Internal Mechanism Research Articles

Unraveling the Mechanical Response of Short-Chain Branched Amorphous Polyethylene: Insights from Molecular Dynamics Simulations

The deformation behavior of short-chain branched, amorphous polyethylene (PE) was investigated using united atom molecular dynamics (MD) simulations under uniaxial stretching. Our research reported in this paper examined the internal mechanisms underlying the mechanical response of the PE by analyzing potential energies, dihedral angle distributions, chain orientation and entanglements. The findings indicated that the short-chain branching generally enhanced Young’s modulus, except for sample B60 with a critical ethyl branch content of 60/1000 (60 ethyl branches per 1000 backbone carbons). The Young’s modulus of B60 was the local minimum value in the various samples. Analysis of the evolution of dihedral angles shows that the short-chain branching hinders the transition from gauche to trans configurations, leading to a decrease in the trans population as the branch content increases. Regarding the sample B60 with critical branch content 60/1000, the orientation parameter and entanglement parameter of the molecular chains along the backbone were lowest in the initial structure, and the efficiency of chain disentanglement was at its lowest during deformation. The simulation results elucidated the deformation mechanism related to the conformational evolution of the molecular chains, which can provide atomic-scale insights into the mechanical properties and processability of branched polyethylene, thereby allowing for optimization in its design and application.

Impact of geopolitical risk on green international technology spillovers: FDI and import channels

This study investigates the impact of geopolitical risk (GPR) on green international technology spillovers through foreign direct investment (FDI) and import channels. The research aims to understand how GPR influences the transfer of green technologies, which are crucial for sustainable development, particularly in developing countries. Utilizing data from 30 Chinese provinces from 2003 to 2019, our findings indicate that increased GPR significantly hinders green technology spillovers through both FDI and imports. Additionally, the research demonstrates that advancements in local green technology levels (GTL), marketization (Mark), and intellectual property rights protection (IPRP) can mitigate the adverse effects of GPR. These results underscore the critical importance of political stability and robust internal mechanisms in promoting international technological exchanges. This study contributes to the literature on the intersection of political risks and technological advancement and provides actionable insights for policymakers to enhance resilience against geopolitical uncertainties, thereby fostering sustainable economic development.

Adaptation of Chinese traditional villages from the perspective of locality preservation

Recently, traditional villages in China have received multiple policy protections and capital injections. While these external interventions protect the cultural heritage of traditional villages and provide opportunities for development, they have gradually eroded their intrinsic adaptive cycle system. This study focused on 6819 traditional villages, revealed the characteristics and transitions of the adaptive cycle stages in traditional villages and described the internal mechanisms of adaptability within these villages. The research findings are as follows: Regarding adaptability characterisation, the adaptability of traditional villages has developed a regionally significant imbalance. The highest proportion of adaptability cycle stages was found in the poverty trap, exploration (r) and gambling traps. Traditional villages are mostly in a rigid state, with a few adaptive processes shifting between the poverty trap and exploration (r), requiring a new impetus to guide positive cycles. In terms of adaptability mechanisms, there was a significant negative relationship between locality, mobility and resilience in traditional villages. Intrinsic adaptive mechanisms gradually disintegrate, and there is an increasing risk of falling into a lock-in trap. This study provides empirical evidence for analysing the issues in traditional villages and offers recommendations for long-term planning and policy formulation of locality protection in traditional villages.

Leaching process assisted by cetyltrimethylammonium chloride for weathered crust elution-deposited rare earth ores: Leaching behavior, kinetic analysis and interfacial regulation

