Processing Time Requirements Research Articles

Securing the Internet of Things with Ascon-Sign

With a Cryptographically-Relevant Quantum Computer (CRQC) estimated to be viable within the next 15 years, the development of post-quantum security is imperative. Previously secure networks may soon fall victim to these CRQCs as they will likely attack the weakest link in a network. In modern networks, these weak-links are often present in the form of Internet of Things (IoT) devices, as the resource constrains imposed by these wireless nodes leads to lowered security. We offer the first Ascon-Sign implementation for resource constrained FPGAs, which allows a wireless sensor network to verify nodes. Our design runs twice as fast as similarly-area constrained devices, and shows a 33% reduction in power per operation. We demonstrate the capability of our design by integrating it with a wireless sensor network for weather detecting. We also propose an amendment to the Ascon-Sign specification that allows for shortened processing time and lower memory requirements.

An online dynamic dual bin packing with lookahead approach for server-to-cell assignment in computer server industry

Efficient production scheduling in computer server industry remains a critical challenge. This challenge becomes particularly acute during the testing phase, where servers are assigned for testing in a modular environment consisting of test banks. Unique characteristics such as online arrival of servers known only over a short time window, skewed arrival pattern, power and cooling compatibility constraints, and dynamic assignment complicate decision making, a situation we observed at our industry partner’s site, which often leads to missing due dates and loss of customer trust. Motivated by this challenge, we introduce an Online Dynamic Dual Bin Packing with Lookahead problem and propose an integer linear programming model that maximizes the number of assigned servers. To efficiently solve this model, we decompose the problem into a set of subproblems for a given lookahead length. A ‘2-phase’ computational framework is proposed that seamlessly integrates mathematical programming with genetic algorithm. Based on realistic data available from a server manufacturer, our findings suggest that solutions are sensitive to (i) the length of the lookahead window, (ii) testing capacity and server arrival pattern, (iii) test processing time requirement, and (iv) physical allocation of servers.

Fractional Boundary Element Solution for Nonlinear Nonlocal Thermoelastic Problems of Anisotropic Fibrous Polymer Nanomaterials

This paper provides a new fractional boundary element method (BEM) solution for nonlinear nonlocal thermoelastic problems with anisotropic fibrous polymer nanoparticles. This comprehensive BEM solution comprises two solutions: the anisotropic fibrous polymer nanoparticles problem solution and the nonlinear nonlocal thermoelasticity problem. The nonlinear nonlocal thermoelasticity problem solution separates the displacement field into complimentary and specific components. The overall displacement is obtained using the boundary element methodology, which solves a Navier-type problem, and the specific displacement is derived using the local radial point interpolation method (LRPIM). The new modified shift-splitting (NMSS) technique, which minimizes memory and processing time requirements, was utilized to solve BEM-created linear systems. The performance of NMSS was evaluated. The numerical results show how fractional and graded parameters influence the thermal stresses of nonlinear nonlocal thermoelastic issues involving anisotropic fibrous polymer nanoparticles. The numerical findings further reveal that the BEM results correlate very well with the finite element method (FEM) and analytical results, demonstrating the validity and correctness of the proposed methodology.

