Sequential Rules Research Articles

Modelling of transport processes: Theory and simulations

The transport processes, being a non-equilibrium system, have been a point of interest for physicists since many years revealing and explaining several unexpected effects. Such systems are often dealt with an archetypal model, known as totally asymmetric simple exclusion process, with two different types of boundary conditions: open and periodic. Moreover, these models are analyzed in two varieties of dynamics, random sequential and parallel updates, even at the micro level which play an important role in the global dynamics of the system. On contrary to the random sequential rule, the parallel updates introduce correlations in the system. Using theoretical and numerical methods in the framework based on mean-field approaches, the system properties are analyzed in both transient and steady state.•Both the updating rules are realized using Monte Carlo simulations.•In simplest form, mean-field approach ignores all the correlations and the results coincide with the random sequential update.•Correlations are induced in the system due to parallel update, therefore, a cluster mean-field theory is also discussed to handle them.

Off the deep end: What can deep learning do for the gene expression field?

After a COVID-related hiatus, the fifth biennial symposium on Evolution and Core Processes in Gene Regulation met at the Stowers Institute in Kansas City, Missouri July 21 to 24, 2022. This symposium, sponsored by the American Society for Biochemistry and Molecular Biology (ASBMB), featured experts in gene regulation and evolutionary biology. Topic areas covered enhancer evolution, the cis-regulatory code, and regulatory variation, with an overall focus on bringing the power of deep learning (DL) to decipher DNA sequence information. DL is a machine learning method that uses neural networks to learn complex rules that make predictions about diverse types of data. When DL models are trained to predict genomic data from DNA sequence information, their high prediction accuracy allows the identification of impactful genetic variants within and across species. In addition, the learned sequence rules can be extracted from the model and provide important clues about the mechanistic underpinnings of the cis-regulatory code.

Improving Urgency-Based Backlog Sequencing of Jobs: An Assessment by Simulation

When order release is applied, jobs are withheld in a backlog from where they are released to meet certain performance targets. The decision that selects jobs for release is typically preceded by a sequencing decision. It was traditionally assumed that backlog sequencing is only responsible for releasing jobs on time, whereas more recent literature has argued that it can also support load balancing. Although the new load-based rules outperform time-based rules, they can be criticized for requiring workload information from the shop floor and for delaying large jobs. While some jobs will inevitably be delayed during periods of high load, we argue that this delaying decision should be under control of management. A simulation study of a wafer fab environment shows that a time-based rule matches the performance of more complex load-based backlog sequencing rules that have recently emerged. The new rule realizes the lowest percentage of tardy jobs if the lower bound that distinguishes between early and urgent jobs is set appropriately. It provides a simpler means of improving release performance, allowing managers to delay jobs that have adjustable due dates.

Testing the Rule of the Earliest Due Date (EDD) in Achieving the Priority of Delivery: A Case Study in the Directorate of Printing Press for the Iraqi Media Network

On-time delivery is a key metric for evaluating the delivery performance of orders and the efficiency of an organization's supply chain. The main reasons behind the late delivery of orders is the lack of real-time data-driven insights, planning, monitoring and operational efficiency. Therefore, the study aims to apply the Earliest Due Date rule, which is one of the sequencing rules for the purpose of achieving the priority of delivery for requests at the study site, as well as evaluating the performance of delivery of requests on time according to the rule of the closest due date, and comparing it with the performance of the rule used in the study site, as The optimal sequence of a group of works leads to its delivery on time. The case study approach was adopted, as a number of completed works were selected as a sample to implement the study in the Directorate of Presses of the Iraqi Media Network. The study found that there is no prior planning for scheduling work in the publications production line, and delays in completing work lead to accumulation of work, delay in delivery and increase in costs. And the optimal sequence was found in the completion of the five work orders, through the sequence achieved in each of the rule of the nearest due date (EDD) and the rule of the critical ratio (CR), in light of which the delay time was addressed, and all work (requests) were delivered according to their specified time. As soon as possible, and the lack of direct coordination between the production department and the printing and correction departments to prepare a detailed scheduling, it also contributes to the completion of work orders for many external parties, and the exposure of the materials used in printing the publication to damage, due to its accumulation and waiting for processing on the machines, which leads to an increase Costs, delay in completing work, and the closest accrual rule has priority in completing work and achieving on-time delivery. The results of the relationship and impact analysis showed a strong inverse relationship between the on-time delivery measure and both the total completion time measure, and the average early completion time. The on-time delivery scale is directly related to determining the priority of completing work for the purpose of achieving certain goals, such as achieving the priority of delivery. The study reached a set of results, the most important of which was the great superiority of the results achieved by the proposed succession rules in finding the optimal sequence, as the closest due date rule (EDD) and the (CR) rule achieved preference in completing and delivering the works according to the specified time, and the speed in their delivery, compared to the results achieved when completing work orders on the basis of first-come-first-served (FCFS) used in the production line, as it did not have an effective role in improving delivery performance according to the specified time and speed of delivery.

