Multiple Operations Research Articles

An Operations Chain Model for Automatic Assessment of Operation Procedure for Equipment Operators

Automatic assessment of the operation ability of operators based on computers is an essential approach for improving the effectiveness of equipment operation training and enhancing equipment safety. Present methods primarily focus on the operation results but pay less attention to the operation procedure. One reason is that there is a lack of a model that has the ability to describe all probable paths to accomplish the same task. Therefore, an operations chain model is put forward for the first time to describe the standard operation procedure and relationships among operations based on the decomposition of operational tasks and the relationships among the various operations required to fulfill the task. A specific operation task corresponds to an operations chain, which will form one or multiple standard operation sequences that will allow trainees to complete the same task through different paths. The Needleman–Wunsch sequence alignment algorithm is introduced to match the trainees’ operation sequence with all standard sequences. The maximum alignment result is the score of the trainees’ operations. An example shows that the operations chain model can accurately describe the complex structure of the standard operating procedures. The Needleman–Wunsch sequence alignment algorithm can objectively evaluate the trainee’s operation capabilities. The combination of the operations chain model and sequence alignment algorithm can form a complete operation procedure assessment method that is friendlier to trainees and has more objective evaluation results. The method will help to improve the effectiveness of the competency assessment of equipment operators.

Machine Learning Predictions of Methane Storage in MOFs: Diverse Materials, Multiple Operating Conditions, and Reverse Models.

A machine learning (ML) model is developed for predicting useable methane (CH4) capacities in metal-organic frameworks (MOFs). The model applies to a wide variety of MOFs, including those with and without open metal sites, and predicts capacities for multiple pressure swing conditions. Despite its wider applicability, the model requires only 5 measurable structural features as input, yet achieves accuracies that surpass less-general models. Application of the model to a database of more than a million hypothetical MOFs identified several hundred whose capacities surpass that of the benchmark MOF, UMCM-152. Guided by the computational predictions, one of the promising candidates, UMCM-153, was synthesized and demonstrated to achieve superior volumetric capacity for CH4. Feature importance analyses reveal that pore volume and gravimetric surface area are the most important features for predicting CH4 capacity in MOFs. Finally, a reverse ML model is demonstrated. This model predicts the set of elementary MOF structural properties needed to achieve a desired CH4 capacity for a prescribed operating condition.

A Dual-Dimension Convolutional-Attention Module for Remaining Useful Life Prediction of Aeroengines

Remaining useful life (RUL) prediction of aeroengines not only enhances aviation safety and operational efficiency but also significantly lowers operational costs, offering substantial economic and social benefits to the aviation industry. Aiming at RUL prediction, this paper proposes a novel dual-dimension convolutional-attention (DDCA) mechanism. DDCA consists of two branches: one includes channel attention and spatial attention mechanisms, while the other applies these mechanisms to the inverted dimensions. Pooling and feature-wise pooling operations are employed to extract features from different dimensions of the input data. These branches operate in parallel to capture more complex temporal and spatial feature correlations in multivariate time series data. Subsequently, an end-to-end DDCA-TCN network is constructed by integrating DDCA with a temporal convolutional network (TCN) for RUL prediction. The proposed prediction model is evaluated using the C-MAPSS dataset and compared to several state-of-the-art RUL prediction models. The results show that the RMSE and SCORE metrics of DDCA-TCN decreased by at least 12.8% and 4.6%, respectively, compared to other models on the FD002 subset, and by at least 10.6% and 18.4%, respectively, on the FD004 subset, which demonstrates that the DDCA-TCN model exhibits excellent performance in RUL prediction, particularly under multiple operating conditions.

Optimization of steel plate quality inspection driven by PscSE and SPPFELAN

AbstractBased on the improved YOLOv8n, a steel plate defect detection and recognition method is proposed to address the high labor costs and workload of traditional tasks. SPPFELAN processes inputs in parallel to enhance computational efficiency by executing multiple pooling operations simultaneously. The parallel feature fusion module PscSE, using a mixed‐dimension SE attention mechanism (scSE), captures global and channel‐related information better, improving characterization capability. The EIOU loss function addresses the ambiguous aspect ratio definition of CIOU loss, enhancing detection accuracy and accelerating convergence. Results show the YOLOv8n‐PscSE‐SPPFELAN model achieves 76.9% mAP@0.5 on the Northeastern University steel plate dataset, a 4.6% improvement over the original YOLOv8n, with a computation amount of 7.7 GFLOPs, reducing resource usage and greatly improving detection speed.

