Winter diets of round whitefish Prosopium cylindraceum in the St. Marys River

The round whitefish (Prosopium cylindraceum) is a coregonine native to numerous northern temperate lentic and lotic systems across North America. The St. Marys River, located between the United States and Canadian border, is one such system where the round whitefish supports a recreational fishery and is a prey source for piscivores. Round whitefish are generally known to prey upon benthic macroinvertebrates, but diets of round whitefish in the St. Marys River have not been characterized. We analyzed stomach contents from 107 round whitefish sampled from the St. Marys River during the winter months of 2020 and 2021 to describe their diets, examine their feeding strategies, and determine if they fed selectively. Ephemeropterans were the most important prey numerically, followed by megalopterans, trichopterans, and dipterans. The round whitefish population sampled from the St. Marys River exhibited a specialized feeding strategy that most often specialized on ephemeropterans. Patterns in the importance of prey types and feeding strategy were consistent between both years sampled. Selective feeding by round whitefish was examined using diet and environmental data from 2021 only, and we found that round whitefish selected both ephemeropterans and megalopterans, but avoided trichopterans and dipterans. Rather than being opportunistic invertivores, our results indicate round whitefish are specialized and selective feeders. Our findings can be used to inform conservation and management of round whitefish, as diets represent an integration of multiple ecological components such as prey availability, predator behavior, and habitat quality.     

Development of a stakeholder identification and analysis method for human factors integration in work system design interventions – Change Agent Infrastructure

In any work system design intervention—for example, a physical workplace re-design, a work process change, or an equipment upgrade—it is often emphasized how important it is to involve stakeholders in the process of analysis and design, to gain their perspectives as input to the development, and ensure their future acceptance of the solution. While the users of an artifact or workplace are most often regarded as being the most important stakeholders in a design intervention, in a work-system context there may be additional influential stakeholders who influence and negotiate the design intervention's outcomes, resource allocation, requirements, and implementation. Literature shows that it is uncommon for empirical ergonomics and human factors (EHF) research to apply and report the use of any structured stakeholder identification method at all, leading to ad-hoc selections of whom to consider important. Conversely, other research fields offer a plethora of stakeholder identification and analysis methods, few of which seem to have been adopted in the EHF context. This article presents the development of a structured method for identification, classification, and qualitative analysis of stakeholders in EHF-related work system design intervention. It describes the method's EHF-related theoretical underpinnings, lessons learned from four use cases, and the incremental development of the method that has resulted in the current method procedure and visualization aids. The method, called Change Agent Infrastructure (abbreviated CHAI), has a mainly macroergonomic purpose, set on increasing the understanding of sociotechnical interactions that create the conditions for work system design intervention, and facilitating participative efforts.     

基于多通道的ICU脑血管疾病死亡风险预测模型

死亡风险预测指根据病人临床体征监测数据来预测未来一段时间的死亡风险。对于ICU病患，通过死亡风险预测可以有针对性地对病人做出临床诊断，以及合理安排有限的医疗资源。基于临床使用的MEWS和Glasgow昏迷评分量表，针对ICU病人临床监测的17项生理参数，提出一种基于多通道的ICU脑血管疾病死亡风险预测模型。引入多通道概念应用于BiLSTM模型，用于突出每个生理参数对死亡风险预测的作用。采用Attention机制用于提高模型预测精度。实验数据来自MIMIC [Ⅲ]数据库，从中提取3?080位脑血管疾病患者的16?260条记录用于此次研究，除了六组超参数实验之外，将所提模型与LSTM、Multichannel-BiLSTM、逻辑回归（logistic regression）和支持向量机（support vector machine, SVM）四种模型进行了对比分析，准确率Accuracy、灵敏度Sensitive、特异性Specificity、AUC-ROC和AUC-PRC作为评价指标，实验结果表明，所提模型性能优于其他模型，AUC值达到94.3%。     

