Static Aspects Research Articles

Enhancing Programs Efficiency through a Machine Learning-Based Model for Tile Size Selection

This work presents an innovative machine-learning approach to improve loop tiling in computational programming. Loop tiling is a crucial strategy for boosting speed by promoting data locality and reducing cache misses. Conventional methods frequently have difficulties in accurately calculating the most suitable tile size, which is a crucial component impacting the performance of the program. The combination of Multi-Output Generalized Regression Neural Networks (MOGRNN) and Linear Regression in researchers’ techniques allows for precise prediction of optimal tile sizes for various computing workloads. The study entails an extensive gathering of data from 22 benchmark programs, which encompass a diverse set of computational patterns and issue sizes. This data collection is further enhanced by incorporating both static and dynamic program aspects. By employing meticulous preprocessing and doing dual-model analysis, the researchers’ approach effectively captures both linear and intricate non-linear correlations present in the data. The method’s usefulness in boosting prediction accuracy for ideal tile sizes and enhancing overall program performance has been demonstrated through extensive testing on an Intel Core i7 CPU. This novel approach provides a viable avenue for advanced study in code optimization approaches.

Static and dynamic interactions within the triple-network model in stroke patients with multidomain cognitive impairments

BackgroundInternal capsule strokes often result in multidomain cognitive impairments across memory, attention, and executive function, typically due to disruptions in brain network connectivity. Our study examines these impairments by analyzing interactions within the triple-network model, focusing on both static and dynamic aspects. MethodsWe collected resting-state fMRI data from 62 left (CI_L) and 56 right (CI_R) internal capsule stroke patients, along with 57 healthy controls (HC). Using independent component analysis to extract the default mode (DMN), executive control (ECN), and salience networks (SAN), we conducted static and dynamic functional network connectivity analyses (DFNC) to identify differences between stroke patients and controls. For DFNC, we used k-means clustering to focus on temporal properties and multilayer network analysis to examine integration and modularity Q, where integration represents dynamic interactions between networks, and modularity Q measures how well the network is divided into distinct modules. We then calculated the correlations between SFNC/DFNC properties with significant inter-group differences and cognitive scales. ResultsCompared to HC, both CI_L and CI_R patients showed increased static FCs between SAN and DMN and decreased dynamic interactions between ECN and other networks. CI_R patients also had heightened static FCs between SAN and ECN and maintained a state with strongly positive FNCs across all networks in the triple-network model. Additionally, CI_R patients displayed decreased modularity Q. ConclusionThese findings highlight that stroke can result in the disruption of static and dynamic interactions in the triple network model, aiding our understanding of the neuropathological basis for multidomain cognitive deficits after internal capsule stroke.

Searching for the Effects of Momentum in Tennis and its Applications

In sports, a team or athlete may feel they have momentum, or “power/strength” during a game, but this phenomenon is difficult to measure. Furthermore, it is not clear how various events during a match create or change momentum. This paper presents an approach to quantifying ‘momentum’ by introducing a composite model integrating a Logistic Regression Model and a LASSO-based Sparse ARMAX Model to predict momentum shifts and guide strategic decisions during games. The combined model addresses both static and dynamic aspects of momentum, culminating in a comprehensive prediction of momentum trends. The model’s robustness is evidenced by its high accuracy rates across different tennis matches and gender scenarios (95.6%, 94.9%, and 95.5%). This paper’s findings confirm momentum’s measurable impact on sports outcomes, offering a scientific basis for tactical decision-making.

Method of interacting between humans and conversational voice agent systems

Voice agent systems are deployed widely in various devices such as mobile phones, smart speakers, televisions, and refrigerators. Although the market for voice agent systems has expanded significantly and the systems themselves are becoming intelligent, their usability remains limited. This study provides general and practical guidelines for voice interaction to help VUI (Voice User Interface) designers and developers in the process of designing voice agent systems. The current guidelines only offer generic information for VUI designers, as they only provide the static aspect of voice interaction rather than consider the entire conversation flow. Studies on designing voice interaction systems have shown that VUI designers and developers need more guidance. The new interaction guidelines were developed based on users’ cognitive strategy in the course of using voice agent systems. We proposed and evaluated 12 practical voice interaction design guidelines for conversational voice agent systems. To evaluate the guidelines, we recruited 60 experts with different levels of voice interaction. Participants evaluated the usefulness of the guidelines and provided insightful opinions on them.

