Selenium, a trace element of significant importance for human health and the environment, can be introduced into the environment through coal combustion. Accurate determination of selenium in coal and coal-bearing strata is essential for implementing effective management strategies and control measures to minimize potential risks to human health and the environment. This study introduces an improved approach for the determination of ⁷⁷Se in the medium resolution mode using HR-ICP-MS, effectively separating interference from doubly charged ions and enabling precise determination of selenium in coal-bearing strata. The relative errors of the standard reference samples obtained by HR-ICP-MS are between 0.65% and 6.33%, comparing to that of ICP-CCT-MS (1.58%–17.27%), prove the reliability of this method. Additionally, the X (bar)—S control charts obtained from HR-ICP-MS compared to ICP-CCT-MS demonstrate the superior stability of HR-ICP-MS in continuous determination. Consequently, though ICP-CCT-MS has better instrumental stability reflected through the internal standard recovery (ICP-CCT-MS:104.81%; HR-ICP-MS:80.54%), HR-ICP-MS is recommended as the preferred method for selenium determination in coal-bearing strata because of its high accuracy and good stability. Full article

Seismic reflection data and implicitly sonic velocity are undoubtedly the most important source of information for large-scale subsurface characterization. Yet, deriving reservoir and fluid flow properties from acoustic data is still challenging in carbonates, which display large acoustic velocity variations that contest many of the conventional assumptions regarding wave propagation in porous media. In this comprehensive study on 370 carbonate samples (247 limestones and 123 dolomites), we re-evaluate the impact of mineral velocity on bulk rock acoustic properties of dolomite and limestone by assessing the link between sonic velocity and the rock’s pore geometry. We quantify pore size and pore network complexity using parameters from both digital image analysis (DIA) and the extended Biot theory (EBT). We then compare DIA and EBT parameters to assess the impact of pore network geometry versus mineral velocity on the acoustic velocity of carbonate rocks. We explore the usefulness of EBT parameter γ_k in improving permeability estimates. Published values of velocity indicate that dolomites exhibit higher velocities than limestones at any given porosity. Our laboratory measurements of acoustic velocity, however, reveal that both dolomites and limestones show extreme variations in sonic velocities where samples with compressional velocity of ~5000 m/s may range in porosity from 5% to 25% and samples with porosity of ~20% may range in velocity from ~4000 m/s to 5700 m/s. Through the quantitative assessment of the pore network in our samples we document that pore network geometry has much more impact on the acoustic velocity of carbonates than variations in mineralogy, in this case dolomite and calcite. Most of the dolostone samples studied are dominated by small pores, resulting in relatively low velocities for their given porosity, while limestones with similar velocity–porosity values often possess simpler pore networks with larger pores. This pore size difference offsets the faster velocity of dolomite. The extended Biot theory parameter γ_k, captures this variation in pore size and internal geometry and exhibits a strong correlation to specific surface. Moreover, γ_k captures the impact of internal pore geometry on acoustic velocity, providing the basis for challenging existing assumptions regarding the importance of mineral velocity. By quantifying internal geometry, γ_k can improve permeability estimates in reservoir characterization and enhance evaluations of producibility and injectability. With that, it has direct implications on general geophysics, hydrocarbon exploration, and CCS initiatives. Full article

The performance consistency of an RF MEMS switch matrix is a crucial metric that directly impacts its operational lifespan. An improved crossbar-based RF MEMS switch matrix topology, SR-Crossbar, was investigated in this article. An optimized port configuration scheme was proposed for the RF MEMS switch matrix. Both the utilization probability of individual switch nodes and the path lengths in the switch matrix achieve their best consistency simultaneously under the proposed port configuration scheme. One significant advantage of this scheme lies in that it only adjusts the positions of the input and output ports, with the topology and individual switch nodes kept unchanged. This grants it a high level of generality and feasibility and also introduces an additional degree of freedom for optimizations. In this article, a universal utilization probability function of single nodes was constructed and an optimization objective function for the SR-Crossbar RF MEMS switch matrix was formulated, which provide a convenient approach to directly solving the optimized port configuration scheme for practical applications. Simulations to demonstrate the optimized dynamic and static consistencies were conducted. For an 8×8 SR-Crossbar switch matrix, the standard deviations of contact resistances of 128 units and losses of all 64 paths decreased from 1.00 and 0.42 to 0.51 and 0.23, respectively. These results aligned closely with theoretical calculations derived from the proposed model. Full article

