Purines are the building blocks of DNA/RNA, energy substrates, and cofactors. Purine metabolites, including ATP, GTP, NADH, and coenzyme A, are essential molecules in diverse biological processes such as energy metabolism, signal transduction, and enzyme activity. When purine levels increase, excess purines are either recycled to synthesize purine metabolites or catabolized to the end product uric acid. Purine catabolism increases during states of low oxygen tension (hypoxia and ischemia), but this metabolic pathway is incompletely understood in the context of histotoxic hypoxia (i.e., inhibition of oxygen utilization despite normal oxygen tension). In rabbits exposed to cyanide—a classical histotoxic hypoxia agent—we demonstrated significant increases in several concordant metabolites in the purine catabolic pathway (including plasma levels of uric acid, xanthosine, xanthine, hypoxanthine, and inosine) via mass spectrometry-based metabolite profiling. Pharmacological inhibition of the purine catabolic pathway with oxypurinol mitigated the deleterious effects of cyanide on skeletal muscle cytochrome c oxidase redox state, measured by non-invasive diffuse optical spectroscopy. Finally, plasma uric acid levels correlated strongly with those of lactic acid, an established clinical biomarker of cyanide exposure, in addition to a tissue biomarker of cyanide exposure (skeletal muscle cytochrome c oxidase redox state). Cumulatively, these findings not only shed light on the in vivo role(s) of cyanide but also have implications in the field of medical countermeasure (MCM) development. Full article

Objectives: This study aims to develop and validate the Serious Educational Game in Nursing Appraisal Scale (SEGiNAS), a tool designed to evaluate the implementation of serious games within nurse education contexts of quantity of process, quality of process, and learning outcomes. Methods and Materials: This methodological and psychometric study aimed to develop and validate a scale. The item generation phase was based on the cognitive theory of multimedia learning, resulting in a 20-item scale. The validation phase involved evaluating the psychometric scale by surveying 160 Portuguese nurses. Results: A factor analysis revealed a three-factor structure corresponding to the scale’s designed dimensions, explaining a total variance of 64.5%. The scale demonstrated high internal consistency for all factors, including engagement and teaching effectiveness (0.925), learning impact and practical application (0.883), and content relevance and clarity (0.848). The dimensions were engagement and teaching effectiveness, learning impact and practical application, and content relevance and clarity. Conclusions: The SEGiNAS scale represents a valid and reliable tool for evaluating serious games in nursing education. Its development fills an existing gap in assessing the teaching–learning process with serious games. This study was not registered. Full article

In cation–anion co-doping, rare earth elements excel at regulating the electronic structure of perovskites, leading to their improved photocatalytic performance. In this regard, the impact of co-doping rare earth elements at the Ba and Ti sites in BaTiO₃ on its electronic and photocatalytic properties was thoroughly investigated based on 2 × 2 × 2 supercell structures of BaTiO₃ with different La concentrations of 12.5% and 25% using DFT calculations. The band structure, density of states, charge density difference, optical properties, and the redox band edge of the co-doped models mentioned above were analyzed. The results indicated that the BaTiO₃ structure co-doped with 25% La at the Ti site exhibited higher absorption in the visible range and displayed a remarkable photocatalytic water-splitting performance. The introduced La dopant at the Ti site effectively reduced the energy required for electronic transitions by introducing intermediate energy levels within the bandgap. Our calculations and findings of this study provide theoretical support and reliable predictions for the exploration of BaTiO₃ perovskites with superior photocatalytic performances. Full article

This study aims to present a comparative analysis of the Bayesian regularization backpropagation and Levenberg–Marquardt training algorithms in neural networks for the metrics prediction of damaged archaeological artifacts, of which the state of conservation is often fragmented due to different reasons, such as ritual, use wear, or post-depositional processes. The archaeological artifacts, specifically laminar blanks (so-called blades), come from different sites located in the Southern Levant that belong to the Pre-Pottery B Neolithic (PPNB) (10,100/9500–400 cal B.P.). This paper shows the entire procedure of the analysis, from its normalization of the dataset to its comparative analysis and overfitting problem resolution. Full article