Exact algorithms based on a constrained shortest path model for robust serial-batch and parallel-batch scheduling problems

We study robust single-machine batch scheduling problems under uncertain processing times to minimize total flow time. Two types of batches are considered: serial batch (s-batch) and parallel batch (p-batch). These problems can model many on-site production and logistics applications which involve uncertain factors such as defect rates. We first prove that a sequencing rule for the shortest nominal processing time is optimal for both s-batch and p-batch problems. We then propose polynomial-time algorithms based on the observation that the robust batch scheduling problems are reducible or partially reducible to a constrained shortest path problem through worst-case scenario analysis. We further present more efficient algorithms for the special case of uniform maximum deviation times for all jobs. The algorithms are evaluated computationally, and the results show that their performance is satisfactory on the tested instances.

Milnacipran Ameliorates Executive Function Impairments following Frontal Lobe Traumatic Brain Injury in Male Rats: A Multimodal Behavioral Assessment.

Traumatic brain injuries (TBIs) affect more than 10 million patients annually worldwide, causing long-term cognitive and psychosocial impairments. Frontal lobe TBIs commonly impair executive function, but laboratory models typically focus primarily on spatial learning and declarative memory. We implemented a multi-modal approach for clinically relevant cognitive-behavioral assessments of frontal lobe function in rats with TBI and assessed treatment benefits of the serotonin-norepinephrine reuptake inhibitor, milnacipran (MLN). Two attentional set-shifting tasks (AST) evaluated cognitive flexibility via the rats' ability to locate food-based rewards by learning, unlearning, and relearning sequential rule sets with shifting salient cues. Adult male rats reached stable pre-injury operant AST (oAST) performance in 3-4 weeks, then were isoflurane-anesthetized, subjected to a unilateral frontal lobe controlled cortical impact (2.4 mm depth, 4 m/sec velocity) or Sham injury, and randomized to treatment conditions. Milnacipran (30 mg/kg/day) or vehicle (VEH; 10% ethanol in saline) was administered intraperitoneally via implanted osmotic minipumps (continuous infusions post-surgery, 60 μL/h). Rats had a 10-day recovery post-TBI/Sham before performing light/location-based oAST for 10 days and, subsequently, odor/media-based digging AST (dAST) on the last test day (26-27 days post-injury) before sacrifice. Both AST tests revealed significant deficits in TBI+VEH rats, seen as elevated total trials and errors (p < 0.05), which generally normalized in MLN-treated rats (p < 0.05). This first simultaneous dual AST assessment demonstrates oAST and dAST are sufficiently sensitive and robust to detect subtle attentional and cognitive flexibility executive impairments after frontal lobe TBI in rats. Chronic MLN administration shows promise for attenuation of post-TBI executive function deficits, thus meriting further investigation.

Are song sequencing rules learned by song sparrows?

Although the effects of learning on song structure have been extensively studied in songbirds, little attention has been given to the learning of syntax at the level of song sequences. Here we investigate song syntax learning in two cohorts of hand-reared song sparrows, Melospiza melodia : an isolate group, consisting of four males raised with no exposure to external song models, and a trained group, consisting of 17 males exposed to recorded song sequences during the sensitive period for song learning. The isolate males followed three syntactical rules previously described for field-recorded song sparrows: (1) they produced their song type repertoires with eventual variety, repeating a song type multiple times before switching to another; (2) they cycled through their repertoires using close to the minimum number of bouts; and (3) they showed consistent preferences for singing certain of their song types more than others. The trained males were tutored with sequences with exaggerated eventual variety and cycling patterns and no usage preferences, but their syntax was little affected by any of these training features. One syntactical pattern that was affected by external experience was the rule that long bouts of a song type are followed by long recurrence intervals before that type is produced again. Isolate males showed no bout length/recurrence interval correlations while trained males showed reduced correlations relative to field-recorded males, implicating learning in the development of the normal pattern. Other songbird species have been found to preferentially use song type transitions as adults that they were tutored with as juveniles, but the trained song sparrows in this study showed no evidence of such effects. • We examined the development of between-song syntax in song sparrows. • Singing with eventual variety is one syntactical pattern little affected by learning. • A second is the tendency to cycle efficiently through the song type repertoire. • Song sparrows do not learn first-order transition preferences from tutor sequences. • Usage learning is thus less prevalent than vocal production learning in this species.

