Search Results (58)

The promotion of enhanced well-being among children and collaboration among families, schools, and communities is paramount and is a pressing concern in the global education sector. This necessitates that preschool teachers possess the necessary competencies for effective family-preschool partnerships (FPPs). This study explored the competencies necessary for Chinese kindergarten teachers to engage in FPP using behavioral event interviews with 30 participants. Thematic analysis identified key competency traits, and independent samples t-tests with Bonferroni correction compared collaboration competencies between outstanding and typical teachers, as well as across different career stages. Consequently, a comprehensive crisscrossing competency framework consisting of four quadrants was developed. This framework distinguishes between high-performance and general traits, as well as between stable and variable traits that may evolve across career stages. High-performance traits such as communication, expression, and relationship management should be prioritized in the training and recruitment of early childhood educators involved in FPP. In contrast, intrinsic qualities that foster successful FPP, such as child orientation, should be cultivated early and sustained throughout a teacher’s career. From a developmental perspective, this framework provides a crucial foundation for evaluating and training kindergarten teachers in the competencies essential for fostering effective FPP. Full article

Numerous preclinical and clinical studies indicate that CBD possesses various therapeutic properties, including antipsychotic, analgesic, anticonvulsant, antineoplastic, and antioxidant effects. Recent research has also highlighted its potential anxiolytic effects. This study aimed to evaluate the impact of CBD treatment in a PTSD induction model. To determine CBD’s efficacy, behavioral tests assessing anxiety and memory were conducted. Additionally, two oxidative stress markers were measured to explore its antioxidant properties. Forty adult male rats were used for PTSD induction. The procedure involved exposure to predator odor on day 10, followed by a second exposure on day 20. A secondary stressor, consisting of daily cage partner changes, was also applied. The animals were randomized into four groups: two non-stressed and two stressed groups. CBD was administered at 10 mg/kg. Behavioral effects were evaluated using the open field (OF), elevated plus maze (EPM), novel object recognition (NOR), and Morris Water Maze (MWM) tests. Malondialdehyde and the GSH/GSSG ratio were assessed using liquid chromatography. CBD treatment did not significantly alter anxiety-like behavior in the EPM, though a trend toward increased vertical exploration was observed in the OF test. In memory-related assessments, no significant differences were found in the NOR test, while performance in the MWM indicated improved spatial memory, with CBD-treated rats spending more time in the target quadrant. In addition, malondialdehyde levels decreased in the CBD groups. Elevated cortisol levels in the stressed CBD group suggest a potential anxiolytic effect, warranting further research. Full article

The Morris Water Maze (MWM) is a widely used behavioral test to assess the spatial learning and memory of animals, particularly valuable in studying neurodegenerative disorders such as Alzheimer’s disease. Traditional methods for analyzing MWM experiments often face limitations in capturing the complexity of animal behaviors. In this study, we present a novel AI-based automated framework to process and evaluate MWM test videos, aiming to enhance behavioral analysis through machine learning. Our pipeline involves video preprocessing, animal detection using convolutional neural networks (CNNs), trajectory tracking, and postprocessing to derive detailed behavioral features. We propose concentric circle segmentation of the pool next to the quadrant-based division, and we extract 32 behavioral metrics for each zone, which are employed in classification tasks to differentiate between younger and older animals. Several machine learning classifiers, including random forest and neural networks, are evaluated, with feature selection techniques applied to improve the classification accuracy. Our results demonstrate a significant improvement in classification performance, particularly through the integration of feature sets derived from concentric zone analyses. This automated approach offers a robust solution for MWM data processing, providing enhanced precision and reliability, which is critical for the study of neurodegenerative disorders. Full article

Wandering dogs are a threat to Aotearoa New Zealand’s endemic kiwi bird species because kiwi have not evolved defenses against mammalian predators. New Zealanders can protect kiwi by reporting wandering dogs, and this also protects the dogs themselves. This study explored New Zealanders’ reporting intentions and behavior, as well as attitudes and beliefs about wandering dogs, harm to kiwi, and dog welfare. Guided by the I₃ framework, we also assessed respondents’ involvement with the intervention of reporting, and involvement with both dog welfare and kiwi welfare. A sample of 250 New Zealanders completed the questionnaire online. Attitudes and behavior were consistent with involvement, and patterns for involvement with kiwi welfare and involvement with dog welfare were similar. The results indicated that almost all respondents fell into one of two quadrants based on involvement—both quadrants were highly involved with dog and kiwi welfare but varied on involvement with reporting. This pattern suggests that promotion campaigns are likely to be an effective intervention to increase the reporting of wandering dogs. Promotional campaigns might focus on dog welfare or kiwi welfare, and should highlight the link between reporting, dogs, and harm to kiwi. Full article