This paper deals with the possibility of smelting quasi-high-entropy alloys (QHEAs) with the partial use of ferroalloys in the charge instead of pure metals. The Cantor alloy (CoCrFeMnNi) was used as the base alloy and the comparison sample, into which niobium was introduced in the amount of 14 to 18% by weight. The structure, hardness, strength, and tribological properties of prototypes were studied. The results obtained showed, on the one hand, the possibility of using ferroalloys as charge components in the smelting of QHEAs and, on the other hand, the positive effect of niobium in the amount of 14–17% on the strength and wear resistance of the alloy. Increasing the niobium content above 18% leads to its uneven distribution in the structure, consequently decreasing the strength and wear resistance of the alloy. The structure of the studied alloys is represented by a solid solution of FCC, which includes all metals, and the niobium content varies widely. In addition, the structure is represented by the phases of implementation: niobium carbide NbC 0.76–1.0, manganese carbide Mn₇C₃, and a CrNi intermetallic compound with a cubic lattice. Full article

Accurate photovoltaic (PV) power prediction plays a crucial role in promoting energy structure transformation and reducing greenhouse gas emissions. This study aims to improve the accuracy of PV power generation prediction. Extreme learning machine (ELM) was used as the core model, and enhanced and improved beluga whale optimization (EIBWO) was proposed to optimize the internal parameters of ELM, thereby improving its prediction accuracy for PV power generation. Firstly, this study introduced the chaotic mapping strategy, sine dynamic adaptive factor, and disturbance strategy to beluga whale optimization, and EIBWO was proposed with high convergence accuracy and strong optimization ability. It was verified through standard testing functions that EIBWO performed better than comparative algorithms. Secondly, EIBWO was used to optimize the internal parameters of ELM and establish a PV power prediction model based on enhanced and improved beluga whale optimization algorithm–optimization extreme learning machine (EIBWO-ELM). Finally, the measured data of the PV output were used for verification, and the results show that the PV power prediction results of EIBWO-ELM were more accurate regardless of whether it was cloudy or sunny. The R2 of EIBWO-ELM exceeded 0.99, highlighting its efficient ability to adapt to PV power generation. The prediction accuracy of EIBWO-ELM is better than that of comparative models. Compared with existing models, EIBWO-ELM significantly improves the predictive reliability and economic benefits of PV power generation. This study not only provides a technological foundation for the optimization of intelligent energy systems but also contributes to the sustainable development of clean energy. Full article

Modern UAVs (unmanned aerial vehicles) equipped with video cameras can provide large-scale high-resolution video data. This poses significant challenges for structure from motion (SfM) and simultaneous localization and mapping (SLAM) algorithms, as most of them are developed for relatively small-scale and low-resolution scenes. In this paper, we present a video-based SfM method specifically designed for high-resolution large-size UAV videos. Despite the wide range of applications for SfM, performing mainstream SfM methods on such videos poses challenges due to their high computational cost. Our method consists of three main steps. Firstly, we employ a visual SLAM (VSLAM) system to efficiently extract keyframes, keypoints, initial camera poses, and sparse structures from downsampled videos. Next, we propose a novel two-step keypoint adjustment method. Instead of matching new points in the original videos, our method effectively and efficiently adjusts the existing keypoints at the original scale. Finally, we refine the poses and structures using a rotation-averaging constrained global bundle adjustment (BA) technique, incorporating the adjusted keypoints. To enrich the resources available for SLAM or SfM studies, we provide a large-size (3840 × 2160) outdoor video dataset with millimeter-level-accuracy ground control points, which supplements the current relatively low-resolution video datasets. Experiments demonstrate that, compared with other SLAM or SfM methods, our method achieves an average efficiency improvement of 100% on our collected dataset and 45% on the EuRoc dataset. Our method also demonstrates superior localization accuracy when compared with state-of-the-art SLAM or SfM methods. Full article

