Algorithm Research Articles

Revisiting the y-ome of Escherichia coli.

The model organism Escherichia coli K-12 has one of the most extensively annotated genomes in terms of functional characterization, yet a significant number of genes, ∼35%, are still considered poorly characterized. Initially genes without known functional understanding were given 'y' gene names. However, due to inconsistency in changing 'y' names to non-'y' names over the years, gene name alone does not provide sufficient information as to the characterization level of genes. Attempts to characterize y-ome genes, i.e. those that lack experimental evidence for function, are ongoing, and recent categorization based on the level of experimental evidence has helped clarify those genes that are well characterized versus uncharacterized. EcoCyc, the most comprehensive, curated genome database for E.coli K-12 substr. MG1655, has updated this approach by expanding the categories to include Partially characterized genes using a set of computational rules that includes keywords, experimental evidence codes and Gene Ontology terms. Approximately half of the previously categorized y-ome genes are now categorized as Partially characterized, leaving 15.5% (738) as Uncharacterized genes in EcoCyc. This new categorization scheme is searchable in the EcoCyc database, will be updated as new experimental evidence is curated and provides important information for research decisions.

Ai] Explore!: An Educational Game for Developing Programming Skills and Algorithmic Thinking

The game [ai] explore! was developed to introduce the algorithm behind the popular science exhibition. The idea behind the [ai] explore! exhibition was to show the application of mathematics and computer science in solving real engineering problems by creating the exhibit using the mathematical algorithm Heat Equation Driven Area Coverage (HEDAC). The algorithm is based on scientific research and has many applications: exploration and search in unknown space, painting, planning agricultural spraying, scanning 3D objects, etc. The exhibits show the process by which the algorithm paints objects with a virtual brush and explores their shape. By surveying the high school students, it was shown that new mathematical and engineering results can be explained and turned into a playable game. After playing the game, students respond positively to the game presented and state that they have a good understanding of the HEDAC algorithm presented and how it works. This motivated us to investigate [ai] explore! as an educational game for teaching mathematics and computer science. The aim of the research described in this paper is to investigate the potential of the developed game as an educational game. The game-based learning (GBL) approach is increasingly used to increase student motivation and engagement in STEM lessons. The main research question is: How do primary school teachers and students perceive the usefulness of the game [ai] explore! in supporting teaching and learning of computer science concepts? We have developed and implemented teaching scenarios on how the game [ai] explore! can be used as an educational game in computer science lessons. We show that the game has great potential to teach primary school students programming. In addition, the game promotes algorithmic thinking and computational thinking skills in general, which are valuable not only in computer science but also in other fields such as mathematics, where a systematic and analytical approach to problem-solving is required.

Computer Models Assist Innovative Design of Graphic Arts

In order to study the theory and application of computer aided design (CAD) related to the composition of art, the theory of symbolic matrix was introduced to realize the purpose of the study of the interaction between perceptual and rational problems. In this paper, the original image was extracted from the design object, and the transformation between the original image and the symbol matrix was realized by the correspondence with the elements in the matrix. Based on the research route, four kinds of mathematical models were established, namely random distribution, symmetric distribution, central diffusion, and reorganization. By applying the mathematical model constructed in this paper, the picture was transformed, the combination of various styles of graphics was added, and how to put the simple image picture into a meaningful integrated image picture was discussed. The computer-aided design method of the existing verification examples was summarized in this paper, and the design method was accurate and effective. Compared with traditional graphic art design methods, the computer-aided design method mentioned in the paper has the following advantages. Computer-aided design can quickly process and analyze images, convert and combine them through mathematical models and algorithms, greatly saving designers time and energy. By introducing symbol matrix theory and establishing various mathematical models, computer-aided design can provide diverse graphic combinations and style transformations, helping designers explore more innovative design solutions.

