Systematic Exploration Research Articles

Enhanced production of naphtha-range compounds from arundo donax via catalytic fast pyrolysis using SAPO-11

Arundo donax, acknowledged for its elevated energy content, substantial biomass yield and rapid growth, stands as an alluring candidate for biofuel production. In this investigation, a molecular sieve-based catalyst was utilized to scrutinize the catalytic fast pyrolysis (CFP) of Arundo donax, aiming for the production of naphtha-range compounds. The study also encompassed the evaluation of pyrolysis product yields within a fixed-bed reactor. The systematic exploration included an assessment of the impacts arising from catalyst type, temperature, weight hourly space velocity (WHSV), and N2 flow rate. The results demonstrated that at 500 °C, WHSV of 1 h−1 and N2 flow rate of 300 ml/min, SAPO-11 exhibited a selectivity of 64.55% to naphtha-range compounds and 64.55% to isomerous naphtha-range compounds. This was in stark contrast to the control group, which contained no catalyst (with a selectivity of 41.40% and 3.19%, respectively), SAPO-41 (with a selectivity of 11.02% and 9.55%, respectively) and HZSM-5 (with a selectivity of 59.89% and 53.51%, respectively). Additionally, SAPO-11 yielded the highest bio-oil compared to other catalysts (SAPO-11 of 28.52 wt% > none of 19.48 wt% > HZSM-5 of 18.90 wt% > SAPO-41 of 13.11 wt%). The conclusions drawn from these findings imply that the synthesized SAPO-11 catalyst exhibited superior catalytic activity and isomerization proficiency in contrast to other catalysts (SAPO-41 and HZSM-5). Remarkably, under the specified conditions of SAPO-11 catalyst, temperature = 500 °C, WHSV = 1 h−1 and N2 flow rate = 200 ml/min, the CFP process yielded a remarkable maximum absolute yield of the naphtha-range compound at 25.82 wt%, with an isomerized naphtha-range compound absolute yield of 25.35 wt%.

Evolution of Single-Session Therapy: A Bibliometric Analysis.

This bibliometric analysis aimed to explore the publication and citation metrics of the research literature on single-session therapy (SST) to understand its current status, trends, and future prospects. Seventy-five keywords were validated by subject matter experts. Publications from 1972 through September 2023 were extracted from the bibliometric website Lens.org. Publication trends, citation patterns, prominent journals, and influential authors were examined as part of the bibliometric analysis. Citation network analysis, bibliographic coupling of authors, and coauthorship network analysis were also performed. A total of 301 SST publications, including 18 books, 85 book chapters, and 176 journal articles, were found, published by 493 authors. The citation- and publication-related metrics suggested a growing level of subject matter expertise over time. Initially, single-author publications held more prominence than collaborative work, but this pattern shifted. From 2011 to 2020, scholarly interest surged, resulting in 144 publications during this period. This bibliometric analysis, the first systematic exploration of the SST knowledge base, can be used to expand and enrich future SST research.

An all phosphorene lattice nanometric spin valve

Phosphorene is a unique semiconducting two-dimensional platform for enabling spintronic devices integrated with phosphorene nanoelectronics. Here, we have designed an all phosphorene lattice lateral spin valve device, conceived via patterned magnetic substituted atoms of 3d-block elements at both ends of a phosphorene nanoribbon acting as ferromagnetic electrodes in the spin valve. Through First-principles based calculations, we have extensively studied the spin-dependent transport characteristics of the new spin valve structures. Systematic exploration of the magnetoresistance (MR) of the spin valve for various substitutional atoms and bias voltage resulted in a phase diagram offering a colossal MR for V and Cr-substitutional atoms. Such MR can be directly attributed to their specific electronic structure, which can be further tuned by a gate voltage, for electric field controlled spin valves. The spin-dependent transport characteristics here reveal new features such as negative conductance oscillation and switching of the sign of MR due to change in the majority spin carrier type. Our study creates possibilities for the design of nanometric spin valves, which could enable integration of memory and logic elements for all phosphorene 2D processors.

The bone marrow lipidomics of mice reveal sex-related differences.

