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Many definitions and delineation methods exist for identifying flash droughts (FDs), which are events of rapid and unusual large depletion of root-zone soil moisture, in comparison to average moisture conditions, due to climatic compound conditions over a short period of several weeks. Six FD identification methods were compared to analyse their functioning using data from several experimental cropland sites across Central Europe. Co- and misidentification of the FD time series were assessed using confusion and synchronicity metrics on a local scale. Even though a large degree of synchronicity of individual FD events was observed, some divergence in drought periods was detected, which was related to four intrinsic differences in the underlying FD definitions: (1) type of critical variable; (2) velocity of drought intensification; (3) pre-set threshold values for final depletion and/or (4) minimum length of the duration of FDs. To balance the strengths and weaknesses of those methods that are not based on soil moisture, we suggest using an ensemble approach for event identification, which is validated in this study for the temperate central European region. In doing so, the current unclearly defined sub-types of FDs can be detected, regardless of the different combinations of compound drivers and differences in intensification dynamics. All methods were implemented in an R package and are available as a Shiny app for the public.

Although the significance of mascots has been demonstrated in numerous fields, studies specifically examining the pivotal role of mascots in sports marketing remain scarce. This study employed a mixed research approach to analyse the influence of sporting mascots on consumers’ attitudes towards the event. Study 1 utilized online review data from social media platforms to construct perceived dimensions of sports event mascot attractiveness through LDA (Latent Dirichlet Allocation) topic modeling. Study 2 ( N  = 400) employed structural equation modelling (SEM) to elucidate the primary effect of mascot attractiveness and the mediating role of sport event identification. The findings of this study substantiated the affective associations between mascots as a kind of event spokesperson and consumers’ psychological mechanisms. Additionally, it was demonstrated that event impact, event expertise, mascot uniqueness, mascot anthropomorphism, mascot intimacy and mascot consistency all foster positive event attitudes among consumers.

Precipitation occurs in individual events, but the event characteristics of precipitation are often neglected. This work seeks to identify the precipitation events on both spatial and temporal scales, explore the event characteristics of precipitation, and reveal the relationships between the different characteristics of precipitation events. To do this, we combined the Forward-in-Time (FiT) algorithm with the gridded hourly precipitation product to detect precipitation events in time and space over China. The identified precipitation events were analyzed to determine their characteristics. The results indicate that precipitation events can be detected and identified in time and space scales based on the FiT algorithm and the gridded hourly precipitation product. The precipitation total, duration, and intensity of these events decrease gradually from the southern (eastern) coastal regions to northern (western) inland areas of China. The event precipitation totals are strongly correlated with event duration and event maximum intensity; the totals are more strongly correlated with event maximum intensity and event intensity in the regions with lower precipitation than the regions with higher precipitation. More than 90% of precipitation events are shorter than 6 h, and events with long duration normally occur in temperate monsoon (TM) and subtropical/tropical monsoon (ST) climate zones. Heavy precipitation events with a duration longer than 7 h generally occur more than seven times per year in TM and ST climate zones. Our results suggest that precipitation analyses should sufficiently consider the characteristics of events across different regions.

Calcium imaging has gained substantial popularity as a tool to profile the activity of multiple simultaneously active cells at high spatiotemporal resolution. Among the diverse approaches to processing of Ca 2+ imaging data is an often subjective decision of how to quantify baseline fluorescence or F 0 . We examine the effect of popular F 0 determination methods on the interpretation of neuronal and astrocyte activity in a single dataset of rats trained to self-administer intravenous infusions of cocaine and compare them with an F 0 -independent wavelet ridgewalking event detection approach. We find that the choice of the processing method has a profound impact on the interpretation of widefield imaging results. All of the d F / F 0 thresholding methods tended to introduce spurious events and fragment individual transients, leading to smaller calculated event durations and larger event frequencies. Analysis of simulated datasets confirmed these observations and indicated substantial intermethod variability as to the events classified as significant. Additionally, most d F / F 0 methods on their own were unable to adequately account for bleaching of fluorescence, although the F 0 smooth approach and the wavelet ridgewalking algorithm both did so. In general, the choice of the processing method led to dramatically different quantitative and sometimes opposing qualitative interpretations of the effects of cocaine self-administration both at the level of individual cells and at the level of cell networks. Significantly different distributions of event duration, amplitude, frequency, and network measures were found across the majority of d F / F 0 approaches. The wavelet ridgewalking algorithm broadly outperformed d F / F 0 -based methods for both neuron and astrocyte recordings. These results indicate the need for heightened awareness of the limitations and tendencies associated with decisions to use particular Ca 2+ image processing pipelines. Both quantification and interpretation of the effects of experimental manipulations are strongly sensitive to such decisions.