Magnesium salts are commonly employed as ammonium-free leaching agents in the extraction of weathered crust elution-deposited rare earth ores (WCE-REO). Nonetheless, these salts frequently face technical challenges, including relatively low leaching rates and inadequate permeability in the in-situ leaching process. In this study, cetyltrimethylammonium chloride (CTAC) was used as a novel leaching aid agent to improve the rare earth leaching process. The effects of CTAC and pH on the rare earth leaching behavior and permeability was investigated. The leaching kinetic model was established and the regulation mechanism by which CTAC interacts with clay mineral surfaces was revealed for rare earth leaching process enhanced by CTAC. The results indicate that the appropriate amount of CTAC can increase the permeation velocity by 1.16×10−4 cm·s−1. Moreover, the rare earth leaching rate increased from 83.75 % to 95.65 %, accompanied by a reduction in leaching equilibrium time under optimal conditions. However, a higher concentration of CTAC results in a lower permeation velocity due to the increasing viscosity and the hydrophilicity. The relatively low pH will affect permeability of composite leaching agent in the ore pillar, diminishing its beneficial impact on rare earth leaching. The kinetic equation, fitted by the shrinking core model, suggests that the leaching adheres to an internal diffusion control mechanism, underscoring mass transfer as the pivotal limiting factor in rare earth leaching. CTAC reduces surface tension and viscosity, facilitating the permeation of the leaching solution into the rare earth ore. Furthermore, CTAC can be adsorbed on the surface of rare earth mineral particles, increasing the contact angle of clay minerals, elevating the Zeta potential, and diminishing the thickness of the diffusion layer, thus promoting mass transfer during leaching. The results of atomic force microscopy (AFM) analysis shows that the appropriate amount of CTAC adsorbed on rare earth ores can decreases the thickness of the hydration film on the particles’ surface, further improving the mass transfer efficiency during the leaching process. The findings of this study offer new insights into the green and efficient extraction of rare earth resources from both theoretical and technical perspectives.

The Contingent Effect of Organizational Artificial Intelligence Adoption on Employees’ Taking Charge: Based on Social Cognitive Theory

The advent of Artificial intelligence (AI) is catalyzing significant transformations in human work dynamics. Existing research has not yet provided a clear picture of how organizational AI adoption will affect employees’ taking charge. Based on this background, this study explores the internal mechanisms by which organizational AI adoption affects employees’ taking charge based on social cognitive theory. The results of the empirical analysis of the 342 samples indicate that: organizational AI adoption leads to a decline in employees’ organizational-based self-esteem; organization-based self-esteem plays a mediating role between organizational AI adoption and employees’ taking charge; future focus is paid to moderating the relationship between organizational-based self-esteem and taking charge and is a determinant of how their organizational-based self-esteem affects taking charge; future focus is paid to moderating the impact of organizational AI adoption through the indirect effects of organizational-based self-esteem on taking charge. This study highlights the importance of considering individual characteristics (e.g., future focus) when analyzing how organizational AI adoption affects employees’ behaviors.

Internal Control Unit Strategy in Implementing Internal Control

This study aims to describe the implementation of the Government Internal Control System (SPIP) at Dr. Radjiman Wediodiningrat Mental Hospital, Lawang, Malang and to develop a strategic plan from the Internal Audit Unit (SPI) in conducting supervision of the Mental Hospital. This research is qualitative research. The informants are the Head of SPI and other respondents. In formulating alternative strategies, SWOT analysis is used with the IFAS and EFAS approach methods. The results showed that SPI is in quadrant I (supporting growth strategy), which has strong internal strengths to grow in seizing existing opportunities. The resulting strategy is to improve internal control in the RSJ through the formation of the SPIP Task Force in organizing internal control in the hospital, increasing the knowledge and understanding of the heads of work units, on all matters relating to inherent supervision within the scope of their work units and utilizing information technology by creating a Supervisory Information System application to support supervisory performance, especially in terms of organizing SPIP in the hospital. The implications of this research are significant for enhancing internal control systems and strategic planning within healthcare institutions. By identifying SPI's current position and proposing targeted strategies, this study provides actionable recommendations for improving internal oversight and control mechanisms. The formation of a dedicated SPIP Task Force and the development of a Supervisory Information System application are expected to strengthen internal controls, streamline supervision processes, and enhance overall organizational efficiency.

