Sequence Of Activities Research Articles

Upaya Peningkatan Kualitas dengan Menggunakan Analisis Siklus PDCA pada Perusahaan Otomotif

In this era of globalization, the competition in the automotive industry is getting more challenging, and consumer demands are also continually increasing. This is what encourages the automotive industry to make products as perfect as possible, one of which is minimizing the level of defects in their production lines. By reducing defects, the process will flow faster without going through the repair process. But in actuality, there are still defects found in the poses of its production. This research was conducted in the welding process in the automotive industry. This paper aims to identify defects in the welding production process in the automotive industry. Then analyze the most dominant defect, find the root cause, and improve. This study uses the PDCA cycle method. The PDCA cycle is a continuous improvement model consisting of four sequential components, Plan, Do, Check, and Action. The results of this study identified five major defects in the welding process: hole nut not centered, dents, NG stud bolts, NG parts, and non-stick spots. The average defect per month is 110 defects, and the dominant defect is the non-centered nut hole of 63.8 defects (58%). The root cause is when the part setting stopper position is unstable. The improvement made is adding a locator pin cylinder hole nut to stabilize the position. The results of this improvement can reduce welding defects to 58.5 defects per month, and the average defect hole nut is not centered by 3.5 defects (6%). So the PDCA cycle is very effective in efforts to improve product quality.

Regular routine aware routing in opportunistic mobile social networks

AbstractDue to intermittent connectivity, routing protocols in opportunistic mobile social networks (OMSN) forward messages through opportunistic contacts. Previous opportunistic routing protocols focus on the past contact history of nodes to make a forwarding decision. It was found that predicting temporally evolving contacts and communities according to different times of the day would improve the accuracy of the prediction. In this paper, a routing paradigm called RRoutine is proposed that allows message holders to exchange multiple copies of the message based on the predicted number of communities. This distribution continues until the remaining copies of the message become one. Each message holder applies either inter or intra‐community forwarding and selects a set of potential forwarders based on the prediction of future encounters in the community and an expected remaining duration between two nodes (ERIMD). The estimation of ERIMD takes into account the sequence of daily activities, the time scale of current activities, and the return time to regular meetings of the nodes. Also, unlike traditional routing, where a single forwarder is selected, a set of potential candidates is selected and prioritized according to their contact prediction, with the most likely candidate receiving the highest priority. Simulation results obtained from synthetic and real mobility data show that the proposed paper improves the performance of the system in terms of message delivery rate, throughput, and latency.

A graph empowered insider threat detection framework based on daily activities

While threats from outsiders are easier to alleviate, effective ways seldom exist to handle threats from insiders. The key to managing insider threats lies in engineering behavioral features efficiently and classifying them correctly. To handle challenges in feature engineering, we propose an integrated feature engineering solution based on daily activities, combining manually-selected features and automatically-extracted features together. Particularly, an LSTM auto-encoder is introduced for automatic feature engineering from sequential activities. To improve detection, a residual hybrid network (ResHybnet) containing GNN and CNN components is also proposed along with an organizational graph, taking a user-day combination as a node. Experimental results show that the proposed LSTM auto-encoder could extract hidden patterns from sequential activities efficiently, improving F1 score by 0.56%. Additionally, with the designed residual link, our ResHybnet model works well to boost performance and has outperformed the best of other models by 1.97% on the same features. We published our code on GitHub: https://github.com/Wayne-on-the-road/ResHybnet.

Postnatal Maturation of Membrane Potential Dynamics during in Vivo Hippocampal Ripples.

Sharp-wave ripples (SWRs) are transient high-frequency oscillations of local field potentials (LFPs) in the hippocampus and play a critical role in memory consolidation. During SWRs, CA1 pyramidal cells exhibit rapid spike sequences that often replay the sequential activity that occurred during behavior. This temporally organized firing activity gradually emerges during 2 weeks after the eye opening; however, it remains unclear how the organized spikes during SWRs mature at the intracellular membrane potential (Vm) level. Here, we recorded Vm of CA1 pyramidal cells simultaneously with hippocampal LFPs from anesthetized immature mice of either sex after the developmental emergence of SWRs. On postnatal days 16 and 17, Vm dynamics around SWRs were premature, characterized by prolonged depolarizations without either pre- or post-SWR hyperpolarizations. The biphasic hyperpolarizations, features typical of adult SWR-relevant Vm, formed by approximately postnatal day 30. This Vm maturation was associated with an increase in SWR-associated inhibitory inputs to pyramidal cells. Thus, the development of SWR-relevant inhibition restricts the temporal windows for spikes of pyramidal cells and allows CA1 pyramidal cells to organize their spike sequences during SWRs.SIGNIFICANCE STATEMENT Sharp-wave ripples (SWRs) are prominent hippocampal oscillations and play a critical role in memory consolidation. During SWRs, hippocampal neurons synchronously emit spikes with organized temporal patterns. This temporal structure of spikes during SWRs develops during the third and fourth postnatal weeks, but the underlying mechanisms are not well understood. Here, we recorded in vivo membrane potentials from hippocampal neurons in premature mice and suggest that the maturation of SWR-associated inhibition enables hippocampal neurons to produce precisely controlled spike times during SWRs.

