Modeling Studies Research Articles

Biomechanics of the Human Knee Joint in Maximum Voluntary Isometric Flexion: Study of Changes in Applied Moment, Agonist-Antagonist Participations, Joint Center, and Flexion Angle.

Estimation of the knee joint strength by maximum voluntary isometric contraction (MVIC) is a common practice to assess strength, coordination, safety to return to work or engage in sports after an injury, and to evaluate the efficacy of treatment modalities and rehabilitation strategies. In this study, we utilize a previously validated coupled finite element-musculoskeletal model of the lower extremity to explore the sensitivity of output measures (posterior cruciate ligament [PCL]/muscle/contact forces and passive moments) in knee MVIC flexion exercises at seated position. To do so, at three knee flexion angles (KFA), input measures (resistance moment and contribution moments of quadriceps and gastrocnemii) were varied at four levels each using the Taguchi design of experiment. Our findings reveal significant increases in PCL forces with KFA (p < 0.01), net MVIC moment (p < 0.01), and resistance moment of quadriceps (p < 0.01). In contrast, they drop at larger activity in gastrocnemii (p < 0.01). Tibiofemoral (TF) contact forces increase with the net MVIC moment (p < 0.01). The passive knee flexion moment, while highly dependent on the location at which computed, also increases with the net MVIC moment (p < 0.01). Changes in KFA, MVIC moment, and proportions thereof carried by quadriceps and/or gastrocnemii substantially affect biomechanics of the joint. Compared with level walking and stair ascent, slightly larger contact forces/stresses and much greater PCL forces are computed. This study improves our understanding of the knee joint behavior during MVIC in effective evaluation and rehabilitation interventions. Besides, it emphasizes the importance of positioning the joint center in model studies.

B Cell and Antibody Responses in Bovine Tuberculosis

The development of vaccines and effective diagnostic methods for bovine tuberculosis requires an understanding of the immune response against its causative agent, Mycobacterium bovis. Although this disease is primarily investigated and diagnosed through the assessment of cell-mediated immunity, the role of B cells and antibodies in bovine tuberculosis has been relatively undervalued and understudied. Current evidence indicates that circulating M. bovis-specific antibodies are not effective in controlling the disease. However, local humoral immune responses may contribute to either defence or pathology. Recent studies in animal models and cattle vaccine trials suggest a potential beneficial role of B cells in tuberculosis control. This review discusses the role of B cells and antibodies in bovine tuberculosis and explores antibody-based diagnostics for the disease, including traditional techniques, such as different ELISA, new platforms based on multiple antigens and point-of-care technologies. The high specificity and sensitivity values achieved by numerous antibody-based tests support their use as complementary tests for the diagnosis of bovine tuberculosis, especially for identifying infected animals that may be missed by the official tests.

Cell membrane camouflaged Cu-doped mesoporous polydopamine for combined CT/PTT/CDT synergistic treatment of breast cancer

Currently, traditional monotherapy for cancer often results in indiscriminate attacks on the body, leading to the emergence of new health problems. To confront these challenges, multimodal combination therapy has become necessary. However, how to develop new smart nanomaterials through green synthesis methods, delivering drugs while simultaneously synergizing multimodal combination therapies for tumor treatment, remains a topic of great significance. In this study, a biomimetic composite nanomaterial (RM-Cu/P) composed of mesoporous polydopamine (MPDA) as the core and red blood cell membranes (RBCMs) as the shell was synthesized as a drug carrier to deliver doxorubicin (DOX) while achieving synergistic chemotherapy, photothermal and chemodynamic therapy (CT/PTT/CDT). Herein, the nanoparticles were extensively characterized to examine their morphological characteristics, elemental composition, and drug-carrying capacity. Notably, the coating of RBCM reduced the toxicity of the RM-Cu/P@DOX nanoparticles, improved their targeting ability and prolonged their circulation time in vivo. The Cu-doped nanoparticles were capable of initiating a Fenton-like reaction to generate reactive oxygen species (ROS) for CDT, while the photothermal conversion efficiency (η) reached 45.20 % under NIR laser irradiation. Subsequently, the particles were examined by in vivo and in vitro experimental studies in cytotoxicity, cellular uptake, ROS levels, lysosomal escape, and mouse tumor model to evaluate their potential application in antitumor. Compared with monotherapy, the RM-Cu/P@DOX nanoparticles had multiple-stimulation response properties under redox, pH, and NIR, which exhibited the advantage of combined trimodal therapy, resulting in remarkable synergistic antitumor efficacy. In conclusion, this innovative platform exhibited promising applications in smart drug delivery and synergistic treatment of cancer.

