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An optimized hybrid deep intrusion detection model (HD-IDM) for enhancing network security

  • Authors: Ahmad, Iftikhar , Imran, Muhammad , Qayyum, Abdul , Ramzan, Muhammad , Alassafi, Madini
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Mathematics Vol. 11, no. 21 (2023), p.
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  • Description: Detecting cyber intrusions in network traffic is a tough task for cybersecurity. Current methods struggle with the complexity of understanding patterns in network data. To solve this, we present the Hybrid Deep Learning Intrusion Detection Model (HD-IDM), a new way that combines GRU and LSTM classifiers. GRU is good at catching quick patterns, while LSTM handles long-term ones. HD-IDM blends these models using weighted averaging, boosting accuracy, especially with complex patterns. We tested HD-IDM on four datasets: CSE-CIC-IDS2017, CSE-CIC-IDS2018, NSL KDD, and CIC-DDoS2019. The HD-IDM classifier achieved remarkable performance metrics on all datasets. It attains an outstanding accuracy of 99.91%, showcasing its consistent precision across the dataset. With an impressive precision of 99.62%, it excels in accurately categorizing positive cases, crucial for minimizing false positives. Additionally, maintaining a high recall of 99.43%, it effectively identifies the majority of actual positive cases while minimizing false negatives. The F1-score of 99.52% emphasizes its robustness, making it the top choice for classification tasks requiring precision and reliability. It is particularly good at ROC and precision/recall curves, discriminating normal and harmful network activities. While HD-IDM is promising, it has limits. It needs labeled data and may struggle with new intrusion methods. Future work should find ways to handle unlabeled data and adapt to emerging threats. Also, making HD-IDM work faster for real-time use and dealing with scalability challenges is key for its broader use in changing network environments. © 2023 by the authors.

An overview of Australian exercise and sport science degrees

  • Authors: Kittel, Aden , Stevens, Christopher , Lindsay, Riki , Spittle, Sharna , Spittle, Michael
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Frontiers in Education Vol. 8, no. (2023), p.
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  • Description: Exercise and Sport Science (EXSS) is a common degree offered at Australian universities, yet there is no systematic overview of this multidisciplinary field of study. This study aimed to determine the broad curriculum content of Australian EXSS degrees by summarizing the units offered, identify most commonly delivered content areas, and capture course information such as work-integrated learning (WIL) requirements and majors offered. Data were gathered through publicly available university course pages, with 30 EXSS courses included and only core units identified. The most common Australian EXSS units were “Exercise Physiology,” “Biomechanics,” “Research Methods and Data Analysis,” “Exercise Prescription and Delivery,” and “Exercise and Sport Psychology.” WIL requirements ranged from 140 to 300 h per course, and five courses offered majors. This study provides an overview of Australian EXSS courses, with the focus on exercise-related components reflecting accreditation requirements. Future research should examine how these courses equip students for the multidisciplinary EXSS industry. Copyright © 2023 Kittel, Stevens, Lindsay, Spittle and Spittle.

An overview of long covid support services in australia and international clinical guidelines, with a proposed care model in a global context

  • Authors: Luo, Shiqi , Zheng, Zhen , Bird, Stephen , Plebanski, Magdalena , Figueiredo, Bernardo , Jessup, Rebecca , Stelmach, Wanda , Robinson, Jennifer , Xenos, Sophia , Olasoji, Micheal , Wan, Dawn , Sheahan, Jacob , Itsiopoulos, Catherine
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Public Health Reviews Vol. 44, no. (2023), p.
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  • Description: Objective: To identify gaps among Australian Long COVID support services and guidelines alongside recommendations for future health programs. Methods: Electronic databases and seven government health websites were searched for Long COVID-specific programs or clinics available in Australia as well as international and Australian management guidelines. Results: Five Long COVID specific guidelines and sixteen Australian services were reviewed. The majority of Australian services provided multidisciplinary rehabilitation programs with service models generally consistent with international and national guidelines. Most services included physiotherapists and psychologists. While early investigation at week 4 after contraction of COVID-19 is recommended by the Australian, UK and US guidelines, this was not consistently implemented. Conclusion: Besides Long COVID clinics, future solutions should focus on early identification that can be delivered by General Practitioners and all credentialed allied health professions. Study findings highlight an urgent need for innovative care models that address individual patient needs at an affordable cost. We propose a model that focuses on patient-led self-care with further enhancement via multi-disciplinary care tools. Copyright © 2023 Luo, Zheng, Bird, Plebanski, Figueiredo, Jessup, Stelmach, Robinson, Xenos, Olasoji, Wan, Sheahan and Itsiopoulos.