Категория «частноправовой механизм защиты права»: эвристический потенциал использования в цивилистической теории и практике

The article discusses the genesis of the metaphorical category of “mechanism” in relation to the protection of civil rights. The natural character of the appearance of this category in the arsenal of the methodology of civilis-tics, due to the property of metaphorical legal consciousness is demonstrated. The heuristic potential of the category “mechanism” at its instrumental use in private law sciences is revealed, due to the fact that it initially assumes a plurality of interpretations and thus is already adapted to the conceptualization of any empirical ma-terial that needs a new comprehension from the point of view of de lege ferenda, with the subsequent for-mation of enforcement practice; it allows to build more clearly the inter-branch relations and structure the legal regulation as a whole – both at the national and interstate level; contributes to a more reliable testing of any new tools that are gradually introduced into the legal system in connection with the emergence of new realities, the concepts of regulation of which have not yet been fully formed. It is concluded that the category “private law mechanism of law protection” emphasizes the dialectical unity of static and dynamic aspects of legal regula-tion, as well as provides the genesis of a new subject of legal regulation, objectively inevitable in the rapidly changing post-industrial society.

HCGCCDA: Prediction of circRNA-disease associations based on the combination of hypergraph convolution and graph convolution

With the advancement of modern biological experiments, an increasing body of evidence indicates a complex relationship between circRNA and diseases, which holds significant implications for disease research and treatment. However, traditional biological experiments have shown low efficiency and high costs when studying the association between circRNA and diseases. To address this issue, researchers have proposed various methods for predicting the correlation between circRNA and diseases. Nevertheless, existing methods mainly focus on feature extraction from individual nodes, leaving the feature extraction from edge information largely unexplored. In this research, we use an advanced method that performs information extraction from both edges and nodes for edge prediction. Specifically, we construct similarity networks for circRNA and diseases from multiple perspectives, including circRNA and disease feature networks obtained through matrix factorization, as well as similarity networks based on Pearson correlation and Gaussian kernel. We also leverage circRNA sequences and disease semantic information to construct similarity networks. Subsequently, we refine node feature information by extracting and optimizing features from both the nodes themselves and their neighbors. Next, we employ hypergraph convolutional networks and graph convolutional networks to learn and embed features from both dynamic and static aspects. Finally, we score the obtained comprehensive embedding features and utilize the model’s training results for prediction. Our model demonstrates excellent performance in five-fold cross-validation, achieving an AUC value of 0.9833, surpassing other advanced models. The results of the case study further validate its high precision. Consequently, our model provides more valuable foundational information for biological research, with the potential to make significant impacts in disease research and treatment.

Mental health therapy for veterans with PTSD as a family affair: A qualitative inquiry into how family support and social norms influence veteran engagement in care.

Social support is important for posttraumatic stress disorder (PTSD) recovery and emerging literature indicate that social support could increase engagement in PTSD therapy. However, there is a need to understand how and why family involvement can increase treatment engagement to inform strategies used in clinical practice. This study explores how individuals with PTSD and family members of individuals with PTSD experience therapy and how social interactions help or hinder therapy engagement. We interviewed 18 U.S. military veterans who had been referred for psychotherapy for PTSD in the Veterans Health Administration and 13 family members and used rapid content analysis to identify themes. We found that engaging in therapy was a family-level decision that participants expected to improve family life. Veterans were motivated to seek treatment to protect their relationships with loved ones. Family members generally encouraged veterans to seek treatment. Specifically, family members who viewed PTSD as a treatable illness versus a static aspect of the veteran's personality expressed positive attitudes about the effectiveness of therapy for reducing symptoms. Veterans whose social networks included individuals with prior military or trauma-related experiences reported that their loved ones possessed more understanding of PTSD and described positive subjective norms around therapy. Family members are often embedded in the therapy process because PTSD has a profound impact on the family. Positive subjective norms for therapy are created by family encouragement and may influence veteran perceptions about the value of treatment. Family members should be engaged early in mental health therapy and to the extent desired by the patient and family member. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Many-Exciton Quantum Dynamics in a Ruddlesden-Popper Tin Iodide.

