This study proposes a novel approach that investigates the dynamic dependency among exchange rates by extending time-varying copulas' parameters following an autoregressive moving average (ARMA) process. The process consists of an autoregressive part that explains the effect of the previous parameters and a forcing variable that measures the dependence structure between marginal variables. We apply this model to the daily data of the exchange rates of five Asian countries with the strongest economies before and during the 2020 pandemic, namely CNY/USD, IDR/USD, INR/USD, JPY/USD, and KRW/USD. The ARIMA-GARCH model was used to model the exchange rates data and estimate the dynamic dependence using time-varying copulas with the extended parameters. The dynamic dependencies between Chinas and the four countries' exchange rates before and during the 2020 pandemic was evidenced. Moreover, India is the country whose exchange rate has been most strongly affected by the pandemic. Some of the highlights of the proposed approach are: • This paper provides two algorithms to investigate the dynamic dependencies among exchange rates data during a crisis and forecast the data using time-varying copulas with the extended parameters. • There are four extended time-varying copulas' parameters which can measure the dynamic dependencies between variables. • The computation procedure is easy to implement.

Adaptive reuse of vacant houses in Japan offers an innovative and sustainable solution to the increase in vacant houses. This approach aligns with principles of sustainable architecture and the circular economy by reducing waste, lowering energy consumption, and extending the lifecycle of existing structures. This study uses purposive sampling, analyzing 262 adaptive reuse cases across Japanese prefectures through partial surveys, municipal records, and online maps. K-prototype clustering identified three distinct patterns. Cluster 1 emphasizes modern businesses, such as food, beverage, and accommodation services, within urban areas to address the needs of densely populated regions and tourist hubs. Cluster 2 blends urban and rural contexts, balancing historical preservation with modern functionality. Cluster 3 highlights rural and scenic accommodations that cater to tourists seeking cultural immersion and authentic experiences, despite challenges like low population density and limited accessibility. These findings contribute to the theoretical understanding of adaptive reuse as a key strategy for repurposing underutilized spaces, promoting both economic and social resilience. In practical terms, it demonstrates how adaptive reuse advances circular economy objectives by preserving cultural heritage, enhancing environmental sustainability, and creating economic opportunities. Further investigation is needed to unlock the unexplored potential of adaptive reuse in broader contexts and functions.

Purpose: Farmer exchange rate (FER) is the ratio between a farmer's income and expenditure and is also an indicator of farmers’ welfare. There is little research regarding its use in risk modeling in crop insurance. This study seeks to propose a design for a household margin insurance scheme of the agricultural sector based on FER.Design/methodology/approach: This research employs various risk modeling concepts, i.e. value at risk, loss models and premium calculation, to construct the proposed model. The standard linear, static and time-varying copula models are used to identify the dependency between variables involved in calculating FER.Findings: First, FER can be considered as the primary variable for risk modeling in agricultural household margin insurance because it demonstrates farmers’ financial ability. Second, temporal dependence estimated using the time-varying copula can minimize errors, reduce the premium rate and result in a tighter guarantee's level of security.Originality/value: This research extends the previous similar studies related to the use of index ratio in margin insurance loss modeling. Its authenticity is in the use of FER, which represents the farmers' trading capability. FER determines farmers’ losses by considering two aspects: the farmers’ income rate and their ability to fulfill their life and farming needs. Also, originality exists in the use of the time-varying copulas in identifying the dependence of the indices involved in calculating FER.

As one of the law resources of Muslim society, hadith is very important to learn. Unlike most hadith-related research, which studies more about content, we examine the relationship pattern between hadith narrators. In the study of hadith science, a series of hadith narrators who narrate a hadith is referred to as a sanad. This hadith sanad must be connected to the Prophet as the primary source of a hadith. Therefore, research related to the relationship between narrators is fundamental because it affects the quality and validity of a hadith. This paper analyzes the pattern of hadith narrators using Sequential Pattern Discovery using Equivalence Classes (SPADE). We separate the data of the narrators from the content, whereas, in the hadith books we use, the two are still mixed. This study, therefore, provides detailed information on the steps in the analysis of the patterns of hadith narrators. Some of the highlights of this paper are: • Algorithm 1 provides the detailed steps in data preprocessing to obtain the “clean data” needed in analyzing the pattern of narrator relationships. • Algorithm 2 provides a detailed description of analyzing the pattern between hadith narrators using SPADE.

Setiap Negara memerlukan sumber penerimaan untuk mewujudkan pembangunan nasional dan membiayai segala keperluannya. Di Indonesia, terdapat tiga sumber utama penerimaan Negara; penerimaan pajak, penerimaan bukan pajak, serta penerimaan hibah baik dari dalam maupun luar negeri. Untuk mengantisipasi berbagai keperluan serta mengoptimalkan penggunaan penerimaan Negara, maka perlu adanya proyeksi realisasi penerimaan Negara dari ketiga sumber tersebut. Proyeksi tersebut dapat dilakukan dengan menggunakan model deret waktu klasik baik model deterministik maupun model stokastik. Namun demikian, pada model deret waktu klasik terdapat beberapa asumsi yang harus dipenuhi seperti pola data atau jumlah minimal data. Sebagai alternatif, pada penelitian ini akan dilakukan peramalan dengan menggunakan model Grey (1,1) dan Grey-Markov; perpaduan antara model Grey dengan analisis Rantai Markov. Model ini memiliki keunggulan dibandingkan model deret waktu klasik, yaitu tidak perlu adanya asumsi mengenai pola data serta peramalan dapat dilakukan meskipun data yang dimiliki cukup kecil (minimal 4 data). Hasil analisis menunjukkan bahwa model Grey-Markov secara umum memberikan akurasi peramalan yang lebih baik dibandingkan dengan model Grey (1,1). [Every state requires a source of revenue to realize its national development and fund its needs. In Indonesia, there are three main sources of state revenues; tax, non-tax, and grant revenues both from within and outside the country. To anticipate various purposes and optimize the use of state revenues, it is necessary to project the realization of the revenues from these three sources. The projection can be done using a classical time series model in both deterministic and stochastic models. However, in the classical time series model there are several assumptions that must be met such as data patterns or minimal amount of data. Alternatively, in this study the data will be forecasted using Grey (1,1) model and Grey-Markov model; a combination of the Grey model with Markov Chain analysis. This models have advantages over the classical time series models, ie no need for assumption about data pattern and forecasting can be done even though there are small size data (at least 4 data). The results of the analysis show that generally Grey-Markov model provides better forecasting accuracy compared with Grey (1,1) model.]