Heuristic Search for Physics-Based Problems: Angry Birds in PDDL+ [Extended Abstract

Angry Birds is a very popular game that requires reasoning about sequential actions in a continuous world with discrete exogenous events. Different versions of the game are hard computationally, and the reigning world champion is still a human despite a long-running yearly competition in IJCAI conferences. In this work, we present the Hydra, the first successful game-playing agent for Angry Birds that uses a domain-independent planner and combinatorial search techniques. Hydra models the game using PDDL+, a rich planning language designed for mixed discrete/continuous domains. To reason about continuous aspects of the domain, Hydra employs time discretization techniques that raise a combinatorial search challenge. To meet this challenge, we propose domain-specific heuristics and a novel "preferred states" mechanism similar to the preferred operators mechanism from classical planning. We compared Hydra with state-of-the-art Angry Birds agents. The results show Hydra can solve a greater diversity of Angry Birds levels compared to other agents and highlight its current limitations.

A Survey on Radar-Based Continuous Human Activity Recognition

Radar-based human motion and activity recognition is currently a topic of great research interest, as the aging population increases and older individuals prefer an independent lifestyle. This technology has a wide range of applications, such as fall detection in assisted living, gesture recognition for human-machine interfaces, and many more. Numerous studies exist on various approaches for radar-based activity capture and classification. However, most of these employ rather artificial data, often obtained in laboratory environments, and typically collected under particular conditions. Specifically, most research so far has aimed at distinguishing a predefined set of single activities with a defined start, stop and duration. This paper aims at drawing the attention to a so far less researched issue, one that will be of vital importance for future real-world application of radar-based human activity recognition: continuous activity recognition, i.e. recognizing specific activities in a stream of several sequential activities with unknown duration and arbitrary transitions between different classes of activities. A review on the current state of the art in this relatively new topic is given, followed by a discussion on future research directions.

Passive User Authentication Utilizing Two-Dimensional Features for IIoT Systems

Passive user authentication is critical for the secure operation of Industrial Internet of Things (IIoT) systems. By jointly utilizing both the time-varying characteristics of the user sequential operation actions and spatial variation characteristics of channel state information (CSI) caused by these actions, this paper proposes a novel two-dimensional passive authentication framework for IIoT systems. In particular, we construct the time-varying operation action sequences from the routine work process of a user and apply the Hidden Markov Model to characterize behavioral biometric characteristics of the user, and also employ the eXtreme Gradient Boosting model to depict the spatial variation characteristics of CSI related to the user. By designing two classifiers corresponding these two characteristics and assigning each classifier an appropriate weight, we propose a two-dimensional user authentication framework for continuous and non-intrusive user authentication in IIoT scenarios. Extensive experiments are conducted to illustrate the authentication performance of the proposed authentication framework in terms of false acceptance rate, false rejection rate and equal-error rate. We further investigate the related authentication efficiency issues like the sensitivity to the weights for classifiers, the sensitivity to authentication time and the capability of resisting against impersonation attacks.

Learning business process simulation models: A Hybrid process mining and deep learning approach

Business process simulation is a well-known approach to estimate the impact of changes to a process with respect to time and cost measures – a practice known as what-if process analysis. The usefulness of such estimations hinges on the accuracy of the underlying simulation model. Data-Driven Simulation (DDS) methods leverage process mining techniques to learn business process simulation models from event logs. Empirical studies have shown that, while DDS models adequately capture the observed sequences of activities and their frequencies, they fail to accurately capture the temporal dynamics of real-life processes. In contrast, generative Deep Learning (DL) models are better able to capture such temporal dynamics. The drawback of DL models is that users cannot alter them for what-if analysis due to their black-box nature. This paper presents a hybrid approach to learn process simulation models from event logs wherein a (stochastic) process model is extracted via DDS techniques, and then combined with a DL model to generate timestamped event sequences. The proposed approach allows us to simulate different types of changes, including the addition of new activity types to a process. This latter capability is achieved by encoding the activities by means of embeddings, rather than representing them as one-hot-encoded categories. An experimental evaluation shows that the resulting hybrid simulation models match the temporal accuracy of pure DL models, while partially retaining the what-if analysis capability of DDS approaches. The evaluation also sheds light into the relative performance of multiple embedding approaches to represent the activities.

