Input Options Research Articles

Program Directors' Selection Criteria for Urology Residency Match in a United States Medical Licensing Examination Step 1 Pass/Fail Era.

Program directors of urology residencies have historically weighted the United States Medical Licensing Examination Step 1 heavily to select interview candidates. In January 2022, the 3-digit Step 1 score changed to pass/fail, aiming to reduce the burden of examination preparation and promote more holistic review, yet, in doing so, abolished a key objective metric. This study examines the criteria now prioritized in the selection of urology candidates to interview. A survey was distributed to program directors of 150 nationally accredited urology residency programs, consisting of 26 factors across 4 domains: (1) academics, (2) extracurriculars, (3) virtual etiquette, and (4) applicant diversity. Respondents rated each factor on a 1 to 10 scale (from no importance to most important), with free-text options for additional input. Forty-six program directors completed the survey. The top 5 factors were (1) urology letters of recommendation (mean ± SD, 8.58 ± 1.57), (2) signaling (8.56 ± 1.69), (3) virtual professionalism (7.33 ± 2.71), (4) completion of a subinternship at the director's program (7.22 ± 2.20), and (5) camera on during virtual meetings (7.04 ± 2.75). Notably, a Step 2 score ≥ 250 (5.84 ± 2.45) ranked sixth among 9 academic factors. The shift to a pass/fail Step 1 has reshaped urology applicant selection, where Step 2 scores do not seem to replace Step 1 as a primary metric. The implications of these changes, including their impact on holistic application review and candidate selection, remain to be determined.

NaturePred: A Tool for Revolutionizing Natural Product Classification with Artificial Intelligence

Background: The identification and classification of natural products are vital in drug discovery and bioactive compound exploration. Traditional methods are laborious and timeconsuming, necessitating innovative tools for accurate predictions using advanced AI techniques. Objectives: This paper presents NaturePred, a user-friendly tool designed to predict the class of natural products and calculate eight physicochemical properties of protein sequences. It aims to accurately predict five distinct classes of natural product biosynthetic gene clusters (BGCs): Polyketide Synthases (PKS), Non-ribosomal Peptide Synthetases (NRPS), Ribosomally Synthesized and Post- Translationally Modified Peptides (RiPPs), Terpenes, and PKS-NRPS Hybrids. It also addresses reliability in multi-class classification with a 90% confidence score threshold. Method: NaturePred offers three input options: single protein sequence, CSV file, or GenBank (.gbk) file. It uses a pipeline with a Natural Language Processing model based on TF-IDF (Term Frequency- Inverse Document Frequency) and a Logistic Regression classifier. Predictions are made if the confidence score exceeds 90%; otherwise, "None of the above class" is predicted. Evaluation with unseen data from the MiBIG database shows high accuracy (~96%) in assigning BGCs. Results: NaturePred provides accurate predictions with high confidence scores, demonstrating reliability across different datasets. It calculates eight physicochemical properties of protein sequences, offering valuable insights for further analysis. Conclusion: NaturePred's integrated features, including versatile input options, accurate predictions, and physicochemical property calculations, make it an indispensable tool in natural product research. By addressing classification challenges, NaturePred facilitates drug discovery and bioactive compound exploration, advancing the field. Tool available: (http://login1.cabgrid.res.in:5101/).

Enhancing Growth and Zinc Bioavailability in Rice (Oryza sativa L.) Cultivars through Agronomic Biofortification Strategies

