bluuebunny/crossref_metadata_2025 huggingface dataset:
Beauty and Social Interest Matter: Effects of Male’s Facial Attractiveness, Vocal Attractiveness and Social Interest on Female’s Decisions in Three-Person Games
Authors: Shang Junchen , Zhang Yizhuo
Published: December 2024

Facial attractiveness, vocal attractiveness, and social interest influence two-person decision making. However, it remains unclear how these three factors jointly influence three-person bargaining. We investigated the impact of facial attractiveness, vocal attractiveness, and social interest on fairness decisions in a three-person ultimatum game and a third-party punishment dictator game. The results of the ultimatum game showed that in the condition of positive social interest, the acceptance rate of unfair/fair offers was higher when third players had attractive faces or attractive voices. Attractive faces of third players also increased acceptance rates of unfair/unfair offers than unattractive faces when third players expressed negative social interest. In the third-party punishment game, participants rated unfair allocations from unattractive-voice proposers with attractive faces (compared to unattractive faces) and proposers who expressed negative social interest (compared to positive social interest) as more reasonable. Regarding the punishment intentions, among the three factors, both the effect of vocal attractiveness and social interest were modulated by the combinations of the other two factors, while the effect of facial attractiveness remains robust across all conditions. These findings suggest that fairness decisions in three-person bargaining games are affected by beauty premium and social interest, while these effects vary across different decision contexts.
Towards Age-Friendly Urban Spaces: Analyzing Elderly Facility Proximity Preference Needs in Dubai
Authors: Jung Chuloh , El Samanoudy Gamal , Porcinari Massimiliano Gotti , Abdelaziz Mahmoud Naglaa Sami
Published: December 2024

This study addresses the critical challenge of optimizing spatial arrangements for the elderly in rapidly aging societies. It investigates the proximity preference preferences among nine types of facilities frequently used by senior citizens in Dubai. The research is set against demographic shifts and policy paradigms that are shifting towards aging-in-community, emphasizing the need for comprehensive urban planning tailored to the elderly. The study classified individuals aged 65 and above as seniors and surveyed 180 participants at Dubai’s Al Safa Park. Researchers analyzed the proximity preference, termed ‘adjacency,’ of facilities, including residential, rest, public, cultural, transportation, welfare, medical, commercial, and religious facilities. Data collection spanned nine days and employed cross-analysis and multidimensional scaling (MDS) to interpret the findings. The results revealed a high preference for proximity between residential spaces and parks, supermarkets, and bus stops, indicating a desire for accessible facilities. MDS analysis showed residential, rest, and transportation facilities were spatially closer, while religious facilities were distinct in location. Income levels significantly influenced facility proximity preferences, with high-income seniors preferring commercial facilities near residential areas but medical facilities farther away. This study highlights the importance of considering income levels in urban planning for the elderly. Recommendations include planning residential, transportation, and green spaces in proximity preference while acknowledging varying preferences for religious facilities. Future research should focus on diverse regions and consider individual circumstances. This study contributes to urban planning by providing insights into senior citizens’ spatial preferences, which is crucial for enhancing facility usage and satisfaction in aging societies.
Numerical Seismic Fragility Analysis of Glass Curtain Walls: Gaps and Challenges in Modelling Optimization and Limit Performance Indicators
Authors: Cella Nicola , Bedon Chiara
Published: December 2024

Glass facades are known to be fascinating building systems that require specific design strategies, experimental protocols and simulation tools. Especially for seismic-resistant buildings, their mechanical performance should be verified against possible failure mechanisms. For this, both numerically optimized and robust approaches are needed, along with tools to support designers. Fragility curves represent, in this sense, a practical approach for many structural typologies and systems. In this paper, attention is given to the development and assessment of a geometrically simplified and mechanically optimized FE numerical model for the non-linear dynamic analysis of glass curtain walls (GCWs). Its potential and gaps in its calibration and prediction capacity, both at the global and local level, are addressed on the base of earlier experimental and numerical studies. A fragility analysis is then carried out by taking advantage of the cloud analysis method to verify the real capacity of a typical GCW and the performance restrictions that are presently recommended by existing standards for construction. A total of 60 non-linear dynamic analyses are carried out for GCWs under real seismic acceleration to capture the maximum effects and possible failure mechanisms. An analysis of the parametric results is then carried out for several performance indicators of practical interest and various technical documents of the literature. As shown, there is a major effect of global and local mechanisms that optimized numerical models should properly capture. At the same time, according to existing technical documents, there is a clear need for more efficient limit values and performance indicators for the design of safe and optimized seismic-resistant GCWs.
Emergence of AI—Impact on Building Condition Index (BCI)
Authors: West Jye , Siddhpura Milind , Evangelista Ana , Haddad Assed
Published: December 2024

