Unmanned aerial vehicles (UAVs) equipped with remote-sensing object-detection devices are increasingly employed across diverse domains. However, the detection of small, densely-packed objects against complex backgrounds and at various scales presents a formidable challenge to conventional detection algorithms, exacerbated by the computational constraints of UAV-embedded systems that necessitate a delicate balance between detection speed and accuracy. To address these issues, this paper proposes the Efficient Multidimensional Global Feature Adaptive Fusion Network (MGFAFNET), an innovative detection method for UAV platforms. The novelties of our approach are threefold: Firstly, we introduce the Dual-Branch Multidimensional Aggregation Backbone Network (DBMA), an efficient architectural innovation that captures multidimensional global spatial interactions, significantly enhancing feature distinguishability for complex and occluded targets. Simultaneously, it reduces the computational burden typically associated with processing high-resolution imagery. Secondly, we construct the Dynamic Spatial Perception Feature Fusion Network (DSPF), which is tailored specifically to accommodate the notable scale variances encountered during UAV operation. By implementing a multi-layer dynamic spatial fusion coupled with feature-refinement modules, the network adeptly minimizes informational redundancy, leading to more efficient feature representation. Finally, our novel Localized Compensation Dual-Mask Distillation (LCDD) strategy is devised to adeptly translate the rich local and global features from the higher-capacity teacher network to the more resource-constrained student network, capturing both low-level spatial details and high-level semantic cues with unprecedented efficacy. The practicability and superior performance of our MGFAFNET are corroborated by a dedicated UAV detection platform, showcasing remarkable improvements over state-of-the-art object-detection methods, as demonstrated by rigorous evaluations conducted using the VisDrone2021 benchmark and a meticulously assembled proprietary dataset. Full article

With the increasing availability and volume of remote sensing data, imaging spectroscopy is an expanding tool for agricultural studies. One of the fundamental applications in agricultural research is crop mapping and classification. Previous studies have mostly focused at local to regional scales, and classifications were usually performed for a limited number of crop types. Leveraging fine spatial resolution (60 cm) imaging spectroscopy data collected by the Global Airborne Observatory (GAO), we investigated canopy-level spectral variations in 16 crop species from different agricultural regions in the U.S. Inter-specific differences were quantified through principal component analysis (PCA) of crop spectra and their Euclidean distances in the PC space. We also classified the crop species using support vector machines (SVM), demonstrating high classification accuracy with a test kappa of 0.97. A separate test with an independent dataset also returned high accuracy (kappa = 0.95). Classification using full reflectance spectral data (320 bands) and selected optimal wavebands from the literature resulted in similar classification accuracies. We demonstrated that classification involving diverse crop species is achievable, and we encourage further testing based on moderate spatial resolution imaging spectrometer data. Full article

Commercially available and air- and moisture-stable rhodium complex [Rh(OH)(cod)]₂ (2) was utilized in the synthesis of [RhX(cod)(NHC)] (3). The presence of an OH group in complex 2 serves as an internal base, facilitating the deprotonation of the C–H bond of the azolium ring in the hydroxyamide-substituted benzimidazolium salt 1. This reaction between 1 and 2 proceeded in THF at room temperature without temperature control, affording the desired NHC/Rh complex 3 in excellent yield. The characterization of complex 3 was accomplished through NMR and HRMS analyses, revealing its existence as a diastereomeric mixture of two NHC/Rh complexes. Furthermore, its catalytic performance was briefly evaluated in the reaction between 2-naphthaldehyde (5) and phenylboronic acid (6). Full article

