Manganese oxide-cerium oxide supported on titanate nanotubes (i.e., MnCe/TiNTs) were prepared and their catalytic activities towards NH₃-SCR of NO were tested. The results indicated that the MnCe/TiNT catalyst can achieve a high NO removal efficiency above 95% within the temperature range of 150–350 °C. Even after exposure to a HCl-containing atmosphere for 2 h, the NO removal efficiency of the MnCe/TiNT catalyst maintains at approximately 90% at 150 °C. This is attributed to the large specific surface area as well as the unique hollow tubular structure of TiNTs that exposes more Ce atoms, which preferentially react with HCl and thus protect the active Mn atoms. Moreover, the abundant OH groups on TiNTs serve as Brønsted acid sites and provide H protons to expel Cl atom from the catalyst surface. The irreversible deactivation caused by HCl can be alleviated by H₂O. That is because the dissociated adsorption of H₂O on TiNTs forms additional OH groups and relieves HCl poisoning. Full article

Managing cellular networks in the Beyond 5G (B5G) era is a complex and challenging task requiring advanced deep learning approaches. Traditional models focusing on internet traffic (INT) analysis often fail to capture the rich temporal and spatial contexts essential for accurate INT predictions. Furthermore, these models do not account for the influence of external factors such as weather, news, and social trends. This study proposes a multi-source CNN-RNN (MSCR) model that leverages a rich dataset, including periodic, weather, news, and social data to address these limitations. This model enables the capture and fusion of diverse data sources for improved INT prediction accuracy. An advanced deep learning model, the transformer-enhanced CNN-RNN (TE-CNN-RNN), has been introduced. This model is specifically designed to predict INT data only. This model demonstrates the effectiveness of transformers in extracting detailed temporal-spatial features, outperforming conventional CNN-RNN models. The experimental results demonstrate that the proposed MSCR and TE-CNN-RNN models outperform existing state-of-the-art models for traffic forecasting. These findings underscore the transformative power of transformers for capturing intricate temporal-spatial features and the importance of multi-source data and deep learning techniques for optimizing cell site management in the B5G era. Full article

We investigated the impact of surface treatments on Si-based electrolyte-gated transistors (EGTs) for detecting urea. Three types of EGTs were fabricated with distinct gate electrodes (Ag, Au, Pt) using a top-down method. These EGTs exhibited exceptional intrinsic electrical properties, including a low subthreshold swing of 80 mV/dec, a high on/off current ratio of 10⁶, and negligible hysteresis. Three surface treatment methods ((3-amino-propyl) triethoxysilane (APTES) and glutaraldehyde (GA), 11-mercaptoundecanoic acid (11-MUA), 3-mercaptopropionic acid (3-MPA)) were individually applied to the EGTs with different gate electrodes (Ag, Au, Pt). Gold nanoparticle binding tests were performed to validate the surface functionalization. We compared their detection performance of urea and found that APTES and GA exhibited the most superior detection characteristics, followed by 11-MUA and 3-MPA, regardless of the gate metal. APTES and GA, with the highest pKa among the three surface treatment methods, did not compromise the activity of urease, making it the most suitable surface treatment method for urea sensing. Full article

Medical and wellness tourism has emerged as a pivotal sector with significant economic implications globally, especially after the COVID-19 pandemic. This study delves into the landscape of Saudi Arabia’s medical and wellness tourism, recognizing its importance as a key player in the tourism industry. The study aims to elevate this sector to new heights on the global stage by employing the Business Model Canvas (BMC) as a strategic tool. BMC allows for a comprehensive analysis of the medical tourism industry in Saudi Arabia, breaking down key elements across its nine blocks: key partners, key activities, key resources, value propositions, customer segments, channels, customer relationships, cost structure, and revenue streams. The results of this study shed light on the unique selling proposition (USP) as a crucial strategic step for Saudi Arabia to distinguish itself and enhance its position in the international medical tourism arena. By identifying and maximizing the unique aspects within each BMC block, the study presents a roadmap for Saudi Arabia, navigating the challenges and capitalizing on the potential of the medical and wellness tourism sector. This research serves as a guide, emphasizing the strategic importance of a well-defined business model to shape the future of medical and wellness tourism in Saudi Arabia and establish a prominent global presence. Full article

