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To validate safety-related automotive software systems, experimental tests are conducted at different stages of the V-model, which are referred as "X-in-the-loop (XIL) methods". However, these methods have significant drawbacks in terms of cost, time, effort and effectiveness. In this study, based on hardware-in-the-loop (HIL) simulation and real-time fault injection (FI), a novel testing framework has been developed to validate system performance under critical abnormal situations during the development process. The developed framework provides an approach for the real-time analysis of system behavior under single and simultaneous sensor/actuator-related faults during virtual test drives without modeling effort for fault mode simulations. Unlike traditional methods, the faults are injected programmatically and the system architecture is ensured without modification to meet the real-time constraints. Moreover, a virtual environment is modeled with various environmental conditions, such as weather, traffic and roads. The validation results demonstrate the effectiveness of the proposed framework in a variety of driving scenarios. The evaluation results demonstrate that the system behavior via HIL simulation has a high accuracy compared to the non-real-time simulation method with an average relative error of 2.52. The comparative study with the state-of-the-art methods indicates that the proposed approach exhibits superior accuracy and capability. This, in turn, provides a safe, reliable and realistic environment for the real-time validation of complex automotive systems at a low cost, with minimal time and effort.

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The complexity and the criticality of automotive electronic implanted systems are steadily advancing and that is especially the case for automotive software development. ISO 26262 describes requirements for the development process to confirm the safety of such complex systems. Among these requirements, fault injection is a reliable technique to assess the effectiveness of safety mechanisms and verify the correct implementation of the safety requirements. However, the method of injecting the fault in the system under test in many cases is still manual and depends on an expert, requiring a high level of knowledge of the system. In complex systems, it consumes time, is difficult to execute, and takes effort, because the testers limit the fault injection experiments and inject the minimum number of possible test cases. Fault injection enables testers to identify and address potential issues with a system under test before they become actual problems. In the automotive industry, failures can have serious hazards. In these systems, it is essential to ensure that the system can operate safely even in the presence of faults. We propose an approach using natural language processing (NLP) technologies to automatically derive the fault test cases from the functional safety requirements (FSRs) and execute them automatically by hardware-in-the-loop (HIL) in real time according to the black-box concept and the ISO 26262 standard. The approach demonstrates effectiveness in automatically identifying fault injection locations and conditions, simplifying the testing process, and providing a scalable solution for various safety-critical systems.

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Recently, remarkable successes have been achieved in the quality assurance of automotive software systems (ASSs) through the utilization of real-time hardware-in-the-loop (HIL) simulation. Based on the HIL platform, safe, flexible and reliable realistic simulation during the system development process can be enabled. However, notwithstanding the test automation capability, large amounts of recordings data are generated as a result of HIL test executions. Expert knowledge-based approaches to analyze the generated recordings, with the aim of detecting and identifying the faults, are costly in terms of time, effort and difficulty. Therefore, in this study, a novel deep learning-based methodology is proposed so that the faults of automotive sensor signals can be efficiently and automatically detected and identified without human intervention. Concretely, a hybrid GRU-based denoising autoencoder (GRU-based DAE) model with the k-means algorithm is developed for the fault-detection and clustering problem in sequential data. By doing so, based on the real-time historical data, not only individual faults but also unknown simultaneous faults under noisy conditions can be accurately detected and clustered. The applicability and advantages of the proposed method for the HIL testing process are demonstrated by two automotive case studies. To be specific, a high-fidelity gasoline engine and vehicle dynamic system along with an entire vehicle model are considered to verify the performance of the proposed model. The superiority of the proposed architecture compared to other autoencoder variants is presented in the results in terms of reconstruction error under several noise levels. The validation results indicate that the proposed model can perform high detection and clustering accuracy of unknown faults compared to stand-alone techniques.

