Software Innovation Research Articles

Innovation in Cross-Linking Mass Spectrometry Workflows: Toward a Comprehensive, Flexible, and Customizable Data Analysis Platform.

Cross-linking mass spectrometry (XL-MS) is widely used in the analysis of protein structure and protein-protein interactions (PPIs). Throughout the entire workflow, the utilization of cross-linkers and the interpretation of cross-linking data are the core steps. In recent years, the development of cross-linkers and analytical software mostly follow up on the classical models of non-cleavable cross-linkers such as BS3/DSS and MS-cleavable cross-linkers such as DSSO. Although such a paradigm promotes the maturity and robustness of the XL-MS field, it confines the innovation and flexibility of new cross-linkers and analytical software. This critical insight will discuss the classification, advantages, and disadvantages of existing data analysis search engines. Take the new platinum-based metal cross-linker as an example, potential pitfalls in characterization of cross-linked peptides using existing software are discussed. Finally, ideas on developing more flexible, comprehensive, and user-friendly cross-linkers and software tools are proposed.

Bayesian reconstruction of magnetic resonance images using Gaussian processes

A central goal of modern magnetic resonance imaging (MRI) is to reduce the time required to produce high-quality images. Efforts have included hardware and software innovations such as parallel imaging, compressed sensing, and deep learning-based reconstruction. Here, we propose and demonstrate a Bayesian method to build statistical libraries of magnetic resonance (MR) images in k-space and use these libraries to identify optimal subsampling paths and reconstruction processes. Specifically, we compute a multivariate normal distribution based upon Gaussian processes using a publicly available library of T1-weighted images of healthy brains. We combine this library with physics-informed envelope functions to only retain meaningful correlations in k-space. This covariance function is then used to select a series of ring-shaped subsampling paths using Bayesian optimization such that they optimally explore space while remaining practically realizable in commercial MRI systems. Combining optimized subsampling paths found for a range of images, we compute a generalized sampling path that, when used for novel images, produces superlative structural similarity and error in comparison to previously reported reconstruction processes (i.e. 96.3% structural similarity and < 0.003 normalized mean squared error from sampling only 12.5% of the k-space data). Finally, we use this reconstruction process on pathological data without retraining to show that reconstructed images are clinically useful for stroke identification. Since the model trained on images of healthy brains could be directly used for predictions in pathological brains without retraining, it shows the inherent transferability of this approach and opens doors to its widespread use.

INOVASI SOFTWARE MATEMATIKA LIMIT DAN INTEGRAL BAGI CALON GURU

This community service activity aims to improve the skills of prospective mathematics teachers on limit and integral mathematical software. The activities include training in mathematical software innovation on limit and integral. The training materials include the definition of mathematical software, installation, use of mathematical software in the form of maple math, material delivery, interactive dialogue, presentations, and evaluation. This is an indicator of the successful implementation of community service activities. The results of community service activities are mathematical software innovation modules on limit and integral. The module describes the use of mathematical software on limits and integrals for mathematical calculations and graphs. This community service activity received positive responses, and the results of prospective mathematics teachers’ presentations in applying mathematical software on limits and integrals were very good.

Digitalization of Accounting: Implementation Features and Efficiency Assessment

The digitalization of accounting processes offers significant benefits, including increased efficiency and reduced errors in accounting data. However, the successful implementation of new and modernization of existing computer programs in the accounting system requires consideration of cost, technical, professional, and managerial factors. This study aims to provide a comprehensive analysis of qualitative and quantitative parameters that can form the basis for making decisions on the integration of automated accounting data processing, robotic process automation, artificial intelligence, blockchain, or their combinations into existing systems for the full digitalization of accounting. The integration of software robots and artificial intelligence into accounting systems offers various benefits related to the creation of a non-standard environment where the generation, transfer, and exchange of traditional accounting information is accelerated and improved through the integration of data from other management systems and databases. These improvements contribute to the efficiency and reliability of the automated accounting system. The introduction of software components such as robotic process automation and artificial intelligence helps to free accounting staff from the routine of repetitive operations and ensures automatic prioritization and sequencing of accounting registration in the digital space. Integration of bots into an automated accounting system also facilitates document sharing and provides remote automated monitoring. This increases interaction between departments and improves control over operations that require additional attention from managers.To assess the effectiveness of such software solutions, the authors critically evaluate existing approaches to determining the qualitative indicators that are expected to be obtained after the introduction of software innovations and propose to supplement the performance criteria with quantitative indicators, among which are the time-saving ratio in document processing, the error rate and the resource-saving ratio. In conclusion, this study substantiates the need to consider quantitative parameters and coefficients that can be used to assess the effectiveness of implementing software solutions in automated accounting systems for the digitalization of financial information. The results emphasize the need for a holistic approach to assessing the effectiveness, reliability, and security of these solutions. Improved technical knowledge and training of accounting professionals in security assessment are crucial for the successful implementation and use of digitalization of accounting systems.

