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

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.

Methods for supplying medical personnel, especially in isolated areas, with the same sophisticated medical information and computational facilities that are available in major medical centers, were investigated. Inexpensive, readily available input/output terminals, including the pushbutton telephone, were tested with several biomedical application programs. An experimental network, consisting of several independent commercial time-sharing computers linked to user terminals through a small communication control computer, was constructed to demonstrate an efficient and flexible approach to remote computer access. The communication control computer, acting as a message handling and translating interface, allowed the use of a uniform terminal control language and common data handling conventions. By linking to the most powerful commercial time-sharing systems available, a reliable medical data processing network was established without a major investment in computer hardware or system software. This experimental work is a major part of the Medical Information Telecommunication Project in the Division of Computer Research and Technology (DCRT) at the National Institutes of Health (NIH).

Editorial: Endoscopic coronary artery bypass grafting—The first steps on a long journey

Robotic handling systems are increasingly applied in distribution centers. They require little space, provide flexibility in managing varying demand requirements, and are able to work 24/7. This makes them particularly fit for e-commerce operations. This paper reviews new categories of automated and robotic handling systems, such as shuttle-based storage and retrieval systems, shuttle-based compact storage systems, and robotic mobile fulfillment systems. For each system, we categorize the literature in three groups: system analysis, design optimization, and operations planning and control. Our focus is to identify the research issue and operations research modeling methodology adopted to analyze the problem. We find that many new robotic systems and applications have hardly been studied in academic literature, despite their increasing use in practice. Because of unique system features (such as autonomous control, flexible layout, networked and dynamic operation), new models and methods are needed to address the design and operational control challenges for such systems, in particular, for the integration of subsystems. Integrated robotic warehouse systems will form the next category of warehouses. All vital warehouse design, planning, and control logic, such as methods to design layout, storage and order-picking system selection, storage slotting, order batching, picker routing, and picker to order assignment, will have to be revisited for new robotized warehouses.

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.

Beef cattle farming faces challenges in optimizing production while meeting demands for meat quality and animal welfare. For the Nelore breed, which represents a significant portion of the Brazilian herd, accurately determining the optimal slaughter time is essential to maximize carcass yield and ensure production efficiency. However, traditional monitoring methods, such as manual weighing and visual inspection, are often imprecise, potentially resulting in slaughter outside the ideal point, compromising meat quality and increasing costs. In this research, an intelligent monitoring system based on computer vision and artificial intelligence was developed to identify the optimal slaughter time of Nelore cattle, reducing waste and improving the quality of the final product. The study employed 3D cameras and deep learning algorithms to monitor animal development in real time. For the cost-benefit analysis, initial investments in hardware and software were considered, as well as operational costs and economic gains from increased precision in slaughter decisions. The results indicated improvements in productivity and meat quality, with a return on investment (ROI) estimated at 268.42% over five years. The implementation of the system proved to be financially viable and sustainable, contributing to waste reduction and better animal welfare. These findings highlight the importance of applying advanced technologies to modernize beef cattle production in Brazil, making it more efficient and competitive.

Although the specialty of information management and information system has already started mobile development courses, the current course orientation is not clear, and therefore the teaching plan, teaching content and experiment plan are not targeted. Aiming at this problem,we studies the teaching purpose and content of the mobile development course for specialty of information management and information system, and designs the experimental case base of information management and information system. By proposing training program for mobile device information system design curriculum, we designs and improves information system case base for mobile device, making students majoring in information management and information system master the basic function of mobile development, and design and develop multiple system oriented information system client and server. For information management and information system majors who have good information system development capabilities, they need not to increase investment in hardware and software or modify other teaching content. They can easily transfer to mobile platform oriented information system design and development, integrating with other development technology course as a whole, so as to improve the current isolating situation of mobile development technology course.

Although software and computing process have long been regarded as products among consumers (Armbrust et al., 2010; Skiba, 2011), the advancement of technologies (e.g., high-speed internet) and infrastructures leads to the notion that software and computing can be delivered as a type of service through an innovative technology called cloud computing. Cloud Computing Service (CCS) delivers applications and processing as services via the Internet, while the software and data are stored on servers at a remote location (Armbrust et al., 2010). In other words, under the context of CCS, software and computing process have become a utility such as electricity and water. Advantages of CCS include reduced investment in hardware and software, immediately elevated performance, etc. CCS is believed to be a billion dollar market and growing (Buyya, Yeo, Venugopal, Broberg, & Brandic, 2009). Nevertheless, CCS will reshape the way in which consumers perceive software and computing process (Armbrust et al., 2010) and make purchase decisions. How the notion of CCS diffuses among individual consumers remains unexplored.

The aim of this article is to present an example of a novel cloud computing infrastructure for big data analytics in the Process Control Industry. Latest innovations in the field of Process Analyzer Techniques (PAT), big data and wireless technologies have created a new environment in which almost all stages of the industrial process can be recorded and utilized, not only for safety, but also for real time optimization. Based on analysis of historical sensor data, machine learning based optimization models can be developed and deployed in real time closed control loops. However, still the local implementation of those systems requires a huge investment in hardware and software, as a direct result of the big data nature of sensors data being recorded continuously. The current technological advancements in cloud computing for big data processing, open new opportunities for the industry, while acting as an enabler for a significant reduction in costs, making the technology available to plants of all sizes. The main contribution of this article stems from the presentation for a fist time ever of a pilot cloud based architecture for the application of a data driven modeling and optimal control configuration for the field of Process Control. As it will be presented, these developments have been carried in close relationship with the process industry and pave a way for a generalized application of the cloud based approaches, towards the future of Industry 4.0.

Looking toward the future of computer networking, we see that the need for increased interconnectivity will require continual growth in speed and features. Today there are several successful LANs competing in the 100 Mbps arena. Now we are considering the next generation of network. Ideally, users need a solution that provides the necessary bandwidth and connectivity while preserving their investments in hardware and software. This paper will present an architecturally different design for a new more effective LAN. The new architecture will provide a convenient solution to the need for higher total network capacity by interconnecting multiple FDDI networks (and/or network segments). The underlying network architecture is based on the FDDI protocols, which is an attractive feature for those who have invested resources in FDDI. Scalability is a required feature for any future network design and is addressed in detail by this architecture. This alternative next-generation networking solution has been presented to the ANSI standards committee X3T12 as a possible FDDI follow-on standard (FFOL).

This paper provides an overview of the various applications of Artificial Intelligence (AI) in precision irrigation. It covers key research areas, methodologies, challenges, and future prospects in the field. The methodology is based on exploring how AI technologies are being used to optimize water management in agriculture and examines the growing body of research on the application of AI in irrigation systems. Deep investigation was conducted to explore how AI technologies can enhance water management in agriculture, leading to improved water management and crop yield in addition to resource efficiency. The paper discusses AI-based methods for monitoring soil conditions, weather forecasting, and real-time decision-making in irrigation. However, integration of AI systems with existing irrigation infrastructure and farming practices can be challenging, requiring significant investment in hardware and software.

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.