Xiahai Wei, Chenyu Zeng and Yao Wang
In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the…
Abstract
Purpose
In the process of making agricultural production decisions in rural households, severe weather conditions, either extreme cold or heat, may squeeze the labor input in the agricultural sector, leading to a reallocation of labor between the agricultural and non-agricultural sectors. By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, reveal the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.
Design/methodology/approach
This study utilizes data from Chinese meteorological stations and two waves of China Household Income Project to examine the impact and behavioral mechanism of extreme temperatures on rural labor reallocation.
Findings
(1) Extremely high and low temperatures had led to a reallocation of labor force from agricultural activities to non-farm employment, with a more pronounced effect from extreme high temperature events. (2) Extreme temperatures influence famers’ decision in abandoning farmland and reducing investment in agricultural machinery, thus creating an interconnected impact on labor mobility. (3) The reallocation effect of rural labor induced by extreme temperatures is particularly evident for males, persons that perceives economic hardship or labor in economically active areas.
Originality/value
By applying a dataset with a wide latitude range, this study empirically confirms the influence of extreme temperatures on the agricultural labor reallocation, and reveals the mechanism of farmers’ adaptive behavioral decision and therefore enriches the research on the impact of climate change on rural labor markets and livelihood strategies.
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Feng Qian, Yongsheng Tu, Chenyu Hou and Bin Cao
Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods…
Abstract
Purpose
Automatic modulation recognition (AMR) is a challenging problem in intelligent communication systems and has wide application prospects. At present, although many AMR methods based on deep learning have been proposed, the methods proposed by these works cannot be directly applied to the actual wireless communication scenario, because there are usually two kinds of dilemmas when recognizing the real modulated signal, namely, long sequence and noise. This paper aims to effectively process in-phase quadrature (IQ) sequences of very long signals interfered by noise.
Design/methodology/approach
This paper proposes a general model for a modulation classifier based on a two-layer nested structure of long short-term memory (LSTM) networks, called a two-layer nested structure (TLN)-LSTM, which exploits the time sensitivity of LSTM and the ability of the nested network structure to extract more features, and can achieve effective processing of ultra-long signal IQ sequences collected from real wireless communication scenarios that are interfered by noise.
Findings
Experimental results show that our proposed model has higher recognition accuracy for five types of modulation signals, including amplitude modulation, frequency modulation, gaussian minimum shift keying, quadrature phase shift keying and differential quadrature phase shift keying, collected from real wireless communication scenarios. The overall classification accuracy of the proposed model for these signals can reach 73.11%, compared with 40.84% for the baseline model. Moreover, this model can also achieve high classification performance for analog signals with the same modulation method in the public data set HKDD_AMC36.
Originality/value
At present, although many AMR methods based on deep learning have been proposed, these works are based on the model’s classification results of various modulated signals in the AMR public data set to evaluate the signal recognition performance of the proposed method rather than collecting real modulated signals for identification in actual wireless communication scenarios. The methods proposed in these works cannot be directly applied to actual wireless communication scenarios. Therefore, this paper proposes a new AMR method, dedicated to the effective processing of the collected ultra-long signal IQ sequences that are interfered by noise.
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Qing Han, Yanguo Qin, Yun Zou, Chenyu Wang, Haotian Bai, Tao Yu, Lanfeng Huang and Jincheng Wang
Although proximal row carpectomy, wrist arthrodesis and even total wrist arthroplasty were developed to treat wrist disease using bone and cartilage of the wrist, severe and…
Abstract
Purpose
Although proximal row carpectomy, wrist arthrodesis and even total wrist arthroplasty were developed to treat wrist disease using bone and cartilage of the wrist, severe and complicated bone defects caused by ferocious trauma and bone tumors remain a stubborn problem for surgeons. Development and application of the three-dimensional (3D) printing technology may provide possible solutions.
Design/methodology/approach
Computed tomography (CT) data of three cases with severe bone defects caused by either trauma or bone tumor were collected and converted into three-dimensional models. Prostheses were designed individually according to the residual anatomical structure of the wrist based on the models. Both the models and prostheses were produced using 3D printing technology. A preoperative design was prepared according to the models and prostheses. Then arthroplasty was performed after preoperative simulation with printed models and prostheses.
