Abstract:Background: Successful psychological treatment requires addressing both external and internal obstacles. Further, addressing these obstacles can improve the quality of care and facilitate effective treatment. Objective: This study aimed to develop a scale of obstacles to successful psychotherapy programs, designed for psychologists in Saudi Arabia. Methods: Three hundred psychologists recruited from Saudi Arabia, completed the questionnaires of Obstacles Successful Psychotherapy Programs Scale (OSPPS). Results: The obtained results revealed favorable psychometric characteristics and factorial construction of the scale. Reliability coefficients, assessed through various methods including Cronbach`s alpha, Split-half method, and Guttman coefficient, demonstrated consistently good results. The internal consistency of the items was found to be high, with values ranging between (85, 90). Exploratory Factor Analysis (EFA) showed satisfactory saturations for all scale items, exceeding the recommended threshold of 0.3. "Z" values in EFA were statistically significant at a 0.01 level. Furthermore, Confirmatory Factor Analysis (CFA) indicated a robust fit for the model. Conclusion: The study concludes by recommended the utilization of the OSPPS in future research within the Arab context, specifically for psychologists interested in the field.
Abstract:Fake news classification plays a vital role in mitigating misinformation across digital platforms. This research adopts a structured methodology for developing a deep learning-based fake news classifier using the Grapevine Software Development Life Cycle (SDLC) model. The classifier utilizes Bidirectional Encoder Representations from Transformers (BERT) to analyze and categorize news articles as either real or fake. We employ a supervised learning approach incorporating fine-tuned BERT embeddings, dropout layers for regularization, and fully connected layers with the hyperbolic tangent activation function (tanh). Model performance is assessed using accuracy, ROC-AUC score, precision-recall curves, and other statistical metrics, validating the effectiveness of the proposed framework.
Abstract:The increasing adoption of distributed applications and cloud-native architectures has transformed the landscape of Wide Area Networks (WANs), introducing challenges such as encrypted traffic, evolving patterns, and dynamic network states. Accurate classification, anomaly detection, and capacity prediction of WAN traffic are critical for enhancing network performance, reducing downtime, and optimising resource utilisation. This survey provides a comprehensive overview of the state-of-the-art methodologies in these domains, focusing on hybrid approaches that integrate deep learning, statistical methods, and data-driven models. The paper reviews existing datasets, algorithms, and evaluation metrics, highlighting the limitations of current techniques, particularly in handling encrypted traffic and achieving generalisation across diverse environments. Emerging trends, such as edge computing and explainable AI, are discussed to showcase their potential to overcome these challenges. The survey also outlines critical research gaps, including dataset diversity, real-time applicability, and efficient computation, and proposes future directions to advance the field. This work is a foundation for researchers and practitioners seeking to address the dynamic nature of WANs and improve traffic management through innovative machine learning and deep learning techniques.
Abstract:The rapid expansion of the global population highlights the urgent need to enhance agricultural productivity as a fundamental pillar of food security. This imperative is especially critical in Africa, where many countries face persistent challenges associated with developing economies. Against this backdrop, this study conducts a Life Cycle Assessment (LCA) of corn production in Huíla Province, Angola. The research evaluates the corn production lifecycle—from cultivation to consumption—by identifying key environmental and social impacts and proposing strategies to improve the sustainability of the production system. The analysis covers both grain and seed production, providing a holistic view of the processes involved. The findings reveal that 99.5% of the pesticides used in agricultural activities have a moderate to high potential for environmental impact, with only 30% posing a low toxicity risk. Herbicides and insecticides were identified as the most hazardous, both in terms of environmental consequences and risks to human health, emphasizing the critical importance of stringent handling and application protocols. The study employed a qualitative methodology, utilizing a systematic literature review to gather relevant data and facilitate a critical analysis of the assessed processes. This research underscores the urgent need for Angola to adopt sustainable agricultural practices to mitigate environmental impacts, strengthen food security, and promote sustainable development in the region.
Abstract:The goal of this paper was to explore the impact of Somatic Symptom Disorder on the level of executive functioning in individuals diagnosed with this disorder. This research aimed to investigate the connection between executive functioning and SSD. A cross-sectional design was employed, and a comparative analysis was conducted on a sample of 100 participants, with an average age range of 18 to 40 years (M = 2.05, SD = 1.94). Participants were recruited from various clinics in Peshawar, KPK. A demographic questionnaire was used to gather information on socioeconomic status and gender. The Somatic Symptom Scale (SSS-8) was utilized to screen for somatic symptoms, while the BRIEF-A was administered to assess executive functioning. Data was analyzed using simple linear regression. The results indicated that somatic symptom disorder significantly affects executive functioning. The study concluded that effectively addressing executive functioning and emotional distress can contribute to early recovery from somatic symptom disorder, and vice versa.
