The goal of the study was to compare the surface characteristics of typical implant materials used in orthopedic surgery and traumatology, as these determine their successful biointegration. The morphological and chemical structure of Vortex plate anodized titanium from commercially pure (CP) Grade 2 Titanium (Ti2) is generally used in the following; non-cemented total hip replacement (THR) stem and cup Ti alloy (Ti6Al4V) with titanium plasma spray (TPS) coating; cemented THR stem Stainless steel (SS); total knee replacement (TKR) femoral component CoCrMo alloy (CoCr); cemented acetabular component from highly cross-linked ultrahigh molecular weight polyethylene (HXL); and cementless acetabular liner from ultrahigh molecular weight polyethylene (UHMWPE) (Sanatmetal, Ltd., Eger, Hungary) discs, all of which were examined. Visualization and elemental analysis were carried out by scanning electron microscopy (SEM), energy dispersive spectroscopy (EDS) and X-ray photoelectron spectroscopy (XPS). Surface roughness was determined by atomic force microscopy (AFM) and profilometry. TPS Ti presented the highest R_a value (25 ± 2 μm), followed by CoCr (535 ± 19 nm), Ti2 (227 ± 15 nm) and SS (170 ± 11 nm). The roughness measured in the HXL and UHMWPE surfaces was in the same range, 147 ± 13 nm and 144 ± 15 nm, respectively. EDS confirmed typical elements regarding the investigated prosthesis materials. XPS results supported the EDS results and revealed a high % of Ti⁴⁺ on Ti2 and TPS surfaces. The results indicate that the surfaces of prosthesis materials have significantly different features, and a detailed characterization is needed to successfully apply them in orthopedic surgery and traumatology. Full article

Arterial blood pressure (ABP) serves as a pivotal clinical metric in cardiovascular health assessments, with the precise forecasting of continuous blood pressure assuming a critical role in both preventing and treating cardiovascular diseases. This study proposes a novel continuous non-invasive blood pressure prediction model, DSRUnet, based on deep sparse residual U-net combined with improved SE skip connections, which aim to enhance the accuracy of using photoplethysmography (PPG) signals for continuous blood pressure prediction. The model first introduces a sparse residual connection approach for path contraction and expansion, facilitating richer information fusion and feature expansion to better capture subtle variations in the original PPG signals, thereby enhancing the network’s representational capacity and predictive performance and mitigating potential degradation in the network performance. Furthermore, an enhanced SE-GRU module was embedded in the skip connections to model and weight global information using an attention mechanism, capturing the temporal features of the PPG pulse signals through GRU layers to improve the quality of the transferred feature information and reduce redundant feature learning. Finally, a deep supervision mechanism was incorporated into the decoder module to guide the lower-level network to learn effective feature representations, alleviating the problem of gradient vanishing and facilitating effective training of the network. The proposed DSRUnet model was trained and tested on the publicly available UCI-BP dataset, with the average absolute errors for predicting systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean blood pressure (MBP) being 3.36 ± 6.61 mmHg, 2.35 ± 4.54 mmHg, and 2.21 ± 4.36 mmHg, respectively, meeting the standards set by the Association for the Advancement of Medical Instrumentation (AAMI), and achieving Grade A according to the British Hypertension Society (BHS) Standard for SBP and DBP predictions. Through ablation experiments and comparisons with other state-of-the-art methods, the effectiveness of DSRUnet in blood pressure prediction tasks, particularly for SBP, which generally yields poor prediction results, was significantly higher. The experimental results demonstrate that the DSRUnet model can accurately utilize PPG signals for real-time continuous blood pressure prediction and obtain high-quality and high-precision blood pressure prediction waveforms. Due to its non-invasiveness, continuity, and clinical relevance, the model may have significant implications for clinical applications in hospitals and research on wearable devices in daily life. Full article

