Vehicle detection and tracking by computer vision for intelligent transportation applications.

The vehicle routing problem (VRP) deals with the allocation of vehicles to the customers that have requested products from a main depot. When the postal service, the bus system, or trucking industry plan for their everyday tasks of delivery goods or providing basic transportation needs to people, they are attempting to solve the VRP. However, this can be a daunting task because the VRP is computationally intensive. In this paper, we will address two areas of the VRP that have been relatively unexplored by previous research, yet play an important part in real-world_ applications of the VRP as well as define and create an evolutionary approach based on these ideas. The first area that we will address deals with the lack of real-world data sets when calculating the times between customers. This new implementation will allow the algorithm to calculate the actual time between customers at a given time of day, thus providing a final solution that is closer to a true optimal set of routes. The second addition to the VRP model will be contributed in the area of precedence relationships. These relationships often exist in real-world applications and are necessary in order ��o help the algorithm establish routes that are desired by the customers. The evolutionary approach provides global optimization insight to the problem

Adaptive behaviors can improve the system consilience of a network system

As a recently reported network property, consilience degree (CSD) indicates how well a network system integrates its topology and node activities together to serve a specific systemic goal. As is well known, many natural and man-made systems are complex networks where, besides network topology, node activity states also play an important role in determining system performance. For example, a collaborative project involving friends is more likely to succeed than one involving enemies, even though the topology of network organization is the same. The concept of CSD can quantitatively distinguish the difference between the involvement of friends and the involvement of enemies. This article reports a simulation study on the adaptive behaviors of nodes based on the selfish rule and the following-others rule, and the simulation results show that based on such adaptive behaviors of nodes, a network system will automatically evolve to a high level of system consilience. The simulation study also demonstrates that a high level of system consilience resulting from adaptive behaviors will contribute to increased system resistance to external disturbances. The generality of adaptive behaviors in reality implies that CSD is an inherent attribute of real-world network systems, and therefore, the concept of CSD has significant application potential in the study of adaptive behaviors in network systems

2-[(4-Chloro­phen­yl)(2-hy­droxy-5-oxo­cyclo­pent-1-en-1-yl)meth­yl]-3-hy­droxy­cyclo­pent-2-en-1-one

There are two mol­ecules in the asymmetric unit of the title compound, C17H15ClO4, in which the dihedral angles between the five-membered rings are 57.3 (1) and 51.4 (1)°. An intra­molecular O—H⋯O hydrogen bond occurs in each mol­ecule. In the crystal, O—H⋯O and C—H⋯O hydrogen bonds link the moleclues into chains along the b axis

Higher education reform and the changes of college students\u27educational experiences in China

One of the foci of higher education reform in China is related to how students\u27learning experiences are channeled and guided. Chinese universities have been criticised for their excessive specialization. Attempts are made now to expose students to a wider range of knowledge. In this paper I choose Peking University, Tsinghua University and Fudan University for the case study. These three universities are very progressive in China, and we will see the tendency of higher education through these three universities. From the results of the study, a conclusion can be made that the reforms have brought out a variety of changes in higher education and these have enriched students\u27experience

Trend of near-surface maximum wind speed in China: under a shifted East Asian monsoon scenario

Póster presentado en: EGU General Assembly 2018 celebrada del 8 al 13 de abril en Viena, Austria.The current global climate research has traditionally focused on changes in air temperature and precipitation. As a key climate parameter, changes of winds have a very significant impact on the environment, such as soil wind erosion, air pollution diffusion, wind power energy, etc. In particular, changes of extreme wind speed (i.e., wind gusts) are poorly analyzed and deserve further investigation. In this study we assess trends in máximum wind speed (MWS) across China for 1975-2016, using observed daily wind datasets, and also analyze its relationship with the East Asian monsoon. The raw observed MWS dataset was subject to aquality control and robust homogenization protocol using the Climatol package. The results reveal a statistically significant (p0.10). Even though MWS declines dominated across much of the country through out the year, only as mal number of stations showed statistically significant negative trends in summer (37.7 %) and spring (29.0 %). Our preliminary analyses show that the weakened East Asian monsoon, particularly in winter, positively correlates with the observed changes in MWS. However, statistical significant correlations are too few and further attribution analyses are strongly needed.This research is funded by (i) the National Natural Science Foundation of China (Grant No.41621061); (ii) the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant (Grant No. 703733); and (iii) the Swedish Research Council by the project “Detection and attribution of changes in extreme wind gusts over land” (2017-03780)

