Tracking abundant gene transcripts quantification over continuous cancer progression stages may reveal the mechanism of disease advancement. In this work, we profile the transcript quantification over the stages using a time-series approach, in which the stages/sub-stages of the disease are the time points, and the quantification measurements are the values. The values over time points are used to interpolate the growth of the progression using the cubic spline function. Then, the transcripts profiles are universally aligned and clustered using the time-series profile hierarchical clustering method based on the area between each pair of the aligned profiles; the method is named (HC-UAP). We compare the proposed method with a hierarchical clustering method based on Euclidean distance (HC-ED). Both methods were applied on two next-generation sequencing (NGS) prostate cancer datasets, the first from the Chinese and the second from the North American population. HC-ED clusters the dataset to find patterns while HC-UAP was able to single out outliers that trend differently in both datasets. While finding patterns in gene expression that trend over stages is the standard approach for analyzing time-series models, identifying outlier transcripts that grow differently than other transcripts can provide more details about the contribution of the mRNA transcriptional activity to the disease. They also can be a potential biomarker for the disease progression.

The chemistry of Schiff base has received remarkable attention in different applications, both organic synthesis and industries. Over the past few years many reports have been on the synthesis, characterization and application of vanadium Schiff base complexes. However, heterogeneous vanadium Schiff base catalysts are active for various oxidation reactions, making catalytic oxidation of hydrocarbons a great interest. This review summarizes the recent development of organic substrate oxidation with heterogeneous vanadium Schiff base catalysts.

The Indonesian creative economy has been on the rise since 2015 when it has started being measured and prioritized by the government. Its contribution towards the Indonesian GDP has risen significantly as well. A small part of that creative sector is the video game industry and market. The video game global market will be worth USD 200 Billion in 2023. Indonesia currently is ranked 16th in terms of market size. Although having an enormous market opportunity, local video game producers only contribute 1%. Growth opportunities exist, however local game studios are facing the difficulty of recruiting quality game developers. Higher education institutions need to produce graduates having the knowledge, competences, and skills relevant for their work. This study is done to identify and prioritize attributes for the design of a university level program in game development that ensures employability in the sector. A qualitative thematic analysis is done in identifying the important factors for an academic program, followed by an analytical hierarchical process in determining the factors. Result of the study shows that a curriculum with internships in game studios, ensuring students are knowledgeable on the business models & video game market, having practitioners teach in the program, and creating a community of practice in the university is essential in producing quality graduates.

This study aims to determine an accurate forecasting model, especially an error rate of around 0, and to examine how the automatic rejection system reacts to stock price as a result of the pandemic. The statistical clustering method is used for the dataset in form of daily observations, while the sample covers the period of cases before and after COVID-19 pandemic from 02 January 2019 to 20 June 2020 at the Trinitan Minerals and Metal Company. Furthermore, the data used in the estimation are the opening and closing price of returns, which are later processed using SAS analysis tools. It is shown that the most appropriate decision-making processes are those proven to be most effective. Therefore, predicting future events based on a suitable time series model will help policymakers and strategists make decisions and develop appropriate strategic plans regarding the stock market. Meanwhile, 98% of the ARIMA (1,1,1) is a forecasting model which can be applied to predict stock prices. The new approach of this study is an integrated autoregressive moving average used as an attempt to accurately predict stock prices during a pandemic.

Micro/mesopore composite catalysts containing platinum with HZSM-5 zeolite and three different silicate mesopores (SBA-3, KIT-6, and KCC-1) were prepared to investigate the influence of different silicate mesopores in the isomerization reaction of n-heptane at the temperature of 200-350 °C The structural characteristics and morphology of the prepared catalysts were studied by various methods such as XRD, FT-IR, N2 adsorption-desorption, XRF, and SEM. The obtained results indicate that the prepared catalysts have high conversion and high selectivity of multi-branched isomers compared to mono-branched isomers and low development of aromatic compounds. The most suitable results and selectivity were obtained in the n-heptane isomerization reaction related to the Pt/HZSM-5-SBA-3 catalyst.

This work was designed to study the effect of major drains, such as El Rahawy, on the Nile's water quality over time. The data in this study includes six years of physical and chemical variables data, starting from 2015 to 2020, covering three different water streams. The present results showed that the Rosetta branch and Khandaq sharky canal were directly affected by the agricultural drainage of the Rahway drain, with the change over time for the worse. Rayah El Nasery canal enjoyed complete independence from the negative impact of the drain while maintaining the quality at an appropriate level over time. The study predicts a decline in the water quality of the Rosetta branch in the future and recommends exerting more efforts to treat all pollutants that are discharged on the Nile and raise the efficiency of the Rahawy drain.

This study updates Humanitarian Logistics Digital Business Ecosystem framework coupled with the development of a proposed integrated CCSD-CoCoSo MCDM method to rank factors used in assessing humanitarian and business logistics actor’s propensity to use, diffuse, and adopt a collaborative digital business ecosystem platform for their future operational use. Employing nine criteria derived from technology innovation theories and institutional theory, and 28 experts comprising our decision matrix. The findings report perceived relative advantage, perceived safety and security, and infrastructure and expertise as the top three vital criteria that experts believe when addressed in an ecosystem platform for humanitarian and business logistics actors it would encourage a collaboration for their sustainable future operations. With organisational culture and structure as the least prioritised criteria. The study concludes that the CCSD-CoCoSo obtained results are objective, validating, and that this model is useful and suitable for MCDM analysis and policy making.

Proficient routes were devised for coupling different aromatic/aliphatic acids with amines to form amide linkage using various catalysts. Under the optimized reaction conditions, highest conversion was possible without formation of any by-products. All synthesized compounds were purified using column chromatography and characterized by mass spectrometry, nuclear magnetic resonance spectrometry and liquid chromatography-mass spectrometric analysis.

Tourist village program is one development priority program for rural development. Despite numerous opportunities to develop tourist villages such as the availability of natural resources and high demand for tourist villages recently, some challenges are still faced to develop tourist villages, especially in a developing country such as Indonesia. Governance problems, infrastructure, and effective partnership are among other factors that remain challenging in developing tourist villages. This study attempts to identify factors that determine the state of tourist villages in Indonesia and determine the appropriate strategies for better tourist village development. Using the case of tourist villages in Kedung Ombo, Central Java, a water based attractive tourist village, this study uses both machine learning and multicriteria approaches by means of Promethee in order to address the objective of the study. This study shows that government support, application of information technology, infrastructure, local participation, partnership, and attractive variations, are among the determinant factors that affect tourist village development. The study also reveals that the appropriate strategies for tourist village development include, improving infrastructure, institutional strengthening, and capacity building. This study could be used to assist local national as well as sub-national governments to effectively manage tourist villages in Indonesia.

The Nile River is an important natural and exclusive source of fresh water in Egypt. Water samples were taken monthly from twelve sites from 2015 to 2020 in El Beheira Governorate and eighteen physicochemical parameters were measured. The results show that the Rahawy drain recorded the highest values for most of the physicochemical parameters. The HPI and MI indicators in Rahawy drain were higher (70 % & 100 %) than in other sites, especially in the summer and winter seasons. The Rahawy WQI values were classified as poor quality. The IWQI results indicate that the water quality for the Rahawy was within the "severe restriction" class, with many restrictions to be used in agriculture. The water quality of the Nile River in the south of Egypt is better than that of the north and the water quality recovery takes more time and distance.