Social Work Education – Specific Aspects of Social Activities with Unemployed Persons

Social work education is faced with the serious challenges of adequately reflecting the main trends of practical activity oriented towards individual target groups. In this context, the essential characteristics and specific features of social work with unemployed persons as a mandatory element and basic form for the realization of the functions of social protection in case of unemployment, should be adequately reflected in the content of the training of future specialists. The article examines the conceptual view of social work with unemployed persons as a certain sequence of social activities and a variety of social interventions aimed at individuals, families or certain groups with the aim of material support and employment creation. Focusing on the content characteristics of social work with this target group as a sequence of targeted social activities differentiated by various criteria in the individual components of the unemployment protection system, the research focuses on systematizing basic requirements for the content of training in the professional direction “Social Activities”.

RME-based local instructional theory for translation and reflection using of South Sumatra dance context

A learning trajectory as a sequence of mathematical activities that could facilitate the growth of students’ understanding of learning goals. This research aimed to produce an instructional sequence to learn the concepts of translation and reflection and to investigate how students develop their understanding informally using a South Sumatra dance context. The research employed the design research method, which consisted of three stages: preliminary design, design experiment (pilot experiment and teaching experiment), and retrospective analysis. This research involved six students who have the high, medium, low capability at the first cycle and 32 students ninth graders at the second cycle at Junior High School 1 Palembang. Data from observations, tests, interviews, and documentation were analyzed descriptively. The tests used were aimed to determine the improvement made by the students; while the classroom observations, interviews, and documentations were used to develop the local instructional theory. This study produced a learning trajectory with three activities include: the students watched a dance video to describe the dancer’s’ positions in relation to floor tiles; the students were asked to determine the starting points of the dancers in Cartesian coordinates to learn concepts; and the students developed a formal concept based on their own knowledge.

Game-based reflective learning: a modular experience for teachers and students

Video games may represent an experience mediated by digital environments in which an immersive learning happens. Game-based learning (GBL) entails a high degree of metacognitive skills: learning is deeper when gamers are prompted to explain their thoughts and actions, to reflect on learning and on their self-regulation. To foster the metacognitive nature of GBL a modular reflective design framework (MO.RE.GBL) is proposed, focusing the phased construction of the learning process, the role of teachers and students’ autonomy and self-regulation, through a reflective approach. The framework can be used by teachers to design GBL activities or with their students to focus the learning process. Students may use it to self-regulate and personalize their GBL experience through the selection of their sequence of activities.

Learning and organization of within-session sequences by pigeons (Columba livia).

Most animals engage in complex activities that are the combination of simpler actions expressed over a period of time. The mechanisms organizing such sequential behavior have been of long-standing biological and psychological interest. Previously, we observed pigeons' anticipatory behavior with a within-session sequence involving four choice alternatives suggestive of a potential understanding of the overall order and sequence of the items within a session. In that task, each colored alternative was correct for 24 consecutive trials as presented in a predictable sequence (i.e., A first, then B, then C, then D). To test whether these four already-trained pigeons possessed a sequential and linked representation of the ABCD items, we added a second four-item sequence involving new and distinct colored choice alternatives (i.e., E first for 24 trials, then F, then G, then H) and then alternated these ABCD and EFGH sequences over successive sessions. Over three manipulations, we tested and trained trials composed of combinations of elements from both sequences. We determined that pigeons did not learn any within-sequence associations among the elements. Despite the availability and explicit utility of such sequence cues, the data suggest instead that pigeons learned the discrimination tasks as a series of temporal associations among independent elements. This absence of any sequential linkage is consistent with the hypothesis that such representations are difficult to form in pigeons. This pattern of data suggests that for repeated sequential activities in birds, and potentially other animals including humans, there are highly effective, but underappreciated, clock-like mechanisms that control the ordering of behaviors.

