OBJECTIVE: To evaluate the effect of the orthokeratology (OK) lens design, used in the Montreal Experience cohort, on corneal treatment zone characteristics and their relationship to the pupil. METHODS: This retrospective study follows previously published work and refers to the analysis of 4 different OK lenses. Tangential topography maps were obtained at baseline and after 1 month of OK lens wear. The extracted parameters are: distance treatment zone diameter (DTZD (mm)); relative peripheral power (RPP (D)); mid-peripheral width (MPW (mm)); a new concept, the plus power ratio (PPR (%)), corresponding to the coverage of the pupil area by the positive power zones. RESULTS: DTZD and MPW were significantly different between the lens designs (Welch’s ANOVA). (DTZD (OK 1: 3.68 ± 0.46 mm; OK 2: 3.06 ± 0.67; OK 3: 2.83 ± 0.54; OK 4: 3.20 ± 0.53) MPW (OK 1: 1.65 ± 0.21 mm; OK 2: 1.31 ± 0.40 mm; OK 3: 1.46 ± 0.17 mm; OK 4: 1.57 ± 0.17 mm)). PPR was significantly lower in OK 1 (40.1 ± 22.1%) than the other designs (OK 2: 53.8 ± 18.4%; OK 3: 60.3 ± 13.6; OK 4: 54.7 ± 15.3). CONCLUSION: This study shows that the corneal response to OK lens wear varies with lens design. When analyzed, topographic analysis shows that OK 1 is associated with a larger DTZD, which produces a lower PPR. This may explain why previously published results showed significantly faster axial length (AL) progression with this lens. Full article

This study outlines the creation of hiking routes in Slovakia’s cultural landscape, focusing on regions with marginal interest, low tourism engagement, and predominant monocultural blocks. The methodology was systematically applied to the Zemplín Geopark in eastern Slovakia, drawing upon historical cartographic records from the Josephine mapping period (1764–1787) to the present day. The investigation identified and delineated 14 hiking trails, offering historical and tourism significance while promoting multifunctionality. Our research introduces sustainable development avenues for regions with marginal interest, providing ecological and tourist benefits that enhance the overall quality of life. The findings align with the Common Agricultural Policy’s objectives for 2021–2027, addressing challenges related to large-scale field fragmentation. Two identified obstacles include property-legal challenges and issues arising from inadequate map registration, which current methods, unfortunately, fail to address. Full article

In response to the numerous challenges faced by traditional human pose recognition methods in practical applications, such as dense targets, severe edge occlusion, limited application scenarios, complex backgrounds, and poor recognition accuracy when targets are occluded, this paper proposes a YOLO-Pose algorithm for human pose estimation. The specific improvements are divided into four parts. Firstly, in the Backbone section of the YOLO-Pose model, lightweight GhostNet modules are introduced to reduce the model’s parameter count and computational requirements, making it suitable for deployment on unmanned aerial vehicles (UAVs). Secondly, the ACmix attention mechanism is integrated into the Neck section to improve detection speed during object judgment and localization. Furthermore, in the Head section, key points are optimized using coordinate attention mechanisms, significantly enhancing key point localization accuracy. Lastly, the paper improves the loss function and confidence function to enhance the model’s robustness. Experimental results demonstrate that the improved model achieves a 95.58% improvement in mAP50 and a 69.54% improvement in mAP50-95 compared to the original model, with a reduction of 14.6 M parameters. The model achieves a detection speed of 19.9 ms per image, optimized by 30% and 39.5% compared to the original model. Comparisons with other algorithms such as Faster R-CNN, SSD, YOLOv4, and YOLOv7 demonstrate varying degrees of performance improvement. Full article