Decoding Silent Speech Cues From Muscular Biopotential Signals for Efficient Human‐Robot Collaborations

AbstractSilent speech interfaces offer an alternative and efficient communication modality for individuals with voice disorders and when the vocalized speech communication is compromised by noisy environments. Despite the recent progress in developing silent speech interfaces, these systems face several challenges that prevent their wide acceptance, such as bulkiness, obtrusiveness, and immobility. Herein, the material optimization, structural design, deep learning algorithm, and system integration of mechanically and visually unobtrusive silent speech interfaces are presented that can realize both speaker identification and speech content identification. Conformal, transparent, and self‐adhesive electromyography electrode arrays are designed for capturing speech‐relevant muscle activities. Temporal convolutional networks are employed for recognizing speakers and converting sensing signals into spoken content. The resulting silent speech interfaces achieve a 97.5% speaker classification accuracy and 91.5% keyword classification accuracy using four electrodes. The speech interface is further integrated with an optical hand‐tracking system and a robotic manipulator for human‐robot collaboration in both assembly and disassembly processes. The integrated system achieves the control of the robot manipulator by silent speech and facilitates the hand‐over process by hand motion trajectory detection. The developed framework enables natural robot control in noisy environments and lays the ground for collaborative human‐robot tasks involving multiple human operators.

Long Term Outcomes after Repeat Lymph Node Dissections for Persistent or Recurrent Differentiated Thyroid Cancer

BackgroundThe primary treatment for locoregional recurrent/persistent differentiated thyroid cancer (DTC) is repeated lymph node dissection; however, there are limited reports on the safety and long-term efficacy of multiple operations. MethodsPatients who underwent a cervical lymph node dissection between 1998-2022 were included in this study. Demographics, initial thyroid surgery, subsequent lymph node dissections, follow up information, and response to therapy were acquired. ResultsAfter one, two, three and four re-operations, 112/314 (35.7%), 16/79 (20.3%), 3/25 (12%), and 0/3 (0%) patients (p < 0.001) had an excellent response, respectively, resulting in a cumulative rate of 41.7% (131/314). The risk for permanent hypoparathyroidism (2.9%) or recurrent laryngeal nerve injury (2.2%) was 5.1% (14/272). This was higher in patients undergoing re-operative central neck dissection (CNDx) (8.7%, 10/114) versus those who did not undergo a previous CNDx (2.5%, 4/158, p < 0.02). ConclusionsSurgery remains the primary treatment for recurrent/persistent DTC, however, the likelihood of an excellent response decreases with additional operations. The risk of permanent complications is low but is more likely to occur during redo CNDx.

ANXA3 as a novel biomarker for sepsis diagnosis: Evidence from integrative WGCNA analysis

Sepsis is a dysregulated immune response to infection that comes with multiple organ dysfunction and high mortality. The management of sepsis relies heavily on early recognition and diagnosis, but current diagnostic methods have limitations in timeliness, sensitivity, and discriminability. This study aims to discover novel biomarkers for sepsis diagnosis. Four datasets from different regions were analyzed using weighted gene co-expression network analysis (WGCNA), and genes with high Gene Significance values across these datasets were overlapped. Finally, two genes, CD177 and ANXA3, were identified. ANXA3 was validated as a potential sepsis biomarker by checking multiple datasets and Receiver Operating Characteristic (ROC) Curve Analysis. Of note, ANXA3 could distinguish not only between adult and child sepsis patients and healthy controls, but also between septic shock and cardiogenic shock. Moreover, a murine sepsis model was established and the results showed that the transcription of ANXA3 in peripheral blood of septic mice was significantly higher than that of healthy controls, while Escherichia coli infection alone did not significantly increase the transcription level of this gene. Subsequent studies of sepsis in mice revealed that the predictive effect of Anxa3 on sepsis could be observed as early as 6 h post-modeling. Interestingly, ANXA3 expression was predominantly up-regulated in myeloid cells, up-regulated in spleen, down-regulated in lung, and not detected in liver after sepsis modeling. Taken together, this study provides a way for the discovery of biomarkers and finds that ANXA3 may be a novel diagnostic biomarker for sepsis.