Real-Time Human Activity Recognition on Embedded Equipment: A Comparative Study

As living standards improve, the growing demand for energy, comfort, and health monitoring drives the increased importance of innovative solutions. Real-time recognition of human activities (HAR) in smart homes is of significant relevance, offering varied applications to improve the quality of life of fragile individuals. These applications include facilitating autonomy at home for vulnerable people, early detection of deviations or disruptions in lifestyle habits, and immediate alerting in the event of critical situations. The first objective of this work is to develop a real-time HAR algorithm in embedded equipment. The proposed approach incorporates the event dynamic windowing based on space-temporal correlation and the knowledge of activity trigger sensors to recognize activities in the case of a record of new events. The second objective is to approach the HAR task from the perspective of edge computing. In concrete terms, this involves implementing a HAR algorithm in a “home box”, a low-power, low-cost computer, while guaranteeing performance in terms of accuracy and processing time. To achieve this goal, a HAR algorithm was first developed to perform these recognition tasks in real-time. Then, the proposed algorithm is ported on three hardware architectures to be compared: (i) a NUCLEO-H753ZI microcontroller from ST-Microelectronics using two programming languages, C language and MicroPython; (ii) an ESP32 microcontroller, often used for smart-home devices; and (iii) a Raspberry-PI, optimizing it to maintain accuracy of classification of activities with a requirement of processing time, memory resources, and energy consumption. The experimental results show that the proposed algorithm can be effectively implemented on a constrained resource hardware architecture. This could allow the design of an embedded system for real-time human activity recognition.

Hurdle approaches using conventional thermal, microwave heating and ginger essential oil in vegetable juice mixture: Heat transfer modeling and microbial inactivation kinetics

In this study, microwave (1000 W; 30 s) and conventional thermal treatments were used in combination with ginger essential oil (EO; 1, 2, 3 % (v/v)) to inactivate Salmonella Typhi and Listeria monocytogenes in a mixture of vegetable (lettuce, spinach, celery, and parsley) juice. The momentum, heat, and Maxwell equations were solved to obtain the electromagnetic and thermal fields simultaneously. Besides, the Weibull model was implemented to simulate microorganisms' inactivation and determine the initiation time and rate. Based on the results, microwave heating with 3% EO reduced the inactivation initiation time by 9.82 and 8.87 s for S. Typhi and L. monocytogenes, respectively (compared to 0 % EO). At the initial stages of heating, where the temperature level has not increased to the desired value, the addition of EO can effectively initiate the inactivation process. The maximum value of resistive losses was reported to be approximately 28 MW/m3. Microwave heating lowered the required process time from 240 to 30 s and 67 % of the consumed power compared to the conventional method. Using microwave and EO in combination can synergistically affect the inactivation of microorganisms.

Rapid and High-Yield Recovery of Sodium Alginate from Undaria pinnatifida via Microwave-Assisted Extraction

Alginate, a promising biopolymer in the food, biomedical, pharmaceutical, and electronic materials industries, is characterized by its biodegradability, biocompatibility, low toxicity, and gel-forming properties. It is most abundantly found in brown algae. However, conventional dilute acid and alkali extraction methods face limitations in commercialization due to their long processing time, low throughput, and high solvent requirements. In this study, a microwave-assisted extraction (MAE) process for sodium alginate was designed to improve extraction efficiency. The solid/liquid ratio, extraction temperature, and extraction solvent concentration were major variables affecting sodium alginate extraction from Undaria pinnatifida (sea mustard). They were then statistically optimized using response surface methodology. Under optimal conditions (13.27 g/L, 91.86 °C, 2.51% (w/v), and 15 min), the yield was 38.41%, which was 93.43% of the theoretical content of sodium alginate in Undaria pinnatifida. Our work has confirmed the productivity and industrial feasibility of the efficient extraction of sodium alginate from marine biomass, and we hope that it will serve as an encouraging case for the application of biopolymers as one of the desirable options for alternative petrochemicals to construct a sustainable society.

A Graph-Based Neural Approach to Linear Sum Assignment Problems.