Polyether ether ketone (PEEK) is esteemed as a high-performance engineering polymer renowned for its exceptional mechanical properties and thermal stability. Nonetheless, the majority of polymer-based lubricating materials fail to meet the contemporary industrial demands for motion components regarding high speed, heavy loading, temperature resistance, and precise control. Utilizing 3D printing technology to design and fabricate intricately structured components, developing high-performance polymer self-lubricating materials becomes imperative to fulfill the stringent operational requirements of motion mechanisms. This study introduces a novel approach employing 3D printing technology to produce PEEK with varying filling densities and conducting in situ synthesis of zeolitic imidazolate framework (ZIF-8) nanomaterials on its surface to enhance PEEK’s frictional performance. The research discusses the synthetic methodology, characterization techniques, and tribological performance evaluation of in situ synthesized ZIF-8 nanomaterials on PEEK surfaces. The findings demonstrate a significant enhancement in frictional performance of the composite material under low-load conditions, achieving a minimum wear rate of 4.68 × 10⁻⁶ mm³/N·m compared to the non-grafted PEEK material’s wear rate of 1.091 × 10⁻⁵ mm³/N·m, an approximately 1.3 times improvement. Detailed characterization and analysis of the worn surface of the steel ring unveil the lubrication mechanism of the ZIF-8 nanoparticles, thereby presenting new prospects for the diversified applications of PEEK. Full article

The hospital environment is increasingly becoming an important reservoir for multi-drug-resistant (MDR) Gram-negative bacteria, posing serious challenges to efforts to combat antimicrobial resistance (AMR). This study aimed to investigate the role of hospital waste as a potential source of MDR ESBL-producing bacteria. Samples were collected from multiple sources within a hospital and its vicinity, including surface swabs, houseflies, and sewage samples. The samples were subsequently processed in a microbiology laboratory to identify potential pathogenic bacteria and confirmed using MALDI-TOF MS. Bacteria were isolated from 87% of samples, with the predominant isolates being E. coli (30.5%), Klebsiella spp. (12.4%), Providencia spp. (12.4%), and Proteus spp. (11.9%). According to the double disc synergy test (DDST) analysis, nearly half (49.2%) of the bacteria were identified as ESBL producers. However, despite exhibiting complete resistance to beta-lactam antibiotics, 11.8% of them did not test positive for ESBL production. The characterization of E. coli revealed that 30.6% and 5.6% of them carried blaCTX-M group 1 type-15 and blaNDM genes, respectively. This finding emphasizes the importance of proper hospital sanitation and waste management practices to mitigate the spread of AMR within the healthcare setting and safeguard the health of both patients and the wider community. Full article

The smart transformer (ST) is a multiport and multi-stage converter that allows for the formation of meshed hybrid microgrids (MHMs) by enabling AC-DC ports in medium and low voltage. This type of microgrid has advantages over the performance of conventional hybrid AC-DC microgrids (HMGs); however, the number of degrees of freedom of the ST increases the complexity of the energy management systems (EMSs), which require adequate and accurate modeling of the power flow of the converters and the MG to find the feasible solution of optimal power flow (OPF) problems in the MHM. An ST’s equivalent power flow model is proposed for formulating the MHM OPF problem and developing low-frequency equivalent models integrated with a decoupled hierarchical control architecture under a real-time simulation approach to the ST-based MHM. A simulation model of the MHM in the Simulink^® environment of Matlab^{® 9.12} is developed and implemented under a digital real-time simulation (DRTS) approach on the OPAL-RT^® platform. This model allows for determining the accuracy of the developed equivalent models, both low-frequency and power flow, and determining the MHM performance based on optimal day-ahead scheduling. Simulation test results demonstrated the ST equivalent model’s accuracy and the MHM’s accuracy for OPF problems with an optimal day-ahead scheduling horizon based on the model-in-the-loop (MIL) and DRTS approach. Full article