A field experiment was conducted using four rice varieties viz., Uma, Pournami, Gouri and DRR Dhan 45 under varying levels of ZnSO4 foliar application (control, 0.1 per cent, 0.5 per cent and 1.0 per cent) in a randomized complete block design. ZnSO4 @ 1.0 per cent recorded taller plants at panicle initiation (80.5 cm) and harvest stages (113 cm) and was comparable with ZnSO4 @ 0.5 per cent. Higher tillers per hill and dry matter production were also recorded at panicle initiation and harvest stages with ZnSO4 @ 1.0 per cent which was statistically similar to ZnSO4 @ 0.5 per cent. Zinc application at 0.5 per cent and 1.0 per cent enhanced Zn concentration in rough rice, brown rice, white rice, and rice bran. The highest Zn accumulation in white rice (21.2 mg kg⁻¹) was achieved with 1.0 per cent ZnSO₄ foliar spray, which was comparable to 0.5 per cent ZnSO₄ spray. Application of 1 per cent ZnSO₄ during maximum tillering and milk stage led to substantial reductions in the phytate: Zn molar ratio in rough rice (28.3), brown rice (32.7), white rice (6.31), and rice bran (25.9). Dhan 45 treated with 1.0 per cent ZnSO₄ achieved the lowest ratios in rough rice (18.9) and brown rice (21.8). Although the 1.0 per cent treatment yielded greater reductions in the phytate: Zn molar ratio, the 0.5 per cent ZnSO₄ treatment produced notable decrease in rough rice (30.2), brown rice (34.8), white rice (6.72), and rice bran (27.6) making it a viable option for lower Zn input. Overall, foliar application of Zn improved Zn bioavailability in both whole grains and milled rice, aligning phytate: Zn molar ratios closer to optimal levels for human nutrition.

E-participation in energy transitions: What does it mean? Chances and challenges within Germany's Energiewende

Does e-participation generate more democratic legitimacy in the context of the German Energiewende through more input and procedural justice, better outputs and environmental outcomes that are accepted by the public? In recent years, many forms of e-participation have emerged, and space for experiments with visual and interactive technologies has been created. However, research evaluating e-participation technologies in energy transitions is lacking. This study uses interviews with 33 stakeholders in a planned wind farm project, combined with online survey results, to derive empirical insights into attitudes towards and preferences for e-participation. On the one hand, we observe openness towards new options for creative input and visualization-based online tools and virtual realities. On the other hand, stakeholders fear vulnerabilities and are skeptical about the efficacy of online discourse. Institutional stakeholders primarily see risks in civic participation, while citizens themselves see the benefits. Unexploited potential for creative input, deliberation, and collaborative planning could be leveraged to increase democratic legitimacy. In this context, better outputs are not synonymous with accepting outcomes, but can include changes to or even rejection of plans based on public preferences and concerns. This potential remains largely untapped, however, and requires a willingness to participate, thus calling for mobilization strategies.

Numerical simulation of the thermo-mechanical coupling behavior of skin tissue exposed to repetitive pulse laser irradiation

A thorough understanding of the thermo-mechanical coupling behavior during the interaction between pulsed laser and skin tissue is a prerequisite for successful application of pulsed laser technology in the treatment of various skin diseases and injuries. Taking into account the complex physiological structure of skin tissue, a theoretical model is established to characterize the thermo-mechanical response of multi-layer skin tissue with variable physical properties, in which the non-linear governing equations of bio-thermo-mechanics with dual-phase lag mechanism and Henrique burn equation are involved. The finite difference method was employed to obtain the time-space distribution of skin temperature, displacement and stress under repeated pulse laser action, and the incidental thermal damage was further evaluated on this basis. The numerical results stated that: i) repeated pulse laser can significant reduce the peak temperature of skin tissue and further reduce or even eliminate thermal damage under the premise of required energy threshold, ii) the thermo-mechanical coupling response and potential thermal damage will be inhibited when the temperature dependence of physical parameter is considered, in which the thermal stress is more sensitive to the temperature dependence than others, iii) the thermal damage is more sensitive to the input parameters than that of thermo-mechanical response, and the burn time can be effectively delayed or even avoided for appropriate input options.

TitrationAnalysis: a tool for high throughput binding kinetics data analysis for multiple label-free platforms

Label-free techniques including Surface Plasmon Resonance (SPR) and Biolayer Interferometry (BLI) are biophysical tools widely used to collect binding kinetics data of bimolecular interactions. To efficiently analyze SPR and BLI binding kinetics data, we have built a new high throughput analysis tool named the TitrationAnalysis. It can be used as a package in the Mathematica scripting environment and ultilize the non-linear curve-fitting module of Mathematica for its core function. This tool can fit the binding time course data and estimate association and dissociation rate constants (ka and kd respectively) for determining apparent dissociation constant (KD ) values. The high throughput fitting process is automatic, requires minimal knowledge on Mathematica scripting and can be applied to data from multiple label-free platforms. We demonstrate that the TitrationAnalysis is optimal to analyze antibody-antigen binding data acquired on Biacore T200 (SPR), Carterra LSA (SPR imaging) and ForteBio Octet Red384 (BLI) platforms. The ka , kd and KD values derived using TitrationAnalysis very closely matched the results from the commercial analysis software provided specifically for these instruments. Additionally, the TitrationAnalysis tool generates user-directed customizable results output that can be readily used in downstream Data Quality Control associated with Good Clinical Laboratory Practice operations. With the versatility in source of data input source and options of analysis result output, the TitrationAnalysis high throughput analysis tool offers investigators a powerful alternative in biomolecular interaction characterization.

