ALBERT

Description: Recently, integrated energy systems have become a new type of energy supply model. It is clear that integrated energy systems can improve energy efficiency and reduce costs. However, the use of a battery energy storage system (BESS) as a backup power source will affect the operating costs of a regional integrated energy system (RIES) in different situations. In this paper, a regional integrated energy system including wind turbines, photovoltaics, gas turbines and battery energy storage was introduced. In order to obtain the minimum operation cost, an operation optimization model was built. The schedule plans of each unit were optimized by a moth flame optimization (MFO) algorithm. Finally, three different scenarios were proposed for the simulation optimization. The simulation optimization results show that when the BESS is used as a backup power source, the operating cost of the system and the resulting pollutant emissions are less than the diesel generator (DG) set. Therefore, it is worthwhile to use BESS instead of DG as the backup power source in RIES.

Description: Potassium (K) deficiency is one of the main limiting factors in cotton ( Gossypium hirsutum L.) production. To study the mechanism of high K-use efficiency of cotton, a pot experiment was conducted. The experiment consisted of two cotton genotypes differing in K-use efficiency (H103 and L122) and two K-application levels (K 0 : 0 g (kg soil) –1 ; K 1 : 0.40 g (kg soil) –1 ). Root-hair density and length, partitioning of biomass and K in various organs, as well as K-use efficiency of the two cotton genotypes were examined. The results show that there was no significant difference in K uptake between the two genotypes at both treatments, although the genotype H103 (high K-use efficiency) exhibited markedly higher root-hair density than genotype L122 in the K 1 treatment. Correlation analysis indicates that neither root-hair density nor root-hair length was correlated with plant K uptake. Furthermore, the boll biomass of genotype H103 was significantly higher than that of genotype L122 in both treatments, and the K accumulation in bolls of genotype H103 was 39%–48% higher than that of genotype L122. On the other hand, the litter index (LI) and the litter K-partitioning index (LKPI) of genotype H103 were 14%–21% and 22%–27% lower than that of genotype L122. Lastly, the K-use efficiency of total plant (KUE-P) of genotype H103 was comparable with that of genotype L122 in both treatments, but the K-use efficiency in boll yield (KUE-B) of genotype H103 was 24% and 41% higher than that of genotype L122 in K 0 and K 1 treatments. Pearson correlation analysis indicated that KUE-P was positively correlated with BKPI and negatively correlated with LKPI, while KUE-B was positively correlated with BKPI and boll-harvest index (HI B ), and negatively correlated with LKPI. It is concluded that there were no pronounced effects of root-hair traits on plant K uptake of the two genotypes. The difference in K-use efficiency was attributed to different patterns of biomass and K partitioning rather than difference in K uptake of the two genotypes.

Description: Potassium (K) deficiency is one of the main limiting factors in cotton ( Gossypium hirsutum L.) production. To study the mechanism of high K-use efficiency of cotton, a pot experiment was conducted. The experiment consisted of two cotton genotypes differing in K-use efficiency (H103 and L122) and two K-application levels (K 0 : 0 g (kg soil) –1 ; K 1 : 0.40 g (kg soil) –1 ). Root-hair density and length, partitioning of biomass and K in various organs, as well as K-use efficiency of the two cotton genotypes were examined. The results show that there was no significant difference in K uptake between the two genotypes at both treatments, although the genotype H103 (high K-use efficiency) exhibited markedly higher root-hair density than genotype L122 in the K 1 treatment. Correlation analysis indicates that neither root-hair density nor root-hair length was correlated with plant K uptake. Furthermore, the boll biomass of genotype H103 was significantly higher than that of genotype L122 in both treatments, and the K accumulation in bolls of genotype H103 was 39%–48% higher than that of genotype L122. On the other hand, the litter index (LI) and the litter K-partitioning index (LKPI) of genotype H103 were 14%–21% and 22%–27% lower than that of genotype L122. Lastly, the K-use efficiency of total plant (KUE-P) of genotype H103 was comparable with that of genotype L122 in both treatments, but the K-use efficiency in boll yield (KUE-B) of genotype H103 was 24% and 41% higher than that of genotype L122 in K 0 and K 1 treatments. Pearson correlation analysis indicated that KUE-P was positively correlated with BKPI and negatively correlated with LKPI, while KUE-B was positively correlated with BKPI and boll-harvest index (HI B ), and negatively correlated with LKPI. It is concluded that there were no pronounced effects of root-hair traits on plant K uptake of the two genotypes. The difference in K-use efficiency was attributed to different patterns of biomass and K partitioning rather than difference in K uptake of the two genotypes.

