Advanced Software Research Articles

How does obsolescence risk influence consumer resistance to smartwatches?

PurposeDue to advances in both software and hardware, obsolescence risk refers to the fear that a product will soon become obsolete, which can be very high for technological products such as smartwatches or smartphones. Drawing on the perceived risk theory and innovation resistance, this study examines the effects of different obsolescence risks on consumer resistance to smartwatches.Design/methodology/approachA sequential explanatory approach using a mixed method was adopted in this study. In Study 1, we conducted semi-structured and in-depth face-to-face interviews with 16 individuals to identify the most important obsolescence risks affecting consumers’ resistance to smartwatches. This qualitative study develops a novel theoretical model based on interpretive results, including technological, economic, functional, and aesthetic obsolescence risks. In Study 2, we tested our theoretical model by applying partial least-squares structural equation modeling to a sample of 298 smartwatch users.FindingsThe results show that consumer resistance to smartwatches is affected by technological, economic, functional, and aesthetic obsolescence risks.Originality/valueAlthough most extant studies have focused on the factors influencing the adoption and use of consumer electronics, little is known about the role of obsolescence risk in consumers’ resistance to these products.

Smart parallel automated cryo-electron tomography.

In situ cryo-electron tomography enables investigation of macromolecules in their native cellular environment. Samples have become more readily available owing to recent software and hardware advancements. Data collection, however, still requires an experienced operator and appreciable microscope time to carefully select targets for high-throughput tilt series acquisition. Here, we developed smart parallel automated cryo-electron tomography (SPACEtomo), a workflow using machine learning approaches to fully automate the entire cryo-electron tomography process, including lamella detection, biological feature segmentation, target selection and parallel tilt series acquisition, all without the need for human intervention. This degree of automation will be essential for obtaining statistically relevant datasets and high-resolution structures of macromolecules in their native context.

Software stewardship and advancement of a high-performance computing scientific application: QMCPACK

We provide an overview of the software engineering efforts and their impact in QMCPACK, a production-level ab-initio Quantum Monte Carlo open-source code targeting high-performance computing (HPC) systems. Aspects included are: (i) strategic expansion of continuous integration (CI) targeting CPUs, using GitHub Actions own runners, and NVIDIA and AMD GPUs used in pre-exascale systems, (ii) incremental reduction of memory leaks using sanitizers, (iii) incorporation of Docker containers for CI and reproducibility, and (iv) refactoring efforts to improve maintainability, testing coverage, and memory lifetime management. We quantify the value of these improvements by providing metrics to illustrate the shift towards a predictive, rather than reactive, maintenance approach. Our goal, in documenting the impact of these efforts on QMCPACK, is to contribute to the body of knowledge on the importance of research software engineering (RSE) for the stewardship and advancement of community HPC codes to enable scientific discovery at scale.

Effect of digital competence on scan time and scan error in intraoral scanning

Objectives: This study aimed to test whether the digital competence of dental hygienists can affect their intraoral scanning potential in terms of scan time and error. Methods: Dental hygienists and dental hygiene students who had never used an intraoral scanner were surveyed to determine their digital competence. Individual data collected using an intraoral scanner was compared with reference data collected using a model scanner to identify scanning errors, and participants’ scanning times were measured. Results: A significant decrease in scanning time was observed as the overall level of digital competence increased. The increase in digital skills and digital knowledge led to a decrease in scanning time by 3.73 and 3.98 minutes, respectively. Conclusions: This study found that digital competence was associated with reduced scan times, but less so with scan errors. This may be due to recent advances in scanning software, and future studies may need to develop a digital competence assessment tool that is more appropriate for the dental field.

Whole-Body SPECT/CT: Protocol Variation and Technical Consideration-A Narrative Review.

Introducing a hybrid imaging approach, such as single-photon emission computerized tomography with X-ray computed tomography (SPECT)/CT, improves diagnostic accuracy and patient management. The ongoing advancement of SPECT hardware and software has resulted in the clinical application of novel approaches. For example, whole-body SPECT/CT (WB-SPECT/CT) studies cover multiple consecutive bed positions, similar to positron emission tomography-computed tomography (PET/CT). WB-SPECT/CT proves to be a helpful tool for evaluating bone metastases (BM), reducing equivocal findings, and enhancing user confidence, displaying effective performance in contrast to planar bone scintigraphy (PBS). Consequently, it is increasingly utilized and might substitute PBS, which leads to new questions and issues concerning the acquisition protocol, patient imaging time, and workflow process. Therefore, this review highlights various aspects of WB-SPECT/CT acquisition protocols that need to be considered to help understand WB-SPECT/CT workflow processes and optimize imaging protocols.