This study systematically explores and compares the performance of various artificial-intelligence (AI)-based models to predict the electrical and thermal efficiency of photovoltaic–thermal systems (PVTs) cooled by nanofluids. Employing extreme gradient boosting (XGB), extra tree regression (ETR), and k-nearest-neighbor (KNN) regression models, their accuracy is quantitatively evaluated, and their effectiveness measured. The results demonstrate that both XGB and ETR models consistently outperform KNN in accurately predicting both electrical and thermal efficiency. Specifically, the XGB model achieves remarkable correlation coefficient (R²) values of approximately 0.99999, signifying its superior predictive capabilities. Notably, the XGB model exhibits a slightly superior performance compared to ETR in estimating electrical efficiency. Furthermore, when predicting thermal efficiency, both XGB and ETR models demonstrate excellence, with the XGB model showing a slight edge based on R² values. Validation against new data points reveals outstanding predictive performance, with the XGB model attaining R² values of 0.99997 for electrical efficiency and 0.99995 for thermal efficiency. These quantitative findings underscore the accuracy and reliability of the XGB and ETR models in predicting the electrical and thermal efficiency of PVT systems when cooled by nanofluids. The study’s implications are significant for PVT system designers and industry professionals, as the incorporation of AI-based models offers improved accuracy, faster prediction times, and the ability to handle large datasets. The models presented in this study contribute to system optimization, performance evaluation, and decision-making in the field. Additionally, robust validation against new data enhances the credibility of these models, advancing the overall understanding and applicability of AI in PVT systems. Full article

Convolutional Neural Networks have emerged as a predominant tool in musculoskeletal medical image segmentation. It enables precise delineation of bone and cartilage in medical images. Recent developments in image processing and network architecture desire a reevaluation of the relationship between segmentation accuracy and the amount of training data. This study investigates the minimum sample size required to achieve clinically relevant accuracy in bone and cartilage segmentation using the nnU-Net methodology. In addition, the potential benefit of integrating available medical knowledge for data augmentation, a largely unexplored opportunity for data preprocessing, is investigated. The impact of sample size on the segmentation accuracy of the nnU-Net is studied using three distinct musculoskeletal datasets, including both MRI and CT, to segment bone and cartilage. Further, the use of model-informed augmentation is explored on two of the above datasets by generating new training samples implementing a shape model-informed approach. Results indicate that the nnU-Net can achieve remarkable segmentation accuracy with as few as 10–15 training samples on bones and 25–30 training samples on cartilage. Model-informed augmentation did not yield relevant improvements in segmentation results. The sample size findings challenge the common notion that large datasets are necessary to obtain clinically relevant segmentation outcomes in musculoskeletal applications. Full article

A robust predictive model was developed using 136 novel peroxisome proliferator-activated receptor delta (PPARδ) agonists, a distinct subtype of lipid-activated transcription factors of the nuclear receptor superfamily that regulate target genes by binding to characteristic sequences of DNA bases. The model employs various structural descriptors and docking calculations and provides predictions of the biological activity of PPARδ agonists, following the criteria of the Organization for Economic Co-operation and Development (OECD) for the development and validation of quantitative structure–activity relationship (QSAR) models. Specifically focused on small molecules, the model facilitates the identification of highly potent and selective PPARδ agonists and offers a read-across concept by providing the chemical neighbours of the compound under study. The model development process was conducted on Isalos Analytics Software (v. 0.1.17) which provides an intuitive environment for machine-learning applications. The final model was released as a user-friendly web tool and can be accessed through the Enalos Cloud platform’s graphical user interface (GUI). Full article

A rapid and accurate monitoring of hazardous formaldehyde (HCHO) gas is extremely essential for health protection. However, the high-power consumption and humidity interference still hinder the application of HCHO gas sensors. Hence, zeolitic imidazolate framework-8 (ZIF-8)-loaded Pt-NiO/In₂O₃ hollow nanofibers (ZPNiIn HNFs) were designed via the electrospinning technique followed by hydrothermal treatment, aiming to enable a synergistic advantage of the surface modification and the construction of a p-n heterostructure to improve the sensing performance of the HCHO gas sensor. The ZPNiIn HNF sensor has a response value of 52.8 to 100 ppm HCHO, a nearly 4-fold enhancement over a pristine In₂O₃ sensor, at a moderately low temperature of 180 °C, along with rapid response/recovery speed (8/17 s) and excellent humidity tolerance. These enhanced sensing properties can be attributed to the Pt catalysts boosting the catalytic activity, the p-n heterojunctions facilitating the chemical reaction, and the appropriate ZIF-8 loading providing a hydrophobic surface. Our research presents an effective sensing material design strategy for inspiring the development of cost-effective sensors for the accurate detection of indoor HCHO hazardous gas. Full article