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The present study examines the effects of social interactions' situational characteristics, emotions, and personality on self-perceived learning from social interactions at work based on diary and survey data. The sample comprises 43 German vocational education and training (VET) trainees in various apprenticeship programs. During the diary period of ten working days, the participants were instructed to record five typical social interactions at work every day. Quantitative data of 1,328 social interactions were analyzed by means of multilevel analysis. Regarding social interactions' characteristics, the analysis revealed the baseline level of instrumentality, an interruption of the social interaction, its instrumentality and questions asked by the trainee during the interaction as positive predictors of self-perceived learning. A trainee's higher speech proportion, however, was a negative predictor. Regarding state emotions, the emotional experiences of bored and motivated were identified as significant positive predictors of learning from social interactions at work. Emotions' baseline level as well as personality traits had no significant influence. The results indicate that social interactions' situational characteristics have the biggest influence on self-perceived learning from social interactions.

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Hardware-in-the-Loop (HIL) has been recommended by ISO 26262 as an essential test bench for determining the safety and reliability characteristics of automotive software systems (ASSs). However, due to the complexity and the huge amount of data recorded by the HIL platform during the testing process, the conventional data analysis methods used for detecting and classifying faults based on the human expert are not realizable. Therefore, the development of effective means based on the historical data set is required to analyze the records of the testing process in an efficient manner. Even though data-driven fault diagnosis is superior to other approaches, selecting the appropriate technique from the wide range of Deep Learning (DL) techniques is challenging. Moreover, the training data containing the automotive faults are rare and considered highly confidential by the automotive industry. Using hybrid DL techniques, this study proposes a novel intelligent fault detection and classification (FDC) model to be utilized during the V-cycle development process, i.e., the system integration testing phase. To this end, an HIL-based real-time fault injection framework is used to generate faulty data without altering the original system model. In addition, a combination of the Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) is employed to build the model structure. In this study, eight types of sensor faults are considered to cover the most common potential faults in the signals of ASSs. As a case study, a gasoline engine system model is used to demonstrate the capabilities and advantages of the proposed method and to verify the performance of the model. The results prove that the proposed method shows better detection and classification performance compared to other standalone DL methods. Specifically, the overall detection accuracies of the proposed structure in terms of precision, recall and F1-score are 98.86%, 98.90% and 98.88%, respectively. For classification, the experimental results also demonstrate the superiority under unseen test data with an average accuracy of 98.8%.

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In the context of office work, learning to handle an Enterprise Resource Planning (ERP) system is important as implementation costs for such systems and associated expectations are high. However, these expectations are often not met because the users are not trained adequately. Electronic Performance Support Systems (EPSS) are designed to support employees' ERP-related problem-solving and informal learning. EPSS are supposed to enhance employees' performance and informal workplace learning through task-specific and granular help in task performance and problem-solving. However, there is little empirical research on EPSS. Two survey studies addressed this research gap. In the first study, 301 people working in Human Resource (HR)-related positions and functions evaluated the learning potential of EPSS as well as potential advantages and obstacles concerning the implementation and use of EPSS. Though other measures are currently assessed as more important for learning, HR employees expect a strong increase in the significance of EPSS for employee learning. In the second study, 652 users of ERP software completed a questionnaire on characteristics of their daily work tasks, team characteristics, individual dispositions, their coping with ERP-related problems, and characteristics of EPSS. Findings indicate that the most frequently available and used approach when dealing with an ERP-related problem is consulting colleagues. Three EPSS types can be distinguished by their increasing integration into the user interface and their context-sensitivity (external, extrinsic, and intrinsic EPSS). While external and extrinsic EPSS are available to many users, intrinsic EPSS are less common but are used intensively if available. EPSS availability is identified to be a strong positive predictor of frequency of EPSS use, while agreeableness as well as the task complexity and information-processing requirements show small negative effects. Moreover, more intensive ERP users use EPSS more frequently. In general, ERP users value, features such as context-sensitivity, an integration of the EPSS into the ERP system's user interface, the option to save one's own notes, and information displayed in an extra window. It is expected that EPSS will play an important role in workplace learning in the future, along with other measures.

