Copula models are increasingly recognized for their ability to capture complex dependencies among random variables. In this study, we introduce three innovative bivariate models utilizing copula functions: the XLindley (XL) distribution with Frank, Gumbel, and Clayton copulas. The results highlight the fundamental characteristics and effectiveness of these newly introduced bivariate models. Statistical inference for the distribution parameters is conducted using a Type II censored sampling design. This employs maximum likelihood and Bayesian estimation techniques. Asymptotic and credible confidence intervals are calculated, and numerical analysis is performed using the Markov Chain Monte Carlo method. The proposed methodology’s applicability is illustrated by analyzing several real-world datasets. The initial dataset examines burr formation occurrences and consists of two observation sets. Additionally, the second and third datasets contain medical information. The second dataset focuses on diabetic nephropathy, while the third dataset explores infection and recurrence time among kidney patients. Full article

Endometriosis in half of affected women is closely related to problems with fertility. Endometriosis-associated infertility is caused by a wide range of abnormalities affecting the female reproductive tract, from oocyte quality impairment to disturbances in the eutopic endometrium or mechanical abnormalities resulting from disease progression. Since supportive antioxidant therapies, in addition to surgical treatment or assisted reproductive techniques (ARTs), have overall been proven to be effective tools in endometriosis management, the objective of our review was to analyze the role of antioxidant substances, including vitamins, micronutrients, N-acetylcysteine (NAC), curcumin, melatonin, and resveratrol, in endometriosis-related infertility. Most of these substances have been proven to alleviate the systemic oxidant predominance, which has been expressed through decreased oxidative stress (OS) markers and enhanced antioxidative defense. In addition, we demonstrated that the predominant effect of the aforementioned substances is the inhibition of the development of endometriotic lesions as well as the suppression of pro-inflammatory molecules. Although we can undoubtedly conclude that antioxidants are beneficial in fertility support, further studies explaining the detailed pathways of their action are needed. Full article

Coastal concrete structures, such as cross-sea bridges and tunnels, are susceptible to the penetration of chloride ions, which can lead to the deterioration of the passive film on the rebar surface, consequently accelerating the corrosion process. Conventional methods for monitoring chloride ions typically require in situ drilling for sample collection, thereby compromising efficiency and accuracy. Additionally, real-time monitoring and early warning cannot be achieved. To address these challenges, this work introduces a fluorescent-probe-based fiber optic sensor for monitoring chloride levels in concrete structures. Quinine sulfate was chosen as the fluorescent material due to its exceptional sensitivity to chloride ions and its stability in concrete environments. The proposed sensor was manufactured using sol–gel and 3D-printing techniques. Tests were conducted using concrete simulation fluid and cement mortar specimens. The results demonstrate that the sensitivity of the proposed sensor is greater than 0.01 M, and its accuracy in penetration depth measurement is better than 3 mm. The findings confirm that the designed fiber optic sensor based on quinine sulfate enables real-time monitoring of chloride ions in concrete structures, offering high sensitivity (0.1% in concentration and 2.7 mm in terms of penetration depth), unique selectivity (as it is immune to other ions whose concentrations are 10 times higher than those of Cl⁻), and a compact size (10 × 20 mm). These attributes render it promising for practical engineering applications. Full article

Liposarcomas are some of the most challenging soft tissue tumors and are subclassified into multiple subtypes with special histologic and molecular features. The peculiarities of each histopathological subtype influence the clinical behavior, management, and treatment of these neoplasms. For instance, well-differentiated liposarcomas are common soft tissue malignancies and usually display a favorable outcome. On the other hand, pleomorphic liposarcoma is the rarest, yet the most aggressive subtype of liposarcoma. This histopathological diagnosis may be challenging due to the scarce available data and because pleomorphic liposarcomas can mimic other pleomorphic sarcomas or other neoplasms of dissimilar differentiation. Nevertheless, the correct diagnosis of pleomorphic liposarcoma is of utmost importance as such patients are prone to develop local recurrences and metastases. Treatment usually consists of surgical excision along with radiotherapy and follow-up of the patients. Therefore, this review aims to assess the complex clinical, histological, and immunohistochemical features of liposarcomas in order to establish how these characteristics influence the management and prognosis of the patients, emphasizing the particularities of pleomorphic liposarcoma. Full article

