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Drawing on signaling theory, the purpose of this study is to investigate the effects of the strength and framing of firm signals sent to repair relationships following relationship violations.

Three 2 × 2 scenario-based experiments (total n = 527) manipulate signal strength × violation type (Study 1); signal frame × violation type (Study 2); and signal strength × brand familiarity (Study 3) to examine their dynamic impacts on relationship recovery efforts.

Stronger signals are more effective at relationship repair and are especially important following integrity (vs competence) violations. Signals framed as customer gains (vs firm costs) lead to more favorable relationship outcomes. Finally, brands that are less (vs more) familiar see greater benefits from strong signals.

The three experiments were scenario-based, which may not replicate real-life behavior or capture participants’ actual emotions following a violation, thus future research should extend into real-world recovery efforts.

Managers should send strong signals (communicating the level of resources invested in the recovery efforts) framed as benefits to the customer, rather than costs to the firm. Strong signals are especially important when brand familiarity is low or an integrity violation has occurred.

This is the first research to directly apply signaling theory to the relationship recovery process and contributes to theory by examining the role of signal strength; framing of the signal as a customer gain vs firm cost; and the interplay of signal strength and brand familiarity on the relationship recovery effort.

Keeping in mind the growing significance of online reviews, management of responses to the customer reviews – webcare – is becoming important in recent times. How a firm responds to online reviews can send a signal to the readers of the reviews contributing to their brand evaluations. From a strategic perspective, a firm should decide if they should respond to all reviews or respond to only a select few reviews. This study aims to provide an understanding of how exhaustive and selective webcare influence brand evaluations. It also explores the role of review balance and review frame, which potentially act as moderators, on such influences.

Three scenario-based experiments were used to manipulate the webcare strategy (exhaustive-selective) and the potential moderators (review balance and review frame). The 910 participants of the single-stage experiments were identified using an online panel managed by UK-based Prolific Academic.

Exhaustive webcare is found to be the most effective strategy for influencing brand evaluations in all conditions. Also, two interesting results were found, which can have practical implications. A selective negative strategy is as effective as an exhaustive webcare in almost all cases, and a selective positive webcare is as good as not having a webcare in nearly all cases. Changes in webcare effectiveness due to the influence of review balance and review frame were established.

With the review reader perspective and focus on brand management, this study may trigger enquiries into effects of webcare strategies on brand evaluations and other outcomes such as word-of-mouth. The interaction effects of the various strategies adopted together on brand evaluation and loyalty have not been explored and would be of interest to academicians and managers.

Firms need to plan a careful resource deployment while responding to the online consumer reviews as responding to a select few reviews may yield the same effects as that of exhaustive webcare. Brand managers may find responding only to positive reviews futile, as it could be as good as having no webcare. Also, the strategy of responding to reviews needs to be adapted based on the online review platform where the set in which the review is read is different.

This is one of the few studies focusing on the effects of webcare on brand evaluations from a review reader perspective as against the dominant reviewer perspective. This research also presents hitherto unexplored effects of an exhaustive-selective webcare strategy on brand evaluations.

Road accidents, an inadvertent mishap can be detected automatically and alerts sent instantly with the collaboration of image processing techniques and on-road video surveillance systems. However, to rely exclusively on visual information especially under adverse conditions like night times, dark areas and unfavourable weather conditions such as snowfall, rain, and fog which result in faint visibility lead to incertitude. The main goal of the proposed work is certainty of accident occurrence.

The authors of this work propose a method for detecting road accidents by analyzing audio signals to identify hazardous situations such as tire skidding and car crashes. The motive of this project is to build a simple and complete audio event detection system using signal feature extraction methods to improve its detection accuracy. The experimental analysis is carried out on a publicly available real time data-set consisting of audio samples like car crashes and tire skidding. The Temporal features of the recorded audio signal like Energy Volume Zero Crossing Rate 28ZCR2529 and the Spectral features like Spectral Centroid Spectral Spread Spectral Roll of factor Spectral Flux the Psychoacoustic features Energy Sub Bands ratio and Gammatonegram are computed. The extracted features are pre-processed and trained and tested using Support Vector Machine (SVM) and K-nearest neighborhood (KNN) classification algorithms for exact prediction of the accident occurrence for various SNR ranges. The combination of Gammatonegram with Temporal and Spectral features of the validates to be superior compared to the existing detection techniques.

Temporal, Spectral, Psychoacoustic features, gammetonegram of the recorded audio signal are extracted. A High level vector is generated based on centroid and the extracted features are classified with the help of machine learning algorithms like SVM, KNN and DT. The audio samples collected have varied SNR ranges and the accuracy of the classification algorithms is thoroughly tested.

Denoising of the audio samples for perfect feature extraction was a tedious chore.

The existing literature cites extraction of Temporal and Spectral features and then the application of classification algorithms. For perfect classification, the authors have chosen to construct a high level vector from all the four extracted Temporal, Spectral, Psycho acoustic and Gammetonegram features. The classification algorithms are employed on samples collected at varied SNR ranges.