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A well-known challenge in the development of safety-critical systems in vehicles today is that reliability and safety assessment should be rigorously addressed and monitored. As a matter of fact, most safety problems caused by system failures can lead to serious hazards and loss of life. Notwithstanding the existence of several traditional analytical techniques used for evaluation based on specification documents, a complex design, with its multivariate dynamic behavior of automotive systems, requires an effective method for an experimental analysis of the system's response under abnormal conditions. Simulation-based fault injection (FI) is a recently developed approach to simulate the system behavior in the presence of faults at an early stage of system development. However, in order to analyze the behavior of the system accurately, comprehensively and realistically, the real-time conditions, as well as the dynamic system model of the vehicle, should be considered. In this study, a real-time FI framework is proposed based on a hardware-in-the-loop (HiL) simulation platform and a real-time electronic control unit (ECU) prototype. The framework is modelled in the MATLAB/Simulink environment and implemented in the HiL simulation to enable the analysis process in real time during the V-cycle development process. With the objective of covering most of the potential faults, nine different types of sensor and actuator control signal faults are injected programmatically into the HiL system as single and multiple faults without changing the original system model. Besides, the model of the whole system, containing vehicle dynamics with the environment system model, is considered with complete and comprehensive behavioral characteristics. A complex gasoline engine system is used as a case study to demonstrate the capabilities and advantages of the proposed framework. Through the proposed framework, transient and permanent faults are injected in real time during the operation of the system. Finally, experimental results show the effects of single and simultaneous faults on the system performance under a faulty mode compared to the golden running mode.

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Sustainability is one of the most critical issues today. Thus, the unsustainable consumption of resources, such as raw materials, CO2 emissions, and the Linear Economy needs to be changed. One framework for a more sustainable economy is the Circular Economy. Although the concept of the Circular Economy has been around since the 1990s, yet we are still far from enabling a Circular Economy. Therefore, a turnaround to the current linear economy as well as a change in society is necessary. In this paper, we get down to the essence of the status quo in the Circular Economy, identify the main barriers, such as lack of information, unsustainable economic models, ignorance, missing incentives, and propose software-driven solutions to tackle these challenges. Our solution extends the service description language by introducing the sustainability impact factor. The goal is to motivate end-users towards a more sustainable behavior without making massive restrictions on their lives.

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[This corrects the article DOI: 10.3389/frai.2021.703504.].

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Trust is the foundation of successful human collaboration. This has also been found to be true for human-robot collaboration, where trust has also influence on over- and under-reliance issues. Correspondingly, the study of trust in robots is usually concerned with the detection of the current level of the human collaborator trust, aiming at keeping it within certain limits to avoid undesired consequences, which is known as trust calibration. However, while there is intensive research on human-robot trust, there is a lack of knowledge about the factors that affect it in synchronous and co-located teamwork. Particularly, there is hardly any knowledge about how these factors impact the dynamics of trust during the collaboration. These factors along with trust evolvement characteristics are prerequisites for a computational model that allows robots to adapt their behavior dynamically based on the current human trust level, which in turn is needed to enable a dynamic and spontaneous cooperation. To address this, we conducted a two-phase lab experiment in a mixed-reality environment, in which thirty-two participants collaborated with a virtual CoBot on disassembling traction batteries in a recycling context. In the first phase, we explored the (dynamics of) relevant trust factors during physical human-robot collaboration. In the second phase, we investigated the impact of robot's reliability and feedback on human trust in robots. Results manifest stronger trust dynamics while dissipating than while accumulating and highlight different relevant factors as more interactions occur. Besides, the factors that show relevance as trust accumulates differ from those appear as trust dissipates. We detected four factors while trust accumulates (perceived reliability, perceived dependability, perceived predictability, and faith) which do not appear while it dissipates. This points to an interesting conclusion that depending on the stage of the collaboration and the direction of trust evolvement, different factors might shape trust. Further, the robot's feedback accuracy has a conditional effect on trust depending on the robot's reliability level. It preserves human trust when a failure is expected but does not affect it when the robot works reliably. This provides a hint to designers on when assurances are necessary and when they are redundant.