Augmented and mixed reality in the medical field is becoming increasingly important. The creation and visualization of digital models similar to reality could be a great help to increase the user experience during augmented or mixed reality activities like surgical planning and educational, training and testing phases of medical students. This study introduces a technique for enhancing a 3D digital model reconstructed from cone-beam computed tomography images with its real coloured texture using an Intel D435 RGBD camera. This method is based on iteratively projecting the two models onto a 2D plane, identifying their contours and then minimizing the distance between them. Finally, the coloured digital models were displayed in mixed reality through a Microsoft HoloLens 2 and an application to interact with them using hand gestures was developed. The registration error between the two 3D models evaluated using 30,000 random points indicates values of: 1.1 ± 1.3 mm on the x-axis, 0.7 ± 0.8 mm on the y-axis, and 0.9 ± 1.2 mm on the z-axis. This result was achieved in three iterations, starting from an average registration error on the three axes of 1.4 mm to reach 0.9 mm. The heatmap created to visualize the spatial distribution of the error shows how it is uniformly distributed over the surface of the pointcloud obtained with the RGBD camera, except for some areas of the nose and ears where the registration error tends to increase. The obtained results indicate that the proposed methodology seems effective. In addition, since the used RGBD camera is inexpensive, future approaches based on the simultaneous use of multiple cameras could further improve the results. Finally, the augmented reality visualization of the obtained result is innovative and could provide support in all those cases where the visualization of three-dimensional medical models is necessary. Full article

This paper contains research on the development of a fault-tolerant six-phase synchronous reluctance motor (SynRM) based on the stator adopted from a general-purpose three-phase induction motor. In the design and calculation process, an extended Clarke transformation was developed for a six-phase asymmetrical system. To verify the proposed design approach, a field–circuit model of electromagnetic phenomena in the studied motor was developed and used to study the motor performance. The increased torque value and reduction in torque ripples were confirmed by comparison to the classical three-phase SynRM design. To illustrate fault tolerance capabilities, the operation of the studied three- and six-phase synchronous reluctance motors under inverter-fault conditions was examined. The conducted analysis shows, among other things, that from the electromagnetic performance point of view, only the proposed six-phase machine is able to properly operate under inverter-fault conditions. The results of the winding design calculations, the performed simulations of six-phase motor operation, and the preliminary tests of the prototype motor are presented and discussed. Full article

Background: Pulmonary superinfections with Herpesviridae and Aspergillus spp. are common in severe coronavirus disease 2019 (COVID-19) pneumonia but their epidemiology and impact remain poorly understood. Methods: We conducted a retrospective observational study of 61 mechanically ventilated COVID-19 patients at Deventer Hospital’s ICU (2020–2021) who underwent bronchoalveolar lavage (BL) due to clinical deterioration. We analyzed blood and respiratory samples, treatment, and clinical outcomes. Results: Among 61 mechanically ventilated COVID-19 patients who underwent BL, 34 (55.7%) had superinfections, with 18 having COVID-19-associated pulmonary aspergillosis (CAPA), 7 having herpes simplex virus (HSV) infection, and 9 having both. Patients with HSV had later diagnoses (median 14 vs. 8 days, p = 0.014), longer mechanical ventilation (median 47 vs. 18.5 days, p = 0.015), and longer ICU stays (median 74 vs. 24 days, p = 0.021) compared to CAPA patients. At baseline, laboratory parameters and treatment (dexamethasone or tocilizumab) showed no significant association with superinfections. Mortality did not differ significantly among groups. Conclusion: In mechanically ventilated COVID-19 patients undergoing bronchoalveolar lavage, HSV reactivation occurred later in the course of illness and was associated with longer mechanical ventilation and ICU stays compared to CAPA. Baseline parameters did not predict superinfections. Full article

Researchers have created cryptography algorithms that encrypt data using a public or private key to secure it from intruders. It is insufficient to protect the data by using such a key. No research article has identified an algorithm capable of protecting both the data and the associated key, nor has any mechanism been developed to determine whether access to the data is permissible or impermissible based on the authentication of the key. This paper presents a WEDEx-Kerberotic Framework for data protection, in which a user-defined key is firstly converted to a cipher key using the “Secure Words on Joining Key (SWJK)” algorithm. Subsequently, a WEDEx-Kerberotic encryption mechanism is created to protect the data by encrypting it with the cipher key. The first reason for making the WEDEx-Kerberotic Framework is to convert the user-defined key into a key that has nothing to do with the original key, and the length of the cipher key is much shorter than the original key. The second reason is that each ciphertext and key value are interlinked. When an intruder utilizes the snatching mechanism to obtain data, the attacker obtains data or a key unrelated to the original data. No matter how efficient the algorithm is, an attacker cannot access the data when these methods and algorithms are used to protect it. Finally, the proposed algorithm is compared to the previous approaches to determine the uniqueness of the algorithm and assess its superiority to the previous algorithms. Full article

