Off-line Processes Research Articles

Resilience against IT attacks in hospitals : Results from an exercise in aGerman university hospital

According to the legal definition healthcare systems and their components (e.g., hospitals) are part of the critical infrastructure of modern industrial nations. During the last few years hospitals increasingly became targets of cyber attacks causing severe impairment of their operability for weeks or even months. According to the German federal strategy for protection of critical infrastructures (KRITIS strategy), hospitals are obligated to take precautions against potential cyber attacks or other IT incidents. This article describes the process of planning, execution and results of an advanced table-top exercise which took place in a university hospital in Germany and simulated the first 3 days after acyber attack causing atotal failure of highly critical IT systems. During afirst stage lasting about 8 months IT-dependent processes within the clinical routine were identified and analyzed. Then paper-based and off-line back-up processes and workarounds were developed and department-specific emergency plans were defined. Finally, selected central facilities such as pharmacy, laboratory, radiology, IT and the hospitals crisis management team took part in the actual disaster exercise. Afterwards the participants were asked to evaluate the exercise and the hospitals cyber security using aquestionnaire. On this basis the authors visualized the hospital's resilience against cyber incidents and defined short-term, medium-term and long-term needs for action. Of the participants 85% assessed the exercise as beneficial, 97% indicated that they received adequate support during the preparations and 75% had received sufficient information; however, only 34% had the opinion that the hospital's and their own preparedness against critical IT failures were sufficient. Before the exercise took place, IT-specific emergency plans were present only in 1.7% of the hospital facilities but after the exercise in 86.7% of the clinical and technical departments. The highest resilience against cyber attacks was not surprisingly reported by facilities that still work routinely with paper-based or off-line processes, the IT department showed the lowest resilience as it would come to acomplete shutdown in cases of atotal IT failure. The authors concluded that the planning phase is the most important stage of developing the whole exercise, giving the best opportunity for working out fallback levels and workarounds and through this strengthen the hospitals resilience against cyber attacks and comparable incidents. Ameticulous preparedness can minimize the severe effects atotal IT failure can cause on patient care, staff and the hospital as awhole.

Analysis of Offline and Online Learning Processes During the Covid-19 Pandemic

The purpose of this study is to assess the offline and online learning process at Namlea State Primary School 5 (SDN 5 Namlea) during the Covid-19 pandemic and to identify the teacher's approach for integrating offline and online learning during the Covid-19 pandemic at SDN 5 Namlea. This study employed a qualitative descriptive methodology with a phenomenological approach. Principals, instructors, students in Classes I, III, IV, and VI, and students in Classes III, IV, and V served as data sources for both primary and secondary data. This research employs nonparticipant observation employing field survey techniques (field study), interviews (interviews), documentary studies, and literature studies as its methodology. During the Covid-19 epidemic, the results of the study demonstrate that the offline and online learning processes at SDN 5 Namlea are very efficient and effective. The school incorporates offline learning through 1) shift (alternating teaching and learning), 2) television, 3) study groups, and 4) home visits. In the process of online education, teachers utilize two media platforms, namely WhatsApp and YouTube. During the Covid-19 pandemic, SDN 5 Namlea implemented offline and online learning strategies by 1) conducting learning activities (Blended Learning, Offline, Online); 2) using technological facilities such as gadgets, laptops, internet, and wifi; and 3) utilizing application media, namely WhatsApp and YouTube.

The expanded circuitry of hippocampal ripples and replay

The place cells and well-defined oscillatory population rhythms of the rodent hippocampus have served as a powerful model system in linking cells and circuits to memory function. While the initial three decades of place cell research primarily focused on the activity of neurons during exploration, the last twenty-five years have seen growing interest in the physiology of the hippocampus at rest. During slow-wave sleep and quiet wakefulness the hippocampus exhibits sharp-wave ripples (SWRs), short high-frequency, high-amplitude oscillations, that organize the reactivation or ‘replay’ of sequences of place cells, and interventions that disrupt SWRs impair learning. While the canonical model of SWRs generation have emphasized CA3 input to CA1 as the source of excitatory drive, recent work suggests there are multiple circuits, including the CA2 region, that can both influence, generate and organize SWRs, both from the oscillatory and information content perspectives in a task and state-dependent manner. This extended circuitry and its function must be considered for a true understanding of the role of the hippocampus in off-line processes such as planning and consolidation.

Age-related changes in fast spindle clustering during non-rapid eye movement sleep and their relevance for memory consolidation.

