Reviewerly/exHarmony huggingface dataset:
Intergenerational Interventions to Cultivate DEI-Champions: Increased Resilience and Accountability Through “Shipboard” Experiences

We developed All-ABOARD (Alliance Building Offshore to Achieve Resilience and Diversity) to meet the ever-increasing needs of cultivating a diverse geoscience workforce. All-ABOARD incorporates the Be the Messenger theoretical framework in all programmatic aspects to encourage participants to think about their own identities, positionalities, and privileges. Drawing from US-based institutions, we recruited four teams of four to five members who spanned a spectrum of positionality and career stages. To evaluate the efficacy of the program, we collected both quantitative and qualitative data at different intervals to measure changes in participants’ understanding and perception of identity, culture, respect, and diversity. The year-long core programming included regular webinars via Zoom and an in-person retreat. We found that immersive experiences and intergenerational teams led to the cultivation of a strong identity as a DEI-champion, enhanced group cohesion, and promoted feelings of resilience among participants. Our participants reported they felt most accountable to themselves and their teams, and that learning was accelerated by bringing together teams from multiple institutions to collaborate across intergenerational boundaries. Our program provides a model for training DEI-champions in geoscience who can advance strategic objectives in their home environments and demonstrates how frameworks from the social sciences can be effectively leveraged to transform geoscience.
NewsInsight: A Comprehensive Video Event Retrieval System with Spatial Insights and Query Assistance

Video event retrieval is the task of finding videos that are relevant to a given query. It is a challenging problem because videos are typically much larger than images, and they can contain a variety of different objects and scenes. However, there are a number of different approaches to video retrieval, and the field is rapidly evolving. Some of the most promising research directions include the use of deep learning and multimodal features. In this paper, we introduce NewsInsight – a comprehensive video event retrieval system developed for participating AI Challenge 2023. The system under investigation leverages the Bootstrapping Language-Image Pre-training (BLIP) model for zero-shot image-text retrieval, demonstrating superior recall scores on the Flickr30K dataset compared to the Contrastive Language–Image Pretraining (CLIP) model. In addition, it employs an Elastic Search filtering mechanism to discard irrelevant images. Beyond semantic search mechanisms, the system supports visual similarity search by calculating the inner product distance between vectors in the video frames corpus and the query image. The system also incorporates an explicit relevance feedback function, AI-based query description rewriting, and visual-example-generating features, enhancing the precision of the query description and aiding end-users in formulating a more accurate depiction of the targeted image for retrieval.
Enhancement of multimode entanglement and asymmetric steering by noiseless linear amplification

Abstract Quantum entanglement and Einstein-Podolsky-Rosen (EPR) steering is an important resource for quantum information. However, it is very fragile and prone to decoherence. Recently, it has been shown that the noiseless linear amplification (NLA) which can be applied to any channel with loss and noise can effectively counteract the effect of quantum decoherence. Besides, the initial quantum correlation can even be exceed by using this method. Thus, it is a useful tool in enhancing quantum entanglement. In this paper, we apply NLA to the tripartite GHZ entangled state and analyze quantum entanglement and steering after NLA. The results showed that the NLA can effectively improve quantum entanglement and expand the range of quantum steering. Simultaneously, they also provide useful direction for the distillation of quantum state using NLA in practical quantum communication.
Exploring Methodological Approaches in Secondary Education in Castellón, Spain: A Comparative Analysis of Traditional, Blended, and Innovative Teaching

The traditional model of teaching-learning process, concerned with the knowledge acquisition within the confines of a classroom, has long prioritised exam preparation and measurable academic success through standardised tests. In contrast, hybrid education integrates in-person teaching with online learning, leveraging Information and Communication Technologies (ICTs) in order to create adaptable and comprehensive learning environments. This paradigm shift acknowledges students individuality, tailoring instruction to their unique learning needs. This research investigated the methodologies employed by secondary education instructors in the province of Castellón (Valencia, Spain), drawing insights from the experiences of 45 master's students specialising in Language and Literature and Language Teaching. Participants were prompted to share their experiences, focusing on identifying whether their supervisors utilised traditional, hybrid, or innovative teaching methodologies. The Padlet platform was employed in the research to collect qualitative data through open-ended responses, providing a rich source of information on the teaching and learning processes observed during 200-hours of internship. Data analysis revealed intriguing insights into the methodologies employed by supervisors. The results indicated a spectrum of teaching methodologies in Castellón's secondary schools, emphasising the importance of considering both traditional and hybrid approaches, while also promoting innovation in educational practices. In conclusion, the findings not only offered a picture of current teaching methods but also provided valuable insights for future discussions on curriculum development, teacher training, and the overall enhancement of the teaching-learning experience.
Focus on Physics: Aha! Moments When Teaching Acceleration