An update on the influence of natural climate variability and anthropogenic climate change on tropical cyclones

  • Authors: Camargo, Suzana , Murakami, Hiroyuki , Bloemendaal, Nadia , Chand, Savin , Deshpande, Medha , Dominguez-Sarmiento, Christian , González-Alemán, Juan , Knutson, Thomas , Lin, I. , Moon, Il-Ju , Patricola, Christian , Reed, Kevin , Roberts, Malcolm , Scoccimarro, Enrico , Tam, Chi , Wallace, Elizabeth , Wu, Liguang , Yamada, Yohei , Zhang, Wei , Zhao, Haikun
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Tropical Cyclone Research and Review Vol. 12, no. 3 (2023), p. 216-239
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  • Description: A substantial number of studies have been published since the Ninth International Workshop on Tropical Cyclones (IWTC-9) in 2018, improving our understanding of the effect of climate change on tropical cyclones (TCs) and associated hazards and risks. These studies have reinforced the robustness of increases in TC intensity and associated TC hazards and risks due to anthropogenic climate change. New modeling and observational studies suggested the potential influence of anthropogenic climate forcings, including greenhouse gases and aerosols, on global and regional TC activity at the decadal and century time scales. However, there are still substantial uncertainties owing to model uncertainty in simulating historical TC decadal variability in the Atlantic, and the limitations of observed TC records. The projected future change in the global number of TCs has become more uncertain since IWTC-9 due to projected increases in TC frequency by a few climate models. A new paradigm, TC seeds, has been proposed, and there is currently a debate on whether seeds can help explain the physical mechanism behind the projected changes in global TC frequency. New studies also highlighted the importance of large-scale environmental fields on TC activity, such as snow cover and air-sea interactions. Future projections on TC translation speed and medicanes are new additional focus topics in our report. Recommendations and future research are proposed relevant to the remaining scientific questions and assisting policymakers. © 2023 The Shanghai Typhoon Institute of China Meteorological Administration

Analysis and design of a cost-effective single-input and regulatable multioutput WPT system

  • Authors: Li, Xiaofei , Zheng, Fan , Wang, Heshou , Dai, Xin , Sun, Yue , Hu, Jiefeng
  • Date: 2023
  • Type: Text , Journal article
  • Relation: IEEE Transactions on Power Electronics Vol. 38, no. 6 (2023), p. 6939-6944
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  • Description: In this letter, a single-input and regulatable multioutput wireless power transfer system is presented. In particular, the system uses positive and negative half-wave rectifiers and a synchronous rectifier to realize multiple output channels. Each output channel is controllable, providing a flexible wireless charger to meet various charging requirements. Moreover, this system utilizes the inherent half-wave-rectifier channels (#B and #C) to detect synchronous signals for the rectifiers rather than using additional synchronous detection circuits, thereby leading to a cost-effective system. Finally, a 300 W laboratory prototype is contrasted with three voltage levels, i.e., 48, 30, and 24 V. With the help of the control logic, this system shows excellent robustness against different occasions, such as load variations, input disturbance, and misalignment. The overall efficiency ranges from 86.7% to 90.6%. © 1986-2012 IEEE.