Estimation of battery temperature during drive cycle operation by the time evolution of voltage and current

During the operation of electric vehicles, accurate temperature monitoring of lithium-ion batteries by battery management system is crucial to their safety. However, temperature sensors possess limitations as they necessitate regular calibration to avoid inaccuracies, which can be challenging to accomplish in certain scenarios. As an alternative, a general sensorless temperature estimation framework is developed in this study to either serve as a complementary approach alongside sensors or function independently to ensure reliable temperature measurement. The proposed framework relies solely on data regarding battery voltage and current measurements, and this information is employed to determine the parameters of the equivalent circuit model via the vector-type recursive least squares algorithm in the first step. In the second step, these parameters and the voltage/current data serve as the input to the deep neural network (DNN) to estimate the instantaneous battery surface temperature during dynamic driving cycles under various ambient temperatures and degradation scenarios. To reflect the effect of past battery operation on the current temperature, a newly defined current integral function is proposed, by which the input time length of the DNN model is suggested. Four combinations of the model input are studied, and the best input option yields an average estimation error of the battery temperature that falls below 0.5 °C.

Eye-Hand Typing: Eye Gaze Assisted Finger Typing via Bayesian Processes in AR.

Nowadays, AR HMDs are widely used in scenarios such as intelligent manufacturing and digital factories. In a factory environment, fast and accurate text input is crucial for operators' efficiency and task completion quality. However, the traditional AR keyboard may not meet this requirement, and the noisy environment is unsuitable for voice input. In this article, we introduce Eye-Hand Typing, an intelligent AR keyboard. We leverage the speed advantage of eye gaze and use a Bayesian process based on the information of gaze points to infer users' text input intentions. We improve the underlying keyboard algorithm without changing user input habits, thereby improving factory users' text input speed and accuracy. In real-time applications, when the user's gaze point is on the keyboard, the Bayesian process can predict the most likely characters, vocabulary, or commands that the user will input based on the position and duration of the gaze point and input history. The system can enlarge and highlight recommended text input options based on the predicted results, thereby improving user input efficiency. A user study showed that compared with the current HoloLens 2 system keyboard, Eye-Hand Typing could reduce input error rates by 28.31 % and improve text input speed by 14.5%. It also outperformed a gaze-only technique, being 43.05% more accurate and 39.55% faster. And it was no significant compromise in eye fatigue. Users also showed positive preferences.

Integration into 3D Printing for Image Processing using AI ML

The challenges of physical measurement for prosthetic development and highlight how our integrated tool offers a remote solution to address these challenges, promoting accessibility, convenience, and active participation in the prosthetic design process. Our Measurement Module for Prosthetic Hand Dimensions is a comparison of Image Processing and Distance Measurement Methods The creation of a module to precisely measure finger dimensions for the creation of prosthetic hands is the main goal of this research project. The module provides distance measurement and image-based input as two different ways to get dimensions. Users place their hands at predetermined distances from a screen in the distance measurement option, while users submit photographs of their hands with reference objects for scale in the image based input option. In order To provide a proper fit and functionality, the collected dimensions are an essential component of the prosthetic hand construction process. The study contrasts the efficiency of distance measurement methods employing cameras or other cameras with image processing techniques like edge recognition and contour analysis. Accuracy, usability, and efficiency are among the factors that are assessed to identify the best method for acquiring dimensions. OpenCV for image processing, TensorFlow for machine learning-based analysis, Tkinter for UI design, Mediapipe for landmark identification and hand tracking, and NumPy for numerical operations are just a few of the open-source libraries that are used in this module. The module's possible influence on improving the creation and customization of prosthetic hands is also covered. By offering insights into effective and precise dimension measurement methodologies for customized prosthetic hand design, the study's findings advance the area of prosthetics

