Laptop Research Articles

Enhancing a Display’s Sunlight Readability with Tone Mapping

The sunlight readability of display devices, such as notebook computers, transparent displays, vehicle displays, and augmented reality, is a significant technical challenge due to degraded image quality. To mitigate this problem, by fitting the human eye function, we propose a tone mapping method on a mobile phone display panel to enhance low grayscale image readability under bright ambient light. Additionally, we adapt a mini-LED backlight model to simulate real images under different ambient lighting conditions. Both experimental and simulated results indicate that high luminance displays with an optimized gamma value significantly enhance sunlight readability and image quality. Moreover, global color rendering can alleviate color shift. Such a method is also valid for the optimization of optical see-through devices under diverse environmental conditions.

Extending Jupyter with Multi-Paradigm Editors

Computational notebooks like the Jupyter programming environment have been popular, particularly for developing data-driven applications. One of its main benefits is that it easily supports different programming languages with exchangeable kernels. Thus, it makes the user interface of computational notebooks broadly accessible. While their literate programming paradigm has advantages, we can use this infrastructure to make other paradigms similarly easily and broadly accessible to developers. In our work, we demonstrate how the Jupyter infrastructure can be utilized with different interfaces for different programming paradigms, enabling even greater flexibility for programmers and making it easier for them to adopt different paradigms when they are most suitable. We present a prototype that adds graphical programming and a multi-paradigm editor on top of the Jupyter system. The multi-paradigm editor seamlessly combines the added graphical programming with the familiar notebook interface side-by-side, which can further help developers switch between programming paradigms when desired. A subsequent user evaluation demonstrates the benefits not only of alternate interfaces and paradigms but also of the flexibility of seamlessly switching between them. Finally, we discuss some of the challenges in implementing these systems and how these can enhance the software development process in the future.

Persist: Persistent and Reusable Interactions in Computational Notebooks

AbstractComputational notebooks, such as Jupyter, support rich data visualization. However, even when visualizations in notebooks are interactive, they are a dead end: Interactive data manipulations, such as selections, applying labels, filters, categorizations, or fixes to column or cell values, could be efficiently applied in interactive visual components, but interactive components typically cannot manipulate Python data structures. Furthermore, actions performed in interactive plots are lost as soon as the cell is re‐run, prohibiting reusability and reproducibility. To remedy this problem, we introduce Persist, a family of techniques to (a) capture interaction provenance, enabling the persistence of interactions, and (b) map interactions to data manipulations that can be applied to dataframes. We implement our approach as a JupyterLab extension that supports tracking interactions in Vega‐Altair plots and in a data table view. Persist can re‐execute interaction provenance when a notebook or a cell is re‐executed, enabling reproducibility and re‐use. We evaluate Persist in a user study targeting data manipulations with 11 participants skilled in Python and Pandas, comparing it to traditional code‐based approaches. Participants were consistently faster and were able to correctly complete more tasks with Persist.

Deconwolf enables high-performance deconvolution of widefield fluorescence microscopy images

Microscopy-based spatially resolved omic methods are transforming the life sciences. However, these methods rely on high numerical aperture objectives and cannot resolve crowded molecular targets, limiting the amount of extractable biological information. To overcome these limitations, here we develop Deconwolf, an open-source, user-friendly software for high-performance deconvolution of widefield fluorescence microscopy images, which efficiently runs on laptop computers. Deconwolf enables accurate quantification of crowded diffraction limited fluorescence dots in DNA and RNA fluorescence in situ hybridization images and allows robust detection of individual transcripts in tissue sections imaged with ×20 air objectives. Deconvolution of in situ spatial transcriptomics images with Deconwolf increased the number of transcripts identified more than threefold, while the application of Deconwolf to images obtained by fluorescence in situ sequencing of barcoded Oligopaint probes drastically improved chromosome tracing. Deconwolf greatly facilitates the use of deconvolution in many bioimaging applications.

