Higher Performance Scores Research Articles

CTNI-54. RESPONSE BY RANO2.0 CRITERIA IN ONC201 (DORDAVIPRONE)-TREATED PATIENTS WITH RECURRENT H3 K27M-MUTANT DIFFUSE MIDLINE GLIOMA

Abstract PURPOSE Response assessment in neuro-oncology (RANO) 2.0 is a recently established response assessment for glial tumors that is agnostic to WHO grade. This updated response assessment includes quantitative evaluation of enhancing and non-enhancing disease and recommendations to account for pseudoprogression. This analysis evaluated response to ONC201 by RANO2.0 in recurrent, H3K27M-mutant diffuse midline glioma (DMG), which was previously assessed by RANO-HGG/RANO-LGG (Arrillaga-Romany, JCO, 2024). METHODS Fifty patients with recurrent H3 K27M-mutant DMG were selected based on criteria designed with input from regulators to evaluate objective response to ONC201 monotherapy. Patients had contrast-enhancing, measurable, progressive disease, and were ≥90 days from radiation, with adequate washout from other anti-cancer therapies. Diffuse intrinsic pontine glioma, primary spinal tumors, leptomeningeal spread, or CSF-disseminating tumors were ineligible. Response was assessed using RANO2.0 criteria by two-party blinded independent central review. RESULTS Best response by RANO2.0 included one complete response (CR, 2.0%), nine partial responses (PR, 18.0%), and four minor responses (MR, 8.0%). Overall response rate (CR+PR+MR) by RANO2.0 criteria was 28.0% (n=14; 95%CI, 16-42). Disease control rate (CR+PR+MR+SD) was 40.0% (n=20; 26-55). Median duration of response was 10.4 months (95%CI, 7.4-15.4). Median time to response was 4.6 months (range, 1.6-15.9). 6- and 12-month progression free survival rates were 32.0% (95%CI, 19-47) and 27.0% (15-41), respectively. RANO2.0 response as a time-varying covariate was significantly associated with overall survival in a multivariate analysis considering baseline performance score, multiple enhancing target lesions, enhancing tumor size, and number of prior recurrences (HR [95%CI], 0.22 [0.08–0.58]; p=0.0023). Patients with an objective response by RANO2.0 were more likely to be alive at 12 (100%) and 24 (86%) months, have reduced corticosteroid usage, and have improved performance status. Responses occurred at a higher rate among patients with a higher performance score and fewer target lesions. CONCLUSIONS RANO2.0 assessment of ONC201-treated patients suggests durable and clinically meaningful efficacy in recurrent H3K27M-mutant DMG.

Application of the online teaching model based on BOPPPS virtual simulation platform in preventive medicine undergraduate experiment.

As online teaching gains prevalence in higher education, traditional face-to-face methods are encountering limitations in meeting the demands of medical ethics, the availability of experimental resources, and essential experimental conditions. Consequently, under the guidance of the BOPPPS (bridge-in, objective, preassessment, participatory learning, postassessment, summary) teaching model, the application of virtual simulation platform has become a new trend. The purpose of this study is to explore the effect of BOPPPS combined with virtual simulation experimental teaching on students' scores and the evaluation of students' participation, performance and teachers' self-efficacy in preventive medicine experiment. Students from Class 1 and Class 2 of 2019 preventive medicine major in Binzhou Medical University were selected as the research objects. The experimental group (class 2) (n = 51) received the teaching mode combined with BOPPPS and virtual simulation platform, while the control group (class 1) (n = 49) received the traditional experimental teaching method. After class, the experimental report scores, virtual simulation scores, students' engagement scale (SES), Biggs questionnaires, and teachers' sense of self-efficacy (TSES) questionnaires were analyzed. The experimental report results demonstrated a significant increase in the total score of the experimental group and the scores of each of the four individual experiments compared to the control group (P < 0.05). To investigate the impact of the new teaching model on students' learning attitudes and patterns, as well as to evaluate teachers' self-efficacy, a questionnaire survey was administered following the course. The SES results showed that students in the experimental group had high performance scores on the two dimensions of learning methods and learning emotions (t = 2.476, t = 2.177; P = 0.015, P = 0.032). Furthermore, in the Biggs questionnaire, the total deep learning score of the experimental group was higher than that of the control group (t = 2.553, P = 0.012), and the deep learning motivation score of the experimental group was higher than that of the control group (t = 2.598, P = 0.011). The TSES questionnaire shows that most teachers think it is easier to manage students and the classroom and easier to implement teaching strategies under this mode. The combination of BOPPPS and the virtual simulation platform effectively enhances the experimental environment for students, thereby improving their academic performance, engagement and learning approach in preventive medicine laboratory courses.