The development of monitoring and diagnostic methods for electrical machines requires the use of transient models capable of operating in real time and producing signal signatures with high precision. In this context, coupled-circuit models offer numerous advantages due to their speed of execution and accuracy. They have been successfully employed to create real-time digital twins of electrical machines. The main challenge of this modeling method lies in the preparation of the model, which involves numerous preliminary calculations and takes time to identify all its parameters. This is particularly due to the variation in inductances based on the rotor position. To determine these inductance values with great precision, the classical approach involves using finite-element field calculation software. However, the computation time quickly becomes an issue due to the large number of values to calculate and simulations to perform. This article introduces an innovative experimental approach to identify a coupled-circuit model and develop a digital twin of a wound-rotor induction machine. This method relies solely on simple electrical measurements and tests conducted at extremely low rotation speeds (1 rpm) to obtain inductance variations as a function of the rotor position. By employing this technique, the need for analytical models or finite-element field calculation simulations, which typically require precise knowledge of the machine’s geometry and materials, is circumvented. The measurement processing employs optimization methods to extract the inductances as a function of the rotor position, which are then used as input data for the coupled-circuit model. The final parameters are specific to each machine and replicate all its manufacturing imperfections such as eccentricity and geometric or winding defects. This experimental identification method significantly enhances the model’s accuracy and reduces the usually required preliminary calculation time in a finite-element-based identification process. Full article

Positioning based on Global Navigation Satellite Systems (GNSSs) in urban environments always suffers from multipath and Non-Line-of-Sight (NLoS) effects. In such conditions, the GNSS pseudorange measurements can be affected by biases disrupting the GNSS-based applications. Many efforts have been devoted to detecting and mitigating the effects of multipath/NLoS, but the identification and classification of such events are still challenging. This research proposes a method for the post-processing estimation of pseudorange biases resulting from multipath/NLoS effects. Providing estimated pseudorange biases due to multipath/NLoS effects serves two main purposes. Firstly, machine learning-based techniques can leverage accurately estimated pseudorange biases as training data to detect and mitigate multipath/NLoS effects. Secondly, these accurately estimated pseudorange biases can serve as a benchmark for evaluating the effectiveness of the methods proposed to detect multipath/NLoS effects. The estimation is achieved by extracting the multipath/NLoS biases from pseudoranges using a clustering algorithm named Density-Based Spatial Clustering of Applications with Noise (DBSCAN). The performance is demonstrated using two real-world data collections in multipath/NLoS scenarios for both static and dynamic conditions. Since there is no ground truth for the pseudorange biases due to the multipath/NLoS scenarios, the proposed method is validated based on the positioning performance. Positioning solutions are computed by subtracting the estimated biases from the raw pseudoranges and comparing them to the ground truth. Full article

Mammary cancer is a frequent disease in female dogs, where a high proportion of cases correspond to malignant tumors that may exhibit drug resistance. Within the mammary tumor microenvironment, there is a cell subpopulation called cancer stem cells (CSCs), which are capable of forming spheres in vitro and resisting anti-tumor treatments, partly explaining the recurrence of some tumors. Previously, it has been described that spheres derived from canine mammary carcinoma cells CF41.Mg and REM 134 exhibit stemness characteristics. Melatonin has shown anti-tumor effects on mammary tumor cells; however, its effects have been poorly evaluated in canine mammary CSCs. This study aimed to analyze the effect of melatonin on the chemoresistance exhibited by stem-like neoplastic cells derived from canine mammary carcinoma to cytotoxic drugs such as doxorubicin and mitoxantrone. CF41.Mg and REM 134 cells were cultured in high-glucose DMEM supplemented with fetal bovine serum and L-glutamine. The spheres were cultured in ultra-low attachment plates in DMEM/F12 medium without fetal bovine serum and with different growth factors. The CD44⁺/CD24^−/low phenotype was analyzed by flow cytometry. The viability of sphere-derived cells (MTS reduction) was studied in the presence of melatonin (0.1 or 1 mM), doxorubicin, mitoxantrone, and luzindole. In addition, the gene (RT-qPCR) of the multidrug resistance bombs MDR1 and ABCG2 were analyzed in the presence of melatonin. Both cell types expressed the MT1 gene, which encodes the melatonin receptor MT1. Melatonin 1 mM does not modify the CD44⁺/CD24^−/low phenotype; however, the hormone reduced viability (p < 0.0001) only in CF41.Mg spheres, without inducing an additive effect when co-incubated with cytotoxic drugs. These effects were independent of the binding of the hormone to its receptor MT1, since, by pharmacologically inhibiting them, the effect of melatonin was not blocked. In CF41.Mg spheres, the relative gene expression of ABCG2 and MDR1 was decreased in response to the hormone (p < 0.001). These results indicate that melatonin negatively modulates the cell survival of spheres derived from CF41.Mg cells, in a way that is independent of its MT1 receptor. These effects did not counteract the resistance to doxorubicin and mitoxantrone, even though the hormone negatively regulates the gene expression of MDR1 and ABCG2. Full article