Understanding the effects of various forest management systems, including logging practices, on soil properties is essential for implementing sustainable management strategies. In Malaysia, two types of forest management systems were commonly used: Malayan Uniform System (MUS) and Selective Management System (SMS) practices. However, their effects on soil quality remained elusive, especially after decades of recovery. To address this need, we selected three plots for the MUS and SMS in Johor, Malaysia, to assess soil properties in logged-over forest plots. All the plots were natural forest reserves. Soil properties analyzed include soil acidity, electrical conductivity, cation exchange capacity, selected nutrient contents, and soil compaction. Generally, the results of the study indicate that forests logged using the SMS exhibit superior soil quality compared to those logged using the MUS according to several key soil properties. Specifically, significantly higher cation exchange capacity, potassium content, calcium content, and magnesium content with lower soil compaction was observed in the SMS when compared to MUS plots. In short, the SMS enhances soil quality more effectively than the MUS, even with a shorter logging cycle. This is because the SMS does not harvest all trees and distributes the impact of harvesting more evenly over time, rather than concentrating it at a single time point. Ultimately, this highlights that the SMS can play a significant role in promoting sustainable forest management practices by preserving soil quality. Full article

Darier disease (DD) is an autosomal dominant disorder due to pathogenic variants of the ATP2A2 gene that causes an isolated skin manifestation based on keratinocyte disconnection and apoptosis. Systemic manifestations of DD have not been demonstrated so far, although a high incidence of neuropsychiatric syndromes suggests an involvement of the central nervous system. We report that the pathogenic ATP2A2 gene variant c.118G>A may cause cardiac involvement in patients with DD, consisting of keratinocyte and cardiomyocyte disconnection. Their common pathologic pathway, still unreported, was documented by both skin and left ventricular endomyocardial biopsies because cardiac dilatation and dysfunction appeared several decades after skin manifestations. Keratinocyte disconnection was paralleled by cardiomyocyte separation at the lateral junction. Cardiomyocyte separation was associated with cell disarray, sarcoplasmic reticulum dilatation, and increased myocyte apoptosis. Clinically, hyperkeratotic skin papules are associated with chest pain, severe muscle exhaustion, and ventricular arrhythmias that improved following administration of aminophylline, a phosphodiesterase inhibitor enhancing SERCA2 protein phosphorylation. Cardiac pathologic changes are similar to those documented in the skin, including cardiomyocyte disconnection that promotes precordial pain and cardiac arrhythmias. Phosphodiesterase inhibitors that enhance SERCA2 protein phosphorylation may substantially attenuate the symptoms. Full article

The building sector contributes significantly to global final energy consumption and energy-related CO${}_2$ emissions. The demand for sustainable and energy-efficient passive buildings with a minimal ecological footprint has increased due to the global energy crisis, climate change, and environmental concerns. This need can be met by constructing passive buildings. However, to develop a building that is truly passive, it is required to meet many passive house conditions, negligible for typical buildings, which increase the project complexity and pose challenges and risks threatening its successful completion. The aim of this work is to present the findings from a quantitative risk analysis in passive construction based on the results of expert surveys that were carried out using a Computer-Assisted Web Interview. Feedback from expert surveys covering the experience of 748 passive buildings projects from seven countries (Poland, Germany, Great Britain, the United States, Australia, Spain, and Austria) allowed us to access the frequency of occurrence, severity, detectability, and Risk Priority Numbers of the 32 risk factors identified in passive buildings projects. Those risk factors were identified based on literature research, risk interviews, scenario analysis, brainstorm sessions with passive buildings specialists, and our own observations of passive buildings projects. This study revealed that incorrect costing was the most frequent issue; complicated, non-compact building shapes with an unfavorable area-to-volume ratio had the highest severity of effects; the wrong interpretation of correctly prepared drawings and details obtained from the designer had the lowest detectability; and incorrect costing had the highest Risk Priority Number. In addition, this study allowed us to identify a narrow group of critical risk factors that are the most significant (have the highest RPN) and to which special attention should be paid in the risk-management process. Full article