The Building Condition Index (BCI) is a widely adopted quantitative metric for assessing various aspects of a building’s condition, as it facilitates decision-making regarding maintenance, capital improvements and, most importantly, the identification of investment risk. In practice, longitudinal BCI scores are typically used to identify maintenance liabilities and trends and proactively provide indications when maintenance strategies need to be altered. This allows for a more efficient resource allocation and helps maximise the lifespan and functionality of buildings and their assets. Given the historical ambiguity concerns because of the reliance on visual inspections, this research investigates how AI and using ANN, DNN and CNN can improve the predictive accuracy of determining a recognisable Building Condition Index. It demonstrates how ANN and DNN perform over asset classes (apartment complexes, education and commercial buildings). The results suggest that DNN architecture is adept at dealing with diverse and complex datasets, thus enabling a more versatile BCI prediction model over various building categories. It is envisaged that with the expansion and maturity of ANN, DNN and CNN, the BCI calculation methodologies will become more sophisticated, automated and integrated with traditional assessment approaches.
Reinforcement of RC Two-Way Slabs with CFRP Laminates: Plastic Limit Method for Carbon Emissions and Deformation Control
Authors: Sharhan Zahraa Saleem , Cucuzza Raffaele , Domaneschi Marco , Ghodousian Oveys , Movahedi Rad Majid
Published: December 2024

Carbon-fiber-reinforced polymer (CFRP) laminates have gained attention for their potential to reduce carbon emissions in construction. The impact of carbon-fiber-reinforced polymer (CFRP Laminate) on carbon emissions and the influence of elasto-plastic analysis on this technique were studied in this research. This study focuses on how CFRP can affect the environmental footprint of reinforced concrete structures and how elasto-plastic analysis contributes to optimizing this strengthening method. Four flat RC slabs were created to evaluate this technique in strengthening. One slab was used as a reference without strengthening, while the other three were externally strengthened with CFRP. The slabs, which were identical in terms of their overall (length, width, and thickness) as well as their flexural steel reinforcement, were subjected to concentrated patch load until they failed. The strength of two-way RC slabs was analyzed using a concrete plastic damage constitutive model (CDP). Additionally, CFRP strips were applied to the tension surface of existing RC slabs to improve their strength. The load–deflection curves obtained from the simulations closely match the experimental data, demonstrating the validity and accuracy of the model. Strengthening concrete slabs with CFRP sheets reduced central deflection by 17.68% and crack width by 40%, while increasing the cracking load by 97.73% and the ultimate load capacity by 134.02%. However, it also led to a 15.47% increase in CO2 emissions. Also, the numerical results show that increasing the strengthening ratio significantly impacts shear strength and damage percentage.
Effect of an Exercise Intervention Program on BMI and Distance Covered in the 6 Min Walking Test (6MWT) in Obese Children
Authors: Papadopoulou Ourania , Desli Evangelia , Chrysochoou Elisavet-Anna , Kogias Christos , Liakos Vasileios , Sopiadou Athina , Vantsi Petrina , Kallistratos Ilias , Iakovidis Paris , Tsiroukidou Kiriaki , Hatziagorou Elpis
Published: December 2024

Background/Objectives: Childhood obesity appears to be an alarmingly growing global threat. Current evidence has shown that obesity can be successfully managed with interventions targeting movement skills, motor coordination and physical activity. However, data concerning physiotherapy practice are limited. We aimed to assess the impact of therapeutic exercise on the 6-minute walk test (6-MWT) in obese children and adolescents. Methods: A total of 31 obese children and adolescents (BMI > 30 kg/m2), 18 males and 13 females, aged from 8 to 18 years, were enrolled. Two study groups were formed: the control group (Group A), comprising ten children; and the intervention group (Group B), comprising 21 children. Daily exercise habits were obtained via interview; anthropometric data (weight, height and BMI) were recorded; and 6-MWT parameters, blood pressure and oxygen saturation (SaO2) were assessed before and after a 4-month intervention program consisting of breathing and aerobic exercises. Results: There was a statistically significant increase in 6-minute walk distance (+43.34, p < 0.001) and an improvement in body weight (p < 0.01), blood pressure (p < 0.01) and oxygen saturation (p < 0.03) in Group B after the 4-month training program. Conclusions: All results highlight the potential of physical exercise in clinical practice to improve functional status and achieve weight loss. Future randomized controlled trials, including individualized therapeutic exercise programs in larger samples of obese children, are much anticipated.
Accessing the Lived Experiences of Children with Illness in Sweden for Health Care Research
Authors: Darcy Laura , Israelsson-Skogsberg Åsa , Kleye Ida , Karlsson Katarina
Published: December 2024

Background: Children are a relatively marginalized group when doing research in the context of illness, young children particularly so. This even though children can and should contribute their point of view in providing evidence-based care. This article contributes the experiences of Nurse Researchers in gathering research data in Sweden on the lived experiences of children undergoing needle-related medical procedures, living with home mechanical ventilation or undergoing treatment for cancer. Methods: Nine published articles from four unique Swedish PhD dissertations in Caring Science formed the basis for the present paper where various open and critical qualitative approaches for gathering data were used. Results: Accessing and interpreting the lived experiences of ill children in healthcare research presents methodological, ethical, and cultural challenges. As with health care contexts in other countries, capturing the ill child’s experiences in Sweden requires several different methods. Health researchers in Sweden must navigate a complex landscape of communication barriers, emotional and psychological challenges, and structural issues within the healthcare system to effectively access and understand the lived experiences of children. Conclusions: This paper adds to the knowledge base of research with a focus on gathering the experiences of children with illness within a Swedish health care context. These insights underscore the importance for all researchers of using child-friendly, inclusive methods to understand children’s lived experiences thus ensuring their voices are heard and respected in health research. Children’s and families’ inability to speak the native language of a country cannot be a hinder for inclusion, but rather be encouraged.
It Is Normal: The Probability Distribution of Temperature Extremes
Authors: Krakauer Nir Y.
Published: December 2024