Wavelength-multiplexed multi-mode EUV reflection ptychography based on automatic differentiation

Ptychographic extreme ultraviolet (EUV) diffractive imaging has emerged as a promising candidate for the next generationmetrology solutions in the semiconductor industry, as it can image wafer samples in reflection geometry at the nanoscale. This technique has surged attention recently, owing to the significant progress in high-harmonic generation (HHG) EUV sources and advancements in both hardware and software for computation. In this study, a novel algorithm is introduced and tested, which enables wavelength-multiplexed reconstruction that enhances the measurement throughput and introduces data diversity, allowing the accurate characterisation of sample structures. To tackle the inherent instabilities of the HHG source, a modal approach was adopted, which represents the cross-density function of the illumination by a series of mutually incoherent and independent spatial modes. The proposed algorithm was implemented on a mainstream machine learning platform, which leverages automatic differentiation to manage the drastic growth in model complexity and expedites the computation using GPU acceleration. By optimising over 200 million parameters, we demonstrate the algorithm's capacity to accommodate experimental uncertainties and achieve a resolution approaching the diffraction limit in reflection geometry. The reconstruction of wafer samples with 20-nm high patterned gold structures on a silicon substrate highlights our ability to handle complex physical interrelations involving a multitude of parameters. These results establish ptychography as an efficient and accurate metrology tool.

Virtual reality as a treatment for chronic musculoskeletal pain syndromes

Chronic musculoskeletal pain syndromes, including fibromyalgia, are often resistant to conventional medications and invasive therapies. Central hypersensitization, neurotransmitter dysregulation, and autonomic nervous system abnormalities are key pathomechanisms, frequently resulting in widespread pain and a variety of psychosomatic symptoms. Virtual Reality (VR) applications have demonstrated effectiveness in reducing pain, both during and after interventions, and in chronic conditions such as fibromyalgia and back pain. The proposed mechanisms behind VR's effectiveness include distraction and immersion, coupled with cognitive behavioral therapy, which promote neuroplasticity and alter pain perceptions. Functional MRI studies have shown the impact of VR interventions on specific brain regions. Advances in hardware and software, potentially combined with treatments like biofeedback, could enhance VR's role in managing chronic pain. Currently, VR for musculoskeletal pain syndromes is primarily used within multimodal programs, but it is also available for home use as a standalone health application. Future research should focus on the ‘drug-like’ effects of VR, requiring controlled trials with comparable study populations and appropriate sham interventions.

Ten questions concerning statistical data analysis in human-centric buildings research: A focus on thermal comfort investigations

Given the large amount of time we spend indoors, designing and operating buildings that are safe, comfortable, and conducive to productivity and well-being is essential. To achieve this goal, in the past decades, research has been conducted to investigate the influence of the indoor environment on occupants. Thermal comfort has been the subject of most investigations in this field. However, despite being a consolidated research topic since the 1920s, statistical practices for analysing thermal comfort data often rely on simplified premises, which may be due to several possible factors (e.g., limited computational capabilities and lack of training). Consequently, important aspects of data analysis are often absent or overlooked. Recent statistics and statistical software advances have provided more options for effectively modelling complex issues. However, properly using these tools requires a solid understanding of statistical analysis, increasing the risk of misuse in practice. This paper presents ten questions highlighting the most critical issues regarding statistical analysis for thermal comfort research and practice. The first four questions provide general perspectives concerning statistical data analysis, while the remaining ones address specific problems related to thermal comfort research, but that can extend to all human-centric research in the built environment. Additionally, the last five questions demonstrate the practical significance of analysis pitfalls (i.e., sampling variability, selection bias, variable selection, clustered/nested observations, and measurement error) through examples with synthetic data. This study provides insights into the current statistical ‘habits’ in thermal comfort research and, more importantly, help researchers better define and conduct their statistical analyses.

MolGC: molecular geometry comparator algorithm for bond length mean absolute error computation on molecules.

Density Functional Theory (DFT) is extensively used in theoretical and computational chemistry to study molecular and crystal properties across diverse fields, including quantum chemistry, materials physics, catalysis, biochemistry, and surface science. Despite advances in DFT hardware and software for optimized geometries, achieving consensus in molecular structure comparisons with experimental counterparts remains a challenge. This difficulty is exacerbated by the lack of automated bond length comparison tools, resulting in labor-intensive and error-prone manual processes. To address these challenges, we propose MolGC, a Molecular Geometry Comparator algorithm that automates the comparison of optimized geometries from different theoretical levels. MolGC calculates the mean absolute error (MAE) of bond lengths by integrating data from various DFT software. It provides interactive and customizable visualization of geometries, enabling users to explore different views for enhanced analysis. In addition, it saves MAE computations for further analysis and offers a comprehensive statistical summary of the results. MolGC effectively addresses complex graph labeling challenges, ensuring accurate identification and categorization of bonds in diverse chemical structures. It achieves a 98.91% average rate in correct bond label assignments on an antibiotics dataset, showcasing its effectiveness for comparing molecular bond lengths across geometries of varying complexity and size. The executable file and software resources for running MolGC can be downloaded from https://github.com/AbimaelGP/MolGC/tree/main .

