Single Code Research Articles

PhasicFlow: A parallel, multi-architecture open-source code for DEM simulations

PhasicFlow is an open-source code for DEM simulations developed in C++, licensed under GPL V-3.0 [www.github.com/PhasicFlow/phasicFlow]. It is parallelized based on shared-memory model and can be executed on multi-core CPUs or GPUs. The philosophy of its development is to provide an efficient, easy-to-use, and easy-to-extend tool for a wide range of hardware including cheap to high-end desktop computers. Here, the code structure, the most important features, its performance for simulating small to large problems (250,000 to 80,000,000 particles) on various hardware are presented. In all cases, the execution time varied linearly with number of particles that is ideal for DEM. The computation load per particle was almost constant for all problem sizes except for small simulations (higher). For CPU simulations, a good scaling was observed with number of cores, and scaling was closer to ideal state for larger simulations. The performance of PhasicFlow was compared with similar tools, wherever possible. It was shown that the performance of PhasicFlow is much higher than the similar tools in CPU execution mode and it is around 10–15% faster than similar tools in GPU execution mode. This new tool broadens the application of computer simulation to larger problems and allows researchers to perform DEM simulations on a wider range of hardware. Program summaryProgram title: PhasicFlowCPC Library link to program files:https://doi.org/10.17632/fs7w7jb8mr.1Developer's repository link:www.github.com/PhasicFlow/phasicFlowLicensing provisions: GNU General Public License 3Programming language: C++Nature of problem: The code covers the simulation of granular matter in powder, gas-solid and liquid-solid flows. The formulation is based on the soft-sphere discrete element method (DEM) which is a powerful tool for simulating solid powder flows. Solid particles are considered distinct bodies that may interact with solid surfaces, other particles and fluid around them.This method can be purely used for investigating various flow problems or can be coupled with proper fluid flow simulation code to be used in multiphase flow systems. The main goal here is to provide a powerful tool for researchers to perform large-scale powder and multiphase flows on cheaper and more accessible hardware.Solution method: The solution method is based on the high-order explicit integration of equations of motions for discrete particles over time to obtain their linear and rotational position and velocity. Triangulation is used for representing solid walls and various motion models are implemented, which cover a wide range of processing units in the real-life application. Particle-particle and particle-wall interactions are modeled using linear and non-linear models accompanied with friction models.The code is designed to be executed fast, to efficiently use computational resources, and to be easily understood, altered and extended for specific uses. It is multi-architecture and can be executed in parallel on both CPU and GPU, depending on the available hardware. It uses Kokkos as the backend code for managing memory resources and dispatching parallel work on computational resources. Accordingly, a single code can be compiled for various hardware types. The parallelization is performed on high-level and with negligible overhead costs, so the developers of the code are not facing hardware-specific programming models when parallelizing the computations workloads.Additional comments including restrictions and unusual features: The simulation setup in the code is based on human readable text-based files that are supplied by the user. To use the code, no knowledge of programming is required. The GitHub repository contains a wiki page that provides a full set of tutorials to show researchers how to simulate various granular flows. A step-by-step procedure for building the code is also provided in the repository that has been tested on various version of Ubuntu, including versions 18.04 LTS, 20.04 LTS and 22.04 LTS.

Symbol superiority: Why $ is better remembered than ‘dollar’

Memory typically is better for information presented in picture format than in word format. Dual-coding theory (Paivio, 1969) proposes that this is because pictures are spontaneously labelled, leading to the creation of two representational codes—image and verbal—whereas words often lead to only a single (verbal) code. With this perspective as motivation, the present investigation asked whether common graphic symbols (e.g.,!@#$%&) are afforded primarily verbal coding, akin to words, or whether they also invoke visual imagery, as do pictures. Across four experiments, participants were presented at study with graphic symbols or words (e.g., $ or ‘dollar’). In Experiment 1, memory was assessed using free recall; in Experiment 2, memory was assessed using old-new recognition. In Experiment 3, the word set was restricted to a single category. In Experiment 4, memory for graphic symbols, pictures, and words was directly compared. All four experiments demonstrated a memory benefit for symbols relative to words. In a fifth experiment, machine learning estimations of inherent stimulus memorability were found to predict memory performance in the earlier experiments. This study is the first to present evidence that, like pictures, graphic symbols are better remembered than words, in line with dual-coding theory and with a distinctiveness account. We reason that symbols offer a visual referent for abstract concepts that are otherwise unlikely to be spontaneously imaged.

