Basic Research Research Articles

Bioactivity-guided isolation and molecular modeling of the anti-inflammatory constituents from the leaves of Duranta erecta Linn.

Duranta erecta Linn. belongs to the Verbenaceae family and is primarily found in subtropical, tropical, and temperate climates. The plant has been reported to contain a variety of phytoconstituents, including iridoid glycosides, flavonoids, flavonoid glycosides, alkaloids, phenolics, tannins, terpenoids, steroids, and saponins. In this investigation, a bioactivity-guided isolation was used to isolate cyclooxygenases (COXs) and lipoxygenase (LOX)-inhibiting compounds from the leaves of Duranta erecta Linn. Duranterectoside A (1), lamiide (2), and apigenin 4’,7-dimethyl ether (3) were identified employing spectroscopic methods (including ESI-MS, 1H-NMR, and 13C-NMR), and by comparing with existing literature data. This is the first report of metabolitrs from D. erecta inhibiting both LOX and COX enzymes. Furthermore, the isolated compounds were analyzed using computerized virtual screening, which enabled the modelling ADME characteristics, molecular docking, and dynamics simulation. The results demonstrated that compound 1 had greater docking scores than the docked lead compounds. Overall, the data reported in this study add to our understanding of the pharmacological properties of the examined plant and Duranterectoside A (1) and pave the way for future research and investigation in inflammation and drug discovery.

Discovery of new topological insulators and semimetals using deep generative models

Topological materials possess unique electronic properties and hold immense attraction to both fundamental physics research and practical applications. Over the past decades, the discovery of new topological materials has relied on the symmetry-based analysis of the quantum wave function. In this study, we propose an efficient inverse design method CTMT (CTMT: CDVAE, Topogivity, interatomic potentials (IAPs) as realized in M3GNet, and TQC) utilizing deep generative machine learning models to discover novel topological insulators and semimetals in a much-fast and low-cost manner. This method covers the entire process of new crystal structure generation, heuristic rule screening, fast stability estimation, and topology type diagnosis, resulting in 4 topological insulators and 16 topological semimetals. Especially, the newly discovered topological materials include several chiral Kramers-Weyl fermion semimetals and chiral materials with low symmetry, whose topology is previously considered challenging to discern. These findings demonstrate the capability of CTMT in discovering topological materials and its great potential for data-driven inverse design of advanced functional materials.

Ion-Ion Structural Correlation and Dynamics of Water in Aqueous NaCl Solutions with a Wide Range of Concentrations.

The behavior of water in concentrated ionic solutions, including supersaturated conditions, is crucial for numerous material and energy conversion processes and fundamental research. All electrolytes whether they "structure-make" or "structure-break" the water structure lead to slower water motion. This study investigates the structure and dynamics of aqueous NaCl solutions across a wide range of concentrations. On the structural side, the primary focus is on ion-ion correlations. In terms of dynamics, we demonstrate that the slowing down of water dynamics continues even beyond the saturated state. We identify three distinct types of dynamics at large concentrations: ballistic, trapped, and diffusive. The van Hove correlation function exhibits no signs of relaxation within a time interval where particle motion is effectively halted. The system displays dynamical heterogeneities, confirmed by evaluating non-Gaussian parameters for the self-part of the van Hove function and identifying the mobile particles. These particles form clusters, with the largest sizes occurring when the non-Gaussian parameters are at their maximum. Additionally, we discuss the relaxation times associated with these systems using the incoherent intermediate scattering function and establish a connection with the mode-coupling theory.

Nitric oxide as integral element in priming- induced tolerance and plant stress memory.

The beneficial effects of priming technology are aimed at the promotion of growth and development and stress tolerance in plants. Different seed pre-treatment and vegetative priming approaches (osmo-, chemical, physical, hormonal, redox treatments) increase the level of nitric oxide (NO) being an active contributor to growth regulation and defence responses. On the other hand, seed pre-treatment or vegetative priming mainly with the NO donor, sodium nitroprusside (SNP) helps to mitigate different abiotic stresses like salinity, cold, drought, excess metals. The effect of SNP/NO covers the alleviation of stress-specific effects (e.g. reduction of cadmium uptake in case of cadmium loading, improvement of water balance in case of drought), as well as general effects, such as alleviating oxidative stress. Seed pre-treatment or vegetative priming with SNP/NO up-regulates antioxidant enzymes at the activity level and increases the amount of a wide range of non-enzymatic antioxidants. However, due to adverse effects of SNP we urge the testing of other conventional NO donors (e.g. S-nitrosoglutathione) as well as new substances with more favourable properties (e.g. NO-releasing nanomaterials, plasmas, plasma treated liquids, combined donors) as seed pre-treating and vegetative priming materials. This review gives further suggestions on the methodology and future directions of fundamental research in relation to NO-associated pre-treatment and priming.