Background: Frailty is a globally recognized issue. However, there is a lack of evidence exploring factors associated with frailty among older residents in community and nursing-home settings. Methods: To explore the prevalence and factors associated with frailty among older adults in community and nursing-home settings, we conducted a systematic search following the PRISMA guidelines across Web of Science, MEDLINE, EMBASE, PubMed, and Cochrane databases up until January 2024, selecting 38 studies which encompassed 150,642 participants. Results: Our findings showed higher frailty prevalence in nursing homes compared to communities. Frailty was significantly associated with sociodemographic (living alone, poor self-reported health), physiological (poor sleep, low activity of daily living), behavioral (physical inactivity) and disease (chronic conditions, depression) factors in both community and nursing-home settings. Conclusions: There are numerous factors associated with frailty in older adults in nursing-home and community settings. These factors underscore the significance of promptly identifying high-risk individuals and devising appropriate interventions to mitigate frailty among them. Full article

Reducing energy consumption in residential and commercial buildings is an important research topic. Thermally activated building systems are a promising technology for significantly reducing energy consumption. The high thermal inertia, large surfaces, and radiative nature are advantages of these systems, but, on the other hand, this makes the system control and design complex. A transient simulation is also required to address the dynamic behavior of the system. The influence of 19 factors (material, design, and operating parameters) on the air temperature and mean radiant temperature inside the room as well as the required cooling equipment power were analyzed to better understand the system. The screening experiment was conducted using the random balance design method, and measurement data were used to validate the resistance–capacitance model. The analysis was performed using the Plackett–Burman design and a design with randomly selected points from a full factorial experiment. The results show that internal heat gains and the inlet water temperature have a significant influence on the system, and the influence of the screed’s properties is insignificant compared to other parameters. It should be borne in mind that the obtained results and conclusions are valid for the assumed range of factors’ variability. Full article

The aim of this research is to propose a more sustainable swimming pool, in terms of water and energy, in the Costa Brava area. The methodology is focused on the creation of a descriptive water and energy model for swimming pools and their monitoring, to propose the transition to a more sustainable swimming pool, in the context of climate change. The research is characterized by an exploratory, descriptive, and simulation approach to adapt swimming pools to the new requirements. Two significant impacts are highlighted from the perspective of water and energy stress—the carbon footprint, related to environmental impact and climate change, and the new business models of the sector, for a more sustainable tourism, with more sustainable pools. In terms of water balance, evaporation is an important variable and there are technical solutions on the market to control it, such as the use of covers. Furthermore, the modeling and simulation carried out helps to calculate the variable as a tool for improvement. For energy balance, in outdoor pools, pumping is an important variable and there are technologies, such as speed variation, that make it possible to reduce this. Furthermore, it should be noted that this research represents an important tool for the improvement of sustainability and operability for the various stakeholders, especially owners and governments, to face climate change, which is becoming increasingly critical for many regions. Full article