Investigating the Relationship between the General Health of Mothers and their Maternal Performance following Vaginal Childbirth

Background Birthing parents need to use specialized skills as the first caregivers of the newborn. Several factors may affect maternal performance, the general health of the birthing parent (e.g., mother) being one of them. Yet, there is a paucity of research in this area, particularly in Iran, and evidence remains inconsistent. Consequently, this study aimed to determine any correlations between maternal performance and the general health of mothers following vaginal childbirth in the context of Iran. Methods This cross-sectional study was conducted with people identifying as mothers (n = 450) who had given birth (&lt; two months) and been referred for the vaccination of their newborn. The multi-stage sampling method was carried out from April 2022 to February 2023. Participants who met the inclusion criteria completed a demographic and obstetric information questionnaire, along with the Barkin maternal performance and general health questionnaire. To investigate the relationship between maternal performance and the general health of the mothers, an independent t-test analysis was conducted . The level of statistical significance was set at p &lt; 0.05. Results The mean age of participants was 26.78, and the mean total score of maternal performance was 91.04 (0 - 120). The highest and lowest scores related to the ‘maternal competence’ and the ‘maternal needs’ domains, respectively. Overall, 76.89% of participants had a favorable general health condition (a score of less than 3.5 is considered favorable). There was no statistically significant difference identified in terms of maternal performance scores between those with a favorable and those with an unfavorable general health status. Conclusion Although the results of the present study did not show a significant relationship between the general health of mothers and maternal performance, this research builds on a growing body of evidence in the context of Iran. Despite recording high maternal performance scores in this sample, it will be important to enhance and maintain good mental health in all childbearing people, particularly following vaginal childbirth, as it is this which will enhance care and outcomes overall.

Performance Analysis of EMG Signal Classification Methods for Hand Gesture Recognition in Stroke Rehabilitation

This study evaluates the performance of different classification methods in classifying healthy individuals and stroke patients. The hand gesture variations of the subjects were also analyzed based on electromyography (EMG) signals. Several classification methods were tested in this analysis to find out which method had the most suitable performance. The results showed that Decision Tree and Naive Bayes classifiers achieved the highest performance in classifying EMG signals from healthy individuals and stroke patients, with both methods showing high accuracy, precision, recall, and F1 score. Specifically, Decision Tree excelled in overall accuracy and recall, while Naive Bayes showed superior precision. For hand gesture recognition, SVM, KNN, and Random Forest classifiers showed similarly high performance, achieving accuracy, precision, recall, and F1 score above 82%. Naive Bayes also performed well, especially in precision, while Decision Tree performed poorly compared to other methods. This insight can form the basis for the development of more effective and personalized rehabilitation systems for stroke patients, by utilizing reliable and accurate EMG signal classification

Study on the overall performance of e-waste dismantling industry polluted cultivated land utilization from a holistic view

The utilization of heavy metal-organic polluted cultivated land caused by the e-waste dismantling industry was affected by multi-dimensional factors. Thus a comprehensive evaluation of these multi-dimensional impacts was necessary for the technical selection and solution of cultivated land utilization. This study attempted to conduct a total benefit simulation analysis on the redevelopment of polluted cultivated land, which was of great significance to decision-making. With a systematic framework designed including the casual relations between components, a system dynamics model was constructed to simulate the technological, economic, and ecological benefits of different polluted cultivated land-used modes, along with which a questionnaire was designed to investigate and analyze the associated social effects. Based on these, the total benefits of the land-use mode were evaluated and optimized within the framework of the “technological-economic-ecological-social” evaluation system. This study included microscale, mesoscale, and macroscale, with multi-dimensional and complex influencing factors, and had advanced research value. The results of the system dynamics model simulation showed that remediation plants played a decisive role in pollutants removal. The flower industry had the highest net present value, but its ecological performance was poor. The overall analysis showed that the scenario of utilization after remediation had the highest performance score, which was attributed to high pollutant removal, low cost, and high carbon sink. This study was meaningful for providing research ideas of comprehensive benefits of polluted cultivated land utilization and also helpful for decision-making. This paper also provided research ideas for other industries such as contaminated site remediation and sewage sludge treatment.

