Processing Delay Research Articles

Leadership styles in improving the performance of civil servants at the secretariat of the North Maluku Province

In this research, the author employed a qualitative research approach to explore the role of leadership styles in improving civil servant performance at the Secretariat of the North Maluku Province. The study found that the Secretary of the North Maluku Province employed a democratic leadership style, which involved effective communication, collaborative decision-making, and motivation. This leadership style positively influenced civil servant performance and contributed to the achievement of organizational goals. The research also identified challenges in improving civil servant performance, including issues related to employee discipline, limited supporting facilities, and delays in salary processing. These challenges underscore the importance of addressing discipline-related issues and providing necessary resources to enhance performance. In conclusion, the research highlighted the significance of leadership styles in government organizations and their impact on civil servant performance. It emphasized the need for leaders to prioritize employee discipline and create a conducive work environment. Additionally, it called for continuous efforts from civil servants to improve their performance and contribute to organizational success.

A-184 Racing Against the Clock: Process Improvement Project to Reduce Cardiac Troponin Turnaround Time Through Autoverification of Results

Abstract Background Turnaround time (TAT), defined as time of collection to time of result verification, is monitored monthly for cardiac Troponin (cTnT). Within our STAT laboratory, our TAT goal is 85% of cTnT results verified within 45 min. In October 2021, our lab began observing an increase in TAT. A quality improvement project was launched to identify a root cause and possible solutions. Methods cTnT TAT data from the Hospital Clinical Laboratory (STAT lab) at Mayo Clinic, Rochester, MN was extracted from our Laboratory Information System (LIS) using Tableau (Salesforce Inc., Seattle, WA). The data were categorized into three components: collection time, in lab processing and testing time, and time to result verification. Goal metrics for each component are: collection <10 min, in lab processing and testing <25 min, and result verification to LIS <5 min. The average time for each component and percentage not meeting TAT goals were calculated in Microsoft Excel. TAT while performing manual verification of results (January 2022) and after autoverification was implemented (December 2022) were compared. Results Prior to implementation of cTnT autoverification, total orders for January 2022 were 1408 with 82.0% meeting TAT of 45 min. Of the 18.0% not meeting TAT, 207 (14.7%) orders exceeded goals for one or more components (percentages do not total 100%): collection delays 131 (63.0%), in lab processing and testing delay 175 (85.0%), and delay in result verification to LIS 80 (39.0%). For those orders not meeting the 45 min TAT goal, mean times were 17.6, 40.3, and 8 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Post implementation of autoverification, total orders for December 2022 were 1444 with 88.6% meeting TAT of 45 min. Of the 11.4% not meeting TAT, 93 (6.4%) orders were outside goals for multiple components: collection delays 51 (55.0%), in lab processing and testing delay 67 (72.0%), and result verification to LIS 0 (0.0%) orders. For those orders not meeting the 45 min TAT goal, mean times 16.6, 39.7, and 0 min for collection time, in lab processing and testing, and result verification to LIS, respectively. Conclusion Achieving TAT goals agreed upon by the laboratory and clinical practice is critical to quality patient care. An investigation into our delayed TAT for cTnT highlighted several opportunities for intervention within the control of the lab. We chose to implement autoverification of results to easily and consistently eliminate the time required for manual result verification. TAT was improved by 6.6% (82.0% to 88.6%) through autoverification of results, allowing technologists to focus on other clinical work.

A-099 Assessment of Stability of Routine Clinical Biochemical Results in 24-h Urine Specimen Under Conditions Simulating Various Preanalytical Variables

