. Finally, pressurized acid leaching method is preferred to leach the GaN waste, and hydrochloric acid is used as the leaching solution because of its better leaching efficiency of gallium. Eventually, the leaching efficiency of the gallium can reach to 98%.

Control charts have been widely used for monitoring process quality in manufacturing and play an important role in triggering a signal in time when detecting a change in process quality. Many control charts in literature assume that the in-control distribution of the univariate or multivariate process data is continuous and not categorical. This research develops two exponentially weighted moving average (EWMA) proportion control charts for monitoring a process with multinomial proportions when considering both large and small sample sizes. For a large sample size , the charting statistic depends on the well-known Pearson χ2 statistic, and the control limit of the EWMA proportion chart is determined by an asymptotical chi-square distribution. For a small sample size, we derive the exact mean and variance of the Pearson χ2 statistic. Hence, the exact EWMA proportion chart is determined. The proportion chart can also be applied to monitor the distribution-free continuous multivariate process as long as each categorical proportion associated with specification limits of each quality variable is known or estimated. Lastly, we investigate the detection performance of the proposed EWMA proportion chart by numerical analyses. Real data analysis demonstrates the beneficial application of the proposed EWMA proportion charts.

Candidate vaccines against African swine fever virus (ASFV) based on naturally attenuated or genetically modified viruses have the potential to generate protective immune responses, although there is no consensus on what defines a protective immune response against ASFV. Studies, espe-cially in sensible host species, focused on unravelling protective mechanisms will contribute to the development of safer and more effective vaccines. The present study provides a detailed analysis of phenotypic and functional data on cellular responses induced by intradermal immunization and subsequent boosting of domestic pigs with the naturally attenuated field strain Lv17/WB/Rie1, as well as the mechanisms underlying protection against intramuscular challenge with the virulent genotype II Armenia/07 strain. The transient mild to moderate increase of IL-8 and IL-10 in serum observed after immunization might be directly correlated with survival. Protection was also associated with a robust ASFV-specific polyfunctional memory T cell response, where CD4CD8 and CD8 T cells were identified as the main cellular sources of virus-specific IFNγ and TNFα. In parallel to cytokine response, these T-cell subsets also showed specific cytotoxic activity as evidenced by the increased expression of the CD107a degranulation marker. Along with virus-specific multifunctional CD4CD8 and CD8 T responses, the increased levels of antigen experienced cytotoxic CD4 T cells observed after challenge in immunized pigs might also contribute to control virulent infection by killing mechanisms targeting infected antigen-presenting cells. Future studies should elucidate whether the memory T-cell responses evidenced in the present study persist and provide long-term protection against further ASFV infections.

Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fuelled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19's informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.

Introduction: Over decades, much have been said and done regarding under-five mortality in Ethiopia. The country has been following the lead of sustainable development goals and UNICEF with its transformation plan targets. However, unless the efforts supported by status assessing studies, it might be difficult for the country to progress. Thus, the current study was directed to identify the prevalence and associated factors of under-five mortality in 2019. Methods: According to the study criteria, we extract and cleaned data in STATA v. 15.0. The data then weighted as per the sampling weight, primary sampling unit, and strata before analyzing in STATA 15.0. Data management consisted of descriptive (mean, standard deviation, and proportion or percent) and association statistics. We deliberated binary logistic regression for this analysis and we checked each variable at 0.25 p-values to include in the model. The final p-value to declare association was p <0.05 and AOR with 95% CI was also applied to describe the results. The data source was the Ethiopian Mini Demographic Health Survey (EMDHS) 2019. EMDHS collected the data from 8,885 in a face-to-face manner with a 99% response rate. Results: From 5,527 numbers of weighted women with under-five analysed in this study, the proportion of under-five mortality was 277.23(5.02%). Factors like 2nd birth order 0.52(0.35, 0.79), 3rd-4th 0.49(0.28, 0.84), 1-2 ANC visits 0.24(0.12, 0.49, ANC visit three’ 0.14(0.07, 0.28), ANC visit four and above 0.22(0.14, 0.36), in marriage mother 0.43(0.19, 0.96), ‘1-2 under-five children 0.02(0.011, 0.03), and greater than three under-five children 0.007(0.0007, 0.004) were all negatively associated with under-five mortality rate. Conclusion: To obtain the exalted outcome out of this study, the government might need to increase antenatal care, women education, institutional delivery, and the modern contraceptive methods use through enhanced community mobilization, health education using community health workers, increasing access to essential cares of mothers and children, and the policy commitment for the issues related to family size, birth order, and birth interval.

Computer vision is a rapidly developing field that focuses on highly sophisticated picture analysis, manipulation, and comprehension. Its objective is to analyze what is happening in front of a camera and utilize that understanding to control a computer or robotic system or to present users with fresh visuals that are more enlightening or appealing than the original camera images. Computer vision technologies make it feasible for new user interfaces, augmented reality gaming, biometrics, automobile, photography, movie creation, Web search, and many more applications. This essay seeks to explain how computer vision can be utilized to play blackjack successfully.