Findings
The diameter of the stem and radial medullary cavity, the direction and location of the prosthesis, and other components were checked during the preoperative design and simulation process phases. The three cases with 3D printed wrist all regained reconstruction of normal anatomy and part of the function after surgery. The average increasing Cooney score rate of Cases 2 and 3 was 133.34 ± 23.57 per cent, and that of Case 1 reached 85 per cent. The average declining rate of the Gartland and Werley Score in Cases 2 and 3 was 65.21 ± 18.89 per cent, and that of Case 1 dropped to 5 per cent in the last follow-up. The scores indicated that patients experienced pain relief and function regain. In addition, the degree of patient satisfaction improved.
Originality/value
3D printed wrist arthroplasty may provide an effective method for severe and complicated cases without sacrificing other bones. Personal customization can offer better anatomy and function than arthrodesis or other traditional surgical techniques.
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Xiuyun Yang and Qi Han
The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital…
Abstract
Purpose
The purpose of this study is to investigate whether the corporate environmental, social and governance (ESG) performance of enterprise is influenced by the enterprise digital transformation. In addition, this study explains how enterprise digital transformation affects ESG performance.
Design/methodology/approach
The sample covers 4,646 nonfinancial companies listed on China’s A-share market from 2009 to 2021. The study adopts the fixed-effects multiple linear regression to perform the data analysis.
Findings
The study finds that enterprise digital transformation has a significant inverted U-shaped impact on ESG performance. Moderate digital transformation can improve enterprise ESG performance, whereas excessive digital transformation will bring new organizational conflicts and increase enterprise costs, which is detrimental to ESG performance. This inverted U-shaped effect is more pronounced in industrial cities, manufacturing industries and enterprises with less financing constraints and executives with financial backgrounds. Enterprise digital transformation mainly affects ESG performance by affecting the level of internal information communication and disclosure, the level of internal control and the principal-agent cost.
Practical implications
The government should take multiple measures to encourage enterprises to choose appropriate digital transformation based on their own production behaviors and development strategies, encourage them to innovate and upgrade their organizational management and development models in conjunction with digital transformation and guide them to use digital technology to improve ESG performance.
Social implications
This study shows that irrational digital transformation cannot effectively improve the ESG performance of enterprises and promote the sustainable development of the country. Enterprises should carry out reasonable digital transformation according to their own development needs and finally improve the green and sustainable development ability of enterprises and promote the sustainable development of society.
Originality/value
This study examines the relationship between enterprise digital transformation and ESG performance. Different from the linear relationship between the two in previous major studies, this study proves the inverse U-shaped relationship between enterprise digital transformation and ESG performance through mathematical theoretical model derivation and empirical test. This study also explores in detail how corporate digital transformation affects ESG performance, as well as discusses heterogeneity at the city, industry and firm levels. It is proposed that enterprises should take into account their own characteristics and carry out reasonable digital transformation according to their development needs.
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Pengyang Li, Qiang Chen, Qingyu Peng and Xiaodong He
This paper aims to study the synergistic effect of graphene sponge on the thermal properties and shape stability of composite phase change material (PCM).
Abstract
Purpose
This paper aims to study the synergistic effect of graphene sponge on the thermal properties and shape stability of composite phase change material (PCM).
Design/methodology/approach
Graphene oxide sponge is first prepared from an aqueous solution of graphene oxide by freeze-drying method. The oxidized graphene sponge is reduced by hydrazine hydrate. Finally, use vacuum impregnation method to introduce paraffin into graphene sponge to prepare composite PCM.
Findings
Graphene sponge is used to improve the shape stability of paraffin wax and improves the thermal conductivity and latent heat of the composite PCM. The thermal conductivity increases by 200 per cent and the composite PCM has excellent reliability in 100 melt-freezing cycles.
Research limitations/implications
A simple way for fabricating composite PCM with high thermal conductivity and latent heat which has the potential to be used as thermal storage materials without container encapsulation has been developed by using graphene sponge and paraffin.
Originality/value
The materials and preparation methods with special structure and properties in this paper provide a new idea for the research of PCM, which can be widely used in the fields of energy conversion and storage.