What Are They Looking for? Exploring the Motivation of Sales Staff in Beijing

Among business practitioners, it is a conventional wisdom that motivating sales staff is essential for any organisation aspiring to succeed. However, the process of motivating is complicated because of the diversity of individual’s needs. Besides, differences in culture may lead to different job attribute preferences across countries. Based on the content theories on motivation, this paper explores the non-financial motivational factors on sales staff in China. This study also illustrates how Chinese cultural heritages, specifically Confucius ideology have affected the needs and the job attribute preferences of sales staff in modern Chinese societies. In addition, the dissertation provides information on the relationships between leadership styles and work motivation among the Chinese sales staff. The findings of this dissertation are based on a qualitative research, in which 20 semi-structured interviews were conducted with sales staff from three companies in Beijing. The study revealed both compliances and discrepancies with traditional perceptions of Chinese employees’ work motivation derived from Hofstede’s cultural dimensions as well as Confucianism studies. This indicates that Chinese sales staff’s work motivations are undergoing changes with the development of economy. The sales staff have played a revenue-producing role in enterprises, therefore, it is important for management to understand how to motivate them. Furthermore, China is emerging as one of the dominant economic powers in the world, a thorough understanding of Chinese employees’ needs and job attribute preferences is important for management researchers and international business practitioners to enhance managerial efficiency in China

Feasibility evaluation of a wind/P2G/SOFC/GT multi-energy microgrid system with synthetic fuel based on C-H-O elemental ternary analysis

Power to gas (P2G) uses electrical energy from access renewable power and captured carbon dioxide (CO2) to generate methane (CH4). The technology provides opportunity for replacing fossil fuels with green-powered hydrocarbon, benefiting the reducing of carbon emission. However, the methanation process in P2G requires high H2/CO2 ratio with available amount of hydrogen (H2) restricted by fluctuation of renewable power, bringing limits to the reusing of captured CO2. This paper presents a feasibility analysis of a novel wind/P2G/SOFC/GT multi-energy system (MES) for microgrid. Green-powered CH4 generated from P2G is mixed with captured CO2, bringing additional flexibility to balancing the overall H2/CO2 ratio for utilization. To comprehensively analyze the feasibility of synthesis CH4/CO2 fuel, evaluation of MES is carried out from both design and off-design conditions. For the design condition, a methodology of C-H-O elemental ternary analysis is applied to reflect the process of fuel utilization and reveal its connection with the trade-off feature of multiple components. For the off-design condition, fluctuations of user’s load and renewable source during winter and summer scenarios are considered in a case study. Results show that under C-H-O distribution of 5.8%, 61.2% and 33.0%, the SOFC/GT could operate safety with electrical efficiency of 62%, capable of participating as a secondary power source for MES. Meanwhile, the overall H2/CO2 utilization ratio of the system is reduced from 4:1 to 12:5, where extremes conditions during winter and summer scenarios are evaluated with renewable penetration level of 94% and wind curtailment rate below 5% reached

Effect of Evaporation Temperature on the Performance of Organic Rankine Cycle in Near-Critical Condition

Considering the large variations of working fluid's properties in near-critical region, this paper presents a thermodynamic analysis of the performance of organic Rankine cycle in near-critical condition (NORC) subjected to the influence of evaporation temperature. Three typical organic fluids are selected as working fluids. They are dry R236fa, isentropic R142b, and wet R152a, which are suited for heat source temperature from 395 to 445 K. An iteration calculation method is proposed to calculate the performance parameters of organic Rankine cycle (ORC). The variations of superheat degree, specific absorbed heat, expander inlet pressure, thermal efficiency, and specific net power of these fluids with evaporation temperature are analyzed. It is found that the working fluids in NORC should be superheated because of the large slope variation of the saturated vapor curve in near-critical region. However, the use of dry R236fa or isentropic R142b in NORC can be accepted because of the small superheat degree. The results also indicate that a small variation of evaporation temperature requires a large variation of expander inlet pressure, which may make the system more stable. In addition, due to the large decrease of latent heat in near-critical region, the variation of specific absorbed heat with evaporation temperature is small for NORC. Both specific net power and thermal efficiency for the fluids in NORC increase slightly with the rise of the evaporation temperature, especially for R236fa and R142b. Among the three types of fluids, dry R236fa and isentropic R142b are better suited for NORC. The results are useful for the design and optimization of ORC system in near-critical condition