Synthesis of a dehydrodieugenol B derivative as a lead compound for visceral leishmaniasis-mechanism of action and in vivo pharmacokinetic studies.

Leishmaniasis is a parasitic neglected tropical disease, affecting 12 million people. Available treatments present several limitations, with an increasing number of resistance cases. In the search for new chemotherapies, the natural product dehydrodieugenol B was used as a scaffold for the synthesis of a series of derivatives, resulting in the discovery of the promising analog [4-(4-(5-allyl-3-methoxy-2-((4-methoxybenzyl)oxy)phenoxy)-3-methoxybenzyl)morpholine, 1]. In this work, we investigated the effect of compound 1 on cell signaling in Leishmania (L.) infantum, culminating in cell death, as well as its immunomodulatory effect in the host cell. Additionally, we performed a pharmacokinetic profile study in an animal model. After treatment, compound 1 induced the alkalinization of acidocalcisomes and concomitant Ca2+ release in the parasite. These events may induce depolarization of the mitochondrial potential, with successive collapse of the bioenergetic system, leading to a reduction of ATP and reactive oxygen species (ROS) levels. The analysis of total proteins and protein profile by matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF/MS) demonstrated that compound 1 also altered the parasite proteins after treatment. Transmission electron microscopy studies revealed ultrastructural damage to mitochondria; together, these data suggest that compound 1 may promote autophagic cell death. Additionally, compound 1 also induced an immunomodulatory effect in host cells, with a reduction of Th1 and Th2 cytokine response, characterizing an anti-inflammatory compound. The obtained pharmacokinetic profile in rats enhances the potential of the compound, with a mean plasma half-life (T1/2) of 21 h. These data reinforce the potential of compound 1 as a new lead for future efficacy studies.

From defense to offense: antimicrobial peptides as promising therapeutics for cancer

Antimicrobial peptides (AMPs), naturally occurring components of innate immunity, are emerging as a promising new class of anticancer agents. This review explores the potential of AMPs as a novel class of anticancer agents. AMPs, naturally occurring peptides with broad-spectrum antimicrobial activity, exhibit several characteristics that make them attractive candidates for cancer therapy, including selectivity for cancer cells, broad-spectrum activity, and immunomodulatory effects. Analysis of a dataset of AMPs with anticancer activity reveals that their effectiveness is influenced by various structural properties, including net charge, length, Boman index, and hydrophobicity. These properties contribute to their ability to target and disrupt cancer cell membranes, interfere with intracellular processes, and modulate the immune response. The review highlights the promising potential of AMPs as a new frontier in cancer treatment, offering hope for more effective and less toxic therapies. AMPs demonstrate promising potential in cancer therapy through multiple mechanisms, including direct cytotoxicity, immune response modulation, and targeting of the tumor microenvironment, as evidenced by extensive preclinical studies in animal models showing tumor regression, metastasis inhibition, and improved survival rates. AMPs show significant potential as cancer therapeutics through their direct cytotoxicity, immune response modulation, and tumor microenvironment targeting, with promising results from preclinical studies and early-phase clinical trials. Future research should focus on optimizing AMP properties, developing novel delivery strategies, and exploring synergistic combination therapies to fully realize their potential as effective cancer treatments, while addressing challenges related to stability, delivery, and potential toxicity.