Traffic accidents, which cause loss of life and pollution, are a social concern. The complex traffic environment on mountain roads increases the harm caused by traffic accidents. This study aimed to identify safety-critical events related to accidents on mountain roads to understand the causes of the accidents, improve traffic safety, and protect the environment. In this study, a naturalistic-driving data collection system, consisting of approximately 8000 km of naturalistic-driving data from 20 drivers driving on mountain roads, was developed. Using these data, a comparative analysis of the identification performance of the support vector machine (SVM), backpropagation neural network (BPNN), and convolutional neural network (CNN) methods was conducted. The SVM was found to yield optimal performance. To improve the identification performance, the yaw rate and information entropy of the data were added as input variables. The improved SVM method yielded an identification accuracy of 90.64%, which was approximately 15% higher than that yielded by the traditional SVM. Moreover, the false positive and false negative rates of the improved SVM were reduced by approximately 10% and 20%, respectively, compared with the traditional SVM. The results demonstrated that the improved SVM method can identify safety-critical events on mountain roads accurately and efficiently.

PurposeThis study aims to examine the effects of motives for attending charity sport events on perceptions of self-congruity and charity sport event identification. It also examined the mediating role of self-congruity on the relationships between motives for attending charity sport events and charity sport event identification.Design/methodology/approachData were collected through an online self-administered survey of 330 participants who had attended charity sport events in Thailand. A series of multiple regressions and the PROCESS macro method were used for analysing direct and indirect effects.FindingsThe results clearly indicated that physical and charitable motives had a significant impact on event identification. While physical, social and charitable motives had an impact on self-congruity, self-congruity had a greater impact on event identification. The role of self-congruity, meanwhile, mediated the relationship between physical, social, enjoyment and charitable motives and the event identification.Research limitations/implicationsThe results of this study contribute to the extension of the body of knowledge, especially in regard to special events and charitable foundations where the proposed relationships have yet to be studied.Originality/valueUsing the social identity theory as a theoretical background, the study adds to the comprehensive understanding of social and psychological motives to build an identity and enhance a strong sense of identification and belonging to a charity sport event.

PurposeThe current study was designed to (1) identify core and peripheral market demand for a recurring hallmark sporting event, testing their impact on event identification and behavioral intentions; and to (2) explore the effect of core and peripheral market demand on event identification between first-time and repeat spectators.Design/methodology/approachResearch participants (N = 540) were spectators at the Shanghai Masters over a span of seven days. Data were analyzed using partial least squares structural equation modeling (PLS-SEM) and partial least squares multi-group analysis (PLS-MGA).FindingsSignificant, positive relationships were found between core market demand and event identification, and between core market demand and behavioral intentions. In contrast, peripheral market demand only had significant, positive effect on event identification; however, findings revealed that event identification fully mediated the relationships between peripheral market demand and behavioral intentions. Additionally, the effect of peripheral market demand on event identification was greater among first-time spectators than repeat spectators.Originality/valueThis study contributed to the application of PLS-SEM in sport management research by adopting a formative-formative hierarchical component model (HCM) to address the prevailing measurement model misspecification of market demand constructs. The findings highlighted the merits of promoting market demand associated with recurring hallmark sporting events and the importance of enhancing event identification through differential market penetration schemes across different spectator groups.