A resilient building environment is crucial for securing sustainable development in urban areas, as the 2030 Agenda for Sustainable Development Goal 11 stresses. In developing countries in particular, the risk of disasters is increasing due to the poorly built environment caused by urbanization. However, building disaster resilience in vulnerable urban environments characterized by aging houses, limited public spaces, and complex land rights and tenancy issues poses a major challenge. This study aims to identify critical factors influencing effective disaster-resilient urban development by examining Japan’s experience, with a focus on approaches facilitating public–private partnerships. Driven by disasters like the 1995 Kobe Earthquake, Japan has promoted innovative strategies to improve urban resilience and mitigate disaster impacts. The Disaster Mitigation Zone Implementation Program represents a novel program designed to revitalize densely populated areas with aging wooden structures highly vulnerable to disasters. Through semi-structured interviews, a literature review, and an in-depth case study in Tokyo, this research analyzes the development and effectiveness of this targeted redevelopment approach. Findings underscore the pivotal role of policies promoting public–private collaboration, consensus-building mechanisms among stakeholders, flexibility in project formulation, and financial incentives via government subsidies. Engaging the private sector ensures project feasibility through urban development expertise, while simpler, smaller-scale projects attract greater private investment. Japan’s experience offers valuable insights into collaborative, context-sensitive strategies for enhancing urban disaster resilience through targeted redevelopment of high-risk areas. Full article

Amidst a backdrop of global economic challenges and shifting market dynamics, this study highlights the transformative role of data elements in enhancing enterprise performance within capital markets, particularly focusing on China’s leading position in the digital economy as a model with implications for global markets. This study utilized a panel data set consisting of 10,493 observations from 2687 listed enterprises in Shanghai and Shenzhen A-shares from 2015 to 2023. An econometric analysis was conducted using a two-way fixed effects model to explore the impact of enterprise data elements on capital market performance in the digital economy and its underlying mechanisms. The research reveals that the digitization of enterprise production factors can significantly enhance performance in the capital market. The study further suggests that enterprise innovation and enterprise value play a crucial role in mediating this effect. This paper introduces a new concept called “data elements”, which expands the definition and assessment methods of enterprise data capabilities. It goes beyond just digital transformation at the application level and includes data governance at the basic ability level. This approach provides a more accurate and comprehensive understanding of the different elements of data. Moreover, the research expands the research scope of microeconomic entities’ economic benefits, thereby extending the value contributed by enterprise data elements to their performance in the capital market. Additionally, this study reveals the relationship between enterprise data elementization and capital market performance through intermediary analysis of enterprise innovation performance and enterprise value, which unveils the “black box” and clarifies the transmission pathway. The findings of this research hold considerable theoretical value and have far-reaching practical implications for government policies concerning data elements and the development of high-quality enterprises, suggesting pathways for global markets to leverage data for enhanced enterprise performance and economic resilience. The results are particularly useful for policymakers, enterprise managers, and scholars in understanding and implementing data-driven strategies in capital markets. Full article

Background: In infants treated with a low-flow nasal cannula (LFNC), the oxygen concentration delivered to the lungs (i.e., the effective FiO₂) is difficult to estimate. The existing mathematical formulas rely on important assumptions regarding the values of respiratory parameters and, thus, may be inaccurate. We aimed to assess oxygen delivery by LFNC to small infants using realistic simulations on a mechanical breathing model. Methods: A mechanical breathing simulator (infant upper-airway replica, single-space breathing compartment, electric motor, microcontroller) was developed. Breathing simulations (n = 1200) were performed at various tidal volume (VT), inspiratory time (Ti), and respiratory rate (RR) combinations and different cannula flows. Results: Minute ventilation (MV) was the most significant predictor of effective FiO₂. FiO₂ was higher at lower VT and higher Ti values. Benaron and Benitz’s formula underestimated the effective FiO₂ at lower MV values, while Finer’s formula significantly overestimated it. A set of predictive FiO₂ charts was developed based on cannula flow, infant body weight, and RR. Conclusions: The effective FiO₂ delivered by LFNC to small infants critically depends on VT, Ti, and RR. However, since VT and Ti values are not available in clinical practice, the existing mathematical formulas may be inaccurate. Our novel predictive FiO₂ charts could assist in optimizing oxygen delivery by LFNC using easy-to-obtain parameters, such as infant body weight and RR. Full article