Advanced Technologies in Plant Factories: Exploring Current and Future Economic and Environmental Benefits in Urban Horticulture

Abstract Plant factories (PFs), also known as vertical farms, are advanced agricultural production systems that operate independently of geographical and environmental conditions. They utilize artificial light and controlled environments to produce horticultural plants year-round. This approach offers a promising solution for the stable and efficient supply of high-quality horticultural produce in urban areas, enhancing resilient urban food systems. This review explores the economic and environmental impacts and potential of PFs. Breakthroughs in PF research and development are highlighted, including increased product yields and quality, reduced energy input and CO2 emissions through optimized growing conditions and automation systems, transitioning to clean energy, improved resource use efficiency, and reduced food transport distances. Moreover, innovations and applications of PFs have been proposed to address challenges from both economic and environmental perspectives. The proposed development of PF technologies for economic and environmental benefits represents a comprehensive and promising approach to urban horticulture, significantly enhancing the impact and benefits of fundamental research and industrial applications.

Dynamic Robot Routing and Destination Assignment Policies for Robotic Sorting Systems

Robotic sorting systems (RSSs) use mobile robots to sort items by destination. An RSS pairs high accuracy and flexible capacity sorting with the advantages of a flexible layout. This is why several express parcel and e-commerce retail companies, who face heavy demand fluctuations, have implemented these systems. To cope with fluctuating demand, temporal robot congestion, and high sorting speed requirements, workload balancing strategies such as dynamic robot routing and destination reassignment may be of benefit. We investigate the effect of a dynamic robot routing policy using a Markov decision process (MDP) model and dynamic destination assignment using a mixed integer programming (MIP) model. To obtain the MDP model parameters, we first model the system as a semiopen queuing network (SOQN) that accounts for robot movement dynamics and network congestion. Then, we construct the MIP model to find a destination reassignment scheme that minimizes the workload imbalance. With inputs from the SOQN and MIP models, the Markov decision process minimizes parcel waiting and postponement costs and helps to find a good heuristic robot routing policy to reduce congestion. We show that the heuristic dynamic routing policy is near optimal in small-scale systems and outperforms benchmark policies in large-scale realistic scenarios. Dynamic destination reassignment also has positive effects on the throughput capacity in highly loaded systems. Together, in our case company, they improve the throughput capacity by 35%. Simultaneously, the effect of dynamic routing exceeds that of dynamic destination reassignment, suggesting that managers should focus more on dynamic robot routing than dynamic destination reassignment to mitigate temporal congestion. Funding: This work was supported by The Fundamental Research Funds for the Central Universities [Grant WK2040000094] and the National Natural Science Foundation of China [Grants 71921001 and 72091215/72091210]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0458 .

Simulation analysis of radiometer effect and outgassing based on molecular particle source perturbation

The Laser Interferometer Space Antenna (LISA) mission is designed to detect space gravitational wave sources in the millihertz band. A critical factor in the success of this mission is the residual acceleration noise metric of the internal test mass (TM) within the ultra-precise inertial sensors. Existing studies indicate that the coupling effects of residual gas and temperature gradient fluctuations significantly influence this metric, primarily manifesting as the radiometer effect and the outgassing effect. However, current theoretical research methods are inadequate for accurately decoupling and predicting the contributions of these two effects. To this end, this paper conducts an in-depth decoupling analysis of the impacts of the radiometer effect and outgassing effect using a simulation method based on molecular particle source perturbation. By constructing a finite element simulation model that couples residual gas and temperature gradient fluctuations, we simulate molecular thermal motion based on fundamental theories such as Maxwell’s distribution function, the free path distribution law, and Knudsen’s adsorption layer hypothesis, in order to study the two manifestations of the radiometer effect and the outgassing. By analyzing and comparing the simulation results, theoretical results, and ground torsion results, we find that the simulation results align effectively with the measured ground torsion results. This alignment enhances our understanding of how the radiometer effect and outgassing impact the internal pressure changes in the sensitive probe, which cannot be sufficiently decoupled by theoretical calculations alone. This conclusion demonstrates that the simulation method can effectively analyze the coupling effects of temperature gradient fluctuation and residual gas in inertial sensors, providing an important theoretical basis and practical significance for developing physical models of inertial sensors aimed at predicting and optimizing overall performance.