Distance Learning has become the “new normal”, especially during the pandemic and due to the technological advances that are incorporated into the teaching procedure. At the same time, the augmented use of the internet has blurred the borders between distance and conventional learning. Students interact mainly through LMSs, leaving their digital traces that can be leveraged to improve the educational process. New knowledge derived from the analysis of digital data could assist educational stakeholders in instructional design and decision making regarding the level and type of intervention that would benefit learners. This work aims to propose an analysis model that can capture the students’ behaviors in a distance learning course delivered fully online, based on the clickstream data associated with the discussion forum, and additionally to suggest interpretable patterns that will support education administrators and tutors in the decision-making process. To achieve our goal, we use Social Network Analysis as networks represent complex interactions in a meaningful and easily interpretable way. Moreover, simple or complex network metrics are becoming available to provide valuable insights into the students’ social interaction. This study concludes that by leveraging the imprint of these actions in an LMS and using metrics of Social Network Analysis, differences can be spotted in the communicational patterns that go beyond simple participation recording. Although HITS and PageRank algorithms were created with completely different targeting, it is shown that they can also reveal methodological features in students’ communicational approach. Full article

Piezoelectric vibration sensors (PVSs) are widely used in high-temperature environments, such as vibration measurements in aero-engines, because of their high accuracy, small size, and high temperature resistance. Accurate prediction of its RUL (Remaining Useful Life) is essential for applying and maintaining PVSs. Based on PVSs’ characteristics and main failure modes, this work combines the Digital-Twin (DT) and Long Short-Term Memory (LSTM) networks to predict the RUL of PVSs. In this framework, DT can provide rich data collection, analysis, and simulation capabilities, which have advantages in RUL prediction, and LSTM network has good results in predicting time sequence data. The proposed method exploits the advantages of those techniques in feature data collection, sample optimization, and RUL multiclassification. To verify the prediction of this method, a DT platform is established to conduct PVS degradation tests, which generates sample datasets, then the LSTM network is trained and validated. It has been proved that prediction accuracy is more than 99.7%, and training time is within 94 s. Based on this network, the RUL of PVSs is predicted using different test samples. The results show that the method performed well in prediction accuracy, sample data utilization, and compatibility. Full article

This study aims to describe the century-long trajectory of educational leadership research (ELR), including changes over time in its main and subsidiary themes, as well as its most influential authors, papers, and journals. The study combines the bibliometric performance and science mapping analysis of 7282 articles retrieved from the Scopus and WoS databases. SciMAT software (version 1.1.04) was used to analyze changes over four sequential time periods and to exhibit the thematic evolution of the field—Period 1 (1907 to 2004), Period 2 (2005 to 2012), Period 3 (2013 to 2019), and Period 4 (2020–2023). Research during Period 1 focused on principals and included efforts to distinguish between their administrative functions and forms of ‘strong’ leadership contributing to school improvement. Period 2 included research aimed at understanding what strong principal leadership entailed, including the development and testing of more coherent models of such leadership. While instructional and transformational leadership models were prominent during Periods 1 and 2, Period 3 research invested heavily in conceptions of leadership distribution. Early research about ‘social justice leadership’ appeared during this period and eventually flourished during Period 4. While principals were an active focus through all Periods, the leadership of others gradually dominated ELR and accounted for the broader leadership theme found in all four periods. The results point to the evolutionary nature of ELR development, which eventually produced a relatively robust knowledge base. Experiences with the COVID-19 pandemic suggest that crises such as this might prompt more revolutionary orientations in the ELR field. Full article

