Software Innovation Research Articles

Analysis methods for large-scale neuronal recordings.

Simultaneous recordings from hundreds or thousands of neurons are becoming routine because of innovations in instrumentation, molecular tools, and data processing software. Such recordings can be analyzed with data science methods, but it is not immediately clear what methods to use or how to adapt them for neuroscience applications. We review, categorize, and illustrate diverse analysis methods for neural population recordings and describe how these methods have been used to make progress on longstanding questions in neuroscience. We review a variety of approaches, ranging from the mathematically simple to the complex, from exploratory to hypothesis-driven, and from recently developed to more established methods. We also illustrate some of the common statistical pitfalls in analyzing large-scale neural data.

Research on Medical Device Software Based on Artificial Intelligence and Machine Learning Technologies

With the rapid development of Artificial Intelligence (AI) and Machine Learning (ML) technologies, new opportunities for innovation and application in medical device software have emerged. This paper explores the current research on medical device software based on AI and ML, analyzing the practical applications and outcomes of these technologies in the medical field. First, the basic concepts of AI and ML and their significance in medical devices are introduced. Then, an in-depth analysis of the functional requirements of medical device software, user experience, and relevant regulations is provided. Next, the paper discusses the specific applications of AI and ML in data processing, model training, and software implementation, showcasing their practical effects through case studies. Finally, the paper summarizes the current technical challenges and future opportunities, hoping to provide insights for further development in the medical device industry.

Development and Initial Testing of an Artificial Intelligence-Based Virtual Reality Companion for People Living with Dementia in Long-Term Care.

Background/Objectives: Feelings of loneliness are common in people living with dementia (PLWD) in long-term care (LTC). The goals of this study were to describe the development of a novel virtual companion for PLWD living in LTC and assess its feasibility and acceptability. Methods: The computer-generated virtual companion, presented using a head-mounted virtual reality display, was developed in two stages. In Stage 1, the virtual companion asked questions designed to encourage conversation and reminiscence. In Stage 2, more powerful artificial intelligence tools allowed the virtual companion to engage users in nuanced discussions on any topic. PLWD in LTC tested the application at each stage to assess feasibility and acceptability. Results: Ten PLWD living in LTC participated in Stage 1 (4 men and 6 women; average 82 years old) and Stage 2 (2 men and 8 women; average 87 years old). Session lengths ranged from 0:00 to 5:30 min in Stage 1 and 0:00 to 53:50 min in Stage 2. Speech recognition issues and a limited repertoire of questions limited acceptance in Stage 1. Enhanced conversational ability in Stage 2 led to intimate and meaningful conversations with many participants. Many users found the head-mounted display heavy. There were no complaints of simulator sickness. The virtual companion was best suited to PLWD who could engage in reciprocal conversation. After Stage 2, response latency was identified as an opportunity for improvement in future versions. Conclusions: Virtual reality and artificial intelligence can be used to create a virtual companion that is acceptable and enjoyable to some PLWD living in LTC. Ongoing innovations in hardware and software will allow future iterations to provide more natural conversational interaction and an enhanced social experience.

Data-driven Agriculture: Software Innovations for Enhanced Soil Health, Crop Nutrients, Disease Detection, Weather Forecasting and Fertilizer Optimization in Agriculture

Data-driven Agriculture: Software Innovations for Enhanced Soil Health, Crop Nutrients, Disease Detection, Weather Forecasting and Fertilizer Optimization in Agriculture

Corporate strategies to protect software innovation: Empirical evidences from South Korea

AbstractThe purpose of this study is to determine whether there are blind spots in decision making to protect innovation by examining how formal and informal protection systems affect software innovation. We empirically compared the innovation activities of 218 software companies with 261 information and communication technology (ICT) manufacturing companies in South Korea. Our results confirm that formal protection system alone is insufficient to protect software innovation, but its effect may vary depending on the corporate size. While, combination of patent with complicated designs is effective. More sophisticated policy to complement informal protections for small and medium‐sized enterprises with less than 100 employees is needed.

Pan-Evo: The Evolution of Information and Biology's Part in This.