Fungi are eukaryotic organisms with relatively few pathogenic members dangerous for humans, usually acting as opportunistic infections. In the last decades, several life-threatening fungal infections have risen mostly associated with the worldwide extension of chronic diseases and immunosuppression. The available antifungal therapies cannot combat this challenge because the arsenal of compounds is scarce and displays low selective action, significant adverse effects, and increasing resistance. A growing isolation of outbreaks triggered by fungal species formerly considered innocuous is being recorded. From ancient times, natural substances harvested from plants have been applied to folk medicine and some of them recently emerged as promising antifungals. The most used are briefly revised herein. Combinations of chemotherapeutic drugs with natural products to obtain more efficient and gentle treatments are also revised. Nevertheless, considerable research work is still necessary before their clinical use can be generally accepted. Many natural products have a highly complex chemical composition, with the active principles still partially unknown. Here, we survey the field underlying lights and shadows of both groups. More studies involving clinical strains are necessary, but we illustrate this matter by discussing the potential clinical applications of combined carnosic acid plus propolis formulations. Full article

Accurately estimating vegetation biomass in urban forested areas is of great interest to researchers as it is a key indicator of the carbon sequestration capacity necessary for cities to achieve carbon neutrality. The emerging vegetation biomass estimation methods that use AI technologies with remote sensing images often suffer from arge estimating errors due to the diversity of vegetation and the complex three-dimensional terrain environment in urban ares. However, the high resolution of Light Detection and Ranging (i.e., LiDAR) data provides an opportunity to accurately describe the complex 3D scenes of urban forests, thereby improving estimation accuracy. Additionally, deep earning foundation models have widely succeeded in the industry, and show great potential promise to estimate vegetation biomass through processing complex and arge amounts of urban LiDAR data efficiently and accurately. In this study, we propose an efficient and accurate method called 3D-CiLBE (3DCity Long-term Biomass Estimation) to estimate urban vegetation biomass by utilizing advanced deep earning foundation models. In the 3D-CiLBE method, the Segment Anything Model (i.e., SAM) was used to segment single wood information from a arge amount of complex urban LiDAR data. Then, we modified the Contrastive Language–Image Pre-training (i.e., CLIP) model to identify the species of the wood so that the classic anisotropic growth equation can be used to estimate biomass. Finally, we utilized the Informer model to predict the biomass in the ong term. We evaluate it in eight urban areas across the United States. In the task of identifying urban greening areas, the 3D-CiLBE achieves optimal performance with a mean Intersection over Union (i.e., mIoU) of 0.94. Additionally, for vegetation classification, 3D-CiLBE achieves an optimal recognition accuracy of 92.72%. The estimation of urban vegetation biomass using 3D-CiLBE achieves a Mean Square Error of 0.045 kg/m², reducing the error by up to 8.2% compared to 2D methods. The MSE for biomass prediction by 3D-CiLBE was 0.06kg/m² smaller on average than the inear regression model. Therefore, the experimental results indicate that the 3D-CiLBE method can accurately estimate urban vegetation biomass and has potential for practical application. Full article

We explore an idea put forward many years ago by Zeldovich and Novikov concerning the existence of compact objects endowed with arbitrarily small mass. The energy density of such objects, which we call “ghost stars”, is negative in some regions of the fluid distribution, producing a vanishing total mass. Thus, the interior is matched on the boundary surface to Minkowski space–time. Some exact analytical solutions are exhibited and their properties are analyzed. Observational data that could confirm or dismiss the existence of this kind of stellar object are discussed. Full article

