Limited Processing Capacity Research Articles

Waste converting through by-product synergy: an insight from three-echelon supply chain.

By-product synergy (BPS) is an innovative method to convert waste into valuable by-products effectively. Based on a three-echelon supply chain composed of an upstream manufacturer, a processing plant with limited processing capacity, and a downstream manufacturer, this study derives the production quantity and waste disposal decisions of the upstream and downstream manufacturers as well as the optimal transfer price decision of the processing plant. Moreover, we assess the environmental performance of BPS. Analytical results suggest that the upstream manufacturer's production quantity and waste disposal decisions and the processing plant's transfer price decision are threshold dependent on the processing plant's capacity, whereas the downstream manufacturer's production quantity decision is threshold dependent on the processing plant's capacity and price of raw materials. BPS is beneficial for all members of the supply chain to increase profit. The production promotion and cost-saving effects ensure that the supply chain members maximize their profit. However, BPS does not always have a positive effect on the environment; when the processing plant's capacity and price of raw materials are below the threshold, implementing BPS results in a win-win situation of economic and environmental benefits.

An event-driven multi-agent based distributed optimal control strategy for HVAC systems in IoT-enabled smart buildings

Smart buildings generally adopt a centralized optimal control for heating, ventilation and air-conditioning (HVAC) systems to improve the building system performance. As a crucial technology adopted in smart buildings, Internet of Things (IoT) based wireless sensor networks (WSNs) are promising platforms for implementing novel distributed optimal control to achieve distributed intelligence effectively. Such a future mist computing paradigm can be hindered by the limited energy resource of WSNs. The event-driven optimization method activates the optimization only when events occur. It can save the energy resource of WSNs, and thus pave the way for implementing the distributed optimal control architecture in IoT-based WSNs. This study therefore proposes an event-driven multi-agent based distributed optimal control strategy for HVAC systems for implementation in IoT-based battery-powered WSNs. The strategy consists of two novel schemes. First, an event determination scheme determines the event threshold by comprehensively considering the system performance and the total energy consumption of individual sensors implementing the distributed optimal control architecture. Second, an event-driven distributed optimization scheme solves the optimization problems with distributed optimization algorithms in IoT sensors of limited data processing capacity when an event occurs. Comparison and case studies are conducted to compare different strategies and validate the proposed strategy. Results show that different strategies require very different sensor energy consumption. The proposed strategy can provide satisfactory system performance while reducing the energy consumption of IoT sensors.

Users of open Big Earth data – An analysis of the current state

The broad dissemination of open data policies for Big Earth data leads to a diversification of users. The literacy in data and data handling differs for each user group, resulting in different needs and requirements. In addition, the development of cloud-based data systems challenges traditional workflows of all users. In order to tailor new cloud-based data systems for Earth data users, it is of utmost importance to obtain a better understanding of users in terms of the type of data they explore, the applications they need the data for, the way they access and process data, and the challenges they face. This is an indispensable prerequisite to ensure an increased uptake of Big Earth data in the future.In order to get a better insight in the requirements and challenges of users coping with Big Earth data, we run a comprehensive web-based user survey. Our results, inclined to users of Big Earth data in Europe and the North American continent, reveal that a majority of survey respondents still download copies of data onto their local machine and handle and process data locally with a combination of programming and desktop-based software. However, survey respondents are facing severe problems related to the growing data volumes, the data heterogeneity and the limited processing capacities for their demanding applications. Thus, they show a specific interest in using cloud-based data services in the near future but express the need for an easier data discovery and the interoperability of data systems. Based on the survey findings, we draw a set of recommendations to make Big Earth data more FAIR (findable, accessible, interoperable and re-useable).

Neurocognitive functioning of children with mild to borderline intellectual disabilities and psychiatric disorders: profile characteristics and predictors of behavioural problems.