Polyurethane (PU) coatings are applied on technical textiles for their superior properties. Up to now, PU-coated textiles are not recycled at end of life. Landfilling is still the most occurring way of processing PU waste. Next to looking to sustainable routes for processing PU waste, there is the drive towards bio-based polymers. With this regard, a bio-based trigger degradable PU coating specifically designed for textiles was developed. The PU was characterized via FT-IR, TGA, and DSC. The performance of the coating was assessed by examining the mechanical properties and the resistance to hydrostatic pressure initially and after washing. The developed bio-based PU coatings had a high tensile strength, were waterproof, and had excellent wash fastness at 40 °C. The coating could be easily debonded from the textile by immersion in a tetra-n-butylammoniumfluoride solution. FT-IR and microscopic analysis indicated that the coating was completely removed and that the polyester fabric was not degraded. Full article

Over the past decade, significant advancements have been made in low-light image enhancement (LLIE) methods due to the robust capabilities of deep learning in non-linear mapping, feature extraction, and representation. However, the pursuit of a universally superior method that consistently outperforms others across diverse scenarios remains challenging. This challenge primarily arises from the inherent data bias in deep learning-based approaches, stemming from disparities in image statistical distributions between training and testing datasets. To tackle this problem, we propose an unsupervised weight map generation network aimed at effectively integrating pre-enhanced images generated from carefully selected complementary LLIE methods. Our ultimate goal is to enhance the overall enhancement performance by leveraging these pre-enhanced images, therewith culminating the enhancement workflow in a dual-stage execution paradigm. To be more specific, in the preprocessing stage, we initially employ two distinct LLIE methods, namely Night and PairLIE, chosen specifically for their complementary enhancement characteristics, to process the given input low-light image. The resultant outputs, termed pre-enhanced images, serve as dual target images for fusion in the subsequent image fusion stage. Subsequently, at the fusion stage, we utilize an unsupervised UNet architecture to determine the optimal pixel-level weight maps for merging the pre-enhanced images. This process is adeptly directed by a specially formulated loss function in conjunction with the no-reference image quality algorithm, namely the naturalness image quality evaluator (NIQE). Finally, based on a mixed weighting mechanism that combines generated pixel-level local weights with image-level global empirical weights, the pre-enhanced images are fused to produce the final enhanced image. Our experimental findings demonstrate exceptional performance across a range of datasets, surpassing various state-of-the-art methods, including two pre-enhancement methods, involved in the comparison. This outstanding performance is attributed to the harmonious integration of diverse LLIE methods, which yields robust and high-quality enhancement outcomes across various scenarios. Furthermore, our approach exhibits scalability and adaptability, ensuring compatibility with future advancements in enhancement technologies while maintaining superior performance in this rapidly evolving field. Full article

Conventional 5-level active neutral-point-clamped (5L-ANPC) topology and state-of-the-art 5-level hybrid active neutral-point-clamped (5L-HANPC) topology are popular for inverter applications. However, their dc-link voltage utilization is limited to only 50%. With the maximum voltage level generated by only half dc-link voltage, these inverters are not capable of boosting voltage in their ac output. To resolve these drawbacks, this paper proposes a family of four novel 5-level boost-active neutral-point-clamped (5L-BANPC) inverters. Without requiring any flying capacitors, the proposed topologies can generate five voltage levels by effectively using the dc-link capacitors. The dc-link voltage utilization of the proposed 5L-BANPC inverters is twice that of the 5L-ANPC and 5L-HANPC topologies. While generating the five-level ac output voltage, natural voltage balancing of both dc-link capacitors and voltage boosting are achieved. Ease of implementation is another noteworthy merit of the proposed 5L-BANPC inverters because they can be implemented using six widely available commercial half-bridge modules without requiring a dedicated circuit design. The operation of the proposed topologies is analyzed. Experimental results are presented for verification. Full article