Click to increase image sizeClick to decrease image size Additional informationNotes on contributorsPaul G. HewittPaul G. Hewitt (pghewitt@aol.com) is the author of Conceptual Physics, new 13th edition; Conceptual Physical Science, 6th edition, coauthored with daughter Leslie Hewitt and nephew John Suchocki; and Conceptual Integrated Science, 3rd edition, with coauthors Suzanne Lyons, John Suchocki, and Jennifer Yeh.
CBHIR-based approach for histological image analysis on large scale datasets

Automatic analysis of histopathological images is a very challenging topic and it involves various tasks. While interpreting Whole Slide Images (WSI), pathologists require advanced tools to perform their analysis. Thus, we propose a content-based histopathological image retrieval (CBHIR) based on incremental phenotyping approach. The CBHIR aims to assist practitioners in the decision making into a diagnosis process while providing similar cases which clinicians can refer to interpret a current one. The phenotyping approach consists mainly in delving into the large histological image in order to discover the correlations that exist between the different regions. The phenotyping graph will be applied in the CBHIR system.
US states will step up moves on AI laws

Headline US: US states will step up moves on AI laws
Player detection method based on scale attention and scale equalization algorithm

Introduction Object detection methods for team ball games players often struggle due to their reliance on dataset scale statistics, resulting in missed detections for players with smaller bounding boxes and reduced accuracy for larger bounding boxes. Methods This study introduces a two-fold approach to address these challenges. Firstly, a novel multi-scale attention mechanism is proposed, aiming to reduce reliance on scale statistics by utilizing a specially created SIoU (Similar to Intersection over Union) label that explicitly represents multi-scale features. This label guides the training of multi-scale attention network modules at two granularity levels. Secondly, an integrated scale equalization algorithm within SIoU labels enhances the detection ability of multi-scale targets in imbalanced samples. Results and discussion Comparative experiments conducted on basketball, volleyball, and ice hockey datasets validate the proposed method. The relative optimal approach demonstrated improvements in the detection accuracy of players with smaller and larger scale bounding boxes by 11%, 7%, 15%, 8%, 9%, and 4%, respectively.
Efficiency and Quality Improvement In Pathology Diagnostic by Using Computational Pathology: Software-Based Analysis of Perineural Invasion in Colon Carcinoma

Introduction: Many pathology laboratories are transitioning from diagnostics with glass slides to diagnostics with whole-slide images (WSI). One of the advantages of digital pathology is the possibility of using the software. Annotations made by software could increase the efficiency and the quality of pathologists´ work. In this research, annotations for nerves were used in WSI´s of colon carcinoma cases to help the detection of perineural invasion to improve the efficiency (time needed to find perineural invasion) and quality (the possibility of finding more perineural invasion in colon carcinoma cases and standardization) of the diagnostics. Materials &amp; Methods: 4 pathologists got 148 colon carcinoma cases without annotations made by software to screen the slides for perineural invasion. After a minimum of 4 weeks of washing out, they got the same instances with annotations for nerves made by software to find perineural invasion. We compared the time they needed for the cases with and without annotations and the percentage of perineural invasion found with and without annotations. Results: All 4 pathologists took advantage of the annotations made by software regarding the time they needed to conclude the perineural invasion, and the difference between the average time of all 4 pathologists without and with the use of software was statistically significant. All 4 pathologists found individual more perineural invasion. However, the average % of found perineural invasion without and with the help of software needed to be more substantial.
HIGH DYNAMIC RANGE IMAGE COMPRESSION ON COMMODITY HARDWARE FOR REAL-TIME MAPPING APPLICATIONS

Abstract. This paper describes a lossy compression scheme for high dynamic range graylevel and color imagery for data transmission purposes in real-time mapping scenarios. The five stages of the implemented non-standard transform coder are written in portable C++ code and do not require specialized hardware to run. Storage space occupied by the bitmaps is reduced via a color space change, 2D integer discrete cosine transform (DCT) approximation, coefficient quantization, two-size run-length encoding and dictionary matching hinged on the LZ4 algorithm. Quantization matrices to eliminate insignificant DCT coefficients are derived from a representative image set through genetic optimization. The underlying fitness function incorporates the obtained output size, classic image quality metrics and the unique color count. Together with a zone-based adaptation mechanism, this allows to specify target bitrates instead of percentage values or abstract quality factors for the reduction rate to be directly matched to the available communication channel capacities. Results on a camera control unit of a fixed-wing unmanned aircraft system built around entry-level PC hardware revealed single-thread compression and decompression throughputs of several hundred mebibytes per second for full-swing 16 and 32 bit RGB imagery at medium compression ratios. A degradation in image quality compared to popular compression libraries could be identified, however, at acceptable levels statistically and visually.
Design and Implementation of Puzzle Adventure Game Based on Unity