A Multi-Global Navigation Satellite System (GNSS) Time Transfer Method with Federated Kalman Filter (FKF)

Relative to single Global Navigation Satellite System (GNSS) measurements, i.e., the measurements from a single GNSS system, a single GNSS code, and a single GNSS receiver, multi-GNSS measurements for time transfer could improve reliability and provide better short-term stability. Previous studies applied equal weighting to different GNSS systems or different GNSS time transfer receivers, which, to some extent, revealed the improvement in the additional short-term stability from the combination of two or more kinds of GNSS measurements. In this study, the effects of the different weight allocation for multi-measurements of GNSS time transfer were analyzed, and a federated Kalman filter was designed and applied to fuse multi-GNSS measurements combined with the standard-deviation-allocated weight. Tests with real data showed that the proposed approach can reduce the noise level to well below about 250 ps for short averaging times.

Learning and Fusing Multi-View Code Representations for Function Vulnerability Detection

The explosive growth of vulnerabilities poses a significant threat to the security of software systems. While various deep-learning-based vulnerability detection methods have emerged, they primarily rely on semantic features extracted from a single code representation structure, which limits their ability to detect vulnerabilities hidden deep within the code. To address this limitation, we propose S2FVD, short for Sequence and Structure Fusion-based Vulnerability Detector, which fuses vulnerability-indicative features learned from the multiple views of the code for more accurate vulnerability detection. Specifically, S2FVD employs either well-matched or carefully extended neural network models to extract vulnerability-indicative semantic features from the token sequence, attributed control flow graph (ACFG) and abstract syntax tree (AST) representations of a function, respectively. These features capture different perspectives of the code, which are then fused to enable S2FVD to accurately detect vulnerabilities that are well-hidden within a function. The experiments conducted on two large vulnerability datasets demonstrated the superior performance of S2FVD against state-of-the-art approaches, with its accuracy and F1 scores reaching 98.07% and 98.14% respectively in detecting the presence of vulnerabilities, and 97.93% and 97.94%, respectively, in pinpointing specific vulnerability types. Furthermore, with regard to the real-world dataset D2A, S2FVD achieved average performance gains of 6.86% and 14.84% in terms of accuracy and F1 metrics, respectively, over the state-of-the-art baselines. This ablation study also confirms the superiority of fusing the semantics implied in multiple distinct code views to further enhance vulnerability detection performance.

Quadrature Phase-Shift Keying Modulation With Random Coding Pulse for Long-Range ϕ-OTDR

Transient effect in erbium-doped fiber amplifier (EDFA) restricts the application of the phase-sensitive optical time-domain reflectometry (ϕ-OTDR) system with coding probe pulse (CPP) in long-distance vibration sensing. In this paper, a CPP modulation scheme based on phase coding is proposed to suppress the amplitude distortion after optical amplification. Quadrature phase-shift keying (QPSK), which has a high spectrum utilization efficiency, is designed on a I/Q modulator. The random sequence CPP is used in this system, because of the single coding sequence, the signal-to-noise ratio (SNR) can be improved without loss of the sensing bandwidth. Experimental results prove the effectiveness of the scheme. Compared to the conventional modulation scheme at a 50 mW pump power, the distortion coefficient of the CPP after amplification decreases from 57.67% to 9.23%, and the signal-to-interference ratio (SIR) increases from 13.89 dB to16.67 dB. Without distributed amplification, the proposed system successfully located the vibration at 85.652 km under the carrier period of 40 ns, and the SNR of the demodulated signal is up to 21.99 dB.