Operations of the Cognitive-Metacognitive System in Promoting Learning: a Brief Theoretical Analysis

Metacognition helps educators develop better teaching and learning strategies, contexts, and materials. But, the development and use of good teaching and learning strategies and materials as guided by metacognition require understanding of how it promotes the efficiency of the cognitive system in learning. This article proposes a cognitive-metacognitive system that illustrates how the metacognitive system promotes the efficiency of the cognitive system based on pioneers’ research on the subject (1979 to 2009). Cognitive system performs cognitive activities of knowing, understanding, applying, analyzing, synthesizing, and evaluating of cognitive inputs to reach at cognitive goals. Metacognitive system performs metacognitive activities of assisting the cognitive system to promote its efficiency. The proposed cognitive-metacognitive system enters into a six-step sequence of mental activities to process cognitive inputs (i.e., data, information, lessons, observations, problems, tasks, and other inputs) and yield cognitive outputs (i.e., actions, artifacts, and behaviors). Metacognition promotes learning by identifying and deploying appropriate and effective cognitive strategies and tools for cognitive processing and metacognitive strategies for regulating cognitive processing. This article also proposes that the effectiveness of the cognitive-metacognitive system in promoting learning depends on its ability in helping learners’ generate manageable meta-level models of cognitive inputs for cognitive processing.

Newton’s cradle and Astro-Blaster: toy hybridization for studying chain collisions

We present a multiple-collision apparatus, based on low-cost and easy-to-find materials, which offers the opportunity to plan and conduct a sequence of experimental activities aimed at studying, in a very simple way, the energy transfer in multiple collisions. Our apparatus is based on the use of everyday objects such as toys, which stimulate students’ interest by creating a familiar learning context. Two significant experiments on multiple collisions are described and the analyses of the corresponding experimental results are discussed. The first experiment makes it possible to analyze the kinetic energy transfer along a chain of adjacent balls. The second experiment offers the opportunity to give an analogical conceptual illustration of the bounce of a supernova core after gravitational collapse. Each experiment is pedagogically introduced by asking students to answer an appropriate stimulus questions, which were identified by instructors in a preliminary brainstorm session with students. Last, a theoretical model is given for kinetic energy transfer along a chain of balls and the experimental results are compared with the model predictions.

A Study of Complex Cell Disruption Methods of Intracellular Water-Soluble Protein Extraction from Chlorella sorokiniana

Microalgae biomass is a promising source of plant protein that has potential applications in the food, cosmetics and pharmaceutical industries. However, the presence of a strong cell wall in microalgae cells creates difficulties in obtaining these proteins. This study aims to establish an efficient protocol for disruption of Chlorella sorokiniana microalgae cells to improve the extraction of intracellular water-soluble proteins. C. sorokiniana microalgae cells were disintegrated using ultrasound, microwave radiation, and enzymatic lysis with lysozyme. Ultrasound was the most effective of the cell disruption methods applied separately, increasing the yield of water-soluble proteins by up to 11.4% compared to the control sample. When ultrasound and enzyme were used consecutively, a synergetic effect was observed, as the yield of intracellular water-soluble proteins significantly exceeded (up to 35%) the result achieved by using a single disruption method. The combined effect of physical and biochemical cell disruption methods on the extraction of intracellular water-soluble proteins was also investigated. It was established that several methods applied sequentially to disrupt cells produced a combined, synergetic effect. A mechanism of sequential action of two cell disruption methods, explaining the synergy and increased yield of water-soluble intracellular proteins, is proposed.

Metamorphic Domes in Northern Tunisia: Exhuming the Roots of Nappe Belts by Widespread Post‐Subduction Delamination in the Western Mediterranean

AbstractCenozoic extension in the Western Mediterranean has been related to the dynamics of back‐arc domains. Although, in most of its orogenic belts extension propagated into the fore‐arc nappe domains. Here we revisit the structure, metamorphism and radiometric ages of the Tunisian Tell, where HP/LT rocks (350°C at 0.8 GPa), were exhumed by the sequential activity of extensional detachments after heating and decompression (410°C–440°C at 0.6–0.3 GPa) in a plate convergent setting. Normal faults thinning the Tunisian Tell detached at two different crustal levels. The shallower one cuts down into the Atlas Mesozoic sequence, involving Tellian Triassic evaporites in the hanging‐wall forming halokinetic structures in the Mejerda basin late Miocene. The deeper‐detachment bounds metamorphic domes formed by marbles and metapsammites from the Atlas domain. Illite crystallinity on Triassic rocks shows epizonal to anchizonal values, at deep and intermediate structural depths of the Tell‐Atlas nappe belt, respectively. New U‐Pb 49.78 ± 1.28 Ma rutile ages from Tellian metabasites, together with existing phlogopite 23–17 Ma K‐Ar ages in Atlas marbles from the footwall of the deepest detachment, indicate a polymetamorphic evolution. The Tell rocks underthrusted the Kabylian flysch in the early Eocene. Further, early Miocene shortening thrusted the metabasites over lower‐grade sediments, producing HP/LT metamorphism and ductile stretching at the base of the Atlas belt. The exhumation of midcrustal roots of Western Mediterranean nappe belts after tectonic shortening is a common feature related to tearing at the edges of the subduction systems and inboard delamination of their subcontinental lithospheric mantle.