With the progress of Gaming industry, the classic genre Puzzle adventure games, due to their friendly difficulty level, low learning curve, and interesting game mechanics, have been favored by a large number of players. This paper follows the guiding principles of software engineering and game development, designs and implements an adventure puzzle game using the Unity game engine. The game design is carried out through multiple aspects such as game art style, plot, sound effects, and game puzzles to provide players with an interesting gameplay experience. For the implementation part of the game, various plugins and functions of the Unity engine are integrated to complete the development of the game system, such as using Unitys navigation system to bake navigation meshes and implement automatic pathfinding for game characters. The animation state machine is used to arrange and control game character animations, and the Unity event system is used to implement click-based interactive functions in the game. At the same time, optimization was carried out based on the test results, improving the experience of user interface and the stability of game program, further enhancing the players gameplay experience. Finally, players can experience thrilling adventure in a mysterious world, and achieve a sense of accomplishment by solving puzzles and overcoming obstacles.
How Job Satisfaction is Influenced by Work Place Environment: An Empirical Study

This study refers to how job characteristics, work environment, pay satisfaction, and advancement opportunities influence employee’s job satisfaction. The study results showed that job clarity, effective communications with management, a participatory management approach, organizational support of career development, opportunities for advancement, and family-friendly policies are all significant variables affecting the job satisfaction of employees. The effect of pay satisfaction on employee job satisfaction was not significant. This study also found gender differences in factors affecting employee’s job satisfaction.&#x0D; The purpose of this research was to measure the effect of work motivation on employee job satisfaction in Madhyanchal Vidyut Vitran Nigam Limited (MVVNL), Lucknow. A Self-administered questionnaire consisting of several measures was employed to gather data from the technical and supporting employees who work in different sections of the MVVNL like the distribution, maintenance, office, area chief engineer and sub Stores of Lucknow. The gathered data were utilized to test the model using the PLS-SEM approach with Smart PLS. The results showed that the essential features of work motivation are manager’s leadership styles, motivation practices, employee job expectations, reward management system and working environment, whereas the identified consequence was employee job satisfaction. All five variables had a positive effect on job satisfaction and among them, employee job expectations and manager’s leadership style had the highest influence and the reward management system had the least influence on job satisfaction. Further, this study offers thoughts for managers by pinpointing the critical factors influencing that motivate employees in MVVNL.
Use of ICT by EFL Teachers in Young Learner Classroom during Covid-19 Pandemic at A State School in Garut

Since the COVID-19 pandemic, online learning has become familiar to teachers and students, including in learning English for young learners. ICT is useful for student presentation learning media, such as Power Point, and learning to use Microsoft Word, Excel, and others. ICT is also useful as an independent learning medium such as E-Learning which can be done at school or at home. The various types of ICT have begun to be used by teachers and students of ELT media. The type of ICT that teachers use in English learning activities for young learners is software, including WhatsApp and YouTube. From this study, the researchers can find out the perceptions of teachers and parents regarding the advantages and disadvantages of using ICT in learning English for young learners. This study used the descriptive method to reveal the research problems. The interview is used to collect the data was conducted to English teachers. One of them, there are 32.2% who have not been able to fully operate ICT as a learning medium. This becomes a difficulty for young learners because they have not been able to master technology properly and correctly due to limited understanding.
Simultaneous Localization and Actuation Using Electromagnetic Navigation Systems

Remote magnetic navigation provides a promising approach for improving the maneuverability and safety of surgical tools, such as catheters and endoscopes, in complex anatomies. The lack of existing localization systems compatible with this modality, beyond fluoroscopy and its harmful ionizing radiation, impedes its translation to clinical practice. To address this challenge, we propose a localization method that achieves full pose estimation by superimposing oscillating magnetic fields for localization onto actuation fields generated by an electromagnetic navigation system. The resulting magnetic field is measured using a three-axis magnetic field sensor embedded in the magnetic device to be localized. The method is evaluated on a three-coil system, and simultaneous actuation and localization is demonstrated with a magnetic catheter prototype with a Hall-effect sensor embedded at its tip. We demonstrate position estimation with mean accuracy and precision below 1 mm, and orientation estimation with mean errors below 2 <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$^\circ$</tex-math></inline-formula> at 10 Hz in a workspace of 80 × 80 × 60 mm. This contribution aims to advance the clinical adoption of remote magnetic navigation in minimally invasive surgery.
PS-NET: An End-to-End Phase Space Depth Estimation Approach for Computer-Generated Holograms