Biofilm Prevention and Removal in Non-Target Pseudomonas Strain by Siphovirus-like Coliphage

Background/Objectives. Bacteriophages have gained significant interest as a potential solution to combat harmful bacteria, especially in the fight against antimicrobial resistance. With the rise in drug-resistant microorganisms, the medical community is increasingly exploring new alternatives to traditional antibiotics, and bacteriophages offer several advantages in this regard. However, phage applications still face some challenges, such as host specificity. Methods. In this study, a somatic Siphovirus-like coliphage (SOM7) was tested for inhibiting the biofilm-forming capacity of the non-target strain Pseudomonas aeruginosa (ATTC 10145). The phage-sensitive strain E. coli WG5 was used as a control. The selected microorganisms were first tested for growth in the presence of SOM7 at three different concentrations (105, 107, and 109 PFU/mL). Results. As expected, the phage-sensitive E. coli WG5 was fully inhibited by the coliphage, and no phage-related affection on the growth rate was observed for the SOM7-resistant P. aeruginosa. More notably, increasing concentrations of SOM7 significantly reduced both the biofilm-forming capacity and the amount of pre-established bacterial biofilm of the phage-insensitive P. aeruginosa (24.9% and 38.8% reduction in the biofilm-forming ability, and 18.8% and 28.0% biofilm degradation for 107 PFU/mL and 109 PFU/mL SOM7, respectively; p &lt; 0.05). These results were supported by transmission electron microscopy (TEM) imaging, providing unprecedent evidence for the interaction of the somatic coliphage with the non-host strain. Conclusions. Although more studies in other biofilm models are necessary, our results show for the very first time that bacteriophages could potentially be used as an alternative to achieve desired anti-biofilm and biofilm-degrading activity in non-host bacterial strains.

SGLT2 Inhibitors and Their Effect on Urolithiasis: Current Evidence and Future Directions

Urolithiasis (UL) is increasingly prevalent due to rising cardiorenometabolic diseases, posing significant management challenges despite advances in urological techniques. Sodium-glucose cotransporter-2 (SGLT2) inhibitors, primarily used for type 2 diabetes mellitus, chronic kidney disease, and heart failure, have emerged as a potential novel approach for UL treatment. These inhibitors may help reduce the risk of urolithiasis, particularly in patients with diabetes, by improving glycemic control and altering urinary chemistry, which are crucial factors in stone formation. However, the changes in urinary composition induced by SGLT2 inhibitors might also increase the risk of uric acid stone formation. This review evaluates the potential of SGLT2 inhibitors in managing UL, highlighting both the benefits and the risks. While these inhibitors show promise in reducing new and recurrent urinary stones in patients with diabetes, data on their effects in patients without diabetes who form stones are limited. Current human evidence largely comes from post hoc analyses of randomized controlled trials (RCTs) and large-scale database studies, with only one study providing detailed stone composition data. Experimental studies in animal models and cell lines have focused on calcium oxalate (CaOx) stones, showing that SGLT2 inhibitors specifically target CaOx stone formation and related renal inflammation. Although primarily studied for CaOx stones, their potential impact on other calcium-containing stones, such as calcium phosphate, remains promising. Further research is needed to explore their therapeutic potential and optimize treatment strategies.

Forecasting ETF Performance: A Comparative Study of Deep Learning Models and the Fama-French Three-Factor Model

The global financial landscape has witnessed a significant shift towards Exchange-Traded Funds (ETFs), with their market capitalization surpassing USD 10 trillion in 2023, due to advantages such as low management fees, high liquidity, and broad market exposure. As ETFs become increasingly central to investment strategies, accurately forecasting their performance has become crucial. This study addresses this need by comparing the efficacy of deep learning models against the traditional Fama-French three-factor model in predicting daily ETF returns. The methodology employs eight artificial neural network architectures, including ANN, LSTM, GRU, CNN, and their variants, implemented in Python and applied to data ranging from 2010 to 2020, while also exploring the impact of additional factors on forecast accuracy. Empirical results reveal that LSTM and the Fama-French three-factor model exhibit a superior performance in ETF return prediction. This study contributes to the literature on financial forecasting and offers practical insights into investment decision making. By leveraging advanced artificial intelligence techniques, this study aims to enhance the toolkit available for ETF performance analysis, potentially improving investment strategies in this dynamic market segment.