Customised product design optimisation considering module synergy effects and expert preferences

ABSTRACT Fierce marketing competition and varied customer requirements on products make a lot of companies transfer to the customisation strategy. However, customers may be overwhelmed by the vast assortment of products and options. This paper proposes a methodology aimed at optimising the range of customised product varieties considering synergy effects of modules and preferences of experts. The methodology outlines key steps required for a successful customised product configuration. Firstly, a questionnaire based on the Kano model is designed to identify customer preferences for product module customisation. The potential customers can be segmented based on the distance among their multidimensional preferences. Secondly, within each segmented market, the customer satisfaction function that considers module synergies is established using the quantified Kano model and coefficient matrix. Thirdly, the cost function that considers experts' preferences is established based on the prospect theory. Finally, a bi-objective optimisation model is formulated to maximise customer satisfaction and minimise cost under technical constraints. Pareto solutions are obtained by solving the model with the non-dominated sorting genetic algorithm II (NSGA-II). Modules and attributes available to customers can be determined by the solutions. An illustrative example of modular laptop computers confirms the methodology's effectiveness in optimising product configuration.

79‐1: Invited Paper: Adaptive Local Backlight Dimming Control with Local Boosting

The local backlight dimming (LBD) technology has attracted significant interest for liquid crystal displays. In automotive, notebook PC, and virtual reality applications, LBD should treat the trade‐off between power saving and visual quality enhancement. However, it requires the cost of the high logic complexity to consider the optimal solution at a higher resolution of the image. This paper proposes the adaptive local backlight dimming control, which can reduce the power consumption of backlight and improve the quality of the display. The proposed method analyzes an image contents with a low complexity algorithm, and it applies a local boosting to achieve a target intensity in backlight control. In performance evaluation using FPGA test system, the designed local dimming processor achieved 7.54% higher power‐saving rate with visual quality enhancement in high‐contrast region.

Tstrait: a quantitative trait simulator for ancestral recombination graphs.

Ancestral recombination graphs (ARGs) encode the ensemble of correlated genealogical trees arising from recombination in a compact and efficient structure and are of fundamental importance in population and statistical genetics. Recent breakthroughs have made it possible to simulate and infer ARGs at biobank scale, and there is now intense interest in using ARG-based methods across a broad range of applications, particularly in genome-wide association studies (GWAS). Sophisticated methods exist to simulate ARGs using population genetics models, but there is currently no software to simulate quantitative traits directly from these ARGs. To apply existing quantitative trait simulators users must export genotype data, losing important information about ancestral processes and producing prohibitively large files when applied to the biobank-scale datasets currently of interest in GWAS. We present tstrait, an open-source Python library to simulate quantitative traits on ARGs, and show how this user-friendly software can quickly simulate phenotypes for biobank-scale datasets on a laptop computer. tstrait is available for download on the Python Package Index. Full documentation with examples and workflow templates is available on https://tskit.dev/tstrait/docs/, and the development version is maintained on GitHub (https://github.com/tskit-dev/tstrait).

Renormalizing the high-harmonic interference via double-slit and grating

A method to utilize high-harmonic interference in Young’s double-slit followed by a grating holds promise for precisely measuring the refractive-index spectrum in the extreme ultraviolet (EUV) region. The measurement is currently bottlenecked by the time it takes to fit the Fresnel interference patterns. The conventional fitting procedure, involving multiple numerical integrals for evaluating the EUV propagation through the slit and grating, takes ≳30min on a standard laptop computer, while the data acquisition time is ∼200s. Here, we apply an analytic Gaussian integral and bypass one of the numerical integrals associated with the grating. The analysis reduces to evaluating a renormalized EUV propagation in a simple double-slit interferometer, and the estimated fitting time on a laptop becomes as short as 112 s. Our study enables real-time analysis during measurements, facilitating mass optical-data collection of EUV lithography materials.