Developmental patterns and computational mechanisms of school-aged children’s fairness-related decision making under social comparison

The developmental patterns and computational mechanisms underlying the impact of unfair offers and social comparisons on school-aged children’s fairness-related decision making remain unclear. To address this, we recruited 190 children aged 8 to 12 years (52.1% female) in a multi-responder ultimatum game. Results revealed an age-related decline in children’s tendency to reject unfair offers, partially mediated by emotions, alongside a slight increase in rejecting inferior social comparisons. Computational modeling identified two distinct motivations guiding children’s rejection behavior: inequity aversion and inferior social comparison avoidance. Furthermore, there was significant variability in responses to superior social comparisons, with some children displaying aversion and others seeking. Our refined model enhances the explanatory power of inequity aversion theory in complex multi-player social scenarios, validating and refining existing theories. In addition, the exploration of superior social comparison tendencies reveals individual heterogeneity, enriching our understanding of children’s social comparisons. These findings contribute to elucidating the developmental patterns and internal mechanisms of children’s socialization processes, offering implications for promoting their social adaptation and mental health.

Can we trust explainable artificial intelligence in wind power forecasting?

Advanced artificial intelligence (AI) models typically achieve high accuracy in wind power forecasting, but their internal mechanisms lack interpretability, which undermines user confidence in forecast value and strategy execution. To this end, this paper aims to investigate the interpretability of AI models, which is crucial but usually overlooked in wind power forecasting. Specifically, four model-agnostic explainable artificial intelligence (XAI) techniques (i.e., Shapley additive explanations, permutation feature importance, partial dependence plot, and local interpretable model-agnostic explanations) are tailored to provide global and instance interpretability for AI models in wind power forecasting. Then, several metrics are proposed to evaluate the trustworthiness of interpretations provided by XAI techniques. Simulation results demonstrate that the proposed XAI techniques can not only identify important features from wind power datasets, but also enable the understanding of the contribution of each feature to the forecast power output for a specific sample. Furthermore, the proposed evaluation metrics aid users in comprehensively assessing the trustworthiness of XAI techniques in wind power forecasting, enabling them to judiciously select suitable XAI techniques for their AI models.

Unveiling the Stability Mechanism of Oriented Ni-Rich Layered Oxides.

Single-crystal and polycrystalline structures are the two main structural forms of the Ni-rich layered cathode for lithium-ion batteries. The structural difference is closely related to the electrochemical performance and thermal stability, but its internal mechanism is unclear and is worthy of further exploration. In this study, both polycrystalline and single-crystal LiNi0.83Co0.12Mn0.05O2 cathodes were prepared by adjusting the calcination temperature and mechanical post-treatment, respectively. Systematic comparisons were made to assess the effects of different grain structures on the electrochemical performance and thermal stability. The study revealed the superior thermal stability of monocrystalline cathodes, attributing it to oxygen vacancies and phase transitions. From the perspective of grain boundaries, it was demonstrated that the diffusion of oxygen vacancies and the reduction of Ni in polycrystalline cathodes exhibit anisotropy. This research elucidates the origins of the superior thermal stability of monocrystalline cathodes in lithium-ion batteries, providing valuable insights into battery material design.

Ultrafast dynamics and internal processing mechanism of silica glass under double-pulse femtosecond laser irradiation

Femtosecond laser-induced plasma filaments have potential for various applications including attosecond physics, spectroscopy, and microprocessing. However, the use of plasma filaments to generate high-aspect-ratio internal modifications remains low-efficiency. Here, we experimentally demonstrated high-efficiency internal processing using plasma filaments induced by a double-pulse femtosecond laser. The processing mechanism was revealed through an investigation of the ultrafast dynamics of plasma filaments in experiments and simulations. We found that the excitation region of the first pulse (P1) exerted a temporal effect on the propagation and absorption of the second pulse (P2) due to the evolution of excited electrons, thus resulting in different processing characterizations. At a smaller inter-pulse delay (IPD), electrons and self-trapped excitons induced by P1 improved the absorption of P2 in the shallow region. Consequently, the main excitation regions of P1 and P2 were separated, resulting in a lower density of energy deposition and weak modifications. Whereas, at a larger IPD, P2 penetrated a deeper region with the relaxation of electrons and excitons induced by P1, leading to a better overlap of excitation regions between P2 and P1, thus improving the density of energy deposition and achieving efficient microprocessing. Besides, at an infinite IPD, P2 behaved like P1, but no modification was obtained owing to the complete energy diffusion of P1. Therefore, controlling the electron dynamic and energy diffusion contributes to the improvement of modification efficiency. Furthermore, the distribution of electron densities on the cross section was estimated to precisely analyze the microprocessing. These results are expected to aid in a better understanding of the interaction mechanism between dielectrics and intense ultrafast lasers and be useful for microprocessing applications.

