Suchergebnisse

PurposeThis paper aims to effectively explore the application effect of big data techniques based on an α-support vector machine-stochastic gradient descent (SVMSGD) algorithm in third-party logistics, obtain the valuable information hidden in the logistics big data and promote the logistics enterprises to make more reasonable planning schemes.Design/methodology/approachIn this paper, the forgetting factor is introduced without changing the algorithm's complexity and proposed an algorithm based on the forgetting factor called the α-SVMSGD algorithm. The algorithm selectively deletes or retains the historical data, which improves the adaptability of the classifier to the real-time new logistics data. The simulation results verify the application effect of the algorithm.FindingsWith the increase of training times, the test error percentages of gradient descent (GD) algorithm, gradient descent support (SGD) algorithm and the α-SVMSGD algorithm decrease gradually; in the process of logistics big data processing, the α-SVMSGD algorithm has the efficiency of SGD algorithm while ensuring that the GD direction approaches the optimal solution direction and can use a small amount of data to obtain more accurate results and enhance the convergence accuracy.Research limitations/implicationsThe threshold setting of the forgetting factor still needs to be improved. Setting thresholds for different data types in self-learning has become a research direction. The number of forgotten data can be effectively controlled through big data processing technology to improve data support for the normal operation of third-party logistics.Practical implicationsIt can effectively reduce the time-consuming of data mining, realize the rapid and accurate convergence of sample data without increasing the complexity of samples, improve the efficiency of logistics big data mining, reduce the redundancy of historical data, and has a certain reference value in promoting the development of logistics industry.Originality/valueThe classification algorithm proposed in this paper has feasibility and high convergence in third-party logistics big data mining. The α-SVMSGD algorithm proposed in this paper has a certain application value in real-time logistics data mining, but the design of the forgetting factor threshold needs to be improved. In the future, the authors will continue to study how to set different data type thresholds in self-learning.

BACKGROUND: With the COVID-19 outbreak in China, the Chinese government took measures to prevent and control the spread of the virus. In-person teaching was replaced by distance learning, which was an unknown challenge for students. In this context, little is known about the perceived distress of nursing students and the relationship between psychological capital, perceived distress, and psychological stress. This study examined the relationship between psychological capital, psychological distress, and perceived stress, and the mediating role of psychological capital in the relationship between perceived stress and psychological distress among nursing students. METHODS: This cross-sectional survey was conducted between January and December 2020 using a convenience sampling method involving 359 undergraduate and specialist nursing students at a tertiary hospital in Shandong Province. Standardised instruments were used to measure psychological capital, psychological stress, and perceived stress. We used SPSS 24.0 and PROCESS macro to analyse the data. RESULTS: There was a statistically significant difference in perceived stress among students based on whether they liked the nursing profession (P < 0.01). Relative to nursing college students, undergraduates experienced significantly higher levels of perceived stress (P < 0.01). Nevertheless, there were no significant differences in perceived stress according to gender, place of residence, and being an only child. Psychological distress was positively correlated (r = 0.632, p < 0.001) with perceived stress (r =-0.662, p < 0.001), whereas it was negatively correlated with psychological capital. Psychological capital played a potential mediating role in the relationship between psychological distress and perceived stress. CONCLUSIONS: Psychological distress was negatively correlated with psychological capital, and positively correlated with perceived stress. Mediation analyses indicated that psychological capital partially mediated the relationship ...

Abstract
Background
Due to their widespread use, sulfonamide antibiotics (SAs) have become ubiquitous environmental contaminants and thus a cause of public concern. However, a complete understanding of the behavior of these pollutants in complex environmental systems has been hampered by the unavailability and high cost of isotopically labeled SAs.

Results
Using commercially available uniformly [14C]- and [13C]-labeled aniline as starting materials, we synthesized [phenyl-ring-14C]- and [phenyl-ring-13C]-labeled sulfamethoxazole (SMX), sulfamonomethoxine (SMM), and sulfadiazine (SDZ) in four-step (via the condensation of labeled N-acetylsulfanilyl chloride and aminoheterocycles) or five-step (via the condensation of labeled N-acetylsulfonamide and chloroheterocycles) reactions, with good yields (5.0–22.5% and 28.1–54.1% for [14C]- and [13C]-labeled SAs, respectively) and high purities (> 98.0%).

Conclusion
The synthesis of [14C]-labeled SAs in milligram amounts enables the preparation of labeled SAs with high specific radioactivity. The efficient and feasible methods described herein can be applied to the production of a variety of [14C]- or [13C]-labeled SAs for studies on their environmental behavior, including the fate, transformation, and bioaccumulation of these antibiotics in soils and aqueous systems.