基于RGB颜色信息聚类的光谱反射率重建

针对光谱反射率研究中因训练样本数据量大造成的冗杂等问题，提出了一种基于RGB信息进行聚类的样本分类方法。首先对颜色进行聚类并确定聚类个数，后对每一类光谱反射率使用BP神经网络分别进行重建。对于实验结果，使用ΔE₀₀、均方根误差（RMSE）以及适应度系数等标准进行评价，同时与主成分分析算法进行对比。从实验分析可得出，在聚类数目为7时光谱反射率重建效果最好，这时的平均CIE2000的色差为0.836，平均RMSE为0.0149，平均适应度系数为99.82%，并且，在最后对重建色差较大的色块进行了优化处理。实验证明，颜色聚类方法可以很好的应用于光谱反射率重建。     

Multi-criteria analysis for screening of reversible metal hydrides in hydrogen gas storage and high pressure delivery applications

Metals and alloys forming reversible hydrides with hydrogen gas are potential building blocks for compact, solid state hydrogen storage systems. Based on the materials’ thermodynamic characteristics, their use as temperature-swing gas compression and delivery systems in the hydrogen economy is also possible. Given the wide variety of materials developed and tested at laboratory and pilot scales, a harmonized method of selecting the feasible material(s) for a particular real-life application is required. This study proposes a system selection framework based on a normalized, multi-criteria metric. Using calculated values of multi-criteria metric, multi-criteria screening and ranking of potential materials has been demonstrated for a particular use case. It is found that the alloy TiMn_1.52 having value of additive metric between 0.25 and 0.35 represents the best material for a single stage system. The alloy pair CaNi₅–Ti_1.5CrMn represents the best alternative for a two-stage system with additive metric values between 0.63 and 0.82. Energy and economic characteristics of the metal hydride gas compression and delivery systems are evaluated and compared with an equivalent mechanical compression system producing the same final effect (i.e., delivery of a given quantity of gas at a defined pressure).     

Optimization of hydrogen enrichment via palladium membrane in vacuum environments using Taguchi method and normalized regression analysis

In this study, the separation of hydrogen from gas mixtures using a palladium membrane coupled with a vacuum environment on the permeate side was studied experimentally. The gas mixtures composed of H₂, N₂, and CO₂ were used as the feed. Hydrogen permeation fluxes were measured with membrane operating temperature in the range of 320–380 °C, pressures on the retentate side in the range of 2–5 atm, and vacuum pressures on the permeate side in the range of 15–51 kPa. The Taguchi method was used to design the operating conditions for the experiments based on an orthogonal array. Using the measured H₂ permeation fluxes from the Taguchi approach, the stepwise regression analysis was also employed for establishing the prediction models of H₂ permeation flux, followed by the analysis of variance (ANOVA) to identify the significance and suitability of operating conditions. Based on both the Taguchi approach and ANOVA, the H₂ permeation flux was mostly affected by the gas mixture composition, followed by the retentate side pressure, the vacuum degree, and the membrane temperature. The predicted optimal operating conditions were the gas mixture with 75% H₂ and 25% N₂, the membrane temperature of 320 °C, the retentate side pressure of 5 atm, and the vacuum degree of 51 kPa. Under these conditions, the H₂ permeation flux was 0.185 mol s^?1 m^?2. A second-order normalized regression model with a relative error of less than 7% was obtained based on the measured H₂ permeation flux.     

Modeling and Verification of a Sentiment Analysis System Using Aspect-Oriented Petri Nets

An increasing number of social media and networking platforms have been widely used. People usually post the online comments to share their own opinions on the networking platforms with social media. Business companies are increasingly seeking effective ways to mine what people think and feel regarding their products and services. How to correctly understand the online customers’ reviews becomes an important issue. This study aims to propose a method with the aspect-oriented Petri nets (AOPN) to improve the examination correctness without changing any process and program. We collect those comments from the online reviews with Scrapy tools, perform sentiment analysis using SnowNLP, and examine the analysis results to improve the correctness. In this paper, we apply our method for a case of the online movie comments. The experimental results have shown that AOPN is helpful for the sentiment analysis and verifying its correctness.