Discrete chaotic maps are a mathematical basis for many useful applications. One of the most common is chaos-based pseudorandom number generators (PRNGs), which should be computationally cheap and controllable and possess necessary statistical properties, such as mixing and diffusion. However, chaotic PRNGs have several known shortcomings, e.g., being prone to chaos degeneration, falling in short periods, and having a relatively narrow parameter range. Therefore, it is reasonable to design novel simple chaotic maps to overcome these drawbacks. In this study, we propose a novel fractal chaotic tent map, which is a generalization of the well-known tent map with a fractal function introduced into the right-hand side. We construct and investigate a PRNG based on the proposed map, showing its high level of randomness by applying the NIST statistical test suite. The application of the proposed PRNG to the task of generating surrogate data and a surrogate testing procedure is shown. The experimental results demonstrate that our approach possesses superior accuracy in surrogate testing across three distinct signal types—linear, chaotic, and biological signals—compared to the MATLAB built-in randn() function and PRNGs based on the logistic map and the conventional tent map. Along with surrogate testing, the proposed fractal tent map can be efficiently used in chaos-based communications and data encryption tasks. Full article

The growing number of end-of-life (EoL) photovoltaic (PV) panels as waste materials is forcing many countries to face the challenge of addressing this issue. The presented research explores the utilization of a by-product of this waste material, namely glass powder, with gypsum in geotechnical engineering to improve clay-soil properties. The approach is to integrate these materials to address the sustainable management of EoL PV panels, an underutilized resource in geotechnical applications. Furthermore, the study extensively examines the physical properties of clay soil, gypsum, and glass powder. Composite samples are created by adjusting the proportions of gypsum (0%, 5%, 10%, and 15%) and glass powder (0%, 4%, 8%, and 12%) relative to the soil’s dry mass. Compaction processes are performed at dry densities of 1500 and 1700 kg/m³, with 7, 28, and 56 days of curing duration. Various tests, including ultrasonic pulse velocity (UPV), unconfined compressive strength (UCS), assessments of wet and dry cycle durability, scanning electron microscope (SEM) analyses, and X-ray diffraction (XRD) analyses, are conducted. The results reveal that gypsum consistently improves the soil’s strength and stiffness features, while initially adding glass powder reduces these properties before showing improvement at a 12% content. Correlations have been proposed to determine the unconfined compressive strength (q_u), initial shear modulus (G₀), and modulus of elasticity (E) to be acquired utilizing just a single test. Moreover, a correlation has been developed to predict the unconfined compressive strength and elastic modulus of any specimen through non-destructive testing. Additionally, microstructural analyses unveil intricate interactions, showcasing the progress of pozzolanic reactions, identifying silicon-rich compounds from glass powder, and elucidating how additives transform soil structure. Full article

The appearance of dried fruit clearly influences the consumer’s perception of the quality of the product but is a subtle and nuanced characteristic that is difficult to quantitatively measure, especially online. This paper describes a method that combines several simple strategies to assess a suitable surrogate for the elusive quality using imaging, combined with multivariate statistics and machine learning. With such a convenient tool, this study also shows how one can vary the pretreatments and drying conditions to optimize the resultant product quality. Specifically, an image batch processing method was developed to extract color (hue, saturation, and value) and morphological (area, perimeter, and compactness) features. The accuracy of this method was verified using data from a case study experiment on the pretreatment of hot-air-dried kiwifruit slices. Based on the extracted image features, partial least squares and random forest models were developed to satisfactorily predict the moisture ratio (MR) during drying process. The MR of kiwifruit slices during drying could be accurately predicted from changes in appearance without using any weighing device. This study also explored determining the optimal drying strategy based on appearance quality using principal component analysis. Optimal drying was achieved at 60 °C with 4 mm thick slices under ultrasonic pretreatment. For the 70 °C, 6 mm sample groups, citric acid showed decent performance. Full article