Development of an open-source solution to facilitate the use of one-button wearables in experience sampling designs

The experience sampling method (ESM) allows for a high degree of ecological validity compared to laboratory research, at the cost of greater effort for participants. It would therefore benefit from implementations that reduce participant effort. In the present paper, we introduce a screenless wrist-worn one-button wearable as an unobtrusive measurement method that can be employed in ESM designs. We developed an open-source Android application to make this commercially available wearable easily configurable and usable. Over the course of six pilot studies, we explored the technical viability (e.g., battery life, reliability of inputs) of this wearable. We compared data quality between wearables and smartphones in a within-subjects design, exploring both the input options of using the number of button presses as a Likert scale, as well as using the angle of the device as a Physical Analogue Scale. Assessments of Extraversion made with either of these methods were highly correlated to comparable assessments made with comparable methods on a smartphone (i.e., Likert scale or a Visual Analogue Scale, respectively). Furthermore, in a preregistered ESM field experiment (N = 134, 4 weeks), we compared compliance to real-life event triggers between wearable devices and smartphones. We found higher numbers of logged events in the wearable group, indicating better adherence to the event-contingent scheduling. Overall, despite the device’s minimal capabilities and resulting limitations, one-button wearables can be beneficial for use in ESM designs.

Exploring the impacts of using the artificial intelligence voice-enabled chatbots on customers interactions in the United Arab Emirates

<p>Over the past decade, chatbots have experienced a significant increase in<br />popularity, especially since the outbreak of COVID-19. In the United Arab<br />Emirates, most businesses have accelerated their digital transformation and<br />are relying on chatbots as a primary way to interact with customers. However,<br />many of these chatbots lack a voice input option for customers. This study<br />investigates the benefits and challenges of incorporating artificial intelligence<br />(AI) voice-enabled chatbots into United Arab Emirates (UAE) businesses and<br />how this impacts customer experience. Qualitative research techniques were<br />used to gather information, and the results demonstrate that implementing AI<br />chatbots with voice input and sentiment analysis features can enhance the<br />customer experience by making it more efficient and convenient.<br />Additionally, the study found that AI chatbots can ultimately save businesses<br />time and money, and while they may reduce the need for human agents, they<br />will not replace them entirely. Finally, an implementation framework and<br />suggestions are provided for businesses that are interested in adopting AI<br />voice-enabled chatbots for customer interactions.</p>

Mechanical Device or Touchscreen Widget: The Effects of Input Device and Task Size on Data Entry on the Primary Flight Display

Due to their customizability, touchscreens continue to advance as a device of choice when designing aircraft cockpits. Previous studies investigated the effect of turbulence on human performance when using touchscreens, but few have evaluated its performance for realistic aviation-specific tasks. In this study, we compared four touchscreen widgets and three mechanical devices during realistic data input on a primary flight display (PFD). Twenty participants took part in the experiment at a constant level of vibration, while simultaneously completing a secondary tracking task. Results indicated that virtual keypads lead to faster completion time for medium to large changes while keeping error rates low. Rotary knobs were fastest for small changes. Virtual keypads also had lower workload and discomfort compared to rotary knobs and drag-based widgets. We found the completion time to be the most important factor in tracking task performance, which translated in higher precision for keypads. These findings suggest that virtual keypads represent an efficient and secure option for numerical data input at low-to-medium vibration.

Towards Greater Inclusion and Accessibility for Physically Disabled Players in Location-Based Games

Most research on Location-Based Games (LBGs) has been conducted on non-disabled players, meaning the experiences of disabled players are not well understood and potentially overlooked. As such, this research aimed to address the following questions: 1). What are the barriers and needs of disabled LBGs players? 2). How can LBGs be made more inclusive and accessible? To address these questions, two studies were conducted in the form of an online qualitative questionnaire and a series of co-design workshops. Results articulate the needs, barriers, and desired features of 60 participants. Based on our findings and through the collaborative work with disabled players we articulate 4 guidelines for game designers and developers aimed at improving the accessibility of LBGs: 1) Adding avatar features that better represent disabled characteristics, 2). Providing different input options for phone interactions, 3) Allowing for disabled specific accounts and 4) Introducing a messenger feature to LBGs.