The study evaluated the effect of adding dandelion extract on the characteristics of raw-ripening pork sausages while reducing the nitrite addition from 150 to 80 mg/kg. The sausages were made primarily from pork ham (80%) and pork jowl (20%). The process involved curing, preparing the meat stuffing, forming the links, and then subjecting the sausages to a 21-day ripening period. Physicochemical parameters such as pH, water activity, and oxidation-reduction potential were compared at the beginning of production and after the ripening process. The study also examined the impact of ripening on protein metabolism in pork sausages and compared the protein profiles of different sausage variants. The obtained research results indicate that dandelion-leaf extract (Taraxacum officinale) were rich in phenolic acids, flavonoids, coumarins, and their derivatives (LC-QTOF-MS method). Antiradical activity test against the ABTS^+* and DPPH radical, and the TBARS index, demonstrated that addition of dandelion (0.5–1%) significantly improved the oxidative stability of raw-ripening sausages with nitrite content reduction to 80 mg/kg. A microbiological evaluation of the sausages was also carried out to assess the correctness of the ripening process. The total number of viable bacteria, lactic acid bacteria, and coliforms were evaluated and subsequently identified by mass spectrometry. Full article

The advancement of genetic knowledge and the discovery of an increasing number of genetic disorders has made the role of the geneticist progressively more complex and fundamental. However, most genetic disorders present during childhood; thus, their early recognition is a challenge for the pediatrician, who will be also involved in the follow-up of these children, often establishing a close relationship with them and their families and becoming a referral figure. In this review, we aim to provide the pediatrician with a general knowledge of the approach to treating a child with a genetic syndrome associated with dysmorphic features. We will discuss the red flags, the most common manifestations, the analytic collection of the family and personal medical history, and the signs that should alert the pediatrician during the physical examination. We will offer an overview of the physical malformations most commonly associated with genetic defects and the way to describe dysmorphic facial features. We will provide hints about some tools that can support the pediatrician in clinical practice and that also represent a useful educational resource, either online or through apps downloaded on a smartphone. Eventually, we will offer an overview of genetic testing, the ethical considerations, the consequences of incidental findings, and the main indications and limitations of the principal technologies. Full article

Down syndrome (DS) is characterised by a duplication of chromosome-21 and is linked to co-occurring physical and mental health conditions, including low self-efficacy and disturbed mood states. The purpose of this study was to investigate the effects of an eight-week prescribed physical and/or cognitive training intervention on measures of mood disturbance, life satisfaction and self-efficacy in a population of adults with DS. Eighty-three participants (age 27.1 ± 8.0 years) from across five continents volunteered. Participants were assigned using matched groups based upon performance in a modified six-minute walk test to either an exercise (EXE) 3 × 30 min of walking/jogging per week, cognitive training (COG) 6 × 20 min per week, a combined group (COM) or the control (CON) who did not complete any intervention. Profile of Mood States (POMS) were assessed using a five-point scale across 65 categories pre- and post-study as well as upon completion of each week of the intervention. In addition, Satisfaction with Life Scale (SWLS) and self-efficacy using the Generalised Self-Efficacy scale (GSE) were recorded before and after the intervention. GSE increased for all participants by 1.9 ± 5.2 (p = 0.002) from pre- to post-intervention, while POMS showed significant changes for the whole group from pre- to post-intervention for tension (p < 0.001), depression (p < 0.001) and for anger (p < 0.001). In addition, significant correlations were observed between SWLS and ΔTMD, Δtension, Δanger, and Δfatigue (p < 0.05) for EXE. Both COG and EXE provide a framework for empowering enhancements in life satisfaction, self-efficacy and mood states fostering improvements in quality of life. Full article