Mathematics Learning Model for Children with Dyscalculia through Special Intervention

This research aimed to formulate a model of mathematical learning difficulties for children with dyscalculia to help children with dyscalculia overcome mathematics problems at school. This research is important to carry out considering that dyscalculia is a serious problem experienced by many students throughout the world. The SDTA model was built by analogizing the STIR model to non-communicable diseases. In this study, of 1247 students who conducted a series of tests (screening tests), there were 121 students, or 9.7% were initially identified as having dyscalculia. Furthermore, observations were carried out and confirmation was carried out with parents and teachers so that 119 students tested positive for dyscalculia. The research results showed that after intervention through treatment by the teacher, dyscalculia students experienced a very drastic decrease, especially in the material on recognizing and ordering numbers. Likewise, the results of model simulations on multiplication operation material illustrated the decline in students with dyscalculia. Specifically for division operations, the treatment process took relatively longer. Therefore, the simulation results in the SDTA model reflected students who are identified as having dyscalculia, along with planned intervention/ treatment, students could pass through the mathematics learning phase successfully.

S2VSNet: Single stage V-shaped network for image deraining & dehazing

Producing high-quality, noise-free images from noisy or hazy inputs relies on essential tasks such as single image deraining and dehazing. In many advanced multi-stage networks, there is often an imbalance in contextual information, leading to increased complexity. To address these challenges, we propose a simplified method inspired by a U-Net structure, resulting in the “Single-Stage V-Shaped Network” (S2VSNet), capable of handling both deraining and dehazing tasks. A key innovation in our approach is the introduction of a Feature Fusion Module (FFM), which facilitates the sharing of information across multiple scales and hierarchical layers within the encoder-decoder structure. As the network progresses towards deeper layers, the FFM gradually integrates insights from higher levels, ensuring that spatial details are preserved while contextual feature maps are balanced. This integration enhances the image processing capability, producing noise-free, high-quality outputs. To maintain efficiency and reduce system complexity, we replaced or removed several non-essential non-linear activation functions, opting instead for simple multiplication operations. Additionally, we introduced a “Multi-Head Attention Integrated Module” (MHAIM) as an intermediary layer between encoder-decoder levels. This module addresses the limited receptive fields of traditional Convolutional Neural Networks (CNNs), allowing for the capture of more comprehensive feature-map information. Our focus on deraining and dehazing led to extensive experiments on a wide range of synthetic and real-world datasets. To further validate the robustness of our network, we implemented S2VSNet on a low-end edge device, achieving deraining in 2.46 seconds.

Use of Radioguided Surgery for Small and Difficult-to-Locate Relapsed MIBG (+) High-Risk Neuroblastoma Lesions.

High-risk neuroblastoma, particularly in the relapse/refractory (R/R) setting, poses unique challenges to obtaining the representative-quality tissue that is mostly required for molecular analysis. This study explores the use of 123I-MIBG radioguided surgery to access complex locations of MIBG-positive neuroblastoma as a tool to overcome the difficulties associated with repeated surgeries in these patients. This study is a retrospective review of all patients with R/R neuroblastoma and MIBG-uptaking lesions who underwent radioguided surgery between February 2020 and 2023 at SJD Barcelona Children's Hospital. The Europrobe 3.2 gamma probe was used to identify neuroblastoma tissue in the operating room. Ten patients were identified. Radioguided surgery was useful in all patients. One patient with previous multiple operations developed an entero-cutaneous fistula with posterior full recovery. Mean surgical time was 111.7 min. The gamma probe identified 100% of neuroblastoma lesions which were all completely removed (123I-MIBG-SPECT/CT negative post-surgery). Pathology and molecular studies could be successfully performed in all samples. 123I-MIBG radioguided surgery proved effective in obtaining viable tissue from difficult-to-access sites in high-risk relapsed neuroblastoma.

