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Background: Climate change denial (CCD) has been found to be more pronounced among individuals with a conservative political orientation. Terror Management Theory posits that an individual's worldview serves as a primary defense mechanism in coping with existential threats, such as the reality of climate change. Drawing on these premises, we conducted preliminary research investigating the impact of death priming on CCD from the Terror management theory perspective. Methods and results: We administered a death priming task and measured CCD in its immediate wake or following a delay task. We confirmed among 219 Amazon's MTurk participants that immediately following death priming, CCD was reduced among all participants. In the delayed death priming condition, we acquired preliminary evidence that CCD was heightened among Republican participants. Conclusions: These findings demonstrate the relevance of death priming to CCD tendencies and potentially spawn future research regarding CCD as a particular form of coping with existential threats.

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In order to analyze the influence of deep learning model on detecting denial-of-service (DoS) attacks, this article first examines the concepts and attack strategies of DoS assaults before looking into the present detection methodologies for DoS attacks. A distributed DoS attack detection system based on deep learning is established in response to the investigation's limitations. This system can quickly and accurately identify the traffic of distributed DoS attacks in the network that needs to be detected and then promptly send an alarm signal to the system. Then, a model called the Improved Conditional Wasserstein Generative Adversarial Network with Inverter (ICWGANInverter) is proposed in response to the characteristics of incomplete network traffic in DoS attacks. This model automatically learns the advanced abstract information of the original data and then employs the method of reconstruction error to identify the best classification label. It is then tested on the intrusion detection dataset NSL-KDD. The findings demonstrate that the mean square error of continuous feature reconstruction in the sub-datasets KDDTest+ and KDDTest-21 steadily increases as the noise factor increases. All of the receiver operating characteristic (ROC) curves are shown at the top of the diagonal, and the overall area under the ROC curve (AUC) values of the macro-average and micro-average are above 0.8, which demonstrates that the ICWGANInverter model has excellent detection performance in both single category attack detection and overall attack detection. This model has a greater detection accuracy than other models, reaching 87.79%. This demonstrates that the approach suggested in this article offers higher benefits for detecting DoS attacks.

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A lightweight aircraft visual navigation algorithm that fuses neural networks is proposed to address the limited computing power issue during the offline operation of aircraft edge computing platforms in satellite-denied environments with complex working scenarios. This algorithm utilizes object detection algorithms to label dynamic objects within complex scenes and performs dynamic feature point elimination to enhance the feature point extraction quality, thereby improving navigation accuracy. The algorithm was validated using an aircraft edge computing platform, and comparisons were made with existing methods through experiments conducted on the TUM public dataset and physical flight experiments. The experimental results show that the proposed algorithm not only improves the navigation accuracy but also has high robustness compared with the monocular ORB-SLAM2 method under the premise of satisfying the real-time operation of the system.

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The number of connected devices or Internet of Things (IoT) devices has rapidly increased. According to the latest available statistics, in 2023, there were approximately 17.2 billion connected IoT devices; this is expected to reach 25.4 billion IoT devices by 2030 and grow year over year for the foreseeable future. IoT devices share, collect, and exchange data via the internet, wireless networks, or other networks with one another. IoT interconnection technology improves and facilitates people's lives but, at the same time, poses a real threat to their security. Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks are considered the most common and threatening attacks that strike IoT devices' security. These are considered to be an increasing trend, and it will be a major challenge to reduce risk, especially in the future. In this context, this paper presents an improved framework (SDN-ML-IoT) that works as an Intrusion and Prevention Detection System (IDPS) that could help to detect DDoS attacks with more efficiency and mitigate them in real time. This SDN-ML-IoT uses a Machine Learning (ML) method in a Software-Defined Networking (SDN) environment in order to protect smart home IoT devices from DDoS attacks. We employed an ML method based on Random Forest (RF), Logistic Regression (LR), k-Nearest Neighbors (kNN), and Naive Bayes (NB) with a One-versus-Rest (OvR) strategy and then compared our work to other related works. Based on the performance metrics, such as confusion matrix, training time, prediction time, accuracy, and Area Under the Receiver Operating Characteristic curve (AUC-ROC), it was established that SDN-ML-IoT, when applied to RF, outperforms other ML algorithms, as well as similar approaches related to our work. It had an impressive accuracy of 99.99%, and it could mitigate DDoS attacks in less than 3 s. We conducted a comparative analysis of various models and algorithms used in the related works. The results indicated that our proposed approach outperforms others, showcasing its effectiveness in both detecting and mitigating DDoS attacks within SDNs. Based on these promising results, we have opted to deploy SDN-ML-IoT within the SDN. This implementation ensures the safeguarding of IoT devices in smart homes against DDoS attacks within the network traffic.

