Hypertension is a pervasive and widespread health condition that poses a significant risk factor for cardiovascular disease, which includes conditions such as heart attack, stroke, and heart failure. Despite its widespread occurrence, the exact cause of hypertension remains unknown, and the mechanisms underlying the progression from prehypertension to hypertension require further investigation. Recent proteomic studies have shown promising results in uncovering potential biomarkers related to disease development. In this study, serum proteomic data collected from Qatar Biobank were analyzed to identify altered protein expression between individuals with normal blood pressure, prehypertension, and hypertension and to elucidate the biological pathways contributing to this disease. The results revealed a cluster of proteins, including the SRC family, CAMK2B, CAMK2D, TEC, GSK3, VAV, and RAC, which were markedly upregulated in patients with hypertension compared to those with prehypertension (fold change ≥ 1.6 or ≤−1.6, area under the curve ≥ 0.8, and q-value < 0.05). Pathway analysis showed that the majority of these proteins play a role in actin cytoskeleton remodeling. Actin cytoskeleton reorganization affects various biological processes that contribute to the maintenance of blood pressure, including vascular tone, endothelial function, cellular signaling, inflammation, fibrosis, and mechanosensing. Therefore, the findings of this study suggest a potential novel role of actin cytoskeleton-related proteins in the progression from prehypertension to hypertension. The present study sheds light on the underlying pathological mechanisms involved in hypertension and could pave the way for new diagnostic and therapeutic approaches for the treatment of this disease. Full article

In brain imaging segmentation, precise tumor delineation is crucial for diagnosis and treatment planning. Traditional approaches include convolutional neural networks (CNNs), which struggle with processing sequential data, and transformer models that face limitations in maintaining computational efficiency with large-scale data. This study introduces MambaBTS: a model that synergizes the strengths of CNNs and transformers, is inspired by the Mamba architecture, and integrates cascade residual multi-scale convolutional kernels. The model employs a mixed loss function that blends dice loss with cross-entropy to refine segmentation accuracy effectively. This novel approach reduces computational complexity, enhances the receptive field, and demonstrates superior performance for accurately segmenting brain tumors in MRI images. Experiments on the MICCAI BraTS 2019 dataset show that MambaBTS achieves dice coefficients of 0.8450 for the whole tumor (WT), 0.8606 for the tumor core (TC), and 0.7796 for the enhancing tumor (ET) and outperforms existing models in terms of accuracy, computational efficiency, and parameter efficiency. These results underscore the model’s potential to offer a balanced, efficient, and effective segmentation method, overcoming the constraints of existing models and promising significant improvements in clinical diagnostics and planning. Full article

Favipiravir (FP) and ebselen (EB) belong to a diverse class of antiviral drugs known for their significant efficacy in treating various viral infections. Utilizing molecular dynamics (MD) simulations, machine learning, and van der Waals density functional theory, we accurately elucidate the binding properties of these antiviral drugs on a phosphorene single-layer. To further investigate these characteristics, this study employs four distinct machine learning models—Random Forest, Gradient Boosting, XGBoost, and CatBoost. The Hamiltonian of antiviral molecules within a monolayer of phosphorene is appropriately trained. The key aspect of utilizing machine learning (ML) in drug design revolves around training models that are efficient and precise in approximating density functional theory (DFT). Furthermore, the study employs SHAP (SHapley Additive exPlanations) to elucidate model predictions, providing insights into the contribution of each feature. To explore the interaction characteristics and thermodynamic properties of the hybrid drug, we employ molecular dynamics and DFT calculations in a vacuum interface. Our findings suggest that this functionalized 2D complex exhibits robust thermostability, indicating its potential as an effective and enabled entity. The observed variations in free energy at different surface charges and temperatures suggest the adsorption potential of FP and EB molecules from the surrounding environment. Full article