Urban growth, a pivotal characteristic of economic development, brings many environmental and ecological challenges. Modeling urban growth is essential for understanding its spatial dynamics and projecting future trends, providing insights for effective urban planning and sustainable development. This study aims to assess the spatiotemporal patterns of urban growth and morphological evolution in mainland Shanghai from 2016 to 2060 using the SLEUTH model under multiple growth scenarios based on the Shanghai Urban Master Plan (2017–2035). A comprehensive set of urban growth metrics and quadrant analysis were employed to quantify the magnitude, rate, intensity, and direction of urban growth, as well as morphological evolution, over time. We found that (1) significant urban growth was observed across most scenarios, with the exception of stringent land protection. The most substantial growth occurred prior to 2045 with an obvious north–south disparity, where southern regions demonstrated more pronounced increases in urban land area and urbanization rates. (2) The spatiotemporal patterns of the rate and intensity of urban growth exhibited similar characteristics. The spatial pattern followed a “concave shape” pattern and displayed anisotropic behavior, with the high values for these indicators primarily observed before 2025. (3) The urban form followed a diffusion–coalescence process, with patch areas dominated by the infilling mode and patch numbers dominated by the edge-expansion mode. This resulted in significant alternating urban growth models in the infilling, edge-expansion, and leapfrog modes over time, influenced by varying protection intensities. These findings provide valuable insights for forward-looking urban planning, land use optimization, and the support of sustainable urban development. Full article

Background/Objectives: Insular gliomas are rare entities whose surgical resection presents a significant challenge due to their close relationship with crucial white matter bundles and deep perforating arteries. The Berger–Sanai classification is a well-established system based on dividing the insula into four quadrants. In contrast, the Kawaguchi grading system focuses on the tumor’s behavior and vascular infiltration. In this study, we compared these two systems in terms of surgical and clinical outcomes. Methods: A retrospective single-center consecutive series of patients treated for insular gliomas was analyzed. Results: A total of 43 patients were analyzed, with a mean age of 47 years. The average preoperative KPS was 92.6, and seizures were the most common presenting symptom. The mean extent of resection (EOR) was 92.2%. Both the Berger–Sanai and Kawaguchi classifications influenced the EOR, but the former showed a moderate-to-strong correlation with expected resection rates. Postoperative clinical outcomes were related to both classification systems, but this association was lost at the 6-month follow-up. Additionally, lower Kawaguchi grades were associated with significant postoperative MRI hyperintensities on DWI sequences and higher rates of intraoperative neuromonitoring (IONM) alterations, which translated into worse postoperative clinical conditions. Conclusions: The Berger–Sanai and Kawaguchi classification systems are both significantly related to the EOR in insular gliomas; however, only the Berger–Sanai system shows strong predictive value regarding resection rates. Both systems serve as good prognostic tools for determining short-term postoperative clinical outcomes, but significant DWI hyperintensities and IONM alterations are specifically associated with the Kawaguchi system. Full article

This study discusses financial sustainability in retirement via a comprehensive bibliometric and content analysis on financial literacy and retirement preparedness for 1970–October 2024. Data for this study are extracted from the Scopus database. In line with the PRISMA guideline, the inclusion and exclusion criteria were applied, and the final corpus of articles used in the analysis is 518 articles. These articles are analyzed using the bibliometric methods: Bibliometrix R-package (Biblioshiny) version 4.0 and VOSviewer 1.6.20. The descriptive analysis identified the publication trends, prominent authors, articles, sources, country collaboration, and quadrant analysis. Based on the bibliographic coupling, the following themes were identified: (1) financial literacy and retirement planning, (2) behavioral finance and psychological determinants of retirement planning and preparedness, (3) financial risk, decision making, and retirement preparedness, and (4) gender, socioeconomic factors, and financial literacy disparities. This study has consolidated and enriched the understanding of how financial literacy, education, psychology and behaviors, and financial risk play a pivotal and central role in shaping long-term financial sustainability, as well as retirement planning and preparedness. From a practical perspective, an all-inclusive understanding of the above is essential for guiding regulators and policymakers in designing policies, targeted interventions, and programs that empower individuals towards retirement preparedness, considering regional, socioeconomic, and gender-related factors. Full article

