An Experimental Computer Network for Mcdical Data Processing

The use of minicomputers in structural analysis

Contributions of immigrants to the field of US psychiatry

Productivity of programmable automation—A simulation-based analysis of a robotic system for component insertion

Patients' rights for privacy and confidentiality extend to publications. This author describes how authors, reviewers, and editors can ensure patient confidentiality in manuscripts.

The Department of Energy (DOE) Office of Science (SC) facilities including accelerators, light sources and neutron sources and sensors that study, the environment, and the atmosphere, are producing streaming data that needs to be analyzed for next-generation scientific discoveries. There has been an explosion of new research and technologies for stream analytics arising from the academic and private sectors. However, there has been no corresponding effort in either documenting the critical research opportunities or building a community that can create and foster productive collaborations. The two-part workshop series, STREAM: Streaming Requirements, Experience, Applications and Middleware Workshop (STREAM2015 and STREAM2016), were conducted to bring the community together and identify gaps and future efforts needed by both NSF and DOE. This report describes the discussions, outcomes and conclusions from STREAM2016: Streaming Requirements, Experience, Applications and Middleware Workshop, the second of these workshops held on March 22-23, 2016 in Tysons, VA. STREAM2016 focused on the Department of Energy (DOE) applications, computational and experimental facilities, as well software systems. Thus, the role of “streaming and steering” as a critical mode of connecting the experimental and computing facilities was pervasive through the workshop. Given the overlap in interests and challenges with industry, the workshop had significant presence from several innovative companies and major contributors. The requirements that drive the proposed research directions, identified in this report, show an important opportunity for building competitive research and development program around streaming data. These findings and recommendations are consistent with vision outlined in NRC Frontiers of Data and National Strategic Computing Initiative (NCSI) [1, 2]. The discussions from the workshop are captured as topic areas covered in this report's sections. The report discusses four research directions driven by current and future application requirements reflecting the areas identified as important by STREAM2016. These include (i) Algorithms, (ii) Programming Models, Languages and Runtime Systems (iii) Human-in-the-loop and Steering in Scientific Workflow and (iv) Facilities.

Patient-Centered Outcomes Research: Early Evidence From A Burgeoning Field.

Editorial: Australian Chemistry under the Spotlight

Epidemiologic surveys have mapped the terrain of psychiatric disorders.Personality disorders have bedeviled the clinician's practice.Rarely have these two been rearranged in a meaningful clinical dialogue.Using the largest psychiatric epidemiologic survey ever, the National Epidemiologic Survey on Alcoholism and Related Conditions, and among the few to venture into axis II disorders, Skodol et al. (1), in this issue of the Journal, give a community-based national view of a common clinical question: What is the effect of specific personality disorder comorbidity on the course of major depression?The original sample included over 40,000 adults, and 2,422 met criteria for DSM-IV current major depressive disorder.Three years later, 1,996 of the original currently depressed subjects were available for reinterviewing, which makes both a respectable sample size and response rate for generalizability.However, some caution is needed, since the sample was over-represented with Caucasian, college-educated, and married respondents.Fifteen percent of participants had persistent major depressive disorder, and 7.3% of those who remitted had a recurrence over the follow-up period.These figures are within the range of longitudinal studies of patients with major depressive disorder (2).While the presence of any personality disorder elevated the risk for persistence of major depressive disorder, when all axis I and II disorders, age of onset of major depressive disorder, number of previous episodes, family history, treatment, and duration of illness were controlled, borderline personality disorder remained the most robust predictor of major depressive disorder persistence.Neither personality disorders nor other clinical variables predicted recurrence of major depressive disorder.Thus, an epidemiologic survey yielded a practical jewel.The finding, undoubtedly, does not surprise the clinician but is now confirmed nationally.As the authors conclude, borderline personality disorder should be assessed in all depressed patients and considered in prognosis and addressed in treatment.One can raise a number of methodologic issues about this study, including the use of lay interviewers or the instrument for assessing axis II disorders.The diagnostic interview, the Alcohol Use Disorder and Associated Disabilities Interview Schedule, DSM-IV version (3), was developed for this survey.The personality disorders included were adapted from items in the Structured Clinical Interview for DSM-IV Personality Disorders.The test-retest and internal consistency results reported for all personality disorders are fair to good, not great.However, the agreement with clinician interviews for borderline personality disorder (kappa=0.71) is about as good as it gets (4).The only other national survey to venture into assessing all axis II disorders was the National Comorbidity Survey Replication (5), which used the International Personality Disorder Examination.The investigators carried out a clinical reappraisal in a sample of 214 subjects using clinically trained interviewers to follow up screened, positive subjects and reported excellent predictions of classification.They also noted that the International Personality Disorder Examination is commonly regarded as