Here, we review the literature on neurotypical individuals and individuals with post-stroke aphasia showing that right-hemisphere regions homologous to language network and other regions, like the right cerebellum, are activated in language tasks and support language even in healthy people. We propose that language recovery in post-stroke aphasia occurs largely by potentiating the right hemisphere network homologous to the language network and other networks that previously supported language to a lesser degree and by modulating connection strength between nodes of the right-hemisphere language network and undamaged nodes of the left-hemisphere language network. Based on this premise (supported by evidence we review), we propose that interventions should be aimed at potentiating the right-hemisphere language network through Hebbian learning or by augmenting connections between network nodes through neuroplasticity, such as non-invasive brain stimulation and perhaps modulation of neurotransmitters involved in neuroplasticity. We review aphasia treatment studies that have taken this approach. We conclude that further aphasia rehabilitation with this aim is justified. Full article

Cellular Vehicle-to-Everything (C-V2X) communications is a technology that enables intelligent vehicles to exchange information and thus coordinate with other vehicles, road users, and infrastructure. However, despite advancements in cellular technology for V2X applications, significant challenges remain regarding the ability of the system to meet stringent Quality-of-Service (QoS) requirements when deployed at scale. Thus, smaller-scale V2X use case deployments may embody a necessary stepping stone to address these challenges. This work assesses network architectures for an Intelligent Perception System (IPS) blind road junction or blind corner scenarios. Measurements were collected using a private 5G NR network with Sub-6GHz and mmWave connectivity, evaluating the feasibility and trade-offs of IPS network configurations. The results demonstrate the feasibility of the IPS as a V2X application, with implementation considerations based on deployment and maintenance costs. If computation resources are co-located with the sensors, sufficient performance is achieved. However, if the computational burden is instead placed upon the intelligent vehicle, it is questionable as to whether an IPS is achievable or not. Much depends on image quality, latency, and system performance requirements. Full article

Background: Though out-of-hospital CA (OHCA) is widely reported, data on in-hospital CA (IHCA) and especially cardiac arrest (CA) in the emergency department (CAED) are scarce. This study aimed to determine the frequency, prevalence, and clinical features of unexpected CAED and compare the data with those of expected CAED. Methods: We defined unexpected CAED as CA occurring in patients in non-critical ED-care areas; classified as not requiring strict monitoring. This classification was the modified Japanese Triage and Acuity Scale and physician assessment. A retrospective analysis of cases from 2016 to 2018 was performed, in comparison to other patients experiencing CAED. Results: The 38 cases of unexpected CA in this study constituted 34.5% of CA diagnosed in the ED and 8.4% of all CA treated in the ED. This population did not differ significantly from other CAED regarding demographics, comorbidities, and survival rates. The commonest symptoms were dyspnoea, disorders of consciousness, generalised weakness, and chest pain. The commonest causes of death were acute myocardial infarction, malignant neoplasms with metastases, septic shock, pulmonary embolism, and heart failure. Conclusions: Unexpected CAED represents a group of potentially avoidable CA and deaths. These patients should be analysed, and ED management should include measures aimed at reducing their incidence. Full article

CubeSats are designed to optimize applications within the strict constraints of space and power. This paper presents an On-Board Image Enhancement technique for remote sensing payloads, focusing on achieving Auto White Balance (AWB) with limited resources and enhancing the capabilities of small/microsatellites. The study introduces hardware-based techniques, including histogram adjustment, De-Bayer processing, and AWB, all tailored to minimize hardware resource consumption on CubeSats. The integrated 1U CubeSat system comprises a sensor board, an Image Data Processor (IDP) unit, and onboard computing, with a total power consumption estimated at 2.2 W. This system facilitates image capture at a resolution of 1920 × 1200 and utilizes the proposed algorithm for image enhancement on remote sensing payloads to improve the quality of images captured in low-light environments, thereby demonstrating significant advancements in satellite image processing and object-detection capabilities. Full article