Linear assignment problems are well-known combinatorial optimization problems involving domains such as logistics, robotics and telecommunications. In general, obtaining an optimal solution to such problems is computationally infeasible even in small settings, so heuristic algorithms are often used to find near-optimal solutions. In order to attain the right assignment permutation, this study investigates a general-purpose learning strategy that uses a bipartite graph to describe the problem structure and a message passing Graph Neural Network (GNN) model to learn the correct mapping. Comparing the proposed structure with two existing DNN solutions, simulation results show that the proposed approach significantly improves classification accuracy, proving to be very efficient in terms of processing time and memory requirements, due to its inherent parameter sharing capability. Among the many practical uses that require solving allocation problems in everyday scenarios, we decided to apply the proposed approach to address the scheduling of electric smart meters access within an electricity distribution smart grid infrastructure, since near-real-time energy monitoring is a key element of the green transition that has become increasingly important in recent times. The results obtained show that the proposed graph-based solver, although sub-optimal, exhibits the highest scalability, compared with other state-of-the-art heuristic approaches. To foster the reproducibility of the results, we made the code available at https://github.com/aircarlo/GNN_LSAP.

Enhanced Analysis of Hierarchical Clustering Techniques for Recommendation Systems using Integrated Deep Learning

Machine learning is an effective technique for optimizing real-time electronics product data analysis. It can efficiently handle large electronics product datasets, reducing processing time and resource requirements for generating insights. This study assesses the current status of methods and applications for optimizing real-time data analysis by examining existing research in machine learning-based recommendation systems for electronic products. The indicated subjects encompass using machine learning algorithms to discern characteristics and correlations from large datasets, applying machine learning for prognostic analytics and projection, and utilizing machine learning to identify anomalies. The paper provides examples of machine learning-based evaluation optimization solutions that focus on utilizing unorganized data and delivering real-time dashboards. Presented here is a discussion on the complex challenges and potential benefits associated with utilizing machine learning to optimize real-time data processing. Machine learning may efficiently expedite real-time data assessment while delivering precise and timely outcomes

Time demands of traditional hand-hewing techniques for sustainable repair of wooden heritage structures: A comparative study

The hand-hewing of timber has been a fundamental skill for carpenters throughout history. Inevitably though, the majority of wood used in construction at the present time is processed with the use of machines. Repairing heritage objects with machined wood however, as opposed to working replacement timber using similar craft methods and tools as those used in the original, is often in conflict with the principles set forth in the Venice Charter for the Conservation and Restoration of Monuments and Sites. Ultimately, relying on mechanization is a missed opportunity to contribute to the preservation of valued intangible cultural heritage.Whereas the sustainability of traditional woodworking techniques and technologies can be promoted by applying them in the conservation of historical wooden buildings, the labor-intensive and time-consuming nature of manual woodworking makes feasibility questionable. In order to provide valuable insight for planners and monument care workers, the paper presents a study on the time demands associated with processing hand-hewn beams intended for use in the structural repair of wooden heritage objects.A series of experiments was conducted wherein a total of 94 beams 18×22 cm in profile and 2.5 m in length were hewn from spruce logs of various diameters. The relationship between initial log diameter and required processing time is presented as a linear function. The relative proportions of the individual steps to the hewing process as a whole were also documented and showed a clear dependency on the total amount of material removal required. The overall processing time was significantly reduced in the case of timber that was soaked for several months before being hewn into beams, and the low-work hewing technique was shown to be more efficient in comparison to high work on trestles.This paper has been published in memory of Petr Růžička, the great pioneer of forgotten carpentry techniques whose work opened a window to our past for future generations.

Estimating the Parameters of a Three-Phase Induction Motor using the Vortex Search Algorithm

In this paper, we present a new optimization method to estimate the parameters and torques of an induction motor (IM). The proposed method is known as the Vortex Search Algorithm (VSA), whose performance is based on the behavior of the vortices created by stirred fluids. This algorithm was compared with other four optimization methods reported in the specialized literature (grasshopper optimization algorithm, particle swarm optimization, salp swarm algorithm and sine cosine algorithm), and its solution quality, precision, and robustness were validated using two test motors. During the tests, we analyzed the minimum error between the estimated values and the values provided by the manufacturer, as well as the global error of each method and their required processing time. The results show that the VSA is an excellent alternative to estimate the parameters of an IM, as it exhibited the best performance when compared to the other optimization methods.