Mathematical model and general algorithm for solving the problem of processing messages taking into account their value and aging in aircraft systems

Mathematical model and general algorithm for solving the problem of processing messages taking into account their value and aging in aircraft systems

Detailed biogeographical mapping as a useful novel tool for the conservation of endemic taxa: a case of study for Iberian orchids

Background Iberian orchids occur in plant communities designated as habitats of interest in the European Union, particularly in Mediterranean forests. Their ecological importance highlights the need for a deeper understanding of their distribution and ecological requirements in order to develop effective conservation and management policies. Methods This study focuses on the biogeographical mapping and characterisation of five Iberian and Balearic endemic orchid species at district level. Distribution data were collected from existing biodiversity databases and integrated into a Geographic Information System (GIS). In order to assign the correct biogeographical epithet to each taxon, a set of rules and criteria was developed to ensure an objective, simple and universal classification based on the species' distribution areas. Results For each orchid species, the study identified its phytosociological assignment, bioclimatic range and current conservation status. Detailed maps were produced, providing insights into the biogeographical, bioclimatic and phytosociological attributes of these taxa. Conclusions The biogeographical mapping and ecological classification presented in this study provide a basis for effective decision-making regarding the conservation and management of these orchid species. In addition, the results can be used to update their conservation status to better reflect their ecological needs and threats.

From computational models of the splicing code to regulatory mechanisms and therapeutic implications.

Since the discovery of RNA splicing and its role in gene expression, researchers have sought a set of rules, an algorithm or a computational model that could predict the splice isoforms, and their frequencies, produced from any transcribed gene in a specific cellular context. Over the past 30 years, these models have evolved from simple position weight matrices to deep-learning models capable of integrating sequence data across vast genomic distances. Most recently, new model architectures are moving the field closer to context-specific alternative splicing predictions, and advances in sequencing technologies are expanding the type of data that can be used to inform and interpret such models. Together, these developments are driving improved understanding of splicing regulatory mechanisms and emerging applications of the splicing code to the rational design of RNA- and splicing-based therapeutics.

B-115 Software Data Processing and Review LC-MS/MS Workflow Improvements for Clinical Research

Abstract Background In the field of quantitative LC-MS/MS, the need for efficient, accurate and user-friendly data review software is paramount, with the data generated in the clinical laboratory meeting local guidelines, as well as rigorous method performance characteristics defined by the FDA Bioanalytical Method Validation and CLSI C62-Ed2 guidelines. The quantitative LC-MS/MS workflow is dependent on interfacing to LMS/LIMS for sample traceability, batch analysis using matrix-matched external calibrators and quality controls for quantification and acceptance, and stable-isotope labeled internal standards to compensate for sample extraction, recovery, matrix effects and analytical variability. The demands on the laboratory to run multiplexed analyte panels and analyze a greater number of samples produces large amounts of analytical data which must still go through batch and quality review before release from the laboratory to the clinicians. The Waters Quan Review Software significantly reduces the burdensome aspects of batch quality control by introducing a series of innovative features. These include improvements in workflow, a modernized interface, and the ability to flag exceptions based on acceptance criteria. Methods Samples for three multiplexed LC-MS/MS panels with previously defined analytical method performance characteristics were acquired using Waters IVD Systems controlled by MassLynx software. The data was processed, quantified, and reviewed using both the existing TargetLynx XS IVD Application Manager and the new Quan Review Software. The three quantitative LC-MS/MS methods were the analysis of four immunosuppressants drugs in whole blood, a twelve steroid panel in plasma and a urine dilute and shoot method for a drugs of abuse panel containing greater than 80 analytes. Each method used matrix matched calibrators and QCs for quantification and batch control with performance characteristics including calibration linearity, carryover, precision and accuracy. Results The analytical performance data generated using the new MS Quan data review software met the same performance criteria as the TargetLynx XS data. For immunosuppressants, the % difference of the mean repeatability and total precision (%CV) for the individual analyte QCs processed using both software was within ±1.3% and ±4.4% respectively. The mean % bias of External Quality Assurance Samples (n=40) for each immunosuppressant processed using both software was within ±7.9%. The modernized, task-oriented accelerated workflows and focused review by exception reduced review time by up to 50%. Conclusions The new Quan Review Software generated results equivalent to the existing TargetLynx XS Application Manager. The software’s enhanced workflow capabilities streamlined the review process in a new user-friendly interface which reduced the training burden enabling scientists to generate high-quality data quickly and easily. The exception focused review functionality allowed tailored rule sets which reduced the time spent reviewing data by up to 50%. Whilst the batch-level review and task-oriented workflow improved batch acceptance and simplified the review of complicated datasets into easy tasks. By integrating these features, Data Review software offers a comprehensive solution that not only improves the efficiency and accuracy of batch quality control but also contributes to the overall reliability and integrity of quantitative LC-MS/MS analysis. For Research Use Only. Not for Use in Diagnostic Procedures.

