Search Results (1,106)

We systematically investigated the parent anthracene (abbreviated as en-1, C₁₄H₁₀) and three N,N′-disubstituted derivatives: the 1,5-diethylanthracene (en-2, C₁₈H₁₈), the 1,5-divinylanthracene (en-3, C₁₈H₁₄), and the 1,5-diphenylanthracene (en-4, C₂₆H₁₈), using a rigorous density functional theory (DFT)/time-dependent density functional theory (TD-DFT) approach. Following full geometric optimization and frequency validation (no imaginary frequencies), frontier molecular orbital analysis revealed an inverse correlation between conjugation extent and the HOMO-LUMO energy gap. Electrostatic potential (ESP) analysis further indicated a progressive increase in surface potential variance upon substitution, reflecting charge redistribution. TD-DFT calculations yielded vertical excitation wavelengths of 438 nm, 441 nm, 464 nm, and 496 nm for en-1, en-2, en-3, and en-4, respectively. Complementary color theory predicts visual colors of yellow, yellow, red, and orange for these compounds based on their absorption characteristics. This work establishes a closed-loop “computation-spectra-color” model for anthracene-based dyes, providing a transferable design paradigm for novel functional pigments with high molar extinction coefficients. Full article

The objective of this study is to examine the impact of Chief Executive Officer (CEO) attributes on corporate performance in Jordan, a representative frontier market. The analysis focuses on four key CEO attributes, comprising two socio-demographic variables—age and educational—and two corporate governance-related ones—tenure and origin. Return on assets (ROA) and return on equity (ROE) are used as proxies for firm performance. Using a sample of 416 firm-year observations from companies listed on the Amman Stock Exchange (ASE) during 2015–2023, the study employs the system GMM methodology to estimate dynamic panel data models, addressing potential endogeneity and capturing the dynamic nature of firm performance. The results show that CEO age has a positive but insignificant effect, whereas CEO education and tenure significantly enhance firm performance. Conversely, CEO origin has a statistically negative impact on firm performance, reflecting the value of insider CEOs. The significant effects of CEO education, tenure, and origin—observed within the models that also incorporated firm- and country-level controls—reflect their incremental contribution to firm performance in frontier markets. Robustness checks, including controls for the COVID-19 pandemic and industry effects, confirm these findings. The study contributes to the literature by demonstrating the applicability of established theories—namely Upper Echelons, Stewardship, Resource Dependence, and Human Capital Theories—while identifying the CEO traits that drive success in frontier markets. It also offers practical guidance for shareholders, board directors, and policymakers in designing effective leadership and governance strategies. Full article

Background: Infertility remains a significant global health challenge, affecting approximately 15% of couples worldwide. In vitro fertilization (IVF) has transformed reproductive medicine; however, challenges such as low success rates in older patients, ovarian insufficiency, endometrial dysfunction, and male infertility continue to limit outcomes. Objective: This review aims to summarize the principles of IVF and explore the potential role of stem cells in enhancing IVF outcomes, with particular attention to applications in both women and men, as well as the accompanying ethical considerations. Summary: Stem cell research has introduced novel therapeutic opportunities, including ovarian rejuvenation, endometrial regeneration, sperm quality enhancement, and the development of synthetic embryo models. Mesenchymal stem cells (MSCs), embryonic stem cells (ESCs), and induced pluripotent stem cells (iPSCs) demonstrate regenerative properties that may help to overcome current reproductive limitations. Despite encouraging findings from preclinical and early clinical studies, challenges such as tumorigenesis, genetic instability, and ethical controversies remain major barriers to translation. Conclusions: IVF continues to serve as a cornerstone of assisted reproductive technology (ART). Stem cell-based approaches represent an exciting frontier that could expand the therapeutic possibilities of IVF. Careful clinical validation, international regulatory harmonization, and robust ethical oversight will be essential to ensuring safe and equitable implementation. Full article