Computed Tomography 2.0: New Detector Technology, AI, and Other Developments.

Computed tomography (CT) dramatically improved the capabilities of diagnostic and interventional radiology. Starting in the early 1970s, this imaging modality is still evolving, although tremendous improvements in scan speed, volume coverage, spatial and soft tissue resolution, as well as dose reduction have been achieved. Tube current modulation, automated exposure control, anatomy-based tube voltage (kV) selection, advanced x-ray beam filtration, and iterative image reconstruction techniques improved image quality and decreased radiation exposure. Cardiac imaging triggered the demand for high temporal resolution, volume acquisition, and high pitch modes with electrocardiogram synchronization. Plaque imaging in cardiac CT as well as lung and bone imaging demand for high spatial resolution. Today, we see a transition of photon-counting detectors from experimental and research prototype setups into commercially available systems integrated in patient care. Moreover, with respect to CT technology and CT image formation, artificial intelligence is increasingly used in patient positioning, protocol adjustment, and image reconstruction, but also in image preprocessing or postprocessing. The aim of this article is to give an overview of the technical specifications of up-to-date available whole-body and dedicated CT systems, as well as hardware and software innovations for CT systems in the near future.

Synergic Combination of Hardware and Software Innovations for Energy Efficiency and Process Control Improvement: A Steel Industry Application

The present paper proposes a steel industry case study focused on a reheating furnace and a rolling mill. Hardware and software innovations were successfully combined in order to obtain process control and energy efficiency improvement. The reheating furnace at study is pusher type and processes billets. The hardware innovation is related to the installation of an insulated tunnel at the end of the reheating furnace, in order to guarantee a higher heat retention of the billets before their path along the rolling mill stands. The software innovation refers to the design and the installation of an Advanced Process Control system which manipulates the gas flow rate and the stoichiometric ratio of the furnace zones in order to satisfy the control specifications on billets and furnace variables. The control system is based on Model Predictive Control strategy and on a virtual sensor which tracks and estimates the billet features inside/outside the furnace. The designed controller was commissioned on the real plant, providing significant performances in terms of service factor, process control, and energy efficiency.

Discussing early innovation software by example of the PERPL software

Discussing early innovation software by example of the PERPL software Dr Joanna Leng, School of Computing, University of Leeds, UK, presents a project from the fellowship, on the PERPL (pattern extraction from relative positions of localisations) software which analyses super-resolution light microscopy (SRLM) data. Alistair developed the initial capabilities of PERPL with knowledge of the Python programming language. Still, with limited experience in software engineering, he realised it was becoming difficult to extend and for others to use. As the software and the underlying science were complex, the best way forward was for Alistair and me to work together. However, this close collaboration involving teaching software engineering skills is not a commonly recognised RSE practice.

Analyzing the limitations of parallelism in hardware and software through threaded programming

Parallel computing is applied across various technology industries for solving complex scientific problems that desire high performance and efficiency while can help utilize the resource to save time, money, and energy. This technique has been developed over decades as the physical limits of chips have been reached. The development of parallel computing is closely bonded with the evolution of hardware and software implementation, and there are many forms of parallelism applied to different systems to improve their performance. This paper focused on threaded programming by applying parallelism to a decision tree program and testing the program performance with variables of thread number and device configuration while aiming to demonstrate the limitation of parallelism from both software and hardware aspects. The experiment's outcome in this paper shows that the overall trend of the performance variation for different devices is similar. Still, an improvement in the performance will be affected by the hardware implementation. In contrast, a device with a better configuration performs better. To summarize this research, parallelism can only be fully utilized when applied to a properly designed program running on a robust machine. Therefore, the innovation of hardware and software is essential to the future development of parallel computing.

