Xiaobing Tang. Global space and the nationalist discourse of modernity : the historical thinking of Liang Qichao

This article reviews the book Global Space and the Nationalist Discourse of Modernity: The Historical Thinking of Liang Qichao written by Xiaobing Tang

Travel in Europe

Postcard from Ziyun Liang, during the Linfield College Semester Abroad Program at Centre International d\u27Études Françaises, L\u27Université Catholique De L\u27Ouest in Angers, Franc

Liang Luang

Gong Luang sebagai konvensi seni diikat oleh ketentuan-ketentuan yang sekaligus menjadi ciri terhadap repertoar Luang itu sendiri. Akan tetapi jika kita melihatnya sebagai kumpulan alat-alat gamelan, maka Gong Luang tersebut masih memberikan kemungkinan-kemungkinan lain untuk dikembangkan baik dari segi teknik permainan, fungsi maupun komposisi lagunya. Teknik permainan gamelan Gong Luang dapat dikembangkan dengan memasukkan teknik-teknik gamelan lain seperti Gong Kebyar, Semar Pegulingan dan lain-lain. Seperti misalnya gangsa jongkok yang biasa dimainkan dengan teknik kekenyongan dapat dikembangkan menjadi teknik kotekan. Bila dikaji dari segi fungsi gamelan Gong Luang dapat dikembangkan di luar konteks upacara untuk memperkaya fungsi dan maknanya. (Adi Adnyana:1999:3) Meskipun gamelan Gong Luang berlaras pelog tujuh nada, akan tetapi ia memiliki nuansa yang berbeda jika dibandingkan dengan gamelan berlaras pelog tujuh nada lainnya. Seperti misalnya gamelan Semar Pegulingan, Selonding, Gambang, Semarandhana dan yang lainnya. Hal inilah yang membuat gamelan Gong Luang menjadi unik dan sangat menarik minat penata untuk menggarapnya menjadi sebuah komposisi musik inovatif yang penata beri judul Liang Luang. Kata Liang jika dicari di dalam kamus bahasa Indonesia berarti lubang (KBBI:590:1997), dan kata Luang berarti kosong (KBBI:603:1997). Jadi, Liang Luang kalau diartikan menjadi dari kosong kembali ke kosong atau dari lobang kembali ke lobang. Maksudnya adalah manusia yang lahir di bumi, semuanya akan mengalami tiga fase yaitu lahir, hidup dan mati. Ketiga proses dalam siklus kehidupan tersebut dalam ajaran agama hindu disebut dengan Tri kona

Pengelolaan objek Wisata Pantai Liang Selama Masa Pandemi Covid-19

Bagaimana pengelolaan objek wisata Pantai Liang selama masa pandemi Covid-19 di Desa Liang, Kecamatan. Salahutu, Kabupaten. Maluku Tengah. Tujuan  penelitian ini adalah untuk mengetahui bagaimana pengelolaan objek wisata pantai Liang selama masa pandemi Covid-19  di Desa Liang, Kecamatan. Salahutu, Kabupaten. Maluku Tengah. Penelitian ini dilaksanakan di Desa Liang Kecamatan Salahutu, Kabupaten Maluku Tengah pada tangal 3 Oktober -3 November 2022. Populasi adalah Populasi dalam penelitian ini adalah sebanyak 100 orang. Sampel dalam penelitian yaitu 100 orang yang di acak . Variabel yang digunakan Pengelolaan fasilitas, Fasilitas Utama, Fasilitas pendukung, Fasilitas penunjang. Teknik analisis data menggunakan model Miles dan Huberman yaitu datareduction, data display, dan conclusion drawing/verivication.   Kata Kunci :Pengelolaan Objek Wisata Pantai Selama Pandemi Covid-1

Correction:Repurposing dextromethorphan and metformin for treating nicotine-induced cancer by directly targeting CHRNA7 to inhibit JAK2/STAT3/SOX2 signaling (Oncogene, (2021), 40, 11, (1974-1987), 10.1038/s41388-021-01682-z)

Only after the article was published online did the authors notice the misspelling of the second author’s name. It should be “Liang Du” instead of “Du Liang”. The authors sincerely apologize for any inconvenience this might have caused. The original article has been corrected

Editor ’s Introduction

Although business intelligence has been a research area in information systems for a long time, it becomes a bandwagon recently after combining with the ever-increasing online data and big data analytics. It allows managers to uncover useful knowledge from a large volume of dynamic data. I think one major reason for such a movement is that scholars can analyze real world behavioral data, rather than keep predicting behavioral intention. I am not saying intention is not useful, but it is interesting if we can go beyond intention to see more real behavior

Guest Editorial: Social and human aspects of cyber-physical systems

open6siIn the vision of Industry 4.0, the new industrial revolution is a revolution of cyber-physical systems, of which the Internet of Things forms a key foundation that has a great impact on the way people live, and the way businesses are organised. Cyber-physical systems are often considered feedback systems that integrate computation, networking, and physical processes, and more recently with ‘human-in-the-loop’ as one of the key research topics. The advances in social computing have connected human-inthe-loop in cyber-social systems such as Facebook and Twitter, while their social-physical activities are supported by the cyberphysical systems on or near their bodies and in their interconnected environments. Cyber-physical systems become an integral part of social-cyber-physical systems (SCPS) that weave into the sociotechnical fabric of human society. These hybrid systems, exhibiting both continuous (in physical and social spaces) and discrete (in cyberspaces) dynamic behaviour, give rise to not only new opportunities but also new challenges in designing products and services where human and technical aspects are massively intertwined. This Special Issue aims to present state-of-the-art research attempts and results on the topic of SCPS.openopenHu J.; Liang R.-H.; Shih C.-S.; Catala A.; Marcenaro L.; Osawa H.Hu, J.; Liang, R. -H.; Shih, C. -S.; CATALA MALLOFRE, Andreu; Marcenaro, L.; Osawa, H

EMPIRICAL RESULTS FROM VAR PREDICTION USING PEARSON?S TYPE IV DISTRIBUTION

Two most important characteristics of equity returns time series data are volatility clustering and non-normality. GARCH model has been widely used to forecast dynamic volatilities and hence has been used for value-at-risk (VaR) estimation. (Bhattacharyya et al 2008) has developed a new VaR estimation model for equity return time series using a combination of the Pearson?s Type IV distribution and the GARCH(1,1) approach which showed superior predictive abilities. This new model was tested on indices of eighteen countries [3] on daily return up to March 1st, 2005. In this project, we replicate the results in [3], and test the model for its predictive power over a more volatile period (i.e. 350 trading days prior to July 18th, 2008). We backtest the validity of the VaR estimations and compare the predictive power of this model over both of the above time periods on indices of eight countries. We discover that the Pearson?s type IV model still remains a good predictive ability during the more volatile period