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To measure non-cognitive facets of competence, we developed and tested a new method that we refer to as Embedded Experience Sampling (EES). Domain-specific problem-solving competence is a multi-faceted construct that is not limited to cognitive facets such as domain knowledge or problem-solving strategies but also comprises non-cognitive facets in the sense of domain-specific emotional and motivational dispositions such as, for instance, interest and self-concept. However, in empirical studies non-cognitive facets are usually either neglected or measured by generalized self-report questionnaires that are detached from the performance assessment. To enable an integrated measurement, we developed the EES method to collect data on non-cognitive facets during scenario-based low-stakes assessments. Test-takers are requested to stop at certain times and spontaneously answer short items (EES items) regarding their actual experience of the problem situation. These EES items are embedded in an EES event that resembles typical social interactions with non-player characters. To evaluate the feasibility and validity of the method, we implemented EES in a series of three studies in the context of commercial vocational education and training (VET): A feasibility study with 77 trainees, a pilot study with 20 trainees, and the main study with 780 trainees who worked on three complex problem scenarios in a computer-based office simulation. In the present paper, we investigate how test-takers perceived the EES events, and whether social desirability biased their answers, and investigate the internal structure of the data and the relationship between EES data and data from several other sources. Interview data and survey data indicated no biases due to social desirability and no additional burden for the test-takers due to the EES events. A correlation analysis following the multitrait-multimethod approach as well as the calibration of a multidimensional model based on Item Response Theory (IRT) also supported the construct validity. Furthermore, EES data shows substantial correlations with test motivation but almost zero correlations with data from generalized retrospective self-report questionnaires on non-cognitive facets. Altogether, EES offers an alternative approach to measuring non-cognitive facets of competence under certain conditions. For instance, EES is also based on self-reporting and thus might not be suitable for high-stakes testing.

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Due to the rapid globalization there is an increasing danger for pandemic outbreaks. The high death toll of fast spreading diseases like the Ebola infection demand the fast development of new medicines. Thus, the automation of pharmaceutical processes is an indispensable but challenging task. In cooperation with the Institute for Virology, Philipps-University, Marburg, Germany, recently, algorithms to detect and evaluate subviral particle tracks in live-cell fluorescence image sequences were developed. In steady interdisciplinary exchange between pharmacists and engineers it turned out that new measures to identify and classify subviral particle motion are required. This article focuses on the evaluation and optimization of a new method to classify subviral particle motion using fractal dimension estimation. The influence of global and local interpolation methods on fractal dimension estimation is investigated. The methods are tested on simulated data and applied to real image sequences. The results prospect a high benefit of using the presented methods for an effective classification of subviral particle behavior.

Asunto(s)

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The complexes [Cu(I)(POP)(dmbpy)][BF4] (1) and [Cu(I)(POP)(tmbpy)][BF4] (2) (dmbpy = 4,4'-dimethyl-2,2'-bipyridyl; tmbpy = 4,4',6,6'-tetramethyl-2,2'-bipyridyl; POP = bis[2-(diphenylphosphino)-phenyl]ether) have been studied in a wide temperature range by steady-state and time-resolved emission spectroscopy in fluid solution, frozen solution, and as solid powders. Emission quantum yields of up to 74% were observed for 2 in a rigid matrix (powder), substantially higher than for 1 of around 9% under the same conditions. Importantly, it was found that the emission of 2 at ambient temperature represents a thermally activated delayed fluorescence (TADF) which renders the compound to be a good candidate for singlet harvesting in OLEDs. The role of steric constraints within the complexes, in particular their influences on the emission quantum yields, were investigated by hybrid-DFT calculations for the excited triplet state of 1 and 2 while manipulating the torsion angle between the bipyridyl and POP ligands. Both complexes showed similar flexibility within a ±10° range of the torsion angle; however, 2 appeared limited to this range, whereas 1 could be further twisted with little energy demand. It is concluded that a restricted flexibility leads to a reduction of nonradiative deactivation and thus an increase of emission quantum yield.

