Evaluation of human workload in a hybrid order picking system

Order picking is a labor-intensive and costly process in supply chains, which is performed manually in most cases. Recently, picking robots have been developed which are capable of working together with human pickers in a shared working space. Such hybrid order picking system can ease human pickers’ workload and provide ergonomic improvements, because it partially automates the order picking process. We propose a simulation model to measure the energy expenditure of human pickers who work with the support of picking robots. The hybrid order picking system is evaluated based on its operational costs, efficiency, and ergonomic characteristics. Preliminary results presented in this study show that there are assignment rules for items to workers and robots that reduce human energy expenditure and costs per pick, as well as maintain average throughput time at a certain level. The aim of this preliminary study is to closely analyze the hybrid order picking system, evaluate managerial implications, and detect research opportunities for future works

Potential of mobile applications in human-centric production and logistics management

With the increasing market penetration of smart devices (smartphones, smartwatches, and tablets), various mobile applications (apps) have been developed to fulfill tasks in daily life. Recently, efforts have been made to develop apps to support human operators in industrial work. When apps installed on commercial devices are utilized, tasks that were formerly done purely manually or with the help of investment-intensive specific devices can be performed more efficiently and/or at a lower cost and with reduced errors. Despite their advantages, smart devices have limitations because embedded sensors (e.g., accelerometers) and components (e.g., cameras) are usually designed for nonindustrial use. Hence, validation experiments and case studies for industrial applications are needed to ensure the reliability of app usage. In this study, a systematic literature review was employed to identify the state of knowledge about the use of mobile apps in production and logistics management. The results show how apps can support human centricity based on the enabling technologies and components of smart devices. An outlook for future research and applications is provided, including the need for proper validation studies to ensure the diversity and reliability of apps and more research on psychosocial aspects of human-technology interaction

Distribution and Abundance of Archaea in South China Sea Sponge Holoxea sp. and the Presence of Ammonia-Oxidizing Archaea in Sponge Cells

Compared with bacterial symbionts, little is known about archaea in sponges especially about their spatial distribution and abundance. Understanding the distribution and abundance of ammonia-oxidizing archaea will help greatly in elucidating the potential function of symbionts in nitrogen cycling in sponges. In this study, gene libraries of 16S rRNA gene and ammonia monooxygenase subunit A (amoA) genes and quantitative real-time PCR were used to study the spatial distribution and abundance of archaea in the South China Sea sponge Holoxea sp. As a result, Holoxea sp. specific AOA, mainly group C1a (marine group I: Crenarchaeota) were identified. The presence of ammonia-oxidizing crenarchaea was observed for the first time within sponge cells. This study suggested a close relationship between sponge host and its archaeal symbionts as well as the archaeal potential contribution to sponge host in the ammonia-oxidizing process of nitrification

Reciprocal Learning in Production and Logistics

Integration of AI technologies and learnable systems in production and logistics transforms the concepts of work organization and task assignments to human and machine agents. Thus, the question arises of what intelligent machines and human workers may be able to achieve as teammates. One answer may be guiding and training the workforce at the workplace to cope with emerging skill mismatches, emphasized by concepts of work-based learning. The extension of cyber-physical production systems towards becoming human-centered and social systems enabling human-machine interaction, creates opportunities for human-machine symbiosis by complementing each other's strengths. In this way, the concept of “Reciprocal Learning” (RL) between humans and intelligent machines has emerged, which is still rather ambiguous and lacks a profound knowledge base. Especially in production and logistics, literature is fragmented. Hence, the objective of this paper is to conduct a systematic literature review to elicit and cluster the knowledge base in RL represented by adjacent interdisciplinary fields of research, such as social and computer sciences. This work contributes to the literature by developing a comprehensive knowledge base on the concept of RL enabling to pursue future research directions towards the realization of human-machine symbiosis through RL in production and logistics

Hybrid order picking : A simulation model of a joint manual and autonomous order picking system

Order picking is a key process in supply chains and a determinant of business success in many industries. Order picking is still performed manually by human operators in most companies; however, there are also increasingly more technologies available to automate order picking processes or to support human order pickers. One concept that has not attracted much research attention so far is hybrid order picking where autonomous robots and human order pickers work together in warehouses within a shared workspace for a joint target. This study presents a simulation model that considers various system characteristics and parameters of hybrid order picking systems, such as picker blocking, to evaluate the performance of such systems. Our results show that hybrid order picking is generally capable of improving pure manual or automated order picking operations in terms of throughput and total costs. Based on the simulation results, promising future research potentials are discussed

DeepFlame: A deep learning empowered open-source platform for reacting flow simulations

In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynamics library OpenFOAM, machine learning framework Torch, and chemical kinetics program Cantera. The complexity of cross-library function and data interfacing (the core of DeepFlame) is minimised to achieve a simple and clear workflow for code maintenance, extension and upgrading. As a demonstration, we apply our recent work on deep learning for predicting chemical kinetics (Zhang et al. Combust. Flame vol. 245 pp. 112319, 2022) to highlight the potential of machine learning in accelerating reacting flow simulation. A thorough code validation is conducted via a broad range of canonical cases to assess its accuracy and efficiency. The results demonstrate that the convection-diffusion-reaction algorithms implemented in DeepFlame are robust and accurate for both steady-state and transient processes. In addition, a number of methods aiming to further improve the computational efficiency, e.g. dynamic load balancing and adaptive mesh refinement, are explored. Their performances are also evaluated and reported. With the deep learning method implemented in this work, a speed-up of two orders of magnitude is achieved in a simple hydrogen ignition case when performed on a medium-end graphics processing unit (GPU). Further gain in computational efficiency is expected for hydrocarbon and other complex fuels. A similar level of acceleration is obtained on an AI-specific chip - deep computing unit (DCU), highlighting the potential of DeepFlame in leveraging the next-generation computing architecture and hardware