Hydrogels consist of a network of highly porous polymeric chains with the potential for use as a wound dressing. Propolis is a natural product with several biological properties including anti-inflammatory, antibacterial and antioxidant activities. This study was aimed at synthesizing and characterizing a polyacrylamide/methylcellulose hydrogel containing propolis as an active ingredient, to serve as a wound dressing alternative, for the treatment of skin lesions. The hydrogels were prepared using free radical polymerization, and were characterized using scanning electron microscopy, infrared spectroscopy, thermogravimetry, differential scanning calorimetry, swelling capacity, mechanical and rheological properties, UV-Vis spectroscopy, antioxidant activity by the DPPH, ABTS and FRAP assays and biocompatibility determined in Vero cells and J774 macrophages by the MTT assay. Hydrogels showed a porous and foliaceous structure with a well-defined network, a good ability to absorb water and aqueous solutions simulating body fluids as well as desirable mechanical properties and pseudoplastic behavior. In hydrogels containing 1.0 and 2.5% propolis, the contents of total polyphenols were 24.74 ± 1.71 mg GAE/g and 32.10 ± 1.01 mg GAE/g and those of total flavonoids 8.01 ± 0.99 mg QE/g and 13.81 ± 0.71 mg QE/g, respectively, in addition to good antioxidant activity determined with all three methods used. Therefore, hydrogels containing propolis extract, may serve as a promising alternative wound dressing for the treatment of skin lesions, due to their anti-oxidant properties, low cost and availability. Full article

The purpose of this study is to evaluate how the ChopMelon Net online learning platform can contribute to the effectiveness of sustainable education by incorporating real social issues. The core innovation of ChopMelon Net is that it provides a learning environment that connects learners directly to real-world challenges and aims to enhance learners’ understanding of sustainable development theory. The study employed the technology acceptance model (TAM), theory of planned behavior (TPB), and knowledge–Behavior gap theory (KBGT) to design the questionnaires and semi-structured interviews in order to comprehensively assess the impact of ChopMelon Net on learners’ skill enhancement. The results showed that learners not only showed significant improvement in critical thinking and problem-solving skills after using ChopMelon Net, but also expressed a high level of satisfaction with the educational content and interactive experience provided by the platform. In addition, the study found that ChopMelon Net effectively promoted learners’ awareness of the importance of sustainable education and behavioral change through its innovative educational model, which provides important empirical support for future sustainable education practices and the design of online education platforms. This study highlights the importance and effectiveness of incorporating real social issues into the teaching and learning process, with far-reaching implications for sustainable innovation in education. Full article

The endo-1,4-β-xylanases (EC 3.2.1.8) are the largest group of hydrolytic enzymes that degrade xylan, the major component of hemicelluloses, by catalyzing the hydrolysis of glycosidic bonds β-1,4 in this polymer, releasing xylooligosaccharides of different sizes. Xylanases have considerable potential in producing bread, animal feed, food, beverages, xylitol, and bioethanol. The fungus Aspergillus tamarii Kita produced xylanases in Adams’ media supplemented with barley bagasse (brewer’s spent grains), a by-product from brewery industries. The culture extract exhibited two xylanase activities in the zymogram, identified by mass spectrometry as glycosyl hydrolase (GH) families 10 and 11 (GH 10 and GH 11). The central composite design (CCD) showed excellent predictive capacity for xylanase production (23.083 U mL⁻¹). Additionally, other enzyme activities took place during the submerged fermentation. Moreover, enzymatic saccharification based on a mixture design (MD) of three different lignocellulosic residues was helpful in the production of fermentable sugars by the A. tamarii Kita crude extract. Full article