HAETAE on ARMv8

In this work, we present the highly optimized implementation of the HAETAE algorithm, submitted to the second round of the Korean Post-Quantum Cryptography (KpqC) competition and to the first round of NIST’s additional post-quantum standardization for digital signatures on 64-bit ARMv8 embedded processors. To the best of our knowledge, this is the first optimized implementation of the HAETAE algorithm on 64-bit ARMv8 embedded processors. We apply various optimization techniques to enhance the multiplication operations in the HAETAE algorithm. We utilize parallel operation techniques involving vector registers and NEON (Advanced SIMD technology used in ARM processors) instructions of ARMv8 embedded processors. In particular, we achieved the best performance of the HAETAE algorithm on ARMv8 embedded processors by applying all the state-of-the-art NTT (Number Theoretic Transform) implementation techniques. Performance improvements of up to 3.07×, 3.63×, and 9.15× were confirmed for NTT, Inverse-NTT, and pointwise Montgomery operations (Montgomery multiplication used in modular arithmetic), respectively, by applying the state-of-the-art implementation techniques, including the proposed techniques. As a result, we achieved a maximum performance improvement of up to 1.16× for the key generation algorithm, up to 1.14× for the signature algorithm, and up to 1.25× for the verification algorithm.

Asymptotic Synchronization for Caputo Fractional-Order Time-Delayed Cellar Neural Networks with Multiple Fuzzy Operators and Partial Uncertainties via Mixed Impulsive Feedback Control

To construct Caputo fractional-order time-delayed cellar neural networks (FOTDCNNs) that characterize real environments, this article introduces partial uncertainties, fuzzy operators, and nonlinear activation functions into the network models. Specifically, both the fuzzy AND operator and the fuzzy OR operator are contemplated in the master–slave systems. In response to the properties of the considered cellar neural networks (NNs), this article designs a new class of mixed control protocols that utilize both the error feedback information of systems and the sampling information of impulse moments to achieve network synchronization tasks. This approach overcomes the interference of time delays and uncertainties on network stability. By integrating the fractional-order comparison principle, fractional-order stability theory, and hybrid control schemes, readily verifiable asymptotic synchronization conditions for the studied fuzzy cellar NNs are established, and the range of system parameters is determined. Unlike previous results, the impulse gain spectrum considered in this study is no longer confined to a local interval (−2,0) and can be extended to almost the entire real number domain. This spectrum extension relaxes the synchronization conditions, ensuring a broader applicability of the proposed control schemes.

Economic and Environmental Aspects of Applying the Regeneration of Spent Moulding Sand

This article presents issues related to the rational management of foundry sand in the context of sustainable development. Attention was drawn to the need to take appropriate measures to protect available natural deposits of good foundry sands in terms of their depletion. The main objective of the analyses undertaken was to find out whether more expensive but more efficient thermal regeneration can compensate for the higher energy consumption in relation to mechanical regeneration of spent moulding sand with an organic binder. This aspect was considered from the point of view of the multiple operations performed to clean the grain matrix from the spent binder, taking into account the direct and indirect costs of the process. This paper presents a comparative analysis of the mechanical and thermal regeneration of spent moulding sand on equipment offered by an exemplary manufacturer. Attention was drawn to the successively increasing price of the regeneration process. When analysing the grain matrix recovery process for sustainability reasons, attention was drawn to an important factor in grain matrix management related to its yield in different regeneration methods. Based on an analysis of the costs of regenerating 1 tonne of spent moulding sand, it was concluded that, in the long term, thermal regeneration, which is more expensive due to the cost of equipment and energy consumption, can offset the outlay incurred. Sand consumption was found to be 4.6 times higher by mechanical regeneration in the case studied. At the same time, the grain matrix after thermal regeneration was found to have significantly better and more stable technological parameters in subsequent sand mould preparation cycles. The reproducibility and stability of the technological process can also be an important component of economic growth as part of sustainable development.