Abstract:Wildlife crime, also known as wildlife trafficking or illegal wildlife trade (IWT), involves unlawful, unregulated, and unsustainable activities involving the capture, use, acquisition, and killing of animals or plants. Gender is significant in wildlife crime participation, practices, outcomes, and motivations. Beyond the gender dimensions of IWT, ethnic-based underpinnings are beginning to find space in wildlife literature, though they remain widely under-reported, especially in developing contexts. However, gender and ethnic-based underpinnings are often under-reported in wildlife literature, especially in developing contexts. Qualtrics sample size determination formula generated a sample size of 270 taken from a study population of 212,779, representing the students’ population in six tertiary education institutions, two each located in each of the three major ethnic groups, namely Hausa, Igbo, and Yoruba. Using descriptive survey design and stratified random sampling technique, we found that males participate more in IWT than females (mean value - 4.052 for males; females - 2.661). Human-wildlife risk perception differs between males and females (mean value-3.730), and cultural diversities in perception and participation promote IWT. Ethnicity and gendered preferences for specific wildlife species are high among males and females (mean value-4.120). The ordinal regression test showed a weak relationship between ethnicity and wildlife demand and use (coefficient value - 0.1918, p-value - 0.231), indicating ethnicity has a non-significant effect on wildlife demand and use. Kruskal-Wallis test showed a non-significant difference in the distribution of wildlife crime among the three major ethnic-based groups (p=0.114). Tertiary education institutions integrate gender- and ethnic-based wildlife crime subjects into their academic curriculum, as well as the research and training of students and staff. The study confirms gender and ethnic-based dimensions of wildlife crime, which can help expand research, education, and knowledge production to other jurisdictions, where such evaluations are lacking, to enhance integrated and sustainable wildlife management.
Abstract:The present study examines the tonal variations exhibited by Squliq Atayal Mandarin speakers, who have suffered from language policy since 1950 in Taiwan. 20 Squliq Atayal natives and 20 Taiwan Mandarin (TM) natives were recruited to read 80 bi-syllabic words. A mixed random effect model and several Mann-Whitney U tests were performed to compare different tonal combinations between the two groups. Results showed that Squliq Atayal Mandarin speakers manifest exaggeration of anticipatory effects, pre-low/pre-high rising, resulting in a comparatively high F0 contour of the 1st syllable. In addition, the habitual stress placement on final syllables in Squliq Atayal engenders instances of final falling in select bi-syllabic tonal combinations. This phenomenon could be explained either by constraints inherent to phonation or by an intent to mitigate the confusion of declination and tone values. Moreover, our findings lend credits to the SLM-r proposition asserting that the phonetic constituents comprising the L1 and L2 phonetic subsystems of bilingual individuals inevitably interact, yielding a Squliq Atayal accented Mandarin.
Abstract:In self-creation theory and general relativity, we study the Bianchi type V I0 cosmological model in the presence of a perfect fluid with anisotropic pressure. An exact solution of the Einstein field equations is allowed by considering the model yields a constant deceleration parameter. With anisotropic pressure, the entropy and thermodynamics functions in different directions (x, y and z) are introduced and studied. Physical and geometrical properties in different directions of the obtained models are discussed.
Abstract:In this research study, we have proposed a new eighth-order optimal method. The new method consists of three steps: the Newton step, an optimal fourth-order iteration scheme, and a simply structured third step that improves the convergence order up to at least eight and ensures an efficiency index of 1.682. The discussion focused on the convergence analysis of the new method. We provide numerical examples to illustrate the effectiveness of our proposed method by comparing it to eighth-order numerical methods. Furthermore, we examine the complex dynamics and Attraction basins (AB), comparing them with various methods of the same order, and we present the results in summary tables.
Abstract:The objective of this study is to examine the factors that impact the inclination of Indian residents towards savings and their adoption of digital financial services. Specifically, the research focuses on identifying the determinants that influence the accessibility and utilisation of financial services among the population. This research endeavour aims to enhance comprehension of financial inclusion and its policy and institutional implications. The paper employs data from the 2021 Global Findex Database, a survey conducted by the World Bank, to evaluate financial inclusion in India. The survey involved 1,000 participants and was conducted with the aim of achieving the paper`s objectives. The present study utilises logistic regression and multinomial logistic analysis as its methodological approach to investigate the correlation between diverse factors and saving behaviour and digital financial inclusion. The research highlights key factors affecting saving behavior and digital financial inclusion, such as digital payments, gender, age, education, and income level. It shows that higher income, education, and engagement in digital payments positively impact savings and financial inclusion. Government transfers and agricultural payments also promote savings and digital payments. Policymakers should focus on targeted interventions to support disadvantaged groups and expand access to formal financial institutions, digital payment platforms, and financial literacy programs. Additionally, targeted social protection policies can improve financial wellbeing for vulnerable populations.