Analysis of microalgal density estimation by using lasso and image texture features

  • Authors: Nguyen, Linh , Nguyen, Dung , Nguyen, Thang , Nguyen, Binh , Nghiem, Truong
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Sensors Vol. 23, no. 5 (2023), p.
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  • Description: Monitoring and estimating the density of microalgae in a closed cultivation system is a critical task in culturing algae since it allows growers to optimally control both nutrients and cultivating conditions. Among the estimation techniques proposed so far, image-based methods, which are less invasive, nondestructive, and more biosecure, are practically preferred. Nevertheless, the premise behind most of those approaches is simply averaging the pixel values of images as inputs of a regression model to predict density values, which may not provide rich information of the microalgae presenting in the images. In this work, we propose to exploit more advanced texture features extracted from captured images, including confidence intervals of means of pixel values, powers of spatial frequencies presenting in images, and entropies accounting for pixel distribution. These diverse features can provide more information of microalgae, which can lead to more accurate estimation results. More importantly, we propose to use the texture features as inputs of a data-driven model based on L1 regularization, called least absolute shrinkage and selection operator (LASSO), where their coefficients are optimized in a manner that prioritizes more informative features. The LASSO model was then employed to efficiently estimate the density of microalgae presenting in a new image. The proposed approach was validated in real-world experiments monitoring the Chlorella vulgaris microalgae strain, where the obtained results demonstrate its outperformance compared with other methods. More specifically, the average error in the estimation obtained by the proposed approach is 1.54, whereas those obtained by the Gaussian process and gray-scale-based methods are 2.16 and 3.68, respectively © 2023 by the authors.

Animal population decline and recovery after severe fire: Relating ecological and life history traits with expert estimates of population impacts from the Australian 2019-20 megafires

Anti-aliasing deep image classifiers using novel depth adaptive blurring and activation function

  • Authors: Hossain, Md Tahmid , Teng, Shyh , Lu, Guojun , Rahman, Mohammad Arifur , Sohel, Ferdous
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Neurocomputing Vol. 536, no. (2023), p. 164-174
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  • Description: Deep convolutional networks are vulnerable to image translation or shift, partly due to common down-sampling layers, e.g., max-pooling and strided convolution. These operations violate the Nyquist sampling rate and cause aliasing. The textbook solution is low-pass filtering (blurring) before down-sampling, which can benefit deep networks as well. Even so, non-linearity units, such as ReLU, often re-introduce the problem, suggesting that blurring alone may not suffice. In this work, first, we analyse deep features with Fourier transform and show that Depth Adaptive Blurring is more effective, as opposed to monotonic blurring. To this end, we propose a novel Depth Adaptive Blur-pool (DAB-pool) module to replace existing down-sampling methods. Second, we introduce a novel activation function – with a built-in low pass filter, as an additional measure, to keep the problem from reappearing. From experiments, we observe generalisation on other forms of transformations and corruptions as well, e.g., rotation, scale, and noise. We evaluate our method under three challenging settings: (1) a variety of image translations; (2) adversarial attacks – both

Antisocial and prosocial online behaviour : exploring the roles of the dark and light triads

Aphasia rehabilitation when speech pathologists and clients do not share the same language: a scoping review

  • Authors: Larkman, Chelsea , Mellahn, Kathleen , Han, Weifeng , Rose, Miranda
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Aphasiology Vol. 37, no. 4 (2023), p. 635-657
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  • Description: Growing cultural and linguistic diversity globally is increasingly requiring speech pathologists to provide effective and equitable aphasia rehabilitation to clients with whom they do not share a language. Little is known about how rehabilitation is being adapted and provided when a language mismatch arises between the therapist and the client. This scoping review aims to systematically map the evidence related to aphasia rehabilitation when the speech pathologist and the client do not share a language. A comprehensive search was conducted in September 2020. Twenty studies comprising surveys and/or interviews and descriptive publications were reviewed. Speech pathologists frequently report a lack of confidence, skill, and preparation to work with culturally and linguistically diverse clients with aphasia. Furthermore, there is a shortage of published evidence, guidelines, resources, and access to interpreters to support their practice. Further research is needed into aphasia rehabilitation when there is no shared language between client and speech pathologist, with particular attention to the therapy approach selected and adaptations required for the target language and culture. Information is needed concerning how the speech pathologist and interpreter work together, as well as the experiences of interpreters and people with aphasia and their families.

