双碳目标下的钢铁节能理念创新与能源结构重塑探讨

在“碳达峰”“碳中和”背景下,中国钢铁必须走脱碳化发展道路,提高能效是当前最为行之有效的举措之一。通过分析钢铁行业存在的较大节能潜力及拥有丰富二次能源的用能特点,在热力学定律、系统最小外部损失、最佳推动力、能源梯级利用等几项基本原则的基础上,提出钢铁行业多能互补与储能相结合的创新理念,包括钢铁二次能源与可再生能源的互补、钢铁不同高低品位能源的互补、储能系统的创新应用等。从中长期看,需以更高的站位,走能源资源环境、高效清洁低碳一体化协同创新道路,构建化工-能源-生态一体化用能系统,进行合理优化与集成,实现能效提升及协同碳减排。     

ED-SWE: Event detection based on scoring and word embedding in online social networks for the internet of people

Online social media networks are gaining attention worldwide, with an increasing number of people relying on them to connect, communicate and share their daily pertinent event-related information. Event detection is now increasingly leveraging online social networks for highlighting events happening around the world via the Internet of People. In this paper, a novel Event Detection model based on Scoring and Word Embedding (ED-SWE) is proposed for discovering key events from a large volume of data streams of tweets and for generating an event summary using keywords and top-k tweets. The proposed ED-SWE model can distill high-quality tweets, reduce the negative impact of the advent of spam, and identify latent events in the data streams automatically. Moreover, a word embedding algorithm is used to learn a real-valued vector representation for a predefined fixed-sized vocabulary from a corpus of Twitter data. In order to further improve the performance of the Expectation-Maximization (EM) iteration algorithm, a novel initialization method based on the authority values of the tweets is also proposed in this paper to detect live events efficiently and precisely. Finally, a novel automatic identification method based on the cosine measure is used to automatically evaluate whether a given topic can form a live event. Experiments conducted on a real-world dataset demonstrate that the ED-SWE model exhibits better efficiency and accuracy than several state-of-art event detection models.     

Hybridizing adaptive large neighborhood search with kernel search: a new solution approach for the nurse routing problem with incompatible services and minimum demand

The average age of the population has grown steadily in recent decades along with the number of people suffering from chronic diseases and asking for treatments. Hospital care is expensive and often unsafe, especially for older individuals. This is particularly true during pandemics as the recent SARS-CoV-2. Hospitalization at home has become a valuable alternative to face efficiently a huge increase in treatment requests while guaranteeing a high quality of service and lower risk to fragile patients. This new model of care requires the redefinition of health services organization and the optimization of scarce resources (e.g., available nurses). In this paper, we study a Nurse Routing Problem that tries to find a good balance between hospital costs reduction and the well-being of patients, also considering realistic operational restrictions like maximum working times for the nurses and possible incompatibilities between services jointly provided to the same patient. We first propose a Mixed Integer Linear Programming formulation for the problem and use some valid inequalities to strengthen it. A simple branch-and-cut algorithm is proposed and validated to derive ground benchmarks. In addition, to efficiently solve the problem, we develop an Adaptive Large Neighborhood Search hybridized with a Kernel Search and validate its performance over a large set of different realistic working scenarios. Computational tests show how our matheuristic approach manages to find good solutions in a reasonable amount of time even in the most difficult settings. Finally, some interesting managerial insights are discussed through an economic analysis of the operating context.     

Failure load prediction and optimisation for adhesively bonded joints enabled by deep learning and fruit fly optimisation

Adhesively bonded joints have been extensively employed in the aeronautical and automotive industries to join thin-layer materials for developing lightweight components. To strengthen the structural integrity of joints, it is critical to estimate and improve joint failure loads effectually. To accomplish the aforementioned purpose, this paper presents a novel deep neural network (DNN) model-enabled approach, and a single lap joint (SLJ) design is used to support research development and validation. The approach is innovative in the following aspects: (i) the DNN model is reinforced with a transfer learning (TL) mechanism to realise an adaptive prediction on a new SLJ design, and the requirement to re-create new training samples and re-train the DNN model from scratch for the design can be alleviated; (ii) a fruit fly optimisation (FFO) algorithm featured with the parallel computing capability is incorporated into the approach to efficiently optimise joint parameters based on joint failure load predictions. Case studies were developed to validate the effectiveness of the approach. Experimental results demonstrate that, with this approach, the number of datasets and the computational time required to re-train the DNN model for a new SLJ design were significantly reduced by 92.00% and 99.57% respectively, and the joint failure load was substantially increased by 9.96%.     