The probability of heat extremes is often estimated using the non-stationary generalized extreme value distribution (GEVD) applied to time series of annual maximum temperature. Here, this practice was assessed using a global sample of temperature time series, from reanalysis (both at the grid point and the region scale) as well as station observations. This assessment used forecast negative log-likelihood as the main performance measure, which is particularly sensitive to the most extreme heat waves. It was found that the computationally simpler normal distribution outperforms the GEVD in providing probabilistic year-ahead forecasts of temperature extremes. Given these findings, it is suggested to consider alternatives to the GEVD for assessing the risk of extreme heat.
Application of Environmental Protection Agency (EPA) Model in Occupational Hazard Evaluation of UV-Curable Coating Production Project
Authors: Zhao Wu , Qing Huiguang , Zhou Zhe , Jiang Haibo
Published: December 2024

This paper applies the EPA inhalation risk evaluation model—which has the advantage of the high safety margin value of the RfC value and considers long-term contact exposure to chemical toxicants for all protected populations—to objectively assess the risk level of acute and chronic occupational poisoning and tumors caused by respiratory exposure to chemical toxicants in a UV-curable coating production project, which is of guiding significance. The results reveal that the hazardous chemical elements in the air in the workshop of this UV-curable coating production project have no substantial influence on worker health; therefore, no further design is required.
Interpretable Conversation Routing via the Latent Embeddings Approach
Authors: Maksymenko Daniil , Turuta Oleksii
Published: December 2024

Large language models (LLMs) are quickly implemented to answer question and support systems to automate customer experience across all domains, including medical use cases. Models in such environments should solve multiple problems like general knowledge questions, queries to external sources, function calling and many others. Some cases might not even require a full-on text generation. They possibly need different prompts or even different models. All of it can be managed by a routing step. This paper focuses on interpretable few-shot approaches for conversation routing like latent embeddings retrieval. The work here presents a benchmark, a sorrow analysis, and a set of visualizations of the way latent embeddings routing works for long-context conversations in a multilingual, domain-specific environment. The results presented here show that the latent embeddings router is able to achieve performance on the same level as LLM-based routers with additional interpretability and higher level of control over model decision-making.
Frequently Suspected, Rarely Confirmed: The Complex Diagnostic Journey of Adult-Onset MELAS—Clinical Evaluation and Cost Implications
Authors: Finkener Sebastian , Ovchinnikov Arkady , Bauer Ronald , Diepers Michael , Gschwind Markus
Published: December 2024

Mitochondrial encephalomyopathy, lactic acidosis, and stroke-like episodes (MELAS) is a rare mitochondrial disorder primarily presenting in pediatric patients, with onset after 40 years being exceptionally rare (1–6%). Here, we report a complex diagnostic journey of a 47-year-old male presenting with new-onset seizures, hemiparesis, and neurocognitive deficits. Initial work-up, including MRI, CSF analysis, and extensive antibody screening, yielded inconclusive results, prompting differential considerations such as autoimmune encephalitis and neoplastic conditions. Finally muscle biopsy findings, coupled with genetic confirmation of the m.3243A>G mutation in the MT-TL1 gene, ultimately established the diagnosis of MELAS. This case depicts the atypical presentation of adult-onset MELAS without initial lactic acidemia, diabetes, or hearing impairment. The prolonged diagnostic process underscores the challenges of identifying rare diseases under today’s financial and administrative constraints. Still ee emphasize the importance of comprehensive diagnostics in rare cases to advance generall understanding and improve future patient outcomes, also amidst resource limitations.
Diagnostic Performance of Clinical and Routine Laboratory Data in Acute Mesenteric Arterial Occlusion—An International Multicenter Study
Authors: Soltanzadeh-Naderi Yasmin , Reintam Blaser Annika , Björck Martin , Nuzzo Alexandre , Starkopf Joel , Forbes Alastair , Murruste Marko , Tamme Kadri , Talving Peep , Voomets Anna-Liisa , Koitmäe Merli , Bala Miklosh , Bodnar Zsolt , Casian Dumitru , Demetrashvili Zaza , D’Oria Mario , Dúran Muñoz-Cruzado Virginia , Fuglseth Hanne , Itzhaki Moran Hellerman , Hess Benjamin , Kase Karri , Lein Kristoffer , Lindner Matthias , Loudet Cecilia I. , Mole Damian J. , Saar Sten , Scheiterle Maximilian , Voon Kenneth , Tverring Jonas , Acosta Stefan
Published: December 2024