Abstract Background The preanalytical phase has long been recognized as a source of substantial variability for 24-h urine samples. Hence, we examined the stability of analyte concentrations of 14 clinical biochemistry parameters commonly requested in a 24-h urine sample. The effects of various preanalytical variables simulating preexamination processes of the testing process were assessed. Methods We aimed to examine the effect of time delay in sample processing (up to 7 days), storage temperature (at 4 °C and room temperature), storage container (24-h urine bag, styrofoam box), and the effect of addition of toluene as preservative. The effect of the variables on clinical biochemistry parameters (uric acid, calcium, amylase, LDH, total protein, urea nitrogen, magnesium, phophorus, glucose, creatinine, sodium, potassium, chloride, and micro-albumin) in 24-h urine samples were assessed. Results Most of the analytes (uric acid, calcium, amylase, total protein, urea nitrogen, magnesium, creatinine, sodium, chloride, and micro-albumin) showed values not significantly different (≤± 5%) from baseline after 7 days of storage regardless of the storage temperature or addition of preservative. Three analytes were stable ≤24-h: phosphorus and glucose were stable up to 24-h and potassium up to 4-h. The addition of toluene had significant positive interference with LDH measurements. Conclusion According to our results, major analytes (uric acid, calcium, amylase, total protein, urea nitrogen, magnesium, creatinine, sodium, chloride, and micro-albumin) were stable up to 7 days in ambient storage conditions and toluene as a preservative was not necessary. However, a few analytes (phosphate, glucose, and potassium) were much more unstable and allowed ≤24-h or ≤4-h of storage in ambient or refrigerated conditions.

Fish-inspired robotic algorithm: mimicking behaviour and communication of schooling fish.

This study aims to present a novel flocking algorithm for robotic fish that will aid the study of fish in their natural environment. The algorithm, fish-inspired robotic algorithm (FIRA), amalgamates the standard flocking behaviors of attraction, alignment, and repulsion, together with predator avoidance, foraging, general obstacle avoidance, and wandering. The novelty of the FIRA algorithm is the combination of predictive elements to counteract processing delays from sensors and the addition of memory. Furthermore, FIRA is specifically designed to work with an indirect communication method that leads to superior performance in collision avoidance, exploration, foraging, and the emergence of realistic behaviors. By leveraging a high-latency, non-guaranteed communication methodology inspired by stigmergy methods inherent in nature, FIRA successfully addresses some of the obstacles associated with underwater communication. This breakthrough enables the realization of inexpensive, multi-agent swarms while concurrently harnessing the advantages of tetherless communication. FIRA provides a computational light control algorithm for further research with low-cost, low-computing agents. Eventually, FIRA will be used to assimilate robots into a school of biological fish, to study or influence the school. This study endeavors to demonstrate the effectiveness of FIRA by simulating it using a digital twin of a bio-inspired robotic fish. The simulation incorporates the robot's motion and sensors in a realistic, real-time environment with the algorithm used to direct the movements of individual agents. The performance of FIRA was tested against other collective flocking algorithms to determine its effectiveness. From the experiments, it was determined that FIRA outperformed the other algorithms in both collision avoidance and exploration. These experiments establish FIRA as a viable flocking algorithm to mimic fish behavior in robotics.

Intergenerational perceptions of the collective action challenges facing Mexican community forests

Rural commons globally are facing environmental, demographic, and economic changes that challenge their sustainability. In addition, political and regulatory burdens can complicate the running of communal enterprises that provide income and livelihoods for community members, young and old. When these challenges combine with the cultural changes driven by globalization, youth in rural areas– especially those without access to land–may look outside of their communities for opportunities. The labor shortages and detachment from place that follow can undermine local capacities to manage communal lands for natural resource-based livelihoods. Researchers and NGOs that have studied and supported community-based resource management, including community forest enterprises (CFEs), argue that building community capacities and improving access to markets are necessary for forest commons to be sustainable. Yet, the role of young people in all of this remains understudied, including how their perceptions and motivations vis a vis forest livelihoods and work compare to those of older members who enjoy tenure rights and associated benefits. To help address this knowledge gap, we conducted semi-structured interviews with youth/young adults and older rights-holders in communities in different regions of Mexico with their own forestry enterprise, along with a small number of non-community members working in or supporting the country’s community forestry sector. We found that youth held a degree of attachment to traditional rural life, but perceived limited economic opportunities locally, low and unstable incomes, and few retirement benefits associated with forest work. This, along with the physical strain of land-based livelihoods, a lack of access to credit, processing delays for harvesting permits, and outdated logging equipment, contributed to diminished interest in the forestry sector and enhanced motivations to out-migrate. To cope with internal labor shortages, communities seek labor from afar, offering short-term contractual wages, and lease land and rights to outsiders. Such actions occur outside of federal commons law, suggesting that the regulatory context in Mexico is out of step with the emergent realities and needs of contemporary forest communities.

