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Innovation is the fundamental driving force for the long-term sustainable development of an economy. After four decades of rapid economic growth, China is facing crises related to a demographic structure of “aging before getting rich,” industrial overcapacity of low-end products and environmental and resources constraints. This paper aims to discuss these issues.

Based on logical analysis and recapitulation of previous empirical research, this study presents the conclusion.

Scientific and technological innovation, as strategic support to improve social productivity and overall national strength, must be placed at the center of the country’s overall development.

The development model that preys upon cheap resources for extensive growth is unsustainable. Thus, the country needs an urgent strategic switch to drive its economic growth through research and development innovation and original technological advancement.

Minimizing the energy consumption in a wireless sensor node is important for lengthening the lifetime of a battery. Radio transmission is the most energy-consuming task in a wireless sensor node, and by compressing the sensor data in the online mode, it is possible to reduce the number of transmission periods. This study aims to demonstrate that temporal compression methods present an effective method for lengthening the lifetime of a battery-powered wireless sensor node.

In this study, the energy consumption of LoRa-based sensor node was evaluated and measured. The experiments were conducted with different LoRaWAN data rate parameters, with and without compression algorithms implemented to compress sensor data in the online mode. The effect of temporal compression algorithms on the overall energy consumption was measured.

Energy consumption was measured with different LoRaWAN spreading factors. The LoRaWAN transmission energy consumption significantly depends on the spreading factor used. The other significant factors affecting the LoRa-based sensor node energy consumption are the measurement interval and sleep mode current consumption. The results show that temporal compression algorithms are an effective method for reducing the energy consumption of a LoRa sensor node by reducing the number of LoRa transmission periods.

This paper presents with a practical case that it is possible to reduce the overall energy consumption of a wireless sensor node by compressing sensor data in online mode with simple temporal compression algorithms.

Cambodia's sustained and robust growth performance since the post-reform era in 1993 has been attributed to the boom in inward foreign direct investment (FDI) attracted to the country's labor-intensive industries, where it has comparative advantages. The purpose of this study is twofold. First, it aims to assess the consistency between Cambodia's revealed comparative advantage in exports and its sectoral inward FDI. Second, it examines the relationship between industry-level FDI and growth performance by accounting for heterogeneity across industries.

The paper uses descriptive methods and an industry-level dataset provided by the Council for the Development of Cambodia to elucidate the issue. Additionally, it applies instrumental variable two-stage least squares (IV-2SLS) regression to investigate the impact of industry-specific FDI on economic growth from 1994 to 2017, which also aims to address the endogeneity issue.

On the one hand, our research finds that Cambodia's FDI has been attracted to sectors in which it has a comparative advantage during the aforementioned period. On the other hand, both FDI and the comparative advantage index significantly impact economic growth in Cambodia. The greater the flow of foreign investment into sectors with comparative advantage, the stronger the impetus for growth.

This study fills a gap in the literature and contributes to a better understanding of the relationship between FDI and economic growth in Cambodia. It is the first paper to investigate the heterogeneity of industry-specific FDI and provides practical recommendations for policymakers to effectively harness foreign investments and avoid malign FDI inflows.

Science policy and practice for open access (OA) books is a rapidly evolving area in the scholarly domain. However, there is much that remains unknown, including how many OA books there are and to what degree they are included in preservation coverage. The purpose of this study is to contribute towards filling this knowledge gap in order to advance both research and practice in the domain of OA books.

This study utilized open bibliometric data sources to aggregate a harmonized dataset of metadata records for OA books (data sources: the Directory of Open Access Books, OpenAIRE, OpenAlex, Scielo Books, The Lens, and WorldCat). This dataset was then cross-matched based on unique identifiers and book titles to openly available content listings of trusted preservation services (data sources: Cariniana Network, CLOCKSS, Global LOCKSS Network, and Portico). The web domains of the OA books were determined by querying the web addresses or digital object identifiers provided in the metadata of the bibliometric database entries.

In total, 396,995 unique records were identified from the OA book bibliometric sources, of which 19% were found to be included in at least one of the preservation services. The results suggest reason for concern for the long tail of OA books distributed at thousands of different web domains as these include volatile cloud storage or sometimes no longer contained the files at all.

Data quality issues, varying definitions of OA across services and inconsistent implementation of unique identifiers were discovered as key challenges. The study includes recommendations for publishers, libraries, data providers and preservation services for improving monitoring and practices for OA book preservation.

This study provides methodological and empirical findings for advancing the practices of OA book publishing, preservation and research.