This study aimed to assess and compare the utilization of preventive and other health services and the cost or availability in different regions of Europe, before and during the economic crisis. The data used in the study were obtained from Wave 8 of the Survey of Health, Ageing and Retirement in Europe (2019/2020) and Wave 1 data (2004/5), with a sample size of 46,106 individuals aged ≥50 across 27 countries, adjusted to represent a population of N = 180,886,962. Composite scores were derived for preventive health services utilization (PHSU), health care services utilization (HCSU), and lack of accessibility/availability in health care services (LAAHCS). Southern countries had lower utilization of preventive services and higher utilization of other health services compared to northern countries, with a significant lack of convergence. Moreover, the utilization of preventive health services decreased, whereas the utilization of secondary care services increased during the austerity period. Southern European countries had a significantly higher prevalence of lack of accessibility. An increase in the frequency of lack of accessibility/availability in health care services was observed from 2004/5 to 2019/20. In conclusion, our findings suggest that health inequalities increase during crisis periods. Therefore, policy interventions could prioritize accessibility and expand health coverage and prevention services. Full article

In this paper, we introduce a model designed to predict human error probability (HEP) in the context of fishing boat operations utilizing the cognitive reliability and error analysis method (CREAM). We conducted an analysis of potential accidents on fishing boats and calculated the cognitive failure probability (CFP) for each identified accident. The common performance conditions (CPCs) from the original CREAM were adapted to better reflect the conditions on fishing boats, with the adapted CPCs’ validity confirmed through expert consultations. To apply CREAM, data were gathered via a survey of fishermen, with the uncertainty in the collected data addressed through the application of fuzzy set theory (FST). We then established a Bayesian network (BN) model to elucidate the relationship between the fuzzy data and HEP, utilizing a weighted sum algorithm to determine conditional probabilities within the BN. Both basic and extended versions of CREAM were applied to analyze the most common accidents among fishermen, calculating the CFP for each type of accident. According to our analysis, the poorer the dynamic CPC, the higher the probability that a fall accident will occur inside the boat due to human error, necessitating a countermeasure. The paper proposes safety enhancements for small fishing boats and illustrates the increased precision of human reliability analysis (HRA) models in forecasting human error by incorporating quantitative methods. It calls for further data collection and refinement of the model for more accurate operational risk assessments. Full article

This study aims to analyze the impact of uniform and eccentric load conditions on the performance of internal feedback hydrostatic thrust and journal bearing. Two distinct models are established: a three-degrees-of-freedom uniform load model and a five-degrees-of-freedom eccentric load model. The support stiffness, overturning stiffness, and flow rate for both thrust and journal bearings are calculated. Additionally, numerical analysis is conducted to examine the influence of oil film thickness, inlet pressure, and restrictor size on the operational characteristics of the bearings, revealing the interplay between an eccentric load and journal bearing speed. The validity of the theoretical algorithm is verified through finite element simulation. The research outcomes hold significant guiding implications for the design and application of internal feedback hydrostatic bearings. Full article

The meaning of justice can be defined according to a juridical, human, theological, ethical, biomedical, or social perspective. It should guarantee the protection of life and health, personal, civil, political, economic, and religious rights, as well as non-discrimination, inclusion, protection, and access to care. In this review, we deal with three theoretical concepts that define justice in all its aspects. (1) The utilitarian theory, which justifies moral statements on the basis of the evaluation of the consequences that an action produces, elaborating a pragmatic model of medical science. (2) The libertarian theory, which considers freedom as the highest political aim, thus absolutizing the rights of the individual; here, the principle of self-determination, with respect to which the principle of permission/consent is the fundamental presupposition, plays a central role in the definition of the person. (3) The iusnaturalist theory, in which man’s moral freedom is identified with the ability to act by choosing what the intellect indicates to him as good; the natural moral law that drives every conscience to do good is therefore realized in respect for the person in the fullness of his rights. In conclusion, different forms and conceptions of justice correspond to different organizations of society and different ways of addressing ethical issues in the biomedical domain. Full article