Evaluation of a digital Cognitive Behavioural Therapy programme for prison residents with histories of substance misuse (“Breaking Free”): Comparison of delivery models employed in the United Kingdom and the United States

Substance misuse is a criminogenic risk factor—however, substance-involved prison residents may have limited access to support and services that might be able to help them overcome their substance misuse. Research around the efficacy of the “Breaking Free” digital Cognitive Behavioural Therapy programme for substance-involved prison residents demonstrates that this digital technology may overcome barriers to support. Breaking Free is the first digital programme for substance misuse that has been implemented in prisons. In the United Kingdom (U.K.) the programme is delivered via secure desktop and laptop computers in staff-facilitated group sessions, and in the United States (U.S.) is delivered via individual secure tablets that prison residents can access independently, without staff support. This study compares U.K. and U.S. prison residents who have engaged with Breaking Free on a number of baseline measures (sociodemographic factors, types of problem substances reported, clinical complexity, and criminogenic risk factors). The delivery models employed in these two jurisdictions are compared in relation to their associations with prison residents’ engagement with the programme and the clinical outcomes experienced. Differences between U.K. and U.S. prison residents in baseline clinical and psychosocial factors were identified, including greater clinical complexity in the U.K. group. U.K. prison residents, who were supported by staff when using Breaking Free, demonstrated higher levels of engagement with the programme than U.S. prison residents who were not supported by staff. The different delivery models employed in the United Kingdom and United States appeared to be associated with group differences in clinical outcomes. Guidance for the optimal implementation of Breaking Free, based on findings from this study, is provided. Further analyses will explore how the different approaches to the delivery of Breaking Free might interact with the differences in prison resident characteristics identified in this study (sociodemographic, clinical and criminogenic), in order to influence prison resident engagement with the programme and clinical outcomes.

SwivelTouch: Boosting Touchscreen Input with 3D Finger Rotation Gesture

Today, touchscreens stand as the most prevalent input devices of mobile computing devices (smartphones, tablets, smartwatches). Yet, compared with desktop or laptop computers, the limited shortcut keys and physical buttons on touchscreen devices, coupled with the fat finger problem, often lead to slower and more error-prone input and navigation, especially when dealing with text editing and other complex interaction tasks. We introduce an innovative gesture set based on finger rotations in the yaw, pitch, and roll directions on a touchscreen, diverging significantly from traditional two-dimensional interactions and promising to expand the gesture library. Despite active research in estimation of finger angles, however, the previous work faces substantial challenges, including significant estimation errors and unstable sequential outputs. Variability in user behavior further complicates the isolation of movements to a single rotational axis, leading to accidental disturbances and screen coordinate shifts that interfere with the existing sliding gestures. Consequently, the direct application of finger angle estimation algorithms for recognizing three-dimensional rotational gestures is impractical. SwivelTouch leverages the analysis of finger movement characteristics on the touchscreen captured through original capacitive image sequences, which aims to rapidly and accurately identify these advanced 3D gestures, clearly differentiating them from conventional touch interactions like tapping and sliding, thus enhancing user interaction with touch devices and meanwhile compatible with existing 2D gestures. User study further confirms that the implementation of SwivelTouch significantly enhances the efficiency of text editing on smartphones.

A real-time virtual outing using virtual reality for a hospitalized terminal cancer patient who has difficulty going out: a case report