We present a study on the many-body exciton interactions in a Ruddlesden-Popper tin halide, namely, (PEA)2SnI4 (PEA = phenylethylammonium), using coherent two-dimensional electronic spectroscopy. The optical dephasing times of the third-order polarization observed in these systems are determined by exciton many-body interactions and lattice fluctuations. We investigate the excitation-induced dephasing (EID) and observe a significant reduction of the dephasing time with increasing excitation density as compared to its lead counterpart (PEA)2PbI4, which we have previously reported in a separate publication [J. Chem. Phys.2020, 153, 164706]. Surprisingly, we find that the EID interaction parameter is four orders of magnitude higher in (PEA)2SnI4 than that in (PEA)2PbI4. This increase in the EID rate may be due to exciton localization arising from a more statically disordered lattice in the tin derivative. This is supported by the observation of multiple closely spaced exciton states and the broadening of the linewidth with increasing population time (spectral diffusion), which suggests a static disordered structure relative to the highly dynamic lead-halide. Additionally, we find that the exciton nonlinear coherent lineshape shows evidence of a biexcitonic state with low binding energy (<10 meV) not observed in the lead system. We model the lineshapes based on a stochastic scattering theory that accounts for the interaction with a nonstationary population of dark background excitations. Our study provides evidence of differences in the exciton quantum dynamics between tin- and lead-based Ruddlesden-Popper metal halides (RPMHs) and links them to the exciton-exciton interaction strength and the static disorder aspect of the crystalline structure.

Творчество В.В. Розанова: техника описания и теория изменения вещей как образ исторического осмысления действительности

The creativity of V.V. Rozanov is a special kind of theory (speculation), allowing one to see things as potencies. The thinker perceives the phenomena and processes of the material and spiritual plane in the context of this speculation. Istria defines the internal and external relationship of these processes as a set of external and internal changes. External changes are defined by the concept of uncertainty, and internal definitions of a certain potency. Rozanov considers the interaction of these potencies simultaneously statically and dynamically. Statically, it presupposes the existence of regularities and the victory of order over chaos. Dynamically manifested as the primacy of randomness in life, since all potencies can not be realized. The interaction of these two forms of change creates life in all its complexity, Rozanov's theory helps us comprehend, therefore, at different periods of the thinker's creativity, both static and dynamic aspects of potentiality that create the history of the world as a sounding word and as a silence. This avoids subjective and one-sided interpretations of the thinker's philosophical heritage. The author uses the method of reconstruction in the work, which makes it possible to show that the philosophical heritage of Rozanov is a semantic field formed around the concept of "potentiality." The scientific novelty of the study lies in the possibility of isolating broad contexts of Rozanov's work and considering its various aspects as a complete metaphysical and dialectical theory, that helps to integrate different aspects and sides of Rozanov’s creativity and makes it more understandable.

How multiple levels of metacognitive awareness operate in collaborative problem solving

Metacognitive awareness is knowing about learners’ own thinking and learning, facilitated by introspection and self-evaluation. Although metacognitive functions are personal, they cannot be explained simply by individual conceptions, especially in a collaborative group learning context. This study considers metacognitive awareness on multiple levels. It investigates how metacognitive awareness at the individual, social, and environmental levels are associated with collaborative problem solving (CPS). Seventy-seven higher education students collaborated in triads on a computer-based simulation about running a fictional company for 12 simulated months. The individual level of metacognitive awareness was measured using the Metacognitive Awareness Inventory. The social level of metacognitive awareness was measured multiple times during CPS through situated self-reports, that is, metacognitive judgements and task difficulty. The environmental level of metacognitive awareness was measured via a complex CPS process so that group members’ interactions were video recorded and facial expression data were created by post-processing video-recorded data. Perceived individual and group performance were measured with self-reports at the end of the CPS task. In the analysis, structural equation modelling was conducted to observe the relationships between multiple levels of metacognitive awareness and CPS task performance. Three-level multilevel modelling was also used to understand the effect of environmental-level metacognitive awareness. The results reveal that facial expression recognition makes metacognitive awareness visible in a collaborative context. This study contributes to research on metacognition by displaying both the relatively static and dynamic aspects of metacognitive awareness in CPS.