Background: There are no clinical or laboratory markers that can diagnose acute mesenteric ischemia (AMI) accurately. This study aimed to find differences in clinical and laboratory markers between arterial occlusive AMI and other acute abdominal diseases where AMI was initially suspected. Methods: This was a post hoc study of an international prospective multicenter study where data on patients with suspected AMI were collected. Independent factors associated with arterial occlusive AMI were evaluated in a multivariable logistic regression analysis. Results: The number of patients with arterial occlusive AMI was 231, consisting of thrombotic (n = 104), embolic (n = 61), and indeterminate (n = 66) occlusions. The non-AMI group included 287 patients, of whom 128 had strangulated bowel obstruction. Current smoking (odds ratio [OR] 2.56, 95% confidence interval [CI] 1.31–5.03), hypertension (OR 2.08, 95% CI 1.09–3.97), bowel emptying (OR 3.25, 95% CI 1.59–6.63), and leukocytosis (OR 1.54, 95% CI 1.14–2.08) at admission were independently associated with arterial occlusive AMI compared to the non-AMI group. Conclusions: This study found clinical and laboratory data to be associated with arterial occlusive AMI in patients with suspicion of AMI, which can possibly be of value in screening for arterial occlusive AMI at the emergency department. Further studies are needed to find more accurate diagnostic markers.
Cardioish: Lead-Based Feature Extraction for ECG Signals
Authors: Tuncer Turker , Baig Abdul Hafeez , Aydemir Emrah , Kivrak Tarik , Tuncer Ilknur , Tasci Gulay , Dogan Sengul
Published: December 2024

Background: Electrocardiography (ECG) signals are commonly used to detect cardiac disorders, with 12-lead ECGs being the standard method for acquiring these signals. The primary objective of this research is to propose a new feature engineering model that achieves both high classification accuracy and explainable results using ECG signals. To this end, a symbolic language, named Cardioish, has been introduced. Methods: In this research, two publicly available datasets were used: (i) a mental disorder classification dataset and (ii) a myocardial infarction (MI) dataset. These datasets contain ECG beats and include 4 and 11 classes, respectively. To obtain explainable results from these ECG signal datasets, a new explainable feature engineering (XFE) model has been proposed. The Cardioish-based XFE model consists of four main phases: (i) lead transformation and transition table feature extraction, (ii) iterative neighborhood component analysis (INCA) for feature selection, (iii) classification, and (iv) explainable results generation using the recommended Cardioish. In the feature extraction phase, the lead transformer converts ECG signals into lead indexes. To extract features from the transformed signals, a transition table-based feature extractor is applied, resulting in 144 features (12 × 12) from each ECG signal. In the feature selection phase, INCA is used to select the most informative features from the 144 generated, which are then classified using the k-nearest neighbors (kNN) classifier. The final phase is the explainable artificial intelligence (XAI) phase. In this phase, Cardioish symbols are created, forming a Cardioish sentence. By analyzing the extracted sentence, XAI results are obtained. Additionally, these results can be integrated into connectome theory for applications in cardiology. Results: The presented Cardioish-based XFE model achieved over 99% classification accuracy on both datasets. Moreover, the XAI results related to these disorders have been presented in this research. Conclusions: The recommended Cardioish-based XFE model achieved high classification performance for both datasets and provided explainable results. In this regard, our proposal paves a new way for ECG classification and interpretation.
Diagnostic Ability of Quantitative Parameters of Whole-Body Bone SPECT/CT Using a Full-Ring 360° Cadmium-Zinc-Telluride Camera for Detecting Bone Metastasis in Patients with Prostate Cancer
Authors: Yoo Ik Dong , Hong Sun-pyo , Lee Sang Mi , Yang Hee Jo , Kim Ki Hong , Kim Si Hyun , Lee Jeong Won
Published: December 2024

Background/Objectives: This study aimed to assess the diagnostic capability of quantitative parameters from whole-body bone single-photon emission computed tomography/computed tomography (SPECT/CT) in detecting bone metastases in prostate cancer patients; Methods: We retrospectively analyzed 82 prostate cancer patients who underwent staging bone scintigraphy with a full-ring 360° Cadmium-Zinc-Telluride (CZT) SPECT/CT system. From the SPECT/CT images, we measured the maximum (SUVmax) and mean (SUVmean) standardized uptake values at six normal bone sites (skull, humerus, thoracic spine, lumbar spine, iliac bone, and femur), and the SUVmax for both metastatic and benign bone lesions. Ratios of lesion SUVmax-to-maximum and mean uptake values at the skull, humerus, and femur were computed for each lesion; Results: SUVmax and SUVmean at the skull and femur exhibited significantly lower variance compared to those at the thoracic spine, lumbar spine, and iliac bone, and revealed no significant differences between patients with and without bone metastasis. In receiver operating characteristic curve analysis for detecting bone metastasis among 482 metastatic lesions, 132 benign bone lesions, and 477 normal bone sites, the lesion-to-femur mean uptake ratio demonstrated the highest area under the curve value (0.955) among SPECT/CT parameters. Using a cut-off value of 5.38, the lesion-to-femur mean uptake ratio achieved a sensitivity of 94.8% and a specificity of 86.5%; Conclusions: The bone lesion-to-femur mean uptake ratio was the most effective quantitative bone SPECT/CT parameter for detecting bone metastasis in prostate cancer patients. Quantitative analysis of bone SPECT/CT images could thus play a crucial role in diagnosing bone metastasis.
Predictors of Post-Hepatectomy Liver Failure in Klatskin Tumors: The Role of Preoperative Glucose, Future Liver Remnant to Spleen Ratio, and Early Bilirubin Monitoring
Authors: Kim Suyeon , Ku Hyung June , Moon Hyung Hwan , Song Sang Hwa , Choi Young Il , Shin Dong Hoon , Koh Yang Seok , Oh Namkee , Rhu Jinsoo , Lee Garam , Yang Won Jong , Song Junho , Kang Chol Min , Ku Seoyeong , Choi Amy
Published: December 2024