This paper aims to present a system framework for classifying different models of military aircrafts, which is based on the sound they produce.

The technique is based on extracting a compact feature set, of only two features, extracted from the frequency domain of the aircrafts’ sound signals produced by their engines, namely, the spectral centroid and the signal bandwidth. These features are then introduced to an artificial neural network to classify the aircraft signals.

The current system identifies the aircraft type among four military aircrafts: Mirage 2000, F-16 Fighting Falcon, F-4 Phantom II and F-104 Starfighter. The experimental results show that the aforementioned types of aircrafts can be accurately classified up to 96.2 per cent via the proposed method.

The proposed system can be used as a low-cost assistive tool to the already existing radar systems to avoid cases of missed detection or false alarm. More importantly, the same method can be used for aircrafts that use stealth technology that cannot be detected using radar devices.

The proposed method constitutes a novel approach to classifying military aircrafts based on their sound signature. It utilizes only two spectral features extracted from the sound of the aircraft engine; these features are then introduced to a neural network classifier.

Aiming at the shortcomings of EEG signals generated by brain's sensorimotor region activated tasks, such as poor performance, low efficiency and weak robustness, this paper proposes an EEG signals classification method based on multi-dimensional fusion features.

First, the improved Morlet wavelet is used to extract the spectrum feature maps from EEG signals. Then, the spatial-frequency features are extracted from the PSD maps by using the three-dimensional convolutional neural networks (3DCNNs) model. Finally, the spatial-frequency features are incorporated to the bidirectional gated recurrent units (Bi-GRUs) models to extract the spatial-frequency-sequential multi-dimensional fusion features for recognition of brain's sensorimotor region activated task.

In the comparative experiments, the data sets of motor imagery (MI)/action observation (AO)/action execution (AE) tasks are selected to test the classification performance and robustness of the proposed algorithm. In addition, the impact of extracted features on the sensorimotor region and the impact on the classification processing are also analyzed by visualization during experiments.

The experimental results show that the proposed algorithm extracts the corresponding brain activation features for different action related tasks, so as to achieve more stable classification performance in dealing with AO/MI/AE tasks, and has the best robustness on EEG signals of different subjects.

This paper seeks to propose a new non‐intrusive method for the assessment of speech quality of voice communication systems and evaluate its performance.

The method is based on measuring perception‐based objective auditory distances between the voiced parts of the output speech to appropriately matching references extracted from a pre‐formulated codebook. The codebook is formed by optimally clustering a large number of parametric speech vectors extracted from a database of clean speech records. The auditory distances are then mapped into equivalent subjective mean opinion scores (MOSs). The required clustering and matching processes are achieved by an efficient data‐mining tool known as the self‐organizing map (SOM). The proposed method was examined using a wide range of distortion including speech compression, wireless channel impairments, VoIP channel impairments, and modifications to the signal from features such as AGC.

The experimental results reported indicate that the proposed method provides a high level of accuracy in predicting the actual subjective quality of the speech. Specifically, the second version of the method, which is based on the use of bark spectrum (BS) analysis, is more accurate in predicting the MOS scores compared with its first and third versions (which are based on BS analysis and mel frequency cepstrum coefficients (MFCC), respectively), and outperforms the ITU‐T PESQ in a large number of test cases, particularly those related to distortion caused by channel impairments and signal level modifications.

It is believed that the prototype developed of the proposed objective speech quality measure is sufficiently accurate and robust against speaker, utterance and distortion type variations. Nevertheless, there are still possible directions for further improvements and enhancement. In general there are three areas that could be pursued for further improvements: widening the coverage of speaker variations of the system's codebook; formulating and using a perceptual speech model that provides true speaker‐independent representation of speech; and implementing the proposed measure as a stand‐alone system, preferably for real‐time applications.

Being an output‐based method, the proposed method can be employed for monitoring and assessing telecommunications networks under both live traffic conditions and off‐line evaluation.

The main contribution of this paper is the introduction of a new output‐based, non‐intrusive method for the assessment of speech quality that is sufficiently accurate and robust. To the best of the author's knowledge, no reliable output‐based objective speech quality assessment method has to date been reported or formally recognised.

The purpose of this paper is to analyze to what extent and in what ways signaling theory has been explored within the field of international marketing. This paper systematically reviews the use of signaling theory in the field of international marketing. Communication is a core aspect of the international marketing process. Research in this field has explored effective and unique ways of improving the communication flow to reduce the asymmetry of information between international consumers and the firm. This notion is adopted, enhanced and strengthened by signaling theory. Signaling theory has recently received the attention of international marketing scholars.

The systematic review methodology was applied for the purpose of identifying the relevant studies. We extracted academic articles over the last 23 years from the domain of international marketing that directly contribute to signaling theory based on 57 journal articles extracted through the systematic review process.

Based on systematic research the results reveal that the topic has grown and continues to expand within the broader international marketing field. We offer a theoretical conceptual framework to better understand signaling theory in the context of international marketing.