This paper introduces a novel four-parameter discrete bivariate distribution, termed the bivariate discretized Fréchet–Weibull distribution (BDFWD), with marginals derived from the discretized Fréchet–Weibull distribution. Several statistical and reliability properties are thoroughly examined, including the joint cumulative distribution function, joint probability mass function, joint survival function, bivariate hazard rate function, and bivariate reversed hazard rate function, all presented in straightforward forms. Additionally, properties such as moments and their related concepts, the stress–strength model, total positivity of order 2, positive quadrant dependence, and the median are examined. The BDFWD is capable of modeling asymmetric dispersion data across various forms of hazard rate shapes and kurtosis. Following the introduction of the mathematical and statistical frameworks of the BDFWD, the maximum likelihood estimation approach is employed to estimate the model parameters. A simulation study is also conducted to investigate the behavior of the generated estimators. To demonstrate the capability and flexibility of the BDFWD, three distinct datasets are analyzed from various fields, including football score records, recurrence times to infection for kidney dialysis patients, and student marks from two internal examination statistical papers. The study confirms that the BDFWD outperforms competitive distributions in terms of efficiency across various discrete data applications. Full article

A squamous odontogenic tumor (SOT) is an epithelial locally benign neoplasia derived from the periodontium of the jaws. It is considered a lesion of low incidence. Predominantly, it affects the mandible, although both jaw bones may be involved. Here, we discuss the malignant clinical evolution of an SOT lesion in an 80-year-old female patient. The patient exhibited an expansive triangular lesion at the inferior right quadrant. Surgery was performed and an SOT was diagnosed (2019). Two years after, the lesion grew, and the analysis of the biopsy revealed SOT malignization with pleomorphic atypical squamous cells, characteristics of a squamous cell carcinoma (2021). Massive DNA sequencing of formalin-fixed–paraffin-embedded specimens of the initial and relapsed tumors indicated pathogenic mutations in RET and POLE genes in both tumors, loss of ALK, and gain of CDKN1B and MAP2K in the relapse. In addition, the clinical, radiographic, and microscopic features of this neoplasm are discussed and compared with those already published. The case presented contributes to the better understanding of this SOT tumor entity and to indicates its malignant evolution, together with its biological behavior and its histologic, clinical, and radiographic features. Also, it aims to stress the importance of deeper genetic analyses in rare diseases to uncover mutations that help to select a personalized treatment. Full article

Alzheimer’s disease (AD) poses a major worldwide health challenge because of its profound impact on cognitive abilities and overall well-being. Despite extensive research and numerous clinical trials, therapeutic options remain limited. Our study aimed to investigate the potential of Kelulut honey (KH) as a novel therapeutic agent for addressing the multifactorial pathology of AD. We tried to evaluate the disease-attenuating and neuroprotective potential of KH in the intrahippocampally induced AD rat model by utilizing histochemistry and enzyme-linked immunosorbent assay (ELISA) studies. A total of 26 male Sprague Dawley rats weighing ~280–380 g were randomly divided into three groups: Control, AD-induced (Aβ), and AD-induced and treated with KH (Aβ+KH). The latter two groups underwent stereotaxic surgery, where 6.25 µg of amyloid β_1–42 peptides were injected intrahippocampally. One-week post-surgery, KH was administered to the treatment group at a dose of 1 g/kg body weight for a period of four weeks, after which the rats went through behavior tests. After completion of behavior analysis, the rats were sacrificed, and the brains were processed for histochemistry and ELISA studies. The open field test analysis demonstrated that KH improved the locomotion of Aβ+KH compared to Aβ (p = 0.0013). In comparison, the Morris water maze did not show any nootropic effects on cognition with a paradoxical increase in time spent in the target quadrant by the Aβ group (p = 0.029). Histochemical staining showed markedly increased Congo-red-stained amyloid plaques, which were significantly reduced in dentate gyrus of Aβ+KH compared to Aβ (p < 0.05). Moreover, significantly higher apoptosis was seen in the Aβ group compared to Aβ+KH (p < 0.01) and control groups (p < 0.001). Furthermore, the ELISA studies deduced more phosphorylated tau in the diseased group compared to Aβ+KH (p = 0.038) and controls (p = 0.016). These findings suggest that KH consumption for twenty-eight days has the potential to attenuate the pathological burden of disease while exerting neuroprotective effects in rodent models of AD. Full article