КОНЦЕПТУАЛЬНАЯ КАРТИНА МИРА КАК КОГНИТИВНЫЙ КОМПОНЕНТ ЯЗЫКОВОЙ ЛИЧНОСТИ РЕБЕНКА С РАССТРОЙСТВОМ АУТИСТИЧЕСКОГО СПЕКТРА

The article considers the possibility of expanding the methods of correcting the speech and cognitive skills of a child with autism spectrum disorder (ASD) by referring to the analysis of the conceptual picture of the world as a component of the child's linguistic personality. The basis of the conceptual picture of the world is a unique mental product associated with the peculiarities of the transformation and interpretation of information by an individual. This peculiarity of the conceptual picture of the world provides rich ground for research in the field of cultural linguistics, however, the application of the theory of the conceptual picture of the world can be no less effective in psychological and pedagogical practice, in particular, in working with children with autism spectrum disorder. The results of the study of the conceptual picture of the world are presented and a model of its development in children with ASD, who have the initial skills of expressive speech, is developed. The significance of an integrated approach to the development of the language personality of a child with ASD is substantiated, which implies a simultaneous impact on all language levels and cognitive activity of the child. Particular emphasis is placed on the sequence of correctional activities of a specialist in the context of the development of the linguistic personality of a child with ASD, taking into account the peculiarities of his conceptual picture of the world.

Complex thinking through a Transition Design-guided Ideathon: testing an AI platform on the topic of sharing economy

RationaleThe development of the complex thinking meta-competency in the education of university students potentially promotes high capacities, where artificial intelligence (AI) might work as a supporting structure.ObjectiveThis proof-of-concept study of an AI-based platform aimed to integrate a sequence of activities into the design of an online platform to assess the development of complex thinking competency in higher education students.MethodThe Transition Design method was used within an Ideathon event supported by an AI platform to provide students with a sharing economy challenge. A total of 31 university students from five university campuses in Mexico synchronously participated. An instrument was applied in the pre-test and post-test phases to explore the complex thinking meta-competency and its sub-competencies of critical, innovative, scientific, and systemic thinking.ResultsTwo hypotheses were tested, both of which turned out to be true: (a) the functioning of the constructed learning situation and (b) the model of the learning situation.ConclusionThese findings may be of value to scientific and academic communities, as well as social and business organizations interested in developing high capacities of complex thinking and educational innovations using digital platforms.

Electroacoustic Effect and Electrical Superconductivity of Metals

The paper discusses the conditions under which superconductivity of metals would be detected at atmospheric pressure and at room temperature. One of the possible directions of research to achieve these conditions is to change the phonon spectrum of metals in order to increase the Debye temperature, and therefore to increase the temperature of the superconducting transition Tc. According to the idea of the work and to the estimates made in it, the maximum frequency of phonons nmax in metals can be increased by the short-term action of an external constant electric field (electric pulse). The duration of the pulse should be (10–5–10–7) s. The voltage of the constant electric field source is U » 1V. A decrease in the crystal lattice parameter should be accompanied by an increase in the maximum phonon frequency nmax and, accordingly, by an increase in the Debye temperature. The characteristic size of the sample, in which the change in electron concentration can be realized, should not exceed the length of electron shielding in metals, i. e. ≈ 10–8 m. The sequential action of a certain number of electric field pulses can maintain a superconducting state in a metal sample for some finite time.

Ketols Emerge as Potent Oxylipin Signals Regulating Diverse Physiological Processes in Plants.

Plants produce an array of oxylipins implicated in defense responses against various stresses, with about 600 oxylipins identified in plants to date. Most known oxylipins are the products of lipoxygenase (LOX)-mediated oxygenation of polyunsaturated fatty acids. One of the most well-characterized oxylipins produced by plants is the hormone jasmonic acid (JA); however, the function of the vast majority of oxylipins remains a mystery. One of the lesser-studied groups of oxylipins is comprised of ketols produced by the sequential action of LOX, allene oxide synthase (AOS), followed by non-enzymatic hydrolysis. For decades, ketols were mostly considered mere by-products of JA biosynthesis. Recent accumulating evidence suggests that ketols exhibit hormone-like signaling activities in the regulation of diverse physiological processes, including flowering, germination, plant-symbiont interactions, and defense against biotic and abiotic stresses. To complement multiple reviews on jasmonate and overall oxylipin biology, this review focuses specifically on advancing our understanding of ketol biosynthesis, occurrence, and proposed functions in diverse physiological processes.