Simulated-annealing-based hyper-heuristic for flexible job-shop scheduling

The flexible job-shop scheduling problem (FJSP) is common in high-mix industries such as semiconductor manufacturing. An FJSP is initiated when an operation can be executed on a machine assigned from a set of alternative machines. Thus, an FJSP consists of the machine assignment and job sequencing sub-problems, which can be resolved using a pair of problem-dependent machine assignment rules (MARs) and job sequencing rules (JSRs). Selecting an MAR–JSR pair that performs efficiently is a challenge. This study proposes a simulated-annealing-based hyper-heuristic (SA-HH) for assembling an heuristic scheme (HS) consisting of MAR–JSR pairs with a set of problem state features. Two variants of SA-HH, i.e. SA-HH based on HS with problem state features (SA-HH) and without problem state features (SA-HH), are investigated. In terms of the best makespan, SA-HH outperforms or is comparable with over 75% of benchmark algorithms on 8 out of 10 instances in the Brandimarte dataset.

GA-AHP Method to Support Robotic Polishing Process Planning

The finishing stage of mould manufacturing is generally completed via mechanical polishing and manually conducted. Not only is this the most expensive phase of the process but also is currently struggling with a deficit of skilled workers. To address these issues, and support the wider needs of Industry 4.0, the manufacturing community has investigated robotic technologies to support the polishing process. The work reported here, investigating the polishing process planning and optimization for mould manufacture is part of a larger project aiming to automate 80% of the current manual process. Presented in this article is an optimization strategy for robotic polishing process sequencing aiming at satisfying polishing sequence rules and the shortest polishing time simultaneously. A hybrid approach combining both genetic algorithm (GA) and analytical hierarchical process (AHP) is proposed based on the specific characteristics of polishing process sequencing. A multi-objective fitness function is defined using AHP including the calculation of polishing time and evaluation of polishing process rules. The proposed GA-based process sequencing has been successfully demonstrated on test piece examples.

The Sandwich Rule for Sequences of Self-Adjoint Operators and Some Applications

In this paper, we mainly deal with sequences of bounded linear operators on Hilbert space. The main result is the so-called squeeze theorem (or sandwich rule) for convergent sequences of self-adjoint operators. We show that this theorem remains valid for all three main topologies on B(H). Some interesting consequences are given.

The Complexity of Learning Approval-Based Multiwinner Voting Rules

We study the PAC learnability of multiwinner voting, focusing on the class of approval-based committee scoring (ABCS) rules. These are voting rules applied on profiles with approval ballots, where each voter approves some of the candidates. According to ABCS rules, each committee of k candidates collects from each voter a score, that depends on the size of the voter's ballot and on the size of its intersection with the committee. Then, committees of maximum score are the winning ones. Our goal is to learn a target rule (i.e., to learn the corresponding scoring function) using information about the winning committees of a small number of sampled profiles. Despite the existence of exponentially many outcomes compared to single-winner elections, we show that the sample complexity is still low: a polynomial number of samples carries enough information for learning the target rule with high confidence and accuracy. Unfortunately, even simple tasks that need to be solved for learning from these samples are intractable. We prove that deciding whether there exists some ABCS rule that makes a given committee winning in a given profile is a computationally hard problem. Our results extend to the class of sequential Thiele rules, which have received attention due to their simplicity.

Mixed-model sequencing versus car sequencing: comparison of feasible solution spaces

ABSTRACT In the automotive industry, a great challenge of production scheduling is to sequence cars on assembly lines. Among a wide variety of scheduling approaches, academics and manufacturers pay close attention to two specific models: Mixed-Model Sequencing (MMS) and Car Sequencing (CS). Whereas MMS explicitly considers the assembly line balance, CS operates with sequencing rules to find the best car sequence fulfilling the assembly plant requirements, like minimising work overload for assembly workers. Meanwhile, automakers including Renault Group are increasingly willing to consider other requirements, like end-to-end supply chain matters, in production planning and scheduling. In this context, this study compares MMS- and CS-feasible solution spaces to determine which workload-oriented sequencing model would be the most appropriate to later integrate new optimisation. We introduce two exact methods based on Dynamic Programming to assess the gap between both models. Numerical experiments are carried out on real-life manufacturing features from a Renault Group assembly plant. They show that MMS generates more feasible sequences than CS regardless of the sequencing rule calculation method. Only the sequencing rules used by real-life production schedulers result in a higher number of distinct feasible sequences for CS, highlighting that the plant might select a sequence with work overload situations.