Pituitary apoplexy (PA) is an acute, life-threatening clinical syndrome caused by hemorrhage and/or infarction of the pituitary gland. It is clinically characterized by the sudden onset of headache. Depending on the severity, it may also be accompanied by nausea, vomiting, visual disturbances, varying degrees of adenohypophyseal hormone deficiency, and decreased level of consciousness. Corticotropic axis involvement may result in severe hypotension and contribute to impaired level of consciousness. Precipitating factors are present in up to 30% of cases. PA may occur at any age and sometimes develops during pregnancy or the immediate postpartum period. PA occurs more frequently in men aged 50–60, being rare in children and adolescents. It can develop in healthy pituitary glands or those affected by inflammation, infection, or tumor. The main cause of PA is usually spontaneous hemorrhage or infarction of a pituitary adenoma (pituitary neuroendocrine tumor, PitNET). It is a medical emergency requiring immediate attention and, in many cases, urgent surgical intervention and long-term follow-up. Although the majority of patients (70%) require surgery, about one-third can be treated conservatively, mainly by monitoring fluid and electrolyte levels and using intravenous glucocorticoids. There are scoring systems for PA with implications for management and therapeutic outcomes that can help guide therapeutic decisions. Management of PA requires proper evaluation and long-term follow-up by a multidisciplinary team with expertise in pituitary pathology. The aim of the review is to summarize and update the most relevant aspects of the epidemiology, etiopathogenesis, pathophysiology, clinical presentation and clinical forms, diagnosis, therapeutic strategies, and prognosis of PA. Full article

Senior high school (SHS) students are at risk of stress and other adverse exposures that may negatively affect their well-being and possibly cause attrition. The concepts of academic resilience and well-being share commonality as psychological attributes linked to positive functioning among students. Despite this connection, there seems to be limited research exploring these concepts across genders among SHS students in developing regions. This study examined the gender difference in academic resilience and well-being among SHS students in Ghana. Using a cross-sectional survey design, 190 SHS students in three schools from Kwahu North and South district (i.e., Afram Plains) of Ghana’s Eastern Region completed the Academic Resilience Scale (ARS-30) and College Student Subjective Wellbeing Questionnaire (CSSWQ). The sample consists of 102 males and 88 females, with a mean age of 17.83 years. The data were analyzed using independent samples t-tests and hierarchical regression. The study established that students have a moderate level of academic resilience and a higher level of well-being, with no statistically significant variation in students’ academic resilience (t = 0.718; p = 0.474) or well-being (t = −1.596; p = 0.112) across gender. Further, the study discovered that resilience significantly predicted academic well-being (B = 0.425; SE = 0.050; t = 8.50; p < 0.001). This study highlights the importance of promoting gender-sensitive intervention strategies that enhance the academic resilience and well-being of SHS students and help boost their educational attainment. Full article

This study aims to investigate the impact of the banking sector on economic growth and commodity prices. Through panel data analysis, the research explores the relationship between the banking sector and economic growth in Spain, France, and Romania from 2000 to 2020. The findings reveal a positive correlation between the strength of the banking sector and economic growth across these nations, underscoring its pivotal role in fostering economic expansion and subsequently improving commodity prices. Additionally, this study evaluates various regulatory measures crucial ensuring the banking sector’s sustainability and preventing financial crises, including credit risk management, lending policies, liquidity constraints, and international financing and investment strategies. By analyzing the interplay between regulatory measures and banking sector performance, incorporating variables such as non-performing loans, household debt, liquid liabilities, government consumption expenditure, foreign investments, and trade openness, this research provides policymakers with valuable insights to formulate effective strategies for promoting economic stability and ensuring the sustainability and growth of the banking sector. Full article