Seasonal morphological changes and grain size distribution mapping on the Massa beaches using a tachometer and GIS

Sandy beaches are complex, highly dynamic, fragile environments that are constantly evolving. Understanding the dynamics of these areas is crucial, both for fundamental science and for coastal zone management. This study presents the results of a seasonal morphological and sedimentological monitoring of the sandy beaches of Massa, located in the central-western region of Morocco. This monitoring was conducted using both an electronic total station and the spatialization of grain size indices (median, sorting index So, and skewness) through interpolation within a Geographic Information System (GIS). A total of 15 topographic profiles were surveyed perpendicular to the coastline during three field campaigns conducted between September 2019 and July 2020. The results indicate a morphodynamic equilibrium of the beaches, characterized by a normal morphological cycle with erosion during winter and accretion during summer. This balance is primarily attributed to the physical behavior of the beaches in response to wave energy, as well as the presence of a highly developed coastal dune capable of supplying sediment to the beaches during periods of shortage. Additionally, the spatialization of grain size indices from 90 samples clearly highlights the significance of hydrodynamic factors, such as waves, tides, currents, littoral drift, and winds, in the distribution of sandy sediments on the Massa beaches. Atlantic waves from the NNW sector, accompanied by a North-South littoral drift, govern the transport and deposition of sediments along beaches, while tides, currents, and local winds contribute to their redistribution across the different beach zones (foreshore, backshore, and coastal dune). Indeed, seasonal hydrodynamic conditions, as well as the proximity or distance of sediment source zones, play a critical role in sediment distribution along these beaches, providing valuable insights for coastal management strategies aimed at mitigating erosion, preserving beach morphology, and ensuring sustainable development in coastal zones.

A Descriptive Study on Interconnection Networks for Parallel Computing and Algorithm Models in Parallel Computing

In parallel computing, Interconnection networks are very crucial for efficient communication among all processors within a similar system. Parallel computing has become a crucial topic in the concern of computer science and also it is revealed to be critical when researching in high performance. The evolution of computer architectures towards an improved number of nodes, where parallelism could be the approach to option for speeding up an algorithm within the last few decades. Efficient data transfer between processors is an essential component in any large scale parallel computation. Motivated by the growing interest in parallel computers, a significant amount of theoretical research has been devoted to the area of interconnection networks for parallel computers, most of it to the packet routing (or store-and-forward) model of communication. We survey some of the major developments in this field, and discuss several new alternative models of communication, In many large scale applications, communication time dominates the execution time of the whole parallel computation. Thus, the performance of a large scale parallel computer is highly correlated with the efficiency of its network and communication algorithm. The combination of processing units build a model of computation (circuits) has gained an essential place in the area of high performance computing (HPC) due to its configuration and considerable processing supremacy that is parallel, series, etc. The aim of the Presenting this paper is study on the idea of parallel computing and its programming models and also explore some theoretical and technical concepts which can be often needed to understand the Interconnection network. In particular, we show how this technology is new in assisting the field of computational physics, especially when the issue is data parallel. In the real-life example of parallel computing, there are two queues to get a ticket of anything; if two cashiers are giving tickets to 2 persons simultaneously, it helps to save time as well as reduce complexity

Research on Trajectory Planning Method Based on Bézier Curves for Dynamic Scenarios