Soil health metrics reflect yields in long-term cropping system experiments

Soil health metrics with strong links to ecological function and agricultural productivity are needed to ensure that future management of agricultural systems meets sustainability goals. While ecological metrics and crop yields are often considered separately from one another, our work sought to assess the links between the two in an agricultural context where productivity is a key consideration. Here, we investigated the value of soil health tests in terms of their relevance to agricultural management practices and crop yields at contrasting long term cropping systems experiments. One site was on a sandy loam Leptic Podzol and the other on a sandy clay loam Endostagnic Luvisol. Furthermore, the experiments had different management systems. One contained legume-supported rotations with different grass-clover ley durations and organic amendment usage, while the other compared a range of nutrient input options through fertiliser and organic amendments on the same rotation without ley periods. Metrics included field tests (earthworm counts and visual evaluation of soil structure scores) with laboratory analysis of soil structure, chemistry and biology. This analysis included bulk density, macroporosity, pH, available phosphorus, exchangeable potassium, soil organic matter and potentially mineralizable nitrogen. Using a novel combination of long-term experiments, management systems and distinctive soil types, we demonstrated that as well as providing nutrients, agricultural management which resulted in better soil organic matter, pH, potassium and bulk density was correlated with higher crop yields. The importance of ley duration and potentially mineralizable nitrogen to yield in legume-supported systems showed the impact of agricultural management on soil biology. In systems with applications of synthetic fertiliser, earthworm counts and visual evaluation of soil structure scores were correlated with higher yields. We concluded that agricultural management altered yields not just through direct supply of nutrients to crops, but also through the changes in soil health measured by simple metrics.

WAVE TRANSFORMATION AND RUNUP VARIABILITY DUE TO WAVE PHASE UNCERTAINTY

Wave runup is a significant portion of total water levels, particularly during storms (Sallenger, 2000). This makes accurate prediction of wave runup paramount. Wave runup can be estimated using simplified empirical models (e.g. Stockdon et al 2006) or simulated using phase-resolved Bousinesq models (Shi et al 2007, Lynett et al 2002) or non-hydrostatic models (e.g. Zijlema et al 2011). These models typically have a variety of offshore boundary input options ranging from spectral to time series, with the most broadly used being spectral parameterizations (e.g. JONSWAP, TMA, etc.) or direct spectral input, both of which neglect phase information at the offshore boundary. Recent research has highlighted the importance of the offshore boundary condition and wave phase at modeling wave runup (e.g. Fiedler et al 2019, Rutten 2021). This work pays careful attention to the consequences of the bound infragravity (IG) wave and explores the influence that unknown phase information can have on predicted wave transformation and resultant wave runup in the context of field observations.

On Some Issues of Analyzing Handwriting Objects Submitted for Examination in Electronic Form

The article discusses the issues of analyzing objects of handwriting examination provided in electronic form. The authors describe the ways of obtaining such manuscripts and their features determined by the type of the electronic documents submitted for examination. They also provide some information on the possibility of establishing if the manuscript was made using the handwriting input option, the specifics of such objects, and their differences from digitized manuscripts. The peculiarities of the stage of separate research of manuscripts presented in electronic files are outlined, as well as the problems of formulating conclusions are addressed. Finally, the authors determine the directions for further scientific research on the matters raised.

Flame (v2.0): advanced integration and interpretation of functional enrichment results from multiple sources.

. http://flame.pavlopouloslab.info. https://github.com/PavlopoulosLab/Flame. https://hub.docker.com/r/pavlopouloslab/flame. Supplementary data are available at Bioinformatics online.

TitrationAnalysis: a tool for high throughput binding kinetics data analysis for multiple label-free platforms.