Background/Objectives: Cancer progression is characterized by the suppression of apoptosis, activation of metastatic processes, and dysregulation of cell proliferation. The proper functioning of these mechanisms relies on critical signaling pathways, including Phosphoinositide 3-kinase/Protein kinase B/mammalian Target of Rapamycin (PI3K/Akt/mTOR), Mitogen-Activated Protein Kinase (MAPK), and Signal Transducer and Activator of Transcription 3 (STAT3). Although curcumin and resveratrol exhibit anticancer properties and affect these pathways, their pharmacokinetic limitations, including poor bioavailability and low solubility, restrict their clinical application. The aim of our study was to evaluate the synergistic anticancer potential of curcumin and resveratrol through hybrid molecules rationally designed from these compounds to mitigate their pharmacokinetic limitations. Furthermore, we analyzed the multi-target anticancer effects of these hybrids on the AKT serine/threonine kinase 1 (AKT1), MAPK, and STAT3 pathways using in silico molecular modeling approaches. Methods: Three hybrid molecules, including a long-chain (ELRC-LC) and a short-chain (ELRC-SC) hybrid, an ester-linked hybrid, and an ether-linked hybrid (EtLRC), were designed using the Avogadro software (v1.2.0), and their geometry optimization was carried out using Density Functional Theory (DFT). The electronic properties of the structures were characterized through Frontier Molecular Orbital (FMO), Molecular Electrostatic Potential (MEP), and Fourier Transform Infrared (FTIR) analyses. The binding energies of the hybrid molecules, curcumin, resveratrol, their analogs, and the reference inhibitor were calculated against the AKT1, MAPK, and STAT3 receptors using molecular docking. The stabilities of the best-fitting complexes were evaluated through 100 ns molecular dynamics (MD) simulations, and their binding free energies were estimated using the Molecular Mechanics/Poisson–Boltzmann Surface Area (MM/PBSA) method. Results: DFT analyses demonstrated stable electronic characteristics for the hybrids. Molecular docking analyses revealed that the hybrids exhibited stronger binding compared to curcumin and resveratrol. The binding energy of −11.4 kcal/mol obtained for the ELRC-LC hybrid against AKT1 was particularly remarkable. Analysis of 100 ns MD simulations confirmed the conformational stability of the hybrids. Conclusions: Hybrid molecules have been shown to exert multi-target mechanisms of action on the AKT1, MAPK, and STAT3 pathways, and to represent potential anticancer candidates capable of overcoming pharmacokinetic limitations. Our in silico-based study provides data that will guide future in vitro and in vivo studies. These rationally designed hybrid molecules, owing to their receptor affinity, may serve as de novo hybrid inhibitors. Full article

The diesel particulate filter (DPF) is among the most effective measures for controlling particulate emissions from diesel vehicles. Therefore, resource-efficient DPF design and operation are critical to sustainable deployment. In practical engineering, the pursuit of high filtration efficiency inevitably leads to excessively high pressure drop, which in turn impairs the fuel economy and operational reliability of the engine. To address this pair of conflicting objectives, this study introduces a hybrid GRA-MLR-WOA approach, with the initial filtration efficiency and pressure drop at an 80 g soot capture amount as the optimization objectives, to optimize the structural parameters of the DPF. Firstly, based on a computational fluid dynamics (CFD) model and orthogonal experimental design, combined with grey relational analysis (GRA), the effects of key structural parameters on filtration efficiency and pressure drop were evaluated. Secondly, Box–Behnken Design (BBD) was integrated with multiple linear regression (MLR) to establish mathematical regression models describing the relationships between structural parameters, filtration efficiency, and pressure drop. Finally, the whale optimization algorithm (WOA) was employed to obtain the Pareto frontier of the regression models. Through screening with the goal of maximizing initial filtration efficiency, the optimized DPF achieved a 46.85% increase in initial filtration efficiency and a 34.88% reduction in pressure drop compared to the original model. This study targets sustainable filtration design and proposes an optimization framework that jointly optimizes pressure drop and the initial filtration efficiency. The results provide a robust empirical basis for engineering practice and demonstrate strong reproducibility. Full article