Widespread adoption of mpMRI has led to a decrease in the number of patients requiring prostate biopsies. ⁶⁸Ga-PSMA-11 PET/CT has demonstrated added benefits in identifying csPCa. Integrating the use of these imaging techniques may hold promise for predicting the presence of csPCa without invasive biopsy. A retrospective analysis of 42 consecutive patients who underwent mpMRI, ⁶⁸Ga-PSMA-11 PET/CT, prostatic biopsy, and radical prostatectomy (RP) was carried out. A lesion-based model (n = 122) using prostatectomy histopathology as reference standard was used to analyze the accuracy of ⁶⁸Ga-PSMA-11 PET/CT, mpMRI alone, and both in combination to identify ISUP-grade group ≥ 2 lesions. ⁶⁸Ga-PSMA-11 PET/CT demonstrated greater specificity and positive predictive value (PPV), with values of 73.3% (vs. 40.0%) and 90.1% (vs. 82.2%), while the mpMRI Prostate Imaging Reporting and Data System (PI-RADS) 4–5 had better sensitivity and negative predictive value (NPV): 90.2% (vs. 78.5%) and 57.1% (vs. 52.4%), respectively. When used in combination, the sensitivity, specificity, PPV, and NPV were 74.2%, 83.3%, 93.2%, and 51.0%, respectively. Subgroup analysis of PI-RADS 3, 4, and 5 lesions was carried out. For PI-RADS 3 lesions, ⁶⁸Ga-PSMA-11 PET/CT demonstrated a NPV of 77.8%. For PI-RADS 4–5 lesions, ⁶⁸Ga-PSMA-11 PET/CT achieved PPV values of 82.1% and 100%, respectively, with an NPV of 100% in PI-RADS 5 lesions. A combination of ⁶⁸Ga-PSMA-11 PET/CT and mpMRI improved the radiological diagnosis of csPCa. This suggests that avoidance of prostate biopsy prior to RP may represent a valid option in a selected subgroup of high-risk patients with a high suspicion of csPCa on mpMRI and ⁶⁸Ga-PSMA-11 PET/CT. Full article

This paper presents the reconstruction framework of the Roman mosaic with the central scene from the abduction of Europa. The mosaic depicting Europa, discovered in Ptuj (Slovenia) and dated from the second half of the third to the beginning of the fourth century AD, once decorated the representative room of a Roman villa. The experimental section addresses the materials and methods used in the 2D reconstruction of the mosaic, including the creation of line drawings of the mosaic based on the preserved part of the mosaic, photogrammetric acquisition, and the creation and processing of 1:1 raster reconstructions of the entire mosaic. This is followed by color management and interpretation approaches which allow the mosaic elements to be implemented in a 3D animation. The presented approaches could be implemented in the reconstruction process of other mosaics and archaeological objects with adaptations to the specifics of related objects. Full article

Iron redox flow batteries (IRFBs) are cost-efficient RFBs that have the potential to develop low-cost grid energy storage. Electrode kinetics are pivotal in defining the cycle life and energy efficiency of the battery. In this study, graphite felt (GF) is heat-treated at 400, 500 and 600 °C, and its physicochemical and electrochemical properties are studied using XPS, FESEM, Raman and cyclic voltammetry. Surface morphology and structural changes suggest that GF heat-treated at 500 °C for 6 h exhibits acceptable thermal stability while accessing the benefits of heat treatment. Specific capacitance was calculated for assessing the wettability and electrochemical properties of pristine and treated electrodes. The 600 °C GF has the highest specific capacitance of 34.8 Fg⁻¹ at 100 mV s⁻¹, but the 500 °C GF showed the best battery performance. The good battery performance of the 500 °C GF is attributed to the presence of oxygen functionalities and the absence of thermal degradation during heat treatment. The battery consisting of 500 °C GF electrodes offered the highest voltage efficiency of ~74%, Coulombic efficiency of ~94%, and energy efficiency of ~70% at 20 mA cm⁻². Energy efficiency increased by 7% in a battery consisting of heat-treated GF in comparison to pristine GF. The battery is capable of operating for 100 charge–discharge cycles with an average energy efficiency of ~ 67% for over 100 cycles. Full article

This study aimed to investigate the role of the thymus in influencing long-term outcomes of COVID-19 by comparing the thymic appearance in patients with and without COVID-19 pulmonary sequelae at chest computed tomography (CT). A total of 102 adult patients previously hospitalized for COVID-19 underwent a follow-up chest CT three months after discharge. Pulmonary sequelae and thymic appearance were independently assessed by two experienced radiologists. The thymus was detectable in 55/102 patients (54%), with only 7/55 (13%) having any kind of pulmonary sequelae, compared to 33 out of 47 (70%, p < 0.001) in patients without thymic visibility, as confirmed in age-stratified analysis and at logistic regression analysis, where thymic involution had a 9.3 odds ratio (95% CI 3.0–28.2, p < 0.001) for the development of pulmonary sequelae. These results support the hypothesis that thymic reactivation plays a protective role against adverse long-term outcomes of COVID-19. Full article

