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In this study, we employ a dual-objective optimization model, utilizing the Implicit Modified Coverage Location Problem (MCLP-Implicit) approach, to determine an appropriate fire station allocation. Our objectives encompass maximizing coverage in areas with a heightened projected demand, based on socioeconomic predictors of building fires, and concurrently minimizing the distance of uncovered demand zones to the closest fire station. The challenges of this model reflect the criticality of strategic placement, aiming for not just swift response times but also the complete coverage of a region with priority given to subregions with a pronounced potential for incidents. The applicability of the proposed approach is demonstrated through a case study in south-east Queensland. Our findings indicate that there is a tangible justification for adopting this model. The broader coverage and wider spread of locations it produces can greatly aid in the dynamic deployment of personnel during surges caused by seasonal fluctuations or unforeseen calamities. By weaving in socioeconomic aspects into demand predictions, our model has also maintained appropriate coverage to socioeconomically disadvantaged communities in the region. Nevertheless, it is paramount to underline the recommendation that further research should account for road connectivity—a pivotal factor when pinpointing these locations in the real world. This research paves the way for enriched insights in long-term urban planning and fire response protocol crafting, especially in regions mirroring similar socioeconomic profiles or risks of natural disasters, not to mention its commercial implications to the relevant industry servicing the fire and rescue authorities. [ABSTRACT FROM AUTHOR]

Fires in buildings are significant public safety hazards and can result in fatalities and substantial financial losses. Studies have shown that the socioeconomic makeup of a region can impact the occurrence of building fires. However, existing models based on the classical stepwise regression procedure have limitations. This paper proposes a more accurate predictive model of building fire rates using a set of socioeconomic variables. To improve the model's forecasting ability, a backward elimination by robust final predictor error (RFPE) criterion is introduced. The proposed approach is applied to census and fire incident data from the South East Queensland region of Australia. A cross-validation procedure is used to assess the model's accuracy, and comparative analyses are conducted using other elimination criteria such as p-value, Akaike's information criterion (AIC), Bayesian information criterion (BIC), and predicted residual error sum of squares (PRESS). The results demonstrate that the RFPE criterion is a more accurate predictive model based on several goodness-of-fit measures. Overall, the RFPE equation was found to be a suitable criterion for the backward elimination procedure in the socioeconomic modeling of building fires. [ABSTRACT FROM AUTHOR]

Simple Summary: Young survivors of bone tumors and soft-tissue sarcoma are at risk of cognitive impairment due to their previous cancer therapies and the development of cancer-/treatment-related chronic health conditions as they age. We postulate that a compromised health status, coupled with the continued engagement in an unhealthy lifestyle during survivorship, may further exacerbate cognitive impairment among survivors. Our study found that survivors of bone tumors and soft-tissue sarcoma demonstrated impairment in attention, processing speed and executive function (higher order thinking ability). Low physical activity and smoking were associated with inattention. Survivors who worked >9 h per day had worse executive function than those with shorter working hours. Of note is that survivors who had a chronic health condition and were physically inactive demonstrated the worst attention and executive function performance. Our findings support continual efforts to investigate intervention targets and leverage health behaviors as modifiable risk factors to prevent cognitive dysfunction in this population. Background: The effect of lifestyle on neurocognitive impairment among cancer survivors remain an understudied area. This study explored the association between lifestyle factors and neurocognitive outcomes (specifically, attention, memory, processing speed and cognitive flexibility) in AYA survivors (aged 15–39 years) of sarcoma. Methods: This study recruited 116 AYA survivors (age 28.2 (SD = 8.2) years), who were diagnosed with osteosarcoma (49%) or soft-tissue sarcoma (51%) at age 13.3 (SD = 7.2) years. The neurocognitive battery included measures of attention, memory, motor-processing speed, and cognitive flexibility. Survivors reported health-damaging practices, which included: physical inactivity, smoking, alcohol intake, inadequate sleep (<7 h of actual sleep/day), sleep-related fatigue (Multidimensional Fatigue Scale) and long working hours (>9 h/day). General linear modeling was conducted to examine the association between lifestyle factors and neurocognitive outcomes, adjusting for age at diagnosis, sex, education attainment and clinical/treatment variables. Results: At 14.9 (SD = 7.6) years post-diagnosis, survivors demonstrated impairment in attentiveness (4.3–13.0%), processing speed (34.5%) and cognitive flexibility (18.1%). Nearly half (45.7%) had developed a chronic health condition (CHC). Low physical activity (estimate = −0.97, p = 0.003) and sleep-related fatigue (estimate = −0.08, p = 0.005) were associated with inattention. Survivors who worked >9 h/day (n = 15) demonstrated worse attention (estimate = 5.42, p = 0.023) and cognitive flexibility (estimate = 5.22, p = 0.005) than survivors who worked ≤9 h/day (n = 66). Interaction analysis (CHCs*physical activity) showed that survivors who developed CHCs and reported low physical activity had worse attention (p = 0.032) and cognitive-flexibility (p = 0.019) scores than other subgroups. Conclusion: Treatment-related CHCs, coupled with continued physical inactivity, may exacerbate inattention and executive dysfunction among survivors. Long working hours and sleep-related fatigue are associated with worse functioning; this finding should be validated with prospective assessment of work-related stressors and objective sleep measures. [ABSTRACT FROM AUTHOR]