Does the Level of Economic Development Affect IFRS Information Comparability?

This study examines whether levels of economic development affect IFRS information comparability. Since the extant literature indicates that accounting information comparability is also affected by code law versus common law legal origins, we hold the legal system variable constant by focusing on a single code law legal jurisdiction, and document significant evidence on the effect of variations in economic development across regions in China on IFRS information comparability. Specifically, we find comparability is higher for firms from more developed regions in the pre-IFRS convergence period, but not in the post-IFRS convergence periods; and the magnitude of improvement in information comparability upon IFRS convergence is greater for firms from less developed regions. Taken together, our findings suggest that firms from less developed regions in a code law legal jurisdiction actually have more to gain from IFRS convergence.

How Can IJDS Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

How Can <i>IJDS</i> Authors, Reviewers, and Editors Use (and Misuse) Generative AI?

Putting team formations in association football into context

Choosing the right formation is one of the coach’s most important decisions in football. Teams change formation dynamically throughout matches to achieve their immediate objective: to retain possession, progress the ball up-field and create (or prevent) goal-scoring opportunities. In this work we identify the unique formations used by teams in distinct phases of play in a large sample of tracking data. This we achieve in two steps: first, we train a convolutional neural network to decompose each game into non-overlapping segments and classify these segments into phases with an average F1-score of 0.76. We then measure and contextualize unique formations used in each distinct phase of play. While conventional discussion tends to reduce team formations over an entire match to a single three-digit code (e.g. 4-4-2; 4 defender, 4 midfielder, 2 striker), we provide an objective representation of team formations per phase of play. Using the most frequently occurring phases of play, mid-block, we identify and contextualize six unique formations. A long-term analysis in the German Bundesliga allows us to quantify the efficiency of each formation, and to present a helpful scouting tool to identify how well a coach’s preferred playing style is suited to a potential club.

Modelling the spatialisation of pesticide sales to monitor environmental policies in France

Environmental policy monitoring requires data with suitable spatial and temporal resolutions. We present a new method of spatialising pesticide sales in France to monitor environmental policies on a territorial scale. Data on pesticide sales in France are available yearly at a postal code resolution. Our spatialisation model combines parcel-scale land use data with information on pesticide types and applied doses, based on legal authorisations for pesticide use in France. We compared the performance of our new model with that of a simple surface fraction model in two distinct territories: Bourville (830 ha, Normandy), a small catchment composed of two small postal code fractions with available ground truth data about pesticide application, and Pleine-Fougères (12,007 ha, Britany), a larger area with a single postal code comprising seven municipalities with contrasting inter-municipal land uses and no ground truth information about pesticide application. For the Bourville site, due to homogeneous land use, the new model was no better than the simple model for global pesticide use, but it performed better when the results were analysed by crop type. For the Pleine-Fougères site, the new model was effective in distinguishing the use of pesticides by municipality, whereas the simple model was not. Our results demonstrate that our new model improves pesticide use quantification at a territorial level and can provide valid support for monitoring public policies regarding water quality and pesticide use over time and space.

Classification of intracranial hemorrhage CT images based on texture analysis using ensemble-based machine learning algorithms: A comparative study