Neurological damage is the pathological substrate of permanent disability in various neurodegenerative disorders. Early detection of this damage, including its identification and quantification, is critical to preventing the disease’s progression in the brain. Tau, glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL), as brain protein biomarkers, have the potential to improve diagnostic accuracy, disease monitoring, prognostic assessment, and treatment efficacy. These biomarkers are released into the cerebrospinal fluid (CSF) and blood proportionally to the degree of neuron and astrocyte damage in different neurological disorders, including stroke, traumatic brain injury, multiple sclerosis, neurodegenerative dementia, and Parkinson’s disease. Here, we review how Tau, GFAP, and NfL biomarkers are detected in CSF and blood as crucial diagnostic tools, as well as the levels of these biomarkers used for differentiating a range of neurological diseases and monitoring disease progression. We also discuss a biosensor approach that allows for the real-time detection of multiple biomarkers in various neurodegenerative diseases. This combined detection system of brain protein biomarkers holds significant promise for developing more specific and accurate clinical tools that can identify the type and stage of human neurological diseases with greater precision. Full article

We examined whether the administration of growth hormone (GH) improves insulin resistance in females of a non-obese hyperglycemic mouse model after birth with low birth weight (LBW), given that GH is known to increase muscle mass. The intrauterine Ischemia group underwent uterine artery occlusion for 15 min on day 16.5 of gestation. At 4 weeks of age, female mice in the Ischemia group were divided into the GH-treated (Ischemia-GH) and non-GH-treated (Ischemia) groups. At 8 weeks of age, the glucose metabolism, muscle pathology, and metabolome of liver were assessed. The insulin resistance index improved in the Ischemia-GH group compared with the Ischemia group (p = 0.034). The percentage of type 1 muscle fibers was higher in the Ischemia-GH group than the Ischemia group (p < 0.001); the muscle fiber type was altered by GH. In the liver, oxidative stress factors were reduced, and ATP production was increased in the Ischemia-GH group compared to the Ischemia group (p = 0.014), indicating the improved mitochondrial function of liver. GH administration is effective in improving insulin resistance by increasing the content of type 1 muscle fibers and improving mitochondrial function of liver in our non-obese hyperglycemic mouse model after birth with LBW. Full article

(1) Background: Cardiometabolic disease progression can be delayed if patients engage in healthy lifestyle behaviors, adherence to which is highly influenced by psychosocial factors. The present study aimed at investigating the association of self-efficacy with the adherence level to healthy lifestyle behaviors among patients with cardiometabolic diseases in Greece. (2) Methods: 1988 patients (1180 females) with cardiometabolic diseases participated. Anthropometric, demographic, socioeconomic, clinical, and lifestyle characteristics were recorded. Patients were also asked to evaluate their efficacy to comply with healthy lifestyle behaviors. (3) Results: The majority exhibited unhealthy lifestyle behaviors. A subgroup demonstrated elevated self-efficacy in maintaining healthy habits despite facing diverse psychosocial challenges. Individuals with higher educational attainment, socioeconomic status, and rural/semi-urban residency had significantly elevated self-efficacy. Those with heightened self-efficacy exhibited significantly lower BMI and reduced prevalence of certain health conditions. Self-efficacy significantly influenced adherence to the Mediterranean diet, physical activity engagement, and smoking cessation, even in challenging circumstances. (4) Conclusions: This study represented an innovative approach in examining the role of self-efficacy in shaping health behaviors and outcomes within a Greek population. By integrating specific psychosocial circumstances into the analysis, valuable insights were provided into the contextual factors influencing self-efficacy and adherence to healthy lifestyle behaviors. Full article

This study reviews the application of artificial intelligence (AI) throughout the food value chain and how it can be leveraged to help companies become more sustainable. A literature review across different parts of the food value chain was conducted to provide an overview of the main themes of current and future AI applications throughout the food industry. Moreover, the paper focuses on the benefits and challenges of change management when integrating AI. A documentary Systematic Review using PRISMA research was conducted to find and analyze the aforementioned applications. The key insight is that change progress varies significantly. Today’s applications are primarily found within food inspection and quality assurance due to relatively straightforward AI applications in the value chain. Such technology is mainly image-based. Companies can use the interconnectedness of AI and sustainability by becoming more efficient through AI and simultaneously saving emissions and resources through optimizing processes. Full article