This study investigates the association between the compensation of Remuneration Committee Chairpersons (RCCs) and their characteristics. Utilizing data from firms listed on the UK FTSE350 index between 2010 and 2020, the research unveils that RCC remuneration is influenced by factors such as observable efforts, time commitment, and accumulated experience. Notably, the analysis reveals a substantial gender gap in RCCs' pay. The results suggest that the contractual pricing of individual director-level attributes plays a role in explaining disparities in compensation for roles with similar responsibilities. Furthermore, the study sheds light on the intricate process of determining compensation within the directorial hierarchy. It delves into how differences in pay among individuals occupying similar positions across various companies can be elucidated by the distinct attributes and qualifications of each individual. Ultimately, the findings advocate for a nuanced examination of directorial roles, highlighting the necessity of distinguishing between different director roles rather than treating them as a homogeneous entity. Full article

The agriculture industry in the Western world is increasingly using biofertilizers, considering the environmental aspects and organic food. Sustainability in agriculture is the primary priority of the government of the Sultanate of Oman. In order to improve and develop the agricultural sector for community development, the government of Oman is paying special attention to its Vision 2040 in line with sustainable development goals. Hence, the aim of the research is to analyze the behavioral aspects of farmers and farmholders towards utilizing biofertilizers for saving the environment as well as providing organic food and bringing sustainability to the agriculture sector of the country. In order to meet the objectives of the study mixed method research has been used. An interview guide has been developed, a questionnaire has also been developed, and the instruments have been approved by the experts. The interview data were analyzed, and afterward, primary data were collected. To test the hypothesis and the framework, Smart PLS 3 has been used. The findings identified that farmers in Oman are reluctant to use biofertilizers because of a lack of awareness, but yet they are using it up to some extent and the proposed model has proven to be significant. The findings are useful not only for the policymakers but also for the practitioners who can obtain guidance about the benefits they can gain from the use of biofertilizers. Full article

Power shift tractors have been widely used in agricultural tractors in recent years because of their advantages of uninterrupted power during shifting, high transmission efficiency and high stability. As one of the indispensable driving states of the power shift tractor, the starting process requires a small impact and a starting speed that meets the driver’s requirements. In this paper, aiming at such contradictory requirements, the starting control strategy of a power shift tractor is formulated with the goal of starting quality and the driver’s intention. Firstly, the identification characteristics of the driver under three starting intentions are obtained by a real vehicle test. An extreme learning machine with fast identification speed and short training time is used to establish the basic driver’s intention identification model. For the instability of the identification results of the Extreme Learning Machine (ELM), the particle swarm optimization algorithm (PSO) is used to optimize the ELM. The optimized extreme learning machine model has an accuracy of 96.891% for driver’s intention identification. The wet clutch is an important part of the power shift gearbox. In this paper, the starting control strategy knowledge base of the starting clutch is established by a combination of bench tests and simulation tests. Through the fuzzy algorithm, the driver’s intention is combined with the starting control strategy. Different drivers’ intentions will affect the comprehensive evaluation model of the clutch (the single evaluation index of the clutch is: the maximum sliding power, the sliding power, the speed stability time, the impact degree), thus affecting the final choice of the starting clutch control strategy considering the driver’s intention. On this basis, this paper studies and establishes the MPC starting controller for the power shift gearbox. Compared with the linear control strategy, the PSO-ELM-fuzzy weight starting strategy proposed in this paper can reduce the maximum sliding friction power by 45%, the sliding friction power by 69.45%, and the speed stabilization time by 0.11 s. The effectiveness of the starting control strategy considering the driver’s intention proposed in this paper to improve the starting quality of the power shift tractor is verified. Full article

