Protein Denaturation in Pork Longissimus Muscle of Different Quality Groups

Differential scanning calorimetry (DSC) was used to investigate the protein denaturation characteristics of pork muscles from four quality groups namely RFN (red, firm, and non-exudative), RSE (red, soft, and exudative), PFN (pale, firm, and non-exudative), and PSE (pale, soft, and exudative). The thermograms indicated three endothermic peaks between 45°C to 90°C, corresponding to denaturation of myosin (peak I), sarcoplasmic proteins (peak II), and actin (peak III). The myosin peak was much reduced in PSE samples, while the actin peak remained almost identical in all groups. RFN and RSE samples were found to have very similar protein denaturation characteristics and were not significantly different in their thermodynamic protein denaturation parameters. PFN samples showed similar myofibrilar protein denaturation but significantly different sarcoplasmic protein denaturation characteristics compared to normal (RFN) samples according to their DSC thermograms. Based on these findings, it was suggested that the pale color in PFN pork is linked to sarcoplasmic protein denaturation.     

Recent Advances in Lipase-Mediated Preparation of Pharmaceuticals and Their Intermediates

Biocatalysis offers an alternative approach to conventional chemical processes for the production of single-isomer chiral drugs. Lipases are one of the most used enzymes in the synthesis of enantiomerically pure intermediates. The use of this type of enzyme is mainly due to the characteristics of their regio-, chemo- and enantioselectivity in the resolution process of racemates, without the use of cofactors. Moreover, this class of enzymes has generally excellent stability in the presence of organic solvents, facilitating the solubility of the organic substrate to be modified. Further improvements and new applications have been achieved in the syntheses of biologically active compounds catalyzed by lipases. This review critically reports and discusses examples from recent literature (2007 to mid-2015), concerning the synthesis of enantiomerically pure active pharmaceutical ingredients (APIs) and their intermediates in which the key step involves the action of a lipase.     

Studying software logging using topic models

Software developers insert logging statements in their source code to record important runtime information; such logged information is valuable for understanding system usage in production and debugging system failures. However, providing proper logging statements remains a manual and challenging task. Missing an important logging statement may increase the difficulty of debugging a system failure, while too much logging can increase system overhead and mask the truly important information. Intuitively, the actual functionality of a software component is one of the major drivers behind logging decisions. For instance, a method maintaining network communications is more likely to be logged than getters and setters. In this paper, we used automatically-computed topics of a code snippet to approximate the functionality of a code snippet. We studied the relationship between the topics of a code snippet and the likelihood of a code snippet being logged (i.e., to contain a logging statement). Our driving intuition is that certain topics in the source code are more likely to be logged than others. To validate our intuition, we conducted a case study on six open source systems, and we found that i) there exists a small number of “log-intensive” topics that are more likely to be logged than other topics; ii) each pair of the studied systems share 12% to 62% common topics, and the likelihood of logging such common topics has a statistically significant correlation of 0.35 to 0.62 among all the studied systems; and iii) our topic-based metrics help explain the likelihood of a code snippet being logged, providing an improvement of 3% to 13% on AUC and 6% to 16% on balanced accuracy over a set of baseline metrics that capture the structural information of a code snippet. Our findings highlight that topics contain valuable information that can help guide and drive developers’ logging decisions.     

The impact of big data analytics on firms’ high value business performance

Big Data Analytics (BDA) is an emerging phenomenon with the reported potential to transform how firms manage and enhance high value businesses performance. The purpose of our study is to investigate the impact of BDA on operations management in the manufacturing sector, which is an acknowledged infrequently researched context. Using an interpretive qualitative approach, this empirical study leverages a comparative case study of three manufacturing companies with varying levels of BDA usage (experimental, moderate and heavy). The information technology (IT) business value literature and a resource based view informed the development of our research propositions and the conceptual framework that illuminated the relationships between BDA capability and organizational readiness and design. Our findings indicate that BDA capability (in terms of data sourcing, access, integration, and delivery, analytical capabilities, and people’s expertise) along with organizational readiness and design factors (such as BDA strategy, top management support, financial resources, and employee engagement) facilitated better utilization of BDA in manufacturing decision making, and thus enhanced high value business performance. Our results also highlight important managerial implications related to the impact of BDA on empowerment of employees, and how BDA can be integrated into organizations to augment rather than replace management capabilities. Our research will be of benefit to academics and practitioners in further aiding our understanding of BDA utilization in transforming operations and production management. It adds to the body of limited empirically based knowledge by highlighting the real business value resulting from applying BDA in manufacturing firms and thus encouraging beneficial economic societal changes.     