Software Training in High Energy Physics

Among the upgrades in current high energy physics (HEP) experiments and the new facilities coming online, solving software challenges has become integral to the success of the collaborations. The demand for human resources skilled in both HEP and software domains is increasing. With a highly distributed environment in human resources, the sustainability of the HEP ecosystem requires a continuous effort in the equipment of physicists with the required abilities in software development. In this paper, the collective software training program in HEP and its activities led by the HEP Software Foundation (HSF) and the Institute for Research and Innovation in Software in HEP (IRIS-HEP) are presented. Experiment-agnostic, open, and accessible modules for training have been developed, focusing on common software material with ranges from core software skills needed by everyone to advanced training required to produce high-quality sustainable software. A basic software curriculum was built, and an introductory software training event has been prepared to serve HEP entrants. This program serves individuals with transferable skills that are becoming increasingly important to careers in the realm of software and computing, whether inside or outside HEP.

Investigating Job Mismatch in Software Industry through News Big Data

The purpose of this study is to identify issues related to software manpower, which became more important in the era of the Fourth Industrial Revolution in Korea. The results of this study can provide guidelines for those who establish software manpower training policies for solving the software industry’s human resource paradox. As for the research method, the quantitative text network and qualitative analyses from industry experts were used to interpret the results. A total of 14,752 news data mentioning software manpower were extracted, and data pre-processing for the synonyms and negative words were performed. The network was non-directional and consisted of 14,074 words (nodes) and 1,542,383 word combinations (edges). In addition, the network was clustered based on Modularity, and the degree of connection and eigenvector centrality were used to determine the importance of nodes. The analysis of the results showed that the government’s efforts through the Korean Ministry of Science and ICT were vital in creating jobs that fueled software innovation growth, and that software education was actively promoted to develop software talent. This study had the following implications. It was confirmed that software is making a high contribution to the expansion of business opportunities and job creation in the fields of new technology and software convergence technology. To resolve the software manpower supply-demand mismatch, it is necessary to cultivate high-quality software talent and provide mid- to long-term activities to attract competent human resources. In addition, it is necessary to develop and expand programs that link education and recruitment in terms of public-private cooperation along with government-led investment to strengthen national software competitiveness.

Research on resource analysis method based on attribute similarity and position weight for conceptual design

Resource analysis is one of the important methods and tools to solve inventive problems in TRIZ. Effective use of resources is the key to form product functional principle innovations and generate technical solutions. In view of the problems that the conceptual design process requires too much experience of designers and is not systematic, the conceptual design is combined with resource analysis method in this paper. Considering the influence of attribute similarity and position weight between components in technical system, an innovative resource analysis method is proposed to accurately exploit available resources in system and super-system to assist the process of conceptual design. The abstract process of resource exploiting in conventional resource analysis methods can be transformed into a specific process of calculating resource similarity by this method. It has the advantages of high computability, strong standardisation and systematisation. The method can not only effectively improve the efficiency and quality of conceptual design, but also lay a theoretical research foundation for the development of computer aided innovation (CAI) software in the future.

The future of cross-border wholesale payments: Mobilising central bank money across borders

Large-value cross-border payments constitute the life-blood of international trade and commerce and are thus crucial to national economies. This paper explores this high-value payment ecosystem, which has become more complex and expensive following the prudential regulatory reform rolled out in response to the global financial crisis. Recent technology innovations, in particular the emergence of blockchain and distributed ledger technology, have led to the creation of a parallel system of value transfers based on crypto currencies. The combination of cloud computing, software innovations and the increased use of application programming interfaces has enabled the creation of new business and technology models that are innovating this space. On top of this, the arrival of central bank digital currencies may have the potential to support cross-border payments, both retail and wholesale. This paper explores how far these developments can support a better system for large-value cross-border payments, providing a number of examples of real-world business approaches. A critical lens is applied to gaps outside of the technology field, such as central bank policies and liquidity management, which will need to be closed before true innovation can occur at scale.