Atherosclerotic disease, including coronary heart disease (CHD), is one of the chronic inflammatory conditions, and an imbalance between pro-inflammatory and anti-inflammatory cytokines plays a role in the process of atherosclerosis. Interleukin (IL)-27, one of the IL-12 family members, is recognized to play a dual role in regulating immune responses with both pro-inflammatory and anti-inflammatory properties. IL-27 is secreted from monocytes, T cells, and endothelial cells, and its expression is upregulated in atherosclerotic plaques. We previously reported that no significant difference was observed in plasma IL-27 levels between patients with stable CHD and those without it. However, the prognostic value of IL-27 levels has not been fully elucidated. We studied the relation of plasma IL-27 levels to cardiovascular events in 402 patients undergoing elective coronary angiography for suspected CHD. We defined cardiovascular events as cardiovascular death, myocardial infarction, unstable angina, stroke, or coronary revascularization. Of the 402 study patients, CHD was present in 209 (52%) patients. Plasma IL-27 levels were not markedly different between patients with CHD and those without it (median 0.23 vs. 0.23 ng/mL). During a follow-up of 7.6 ± 4.5 years, cardiovascular events were observed in 70 patients (17%). In comparison to the 332 patients with no event, the 70 patients who had cardiovascular events showed significantly higher IL-27 levels (median 0.29 vs. 0.22 ng/mL) and more frequently had an IL-27 level of >0.25 ng/mL (59% vs. 40%) (p < 0.01). The Kaplan–Meier analysis demonstrated a lower event-free survival rate in patients with an IL-27 level >0.25 ng/mL than in those with an IL-27 level ≤0.25 ng/mL (p < 0.02). The multivariate Cox proportional hazards regression analysis showed that IL-27 level (>0.25 ng/mL) was a significant predictor for cardiovascular events (hazard ratio: 1.82; 95%CI: 1.13–2.93, p < 0.02), independent of CHD. Thus, high IL-27 levels in plasma were related to an increased risk of further cardiovascular events in patients who underwent elective coronary angiography. Full article

Oncological patients show intense catabolic activity, as well as a susceptibility to higher nutritional risk and clinical complications. Thus, tools are used for monitoring prognosis. Our objective was to analyze the nutrition prognosis of patients who underwent radiotherapy, correlating it with outcomes and complications. We performed a retrospective transversal study based on secondary data from hospital records of patients who started radiotherapy between July 2022 and July 2023. We established Prognostic Scores through a combination of Prognostic Nutritional Index (PNI) and a Subjective Global Assessment (SGA), assessed at the beginning and end of treatment. Score 3 patients, with PNI ≤ 45.56 and an SGA outcome of malnutrition, initially presented a higher occurrence of odynophagia, later also being indicative of reduced diet volume, treatment interruption, and dysphagia. SGA alone showed sensitivity to altered diet volume, dysphagia, and xerostomia in the second assessment. Besides this, PNI ≤ 45.56 also indicated the use of alternative feeding routes, treatment interruption, and hospital discharge with more complications. We conclude that the scores could be used to indicate complications; however, further studies on combined biomarkers are necessary. Full article

This article examines the involvement of the brain-derived neurotrophic factor (BDNF) in the control of nociception and pain. BDNF, a neurotrophin known for its essential role in neuronal survival and plasticity, has garnered significant attention for its potential implications as a modulator of synaptic transmission. This comprehensive review aims to provide insights into the multifaceted interactions between BDNF and pain pathways, encompassing both physiological and pathological pain conditions. I delve into the molecular mechanisms underlying BDNF’s involvement in pain processing and discuss potential therapeutic applications of BDNF and its mimetics in managing pain. Furthermore, I highlight recent advancements and challenges in translating BDNF-related research into clinical practice. Full article