Application of a universal parasite diagnostic test to biological specimens collected from animals

  • Authors: Lane, Meredith , Kashani, Mitra , Barratt, Joel , Qvarnstrom, Yvonne , Yabsley, Michael , Garrett, Kayla , Bradbury, Richard
  • Date: 2023
  • Type: Text , Journal article
  • Relation: International Journal for Parasitology: Parasites and Wildlife Vol. 20, no. (2023), p. 20-30
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  • Description: A previously described universal parasite diagnostic (nUPDx) based on PCR amplification of the 18S rDNA and deep-amplicon sequencing, can detect human blood parasites with a sensitivity comparable to real-time PCR. To date, the efficacy of this assay has only been assessed on human blood. This study assessed the utility of nUPDx for the detection of parasitic infections in animals using blood, tissues, and other biological sample types from mammals, birds, and reptiles, known to be infected with helminth, apicomplexan, or pentastomid parasites (confirmed by microscopy or PCR), as well as negative samples. nUPDx confirmed apicomplexan and/or nematode infections in 24 of 32 parasite-positive mammals, while also identifying several undetected coinfections. nUPDx detected infections in 6 of 13 positive bird and 1 of 2 positive reptile samples. When applied to 10 whole parasite specimens (worms and arthropods), nUPDx identified all to the genus or family level, and detected one incorrect identification made by morphology. Babesia sp. infections were detected in 5 of the 13 samples that were negative by other diagnostic approaches. While nUPDx did not detect PCR/microscopy-confirmed trichomonads or amoebae in cloacal swabs/tissue from 8 birds and 2 reptiles due to primer template mismatches, 4 previously undetected apicomplexans were detected in these samples. Future efforts to improve the utility of the assay should focus on validation against a larger panel of tissue types and animal species. Overall, nUPDx shows promise for use in both veterinary diagnostics and wildlife surveillance, especially because species-specific PCRs can miss unknown or unexpected pathogens. © 2022

Application of KRR, K-NN and GPR algorithms for predicting the soaked CBR of fine-grained plastic soils

  • Authors: Verma, Gaurav , Kumar, Brind , Kumar, Chintoo , Ray, Arunava , Khandelwal, Manoj
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Arabian Journal for Science and Engineering Vol. 48, no. 10 (2023), p. 13901-13927
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  • Description: California bearing ratio (CBR) test is one of the comprehensive tests used for the last few decades to design the pavement thickness of roadways, railways and airport runways. Laboratory-performed CBR test is considerably rigorous and time-taking. In a quest for an alternative solution, this study utilizes novel computational approaches, including the kernel ridges regression, K-nearest neighbor and Gaussian process regression (GPR), to predict the soaked CBR value of soils. A vast quantity of 1011 in situ soil samples were collected from an ongoing highway project work site. Two data divisional approaches, i.e., K-Fold and fuzzy c-means (FCM) clustering, were used to separate the dataset into training and testing subsets. Apart from the numerous statistical performance measurement indices, ranking and overfitting analysis were used to identify the best-fitted CBR prediction model. Additionally, the literature models were also tried to validate through present study datasets. From the results of Pearson’s correlation analysis, Sand, Fine Content, Plastic Limit, Plasticity Index, Maximum Dry Density and Optimum Moisture Content were found to be most influencing input parameters in developing the soaked CBR of fine-grained plastic soils. Experimental results also establish the proficiency of the GPR model developed through FCM and K-Fold data division approaches. The K-Fold data division approach was found to be helpful in removing the overfitting of the models. Furthermore, the predictive ability of any model is considerably influenced by the geological location of the soils/materials used for the model development. © 2023, The Author(s).

