Development and Characterization of Polymorphic Microsatellite Markers (SSRs) for an Endemic Plant, Pseudolarix amabilis (Nelson) Rehd. (Pinaceae)

Pseudolarix (Pinaceae) is a vulnerable (sensu IUCN) monotypic genus restricted to southeastern China. To better understand levels of genetic diversity, population structure and gene flow among populations of P. amabilis, we developed five compound SSR markers and ten novel polymorphic expressed sequence tags (EST) derived microsatellites. The results showed that all 15 loci were polymorphic with the number of alleles per locus ranging from two to seven. The expected and observed heterozygosities varied from 0.169 to 0.752, and 0.000 to 1.000, respectively. The inbreeding coefficient ranged from −0.833 to 1.000. These markers will contribute to research on genetic diversity and population genetic structure of P. amabilis, which in turn will contribute to the species conservation

A Novel Data-Driven Prediction Framework for Ship Navigation Accidents in the Arctic Region

Arctic navigation faces numerous challenges, including uncertain ice conditions, rapid weather changes, limited communication capabilities, and lack of search and rescue infrastructure, all of which increase the risks involved. According to an Arctic Council statistical report, a remarkable 2638 maritime accidents were recorded in Arctic waters between 2005 and 2017, showing a fluctuating upward trend. This study collected and analyzed ship accident data in Arctic waters to identify the various accident scenarios and primary risk factors that impact Arctic navigation safety. By utilizing data-driven algorithms, a model for predicting ship navigation accidents in Arctic waters was constructed, providing an in-depth understanding of the risk factors that make accidents more likely. The research findings are of practical significance for enhancing quantitative risk assessment, specifically focusing on the navigational risks in Arctic waters. The results of this study can assist maritime authorities and shipping companies in conducting risk analysis and implementing accident prevention measures for safe navigation in Arctic waters.publishedVersio

Personalized Drug Dosage – Closing the Loop

A brief account is given of various approaches to the individualization of drug dosage, including the use of pharmacodynamic markers, therapeutic monitoring of plasma drug concentrations, genotyping, computer-guided dosage using ‘dashboards’, and automatic closed-loop control of pharmacological action. The potential for linking the real patient to his or her ‘virtual twin’ through the application of physiologically-based pharmacokinetic modeling is also discussed

Perceived Parenting Style and Adolescents’ Social Anxiety in Selangor, Malaysia

Anxiety, especially social anxiety, is the most common mental health issue among Malaysian adolescents, and parenting styles have been suggested to play a crucial role in the development of adolescents’ anxiety symptoms. Therefore, this paper investigates the relationship between Malaysian adolescents’ perceptions of their parents’ parenting styles and their measured level of social anxiety, including differences by age and race. A total of 327 adolescents from international and national secondary schools in Selangor participated in this study. The Parental Perception Questionnaire and Kutcher Generalized Social Anxiety Disorder Scale for Adolescents were used to measure the adolescents’ perceptions of parenting styles and social anxiety, respectively. The results showed no significant correlations between parenting styles and social anxiety. In addition, parenting styles did not significantly predict the adolescents’ social anxiety. However, there were significant racial and age group differences in the categories of parenting style and levels of social anxiety. In conclusion, the parenting style received by Malaysian adolescents was not significantly related to their social anxiety. Interventions should focus on high-risk groups of adolescents (i.e., Malay adolescents and those aged 15–16 years old) to reduce their social anxiety

Formulation of a deep learning model for automated detection via segmentation of lung cancer