Application of lithium cationization tandem mass spectrometry for structural analysis of lignin model oligomers with β-β' and β-O-4' linkages.

Structural elucidation of lignin degradation products is a requirement for successfully developing lignin valorization technology. Most of mass spectrometry-based techniques have utilized negative ion mode mass spectrometry for structural elucidation of lignin-derived compounds. Unfortunately, simple deprotonation can lead to in-source fragmentation and may not be suitable for condensed lignin structures without acidic moieties. Herein, we present a lithium cationization methodology for mass spectrometry sequencing of advanced lignin oligomers having β-β' and β-O-4' bonding motifs. To do so, two advanced lignin oligomers were first synthesized through a step-by-step synthetic route, and then subjected to two different ESI mass spectrometry techniques in positive ion mode using lithium cations for ionization. An orbitrap mass spectrometer was used to obtain exact mass information, and higher-energy collisional dissociation (HCD) was used to sequence the lignin model oligomers. Based on the sequence-specific fragment ions, sequence rules were proposed. Multi-stage (MSn) collision-induced dissociation (CID) using an ion trap mass spectrometer provided data to investigate the origin of each fragment ion and to further confirm proposed fragmentation pathways. In addition to β-O-4' bond cleavage, the presented lithium cationization approach led to cleavage of β-β' bonds on the model oligomers in both ion trap and orbitrap mass spectrometry experiments. Additionally, MSn experiments were used to investigate possible lithium cationization sites on the model oligomers. Lithium cationization in positive ion mode mass spectrometry proved to be a robust tool for characterization and sequencing of advanced lignin oligomers with different bonding motifs.

Predict, then schedule: Prescriptive analytics approach for machine learning-enabled sequential clinical scheduling

This research considers the design of an appointment system (AS) for sequentially scheduling patients in the presence of stochastic factors (no-show and service time uncertainty) and two patient classes. Although studies on AS design are prevalent in literature, most prior works do not consider heterogeneous patient characteristics (e.g., patient-specific no-show risk and service time duration) and sequential patient call-ins together. This research integrates patient-specific uncertainty estimates into the AS design and assess its impact on AS efficiency (measured as the weighted sum of patient waiting time, doctor idle time, and overtime). Specifically, the patients are scheduled as they sequentially call the clinic for appointments based on their risk of no-show and estimated consultation duration. For this setting, a predict-then-schedule framework is proposed. In the predict step, patient-specific no-show risk and service duration are estimated using a machine learning model. The schedule step determines each patient’s appointment time and interval by integrating the predictions with three scheduling decisions (allocation, sequencing, and overbooking). As a result, new sequencing rules are employed. Results indicate that adopting the predict-then-schedule approach in a sequential framework always dominates conventional approaches and could improve the efficiency by 60%.

Performance Evaluation of Sequential Rule Mining Algorithms

Data mining techniques are useful in discovering hidden knowledge from large databases. One of its common techniques is sequential rule mining. A sequential rule (SR) helps in finding all sequential rules that achieved support and confidence threshold for help in prediction. It is an alternative to sequential pattern mining in that it takes the probability of the following patterns into account. In this paper, we address the preferable utilization of sequential rule mining algorithms by applying them to databases with different features for improving the efficiency in different fields of application. The three compared algorithms are the TRuleGrowth algorithm, which is an extension sequential rule algorithm of RuleGrowth; the top-k non-redundant sequential rules algorithm (TNS); and a non-redundant dynamic bit vector (NRD-DBV). The analysis compares the three algorithms regarding the run time, the number of produced rules, and the used memory to nominate which of them is best suited in prediction. Additionally, it explores the most suitable applications for each algorithm to improve the efficiency. The experimental results proved that the performance of the algorithms appears related to the dataset characteristics. It has been demonstrated that altering the window size constraint, determining the number of created rules, or changing the value of the minSup threshold can reduce execution time and control the number of valid rules generated.

Revitalizing minority languages using music: Three South-Guan languages of Ghana in focus

Several interdisciplinary researchers have been interested in the relationship between language and music thus producing several works in quest of this interdisciplinary connection. This study, on the other hand, attempted to examine how music may be utilized to reinvigorate the understudied minority languages. It focuses on three South Guan languages: Leteh, Kyerepong and Efutu. Studies done in this area have observed that in notable respects, language and music are systematically comparable. For instance, the two are similarly constructed of functional and meaningful units; both possess phonemic and morphemic properties. Furthermore, the phonemic and morphemic units/properties are used to produce utterances by rules of sequencing and re-combination. This study draws on the Affective Filter Hypothesis (Krashen, 1982) which emphasizes the importance of positive emotions in language acquisition. Language surveys were conducted in Winneba, Larteh and Adukrom where the three languages are spoken. The study concludes that the principle behind the use of songs in teaching a second language can be extended to the teaching/learning of less-studied languages/minority languages and ultimately, their revitalization.

