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Ibrexafungerp for the treatment of vulvovaginal candidiasis:A systematic review and meta-analysis of randomized placebo-controlled trials
No full textIntroduction: Vulvovaginal candidiasis (VVC) is a prevalent fungal infection affecting millions of women globally, primarily caused by Candida species, most notably Candida albicans. Ibrexafungerp emerges as a promising candidate in the treatment arsenal against VVC, presenting a novel approach to combating this prevalent fungal infection. Methods: A systematic literature search was conducted across major databases, including PubMed, EMBASE, and the Cochrane Library, to identify relevant randomized controlled trials (RCTs) evaluating the efficacy and safety of ibrexafungerp in the treatment of VVC. Following rigorous methodology, data extraction, risk of bias assessment using Cochrane's RoB 2 tool, and meta-analysis were conducted. Results: Four RCTs were included in the analyses. The ibrexafungerp regimen utilized across the studies were 300 mg administered twice daily for one day. Meta-analysis revealed that ibrexafungerp was associated with significantly higher clinical cure rates compared to placebo in patients with VVC (pooled odds ratio (OR) 2.32; 95 % confidence interval (CI) 1.80 to 2.98). Complete symptom resolution was achieved in a greater proportion of participants receiving ibrexafungerp (pooled OR 2.76; 95 % CI 1.62 to 4.71). Analysis of treatment-emergent adverse events revealed a significant higher incidence of at least one treatment-emergent adverse event with ibrexafungerp compared to placebo (pooled OR 2.83; 95 % CI 2.06 to 3.88). Conclusion: This study provides robust support for the efficacy of ibrexafungerp in the treatment of VVC. While the safety profile of ibrexafungerp appears favorable with mostly mild adverse events reported, decision-making in the clinical context should be guided by individual patient factors.</p
Snapshot high dynamic range imaging based on adaptive frequency stripe masks
No full textA novel snapshot method based on adaptive frequency stripe masks is proposed to realize high dynamic range (HDR) imaging for highly reflective surfaces. The high refresh rate of the digital micromirror device is used to load modulation masks, and superimposed modulation of the target scene is performed to achieve snapshot HDR imaging by using a single superimposed image. Adaptive frequency stripe masks are generated based on the energy spectrum of the scene to enhance the lateral resolution of the demodulated sub-images. Then the sub-images are separated through a frequency domain reconstruction algorithm, and the final HDR image is obtained by using a multi-exposure image fusion technique. Experimental results demonstrate that the proposed method produces ghost-free HDR images with enhanced lateral resolution and contrast while effectively avoiding overexposure and underexposure. This approach provides a valuable solution for visual imaging of highly reflective surfaces.</p
Gender, ethnicity and SMEs’ access to finance:a systematic literature review of global empirical evidence
No full textWe systematically survey the global empirical evidence on gender and ethnicity implications of small and medium enterprises’ (SMEs) access to finance in the last two decades. We find overwhelming evidence that women-owned SMEs encounter greater financial constraints, and seek less financing, in comparison to men-owned SMEs. Borrowing discouragement and fear of being rejected by creditors are identified as leading causes of women’s non-participation in external borrowing. We find scarce evidence of systematic gender-based discrimination by lenders. However, there is evidence that women face higher interest and rejection rates and stringent lending criteria compared to men. We find that ethnic-minority-owned SMEs experience greater financial constraints. In the USA, evidence of ethnicity-based-discrimination is found; however, it is not common across the world. Our findings also show that ethnic-minority-owned SMEs demand for and ability to obtain external finance decreases further during and after economic crisis periods. We provide avenues for further research
Recycled Red Brick Masonry Demolition Waste as a Sustainable Cement Replacement Alternative:A DoE-Based Approach
No full textThis study presents the findings of an experimental and numerical investigation into the recycling of construction waste, introducing an innovative approach to repurpose red bricks from masonry demolition waste as a sustainable substitute for cement. The experimental work involved mechanical characterization of different mortar mixes at 3, 7, and 28 days after casting, along with the measurement of water absorption at 28 days. The design process utilized the "Mixture Design" technique, which analyzed 14 mixes comprising Portland cement, ground brick, and ground mortar. A predictive model was developed to estimate compressive strength, flexural strength, and water absorption over the curing period, yielding high correlation coefficients that validate its reliability. Additionally, the study proposes optimized cement-to-waste ratios tailored to different ground brick and ground mortar compositions, providing a novel approach to material formulation. These findings significantly contribute to advancing the circular economy by valorizing demolition waste, while simultaneously improving the durability and sustainability of construction materials, offering a practical and environmentally conscious alternative for the construction industry