3,8-Disubstituted Pyrazolo[1,5-a]quinazoline as GABAA Receptor Modulators: Synthesis, Electrophysiological Assays, and Molecular Modelling Studies

As a continuation of our study in the field of GABAA receptor modulators, we report the design and synthesis of new pyrazolo[1,5-a]quinazoline (PQ) bearing at the 8-position an oxygen or nitrogen function. All the final compounds and some intermediates, showing the three different forms of the pyrazolo[1,5-a]quinazoline scaffold (5-oxo-4,5-dihydro, -4,5-dihydro, and heteroaromatic form), have been screened with an electrophysiological technique on recombinant GABAAR (α1β2γ2-GABAAR), expressed in Xenopus laevis oocytes, by evaluating the variation in produced chlorine current, and permitting us to identify some interesting compounds (6d, 8a, 8b, and 14) on which further functional assays were performed. Molecular modelling studies (docking, minimization of complex ligand–receptor, and MD model) and a statistical analysis by a Hierarchical Cluster Analysis (HCA) have collocated these ligands in the class corresponding to their pharmacological profile. The HCA results are coherent with the model we recently published (Proximity Frequencies), identifying the residues γThr142 and αHis102 as discriminant for the agonist and antagonist profile.

Structure, Properties, and Applications of Silica Nanoparticles: Recent Theoretical Modeling Advances, Challenges, and Future Directions.

Silica nanoparticles (SNPs), one of the most widely researched materials in modern science, are now commonly exploited in surface coatings, biomedicine, catalysis, and engineering of novel self-assembling materials. Theoretical approaches are invaluable to enhancing fundamental understanding of SNP properties and behavior. Tremendous research attention is dedicated to modeling silica structure, the silica-water interface, and functionalization of silica surfaces for tailored applications. In this review, the range of theoretical methodologies are discussed that have been employed to model bare silica and functionalized silica. The evolution of silica modeling approaches is detailed, including classical, quantum mechanical, and hybrid methods and highlight in particular the last decade of theoretical simulation advances. It is started with discussing investigations of bare silica systems, focusing on the fundamental interactions at the silica-water interface, following with a comprehensively review of the modeling studies that examine the interaction of silica with functional ligands, peptides, ions, surfactants, polymers, and carbonaceous species. The review is concluded with the perspective on existing challenges in the field and promising future directions that will further enhance the utility and importance of the theoretical approaches in guiding the rational design of SNPs for applications in engineering and biomedicine.

Contrasting Rainfall Anomaly Patterns Over South America Related to Central and Eastern Atlantic Niño Modes

ABSTRACTThe present study examines the effects of the central Atlantic Niño (CAN) and eastern Atlantic Niño (EAN) events on the seasonal precipitation in South America (SA) during the 1951–2020 period. For the CAN during the summer and autumn, an interhemispheric sea surface temperature (SST) dipole mode induces an anomalous thermally direct circulation in the 10° N–10° S band and is the main factor causing precipitation anomaly patterns with a dipole structure between northern (negative) and northeastern (positive) SA. For winter and spring, the SST pattern featuring a South Atlantic dipole induces meridional and zonal anomalous circulations, which are the mechanisms causing positive precipitation anomalies in tropical SA to the north of 20° S. In contrast, for the EAN, the precipitation anomaly patterns show large areas with anomalous dryness, particularly during summer, autumn, and spring. For summer and autumn, the east–west SST anomaly gradient in the equatorial Atlantic and the associated sea level pressure (SLP) anomalies induce equatorial westerlies and a regional Walker cell with descending motions in most tropical SA, where large areas with anomalous dryness are noted. During spring, a northward SST gradient in the tropical Atlantic induces a meridional cell with descending motions in the 0°–10° S band; meanwhile, the westward SST gradient in the equatorial Atlantic and tropical South Atlantic induces a zonal circulation with descending motions over northeastern and eastern Brazil. These descending motions extend the anomalous dryness over a large area. For the EAN events, the east–west SST gradient and the associated east–west circulation in the South American/Atlantic region are crucial elements to modulate precipitation variability in SA. Therefore, the CAN and EAN events induce distinct precipitation anomaly patterns in SA due to distinct associated regional circulation patterns. The results presented here have not been discussed before and might have relevant implications for climate monitoring and modelling studies.