Osteoporosis is a common skeletal disorder characterized by an imbalance between bone resorption and formation, exhibiting a higher prevalence in women compared with men. While previous studies have primarily focused on genomics and genetics in osteoporosis susceptibility, there is a lack of systematic exploration of sex-specific differences in lipid levels in mouse bone marrow. Multiple reaction monitoring-based liquid chromatography-trandem mass spectrometry(LC-MS/MS) was used to quantify lipidomic profiles in bone marrow samples from three female mice and three male mice. The LC-MS/MS technique based on the multiple reaction monitoring method identified and quantified 184 lipids from 15 lipid classes. The contents of most lipids in the bone marrow cells of female mice were higher than those in male mice, including four polyunsaturated fatty acids, three phospholipids and four sphingolipids. Among all the lipid molecules, lactosylceramide (d18:0/16:0) showed the highest fold change in female mice, while its precursor lipid, glucosylceramide, was the most up-regulated in male mice. This study, focusing on bone marrow lipidomics, elucidates significant sexual dimorphism in lipid levels within bone marrow cells. It provides novel evidence supporting the higher prevalence of osteoporosis in women and enhances our understanding of the connection between sex-specific lipid levels and the risk of osteoporosis.

Li‐Ion Transport Mechanisms in Selenide‐Based Solid‐State Electrolytes in Lithium‐Metal Batteries: A Study of Li8SeN2, Li7PSe6, and Li6PSe5X (X = Cl, Br, I)

To achieve high‐energy‐density and safe lithium‐metal batteries (LMBs), solid‐state electrolytes (SSEs) that exhibit fast Li‐ion conductivity and good stability against lithium metal are of great importance. This study presents a systematic exploration of selenide‐based materials as potential SSE candidates. Initially, Li8SeN2 and Li7PSe6 were selected from 25 ternary selenides based on their ability to form stable interfaces with lithium metal. Subsequently, their favorable electronic insulation and mechanical properties were verified. Furthermore, extensive theoretical investigations were conducted to elucidate the fundamental mechanisms underlying Li‐ion migration in Li8SeN2, Li7PSe6, and derived Li6PSe5X (X = Cl, Br, I). Notably, the highly favorable Li‐ion conduction mechanism of vacancy diffusion was identified in Li6PSe5Cl and Li7PSe6, which exhibited remarkably low activation energies of 0.21 and 0.23 eV, and conductivity values of 3.85 × 10−2 and 2.47 × 10−2 S cm−1 at 300 K, respectively. In contrast, Li‐ion migration in Li8SeN2 was found to occur via a substitution mechanism with a significant diffusion energy barrier, resulting in a high activation energy and low Li‐ion conductivity of 0.54 eV and 3.6 × 10−6 S cm−1, respectively. Throughout this study, it was found that the ab initio molecular dynamics and nudged elastic band methods are complementary in revealing the Li‐ion conduction mechanisms. Utilizing both methods proved to be efficient, as relying on only one of them would be insufficient. The discoveries made and methodology presented in this work lay a solid foundation and provide valuable insights for future research on SSEs for LMBs.

Magnetism and electronic properties of ConMoP (n = 1 ~ 5) cluster: a DFT study.

Hydrogen has emerged as a promising clean energy carrier, underscoring the imperative need to comprehend its adsorption mechanisms. This study delves into the magnetic and electronic properties of Co-Mo-P clusters, aiming to unveil their catalytic potential in hydrogen production. Employing density functional theory (DFT), we optimized cluster configurations and scrutinized their magnetic behaviors. Our investigation unveiled 16 stable configurations of the ConMoP (n = 1 ~ 5) cluster, predominantly in steric forms. The magnetic attributes were primarily ascribed to the d orbitals of Co metal atoms, with Co3MoP exhibiting exceptional magnetic characteristics. Analysis of density of state diagrams revealed the prevalence of spin-up α-electrons in d orbitals, while spin-down β-electrons attenuated overall magnetic properties. Localized orbital (LOL) analysis highlighted stable covalent bonds within the clusters, affirming their catalytic potential. Orbital delocalization index (ODI) analysis revealed diverse spatial distribution ranges for orbitals across different configurations, suggesting a progressive attenuation of off-domain properties with increasing cluster size. Furthermore, infrared spectroscopy unveiled distinct vibrational peaks in various configurations, indicative of unique infrared activities. These findings contribute to a nuanced theoretical understanding of Co-Mo-P clusters and pave the path for future research aimed at augmenting their catalytic efficiency in hydrogen production. This study underscores the viability of Co-Mo-P clusters as alternatives to conventional Pt catalysts, offering insights into the design of novel materials for sustainable energy applications. Further research is warranted to explore the behavior of the Co-Mo-P system under diverse reaction conditions, fostering advancements in materials and energy science. In this study, we harnessed the ConMoP (n = 1 ~ 5) cluster as a simulation platform for probing the local structure of the material. Our aim was to scrutinize the magnetism, electronic characteristics influenced by the varying metal atoms within these clusters. A systematic exploration involved incrementing the number of metal atoms and expanding the cluster size to elucidate the corresponding property variations. Density functional theory (DFT) calculations were pivotal to our methodology, employing the B3LYP hybrid functional implemented in the Gaussian16 software package. The ConMoP (n = 1 ~ 5) cluster underwent optimization calculations and vibrational analysis at the def2-tzvp quantization level, yielding optimized configurations with diverse spin multiplet degrees. To comprehensively characterize and visually represent the stability, electronic features, and catalytic attributes of these configurations, we employed a suite of computational tools. Specifically, quantum chemistry software GaussView and wave function analysis software Multiwfn played integral roles. Through the integrated use of these computational tools, we acquired valuable insights into the magnetism, electronic characteristics of the ConMoP (n = 1 ~ 5) cluster, shedding light on their dependency on distinct metal atoms.