Description: Self-powered skin sensors have attracted significant attention in recent years due to their great potential in medical care, robotics, prosthetics, and sports. More importantly, self-powered skin sensors do not need any energy-supply components like batteries, which allows them to work sustainably and saves them the trouble of replacement of batteries. The self-powered skin sensors are mainly based on energy harvesters, with the device itself generating electrical signals when triggered by the detected stimulus or analyte, such as body motion, touch/pressure, acoustic sound, and chemicals in sweat. Herein, the recent research achievements of self-powered skin sensors are comprehensively and systematically reviewed. According to the different monitoring signals, the self-powered skin sensors are summarized and discussed with a focus on the working mechanism, device structure, and the sensing principle. Based on the recent progress, the key challenges that exist and the opportunities that lie ahead are also discussed.

Description: The implementation of China’s Belt and Road Initiative macro strategy has promoted exchanges and cooperation between China and Europe and countries along the route. The operation of the China Railway Express provides a new transportation platform for China–Europe trade. The sustainable development of the China Railway Express has a great significance in terms of promoting the growth of China–Europe trade and meeting the demand for freight in Asia and Europe. Its time-saving advantage over shipping and its economic advantage over air transport cannot be ignored. This paper is based on the discrete selection model of stochastic utility theory. The paper constructs a multi-logit model based on generalized cost functions, including economics, timeliness, reliability, convenience, safety, and environmental protection. To calculate the market share of the China Railway Express and China–Europe Shipping, the paper conducts a quantitative analysis of the market competitiveness of the China Railway Express. Then, the sensitivity analysis and elastic analysis are carried out on the China Railway Express freight rate, the Chinese road freight rate, the China Railway Express service fee, the China Railway Express travelling speed, the China Railway Express sending operation time at the departure station, the China Railway Express transportation time error, the China Railway Express transportation frequency, and the China Railway Express carbon emissions per unit. Finally, based on the analysis results, suggestions for promoting the sustainable development of the China Railway Express are given.

Description: Abstract In order to investigate how Fe was enriched from parental high‐Ti basaltic magma to form the stratigraphically thick Fe‐Ti oxide ore at the bottom layers, we present a systematic study for Fe isotopic compositions of whole rocks and mineral separates (clinopyroxene, magnetite, and ilmenite) throughout the Panzhihua intrusion. Whole rock δ56Fe ranges from 0 ± 0.02‰ to 0.15 ± 0.04‰, consistent with the range of clinopyroxene (0.01 ± 0.02‰ to 0.16 ± 0.05‰). On the contrary, magnetite (Mt) separates have δ56Fe ranging from 0.17 ± 0.05‰ to 0.62 ± 0.02‰, showing a strikingly complementary trend with coexisting ilmenite (Ilm) separates (−0.52 ± 0.03‰ to −0.09 ± 0.02‰) along the profile. The calculated bulk δ56Fe of Fe‐Ti oxides (Mt + Ilm), however, has a small range from 0.01‰ to 0.16‰, identical to those for clinopyroxene separates and whole rocks. The uniform δ56Fe of clinopyroxene may have resulted from the small Fe isotope fractionation between clinopyroxene and parental magma in early‐stage magma differentiation before substantial crystallization of Fe‐Ti oxides. The complementary trends of δ56Fe for Mt and Ilm along the profile and the uniform bulk δ56Fe of Fe‐Ti oxides are better interpreted as in situ crystallization of Fe‐Ti oxides from the interstitial liquid. Our Fe isotopic data and petrographic observations indicate that the thick Fe‐Ti oxide ore layers in the lower zone of the Panzhihua intrusion may be attributed to in situ crystallization of Mt and Ilm from the interstitial, immiscible Fe‐rich melt in the lower part of the magma chamber.