The Global Malnutrition Composite Score: Impacting malnutrition care.

The presence and impact of malnutrition in adult hospitalized patients has been well documented by a significant body of literature. However, current malnutrition care practices often vary widely and frequently lack standardization. The Global Malnutrition Composite Score (GMCS), the first nutrition-related electronic clinical quality measure, is intended to evaluate the quality of malnutrition care provided to inpatient adults. This measure aims to aid in standardizing malnutrition care through performance measurement. The four components of the measure (nutrition screening, nutrition assessment, malnutrition diagnosis, and nutrition care plans) follow the well-established Nutrition Care Process and encourage the use of standardized terminology. Facilities with high-performance scores on the GMCS are likely to experience improved outcomes associated with high-quality malnutrition care.

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model.

This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment. The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising. The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future research and advancements in this area.

Investigating the Effect of Packaging Conditions on the Properties of Peeled Garlic by Using Artificial Neural Network (ANN)

ABSTRACTThis study investigated the effect of packaging conditions on the properties of peeled garlic during storage, and the results have been evaluated using statistical analysis and artificial neural network (ANN). Peeled garlic was packed with polyethylene (PE) film and polyethylene film equipped with nanoparticles (2% nanoclay) and filled into the packages using ambient and modified atmospheres (1% O2, 5% CO2 and 94% N2). A group of packages was also packed under vacuum conditions. The packaged samples were stored at 25°C, 4°C and −18°C for 35 days. Colour indices (a*, b* and L*), chemical properties (pH and TSS) and mechanical properties (Fmax and Emod) of the peeled garlic were measured during the storage time. The final results showed that the use of nanofilm and modified atmosphere had a positive effect on maintaining the quality of peeled garlic during the storage. On the other hand, the temperature changes showed that the freezing temperature had a negative effect on the garlic quality (properties) during the storage period. The statistical analysis results of the data showed the significant effect of treatments and their interactions on properties at levels of 1% and 5%. The results of ANN showed that the peeled garlic properties (physical, chemical and mechanical) could be predicted with the highest performance scores. The most successful ANN models were identified for each property, with the Trainbr learning algorithm and Tansig transfer function yielding the highest prediction scores for physical (R2 &gt; 0.90) and chemical properties; on the other hand, Logsig was most successful for mechanical properties (R2 &gt; 0.84).

CDK: A novel high-performance transfer feature technique for early detection of osteoarthritis

Knee osteoarthritis (OA) is a prevalent condition causing significant disability, particularly among the elderly, necessitating advancements in diagnostic methodologies to facilitate early detection and treatment. Traditional OA diagnosis, relying on radiography and physical exams, faces limitations in accuracy and objectivity. This underscores the need for more advanced diagnostic methods, such as machine learning (ML) and deep learning (DL), to improve OA detection and classification. This research introduces a novel ensemble learning approach for image data feature extraction which ingeniously combines the strengths of 2 advanced (ML) models with a (DL) method to substantially improve the accuracy of OA detection from radiographic images. This innovative strategy aims to address the limitations of traditional diagnostic tools by leveraging the enhanced sensitivity and specificity of combined ML and DL models. The methodology deployed in this study encompasses the application of 10 ML models to a comprehensive publicly available Kaggle dataset with a total of 3615 samples of knee X-ray images. Through rigorous k-fold cross-validation and meticulous hyperparameter optimization, we also included evaluation metrics like accuracy, receiver operating characteristic, precision, recall, and F1-score to assess our models' performance effectively. The proposed novel CDK (convolutional neural network, decision tree, K-nearest classifier) ensemble approach for feature extraction is designed to synergize the predictive capabilities of individual models, thereby significantly improving the detection accuracy of OA indicators within radiographic images. We applied several ML and DL approaches to the newly created feature set to evaluate performance. The CDK ensemble model outperformed state-of-the-art studies with a high-performance score of 99.72% accuracy. This remarkable achievement underscores the model's exceptional capability in the early detection of OA, highlighting its superiority in comparison to existing methods.