Mount Meager is a deeply eroded quaternary volcanic complex located in southwestern British Columbia (BC) and is known for its frequent large landslides. In 2010, the south face of Mount Meager collapsed, generating a long-runout debris avalanche that was one of the largest landslides (50 × 10⁶ m³) in Canadian history. Over the past 14 years, the landslide deposit has been reworked by stream action, delivering large amounts of sediment to Lillooet River, just downstream. In this study, we investigate 10 years of geomorphic evolution of the landslide deposit using orthophotos and digital elevation models (DEMs) generated using Structure from Motion (SfM) photogrammetry on aerial photographs acquired during unmanned aerial vehicle (UAV) and Global Navigation Satellite System (GNSS) surveys. The SfM products were used to produce a series of precise maps that highlight the geomorphological changes along the lower Meager Creek within the runout area of the landslide. Comparison of DEMs produced from 2010, 2012, 2015, and 2019 imagery allowed us to calculate deposit volume changes related to erosion, transport, and redeposition of landslide material. We estimate that about 1.1 × 10⁶ m³ of sediment was eroded from the landslide deposit over the period 2015–2019. About 5.2 × 10⁵ m³ of that sediment was redeposited inside the study area. About 5.8 × 10⁵ m³ of sediment, mainly sand, silt, and clay, were exported from the study area and are being carried by Lillooet River towards Pemberton, 40 km from Mount Meager, and farther downstream. These remobilized sediments likely reduce the Lillooet River channel capacity and thus increase flood hazards to the communities of Pemberton and Mount Currie. Our study indicates a landslide persistence in the landscape, with an estimated 47-year half-life decay, suggesting that higher flood hazard conditions related to increased sediment supply may last longer than previously estimated. This study shows the value of using SfM in tandem with historic aerial photographs, UAV photos, and high-resolution satellite imagery for determining sediment budgets in fluvial systems. Full article

This study presents a refined approach to spatially identify carbon sequestration assets, crucial for effective climate action planning in Illinois. By integrating landscape analytical methods with species-specific carbon assessment techniques, we deliver a nuanced evaluation of forest area sequestration potential. Our methodology employs a combination of landscape imagery, deep learning analytics, Kriging interpolation, and i-Tree Planting tools to process forest sample data. The results reveal a spatial variability in sequestration capacities, highlighting significant carbon sinks in southern Illinois. This region, known for its historical woodland richness, showcases the distinct carbon sequestration abilities of various tree species. Findings emphasize the role of biodiversity in the carbon cycle and provide actionable insights for forest management and carbon neutral strategies. This study demonstrates the utility of advanced spatial analysis in environmental research, underscoring its potential to enhance accuracy in ecological quantification and conservation efforts. Full article

Pesticides play a critical role in pest management and agricultural productivity; however, their misuse or overuse can lead to adverse effects on human health and the environment, including impacts on ecosystems and contamination. Currently, neonicotinoids (NNIs) are the most widely used systemic insecticides and are questioned worldwide for their possible impacts on pollinators. After NNI application, a substantial portion is not absorbed by the plant and may accumulate in the soil, affecting the soil microbial community. In this review, we explore the main studies carried out either in the laboratory or in the field about this matter. The studies report that the application of NNIs affects soil microbial activity and can act on microbial communities differently due to their unique chemical properties, degradation in soil, soil type, effects on soil properties, and methods of application. NNIs alter the diversity, structure, and abundance of soil microbes, in some cases increasing or decreasing their representativeness in soil. Bacterial phyla like Pseudomonadota, Bacillota, Actinomycetota, and Nitrospirota increase after NNI exposure, just like the families Nitrosomonadaceae, Nitrososphaeraceae, Nitrospiraceae, Sphingomonadaceae, Streptomycetaceae, and Catenulisporaceae. At the bacterial genus level, Nitrospira was associated with a decrease in nitrification processes in soil. The bacterial genera Sphingomonas, Streptomyces, Catenulispora, Brevundimonas, Pedobacter, and Hydrogenophaga are related to NNI degradation after application. Microorganisms could minimize the impacts of NNIs in agricultural soil. Therefore, the use of bioinoculation as a bioremediation tool is explored as an alternative to contribute to agricultural sustainability. Full article