Analytical validation of the LungLB test: a 4-color fluorescence in-situ hybridization assay for the evaluation of indeterminate pulmonary nodules

BackgroundEvaluation of indeterminate pulmonary nodules (IPNs) often creates a diagnostic conundrum which may delay the early detection of lung cancer. Rare circulating genetically abnormal cells (CGAC) have previously demonstrated utility as a biomarker for discriminating benign from malignant small IPNs in the LungLB assay. CGAC are identified using a unique 4-color fluorescence in-situ hybridization (FISH) assay and are thought to reflect early cell-based events in lung cancer pathogenesis and the anti-tumor immune response. LungLB is a prognostic tool that combines the CGAC biomarker and clinical features to aid in IPN evaluation by improving the stratification of patient risk of malignancy.MethodsHerein we describe the analytical performance of the LungLB blood test. Analytical validation was performed according to Clinical and Laboratory Standards Institute (CLSI) guidelines with adaptations for rare cell-based assays. Multiple operators, reagent lots, and assay runs were tested to examine accuracy, precision, reproducibility, and interfering factors.ResultsThe FISH probes used in the LungLB assay demonstrate 100% sensitivity and specificity for their intended chromosomal loci (3q29, 3p22.1, 10q22.3 and 10cen). LungLB demonstrates analytical sensitivity of 10 CGAC per 10,000 lymphocytes analyzed, 100% analytical specificity, and high linearity (R2 = 0.9971). Within run measurements across 100 samples demonstrated 96% reproducibility. Interfering factors normally found in blood (lipemia, biotin) and exposure to adverse temperatures (-20ºC or 37ºC) did not interfere with results. Sample stability was validated to 96 hours.ConclusionThe analytical performance of LungLB in this validation study successfully demonstrates it is robust and suitable for everyday clinical use.

Airside Optimization Framework Covering Multiple Operations in Civil Airport Systems with a Variety of Aircraft: A Simulation-Based Digital Twin

Airside Optimization Framework Covering Multiple Operations in Civil Airport Systems with a Variety of Aircraft: A Simulation-Based Digital Twin

Day-ahead optimal dispatch method of integrated electric-heat-cool-gas energy system based on N-1 safety criterion

With the continuous development of integrated energy system and the deepening of the coupling relationship between multiple energy flows, the safe and stable operation of the system has received widespread attention. At present, the security of integrated energy system is mostly researched from the perspective of power grid and rarely researched from the perspective of equipment. This study proposes a day-ahead optimal dispatch method of integrated electric-heat-cool-gas energy system based on N-1 safety criterion, which ensures the normal operation of integrated energy system after the failure of a power supply equipment by increasing appropriate amount of power supply. In this study, the changes in equipment power and operating cost before and after equipment failure under the dispatch method considering N-1 safety criterion and the changes in equipment power and operating cost before and after equipment failure under the dispatch method without considering N-1 safety criterion are investigated by numerical simulation. The results show that when a power supply equipment fails, the average daily loss of load is 6139.99 kW for the dispatch method without considering the N-1 safety criterion and the average daily loss of load is 0 kW for the dispatch method considering the N-1 safety criterion. Therefore, the dispatch method considering the N-1 safety criterion performs better in terms of reliability. When a power supply equipment fails, the average operating cost is 5127.71 USD for the dispatch method without considering the N-1 safety criterion and the average operating cost is 4125.56 USD for the dispatch method considering the N-1 safety criterion. Therefore, the dispatch method considering the N-1 safety criterion performs better in terms of economy.

Research on Multiplication Routine Based on Reconfigurable Four-Valued Logic Processor

Despite the indispensable role of traditional electronic computers in modern society, their limitations in parallel processing capabilities, bit-width constraints, and processor bit-width are becoming increasingly apparent, especially when handling large-scale datasets and complex computational tasks. Although hardware technology and algorithm optimization continue to advance, the arithmetic units of traditional computers—adders—remain constrained by carry delay and bit-width limitations. This bottleneck is particularly pronounced in multiplication operations, mainly when adders are used for partial product accumulation. However, since 2018, the emergence of a new type of Reconfigurable Four-Valued Logic Electronic Processor (RFLEP) has provided a potential solution to these traditional limitations. With its large processor bit-width, flexible bit grouping capabilities, and dynamic hardware function reconfiguration features, this processor has brought revolutionary changes to the field of computing. In this context, this paper proposes and implements a Reconfigurable Four-Valued Logic Multiplication Routine (RFLMR) tailored explicitly for the RFLEP. The RFLMR utilizes the Modified Signed-Digit (MSD) representation method in multi-valued logic combined with the M transformation in four-valued logic to generate partial products. These partial products are then efficiently summed in parallel using the JW-MSD parallel adder, achieving the rapid execution of multiplication operations. Experimental results demonstrate that the multiplication routine based on the RFLEP performs multiplication operations accurately and meets theoretical expectations regarding implementation efficiency and performance. This research not only provides new ideas for developing next-generation high-performance computing systems but also paves the way for exploring more efficient and powerful computing models, heralding a profound transformation in future computing technology.