Compaction quality significantly influences the strength and durability of concrete in structures. Under-compacting can retain entrapped air, reducing strength, while over-compacting can lead to segregation, creating local variances in strength distribution and modulus of elasticity in the concrete structure. This study examines the widely adopted concept that compaction is optimal when bubbles cease to emerge on the concrete surface. We recorded the surface activity of six comparable concrete specimens during the compaction process using a 4K video camera. Four specimens were compacted using a table vibrator and two with a poker vibrator. From the video frames, we isolated the bubbles for analysis, employing digital image processing techniques to distinguish newly risen bubbles per frame. It was found that the bubbles continuously rose to the surface in all specimens throughout the compaction process, suggesting a need for extended compaction, with some specimens showing a slow in the rate of the bubbles’ emergence. However, upon examining the segregation levels, it was discovered that all the specimens were segregated, some severely, despite the continued bubble emergence. These findings undermine the reliability of using bubble emergence as a principle to stop compaction and support the need for developing online measurement tools for evaluating compaction quality. Full article

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle’s onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle’s onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. Full article

Particle Swarm Optimization (PSO) algorithms within control structures are a realistic approach; their task is often to predict the optimal control values working with a process model (PM). Owing to numerous numerical integrations of the PM, there is a big computational effort that leads to a large controller execution time. The main motivation of this work is to decrease the computational effort and, consequently, the controller execution time. This paper proposes to replace the PSO predictor with a machine learning model that has “learned” the quasi-optimal behavior of the couple (PSO and PM); the training data are obtained through closed-loop simulations over the control horizon. The new controller should preserve the process’s quasi-optimal control. In identical conditions, the process evolutions must also be quasi-optimal. The multiple linear regression and the regression neural networks were considered the predicting models. This paper first proposes algorithms for collecting and aggregating data sets for the learning process. Algorithms for constructing the machine learning models and implementing the controllers and closed-loop simulations are also proposed. The simulations prove that the two machine learning predictors have learned the PSO predictor’s behavior, such that the process evolves almost identically. The resulting controllers’ execution time have decreased hundreds of times while keeping their optimality; the performance index has even slightly increased. Full article

In synthetic aperture radar (SAR) signal processing, compared with the raw data of level-0, level-1 SAR images are more readily accessible and available in larger quantities. However, an amount of level-1 images are affected by radio frequency interference (RFI), which typically originates from Linear Frequency Modulation (LFM) signals emitted by ground-based radars. Existing research on interference suppression in level-1 data has primarily focused on two methods: transforming SAR images into simulated echo data for interference suppression, or focusing interference in the frequency domain and applying notching filters to reduce interference energy. However, these methods overlook the effective utilization of the interference parameters or are confined to suppressing only one type of LFM interference at a time. In certain SAR images, multiple types of LFM interference manifest bright radiation artifacts that exhibit varying lengths along the range direction while remaining constant in the azimuth direction. It is necessary to suppress multiple LFM interference on SAR images when original echo data are unavailable. This article proposes a joint sparse recovery algorithm for interference suppression in the SAR image domain. In the SAR image domain, two-dimensional LFM interference typically exhibits differences in parameters such as frequency modulation rate and pulse width in the range direction, while maintaining consistency in the azimuth direction. Based on this observation, this article constructs a series of focusing operators for LFM interference in SAR images. These operators enable the sparse representation of dispersed LFM interference. Subsequently, an optimization model is developed that can effectively suppress multi-LFM interference and reduce image loss with the assistance of a regularization term in the image domain. Simulation experiments conducted in various scenarios validate the superior performance of the proposed method. Full article

Knee osteoarthritis is the most prevalent type of osteoarthritis. Patients frequently encounter pain triggered by movement that evolves into impaired joint function. Needing persistent rest or having night-time pain signifies advanced disease. Qualitative research is considered the most effective method for comprehending patients’ needs and contexts. Methods: This study employed a qualitative research design, allowing the researchers to acquire insights into the patients’ beliefs and values, and the contextual factors influencing the formation and expression of these beliefs and values. Results: A cohort of nine patients awaiting total knee replacement (TKR) surgery was included and they were interviewed until data saturation was achieved. The results of the phenomenological analysis resulted in the identification of three themes: “The existence of pain impedes the capacity to participate in daily life activities”; “TKR induced fears and uncertainties regarding the progression of the disease”; “Severe nighttime pain compromising sleep quality”. Conclusions: This study analyzes the experiences of people awaiting TKR surgery, emphasizing the importance of addressing their unique needs to improve preoperative education and rehabilitation. In this way, patients’ recovery during the postoperative phase can be improved. Full article