Under the influence of tourism and globalization, heritage production presents a new landscape. As a crucial framework for interpreting heritage, Authorized Heritage Discourse (AHD) has profound significance in discussing its practice characteristics in this context. Taking cities along the Jiangsu–Zhejiang section of the Grand Canal as a case study and drawing upon policy text, this study explores the practice characteristics of AHD in the tourism context. Results indicate that the thematic practices of AHD encompass protection and management, ecological construction, cultural production and inheritance, touristification, infrastructure and services, and marketing and cooperation, forming a clustering pattern with touristification as the central theme. The spatial characteristics manifest as multi-scale practices ranging from global to regional to local, each corresponding to diverse thematic characteristics. This study deepens the understanding of AHD in tourism and advances the research progress of heritage tourism. It also provides practical references for the utilization of urban heritage and the management of heritage tourism. Full article

Background: Lipids, as a fundamental cell component, play an regulating role in controlling the different cellular biological processes involved in viral infections. A notable feature of coronavirus disease 2019 (COVID-19) is impaired lipid metabolism. The function of lipophagy-related genes in COVID-19 is unknown. The present study aimed to investigate biomarkers and drug targets associated with lipophagy and lipophagy-based therapeutic agents for COVID-19 through bioinformatics analysis. Methods: Lipophagy-related biomarkers for COVID-19 were identified using machine learning algorithms such as random forest, Support Vector Machine-Recursive Feature Elimination, Generalized Linear Model, and Extreme Gradient Boosting in three COVID-19-associated GEO datasets: scRNA-seq (GSE145926) and bulk RNA-seq (GSE183533 and GSE190496). The cMAP database was searched for potential COVID-19 medications. Results: The lipophagy pathway was downregulated, and the lipid droplet formation pathway was upregulated, resulting in impaired lipid metabolism. Seven lipophagy-related genes, including ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2, were used as biomarkers and drug targets for COVID-19. Moreover, lipophagy may play a role in COVID-19 pathogenesis. As prospective drugs for treating COVID-19, seven potential downregulators (phenoxybenzamine, helveticoside, lanatoside C, geldanamycin, loperamide, pioglitazone, and trichostatin A) were discovered. These medication candidates showed remarkable binding energies against the seven biomarkers. Conclusions: The lipophagy-related genes ACADVL, HYOU1, DAP, AUP1, PRXAB2, LSS, and PLIN2 can be used as biomarkers and drug targets for COVID-19. Seven potential downregulators of these seven biomarkers may have therapeutic effects for treating COVID-19. Full article

Carbonate rocks are important for the petroleum industry, as they contribute significantly to hydrocarbon reserves, although their analysis is complex due to the high cost of core sampling and their high heterogeneity; for this, synthetic rocks aim to provide relatively homogeneous samples with analogous characteristics to natural rocks. In this research, synthetic carbonate rocks were produced by mixing a fixed ratio between limestone powder, obtained from limestone mining waste, and epoxy resin as a cementing material, using compaction energy for consolidation. The work aimed to produce homogeneous samples with high strength, reproducing the intergranular pore system for future applications in rock–fluid interaction analysis. The characteristics and structure of the samples were investigated through X-ray computed microtomography, petrographic images, petrophysical, chemical and geomechanical tests. Results showed a direct increasing relationship between porosity and permeability and a tendency for mechanical strength (UCS) to decrease with increasing porosity. When compared with the natural carbonate rocks, these presented similarities in their mechanical properties and petrophysical measurements, showing that the methodology can be considered as an alternative for the obtention of a realistic material that can be used for future experiments regarding rock mechanics and rock–fluid interaction for prediction of carbonate rocks’ behavior. Full article

Raphanus sativus L. is a potential source of raw material for biodiesel fuel due to the high oil content in its grains. In Brazil, this species is cultivated in the low rainfall off-season, which limits the productivity of the crop. The present study investigated the effects of water restriction on the physiological and biochemical responses, production components, and oil quality of R. sativus at different development stages. The treatments consisted of 100% water replacement (control), 66%, and 33% of field capacity during the phenological stages of vegetative growth, flowering, and grain filling. We evaluated characteristics of water relations, gas exchange, chlorophyll a fluorescence, chloroplast pigment, proline, and sugar content. The production components and chemical properties of the oil were also determined at the end of the harvest cycle. Drought tolerance of R. sativus was found to be mediated primarily during the vegetative growth stage by changes in photosynthetic metabolism, stability of photochemical efficiency, increased proline concentrations, and maintenance of tissue hydration. Grain filling was most sensitive to water limitation and showed a reduction in yield and oil content. However, the chemical composition of the oil was not altered by the water deficit. Our data suggest that R. sativus is a drought-tolerant species. Full article

