Jyh-Woei Lin

Human cloning involves creating a genetically identical copy of a human cell, tissue, or embryo. That is, human reproductive cloning, producing a genetic copy of an existing person, has never been successfully done and remains globally banned or restricted. While there is no scientific evidence of successful human reproductive cloning, biotechnology combines cloning methods with genetic engineering to drive advancements in medical research and disease treatment. With the development of genetic engineering and cloning technologies, human cloning, as a possible future phenomenon, has sparked extensive ethical and moral discussions. In legal medicine, human cloning presents complex challenges intersecting bioethics, criminal law, civil identity, and forensic science. While the technology remains strictly regulated or banned globally, forensic experts and medical jurists analyze cloning through distinct administrative, civil, and criminal frameworks. Human cloning can be ethically evaluated using the foundational four principles of biomedical ethics established by Tom Beauchamp and James Childress including autonomy, beneficence, non-maleficence and justice. These principles provide a widely used framework to analyze the risks, rights, and moral implications of both reproductive and therapeutic cloning. German philosopher Jürgen Habermas strongly opposes human cloning and genetic enhancement because he believes they destroy the foundations of moral equality and personal autonomy. In his seminal work The Future of Human Nature, Jürgen Habermas argues against human cloning and the forensic implications of genetic duplication. He warns that the made status of a cloned individual fundamentally destroys the subject’s capacity to conceive of themselves as an autonomous, undivided author of their own life.

Hydrogen energy is considered to be one of the most environmentally friendly and potential green energy options due to its characteristics of producing only water and no carbon emissions after combustion. Low‐carbon and zero‐carbon can be achieved using hydrogen energy. Therefore, in the future, it will be the mainstream because it can achieve the goal of net‐zero carbon emissions. Hydrogen can be obtained using hydrogen purification technologies, which are mainly divided into pressure swing adsorption technology, condensation separation technology, metal palladium membrane separation technology, and metal hydride separation technology. Hydrogen storage technologies are mainly divided into compressed hydrogen storage, liquefied hydrogen storage, cryo‐compressed hydrogen storage, metal hydrides hydrogen storage, liquid organic hydrogen carriers, and underground hydrogen storage. Additionally, with the increasing focus on renewable energy, solid‐state hydrogen storage holds great promise for future applications in various sectors.

Taiwan will levy a carbon fee starting in 2025, according to the three-tier carbon accounting model and carbon emissions inventory measures. On 21 October 2024, Taiwan’s Ministry of Environment announced the carbon fee and that Taiwan had officially entered an era in which carbon emissions would be priced. The carbon fee officially took effect on 1 January 2025. Therefore, all manufacturing and power industries with annual carbon emissions exceeding 25,000 tons of carbon dioxide equivalents (tCO₂e) would be billed. The carbon fee system provides various preferential rate options and encourages all companies to propose voluntary reduction plans. Rate differences can help generate substantial carbon reduction action. However, the carbon fee system is flexible and can be adjusted in the future based on implementation status and industry change to help cope with changes in the net-zero transformation process.

The 2 Music Therapies are used for the elderly female insomnias of 2 groups because insomnia is one of the major problems of menopausal women with growing age. Each group has 21 insomnias. The Lambdoma Matrix Harmonic Music (LMHM) and Medical Resonance Therapy Music (MRTM) therapies own around 57% of success rate and around 52% of success rate, respective. Therefore, the LMHM can recognize as the good therapy music to improve the quality of sleeping.

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Two backpropagation neural network (BPNN) models were constructed to predict two historic geomagnetic storms that occurred in September 1999 and October 2003. The Disturbance storm time (Dst) indices from January 1, 1999, to December 31, 2014 (Coordinated Universal Time, UTC), were used as the training and test datasets for cross-validation in order to verify and validate the reliability and robustness of the two BPNN models, and yielded reasonable, predicted results. A large correlation coefficient (R) and low root mean square error (RMSE) were obtained, verifying the reliability of the two BPNN models. The predicted Dst indices can be provided for giving inputs in advance (i.e., any future time). Therefore, this analyzed method can serve as an excellent real-time prediction (RTP). To test the ability and possibility of the long-term prediction (LTP) obtained using the two BPNN models, the Dst indices were examined, which corresponded to two significant historic large geomagnetic storms that occurred in August 1972 and March 1989. For the both BPNN models, after evaluating their learning procedure, the time-dependence of LTP, the dependence of the predicted errors on the time period length of training datasets, and the variance by learning process, we found that they were stable models for the RTP and LTP.

In this study, Global Ionosphere Maps (GIMs) were used to detect all total electron content (TEC) precursors prior to the M_w 7.9 Wenchuan earthquake at 06:28:01 (UTC) on 12 May 2008. The GIMs were converted into GIM-gis between 00.00 (UTC) on 01 December 2004 and 31 March 2005 and were used to build the Spectral-Based Graph Neural Network (SB-GNN) and Adaptive Butterworth Spectral-Based Graph Neural Network (ABSB-GNN) models. A blurred TEC precursor was detected at 2:20 (UTC) on 9 May 2008, i.e. 3 days prior to the Wenchuan earthquake, using the ABSB-GNN model in place of the SB-GNN model, which improved predictive ability. After evaluating the variance in the learning process, the two models were verified for stability with better training and predicted accuracies in the ABSB-GNN model, which might be the reason that a blurred TEC precursor was detectable using the ABSB-GNN model.