BackgroundThe aim of the current study was twofold: first, to uncover a neurocognitive profile of normative and relative strengths and weaknesses that characterises an extremely vulnerable group of children with mild to borderline intellectual disabilities (MBID) and co‐morbid psychiatric disorders, and second, to investigate the relevance of these neurocognitive functions explaining internalising and externalising symptoms.MethodWe recruited 45 children (M age = 9.5, SD age = 1.7; range 6–13 years) with MBID (Full‐Scale IQ 50–85) and at least one psychiatric disorder. Neurocognitive functioning was examined utilising the Wechsler Intelligence Scale for Children – Fifth Edition (WISC‐V) indices and the Cognitive Task Application (COTAPP), a comprehensive computerised self‐paced task designed in such a manner that ‘g’ (an overall tendency of children with MBID to execute tasks with a slower reaction time and a higher error rate) has been corrected for in the administration of the task (i.e. completely self‐paced) and in the operationalisation of outcome measures. Behavioural problems were measured using the CBCL and TRF. One‐sample t‐tests and binomial tests were carried out to compare performance with normative data. Regression analyses were used to examine the relationship between neurocognitive parameters and mental health.ResultsCompared with normative data, very small to very large effect sizes were found, indicating clear heterogeneity amongst neurocognitive domains relevant for children with MBID. Two prominent neurocognitive weaknesses emerged: processing speed – characterised by slowness and unstableness combined with a high drift rate and delayed processing of the previous trial, particularly under higher cognitive demands – and working memory – in terms of a weaker central executive and ‘slave’ systems to temporarily store information. Both domains were not clearly predictive of internalising or externalising problems.ConclusionChildren with MBID and psychiatric disorders are hampered by a strongly diminished processing speed and working memory capacity, together resulting in an overall limited processing capacity that may underlie the general developmental delays on domains that depend on fast and parallel processing of information (i.e. language, reading, mathematics and more complex forms of social cognition). Neurocognitive vulnerabilities are neither necessary nor sufficient to explain internalising and externalising problems; rather, a mismatch between the support needs and adaptations these children need, arising from their diminished processing capacity, and the inadequacy of the environment to compensate for this vulnerability may be of relevance.

Value-based Procurement and Agency Problems in Triadic Healthcare Relationships

This paper adopts an agency theory perspective to analyze a medical devices procurement triad consisting of a centralized purchasing unit, a medical unit of a hospital and an external medical device supplier. Through an in-depth case study of this triad, we examine agency issues and how they hinder the governance of triad-level outcomes i.e., value-based procurement. Our findings contribute to agency theory by positing that each triad member may pursue individual goals creating goal incongruence not as a result of opportunism but rather caused by role-based norms and incentive structures. Furthermore, information asymmetry in triads should be approached as a multilateral phenomenon when all triad members are boundedly rational actors possessing limited information processing capacity and thus suffering from specialized information and hidden (bilateral) actions of the other triad members. Finally, we find social factors such as power asymmetry and institutionalized mindset of unit price myopia to moderate the effects of information asymmetry and goal incongruence on triad-level governance. These factors together appear to curtail moves towards more outcome-based contracts and hence value-based procurement.

The study effect of focus of Attention instructions on motor skills learning: Difference in children’s learning process

The limited information processing capacity of individuals forces coaches in various sports to turn their attention to the main and important parts of the movement by providing instructions. One of the most important factors in the effectiveness of instructions is to focus on individual. It is very important to pay attention to this issue in children. By study the effects of instructions of attention on children's free-throwing learning skills, this study will help determine which type of instructions of attention are most useful for children. Tools and Methods: In this quasi-experimental study, It is chosen 42 girl students randomly who aged 9 to 11 years old in 18 district of Tehran and divided in three groups: internal focus attention, external focus attention, and control. After introduction the primary instructions and watching instructional movie related to basketball free throw, the participates thrown five times in test way then in acquisition phase, made 100 attempts in two sessions during two sequencing days. Each session included two blocks of 10-attempts and recovery time in 2 minute among blocks. The retention test included two blocks of 10-attempt that token 48 hours after last acquisition session. It is used covariance analysis test (3(group)* 10(throw)) in acquisition phase and LSD test to analyze the data. Resuit: The result showed that the internal focus attention group performed better than the control group in acquisition phase (P = 0.012), there isn’t significant difference between external focus attention group's mean (P = 0.084). also there isn’t significant difference between internal focus attention group's mean and external one (P = 0.401). In other words, internal focus led to an improvement in basketball throwing performance in children at the acquisition stage. The aim of this study was to investigate the effects of focus instructions on motor skills learning in children. The results of free throw performance scores did not show any significant difference between the groups during retention, but showed the advantage of using internal attention in the acquisition stage. A theoretical understanding of how different instructions can have practical implications for teachers and educators in helping learners for acquire skills, and changes in performance and learning processes depend on both task and performance, and no definitive conclusion can be drawn as to which The type of focus is useful for children's learning, and educators should take on the role of facilitator in guiding learners, regardless of the type of focus provided in different areas of learning.