Calcium chloride hexahydrate based composite phase change/thermochemical material for wide-temperature range passive battery thermal management
No full textBattery, as the core of the electric vehicle, needs to be thermally managed and protected to avoid decreased performance and thermal runaway. In this study, calcium chloride hexahydrate based composite phase change materials are developed for passive battery thermal management and thermal protection. The composite phase change materials achieve wide-temperature range thermal management based on their high energy density, including 84.89 ∼ 195.5 J/g for pre-heating between 0 and 10 ℃, 99.93 ∼ 179.2 J/g for operation cooling between 25 and 50 ℃, and 326 ∼ 699.5 J/g for thermal runaway elimination between 50 and 120 ℃. Using ceramic fibre as a support material, strontium chloride hexahydrate as a nucleating agent, and hydroxylated cellulose nanofiber to improve the form stability of calcium chloride hexahydrate, the phase transition temperature is increased to 37.1 °C, which meets thermal management requirements. The unique dendrite structure provided by crystalline phase change material and the cross-linked fibre network enhances the tensile strength of the composite to 2.97 MPa. Compared with typical battery wrapping material, Polyvinyl chloride, the battery module based on the developed composite phase change material can reduce the peak temperature and temperature difference during operation cooling by up to 34.9 % and 50.7 %, respectively. In addition, the composite phase change material also provides excellent flame retardancy, with a limiting oxygen index value of 100 % unburned and UL-94 grade reaching V0. In the case of battery thermal runaway, the composite phase change material can absorb the 37,730 J of energy released by the first battery and eliminate the thermal runaway. The results show that the prepared composite phase change material has high performance thermal management and thermal protection, with the advantage of low cost.</p
Mechanical fault diagnosis method based on binary neural network with adaptive depth-to-width ratio
No full textDeep neural network (DNN) has powerful feature extraction and classification abilities and is extensively used in mechanical fault diagnosis. However, in the DNN-based fault diagnosis method, too many model parameters, high computational complexity, and occupying a large amount of storage space make it very difficult to optimize and retrain the DNN model. To overcome this deficiency, a binary neural network (BNN) is introduced into fault diagnosis. In the proposed BNN-based fault diagnosis method, the floating-point weights and activations are binarized as '+1' and '−1' to save storage space, and the floating-point multiply-accumulate operations (OPs) are replaced by bitwise OPs to reduce the computational complexity. The BNN-based fault diagnosis method effectively compresses the structure of DNNs and accelerates model inference. However, the binarization OP may bring about the quantization error and gradient error, which will accumulate with the increase of the network depth, and lead to a degradation in model performance. To solve these deficiencies in the proposed BNN-based fault diagnosis method, an improved BNN fault diagnosis method based on adaptive depth-to-width ratio (ADTWR) is proposed. For the convenience in description, the proposed method is called the BNN-ADTWR method. In the proposed BNN-ADTWR method, the network structure is reshaped by the comparison of the gains brought by the depth and width of the backbone network, and the backbone network is further optimized by the pruning technique. To evaluate the effectiveness of the proposed method, the comparative experiments, such as ablation, anti-noise, and computational complexity, had successfully completed on several rolling bearing datasets. The experiment results show that the proposed BNN-ADTWR method significantly reduces computational complexity and helps with rapid fault diagnosis. The constructed ADTWR backbone network decreases activations in the traditional backbone network and improves absolute accuracy while the computational cost is equivalent
Contemporary Social Movements and Radical Democracy:Insights from the Global South
No full textRadical democracy and social movements have an intricate relationship as the former is often expected to inform, inspire, and guide the latter to challenge oppressive social orders and power structures – yet, not always successfully. This article offers an analysis of contemporary social movements in Colombia and Turkey to show the limits and potential of radical democracy to reimagine new expressions of citizenship and non-capitalist alternatives. It argues that there is a mismatch between various conceptualizations of radical democracy and the ways it has been articulated and practiced by anti-austerity and pro-democracy movements of the twenty-first century. We propose that radical democracy should be rethought in light of novel forms of political activism and visions emerging from the social movements of the Global South in response to the failures of liberal democracy