In this study, the portfolio optimization problem is explored, using a combination of classical and quantum computing techniques. The portfolio optimization problem with specific objectives or constraints is often a quadratic optimization problem, due to the quadratic nature of, for example, risk measures. Quantum computing is a promising solution for quadratic optimization problems, as it can leverage quantum annealing and quantum approximate optimization algorithms, which are expected to tackle these problems more efficiently. Quantum computing takes advantage of quantum phenomena like superposition and entanglement. In this paper, a specific problem is introduced, where a portfolio of loans need to be optimized for 2030, considering `Return on Capital’ and `Concentration Risk’ objectives, as well as a carbon footprint constraint. This paper introduces the formulation of the problem and how it can be optimized using quantum computing, using a reformulation of the problem as a quadratic unconstrained binary optimization (QUBO) problem. Two QUBO formulations are presented, each addressing different aspects of the problem. The QUBO formulation succeeded in finding solutions that met the emission constraint, although classical simulated annealing still outperformed quantum annealing in solving this QUBO, in terms of solutions close to the Pareto frontier. Overall, this paper provides insights into how quantum computing can address complex optimization problems in the financial sector. It also highlights the potential of quantum computing for providing more efficient and robust solutions for portfolio management. Full article

Rapid methods for assessing the fatigue properties of materials have been developed, among which the self-heating method stands out as particularly promising. This approach analyzes the thermal signal of the specimen when subjected to cyclic loading. In this research, the self-heating method was utilized for the first time with laser powder bed fusion (LPBF) of NiTi alloys, examining two specific loading conditions: loading ratios of 0.1 and 10. A thorough examination of the material self-heating behavior was conducted. For comparative purposes, conventional fatigue tests were also conducted, alongside interrupted fatigue tests designed to highlight the underlying mechanisms involved in high cycle fatigue and potentially self-heating behavior. The investigation revealed several key mechanisms at play, including intra-grain misorientation, the emergence and growth of persistent slip bands, and the formation of stress-induced martensite. These findings not only deepen our understanding of the fatigue behavior of LPBF NiTi alloys but also highlight the self-heating method potential as a tool for studying material fatigue. Full article

In the era of social media, online reviews have become a crucial factor influencing the exposure of tourist destinations and the decision-making of potential tourists, exerting a profound impact on the sustainable development of these destinations. However, the influence of review valence on visit intention, especially the role of affective commitment and reputation (ability vs. responsibility), remains unclear. Drawing on emotion as a social information theory, this paper aims to elucidate the direct impact of different review valences on tourists’ visit intentions, as well as mediating mechanisms and boundary conditions. Three experiments indicate that positive (vs. negative) reviews can activate stronger affective commitment and visit intention, with affective commitment also playing a mediating role. Additionally, destination reputation significantly moderates the after-effects of review valences. More specifically, a responsibility reputation (compared with an ability reputation) weakens the effect of negative valence on affective commitment and visit intention. This study provides valuable theoretical insights into how emotional elements in online reviews influence the emotions and attitudes of potential tourists. Particularly for tourism managers, review valence and responsibility reputation hold practical significance in destination marketing. Full article

Antimicrobial peptides (AMPs) are important mediator molecules of the innate defense mechanisms in a wide range of living organisms, including bacteria, mammals, and plants. Among them, peptide protease inhibitors (PPIs) from plants play a central role in their defense mechanisms by directly attacking pathogens or by modulating the plant’s defense response. The growing prevalence of microbial resistance to currently available antibiotics has intensified the interest concerning these molecules as novel antimicrobial agents. In this scenario, PPIs isolated from a variety of plants have shown potential in inhibiting the growth of pathogenic bacteria, protozoans, and fungal strains, either by interfering with essential biochemical or physiological processes or by altering the permeability of biological membranes of invading organisms. Moreover, these molecules are active inhibitors of a range of proteases, including aspartic, serine, and cysteine types, with some showing particular efficacy as trypsin and chymotrypsin inhibitors. In this review, we provide a comprehensive analysis of the potential of plant-derived PPIs as novel antimicrobial molecules, highlighting their broad-spectrum antimicrobial efficacy, specificity, and minimal toxicity. These natural compounds exhibit diverse mechanisms of action and often multifunctionality, positioning them as promising molecular scaffolds for developing new therapeutic antibacterial agents. Full article