Application of various robust techniques to study and evaluate the role of effective parameters on rock fragmentation

  • Authors: Mehrdanesh, Amirhossein , Monjezi, Masoud , Khandelwal, Manoj , Bayat, Parichehr
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Engineering with Computers Vol. 39, no. 2 (2023), p. 1317-1327
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  • Description: In this paper, an attempt has been made to implement various robust techniques to predict rock fragmentation due to blasting in open pit mines using effective parameters. As rock fragmentation prediction is very complex and complicated, and due to that various artificial intelligence-based techniques, such as artificial neural network (ANN), classification and regression tree and support vector machines were selected for the modeling. To validate and compare the prediction results, conventional multivariate regression analysis was also utilized on the same data sets. Since accuracy and generality of the modeling is dependent on the number of inputs, it was tried to collect enough required information from four different open pit mines of Iran. According to the obtained results, it was revealed that ANN with a determination coefficient of 0.986 is the most precise method of modeling as compared to the other applied techniques. Also, based on the performed sensitivity analysis, it was observed that the most prevailing parameters on the rock fragmentation are rock quality designation, Schmidt hardness value, mean in-situ block size and the minimum effective ones are hole diameter, burden and spacing. The advantage of back propagation neural network technique for using in this study compared to other soft computing methods is that they are able to describe complex and nonlinear multivariable problems in a transparent way. Furthermore, ANN can be used as a first approach, where much knowledge about the influencing parameters are missing. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.

Applications of Computed Tomography (CT) in environmental soil and plant sciences

  • Authors: Zhang, Huan , He, Hailong , Gao, Yanjun , Mady, Ahmed , Filipović, Vilim , Dyck, Miles , Lv, Jialong , Liu, Yang
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: Soil and Tillage Research Vol. 226, no. (2023), p.
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  • Description: Computed tomography (CT) in combination with advanced image processing can be used to non-invasively and non-destructively visualize complex interiors of living and non-living media in 2 and 3-dimensional space. In addition to medical applications, CT has also been widely used in soil and plant science for visual and quantitative descriptions of physical, chemical, and biological properties and processes. The technique has been used successfully on numerous applications. However, with a rapidly evolving CT technologies and expanding applications, a renewed review is desirable. Only a few attempts have been made to collate and review examples of CT applications involving the integrated field of soil and plant research in recent years. Therefore, the objectives of this work were to: (1) briefly introduce the basic principles of CT and image processing; (2) identify the research status and hot spots of CT using bibliometric analysis based on Web of Science literature over the past three decades; (3) provide an overall review of CT applications in soil science for measuring soil properties (e.g., porous soil structure, soil components, soil biology, heat transfer, water flow, and solute transport); and (4) give an overview of applications of CT in plant science to detect morphological structures, plant material properties, and root-soil interaction. Moreover, the limitations of CT and image processing are discussed and future perspectives are given. © 2022 Elsevier B.V.

Applications of machine learning and deep learning in antenna design, optimization, and selection : a review

  • Authors: Sarker, Nayan , Podder, Prajoy , Mondal, M. , Shafin, Sakib , Kamruzzaman, Joarder
  • Date: 2023
  • Type: Text , Journal article , Review
  • Relation: IEEE Access Vol. 11, no. (2023), p. 103890-103915
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  • Description: This review paper provides an overview of the latest developments in artificial intelligence (AI)-based antenna design and optimization for wireless communications. Machine learning (ML) and deep learning (DL) algorithms are applied to antenna engineering to improve the efficiency of the design and optimization processes. The review discusses the use of electromagnetic (EM) simulators such as computer simulation technology (CST) and high-frequency structure simulator (HFSS) for ML and DL-based antenna design, which also covers reinforcement learning (RL)-bases approaches. Various antenna optimization methods including parallel optimization, single and multi-objective optimization, variable fidelity optimization, multilayer ML-assisted optimization, and surrogate-based optimization are discussed. The review also covers the AI-based antenna selection approaches for wireless applications. To support the automation of antenna engineering, the data generation technique with computational electromagnetics software is described and some useful datasets are reported. The review concludes that ML/DL can enhance antenna behavior prediction, reduce the number of simulations, improve computer efficiency, and speed up the antenna design process. © 2013 IEEE.