Natural convection of rectangular cavity enhanced by obstacle and fin to simulate phase change material melting process using Lattice Boltzmann method

In recent years, the lattice Boltzmann method has become a powerful method for computational modeling of various complex fluid flow concerns, including the simulation of the melting process in phase change materials. In the present paper, the natural convection of phase change materials in a cavity is simulated, and the effect of adiabatic obstacle and fin is investigated by the lattice Boltzmann method. The obtained results are presented in different Rayleigh numbers (Ra = 103-105), and cavity angles (θ=-90to90) in three scenarios (without adiabatic fin and obstacle, with an adiabatic obstacle, and with adiabatic fin). The investigation across various cavity angles, with adiabatic obstacles and fins, demonstrates a consistent trend of effective melting process delay by up to 50%, underscoring the significant impact of these adiabatic features on PCM behavior. Adiabatic obstacles induce localized melting delays due to unmelted zones around them. Streamlines highlight vortices formed by obstacles, and elevated Nusselt numbers correlate with accelerated melting facilitated by adiabatic fins. Modifying the adiabatic fin height from Yf = 0.1 to Yf = 0.7 leads to a doubling of melting time at around 80% PCM melting. Conversely, decreasing fin height from 0.5 to 0.7 extends the complete melting time by approximately 10%, showcasing the influential role of fin height in shaping PCM melting behaviour.

Secure-fault-tolerant efficient industrial internet of healthcare things framework based on digital twin federated fog-cloud networks

The Industrial Internet of Healthcare Things (IIoHT) is the emerging paradigm in digital healthcare. Context-aware healthcare sensors, local intelligent watches, healthcare devices, wireless communication technologies, fog, and cloud computing are all parts of the IIoHT used in healthcare. The ubiquitous healthcare services it provides to its users in practice. However, the current IIoHT healthcare frameworks have security and failure issues in mobile fog and cloud networks where they are spread out. This paper presents the secure, fault-tolerant IIoHT Framework based on digital twin (DT) federated learning-enabled fog-cloud models. The DT is an effective technology that makes virtual copies of servers at different locations. DT integrated with federated learning inside the fog and cloud environments, where the failure of tasks and execution improved for healthcare sensor data. The study aims to reduce processing time and the risk of task failure. The study presents the Secure and Fault-Tolerant Strategies (SFTS)-enabled IIoHT framework that optimizes wearable sensor data and executes it with the minimum offloading and processing delays. Simulation results show that the proposed work minimized the security risk by 40%, failure risk of tasks risk by 50%, and the training and testing time by 39% for sensor data during the execution of mobile fog cloud networks.

Analysis of thermo-mechanical coupling characteristics of feed system of woodworking center based on fuzzy control algorithm

With the rapid development of science and technology, automatic control systems have been applied in more and more fields. At the same time, the requirements for the stability, accuracy, response speed, and self-regulation ability of the system are also increasing. In the wood processing industry, the heating system of factories is an important link to ensure normal production. Therefore, in order to further improve the production efficiency of the wood processing industry and enhance the stability and controllability of the heating system in wood processing production, this article takes into account the delay and coupling effects in the rapid heating process, and combines fuzzy control with temperature control to study and establish a woodworking thermal mechanical coupling model based on fuzzy control algorithm. The results show that compared with traditional PID control, fuzzy control has advantages such as short response time, small overshoot, high steady-state accuracy, fast steady-state recovery, and good dynamic and static performance. Under the condition of rapid heating, the existence of delay effect weakens the effect of Thermal shock, while the coupling effect not only affects the propagation of thermoelastic waves in the elastic body, but also weakens the weakening effect of delay effect on Thermal shock to a certain extent.