Auxins are chemical compounds of wide interest, mostly due to their role in plant metabolism and development. Since the latter half of the XX century, synthetic auxins have been used as herbicides, presenting notable advantages, such as low toxicity in humans. As of today, extensive descriptions of syntetic and natural auxins have been made; yet studies centered on the molecular recognition process of auxins are scarce. Herein, we present a comprehensive in silico characterization of auxin receptor TIR1, using structure based methods to aid de novo design of putative ligands. In addition, we conducted binding site characterization using cosolvent molecular dynamics and enhanced sampling simulations with reference ligands. We also present a database of putative auxins and decoy compounds which can aid further studies focusing on synthetic auxin design. To the best of our knowledge, this study is the first of its kind focusing on TIR1.

This study is part of a project designed to simulate the long-term landform equilibrium of a rehabilitated mine site. The project utilizes event fine suspended sediment (FSS) fluxes in a receiving stream following a rainfall event as an indicator of landform stability. The aim of this study was to use HEC-HMS to determine sediment and discharge quantity upstream to determine how it affects the downstream development of the catchment landform, in terms of sediment changes and geomorphology. Thus, the study focused on hydrology and sediment modelling of the upper catchment with HEC-HMS (Hydrologic Engineering Center-Hydrologic Modeling System) to determine daily discharge and sediment output at the catchment outlet. HEC-HMS was used to calibrate the stream discharge and FSS quantities at the catchment outlet to observed continuous discharge and FSS values. Calibration of the HEC-HMS model was done for two water years and then the same model parameters were used to validate the model for a third water year. Catchment discharge and FSS were calibrated and validated for continuous rainfall events against observed discharge and FSS data at the catchment outlet. The model was then run for a projected rainfall of 50 years. The denudation rate predicted by the model falls in the range previously determined for the region. The simulated sediment output was compared to the rainfall trends over the years. As a result, the sediment spikes following a rainfall-runoff event gradually decreased over time. Reducing FSS spikes indicate that the landform gradually attains stability. This modeling study can be used for long-term simulations to determine erosion equilibrium over the years and to quantify sediment yield in catchments for projected wet and dry rainfall scenarios.

3D city models are being extensively used in applications such as evacuation scenarios and energy consumption estimation. The main standard for 3D city models is the CityGML data model which can be encoded through the CityJSON data format. CityGML and CityJSON use polygonal modelling in order to represent geometries. True topological data structures have proven to be more computationally efficient for geometric analysis compared to polygonal modelling. In a previous study, we have introduced a method to topologically reconstruct CityGML models while maintaining the semantic information of the dataset, based solely on the combinatorial map (C-Map) data structure. As a result of the limitations of C-Map's semantic representation mechanism, the resulting datasets could suffer either from semantic information loss or the redundant repetition of them. In this article, we propose a solution for a more efficient representation of both geometry, topology and semantics by incorporating the C-Map data structure in the CityGML data model and implementing a CityJSON extension to encode the C-Map data. In addition, we provide an algorithm for the topological reconstruction of CityJSON datasets to append them according to this extension. Finally, we apply our methodology to three open datasets in order to validate our approach when applied to real-world data. Our results show that the proposed CityJSON extension can represent all geometric information of a city model in a lossless way, providing additional topological information for the objects of the model.

: Low-temperature growth of diamond films by chemical vapor deposition (CVD) method is not so widely reported and especially its initial periods of nucleation and growth phenomenon are of particular interest to the researchers. Four sets of substrates were selected for growing diamond films by linear antenna microwave plasma enhanced CVD (LA-MPCVD). Among them, silicon and sapphire substrates were pre-treated with detonation nanodiamond (DND) seeds before diamond growth for enhancement of its nucleation. On the other hand, carbon nanotube (CNT) films on Si substrates were also used as another template for LA-MPCVD diamond growth. In order to enhance the diamond nucleation during CVD growth, some of the CNT films were again pre-treated by electrophoretic deposition (EPD) of diamond nanoparticles. All these substrates were then put inside the LA-MPCVD chamber for growing diamond films under variable processing conditions. Microwave input powers (1100 – 2800 W), input power modes (pulse or continuous), antenna to stage distances (5 - 6.5 cm), process gas recipes (with or without CO2), methane gas percentages (3 - 5%), deposition times (11 – 120 minutes) were altered to investigate their effect on the growth of diamond film on the pre-treated substrates. The substrate temperatures were found to vary from as low as 170oC to a maximum of 307oC during the alteration of the different processing parameters. Contrary to the conventional MPCVD, it was observed that during the first hour of LA-MPCVD diamond growth, DND seeds or the nucleating structures, do not coalesce together to make a continuous film. Deposition time was the most critical factor in fully covering the substrate surfaces with diamond film, since the substrate temperature could not become stable during the first hour of LA-MPCVD. CNT was found to be oxidised rapidly under LA-MPCVD plasma conditions; therefore, a CO2-free process gas recipe was used to reduce CNT burning. Moreover, EPD-coated CNT was found to be less oxidised by the LACVD plasma during diamond growth.