Synthetic scaffolds for the repair of long-segment tracheal defects are hindered by insufficient biocompatibility and poor graft epithelialization. In this study, we determined if extracellular matrix (ECM) coatings improved the biocompatibility and epithelialization of synthetic tracheal grafts (syn-TG). Porcine and human ECM substrates (pECM and hECM) were created through the decellularization and lyophilization of lung tissue. Four concentrations of pECM and hECM coatings on syn-TG were characterized for their effects on scaffold morphologies and on in vitro cell viability and growth. Uncoated and ECM-coated syn-TG were subsequently evaluated in vivo through the orthotopic implantation of segmental grafts or patches. These studies demonstrated that ECM coatings were not cytotoxic and, enhanced the in vitro cell viability and growth on syn-TG in a dose-dependent manner. Mass spectrometry demonstrated that fibrillin, collagen, laminin, and nephronectin were the predominant ECM components transferred onto scaffolds. The in vivo results exhibited similar robust epithelialization of uncoated and coated syn-TG patches; however, the epithelialization remained poor with either uncoated or coated scaffolds in the segmental replacement models. Overall, these findings demonstrated that ECM coatings improve the seeded cell biocompatibility of synthetic scaffolds in vitro; however, they do not improve graft epithelialization in vivo. [ABSTRACT FROM AUTHOR]

Although educational timetabling problems have been studied for decades, one instance of this, the school timetabling problem (STP), has not developed as quickly as examination timetabling and course timetabling problems due to its diversity and complexity. In addition, most STP research has only focused on the educators' availabilities when studying the educator aspect, and the educators' preferences and expertise have not been taken into consideration. To fill in this gap, this paper proposes a conceptual model for the school timetabling problem considering educators' availabilities, preferences and expertise as a whole. Based on a common real-world school timetabling scenario, the artificial bee colony (ABC) algorithm is adapted to this study, as research shows its applicability in solving examination and course timetabling problems. A virtual search space for dealing with the large search space is introduced to the proposed model. The proposed approach is simulated with a large, randomly generated dataset. The experimental results demonstrate that the proposed approach is able to solve the STP and handle a large dataset in an ordinary computing hardware environment, which significantly reduces computational costs. Compared to the traditional constraint programming method, the proposed approach is more effective and can provide more satisfactory solutions by considering educators' availabilities, preferences, and expertise levels. [ABSTRACT FROM AUTHOR]

Extreme learning machine (ELM) is a popular randomization-based learning algorithm that provides a fast solution for many regression and classification problems. In this article, we present a method based on ELM for solving the spectral data analysis problem, which essentially is a class of inverse problems. It requires determining the structural parameters of a physical sample from the given spectroscopic curves. We proposed that the unknown target inverse function is approximated by an ELM through adding a linear neuron to correct the localized effect aroused by Gaussian basis functions. Unlike the conventional methods involving intensive numerical computations, under the new conceptual framework, the task of performing spectral data analysis becomes a learning task from data. As spectral data are typical high-dimensional data, the dimensionality reduction technique of principal component analysis (PCA) is applied to reduce the dimension of the dataset to ensure convergence. The proposed conceptual framework is illustrated using a set of simulated Rutherford backscattering spectra. The results have shown the proposed method can achieve prediction inaccuracies of less than 1%, which outperform the predictions from the multi-layer perceptron and numerical-based techniques. The presented method could be implemented as application software for real-time spectral data analysis by integrating it into a spectroscopic data collection system. [ABSTRACT FROM AUTHOR]

Chinese calligraphy, revered globally for its therapeutic and mindfulness benefits, encompasses styles such as regular (Kai Shu), running (Xing Shu), official (Li Shu), and cursive (Cao Shu) scripts. Beginners often start with the regular script, advancing to more intricate styles like cursive. Each style, marked by unique historical calligraphy contributions, requires learners to discern distinct nuances. The integration of AI in calligraphy analysis, collection, recognition, and classification is pivotal. This study introduces an innovative convolutional neural network (CNN) architecture, pioneering the application of CNN in the classification of Chinese calligraphy. Focusing on the four principal calligraphy styles from the Tang dynasty (690-907 A.D.), this research spotlights the era when the traditional regular script font (Kai Shu) was refined. A comprehensive dataset of 8282 samples from these calligraphers, representing the zenith of regular style, was compiled for CNN training and testing. The model distinguishes personal styles for classification, showing superior performance over existing networks. Achieving 89.5-96.2% accuracy in calligraphy classification, our approach underscores the significance of CNN in the categorization of both font and artistic styles. This research paves the way for advanced studies in Chinese calligraphy and its cultural implications.