HAMBATAN KOMUNIKASI INTERAKSIONAL PADA DPAC PDIP DI KECAMATAN BATU AJI KOTA BATAM

Indonesia is a country that uses a democratic system, so political communication is one of the main keys to the establishment of politics in this democratic country. Communication can be in the form of conveying information to the public by political party candidates to convey their goals to become a politician. Political communication is also intended as a vessel for channeling people's aspirations to prospective political members who will advance as power holders in a democratic country like Indonesia. Therefore the purpose of this study is to find out how the communication barriers that occur in the Batu Aji sub-district community are carried out by the Indonesian Democratic Party of Struggle and to find out what causes the delay in the political communication process. This study used descriptive research with qualitative research methods located in Batu Aji District. Data collection techniques used were observation and interview techniques and continued with documentation. The results of the discussion obtained are that the success achieved by political actors depends on how political communication goes well and smoothly without any obstacles, by building relationships with the community and building trust so that there is no miscommunication that occurs in politics.
 
 Keywords: Obstacles, Politic, Communication, Democratic

Pre-Analytical Variables Influencing Stability of Blood-Based Biomarkers of Neuropathology.

Sample collection and preanalytical protocols may significantly impact the results of large-scale epidemiological studies incorporating blood-based biomarkers of neuropathology. To evaluate the stability and assay variability of several blood-based biomarkers of neuropathology for common preanalytical conditions. We collected serum and plasma samples from 41 participants and evaluated the effect of processing delay of up to 72 h when stored at 4∘C, three freeze-thaw cycles, and a combination of 48-h processing delay when stored at 4∘C and three freeze-thaw cycles on biomarker stability. Using the Simoa assay (Quanterix Inc.), we measured amyloid-β 40 (Aβ40), amyloid-β 42 (Aβ42), neurofilament light (NfL), glial fibrillary acidic protein (GFAP), and phosphorylated tau 181 (p-tau-181). We found that Aβ40 and Aβ42 levels significantly decreased after a 24-h processing delay in both plasma and serum samples, and a single freeze-thaw cycle (p < 0.0001). Nevertheless, serum Aβ42/40 ratio remained stable with a processing delay up to 48 h while plasma Aβ42/40 ratio showed only small but significant increase with a delay up to 72 h. Both plasma and serum GFAP and NfL levels were only modestly affected by processing delay and freeze-thaw cycles. Plasma p-tau-181 levels notably increased with a 24-, 48-, and 72-h processing delay, but remained stable in serum. Intra-individual variation over two weeks was minimal for all biomarkers and their levels were substantially lower in serum when compared with plasma. These results suggest that standardizing preanalytical variables will allow robust measurements of biomarkers of neuropathology in population studies.

Infrastructure sensor-based cooperative perception for early stage connected and automated vehicle deployment

Infrastructure-based sensors provide a potentially promising solution to support the wide adoption of connected and automated vehicles (CAVs) technologies at an early stage. For connected vehicles with lower level of automation that do not have perception sensors, infrastructure sensors will significantly boost its capability to understand the driving context. Even if a full suite of sensors is available on a vehicle with higher level of automation, infrastructure sensors can support overcome the issues of occlusion and limited sensor range. To this end, a cooperative perception modeling framework is proposed in this manuscript. In particular, the modeling focus is placed on a key technical challenge, time delay in the cooperative perception process, which is of vital importance to the synchronization, perception, and localization modules. A constant turn-rate velocity (CTRV) model is firstly developed to estimate the future motion states of a vehicle. A delay compensation and fusion module is presented next, to compensate for the time delay due to the computing time and communication latency. Last but not the least, as the behavior of moving objects (i.e., vehicles, cyclists, and pedestrians) is nonlinear in both position and speed aspects, an unscented Kalman filter (UKF) algorithm is developed to improve object tracking accuracy considering communication time delay between the ego vehicle and infrastructure-based LiDAR sensors. Simulation experiments are performed to test the feasibility and evaluate the performance of the proposed algorithm, which shows satisfactory results.