Revisiting the Potential of Group VI Inorganic Precatalysts for the Ethenolysis of Fatty Acids through a Mechanochemical Approach.

The utilization of biobased feedstocks to prepare useful compounds is a pivotal trend in current chemical research. Among a varied portfolio of naturally available starting materials, fatty acids are abundant, versatile substrates with multiple applications. In this context, the ethenolysis of unsaturated fatty acid esters such as methyl oleate is an atom-economical way to prepare functional C10 olefins with a biobased footprint. Despite the existence of a variety of metathesis catalysts for the latter process, there is a lack of readily available, efficient, and inexpensive catalytic systems based on earth-abundant metals (Mo, W) whose preparation does not require sophisticated syntheses and manipulations. Here, a systematic exploration of homogeneous and heterogeneous inorganic Mo, W (oxy)halides shows that MoOCl4, while inactive as a homogeneous species, forms active and selective silica-supported ethenolysis precatalysts able to reach equilibrium conversion of methyl oleate within a few minutes upon activation with SnMe4. Such heterogeneous MoOCl4-based precatalysts were easily accessed through mechanochemical solvent-free procedures and found to contain, upon characterization by elemental analysis and Raman spectroscopy, isolated (≡SiO)Mo(=O)Cl3 units or polymeric silica-supported [-O(≡SiO)nMoCl4-nO-]m (n = 1, 2) complexes depending on the molybdenum loading. The former isolated species exhibited a higher catalytic performance. The developed heterogeneous precatalysts could be applied to the ethenolysis of various substrates, including polyunsaturated fatty acid esters and industrial fatty acid methyl ester (FAME) mixtures from palm oil transesterification.

Unraveling the influence of channel size and shape in 3D printed ceramic scaffolds on osteogenesis

Bone has the capacity to regenerate itself for relatively small defects; however, this regenerative capacity is diminished in critical-size bone defects. The development of synthetic materials has risen as a distinct strategy to address this challenge. Effective synthetic materials to have emerged in recent years are bioceramic implants, which are biocompatible and highly bioactive. Yet nothing suitable for the repair of large bone defects has made the transition from laboratory to clinic. The clinical success of bioceramics has been shown to depend not only on the scaffold's intrinsic material properties but also on its internal porous geometry. This study aimed to systematically explore the implications of varying channel size, shape, and curvature in tissue scaffolds on in vivo bone regeneration outcomes. 3D printed bioceramic scaffolds with varying channel sizes (0.3 mm to 1.5 mm), shapes (circular vs rectangular), and curvatures (concave vs convex) were implanted in rabbit femoral defects for 8 weeks, followed by histological evaluation. We demonstrated that circular channel sizes of around 0.9 mm diameter significantly enhanced bone formation, compared to channel with diameters of 0.3 mm and 1.5 mm. Interestingly, varying channel shapes (rectangular vs circular) had no significant effect on the volume of newly formed bone. Furthermore, the present study systematically demonstrated the beneficial effect of concave surfaces on bone tissue growth in vivo, reinforcing previous in silico and in vitro findings. This study demonstrates that optimizing architectural configurations within ceramic scaffolds is crucial in enhancing bone regeneration outcomes. Statement of significanceDespite the explosion of work on developing synthetic scaffolds to repair bone defects, the amount of new bone formed by scaffolds in vivo remains suboptimal. Recent studies have illuminated the pivotal role of scaffolds’ internal architecture in osteogenesis. However, these investigations have mostly remained confined to in silico and in vitro experiments. Among the in vivo studies conducted, there has been a lack of systematic analysis of individual architectural features. Herein, we utilized bioceramic 3D printing to conduct a systematic exploration of the effects of channel size, shape, and curvature on bone formation in vivo. Our results demonstrate the significant influence of channel size and curvature on in vivo outcomes. These findings provide invaluable insights into the design of more effective bone scaffolds.