Development of Algebra Test Using the Item Response Theory Approach for Junior High School Students

This research aims to develop valid and reliable measuring tool for students' algebraic abilities that can be used in schools and the general public. The research follows a structured test development design, including stages such as preparing test specifications and items, field testing, revising items, and test development. The questions are aligned with the 2013 curriculum syllabus, ensuring relevance to educational standards. The test was given to 662 junior high school students in Kendari City, Indonesia, and their responses were analyzed using the item response theory (IRT) model with two logistic parameters: item difficulty level and item discriminatory power. The BILOG MG program was employed to estimate item and ability parameters. Before conducting item analysis with IRT, essential assumption tests were conducted, including unidimensional and model fit tests. The results of the development process, based on item analysis using BILOG MG, yielded 15 items covering various aspects of algebraic abilities. These items were derived from indicators such as recognizing algebraic forms, identifying elements within these forms, performing addition, subtraction, multiplication, and division operations on algebraic forms, presenting and solving real-world problems in algebraic contexts, and addressing contextual problems involving algebraic operations. The items demonstrated good fit with the model and exhibited an appropriate level of item difficulty and discriminatory power, making them suitable for use as a reliable assessment tool. Consequently, these developed tests are deemed effective for measuring students' foundational algebraic abilities.

CSAIL2019 Crypto-Puzzle Solver Architecture

tThe CSAIL2019 time-lock puzzle is an unsolved cryptographic challenge introduced by Ron Rivest in 2019, replacing the solved LCS35 puzzle. Solving these types of puzzles requires large amounts of intrinsically sequential computations, with each iteration performing a very large (3,072-bit for CSAIL2019) modular multiplication operation. The complexity of each iteration is several times greater than known field-programmable gate array (FPGA) implementations, and the number of iterations has been increased by about 1,000x compared with LCS35. Because of the high complexity of this new puzzle, a number of intermediate, or milestone, versions of the puzzle have been specified. In this article, we present several FPGA architectures for the CSAIL2019 solver, which we implement on a medium-sized Intel Agilex device. We develop a new multi-cycle modular multiplication method, which is flexible and can fit on a wide variety of sizes of current FPGAs. We introduce a class of multi-cycle squarer-based architectures that allow for better resource and area trade-offs. We also demonstrate a new approach for improving the fitting and timing closure of large, chip-filling arithmetic designs. We used the solver to compute the first 23 out of 28 milestone solutions of the puzzle, which are the first reported results for this problem.

Runway assignment optimisation model for Istanbul Airport considering multiple parallel runway operations

Abstract The aviation industry has rapidly developed in recent years. Due to the increased number of flight operations, managing air traffic has become essential. The air traffic management system aims to reduce the air traffic control workload and use existing resources more efficiently. This study proposed a new mixed integer linear programming model to minimise the total fuel consumption during taxi operations for the runway assignment problem, comparing the actual Istanbul Airport runway assignment data. The average taxi times are calculated using the 30,000-flight operations data for each arrival and departure taxi route. Also, 47 different aircraft types are obtained using the data for the fuel consumption calculation. The International Civil. Aviation Organisation (IACO) aircraft engine emissions databank provides the fuel consumption values for each aircraft according to engine type. This approach allows our model to calculate more realistic fuel consumption for taxi operations, as each aircraft engine type has a different fuel consumption value. The proposed model is implemented at Istanbul Airport, the busiest airport in Turkey, where multiple parallel runway operations are applied. The results showed that the proposed model reduced total fuel consumption for taxi operations between 6.6% and 14.4% compared to the actual Istanbul Airport runway assignment data.