SongComposer: A Large Language Model for Lyric and Melody Composition in Song Generation

We present SongComposer, an innovative LLM designed for song composition. It could understand and generate melodies and lyrics in symbolic song representations, by leveraging the capability of LLM. Existing music-related LLM treated the music as quantized audio signals, while such implicit encoding leads to inefficient encoding and poor flexibility. In contrast, we resort to symbolic song representation, the mature and efficient way humans designed for music, and enable LLM to explicitly compose songs like humans. In practice, we design a novel tuple design to format lyric and three note attributes (pitch, duration, and rest duration) in the melody, which guarantees the correct LLM understanding of musical symbols and realizes precise alignment between lyrics and melody. To impart basic music understanding to LLM, we carefully collected SongCompose-PT, a large-scale song pretraining dataset that includes lyrics, melodies, and paired lyrics-melodies in either Chinese or English. After adequate pre-training, 10K carefully crafted QA pairs are used to empower the LLM with the instruction-following capability and solve diverse tasks. With extensive experiments, SongComposer demonstrates superior performance in lyric-to-melody generation, melody-to-lyric generation, song continuation, and text-to-song creation, outperforming advanced LLMs like GPT-4.Comment: project page: https://pjlab-songcomposer.github.io/ code: https://github.com/pjlab-songcomposer/songcompose

Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model’s SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0–10 cm) from the farmland (2.5 × 104 m2) of Fukang City, Xinjiang Uygur AutonomousRegion, China. The random forest (RF) method and extreme learning machine (ELM) were used to estimate the SMC using six methods of pretreatments combined with four optimal spectral indices. The validation accuracy of the estimated method clearly increased compared with that of linear models. The combination of pretreatments and indices by our assessment effectively eliminated the interference and the noises. Comparing two machine learning algorithms showed that the RF models were superior to the ELM models, and the best model was PIR (R2val = 0.907, RMSEP = 1.477, and RPD = 3.396). The SMC map predicted via the best scheme was highly similar to the SMC map measured. We conclude that combining preprocessed spectral indices and machine learning algorithms allows estimation of SMC with high accuracy (R2val = 0.907) via UAV hyperspectral imagery on a regional scale. Ultimately, our program might improve management and conservation strategies for agroecosystem systems in arid regions

Trend analysis of long-time series habitat quality in Beijing based on multiple models

This study selects Beijing from 1980 to 2020 as the research area, utilizing high temporal resolution land use data to analyze through the habitat quality module of the InVEST model. Unlike previous research, this study employs the Theil-Sen Median method and Mann-Kendall test to analyze the trend changes in habitat quality more accurately. This method has significant advantages in dealing with non-linear and non-normally distributed data over long time series, providing a more accurate and reliable analysis of habitat quality trends. Methodologically, the study first collects and organizes the land use type data of Beijing from 1980 to 2020, then uses the habitat quality module of the InVEST model to process and analyze the data of each year, assessing the impact of different land use types on habitat quality. Subsequently, the Theil-Sen Median method and Mann-Kendall test are used to analyze the time series trend of habitat quality, to identify and quantify the trend and significance of habitat quality changes. The results show that over the past 40 years, the area of construction land in Beijing has significantly expanded, leading to a compression of other types of land. The spatial distribution of habitat quality shows a clear difference between the two sides divided by a line connecting the northeast and southwest, with the west side being the area of good habitat quality and the east side being poorer. In the past 10 years, the overall habitat quality has improved, but most areas still show a decreasing trend, especially in the western and northern mountainous areas where habitat quality has significantly declined. Based on these findings, it is recommended that future urban planning and land management should pay more attention to the protection and improvement of habitat quality, especially the restoration work for areas with poor habitat quality