Background: Post-hepatectomy liver failure (PHLF) is a serious complication following hepatic resection for Klatskin tumors, significantly affecting patient prognosis. Identifying reliable preoperative and early postoperative predictors of PHLF can help optimize patient outcomes and guide surgical planning. Method: We conducted a retrospective review of 34 patients who underwent hemi-hepatectomy for extrahepatic cholangiocarcinoma at Kosin University Gospel Hospital between April 2019 and April 2024, and at Chonnam National University Hwasun Hospital between September 2017 and April 2024. Demographics, laboratory data, and volumetric measurements including spleen volume, were analyzed to assess their roles in predicting PHLF. Logistic regression and receiver operating characteristic (ROC) curve analyses were performed to evaluate the predictive value of these factors. Results: Elevated preoperative glucose levels and reduced future liver remnant to spleen ratio (FLR/SV) were significantly associated with an increased risk of PHLF. Additionally, elevated postoperative day 1 total bilirubin (POD 1 TB) was identified as a key postoperative predictor of PHLF. Multivariable analysis confirmed the significance of these factors, with FLR/SV, preoperative glucose, and POD 1 TB demonstrating good discriminative ability in ROC analysis, with AUC values of 0.779, 0.782, and 0.786 respectively. Conclusion: Preoperative glucose control, evaluation of FLR/SV, and early postoperative monitoring of TB are useful for improving outcomes in patients undergoing major hepatectomy for Klatskin tumors.
Quality of Life Evaluation Before and After Pulmonary Lobe Resection for Benign Diseases: A Comparative Study Among Patients with Tuberculosis, Bronchiectasis, and Benign Lung Nodules
Authors: Nicola Alin , Porosnicu Tamara Mirela , Laitin Sorina Maria Denisa , Oancea Cristian , Tudorache Emanuela
Published: December 2024

Background: Pulmonary lobe resection is a common surgical intervention for various benign lung diseases, including tuberculosis (TB), bronchiectasis, and benign lung nodules. While immediate clinical outcomes are well documented, the impact on patients’ quality of life (QoL) remains less explored. This study aims to evaluate QoL before and after pulmonary lobe resection over a 6-month period among patients with benign lung diseases. Objectives: To assess and compare changes in QoL among patients undergoing pulmonary lobe resection for TB, bronchiectasis, and benign lung nodules, and to identify factors influencing QoL outcomes. Methods: This prospective study included 84 patients who underwent pulmonary lobe resection for benign lung diseases, divided into three groups: TB (n = 22), bronchiectasis (n = 31), and benign lung nodules (n = 31). QoL was assessed using the SF-36 and WHOQOL-BREF questionnaires preoperatively and at 6 months postoperatively. Anxiety and depression were evaluated using the Hospital Anxiety and Depression Scale (HADS), and perceived stress was measured using the Perceived Stress Scale (PSS-10). Statistical analyses included paired t-tests, ANOVA, and Pearson’s correlation, with p-values < 0.05 considered significant. Results: At 6 months postoperatively, all groups showed significant improvements in physical and mental QoL scores (p < 0.05). The TB group exhibited the greatest improvement in physical health domains, while the bronchiectasis group showed significant enhancement in social functioning. Anxiety and depression scores decreased significantly in all groups, with the TB group showing the most substantial reduction (p < 0.01). Perceived stress levels also decreased across all groups. Comparisons revealed that the TB group had significantly higher QoL improvement compared to the other groups (p < 0.05). Conclusions: Pulmonary lobe resection for benign diseases significantly improves QoL over a 6-month period, particularly in patients with TB. The findings suggest that surgical intervention not only alleviates physical symptoms but also enhances psychological well-being. These results underscore the importance of considering QoL outcomes in the management of benign lung diseases requiring surgical intervention.
A Framework for Middle Level Curriculum: A Literature Review to Support the Middle Level Education Research Special Interest Group Research Agenda
Authors: Weiler Christopher , Mertens Steven B. , Nagle James , Pettit Stacie , Wall Amanda
Published: December 2024

This research synthesis, highlighting the middle level curriculum, was created as part of a working group within the American Education Research Association (AERA) Middle level Education Research Special Interest Group (MLER SIG) to advance middle level education research. The literature review that informed this research synthesis included middle level-focused, peer-reviewed journal articles from 2016 to the present. Synthesis of the literature and iterative analysis led to organizing a middle level curriculum framework to inform middle level researchers, which included five focused areas for inquiry: (a) the curriculum and equity of experience and opportunity; (b) stakeholder power and the curriculum (development, implementation, and accountability); (c) goals and purposes for the curriculum; (d) teacher learning, roles, and enactment related to the curriculum; and (e) young adolescent well-being and experiences with the curriculum. Twenty-six research questions were developed to support new research in middle level curriculum; each question was cross-referenced with the five categories in the middle level curriculum framework to create a robust starting point for research questions. Recommendations for middle level research include (a) the need for more longitudinal research studies focusing on varying aspects of middle level education, (b) the need for more large-scale research studies examining the same context (e.g., school) or multiple contexts (e.g., school districts) over time, and (c) the need for the MLER SIG to initiate and support research studies addressing one or more aspects of this research agenda and include large-scale data collection and potentially external funding.
Happiness at School and Its Relationship with Academic Achievement
Authors: Hochschild Ovalle Hernán , Nussbaum Miguel , Claro Susana , Espinosa Pablo , Alvares Danilo
Published: December 2024