P11.70.A EQUITY IN GENOMIC TESTING: LESSONS LEARNT FOLLOWING THE ROLLOUT OF AN AMBITIOUS GENOMIC TESTING STRATEGY FOR NEUROLOGICAL TUMOUR PATIENTS IN THE UNITED KINGDOM

Abstract BACKGROUND The NHS England Genomic Test Directory now offers extensive genomic testing to brain tumour patients including whole genome sequencing (WGS), methylation arrays, gene panels, and other targeted mutation tests. Comprehensive genomic testing enables precise diagnosis and prognosis, tailoring of treatment, and research into new actionable targets. Despite the ambitious plans and potential for significant patient benefits, the Tessa Jowell Brain Cancer Mission (TJBCM) identified variation in deployment of these tests across Neuro-Oncology Centres in the UK. Based on these findings, we aimed to further understand the variation. We aimed to uncover drivers to delivering a successful genomic testing programme and identify local and national barriers to rolling out extensive genomic testing. MATERIAL AND METHODS As part of the TJBCM Centre of Excellence Programme, 90% of Neuro-Oncology Centres in the UK provided self-reported data on volumes of genomic testing for brain tumour patients. Subsequently, structured interviews were conducted with neuro-oncologists, neuropathologists, neurosurgeons, clinical scientists and other stakeholders from these Centres to further evaluate the state of genomic testing. RESULTS Accessibility to WGS varied from no access, to Centres where WGS was available in a limited capacity, to Centres where WGS was accessible but not scalable or timely. Bottlenecks were identified throughout the WGS pathway including inadequate tumour tissue, sample processing and transport delays, and insufficient clinical scientists to interpret results. As such, no UK Centre interviewed had sufficient capacity to offer WGS to all neurological tumour patients in a timely manner using NHS-accredited pathways. Interviews showed geographic variability in access to basic molecular tests such as methylation array and CDKN2A/B which are required for diagnostics and prognostics. There was also a strong reliance on a small number of laboratories to provide methylation array, resulting in backlogs. Common drivers to effective delivery of genomic testing included funded posts for research nurses, innovative systems for delivery of tumour samples from surgery to the laboratory, dedicated staff who advocated for genomic testing, and well-established Genomic Tumour Advisory Boards for test interpretation. CONCLUSION Impressive progress has been made in the last two years yet there remains a mismatch between what the National Genomic Test Directory dictates, and what can currently be delivered in a manageable timeframe. Inconsistencies in infrastructure, funding and training are preventing UK Centres from delivering the intended world-leading genomic testing. Barriers to address vary from simple changes at a local level, to national coordination and funding so that genomic testing will be equally accessible to all brain tumour patients in the UK.

Backup Resource Allocation Model with Probabilistic Protection Considering Service Delay

This paper proposes a backup resource allocation model for virtual network functions (VNFs) to minimize the total allocated computing capacity for backup with considering the service delay. If failures occur to primary hosts, the VNFs in failed hosts are recovered by backup hosts whose allocation is pre-determined. We introduce probabilistic protection, where the probability that the protection by a backup host fails is limited within a given value; it allows backup resource sharing to reduce the total allocated computing capacity. The previous work does not consider the service delay constraint in the backup resource allocation problem. The proposed model considers that the probability that the service delay, which consists of networking delay between hosts and processing delay in each VNF, exceeds its threshold is constrained within a given value. We introduce a basic algorithm to solve our formulated delay-constraint optimization problem. In a problem with the size that cannot be solved within an acceptable computation time limit by the basic algorithm, we develop a simulated annealing algorithm incorporating Yen's algorithm to handle the delay constraint heuristically. We observe that both algorithms in the proposed model reduce the total allocated computing capacity by up to 56.3% compared to a baseline; the simulated annealing algorithm can get feasible solutions in problems where the basic algorithm cannot.

Heuristically Assisted Multiagent RL-Based Framework for Computation Offloading and Resource Allocation of Mobile-Edge Computing

Mobile edge computing (MEC) as a promising technology enables it to satisfy ever-increasing demands for low-latency and ultra-reliable services. However, due to the limitations of computing capability and the dynamic network environment, it is challenging to process massive data with low latency. In this paper, we consider a dynamic MEC network with a high-performance edge server, multiple time-varying channels, and multiple mobile devices. We aim to find a policy that can maximize the processing success rate of computational tasks and the fairness index of the system while minimizing the process delays. To this end, we propose a heuristic-assisted multiagent reinforcement learning (RL) based framework to realize the joint optimization of computation offloading and resource allocation. On one hand, heuristic search is introduced in this framework to find a better resource allocation policy in edge servers and further assist the multiagent RL algorithm to determine offloading policy in mobile devices. On the other hand, a novel parametrized multiagent RL algorithm based on soft actor-critic (SAC) is also proposed to broaden the effectiveness and availability of the proposed framework. Simulation results of the average cumulative reward, success rate, processing delay, and fairness index fully verify the superiority of the proposed framework and algorithm for supporting this problem.

