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Moving the capital of Indonesia from the Jakarta area to the island of Kalimantan is an inevitable necessity due to economic, political, socio-cultural and environmental factors, which require other areas of the archipelago to be developed. It has been decided that the green city and smart city concepts will be applied in the development of the new capital, using a state-of-the-art approach, including metaverse technology. The methodology employed in this study includes in-depth interviews, focus group discussions, direct field observations, and documentation. The findings of this research consist of an analysis of the readiness of IKN and its surrounding area for the implementation of metaverse technology in IKN. This paper examines whether metaverse technology is needed in the construction of the new capital city. If so, what are the needs for the development of the new capital's metaverse, and what are the advantages (and disadvantages) of using the metaverse. The results are expected to provide a more comprehensive view of the use of metaverse technology in the new capital.

Given the tubal origin of high-grade serous ovarian cancer (HGSC), we sought to investigate intrauterine lavage (IUL) as a novel method of biomarker detection. IUL and serum samples were collected from patients with HGSC or benign pathology. Although CA-125 and HE4 concentrations were significantly higher in IUL samples compared to serum, they were similar between IUL samples from patients with HGSC vs benign conditions. In contrast, CA-125 and HE4 serum concentrations differed between HGSC and benign pathology (P =.002 for both). IUL and tumor samples from patients with HGSC were subjected to targeted panel sequencing and droplet digital PCR (ddPCR). Tumor mutations were found in 75 % of matched IUL samples. Serum CA-125 and HE4 biomarker levels allowed for better differentiation of HGSC and benign pathology compared to IUL samples. We believe using IUL for early detection of HGSC requires optimization, and current strategies should focus on prevention until early detection strategies improve.

Free flap procedure provides an overall success rate of 97%, which decreases to 85% in hypercoagulable states. COVID-19, as a pro-thrombotic disorder, therefore seems detrimental to free flap survival. We encountered a case of unique pattern of free flap partial failure in a young male who underwent extremity reconstruction. The patient was diagnosed as COVID-19 positive on the 3rd day post-reconstruction. The flap survived well for the first 7 days post-operatively, but gradually the skin got necrosed and the subcutaneous fat layer was preserved when debriding. To our knowledge, this is the only case in which the skin of the free flap of a COVID-19 positive patient was necrosed almost entirely subsequently, while the subcutaneous fat was relatively preserved.

Background: The diagnosis of multidrug resistant and extensively drug resistant tuberculosis is a global health priority. Whole genome sequencing of clinical Mycobacterium tuberculosis isolates promises to circumvent the long wait times and limited scope of conventional phenotypic antimicrobial susceptibility, but gaps remain for predicting phenotype accurately from genotypic data especially for certain drugs. Our primary aim was to perform an exploration of statistical learning algorithms and genetic predictor sets using a rich dataset to build a high performing and fast predicting model to detect anti-tuberculosis drug resistance. Methods: We collected targeted or whole genome sequencing and conventional drug resistance phenotyping data from 3601 Mycobacterium tuberculosis strains enriched for resistance to first- and second-line drugs, with 1228 multidrug resistant strains. We investigated the utility of (1) rare variants and variants known to be determinants of resistance for at least one drug and (2) machine and statistical learning architectures in predicting phenotypic drug resistance to 10 anti-tuberculosis drugs. Specifically, we investigated multitask and single task wide and deep neural networks, a multilayer perceptron, regularized logistic regression, and random forest classifiers. Findings: The highest performing machine and statistical learning methods included both rare variants and those known to be causal of resistance for at least one drug. Both simpler L2 penalized regression and complex machine learning models had high predictive performance. The average AUCs for our highest performing model was 0.979 for first-line drugs and 0.936 for second-line drugs during repeated cross-validation. On an independent validation set, the highest performing model showed average AUCs, sensitivities, and specificities, respectively, of 0.937, 87.9%, and 92.7% for first-line drugs and 0.891, 82.0% and 90.1% for second-line drugs. Our method outperforms existing approaches based on direct association, with increased sum of sensitivity and specificity of 11.7% on first line drugs and 3.2% on second line drugs. Our method has higher predictive performance compared to previously reported machine learning models during cross-validation, with higher AUCs for 8 of 10 drugs. Interpretation: Statistical models, especially those that are trained using both frequent and less frequent variants, significantly improve the accuracy of resistance prediction and hold promise in bringing sequencing technologies closer to the bedside. Keywords: Mycobacterium tuberculosis, Multidrug-resistance, Extensively drug-resistant tuberculosis, Machine learning, Genome sequencing

This study aimed to investigate the effects of temporal variability on the optimization of the Hydrologiska Byråns Vattenbalansavedlning (HBV) model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed: short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash-Sutcliffe efficiency coefficient (NSE), relative error (RE), root mean square error (RMSE), and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, −0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temporal calibration in hydrological analysis delivers better simulation in general for water balance analysis.

This study aimed to investigate the effect of temporal variability on the optimization of the Hydrologiska Byråns Vattenbalansavedlning (HBV) model, as well as the calibration performance using manual optimization and average parameter values. By applying the HBV model to the Jiangwan Catchment, whose geological features include lots of cracks and gaps, simulations under various schemes were developed: short, medium-length, and long temporal calibrations. The results show that, with long temporal calibration, the objective function values of the Nash-Sutcliffe efficiency coefficient (NSE), relative error (RE), root mean square error (RMSE), and high flow ratio generally deliver a preferable simulation. Although NSE and RMSE are relatively stable with different temporal scales, significant improvements to RE and the high flow ratio are seen with longer temporal calibration. It is also noted that use of average parameter values does not lead to better simulation results compared with manual optimization. With medium-length temporal calibration, manual optimization delivers the best simulation results, with NSE, RE, RMSE, and the high flow ratio being 0.563 6, 0.122 3, 0.978 8, and 0.854 7, respectively; and calibration using average parameter values delivers NSE, RE, RMSE, and the high flow ratio of 0.481 1, 0.467 6, 1.021 0, and 2.784 0, respectively. Similar behavior is found with long temporal calibration, when NSE, RE, RMSE, and the high flow ratio using manual optimization are 0.525 3, −0.069 2, 1.058 0, and 0.980 0, respectively, as compared with 0.490 3, 0.224 8, 1.096 2, and 0.547 9, respectively, using average parameter values. This study shows that selection of longer periods of temporal calibration in hydrological analysis delivers better simulation in general for water balance analysis.

Phosphorus is one of the most important nutrients required to support various kinds of biodegradation processes. As this particular nutrient is not included in the activated sludge model no. 1 (ASM1), this study extended this model in order to determine the fate of phosphorus during the biodegradation processes. When some of the kinetics parameters are modified using observed data from the restoration project of the Xuxi River in Wuxi City, China, from August 25 to 31 in 2009, the extended model shows excellent results. In order to obtain optimum values of coefficients of nitrogen and phosphorus, the mass fraction method was used to ensure that the final results were reasonable and practically relevant. The temporal distribution of the data calculated with the extended ASM1 approximates that of the observed data.