MalSPM: Metamorphic malware behavior analysis and classification using sequential pattern mining

Malware pose a serious threat to the computers of individuals, enterprises and other organizations. In the Windows operating system (OS), Application Programming Interface (API) calls are an attractive and distinguishable feature for malware analysis and detection as they can properly reflect the actions of portable executable (PE) files. In this paper, we propose MalSPM, an intelligent malware detection system based on sequential pattern mining (SPM) for the analysis and classification of malware behavior during executions. A dataset that contains sequences of API calls made by different malware on Windows OS is abstracted into a suitable format. SPM algorithms are first used on the corpus to find frequent API calls and their patterns. Moreover, sequential rules between API calls patterns as well as maximal and closed frequent API calls patterns are discovered. Obtained frequent patterns are then used for the classification of different malware. Seven classifiers are used and their performance is compared by using various metrics. Moreover, the performance of MalSPM is compared with existing state-of-the-art malware detection approaches and obtained results show that MalSPM outperforms these approaches.

Data-driven treatment pathways mining for early breast cancer using cSPADE algorithm and system clustering.

Due to the multidimensional, multilayered, and chronological order of the cancer data, it was challenging for us to extract treatment paths. To determine whether the cSPADE algorithm and system clustering proposed in this study can effectively identify the treatment pathways for early breast cancer. We applied data mining technology to the electronic medical records of 6891 early breast cancer patients to mine treatment pathways. We provided a method of extracting data from EMR and performed three-stage mining: determining the treatment stage through the cSPADE algorithm → system clustering for treatment plan extraction → cSPADE mining sequence pattern for treatment. The Kolmogorov-Smirnov test and correlation analysis were used to cross-validate the sequence rules of early breast cancer treatment pathways. We unearthed 55 sequence rules for early breast cancer treatment, 3 preoperative neoadjuvant chemotherapy regimens, three postoperative chemotherapy regimens, and 2 chemotherapy regimens for patients without surgery. Through 5-fold cross-validation, Pearson and Spearman correlation tests were performed. At the significance level of p<0.05, all correlation coefficients of support, confidence and lift were greater than 0.89. Using the Kolmogorov-Smirnov test, we found no significant differences between the sequence distributions. We have proved that cSPADE algorithm combined system clustering is an effective technique for identifying temporal relationships between treatment modalities, enabling hierarchical and vertical mining of breast cancer treatment models. In addition, we confirmed the robustness of the results by cross-validation of these treatment pathway ordering rules. Through this method, the treatment path of early breast cancer patients can be revealed, and the real-world breast cancer treatment behaviour model can be evaluated, which can provide reference for the redesign and optimization of treatment path.

Combinatorial optimization of mRNA structure, stability, and translation for RNA-based therapeutics

Therapeutic mRNAs and vaccines are being developed for a broad range of human diseases, including COVID-19. However, their optimization is hindered by mRNA instability and inefficient protein expression. Here, we describe design principles that overcome these barriers. We develop an RNA sequencing-based platform called PERSIST-seq to systematically delineate in-cell mRNA stability, ribosome load, as well as in-solution stability of a library of diverse mRNAs. We find that, surprisingly, in-cell stability is a greater driver of protein output than high ribosome load. We further introduce a method called In-line-seq, applied to thousands of diverse RNAs, that reveals sequence and structure-based rules for mitigating hydrolytic degradation. Our findings show that highly structured “superfolder” mRNAs can be designed to improve both stability and expression with further enhancement through pseudouridine nucleoside modification. Together, our study demonstrates simultaneous improvement of mRNA stability and protein expression and provides a computational-experimental platform for the enhancement of mRNA medicines.

Anticipating technical car sequencing rules in the master production scheduling of mixed-model assembly lines

In this paper, we propose a new linear programming-based approach that enables the consideration of technical car sequencing rules in the master production scheduling of mixed-model assembly lines at a much higher level of detail than previous approaches. To this end, we investigate certain interdependencies of car sequencing rules, which have largely been neglected, both in practice and in the research literature. We illustrate the existence and impact of these interdependencies and show that they induce additional implicit constraints, which can be represented by linear inequalities and incorporated into linear optimization models for master production scheduling. In a numerical study, we evaluate the approach and show, that it can significantly reduce sequencing violations compared to existing approaches.