Many people wonder whether biology, including humans, will benefit or experience harm from new developments in information such as artificial intelligence (AI). Here, it is proposed that biological and non-biological information might be components of a unified process, 'Panevolution' or 'Pan-Evo', based on four basic operations-innovation, transmission, adaptation, and movement. Pan-Evo contains many types of variable objects, from molecules to ecosystems. Biological innovation includes mutations and behavioural changes; non-biological innovation includes naturally occurring physical innovations and innovation in software. Replication is commonplace in and outside biology, including autocatalytic chemicals and autonomous software replication. Adaptation includes biological selection, autocatalytic chemicals, and 'evolutionary programming', which is used in AI. The extension of biological speciation to non-biological information creates a concept called 'Panspeciation'. Panevolution might benefit or harm biology, but the harm might be minimal if AI and humans behave intelligently because humans and the machines in which an AI resides might split into vastly different environments that suit them. That is a possible example of Panspeciation and would be the first speciation event involving humans for thousands of years. This event will not be particularly hostile to humans if humans learn to evaluate information and cooperate better to minimise both human stupidity and artificial simulated stupidity (ASS-a failure of AI).

Maximizing Glycoproteomics Results through an Integrated Parallel Accumulation Serial Fragmentation Workflow.

Glycoproteins play important roles in numerous physiological processes and are often implicated in disease. Analysis of site-specific protein glycobiology through glycoproteomics has evolved rapidly in recent years thanks to hardware and software innovations. Particularly, the introduction of parallel accumulation serial fragmentation (PASEF) on hybrid trapped ion mobility time-of-flight mass spectrometry instruments combined deep proteome sequencing with separation of (near-)isobaric precursor ions or converging isotope envelopes through ion mobility separation. However, the reported use of PASEF in integrated glycoproteomics workflows to comprehensively capture the glycoproteome is still limited. To this end, we developed an integrated methodology using timsTOF Pro 2 to enhance N-glycopeptide identifications in complex mixtures. We systematically optimized the ion optics tuning, collision energies, mobility isolation width, and the use of dopant-enriched nitrogen gas (DEN). Thus, we obtained a marked increase in unique glycopeptide identification rates compared to standard proteomics settings, showcasing our results on a large set of glycopeptides. With short liquid chromatography gradients of 30 min, we increased the number of unique N-glycopeptide identifications in human plasma samples from around 100 identifications under standard proteomics conditions to up to 1500 with our optimized glycoproteomics approach, highlighting the need for tailored optimizations to obtain comprehensive data.

The innovation of AI-based software in oral diseases: clinical-histopathological correlation diagnostic accuracy primary study

BackgroundMachine learning (ML) through artificial intelligence (AI) could provide clinicians and oral pathologists to advance diagnostic problems in the field of potentially malignant lesions, oral cancer, periodontal diseases, salivary gland disease, oral infections, immune-mediated disease, and others. AI can detect micro-features beyond human eyes and provide solution in critical diagnostic cases.ObjectiveThe objective of this study was developing a software with all needed feeding data to act as AI-based program to diagnose oral diseases. So our research question was: Can we develop a Computer-Aided Software for accurate diagnosis of oral diseases based on clinical and histopathological data inputs?MethodThe study sample included clinical images, patient symptoms, radiographic images, histopathological images and texts for the oral diseases of interest in the current study (premalignant lesions, oral cancer, salivary gland neoplasms, immune mediated oral mucosal lesions, oral reactive lesions) total oral diseases enrolled in this study was 28 diseases retrieved from the archives of oral maxillofacial pathology department. Total 11,200 texts and 3000 images (2800 images were used for training data to the program and 100 images were used as test data to the program and 100 cases for calculating accuracy, sensitivity& specificity).ResultsThe correct diagnosis rates for group 1 (software users), group 2 (microscopic users) and group 3 (hybrid) were 87%, 90.6, 95% respectively. The reliability for inter-observer value was done by calculating Cronbach’s alpha and interclass correlation coefficient. The test revealed for group 1, 2 and 3 the following values respectively 0.934, 0.712 & 0.703. All groups showed acceptable reliability especially for Diagnosis Oral Diseases Software (DODS) that revealed higher reliability value than other groups. However, The accuracy, sensitivity & specificity of this software was lower than those of oral pathologists (master’s degree).ConclusionThe correct diagnosis rate of DODS was comparable to oral pathologists using standard microscopic examination. The DODS program could be utilized as diagnostic guidance tool with high reliability & accuracy.

Scheduling online oral assessments using an iterative algorithm: A profound software for educational continuity

In response to the socioeconomic effects of the global COVID-19 pandemic, including the disruption of educational continuity, an online oral assessment solution is proposed. While the next pandemic is unpredictable, the need for effective student assessment during lockdown is essential. This paper presents an iterative algorithm for scheduling online oral examinations as a profound software innovation capable of securely assessing students remotely while adhering to COVID-19 safety requirements. The efficacy of the software was tested by university tutors and undergraduate students for 60 days on local and web hosts. The results show that the algorithm offers optimal performance despite extraneous factors such as bandwidth capacity, operating system type, and client-side web browsers. The software manages the systematic random pairing of examiners, sessions, courses, and candidates, as well as the automated generation of question papers with a marking rubric. This invention would largely provide assessment entities with significant pedagogical, financial, and technological benefits while ensuring educational continuity regardless of the pandemic's status.