The authors map and critically evaluate the use of signaling theory in international marketing. Relevance of signaling theory in international marketing is growing and authors present an integrative framework that organizes the existing literature, and provides scholars to further expand on emerging themes of the domain. The paper offers some useful future research directions.

This paper aims to propose an improved multifunctional control strategy for achieving real, reactive power flow control and the mitigation of power quality issues in grid integrated photovoltaic (GIPV) systems.

The paper proposes a dual stage, three phase, multifunctional GIPV system with modified instantaneous reactive power (IRP) theory-based and modified synchronous reference frame (SRF) theory-based control algorithms for reference template generation with continuous load power requirement tracking. The control structure is designed so as to impart virtual distribution static compensator functionality to the photovoltaic inverter. The dual mode operation in active filter and renewable power injection modes provides enhanced capability to the GIPV system. A comprehensive evaluation of the dynamic behaviour of the GIPV system is carried out for various conditions of irradiance and load under MATLAB/Simulink platform. The performance comparison is done considering an uncompensated system and the GIPV system with both proposed control algorithms.

The extensive simulation results demonstrate that the proposed modified SRF theory-based multifunctional control strategy shows superior performance in real and reactive power flow control; reduction in real and reactive burden of the utility grid; and regulation of dc bus voltage under varying scenarios of irradiance and load. Furthermore, there is improvement of grid power factor and reduction in total harmonic distortion of grid currents in compliance with the IEEE 519 standard even with highly non-linear loads at the point of common coupling.

The proposed modified SRF theory-based multifunctional controller offers a viable solution for power quality enhancement as well as the realization of effective real and reactive power flow control in GIPV systems. Thus, the penetration level of distributed generation can be increased in this era of global energy crisis.

With the intensification of market competition, there is a growing demand for weak signal identification and evolutionary analysis for enterprise foresight. For decades, many scholars have conducted relevant research. However, the existing research only cuts in from a single angle and lacks a systematic and comprehensive overview. In this paper, the authors summarize the articles related to weak signal recognition and evolutionary analysis, in an attempt to make contributions to relevant research.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. Framework comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

The research results show that it is necessary to improve the automation level in the process of weak signal recognition and analysis and transfer valuable human resources to the decision-making stage. In addition, it is necessary to coordinate multiple types of data sources, expand research subfields and optimize weak signal recognition and interpretation methods, with a view to expanding weak signal future research, making theoretical and practical contributions to enterprise foresight, and providing reference for the government to establish weak signal technology monitoring, evaluation and early warning mechanisms.

The authors develop a systematic overview framework based on the most classical three-dimensional space model of weak signals. It comprehensively summarizes the current research insights and knowledge from three dimensions of research field, identification methods and interpretation methods.

Authenticating an individual through voice can prove convenient as nothing needs to be stored and cannot easily be stolen. However, if an individual is authenticating under duress, the coerced attempt must be acknowledged and appropriate warnings issued. Furthermore, as duress may entail multiple combinations of emotions, the current f-score evaluation does not accommodate that multiple selected samples possess similar levels of importance. Thus, this study aims to demonstrate an approach to identifying duress within a voice-based authentication system.

Measuring the value that a classifier presents is often done using an f-score. However, the f-score does not effectively portray the proposed value when multiple classes could be grouped as one. The f-score also does not provide any information when numerous classes are often incorrectly identified as the other. Therefore, the proposed approach uses the confusion matrix, aggregates the select classes into another matrix and calculates a more precise representation of the selected classifier’s value. The utility of the proposed approach is demonstrated through multiple tests and is conducted as follows. The initial tests’ value is presented by an f-score, which does not value the individual emotions. The lack of value is then remedied with further tests, which include a confusion matrix. Final tests are then conducted that aggregate selected emotions within the confusion matrix to present a more precise utility value.

Two tests within the set of experiments achieved an f-score difference of 1%, indicating, Mel frequency cepstral coefficient, emotion detection, confusion matrix, multi-layer perceptron, Ryerson audio-visual database of emotional speech and song (RAVDESS), voice authentication that the two tests provided similar value. The confusion matrix used to calculate the f-score indicated that some emotions are often confused, which could all be considered closely related. Although the f-score can represent an accuracy value, these tests’ value is not accurately portrayed when not considering often confused emotions. Deciding which approach to take based on the f-score did not prove beneficial as it did not address the confused emotions. When aggregating the confusion matrix of these two tests based on selected emotions, the newly calculated utility value demonstrated a difference of 4%, indicating that the two tests may not provide a similar value as previously indicated.

This approach’s performance is dependent on the data presented to it. If the classifier is presented with incomplete or degraded data, the results obtained from the classifier will reflect that. Additionally, the grouping of emotions is not based on psychological evidence, and this was purely done to demonstrate the implementation of an aggregated confusion matrix.

The f-score offers a value that represents the classifiers’ ability to classify a class correctly. This paper demonstrates that aggregating a confusion matrix could provide more value than a single f-score in the context of classifying an emotion that could consist of a combination of emotions. This approach can similarly be applied to different combinations of classifiers for the desired effect of extracting a more accurate performance value that a selected classifier presents.