Reduced-order reconstruction of discrete grey forecasting model and its application

Discrete grey forecasting models based on an accumulative operator have been widely used in many practical fields. With the development of grey forecasting models, it is a problem to be solved to further analyze internal mechanisms and unify the structures. This paper aims to reconstruct the model from a perspective of sequence characteristics and simplify the modeling steps under the condition of ensuring the accuracy of the model. First, this paper analyzes dynamic sequence evolution hidden and mines relationship between the structure and original sequence features contained in discrete grey forecasting model. Then, the reconstruction is carried out to prove the equivalence and quantitative relation between reduced-order model and original model. Under order recursive estimation, new parameters are addressed. Finally, theoretical correctness is verified by large-scale numerical simulation. Moreover, the reduced-order model is applied for prediction on the peak of battery incremental capacity and capacity degradation. Results show the effectiveness and superior prediction performance of the reduced-order model, where MAPEs of grey forecasting models have controlled under 4%.

Consumer acceptance of different types of cultural borrowing and its internal mechanisms

In cross-regional cultural interactions, the frequent and controversial practice of cultural borrowing has attracted attention. According to the different relative statuses and comparative directions of the cultural subjects, cultural borrowing can be divided into strong-to-weak (weak-to-strong) cultural borrowing between subjects with strong (weak) status and subjects with weak (strong) status in cultural interactions, as well as equal cultural borrowing between subjects with equal status in cultural interactions. After the concept and types of cultural borrowing are defined, a multisituation experimental method is used to confirm the internal mechanisms and effect boundaries of consumers’ cultural borrowing acceptance. The results of five experiments based on over a thousand subjects indicate that different types of cultural borrowing (strong-to-weak, equal, weak-to-strong) arouse consumers’ perceived cultural threat, thereby reducing their acceptance of cultural borrowing to varying degrees. The influence of different types of cultural borrowing on perceived cultural threat is moderated by the degree of incongruent use and reality of the presentation of the borrowed culture; the influence of perceived cultural threat on cultural borrowing acceptance is moderated by the positioning of the borrowing subjects and the degree of identity in the relationship. By exploring cultural borrowing and consumer acceptance issues in the field of consumption through a series of experiments, this study effectively reveals the differentiated impacts and underlying mechanisms of different types of cultural borrowing on consumers’ acceptance responses.

The tonoplast-localized OsTIP2;1 is involved in aluminum detoxification in rice

Aluminum (Al) stress is a significant issue in acidic soils, severely affecting crop growth and yield. Rice is notably resilient to Al toxicity, yet the internal tolerance mechanisms remain inadequately addressed. Here, we examined the role of OsTIP2;1, a tonoplast-bound intrinsic protein (TIP), in rice's internal Al detoxification. Our findings reveal that OsTIP2;1 expression was quickly and explicitly activated by Al ions in roots but not in shoots. The OsTIP2;1-GFP protein localizes to the tonoplast in plant and yeast cells. Non-functional ostip2;1 rice mutants were more vulnerable to Al toxicity. In the roots, the ostip2;1 mutants exhibited considerably lower levels of Al in the cell sap, primarily the vacuolar contents, than in the wild-type plant. Moreover, the ostip2;1 mutants showed reduced Al accumulation in the roots but increased translocation to the shoots. Heterologous expression of tonoplast-localized OsTIP2;1 in yeast led to enhanced Al tolerance, suggesting that OsTIP2;1 facilitates Al sequestration to the vacuole. These findings indicate that OsTIP2;1 mediates internal detoxification by transporting Al into the vacuole in the root and restricting its transport to above-ground tissues, thus contributing to Al resistance in rice.