Two decades of network meta-analysis: Roadmap to their applications and challenges.

Recently, Ades and colleagues discussed the controversies and advancements in network meta-analysis (NMA) over the past two decades, discussing its reliability, assumptions, novel approaches, and provided some useful recommendations for the conduction of NMAs. The present discussion paper builds on the insights by Ades and colleagues, providing a roadmap for NMA applications, advancements in software and tools, and approaches designed to facilitate the assessment and interpretation of NMA findings. It also discusses the impact of NMA across disciplines, particularly for policymakers and guideline developers. Despite 20 years of NMA history, challenges remain in understanding and assessing assumptions, communicating and interpreting findings, and applying common approaches like network meta-regression and NMA involving non-randomized studies in readily available software. NMA has proven particularly valuable in clinical decision-making, which highlights the need for additional training and interdisciplinary collaboration of knowledge users, including patient engagement, to enhance its adoption and address real-world problems.

Le futur du métier de traducteur face à la révolution numérique : préoccupations et perspectives pour les bilingues

The Future of The Translation Profession in The Age of Digital Revolution: Concerns and perspectives for bilingual professionals The digital revolution has caused a significant shift in labour market policies across all sectors. The rapid advancement and widespread adoption of software and IT tools have led to an increased risk of job displacement for skilled professionals, as these tools enable even non-experts to perform tasks traditionally handled by specialists. This study explores the potential threat to employment faced by professional translators due to these technological advancements. The rise of artificial intelligence and machine translation (MT) has encouraged many bilingual individuals to enter the translation field, a trend that has raised concerns among linguistic researchers. These technological developments have also cast doubt on the value of language experts. This research addresses key questions: To what extent is the future of professional translators at risk? Do professional translators face significant competition in the Information Age? To answer these questions, this study employed both quantitative and qualitative research methods, including field data collection from bilinguals and professional translators. Questionnaires were administered first to bilinguals to gauge their motivations and then to professional translators to understand their concerns and perspectives. The data analysis provided clear insights that supported the study's hypotheses. The research suggests that professional translators may face substantial unemployment risks in the future. This is largely attributed to the increasing involvement of bilinguals in the field, driven by the proliferation of machine translation tools. The study highlights the relevance of connectivism and job prospection theories in understanding the challenges faced by professional translators and in addressing the rise of bilinguals in the workforce.

Development of Ground Control Station for Unmanned Aerial Vehicles

Unmanned aerial vehicles (UAVs) have emerged as important tools in various industries, from military research to civilian applications such as agriculture, disaster management and research. This paper examines the technological development of GCS and current state-of-the-art developments, including advances in hardware, software, and human-machine interfaces. Through a systematic review of recent research, we identify key policy considerations, performance criteria, and challenges associated with GCS development. In addition, we explore autonomy, artificial intelligence integration, cybersecurity, interoperability standards, and other development trends that are changing the UAV industry landscape. Combining insights from academic literature, industry reports, and technology developments, this review provides a comprehensive understanding and new idea of the state of GCS technology and valuable perspectives on future research directions and practical applications in UAV systems.

A Review of Recent Hardware and Software Advances in GPU-Accelerated Edge-Computing Single-Board Computers (SBCs) for Computer Vision.

Computer Vision (CV) has become increasingly important for Single-Board Computers (SBCs) due to their widespread deployment in addressing real-world problems. Specifically, in the context of smart cities, there is an emerging trend of developing end-to-end video analytics solutions designed to address urban challenges such as traffic management, disaster response, and waste management. However, deploying CV solutions on SBCs presents several pressing challenges (e.g., limited computation power, inefficient energy management, and real-time processing needs) hindering their use at scale. Graphical Processing Units (GPUs) and software-level developments have emerged recently in addressing these challenges to enable the elevated performance of SBCs; however, it is still an active area of research. There is a gap in the literature for a comprehensive review of such recent and rapidly evolving advancements on both software and hardware fronts. The presented review provides a detailed overview of the existing GPU-accelerated edge-computing SBCs and software advancements including algorithm optimization techniques, packages, development frameworks, and hardware deployment specific packages. This review provides a subjective comparative analysis based on critical factors to help applied Artificial Intelligence (AI) researchers in demonstrating the existing state of the art and selecting the best suited combinations for their specific use-case. At the end, the paper also discusses potential limitations of the existing SBCs and highlights the future research directions in this domain.