Apprenticeships : the problem of attractiveness and the hindrance of heterogeneity

  • Authors: Smith, Erica
  • Date: 2023
  • Type: Text , Journal article
  • Relation: International Journal of Training and Development Vol. 27, no. 1 (2023), p. 18-38
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  • Description: This paper examines a question posed in 2019 in the International Journal on Training and Development: ‘How do we solve a problem like apprenticeship?’ Data sources covering a substantial number of countries are used to present findings on, and analyse, initiatives that have been implemented or that have been considered, and then to develop some analytical constructs to help address the question. Fundamental issues such as the status of vocational education and training and the status of apprenticed occupations are important, but the nature of the apprenticeship arrangements, within countries and within industries are also major factors affecting perceived attractiveness. The paper therefore argues that the heterogeneity of apprenticeship systems and arrangements is a major barrier to solving the attractiveness problem. Moreover, the heterogeneity of potential apprenticeship applicants means that marketing campaigns or other efforts to attract more, and higher quality, apprentices need to be cognisant of individuals’ backgrounds, characteristics, and aspirations. Some tentative ways of addressing these matters are presented, but the conclusion is that the topic needs large-scale research. © 2022 The Authors. International Journal of Training and Development published by Brian Towers (BRITOW) and John Wiley & Sons Ltd.

Association of fear of negative evaluation with performance anxiety : mediation by negative self-statements and moderation by positive self-statements during performance in adults

  • Authors: Gomez, Rapson , Watson, Shaun , Brown, Taylor
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Journal of Psychopathology and Behavioral Assessment Vol. 45, no. 3 (2023), p. 650-658
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  • Description: Using individual differences constructs, the current study used cross-sectional data to examine the mediating role of negative self-statements during public speaking on the relationship between fear of negative evaluation and public speaking anxiety (a type of performance anxiety), and how this relationship was moderated by positive self-statements during public performance. The sample comprised 319 adults (men = 105, women = 214) from the general Australian community, with ages ranging from 18 years to 65 years. All participants completed questionnaires covering the different study variables. The findings showed that there was partial mediation by negative self-statements on the relationship between fear of negative evaluation and performance anxiety. There were also moderation effects by positive self-statements for this relationship. Additionally, moderation by positive self-statements was evident at all levels of positive self-statements. The theoretical and clinical implications of the findings for public speaking anxiety are discussed. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Associations between smartphone keystroke metadata and mental health symptoms in adolescents: findings from the future proofing study

  • Authors: Braund, Taylor , O'Dea, Bridianne , Bal, Debopriyo , Maston, Kate , Larsen, Mark , Werner-Seidler, Aliza , Tillman, Gabriel , Christensen, Helen
  • Date: 2023
  • Type: Text , Journal article
  • Relation: JMIR Mental Health Vol. 10, no. (2023), p.
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  • Description: Background: Mental disorders are prevalent during adolescence. Among the digital phenotypes currently being developed to monitor mental health symptoms, typing behavior is one promising candidate. However, few studies have directly assessed associations between typing behavior and mental health symptom severity, and whether these relationships differs between genders. Objective: In a cross-sectional analysis of a large cohort, we tested whether various features of typing behavior derived from keystroke metadata were associated with mental health symptoms and whether these relationships differed between genders. Methods: A total of 934 adolescents from the Future Proofing study undertook 2 typing tasks on their smartphones through the Future Proofing app. Common keystroke timing and frequency features were extracted across tasks. Mental health symptoms were assessed using the Patient Health Questionnaire-Adolescent version, the Children's Anxiety Scale-Short Form, the Distress Questionnaire 5, and the Insomnia Severity Index. Bivariate correlations were used to test whether keystroke features were associated with mental health symptoms. The false discovery rates of P values were adjusted to q values. Machine learning models were trained and tested using independent samples (ie, 80% train 20% test) to identify whether keystroke features could be combined to predict mental health symptoms. Results: Keystroke timing features showed a weak negative association with mental health symptoms across participants. When split by gender, females showed weak negative relationships between keystroke timing features and mental health symptoms, and weak positive relationships between keystroke frequency features and mental health symptoms. The opposite relationships were found for males (except for dwell). Machine learning models using keystroke features alone did not predict mental health symptoms. Conclusions: Increased mental health symptoms are weakly associated with faster typing, with important gender differences. Keystroke metadata should be collected longitudinally and combined with other digital phenotypes to enhance their clinical relevance. ©Taylor A Braund, Bridianne O'Dea, Debopriyo Bal, Kate Maston, Mark Larsen, Aliza Werner-Seidler, Gabriel Tillman, Helen Christensen.