Blood vessels in the brain tissue can leak or burst, causing an intracranial hemorrhage, a potentially fatal disorder. One of the critical steps in brain stroke imaging is the categorization of intracranial hemorrhages. This article aims to perform texture-based intracranial hemorrhage computed tomography image classification with ensemble and machine learning classifiers using local binary pattern, local ternary pattern, and Weber local descriptor approach. The most common texture feature extraction method is a local binary pattern; however, other methods such as local ternary pattern, Weber local descriptor, and local binary pattern are also utilized for texture feature extraction. For all the extracted texture codes, histograms are applied, and finally, single feature vector texture codes by performing the concatenation of the bin. The efficacy of feature descriptors for classifying images into abnormal and normal classes was assessed using ten distinct classifiers. The result shows that Weber's local descriptor shows promising results in texture code classification for ensemble-based classification. For all texture-based methods, classification accuracy was better than standard machine learning. According to the findings of the experiment, the Random Forest classifier outperformed all other classifiers in terms of classification accuracy (86.55%), recall (86.31%), precision (87.23%), sensitivity (86.31%), specificity (86.81%), and F1-score (86.77%) for the Weber local descriptor.

Attention-Oriented Deep Multi-Task Hash Learning

Hashing has wide applications in image retrieval at large scales due to being an efficient approach to approximate nearest neighbor calculation. It can squeeze complex high-dimensional arrays via binarization while maintaining the semantic properties of the original samples. Currently, most existing hashing methods always predetermine the stable length of hash code before training the model. It is inevitable for these methods to increase the computing time, as the code length converts, caused by the task requirements changing. A single hash code fails to reflect the semantic relevance. Toward solving these issues, we put forward an attention-oriented deep multi-task hash learning (ADMTH) method, in which multiple hash codes of varying length can be simultaneously learned. Compared with the existing methods, ADMTH is one of the first attempts to apply multi-task learning theory to the deep hashing framework to generate and explore multi-length hash codes. Meanwhile, it embeds the attention mechanism in the backbone network to further extract discriminative information. We utilize two common available large-scale datasets, proving its effectiveness. The proposed method substantially improves retrieval efficiency and assures the image characterizing quality.

PartitionTuner: An operator scheduler for deep‐learning compilers supporting multiple heterogeneous processing units

AbstractRecently, embedded systems, such as mobile platforms, have multiple processing units that can operate in parallel, such as centralized processing units (CPUs) and neural processing units (NPUs). We can use deep‐learning compilers to generate machine code optimized for these embedded systems from a deep neural network (DNN). However, the deep‐learning compilers proposed so far generate codes that sequentially execute DNN operators on a single processing unit or parallel codes for graphic processing units (GPUs). In this study, we propose PartitionTuner, an operator scheduler for deep‐learning compilers that supports multiple heterogeneous PUs including CPUs and NPUs. PartitionTuner can generate an operator‐scheduling plan that uses all available PUs simultaneously to minimize overall DNN inference time. Operator scheduling is based on the analysis of DNN architecture and the performance profiles of individual and group operators measured on heterogeneous processing units. By the experiments for seven DNNs, PartitionTuner generates scheduling plans that perform 5.03% better than a static type‐based operator‐scheduling technique for SqueezeNet. In addition, PartitionTuner outperforms recent profiling‐based operator‐scheduling techniques for ResNet50, ResNet18, and SqueezeNet by 7.18%, 5.36%, and 2.73%, respectively.

Breathing Room: Working Principles of Independent Art Spaces in African Cities

Breathing Room: Working Principles of Independent Art Spaces in African Cities

Colourful 3D anti-counterfeiting label using nanoscale additive manufacturing

ABSTRACT Microprinting is changing anti-counterfeit technology by producing complex micro-/nano-scale labels to increase the security level. However, the current microprinted anti-counterfeiting labels only contain 2D features, thought of as single coding (e.g. mostly in texts or graphs), unable to satisfy the high-end anti-counterfeiting demands of producing multi-coding, high-storage-density labels. Here, we introduce an approach for fluorescence printing to produce a 3D colourful, sophisticated anti-counterfeiting label. In the method, pre-synthetic QDs are printed with photoresists based on two-photon polymerisation, allowing for accurate regulation of fluorescence in both 2D and 3D. The fabricated photoresists exhibit immediate and postprocessing-free colour emissions under UV light. We demonstrated the ultra-high free design and accurate manufacturing capability of the approach by fabricating multi-colour, multi-layer patterns containing both texts and graphs on a 50μm × 50μm × 30μm portable label. The approach can also be applied to multi-dimensional optical data storage and colourful microdisplays.