With the increase in car ownership, traffic congestion, and frequent accidents, autonomous driving technology, especially for dynamic driving scenarios in the whole domain, has become a technological challenge for today’s researchers. Trajectory planning, as a crucial component of the autonomous driving technology framework, is gradually becoming a hot topic in intelligent research. In response to the challenges of planning lane-changing trajectories in complex dynamic driving scenarios under emergency evasive maneuvers, where it is difficult to consider surrounding vehicles and achieve dynamic adaptability, this paper proposes a dynamic adaptive trajectory planning method based on Bézier curves. Firstly, a mathematical model of Bézier curves is established and its curve characteristics are analyzed, which facilitates the correlation between the trajectory control points and the vehicle and the surrounding obstacles. Secondly, a mathematical function representing the Bézier curve is formulated, where the control points serve as the input and the lane-changing control curve as the output. Finally, the proposed method is validated through simulations on a jointly established simulation platform. The results indicate that the proposed method can plan lane-changing trajectories that are both safe and efficient under emergency evasive maneuvers, considering both static and complex dynamic conditions. This provides a novel solution for lane-changing trajectory planning in emergency evasive maneuvers for autonomous vehicles and holds significant theoretical research value.

Stable laser-acceleration of high-flux proton beams with plasma collimation

Laser-plasma acceleration of protons offers a compact, ultra-fast alternative to conventional acceleration techniques, and is being widely pursued for potential applications in medicine, industry and fundamental science. Creating a stable, collimated beam of protons at high repetition rates presents a key challenge. Here, we demonstrate the generation of multi-MeV proton beams from a fast-replenishing ambient-temperature liquid sheet. The beam has an unprecedentedly low divergence of 1° (≤20 mrad), resulting from magnetic self-guiding of the proton beam during propagation through a low density vapour. The proton beams, generated at a repetition rate of 5 Hz using only 190 mJ of laser energy, exhibit a hundred-fold increase in flux compared to beams from a solid target. Coupled with the high shot-to-shot stability of this source, this represents a crucial step towards applications.

Using peer narrative support to promote return to work for cancer survivors: a protocol study of action research

BackgroundChinese cancer survivors are not doing well in returning to work. Peer support, as an external coping resource to help cancer survivors return to work, brings together members of the lay community with similar stressors or problems for mutual support. Peer volunteers have not received systematic training, so inappropriate language in the support process can often cause secondary damage to both the peer and the cancer survivor. High-quality narrative support can help both parties gain positive meaning in communication. Therefore, we draw on narrative-related theoretical techniques to construct a model of peer narrative support to facilitate cancer survivors’ return to work.MethodsThis study is a participatory action research, including three phases: (i) Construction: Construct a peer narrative support model through literature review, theoretical research, and expert meetings. A peer narrative competence training program and a peer narrative support implementation program are constructed on this basis. (ii) Intervention: Firstly, two rounds of training actions will be carried out for 8 weeks. Second, after the peers have passed the training assessment, cancer survivors will be recruited to carry out two rounds of peer narrative support for six months. The model framework will be revised in conjunction with observation and reflection during the action. (iii) Evaluation: A combination of quantitative and qualitative methods will be used to assess the feasibility and scientific validity of the peer narrative support model and to evaluate the effectiveness of the intervention. The research methods used include theoretical research, literature review, expert meeting method, participatory observation method, interview method, and questionnaire survey method.DiscussionChina currently has cancer rehabilitation associations in most provinces, cities, and counties, and peer volunteers in these organisations use their personal experience to help other cancer patients. Currently, the content of their help is mostly limited to disease recovery and financial condolences, and there is a lack of scientific training for peer volunteers. This study aims to help patients cope with their suffering and eventually return to work by narrating the stories of role models. The narrative support covers three aspects: focusing on recovery, rebuilding effectiveness, and adjusting planning. In addition, this study relies on the methodology of action research to develop a peer narrative support model, training program, and implementation plan, which are more applicable to the national context of peer support for cancer patients in China. This will provide scientific guidance for the Chinese Cancer Rehabilitation Association to develop peer narrative support and guide cancer survivors back to work.Trial registrationThis study has been registered at the Chinese Clinical Trial Registry (Registration number ChiCTR2300068403) on Feb 17, 2023. (Retrospectively registered) https://www.chictr.org.cn/bin/project/edit?pid=183766.