Expression of the aromatase cytochrome p450 enzyme, α and β estrogen receptors, and androgens in cells of the spermatogenic lineage, Leydig and Sertoli of Hydrochoerus hydrochaeris (Linnaeus, 1766)

Capybaras (Hydrochoerus hydrochaeris) are the biggest rodents in the world and have a huge importance in conservation, can be a rodent model in reproduction and have a huge role in the food chain in South America. Molecular studies of the reproduction of this animal are important for all animal class conservation, mainly rodents. In this way, hormone signaling can be useful to understand external and internal mechanisms of reproduction. In this work, estrogen receptors α and β, aromatase cytochrome P450 enzyme, and androgens were detected by immunohistochemistry on Leydig cells, Sertoli cells, myoid cells, and seminiferous epithelium H. hydrochaeris testis. Nine animals were sacrificed and it was collected the testicles, and for each staining (hematoxylin and eosin, Mallory, and immunohistochemistry for four antibodies) it was made 5 slides per animal, after that a qualitative analysis was performed. The presence of estrogen receptors α and β in germinal epithelial cells, with stronger expression in spermatogonia and spermatocytes, may indicate the action of these hormones in the initial process of sperm cell development, needing more studies to investigate this first evidence. In other animals is believed that the aromatase cytochrome P450 enzyme can promote the transformation of androgens into estrogen, and in H. hydrochaeris is observed in those cells and androgen receptors. Sertoli cells exhibited moderate immunostaining for androgen and estrogen receptors α and β in the cytoplasm, with poor labeling for the aromatase cytochrome P450 enzyme in the nucleus in H. hydrochaeris. It is suggested that Sertoli cells may be responsible for producing estrogen in adult animals.

The mechanism and effect factors of the combustion cycle‐to‐cycle variations in the spark ignition engine

AbstractThe combustion cycle‐to‐cycle variations (CCV) are the typical combustion phenomena in the internal combustion engine, which will not only affect the combustion efficiency, heat‐work conversion process, and emission formation in the cylinder, but also cause the output torque and power fluctuation, resulting in unstable and even misfire. These phenomena are particularly evident in the spark ignition (SI) engine, especially at idle, acceleration, and high exhaust gas recirculation conditions. Consequently, it is quite important to explore the internal relationship and correlation mechanism between the CCV and the affecting factors. This paper comprehensively reviewed the fundamental reasons and mechanisms of CCV of the SI engine. In addition, the characteristic parameters and characterization methods of the CCV, the laws and influencing factors of the CCV, and the numerical simulation methods of the CCV were introduced in detail to quantitatively analyze the performance, combustion, and emissions characteristics of the SI engine. Each research direction is discussed in detail in various sections. The research status of the CCV of the SI engine from the experimental and numerical simulation aspects was also presented and discussed. Lastly, effective methods and strategies were proposed to improve the combustion process and fuel economy, and reduce exhaust emissions of the SI engine for high efficiency and clean combustion.

Glucose Challenge Uncovers Temporal Fungibility of Metabolic Homeostasis over a day:night cycle.

Rhythmicity is a cornerstone of behavioral and biological processes, especially metabolism, yet the mechanisms behind metabolite cycling remain elusive. This study uncovers a robust oscillation in key metabolite pathways downstream of glucose in humans. A purpose-built 13C6-glucose isotope tracing platform was used to sample Drosophila every 4h and probe these pathways, revealing a striking peak in biosynthesis shortly after lights-on in wild-type flies. A hyperactive mutant (fumin) demonstrates increased Krebs cycle labelling and dawn-specific glycolysis labelling. Surprisingly, neither underlying feeding rhythms nor the presence of food availability explain the rhythmicity of glucose processing across genotypes, suggesting a robust internal mechanism for metabolic control of glucose processing. These results align with clinical data highlighting detrimental effects of mistimed energy intake. Our approach offers a unique insight into the dynamic range of daily metabolic processing and provides a mechanistic foundation for exploring circadian metabolic homeostasis in disease contexts.