Sambuci folium (elderberry leaves) have been used in traditional medicine, mainly externally, to treat skin diseases and wounds. Therefore, the aim of this study was to screen the biological activity of elderberry leaves (antioxidant potential and possibility of inhibition of tyrosinase and hyaluronidase enzymes) combined with phytochemical analysis. For this purpose, a phytochemical analysis was carried out. Elderberry leaves of 12 varieties (“Sampo”, “Obelisk”, “Dwubarwny”, “Haschberg”, “Haschberg 1”, “Koralowy”, “Sambo”, “Black Beauty”, “Black Tower”, “Golden hybrid”, “Samyl”, “Samyl 1”) in two growth stages. The compounds from the selected groups, phenolic acids (chlorogenic acid) and flavonols (quercetin), were chromatographically determined in hydroalcoholic leaf extracts. All tested elderberry leaf extracts showed antioxidant effects, but the most promising potential: very high compounds content (TPC = 61.85 mg GAE/g), antioxidant (e.g., DPPH IC₅₀ = 1.88 mg/mL; CUPRAC IC_0.5 = 0.63 mg/mL) and optimal anti-inflammatory (inhibition of hyaluronidase activity 41.28%) activities were indicated for older leaves of the “Sampo” variety. Additionally, the extract obtained from “Sampo” and “Golden hybrid” variety facilitated the treatment of wounds in the scratch test. In summary, the best multidirectional pro-health effect in treating skin inflammation was specified for “Sampo” leaves II extract (leaves during the flowering period); however, wound treatment was noted as rich in chlorogenic acid younger leaf extracts of the “Golden hybrid” variety. Full article

Organisations encounter a significant challenge in the globalised business landscape, and thus mitigate risk by establishing robust supply chains (SCs) networks is required. In a rapidly changing environment, gaining a competitive edge is imperative. However, the exploration of the essential factors enabling resilient and sustainable supply chain management (RSSCM) in construction projects has been lacking. This study aims to bridge this gap by identifying the enabling factors for resilient and sustainable supply chain management (SSCM). To achieve this, a survey was conducted among Egyptian engineers, involving 32 factors derived from an extensive literature review on RSSCM. The data collected were categorised into four groups, namely Organisational Knowledge and Competence, Risk Management and Security, Collaboration and Communication, and Planning Efficiency and Timing, using brainstorming techniques. Subsequently, the data were analysed utilising a novel hybrid assessment approach that combines evaluation of alternatives and ranking, employing the compromise solution-fuzzy synthetic evaluation methodology, for the first time, offering a unique approach to assessing and prioritising these categories. The findings reveal that ‘Planning Efficiency and Timing’ emerged as the highest-performing category, whereas ‘Collaboration and Communication’ performed the worth. Furthermore, our results indicate that brainstorming enabled the grouping of the enablers into four distinct categories, providing a structured framework for understanding and organising them. The integration of MARCOS and FSE offered a robust decision-making approach, proposing a resilient and comprehensive decision-support system capable of tackling intricate real-world issues. This research outcome offers building administrators valuable insights for comparing different supply chains, considering how supply chain characteristics influence resilience and risk exposure in building SCs. Full article

In today’s world, effective management and the use of spatial data are of great importance in many sectors. Industries such as land management, asset management, and infrastructure management are areas where spatial data are heavily utilized. Advanced technologies such as Geographic Information Systems (GISs) and Building Information Modeling (BIM) are used in the processes of collecting, analyzing, and managing geographically enabled data (geo-data). These technologies enable the effective processing of large datasets, improve decision-making processes based on geographic information, and facilitate more efficient collaboration across sectors. This study conducts an in-depth examination of the existing literature on asset management, infrastructure management, and BIM-GIS integration using bibliometric analysis and systematic literature review methods. Bibliometric analysis is employed to determine statistical values such as current research trends, frequently cited authors, most used keywords, and country performances in the relevant field. This study’s results highlight future research trends and significant gaps in the areas of asset management, infrastructure management, natural gas pipelines, and BIM-GIS integration. In particular, this study demonstrates the critical importance of asset management and BIM-GIS integration for sustainable infrastructure design, construction, and management. In this context, attention is drawn to the importance of data standardization, digitization, systematic integration, and contemporary land management requirements. Full article

Electrochemical impedance spectroscopy (EIS) is becoming more and more relevant for the characterization of biosensors employing interdigitated electrodes. We compare four different sensor topologies for an exemplary use case of ion sensing to extract recommendations for the design optimizations of impedimetric biosensors. Therefore, we first extract how sensor design parameters affect the sensor capacitance using analytical calculations and finite element (FEM) simulations. Moreover, we develop equivalent circuit models for our sensor topologies and validate them using FEM simulations. As a result, the impedimetric sensor response is better understood, and sensitive and selective frequency ranges can be determined for a given sensor topology. From this, we extract design optimizations for different sensing principles. Full article