Objective: Even if hospitalized terminal cancer patients wish to go out, it is sometimes difficult for them to realize this because of various symptoms. We have been providing a virtual outing experience using virtual reality (VR) for terminal cancer patients who have difficulty going out, and have often received requests to “talk with people in the virtual outing,” but there is a problem that a large time lag occurs in conversation in a VR space under the general Internet environment. However, with the advent of systems that enable high-speed, low-latency communications, real-time communication is now possible even in VR spaces. Thus, we aimed to explore the feasibility of implementing the real-time virtual outing.Methods: The patient’s preferred virtual outing was to his daughter’s new home. The study operator used a 360° video real-time sharing system to broadcast the view of the daughter’s home. The patient experienced the images using a VR head-mounted display in his room. The patient’s wife, son, daughter, and grandson participated in this delivery using a laptop computer from a dayroom in the hospital, and his daughter’s husband participated using the 360° video real-time sharing system from the daughter’s home with the researcher. Before and after the virtual outing, changes in symptoms and emotions were assessed using the Edmonton Symptom Assessment System Revised Japanese version and the Numerical Rating Scale for headache, dizziness, pleasure, and satisfaction. In addition, we collected the patients’ impressions of the virtual outing.Results: The patient was a male in his early 70s. After approximately 30 min of real-time virtual outings, “tiredness, drowsiness, depression, and wellbeing” were improved and “pleasure, and satisfaction” were increased, while no side effects or worsening of symptoms were observed. In addition, it was observed from the patient’s comments that he felt a sense of presence, as if he were her home.Discussion: The patient and his family could enjoy smooth conversation without time lag even in the VR space. Therefore, it was suggested that real-time virtual outings using VR could help realize the wishes of hospitalized terminal cancer patients who have difficulty going out as a new approach.

Profiling consumers’ online shopping and following social media influencers behaviors

In order to develop effective digital marketing strategies, this study explores how demographic factors may affect consumers’ online shopping and following social media influencers behaviors. Market segmentation theory and consumer demographics theory provide the theoretical foundation for this study. By analyzing survey data collected from 6,034 U.S. adults with decision tree analysis, there are a number of significant findings. First, educational level, income, and age category are the important predictors for consumers’ buying things online using a desktop or laptop computer behavior. Second, age category, geographic location (urban, suburban, rural) are the predictors for consumers’ buying things online using a smartphone behavior. Third, educational level, age, and geographic location are the predictors for consumers’ preferring online shopping over in-store shopping behavior. Fourth, age category and race-ethnicity are the predictors for following social media influencers behavior. Fifth, gender and age category are the predictors for purchasing something after seeing an influencer’s posts behavior. Finally, age category and gender are the predictors for purchase decisions getting impacted by influencers. The results provide valuable insights about consumer behaviors online, market segmentation, and influencer marketing strategies.

Analysis of the Effect of Peristaltic Transport Flux on Channel Wall: Bingham Fluid as A Model

This study is concerned with analyzing the peristaltic flow of Bingham fluid through an irregular tube with a short wavelength “relative to channel width” and using the governing equations of Bingham fluid in the Navier-Stokes equations in the Cartesian coordinate system. Significant results of the problem were obtained and analyzed using the Wolfram-Mathematica Computational Notebook.

Multiverse Notebook: Shifting Data Scientists to Time Travelers

Computational notebook environments are popular and de facto standard tools for programming in data science, whereas computational notebooks are notorious in software engineering. The criticism there stems from the characteristic of facilitating unrestricted dynamic patching of running programs, which makes exploratory coding quick but the resultant code messy and inconsistent. In this work, we first reveal that dynamic patching is a natural demand rather than a mere bad practice in data science programming on Kaggle. We then develop Multiverse Notebook, a computational notebook engine for time-traveling exploration. It enables users to time-travel to any past state and restart with new code from there under state isolation. We present an approach to efficiently implementing time-traveling exploration. We empirically evaluate Multiverse Notebook on ten real-world tasks from Kaggle. Our experiments show that time-traveling exploration on Multiverse Notebook is reasonably efficient.

Time-course analysis system for leaf feeding marks reveals effects of Arabidopsis trichomes on insect herbivore feeding behavior.