This work is inscribed into a more comprehensive project related to the architectural requalification and restoration of Frinco Castle, one of the most significant fortified medieval structures in the Monferrato area (province of Asti, Italy), that experienced a structural collapse. In particular, this manuscript focuses on data fusion of multi-sensor acquisitions of metric surveys for 3D documenting this structural-risky building. The structural collapse made the entire south front fragile. The metric survey was performed by using terrestrial and aerial sensors to reach every area of the building. Topographically oriented Terrestrial Laser Scans (TLS) data were collected for the exterior and interior of the building, along with the DJI Zenmuse L1 Airborne Laser Scans (ALS) and Zenmuse P1 Photogrammetric Point Cloud (APC). First, the internal alignment in the TLS data set was verified, followed by the intra-technique alignments, choosing TLS as the reference data set. The point clouds from each sensor were analyzed by computing voxel-based point density and roughness, then segmented, aligned, and fused. 3D acquisitions and segmentation processes were fundamental for having a complete and structured dataset of almost every outdoor and indoor area of the castle. The collected metrics data was the starting point for the modeling phase to prepare 2D and 3D outputs fundamental for the restoration process. Full article

The study was conducted within a well-managed beef cattle operation in northeastern Mexico. Each nest was weighed and dissected to obtain the plant and animal material used to build the nests. The number of materials present per nest and relative frequency were determined. Twenty-one building materials were used. Over the years, Muhlenbergia torreyi represented 85.5% of the total biomass of the nests, and Aristida longiseta, Bouteloua gracilis, Brickellia canescens, Purshia mexicana and Cirsium ehrenbergii constituted 2.45, 2.80, 2.44, 1.34 and 1.11% of the total biomass, respectively. The above-mentioned grasses represented 95.62% of the total biomass. Material of animal origin was horse and cow hair, which represented 0.84 and 0.58% of the total biomass, respectively. It was concluded that, at the study site, Muhlenbergia torreyi was a key nest-building material for the Worthen sparrow nest. Full article

Phytocannabinoids, compounds found in Cannabis sativa L., are used in oncology and palliative care to reduce the adverse reactions of standard therapies. Cancer patients use formulations of Cannabis sativa L. to manage the anxiety, pain, and nausea associated with cancer treatment, and there is growing evidence that some of them may exhibit anticancer properties. In this study, we tested the anticancer potential of selected cannabinoids CBD (cannabidiol) and its quinone derivative CBD-HQ (cannabidiol hydroquinone), CBG (cannabigerol) and its acid derivative CBG-A (cannabigerolic acid), as well as a combination of CBD+CBG on the colon cancer cell line SW-620. The MTT assay was used to determine the cannabinoids’ ability to induce colon cancer cell death. All cannabinoids were cytotoxic at the lowest concentration (3 μg/mL). The half maximal inhibitory concentration (IC50) ranged from 3.90 to 8.24 μg/mL, depending on the substance. Cytotoxicity was confirmed in a 3D spheroidal cell culture with calcein and propidium iodide staining. The amount of intracellular reactive oxygen species (ROS) was examined using a DCF-DA assay. CBG showed the lowest antioxidant activity of all the cannabinoids tested. The level of intracellular ROS decreased only by 0.7–18%. However, CBG-A induced the strongest reduction in ROS level by 31–39%. Our results suggest that cannabinoids represent an interesting research direction with great implementation potential. These preliminary results represent the beginning of research into the potential of these substances for anticancer treatment and underscore the potential for further research. Full article

The co-occurrence of myasthenia gravis (MG) and paroxysmal nocturnal hemoglobinuria (PNH) is rare; only one case has been published so far. We report a 63-year-old Caucasian female patient who was diagnosed with MG at the age of 43. Thymoma was also detected, and so it was surgically resected, which resulted in reasonable disease control for nearly 20 years. Slight hemolysis began to emerge, and then myasthenia symptoms progressed, so immunosuppressive therapy was started. Due to progressive disease and respiratory failure, the patient underwent plasmapheresis, and ventilatory support was stopped. Marked hemolysis was present, and diagnostic tests confirmed PNH with type III PNH cells. Her myasthenia symptoms aggravated, mechanical ventilation had to be started again, and due to the respiratory acidosis, massive hemolysis occurred. After two plasmapheresis sessions, the patient received eculizumab at 600 mg, resulting in prompt hemolysis control. After the second dose of the treatment, the patient was extubated. Still, due to their inability to cough, she developed another respiratory failure and pneumonia–sepsis, resulting in the patient’s death. This case highlights the rare association between these two serious diseases and similar immune-mediated pathophysiology mechanisms involving the complement system. Full article