Background: Large Language Models (LLMs) have driven significant advances in artificial intelligence (AI), with transformative applications across numerous scientific fields, including biomedical research and drug development. However, despite growing interest in adjacent domains, their adoption in pharmacometrics, a discipline central to model-informed drug development (MIDD), remains limited. This study aims to systematically explore the potential role of LLMs across the pharmacometrics workflow, from data processing to model development and reporting. Methods: We conducted a comprehensive literature review to identify documented applications of LLMs in pharmacometrics. We also analyzed relevant use cases from related scientific domains and structured these insights into a conceptual framework outlining potential pharmacometrics tasks that could benefit from LLMs. Results: Our analysis revealed that studies reporting LLMs in pharmacometrics are few and mainly limited to code generation in general-purpose programming languages. Nonetheless, broader applications are theoretically plausible and technically feasible, including information retrieval and synthesis, data collection and formatting, model coding, PK/PD model development, support to PBPK and QSP modeling, report writing and pharmacometrics education. We also discussed visionary applications such as LLM-enabled predictive modeling and digital twins. However, challenges such as hallucinations, lack of reproducibility, and the underrepresentation of pharmacometrics data in training corpora limit the actual applicability. Conclusions: LLMs are unlikely to replace mechanistic pharmacometrics models but hold great potential as assistive tools. Realizing this potential will require domain-specific fine-tuning, retrieval-augmented strategies, and rigorous validation. A hybrid future, integrating human expertise, traditional modeling, and AI, could define the next frontier for innovation in MIDD. Full article

With the widespread adoption of large language models (LLMs) in code generation tasks, geospatial code generation has emerged as a critical frontier in the integration of artificial intelligence and geoscientific analysis. This growing trend underscores the urgent need for systematic evaluation methodologies to assess the generation capabilities of LLMs in geospatial contexts. In particular, geospatial computation and visualization tasks in the JavaScript environment rely heavily on the orchestration of diverse frontend libraries and ecosystems, posing elevated demands on a model’s semantic comprehension and code synthesis capabilities. To address this challenge, we propose GeoJSEval—the first multimodal, function-level automatic evaluation framework for LLMs in JavaScript-based geospatial code generation tasks. The framework comprises three core components: a standardized test suite (GeoJSEval-Bench), a code submission engine, and an evaluation module. It includes 432 function-level tasks and 2071 structured test cases, spanning five widely used JavaScript geospatial libraries that support spatial analysis and visualization functions, as well as 25 mainstream geospatial data types. GeoJSEval enables multidimensional quantitative evaluation across metrics such as accuracy, output stability, resource consumption, execution efficiency, and error type distribution. Moreover, it integrates boundary testing mechanisms to enhance robustness and evaluation coverage. We conduct a comprehensive assessment of 20 state-of-the-art LLMs using GeoJSEval, uncovering significant performance disparities and bottlenecks in spatial semantic understanding, code reliability, and function invocation accuracy. GeoJSEval offers a foundational methodology, evaluation resource, and practical toolkit for the standardized assessment and optimization of geospatial code generation models, with strong extensibility and promising applicability in real-world scenarios. This manuscript represents the peer-reviewed version of our earlier preprint previously made available on arXiv. Full article

Climate change is transforming energy use at the residential level by increasing temperature fluctuations and sustaining extreme weather events. This study proposes a climate-reactive, multi-objective approach to integrate the demand response (DR) with distributed generation (DG) and power quality improvement under a multi-objective framework of an integrated climate-adaptive approach to residential energy management. A cognitive neural network combination model with bidirectional long short-term memory networks (bidirectional) and a self-attention mechanism was used to successfully predict temperature-sensitive loads. The hybrid deep learning solution, which applies convolutional and bidirectional long short-term memory (LSTM) networks with attention, predicted the temperature-dependent load profiles optimized with an enhanced modified grey wolf optimizer (MGWO). The results of the experimental studies indicated significant gains in performance: in energy expenditure, the studies reduced it by 32.7%; in peak demand, they were able to reduce it by 45.2%; and in self-generated renewable energy, the results were 28.9% higher. The solution reliability rate provided by the MGWO was 94.5%, and it converged more quickly, thus providing better diversity in the Pareto-optimal frontier than that of traditional metaheuristic algorithms. Sensitivity tests with climate conditions of +2 °C and +4 °C showed strategy changes as high as 18.3%, thus establishing the flexibility of the system. Empirical evidence indicates that the energy and peak demand are to be cut, renewable integration is enhanced, and performance is strong in fluctuating climate conditions, highlighting the adaptability of the system to future resilient smart homes. Full article