一种按拼音首字母对数据库中的汉字进行检索的方法研究

根据SQL Server的排序规则,在不改变查询表结构的条件下,探讨并研究了在SQL Server的数据库服务器端直接按汉字的拼音首字母对汉字进行快速检索的方法。     

Heuristics for solving continuous berth allocation problem considering periodic balancing utilization of cranes

This study investigates a berth allocation problem considering the periodic balancing utilization of quay cranes in container terminals. The proposed model considers that the quay cranes allocated to a work shift should be fully used and other real-world considerations, such as the continuous quay line, the penalties for early arrivals and departure delays. To solve the model, several heuristics are developed: the model for large problems is decomposed into sub-models that are solved by rolling-horizon heuristics; neighborhood search heuristics are used for optimizing a berthing order of vessels; parallel computing is used to improve the algorithmic performance. The method performs well when applied to real-world large-scale instances with promising computation time that is linearly related to the number of vessels.     

A multi-objective approach to optimal placement and sizing of multiple active power filters using a music-inspired algorithm

This paper proposes a new multi-objective framework for optimal placement and sizing of the active power filters (APFs) with satisfactory and acceptable standard levels. total harmonic distortion (THD) of voltage, harmonic transmission line loss (HTLL), motor load loss function (MLLF), and total APFs currents are the four objectives considered in the optimization, while harmonic distortions within standard level, and maximum allowable APF size, are modeled as constraints. The proposed model is one of non-convex optimization problem having a non-linear, mixed-integer nature. Since, a new modified harmony search algorithm (MHSA) is used and followed by a min–max technique in order to obtain the final optimal solution. The harmony search algorithm is a recently developed optimization algorithm, which imitates the music improvisation process. In this process, the Harmonists improvise their instrument pitches searching for the perfect state of harmony. The newly developed method has been applied on the IEEE 18-bus test system and IEEE 30-bus test system by different scenarios and cases to demonstrate the feasibility and effectiveness of the proposed method. The detailed results of the case studies are presented and thoroughly analyzed. The obtained results illustrate the sufficiency and profitableness of the newly developed method in the placement and sizing of the multiple active power filters, when compared with other methods.     

Searching for triangles: An extension to food & packaging

Two laboratory-based visual search experiments (Experiments 1 and 2) and an online survey (Experiment 3) were conducted in order to investigate the visual search for triangular foods. The results of Experiment 1 revealed that the visual search for a downward pointing triangular target was faster than when the same target pointed upward, regardless of whether the stimuli were simple geometric figures or photos of food. Experiment 2 replicated these results using images of both food and non-food packaging. Experiment 3 revealed that the same triangular stimuli were generally rated as less pleasant, less liked, and less familiar when they pointed downward than when they pointed upward. Taken together, these results therefore suggest that the cognitive processing of food images is influenced by incidental aspects of their visual appearance, and that such a pattern of results can also be extended to the case of food packaging.     

改良冷原子荧光光谱法测定土壤中的汞

采用王水水浴消解—冷原子荧光光谱法(AFS)测定土壤中的汞。利用自行研制的峰值积分测定方法与传统峰面积积分测定方法比较,优化了AFS测定条件,峰值测定的加标回收率在98.96%~101.49%之间,RSD%在1.278%~3.605%之间。峰面积测定的加标回收率在98.61%~102.68%之间,RSD%在2.770%~4.358%之间,从加标回收率及RSD的数据分析来看,峰值积分测定均好于峰面积积分测定。与峰面积积分测定法比较,峰值积分测定法样品测定时间减少50%以上、简便、结果准确、可靠。本法适用于大批土壤中汞的测定。     

SIPO专利检索资源之药物专利检索系统

本文主要介绍了SIPO专利检索资源之药物专利检索系统(简称CHMP),包括CHMP旧版和新版。文中对CHMP旧版和新版的基本使用方法做了介绍,有助于用户快速入门和了解其基本功能,提高对药物专利检索的兴趣和效率;文中最后指出目前CHMP两个版本功能上存在差异会导致用户选择困难。