Innovative Financial Management in Higher Education: A Multi-Scale Deep Learning Approach for Risk Reduction and Quality Enhancement

This paper presents a novel university financial management system leveraging multi-scale deep learning. With rising college enrollment and teaching complexities, traditional financial models require adaptation to mitigate risks and improve management quality. The system integrates hardware and software innovations: multiple sensors enhance data scanning, coordinated by a central coordinator, ensuring comprehensive financial database coverage. Software-wise, a structured database establishes attribute-based financial connections, crucial for weight assignment. Employing a multilayer perceptual network topology, a full interconnection model based on multi-scale deep learning facilitates profound data extraction. Experimental evaluations demonstrate the system's superior financial risk assessment capabilities compared to traditional approaches, extracting a broader spectrum of financial parameters for comprehensive risk warnings. By embracing multi-scale deep learning, this system promises significant advancements in university financial management, enhancing adaptability and risk mitigation in college finance departments.

A 3D World Interpreter System for Safe Autonomous Crane Operation

In an effort to improve short-sea shipping in Europe, we present a 3D world interpreter (3DWI) system as part of a robotic container-handling system. The 3DWI is an advanced sensor suite combined with AI-based software and the communication infrastructure to connect to both the crane control and the shore control center. On input of LiDAR data and stereo captures, the 3DWI builds a world model of the operating environment and detects containers. The 3DWI and crane control are the core of an autonomously operating crane that monitors the environment and may trigger an emergency stop while alerting the remote operator of the danger. During container handling, the 3DWI scans for human activity and continuously updates a 3D-Twin model for the operator, enabling situational awareness. The presented methodology includes the sensor suite design, creation of the world model and the 3D-Twin, innovations in AI-detection software, and interaction with the crane and operator. Supporting experiments quantify the performance of the 3DWI, its AI detectors, and safety measures; the detectors reach the top of VisDrone’s leaderboard and the pilot tests show the safe autonomous operation of the crane.

Development of Diagnosis System for Rolling Bearings Faults on Real Time Based on FPGA

The real-time monitoring of events in an industrial plant is an advanced technique that presents the real conditions of operation of the machinery responsible for the manufacturing process. A predictive maintenance program includes various rotating machinery condition monitoring techniques of the machine to determine the conditions of failure. To increase the operational reliability and to reduce preventive maintenance, it is necessary an efficient tool for analysis and process monitoring, in real time, enabling the detection of incipient faults for rolling bearings. Over the past few years there has been a major technological developments related to digital system, including innovations in both hardware and software. These innovations enable the development of new design methodologies that take into account the ease of future modifications, upgrades and expansions of the designed system. This paper presents a study of new design tools for embedded digital systems based on open hardware architecture with reconfigurable logic. Will be discussed a case study in the area of fault detection of rolling bearings, as well as its implementation and testing.

New software innovations designed to accelerate digital practice transformation.

New software innovations designed to accelerate digital practice transformation.

Potential factors contributing to observed sex differences in virtual-reality-induced sickness

Virtual reality (VR) technology has been widely adopted for several professional and recreational applications. Despite rapid innovation in hardware and software, one of the long prevailing issues for end users of VR is the experience of VR sickness. Females experience stronger VR sickness compared to males, and previous research has linked susceptibility to VR sickness to the menstrual cycle (Munafo et al., Exp Brain Res 235(3):889–901). Here we investigated the female versus male experience in VR sickness while playing an immersive VR game, comparing days of the menstrual cycle when hormones peak: day 15 (ovulation—peak estrogen) and day 22 (mid-luteal phase—peak progesterone). We found that immersion duration was greater in the second session than the first, and discomfort was lessened, suggesting a powerful adaptation with repeated exposure. Due to the estrogen levels changing along with the exposure, there was no clear independent impact of that; note, though, that there was a significant difference between self-report and physiological measures implying that GSR is potentially an unreliable measure of motion sickness. Although prior work found a delay over 2 days between session would not allow adaptation and habituation to reduce VR sickness susceptibility, we found that a week delay has potential success.

Comprehensive advancement in endoscopy: optical design, algorithm enhancement, and clinical validation for merged WLI and CBI imaging.