The Effects of Work-Family Conflict and Work-Family Support on Job Performance of Chinese Grassroots Civil Servants

Objective: As an important and special professional group in Chinese society, the career development of grassroots civil servants has received much attention. With the development of information technology and the strengthening of workplace competition, new conflicts between work and family continue to intensify, and their conflict issues have received widespread attention. Theoretical framework: This study focuses on grassroots civil servants and explores the impact of work family relationships on job performance. Theoretical assumptions are proposed based on management science theory and previous research on the relationship between variables. Method: Obtain survey data and interview response texts through questionnaire surveys and semi-structured interviews. The proposed hypothesis was validated based on quantitative and qualitative analysis methods. Results and Conclusion: The empirical results indicate that the quality and balance of work family relationships have a significant impact on job performance, and work engagement and job burnout play a mediating role in the influencing process. Implication of the Study: The research results enrich the systematic exploration of the specific context of work family relationships and the impact of groups on work behavior, which is conducive to improving humanized and organized management mechanisms, providing guidance for the practice of work family coordination and division of labor within the grassroots civil servant team. Originality/Value: The study aims to improve the living and development environment of grassroots civil servants, and stimulating deep work motivation.

Systematic exploration of network pharmacology, in silico modeling and pharmacokinetic profiling for vitamin E in polycystic ovarian syndrome.

Aim: This study seeks to explore the possibility of using vitamin E to alleviate the symptoms of polycystic ovarian syndrome (PCOS). Methods: Various computational methods were employed, including network pharmacology utilizing a compound-target-pathway approach, Swiss ADME, OSIRIS® property explorer, pkCSM, PASS online web resource and MOLINSPIRATION® software. In addition, in silico analysis of vitamin E was performed with ten receptors. Results & discussion: Our findings highlight the diverse potential of vitamin E in alleviating PCOS. The observed influence on hormones is in line with existing PCOS theories regarding cyst development, further enhancing the therapeutic promise of vitamin E. Conclusion: In conclusion, our computational analysis indicates that vitamin E shows potential as a therapeutic agent for alleviating PCOS in adolescents.

Physicochemical Properties of 20 Ionic Liquids Prepared by the Carbonate-Based IL (CBILS) Process.

Ionic liquids (ILs) are an emerging materials' class with applications in areas such as energy storage, catalysis, and biomass dissolution and processing. Their physicochemical properties including surface tension, viscosity, density and their interplay between cation and anion chemistry are decisive in these applications. For many commercially available ILs, a full set of physicochemical data is not available. Here, we extend the knowledge base by providing physicochemical properties such as density (20 and 25 °C), refractive index (20 and 25 °C), surface tension (23 °C, including polar and dispersive components), and shear viscosity (ambient atmosphere, shear rate 1-200 s-1), for 20 commercial ILs. A correlation between the crystal volume, dispersive surface tension, and shear viscosity is introduced as a predictive tool, allowing for viscosity estimation. Systematic exploration of cation/anion alkyl side chain lengths reveals the impact on the IL's physicochemical attributes. Increasing the anion's headgroup decreases surface tension up to 35.7% and consequently shear viscosity. We further demonstrate that the dispersive part of the surface tension linearly correlates with the refractive index of the ionic liquid. While we provide additional physicochemical data, the screening and modeling efforts will contribute to better structure property predictions enabling faster progress in design and applications of ILs.