The marine dinoflagellate Alexandrium is known to form harmful algal blooms (HABs) and produces saxitoxin (STX) and its derivatives (STXs) that cause paralytic shellfish poisoning (PSP) in humans. Cell growth and cellular metabolism are affected by environmental conditions, including nutrients, temperature, light, and the salinity of aquatic systems. Abiotic factors not only engage in photosynthesis, but also modulate the production of toxic secondary metabolites, such as STXs, in dinoflagellates. STXs production is influenced by a variety of abiotic factors; however, the relationship between the regulation of these abiotic variables and STXs accumulation seems not to be consistent, and sometimes it is controversial. Few studies have suggested that abiotic factors may influence toxicity and STXs-biosynthesis gene (sxt) regulation in toxic Alexandrium, particularly in A. catenella, A. minutum, and A. pacificum. Hence, in this review, we focused on STXs production in toxic Alexandrium with respect to the major abiotic factors, such as temperature, salinity, nutrients, and light intensity. This review informs future research on more sxt genes involved in STXs production in relation to the abiotic factors in toxic dinoflagellates. Full article

Aeollanthus repens, native to East Africa, thrives in seasonally dry tropical biomes and boasts qualities ideal for both ornamental and ground cover purposes. However, despite its potential, its current resistance levels remain uncertain. Assessing its adaptability could offer valuable insights for its wider adoption and utilization. In this study, researchers employed 3-month-old cuttings of A. repens, subjecting them to six distinct environments by manipulating the temperature (25/20 °C and 35/30 °C) and soil moisture levels (100%, 20%, and 40%). Their leaf physiological and photosynthetic indices were assessed at intervals of 5, 10, and 15 days following exposure to stress. The findings unveiled that exposure to prolonged moisture, elevated temperatures, or a combination of both led to an increase in osmoregulatory substances in the leaves. This increase was accompanied by heightened enzyme activity and an increased intercellular carbon dioxide concentration, followed by a subsequent decline. Additionally, chlorophyll content, net photosynthetic rate, stomatal conductance, and transpiration rate exhibited a decreasing trend over time. Through a comprehensive assessment of stress tolerance utilizing a composite affiliation function value index, the study concluded that A. repens exhibits optimal growth in a certain high-temperature environments and demonstrates substantial resistance to waterlogging, drought, and simultaneous high-temperature stress. However, the resilience of A. repens appears to diminish under the compounded stresses of high temperature and drought. Full article

This article presents a detailed examination of the methodology and modeling tools utilized to analyze gas flows in pipelines, rooted in the fundamental principles of gas dynamics. The methodology integrates numerical simulations with modern neural network techniques, particularly focusing on the PINN utilizing the continuous symmetry data inherent in PDEs, which is called the symmetry-enhanced Physics-Informed Neural Network. This innovative approach combines artificial neural networks (ANNs) integrating physical equations, which provide enhanced efficiency and accuracy when modeling various complex processes related to physics with a symmetric and asymmetric nature. The presented mathematical model, based on the system of Euler equations, has been carefully implemented using Python language. Verification with analytical solutions ensures the accuracy and reliability of the computations. In this research, a comparative and comprehensive analysis was carried out comparing the outcomes obtained using the symmetry-enhanced PINN method and those from conventional computational fluid dynamics (CFD) approaches. The analysis highlighted the advantages of the symmetry-enhanced PINN method, which produced smoother pressure and velocity fluctuation profiles while reducing the computation time, demonstrating its capacity as a revolutionary modeling tool. The estimated results derived from this study are of paramount importance for ensuring ongoing energy supply reliability and can also be used to create predictive models related to gas behavior in pipelines. The application of modeling techniques for gas flow simulations has the potential to improve the integrity of our energy infrastructure and utilization of gas resources, contributing to advancing our understanding of symmetry principles in nature. However, it is crucial to emphasize that the effectiveness of such models relies on continuous monitoring and frequent updates to ensure alignment with real-world conditions. This research not only contributes to a deeper understanding of compressible gas flows but also underscores the crucial role of advanced modeling methodologies in the sustainable management of gas resources for both current and future generations. The numerical data covered the physics of the process related to the modeling of high-pressure gas flows in pipelines with regard to density, velocity and pressure, where the PINN model was able to outperform the classical CFD method for velocity by 170% and for pressure by 360%, based on $L_{\infty }$ values. Full article