Can the construction of smart cities promote the capital allocation efficiency: Evidence from China

Based on the panel data of 284 prefecture-level city in China from 2005 to 2019, this paper measures the urban capital allocation efficiency and takes smart city construction as a quasi-natural experiment. The multi-period difference-in-differences (DID) model has been built to verify the internal mechanism and practical effect of smart city construction driving the optimal allocation of capital. The results show that the construction of smart cities has significantly improved the efficiency of capital allocation, and the conclusion remains valid after a series of robustness tests; Mechanism analysis shows that strengthening environmental regulations and improving innovation capabilities, together with the construction of smart cities, have promoted the improvement of capital allocation efficiency; The heterogeneity analysis indicates that the effect of smart city construction on capital allocation efficiency is influenced by the administrative level and regional heterogeneity characteristics of the city. This paper provides empirical evidence for the policy effectiveness of smart city construction and provides policy enlightenment on how to optimize capital allocation.

Research on Lithology Identification based on Machine Learning

Machine learning has great potential in lithology identification. Through supervised learning, unsupervised learning, semi-supervised learning, deep learning and other methods, features can be automatically extracted from complex seismic data and logging data to achieve efficient and accurate lithology classification. These methods not only improve the accuracy and efficiency of lithology identification, but also reduce the workload of geologists, allowing them to focus on higher-level analysis and decision making. Despite significant progress, machine learning still faces many challenges in lithology identification. First, data quality and quantity limitations remain a major problem, especially in certain areas where obtaining high-quality seismic and logging data is difficult. Second, the training and interpretability of complex models also need to be addressed, especially because the "black box" nature of deep learning models makes it difficult for geologists to understand their internal mechanisms and predictions. In addition, the generalization ability and overfitting of models, as well as the need for real-time data processing in practical applications, are also urgent challenges to be solved. To address these challenges, this paper proposes several solutions and future directions. Data enhancement and synthesis techniques can extend existing data sets and improve the robustness and accuracy of models. The development of interpretative models and visualization tools helps geologists understand and trust the decision-making process of models. Multi-source data fusion technology can effectively use multi-source heterogeneous data such as seismic data, well logging data and geological map to improve model performance. Online learning and transfer learning technologies can update models in real time, improve the adaptability and generalization ability of models, and develop more accurate and interpretable models.

Analysis of energy dissipation in hyperbolic problems influenced by internal and boundary control mechanisms

ABSTRACT This article primarily focuses on the rational stabilisation of the wave equation, supplied with a second-order dynamical boundary condition of hyperbolic type, while considering an additional internal damping mechanism within the specified ring. To achieve rational decay rates of the associated energy, it is imperative to exponentially stabilise a portion of the domain using a global Ingham's-type estimate. This paper will subsequently illustrate how this partially localised exponential stabilisation, combined with a Bessel analysis, leads to a rational decrease in the overall energy of the system considered.

Research on Urban Resilience from the Perspective of Land Intensive Use: Indicator Measurement, Impact and Policy Implications

Land intensive use reflects the spatial structure, agglomeration characteristics, and internal mechanisms of urban economic, social, and ecological system development, significantly impacting urban resilience. Based on panel data from 287 cities in China from 2010 to 2020, this paper measures the levels of land intensive use and urban resilience, and empirically examines the impact mechanism of land intensive use on urban resilience through baseline regression and panel quantile regression. The results reveal that: (1) During the study period, China’s urban land intensive use level has significantly improved. The land intensive use level shows a trend of “the strong become stronger, and the weak are always weak” and “high in the east and low in the west” spatial differentiation, while the urban resilience level showed a trend of accelerated “catching up” of low-resilience cities towards high-resilience cities and “high in the east and low in the west” spatial differentiation as well. (2) Land intensive use significantly promotes effect on urban resilience, and the effect depends on different conditions. (3) Among all dimensions of land intensive use, both land input intensity and land use benefits significantly promote urban resilience, while land use intensity shows an insignificant effect. (4) The impact of land intensive use on urban resilience demonstrates significant scale heterogeneity and geographic regional heterogeneity. Based on these findings, the paper proposes relevant policy suggestions for land intensive use aimed at improving urban resilience, offering guidance for promoting high-quality land use and sustainable urban resilience development.