This paper presents an examination regarding the DC–DC converter output behavior at variable loads. An example of a circuit with two-quadrant operation with different control methods is shown. The presented mathematical approach is used to determine the main output values and could have an enclosure as the calculation methodology. The examination data for the step-down circuit configuration with a single-ended output LC filter are provided. A modified excel model is used to extract the parameters for the circuit output gain at the rated frequency. The simulations of the load factor variation are shown in four different plots, where the area of the steady operation is presented. The results are derived to estimate the relationship between the circuit output and load characteristics’ deviation. The converter dynamic range of the operation is determined by the presented graphics. A proper LC output filter design is essential for reducing the ripple currents and increasing the overall circuit effectivity. Full article

The infinite series solution to the boundary-value problems of Laplace’s equation with discontinuous Dirichlet boundary conditions was found by using the basic method of separation of variables. The merit of this paper is that the closed-form solution, or the singular similarity solution in the semi-infinite strip domain and the first-quadrant domain, can be generated from the basic infinite series solution in the rectangular domain. Moreover, based on the superposition principle, the infinite series solution in the rectangular domain can be related to the singular similarity solution in the semi-infinite strip domain. It is proven that the analytical source-type singular behavior in the infinite series solution near certain singular points in the rectangular domain can be revealed from the singular similarity solution in the semi-infinite strip domain. By extending the boundary of the rectangular domain, the infinite series solution to Laplace’s equation in the first-quadrant domain can be derived to obtain the analytical singular similarity solution in a direct and much easier way than by using the methods of Fourier transform, images, and conformal mapping. Full article

This article examines a new powertrain system for electric vehicles based on the dual-star induction machine, presented as a promising option due to its significant advantages in terms of performance, energy efficiency, and reliability. This system could play a key role in the evolution of electro-mobility technology. The dual-star induction machine reduces electromagnetic torque fluctuations, limits current harmonics, improves power factor, and enables half-speed operation. Our study focuses on the control strategy and operation of the traction chain for electric vehicles propelled by the dual-star induction machine (DSIM) using Matlab software with version 2017. We integrate the battery as the main energy source, along with three-level static converters for energy conversion in the vehicle’s four operating quadrants. We have opted for sliding mode control, which has proven to be feasible and robust against external disturbances. Although we have modeled driver behavior, we consider it as an aspect of control, to which we add the driving profile to guide our evaluation of the control to be used for vehicle operation. The results of our study demonstrate the reliability and robustness of DSIM for electric vehicle motorization and speed control. Promoting this technology is essential to improve the overall performance and efficiency of electric vehicles, especially in traction and braking modes for energy recovery. This underscores the importance of DSIM in the sustainable development of the electric transportation system. Full article

Data acquisition from a vehicle operating in real driving conditions is extremely useful for analyzing the real-time behavior of the vehicle and its components. A few studies have measured the real-time data for a four-wheeler electric vehicle. However, no attempts have been reported to measure the real-time data and find the inverter efficiency for a two-wheeler electric vehicle. The present work has accomplished successful real-time data acquisition from a two-wheeler electric vehicle. The real-time current and voltage coming in and going out from the inverter, frequency of the motor operation, power factor, distance covered, and velocity have been measured. The inverter efficiency is found to be over 95% for over 80% of the total drive time, and the power factor for the motor is over 0.8 for almost 50% of the total drive time. A few insights on driver behavior and finally the torque-speed characteristics and two quadrant operation of the motor are discussed. Full article

A new normal has been established as a result of the effects of the COVID-19 pandemic on social behavior, technology, and business. This has a significant effect on how technology is used, such as mobile banking services, which offer more hygienic and secure payment alternatives than cash. Mobile banking has been viewed as having the ability to enhance access to unbanked customers in developing economies such as Indonesia, where 100 million people remain unbanked. This study aims to develop strategies using importance-performance analysis (IPA) to improve adoption based on the perceived importance and performance of 1441 mobile banking users during the COVID-19 pandemic. Data were collected using an online questionnaire administered during the period of September 2022 to March 2023 using the mobile banking adoption attributes of Attitude, Perceived Usefulness, Perceived Ease of Use, Compatibility, Subjective Norm, Interpersonal Influence, External Influence, Perceived Behavior Control, facilitating conditions, self-efficacy, firm reputation, trust, disease risk, performance risk, financial risk, privacy risk, time risk, psychological risk, and perceived risk. IPA results were divided into four quadrants: “concentrate here”, “keep up the good work”, “low priority”, and “possible overkill” with a representation that respondents regard as important and well-addressed. The findings show that bank strategists seeking competitive advantage must push innovation efforts to protect users by improving privacy risk and financial risk and enhancing mobile banking security from potential cyberattacks. Digital banks and associated institutions need to educate mobile banking customers on the benefits of security measures for these services, which may improve confidence and trust, and consequently, accelerate mobile banking adoption. Full article