Upgrading Pembuatan Laporan Keuangan dengan Sistem Enterprise Service Bus pada Seven Retail Group

Potential weaknesses faced by the company. So it is necessary to consider the aspects of process inefficiency, risk of work errors, delays in information, lack of transparency, high operational costs, vulnerability to data loss and difficulties in monitoring performance. In the current era of globalization and digitization, companies face pressure to increase operational efficiency and accountability in presenting financial information. By using the PALS (Pasticipatory Action Learning System) method, where this method basically involves accounting staff in the process of upgrading the Enterprise Service Bus (ESB) system from 30 accounting staff and 5 staff will be taken for the interview sample in this research. From the problems currently faced by companies in managing financial reports, they have not been able to carry out systematic recording. With better data integration using ESB, a company's various systems and applications can be connected and communicate well. In real-time reporting compared to manual (conventional) recording which requires processing time, ESB allows for better real-time financial reports.

Multi-objective scheduling of a steelmaking plant integrated with renewable energy sources and energy storage systems: Balancing costs, emissions and make-span

As an energy-intensive industry, the steel industry grapples with increasing energy costs and decarbonisation pressures. Therefore, multi-objective optimisation is widely applied in the production scheduling of the steelmaking plant. However, the optimal solution prioritising energy savings and emission reductions may lead to impractical or less economically efficient solutions, since the processing time requirement (PTR) of steel production orders in real-world production is neglected. This study fills the research gap by discussing the impact of PTR on the make-span of the steelmaking process and incorporating it into the optimisation model. Considering the variability of PTR, the solving of the multi-objective scheduling problem is transformed into the selection from Pareto solutions with different make-spans. To better leverage the temporal flexibility of the steelmaking process, a what-if-analysis-based strategy coupled with the Normal Boundary Intersection method is proposed to generate a series of evenly distributed Pareto solutions. The energy storage system is integrated to improve the time granularity of the steelmaking plant's flexibility. Our case studies demonstrate that the electricity and emission costs are reduced by 68.5%, indirect emissions are reduced by 83.5%, and the on-site renewable energy self-consumption rate increases by 12.1%. The effectiveness of the proposed method implies that it is of great relevance to the development of a cleaner steel industry in the future.

Evaluating The Philippine TVET Competency Assessment and Certification System Using SERVQUAL Model

The TVET program has been successful in providing highly skilled individuals to meet the demands of the labor market. However, it is important to evaluate the delivery of the program to ensure that it continues to meet these demands. This study focused on analyzing the delivery of the TVET competency assessment and certification system in Davao de Oro, Philippines. The study utilized a descriptive survey method and adopted the (Service Quality) SERVQUAL model to assess service quality. The results revealed that there is a gender gap distribution among male and female clients who availed of the process of competency assessment and certification system. The study also found that empathy obtained a negative gap score (-0.03) in the process. This negative score implies that empathy is as important as other dimensions of service quality. However, other dimensions of service quality have met the expectations of clients and obtained a positive score. Overall, respondents have perceived the implementation of the technical vocational education and training TVET competency assessment and certification system to be satisfactory. To improve the service quality, it is necessary to provide appropriate information about processing time and documentary requirements to all clients and equip personnel in handling clients proactively. Relationship-based management should also be improved to promote holistic technical vocational education and training (TVET) service quality, particularly in competency assessment and certification systems.