As endoscopic imaging technology advances, there is a growing clinical demand for enhanced imaging capabilities. Although conventional white light imaging (WLI) endoscopy offers realistic images, it often cannot reveal detailed characteristics of the mucosa. On the other hand, optical staining endoscopy, such as Compound Band Imaging (CBI), can discern subtle structures, serving to some extent as an optical biopsy. However, its image brightness is low, and the colors can be abrupt. These two techniques, commonly used in clinical settings, have complementary advantages. Nonetheless, they require different lighting conditions, which makes it challenging to combine their imaging strengths on living tissues. In this study, we introduce a novel endoscopic imaging technique that effectively combines the advantages of both WLI and CBI. Doctors don't need to manually switch between these two observation modes, as they can obtain the image information of both modes in one image. We calibrated an appropriate proportion for simultaneous illumination with the light required for WLI and CBI. We designed a new illumination spectrum tailored for gastrointestinal examination, achieving their fusion at the optical level. Using a new algorithm that focuses on enhancing specific hemoglobin tissue features, we restored narrow-band image characteristics lost due to the introduction of white light. Our hardware and software innovations not only boost the illumination brightness of the endoscope but also ensure the narrow-band feature details of the image. To evaluate the reliability and safety of the new endoscopic system, we conducted a series of tests in line with relevant international standards and validated the design parameters. For clinical trials, we collected a total of 256 sets of images, each set comprising images of the same lesion location captured using WLI, CBI, and our proposed method. We recruited four experienced clinicians to conduct subjective evaluations of the collected images. The results affirmed the significant advantages of our method. We believe that the novel endoscopic system we introduced has vast potential for clinical application in the future.

Правовое обеспечение цифровизации финансового сектора в контексте достижения национальных целей экономического развития России

The subject of the study is the current state of digitalization of the financial sector in the Russian Federation. The purpose of the work is to establish prospects for the development of the regulatory framework for the digitalization of the financial sector in conjunction with activities to ensure the achievement of national economic development goals of the country. The article reveals the debatable nature of the conceptual apparatus reflecting the digital transformation of the economy, characterizes various approaches to the meaningful content of the terminology of digitalization of the financial sector. Based on information sources, the progressive nature of the increase in spending on the development of the digital economy has been determined. The analysis of the degree of digitalization of the financial sector is carried out. The positive dynamics of Russian financial sector organizations switching to the use of software innovations, digital data protection technologies and encryption tools has been established. The priorities of digitalization development in the context of achieving the national goals of economic development of Russia are determined. The urgency of the need to create a multidisciplinary legal framework as a reliable basis for continuing the systemic digital transformation of the financial sector has been confirmed. The regulatory documents in force and in the process of public discussion are considered, intended for the legal regulation of the digitalization process in the financial sector of the economy. The directions of improving the work on the legal support of the financial sector are substantiated: data protection, the introduction of the digital ruble, SupTech, RegTech technologies, etc. Conclusions are drawn on the expediency of intensifying research in order to accumulate substantive information for the development of regulatory tools and operational decision-making in the field of legal regulation of financial sector institutions in conjunction with activities to achieve national goals of economic development of the Russian Federation.

Perangkat Lunak Komputer

The development of the world of technology at this time has a major influence and impact on the development of educational information systems. In the world of information systems that aim to provide various kinds of information, and with the development of technological progress experiencing very rapid development, it is characterized by the increasing number of computer-based information systems. The objectives of this journal are (1) Analyzing the history and evolution of browsers, (2) Identifying the Challenges Faced by Browsers (3) Reviewing the Latest Innovations in software. The method in writing this journal is in the form of data collection from several journals. (1) The Internet was first created by the United States Department of Defense (DARPA) in the 1960s. Early browser software such as "NCSA Mosaic" appeared in the early 1990s, allowing users to access text and images simply via the HTTP protocol, (2) The challenges faced by browsers are security, user privacy, sustainability of mobile devices etc., (3) The latest innovations in software are the latest web technologies, enhanced security and support for VR/AR.