The European Union faces the pressing challenge of decarbonising the buildings sector to meet its climate neutrality goal by 2050. Buildings are significant contributors to greenhouse gas emissions, primarily through energy consumption for heating and cooling. This study uses the advanced PRIMES-BuiMo model to develop state-of-the-art innovative pathways and strategies to decarbonise the EU buildings sector, providing insights into energy consumption patterns, renovation rates and equipment replacement dynamics in the EU and in two representative Member States, Sweden and Greece. The model-based analysis shows that the EU’s transition towards climate neutrality requires significant investment in energy efficiency of buildings combined with decarbonisation of the fuel mix, mostly through the uptake of electric heat pumps replacing the use of fossil fuels. The Use Case also demonstrates that targeted policy interventions considering the national context and specificities are required to ensure an efficient and sustainable transition to zero-emission buildings. The analysis of transformational strategies in Greece and Sweden provides an improved understanding of the role of country-specific characteristics on policy effectiveness so as to inform more targeted and contextually appropriate approaches to decarbonise the buildings sector across the EU. Full article

Semi-supervised training methods need reliable pseudo labels for unlabeled data. The current state-of-the-art methods based on pseudo labeling utilize only high-confidence predictions, whereas poor confidence predictions are discarded. This paper presents a novel approach to generate high-quality pseudo labels for unlabeled data. It utilizes predictions with high- and low-confidence levels to generate refined labels and then validates the accuracy of those predictions through bi-directional object tracking. The bi-directional object tracker leverages both past and future information to recover missing labels and increase the accuracy of the generated pseudo labels. This method can also substantially reduce the effort and time needed in label creation compared to the conventional manual labeling. The proposed method utilizes a buffer to accumulate detection labels (bounding boxes) predicted by the object detector. These labels are refined for accuracy though forward and backward tracking, ultimately constructing the final set of pseudo labels. The method is integrated in the YOLOv5 object detector and tested on the BDD100K dataset. Through the experiments, we demonstrate the effectiveness of the proposed scheme in automating the process of pseudo label generation with notably higher accuracy than the recent state-of-the-art pseudo label generation schemes. The results show that the proposed method outperforms previous methods in terms of mean average precision (mAP), label generation accuracy, and speed. Using the bi-directional recovery method, an increase in mAP@50 for the BDD100K dataset by 0.52% is achieved, and for the Waymo dataset, it provides an improvement of mAP@50 by 8.7% to 9.9% compared to 8.1% of the existing method when pre-training with 10% of the dataset. An improvement by 2.1% to 2.9% is achieved as compared to 1.7% of the existing method when pre-training with 20% of the dataset. Overall, the improved method leads to a significant enhancement in detection accuracy, achieving higher mAP scores across various datasets, thus demonstrating its robustness and effectiveness in diverse conditions. Full article

This phenomenological qualitative study examined the lived experience of pediatric nurse residents’ transition to practice during the COVID-19 pandemic. The purposive sample included nine pediatric nurses, participating in a nurse residency program, who entered the nursing profession during the first year of the pandemic. The setting was a free-standing, Magnet-recognized, pediatric academic medical center in the Northeastern U.S. Individual interviews were audio recorded and transcribed. Narratives were analyzed using a hermeneutic phenomenological approach. Five themes emerged from the data: Our New Normal; The Rules Keep Changing; I’m Not Ready for This (transition to practice); The Toll of COVID; and Shattered Family-Centered Care. Sub-themes emerged in The Toll of COVID theme: COVID and the Nursing Care Environment, Emotional Toll of COVID, Burnout: A Universal Truth, and The Pandemic within the Pandemic. The nurse residents’ narratives uncovered the essence of their uncertainty, sorrow, growth, and resilience. Through the eyes of pediatric nurse residents, this study illuminated the experiences of these novices as they entered the nursing profession amid a pandemic. Full article