Design and Implementation of Intelligent Inspection and Alarm Flight System for Epidemic Prevention

Unmanned aerial vehicles (UAV) and related technologies have played an active role in the prevention and control of novel coronaviruses at home and abroad, especially in epidemic prevention, surveillance, and elimination. However, the existing UAVs have a single function, limited processing capacity, and poor interaction. To overcome these shortcomings, we designed an intelligent anti-epidemic patrol detection and warning flight system, which integrates UAV autonomous navigation, deep learning, intelligent voice, and other technologies. Based on the convolution neural network and deep learning technology, the system possesses a crowd density detection method and a face mask detection method, which can detect the position of dense crowds. Intelligent voice alarm technology was used to achieve an intelligent alarm system for abnormal situations, such as crowd-gathering areas and people without masks, and to carry out intelligent dissemination of epidemic prevention policies, which provides a powerful technical means for epidemic prevention and delaying their spread. To verify the superiority and feasibility of the system, high-precision online analysis was carried out for the crowd in the inspection area, and pedestrians’ faces were detected on the ground to identify whether they were wearing a mask. The experimental results show that the mean absolute error (MAE) of the crowd density detection was less than 8.4, and the mean average precision (mAP) of face mask detection was 61.42%. The system can provide convenient and accurate evaluation information for decision-makers and meets the requirements of real-time and accurate detection.

Design and Evaluation of a Machine Vision System and Mechatronic Drive for a Pneumatic Seed Meter for Corn

A pneumatic seed meter equipped with a machine vision system and a mechatronic drive was developed to reduce the incidence of missed seeding during mechanical corn planting. The machine vision system monitored the seed plate and checked if seeds did not attach to the seed plate holes. The rotation speed to the seed plate was adjusted by the mechatronic drive based on the presence and absence of seeds attached to the seed plate holes. The reliability of the mechatronic drive in regulating the seeding rate, and the accuracy of the machine vision system in identifying filled and unfilled seed plate holes were both tested. The combined system of the machine vision system and the mechatronic drive was also tested if it can effectively reduce the incidence of missed seeding in a stationary laboratory rig under simulated speed settings of 2kph, 4kph, and 6kph. It was found out that the drive did not maintain a consistent seed delivery rate, which can be attributed to the limited processing capacity of the microcontroller used to control the drive. The machine vision system was highly accurate as it only committed a 0.2564 % error out of 780 images. The machine vision system of the seed meter, at 2kph and 4kph simulated speed settings, committed significantly, at 95% level of confidence, less missed seeding in the conducted treatments in comparison to the setup where the seed meter was operated without the machine vision system. The treatments wherein the machine vision system was used, however, committed more missed seeding at 6kph simulated speed setting.

When two faces are not better than one: Serial limited-capacity processing with redundant-target faces.

Many researchers believe that faces-whether presented alone or as part of an ensemble-are processed automatically. According to this idea, (a) the detection of single or multiple faces is resource free and does not require allocation of attention, and (b) visual search for faces is held in parallel. The current study put these hypotheses under direct scrutiny. Participants performed in a redundant target-detection task, responding according to the presence or absence of a face (or faces) on the display. I used a rigorous methodology known as the system factorial technology (SFT). The SFT methodology afforded simultaneous assessment of (a) architecture (serial vs. parallel), (b) stopping rule (exhaustive vs. self-terminating), and (c) capacity (limited, unlimited, or supercapacity). SFT analyses were held at the level of the mean RTs and at the level of the RT distributions. The analyses pointed conclusively to a serial self-terminating architecture with limited capacity. These findings cast serious doubts on the alleged automaticity of faceperception.

A framework for the prediction of earthquake using federated learning.