Prompt Space Optimizing Few-shot Reasoning Success with Large Language Models

Prompt engineering is an essential technique for enhancing the abilities of large language models (LLMs) by providing explicit and specific instructions. It enables LLMs to excel in various tasks, such as arithmetic reasoning, question answering, summarization, relation extraction, machine translation, and sentiment analysis. Researchers have been actively exploring different prompt engineering strategies, such as Chain of Thought (CoT), Zero-CoT, and In-context learning. However, an unresolved problem arises from the fact that current approaches lack a solid theoretical foundation for determining optimal prompts. To address this issue in prompt engineering, we propose a new and effective approach called Prompt Space. Our methodology utilizes text embeddings to obtain basis vectors by matrix decomposition, and then constructs a space for representing all prompts. Prompt Space significantly outperforms state-of-the-art prompt paradigms on ten public reasoning benchmarks. Notably, without the help of the CoT method and the prompt "Let's think step by step", Prompt Space shows superior performance over the few-shot method. Overall, our approach provides a robust and fundamental theoretical framework for selecting simple and effective prompts. This advancement marks a significant step towards improving prompt engineering for a wide variety of applications in LLMs.Comment: Natural language processing (NLP

Variability of winter haze over the Beijing-Tianjin-Hebei region tied to wind speed in the lower troposphere and particulate sources [Póster]

Póster presentado en: EMS Annual Meeting - European Conference for Applied Meteorology and Climatology 2018, celebrado en Budapest del 3 al 7 de septiembre de 2018.This study analyzes the variability of winter haze days and visibility in the Beijing-Tianjin-Hebei (BTH) region in relation to wind speed changes in the lower troposphere and emissions for 1961-2014. Daily surface meteorological data, NCEP/NCAR atmospheric reanalysis data, and fossil fuel emission data are used in this research. The results reveal a significant increase in winter haze days of +0.8 days decade-1 (p<0.01), and a subsequent decline in visibility of-1.56 km decade-1 (p<0.01). Most interestingly, an accelerated increase in haze days was observed for the last 11-year period (+8.3 days decade-1) of the study (2004-2014). The increase of winter haze occurrence and decrease in visibility are partly attributed to: the significant (p<0.01) declining trend of mean wind speed at the near-surface (-0.19 m s-1 decade-1), 925hPa (-0.23 m s-1 decade-1), and 850hPa (-0.21 m s-1 decade-1); the vertical shear of wind between 1000hPa and 850hPa (-0.07 m s-1 decade-1); and, the declining (dust storm frequency as a proxy,-0.41 days dec-1) surrounding particulate sources and increasing fossil fuel emissions (total carbon emission as a proxy, +4820.6 metric tons dec-1). Specifically, wind speed changes in the lower troposphere explain 41.3% of winter haze days and 71.2% of the visibility variance. These are extended to 51.7% and 81.6% respectively when combined with natural (dust storm frequency) and anthropogenic (fossil fuel emissions) particulate sources. Therefore, the analyses show that wind speed changes in the lower troposphere, together with the varied natural and anthropogenic sources of particulates, play a key role in modulating winter haze and visibility conditions in the BTH area.This research is funded by the National Natural Science Foundation of China (Grant No.41621061), and funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement no. 703733 (STILLING project). This work has been also supported by the VR Project (2017-03780) funded by the Swedish Research Council

Future climate change significantly alters interannual wheat yield variability over half of harvested areas

Climate change affects the spatial and temporal distribution of crop yields, which can critically impair food security across scales. A number of previous studies have assessed the impact of climate change on mean crop yield and future food availability, but much less is known about potential future changes in interannual yield variability. Here, we evaluate future changes in relative interannual global wheat yield variability (the coefficient of variation (CV)) at 0.25° spatial resolution for two representative concentration pathways (RCP4.5 and RCP8.5). A multi-model ensemble of crop model emulators based on global process-based models is used to evaluate responses to changes in temperature, precipitation, and CO2. The results indicate that over 60% of harvested areas could experience significant changes in interannual yield variability under a high-emission scenario by the end of the 21st century (2066–2095). About 31% and 44% of harvested areas are projected to undergo significant reductions of relative yield variability under RCP4.5 and RCP8.5, respectively. In turn, wheat yield is projected to become more unstable across 23% (RCP4.5) and 18% (RCP8.5) of global harvested areas—mostly in hot or low fertilizer input regions, including some of the major breadbasket countries. The major driver of increasing yield CV change is the increase in yield standard deviation, whereas declining yield CV is mostly caused by stronger increases in mean yield than in the standard deviation. Changes in temperature are the dominant cause of change in wheat yield CVs, having a greater influence than changes in precipitation in 53% and 72% of global harvested areas by the end of the century under RCP4.5 and RCP8.5, respectively. This research highlights the potential challenges posed by increased yield variability and the need for tailored regional adaptation strategies

Contribution of uneven warming to the observed wind stilling in North China for 1961-2016

This work has been supported by the project “Detection and attribution of changes in extreme wind gusts over land” (2017-03780) funded by the Swedish Research Council