Sleep plays a crucial role in memory consolidation. Recent data in rodents and young adults revealed that fast spindle band power fluctuates at a 0.02-Hz infraslow scale during non-rapid eye movement (NREM) sleep. These fluctuations result from a periodic temporal clustering of spindles and may modulate sleep maintenance and memory consolidation. With age, sleep undergoes substantial changes but age-related changes in spindle clustering have never been investigated. Polysomnography data were collected in 147 older (mean age ± SD: 69.3 ± 4.1 years) and 32 young-middle aged (34.5 ± 10.9 years) adults. Sleep-dependent memory consolidation was assessed in a subsample of 57 older adults using a visuospatial memory task. We analyzed power fluctuations in fast spindle frequency band, detected fast spindles, and quantified their clustering during the night separating encoding and retrieval. Fast spindle band power fluctuated at a 0.02-Hz infraslow scale in young-middle aged and older adults. However, the proportion of clustered fast spindles decreased non-linearly with age (p < .001). This effect was not mediated by NREM sleep fragmentation. The clustering level of fast spindles modulated their characteristics (p < .001). Finally, the mean size of spindle clusters was positively associated with memory consolidation (p = .036) and negatively with NREM sleep micro-arousal density (p = .033). These results suggest that clusters of fast spindles may constitute stable sleep periods promoting off-line processes such as memory consolidation. We emphasize the relevance of considering spindle dynamics, obviously impaired during aging, to understand the impact of age-related sleep changes on memory. Clinical Trial Information: Name: Study in Cognitively Intact Seniors Aiming to Assess the Effects of Meditation Training (Age-Well). URL: https://clinicaltrials.gov/ct2/show/NCT02977819?term=Age-Well&draw=2&rank=1. See STROBE_statement_AGEWELL.doc in supplementary material. Registration: EudraCT: 2016-002441-36; IDRCB: 2016-A01767-44; ClinicalTrials.gov Identifier: NCT02977819.

Defect detection in L-PBF Metal Additive Manufacturing using on-line and off-line inspection processes

Laser powder bed fusion (L-PBF) additive manufacturing enables new industrial possibilities for high precision manufacturing and design optimization while reduces material loses. Manufactured parts complex geometries represent a major challenge for non-destructive testing and quality control. The aim of this study is to assess defect detection precision by means of in-line and off-line inspection techniques. For that, a set of samples of Inconel718 have been manufactured in a RenAM500Q Renishaw machine. The samples include intentionally induced defects in the mid-plane considering a width varying from 0.11 to 0.35mm. Samples have been monitored on-line, trough on-axis optical monitoring system, and off-line, trough ultrasonic testing (UT). Samples have been inspected by UT in an immersion tank and by laser ultrasounds. Defects geometry has been determined through destructive characterization. All inspection methods are able to detect induced defects with resolutions in the range of 0.3 mm.

Targeted Bottom-up Characterization of Recombinant Monoclonal Antibodies by Multidimensional LC/MS.

On-line bottom-up approaches have recently emerged as promising alternatives to standard off-line processes for characterizing post-translational modifications (PTMs) of therapeutic monoclonal antibodies (mAbs). The benefits of on-line processing include reductions in required sample amount and sample handling, as well as reducing the overall turnaround time. However, shortening digestion time for the on-line approach of an intact mAb can cause incomplete peptide cleavages, leading to low sequence coverage and poor repeatability of analyses. For the first time, we describe a novel, automated targeted bottom-up strategy consisting of reducing the complexity of intact mAb by digesting the product into small ∼25 kDa fragments, followed by an on-line peptide mapping analysis of each fragment. For this purpose, a four-dimensional-liquid chromatography/mass spectrometry (4D-LC/MS) method was developed using an immobilized IdeS-high-performance liquid chromatography (HPLC) column as a first dimension (1D) for on-line digestion, followed by a (2D) on-column reversed-phase liquid chromatography (RPLC) for reduction and fragments separation. Then, only one fragment was selected for digestion using a (3D) immobilized trypsin cartridge and, finally, the obtained peptides were analyzed by (4D) RPLC-MS. This strategy considerably improved the on-line digestion efficiency with higher sequence coverages (LC and HC >97%), thus allowing various PTMs including oxidation, deamidation, and isomerization located in the complementarity-determining regions (CDRs), as well as N-glycans present on the Fc/2 fragment, to be monitored with similar sensitivity to those obtained with standard off-line approaches. Additional investigations at a middle-up level were also performed via a three-dimensional-LC/MS (3D-LC/MS) approach within the same system, demonstrating the feasibility to achieve a multilevel comprehensive characterization of mAbs.