ChatGPT in Higher Education: Practical Ideas for Addressing Artificial Intelligence in Nursing Education.

As the use of artificial intelligence (AI) becomes more prevalent in academic settings, there is a growing concern about maintaining a culture of integrity. This article explores the role of academic institutions and programs in fostering a culture of integrity in relation to AI. By implementing specific policies, integrating tools, and utilizing software for AI detection, academic institutions can establish a culture of integrity in relation to AI. These collective efforts foster an environment where ethical AI practices are upheld and reinforce the importance of academic honesty, particularly in the nursing profession. Academic institutions have the capacity to establish integrity-focused policies and integrate anti-AI agent tools in courses to mitigate unethical AI usage, while software advancements assist faculty in identifying AI presence during assessments. Emphasizing the interplay between academic and professional integrity strengthens nurses' dedication to academic honesty. [J Nurs Educ. 2024;63(X):XXX-XXX.].

Enabling analytical power calculations for multilevel models with autocorrelated errors through deriving and approximating the precision matrix.

To unravel how within-person psychological processes fluctuate in daily life, and how these processes differ between persons, intensive longitudinal (IL) designs in which participants are repeatedly measured, have become popular. Commonly used statistical models for those designs are multilevel models with autocorrelated errors. Substantive hypotheses of interest are then typically investigated via statistical hypotheses tests for model parameters of interest. An important question in the design of such IL studies concerns the determination of the number of participants and the number of measurements per person needed to achieve sufficient statistical power for those statistical tests. Recent advances in computational methods and software have enabled the computation of statistical power using Monte Carlo simulations. However, this approach is computationally intensive and therefore quite restrictive. To ease power computations, we derive simple-to-use analytical formulas for multilevel models with AR(1) within-person errors. Analytic expressions for a model family are obtained via asymptotic approximations of all sample statistics in the precision matrix of the fixed effects. To validate this analytical approach to power computation, we compare it to the simulation-based approach via a series of Monte Carlo simulations. We find comparable performances making the analytic approach a useful tool for researchers that can drastically save them time and resources.

Studying the association Between longitudinal non-dense breast tissue and breast cancer risk: a joint modelling approach.

Conflicting results have appeared in the literature on whether the amount of non-dense, adipose tissue in the breast is a risk factor or a protective factor for breast cancer, and biological hypotheses supporting both have been proposed. We suggest here that limitations in study design and statistical methodology could potentially explain the inconsistent results. Specifically, we exploit recent advances in methodology and software developed for the joint analysis of multiple longitudinal outcomes and time-to-event data to jointly analyze dense and non-dense tissue trajectories and the risk of breast cancer in a large, Swedish, screening cohort. We also perform extensive sensitivity analyses by mimicking analyses/designs of previously published studies, e.g. ignoring available longitudinal data. Overall, we did not find strong evidence supporting an association between non-dense tissue and the risk of incident breast cancer. We hypothesize that (1) previous studies have not been able to isolate the effect of non-dense tissue from dense tissue or adipose tissue elsewhere in the body, that (2) estimates of the effect of non-dense tissue on risk are strongly sensitive to modeling assumptions, or that (3) the effect size of non-dense tissue on breast cancer risk is likely to be small/not clinically relevant.

Comparative Analysis of Four Different Intraoral Scanners: An In Vitro Study.