Specification, validation and verification of social, legal, ethical, empathetic and cultural requirements for autonomous agents

Autonomous agents are increasingly being proposed for use in healthcare, assistive care, education, and other applications governed by complex human-centric norms. To ensure compliance with these norms, the rules they induce need to be unambiguously defined, checked for consistency, and used to verify the agent. In this paper, we introduce a framework for formal specification, validation and verification of social, legal, ethical, empathetic and cultural (SLEEC) rules for autonomous agents. Our framework comprises: (i) a language for specifying SLEEC rules and rule defeaters (that is, circumstances in which a rule does not apply or an alternative form of the rule is required); (ii) a formal semantics (defined in the process algebra tock-CSP) for the language; and (iii) methods for detecting conflicts and redundancy within a set of rules, and for verifying the compliance of an autonomous agent with such rules. We show the applicability of our framework for two autonomous agents from different domains: a firefighter UAV, and an assistive-dressing robot.

Prediction of tool wear and surface finish using ANFIS modelling during turning of Carbon Fiber Reinforced Plastic (CFRP) composites

Carbon fiber-reinforced plastics (CFRP) are widely used in various industries due to their high strength to weight ratio, corrosion resistance, durability, and excellent thermo-mechanical properties. The machining of CFRP composites has always been a challenge for the manufacturers. In this study, CNC turning operation with coated carbide tool is used to machine a specific CFRP and the relationship between the cutting parameters (Speed, Feed, Depth of Cut) and response parameters (Vibration, Surface Finish, Cutting Force and Tool Wear) are investigated. An adaptive-network-based fuzzy inference system (ANFIS) model with two multi-input–single-output (MISO) system has been developed to predict the tool wear and surface finish. Speed, feed, depth of cut, vibration and cutting force have been used as input parameters and tool wear and surface finish have been used as output parameters. Three sets of cutting parameter have been used to gather the data points for continuous turning of CFRP composite. The model merged fuzzy inference modeling with artificial neural network learning abilities, and a set of rules is constructed directly from experimental data. This model is capable of predicting the cutting tool wear and surface finish during turning of CFRP composite. The predicted tool wear and surface finish data are compared to the experimental results. The predicted data agreed well with the actual experimental data with 98.96 % accuracy for tool wear and 99.61 % accuracy for surface finish.

The Pillar 2 Directive and the Qualified Domestic (Minimum) Top-up Tax Puzzle

The article argues for using a teleological interpretation of the Pillar 2 Directive to allow EU Member States to comply with Pillar 2’s soft law requirements for the qualified domestic minimum top-up tax (QDMTT) and its safe harbour (SH). This interpretation is necessary because the directive’s text is not aligned with certain requirements under Pillar 2 soft law since the directive was adopted before the Inclusive Framework issued any administrative guidance. Without the teleological interpretation, EU Member States cannot comply with the qualified domestic top-up tax (QDTT) requirements and, therefore, cannot attain its SH. This would decrease taxpayers’ legal certainty and undermine EU Member States’ fiscal interests.To pursue the argument, the article analyses Pillar 2’s soft law, i.e., the model rules, the commentary, and two sets of administrative guidance related to the QDMTT and its SH, and compares their provisions with the relevant provisions of the Pillar 2 Directive. Consequently, the study offers solutions to discrepancies between these two sets of rules and joins a broader discussion regarding the relationship between EU tax law and soft law.