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Little is known about the extent of prior authorization requirements in otolaryngology. We performed a secondary analysis of data comparing prior authorization (PA) policies across 5 major Medicare Advantage insurers to estimate the counterfactual proportion of 2021 Medicare Part B fee-for-service spending and utilization for commonly performed otolaryngologic procedures that would have required PA. The counterfactual proportion of spending (range: 20.4%-27.6%) and utilization (range: 1.8%-4.5%) requiring PA was relatively consistent across insurers and largely attributable to rhinologic procedures. However, PA requirements for specific services varied widely among insurers. Among the 70 (of 196; 35.7%) services subject to PA by any insurer, nearly half were subject to PA by a single insurer (n = 34; 48.6%). Only 10 (14.3%) services were subject to PA by 4 (n = 6; 8.6%) or 5 (n = 4; 5.7%) insurers. These discrepancies illustrate the challenges of navigating discordant insurer policies for otolaryngologists and raise concerns about the validity of certain PA requirements.

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BACKGROUND: The delay time from onset of symptoms of a myocardial infarction to seeking medical assistance can have life-threatening consequences. Women delay significantly more often than men do in calling for medical help, once symptoms of a myocardial infarction occur. OBJECTIVES: The current qualitative study's main aim was to explore psychosocial factors that contribute to Israeli women's delaying calls for medical assistance and, by contrast, the motivational factors that encourage them to do so. DESIGN: A qualitative study. METHOD: In total, 12 women were interviewed shortly after experiencing a myocardial infarction. Qualitative data were subjected to thematic analysis. RESULTS: Two major themes emerged describing barriers to seeking help: (1) the use of denial as a defense mechanism and (2) the need for control. The motivational factor which enhanced help-seeking was "fear of death." CONCLUSION: These findings may help in designing gender-sensitive interventions with the aim of minimizing the symptom onset to call time and thus preventing irreversible and life-threatening health damage.

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Following the French law n° 98-468 of 17 June 1998 relative to the prevention of sexual offenses and the protection of minors, social and judicial follow-up and court-ordered treatment were introduced with the aim of reinforcing the prevention of recidivism. Court-ordered treatment is one of the possible obligatory measures provided for by social and judicial follow-up. However, there is no consensus between the different professionals concerning the criteria of indication and the final purpose of this measure. Most of the few available studies are retrospective. Only rare studies have assessed the influence of criminological factors on the indication of court-ordered treatment. We carried out a nation-wide qualitative comparative study in two populations, psychiatric experts and sentencing judges, by means of e-mail questionnaires. The aim was to determine the criteria for court-ordered treatment according to psychiatric experts and to sentencing judges, to identify the criteria that gave rise to differences in appreciation between these professionals, and to attempt to explain these differences. The secondary aim of the study was to determine the methods and tools used in expert practice to evaluate dangerousness and risk of recidivism. We obtained 20 responses in each of the two populations. The great majority of psychiatric experts and sentencing judges considered that court-ordered treatment was appropriate when the offender presented with psychiatric dangerousness, and so with an underlying mental disorder. When a subject had no identified mental disorder, the psychiatrists were divided in their opinion, whereas the majority of sentencing judges were in favor of court-ordered treatment. Opinions differed particularly significantly between the two populations in four circumstances: a subject with an antisocial/psychopathic personality disorder, a subject who denied the alleged acts, the influence of precarious social circumstances and the influence of instability in intimate relationships. The majority of experts used international classifications (DSM-5 and ICD-10) as a basis for their psychiatric diagnosis. Just under half of those surveyed used structured or semi-structured interview guides and only a few stated that they used standardized actuarial tools to assess risk of recidivism. The concepts of care, dangerousness and mental disorder are associated with multiple representations that certainly play a part in the disagreements between the different professionals. It is of prime importance to define these concepts more clearly in order to encourage the use of a common language and to clarify the indications and purpose of court-ordered treatment. We also hypothesize that disagreements between professionals regarding the criteria for court-ordered treatment may be related to certain difficulties raised by the management of the convicted person. The development of guidelines that could be used by all professionals would help to reduce some of these difficulties. Psychiatric experts remain attached to clinical evaluation. Their limited use of assessment tools may relate to the material constraints and time constraints involved. The issue at stake in court-ordered treatment and social and judicial follow-up is to promote cooperation between the various professionals by creating a space for exchange of ideas where the fundamental questions raised by these measures can be discussed, fears shared and knowledge pooled.