This study contributes to the growing body of research on the relationship between subjective well-being and academic performance in schools by providing a context-specific analysis of Chilean students. Using 2022 SIMCE data—a national standardized assessment in Chile—from 4th and 10th graders (N = 292,725), the research employs hierarchical linear modeling to explore how students’ school happiness relates with performance in language and mathematics, accounting for socioeconomic status, gender, origin, self-efficacy, and previous year’s grades. The findings reveal that students who self-report feeling happy at school tend to perform better academically, though this varies by grade level and subject. In early grades, happiness mitigates the negative effects of low socioeconomic status, with lower-income students benefiting more. By 10th grade, wealthier students and boys show a stronger association between happiness and academic performance, particularly in mathematics. This study also highlights the critical role of self-efficacy in math, showing that students with higher self-efficacy not only perform better but benefit from happiness at school. Additionally, foreign-origin students experience varying benefits, emphasizing the need for culturally responsive and gender-sensitive interventions. These findings underscore the importance of promoting happiness at school and academic self-efficacy to reduce educational inequalities and enhance learning outcomes.
Sulfur-Fumigated Ginger Identification Method Based on Meta-Learning for Different Devices
Authors: Wang Tianshu , He Jiawang , Yan Hui , Hu Kongfa , Yang Xichen , Zhang Xia , Duan Jinao
Published: December 2024

Since ginger has characteristics of both food and medicine, it has a significant market demand worldwide. To effectively store ginger and achieve the drying and color enhancement effects required for better sales, it is often subjected to sulfur fumigation. Although sulfur fumigation methods can effectively prevent ginger from becoming moldy, they cause residual sulfur dioxide, harming human health. Traditional sulfur detection methods face disadvantages such as complex operation, high time consumption, and easy consumption. This paper presents a sulfur-fumigated ginger detection method based on natural image recognition. By directly using images from mobile phones, the proposed method achieves non-destructive testing and effectively reduces operational complexity. First, four mobile phones of different brands are used to collect images of sulfur- and non-sulfur-fumigated ginger samples. Then, the images are preprocessed to remove the blank background in the image and a deep neural network is designed to extract features from ginger images. Next, the recognition model is generated based on the features. Finally, meta-learning parameters are introduced to enable the model to learn and adapt to new tasks, thereby improving the adaptability of the model. Thus, the proposed method can adapt to different devices in its real application. The experimental results indicate that the recall rate, F1 score, and AUC-ROC of the four different mobile phones are more than 0.9, and the discrimination accuracy of these phones is above 0.95. Therefore, this method has good predictive ability and excellent practical value for identifying sulfur-fumigated ginger.
Evaluation of the Protein Profile of a Saccharomyces cerevisiae Strain Immobilized in Biocapsules for Use in Fermented Foods
Authors: García-García Juan C. , G-García Miguel E. , Mauricio Juan C. , Moreno Juan , García-Martínez Teresa
Published: December 2024

Yeast biocapsules are a novel immobilization technology that could be used in fermentation processes. They are spherical structures consisting of yeast cells encapsulated and attached to the hyphae of a filamentous fungus. Yeast biocapsules offer a cutting-edge approach to cell immobilization, with significant potential for advancing fermented food production. By enhancing fermentation control, improving product quality, and increasing process efficiency, these biocapsules represent a key innovation in food fermentation technology, particularly in the production of alcoholic beverages such as beer and wine. Proteomic analysis of two-dimensional gels was carried out to study changes in proteins expressed in (i) co-immobilized yeast cells, and (ii) free-format yeast cells. This analysis showed that the proteins expressed in co-immobilized yeast cells played critical roles in DNA repair, cell cycle regulation, protein synthesis, and translation, whereas the proteins expressed by free yeast cells were mainly related to glycolysis. These findings suggest a defense response of the co-immobilized yeast against fungal interactions, involving regulatory mechanisms at the DNA, RNA, and protein levels. This study opens new avenues for exploring yeast–fungus co-immobilization, including stress responses, the nature of the binding polymers, and the proteomics of biocapsules. Additionally, investigating natural co-immobilization mechanisms between various microorganisms could uncover further biotechnological applications and biocatalytic activities.
Investigation of Efficient Pullulan Synthesis Utilizing Huangjiu Lees as a Substrate
Authors: Lu Peiqi , Liu Tiantian , Ma Jingqiu , Kan Tao , Han Xiao , Ji Zhongwei , Mao Jian
Published: December 2024