Discussion on the Automatic Design Method of Well Trajectory for Cluster Well Platform

Abstract Shale oil and gas and other unconventional oil and gas resources adopt the development mode of cluster well group to achieve economic benefit development. Cluster well groups have the characteristics of small spacing between wellheads and limited space, and generally adjust trajectory parameters by designing individual artificial Wells one by one to meet the needs of geological target hitting and anti-collision and circumnavigation. Trajectory design affects the whole body, and the change of trajectory of one well causes all Wells on the same platform to readjust design parameters, which seriously affects the design efficiency. Therefore, on the basis of summarizing the experience of manual trajectory design, combining classical path planning algorithm and global planning algorithm, an automatic trajectory design model is established. A* path optimization algorithm and ant colony algorithm are used to construct the routing strategy method and rule set, and adapt the efficient manual guidance strategy for different risk classifications to achieve efficient automatic routing. A cluster well trajectory automatic design and routing module has been developed. Automatic trajectory design tested 15 platforms, with an average time of 60s. The efficiency of trajectory design was greatly improved, and it has important guiding significance for the development of automated drilling technology.

Development of an FDD model for an existing building using transfer learning

Building heating, ventilation, and air conditioning (HVAC) systems are affected by several errors that can cause thermal discomfort to occupants and waste energy in buildings. Therefore, early and accurate Fault Detection and Diagnosis (FDD) is vital for enhancing the system’s efficiency. Data-driven FDD is promising because it is convenient compared to the first-principles-based rule set that demands in-depth expertise. However, to realize data-driven FDD for real-life cases, the data-imbalance problem in FDD must be solved. In this study, the authors suggest a novel approach that generates synthetic data from a building system simulation tool, HVACsim+, using them as a source model to apply transfer learning to a target air handling unit system. Even though from the existing target system only the normal operational data were available, the transfer learning approach was satisfactory, confirming that the proposed method effectively mitigates data imbalance in developing data-driven FDD.

Deep Learning Detection of Hand Motion During Microvascular Anastomosis Simulations Performed by Expert Cerebrovascular Neurosurgeons

Deep learning enables precise hand tracking without the need for physical sensors, allowing for unsupervised quantitative evaluation of surgical motion and tasks. We quantitatively assessed the hand motions of experienced cerebrovascular neurosurgeons during simulated microvascular anastomosis using deep learning. We explored the extent to which surgical motion data differed among experts. A deep learning detection system tracked 21 landmarks corresponding to digit joints and the wrist on each hand of 5 expert cerebrovascular neurosurgeons. Tracking data for each surgeon were analyzed over long and short time intervals to examine gross movements and micromovements, respectively. Quantitative algorithms assessed the economy and flow of motion by calculating mean movement distances from the baseline median landmark coordinates and median times between sutures, respectively. Tracking data correlated with specific surgical actions observed in microanastomosis video analysis. Economy of motion during suturing was calculated as 19, 26, 29, 27, and 28 pixels for surgeons 1, 2, 3, 4, and 5, respectively. Flow of motion during microanastomosis was 31.96, 29.40, 28.90, 7.37, and 47.21seconds for surgeons 1, 2, 3, 4, and 5, respectively. Hand tracking data showed similarities among experts, with low movements from baseline, minimal excess motion, and rhythmic suturing patterns. The data revealed unique patterns related to each expert's habits and techniques. The results showed that surgical motion can be correlated with hand motion and assessed using mathematical algorithms. We also demonstrated the feasibility and potential of deep learning-based motion detection to enhance surgical training.

The Dark Side of Forced Positivity: Why Faking Positive Emotions Can Backfire at Work

Abstract: This article explores the potential costs of enforcing strict positivity norms and emotional display rules in organizational settings. It examines the concepts of emotional labor and surface acting through a review of the academic literature documenting the personal and interpersonal tolls of dissociating outward affect from inner feelings over prolonged periods. Two real-world examples are provided of companies where mandated positivity policies backfired by damaging employee well-being, engagement, and productivity. Drawing from emotional labor theory and research on compassionate cultures, guidelines are offered for leaders seeking healthier ways to cultivate optimism without compromising psychological safety or problem-solving capacity. Specifically, the brief recommends permitting genuine expression of a full range of emotions, focusing feedback on competence rather than smiles, fostering employee autonomy, and modeling self-care behaviors. Overall, the potential damage of forcing positivity at work is examined, with practical implications discussed for balancing positivity with safety and performance.