Cloud-Native Applications and Services

This Special Issue presents some of the most recent innovations in cloud-native software and system engineering practices providing a broad and well-grounded picture of what the more and more frequently used term “Cloud-native” is currently used for [...]

Guest Editorial: IEEE TC Special Issue On Software, Hardware and Applications for Neuromorphic Computing

The papers in this special section focus on software, hardware, and computer applications for neuromorphic computing. Inspired by biological neural systems, neuromorphic computing has drawn much attention for its great potential of achieving machine intelligence at extremely low energy dissipation. Bio-inspired computing models have been investigated for information encoding, sparse representation, event driven communication/computation, and online learning. This new computing paradigm triggered a recent wave of innovations in software and hardware architecture and emerging device technology, which consequently enabled many novel applications. This is an exemplar of research area where the application, computing model, architecture and circuit level design are tightly coupled to deliver unprecedented functionality and energy efficiency.

Low-field MRI: Clinical promise and challenges.

Modern MRI scanners have trended toward higher field strengths to maximize signal and resolution while minimizing scan time. However, high-field devices remain expensive to install and operate, making them scarce outside of high-income countries and major population centers. Low-field strength scanners have drawn renewed academic, industry, and philanthropic interest due to advantages that could dramatically increase imaging access, including lower cost and portability. Nevertheless, low-field MRI still faces inherent limitations in image quality that come with decreased signal. In this article, we review advantages and disadvantages of low-field MRI scanners, describe hardware and software innovations that accentuate advantages and mitigate disadvantages, and consider clinical applications for a new generation of low-field devices. In our review, we explore how these devices are being or could be used for high acuity brain imaging, outpatient neuroimaging, MRI-guided procedures, pediatric imaging, and musculoskeletal imaging. Challenges for their successful clinical translation include selecting and validating appropriate use cases, integrating with standards of care in high resource settings, expanding options with actionable information in low resource settings, and facilitating health care providers and clinical practice in new ways. By embracing both the promise and challenges of low-field MRI, clinicians and researchers have an opportunity to transform medical care for patients around the world. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 6.

Novelty and Primacy: A Long-Term Estimator for Online Experiments

Online experiments are the gold standard for evaluating impact on user experience and accelerating innovation in software. However, since experiments are typically limited in duration, observed treatment effects are not always stable, sometimes revealing increasing or decreasing patterns over time. There are multiple causes for a treatment effect to change over time. In this article, we focus on a particular cause, user-learning, which is primarily associated with novelty or primacy. Novelty describes the desire to use new technology that tends to diminish over time. Primacy describes the growing engagement with technology as a result of adoption of the innovation. Estimating user-learning is critical because it holds experimentation responsible for trustworthiness, empowers organizations to make better decisions by providing a long-term view of expected impact, and prevents user dissatisfaction. In this article, we propose an observational approach, based on difference-in-differences technique to estimate user-learning at scale. We use this approach to test and estimate user-learning in many experiments at Microsoft. We compare our approach with the existing experimental method to show its benefits in terms of ease of use and higher statistical power, and to discuss its limitation in presence of other forms of treatment interaction with time.