Effect of Assisted Reproductive Technology (ART) on Babies Born: Compared by IVF Laboratories of Two Countries

Assisted reproductive technology (ART) has been widely used for infertility treatment, but many people have concern about their baby’s health. The objective of this chapter is to provide some detailed data about the effect of ART on human birth babies by analyzing the data from in vitro fertilization (IVF) centers in two countries. All recent records related to a baby’s birth including mother’s age, gestational days, baby’s sex, and birth weight data were collected and analyzed according to fresh or frozen embryo transfer procedure. Normal delivery data without ART were used as control. The result showed that ART patient age is significantly older than non-IVF women; the gestation of fresh and frozen embryo transfer is the same as normal spontaneous conception gestation days, but women pregnant with multiple gestations have shorter gestational period and early birth rate as well as low birth weight; and there is no significant difference in the baby’s weight between ART singleton babies and normal conception babies, but male babies weight is more than female babies, and multiple gestation’s birth weights are significantly lower than singletons, while frozen embryo transfer babies have significantly heavier birth weight than fresh embryo transfer. Also, the frozen embryo transfer technique may significantly decrease premature birth rate. Thus, frozen embryo transfer may be recommended as a health strategy in ART

Human-Robot Collaboration in Intralogistics: Benefits and Effects on Employees

Die Kommissionierung ist eine besonders zeit- und kostenintensive Tätigkeit in der Intralogistik, vor allem wenn diese manuell ausgeführt wird. Deswegen kann es für Unternehmen wirtschaftlich interessant sein, autonome Kommissionierroboter, die mit Menschen zusammenarbeiten, in einem hybriden System einzusetzen. Dieser Artikel gibt einen Überblick über die Vorteile der Mensch-Roboter-Zusammenarbeit in der Intralogistik und quantifiziert diese exemplarisch mit Hilfe eines Simulationsmodells. Daneben werden praxisnahe Herausforderungen bei der Implementierung derartiger hybrider Systeme in Bezug auf Menschenzentrierung, Ergonomie, Technologie-Akzeptanz und wirtschaftliche Arbeitsleistung im Sinne der Industrie 5.0 beleuchtet.Order picking is a particularly time-consuming and cost-intensive activity in intralogistics, especially if it is carried out manually. That is why it can be economically interesting for companies to use autonomous picking robots working together with humans in a hybrid system. This paper provides an overview of the advantages of human-robot collaboration in intralogistics and as an example quantifies them with the help of a simulation model. In addition, practical challenges in the implementation of such hybrid order picking systems are highlighted in terms of humancentricity, ergonomics, technology-acceptance and economic work performance in the sense of Industry 5.0

Facial Structure Analysis Separates Autism Spectrum Disorders Into Meaningful Clinical Subgroups

Varied cluster analysis were applied to facial surface measurements from 62 prepubertal boys with essential autism to determine whether facial morphology constitutes viable biomarker for delineation of discrete Autism Spectrum Disorders (ASD) subgroups. Earlier study indicated utility of facial morphology for autism subgrouping (Aldridge et al. in Mol Autism 2(1):15, 2011). Geodesic distances between standardized facial landmarks were measured from three-dimensional stereo-photogrammetric images. Subjects were evaluated for autism-related symptoms, neurologic, cognitive, familial, and phenotypic variants. The most compact cluster is clinically characterized by severe ASD, significant cognitive impairment and language regression. This verifies utility of facially-based ASD subtypes and validates Aldridge et al.\u27s severe ASD subgroup, notwithstanding different techniques. It suggests that language regression may define a unique ASD subgroup with potential etiologic differences

Bidirectional regulation of angiogenesis and miR-18a expression by PNS in the mouse model of tumor complicated by myocardial ischemia

BACKGROUND: Panax Notoginseng Saponins (PNS) is the major class of active constituents of notoginseng, a natural product extensively used as a therapeutic agent in China. Tumor when accompanied by cardiovascular disorders poses a greater challenge for clinical management given the paradoxical involvement of angiogenesis, therefore gaining increased research attention. This study aim to investigate effects of PNS and its activity components in the mouse model of tumor complicated with myocardial ischemia. METHODS: Tumor complexed with myocardial ischemia mouse model was first established, which was followed by histological and immunohistochemistry examination to assess the effect of indicated treatments on tumor, myocardial ischemia and tissue specific angiogenesis. MicroRNA (miRNA) profiling was further carried out to identify potential miRNA regulators that might mechanistically underline the therapeutic effects of PNS in this complex model. RESULTS: PNS and its major activity components Rg1, Rb1 and R1 suppressed tumor growth and simultaneously attenuated myocardial ischemia. PNS treatment led to decreased expression of CD34 and vWF in tumor and increased expression of these vascular markers in heart. PNS treatment resulted in reduced expression of miR-18a in tumor and upregulated expression of miR-18a in heart. CONCLUSIONS: Our data demonstrated for the first time that PNS exerts tissue specific regulatory effects on angiogenesis in part through modulating the expression of miR-18a, which could be responsible for its bidirectional effect on complex disease conditions where paradoxical angiogenesis is implicated. Therefore, our study provides experimental evidence warranting evaluation of PNS and related bioactive component as a rational therapy for complex disease conditions including co-manifestation of cancer and ischemic cardiovascular disease