Mathematical modeling for predicting electrical energy harvested using piezoelectric composite materials for smart system applications

In the contemporary quest for sustainable energy, the potential of piezoelectric energy harvesters to convert mechanical vibrations into electrical energy has become increasingly important. This study focuses on piezoelectric composites, in particular a BaTiO3/PLA (Barium Titanate/ Polylactic Acid) system with different volume percentages of BaTiO3 ceramic particles (20%, 40% and 60%), with the aim of optimizing energy conversion efficiency. A mathematical model is introduced, encompassing material attributes, mechanical loading frequencies and electrical energy outputs. The central role of mathematical modeling in predicting harvested energy is highlighted, offering insights beyond experimental limitations. The model, which is functionally dependent on the properties of the ceramic and polymer, enables the systematic exploration of various compositions and the identification of optimal material ratios. Experimental validation of the model for different strains (0.4%, 0.8% and 1%) and compositions of BaTiO3/PLA reaffirms its reliability. Notably, the highest power harvest observed is around 4.5 μW under a strain of 1% with a BaTiO3 composition of 60%. With these specific numerical values, this approach merges materials science and energy technology, propelling the advancement of efficient piezoelectric materials for renewable energy applications.

Analysis Of P-N Junction Length of Drain and Source in MOSFET Transistor Through TCAD Simulation

This paper delves into the convergence of mathematical and computer science principles within the realm of electronics engineering, particularly focusing on device-level transistor restructuring. A pressing challenge confronting the semiconductor sector is the persistent escalation of leakage current alongside technological advancements. At the heart of any chip or semiconductor device lies the transistor, serving as its fundamental building block. Among the various forms of leakage current, subthreshold leakage stands out as a dominant component in transistors. Within this study, a systematic exploration of the numerical association governing the physical configuration of MOSFET and leakage amount specifically the subthreshold one is undertaken. One innovative algorithm is crafted to facilitate the automated tracking of transistor structures, laying the groundwork for subsequent analyses. The simulation framework is meticulously constructed by leveraging mathematical formulations derived from the algorithmic outputs. Remarkably, the results obtained from the simulation conducted through TCAD software exhibit a remarkable proximity to established mathematical models. Given the ubiquity of Complementary Metal Oxide Semiconductor (CMOS) technology in contemporary semiconductor fabrication, it serves as the cornerstone for the simulation endeavours herein.

BTS Beyond Beats: Disclosing Semantics Through Lyric Analysis

In the contemporary music landscape, BTS (Bangtan Sonyeondan) has risen as a global phenomenon, captivating audiences not only with mesmerizing beats but also through conveying profound messages in their lyrics. This article explores the multifaceted journey of BTS, emphasizing their impact on global pop culture, significant achievements, and role as cultural ambassadors. The study addresses three key research questions focused on how BTS lyrics reflect profound messages, identifying hidden meanings through semantic analysis, and appreciating music as an art form. "BTS Beyond Beats" serves as an invitation to appreciate music as an intricate art form, transcending the auditory experience to unravel the beauty and depth within BTS's crafted words. The study sheds light on the artistic depth and cultural impact of BTS's lyrics, offering a captivating journey into their semantic landscape. This research contributes to a deeper understanding of the wisdom and hidden meanings in BTS lyrics, fostering a richer appreciation of music as a profound art form that goes beyond mere melodic hums. The semantic revelation invites listeners to connect with BTS on a profound level, recognizing the cultural significance and wisdom encapsulated in each lyrical masterpiece. The research methodology integrates linguistic and cultural semiotics, content analysis, and qualitative research methods, aiming for a systematic exploration of nuanced meanings, metaphors, and connotations within BTS's lyrics. Results consistently highlight themes of self-discovery, resilience, love, and societal critique, showcasing the group's ability to navigate diverse emotional and social landscapes. Cultural nuances and references to Korean history, folklore, and societal norms contribute to BTS's identity as cultural ambassadors.

Association of genetically predicted 486 blood metabolites on the risk of Alzheimer's disease: a Mendelian randomization study.