Quantifying and Assessing Post-Stroke Patients' Functional Capability Level for Independent Daily Activities: A Review

Every year, over 13.7 million stroke episodes occur worldwide, addressing the pressing need for efficient rehabilitation treatments to restore motor function in stroke patients is paramount. The escalating number of stroke cases emphasises the urgency of developing robust rehabilitation methodologies. Currently, therapists rely on subjective and manual assessments to determine subsequent patient interventions, a method fraught with challenges, including subjectivity and dependence on the therapist's experience. This paper critically reviews the quantification and assessment of post-stroke patients' functional capability for independent daily activities. It delves into the existing standards of clinical assessment, explores the integration of sensors in limb rehabilitation prototypes, and observes the mathematical methods and algorithms employed by previous researchers to assess and quantify stroke patients' functional capability automatically. The discussion covers the advantages and disadvantages encountered in developing these prototypes and evaluating their suitability in addressing the complexities of stroke rehabilitation. In conclusion, this paper provides a perception of existing research and identifies crucial gaps. Based on the current research landscape, it presents proposals for prototype development, paving the way for future advancements in stroke rehabilitation methodologies.

Comprehensive stress-driven multi-material problem for heat-sinking heterogeneous structures

In the realm of designing thermoelastic structures, particularly within aerospace and broader engineering fields, complex challenges arise from the diverse behaviors of heterogeneous materials and the intricate requirements of stress-based systems in coupled thermomechanical considerations. This study addresses these challenges by proposing a comprehensive methodology with two main contributions: (i) the development of an effective unified solution for stress-driven designs that tackle heat-sinking problems by accommodating a wide range of materials, from homogeneous to heterogeneous and (ii) the introduction of a robust stress-based optimization approach for multi-material problems within coupled thermomechanical systems. The methodology employs the well-established P-norm approach to consolidate heterogeneous stresses into a unified global metric. Additionally, it integrates effective material interpolation with the generalized Solid Isotropic Material with Penalization (SIMP) framework, resulting in comprehensive multi-material models that include the constitutive matrix, thermal conductivity, thermal stress coefficient, and thermoelastic stress distributions. To further enhance adaptability and flexibility, the methodology incorporates a polygonal discretization technique. Detailed sensitivity analyses, using the adjoint variable technique, are conducted to improve computational efficiency in gradient-based mathematical programming algorithms. The efficiency, robustness, and practicality of the proposed methodology are validated through numerical examples, demonstrating its effectiveness and reliability in real-world applications.

Discovering Dadu: A Ludemic Enigma from South Asia

Abstract Dadu is a traditional board game exclusively played within the Muslim Dawoodi Bohra community in South Asia. Most households have their own hand-stitched cloth board, together with a set of wood-turned playing pieces and cowrie shells for dice. Though formally a two-player game, it is commonly played in large teams during family gatherings. Non-community members rarely take part and the game remains virtually unknown to outsiders. Descriptions of the game are absent from the scholarly literature and it does not find mention in any of the major game encyclopedias. Surviving boards and communal memory trace the game back to the early 20th century, but the hybrid nature of the game and the origin of the Dawoodi Bohra community in Yemen suggest that it may go back several centuries further. The present article uses ludemic theory to analyze the constituent elements of the game and demonstrate their affinity with elements in other games both inside and outside South Asia. The findings are contextualized within the wider history of the Dawoodi Bohra community, suggesting that Dadu may have resulted from the adaptation of a West Asian Tab game to a South Asian single track race game. A detailed set of rules gathered from interviews with members of the Dawoodi Bohra community is appended to the article.