This work presents results on laser-induced surface structuring of AlN ceramic and its application in Surface-Enhanced Raman Spectroscopy (SERS). The laser processing is performed by nanosecond pulses in air and vacuum. Depending on the processing conditions, different surface morphology can be obtained. The ablation process is realized by ceramic decomposition as the formation of an aluminium layer is detected. The efficiency of the fabricated structures as active substrates in SERS is estimated by the ability of the detection of ammonium nitrate (NH₄NO₃). It is conducted for Raman spectrometer systems that operate at wavelengths of 514 and 785 nm where the most common commercial systems work. The obtained structures contribute to enhancement of the Raman signal at both wavelengths, as the efficiency is higher for excitation at 514 nm. The limit of detection (LOD) of ammonium nitrate is estimated to be below the maximum allowed value in drinking water. The analysis of the obtained results was based on the calculations of the near field enhancement at different conditions based on Finite Difference Time Domain simulation and the extinction spectra calculations based on Generalized Mie scattering theory. The structures considered in these simulations were taken from the SEM images of the real samples. The oxidation issue of the ablated surface was studied by X-ray photoelectron spectroscopy. The presented results indicated that laser structuring of AlN ceramics is a way for fabrication of Al structures with specific near-field properties that can be used for the detection of substances with high social impact. Full article

The antioxidant properties of butterfly pea flower (BF), which is rich in natural anthocyanins, have garnered significant attention. The impact of digestion and metabolism on BF extracts and evaluate their subsequent antioxidant activities in vivo were explored in the present study. After in vitro digestion, 42.03 ± 2.74% of total anthocyanins from BF extracts remained, indicating a negative influence of the digestion process on the bioaccessibility of phenolic compounds derived from BF. Furthermore, UPLC-LTQ-Orbitrap-MS² analysis identified a total of four prototypes and twenty-seven metabolites in rat plasma or urine samples following the intake of BF extracts. The kinetics of key metabolites including delphinidin 3-glucoside (D3G), cyanidin-3-glucoside (C3G), and 4-hydroxybenzoic acid were subsequently determined in blood, and the Cmax values were 69.034 ± 8.05 nM and 51.65 ± 3.205 nM. These key metabolites derived from BF anthocyanins, including C3G and D3G, and flavonoid quercetin exhibited main antioxidant attributes that improved the plasmic and hepatic activities of various antioxidant enzymes and the total antioxidant capacity (T-AOC) and malondialdehyde (MDA) in a D-galactose-induced rat model. These findings provide insights into the bioaccessibility and bioavailability of bioactive constitutes derived from BF extracts, which are crucial for determining the actual efficacy of BF as well as developing functional foods based on BF. Full article

There is a nationwide effort to increase the representation and engagement of minoritized students in computer science education. Discourse about the barriers to diversity among computer science majors is often characterized by student pathologies and does not consider the impacts of classroom culture and instructor pedagogies. Amid the push for strategies to recruit and retain minoritized students in computer science, little has been done to transform curriculum and analyze faculty perspectives on curriculum and pedagogy as methods to increase students’ access to the computer science major. This paper presents an example of curriculum redesign for an undergraduate introductory computer science course (ICS) that sought to address issues of inequitable representation by centering student identities and redistributing power in favor of students. The authors draw upon critical sociocultural and the 4Es heuristic for disciplinary literacy to reimagine the ICS course as a space that centers on the important roles of identity and power in solving for diversity in computer science education. We highlight for researchers and practitioners how our work may be used to disrupt meritocratic practices that alienate minoritized and economically disadvantaged students and to expand definitions of mastery and expertise in computer science education. Full article

This paper presents the design of a two-port dual-band dual-circularly-polarized dielectric resonator antenna (DRA). The proposed DRA is formed by stacking two dielectric resonators (DRs) of different shapes, including a hexagonal DR on top and a cross-shaped DR on the bottom. It is designed to resonate at two near-degenerate orthogonal modes of TE₁₁₁ and TE₁₁₃, and an aperture-coupled feeding through a cross-like slot is used to achieve dual-band impedance matching simultaneously for right- and left-handed circular polarizations. Tests were conducted on a prototype working in C-band to verify the design concept. The experiment results demonstrate that the proposed DRA has exceptional performance with measured −10 dB reflection bandwidths of 24.4% and 17.4%, 3 dB axial ratio bandwidths of 21.2% and 16.3%, and maximum gains of 5.64 and 8.13 dBic for the lower and upper bands, respectively. Moreover, the measured channel isolation is more than 15.8 dB. The results obtained from the experiments show good agreement with the simulation, and hence, it can be concluded that the proposed DRA is a promising solution that can be used for various wireless communication applications. Full article