Alcohol consumption, associated with various cancers, mental disorders, and aggressive behavior, leads to three million deaths globally each year. In Brazil, the alcohol per capita consumption among drinkers aged 15 and over is 41.7 g of pure alcohol/day (~1 L beer/day), which falls into the risky consumption category and exceeds the global average by almost 30%. An effective way to mitigate alcohol-related harm is to increase its retail price. This study assesses the costs of consuming leading brands of beer and sugarcane spirit cachaça (Brazil’s most popular alcoholic beverages) against the expenditure on staple foods. Data on food and alcoholic beverage prices were collected in João Pessoa, Brazil, for 2020 and 2021. The cost per gram of pure alcohol and food were considered to establish consumption patterns of 16.8 g/day (moderate), 41.7 g/day, and 83.4 g/day (heavy), distributed in three scenarios involving the beverages alone or combined (64% beer and 36% cachaça), and a balanced 2000 kcal/day staple diet. The study finds that all heavy consumption scenarios cost less or significantly less (cachaça alone) than a 2000 kcal/day staple diet, highlighting an urgent need for fiscal policies, such as a minimum unit pricing for alcohol, to address public health concerns. Full article

 In the realm of accelerated testing within controlled laboratory settings, the fidelity of the service environment assumes paramount importance. It is imperative to replicate real-world conditions while compressing the testing duration to facilitate early evaluations, thereby optimizing time and cost efficiencies. Traditional immersion protocols, reflective solely of full ballast tank conditions, inadequately expedite the corrosion process representative of an average ballast tank environment. Through the integration of immersion with fog/dry conditions, aligning the test protocol more closely with the internal conditions of an average ballast tank, heightened rates of general corrosion are achieved. This augmentation yields an acceleration factor of 7.82 times the standard test duration, under the assumption of a general corrosion rate of 0.4 mm/year for uncoated ballast tank steel, with both sides exposed. Subsequently, the fog/dry test protocol, albeit only resembling the environment of an empty ballast tank, closely trails in terms of acceleration efficacy. The fog/dry test protocol offers cost-effectiveness and replicability compared to the AMACORT CIFD-01 protocol, making it a strong competitor despite the relatively close acceleration factor. Full article

During robot-assisted rehabilitation, failure to recognize lower limb movement may efficiently limit the development of exoskeleton robots, especially for individuals with knee pathology. A major challenge encountered with surface electromyography (sEMG) signals generated by lower limb movements is variability between subjects, such as motion patterns and muscle structure. To this end, this paper proposes an sEMG-based lower limb motion recognition using an improved support vector machine (SVM). Firstly, non-negative matrix factorization (NMF) is leveraged to analyze muscle synergy for multi-channel sEMG signals. Secondly, the multi-nonlinear sEMG features are extracted, which reflect the complexity of muscle status change during various lower limb movements. The Fisher discriminant function method is utilized to perform feature selection and reduce feature dimension. Then, a hybrid genetic algorithm-particle swarm optimization (GA-PSO) method is leveraged to determine the best parameters for SVM. Finally, the experiments are carried out to distinguish 11 healthy and 11 knee pathological subjects by performing three different lower limb movements. Results demonstrate the effectiveness and feasibility of the proposed approach in three different lower limb movements with an average accuracy of 96.03% in healthy subjects and 93.65% in knee pathological subjects, respectively. Full article