Description: Recently, the demand for human activity recognition has become more and more urgent. It is widely used in indoor positioning, medical monitoring, safe driving, etc. Existing activity recognition approaches require either the location information of the sensors or the specific domain knowledge, which are expensive, intrusive, and inconvenient for pervasive implementation. In this paper, a human activity recognition algorithm based on SDAE (Stacking Denoising Autoencoder) and LightGBM (LGB) is proposed. The SDAE is adopted to sanitize the noise in raw sensor data and extract the most effective characteristic expression with unsupervised learning. The LGB reveals the inherent feature dependencies among categories for accurate human activity recognition. Extensive experiments are conducted on four datasets of distinct sensor combinations collected by different devices in three typical application scenarios, which are human moving modes, current static, and dynamic behaviors of users. The experimental results demonstrate that our proposed algorithm achieves an average accuracy of 95.99%, outperforming other comparative algorithms using XGBoost, CNN (Convolutional Neural Network), CNN + Statistical features, or single SDAE.

Description: Urban commercial districts (UCDs) are the concentrated areas for commercial activities in a city, which provide shopping, leisure, business, and other functions. Urban planners usually face problems in how to plan and design UCDs. The layout of UCDs should not only be appropriately concentrated to realize economic benefits, but should also be properly dispersed to accommodate the distribution of the population. Using Beijing as a case study, this study conducted research into UCDs from a microscopic perspective by utilizing open source big data. A recognition and classification method of UCDs was proposed based on the data of POI and road networks. The proposed model combines Huff’s model and the Voronoi method to analyze how various UCDs should be distributed within a city according to the spatial pattern of the population. The results showed that different kinds of UCDs had different spatial distribution features. Problems were also found, for example, UCDs on the urban outskirts served a large population; there were limitations to the spatial distribution of UCDs in the downtown area; and there was incongruity between the UCD types and the population layout. Based on these findings, suggestions regarding the optimization of the urban commercial spatial structure were also put forward.

Description: In this study, we analyzed the temporal-spatial variations of the characteristics of land use change in central Asia over the past two decades. This was conducted using four indicators (change rate, equilibrium extent, dynamic index, and transfer direction) and a multi-scale correlation analysis method, which explained the impact of recent environmental transformations on land use changes. The results indicated that the integrated dynamic degree of land use increased by 2.2% from 1995 to 2015. The areas of cropland, water bodies, and artificial land increased, with rates of 1047 km2/a, 39 km2/a, and 129 km2/a, respectively. On the other hand, the areas of forest, grassland, and unused land decreased, with rates of 54 km2/a, 803 km2/a, and 359 km2/a, respectively. There were significant increases in cropland and water bodies from 1995 to 2005, while the amount of artificial land significantly increased from 2005 to 2015. The increased areas of cropland in Xinjiang were mainly converted from grassland and unused land from 1995 to 2015, while the artificial land increase was mainly a result of the conversion from cropland, grassland, and unused land. The area of cropland rapidly expanded in south Xinjiang, which has led to centroid position to move cropland in Xinjiang in a southwest direction. Economic development and the rapid growth of population size are the main factors responsible for the cropland increases in Xinjiang. Runoff variations have a key impact on cropland changes at the river basin scale, as seen in three typical river basins.

Description: The widespread popularity of smartphones makes it possible to provide Location-Based Services (LBS) in a variety of complex scenarios. The location and contextual status, especially the Indoor/Outdoor switching, provides a direct indicator for seamless indoor and outdoor positioning and navigation. It is challenging to quickly detect indoor and outdoor transitions with high confidence due to a variety of signal variations in complex scenarios and the similarity of indoor and outdoor signal sources in the IO transition regions. In this paper, we consider the challenge of switching quickly in IO transition regions with high detection accuracy in complex scenarios. Towards this end, we analyze and extract spatial geometry distribution, time sequence and statistical features under different sliding windows from GNSS measurements in Android smartphones and present a novel IO detection method employing an ensemble model based on stacking and filtering the detection result by Hidden Markov Model. We evaluated our algorithm on four datasets. The results showed that our proposed algorithm was capable of identifying IO state with 99.11% accuracy in indoor and outdoor environment where we have collected data and 97.02% accuracy in new indoor and outdoor scenarios. Furthermore, in the scenario of indoor and outdoor transition where we have collected data, the recognition accuracy reaches 94.53% and the probability of switching delay within 3 s exceeds 80%. In the new scenario, the recognition accuracy reaches 92.80% and the probability of switching delay within 4 s exceeds 80%.