The hydraulic concrete in the alpine region is subjected to alternating actions of freeze–thaw (F) and abrasion (W) during operation, resulting in significant deterioration of concrete durability. In this paper, the water/binder ratio (W/B) was employed as the test variable, the working condition F group and W group were set as the control group, and the working condition F-W group was used as the test group. Fast-freezing and underwater methods are used for the alternating test. By measuring the mass loss, relative dynamic elastic modulus (RDEM), surface morphological characteristics, fractal dimension of concrete in each alternating cycle, and the evolution law of concrete performance under the alternating action of F and W was explored. The results show that compared with the control group, the alternating action will accelerate the mass loss of concrete, reduce the RDEM, and cause the deterioration of surface wear. The maximum increase in mass loss and RDEM of concrete is 1.92% and 20.11%, respectively. During this process, the fractal dimension of the concrete increases as the number of alternating cycles increases, but it still does not exceed the limit of 2.4. In addition, a relationship function between the fractal dimension and the mass loss rate, volume loss, was established. It was found that the experimental group had a good linear correlation, and the correlation was close to 95%, which was about 20% higher than that of the control group. Full article

This paper is devoted to some selected topics of the theory of special orthogonal group SO(3). First, we discuss the trace of orthogonal matrices and its relation to the characteristic polynomial; on this basis, the partition of SO(3) formed by conjugation classes is described by trace mapping. Second, we show that every special orthogonal matrix can be expressed as the product of three elementary special orthogonal matrices. Explicit formulas for the decomposition as needed for applications in differential geometry and physics as symmetry transformations are given. Full article

We consider a history-dependent variational inequality in a real Hilbert space, for which we recall an existence and uniqueness result. We associate this inequality with a gap function, together with two additional problems: a nonlinear equation and a minimization problem. Then, we prove that solving these problems is equivalent to solving the original history-dependent variational inequality. Next, we state and prove a convergence criterion, i.e., we provide necessary and sufficient conditions which guarantee the convergence of a sequence of functions to the solution of the considered inequality. Based on the equivalence above, we deduce various consequences that present some interest on their own, and, moreover, we obtain convergence results for the two additional problems considered. Finally, we apply our abstract results to the study of an inequality problem in solid mechanics. It concerns the study of a viscoelastic constitutive law with long memory and unilateral constraints, for which we deduce a convergence result and provide the corresponding mechanical interpretations. Full article

This paper illustrates two species in the lichen-forming family Arthoniaceae from southern China, including a new species of Herpothallon, H. fibrosum L.L. Liu & Lu L. Zhang and a new record of Cryptothecia, C. striata G. Thor for China. Herpothallon fibrosum has fluffy, cylindrical pseudoisidia, like a bundle of fiber, and psoromic acid and confluentic acid are present. Furthermore, the new record of Cryptothecia striata has been identified by morphological, anatomical, chemical, and molecular studies. The systematic position of the two species was clarified by the molecular sequence data from the small subunit of the mitochondrial ribosomal DNA (mtSSU). Detailed taxonomic descriptions, chemical characters, comparisons, and discussion of the characteristics of similar species are provided for the two species; the relationship between Cryptothecia and Herpothallon is also discussed here. Full article

Mycoses are one of the major causes of morbidity/mortality among immunocompromised individuals. Considering the importance of these infections, the World Health Organization (WHO) defined a priority list of fungi for health in 2022 that include Candida albicans as belonging to the critical priority group and Pichia kudriavzevii (Candida krusei) to the medium priority group. The existence of few available antifungal drugs, their high toxicity, the acquired fungal resistance, and the appearance of new species with a broader spectrum of resistance, points out the need for searching for new antifungals, preferably with new and multiple mechanisms of action. The cyclam salt H₄[H₂(^4-CF3PhCH₂)₂Cyclam]Cl₄ was previously tested against several fungi and revealed an interesting activity, with minimal inhibitory concentration (MIC) values of 8 µg/mL for C. krusei and of 128 µg/mL for C. albicans. The main objective of the present work was to deeply understand the mechanisms involved in its antifungal activity. The effects of the cyclam salt on yeast metabolic viability (resazurin reduction assay), yeast mitochondrial function (JC-1 probe), production of reactive oxygen species (DCFH-DA probe) and on intracellular ATP levels (luciferin/luciferase assay) were evaluated. H₄[H₂(^4-CF3PhCH₂)₂Cyclam]Cl₄ induced a significant decrease in the metabolic activity of both C. albicans and C. krusei, an increase in Reactive Oxygen Species (ROS) production, and an impaired mitochondrial function. The latter was observed by the depolarization of the mitochondrial membrane and decrease in ATP intracellular levels, mechanisms that seems to be involved in the antifungal activity of H₄[H₂(^4-CF3PhCH₂)₂Cyclam]Cl₄. The interference of the cyclam salt with human cells revealed a CC₅₀ value against HEK-293 embryonic kidney cells of 1.1 mg/mL and a HC₁₀ value against human red blood cells of 0.8 mg/mL. Full article