Damage Localization in a Building Structure during Seismic Excitation

Aging of buildings during their service life has attracted the attention of researchers on structural health monitoring (SHM). This paper is related with detecting damage in building structures at the earliest possible stage during seismic activity to facilitate decision-making on evacuation before physical inspection is possible. For this, a simple method for damage assessment is introduced to identify the damage story of multistory buildings from acceleration measurements under a wave propagation approach. In this work, damage is assumed as reduction in shear wave velocities and changes in damping ratios that are directly related with stiffness loss. Most damage detection methods are off-line processes; this is not the case with this method. First, a real-time identification system is introduced to estimate the current parameters to be compared with nominal values to detect any changes in the characteristics that may indicate damage in the building. In addition, this identification system is robust to constant disturbances and measurement noise. The time needed to complete parameter identification is shorter compared to the typically wave method, and the damage assessment can keep up with the data flow in real time. Finally, using a robust threshold, postprocess of the compared signal is performed to find the location of the possible damage. The performance of the proposed method is demonstrated through experiments on a reduced-scale five-story building, showing the ability of the proposed method to improve early stage structural health monitoring.

Teaching-Playback Navigation Without a Consistent Map

This paper presents a teaching-playback navigation method that does not require a consistent map built using simultaneous localization and mapping (SLAM). Many open source projects related to autonomous navigation including SLAM have been made available recently; however, autonomous mobile robot navigation in large-scale environments is still difficult because it is difficult to build a consistent map. The navigation method presented in this paper uses several partial maps to represent an environment map. In other words, the complex mapping process is not necessary to begin autonomous navigation. In addition, the trajectory that the robot travels in the mapping phase can be directly used as a target path. As a result, teaching-playback autonomous navigation can be achieved without any off-line processes. We tested the navigation method using log data taken in the environment of the Tsukuba Challenge and the testing results show its performance. We provide source code for the navigation method, which includes modules required for autonomous navigation (https://github.com/NaokiAkai/AutoNavi).

The role of complementary learning systems in learning and consolidation in a quasi-regular domain

We examine the role of off-line memory consolidation processes in the learning and retention of a new quasi-regular linguistic system similar to the English past tense. Quasi-regular systems are characterized by a dominance of systematic, regular forms (e.g., walk-walked, jump-jumped) alongside a smaller number of high frequency irregulars (e.g., sit-sat, go-went), and are found across many cognitive domains, from spelling-sound mappings to inflectional morphology to semantic cognition. Participants were trained on the novel morphological system using an artificial language paradigm, and then tested after different delays. Based on a complementary systems account of memory, we predicted that irregular forms would show stronger off-line changes due to consolidation processes. Across two experiments, participants were tested either immediately after learning, 12 h later with or without sleep, or 24 h later. Testing involved generalization of the morphological patterns to previously unseen words (both experiments) as well as recall of the trained words (Experiment 2). In generalization, participants showed ‘default’ regularization across a range of novel forms, as well as irregularization for previously unseen items that were similar to unique high-frequency irregular trained forms. Both patterns of performance remained stable across the delays. Generalizations involving competing tendencies to regularize and irregularize were balanced between the two immediately after learning. Crucially, at both 12-h delays the tendency to irregularize in these cases was strengthened, with further strengthening after 24 h. Consolidated knowledge of both regular and irregular trained items contributed significantly to generalization performance, with evidence of strengthening of irregular forms and weakening of regular forms. We interpret these findings in the context of a complementary systems model, and discuss how maintenance, strengthening, and forgetting of the new memories across sleep and wake can play a role in acquiring quasi-regular systems.

Towards ingredients by combining Supercritical Fluids with other processes

“Crude extracts” obtained from most natural sources cannot be used as is from a supercritical fluid (SCF) plant and have to be reprocessed to eliminate water, pigments and waxes so as to become “clean extracts” that are concentrated in active compounds for direct use as ingredients in food, cosmetics or nutraceuticals/pharmaceuticals.On the other hand, the solid or liquid raw material can be processed as the ingredient itself from which unwanted compounds are eliminated and/or in which a valuable solute is infused.Several on-line or off-line processes are proposed so as to obtain such “ready-for-use” ingredients by combination of supercritical fluid processes (extraction, fractionation, impregnation) and other techniques including vacuum and molecular distillation, centrifugation and dehydration, adsorption on specific adsorbents, cold filtration and winterisation.