An 88-day feeding trial was conducted to evaluate the effects of dietary inosine 5′-monophosphate (5′-IMP) on the growth performance and salinity and oxidative stress resistance in the juvenile gibel carp CAS III (Carassius auratus gibelio; initial body weight: 7.48 g). Four isonitrogenous and isoenergetic diets containing exogenous 5′-IMP were formulated. P1, P2, P3 and P4 were diets containing 5′-IMP at four concentrations (0, 1, 2 and 4 g kg⁻¹). The four diets were randomly allotted to triplicate tanks in a recirculating system. After the feeding trial, six fish per tank were netted randomly and placed into 12‰ saline water to test their response to salinity stress. The results indicated that the feed conversion rate was enhanced by dietary supplementation with 5′-IMP. The appetite, plasma neuropeptide Y level and feeding rate of the P3 group were lower than those in the control treatment group. Dietary supplementation with 5′-IMP improved the osmoregulatory adaptation of gibel carp under acute salinity stress. Six hours after the salinity stress treatment, in the dietary 5′-IMP treatment group, the plasma cortisol and K⁺ concentrations were lower and the Na⁺/K⁺-ATPase activity was greater than that in the control group. Dietary supplementation with 5′-IMP promoted the expression of the glucocorticoid receptors NKA-α1b and NKCC and retarded the expression of Hsp70 in P4-treated gill filaments and kidneys. Dietary supplementation with 5′-IMP resulted in a stable oxidative-stress-resistant phenotype characterized by increased levels of cellular antioxidants, including SOD, catalase, glutathione peroxidase, glutathione reductase and MPO. The above results of the current study demonstrate that supplementation of 5′-IMP can promote feed utilization and have positive influences on the salinity and oxidative stress resistance of gibel carp. Full article

Based on a novel phenylacetylene capped polyimide (PI) with unique high-temperature resistance, its curing kinetics and thermal decomposition behavior were investigated. The curing mechanism and kinetics were studied by differential scanning calorimetry (DSC), and the activation energy (E_a) and pre-exponential factor (A) of the curing reaction were calculated based on the Kissinger equation, Ozawa equation, and Crane equation. According to the curve of conversion rate changing with temperature, the relationship between the dynamic reaction E_a and conversion rate (α) was calculated by the Friedman equation, Starink equation, and Ozawa–Flynn–Wall (O-F-W) equation, and the reaction E_a in different stages was compared with the results of molecular dynamics. Thermogravimetric analysis (TGA) and a scanning electron microscope (SEM) were used to analyze the thermal decomposition behavior of PI resins before and after curing. Temperatures at 5% and 20% mass loss (T_5%, T_20%), peak decomposition temperature (T_max), residual carbon rate (RW), and integral process decomposition temperature (IPDT) were used to compare the thermal stability of PI resins and cured PI resins. The results display that the cured PI has excellent thermal stability. The E_a of the thermal decomposition reaction was calculated by the Coats–Redfern method, and the thermal decomposition behavior was analyzed. The thermal decomposition reaction of PI resins at different temperatures was simulated by molecular dynamics, the initial thermal decomposition reaction was studied, and the pyrolysis mechanism was analyzed more comprehensively and intuitively. Full article

Students with disabilities are increasingly being educated in general education classrooms. This exploratory study investigates the efficacy of using mixed-reality simulation (MRS) to provide deliberate practice on high-leverage practices (HLPs) for pre-service general education teachers. Results indicate significant shifts in pre-service teacher understanding of and perceived readiness to implement HLPs in favor of the mixed-reality treatment group. Examining the influence of this innovative technology on pre-service teacher lesson planning yielded mixed results. Findings hold implications for the preparation of special and general education teachers across all content areas. Full article