Population-level impact of weight loss on predicted healthcare spending and the incidence of obesity-related outcomes in the Asia-Pacific region: a modelling study

Population-level impact of weight loss on predicted healthcare spending and the incidence of obesity-related outcomes in the Asia-Pacific region: a modelling study

Microwave scattering properties of ice crystal particles during the melting process

Ice crystal particles play an important role in the study of cloud resolution, climate models, and radiative forcing. During the melting process, significant changes occur in the microphysical properties of ice crystal particles, such as the ice phase state, morphology, and mixing state. This process further affects the scattering and radiation characteristics properties of ice crystal particles. In this study, we constructed a non-spherical and inhomogeneous particle model based on the melting process of ice crystal particles. The scattering properties of melting ice crystal particles under four selected microwave frequency bands (92 GHz, 220 GHz, 280 GHz, and 340 GHz) are investigated by using discrete dipole approximation (DDA) method. The influence of ice crystal content (ICC) and particle aspect ratio on the scattering properties of ice crystal particles under thin coating and medium coating conditions are emphasized. The results show that the melting process significantly affects the scattering properties of melting ice crystal particles in a frequency dependent manner. Additionally, even slight melting of ice crystal particles leads to drastic changes in their scattering properties. Furthermore, we found that the morphology of ice crystal nuclei has a significant impact on their scattering characteristics even at medium levels of melting degree. In summary, this study confirms that it is essential to consider morphology and inhomogeneous characteristics during the melting process for microwave detection of ice crystal particles. This research may have significant implications for studies related to detection and inversion techniques for ice crystal particles.

Site-Blocking Antisense Oligonucleotides as a Mechanism to Fine-Tune MECP2 Expression.

Rett syndrome (RTT) is a neurodevelopmental disorder caused by loss-of-function mutations in the methyl CpG binding protein 2 (MECP2) gene. Despite its severe phenotype, studies in mouse models suggest that restoring MeCP2 levels can reverse RTT symptomology. Nevertheless, traditional gene therapy approaches are hindered by MECP2's narrow therapeutic window, complicating the safe delivery of viral constructs without overshooting the threshold for toxicity. The 3' untranslated region (3'UTR) plays a key role in gene regulation, where factors like miRNAs bind to pre-mRNA and fine-tune expression. Given that each miRNA's contribution is modest, blocking miRNA binding may represent a potential therapeutic strategy for diseases with high dosage sensitivity, like RTT. Here, we present a series of site-blocking antisense oligonucleotides (sbASOs) designed to outcompete repressive miRNA binding at the MECP2 3'UTR. This strategy aims to increase MECP2 levels in patients with missense or late-truncating mutations, where the hypomorphic nature of the protein can be offset by increased abundance. Our results demonstrate that sbASOs can elevate MECP2 levels in a dose-dependent manner in SH-SY5Y and patient fibroblast cell lines, plateauing at levels projected to be safe. Confirming in vivo functionality, sbASO administration in wild-type mice led to significant MeCP2 upregulation and the emergence of phenotypes associated with MeCP2 overexpression. In a T158M neural stem cell model of RTT, sbASO treatment significantly increased MECP2 expression and levels of the downstream effector protein, brain-derived neurotrophic factor (BDNF). These findings highlight the potential of sbASO-based therapies for MECP2-related disorders and advocate for their continued development.