ARTIFICIAL INTELLIGENCE IN SPORTS: A RETROSPECTIVE OF THE FORMATION AND INTEGRATION PROCESSES IN THE SPORTS INDUSTRY, INTERNATIONAL EXPERIENCE AND STATE OF THE ISSUE IN UKRAINE

In 2020, in Ukraine, the Concept for the development of piece intelligence in Ukraine was developed, prepared by the Ministry of Digital Transformation of Ukraine and approved by the Cabinet of Ministers of Ukraine 02.12.2020 No. 1556-r.. This itself acts as the main instrument for the development of integration processes of piece intelligence and all galuzites It is the main mechanism for its implementation, as a separate strategy. Sports science is also actively involved in this process. There are already dozens of applications for the effective development of artificial intelligence at all stages of sports training. However, the overall picture of research and applied aspects still contains a sufficiently fragmented and chaotic order, which will require an integrated scientific approach and the development of detailed algorithms for the integration of individual intelligence in sports activities and other sports activities. The purpose of the work was to look at the practical stagnation of the capabilities of artificial intelligence systems in sports. Find out and learn the necessary facts about artificial intelligence. Conduct a retrospective analysis of the research and development of human intelligence systems in sports in Ukraine and in the international arena. Research methods – experimental-theoretical (analysis, logical), empirical (development), theoretical (research and analysis, analysis and synthesis). Apparently, prior to the results of the investigation, a retrospective analysis of the formation and integration processes in sportswear, international evidence of the applied science of artificial intelligence in sports and nutrition in Ukraine was carried out.

Concentrated Solar-Driven Catalytic CO2 Reduction: From Fundamental Research to Practical Applications.

Concentrated solar-driven CO2 reduction is a breakthrough approach to combat climate crisis. Harnessing the in-situ coupling of high photon flux density and high thermal energy flow initiates multiple energy conversion pathways, such as photothermal, photoelectric, and thermoelectric processes, thereby enhancing the efficient activation of CO2. This review systematically presents the fundamental principles of concentrated solar systems, the design and classification of solar-concentrating devices, and industrial application case studies. Meanwhile, key technological advances-from theoretical foundations to practical applications-are also discussed. At the microscopic level, a comprehensive analysis of multiscale reaction kinetics within the domain of photothermal synergistic catalysis has been conducted. This analysis elucidates the significance of catalyst design, further detailing the intricate regulatory mechanisms governing reaction pathways and active sites through nanostructured catalysts, single-atom catalysts, and metal-support interactions. However, the transition from laboratory research to industrial-scale application still faces challenges, including the complexity of system integration, energy density optimization, and economic feasibility. This review provides a theoretical framework and practical guidance through a complete investigation of current technological bottlenecks and future development directions, with the aim of driving key advances in concentrated solar-driven CO2 reduction catalysis.

Advances in IgY antibody dosage form design and delivery strategies: Current status and future perspective.

Immunoglobulin Y (IgY), a unique type of antibody found in birds, is attracting increasing attention for a broad range of biomedical applications. Rational IgY protection, dosage form design, and delivery are highly essential to transform functional IgY antibodies into desired IgY products for therapeutic and prophylactic administration. Although progress has been made in this field, it remains in the early stages, highlighting the fundamental research and development needed in this aspect of IgY technology. Hence, this article reviews the conventional and innovative IgY dosage designs and delivery strategies, emphasizes the challenges faced in various IgY delivery systems, discusses the criteria for evaluating IgY dosage form performance, and provides a comprehensive analysis of the current research status and prospects of IgY delivery strategies.

What drives sustainable procurement? Insights from the theory of planned behavior

PurposeA fundamental research question is what leads some organizations, but not others, to be sustainable in their procurement operations. Extant theoretical frameworks, while valuable, do not fully reflect the nuances of decision-making in procurement operations. We aim to illuminate the role of individual attitudes, capabilities, and behavioral intentions in actualizing sustainable procurement.Design/methodology/approachWe develop a framework by adapting the theory of planned behavior (TPB) to the context of sustainable procurement. We test the framework with a sample of 465 procurement professionals based in the EU through partial least squares structural equation modeling.FindingsWe find that sustainable purchasing behavior is predominantly shaped by behavioral intention, that is, willingness to pay for sustainability. This behavioral intention is significantly influenced by individual attitudes and capabilities in addition to awareness of consequences and perceived corporate social responsibility engagement but, interestingly, not by individual subjective norms.Originality/valueThe TPB is one of the most influential models for predicting behavior. However, the application of the theory in operations management is hitherto limited. The present study contributes to understanding individual-level antecedents of operations management practices and offers suggestions to practitioners engaged in fostering sustainable procurement.