In 2020, the International Agency for Research on Cancer recorded nearly 20 million new cases of cancer around the world. It is estimated that cancer will be the second biggest cause of mortality worldwide in 2020, with over 10 million deaths. In Malaysia, the recorded number of new cases and deaths due to cancer in 2020 are 48639 and 29530, respectively. Lung cancer is the third most fre-quent cancer in Malaysia, and it also has the highest mortality rate, at 15.3 per-cent. Lung cancer has become a major public health issue in Malaysia, with only a 11% 5-year survival rate. Computed tomography (CT) scanning is the most common tool for early-stage lung cancer screening. One of the clinical signs of early lung cancer on CT imaging is pulmonary nodules, which are characterized as a small, opaque, roundish growth on the lung with a size of 7-30mm. There are two types of pulmonary nodules: benign and malignant (cancerous). The characteristic difference between malignant and benign nodules had make the pulmonary nodules segmentation significant as the radiologist can classify the malignancy of the nodules with the size of the nodules. Furthermore, radiologist can adjust the dosage of medication for malignant nodules patient, according to the size of the pulmonary nodules. The manual detection of pulmonary nodules in CT images is a tiring job as the radiologist may need to watch over 200 CT imag-es per CT scans. Luckily the advancement in machine learning technologies have paved way to new possibilities of pulmonary nodules detection and segmentation. and can integrate automation in solving repetitive manual intensive tasks. There-fore, this research investigates the diagnosis of lung cancer through CT images by using transfer learning and fine-tuning of the encoder. Hyperparameters such as type of number of epochs, optimizer and loss function are investigated on which combinations of these hyperparameters will yield the highest segmentation dice coefficient and Intersect over Union (IoU). Neural network architectures ResNet101 are evaluated as transfer learning encoder in extracting features from the patient’s CT images. The extracted fea-tures are then fed into the DeepLabV3 segmentation head to form a complete segmentation model. Subsequently, evaluating the combination of various pipe-lines, the loss and dice coefficient graphs are used to find the pipeline which performs the best in pulmo-nary nodules segmentation. This study indicated that the DeepLabV3-ResNet101-Adagrad Optimizer-Dice Loss pipeline yield the best performance. The pulmonary nodule segmentation models achieved a Dice Coefficient of 0.7983. The findings in this research will open new possibilities in screening method of lung cancer screening methods, offering more efficient and accurate detection of pulmonary nodules, ultimately improving patient outcomes

Ruthenium-Catalyzed para-Selective C−H Alkylation of Aniline Derivatives

The para-selective C−H alkylation of aniline derivatives furnished with a pyrimidine auxiliary is herein reported. This reaction is proposed to take place via an N−H-activated cyclometalate formed in situ. Experimental and DFT mechanistic studies elucidate a dual role of the ruthenium catalyst. Here the ruthenium catalyst can undergo cyclometalation by N−H metalation (as opposed to C−H metalation in meta-selective processes) and form a redox active ruthenium species, to enable site-selective radical addition at the para position

Resources Base View of the Video Games Console Manufacturers. Is Intuitive Interactive Social Gamin (IISG) The Future?

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Deep learning approaches to mean-variance portfolio hedging

This study develops and evaluates deep learning-based hedging strategies for power call options under three market models: the Black-Scholes (BS) complete market, the Merton jump-diffusion model, and a Mixed Merton model with multiple jump sources. Power options, characterized by their nonlinear payoff structure, $(S_T^\beta-K)^+$, present unique challenges due to their heightened sensitivity to the underlying asset's dynamics, especially in the presence of discontinuous jumps. We propose a neural network framework employing long short-term memory (LSTM) architectures to dynamically replicate option payoffs by optimizing the initial wealth $\hat{x}_0$ and hedge ratios $\hat{\pi}$, minimizing the mean-squared error between terminal wealth and option claims. Under the BS model, hedging performance is nearly perfect, serving as a benchmark. Numerical results demonstrate that both the Merton and Mixed Merton models effectively capture jump components, achieving low hedging loss and minimal option pricing error. These findings underscore the viability of deep learning for hedging exotic derivatives in incomplete markets, offering a flexible and scalable framework for modeling complex asset dynamics. Future work could extend this approach to other exotic options and investigate alternative architectures such as GRUs or mixed-exponential jump models.Bachelor's degre