Emotion Recognition From Speech and Text using Long Short-Term Memory

Everyday interactions depend on more than just rational discourse; they also depend on emotional reactions. Having this information is crucial to making any kind of practical or even rational decision, as it can help to better understand one another by sharing our responses and providing recommendations on how they may feel. Several studies have recently begun to focus on emotion detection and labeling, proposing different methods for organizing feelings and detecting emotions in speech. Determining how emotions are conveyed through speech has been given major emphasis in social interactions during the last decade. However, the real efficiency of identification needs to be improved because of the severe lack of data on the primary temporal link of the speech waveform. Currently, a new approach to speech recognition is recommended, which couples structured audio information with long-term neural networks to fully take advantage of the shift in emotional content across phases. In addition to time series characteristics, structural speech features taken from the waveforms are now in charge of maintaining the underlying connection between layers of the actual speech. There are several Long-Short-Term Memory (LSTM) based algorithms for identifying emotional focus over numerous blocks. The proposed method (i) reduced overhead by optimizing the standard forgetting gate, reducing the amount of required processing time, (ii) applied an attention mechanism to both the time and feature dimension in the LSTM's final output to get task-related information, rather than using the output from the prior iteration of the standard technique, and (iii) employed a powerful strategy to locate the spatial characteristics in the final output of the LSTM to gain information, as opposed to using the findings from the prior phase of the regular method. The proposed method achieved an overall classification accuracy of 96.81%.

QoS-SLA-aware adaptive genetic algorithm for multi-request offloading in integrated edge-cloud computing in Internet of vehicles

The Internet of Vehicles over vehicular ad hoc network is an emerging technology enabling the development of smart applications focused on improving traffic safety, traffic efficiency, and the overall driving experience. These applications have stringent requirements detailed in the Service Level Agreement. Since vehicles have limited computational and storage capabilities, applications' requests are offloaded onto an integrated edge-cloud computing system. Existing offloading solutions focus on optimizing the application's Quality of Service (QoS) in terms of execution time while respecting a single SLA constraint. They do not consider the impact of overlapped multi-request processing nor the vehicle's varying speed. This paper proposes a novel Artificial Intelligence QoS-SLA-aware adaptive genetic algorithm (QoS-SLA-AGA) to optimize the application's execution time for multi-request offloading in a heterogeneous edge-cloud computing system, which considers the impact of processing multi-requests overlapping and dynamic vehicle speed. The proposed genetic algorithm integrates an adaptive penalty function to assimilate the SLA constraints regarding latency, processing time, deadline, CPU, and memory requirements. Numerical experiments and analysis compare our QoS-SLA-AGA to baseline genetic-based, meta-heuristic Particle Swarm Optimization (PSO), random offloading, All Edge Computing (AEC), and All Cloud Computing (ACC) approaches. Results show QoS-SLA-AGA executes the requests 1.04, 1.23, 1.05, and 9.41 times faster on average compared to the PSO, random offloading, ACC, and AEC approaches respectively. Moreover, the proposed algorithm violates 49.58%, 60.36%, 16.26%, and 80.42% fewer SLAs compared to PSO, random, ACC, and AEC respectively. In contrast, the baseline genetic-based approach increases the requests' performance by 1.14 times, with 24.03% more SLA violations.

Scheduling with Testing of Heterogeneous Jobs

This paper studies a canonical general scheduling model that captures the fundamental trade-off between processing jobs and performing diagnostics (testing). In particular, testing reveals the required processing time and urgency of need-to-schedule jobs to inform future scheduling decisions. The model captures a range of important applications. Prior work focused on special cases (e.g., jobs with independent and identically distributed processing time) to devise optimal policies. In contrast, the current paper studies the most general form of the model and describes two simple heuristics to solve it; adaptive weighted shortest processing time is an adaptive generalization of Smith’s rule that optimally solves several important extensions of previously studied models, whereas index policy optimally solves a closely related stochastic optimization bandit problem. The latter achieves an approximation guarantee that quickly approaches a constant factor that is bounded by two as the number of jobs grows and approaches optimally when the testing time decreases. Extensive numerical experiments suggest that our policies effectively solve the general setting (under 0.1% from optimal on average and under 10% from optimal in rare, worst-case instances). This paper was accepted by Jeannette Song, operations management. Supplemental Material: The data files and online appendix are available at https://doi.org/10.1287/mnsc.2023.4833 .