Two plates of nanostructured ferritic alloy NFA-1 were processed by ball milling atomized Fe-14Cr-3W-0.4Ti-0.2Y (wt.%) with FeO powders, canning, and hot-extrusion at 850 °C, followed by annealing and multipass cross-rolling at 1000 °C. This produces a severe (001) brittle cleavage texture on planes running parallel to the plate faces. In the first plate (P1), pre-existing microcracks (MCs) formed on the cleavage planes during cross-rolling. The second plate (P2) contained far fewer, if any, MCs. Here, we compare the tensile data for out-of-plane (S) and in-plane (L) tensile axis orientations, at temperatures from −196 °C to 800 °C. We also assess the tensile property differences between P1 and P2, and the effect of specimen size. The L-orientation strength and ductility were excellent; for example, the room temperature (RT) yield stress, σ_y ≈ 1042 ± 102 MPa, and the total elongation, ε_t ≈ 12.9 ± 1.5%. In contrast, the S-orientation RT σ_y ≈ 708 ± 57 MPa, and ε_t ≤ 0.2%. These differences were due to cleavage on the brittle (001) planes. Cleavage leads to beneficial delamination toughening, but is deleterious to deformation processing and through-wall heat transfer. Therefore, it is important to quantitatively characterize the pronounced NFA-1 strength anisotropy due to severe crystallographic texturing and cleavage fracture. Full article

Spinal metastasis is exceedingly common in patients with cancer and its prevalence is expected to increase. Surgical management of symptomatic spinal metastasis is indicated for pain relief, preservation or restoration of neurologic function, and mechanical stability. The overall prognosis is a major driver of treatment decisions; however, clinicians’ ability to accurately predict survival is limited. In this narrative review, we first discuss the NOMS decision framework used to guide decision making in the treatment of patients with spinal metastasis. Given that decision making hinges on prognosis, multiple scoring systems have been developed over the last three decades to predict survival in patients with spinal metastasis; these systems have largely been developed using expert opinions or regression modeling. Although these tools have provided significant advances in our ability to predict prognosis, their utility is limited by the relative lack of patient-specific survival probability. Machine learning models have been developed in recent years to close this gap. Employing a greater number of features compared to models developed with conventional statistics, machine learning algorithms have been reported to predict 30-day, 6-week, 90-day, and 1-year mortality in spinal metastatic disease with excellent discrimination. These models are well calibrated and have been externally validated with domestic and international independent cohorts. Despite hypothesized and realized limitations, the role of machine learning methodology in predicting outcomes in spinal metastatic disease is likely to grow. Full article

Air pollution, especially ground-level ozone, poses severe threats to human health and ecosystems. Accurate forecasting of ozone concentrations is essential for reducing its adverse effects. This study aims to use the functional time series approach to model ozone concentrations, a method less explored in the literature, and compare it with traditional time series and machine learning models. To this end, the ozone concentration hourly time series is first filtered for yearly seasonality using smoothing splines that lead us to the stochastic (residual) component. The stochastic component is modeled and forecast using a functional autoregressive model (FAR), where each daily ozone concentration profile is considered a single functional datum. For comparison purposes, different traditional and machine learning techniques, such as autoregressive integrated moving average (ARIMA), vector autoregressive (VAR), neural network autoregressive (NNAR), random forest (RF), and support vector machine (SVM), are also used to model and forecast the stochastic component. Once the forecast from the yearly seasonality component and stochastic component are obtained, both are added to obtain the final forecast. For empirical investigation, data consisting of hourly ozone measurements from Los Angeles from 2013 to 2017 are used, and one-day-ahead out-of-sample forecasts are obtained for a complete year. Based on the evaluation metrics, such as $R^2$, root mean squared error (RMSE), and mean absolute error (MAE), the forecasting results indicate that the FAR outperforms the competitors in most scenarios, with the SVM model performing the least favorably across all cases. Full article