In the intricate dance between viruses and host cells, RNA-binding proteins (RBPs) serve as crucial orchestrators of gene expression and cellular processes. We will delve into the riveting realm of viral hijacking, where viruses deftly exploit host RBPs to manipulate cellular machinery for their replication and pathogenesis. Through a masterstroke of molecular subterfuge, viruses co-opt RBPs involved in various facets of RNA metabolism - from transcription to degradation - to promote viral gene expression and evade host immune defenses. This manipulation leads to global alterations in cellular RNA metabolism, impacting essential host genes vital for immune responses and homeostasis.Unveiling this clandestine alliance between viruses and RBPs is not just a scientific pursuit, but a critical imperative for devising innovative antiviral strategies to disrupt these interactions. By unraveling the intricate interplay between viral proteins and host RBPs throughout the viral life cycle - from entry to assembly - researchers aim to identify vulnerabilities that can be targeted for therapeutic intervention. Strategies such as disrupting viral protein-RBP interactions hold promise for inhibiting viral replication and curbing pathogenesis, offering a beacon of hope in the battle against viral infections.Our manuscript elucidates the indispensable roles played by RBPs in viral replication, translation, and pathogenesis, shedding light on the molecular mechanisms driving viral success. Delving deeper, it explores how viruses intricately entwine with host RBPs, manipulating cellular signaling pathways to create a hospitable environment for viral spread. By dissecting these manipulative tactics, researchers uncover new targets for antiviral therapy, envisioning a future where tailored interventions disrupt viral-host RBP interactions with precision and efficacy.As the narrative unfolds, the therapeutic implications of targeting RBPs or their interactions with viral proteins emerge as a promising frontier in the fight against viral infections. From small molecule inhibitors to RNA-based therapeutics, innovative approaches are on the horizon, offering new avenues for combatting viral diseases. We set the stage for future research, beckoning researchers to delve deeper into the molecular intricacies of viral hijacking of RBPs and charting a course towards novel therapeutic interventions that promise to reshape the landscape of antiviral therapy.\Ultimately, it beckons the scientific community to embark on a voyage of discovery, unraveling the secrets of viral hijacking of RBPs and paving the way for transformative advances in antiviral therapeutics. The stage is set for a new chapter in the battle against viral infections, where knowledge becomes the sword and innovation the shield against the pernicious machinations of viral pathogens.

This paper investigates the effect of shot peening on the strength and corrosion properties of EN AW 6082-T651 aluminium alloy. Surface roughness, microhardness, residual stresses and corrosion performance were investigated and compared with the untreated aluminium alloy. Cracks and delaminations in the surface layer can only be seen on the treated specimens at a working pressure of 4 bar and 8 bar, while no such effect was observed at a working pressure of 1.6 bar. The effect of dislocation densification was confirmed by the microhardness measurement of the hardened layer, where microhardness increased by up to 23 % compared to the untreated aluminium alloy. Shot peening also results in a deeper layer with higher compressive residual stresses compared to untreated specimens. With higher working pressure, surface roughness increases, which can promote localized pitting corrosion. However, all treated specimens improve the corrosion resistance and have a lower degree of anodic dissolution. The current density of the shot peened specimen with the lowest surface roughness was more than twice lower than that of the untreated specimen.

The hot rolling in the process chain of the aluminium rolled products present the critical element of material quality and influence on productivity. The desire to increase the letter demand modifications of hot rolling, consequential changes of microstructure, crystallographic texture, mechanical and formability properties must be knowledge and consistently considered in planning the rolling process and rolled product. Achieving lower thicknesses of the hot-rolled band would enable fewer passes with cold rolling; consequently, hot rolling with the same number of passes will complete with lower temperatures. Microstructural and texture characterization with the optical microscope and scanning electron microscope for the hot-rolled band 3.25 mm the smaller grains appeared in the center of the cross-section, unlike for the hot-rolled band 6 mm, the smaller grains were detected on the upper and bottom position of the cross-section. Furthermore, the comparison shows also that the hot-rolled band 6 mm has 64 % of random texture components and 83 % of recrystallized grains, and the proportional adjustment for the hot-rolled band 3.25 mm has the 42 % of random texture components and 55 % of recrystallized grains. For the mechanical testing results, the elongation values in rolling and transverse directions significantly differ just in the case of a hot rolled band of 3.25 mm. Consequently, the earing results are more than 1.5 % higher for the hot-rolled band, 3.25 mm, than the hot-rolled band, 6 mm.