Bioassay with an insect herbivore is a common approach to studying plant defense. While measuring insect growth rate as a negative indicator of plant defense levels is simple and straightforward, analysing more detailed feeding behavior parameters of insects, such as feeding rates, leaf area consumed per feeding event, intervals between feeding events, and spatio-temporal patterns of feeding sites on leaves, is more informative. However, such observations are generally time consuming and labor-intensive. Here, we provide a semi-automated system for quantifying feeding behavior parameters of insects feeding on plant leaves. Automated photo scanners record the time-course development of feeding marks on leaves. An image analysis pipeline processes the scanned images and extracts leaf area. By analysing changes in leaf area over time, it detects insect feeding events and calculates the leaf area consumed during each feeding event, providing quantitative parameters of the feeding behavior of insects. In addition, it visualizes spatio-temporal changes in feeding sites, providing a measure of the complex behavior of insects on leaves. Using this analysis pipeline, we demonstrate that Arabidopsis trichomes reduce insect feeding rate, but not feeding duration or intervals between feeding events. Our image acquisition system requires only a photo scanner and a laptop computer and does not require any specialized equipment. The analysis software is provided as an ImageJ macro and R package and is available at no cost. Taken together, our work provides a scalable method for quantitative assessment of the feeding behavior of insects on leaves, facilitating understanding of plant defense mechanisms.

ViralFlow v1.0-a computational workflow for streamlining viral genomic surveillance.

ViralFlow v1.0 is a computational workflow developed for viral genomic surveillance. Several key changes turned ViralFlow into a general-purpose reference-based genome assembler for all viruses with an available reference genome. New virus-agnostic modules were implemented to further study nucleotide and amino acid mutations. ViralFlow v1.0 runs on a broad range of computational infrastructures, from laptop computers to high-performance computing (HPC) environments, and generates standard and well-formatted outputs suited for both public health reporting and scientific problem-solving. ViralFlow v1.0 is available at: https://viralflow.github.io/index-en.html.

Prevalence and Effect of Computer Vision Syndrome during COVID-19: among Bangladeshi People

Background: Global pandemic has devastated almost every aspect of life. "Spatial distancing" involves creating and maintaining safe social distance, shifting the world away from public locations and toward isolation. Remote working has become a demand in these historically unique times, and technology is a need for human life as a link to the outside world. People use technology to interact, communicate, and complete activities. Online meetings, audio and video conferencing, and leisure activities like online gaming, blogging, and social networking have virtualized human interaction, leading to a rapid digitalization of human life. The increased usage of video display terminals (VDTs) predisposes to a range of problems. Digital eye strain (DES) or computer vision syndrome (CVS) refers to a group of health disorders that include not only visual issues but also musculoskeletal issues. Objective: The aim of this study is to determine the prevalence of CVS and to examine the use of digital screens, common symptoms, and related variables. The findings could in the future provide vital, detailed health information to all online users. Methods and Materials: A descriptive cross-sectional study was conducted from 1st July to 31st December, 2021. A total of 429 people participated in the study and majority of the participants were students and teachers involved in digital class rooms for academic purposes. A survey questionnaire was distributed on social media channels. Regular users of digital screens were provided with Google forms through social media. It includes both a questionnaire and an informed consent form. Results: Out of 429 participants in, 59.9% were females and 36.13% males. The maximum age group were from between 18-27, 58.9%, while the least were from 58 and above, 2%. The major digital screen platforms used by them were smartphone/tablet, 97.9%, television, 70.8% and computer laptop, 66.8%. The utilization of digital screens was categorized under ..........