Mechanistic partitioning of species richness in diverse tropical forest tree communities with immigration and temporal environmental stochasticity

Abstract Hyperdiverse tropical forest tree communities illustrate a fundamental problem in ecology: How can many species coexist given relatively few limiting resources? Neutral theory provides a solution by positing that species have equal fitness and hence drift to extinction slowly. However, neutral theory seriously under‐predicts temporal changes in species abundances. This can be remedied by breaking neutrality and adding temporal environmental stochasticity (TES), but the mechanisms mediating the effects of TES on species richness remain unclear. Here, we make progress by analysing a local community model with species competing for a common resource under TES, to derive formulae partitioning species richness according to different mechanisms. By applying our formulae to generic parameter sets for tropical forest tree communities, we found that when the autocorrelation time of TES was short, the dominant mechanism driving species richness was non‐linear averaging of the interspecific competition term over time, which reduced the typical strength of interspecific competition and boosted richness relative to the neutral case. However, greater immigration to the community resulted in more species and hence weaker non‐linear averaging due to the law of large numbers. In contrast, when the autocorrelation time of TES was long, the dominant mechanism driving species richness was strong selection between changes in environmental conditions, which increased the typical strength of interspecific competition and reduced richness relative to the neutral case. By applying our formulae to a specific parameter set for a tropical forest tree community in Panama, we found that TES had minor effects on species richness because (i) the immigration rate was sufficiently large for non‐linear averaging of the interspecific competition term to be weak and (ii) the autocorrelation time was sufficiently short to suppress the effects of selection. Synthesis. We provide a novel mechanistic explanation of how TES affects tree species richness in tropical forests, in particular how TES often has minor effects on richness despite having substantial effects on temporal changes in species abundances. This provides a deeper insight into why a neutral model with added TES can accurately capture static and dynamic aspects of tree community diversity in the tropics.

Student Competition (Knowledge Generation) ID 1987946

Background/Objectives NX210c is a 12-amino acid peptide derived from conserved thrombospondin type 1 repeat sequences in the subcommissural organ-spondin, which has a unique multifunctional mechanism of action to ameliorate outcomes following neurological injuries. The aim of this study was to evaluate the efficacy of NX210c to promote functional recovery and tissue repair in a cervical traumatic spinal cord injury (SCI) model. Methods/Overview Adult female Wistar rats were subjected to a C6/C7 clip compression-contusion injury and treated once daily with intraperitoneal injections of NX210c (8 mg/kg) or its vehicle for 8 weeks, beginning 4 hours (h) or 8h post-injury (n=16-17/group), with concurrent neurobehavioural tests. Results Earlier NX210c administration at 4h increased forelimb grip strength (p&amp;lt;0.05) and improved several static and dynamic aspects of locomotion including regularity index and base of support of the forelimbs (CatWalk) (p&amp;lt;0.05). Delaying initial administration of NX210c to 8h, promoted weight gain, accelerated bladder control recovery from 14 to 9 days post-injury, and improved trunk balance (inclined plane) as early as one-week post-injury (p&amp;lt;0.05). 94% of NX210c-treated rats compared to 75% of vehicle controls observed weight support at the delayed initial injection timepoint. Histology (n=6/group) demonstrated greater white matter preservation and reduced cavity size at the injury epicenter, and higher neuronal soma counts caudally, with NX210c starting 8h post-injury compared to the vehicle (p&amp;lt;0.05). Conclusions NX210c targets various aspects of SCI, improving motor function, bladder control, white matter preservation, and neuronal counts, with more benefits observed at the later initial injection timepoint.

Reading and writing in n-dimensional face space

Abstract This manuscript begins with a brief introduction that establishes the theoretical background, followed by a tripartite unfolding that explains the following contents: 1) the path from point zero as a plurimorphic space of semiotic tranquility; 2) the historical trajectory of physiognomy and pathognomy suggestions; and 3) new biometric readings and the digital face. We postulate the neutral state of the face and see a possible manifestation of it in the tranquility illustration and theorization by Le Brun. Together with the passive face recounted by Magli, these are some of the static aspects of that space called the face, full of signs that write and compose it and allow its physiognomic reading. Degree zero, the pre-signal null point from which interpretation originates, determines the semiotic potential of the face, and the expressions of the emotions become the key to pathognomy studies, in their historical-cultural variants. We will then see how, in today’s highly technological and mediatized society, there are new biometric manifestations linked to the digital face. From the Bertillonage of the early 1900s, we conclude with a work of contemporary art that critically revisits the same approach.