Deep Learning for Quality Assessment in Live Video Streaming

Video content providers put stringent requirements on the quality assessment methods realized on their services. They need to be accurate, real-time, adaptable to new content, and scalable as the video set grows. In this letter, we introduce a novel automated and computationally efficient video assessment method. It enables accurate real-time (online) analysis of delivered quality in an adaptable and scalable manner. Offline deep unsupervised learning processes are employed at the server side and inexpensive no-reference measurements at the client side. This provides both real-time assessment and performance comparable to the full reference counterpart, while maintaining its no-reference characteristics. We tested our approach on the LIMP Video Quality Database (an extensive packet loss impaired video set) obtaining a correlation between $78\%$ and $91\%$ to the FR benchmark (the video quality metric). Due to its unsupervised learning essence, our method is flexible and dynamically adaptable to new content and scalable with the number of videos.

Real-Time Classification of Healthy and Apnea Subjects Using ECG Signals With Variational Mode Decomposition

This paper introduces a novel method for the classification of healthy and apnea subjects using variational mode decomposition. The proposed method distinguishes the apnea and normal subjects with the help of an electrocardiogram (ECG) signal. Polysomnogram is the gold standard used for the identification of apnea subjects. This process is complex, expensive, and time-consuming. In this paper, both online and off-line-based feature extraction and classification methods are explored. The proper extraction of suitable features from the signal is done by applying variational mode decomposition. Two features are extracted from the variational mode functions, namely, energy and RR interval of ECG signal. These features are fed in to a support vector machine classifier, where they are classified as healthy and apnea. The accuracy obtained for both online and off-line processes are 97.5% and 95%, respectively.

Dual enhancement mechanisms for overnight motor memory consolidation.

Our brains are constantly processing past events [1]. These off-line processes consolidate memories, leading in the case of motor skill memories to an enhancement in performance between training sessions. A similar magnitude of enhancement develops over a night of sleep following an implicit task, when a sequence of movements is acquired unintentionally, or following an explicit task, when the same sequence is acquired intentionally [2]. What remains poorly understood, however, is whether these similar offline improvements are supported by similar circuits, or through distinct circuits. We set out to distinguish between these possibilities by applying Transcranial Magnetic Stimulation (TMS), over the primary motor cortex (M1) or the inferior parietal lobule (IPL) immediately after learning in either the explicit or implicit task. These brain areas have both been implicated in encoding aspects of a motor sequence, and subsequently supporting offline improvements over sleep [3–5]. Here we show that offline improvements following the explicit task are dependent upon a circuit that includes M1 but not IPL. By contrast, offline improvements following the implicit task are dependent upon a circuit that includes IPL but not M1. Our work establishes the critical contribution made by M1 and IPL circuits to offline memory processing, and reveals that distinct circuits support similar offline improvements.

Post-training Meditation Promotes Motor Memory Consolidation.

Following training, motor memory consolidation is thought to involve either memory stabilization or off-line learning processes. The extent to which memory stabilization or off-line learning relies on post-training wakeful periods or sleep is not clear and thus, novel research approaches are needed to further explore the conditions that promote motor memory consolidation. The present experiment represents the first empirical test of meditation as potential facilitator of motor memory consolidation. Twelve adult residents of a yoga center with a mean of 9 years meditation experience were trained on a sequence key pressing task. Three hours after training, the meditation group completed a 30 min session of yoga nidra meditation while a control group completed 30 min of light work duties. A wakeful period of 4.5 h followed meditation after which participants completed a test involving both trained and untrained sequences. Training performance did not significantly differ between groups. Comparison of group performance at test, revealed a performance benefit of post-training meditation but this was limited to trained sequences only. That the post-training meditation performance benefit was specific to trained sequences is consistent with the notion of meditation promoting motor memory consolidation as opposed to general motor task performance benefits from meditation. Further, post-training meditation appears to have promoted motor memory stabilization as opposed to off-line learning. These findings represent the first demonstration of meditation related motor memory consolidation and are consistent with a growing body of literature demonstrating the benefits of meditation for cognitive function, including memory.

Wash water disinfection of a full-scale leafy vegetables washing process with hydrogen peroxide and the use of a commercial metal ion mixture to improve disinfection efficiency