IJCM_232A: Identification of Kinematic Motifs in Crouch Gait of Children with Cerebral Palsy

Background: Crouch Gait is a fairly common pathological gait adaptation seen in children with Cerebral Palsy, characterized by inadequacy of plantar flexion, knee extension and hip extension in the stance phase of gait. From a kinetic perspective crouch gait severely impairs the ability to generate thrusting forces through the extensor muscles of stance extremity which is necessary for forward progression. Broader goal of this study was to develop and test a wearable robotic exoskeletal device for children with crouch gait, where a knee actuator provides extensor thrust. Artificial Intelligence (AI) models which control the torque output of the knee actuator, needed to be trained to activate, control, or organize the output. Objectives: Rhythmical/repetitive patterns, like the phases of normal gait, needed to be identified in crouch gait, which could be used to train AI models Methodology: A total of 30 children with cerebral palsy were screened of which 7 children aged between 7 and 14 years, who presented with classical crouch gait were recruited. Kinesiological data was collected through accelerometric and goniometric placed bilaterally on thigh, leg, and knee, as well as pressure sensors embedded on to bilateral insoles at the fore-foot and hind foot, while the children walked over a 5 meter course. All sensor data was relayed through an Arduino micro-controller unit for analog to digital conversion and stored on a laptop computer. All Accelerometer and goniometric data was plotted against time on MS Excel to identify specific patterns or motifs. Results: Key kinematic motifs could be identified in crouch gait, which bestowed semblance of rhythmicity, and cyclical pattern, which was difficult to identify under normal observation. Conclusion: Accelerometric, as well as goniometric data suggest the presence of inherent kinematic patterns in the same. These could be used for training AI models for robotic exoskeletal devices.

Identification of Flowing Electrolyte Lead Acid Battery Operating Voltage

Identification of the operating voltage of a lead acid battery with 30% sulfuric acid electrolyte flow has been carried out. The battery consists of six cells with Pb and PbO as electrodes. The battery is equipped with a 1200 ml reservoir system to collect electrolyte and supply electrolyte to each cell. Each cell has electrolyte inlets and outlets at the top and bottom that circulate through each cell using a peristaltic pump. The battery prototype built was tested for five charge-discharge cycles with a constant current of 2 A for the charging process and 0.5 A for the discharging process using Turnigy Accucell. During the charge-discharge cycle test, monitoring and recording of voltage data is carried out using a Laptop PC. Data processing uses WebplotDigitizer and Microsoft Excel for data graphing. The results are analyzed and used to identify the operating voltage of the battery by taking the average voltage over five charge-discharge cycles. The average voltage is 13.98 V for the charging process and 12.11 V for the discharging process. Six-cell battery with full capacity works at a voltage range of 12.11-13.98 V. In the process of charging with a constant current of 2 A, the battery takes an average of 7.49 hours. So, the charging capacity can be estimated at 14,980 mAh. Whereas the battery discharge process takes an average of 11 hours with a constant current of 0.5 A to a voltage drop of 10.81 V. The resulting capacity of the discharged battery is 5500 mAh.
 Keywords: flowing electrolyte, lead acid battery, operating voltage

Impact of war on HIV-related healthcare services and health workers in eastern Ukraine: a qualitative study

ABSTRACT The 2022 Russian invasion of Ukraine has caused serious challenges for healthcare workers (HCWs) and HIV-related healthcare services. This study assessed the effects of the invasion on HCWs wellbeing and on continuity of HIV services, using in-depth interviews with HCWs from facilities offering HIV antiretroviral therapy (ART) and pre-exposure prophylaxis (PrEP) services in the Donetsk region of Eastern Ukraine. A directed content analysis, with both inductive and deductive approaches, was conducted. Ten HCWs (6 [60%] doctors, 4 [40%] nurses; 9 [90%] female) were interviewed. Six respondents were displaced from their homes and worksites, and all described stress and threats to emotional wellbeing. HCWs used online consultations, encrypted mobile communication, and multi-month dispensing to support continuity of ART and PrEP services. They noted immediate needs for psychological and financial support, and access to laptop computers and mobile communications to ensure continuity of HIV services. Priorities for restoration of services include repair of health facilities, restoration of laboratory services and supply chains, and return of personnel and patients. HCWs made innovative, rapid adaptations to HIV services to keep ART and PrEP services running, demonstrating the resolve of Ukrainian HCWs to maintain continuity of HIV services despite the disruptions of war.