Wildlife habitat association over a twelve-year period (2008–2020) in the Greater Mahale Ecosystem, western Tanzania

The Greater Mahale Ecosystem (GME) in western Tanzania hosts high faunal biodiversity, including charismatic species such as elephants and chimpanzees. Across the GME, habitat occurs in differently administered, unprotected and protected areas, including village land, forest reserves, and national parks. Areas of lower protective status are prone to deforestation and land cover changes. These land cover changes influence suitable habitat itself as well as the connectivity between habitat patches, but the impact on different mammal species in this region is unknown. In this study, we investigated changes in habitat availability by creating species distribution models for 11 medium-large mammal species based on species occurrence data collected from 2008 to 2020. We tested habitat associations for each species and evaluated the importance of the static predictor variables elevation, slope, aspect and terrain-ruggedness and the dynamic predictor variables distance to deforestation, percent forest, vegetation intensity, and vegetation variability, retrieved through remotely sensed data analysis. Our models suggest habitat declined for most, but not all species. Whilst elephants and buffalo lost more than 50 % of available habitat during this period, primates exhibited habitat stability. Habitat losses were pronounced in the southeast of the GME. Slope, elevation and median tasseled-cap wetness index (vegetation intensity) and distance to deforestation were the most important predictor variables. Our study presents a spatially and temporally explicit estimation of habitat changes in a critically important, biodiverse region undergoing drastic land cover changes. We call for consideration in land-use planning to foster connectivity and landscape integrity to protect and conserve wildlife across the ecosystem.

Identifying Critical Stations Affecting Vulnerability of a Metro Network Considering Passenger Flow and Cascading Failure: Case of Xi’an Metro in China

Analyzing the importance grade of each metro station is the premise and foundation to ensure the safe and efficient operation of the metro network. This paper explores the identification method of critical stations in the metro network on the basis of considering the influence of passenger flow. Eight indicators were selected from both the static and dynamic aspects to construct the critical station identification index system considering the impact of passenger flow and cascading failure. Then, the triangular fuzzy analytic hierarchy process (TFAHP) method and critical importance through intercriteria correlation (CRITIC) method were used to calculate subjective and objective weight of all indicators, and the combined weighting method based on game theory was applied to calculate the comprehensive weight. The improved technique for order of preference by similarity to ideal solution (TOPSIS) method was developed to calculate the station importance and rank all stations according to their importance. Finally, the Xi’an metro network by mid-2021 was taken as the case study. The results show that there are 21 critical stations in the Xi’an metro network in three time periods (i.e., morning rush hours, evening rush hours, and nonrush hours) on weekdays and weekends, respectively, and those critical stations are divided into three levels. High centrality, high passenger flow intensity, and strong destructive ability are three important characteristics of critical stations in the metro network, and different critical stations have different importance characteristics. The Xi’an metro operation management authority needs to conduct a classification management scheme for those critical stations. The conclusions can provide the basis for the metro operation management authority to formulate accurate station management scheme, so as to increase the robustness of the metro network.

High Step-Up Soft-Switching DC–DC Converter Integrated With Y-Source Network

High step-up dc-dc converter is generally used in micro grid, uninterruptible power supply, hybrid electric vehicle, and other applications. For attaining high boosting and high efficiency, a high step-up soft switching circuit topology with an integrated Y-Source network is presented. Its front end is a Boost structure with the strengths of continuous and low ripple input current. The active clamp circuit may store the energy of leakage inductance in the output capacitor, and then output with reuse. In addition, the switches achieve zero voltage conduction, thereby decreasing its switching loss. At the same time, the coupling inductor achieves zero dc bias, which reduces the core size and core loss. The converter may attain high step-up by selecting the proper turns ratio of the coupling inductor at a reasonable duty cycle. The paper elucidates the working mode of the converter in depth, builds the small signal model, and study the stability of the converter. Finally, an experimental prototype of 200 W 28-380 V 100 kHz is built, and the feasibility of the presented converter is demonstrated in both dynamic and static aspects of the experiment.