Cross-project defect prediction (CPDP) aims to predict software defects in a target project domain by leveraging information from different source project domains, allowing testers to identify defective modules quickly. However, CPDP models often underperform due to different data distributions between source and target domains, class imbalances, and the presence of noisy and irrelevant instances in both source and target projects. Additionally, standard features often fail to capture sufficient semantic and contextual information from the source project, leading to poor prediction performance in the target project. To address these challenges, this research proposes Smote Correlation and Attention Gated recurrent unit based Long Short-Term Memory optimization (SCAG-LSTM), which first employs a novel hybrid technique that extends the synthetic minority over-sampling technique (SMOTE) with edited nearest neighbors (ENN) to rebalance class distributions and mitigate the issues caused by noisy and irrelevant instances in both source and target domains. Furthermore, correlation-based feature selection (CFS) with best-first search (BFS) is utilized to identify and select the most important features, aiming to reduce the differences in data distribution among projects. Additionally, SCAG-LSTM integrates bidirectional gated recurrent unit (Bi-GRU) and bidirectional long short-term memory (Bi-LSTM) networks to enhance the effectiveness of the long short-term memory (LSTM) model. These components efficiently capture semantic and contextual information as well as dependencies within the data, leading to more accurate predictions. Moreover, an attention mechanism is incorporated into the model to focus on key features, further improving prediction performance. Experiments are conducted on apache_lucene, equinox, eclipse_jdt_core, eclipse_pde_ui, and mylyn (AEEEM) and predictor models in software engineering (PROMISE) datasets and compared with active learning-based method (ALTRA), multi-source-based cross-project defect prediction method (MSCPDP), the two-phase feature importance amplification method (TFIA) on AEEEM and the two-phase transfer learning method (TPTL), domain adaptive kernel twin support vector machines method (DA-KTSVMO), and generative adversarial long-short term memory neural networks method (GB-CPDP) on PROMISE datasets. The results demonstrate that the proposed SCAG-LSTM model enhances the baseline models by 33.03%, 29.15% and 1.48% in terms of F1- measure and by 16.32%, 34.41% and 3.59% in terms of Area Under the Curve (AUC) on the AEEEM dataset, while on the PROMISE dataset it enhances the baseline models’ F1- measure by 42.60%, 32.00% and 25.10% and AUC by 34.90%, 27.80% and 12.96%. These findings suggest that the proposed model exhibits strong predictive performance. Full article

The Italian pig sector requires heavy pigs (raised for at least nine months and slaughtered at >160 kg). In order to avoid boar taint and lower the impact on welfare, immunocastration provides an alternative to surgical castration. This study investigated the effects of immunocastration compared to surgical castration on the chemical composition and fatty acid profile of loins (longissimus dorsi muscle) and adipose tissue in Italian heavy pigs raised for dry-cured ham. Twenty-four male pigs were subjected to surgical castration (n = 12) or immunocastration (n = 12). Carcass parameters were monitored at slaughter, and samples of longissimus dorsi muscle and subcutaneous fat were analysed. This study showed no significant differences in carcass characteristics and proximate composition of fresh meat. However, variations were observed in the fatty acid profiles of meat and adipose tissue between groups. Notably, saturated fatty acids, particularly stearic acid (18:0), were higher in the intramuscular fat (IMF) of the immunocastrated pigs compared to the surgically castrated pigs. Conversely, monounsaturated fatty acids, predominantly oleic acid (18:1n-9), were higher in the IMF from the surgically castrated pigs compared to the immunocastrated pigs. While immunocastration may offer benefits in terms of animal growth and carcass composition, it could lead to unfavourable lipid changes in fresh loin meat for Italian heavy pigs. Full article

Neuromuscular blocking agents (NMBAs) are routinely used during anesthesia to relax skeletal muscle. Nicotinic acetylcholine receptors (nAChRs) are ligand-gated ion channels; NMBAs can induce muscle paralysis by preventing the neurotransmitter acetylcholine (ACh) from binding to nAChRs situated on the postsynaptic membranes. Despite widespread efforts, it is still a great challenge to find new NMBAs since the introduction of cisatracurium in 1995. In this work, an effective ensemble-based virtual screening method, including molecular property filters, 3D pharmacophore model, and molecular docking, was applied to discover potential NMBAs from the ZINC15 database. The results showed that screened hit compounds had better docking scores than the reference compound d-tubocurarine. In order to further investigate the binding modes between the hit compounds and nAChRs at simulated physiological conditions, the molecular dynamics simulation was performed. Deep analysis of the simulation results revealed that ZINC257459695 can stably bind to nAChRs’ active sites and interact with the key residue Asp165. The binding free energies were also calculated for the obtained hits using the MM/GBSA method. In silico ADMET calculations were performed to assess the pharmacokinetic properties of hit compounds in the human body. Overall, the identified ZINC257459695 may be a promising lead compound for developing new NMBAs as an adjunct to general anesthesia, necessitating further investigations. Full article