FEATURES OF MULTIPLATFORM APPLICATION DEVELOPMENT ON KOTLIN

A study of the methodology of developing multi-platform applications using the Kotlin programming language was conducted. The method of developing a multi-platform application for Windows, Android, macOS and Linux operating systems is presented, which allows you to create musical melodies in a new format. The principles of declarative programming and frameworks for multi-platform development are studied. For the software implementation of individual native applications, native solutions have been chosen. This solution is facilitated by the use of Kotlin Multiplatform and Compose Multiplatform frameworks. Kotlin Multiplatform allows you to create a universal logic code of a multiplatform application, in combination with the Compose Multiplatform UI framework. Using such a combination makes it possible to write a single logic code and application interface for several platforms at the same time, which helps to save time and avoid a significant number of errors. The MVI architectural pattern is described, which best fits the declarative style of the Compose Multiplatform framework. The Dependency Injection design pattern is considered, as well as other tools for designing multiplatform applications, such as: the Kotlin Coroutines library to support asynchrony and parallel computing in Kotlin, the Gradle Kotlin DSL project assembly system, the Decompose library, and the MVIKotlin framework. A methodology for developing the architecture of Android application components is proposed. The modular structure of the project architecture is divided into two parts: the common module, which contains the main logic of the application, as well as platform implementations of components, and platform modules that perform initialization and launch of the application on a certain platform. Dependency implementation is used to establish access to repositories from application business logic components. The logic of implementing dependencies is described in Koin module files. A methodology is proposed that summarizes the methodology of developing multi-platform applications in the Kotlin programming language for developing user interfaces for several platforms.

The Cost of Doing Business: An Appraisal of Relative Value Units in Plastic Surgery and Other Surgical Subspecialties.

The relationship between procedural complexity and relative value units (RVUs) awarded has been studied within some specialties, but it has not yet been compared across different surgical disciplines. This study aims to analyze the association of RVUs with operative time as a surrogate for complexity across surgical specialties, with a focus on plastic surgery. A retrospective review of surgical cases was conducted with the 2019 National Surgical Quality Improvement Program database. The top 10 most performed procedures per surgical specialty were identified based on case volume. Only cases with a single CPT code were analyzed. A subanalysis of plastic surgery procedures was also conducted to include unilateral and bilateral procedures with a frequency greater than 20. Overall, operative time correlated strongly with work RVUs (R = 0.86). Orthopedic surgery had one of the shortest average operative times with the greatest work RVUs per hour, in contrast to plastic surgery, with the greatest average operative time and one of the lowest work RVUs per hour. Of the plastic surgery procedures analyzed, only five were valued on par with the average calculated from all other specialties. The most poorly rewarded procedure for time spent is unilateral free flap breast reconstruction. Of all the surgical specialties, plastic surgery has the lowest RVUs per hour and the highest average operative time, leading to severe potential undervaluation compared with other specialties. This study suggests that further reevaluation of the current RVU system is needed to account for complexity more equitably as well as encourage value-based care.

Fib Model Code 2020: Enabling advances for new and existing concrete structures

AbstractThe next edition of the fib Model Code for concrete structures, Model Code 2020 (MC2020), is under development with the aim of issuing a first partially harmonized draft for comment in early 2023. MC2020 is a single code dealing with both new and existing concrete structures, that is both the design of new structures and the various activities associated with the assessment, interventions upon and life‐cycle management (LCM) of existing concrete structures. The paper outlines the overarching sustainability framework and the performance‐based or outcomes‐based approaches (PBA) adopted by MC2020. There is a pressing need to reduce barriers to the evolution in concrete construction materials and the technical aspects of design and the LCM of existing structures, in order to meet pressing climate change drivers and the goal of achieving lower carbon construction. MC2020 also addresses significant advances made on a wide range of issues including those relating to structural design and analysis methods, seismic design and assessment procedures, durability, structural monitoring, service life design, structural assessment through‐life and making interventions to adapt existing structures or enhance their performance or extend their useful life.