In order to increase the utilization rate of stainless steel slag, reduce storage needs, and mitigate environmental impacts, this study replaces a portion of limestone with varying amounts of stainless steel slag in the calcination of Portland cement clinker. The study primarily examines the influence of stainless steel slag on the phase composition, microstructure, compressive strength, and free calcium oxide (ƒ-CaO) content of Portland cement clinker. The results show the following: (1) Using stainless steel slag to calcine Portland cement clinker can lower the calcination temperature, reducing industrial production costs and energy consumption. (2) With an increase in the amount of stainless steel slag, the dicalcium silicate (C₂S) and tricalcium silicate (C₃S) phases in Portland cement clinker initially increase and then decrease; the C₃S crystals gradually transform into continuous hexagonal plate-shaped distributions, while the tricalcium aluminate (C₃A) and tetracalcium aluminoferrite (C₄AF) crystal structures become denser. When the stainless steel slag content is 15%, the dicalcium silicate and tricalcium silicate phases are at their peak; the C₃S crystals are continuously distributed with a relatively dense structure, and C₃A and C₄AF crystals melt and sinter together, becoming distributed around C₃S. (3) As stainless steel slag content increases, the compressive strength of Portland cement clinker at 3 days, 7 days, and 28 days increases and then decreases, while ƒ-CaO content decreases and then increases. When the stainless steel slag content is 15%, the compressive strength at 28 days is at its highest, 64.4 MPa, with the lowest ƒ-CaO content, 0.78%. The test results provide a basis for the utilization of stainless steel slag in the calcination of Portland cement clinker. Full article

The present study aimed to investigate the chemical content of Romanian juneberries (Amelanchier lamarckii), their effect on antioxidant and enzyme inhibition activities, and their bioaccessibility after simulated in-vitro digestion. In Amelanchier lamarckii extract (AME), 16 polyphenolic compounds were identified by LC-ESI+-MS analysis. The most representative compounds found in the extract were cyanidin-galactoside, 3,4-dihydroxy-5-methoxybenzoic acid, feruloylquinic acid, and kaempferol, all belonging to the anthocyanins, phenolic acids, and flavonols subclasses. The polyphenols of AME exert quenching abilities of harmful reactive oxygen species, as the CUPRAC antioxidant assay value was 323.99 µmol Trolox/g fruit (FW), whereas the FRAP antioxidant value was 4.10 μmol Fe²⁺/g fruit (FW). Enzyme inhibition assays targeting tyrosinase (IC50 = 8.843 mg/mL), α-glucosidase (IC50 = 14.03 mg/mL), and acetylcholinesterase (IC50 = 49.55 mg/mL) were used for a screening of AME’s inhibitory potential against these key enzymes as a common approach for the discovery of potential antidiabetic, skin pigmentation, and neurodegenerative effects. The screening for the potential antidiabetic effects due to the α-glucosidase inhibition was performed in glucose-induced disease conditions in a human retinal pigmented epithelial cell experimental model, proving that AME could have protective potential. In conclusion, AME is a valuable source of phenolic compounds with promising antioxidant potential and metabolic disease-protective effects, warranting further investigation for its use in the nutraceutical and health industries. Full article

Since SARS-CoV-2 is a highly transmissible virus, alternative reliable, fast, and cost-effective methods are still needed to prevent virus spread that can be applied in the laboratory and for point-of-care testing. Reverse transcription real-time fluorescence quantitative PCR (RT-qPCR) is currently the gold criteria for detecting RNA viruses, which requires reverse transcriptase to reverse transcribe viral RNA into cDNA, and fluorescence quantitative PCR detection was subsequently performed. The frequently used reverse transcriptase is thermolabile; the detection process is composed of two steps: the reverse transcription reaction at a relatively low temperature, and the qPCR performed at a relatively high temperature, moreover, the RNA to be detected needs to pretreated if they had advanced structure. Here, we develop a fast and sensitive one-tube SARS-CoV-2 detection platform based on Ultra-fast RTX-PCR and Pyrococcus furiosus Argonaute-mediated Nucleic acid Detection (PAND) technology (URPAND). URPAND was achieved ultra-fast RTX-PCR process based on a thermostable RTX (exo-) with both reverse transcriptase and DNA polymerase activity. The URPAND can be completed RT-PCR and PAND to detect nucleic acid in one tube within 30 min. This method can specifically detect SARS-CoV-2 with a low detection limit of 100 copies/mL. The diagnostic results of clinical samples with one-tube URPAND displayed 100% consistence with RT-qPCR test. Moreover, URPAND was also applied to identify SARS-CoV-2 D614G mutant due to its single-nucleotide specificity. The URPAND platform is rapid, accurate, tube closed, one-tube, easy-to-operate and free of large instruments, which provides a new strategy to the detection of SARS-CoV-2 and other RNA viruses. Full article