Due to the remarkable growth in online retail sales in New Zealand, a large number of parcels are needed to be delivered to consumers’ doorsteps. Home deliveries in major New Zealand cities (e.g., Christchurch) typically occur between 9 a.m. and 6 p.m. on weekdays, when many home delivery attempts fail. This leads to adverse effects, such as vehicular traffic in residential areas and greater air pollution per parcel delivered. However, home deliveries outside of typical business hours (i.e., before 9 a.m. and after 5 p.m.) might be worthwhile to help subside the above issues. Therefore, this study investigated consumers’ preferences for receiving home deliveries during various times, such as early morning, morning, afternoon, late afternoon, and evening. The data used in this study were obtained via an online survey of 355 residents of Christchurch city. Non-parametric tests, namely the Friedman test, Wilcoxon signed-rank test, and ordinal logistic regression, were carried out to examine consumer preferences for the above time slots. The results showed that consumers preferred the late afternoon (3 p.m. to 6 p.m.) time slot the most for receiving home deliveries. It appeared that the off-peak delivery option is less likely to draw the desired consumer patronage and is thus less likely to assist in lowering the number of unsuccessful home deliveries, the transportation costs incurred by service providers, traffic congestion, and pollution in urban areas. Full article

Synodality, as a determinant of the mentality and style of pastoral activity, has proven to be one of the key themes of Church life in the last few years. The synodal dimension of the Church is seen as the fundamental backbone of all the other important components of pastoral engagement. Religious life and practice of adult believers, on the other hand, remains one of the most challenging pastoral tasks. This claim is so current that concerning many church environments one can legitimately ask whether a mature and developed practice of faith exists at all. The synodal process that is ongoing in the Catholic Church, especially until the fall of 2024, helps to improve so many pastoral activities and most of them concern the practice of faith of adult believers. This paper is dedicated to the theological–pastoral study of the main components of the improvement of this practice in light of the contributions of the synodal process. Full article

We analyze the tight focusing of a generalized Poincaré beam using a Richards–Wolf formalism. Conventional Poincaré beams are superpositions of two Laguerre–Gaussian beams with orthogonal polarization, while the generalized Poincaré beams are composed of two arbitrary optical vortices with rotationally symmetric amplitudes. Analytical relationships for projections of the electric field in the focal plane are derived. Using the superposition of a right-handed circularly polarized plane wave and an optical vortex with a topological charge of −1 as an example, relationships for the intensity distribution and the longitudinal projection of the spin angular momentum vector are deduced. It is theoretically and numerically shown that the original beam has a topological charge of –1/2 and a C-point of circular polarization, and it is generated at the focal plane center, producing an on-axis C-line with a singularity index of –1/2 (a star). Furthermore, when making a full circle of some radius around the optical axis, the major axis vector of polarization ellipse is theoretically and numerically shown to form a one-sided polarization (Möbius) strip of order −3/2, which has three half-twists and a single ‘patching’ in which two oppositely directed vectors of the major axis of polarization ellipse occur close to each other. Full article

Mechanical seals ensure the internal sealing of centrifugal pumps from the surrounding environment. They are one of the most critical components in a centrifugal pump. For this reason, the condition of mechanical seals should be monitored during operation. Mechanical seal friction power is an important component of mechanical losses in centrifugal pumps and is used as an indicator of wear and therefore seal condition. The soft sensor described in this paper is based on temperature measurements at the seal and can be used for determining the frictional power performance. A major factor in determining frictional power performance is the heat transfer between the mechanical seal and the medium inside the pump. For calculating the heat transfer, the stationary temperature fields in the rings of the mechanical seal are described by transmission efficiencies. The root mean squared error was determined for steady-state operating conditions to assess the quality of the soft sensor calculation. The frictional power performance can be determined by recording the temperature at the mechanical seal mating ring and the medium. The algorithm detects when the steady-state operating conditions change but does not map the dynamic changes between the stationary operating conditions. Full article

