Fairview School District

This study investigates the sustainability of mental health programmes in Philippine higher education institutions amid the COVID-19 pandemic, examining the extent to which programmes align with the Philippine Mental Health Act, integrate monitoring and evaluation mechanisms, and respond to the needs of students, faculty and staff. Using a descriptive qualitative design, the study combines a scoping review of 23 institutional documents with focus group discussions involving key stakeholders. The findings reveal that higher education institutions prioritised awareness campaigns, counselling services and wellness initiatives during the COVID-19 pandemic, which accelerated the adoption of online counselling and preventive education. Programme implementation was, however, uneven: monitoring and evaluation systems were inconsistently applied, stigmatisation continued to limit help-seeking and access to services varied across institutions. Limited resources and institutional readiness further constrained programme sustainability. The study highlights the importance of embedding robust monitoring systems, expanding stigmareduction initiatives and ensuring equitable access to mental health services. Sustaining these programmes requires the alignment of systemic, motivational and behavioural strategies, so that mental health support becomes a lasting component of higher education practice

Continuous intra-abdominal pressure (IAP) monitoring is critical for the early detection and management of intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS). Current standard IAP monitoring is based on intravesical pressure (IVP) monitoring, limited by intermittent measurements, infection risks, and labor-intensive procedures. This study introduces the PressureDot® Capsule, an ingestible, wireless device designed for continuous, non-invasive IAP monitoring by gastrointestinal pressure. The PressureDot® Capsule's performance was evaluated through benchtop and in vivo testing by a porcine model. In benchtop tests, the capsule demonstrated a high correlation with a reference pressure meter (two tests performed, R2 = 0.97 and R2 = 0.94, respectively), confirming its measurement accuracy under controlled conditions. In vivo testing further validated its feasibility, with strong correlations observed between the capsule and true IAP (two tests performed, R2 = 0.81 and R2 = 0.94, respectively). Compared to conventional methods, the PressureDot® Capsule offers advantages, including continuous monitoring, reduced infection risk, labor efficiency, and suitability for outpatient care. Its compact design enhances patient compliance, making it a promising solution for clinical and athletic applications. These findings support PressureDot® Capsule's potential as a transformative tool for real-time IAP monitoring across diverse healthcare settings.Clinical Relevance- The PressureDot® Capsule enables continuous, non-invasive intra-abdominal pressure monitoring, reducing infection risks and labor demands while improving patient outcomes in critical care and outpatient settings.

Alzheimer's disease affects millions of people worldwide and is the most common form of dementia. The risk of developing Alzheimer’s increases with age. This neurodegenerative disease is characterized by the deterioration of memory and cognitive functions. Not only does Alzheimer's affect the mental health of patients, but it also affects their immediate family members and caretakers. The later stages of Alzheimer's disease can change the behavior of a person in various ways. Rather than having to witness a family member change so quickly, people often wish they had known that they had AD earlier. Many studies have been conducted regarding methods to predict the development of Alzheimer's disease; however, nothing has been proven definite. One method that has not been discussed enough is the use of biomarkers. Over the years, multiple biomarkers have had success in predicting other major diseases like heart disease, cancer, and diabetes. They are currently being studied for their use in neurological issues. While analyzing multiple studies, this paper aims to establish the effectiveness of using biomarkers to predict signs of Alzheimer’s disease. Ultimately, being able to predict Alzheimer's early on will allow the medical industry to create prevention strategies for the future, along with better treatments.

The prelims comprise: Half-Title Page Title Page Copyright Page Table of Contents About the Editors List of Contributors Preface

Paraphrase generation, a.k.a. paraphrasing, is a common and important task in natural language processing. Emotional paraphrasing, which changes the emotion embodied in a piece of text while preserving its meaning, has many potential applications, including moderating online dialogues and preventing cyberbullying. We introduce a new task of fine-grained emotional paraphrasing along emotion gradients, that is, altering the emotional intensities of the paraphrases in fine-grained settings following smooth variations in affective dimensions while preserving the meaning of the original text. We reconstruct several widely used paraphrasing datasets by augmenting the input and target texts with their fine-grained emotion labels. Then, we propose a framework for emotion and sentiment guided paraphrasing by leveraging pre-trained language models for conditioned text generation. Extensive evaluation of the fine-tuned models suggests that including fine-grained emotion labels in the paraphrase task significantly improves the likelihood of obtaining high-quality paraphrases that reflect the desired emotions while achieving consistently better scores in paraphrase metrics such as BLEU, ROUGE, and METEOR.