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A detailed examination was performed on photophysical properties of phosphorescent cyclometalated (C(^)N)Pt(O(^)O) complexes (ppy)Pt(dpm) (1), (ppy)Pt(acac) (1'), and (bzq)Pt(dpm) (2) and newly synthesized (dbq)Pt(dpm) (3) (C(^)N = 2-phenylpyridine (ppy), benzo[h]quinoline (bzq), dibenzo[f,h]quinoline (dbq); O(^)O = dipivolylmethanoate (dpm), acetylacetonate (acac)). Compounds 1, 1', 2, and 3 were further characterized by single crystal X-ray diffraction. Structural changes brought about by cyclometalation were determined by comparison with X-ray data from model C(^)N ligand precursors. The compounds emit from metal-perturbed, ligand-centered triplet states (E(0-0) = 479 nm, 1; E(0-0) = 495 nm, 2; E(0-0) = 470 nm, 3) with disparate radiative rate constants (kr = 1.4 × 10(5) s(-1), 1; kr = 0.10 × 10(5) s(-1), 2; kr = 2.6 × 10(5) s(-1), 3). Zero-field splittings of the triplet states (ΔE(III-I) = 11.5 cm(-1), 1'; ΔE(III-I) < 2 cm(-1), 2; ΔE(III-I) = 46.5 cm(-1), 3) were determined using high resolution spectra recorded in Shpol'skii matrices. The fact that the E0-0 energies do not correspond to the extent of π-conjugation in the aromatic C(^)N ligand is rationalized on the basis of structural distortions that occur upon cyclometalation using data from single crystal X-ray analyses of the complexes and ligand precursors along with the triplet state properties evaluated using theoretical calculations. The wide variation in the radiative rate constants and zero-field splittings is also explained on the basis of how changes in the electronic spin density in the C(^)N ligands in the triplet state alter the spin-orbit coupling in the complexes.

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This study highlights the potential benefits of using terdentate over bidentate ligands in the construction of organometallic complexes as organic light-emitting diode (OLED) emitters offering better color purity, and explores in detail the molecular origins of the differences between the two. A pair of closely related platinum(II) complexes has been selected, incorporating a bidentate and a terdentate cyclometallating ligand, respectively, namely, Pt(4,6-dFppy)(acac) (1) {4,6-dFppy = 2-(4,6-difluorophenyl)pyridine metalated at C(2) of the phenyl ring} and Pt(4,6-dFdpyb)Cl (2) {4,6-dFdpyb = 4,6-difluoro-1,3-di(2-pyridyl)benzene, metalated at C(2) of the phenyl ring}. The emission properties over the range of temperatures from 1.2 to 300 K have been investigated, including optical high-resolution studies. The results reveal a detailed insight into the electronic and vibronic structures of the two compounds. In particular, the Huang-Rhys parameter S that serves to quantify the degree of molecular distortion in the excited state with respect to the ground state, though small in both cases, is smaller by a factor of 2 for the terdentate than the bidentate complex (S ≈ 0.1 and ≈0.2, respectively). The smaller value for the former reflects the greater degree of rigidity induced by the terdentate ligand, leading to a lesser contribution of intraligand Franck-Condon vibrational modes in the green spectral range of the emission spectra. Consequently, an enhanced color purity with respect to blue light emission results. The high rigidity and the short Pt-C bond in Pt(4,6-dFdpyb)Cl also serve to disfavor nonradiative decay pathways, including those involving higher-lying dd* states. These effects account for the greatly superior luminescence quantum yield of the terdentate complex in fluid solution, amounting to φ(PL) = 80% versus only 2% found for the bidentate complex.

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Phenyl-2,6-bis(oxazole) ligands have been explored for the synthesis of novel palladium(II) and platinum(II) pincer complexes. The materials were characterized by spectroscopic methods and by X-ray crystallography. Investigations of the photophysical properties revealed that the lowest triplet states of the materials are largely centred at the bis(oxazole) ligands. The platinum(II) compounds are moderately emissive in fluid solution at ambient temperature. Introduction of both strong donors and strong acceptors leads to a significant red shift of the emission. Due to the facile synthesis of bis(oxazole) based complexes with electronically tuneable oxazole moieties, these materials might be promising alternatives to the well-established phenyl-2,6-bipyridyl systems.