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As 5G technology becomes more widespread, the significant improvement in network speed and connection density has introduced more challenges to network security. In particular, distributed denial of service (DDoS) attacks have become more frequent and complex in software-defined network (SDN) environments. The complexity and diversity of 5G networks result in a great deal of unnecessary features, which may introduce noise into the detection process of an intrusion detection system (IDS) and reduce the generalization ability of the model. This paper aims to improve the performance of the IDS in 5G networks, especially in terms of detection speed and accuracy. It proposes an innovative feature selection (FS) method to filter out the most representative and distinguishing features from network traffic data to improve the robustness and detection efficiency of the IDS. To confirm the suggested method's efficacy, this paper uses four common machine learning (ML) models to evaluate the InSDN, CICIDS2017, and CICIDS2018 datasets and conducts real-time DDoS attack detection on the simulation platform. According to experimental results, the suggested FS technique may match 5G network requirements for high speed and high reliability of the IDS while also drastically cutting down on detection time and preserving or improving DDoS detection accuracy.

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Due to the large computational overhead, underutilization of features, and high bandwidth consumption in traditional SDN environments for DDoS attack detection and mitigation methods, this paper proposes a two-stage detection and mitigation method for DDoS attacks in SDN based on multi-dimensional characteristics. Firstly, an analysis of the traffic statistics from the SDN switch ports is performed, which aids in conducting a coarse-grained detection of DDoS attacks within the network. Subsequently, a Multi-Dimensional Deep Convolutional Classifier (MDDCC) is constructed using wavelet decomposition and convolutional neural networks to extract multi-dimensional characteristics from the traffic data passing through suspicious switches. Based on these extracted multi-dimensional characteristics, a simple classifier can be employed to accurately detect attack samples. Finally, by integrating graph theory with restrictive strategies, the source of attacks in SDN networks can be effectively traced and isolated. The experimental results indicate that the proposed method, which utilizes a minimal amount of statistical information, can quickly and accurately detect attacks within the SDN network. It demonstrates superior accuracy and generalization capabilities compared to traditional detection methods, especially when tested on both simulated and public datasets. Furthermore, by isolating the affected nodes, the method effectively mitigates the impact of the attacks, ensuring the normal transmission of legitimate traffic during network attacks. This approach not only enhances the detection capabilities but also provides a robust mechanism for containing the spread of cyber threats, thereby safeguarding the integrity and performance of the network.

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This paper is concerned with the secure output consensus problem for the heterogeneous multi-agent systems under the event-triggered scheme in the presence of the denial-of-service attack. Without detecting the attack, the hold-input controller update strategy is adopted when some transmission data may be lost due to the effect of the attack. Based on the tolerable duration of the attack, a novel edge-based event-triggered scheme is developed. The scheme can avoid continuous communication and exclude Zeno behavior. With the aid of the switched system theory, output consensus is preserved. An example shows the effectiveness.

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In order to reduce the position errors of the Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) integrated navigation system during GPS denial, this paper proposes a method based on the Particle Swarm Optimization-Back Propagation Neural Network (PSO-BPNN) to replace the GPS for positioning. The model relates the position information, velocity information, attitude information output by the SINS, and the navigation time to the position errors between the position information output by the SINS and the actual position information. The performance of the model is compared with the BPNN through an actual ship experiment. The results show that the PSO-BPNN can obviously reduce the position errors in the case of GPS signal denial.

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With the escalation in the size and complexity of modern Denial of Service attacks, there is a need for research in the context of Machine Learning (ML) used in attack execution and defense against such attacks. This paper investigates the potential use of ML in generating behavioral telemetry data using Long Short-Term Memory network and spoofing requests for the analyzed traffic to look legitimate. For this research, a custom testing environment was built that listens for mouse and keyboard events and analyzes them accordingly. While the economic feasibility of this attack currently limits its immediate threat, advancements in technology could make it more cost-effective for attackers in the future. Therefore, proactive development of countermeasures remains essential to mitigate potential risks and stay ahead of evolving attack methods.