Abstract BackgroundCurrent methods of COVID-19 detection from other respiratory illnesses using computed tomography (CT) scans are highly inaccurate. However, understanding pathogen-specific immune responses can help reduce inconsistencies and improve the accuracy of COVID-19 and Pneumonia detection. A deep learning model using Relief-based feature selection (RBAs) was developed to detect COVID-19 and Pneumonia. Patient-specific Class Activation Maps (CAMs) were produced to highlight immunopathogenic differences and identify differences between COVID-19 and Pneumonia on CT scans.MethodsTo examine the effect on lung lesions, a COVIDx CT-2 dataset, containing CT scans from 3,745 patients, was examined. We developed an algorithm to convert the 3-D CT scan of each patient into multiple 2-D slices. Altogether, there were 194,344 2-D slices retrieved from 3,745 CT Scans. The distribution of slices was 67%-20%-17% consisting of COVID-19, Pneumonia, and normal CT scan, respectively. An AlexNet architecture was implemented with additional feature extraction layers (containing RBA) and classification layers to perform deep learning. The 2-D slices were divided into 3 groups: Training, Test, and Validation. The training set consisted of 70% of the data, the test set consisted of 20% of the data, and the validation consisted of 10% of the data. After training, unique CAMs were generated on patient CT scans using the immunopathogenic differences to highlight COVID-19 and Pneumonia related abnormalities.ResultsThe model accurately distinguished hyperinflammation in COVID-19 patients from Pneumonia patients and achieved a validation accuracy of 95.60% and a false-positive rate of 4.65%. Additionally, the segmented lung, shown by the patient-specific CAMs, identified higher levels of inflammation in the lung of COVID scans compared to the other two groups.DiscussionThe use of deep learning in disease diagnosis and prevention has provided many avenues to advance current techniques. Likewise, in this analysis, deep learning was shown to successfully predict COVID-19 via CT scan. By providing patient-specific CAMs, the model can be used to not just aid in diagnosis but potentially also to evaluate serial chest CT scans for treatment.

“The most satisfying thing in life is to have been able to give a large part of one's self to others.”—Pierre Teilhard de Chardin1As President of the Teacher Education Network for the past year, I ...

This paper presents the results of an experimental investigation of the parametric stabilization of Rayleigh-Bénard convection through the imposition of sinusoidal vibration. The ability to dynamically stabilize Rayleigh-Bénard convection using acceleration modulation is of interest to groups who design and study thermoacoustic machines as the introduction of parasitic convection can have deleterious effects on the desired operation and thermodynamic efficiency of the device. These performance issues caused by suspected convective instability have been seen both in traveling wave thermoacoustic refrigerators and cryogenic pulse tube chillers. This paper reports the results of an experiment intended to determine the vibratory, fluidic, and geometric conditions under which a small, rectangular container of statically unstable fluid may be stabilized by vertical vibration with comparison to the computational methods of R. M. Carbo [J. Acoust. Soc. Am. 135, 654-668 (2014)]. Measurements are obtained using a large-displacement kinematic shaker of an original design with the convecting gas characterized using both thermal transport measurements and flow visualization employing tracer particles illuminated by a diode laser light sheet phase-locked to the shaker. These experiments are believed to be the first demonstrating the suppression of convection through vibration in rectangular containers.

Abstract The timing of character instruction for beginning Chinese foreign language learners has been a controversial issue in Chinese language instruction for many decades. Some instructors teach characters early and integrate this learning with oral language; other instructors teach characters only after an oral base has been established. To date, the experimental research on this topic has been conducted exclusively with university students, and it is unknown if results can generalize to a younger demographic with different learning needs and instructional contexts. The purpose of this study was to examine the reading, writing, and oral language skills of beginning‐level middle school students with no Chinese language background who were divided into early and delayed instruction groups. In addition, a questionnaire about the students’ attitudes toward character learning was administered. It was found that the early instruction group performed significantly better than the delayed group on reading comprehension and character writing tests; there were no significant differences between the groups on the oral interview and the oral fluency assessments. The attitudes survey showed no significant differences in character learning attitudes between the early and the delayed groups except in regard to the timing of character instruction. Pedagogical applications are suggested.