A Comparative Study of Machine Learning Models for Predicting Customer Churn in Retail Banking: Insights from Logistic Regression, Random Forest, GBM, and SVM

Customer churn poses a significant challenge in the retail banking sector, leading to substantial financial losses and undermining long-term growth. This study explores the effectiveness of various machine learning models, including Logistic Regression, Random Forest, Gradient Boosting Machine (GBM), and Support Vector Machine (SVM), in predicting customer churn. Utilizing a comprehensive dataset derived from a leading bank, we conducted extensive data preprocessing and feature engineering before evaluating model performance through metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. Our findings reveal that the Gradient Boosting Machine outperforms its counterparts, achieving an accuracy of 87.2%, with an AUC-ROC score of 0.91, indicating its exceptional ability to distinguish between churned and non-churned customers. Random Forest follows closely, exhibiting robust performance, while SVM and Logistic Regression demonstrate moderate accuracy levels. This research underscores the transformative potential of machine learning in enhancing customer retention strategies within the banking industry. By identifying at-risk customers and understanding the underlying factors contributing to churn, banks can implement targeted interventions to improve customer satisfaction and loyalty. The study further suggests avenues for future research, including the exploration of real-time data analysis and the integration of qualitative customer insights, to refine predictive models and retention strategies.

Framework for Cyber Risk Loss Distribution of Client‐Server Networks: A Bond Percolation Model and Industry Specific Case Studies

ABSTRACTCyber risk has emerged as a significant threat to businesses that have increasingly relied on new and existing information technologies (IT). Across various businesses in different industries and sectors, a distinct pattern of IT network architectures, such as the client‐server network architecture, may, in principle, expose those businesses, which share it, to similar cyber risks. That is why in this article, we propose a probabilistic structural framework for loss assessments of cyber risks on the class of client‐server network architectures with different client types. To our knowledge, there exist no theoretical models of an aggregate loss distribution for cyber risk in this setting. With this structural framework via the exact mean and variance of losses, we demonstrate how the changing cybersecurity environment of a business's IT network impacts the loss distribution. Furthermore, our framework provides insights into better investment strategies for cybersecurity protection on the client‐server network. Motivated by cyberattacks across industries, we apply our framework to four case studies that utilize the client‐server network architecture. Our first application is implantable medical devices in healthcare. Our second application is the smart buildings domain. Third, we present an application for ride‐sharing services such as Uber and Lyft. The fourth is the application of vehicle‐to‐vehicle cooperation in traffic management. The results are corresponding exact means and variances of cyber risk loss distributions parameterized by various cybersecurity parameters allowing for liability assessments and decisions in cybersecurity protection investments.

Design, Development of Pyrazole-Linked Spirocyclopropyl Oxindole-Carboxamides as Potential Cytotoxic Agents and Type III Allosteric VEGFR-2 Inhibitors.

Tumor progression depends on angiogenesis, which is stimulated by growth factors like VEGF, targeting VEGFR kinase with small molecules is an effective anti-angiogenic therapeutic approach. The rational modification of sunitinib (VEGFR-2 inhibitor) to spirocyclopropyloxindoline carboxamides have been performed and their in vitro cytotoxic profiling was evaluated. The molecular modelling studies enabled the screening of designed analogues and identifying the possible interactions within the type III allosteric inhibitor binding site of VEGFR-2. The biological screening of synthesized compounds 15 a-y, revealed the ability of compound 15 w to inhibit the cell growth in MCF-7 cell line with IC50 value of 3.87±0.19 μM and alongside inhibition of VEGFR-2 kinase at a IC50 concentration of 4.34±0.13 μM was observed. Also, VEGFR-2 inhibition was validated through HUVEC tube formation inhibition assay. The qualitative assessment of apoptosis induction by 15 w in MCF-7 cells was evaluated through staining studies such as AO/EB and DAPI staining, whereas quantification of apoptosis and cell cycle analysis were performed through FACS analysis. The metastatic ability of the cancer cells was evaluated through inhibition of cell migration by a scratch wound healing assay. The current study strives to sequentially optimize the structural attributes of the 3-alkenyl oxindole core to surpass the existing challenges of well-known VEGFR-2 inhibitors. The findings observed from this study highlights that compound 15 w to be a prominent lead towards the development of clinical drug candidates.