We explored the state of the art economic and livelihood valuation of ecosystem services (ES) in the Sundarbans mangroves, including a comparative analysis between the Bangladesh and Indian parts of the region. We identified 145 values from 26 studies to estimate the Sundarbans’ economic and livelihood values. The number of ES valuation studies of the Sundarbans is scant, and it has gradually increased over time, focusing mainly on the estimation of provisioning ES (66.2%), followed by regulating and maintenance (25.5%), and cultural (8.3%) ES. However, recently, attention has been paid to estimation, regulating and maintenance, and cultural ES. The number of studies on ES was higher for the Bangladesh (73%) part of the Sundarbans than the Indian (27%) one. The estimated economic values of the Sundarbans’ provisioning, regulating and maintenance, and cultural ES were US $ 713.30 ha⁻¹ yr⁻¹, US $ 2584.46 ha⁻¹ yr⁻¹, and US $ 151.88 ha⁻¹ yr⁻¹, respectively. Except for cultural ES, the identified values for the other two ES categories were about 1.5 to 2.5 times higher for the Bangladesh Sundarbans compared to the Indian ones. The results of the meta-regression model showed that the estimated economic and livelihood values of ES are affected by the associated variables (e.g., type of ES, valuation methods, study area, population, and GDP). Our study also identified some remarkable gaps and limitations in the economic and livelihood valuation of the ES of the Sundarbans, highlighting the need for further research to find out the values of all ES to help with policy decision-making. Full article

Spectral computed tomography (CT)-reconstructed images often exhibit severe noise and artifacts, which compromise the practical application of spectral CT imaging technology. Methods that use tensor dictionary learning (TDL) have shown superior performance, but it is difficult to obtain a high-quality pre-trained global tensor dictionary in practice. In order to resolve this problem, this paper develops an algorithm called tensor decomposition with total generalized variation (TGV) for sparse-view spectral CT reconstruction. In the process of constructing tensor volumes, the proposed algorithm utilizes the non-local similarity feature of images to construct fourth-order tensor volumes and uses Canonical Polyadic (CP) tensor decomposition instead of pre-trained tensor dictionaries to further explore the inter-channel correlation of images. Simultaneously, introducing the TGV regularization term to characterize spatial sparsity features, the use of higher-order derivatives can better adapt to different image structures and noise levels. The proposed objective minimization model has been addressed using the split-Bregman algorithm. To assess the performance of the proposed algorithm, several numerical simulations and actual preclinical mice are studied. The final results demonstrate that the proposed algorithm has an enormous improvement in the quality of spectral CT images when compared to several existing competing algorithms. Full article

We have developed an innovative thin-film nanocomposite membrane that contains cellulose acetate (CA) with small amounts of TiO₂-decorated graphene oxide (GO) (ranging from 0.5 wt.% to 2 wt.%) sandwiched between two polytetrafluoroethylene (PTFE)-like thin films. The PTFE-like films succeeded in maintaining the bulk porosity of the support while increasing the thermal and chemical robustness of the membrane and boosting the catalytic activity of TiO₂ nanoparticles. The membranes exhibited a specific chemical composition and bonding, with predominant carbon–oxygen bonds from CA and GO in the bulk, and carbon–fluorine bonds on their PTFE-like coated sides. We have also tested the membranes’ photocatalytic activities on azithromycin-containing wastewaters, demonstrating excellent efficiency with more than 80% degradation for 2 wt.% TiO₂-decorated GO in the CA-GO-TiO₂/PTFE-like membranes. The degradation of the azithromycin formulation occurs in two steps, with reaction rates being correlated to the amount of GO-TiO₂ in the membranes. Full article