Associations of reinforcement sensitivity theory personality constructs, cognitive biases for negative and threatening social information, and social anxiety

  • Authors: Gomez, Rapson , Watson, Shaun , Stavropoulos, Vasileios , Typuszak, Natasha
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Current Psychology Vol. 42, no. 17 (2023), p. 14159-14170
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  • Description: Background: Using Kimbrel’s (2008) mediation model of social anxiety as a theoretical framework, the primary aim of the current study was to use path analysis to examine how biased cognitions for negative and threatening social information mediated the relationships for the personality constructs of the reinforcement sensitivity theory (RST) with generalized and specific social anxiety (target mediation model). A secondary aim was to examine reverse mediation testing (RMT) models, in which the social anxiety constructs were viewed as mediating the relations between RST constructs and biased social cognition constructs. Methods: A total of 302 (males = 101, females = 201) adults (age ranging from 18 to 65 years) from the general community completed questionnaires measuring the behavioral inhibition system/fight-flight-freeze system (BIS/FFFS), the behavioral approach system (BAS), social comparison (SC), social ineptness (SI), and generalized and specific social anxiety. Results: The findings for the target mediation model showed that there was support for indirect effects for the BIS/FFFS and the BAS on generalized and specific social anxiety through SC and SI. For the RMT model, there was support for the indirect effect of the RST constructs with SI through generalized social anxiety. However, specific generalized anxiety did not mediate the relations of the BIS/FFFS and BAS to SC. Conclusions: The findings highlight the importance of cognitive therapy that targets SC and SI in the treatment of social anxiety, especially among those with high BIS/FFFS and low BAS. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

Associations of UPPS-P negative urgency and positive urgency with ADHD dimensions : moderation by lack of premeditation and lack of perseverance in men and women

  • Authors: Gomez, Rapson , Watson, Shaun
  • Date: 2023
  • Type: Text , Journal article
  • Relation: Personality and Individual Differences Vol. 206, no. (2023), p.
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  • Description: The study examined how dimensions of Whiteside and Lynam's (2003) UPPS-P model of impulsivity (lack of premeditation, lack of perseverance, negative urgency, and positive urgency) were associated directly and interactively with the attention-deficit/hyperactivity disorder (ADHD) dimensions of inattention and hyperactivity/impulsivity in men and women separately. A total of 550 adults (men = 147, women = 403), ages ranging from 18 to 65 years, from the general community completed questionnaires covering the study variables. For women, there was support for the additive model for the prediction of inattention, and both inattention and hyperactivity/impulsivity were predicted by lack of premeditation × positive urgency. For men, inattention was predicted by lack of premeditation × negative urgency, and lack of premeditation × positive urgency. In all instances, low levels of premeditation reduced the relationships between the urgency dimensions and ADHD dimensions. The theoretical and clinical implications of the findings are discussed. © 2023 The Author(s)