Actuator optimization and deep learning-based control of pediatric knee exoskeleton for community-based mobility assistance

Lightweight and smart exoskeletons offer the potential to improve mobility in children. State-of-the-art pediatric exoskeletons are typically clinic-based since they are either tethered or portable but cumbersome and their design is often not optimized across a range of environments and users. To facilitate pediatric exoskeleton in community settings, we first proposed an actuator optimization framework that identified the optimal design parameters for both motor and transmission while minimizing the actuator mass and satisfying the output torque, speed, bandwidth, and resistance torque requirements. Guided by the optimization results, we customized a simple, lightweight actuator that met all mechatronic constraints for our portable exoskeleton (1.78 kg unilateral). Secondly, we adopted deep learning (Long Short Term Memory) based on gait phase estimation to facilitate stable control for community use. The models accurately estimated the gait phase on irregular walking patterns (accuracy 94.60%) without explicit training in children (typically developing and with cerebral palsy). The controller results demonstrated an elevated ability to adapt to the irregular gait patterns of the child with cerebral palsy. The experimental results in the child with typical development and four healthy adults demonstrated accurate assistive torque tracking performance (accuracy 97.00%) at different walking speeds (i.e., under uncertain torque to wearers). This work presented a holistic solution that includes both hardware innovation (actuator optimization framework) and software innovation (deep learning-based control) towards the paradigm shift of pediatric exoskeletons from clinic to community setting.

The Role of the Warehousing Module in the Building of a Modern Enterprise Resource Planning System

This article presents a developed and functioning Enterprise Resource Planning (ERP) for the warehouse logistics management, designed for the needs of small and medium-sized companies in Bulgaria. The current ERP system aims to satisfy the needs of a specific transport company – to track and manage the main activities. The main thesis of the present development is the leading role of software innovations in the use of data, their summation and distribution among the company's employees in real time. The design methodology of the developed system includes a study and thorough analysis of customer needs and requirements in order to fully cover the processes in the client company. At the same time, the system is flexible enough to adapt easily to future changes in the customer environment. The article presents the main characteristics of the implemented system, such as ease of use, speed, and intuitive design. As a result of its implementation, the management decision-making processes of both the middle and senior management teams of the company have been significantly supported and facilitated. The developed system has a market implementation in Bulgaria, and is in the process of searching for sales representatives, partners and customers in other countries.

Enhanced Adaptive Over-Segmentation Technique for Image Forgery Detection under Noise Attacks

The problem of picture fraud has become more pervasive in the current digital era as a direct result of the ease with which complex image manipulation programs can be accessed. This research study provides a comprehensive analysis of several methodologies and approaches that are used to detect and localize cases of picture fraud. The primary objective is to provide a clear understanding of the most cutting-edge methods that are now being utilized, as well as the problems and opportunities that lie ahead. In the field of picture forensics, the focus of this article is on several distinct types of image fraud, such as copy-move, splicing, retouching, and inpainting forgeries. We study the underlying principles that lie behind frequently used detection methods, such as block matching, feature-based analysis, and approaches that are anchored in deep learning. In addition, we go over the benefits and drawbacks of each strategy, focusing on how they apply to a variety of settings. In addition, this article takes a look at the datasets that are typically utilized for the training and evaluation of forgery detection algorithms, highlighting both the benefits and the limitations of those datasets. In addition to this, we examine the numerous assessment metrics that are utilized in order to evaluate the performance of the various methods, with a particular focus on the requirement for standardized benchmark datasets and evaluation methodologies. In addition, we discuss the obstacles that are presented when attempting to identify picture fraud in the real world. These issues include the need to deal with photos that have been compressed, images that have varying resolutions, and the existence of post-processing effects. In this article, we will discuss the significance of multi-modal analysis and the fusion of information obtained from a variety of sources in order to improve the reliability of counterfeit detection systems. The approaches that are used to detect picture counterfeiting are the topic of the next section of this review. We look at several techniques, such as those based on segmentation, texture analysis, and deep neural networks, in order to determine the precise position of forged sections inside a picture. We go through their precision, the amount of computing complexity they have, and the possible uses they have. In conclusion, we discuss the potential of the future. In order to identify and localize forged images, it is necessary to keep up with the latest innovations in image editing software and build forgery detection systems that are more technologically advanced and effective. This is because picture editing techniques are always being refined and improved. We offer prospective research avenues, such as Explainable AI, Generative Adversarial Networks (GANs) for forgeries production, and hybrid techniques to combine the strengths of various detection methods. Specifically, we focus on hybrid approaches to combine the strengths of diverse detection methods. In conclusion, the purpose of this study is to offer academics and industry professionals in This article provides a comprehensive review of the present state of identifying and localizing picture forgeries, which falls under the umbrella of image forensics as a discipline. It encompasses the most recent developments in forgery detection and localisation, making it possible to gain an in-depth comprehension of the subject matter. It is possible to pave the way for more effective and reliable forgery detection systems to protect the integrity of digital pictures in a variety of applications if we grasp the existing approaches as well as the obstacles that they provide.