Purpose: The aim of this study was to assess the efficacy of an artificial intelligence (AI) algorithm that uses radiomics data to assess recurrence and predict survival in hepatocellular carcinoma (HCC) treated with transarterial chemoembolization (TACE). Methods: A total of 57 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were prospectively enrolled in this study as test data. A total of 100 patients with treatment-naïve HCC or recurrent HCC who were eligible for TACE were retrospectively acquired for training data. Radiomic features were extracted from contrast-enhanced, liver computed tomography (CT) scans obtained before and after TACE. An AI algorithm was trained using the retrospective data and validated using the prospective test data to assess treatment outcomes. Results: This study evaluated 107 radiomic features and 5 clinical characteristics as potential predictors of progression-free survival and overall survival. The C-index was 0.582 as the graph of the cumulative hazard function, predicted by the variable configuration by using 112 radiomics features. The time-dependent AUROC was 0.6 ± 0.06 (mean ± SD). Among the selected radiomics features and clinical characteristics, baseline_glszm_SizeZoneNonUniformity, baseline_ glszm_ZoneVariance and tumor size had excellent performance as predictors of HCC response to TACE with AUROC of 0.853, 0.814 and 0.827, respectively. Conclusions: A radiomics-based AI model is capable of evaluating treatment outcomes for HCC treated with TACE. Full article

Hydrogen, an advanced energy source, is growing quickly in its infrastructure and technological development. Urban areas are constructing convergence-type hydrogen refilling stations utilizing existing gas stations to ensure economic viability. However, it is essential to conduct a risk analysis as hydrogen has a broad range for combustion and possesses significant explosive capabilities, potentially leading to a domino explosion in the most severe circumstances. This study employed quantitative risk assessment to evaluate the range of damage effects of single and domino explosions. The PHAST program was utilized to generate quantitative data on the impacts of fires and explosions in the event of a single explosion, with notable effects from explosions. Monte Carlo simulations were utilized to forecast a domino explosion, aiming to predict uncertain events by reflecting the outcome of a single explosion. Monte Carlo simulations indicate a 69% chance of a domino explosion happening at a hydrogen refueling station if multi-layer safety devices fail, resulting in damage estimated to be three times greater than a single explosion. Full article

Since prostate cancer (PCa) relies on limited therapies, more effective alternatives are required. Essential oils (EOs) and their bioactive compounds are natural products that have many properties including anticancer activity. This review covers studies published between 2000 and 2023 and discusses the anti-prostate cancer mechanisms of the EOs from several plant species and their main bioactive compounds. It also provides a critical perspective regarding the challenges to be overcome until they reach the market. EOs from chamomile, cinnamon, Citrus species, turmeric, Cymbopogon species, ginger, lavender, Mentha species, rosemary, Salvia species, thyme and other species have been tested in different PCa cell lines and have shown excellent results, including the inhibition of cell growth and migration, the induction of apoptosis, modulation in the expression of apoptotic and anti-apoptotic genes and the suppression of angiogenesis. The most challenging aspects of EOs, which limit their clinical uses, are their highly lipophilic nature, physicochemical instability, photosensitivity, high volatility and composition variability. The processing of EO-based products in the pharmaceutical field may be an interesting alternative to circumvent EOs' limitations, resulting in several benefits in their further clinical use. Identifying their bioactive compounds, therapeutic effects and chemical structures could open new perspectives for innovative developments in the field. Moreover, this could be helpful in obtaining versatile chemical synthesis routes and/or biotechnological drug production strategies, providing an accurate, safe and sustainable source of these bioactive compounds, while looking at their use as gold-standard therapy in the close future. Full article