Amplitude prediction from uplink to downlink CSI against receiver distortion in FDD systems

In frequency division duplex (FDD) massive multiple-input multiple-output (mMIMO) systems, the reciprocity mismatch caused by receiver distortion seriously degrades the amplitude prediction performance of channel state information (CSI). To tackle this issue, from the perspective of distortion suppression and reciprocity calibration, a lightweight neural network-based amplitude prediction method is proposed in this paper. Specifically, with the receiver distortion at the base station (BS), conventional methods are employed to extract the amplitude feature of uplink CSI. Then, learning along the direction of the uplink wireless propagation channel, a dedicated and lightweight distortion-learning network (Dist-LeaNet) is designed to restrain the receiver distortion and calibrate the amplitude reciprocity between the uplink and downlink CSI. Subsequently, by cascading, a single hidden layer-based amplitude-prediction network (Amp-PreNet) is developed to accomplish amplitude prediction of downlink CSI based on the strong amplitude reciprocity. Simulation results show that, considering the receiver distortion in FDD systems, the proposed scheme effectively improves the amplitude prediction accuracy of downlink CSI while reducing the transmission and processing delay.

SAMBA: Sparsity Aware In-Memory Computing Based Machine Learning Accelerator

Machine Learning (ML) inference is typically dominated by highly data-intensive Matrix Vector Multiplication (MVM) computations that may be constrained by the memory bottleneck due to massive data movement between processor and memory units. Although analog in-memory computing (IMC) ML accelerators have been proposed to execute MVM with high efficiency, the latency and energy of such analog computing systems can be dominated by the large latency and energy costs from analog-to-digital converters (ADC). Leveraging sparsity in ML workloads, reconfigurable ADCs can improve the MVM energy and latency by reducing the required ADC bit precision. However, such improvement in latency can be hindered by non-uniform sparsity of the weight matrices mapped into hardware. Moreover, data movement between MVM processing cores may become another factor that delays the overall system-level performance. To address these issues, we propose SAMBA, Sparsity Aware IMC Based Machine Learning Accelerator. First, we propose load balancing at the mapping of weight matrices into physical crossbars. The goal of load balancing is to accommodate reconfigurable ADCs and address the possible delay of MVM processing caused by non-uniform sparsity in the weight matrices. Second, we propose optimizations in arranging and scheduling the tiled MVM hardware to minimize the overhead of data movement across multiple processing cores. Our evaluations show that the proposed load balancing technique can achieve performance improvement by eliminating the non-uniformity in the sparsity of mapped matrices. Moreover, we demonstrate that data movement optimizations can further improve both performance and energy efficiency regardless of sparsity condition. With the combination of load balancing and data movement optimization in conjunction with reconfigurable ADCs, our proposed approach provides up to 2.38x speed-up and 1.54x energy efficiency over state-of-art analog IMC based ML accelerators for ImageNet datasets on Resnet-50 architecture.

UCDM: A User-Centric Data Model in Power Blockchain

Background: As innovative information technology, blockchain has combined the advantages of decentralization, immutability, data provenance, and contract operation automatically, which can be used to solve the issues of single point failure, high trading cost, low effectiveness, and data potential risk in power trading. However, in the traditional power blockchain, the design of functional components in blockchain, such as the data structure of the block, does not take the actual features of power into account, thus leading to a performance bottleneck in practical application. Motivated by business characteristics of power trading, a user-centric data model UCDM in consortium blockchain is proposed to achieve efficient data storage and quick data retrieval. Methods: The proposed UCDM is designed by considering the requirements of transaction retrieval and analysis, thus supporting the requirements of concurrent data requests and mass data storage. The ID of each user will independently form its own chain over the blockchain. Results: Compared with the traditional data model, the extensive experimental results demonstrate that the proposed UCDM has shorter processing delay, higher throughput, and shorter response latency, thus having practical value. Conclusion: UCDM is an effective solution to the transaction retrieval and analysis in power blockchain. Furthermore, the participant of the blockchain network has a unique identity over the world, which ensures high security during trading.