The Intrusion Detection System (IDS) is the most widely used network security mechanism for distinguishing between normal and malicious traffic network activities. It aids network security in that it may identify unforeseen hazards in network traffic. Several techniques have been put forth by different researchers for network intrusion detection. However, because network attacks have increased dramatically, making it difficult to execute precise detection rates quickly, the demand for effectively recognizing network incursion is growing. This research proposed an improved solution that uses Long Short-Term Memory (LSTM) and hash functions to construct a revolutionary double-layer security solution for IoT Network Intrusion Detection. The presented framework utilizes standard and well-known real-time IDS datasets such as KDDCUP99 and UNSWNB-15. In the presented framework, the dataset was pre-processed, and it employed the Shuffle Shepherd Optimization (SSO) algorithm for tracking the most informative attributes from the filtered database. Further, the designed model used the LSTM algorithm for classifying the normal and malicious network traffic precisely. Finally, a secure hash function SHA3-256 was utilized for countering the attacks. The intensive experimental assessment of the presented approach with the conventional algorithms emphasized the efficiency of the proposed framework in terms of accuracy, precision, recall, etc. The analysis showed that the presented model attained attack prediction accuracy of 99.92% and 99.91% for KDDCUP99 and UNSWNB-15, respectively.

Pervasive connectivity is an essential underlying substrate for smart cities, leading municipalities to start programs where Wi-Fi is the fundamental building block to develop public Municipal Wireless Networks. Even though hundreds of cities around the world offer some form of Wi-Fi access, there are no widely available results regarding the network Quality of Service (QoS), user Quality of Experience (QoE), and overall utilization profile. The Municipality of São Paulo operates a free Wi-Fi Internet program in 120 public spaces called digital squares. We collected user connection data, network performance, and service availability for more than 2 years from the 120 squares and undertook experiments with video streaming in five squares. We used this unique large dataset to evaluate the impact of current admission control practices in public Wi-Fi networks on the network QoS, user QoE and service availability, also providing insights into the most common QoS/QoE issues and their causes. We also leveraged the data set to establish and verify a correlation between the number of users access in the network and specific events occurring in the area.

Accurate feature identification of drought disaster events is required for proper risk management in agriculture. This study improved the crop water deficit index (CWDI) by including the daily meteorological, crop development stage, soil moisture content, and yield data for 1981–2020 in northeastern China. Two drought characteristic variables (drought duration and intensity) were extracted using the theory of runs to produce the improved crop water deficit index (CWDIwp). Thresholds for the bivariate indicators were also determined for agricultural drought events of varying severity. A joint distribution model for drought variables was constructed based on five types of Archimedean copulas. The joint probability and the joint recurrence period for agricultural drought events were analyzed for drought events with varying intensities in northeast China. The results suggest that the CWDIwp can reliably identify the onset, duration, and intensity of drought events over the study area and can be used to monitor agricultural drought events. The conditional probability of drought intensity (duration) decreased as the drought duration (intensity) threshold increased, whereas the drought recurrence period increased as the threshold for drought duration and intensity rose. In the period (1981–2020), drought intensity in the three Northeastern provinces showed an increasing trend in the order Jilin Province > Liaoning Province > Heilongjiang Province. The spatial distribution of the joint probability and the joint recurrence period was obvious, and the joint probability showed a decreasing distribution trend from west to east. The distribution trend for the joint probability was opposite to that of the joint recurrence period. Furthermore, the areas with high drought probability values corresponded to the areas with low values for the recurrence period, indicating that the drought occurrence probability was higher, and the recurrence period value was lower in the drought-prone areas. The high-risk drought areas (60–87%) were in western Liaoning and western Jilin, with a recurrence period of 1–3 years, whereas the low-risk areas (<40%) were located in the mountainous areas of eastern Liaoning and eastern Jilin. The joint probability and joint recurrence period for agricultural drought events of varying severity were quite different, with the probability following the order light drought > moderate drought > severe drought > extreme drought. The order for the recurrence period was light drought < moderate drought < severe drought < extreme drought. The results provide technical support for disaster prevention and mitigation in drought risk management.