Pullulan is a high-value biopolymer synthesized by Aureobasidium pullulans through the fermentation of starch and sugars. It finds extensive applications in food, packaging, biomedicine, and other sectors. However, the high production costs significantly limit the development and application of pullulan. Consequently, there is an urgent need to identify high-quality fermentation substrates. In recent years, the rapid growth of Huangjiu industry has led to the generation of waste Huangjiu lees, which not only contribute to environmental pollution but also represent a significant waste of resources. As a result, the resource utilization of Huangjiu lees has garnered considerable attention. In this study, Huangjiu lees were employed as raw materials for fermentation to produce pullulan. Following fermentation of Huangjiu lees powder with the primary strain Aureobasidium pullulans LL1, the yield of pullulan was notably reduced. Through adaptive evolution, an evolved strain, Aureobasidium pullulans AP9, was isolated, demonstrating enhanced efficiency in producing pullulan from Huangjiu lees. The impact of Huangjiu lees on pullulan biosynthesis was elucidated via transcriptome analysis. Fermentation conditions were optimized using a single-factor approach, and a multi-strain staged fermentation strategy involving Aspergillus niger and Aureobasidium pullulans was employed to further enhance pullulan yield. Under optimal conditions, the pullulan yield reached 22.06 g/L, with a molecular weight of 1.04 × 106 Da. This study underscores the significant potential of utilizing Huangjiu lees for pullulan production and offers valuable insights for the resource utilization of this byproduct.
How Mental Health Nurses Perceive the Implementation of Batho Pele Principles in a Selected Mental Health Hospital in Limpopo Province, South Africa
Authors: Mabunda Nkhensani Florence
Published: December 2024

Background: Batho Pele is a South African legislative framework initiative introduced to improve the overall delivery of public services. The framework was introduced in 1997 and aimed at bringing a comprehensive transformation of the work ethics of all public servants, including health workers, at all levels of health hospitals. The study aims to investigate nurses’ perceptions of implementing Batho Pele principles in selected mental health hospitals. Methods: A quantitative descriptive survey design using a self-administered questionnaire was used to collect data. Probability-stratified random sampling was used to select the sample of 230 from the population of nurses. Statistical Package for the Social Sciences (SPSS) version 23 was used to analyze descriptive data. Results: The study showed that lack of knowledge, communication and practical skills, and human and material resources negatively affect the effective implementation of Batho Pele principles in the care of mental health service users. Conclusions: The Batho Pele principles are insufficiently implemented in mental health hospitals due to a shortage of staff to facilitate the implementation of Batho Pele principles. The researcher recommended an in-service program to train nurses in implementing Batho Pele principles to enhance the provision of exemplary mental health services.
Short-Term Effects of Conventional Chest Physiotherapy and Expiratory Flow Increase Technique on Respiratory Parameters, Heart Rate, and Pain in Mechanically Ventilated Premature Neonates: A Randomized Controlled Trial
Authors: Macedo Jacqueline Carla de , Olivo Clarice Rosa , Barnabé Viviani , Dias Eduardo Dati , Moraes Íbis Ariana Peña de , Tibério Iolanda de Fátima Lopes Calvo , Carvalho Celso Ricardo Fernandes de , Saraiva-Romanholo Beatriz Mangueira
Published: December 2024

Background/Objectives: Prematurity is a leading cause of infant mortality and mechanical ventilation increases respiratory complication risk. The effects of secretion removal techniques in premature infants remain a topic of ongoing debate. The aim of the study is to compare two secretion removal techniques in premature infants on mechanical ventilation; Methods: The participants were randomized into conventional chest physiotherapy (CPT; n = 22) or expiratory flow increase technique (EFIT; n = 21) groups. Each participant completed four sessions on consecutive days with a minimum of one and a maximum of two sessions per day. We assessed peripheral oxygen saturation (SpO2), heart (HR) and respiratory rates (RR), tidal volume (VT), and pain levels at specific time points: before the intervention, immediately after the intervention, 5 min after the intervention, and 10 min after the intervention. The partial Eta squared (ŋp2) was reported to measure the effect size; Results: HR and RR increased post-intervention in both groups (p < 0.001; HR ŋp2 = 0.51; RR ŋp2 = 0.38); post hoc comparisons showed EFIT group decreased RR from the first to last session (p = 0.045). Both groups exhibited increased VT and SpO2 in all sessions (p < 0.001; VT ŋp2 = 0.40; SpO2 ŋp2 = 0.50). The EFIT group had higher SpO2 values (p = 0.013; ŋp2 = 0.15) and lower pain scores (p < 0.001; ŋp2 = 0.46) post-intervention compared to CPT; Conclusions: CPT and EFIT resulted in similar effects on short-term respiratory parameters and heart rate; however, EFIT had advantages, including lower RR, higher SpO2, and reduced pain, suggesting it may be a more effective, comfortable neonatal respiratory treatment.
Barriers and Facilitators of Physical Activity in Pregnancy and Postpartum Among Iranian Women: A Scoping Review
Authors: May Linda E. , Moss Sarah J. , Szumilewicz Anna , Santos-Rocha Rita , Shojaeian Najmeh A.
Published: December 2024