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PURPOSE: This study aims to describe the phenomenon of unperceived pregnancy followed by neonaticide with a focus on the lack of awareness of reproductive potential in an Austrian sample. METHODS: An explorative comparative study of neonaticide cases with single and repeat perpetrators was conducted using nationwide register-based data from 1995 to 2017. A total number of 55 cases out of 66 were included in the analysis. A standardized coding sheet was used and calculations were performed. RESULTS: 48 women gave birth to 101 children, of which 55 were killed, 23 children lived out of home care and 23 lived with the perpetrator We found a higher fertility rate in both neonaticide perpetrators in the single (1,9) and the repeat group (4,25) in comparison to the general population (1,4). The use of contraception was only 31% among neonaticide perpetrators, deviating substantially from the general Austrian population age group (16-29yrs) which used contraception in 91%. The neonaticide perpetrators used an effective contraception method (pearl-index < 4) in only 2%, whereas 20% of the general population did so. The number of unperceived pregnancies was high in both groups (50/55) 91%. CONCLUSION: Future case reports and forensic evaluations should take reproductive behavior into account, as it may offer valuable insights into the events leading up to neonaticide. Our findings suggest that denial of reproductive potential often precedes unperceived pregnancies. In the Austrian cohort, women who experienced unperceived pregnancies resulting in unassisted births and subsequent neonaticide showed a low prevalence of contraceptive use. This is particularly noteworthy given that the primary motive for neonaticide is unwanted pregnancy.

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Past theories have linked science denial to religiosity but have not explained its geographic variability. We hypothesize that it springs not only from religious intensity but also from religious intolerance, which depends greatly on the experience of religious diversity and hence on geography. The belief that one's religion trumps other faiths precipitates the stance that it trumps science too. This psychological process is most likely to operate in regions or countries with low religious heterogeneity. We measure the rejection of science not only in people's refusal to follow specific health recommendations, such as taking COVID-19 vaccines, but also in general measures of scientific engagement and attainment. We rule out alternative explanations, including reverse causality and spurious correlations, by conducting controlled experiments and running robustness checks on our statistical models.

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The finite-horizon optimal secure tracking control (FHOSTC) problem for cyber-physical systems under actuator denial-of-service (DoS) attacks is addressed in this paper. A model-free method based on the Q-function is designed to achieve FHOSTC without the system model information. First, an augmented time-varying Riccati equation (TVRE) is derived by integrating the system with the reference system into a unified augmented system. Then, a lower bound on malicious DoS attacks probability that guarantees the solutions of the TVRE is provided. Third, a Q-function that changes over time (time-varying Q-function, TVQF) is devised. A TVQF-based method is then proposed to solve the TVRE without the need for the knowledge of the augmented system dynamics. The developed method works backward-in-time and uses the least-squares method. To validate the performance and features of the developed method, simulation studies are conducted in the end.

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This paper studies the control problem for a continuous-time networked system with non-linearity in the state equation as well as in the input, as saturation. The system is considered under denial-of-service (DoS), attacks which cause the blockage of input and/or output components in the overall closed-loop model. An event-triggering scheme that is resilient in nature, along with an observer-based control, has been considered under DoS attacks. The resultant scheme ensures efficient network resources and excludes Zeno behavior naturally due to the presence of a minimum positive interevent delay. Then, an event-based switched non-linear model is presented to address both the event-triggering scheme and the presence of DoS blocking attacks. A piece-wise Lyapunov-Krasovskii functional method on the described non-linear model, resulting in the switched system, is considered for achieving an exponentially stable response by driving the required feasibility conditions. In the presence of a non-linear system with saturation in the actuator, the presented design establishes quantitative relationships among the exponential decay rate, active/sleeping intervals of attacks, parameters of the event-triggering condition, and sampling period of the system. After that, linear matrix inequalities are presented for designing an event-triggered controller with an observer, while the design also includes the region of convergence for dealing with the input non-linearity. Finally, comparative results for an offshore structure model with non-linearity in states as well as in actuator, are demonstrated to verify the results of the control scheme that is developed. It has been verified that our design is less conservative than the previous designs, and can handle the non-linearities in the dynamics of plant and actuator saturation more efficiently, while DoS attacks are also present. By applying our proposed method, the overshoot and undershoot are less than ±2.5 percent, while system states converge to the origin within 55 s.

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While much research has examined the correlates of climate change beliefs from an alarmist perspective, less work has systematically measured climate change skepticism. This study aims to create a comprehensive tool capturing climate skeptics' beliefs and test its association with individual difference variables. 502 European adults completed a 22-item questionnaire on climate change (CC) skepticism as well as measures of ambiguity tolerance, belief in a just world (BJW), dark-side personality traits, and self-esteem. Principal components analysis revealed a four dimension structure of CC. Political ideology was the most consistent and significant predictor across the climate change skepticism factors. Dark-side traits, also played a role. Future research should further validate this measure and explore how climate change information could be tailored to different audiences. Understanding the nuances and causes of climate skepticism can enable more effective communication to promote sustainability.