Pediatric Limb Lengthening and Reconstruction Surgical Coding Survey Results.

In surgical specialties like orthopaedics, documenting the surgery performed involves applying the appropriate current procedural terminology (CPT) code(s). For limb reconstruction surgeons, the wide-ranging types of surgeries and rapid evolution of the field create a variety of factors making it difficult to code the procedures. We sought to (1) assess whether appropriate limb reconstruction codes currently exist and (2) determine whether there is agreement among experienced pediatric orthopaedic surgeons when applying these codes to similar cases. A REDCAP survey comprised of 10 common pediatric limb reconstruction cases was sent to experienced pediatric limb reconstruction surgeons in the United States. Based on the description of each case, the surgeons were asked to code the cases as they usually would in their practice. There were no limitations regarding the number or the types of codes each surgeon could choose to apply to the case. Nine additional demographic and general coding questions were asked to gauge the responding surgeon's coding experience. Survey participants used various codes for each case, ranging from only 1 code to a maximum of 9 codes to describe a single case. The average number of codes per case ranged from 1.2 to 3.6, with an average of 2.5 among all 10 cases. The total number of unique codes provided by the respondents for each case ranged from 5 to 20. Only 3 of the 10 cases had an agreement >75% for any single code, and only 2 of the 10 cases had >50% agreement on any combination of 2 codes. There are dramatic variations in coding methods among pediatric orthopaedic limb reconstruction surgeons. This information highlights the need to improve the current CPT coding landscape. Possible solutions include developing new codes that better represent the work done, developing standardized guidelines with the existing codes to decrease variation, and improving CPT coding education by developing limb reconstruction coding "champions." Level V.

PEMILIHAN KODE TUTUR DALAM INTERAKSI SOSIAL PADA SANTRI PUTRI ASRAMA AL-AISYAH PONDOK PESANTREN DARUSSALAM BLOKAGUNG BANYUWANGI

The purpose of this research is motivated by the choice of speech codes used in the social interaction of the language of students in different Islamic boarding schools. This research method uses a qualitative approach to the type of field research. This research studies intensively the background of the actual situation and the interaction of the social environment of the Blokagung Banyuwangi female students. The techniques used in collecting data are observation, listening to engagement, taking notes, and documentary studies. The data analysis technique uses a qualitative descriptive technique. The results showed that the selection of speech codes or language choices contained in Al-Aisyah Dormitory included 1) Mixed code totaling 24 data, 2) Code switching totaling 16 data, and 3) Single code or single language totaling 10 data. The factors that influence the choice of speech code include: Participants, Settings, Ends, Relationships between Speakers and Speech Partners, and the Main Points of Conversation.

Successive Cancellation Decoding of Single Parity-Check Product Codes: Analysis and Improved Decoding

A product code with single parity-check component codes can be described via the tools of a multi-kernel polar code, where the rows of the generator matrix are chosen according to the constraints imposed by the product code construction. Following this observation, successive cancellation decoding of such codes is introduced. In particular, the error probability of single parity-check product codes over binary memoryless symmetric channels under successive cancellation decoding is characterized. A bridge with the analysis of product codes introduced by Elias is also established for the binary erasure channel. Successive cancellation list decoding of single parity-check product codes is then described. For the provided example, simulations over the binary input additive white Gaussian channel show that successive cancellation list decoding outperforms belief propagation decoding applied to the code graph. Finally, the performance of the concatenation of a product code with a high-rate outer code is investigated via distance spectrum analysis. Examples of concatenations performing within 0.7 dB from the random coding union bound are provided.