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Irradiation into the dye-based absorption band of complexes ((t)Bu(2)bipy)Pt(SR)(2) and ((t)Bu(2)bipy)Pt(OR)(2) where R denotes a coumarine-based thiolate and alkoxolate substituent populates the same excited triplet state as is obtained by excitation into the much weaker (RX)(2)Pt→(t)Bu(2)bipy (X = O, S) charge-transfer band. This paves the way toward more efficient photosensitizers.

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This work describes the synthesis, crystal structure, and detailed photophysical studies of [n-Bu(4)N][Pt(4,6-dFppy)(CN)(2)] (n-Bu = n-butyl, 4,6-dFppy = (4',6'-difluorophenyl)pyridinate). The material can easily be prepared in high yield and purity by the reaction of [Pt(4,6-dFppy)(H-4,6-dFppy)Cl], [n-Bu(4)N]Cl, and KCN in CH(2)Cl(2). Because of the bulky counterion [n-Bu(4)N](+), Pt-Pt interactions, which frequently lead to aggregate formation, are suppressed in the solid state. Thus, monomer emission is observed. The phosphorescence quantum yield of the neat powder amounts to phi(PL) = 60% at ambient temperature and decays with 19 micros. In tetrahydrofuran (THF) solution, on the other hand, the emission decay time is with 0.26 micros distinctly shorter, and the quantum yield is very low. By means of emission decay time studies in frozen THF and investigations of the highly resolved single crystal emission at 1.2 K, we can assign the emitting T(1) state of the compound as being largely of ligand centered ((3)LC, (3)pi pi*) character. The observed differences of the emission properties of the neat powder compared to the fluid solution are rationalized with an energy stabilization of quenching dd* states in solution because of molecular distortions and/or bond elongations.

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The syntheses, crystal structures, and detailed investigations of the photophysical properties of phosphorescent platinum(II) Schiff base complexes are presented. All of these complexes exhibit intense absorption bands with lambda(max) in the range 417-546 nm, which are assigned to states of metal-to-ligand charge-transfer ((1)MLCT) (1)[Pt(5d)-->pi*(Schiff base)] character mixed with (1)[lone pair(phenoxide)-->pi*(imine)] charge-transfer character. The platinum(II) Schiff base complexes are thermally stable, with decomposition temperatures up to 495 degrees C, and show emission lambda(max) at 541-649 nm in acetonitrile, with emission quantum yields up to 0.27. Measurements of the emission decay times in the temperature range from 130 to 1.5 K give total zero-field splitting parameters of the emitting triplet state of 14-28 cm(-1). High-performance yellow to red organic light-emitting devices (OLEDs) using these platinum(II) Schiff base complexes have been fabricated with the best efficiency up to 31 cd A(-1) and a device lifetime up to 77 000 h at 500 cd m(-2).

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Detailed photophysical studies of the emitting triplet state of the highly phosphorescent compound Pt(dpyb)Cl based on high-resolution optical spectroscopy at cryogenic temperatures are presented {dpyb = N--C(2)--N-coordinated 1,3-di(pyridylbenzene)}. The results reveal a total zero-field splitting of the emitting triplet state T(1) of 10 cm(-1) and relatively short individual decay times for the two higher lying T(1) substates II and III, while the decay time of the lowest substate I is distinctly longer. Further evidence for the assignment of the T(1) substates is gained by emission measurements under high magnetic fields. Distinct differences are observed in the vibrational satellite structures of the emissions from the substates I and II, which are dominated by Herzberg-Teller and Franck-Condon activity, respectively. At T = 1.2 K, the individual spectra of these two substates can be separated by time-resolved spectroscopy. For the most prominent Franck-Condon active modes, Huang-Rhys parameters of S approximately 0.1 can be determined, which are characteristic of very small geometry rearrangements between the singlet ground state and the triplet state T(1). The similar geometries are ascribed to the high rigidity of the Pt(N--C--N) system which, unlike complexes incorporating bidentate phenylpyridine-type ligands and exhibiting similar metal-to-ligand charge transfer admixtures, cannot readily distort from planarity. The results provide new insight into strategies for optimizing the performance of platinum-based emitters for applications such as organic light-emitting diode (OLED) technology and imaging.