A landslide runout model for sediment transport, landscape evolution, and hazard assessment applications

Abstract. We developed a new rule-based, cellular-automaton algorithm for predicting the hazard extent, sediment transport, and topographic change associated with the runout of a landslide. This algorithm, which we call MassWastingRunout (MWR), is coded in Python and implemented as a component for the package Landlab. MWR combines the functionality of simple runout algorithms used in landscape evolution and watershed sediment yield models with the predictive detail typical of runout models used for landslide inundation hazard mapping. An initial digital elevation model (DEM), a regolith depth map, and the location polygon of the landslide source area are the only inputs required to run MWR to model the entire runout process. Runout relies on the principle of mass conservation and a set of topographic rules and empirical formulas that govern erosion and deposition. For the purpose of facilitating rapid calibration to a site, MWR includes a calibration utility that uses an adaptive Bayesian Markov chain Monte Carlo algorithm to automatically calibrate the model to match observed runout extent, deposition, and erosion. Additionally, the calibration utility produces empirical probability density functions of each calibration parameter that can be used to inform probabilistic implementation of MWR. Here we use a series of synthetic terrains to demonstrate basic model response to topographic convergence and slope, test calibrated model performance relative to several observed landslides, and briefly demonstrate how MWR can be used to develop a probabilistic runout hazard map. A calibrated runout model may allow for region-specific and more insightful predictions of landslide impact on landscape morphology and watershed-scale sediment dynamics and should be further investigated in future modeling studies.

The effectiveness of secondary-school based interventions on the future physical activity of adolescents in Aotearoa New Zealand: a modelling study.

Secondary schools are important settings for promoting varied physical activity (PA) opportunities for adolescents to promote PA throughout life. However, research on the effect of secondary school-based interventions on future PA is limited. This study examined the potential impact of secondary school-based interventions on the determinants of future PA participation of Aotearoa New Zealand (NZ) adolescents using simulated modelling. We used data from a nationally representative sample of secondary school students (n = 5035) aged 12-17 between 2017 and 2020 in NZ. We modelled four secondary school-based interventions and their projected impact on five determinants of future PA. Modelled interventions were the technological augmentation of physical education (TAPE), a peer-led PA mentoring programme (PL), physically active learning (PAL) and the inclusion of a natural environment play area (NE). Total weekly PA increased the most from the NE intervention (+ 0.2h/week), followed by TAPE (+ 0.08h/week), PAL (+ 0.05h/week) and PL (-0.06h/week). Current number of PA settings increased the most in NE (+ 1.75 settings/week), followed by TAPE (+ 1.29 settings/week), PAL (+ 1.21 settings/week) and PL (+ 0.73 settings/week). Current number of PA types increased the most in NE (+ 1.57 types/week), followed by PL (+ 1.05 types/week), TAPE (+ 0.34 types/week) and PAL (+ 0.15 types/week). Physical literacy scores increased the most from PL (+ 3.6%), followed by PAL (+ 3.3%), TAPE (+ 0.43%) and NE (+ 0.12%). Social support scores increased the most from PAL (+ 5%), followed by PL (+ 1.9%), TAPE (+ 1.46%) and NE (+ 0.57%). On average, all interventions benefitted determinants of future PA participation to a small degree. Results show differing magnitudes of the intervention effect by determinant, indicating the complexities surrounding the promotion of PA adherence. Future interventions could be improved through detailed consultation alongside, and involving, adolescents and stakeholders within schools. Researchers should also prioritise the collection of longitudinal PA data and explore its connection with sociodemographic differences between adolescents.

Studying the influence of the turbulence model on the aerodynamic characteristics of a cruise missile model

The article presents the study of the turbulence model’s influence on aerodynamic characteristics of a cruise missile model obtained during numerical calculations conducted using ANSYS Fluent Release 2023R1 software. Three turbulence models (i.e. Spalart-Allmaras, realizable k-ε and SST k-ω), as well as three computational meshes with a different refinement in a boundary layer region, were investigated. The results were analyzed and compared to characteristics obtained experimentally in the low-speed aerodynamic tunnel at the Military University of Technology (Warsaw, Poland).