Correlation of instantaneous fading in bidirectional channels of atmospheric laser communication

The instantaneous fading correlation of the bidirectional channel is an important characteristic of the atmospheric random channel. The instantaneous channel state of the transmitting end can be directly obtained through the instantaneous channel state of the receiving end, reducing the overhead of the additional feedback branch. In this paper, an autoregressive (AR) model and an inverse transformation method are used to generate time-dependent optical signals obeying an APD exponential Weibull distribution. Based on the channel reciprocity function, a channel cross-correlation model on the laser communication transmission path is established, and the influence of factors such as transmitting and receiving apertures, zenith angle, transmission distance, altitude, wavelength, and detector responsivity on the cross-correlation coefficient is studied. Based on theoretical research, an experimental system was built to verify the heterogeneous reciprocity model, and the experimental results were basically consistent with the simulation results. A channel state information prediction and evaluation are achieved through a relevant reciprocity theory, providing a basis for the development of adaptive technology in atmospheric laser communications.

A bibliometric analysis of exosomes in aging from 2007 to 2023

BackgroundAging is the primary factor contributing to the development of aging-related diseases. As research on exosomes continues to advance, its relationship with aging and aging-related diseases has become a hot topic This article analyzes the research hotspots of exosomes in aging and aging-related diseases, aiming to fill the gap in bibliometric research in this field and help researchers better understand the current status and future trends of both fundamental and clinical research in this field.MethodsThe articles were retrieved and exported from WoSCC on December 18, 2023. The visual analysis of countries and regions, institutions, authors, references, and keywords in exosomes of aging was conducted using VOSviewer 1.6.18, CiteSpace 6.2.R7, and Bibliometrix.ResultsThe bibliometric analysis included 1628 articles. China and the United States emerged as the top two leading countries in this field. A total of 2,321 research institutions from 78 countries and regions were primarily led by China and the United States. Both Kapogiannis D and Goetzl E were active authors in this field. Thery C, Valadi H, and Raposo G were the important promoters in this field. Thery C proposed the method of differential centrifugation and density gradient centrifugation to extract exosomes. Valadi H discovered cells could send RNA-messages to each other by loading them into exosome-vesicles. The journal with the highest number of articles was International Journal of Molecular Sciences, while PLoS One was the most frequently cited journal. The keyword analysis revealed that future research on exosomes in aging will possibly focus on “inflammation, cellular senescence, angiogenesis, insulin resistance, and Alzheimer's disease.”ConclusionWe identified the research trends of exosomes in the field of aging through this bibliometric analysis. The present study provides valuable new perspectives on the history and current status of exosomes in the field of aging and aging-related diseases, and also offering guidance for future research directions.

Stimuli-Responsive Nano Drug Delivery Systems for the Treatment of Neurological Diseases.

Nanomaterials with unparalleled physical and chemical attributes have become a cornerstone in the field of nanomedicine delivery. These materials can be engineered into various functionalized nanocarriers, which have become the focus of research. Stimulus-responsive nanodrug delivery systems (SRDDS) stand out as a sophisticated class of nanocarriers that can release drugs in response to environmental cues. Due to the complex pathogenesis and the multifaceted pathological environment of the nervous system, developing accurate and effective drug therapy with low side-effects is a formidable task. In recent years, SRDDS have been widely used in the treatment of neurological diseases. By customizing SRDDS to align with the specific microenvironment of the nervous system tissues or external stimulation, the efficacy of drug delivery can be enhanced. This review provides an in-depth look at the characteristics of the microenvironment of neurological diseases and highlights case studies of SRDDS tailored to treat these disorders based on theunique stimulation criteria of nervous system tissues or external triggers. Additionally, this review provides a comprehensive overview of the progress and future prospects of SRDDS technology in the treatment of neurological diseases, providing valuable guidance for its transition from fundamental research to clinical application.

Editorial—Focus point on citizen science for physics: from education to crowdsourcing fundamental research

Editorial—Focus point on citizen science for physics: from education to crowdsourcing fundamental research