Earthquakes are a natural phenomenon which may cause significant loss of life and infrastructure. Researchers have applied multiple artificial intelligence based techniques to predict earthquakes, but high accuracies could not be achieved due to the huge size of multidimensional data, communication delays, transmission latency, limited processing capacity and data privacy issues. Federated learning (FL) is a machine learning (ML) technique that provides an opportunity to collect and process data onsite without compromising on data privacy and preventing data transmission to the central server. The federated concept of obtaining a global data model by aggregation of local data models inherently ensures data security, data privacy, and data heterogeneity. In this article, a novel earthquake prediction framework using FL has been proposed. The proposed FL framework has given better performance over already developed ML based earthquake predicting models in terms of efficiency, reliability, and precision. We have analyzed three different local datasets to generate multiple ML based local data models. These local data models have been aggregated to generate global data model on the central FL server using FedQuake algorithm. Meta classifier has been trained at the FL server on global data model to generate more accurate earthquake predictions. We have tested the proposed framework by analyzing multidimensional seismic data within 100 km radial area from 34.708° N, 72.5478° E in Western Himalayas. The results of the proposed framework have been validated against instrumentally recorded regional seismic data of last thirty-five years, and 88.87% prediction accuracy has been recorded. These results obtained by the proposed framework can serve as a useful component in the development of earthquake early warning systems.

Grocery Shopping under Cognitive Constraints and Restrictive Budget: An Algorithmic Approach

This paper presents an algorithmic approach to optimize the grocery shopping behaviour of consumers under specific circumstances: a highly restrictive budget and cognitive constraints. I develop a formal optimization model that accounts for the trade-off between dietary objectives and product preferences during the shopping process. I show how a heuristic algorithm and its implementation in an interactive web platform can be used to find local optima to a consumer’s choice problem, by alleviating the burden from limited processing capacity, especially overchoice. The approach presented in this paper has implications for retailers and policymakers as it points to concrete actions to enhance dietary imbalances and potential underconsumption experienced by low-income and cognitively constrained individuals. I discuss a specific use case of the algorithm for people with intellectual and developmental disabilities.

Chilean salmon farming vulnerability to external stressors: The COVID 19 as a case to test and build resilience

This study addresses the risk and vulnerability of Chilean salmon production to hazards resulting from the COVID-19 pandemic threat, including limited access to farms, limited processing capacity and reduced market demand. The role of different management approaches in reducing risk and vulnerability is also explored. Results suggest that concession areas having the largest accumulated and current biomass have the highest risk, which is also transferred to the municipal level. The scenarios modelled with better management practices that reduce diseases were able to reduce risks by 30–40%. The largest risk reduction is achieved when production biomass is divided in a more equitable manner among concession areas, suggesting the need for strategic improvements in spatial planning of the activity in the marine environment according to ecosystem carrying capacity and better practices. Improving adaptation capacity can reduce vulnerability between 20% and 30% for municipalities; for example, providing local employment can be a win-win management measure under the COVID-19 threat because it reduces movement of people and facilitates handling and responses to emergencies. A larger footprint in local economies and employment can also improve social perception and acceptance of the sector, thus contributing to improve adaptation changes and governance to face the threats. The framework used here to perform a risk and vulnerability assessment of salmon farming to the pandemic-associated threats can also be useful for other aquaculture systems elsewhere, provided that relevant information is available.

An improved list-based task scheduling algorithm for fog computing environment

A high-performance execution of programs predominately depends on the efficient scheduling of tasks. An application consists of a sequence of tasks that can be represented as a directed acyclic graph (DAG). The tasks in the DAG have precedence constraints between them and each task has a different timeline on different processors. In this paper, a new list-based scheduling algorithm is proposed which schedules the tasks which are represented as a DAG structure. The main focus of this algorithm is to schedule the tasks to the suitable processing node in fog environment as the fog nodes have limited processing capacity. The assignment of tasks on the fog node should consider both the computation cost of the node and the execution finishing time of the node. The proposed algorithm has three phases. (1) the level sorting phase, where the independent tasks are identified (2) in the Task prioritization phase the proposed algorithm assigns priority to the task which has more successors so that more tasks in the next level can start their execution and (3) in the task selection phase a balanced combination of local optimal and global optimal approach is considered to assign a task to a suitable processor which further enhances the processor selection phase results in minimizing both the makespan and overall computation cost of the processors. Extensive experiments are carried out using randomly generated graphs and graphs from the real-world to analyze the performance of the proposed algorithm. The results show that the proposed algorithm outperforms all other well-known algorithms like predict earliest finish time, heterogeneous earliest finish time algorithm, minimal optimistic processing time, and SDBBATS in terms of performance matrices like average scheduling length ratio, speedup, and makespan.