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Denial of services (DoS) attacks exist in wind integrated power system. DoS attacks can cause network-induced delay and packages loss in information transmission. Meanwhile, considering the parameter perturbation of controller and system model uncertainty in wind integrated power system, these may cause the system dynamic performances degradation or even instability. Based on the above considerations, the joint non-fragile automatic generation robust control of wind integrated power system under DoS attacks is studied in this paper. In order to ensure the expected system performance and more effectively utilize the limited network communication resources under DoS attacks, a novel dynamic multi-event driven mechanism based joint non-fragile H∞ automatic generation control method is proposed. By constructing a suitable Lyapunov-Krasovskii functional and utilizing the Shur complement lemma to handle nonlinear matrix inequality, the sufficient conditions are derived to guarantee the asymptotic stability of wind integrated power system under DoS attacks. Furthermore, the performance of the proposed non-fragile regulator is demonstrated through a four-area wind integrated power system to show the feasibility and applicability. The analysis result indicates that the proposed scheme provides stronger robustness, higher wind energy utilization efficiency and more efficient communication mechanism.

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The most prominent issue influencing Turkish-Armenian relations is the international recognition of the Armenian genocide. However, there is a notable absence of empirical analyses regarding the perceptions of the genocide among the Turkish population. This study aims to fill this scholarly gap by exploring, for the first time, the perspectives of Turkish Jews. It analyses evidence collected from interviews conducted with 14 Turkish Jews, utilising Stanley Cohen's (2001) theoretical framework, which aids in delineating significant factors by a categorisation of types of acceptance and denial. The findings highlight a diversity of responses linked to political attitudes, which can be broadly categorised into Kayades and Avlaremoz mindsets. They also show that Turkish Jews' views on the Holocaust influence how they perceive the Armenian genocide. Additionally, the results indicate that Cohen's approach is useful in explaining non-denying responses. In conclusion, the study argues that Turkish Jews' perspectives appear to be strongly related to their stance towards the Turkish state and the Holocaust.

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Na esteira das lógicas da economia psíquica e da economia política desenvolvidas por Freud e Lacan, tomamos como cerne um tipo de defesa que aparece em muitos casos da clínica contemporânea - o desmentido da privação - para colocar em evidência sua relação com o discurso capitalista pensado por Lacan. O desmentido da privação é um modo de defesa subjetivo que aparece como um índice do fracasso do pai privador na passagem do segundo para o terceiro tempo do complexo de Édipo. Trata-se de uma forma de se defender da castração buscando satisfação pulsional sem mediação simbólica ­ busca essa que é fracassada e extrapola o princípio de prazer. Conclui-se que esse desmentido contemporâneo é um sintoma do próprio discurso capitalista. Se a for aclusão da castração no discurso capitalista é uma promessa igualmente fracassada, o desmentido da privação aparece na subjetividade tentando solucionar esse fracasso. Ele é um sinal de que o discurso capitalista não cumpre sua promessa - e isso por sua própria engrenagem lógica.

Dans le sillage des logiques d'économie psychique et d'économie politique développées par Freud et Lacan, nous nous concentrons sur un type de défense qui apparaît dans de nombreux cas de pratique clinique contemporaine - le déni de la privation - pour mettre en évidence sa relation avec le discours capitaliste d' accord avec ce qui fut pensé par Lacan. Le déni de la privation est une défense subjective qui apparaît comme un indice de l'échec du père qui prive dans le passage du deuxième au troisième stade du complexe d'Œdipe. C'est une manière de se défendre de la castration en recherchant une satisfaction pulsionnelle sans médiation symbolique ­ une recherche qui échoue et dépasse le principe du plaisir. Nous concluons que ce déni contemporain est un symptôme du discours capitaliste lui-même. Si la for clusion de la castration dans le discours capitaliste est une promesse également ratée, le déni de la privation apparaît dans la subjectivité qui tente de résoudre cet échec. C'est le signe que le discours capitalistene tient pas ses promesses - et cela par sa propre logique.

Following the logics of psychic economy and political economy developed by Freud and Lacan, we target a type of defense that appears in many cases of contemporary clinical practice - the denial of deprivation - to highlight its relationship with the capitalist discourse as thought by Lacan. The denial of deprivation is a subjective defense that appears as an index of the failure of the depriving father in the transition from the second to the third stage of the Oedipus complex. It is a way of defending oneself from castration by seeking instinctual satisfaction without symbolic mediation ­ a search that fails and goes beyond the pleasure principle. The conclusion is that this contemporary denial is a symptom of capitalist discourse itself. If the foreclosure of castration in capitalist discourse is an equally failed promise, the denial of deprivation appears in subjectivity trying to resolve this failure. It is a sign that capitalist discourse does not fulfill its promise - and this by its own logical perspective.

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