Nosocomial infections represent a major threat within healthcare systems worldwide, underscoring the critical need for materials with antimicrobial properties. This study presents the development of polyurethane foam embedded with silver nanoparticles (PUF/AgNPs) using a rapid, eco-friendly, in situ radiochemical synthesis method. The nanocomposites were characterized by UV–vis and FTIR spectroscopy, scanning electron microscopy coupled with energy dispersive X-ray technique (SEM/EDX), differential scanning calorimetry (DSC), dynamic mechanical analysis (DMA), tensile and compression strengths, antimicrobial activity, and foam toxicity tests. The resulting PUF/AgNPs demonstrated prolonged stability (over 12 months) and good dispersion of AgNPs. Also, the samples presented higher levels of hardness compared to samples without AgNPs (deformation of 1682 µm for V1 vs. 4307 µm for V0, under a 5 N force), tensile and compression strength of 1.80 MPa and 0.34 Mpa, respectively. Importantly, they exhibited potent antimicrobial activity against a broad range of bacteria (including Pseudomonas aeruginosa, Staphylococcus aureus, Escherichia coli, and Enterococcus faecalis) and a fungal mixture (no fungal growth on the sample surface was observed after 28 days of exposure). Furthermore, these materials were non-toxic to human keratinocytes, which kept their specific morphology after 24 h of incubation, highlighting their potential for safe use in biomedical applications. We envision promising applications for PUF/AgNPs in hospital bed mattresses and antimicrobial mats, offering a practical strategy to reduce nosocomial infections and enhance patient safety within healthcare facilities. Full article

Accurate traffic flow prediction plays a crucial role in the development of intelligent traffic management. Despite numerous investigations into spatio-temporal methods, achieving high accuracy in traffic flow prediction remains challenging. This challenge arises from the complex dynamic spatio-temporal correlations within the traffic road network and the limitations imposed by the selection of hyperparameters based on experiments and manual experience, which can affect the performance of the network architecture. This paper introduces a novel transformer-based adaptive graph convolutional recurrent network. The proposed network automatically infers the interdependencies among different traffic sequences and incorporates the capability to capture global spatio-temporal correlations. This enables the dynamic capture of long-range temporal correlations. Furthermore, the whale optimization algorithm is employed to efficiently design an optimal network structure that aligns with the requirements of the traffic domain and maximizes the utilization of limited computational resources. This design approach significantly enhances the model’s performance and improves the accuracy of traffic flow prediction. The experimental results on four real datasets demonstrate the efficacy of our approach. In PEMS03, it improves MAE by 2.6% and RMSE by 1.4%. In PEMS04, improvements are 1.6% in MAE and 1.4% in RMSE, with a similar MAPE score to the best baseline. For PEMS07, our approach shows a 4.1% improvement in MAE and 2.2% in RMSE. On PEMS08, it surpasses the current best baseline with a 3.4% improvement in MAE and 1.6% in RMSE. These results confirm the good performance of our model in traffic flow prediction across multiple datasets. Full article

Background/Objectives: Given the limited success in treating functional gastrointestinal disorders (FGIDs) through conventional methods, there is a pressing need for tailored treatments that account for the heterogeneity and biopsychosocial factors associated with FGIDs. Here, we considered the potential of novel subtypes of FGIDs based on biopsychosocial information. Methods: We collected data from 198 FGID patients utilizing an integrative approach that included the traditional Korean medicine diagnosis questionnaire for digestive symptoms (KM), as well as the 36-item Short Form Health Survey (SF-36), alongside the conventional Rome-criteria-based Korean Bowel Disease Questionnaire (K-BDQ). Multivariate analyses were conducted to assess whether KM or SF-36 provided additional information beyond the K-BDQ and its statistical relevance to symptom severity. Questions related to symptom severity were selected using an extremely randomized trees (ERT) regressor to develop an integrative questionnaire. For the identification of novel subtypes, Uniform Manifold Approximation and Projection and spectral clustering were used for nonlinear dimensionality reduction and clustering, respectively. The validity of the clusters was assessed using certain metrics, such as trustworthiness, silhouette coefficient, and accordance rate. An ERT classifier was employed to further validate the clustered result. Results: The multivariate analyses revealed that SF-36 and KM supplemented the psychosocial aspects lacking in K-BDQ. Through the application of nonlinear clustering using the integrative questionnaire data, four subtypes of FGID were identified: mild, severe, mind-symptom predominance, and body-symptom predominance. Conclusions: The identification of these subtypes offers a framework for personalized treatment strategies, thus potentially enhancing therapeutic outcomes by tailoring interventions to the unique biopsychosocial profiles of FGID patients. Full article