Congestion avoidance based on artificial bee colony algorithm for routing and wavelength assignment in optical satellite networks

Massive data traffic demand for satellite networks has been increased with the emergence of global broadband services. Due to limited onboard processing capacity and imbalanced distribution of global traffic, severe link congestion may occur in hot spots, deteriorating the performance of the whole network. A link cost modification applied in load balancing algorithm based on artificial bee colony is proposed for routing and wavelength assignment problem to distribute traffic in advance. Moreover, congestion avoidance based on wavelength utilization is designed for unexpected congestion to minimize the adverse impact of congestion on network performance. Experiment results show that the method we proposed realizes high performance in communication success rate and average transmission delay and also achieves the maximum utilization of wavelength utilization under the condition of low blocking rate and the equalization of traffic load.

Vehicle Assisted Computing Offloading for Unmanned Aerial Vehicles in Smart City

Smart city emerges a promising paradigm for improving operational efficiency of city and comfort of people. With embedded multi-sensors, Unmanned Aerial Vehicles (UAVs) hold great potential for collecting sensing data and providing social services in smart city. However, due to the limited battery lifetime and processing capacities of UAVs, the efficient offloading scheme of UAVs is urgently needed in smart city. Therefore, in this article, a vehicle-assisted computing offloading architecture for UAVs is proposed to improve offloading efficiency by harnessing the moving vehicles in smart city. We first develop an offloading model for UAVs to determine the offloading strategy. Next, to select the optimal vehicles for offloading, we formulate a matching scheme based on the preference lists of UAVs and vehicles to derive the optimal matching between UAVs and vehicles. After that, to improve the offloading efficiency and maximize the utilities of UAVs and vehicles, the transaction process of computing data between UAVs and vehicles is modeled as a bargaining game. Moreover, an offloading algorithm for UAVs and vehicles is proposed to obtain the optimal strategy. Finally, simulations are performed to validate the efficiency of the proposed offloading scheme. The results demonstrate that the proposed offloading scheme can significantly save resource and improve the utilities of UAVs and vehicles.

Health Literacy, Processing Capacity, Illness Knowledge, and Actionable Memory for Medication Taking in Type 2 Diabetes: Cross-Sectional Analysis.

Patient knowledge about the purpose of medications is crucial to ensure safe and correct use, so it is an important index of adherence in patients with chronic illness. We examined how health literacy and its components (processing capacity and knowledge about illness) influence memory for medication purposes. We conducted a cross-sectional study to examine memory for medication purposes in relation to health literacy, processing capacity, and illness knowledge among patients with diabetes in outpatient clinics. Six hundred seventy-four adults who were diagnosed with type II diabetes mellitus, age 40 years or older, taking 5 or more current medications, native speakers of English, and with glycosylated hemoglobin (HbA1c) of 6.0 or more, were recruited to the study. We included measures of processing capacity, illness knowledge, health literacy, and actionable memory for medication taking (memory for medication purpose). Results suggested an association between health literacy and both processing capacity and health knowledge, with some evidence that knowledge can compensate for limited processing capacity in order to maintain health literacy. Furthermore, health literacy was associated with memory for medication purposes, with processing capacity and health knowledge partly mediating this association. This pattern of results supports the process-knowledge model of health literacy. Our findings establish the role of health literacy in medication taking, in relation to broader cognitive abilities and knowledge. Implications for improving the learning of medication purpose among diverse older adults with chronic illness are discussed. NIH trial registry number: NCT01296633.

Optimal Edge Computing for Infrastructure-Assisted UAV Systems

The ability of Unmanned Aerial Vehicles (UAV) to autonomously operate is constrained by the severe limitations of their on-board resources. The limited processing capacity and energy storage of these devices inevitably makes the real-time analysis of complex signals – the key to autonomy – challenging. In urban environments, the UAVs can leverage the communication and computation resources of the surrounding city-wide Internet of Things infrastructure to enhance their capabilities. For instance, the UAVs can interconnect with edge computing resources and offload computation tasks to improve response time to sensor input and reduce energy consumption. However, the complexity of the urban topology and large number of devices and data streams competing for the same network and computation resources create an extremely dynamic environment, where poor channel conditions and edge server congestion may penalize the performance of task offloading. This paper develops a framework enabling optimal offloading decisions as a function of network and computation load parameters and current state. The optimization is formulated as an optimal stopping time problem over a semi-Markov process. We solve the optimization problem using Dynamic Programming and Deep Reinforcement learning at different levels of abstraction and prior knowledge of the system underlying stochastic processes. We validate our results in a realistic scenario, where a UAV performs a building inspection task while connected to an edge server.