Producing high-quality segmentation masks for medical images is a fundamental challenge in biomedical image analysis. Recent research has investigated the use of supervised learning with large volumes of labeled data to improve segmentation across medical imaging modalities and unsupervised learning with unlabeled data to segment without detailed annotations. However, a significant hurdle remains in constructing a model that can segment diverse medical images in a zero-shot manner without any annotations. In this work, we introduce the attention diffusion zero-shot unsupervised system (ADZUS), a new method that uses self-attention diffusion models to segment biomedical images without needing any prior labels. This method combines self-attention mechanisms to enable context-aware and detail-sensitive segmentations, with the strengths of the pre-trained diffusion model. The experimental results show that ADZUS outperformed state-of-the-art models on various medical imaging datasets, such as skin lesions, chest X-ray infections, and white blood cell segmentations. The model demonstrated significant improvements by achieving Dice scores ranging from 88.7% to 92.9% and IoU scores from 66.3% to 93.3%. The success of the ADZUS model in zero-shot settings could lower the costs of labeling data and help it adapt to new medical imaging tasks, improving the diagnostic capabilities of AI-based medical imaging technologies. Full article

EU production is important for global poultry markets and is concentrated in a few countries, including Italy. The aim of this study is to investigate the technical efficiency of Italian poultry farms in 2019–2022, characterized by the COVID-19 pandemic and avian influenza, which occurred almost simultaneously and presented poultry farms with important economic challenges. In particular, this study aims to observe how efficiently poultry farms utilized their inputs with regards to controllable or managerial factors and exogenous shocks and factors beyond the firm’s control. Data was retrieved from the RICA database, the Italian section of the EU Farm Accountancy Data Network. After a descriptive analysis, a stochastic frontier model was applied to the panel data to estimate production frontier and firm-specific inefficiency factors. Results reveal the relevance of certain cost categories (feed, water, fuel, and electricity) and their increase over the observed period. Current and capital costs have positive and significant impacts on the value of production. As regards the determinants of technical efficiency, a greater endowment of some inputs (labor and farm area) and the sizes of farms in terms of livestock units are correlated with an improvement in the technical efficiency of farms. Full article

Background: Psoriasis is a chronic immune-mediated disease affecting approximately 43 million individuals worldwide. While previous studies provide certain insights, there remains different conclusions and a lack of a comprehensive analysis regarding the burden of psoriasis. In response to ongoing therapeutic advances and a growing patient population, this study utilizes the Global Burden of Disease (GBD) 2021 estimates to characterize the spatiotemporal evolution of the psoriasis burden from 1990 through 2021. By integrating these biological, geographic, and socioeconomic determinants, this study aims to inform more targeted and effective health policy planning. Methods: To track changes over time, the Estimated Annual Percentage Change (EAPC) was determined using a linear regression model. In addition, a frontier analysis was utilized to investigate the link between psoriasis burden and socio-demographic progress. Furthermore, geographically weighted regression was used for the spatial econometric assessment of EAPC, age-standardized rates (ASRs), and Human Development Index (HDI) covariance structures across nation-states. Results: Between 1990 and 2021, the global burden of psoriasis increased consistently, with ASRs exhibiting a positive correlation with the Socio-demographic Index (SDI). High-SDI regions reported the highest burden, while high–middle-SDI regions experienced the steepest rise. Conclusions: This study reveals an increasing global psoriasis burden (1990–2021) through systematic analyses, indicating distinct regional progression patterns. These findings advocate for geographically tailored strategies to alleviate healthcare system pressures. Full article

As cyberattacks grow increasingly sophisticated, advanced Network Intrusion Detection Systems (NIDS) have become essential for securing cyberspace. While Machine Learning (ML) is foundational to modern NIDS, its effectiveness is often hampered by a resource-intensive development pipeline involving feature engineering, model selection, and hyperparameter tuning. Automated Machine Learning (AutoML) promises a solution, but its application to the massive, high-speed data streams in NIDS is fundamentally a parallel and distributed computing challenge. This paper argues that the scalability and performance of AutoML in NIDS are governed by the underlying computational paradigm. We introduce a novel taxonomy of AutoML frameworks, uniquely classifying them by their parallel and distributed architectures. Through a comprehensive meta-analysis of over 15 NID methods on benchmark datasets, we demonstrate how the performance of leading systems is a direct consequence of their chosen computational paradigm. Finally, we identify frontier challenges and future research directions at the intersection of AutoML, NIDS, and high-performance distributed systems, focusing on computational scalability, security, and end-to-end automation. Full article