As a highly efficient and flexible data collection device, Unmanned Aerial Vehicles (UAVs) have gained widespread application because of the continuous proliferation of Internet of Things (IoT). Addressing the high demands for timeliness in practical communication scenarios, this paper investigates multi-UAV collaborative path planning, focusing on the minimization of weighted average Age of Information (AoI) for IoT devices. To address this challenge, the multi-agent twin delayed deep deterministic policy gradient with dual experience pools and particle swarm optimization (DP-MATD3) algorithm is presented. The objective is to train multiple UAVs to autonomously search for optimal paths, minimizing the AoI. Firstly, considering the relatively slow learning speed and susceptibility to local minima of neural network algorithms, an improved particle swarm optimization (PSO) algorithm is utilized for parameter optimization of the multi-agent twin delayed deep deterministic policy gradient (MATD3) neural network. Secondly, with the introduction of the dual experience pools mechanism, the efficiency of network training is significantly improved. Experimental results show DP-MATD3 outperforms MATD3 in average weighted AoI. The weighted average AoI is reduced by 33.3% and 27.5% for UAV flight speeds of v = 5 m/s and v = 10 m/s, respectively. Full article

Lakes on the Qinghai Tibet Plateau (QTP) are widely distributed spatially, and they are mostly seasonally frozen. Due to global warming, the thickness and phenology of the lake ice has been changing, which profoundly affects the regional climate evolution. There are a few studies about lake ice in alpine regions, but the understanding of climatological characteristics of lake ice on the QTP is still limited. Based on a field experiment in the winter of 2022, the thermal conductivity of Qinghai Lake ice was determined as 1.64 W·m⁻¹·°C⁻¹. Airborne radar ice thickness data, meteorological observations, and remote sensing images were used to evaluate a quasi-steady ice model (Leppäranta model) performance of the lake. This is an analytic model of lake ice thickness and phenology. The long-term (1979–2017) ice history of the lake was simulated. The results showed that the modeled mean ice thickness was 0.35 m with a trend of −0.002 m·a⁻¹, and the average freeze-up start (FUS) and break-up end (BUE) were 30 December and 5 April, respectively, which are close to the field and satellite observations. The simulated trend of the maximum ice thickness from 1979 to 2017 (0.004 m·a⁻¹) was slightly higher than the observed result (0.003 m·a⁻¹). The simulated trend was 0.20 d·a⁻¹ for the FUS, −0.34 d·a⁻¹ for the BUE, and −0.54 d·a⁻¹ for the ice duration (ID). Correlation and detrending analysis were adopted for the contribution of meteorological factors. In the winters of 1979–2017, downward longwave radiation and air temperature were the two main factors that had the best correlation with lake ice thickness. In a detrending analysis, air temperature, downward longwave radiation, and solar radiation contributed the most to the average thickness variability, with contributions of 42%, 49%, and −48%, respectively, and to the maximum thickness variability, with contributions of 41%, 45%, and −48%, respectively. If the six meteorological factors (air temperature, downward longwave radiation, solar radiation, wind speed, pressure, and specific humidity) are detrending, ice thickness variability will increase 83% on average and 87% at maximum. Specific humidity, wind, and air pressure had a poor correlation with ice thickness. The findings in this study give insights into the long-term evolutionary trajectory of Qinghai Lake ice cover and serve as a point of reference for investigating other lakes in the QTP during cold seasons. Full article