Nanosatellites are proliferating as low-cost, dedicated remote sensing opportunities for small nations. However, nanosatellites’ performance as remote sensing platforms is impaired by low downlink speeds, which typically range from 1200 to 9600 bps. Additionally, an estimated 67% of downloaded data are unusable for further applications due to excess cloud cover. To alleviate this issue, we propose an image segmentation and prioritization algorithm to classify and segment the contents of captured images onboard the nanosatellite. This algorithm prioritizes images with clear captures of water bodies and vegetated areas with high downlink priority. This in-orbit organization of images will aid ground station operators with downlinking images suitable for further ground-based remote sensing analysis. The proposed algorithm uses Convolutional Neural Network (CNN) models to classify and segment captured image data. In this study, we compare various model architectures and backbone designs for segmentation and assess their performance. The models are trained on a dataset that simulates captured data from nanosatellites and transferred to the satellite hardware to conduct inferences. Ground testing for the satellite has achieved a peak Mean IoU of 75% and an F1 Score of 0.85 for multi-class segmentation. The proposed algorithm is expected to improve data budget downlink efficiency by up to 42% based on validation testing. Full article

In addition to the detrimental health consequences, the early stages of the COVID-19 pandemic have yielded unforeseen benefits in terms of reducing air pollution emissions. This study investigated air pollution changes in Novi Sad, Serbia, during the COVID-19 lockdown (March–June 2020) and their correlation with acute exacerbations of chronic obstructive pulmonary disease (AECOPD) hospitalizations. Using quasi-Poisson generalized linear models (GLM) and distributed lag non-linear models (DLNM), we examined the relationship between the number of AECOPD hospitalizations and the concentrations of selected air pollutants (PM₁₀, PM_2.5, SO₂, and NO₂) from March to June of 2019, 2020, and 2021. During the COVID-19 lockdown, significant reductions in most air pollutant concentrations and the number of AECOPD hospitalizations were observed. However, neither the study year nor its interaction with air pollutant concentration significantly predicted AECOPD hospitalizations (p > 0.05). The 95% confidence intervals of the relative risks for the occurrence of AECOPD hospitalizations at each increase in the examined air pollutant by 10 μg/m³ overlapped across years, suggesting consistent effects of air pollution on the risk of AECOPD hospitalizations pre-pandemic and during lockdown. In conclusion, reduced air pollution emissions during the COVID-19 lockdown did not lead to a statistically significant change in the number of AECOPD hospitalizations. Full article

Acute hypoxemic respiratory failure (AHRF) is defined as acute and progressive, and patients are at a greater risk of developing acute respiratory distress syndrome (ARDS). Until now, most studies have focused on prognostic and diagnostic biomarkers in ARDS. Since there is evidence supporting a connection between dysregulated coagulant and fibrinolytic pathways in ARDS progression, it is plausible that this dysregulation also exists in AHRF. The aim of this study was to explore whether levels of soluble endothelial protein C receptor (sEPCR) and plasminogen differentiate patients admitted to the emergency department (ED) with AHRF. sEPCR and plasminogen levels were measured in 130 AHRF patients upon ED presentation by ELISA. Our results demonstrated that patients presenting to the ED with AHRF had elevated levels of sEPCR and plasminogen. It seems that dysregulation of coagulation and fibrinolysis occur in the early stages of respiratory failure requiring hospitalisation. Further research is needed to fully comprehend the contribution of sEPCR and plasminogen in AHRF. Full article

The profound transformations of traditional rural landscapes have heightened attention towards the recovery and valorisation of their buildings, often abandoned, to accommodate new landscape usage needs. This aligns with the principles of sustainable landscape management. However, knowledge of the rural real estate market remains largely unexplored. This research aims to define and examine the key features influencing the purchase of rural buildings, for shedding light on their market. The objective is to provide useful new insight to the property appraisers and real estate agents involved in the sale of traditional rural buildings, even if in conditions of degradation or abandonment and in traditional landscape contexts. Furthermore, these results could serve as a valuable resource for policymakers, enabling them to indirectly evaluate the impacts of urban and landscape policies on buyers’ preferences regarding key features of rural properties. The research focused on the ‘trulli’, traditional buildings located in the Valle d’Itria (Puglia, Southern Italy). First, a detailed market analysis was carried out with the support of local real estate experts, to survey the transactions of trulli and identify the features influencing their purchase. Second, the obtained dataset was analysed through network analysis, which enabled us to explore the role and importance assigned by buyers to the identified features. The results highlighted that the quality of the landscape where trulli are located changed the buyers’ viewpoint on the purchase features. In greater detail, price, area, potable water accessibility and level of maintenance of trulli were the most crucial features, particularly in high and medium landscape value zones, compatible with touristic and recreational activities. On the other hand, the annex agricultural surface covered a central function in low landscape value zone for possible agricultural uses. Full article