Inactivity during pregnancy and postpartum is largely a result of women’s attitudes and misunderstandings of physical activity, especially in Iran. This scoping review critically assesses the barriers and facilitators influencing physical activity among pregnant and postpartum Iranian women to provide the basis for future physical activity interventions. Ten databases and platforms were searched up to 1 June 2024: Medline, SportDISCUS, PsycINFO, EMBASE, CINAHL, Cochrane Review Database, Clinical Trial, SID, ISC, and Web of Science. Grey literature sources were included to retrieve original publications on barriers and facilitators during pregnancy and postpartum among Iranian women. The search resulted in 2470 identified studies screened for inclusion criteria. After screening both abstracts and full texts, 33 of the studies were included, and data were extracted and charted. Findings were summarized in alignment with the objectives. The results show that the basic physical activity barriers are intrapersonal, interpersonal, and environmental factors. Facilitating factors include using E-learning resources and combined interventions to educate women and provide awareness of the existence of exercise classes. Social and emotional support by family members and other women in the same situation can be effective. Overall, the study of obstacles to and enablers of physical activity during pregnancy and postpartum is ongoing. In addition to highlighting the present situation in Iran, this study identifies further opportunities for future research on the development of appropriate interventions to reduce the barriers and strengthen the facilitators for physical activity among pregnant and postpartum Iranian women with trained groups, including skilled healthcare providers.
Leveraging Digital Twins as a Common Operating Picture for Disaster Management: Case of Seismic Hazards
Authors: Korkmaz Mustafa , Zulfikar A. Can , Demirkesen Sevilay
Published: December 2024

Disasters, both natural and man-made, pose significant challenges that necessitate swift and informed decision-making to minimize impacts and enhance recovery efforts. This study explores the integration of digital twins (DTs) with a common operating picture (COP) to improve disaster management practices, particularly during the response phase. Utilizing a comprehensive approach, the research develops a 3D core model of Kocaeli, Turkey, which incorporates real-time data from various sources, including building information models and seismic hazard assessments. The findings reveal that the DT–COP framework significantly enhances situational awareness and resource allocation, allowing for more effective response strategies. However, challenges related to data accuracy, integration, and interoperability persist. The study concludes that leveraging DTs as a COP can transform disaster management practices, ultimately improving response capabilities and community resilience while highlighting the need for ongoing research to address existing challenges.
Estimation of Route-Choice Behavior Along LRT Lines Using Inverse Reinforcement Learning
Authors: Okubo Tomohiro , Kobayashi Akihiro , Kamisaka Daisuke , Morimoto Akinori
Published: December 2024

As the decline of public transportation in rural areas becomes a growing concern, initiatives to introduce attractive next-generation transportation systems to promote public transportation usage are being considered across various regions. In Toyama City, Toyama Prefecture, where the next-generation light rail transit (LRT) system has been introduced, the number of users has significantly increased compared to before its introduction, with some users riding the LRT for the sake of the experience itself. On the other hand, there is a demand for a more micro-level and quantitative evaluation of the impact that the LRT has on the liveliness of areas along its route. Therefore, this study uses inverse reinforcement learning (IRL), a type of machine learning, to build a model that estimates route-choice behavior along the LRT lines based on behavioral trajectories generated from smartphone location data. The model is capable of evaluating the characteristics of location data with high accuracy. The findings indicate that routes along the LRT lines tend to be selected, suggesting that both the appeal of the LRT itself and the attractiveness of the spaces along its route contribute to this tendency.
FastQAFPN-YOLOv8s-Based Method for Rapid and Lightweight Detection of Walnut Unseparated Material
Authors: Li Junqiu , Wang Jiayi , Kong Dexiao , Zhang Qinghui , Qiang Zhenping
Published: December 2024

Walnuts possess significant nutritional and economic value. Fast and accurate sorting of shells and kernels will enhance the efficiency of automated production. Therefore, we propose a FastQAFPN-YOLOv8s object detection network to achieve rapid and precise detection of unsorted materials. The method uses lightweight Pconv (Partial Convolution) operators to build the FasterNextBlock structure, which serves as the backbone feature extractor for the Fasternet feature extraction network. The ECIoU loss function, combining EIoU (Efficient-IoU) and CIoU (Complete-IoU), speeds up the adjustment of the prediction frame and the network regression. In the Neck section of the network, the QAFPN feature fusion extraction network is proposed to replace the PAN-FPN (Path Aggregation Network—Feature Pyramid Network) in YOLOv8s with a Rep-PAN structure based on the QARepNext reparameterization framework for feature fusion extraction to strike a balance between network performance and inference speed. To validate the method, we built a three-axis mobile sorting device and created a dataset of 3000 images of walnuts after shell removal for experiments. The results show that the improved network contains 6071008 parameters, a training time of 2.49 h, a model size of 12.3 MB, an mAP (Mean Average Precision) of 94.5%, and a frame rate of 52.1 FPS. Compared with the original model, the number of parameters decreased by 45.5%, with training time reduced by 32.7%, the model size shrunk by 45.3%, and frame rate improved by 40.8%. However, some accuracy is sacrificed due to the lightweight design, resulting in a 1.2% decrease in mAP. The network reduces the model size by 59.7 MB and 23.9 MB compared to YOLOv7 and YOLOv6, respectively, and improves the frame rate by 15.67 fps and 22.55 fps, respectively. The average confidence and mAP show minimal changes compared to YOLOv7 and improved by 4.2% and 2.4% compared to YOLOv6, respectively. The FastQAFPN-YOLOv8s detection method effectively reduces model size while maintaining recognition accuracy.