Information Inertia and Limited Information Processing Capacity in Selecting Index ETFs

We investigate the role of information inertia and limited information processing capacity in creating an anomaly in the US index ETF industry. We fi nd that tracking errors and long-term returns are significant and consistent predictors of future returns, while there is little evidence that investors' flows are sensitive to them. In line with this finding, we provide a trading strategy that chases small tracking errors, which outperforms the equal-weighted portfolio of all S&P 500 index ETFs by 1.65% annually over 2003-2020. Mutual fund managers who just joined the fund are more likely to switch to better index ETFs than existing managers, suggesting investors face information inertia. Unsophisticated investors have limited information processing capacity as index ETFs held by more institutional investors deliver higher returns. Overall, our results suggest that the anomaly in low-return and low-tracking error sensitivity in the index ETF industry can be explained by information inertia and limited information processing capacity.

IoHT-MBA: An Internet of Healthcare Things (IoHT) Platform based on Microservice and Brokerless Architecture

Internet of Thing (IoT), currently, is one of the technology trends that are most interested. IoT can be divided into five main areas including: Health-care, Environmental, Smart city, Commercial and Industrial. The IoHT-MBA Platform is considered the backbone of every IoT architecture, so the optimal design of the IoHT-MBA Platform is essential issue, which should be carefully considered in the different aspects. Although, IoT is applied in multiple domains, however, there are still three main features that are challenge to improve: i) data collection, ii) users, devices management, and iii) remote device control. Today’s medical IoT systems, often too focused on the big data or access control aspects of participants, but not focused on collecting data accurately, quickly, and efficiently; power redundancy and system expansion. This is very important for the medical sector - which always prioritizes the availability of data for therapeutic purposes over other aspects. In this paper, we introduce the IoHT Platform for Healthcare environment which is designed by microservice and brokerless architecture, focusing strongly on the three aforementioned characteristics. In addition, our IoHT Platform considers the five other issues including (1) the limited processing capacity of the devices, (2) energy saving for the device, (3) speed and accurate of the data collection, (4) security mechanisms and (5) scalability of the system. Also, in order for the IoHT Platform to be suitable for the field of health monitoring, we also add realtime alerts for the medical team. In the evaluation section, moreover, we describe the evaluation to prove the effectiveness of the proposed IoHT Platform (i.e. the proof-of-concept) in the performance, non-error, and non affected by geographical distance. Finally, a complete code solution is publicized on the authors’ GitHub repository to engage further reproducibility and improvement.

A Network Function Parallelism-enabled MEC Framework for Supporting Low-Latency Services

Mobile edge computing (MEC) enables users to offload computing tasks to edge servers for provisioning low-latency and computation-intensive services. To manage heterogeneous resources and improve service flexibility, MEC is entailed by new technologies, \textit{i.e.}, software defined networking (SDN) and network function virtualization (NFV), which allow services running on common commodity hardware instead of proprietary hardware. However, data processing via software on commodity servers may induce high latency due to limited processing capacity, which impedes the quality of service. Meanwhile, MEC is a resource-sharing system and thus fairness should be considered. In this paper, we propose a network function parallelism (NFP)-enabled MEC (NFPMec) framework for supporting low-latency services. To reap the potential benefits of the NFPMec, we formulate the fairness-aware throughput maximization problem (FTMP) with aim of maximizing the fairness-aware system throughput while satisfying the QoS requirements. We propose a relaxation-based generalized benders algorithm (RGBA) to decouple the FTMP into two sub-problems based on the non-linear convex duality theory. After relaxation, the sub-problems are solved by the Karush-Kuhn-Tucker (KKT) approach. The convergence of the RGBA is theoretically proved. The simulation results demonstrate that the proposed NFPMec outperforms SDN-enabled MEC networks in terms of resource utilization, service latency and system throughput.