Studies have reported that metabolic disturbance exhibits in patients with Alzheimer's disease (AD). Still, the presence of definitive evidence concerning the genetic effect of metabolites on AD risk remains insufficient. A systematic exploration of the genetic association between blood metabolites and AD would contribute to the identification of new targets for AD screening and prevention. We conducted an exploratory two-sample Mendelian randomization (MR) study aiming to preliminarily identify the potential metabolites involved in AD development. A genome-wide association study (GWAS) involving 7,824 participants provided information on 486 human blood metabolites. Outcome information was obtained from a large-scale GWAS meta-analysis of AD, encompassing 21,982 cases and 41,944 controls of Europeans. The primary two-sample MR analysis utilized the inverse variance weighted (IVW) model while supplementary analyses used Weighted median (WM), MR Egger, Simple mode, and Weighted mode, followed by sensitivity analyses such as the heterogeneity test, horizontal pleiotropy test, and leave-one-out analysis. For the further identification of metabolites, replication and meta-analysis with FinnGen data, steiger test, linkage disequilibrium score regression, confounding analysis, and were conducted for further evaluation. Multivariable MR was performed to assess the direct effect of metabolites on AD. Besides, an extra replication analysis with EADB data was conducted for final evaluation of the most promising findings. After rigorous genetic variant selection, IVW, complementary analysis, sensitivity analysis, replication and meta-analysis with the FinnGen data, five metabolites (epiandrosterone sulfate, X-12680, pyruvate, docosapentaenoate, and 1-stearoylglycerophosphocholine) were identified as being genetically associated with AD. MVMR analysis disclosed that genetically predicted these four known metabolites can directly influence AD independently of other metabolites. Only epiandrosterone sulfate and X-12680 remained suggestive significant associations with AD after replication analysis with the EADB data. By integrating genomics with metabonomics, this study furnishes evidence substantiating the genetic association of epiandrosterone sulfate and X-12680 with AD. These findings hold significance for the screening, prevention, and treatment strategies for AD.

Making the Brightest Ones Dim: Maximizing the Photothermal Conversion Efficiency of BODIPY-Based Photothermal Agents.

The successful implementation of photothermal therapy (PTT) in cancer treatment hinges on the development of highly effective photothermal agents (PTAs). Boron dipyrromethene (BODIPY) dyes, being well known for their high brightness and quantum efficiencies, are the antithesis of PTAs. Nonetheless, a systematic exploration of the photophysics and photothermal characteristics of a series of π-extended BODIPY dyes with high absorptivity in the near-infrared (NIR) region has achieved superior photothermal conversion efficiencies (>90%), in both monomeric state and nanoparticles after encapsulation in a biocompatible polyethyleneglycol 1,2-distearoyl-sn-glycero-3-phosphoethanolamine-N-[methoxy-(polyethylene glycol)-2000]. Optimal PTA candidates combine strong NIR absorption provided by extended donor-acceptor conjugation and an optimization of the electronic and steric effects of meso-substituents to maximize photothermal conversion performance. The PTT-optimized meso-CF3-BODIPY, TCF3PEn exhibits exceptional efficacy in inducing cancer cell apoptosis and in vivo tumor ablation using low-power NIR laser irradiation (0.3 Wcm-2, 808 nm) as well as excellent biological safety, underscoring its potential for advancing light-induced cancer therapies.

CFD Analysis of Coolant Flow Characteristics in Reactor Pressure Vessel: A Comprehensive Review

This comprehensive review explores the application of Computational Fluid Dynamics (CFD) in analyzing coolant flow within Reactor Pressure Vessels (RPVs) of nuclear power plants. By synthesizing existing research, methodologies, and advancements specific to RPVs, the paper offers in-depth insights into critical aspects such as boundary conditions, turbulence modeling, heat transfer mechanisms, and validation techniques. Examining a range of studies encompassing various reactor types from Pressurized Water Reactors (PWRs) to Integral Pressurized Water Reactors (IPWRs), the review underscores CFD's pivotal role in enhancing safety, efficiency, and performance optimization in nuclear reactors. Through systematic exploration, this study underscores the critical importance of precise modeling in facilitating safety assessments, operational optimization, and design enhancements across various reactor systems. Accurate modeling serves as a cornerstone for informed decision-making processes aimed at maximizing reactor performance while ensuring the highest standards of safety and reliability. The paper navigates through challenges such as computational limitations and turbulence modeling intricacies, while also discussing emerging trends like the porous media method aimed at improving computational efficiency. By offering a comprehensive understanding of thermal-hydraulic behavior in nuclear reactors, the review underscores CFD's contribution to enhancing safety and reliability in nuclear power generation. Overall, this review underscores the indispensable role of CFD in advancing our understanding of nuclear reactor dynamics, thereby contributing significantly to the overarching goals of improved safety and reliability in nuclear power generation.