Label-free techniques including Surface Plasmon Resonance (SPR) and Biolayer Interferometry (BLI) are biophysical tools widely used to collect binding kinetics data of bimolecular interactions. To efficiently analyze SPR and BLI binding kinetics data, we have built a new high throughput analysis tool named the TitrationAnalysis. It can be used as a package in the Mathematica scripting environment and ultilize the non-linear curve-fitting module of Mathematica for its core function. This tool can fit the binding time course data and estimate association and dissociation rate constants ( k a and k d respectively) for determining apparent dissociation constant ( K D ) values. The high throughput fitting process is automatic, requires minimal knowledge on Mathematica scripting and can be applied to data from multiple label-free platforms. We demonstrate that the TitrationAnalysis is optimal to analyze antibody-antigen binding data acquired on Biacore T200 (SPR), Carterra LSA (SPR imaging) and ForteBio Octet Red384 (BLI) platforms. The k a , k d and K D values derived using TitrationAnalysis very closely matched the results from the commercial analysis software provided specifically for these instruments. Additionally, the TitrationAnalysis tool generates user-directed customizable results output that can be readily used in downstream Data Quality Control associated with Good Clinical Laboratory Practice operations. With the versatility in source of data input source and options of analysis result output, the TitrationAnalysis high throughput analysis tool offers investigators a powerful alternative in biomolecular interaction characterization.

Optimizing expert and patient input in pediatric trial design: Lessons learned and recommendations from a collaboration between conect4children and European Patient-CEntric ClinicAl TRial PLatforms.

Advice from multiple stakeholders is required to design the optimal pediatric clinical trial. We present recommendations for acquiring advice from trial experts and patients/caregivers, derived from advice meetings that were performed through a collaboration of the Collaborative Network for European Clinical Trials for Children (c4c) and the European Patient-CEntric ClinicAl TRial PLatforms (EU-PEARL). Three advice meetings were performed: (1) an advice meeting for clinical and methodology experts, (2) an advice meeting for patients/caregivers, and (3) a combined meeting with both experts and patients/caregivers. Trial experts were recruited from c4c database. Patients/caregivers were recruited through a patient organization. Participants were asked to provide input on a trial protocol, including endpoints, outcomes, and the assessment schedule. Ten experts, 10 patients, and 13 caregivers participated. The advice meetings resulted in modification of eligibility criteria and outcome measures. We have provided recommendations for the most effective meeting type per protocol topic. Topics with limited options for patient input were most efficiently discussed in expert advice meetings. Other topics benefit from patient/caregiver input, either through a combined meeting with experts or a patients/caregivers-only advice meeting. Some topics, such as endpoints and outcome measures, are suitable for all meeting types. Combined sessions profit from synergy between experts and patients/caregivers, balancing input on protocol scientific feasibility and acceptability. Both experts and patients/caregivers provided critical input on the presented protocol. The combined meeting was the most effective methodology for most protocol topics. The presented methodology can be used effectively to acquire expert and patient feedback.

MULTIPLE PATH PARTICLE DOSIMETRY MODEL CONCEPT AND ITS APPLICATION TO DETERMINE RESPIRATORY TRACT HAZARDS IN THE 3D PRINTING

The Multiple Path Particle Dosimetry (MPPD) model is computer software that estimates and visualizes the deposition, clearance, and retention of particles in the respiratory tract systems of humans, rats, and other species. The mathematical model provides a broad spectrum of settings and input options. This research aims to explore the MPPD model concept and determine the deposition fraction (DF), clearance, and retained mass in the human respiratory tract (HRT) based on the geometric mean diameter (GMD) and mass concentration (MC) of particulate matter (PM) emitted during the 3D printing process. We used the real-time air sample data collected during the 8-hour working shift in the 3D printing office. Ultrafine PM deposits mainly in lungs (56%), fine PM mostly deposits in the upper respiratory tract (URT) (41%) and lungs (39%), but coarse PM mostly deposits in the URT (81%). The biggest DF in lower respiratory tract is ultrafine PM (487 μg), the smaller DF is coarse PM (185 μg) and the smallest DF is fine PM (123 μg). The biggest DF in lung for all PM - lower lobes (fine PM - 60%, ultrafine PM, coarse PM - 61%). In a model, where exposure was 5 hours a day, five days a week, during one month, followed by one year of post-exposure period, it was shown that retained mass in the tracheobronchial (TB) region was 1% for ultrafine and coarse PM each, 2% for fine PM, and 55% for all PM in the pulmonary region. The MPPD software is an easily accessible and valuable tool for assessing the impact of PM on the HRT. Particulate matter decreasing in diameter, tend to deposit mostly in the deeper levels of HRT. Tracheobronchial region clearance is more rapid than pulmonary region clearance. Potentially for persons using the 3D-printer regularly the worst health impact could be associated with smaller size of PM, due to tendency deposit mostly in pulmonary region where the clearance rate is slower.