109 The Impact of Extended Reality on Surgery: A Scoping Review

Abstract Aim Extended reality (XR) is a spectrum of technologies encompassing augmented reality (AR), virtual reality (VR), augmented virtuality (AV), mixed reality (MR). This scoping review maps out current utilisation and future prospects of XR-assisted surgery. Method A systematic search of PubMed, Scopus, Embase was performed. Primary studies describing surgical procedures on human subjects, dentistry, anaesthetic procedures for surgery were included. Non-surgical, rehabilitation, bedside, veterinary procedures, robotic surgery were excluded. Studies were classified into preoperative planning, intraoperative navigation/guidance, patient pain, patient anxiety, surgical training, surgeon confidence. Results 213 studies were included for analysis. Thirty-six studies on pre-operative planning noted VR improved surgeon's understanding of anatomical sites, leading to reduced operating time and surgical trauma. Fifty-nine studies on intra-operative planning noted AR headsets highlight ‘negative structures’, reducing chance of accidental incision. Fourteen studies on patients’ pain found VR-induced meditative state resulted in less analgesics for patient comfort. Twelve studies on patient anxiety found VR failed to change patients’ physiological parameters such as arterial blood pressure, cortisol levels, heart rate. Sixty-eight studies explored surgical training, with VR being most cost-effective. Thirteen studies documented increased surgeon confidence. Conclusions XR-assisted surgery's growth is fuelled by hardware and software innovations. Training and pre-operative planning are mostly achieved by VR; intraoperative guidance is mostly supplemented with AR. The other sections of XR spectrum, AV and MR, are underexplored. Working time restrictions during surgical training, COVID-19's impact on limiting physical presence and increasing complexity of surgical procedures means that XR-assisted surgery may assume a greater role in coming decades.

The effects of ICT and FDI on CO2 emissions in China.

With the rapid development of information and communication technology (ICT) and counter-cyclical expansion of foreign direct investment (FDI), most foreign-invested companies in China are highly polluting. Meanwhile, new research shows that the impact of ICT on the environment is uncertain. This study is an effort in dividing ICT into hardware and software, aiming to explore its effects on carbon (CO2) emissions from 2003 to 2017 in 31 provinces, autonomous regions, and municipalities in China and further explore the impacts of its application to foreign-invested enterprises on environmental quality. The findings show that ICT software has a significant negative effect on CO2 emissions, but ICT hardware and FDI have significant positive effects on CO2 emissions. However, when ICT software and hardware are applied to foreign-invested enterprises, they can significantly improve the environmental quality. Moreover, the durative innovation of ICT software ensures environmental sustainability. A set of government measures are published to help stimulate the positive effect of ICT software on CO2 emissions, such as taxes and fees cuts, and no-interest loans. This could provide guidelines for other countries.

A Comprehensive Review on Advanced Driver Assistance System

In recent years, automotive industry is experiencing an unprecedented transformation with the rise of digital technologies. While in the past, acceleration, top speed, and mechanical design were the most essential factors for purchasing an automobile, electronics and software innovations define the characteristics of the future. One among such innovations is the Advanced Driver Assistance System (ADAS). This innovation is now considered as the major drive force of the automotive domain with the intelligent electronic and software architectures. ADAS is primarily designed with an objective to assist drivers by providing an alert/automate the manual tasks in any adverse conditions. ADAS functions will also have the capability to obtain the signals from roadways, Road Side Units (RSUs) and other vehicles. This research article attempts to provide a comprehensive review on the research developments and technologies used in design, development and implementation of advanced driver assistance systems. The sections included in the proposed study describe about the different ADAS methods such as adaptive and automated cruise control, smart navigation with collision warning and avoidance system, automated vehicle parking assistance and object detection. The primary goal of this research study is to achieve a collective knowledge of ADAS operational capabilities and limitations, as well as to suggest research requirements for future investigations.

Summary of the 12th Brazilian Conference on Software and the 1st Latin American School on Software Engineering

The main goal of the Brazilian Conference on Software: Practice and Theory (CBSoft) is to bring together researchers, practitioners, and students aiming at fostering the exchange of experience about the most recent research, tendencies, and theoretical and practical innovations in software. The conference is the top conference in the field of Latin America. In 2021, the conference was organized virtually by Dr. Karine Roggia, Dr. Cristiano Vasconcellos (UDESC), and Dr. Paulo Boulsfield (Univille). This year, we had more than 450 participants registered to attend CBSoft. CBSoft 2021 held four traditional Brazilian symposia: (i) 35th Brazilian Symposium on Software Engineering (SBES); (ii) 25th Brazilian Symposium on Programming Languages (SBLP); (iii) 15th Brazilian Symposium on Software Components, Architectures, and Reuse (SBCARS); (iv) 6th Brazilian Symposium on Systematic Automated Software Testing (SAST).