Commercial cache-coherent nonuniform memory access (ccNUMA) systems often require extensive investments in hardware design and operating system support. A different approach to building these systems is to use Standard High Volume (SHV) hardware and stock software components as building blocks and assemble them with minimal investments in hardware and software. This design approach trades the performance advantages of specialized hardware design for simplicity and implementation speed, and relies on application-level tuning for scalability and performance. We present our experience with this approach in this paper. We built a 16-way ccNUMA Intel system consisting of four commodity four-processor Fujitsu® Teamserver™ SMPs connected by a Synfinity™ cache-coherent switch. The system features a total of sixteen 350-MHz Intel® Xeon™ processors and 4 GB of physical memory, and runs the standard commercial Microsoft Windows NT® operating system. The system can be partitioned statically or dynamically, and uses an innovative, combined hardware/software approach to support application-level performance tuning. On the hardware side, a programmable performance-monitor card measures the frequency of remote-memory accesses, which constitute the predominant source of performance overhead. The monitor does not cause any performance overhead and can be deployed in production mode, providing the possibility for dynamic performance tuning if the application workload changes over time. On the software side, the Resource Set abstraction allows application-level threads to improve performance and scalability by specifying their execution and memory affinity across the ccNUMA system. Results from a performance-evaluation study confirm the success of the combined hardware/software approach for performance tuning in computation-intensive workloads. The results also show that the poor local-memory bandwidth in commodity Intel-based systems, rather than the latency of remote-memory access, is often the main contributor to poor scalability and performance. The contributions of this work can be summarized as follows: • The Resource Set abstraction allows control over resource allocation in a portable manner across ccNUMA architectures; we describe how it was implemented without modifying the operating system. • An innovative hardware design for a programmable performance-monitor card is designed specifically for a ccNUMA environment and allows dynamic, adaptive performance optimizations. • A performance study shows that performance and scalability are often limited by the local-memory bandwidth rather than by the effects of remote-memory access in an Intel-based architecture.

Technological investment is a key managerial decision to make by firms. In recent years, firms increasingly invest in software systems to support big data analytics, digital transformation, and AI, which however do not always pay off. Recent research suggests that software availability may influence entry, at the industry level, by increasing labor productivity, reducing scaling costs, and/or facilitating demand forecasting. However, how these mechanisms through which firms' software investment may affect organizations by inducing various changes is unclear. Insights into these mechanisms require microdata at the firm level over years. We construct a unique panel dataset from 13,335 German firms across industry sectors in 2011–2017 to conduct a firm‐level econometric analysis. We find that, on average, firms' software investment improves demand forecasting but, interestingly, may reduce labor productivity and slow down scaling up. We further propose several organizational contexts in which software investment can be more beneficial to remedy such challenges. Labor productivity is improved only if firms reorganize labor work when making software investment, and scaling costs may be reduced if firms facilitate learning through investments in both software and human capital development. Putting together, this research contributes a new understanding of organizational changes induced by software investment and what contingency factors can make software investment more beneficial in the organizational contexts, to guide managers to make value‐increasing decisions.

Purpose: The aim of this research is to explore the virtual tours (VT) are provided by Jakarta Creative Tourism Travel Agents (JCTTA) for tourists to travel during the Covid-19 pandemic. Research methods: The research method used in this study is descriptive qualitative. Data collection was carried out by interview with key informants consisting of virtual tour service managers, visitors as service users. Data analysis was carried out descriptive qualitative form the results of interview which were used to answer problem formulations related to virtual tour products managed by JCCTA. Result and discussion: The results of this study indicate that VT has become an alternative in the tourism business and a viable option for travelers during the COVID-19 pandemic. VT enable travelers to explore tourist destinations without physically traveling, providing visual and interactive experiences through video and Google Earth. The discussion in this research highlights the advantages of VT as a means to sustain the tourism industry and offer a safe option for travelers during the pandemic. Implication: The implications suggest that 1) handling quickly in response to tourist complaints, especially network connections; 2) displays complete tourist attractions found in tourism destinations, so that no tourist attraction is overlooked; 3) presenting more attractive displays on the web site to attract new customers and consumers; 4) improve the quality of software and hardware used to produce VT packages; 5) Managers must try to establish a program for how this virtual tour business can still run in the normal era after the COVID-19 pandemic by conducting surveys of previous customer interests. On the other hand, JCTTA's investment in software and hardware to produce virtual tour packages continues to generate profits for the company. Keywords: virtual tour, covid-19 pandemic, travel agent

Based on the evaluation of the brand value of 21 A-share listed companies engaged in garment and home textile in 2019 using the HIROSE-CNGarments model, and the company's investment in software and hardware and operating efficiency indicators, the impact of informationized investment on the company's brand value was analyzed, and then the currently main influence paths were analyzed to propose Balanced Score Card as management tools by which the informationized investment can be fully used for the company's brand value. The main contribution of this study is to find out the main way that information investment affects the company's brand value. It is found that the supply chain efficiency of China's A-share clothing and home textile listed companies needs to be improved, and companies should make full use of information investment to improve the supply chain efficiency.