A novel estimation procedure for robust CANDECOMP/PARAFAC model fitting

The parameter estimation in CANDECOMP/PARAFAC (CP) is carried out by alternating least squares (ALS) that yields least-squares solutions and provides consistent outcomes. At the same time it has several drawbacks, like sensitivity to the presence of outliers in the data, issues with the computational efficiency in terms of processing time and memory requirements, as well as susceptibility to degeneracy conditions. These weaknesses have been addressed, but there is no outlier-robust procedure that at the same time is highly computationally efficient, especially for large data sets. A novel procedure based on an integrated estimation algorithm is proposed. This is an alternative to ALS, which guards against outliers and is computationally efficient at the same time. The performance of the new method is demonstrated on an extensive simulation study and an empirical example.

Binary Integer Formulations for Task Allocation and Optimal Labor Cost in Small and Medium Apparel Manufacturing

In small apparel manufacturing, unit price determination is often based on production duration given by customers and design complexity rather than information relating to internal labor resources. However, labor expertise and skills are critical factors that outweigh the machinery and technology in small and medium apparel companies. The quality of the product greatly depends on the experience and delicacy of the tailors. Using data on labor skill and wage levels in the planning process will benefit human resource utilization, increasing productivity, and profits effectively. This paper proposes a general mathematical model for task allocation and cost optimization for small and medium apparel companies. The model handles task allocation and cost minimization problems that must ensure processing time requirements and balance workloads for operators. The developed model tests two case studies in a published paper. The results prove that although the proposed model is simple, it has high applicability and efficiency in solving allocation optimization problems. The authors then integrate the formulations into a Standalone desktop app in the MATLAB “App designer” module. With a standalone desktop app, end users can enjoy the application. This app has a user-friendly design. Users unfamiliar with computers or planners with no background in programming can use the app to tackle similar optimization problems. The proposed mathematical model can further expand to include more complex issues in apparel companies and can also be a good reference for other fields.

Design and Implementation of a Hierarchical Digital Twin for Power Systems Using Real-Time Simulation

This paper presents a hierarchical Digital Twin architecture and implementation that uses real-time simulation to emulate the physical grid and support grid planning and operation. With the demand for detailed grid information for automated grid operations and the ongoing transformation of energy systems, the Digital Twin can extend data acquisition by establishing a reliable real-time simulation. The system uses observer algorithms to process model information about the voltage dependencies of grid nodes, providing information about the dynamic behavior of the grid. The architecture implements multiple layers of data monitoring, processing, and simulation to create node-specific Digital Twins that are integrated into a real-time Hardware-in-the-Loop setup. The paper includes a simulation study that validates the accuracy of the Digital Twin, in terms of steady-state conditions, dynamic behavior, and required processing time. The results show that the proposed architecture can replicate the physical grid with high accuracy and corresponding dynamic behavior.

Evaporation kinetics and quality attributes of grape juice concentrate as affected by microwave and vacuum processing

ABSTRACT In this study, grape juice was concentrated through a microwave vacuum-assisted technique. The concentrate was prepared at different powers (30, 50, 80 W) and vacuum pressures (35, 50, 70 kPa) for different time intervals to optimize the processing conditions. The highest evaporation rate (5.32 g/100 g min) and the lowest moisture ratio (0.18) were observed at a higher level of microwave power (80 W) and vacuum pressure (70 kPa). Moreover, different mechanistic models were used to predict the values of the moisture ratio. Among them, the Midilli (R2 = 0.999, SSE = 1.970 × 10−8, RPD = 0.012%) model best fits the experimental values. The effective diffusivity (1.69 × 10−14 m2/s), activation energy (1.56 W/kg), and extraction yield (69.30%) possessed maximum values at 80 W and 70 KPa. While concentrate had exhibited higher antioxidant activities (77.2%) than fresh juice (25.28%) at 30 W. Overall results indicated that the microwave-vacuum technique had significantly reduced the processing time and energy requirement.