The G-modified Helmholtz equation is a partial differential equation that enables us to express gravity intensity g as a series of spherical harmonics having radial distance r in irrational powers. The Laplace equation in three-dimensional space (in Cartesian coordinates, is the sum of the second-order partial derivatives of the unknown quantity equal to zero) is used to express the Earth’s gravity potential (disturbing and normal potential) in order to represent other useful quantities—which are also known as functionals of the disturbing potential—such as gravity disturbance, gravity anomaly, and geoid undulation as a series of spherical harmonics. We demonstrate that by using the G-modified Helmholtz equation, not only gravity intensity but also disturbing potential and its functionals can be expressed as a series of spherical harmonics. Having gravity intensity represented as a series of spherical harmonics allows us to create new Global Gravity Models. Furthermore, a more detailed examination of the Earth’s isogravitational surfaces is conducted. Finally, we tabulate our results, which makes it clear that new Global Gravity Models for gravity intensity g will be very useful for many geophysical and geodetic applications. Full article

As the core component of the wind turbine generation gearbox, the gear-rotor-bearing transmission system typically operates in harsh environments, inevitably leading to the occurrence of composite faults in the system, which exacerbates system vibration. Therefore, it is necessary to study the vibration characteristics of wind turbine helical gear-rotor-bearing transmission systems with composite faults. This paper uses an improved energy method to calculate the theoretical time-varying mesh stiffness of a helical gear with a root crack failure. On the premise of considering the time-varying meshing stiffness of the faulty helical gear, the gear eccentric fault, and the nonlinear support force of the faulty bearing, a multi-degree-of-freedom helical gear-rotor-bearing transmission system with compound faults was established by using the lumped parameter method. The dynamic model of the system was solved based on the Runge–Kutta method, and the vibration response of the system under healthy conditions, single faults with gear eccentricity, single faults with tooth root cracks, and coupled bearing composite faults were simulated and analyzed. The results show that the simulation results based on KISSsoft software 2018 version verify the effectiveness of the improved energy method; the existence of single faults and composite faults will cause the fault characteristics in the time domain and frequency domain responses. In this paper, the influence of a single fault and a complex fault on the time domain and frequency domain of the system is mainly discovered through the fault study of the helical rotor-bearing system, and the influence of the fault degree on the vibration of the gear motion system is discussed. The greater the degree of the fault, the more vibration of the system occurs; accordingly, when the system is under the coupling of tooth root crack and bearing fault, there is a significant difference compared with the healthy system and the single fault system. The system vibration has obvious time domain and frequency domain signal characteristics, including periodic pulse impacts caused by gear faults and time domain impact caused by bearing. The fault characteristic frequencies can also be found in the frequency domain. In this paper, the fault study of a helical gear of wind turbine generation provides a reference for the theoretical analysis of the vibration characteristics of the helical gear-rotor-bearing system under various fault conditions, lays a solid foundation for the simulation and subsequent diagnosis of the composite fault signal of the system, and provides help for the fault diagnosis of wind turbine gearboxes in the future. Full article

Oleogels, which are traditionally utilized to reduce saturated and trans fats in bakery foods, have recently shown promising applications in non-bakery foods, particularly in the enhancement of their food texture and cooking qualities. This study investigates the impact of incorporating stearic acid-containing candelilla wax–groundnut oil oleogel in various proportions on the production of whole wheat pasta. Five different pasta samples were prepared by replacing water with oleogels in varying concentrations (2.5%, 5%, 10%, and 15%), and their physicochemical attributes were evaluated using a range of analytical methods for both cooked and uncooked pasta (like microscopy, colorimetry, dimensional analysis, texture, cooking qualities, moisture content, and FTIR). Significant differences in width, thickness, and color properties were observed between the control sample (0% oleogel) and those containing oleogel, with notable variations in surface texture and color intensities, particularly with the higher oleogel content (p < 0.05). Cooked pasta exhibited lower L* values and higher a* values than uncooked pasta. Stereo zoom microscope and field emission scanning electron microscope (FESEM) micrographs demonstrated a change in the pasta surface topology and microstructures. Dark spots on the pasta with greater oleogel concentrations (samples with 10% and 15% oleogel replacement) suggest the formation of starch–lipid complexes. Cooking induced pore formation, which was more pronounced when the oleogel content was increased, impacted the water absorption capacity, swelling index, and moisture content. The cooked samples exhibited higher moisture content and improved polymer network stability compared to the uncooked ones, indicating the potential of oleogel incorporation to modulate pasta properties in a concentration-dependent manner. These findings underscore the versatility of oleogels when their applications are diversified in non-bakery foods to enhance food texture and quality. Full article