DETERMINATION OF DELIVERY FUEL DISTRIBUTION ROUTES TO MINIMIZE TOTAL DISTRIBUTION COSTS WITH THE SAVING MATRIX METHOD AT PT. ENERGI NIAGA UTAMA

Transportation and distribution are two components that affect a company's competitive advantage because a decrease in distribution costs can indirectly increase the company's profits. The higher the level of competition, it requires companies to be able to make good distribution strategies. Some of the problems that exist in PT. Energi Niaga Utama in distributing diesel fuel to companies is the delay in the distribution process, in which diesel fuel uses a 16 KL tank car. Therefore, this study wants to minimize the total route and distribution costs to provide profits and minimize company losses. In this study, the determination of the saving matrix method helps solve distribution problems. Based on the calculation using the saving matrix method, two stages of delivery were obtained with as many as 12 shipping routes so that each company did not delay the delivery of diesel fuel for the company's initial route distance of 2,018.6 km with a total distribution cost of Rp. 11.959.158, by using the saving matrix method, can provide distance savings of 1,552.8 km with a total distribution cost of Rp. 11.300.240, the percentage of distance savings is 23.07%, and the percentage of distribution cost savings is 5.5%.

Predictor-corrector models for lightweight massive machine-type communications in Industry 4.0

Future Industry 4.0 scenarios are characterized by seamless integration between computational and physical processes. To achieve this objective, dense platforms made of small sensing nodes and other resource constraint devices are ubiquitously deployed. All these devices have a limited number of computational resources, just enough to perform the simple operation they are in charge of. The remaining operations are delegated to powerful gateways that manage sensing nodes, but resources are never unlimited, and as more and more devices are deployed on Industry 4.0 platforms, gateways present more problems to handle massive machine-type communications. Although the problems are diverse, those related to security are especially critical. To enable sensing nodes to establish secure communications, several semiconductor companies are currently promoting a new generation of devices based on Physical Unclonable Functions, whose usage grows every year in many real industrial scenarios. Those hardware devices do not consume any computational resource but force the gateway to keep large key-value catalogues for each individual node. In this context, memory usage is not scalable and processing delays increase exponentially with each new node on the platform. In this paper, we address this challenge through predictor-corrector models, representing the key-value catalogues. Models are mathematically complex, but we argue that they consume less computational resources than current approaches. The lightweight models are based on complex functions managed as Laurent series, cubic spline interpolations, and Boolean functions also developed as series. Unknown parameters in these models are predicted, and eventually corrected to calculate the output value for each given key. The initial parameters are based on the Kane Yee formula. An experimental analysis and a performance evaluation are provided in the experimental section, showing that the proposed approach causes a significant reduction in the resource consumption.

Using interpretive structural modelling and quality cost model to solve project completion delay in shipyard case study in PT X

Purpose – This research proposes an alternative method to solve completion delay issue and optimized the strategy using ISM technical method and Quality Cost Model application. Design/methodology/approach – Data was acquired from 120 questions from company key persons that involved in project for last 5 years. The instruments processed using ISM analysis to measure the relationship among causes, then using Quality Cost Model to solve. Findings – This research shows that factors of late payment from client, delays in bank credit processing, bad weather, and design changes by owners are the root causes of delays. Further analysis using the Theory of Constraint (TOC) shows that financial constraint issues influence the decreased throughput and increase operating cost. Further management use five sequential steps to improve the management and P-A-F models as one of strategic cost management tools applied to the root causes to determine the optimum cost strategy to solve the problem. Research limitations/implications – This research was conducted using a case study method, and the conclusion related to problem and root cause may not be applied totally to other companies, but the framework may be applicable. Future research can investigate other issue using TOC framework, combine TOC with other models to conduct root cause analysis, and the COQ model implementation in shipyard management. Practical implications – This research demonstrates that management can use Interpretative Structural Modelling (ISM) analysis to determine the root cause, then analyse and overcome the issues using TOC framework. COQ model approach to sharpening the decision, and prioritize the resources allocation. Originality/value – This research combine ISM, TOC, and PAF Quality Cost Model approach for decision making which can be a management tool in strategic cost management.