Thin ice with a thickness of less than half a meter produces strong salt and heat fluxes which affect deep water circulation and weather in the polar oceans. The identification of thin ice areas is essential for ship navigation. We have developed thin ice detection algorithms for the AMSR2 and FY-3C MWRI radiometer data over the Arctic Ocean. Thin ice (<20 cm) is detected based on the classification of the H-polarization 89–36-GHz gradient ratio (GR8936H) and the 36-GHz polarization ratio (PR36) signatures with a linear discriminant analysis (LDA) and thick ice restoration with GR3610H. The brightness temperature ($T_B$) data are corrected for the atmospheric effects following an EUMETSAT OSI SAF correction method in sea ice concentration retrieval algorithms. The thin ice detection algorithms were trained and validated using MODIS ice thickness charts covering the Barents and Kara Seas. Thin ice detection is applied to swath $T_B$ datasets and the swath charts are compiled into a daily thin ice chart using 10 km pixel size for AMSR2 and 20 km for MWRI. On average, the likelihood of misclassifying thick ice as thin in the ATIDA2 daily charts is 7.0% and 42% for reverse misclassification. For the MWRI chart, these accuracy figures are 4% and 53%. A comparison of the MWRI chart to the AMSR2 chart showed a very high match (98%) for the thick ice class with SIC > 90% but only a 53% match for the thin ice class. These accuracy disagreements are due to the much coarser resolution of MWRI, which gives larger spatial averaging of $T_B$ signatures, and thus, less detection of thin ice. The comparison of the AMSR2 and MWRI charts with the SMOS sea ice thickness chart showed a rough match in the thin ice versus thick ice classification. The AMSR2 and MWRI daily thin ice charts aim to complement SAR data for various sea ice classification tasks. Full article

Photocatalysis is a fascinating process in which a photocatalyst plays a pivotal role in driving a chemical reaction when exposed to light. Its capacity to harness light energy triggers a cascade of reactions that lead to the formation of intermediate compounds, culminating in the desired final product(s). The essence of this process is the interaction between the photocatalyst’s excited state and its specific interactions with reactants, resulting in the creation of intermediates. The process’s appeal is further enhanced by its cyclic nature—the photocatalyst is rejuvenated after each cycle, ensuring ongoing and sustainable catalytic action. Nevertheless, comprehending the photocatalytic process through the modeling of photoactive materials and molecular devices demands advanced computational techniques founded on effective quantum chemistry methods, multiscale modeling, and machine learning. This review analyzes contemporary theoretical methods, spanning a range of lengths and accuracy scales, and assesses the strengths and limitations of these methods. It also explores the future challenges in modeling complex nano-photocatalysts, underscoring the necessity of integrating various methods hierarchically to optimize resource distribution across different scales. Additionally, the discussion includes the role of excited state chemistry, a crucial element in understanding photocatalysis. Full article

The effects of initial small-scale material nonlinearity on the pre-yield and pre-buckling response of externally pressurized metallic (plane strain) perfect rings (very long cylindrical shells) is investigated. The cylindrically curved 16-node element, based on an assumed quadratic displacement field (in surface-parallel coordinates) and the assumption of linear distribution of displacements through thickness (LDT), is employed to obtain the discretized system equations. The effect of initial small-scale material nonlinearity (assumed hypo-elastic) on the deformation and stress in the pre-yield and pre-buckling regime of a very long relatively thin metallic cylindrical shell (plane strain ring) is numerically investigated. These numerical results demonstrate that the enhanced responses for metallic rings due to initial small-scale nonlinearity are significant enough to not miss attentions from designers and operators of submersibles alike. Full article