Abstract Hydrogen peroxide (H2O2) was used to maintain the microbial wash water quality of a full-scale leafy vegetables (radicchio, sugar loaf, curled endive, lollo, lollo rosso) wash water process. Despite addition of 300 L/h of 1.8% H2O2 to a 450 L washing bath (333 ± 50 kg/h fresh-cut produce introduction speed), the H2O2 quickly decreased and a lower wash water contamination of aerobic psychrotrophic plate count (APC) and enterococci than without addition of H2O2 could not be maintained. There was no significant difference between the APC on fresh-cut leafy vegetables washed with H2O2 and those washed with water. In a second part, lab-scale experiments were performed to assess the impact of a commercial metal ion formulation (Bacsan®, containing a. o. Cu2+, Zn2+, Ag+) on the stability of H2O2 in artificial wash water, made from iceberg lettuce and tap water. Bacsan improved the stability of H2O2 in artificial lettuce wash water and fresh-cut leafy vegetables wash water from a processing company and synergistically increased the disinfection efficiency of APC and Escherichia coli (E. coli) compared to H2O2 or Bacsan. Increasing chemical oxygen demand (COD) had detrimental effect on the H2O2 stability and disinfection efficiency. Addition of Ag+ to Bacsan further synergistically enhanced the H2O2 stability. H2O2 is not suited as an in situ wash water disinfectant to avoid cross-contamination in fresh-cut leafy vegetables washing processes due to the slow water disinfection kinetics and the rapid H2O2 consumption. However, H2O2/Bacsan shows potential for use in off-line processes.

Sleep and the processing of emotions

How emotions interact with cognitive processes has been a topic of growing interest in the last decades, as well as studies investigating the role of sleep in cognition. We review here evidence showing that sleep and emotions entertain privileged relationships. The literature indicates that exposure to stressful and emotional experiences can induce changes in the post-exposure sleep architecture, whereas emotional disturbances are likely to develop following sleep alterations. In addition, post-training sleep appears particularly beneficial for the consolidation of intrinsically emotional memories, suggesting that emotions modulate the off-line brain activity patterns subtending memory consolidation processes. Conversely, sleep contributes unbinding core memories from their affective blanket and removing the latter, eventually participating to habituation processes and reducing aversive reactions to stressful stimuli. Taken together, these data suggest that sleep plays an important role in the regulation and processing of emotions, which highlight its crucial influence on human's abilities to manage and respond to emotional information.

A speeded-up online incremental vision-based loop-closure detection for long-term SLAM

An online incremental method of vision-only loop-closure detection for long-term robot navigation is proposed. The method is based on the scheme of direct feature matching which has recently become more efficient than the Bag-of-Words scheme in many challenging environments. The contributions of the paper are the application of hierarchical k-means to speed-up feature matching time and the improvement of the score calculation technique used to determine the loop-closing location. As a result, the presented method runs quickly in long term while retaining high accuracy. The searching cost has been markedly reduced. The proposed method requires no any off-line dictionary generation processes. It can start anywhere at any times. The evaluation has been done on standard well-known datasets being challenging in perceptual aliasing and dynamic changes. The results show that the proposed method offers high precision-recall in large-scale different environments with real-time computation comparing to other vision-only loop-closure detection methods.

Offline and Simplified Industrial Automation Processes Employing Robotic Manipulators

One of the predominant applications of the theories and principles of robotics is in industrial automation particularly in automotive manufacturing. This paper explores some mini-projects related to industrial automation that will help students and even robotics enthusiasts understand the kinematics of each manipulator. These projects can be as simple as following a straight line to as complex as adding machine vision and online intelligence to the robotics manipulator.

Vision-based all-in-one solution for augmented reality and its storytelling applications

In this paper, we propose a vision-based all-in-one solution for augmented reality where users want to exploit unknown 3D objects in their systems. In our solution, we facilitate two time-consuming off-line processes: obtaining information, such as keyframes and keypoints, for real-time tracking of unknown 3D targets, and estimating local coordinates with a scale for accurate registration of virtual content. The proposed solution only requires images with minimal interactions. The users do not need to know about 3D markerless tracking in depth. At the end, we propose a framework for AR miniatures systems to verify the effectiveness of our solution. As a result, all developed systems worked in real-time, more than 25 fps, and showed reliable registration even in severe viewpoint changes. Our demonstration videos are available in the supplemental materials.

Flow-Injection Spectrophotometric Determination of Bromate in Bottled Drinking Water Samples Using Chlorpromazine Reagent and a Liquid Waveguide Capillary Cell

In this work, aiming to develop a simple, inexpensive method for the determination of low bromate levels in water samples, a liquid waveguide capillary cell (LWCC) was coupled to a FIA system. The long optical path (100 cm) of the LWCC was used to improve the sensitivity and the limit of detection without resorting to any off-line or in-line preconcentration processes. The spectrophotometric determination was based on the oxidation of chlorpromazine by bromate in an acidic medium, resulting in the formation of a colored radical product. Sulfamic acid was added to the reagent for minimizing the interference of nitrite, and a chelating ion exchange resin was used to remove major cationic interferences. The developed system allowed the determination of bromate within the range between 1 - 20 μg L(-1) with a detection limit of 0.2 μg L(-1).