This paper presents a comprehensive analysis of the border city of Jilong in Tibet, China, within the wider context of the global south and the transformation of China’s interior frontier in recent decades. It examines the transformation process of Jilong, identifies the driving factors of its development, and investigates the implementation and impact of relevant policies. Employing a longitudinal case study method, semi-structured interviews, and multi-source data analysis (including policy documents, statistical bulletins, and field notes), this research examines Jilong’s transformation trajectory, the factors behind this change, and policy implementation outcomes. The findings reveal that Jilong has undergone a significant transition from a traditional border trade point to a national strategic hub. Industrial diversification, infrastructure modernization, and governance innovation are recognized as central to this transformation. Additionally, the study also finds challenges such as ecological vulnerability, geological disaster risk, and the necessity for enhancement in cross-border collaboration mechanisms, proposing measures like green development, customs facilitation, and a system for both importing and cultivating local talent. This research emphasizes the transformation of border cities from a complex interplay of national strategy, external shocks, and local initiative. It accordingly advocates for an integrated development model, which combines policy empowerment, resilient infrastructure, cultivation of distinctive industries, and refined border governance. This study adds to research on border cities in the Global South and provides insights for supporting sustainable development in similar cities located in strategic corridors. Full article

Artificial intelligence (AI), particularly machine learning (ML), is equipping laser micro/nano processing with significant intelligent capabilities, demonstrating exceptional performance in areas such as manufacturing process modeling, process parameter optimization, and real-time anomaly detection. This transformative potential is driving the development of next-generation laser micro/nano processing technologies. The key challenges confronting traditional laser manufacturing stem from the complexity of laser–matter interactions, resulting in difficult-to-control processing outcomes and the accumulation of micro/nano defects across multi-step processes, ultimately triggering catastrophic process failures. This review provides an in-depth exploration of how machine learning effectively addresses these challenges through the integration of data-driven modeling with physics-driven modeling, coupled with intelligent in situ monitoring and adaptive control techniques. Systematically, we summarize current representative breakthroughs and frontier advances at the intersection of machine learning and laser micro/nano processing research. Furthermore, we outline potential future research directions and promising application prospects within this interdisciplinary field. Full article

(1) Background: The convergence of digital twin technology, artificial intelligence, and multimodal biomarkers heralds a transformative era in neuropsychological assessment and intervention. Digital twin cognition represents an emerging paradigm that creates dynamic, personalized virtual models of individual cognitive systems, enabling continuous monitoring, predictive modeling, and precision interventions. This systematic review comprehensively examines the integration of AI-driven biomarkers within biomimetic neuropsychological frameworks to advance personalized cognitive health. (2) Methods: Following PRISMA 2020 guidelines, we conducted a systematic search across six major databases spanning medical, neuroscience, and computer science disciplines for literature published between 2014 and 2024. The review synthesized evidence addressing five research questions examining framework integration, predictive accuracy, clinical translation, algorithm effectiveness, and neuropsychological validity. (3) Results: Analysis revealed that multimodal integration approaches combining neuroimaging, physiological, behavioral, and digital phenotyping data substantially outperformed single-modality assessments. Deep learning architectures demonstrated superior pattern recognition capabilities, while traditional machine learning maintained advantages in interpretability and clinical implementation. Successful frameworks, particularly for neurodegenerative diseases and multiple sclerosis, achieved earlier detection, improved treatment personalization, and enhanced patient outcomes. However, significant challenges persist in algorithm interpretability, population generalizability, and the integration of healthcare systems. Critical analysis reveals that high-accuracy claims (85–95%) predominantly derive from small, homogeneous cohorts with limited external validation. Real-world performance in diverse clinical settings likely ranges 10–15% lower, emphasizing the need for large-scale, multi-site validation studies before clinical deployment. (4) Conclusions: Digital twin cognition establishes a new frontier in personalized neuropsychology, offering unprecedented opportunities for early detection, continuous monitoring, and adaptive interventions while requiring continued advancement in standardization, validation, and ethical frameworks. Full article