Local control and survival after stereotactic radiosurgery for colorectal cancer brain metastases: an international multicenter analysis.

The goal of this study was to characterize local tumor control (LC), overall survival (OS), and safety of stereotactic radiosurgery for colorectal brain metastasis (CRBM). Ten international institutions participating in the International Radiosurgery Research Foundation provided data for this retrospective case series. This study included 187 patients with CRBM (281 tumors), with a median age of 62 years and 56.7% being male. Most patients (53.5%) had solitary tumors, although 10.7% had > 5 tumors. The median tumor volume was 2.7 cm3 (IQR 0.22-8.1 cm3), and the median margin dose was 20 Gy (IQR 18-22 Gy). The 3-year LC and OS rates were 72% and 20%, respectively. Symptomatic adverse radiation effects occurred in 1.6% of patients. In the multivariate analysis, age > 65 years and tumor volume > 4.0 cm3 were significant predictors of tumor progression (hazard ratio [HR] 2.6, 95% CI 1.4-4.9; p = 0.003 and HR 3.4, 95% CI 1.7-6.9; p < 0.001, respectively). Better performance status (Karnofsky Performance Scale score > 80) was associated with a reduced risk of tumor progression (HR 0.38, 95% CI 0.19-0.73; p = 0.004). Patient age > 62 years (HR 1.6, 95% CI 1.1-2.3; p = 0.03) and the presence of active extracranial disease (HR 1.7, 95% CI 1.1-2.4; p = 0.009) were significantly associated with worse OS. Stereotactic radiosurgery offers a high LC rate and a low rate of symptomatic adverse radiation effects for the majority of CRBMs. The OS and LC favored younger patients with high functional performance scores and inactive extracranial disease.

Tumors Affect the Metabolic Connectivity of the Human Brain Measured by 18 F-FDG PET.

18 F-FDG PET captures the relationship between glucose metabolism and synaptic activity, allowing for modeling brain function through metabolic connectivity. We investigated tumor-induced modifications of brain metabolic connectivity. Forty-three patients with left hemispheric tumors and 18 F-FDG PET/MRI were retrospectively recruited. We included 37 healthy controls (HCs) from the database CERMEP-IDB-MRXFDG. We analyzed the whole brain and right versus left hemispheres connectivity in patients and HC, frontal versus temporal tumors, active tumors versus radiation necrosis, and patients with high Karnofsky performance score (KPS = 100) versus low KPS (KPS < 70). Results were compared with 2-sided t test ( P < 0.05). Twenty high-grade glioma, 4 low-grade glioma, and 19 metastases were included. The patients' whole-brain network displayed lower connectivity metrics compared with HC ( P < 0.001), except assortativity and betweenness centrality ( P = 0.001). The patients' left hemispheres showed decreased similarity, and lower connectivity metrics compared with the right ( P < 0.01), with the exception of betweenness centrality ( P = 0.002). HC did not show significant hemispheric differences. Frontal tumors showed higher connectivity metrics ( P < 0.001) than temporal tumors, but lower betweenness centrality ( P = 4.5 -7 ). Patients with high KPS showed higher distance local efficiency ( P = 0.01), rich club coefficient ( P = 0.0048), clustering coefficient ( P = 0.00032), betweenness centrality ( P = 0.008), and similarity ( P = 0.0027) compared with low KPS. Patients with active tumor(s) (14/43) demonstrated significantly lower connectivity metrics compared with necroses. Tumors cause reorganization of metabolic brain networks, characterized by formation of new connections and decreased centrality. Patients with frontal tumors retained a more efficient, centralized, and segregated network than patients with temporal tumors. Stronger metabolic connectivity was associated with higher KPS.