漫湾水电站水库水温分布观测与数学模型计算研究

近年来建设的高坝大库越来越多,下泄低温水的影响日趋严重,对水库下泄低温水的影响和减缓措施的研究应给予关注和不断加强。本文是对已投入运行的漫湾水电站库区的水温结构进行研究。通过2004年2月对漫湾水库进行水温分布现场观测,并辅以三维数值模型计算,获得了较详细的水温分布成果。研究结果表明,漫湾水库的水温结构既不属于典型分层型又不属于完全混合型,应属于局部分层型或过渡型。该成果对于流域梯级水电开发对水环境的影响研究,具有一定的代表性,可用于类比分析研究与漫湾水库条件类似的其它水电站。     

多点热式气体质量流量测试方法实验研究

在已研制单点热式气体质量流量计的基础上,针对目前大口径或不规则管道气体质量流量测量中存在的问题,提出了基于多点测量的热式气体质量流量测试方法.文章对敏感元件(热膜探头)温度特性和以热膜探头为测点的多点测试方法进行了大量的试验研究.实验结果表明:多点测试方法中,以对数线性法最好;多点热式气体质量流量测试方法可以明显改善某些单点测量中出现的较大偏差,测量精度可以达到1.5%,扩展不确定度小于3.4%.     

光电二极管有源像素 CMOS图像传感器固定模式噪声分析

在研究有源像素工作机理和制作工艺的基础上,分析了固定模式噪声的产生机制以及对输出信号的影响,建立了噪声产生模型,得出了噪声影响的定量计算的公式.提出了通过优化电路和版图设计抑制电路固定模式噪声的方法.对于无法通过电路设计消除的噪声,提出了双采样电路抑制方法.研究结果对于设计低噪色有源像素具有指导意义.     

供热系统三维仿真建模研究

介绍了虚拟现实(Virtual Reality)技术及其三维建模语言VRML,阐述了虚拟现实技术在供热系统中的应用.将供热系统分为锅炉、换热站及居民小区三部分,对其进行分别建模,最后通过各路管道组成供热系统,实现了虚拟现实技术在热工行业中应用.     

电表脉冲数传仪

研制了一种脉冲电度表.利用传感技术,采集电能的参数,把电度表中的电能量转换为脉冲量,再经脉冲变送电路形成具有一定幅度和一定宽度的脉冲信号.可以经长传导线引入微机监控系统,形成电源智能监测仪器.也可以用无线发射机与中心监控系统联网.用软件系统计算出有功电能、视在电能、无功电能及功率因数等参数.配合其它参数的采集系统,可全面监测电力变压器的运行状态.该技术也可以应用在其它仪表设备中,完成仪表信号数字化和远距离传输.系统抗干扰能力强,是信号采集与变送技术的一项创新.在自动化技术中有较高的实用价值.     

编码器自动监测与故障诊断系统的研究与实现

文中介绍了某大型机电设备编码器的实时监测与故障诊断系统的设计工作.系统采用在线和离线两种工作方式,实现了接触式编码器的监测和诊断.系统硬件主要电路板基于PC104总线结构,18路I/O通道(9路输入,9路输出)完成9位编码器的检测和诊断.系统软件界面友好,巧妙地实现了格雷码与角度值的转换.本系统对其他编码器自动监测和故障诊断系统具有重要的借鉴意义.