In the present work, an end-to-end approach is proposed for recovering an RGB-D scene representation directly from a hologram using its phase space representation. The proposed method involves four steps. First, a set of silhouette images is extracted from the hologram phase space representation. Second, a minimal 3D volume that describes these silhouettes is extracted. Third, the extracted 3D volume is decomposed into horizontal slices, and each slice is processed using a neural network to generate a coarse estimation of the scene geometry. Finally, a third neural network is employed to refine the estimation for higher precision applications. Experimental results demonstrate that the proposed approach yields faster and more accurate results compared to numerical reconstruction-based methods. Moreover, the obtained RGB-D representation can be directly utilized for alternative applications such as motion estimation.
Effects of Dosing and Mechanical Activation on the Performance of Reservoir Sediment-Cement Composite Cementitious Material

To solve the problem of reservoir sediment accumulation, this paper intends to study the feasibility of preparing reservoir sediment-cement composite cementitious material by partially replacing cement with reservoir sediment. Using the single factor test method, the replacement rate of reservoir sediment and mechanical activation time were selected as variables to explore the effects of water requirement of normal consistency, setting time, mortar fluidity, and mechanical properties of mortar specimens. The results show that with the increase of sediment replacement rate, the water requirement of normal consistency of composite cementitious material paste increases, the setting time increases, the fluidity of mortar decreases, and the mechanical properties of mortar specimens decrease significantly. With the increase of mechanical activation time, the water requirement of normal consistency decreases, the setting time decreases, the fluidity of mortar increases, and the mechanical properties of the mortar specimen increase. The microcosmic results show that mechanical activation can promote the formation of C-S-H gel in reservoir sediment, and fill each other with unreacted reservoir sediment particles, resulting in a denser microstructure. The purpose of this paper is to provide theoretical support for the preparation of reservoir mud-cement composite cementitious material.
Artificial Intelligence–Powered Assessment of Pathologic Response to Neoadjuvant Atezolizumab in Patients With NSCLC: Results From the LCMC3 Study

<h2>Abstract</h2><h3>Introduction</h3> Pathologic response (PathR) by histopathologic assessment of resected specimens may be an early clinical end point associated with long-term outcomes with neoadjuvant therapy. Digital pathology may improve the efficiency and precision of PathR assessment. LCMC3 (NCT02927301) evaluated neoadjuvant atezolizumab in patients with resectable NSCLC and reported a 20% major PathR rate. <h3>Methods</h3> We determined PathR in primary tumor resection specimens using guidelines-based visual techniques and developed a convolutional neural network model using the same criteria to digitally measure the percent viable tumor on whole-slide images. Concordance was evaluated between visual determination of percent viable tumor (n = 151) performed by one of the 47 local pathologists and three central pathologists. <h3>Results</h3> For concordance among visual determination of percent viable tumor, the interclass correlation coefficient was 0.87 (95% confidence interval [CI]: 0.84–0.90). Agreement for visually assessed 10% or less viable tumor (major PathR [MPR]) in the primary tumor was 92.1% (Fleiss kappa = 0.83). Digitally assessed percent viable tumor (n = 136) correlated with visual assessment (Pearson r = 0.73; digital/visual slope = 0.28). Digitally assessed MPR predicted visually assessed MPR with outstanding discrimination (area under receiver operating characteristic curve, 0.98) and was associated with longer disease-free survival (hazard ratio [HR] = 0.30; 95% CI: 0.09–0.97, <i>p</i> = 0.033) and overall survival (HR = 0.14, 95% CI: 0.02–1.06, <i>p</i> = 0.027) versus no MPR. Digitally assessed PathR strongly correlated with visual measurements. <h3>Conclusions</h3> Artificial intelligence–powered digital pathology exhibits promise in assisting pathologic assessments in neoadjuvant NSCLC clinical trials. The development of artificial intelligence–powered approaches in clinical settings may aid pathologists in clinical operations, including routine PathR assessments, and subsequently support improved patient care and long-term outcomes.
The Methodological Framework and Meaningful Learning in Thesis Research at the Graduate School of the Peruvian Police

The experience of the human being acquired throughout his life is fundamentally the raw material with what is contributed to society, the research aimed to determine the application of significant learning in the writing of the methodological framework of the thesis. The methodology, due to its nature of study, was applied. It was developed from the perspective of the quantitative approach in logic and deductive reasoning, at the descriptive-comparative level, used a quasi-experimental design, for statistical analysis the Minitab software was used, the samples were (5) for the control group and (5) for the experimental group, the Chi-Square distribution was chosen to analyze the correspondence if they differ significantly from each other. Data collection was carried out using instruments built following the criteria; (a) Pre-Test (First Test) to know the previous knowledge; (b) Post-Test (Final Test) The results allowed us to appreciate that there are significant differences between the control group and the experimental group (Post-Test) noting that the students of the experimental group obtained higher scores surpassing the students of the control group who received the learning with the traditional method.