National Achievement Test (NAT) Results and Academic Performance: A Comparative Analysis of Filipino Proficiency across Two Academic Years

This study thoroughly examines the academic performance and NAT outcomes of sixth-grade students across seven elementary schools in Baclayon, Bohol, during 2013-2014 and 2014-2015. It aims to identify correlations between academic performance and NAT results, specifically in the Filipino subject. Using documentary analysis, the research explores students' learning competencies and provides insights into school performance in Baclayon, a historically significant town with a diverse economic landscape and notable tourist attractions. The focus is on NAT results in the Filipino subject for Grade Six students in seven elementary schools, employing a collaborative approach for data collection and analysis. The findings unveiled intriguing insights, indicating that while School I consistently demonstrated high mean scores and mean performance scores across the two academic years, enrollment fluctuations were observed, notably in School V. Further analysis delineated proficiency levels in the Filipino subject, highlighting areas of strength and potential improvement. The correlation analysis between academic performance and NAT results in the Filipino subject yielded nuanced outcomes, elucidating the multifaceted dynamics at play in educational settings. These findings illuminate pathways for enhancing educational strategies and outcomes in Baclayon's elementary schools, contributing to broader discussions on educational reform and improvement. In 2013-2014 and 2014-2015, Schools I, II, and IV had high enrollment, while VII, VI, III, and VII had low enrollment. Despite good performance in Filipino, NAT results didn't align in Baclayon's elementary schools. This hints that the NAT might not reflect Filipino proficiency accurately. High-enrollment schools likely have effective strategies, while low-performing ones may need to boost enrollment and academics.

Machine learning and IoT based Predictive Maintenance for the industrial motors for sustained Automation in the power plant Industry

The purpose of this research is to develop novel framework based on advanced tools, including machine learning and the Internet of Things and self-attention mechanisms. Traditional and advanced tools were used in the data-driven predictive maintenance mechanism and served as a basis for comparing the tools. At the power plant, thirty-four datasets were collected to monitor three industrial motors continuously. The tools’ predictive ability was analysed using conceptualized features from the sensory data , and the management strategy remained dependent on the network. The study results show that each tool performs at high-performing levels because each exceeded 75% of the performance metric. The study results indicated that the proposed framework gave a consistent high-performance metric, 86.4%, in all the ten experimental scenarios used . The rate determination was attributed to the choice of the long short-term memory architecture, the self-attention mechanisms, and the optimization techniques. The choice of using mean squared logarithmic error also contributed to the outcomes because these tools yield high-performance scores. In addition to these, the test of the forecasting models chosen also influenced this performance. This study’s findings show that this framework is reliable in predicting pending failure equipment and developing relevant management strategies in industrial motors failure. It was found that the use of advanced tools in the development of the framework was a crucial aspect. Scaled tools and optimization techniques are of crucial importance in predictive management in equipment failure as they assist the frameworks in identifying ideal features from the sensory data. It should also be noted that the choice of a loss-function model is also important in the predictability of framework..

A Novel Approach to Detection of Alzheimer’s Disease from Handwriting: Triple Ensemble Learning Model

The irreversible degeneration of nerve cells in the body dramatically affects the motor skills and cognitive abilities used effectively in daily life. There is no known cure for neurodegenerative diseases such as Alzheimer’s. However, in the early diagnosis of such diseases, the progression of the disease can be slowed down with specific rehabilitation techniques and medications. Therefore, early diagnosis of the disease is essential in slowing down the disease and improving patients’ quality of life. Neurodegenerative diseases also affect patients’ ability to use fine motor skills. Losing fine motor skills causes patients’ writing skills to deteriorate gradually. Information about Alzheimer’s disease can be obtained based on the deterioration in the patient’s writing skills. However, manual detection of Alzheimer’s disease (AD) from handwriting is a time-consuming and challenging task that varies from physician to physician. Machine learning-based classifiers are extremely popularly used with high-performance scores to solve the challenging manual detection of AD. In this study, Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Adaptive Boosting (AdaBoost), and eXtreme Gradient Boosting (XGBoost) machine learning classification algorithms were combined with a Voting Classifier and trained and tested on the publicly available DARWIN (Diagnosis Alzheimer’s With haNdwriting) dataset. As a result of the experimental studies, the proposed Ensemble methodology achieved 97.14% Acc, 95% Prec, 100% Recall, 90.25% Spec, and 97.44% F1-score (Dice) performance values. Studies have shown that the proposed work is exceptionally robust.