CodLncScape Provides a Self-Enriching Framework for the Systematic Collection and Exploration of Coding LncRNAs.

Recent studies have revealed that numerous lncRNAs can translate proteins under specific conditions, performing diverse biological functions, thus termed coding lncRNAs. Their comprehensive landscape, however, remains elusive due to this field's preliminary and dispersed nature. This study introduces codLncScape, a framework for coding lncRNA exploration consisting of codLncDB, codLncFlow, codLncWeb, and codLncNLP. Specifically, it contains a manually compiled knowledge base, codLncDB, encompassing 353 coding lncRNA entries validated by experiments. Building upon codLncDB, codLncFlow investigates the expression characteristics of these lncRNAs and their diagnostic potential in the pan-cancer context, alongside their association with spermatogenesis. Furthermore, codLncWeb emerges as a platform for storing, browsing, and accessing knowledge concerning coding lncRNAs within various programming environments. Finally, codLncNLP serves as a knowledge-mining tool to enhance the timely content inclusion and updates within codLncDB. In summary, this study offers a well-functioning, content-rich ecosystem for coding lncRNA research, aiming to accelerate systematic studies in this field.

From desert flora to cancer therapy: systematic exploration of multi-pathway mechanisms using network pharmacology and molecular modeling approaches.

Ovarian cancer, often labeled a "silent killer," remains one of the most compelling and challenging areas of cancer research. In 2019 alone, a staggering 222,240 new cases of ovarian cancer were reported, with nearly 14,170 lives tragically lost to this relentless disease. The absence of effective diagnostic methods, increased resistance to chemotherapy, and the heterogeneous nature of ovarian cancer collectively contribute to the unfavorable prognosis observed in the majority of cases. Thus, there is a pressing need to explore therapeutic interventions that offer superior efficacy and safety, thereby enhancing the survival prospects for ovarian cancer patients. Recognizing this potential, our research synergizes bioinformatics with a network pharmacology approach to investigate the underlying molecular interactions of Saudi Arabian flora (Onopordum heteracanthum, Acacia ehrenbergiana, Osteospermum vaillantii, Cyperus rotundus, Carissa carandas, Carissa spinarum, and Camellia sinensis) in ovarian cancer treatment. At first, phytoconstituents of indigenous flora and their associated gene targets, particularly those pertinent to ovarian cancer, were obtained from open-access databases. Later, the shared targets of plants and diseases were compared to identify common targets. A protein-protein interaction (PPI) network of predicted targets was then constructed for the identification of key genes having the highest degree of connectivity among networks. Following that, a compound-target protein-pathway network was constructed, which uncovered that, namely, hispidulin, stigmasterol, ascorbic acid, octopamine, cyperene, kaempferol, pungenin, citric acid, d-tartaric acid, beta-sitosterol, (-)-epicatechin gallate, and (+)-catechin demonstrably influence cell proliferation and growth by impacting the AKT1 and VEGFA proteins. Molecular docking, complemented by a 20-ns molecular dynamic (MD) simulation, was used, and the binding affinity of the compound was further validated. Molecular docking, complemented by a 20-ns MD simulation, confirmed the binding affinity of these compounds. Specifically, for AKT1, ascorbic acid showed a docking score of -11.1227kcal/mol, interacting with residues Ser A:240, Leu A:239, Arg A:243, Arg C:2, and Glu A:341. For VEGFA, hispidulin exhibited a docking score of -17.3714kcal/mol, interacting with Asn A:158, Val A:190, Gln B:160, Ser A:179, and Ser B:176. To sum up, both a theoretical and empirical framework were established by this study, directing more comprehensive research and laying out a roadmap for the potential utilization of active compounds in the formulation of anti-cancer treatments.

One issue, two interpretations: unpacking the role of issue definition in e-government implementation

ABSTRACT Current research suggests that the way in which governments interpret e-government has the potential to affect e-government implementation but lacks systematic exploration. To address this gap, this study examines the role of issue definition in affecting e-government implementation. Taking Chinese provincial-level digital public service (DPS) as a case, this study finds that Chinese provincial governments prioritize economic potential over democratic potential when defining e-government. An economic-oriented issue definition of e-government can facilitate subsequent DPS implementation, eliciting a larger number of policy documents and better performance. However, defining e-government more as a democratic issue may not exert a sufficient influence.