Research on Optimization of Visual Object Tracking Algorithm Based on Deep Learning

The appearance of deep gaining knowledge has extensively advanced the sphere of visible item tracking, permitting greater robust and accurate monitoring of items throughout complex scenes. This study optimises a visual item tracking set of rules based on the Siamese region inspiration network (Siamese RPN) monitoring algorithm, aiming to beautify its efficiency and effectiveness in actual-time packages. The Siamese RPN algorithm, acknowledged for its stability among accuracy and velocity because of its architecture that mixes the Siamese network for characteristic extraction with a vicinity suggestion community for item localisation, provides a promising basis for improvement. This examination introduces numerous optimisations to the authentic Siamese RPN framework. First, we endorse an improved feature extraction model that leverages a more efficient deep neural community structure, lowering computational load while preserving excessive accuracy. 2nd, we optimise the place concept mechanism by incorporating an adaptive anchor scaling method that dynamically adjusts the scale and ratio of anchors based on the object’s scale variations, enhancing the tracking accuracy across distinctive object sizes and aspect ratios. Moreover, we introduce a unique training method that employs an aggregate of actual global and synthetically generated statistics to beautify the robustness of the monitoring algorithm towards various demanding situations, including occlusions, speedy moves, and illumination adjustments. The effectiveness of the proposed optimizations is evaluated through complete experiments on numerous benchmark datasets, consisting of OTB, VOT, and LaSOT, demonstrating extensive upgrades in tracking accuracy and speed as compared to the authentic Siamese RPN algorithm and different modern-day tracking techniques. The outcomes of this study no longer underscore the potential of optimised Siamese RPN algorithms in visible item monitoring but additionally lay the basis for future explorations into actual-time, green, and strong tracking systems. Those improvements keep great promise for a wide variety of packages, from surveillance and protection to autonomous cars and augmented truth structures, where particular and dependable item monitoring is paramount.

Identifying Crop Distress and Stress-Induced Plant Diseases Using Hyper Spectral Image Analysis

The purpose of this study is to determine whether hyper spectral images can be used to identify plant diseases and crop pressure from aerial photographs. With extensive research on the prevalent methods used closer to the problem, this study offers a potent strategy for identifying crop distress and illnesses using this efficient imaging technique. To identify the spectral fingerprints of common indications and symptoms of plant diseases and crop strains, this study evaluates the available hyper spectral photo datasets. After that, the data are examined using two learning algorithms—the highly randomized trees and the Random Woodland set of rules—to create predictions that are entirely dependent on the results that are discovered. In the end, a benchmarked set of test statistics is used to assess the prediction accuracy. The results of this study show that hyper spectral photo evaluation has a strong and promising utility for crop stress and disease identification. Hyper spectral light evaluation is a method for identifying plant diseases caused by strain on crops. By gathering and evaluating high-dimensional spectral reflection data from satellite or aircraft structures, details regarding the physiological homes of flowers can be identified. The health of plants, illnesses brought on by stress, and agricultural productivity predictions can all be made using these facts. Hyper spectral recordings can also be used to create actions that reduce agricultural losses and enhance the health of plants that are prone to disease.

A novel mathematical model for modeling viscosity and temperature relationship for dead oils

Viscosity is crucial in subsurface and surface transport, used in engineering domains like heat transfer and pipeline design. However, measurements are limited, necessitating predictive viscosity relationships. Existing models lack precision or pertain to limited fluids, and accurately forecasting dead oil viscosity remains challenging due to errors. The study presents a mathematical algorithm to accurately estimate viscosity values in hydrocarbon fluids. It uses a robust non-linear regression technique to establish a reliable relationship between fluid viscosity and temperature within a specific temperature range. The algorithm is applied to extra-heavy to light crude oil samples from Iranian oilfields, revealing viscosity values ranging from 0.29 cp to 5328.74 cp within a dataset of 243 viscosity data points. After modeling each of these five fluids, the highest values obtained for the maximum absolute error and relative error are related to the fluid with an API gravity of 12.92. The maximum absolute error and relative error for this fluid sample are 1.25 cp and 6.04%, respectively. The algorithm offers acceptable precision in outcome models, even with limited training data, demonstrating its effectiveness in training models with less than 30% of available data. Moreover, these models end up with a near-unity coefficient of determination in testing data, reaffirming their proficiency at reflecting empirical data with remarkable accuracy.