Mimicking clinical trials with synthetic acute myeloid leukemia patients using generative artificial intelligence

Clinical research relies on high-quality patient data, however, obtaining big data sets is costly and access to existing data is often hindered by privacy and regulatory concerns. Synthetic data generation holds the promise of effectively bypassing these boundaries allowing for simplified data accessibility and the prospect of synthetic control cohorts. We employed two different methodologies of generative artificial intelligence – CTAB-GAN+ and normalizing flows (NFlow) – to synthesize patient data derived from 1606 patients with acute myeloid leukemia, a heterogeneous hematological malignancy, that were treated within four multicenter clinical trials. Both generative models accurately captured distributions of demographic, laboratory, molecular and cytogenetic variables, as well as patient outcomes yielding high performance scores regarding fidelity and usability of both synthetic cohorts (n = 1606 each). Survival analysis demonstrated close resemblance of survival curves between original and synthetic cohorts. Inter-variable relationships were preserved in univariable outcome analysis enabling explorative analysis in our synthetic data. Additionally, training sample privacy is safeguarded mitigating possible patient re-identification, which we quantified using Hamming distances. We provide not only a proof-of-concept for synthetic data generation in multimodal clinical data for rare diseases, but also full public access to synthetic data sets to foster further research.

Importance-Performance Analysis (IPA) Approach of Marine Park Tourism Destinations: A Case Study of Kapas Island Marine Park, Terengganu

Tourism in the marine park is vital to tourist satisfaction. Tourism and sustainability must be balanced in sensitive regions like marine parks. Tourist feedback helps balance and sustain marine park management. Kapas Island Marine Park (KIMP) in Terengganu receives a high number of tourists annually. The resources and the tourist experience may be adversely affected by excessive utilisation of marine resources. It may have long-term effects on the travel industry. This research has adopted an Importance-Performance Analysis (IPA) to scrutinise how visitors view the execution of 23 attributes of KIMP. The objective of this study is to show that various tourism attributes affect tourist satisfaction, as well as to discover the satisfaction- and dissatisfaction-generating attributes that may be found in the marine tourism experience. In addition, this research discusses how IPA may be used to influence future visitor research and management at marine park sites. The findings of this research were obtained by a questionnaire survey of 310 visitors to the KIMP during May and June 2022. The results are displayed on a grid with four quadrants; facilities are available to ensure the safety and security of the visitors (Quadrant One); attributes are already providing good service, and therefore, performance must be sustained (Quadrant Two); the fundamental amenities or necessities as seen by visitors (Quadrant Three); attributes that have a low significance score but obtained a high-performance score (Quadrant Four). These findings potentially provide a reference for thorough development strategies for a marine park tourism destination.

Prognostic factors and real-life applicability of prognostic models for patients with bone metastases of carcinoma.

This study aimed to investigate the factors affecting the survival of patients with bone carcinoma metastases and assess the clinical applicability of existing prognostic models. We retrospectively evaluated 247 patients who presented to our hospital between 2011 and 2021 diagnosed with bone carcinoma metastasis. Demographic data, general health status, primary diagnoses, laboratory and radiological findings, pathological fracture status, treatment methods, and survival times of the patients were recorded, and the effects of these variables on survival time were evaluated. Previously developed Katagiri, Janssen, 2013-Spring, PathFX, and SORG prognostic models were applied, and the predictive performances of these models were evaluated by comparing the predicted survival time with the actual survival time of our patients. After the multivariate analysis, the following factors were shown to be significantly associated with the survival time of patients: blood hemoglobin and leukocyte levels, lactate dehydrogenase concentration, prognostic nutritional index, body mass index, performance status, medium and fast-growing groups of primary tumors, presence of extraspinal and visceral or brain metastases, and pathological fractures. According to receiver operating characteristics and Brier scores, SORG had the overall highest performance scores, while the Janssen nomogram had the lowest. Our report showed that all prognostic models were clinically applicable, but their performances varied. Among them, the SORG predictive model had the best performance scores overall and is the model the authors suggested for survival prediction among patients with carcinoma bone metastases. Level IV, Prognostic Study.