Survey and Analysis for the Challenges in Computer Science to the Automation of Grading Systems
No full textAssessment is essential to educational system. Automatic grading reduces the time and effort taken by tutors to assess the answers written by the students. To understand recent computational methods used for automatic grading, a review has been conducted. 4,084 articles were initially identified using a keyword search. After filtering, the number was reduced to 57. It was found that statistical models are normally used in Automatic-Short-Answer-Grading (ASAG); vector-based similarity measures are the most popular among projects; pilot datasets are mostly used; standard datasets for evaluation are missing. Evidence shows that machine learning and deep learning are most popularly adopted methods and generative AI, e.g., LLMs and ChatGPT are also jump to the chance, which indicates that integrating AI in education is an inevitable trend. Also, most investigations prefer to adopt multiple approaches to improve computational quality, dataset analysis, and evaluation results. The identified research gaps will be a useful reference guide to users/researchers beneficial to formative/summative assessment. We concluded that the presented outcome, analysis and discussions are informative to academia and pedagogical practitioners who are interested in further developing/using ASAG systems. Although research into ASAG is still rudimentary, it is a promising area with impact on academic circles/commercially educational markets.</p
A theoretical combustion kinetics model for domestic green waste
No full textA thorough understanding of the combustion kinetics of domestic green waste is of great significance for its incineration efficiency and energy harvesting characteristics. The published literature regarding the combustion kinetics of domestic green waste primarily employs experimental techniques. There is a need to develop theoretical basis for combustion kinetics of domestic green waste, leading towards the development of comprehensive dynamic models. In this study, a theoretical combustion kinetics model has been developed for domestic green waste, which is primarily composed of kitchen and garden waste. The developed model is based on Badzioch’s relationship. A correlation between the rate of fuel weight loss and the heating rate has been introduced in the model. Moreover, time parameter has been replaced with temperature which aids in obtaining Thermogravimetric and Differential Thermal curves directly. The theoretical combustion kinetics model has been rigorously tested empirically in order to ascertain its validity and appropriateness. The results show that the theoretical Thermogravimetric and Differential Thermal curves are reasonably accurate, with < 10 % difference compared to experimental data. The developed combustion kinetics model for domestic green waste thus provides invaluable insights into the combustion phenomena and paves the way for the engineering design of corresponding incineration equipment
Adhesive contact problem between thermoelectric material and rigid solid with slightly wavy surface
No full textThe thermoelectric effect is a general term for the electrical effects that arise from a temperature difference, as well as the reversible thermal effects induced by electric current, commonly including the Seebeck effect, Peltier effect, and Thomson effect. Thermoelectric devices based on the advantageous thermoelectric effect are increasing used in practical engineering, such as the temperature measurement and thermoelectric power generation. The periodic wavy contact behavior is crucial for the long-term stability and energy conversion efficiency improvement of thermoelectric devices. This paper investigates the two-dimensional periodic Maugis-Dugdale (MD) adhesion contact behavior of thermoelectric half-plane using the integral equation method. The contact problem is transformed into a singular integral equation (SIE) with the Hilbert kernel, in which the size of cohesive zone becomes the key unknown parameter. Through theoretical analysis and numerical calculation, the relationships between normal load, contact zone and adhesion zone under two dimensionless parameters including classical Tabor parameter and the ratio of the surface energy of the grooved surface to the elastic strain energy when the grooved surface is smoothed-out are analyzed, and their curve distributions during loading and unloading are discussed. The results show that rougher surfaces cause more energy loss due to adhesion hysteresis. As the total current and energy flow increase, the normal load necessary to achieve the same contact halfwidth also rises. This enables us to adjust the surface contact behavior by varying the thermoelectric loads, thereby altering the stress distribution on the contact surface
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