(1) Background: Intraoral scanners undergo rapid advancements in hardware and software, prompting frequent updates by manufacturers. (2) Aim: This study aimed to quantitatively assess the precision of full dental arch digital impressions obtained from four different intraoral scanners: Trios 5-3SHAPE, Copenhagen, Denmark, CEREC Primescan- Dentsply Sirona, New York, NY, USA, Planmeca Emerald S-Planmeca Oy, Helsinki, Finland, and Medit i700-Medit Corp, Seoul, Republic of Korea. (3) Methods: A maxillary virtual dental model (digital master model) was created in accordance with ISO standard 20896-1. Subsequently, a 3D-printed model was obtained from the master model's STL file and scanned 15 times consecutively with each scanner. STL files were aligned with the master model's STL using Medit Link-Medit Design software v.3.1.0. The accuracy was evaluated by measuring deviations in micrometers between each scanner's scans and the master model. (4) Results: The study revealed variations in accuracy ranging from 23 to 32 µm across scans of the same dental arch, irrespective of the scanner used and scanning strategy employed. The anterior regions exhibited higher precision (Mean Absolute Deviation of 112 µm) compared to the posterior regions (Mean Absolute Deviation of 127 µm). Trios 5 demonstrated the smallest deviation (average 112 µm), indicating superior accuracy among the scanners tested. Emerald S and Medit i700 exhibited balanced performance (average 117 µm and 114 µm, respectively), while Primescan consistently displayed high deviation (average 127 µm). (5) Conclusions: Based on clinically accepted thresholds for accuracy in intraoral scanning, which are typically 200 µm for full arch scans, Trios 5 surpasses these benchmarks with its average deviation falling within the 200 µm range. Emerald S and Medit i700 also meet these standards, while Primescan, although showing high overall deviation, approaches the upper limit of clinical acceptability. Considering the limitations of an in vitro investigation, the findings demonstrate that each intraoral scanner under evaluation is capable of reliably and consistently capturing a full arch scan for dentate patients.

Effects of training methods on performance of a scaffolding task for workers with different spatial ability

With software developments and advances in display technologies substantially improved, augmented reality (AR) application has gained popularity. In this study, we discuss using classic PowerPoint and AR for two kinds of scaffolding tasks (task-lifeline assembly and hedge assembly) for users with different spatial ability. We considered both objective and subjective measures of performance, i.e., correct rate and system usability and the ITC-sense of presence inventory (ITC-SOPI) scale. The results of the study show that participants using AR achieved higher operating performance than those using PowerPoint. Furthermore, the users’ learning effect was influenced by spatial ability when using PowerPoint. Participants with high spatial ability achieved higher performance than participants with low spatial ability in PowerPoint. However, participants who used AR as a training method did not show significantly different operating performance at different levels of spatial ability. Consequently, AR was believed to be a potential method for enhancing training performance.

A comparative study of slope stability analysis via Geo5 using IoT framework

In mountainous regions, landslides pose significant and frequent threats, causing extensive damage due to their destructive nature and frequency, therefore determining their likely occurrence sites and environmental factors is essential for hazard assessment. The infinite slope approach characterizes slope stability using a factor of safety (FOS) to assess the likelihood of slope failure and is frequently used to estimate the occurrence of shallow landslides on soil and regolith-covered slopes. Various methods have been used to evaluate and spread uncertainty across such models. Slope failure is a significant geological occurrence brought on by topography and weather, which result in a variety of ground movements. Engineers must plan and implement measures to mitigate hazards, safeguarding both lives and property from potential risks and dangers by using an adequate stabilizing solution. Technology and software advancements have made it simpler than ever to handle challenging issues that used to require a lot of time in every profession. Over the past decade, software utilization in civil engineering has surged. GEO 5 is a versatile program gaining prominence, aiding in the resolution of diverse geotechnical challenges. Through the installation of IoT cameras in various sand and clay areas along the bank of the Mahandi River in Odisha, we have gathered the data necessary to develop the region in this case. a select selection of which we have chosen for our research. In this study, slope stability-related modules have been carefully examined and used for the analysis of slope stability. Using the GEO5 program, the geometry of the issues was established, and the study took into account several stability optimization techniques. Additionally, the cost factor of various reinforcing techniques was calculated and contrasted.

Classification of Cardiovascular Arrhythmia Using Deep Learning Techniques: A Review

Deep Learning (DL), an offshoot of Machine Learning (ML) has emerged as a powerful and feasible solution for medical image analysis due to advancements in robust computer software and hardware technologies. It plays a key role in Cardiovascular disease (CVD) diagnosis by detecting anomalies in Electrocardiogram (ECG) signals. Cardiac arrhythmia, which refers to irregular heartbeat, may signal an early symptom of CVD and can lead to fatal outcomes if ignored. Accurate detection of arrhythmia is very challenging even for experts to distinguish between acute and chronic conditions in ECG readings. This triggered the focus of researchers to explore the application of Artificial Intelligence for ECG classification. Traditional machine learning methods use handcrafted features that require domain knowledge. The new era in DL makes the automatic detection of Cardiovascular Disease (CVD) possible. In this paper, an exhaustive review of DL-based techniques for ECG classification has been presented. Research findings in this survey indicate the challenges and issues with arrhythmia detection, such as single lead and multiple lead ECG signals, choice of the size of the training data set, and the number of arrhythmia classes, etc. The study also signifies that there is great scope for improving the performance of arrhythmia prediction models by employing hybrid ensemble learning, time series analysis using Recurrent Neural Network architectures and identification of unexplored classes of arrhythmia.