Policy, office and votes: An experimental investigation of the dynamic aspect of party goals

AbstractPolitical parties face hard choices when balancing desires to influence public policy, to gain executive office and to win votes. The existing literature examining such party preferences has traditionally focused on rather static aspects of the parties, such as size, policy positions and the level of intraparty democracy. This article argues that party actors' sophisticated estimations of whether to enter into coalition can be affected by fluctuating public opinion, thereby having a more dynamic aspect. Drawing on a survey experiment on youth politicians in Norway, we test how perceived standing in the polls affects how politicians weigh up policy versus office and votes versus office. The experimental effect of perceived standings was investigated in addition to the respondents' positions within the party, as well as their parties' former governing history, political orientation and size. Results show that, in the presence of the treatment condition (party is perceived to do well in the polls), the preference for policy over office is lessened. We find no experimental effect for vote versus office. These results advance our understanding of the dynamic aspects of party goals and coalition formation.

PARAMETERS OF FLOWS OF GRAIN MATERIALS

Solving the issue related to the study of flows of loose materials is due to the development of modern sensors of flow parameters of loose materials. In particular, it can be about flow sensors. This type of sensors is one of the most attractive for use in the control and regulation channels of modern automated production control systems in the agricultural and food industry. The issues considered in this article relate to the mechanics of flows of loose materials. The field of mechanics, which studies the static and dynamic aspects of loose materials, is one of the basic branches of mechanics in general. The main focus is on free gravity flow. Grain material is considered as loose material. Taking into account the peculiarities of loose material and the specifics of various industries, the problem of creating flow parameter sensors was and will be relevant. The definition of the object and process for which the sensor is being developed, namely the study of flows of loose materials, is also relevant. The article considered how loose material behaves during free fall from silos in an airless space and also in an airy one. The consumption of bulk materials from the bottom opening of the silo, in most cases, is not stable and leads to the formation of a variable flow of bulk material. After the grain leaves the silos opening, a gravitational flow is formed, consisting of dispersed grains and an air flow. The formed flow of bulk material can be considered as an information factor for determining the consumption of bulk material and a factor in creating effective sensors for automated control systems of technological processes. Formulas for calculating the flow rate of loose material in a gravity flow and the flow volumes at different states were also given in the article.

A Fuzzy Comprehensive Evaluation Method of Regional Economic Development Quality Based on a Convolutional Neural Network

This paper presents an in-depth research analysis on the evaluation of the development quality of regional economy through an improved convolutional neural network algorithm, and uses it to design a fuzzy comprehensive evaluation model for the practical process. Based on the measured indices of different variables, a spatial econometric model is constructed and provincial panel data are selected to empirically analyze the impact and spatial spillover effects of financial agglomeration and technological innovation on regional economic quality development from both static and dynamic aspects and to examine the spatial correlation of the factors. A new serial data flow model is adopted, which optimizes the control of data flow in convolutional computation, reduces the percentage of clock cycles used to read memory data, and increases the computational efficiency. At the same time, with dynamic data caching, a convolutional computation can be completed in one clock cycle, reducing the memory capacity required for caching intermediate data. The effectiveness of the evaluation system constructed in this paper is further tested. Most of the indicators have a significant positive or negative impact on the quality level of economic development, and the direction of the impact is consistent with the positive and negative attributes of the indicators in this study, which verifies the validity of the evaluation indicator system constructed in this paper. In summary of the study, effective suggestions are made in terms of human capital investment, reasonable allocation of fiscal expenditure, enhancing regional greening development and improving risk prevention measures.

The Role of H-Bonds in the Excited-State Properties of Multichromophoric Systems: Static and Dynamic Aspects.

Given their importance, hydrogen bonds (H-bonds) have been the subject of intense investigation since their discovery. Indeed, H-bonds play a fundamental role in determining the structure, the electronic properties, and the dynamics of complex systems, including biologically relevant materials such as DNA and proteins. While H-bonds have been largely investigated for systems in their electronic ground state, fewer studies have focused on how the presence of H-bonds could affect the static and dynamic properties of electronic excited states. This review presents an overview of the more relevant progress in studying the role of H-bond interactions in modulating excited-state features in multichromophoric biomimetic complex systems. The most promising spectroscopic techniques that can be used for investigating the H-bond effects in excited states and for characterizing the ultrafast processes associated with their dynamics are briefly summarized. Then, experimental insights into the modulation of the electronic properties resulting from the presence of H-bond interactions are provided, and the role of the H-bond in tuning the excited-state dynamics and the related photophysical processes is discussed.