Community-Aware Evolution Similarity for Link Prediction in Dynamic Social Networks

The link prediction problem is a time-evolving model in network science that has simultaneously abetted myriad applications and experienced extensive methodological improvement. Inferring the possibility of emerging links in dynamic social networks, also known as the dynamic link prediction task, is complex and challenging. In contrast to the link prediction in cross-sectional networks, dynamic link prediction methods need to cater to the actor-level temporal changes and associated evolutionary information regarding their micro- (i.e., link formation/deletion) and mesoscale (i.e., community formation) network structure. With the advent of abundant community detection algorithms, the research community has examined community-aware link prediction strategies in static networks. However, the same task in dynamic networks where, apart from the actors and links among them, their community pattern is also dynamic, is yet to be explored. Evolutionary community-aware information, including the associated link structure and temporal neighborhood changes, can effectively be mined to build dynamic similarity metrics for dynamic link prediction. This study aims to develop and integrate such dynamic features with machine learning algorithms for link prediction tasks in dynamic social networks. It also compares the performances of these features against well-known similarity metrics (i.e., ResourceAllocation) for static networks and a time series-based link prediction strategy in dynamic networks. These proposed features achieved high-performance scores, representing them as prospective candidates for both dynamic link prediction tasks and modeling the network growth.

Sustainable Mangrove Ecotourism Performance in Aceh Jaya: Potential and Competitiveness Analysis

This study aimed to analyse potential and competitiveness of sustainable mangroves in Aceh Jaya region as an alternative tourism destination. The ecotourism potential was identified from the existing onsite biodiversity, while the competitiveness level was investigated by distributing a questionnaire to 100 visitors asked about their perceptions of attraction, accessibility, ancillary services, amenities and activities regarding the mangrove site. A qualitative descriptive analysis was used to investigate the ecotourism potential and the competitiveness level was evaluated by scoring the competitiveness attributes on a 1- to 4-point scale where 1.00-1.74 (very low); 1.75-2.49 (low); 2.50–3.24 (high); and 3.25-4.00 (very high). The results on the biodiversity assessment showed that there are six species of mangrove vegetations were detected at this site, including Rhizopora mucronata, Rhizopora apiculata, Rhizopora stylosa, Bruguiera cylindrical, Bruguiera gymnorhiza, and Nypah fruticans. In this ecosystem, the existence of crocodiles in captivity and specific aquatic wildlife (i.e., Cerithidea sp.) was also observed. Regarding the competitiveness level, the highest performance score was the mangrove “attraction” (scored 3.29), followed by “amenities” (scored 3.06) and “ancillary services” (scored 2.99), whereas the others were scored with the lowest performance attributes. To increase competitiveness, it is necessary to improve transportation and public facilities at mangrove ecotourism location.

How to reduce anxiety symptoms through individual sport in youth: A longitudinal study over 8-month observation.

Anxiety symptoms are widely observed among the youth, and engagement in sports has been demonstrated to mitigate these symptoms. Nonetheless, the effectiveness of specific sports and the potential moderating role of psychological factors, such as self-esteem and self-efficacy, on the influence of sports on anxiety, remains to be elucidated. This study was designed to longitudinally assess the impact of sports participation on anxiety symptoms among young individuals. The study encompassed 163 university students, with a male predominance of 81.6%, and explored the influence of sport-related factors (such as mastery of table tennis skills, level and engagement) and psychological aspects (including self-efficacy, self-esteem and resilience) on anxiety symptoms, employing an 8-month longitudinal approach. Physical activity, sedentary and sleep behaviour, along with age, body mass index (BMI), and sex, were accounted for as confounding variables. The study found that high table tennis performance score was found to buffer the development of anxiety symptoms in students with decreased self-esteem in an exploratory moderation model. Self-esteem and self-efficacy were negatively associated with the development of anxiety symptoms, whereas physical activity factors did not have a direct effect. This study highlights the potential of table tennis as a form of sport to alleviate anxiety symptoms in university students, particularly among those with decreased self-esteem. Future research should address the study's limitations and explore the potential moderating effects of other psychological factors. Overall, these findings suggest a potential new approach to treating anxiety symptoms among university students.