Digitalization of HR is a catalyst for profound transformations in business, focusing on optimizing HR processes and ensuring flexibility in response to the dynamic challenges of today. The evolution towards a digital economy creates an environment where task automation, integration of artificial intelligence, use of advanced analytical platforms and cloud technologies become not just options, but imperatives for modernizing operational activities and forming a positive corporate culture. These technological shifts allow us to move from operational, often bureaucratized, HR functions to strategic partnerships capable of anticipating business needs and developing human capital. However, the process of introducing digital innovations into the HR sphere is not without significant obstacles. The key barrier remains the limited financial resources, which often calls into question the necessary investments in software, hardware and staff training. In addition, there is a shortage of qualified personnel who have the necessary digital competencies to work with new tools and analyze data. The development of intelligence and analytical capabilities opens up new horizons. Cloud-based HR systems (e.g., Cloud-based HRIS) provide centralized access to relevant data, increase security and scalability, allowing HR departments to focus on analytics and decision-making based on processing large data sets. Artificial intelligence can be used to automate routine tasks, such as initial resume screening, interview scheduling, providing answers to typical employee questions via chatbots, as well as to personalize training and development programs. The development of the concept of digital personnel development requires not only technical modernization, but also deep organizational changes, including the transformation of corporate culture, the development of leadership qualities and the formation of a strategic vision. An integrated approach that combines technological investments, human capital development and management support is the key to achieving the desired results and ensuring the sustainable competitive development of the company in the context of digital transformation. The work uses systems analysis methods to assess digitalization trends in the HR sector, a comparative analysis of classical and digital models of personnel management. Thus, to effectively digitize HR processes, companies need to not only invest in modern technologies, but also pay due attention to developing the competencies of their employees and forming a clear implementation strategy that is consistent with overall business goals. This will allow maximizing the potential of digital tools and ensuring the sustainable development of the organization in the long term. The current stage of economic development is characterized by the continuous growth of the role of digital technologies in all areas of activity, including personnel management. The digitalization of HR processes is becoming not just a trend, but an imperative for enterprises seeking to increase competitiveness and optimize internal operations. Research in this area is aimed at a comprehensive study of transformational processes that occur under the influence of digital innovations. One of the key areas of digitalization is the automation of routine HR functions, such as recruiting, onboarding, vacation management and timekeeping.

Human factors can play havoc with quality of task performance in safety‐sensitive operations, especially those that run 24‐hours, 7‐days a week. Research across multiple industries indicates common threads of observable and preventable behaviors that can lead to catastrophic events. Fatigue, monotony, overwork and a multitude of other cultural factors and organizational policies can trigger these mental and physical human errors. With recent extensive capital investments in hardware and software to automate continuous operations, the human operating the controls may become complacent and fail to perform as anticipated.Since the chemical plant, maritime, aviation and space disasters of the mid‐1980's, there has been an increased focus on operational procedures. Operational fatigue has been on the National Transportation Safety Board's list of Most Wanted Transportation Safety Improvements since the list's inception in 1990 [1]. Today, it may be time to re‐visit human factors and focus on the human in operational settings in all modes of 24hour operations. The key is to first understand two strategies to prevent shiftwork‐related human error. The first strategy is at the organizational level where we can align human resource and safety policies, address aging needs through better shift scheduling, develop work/rest guidelines and design wellness objectives to enhance 24‐hour safety performance. The second strategy is at the operational level where employees recognize personal set‐ups for human error and take the appropriate measures. These skills are learned through “fatigue counter‐measure training.” Both strategies will be discussed throughout this article.

In this report, we detail direct inject mass spectrometry via a Robotically Automated Vacuum Enabled (RAVE) interface that utilizes commercially available capillary electrophoresis hardware to directly inject samples for mass spectrometry (MS) at a sampling rate of approximately 12s per sample. This system enables direct electrospray ionization from standard 48, 96 or 384-well plates with minimal investment in hardware and software, utilizing a custom developed web app interface that provides autosampler control and analysis of raw extracted data from the mass spectrometer. We show a high level of correlation among results obtained with RAVE coupled MS, acoustic ejection (Echo) MS, and liquid chromatography coupled MS (LCMS) on 384 biocatalytically driven reactions. We additionally utilize RAVE MS on an array of 96 chemocatalytic reaction conditions to show that, while direct MS analysis can be challenging in complex mixtures, simple dilution followed by direct injection is often sufficient for analysis. With these results, we demonstrate the potential for RAVE MS to be utilized as a low-cost, low barrier to entry tool for rapid direct-inject MS analysis.