Reviewerly/exHarmony huggingface dataset:
Application of the variability budget approach to the Dissolution test

The aim. This study aimed to evaluate the completeness of our knowledge about the sources of variation in the Dissolution test with 100 % release by compiling a variability budget.&#x0D; Materials and methods. The study was performed on 500 mg metformin tablets, using pharmacopoeial quality reagents, State Pharmacopoeia of Ukraine (SPhU) Metformin HCl reference standard, Pharmatest DT70 Dissolution apparatus, Perkin Elmer Lambda 35 spectrophotometer, Mettler Toledo XP 204 analytical balance, and ISO class A volumetric glassware. The SPhU metrological approach was employed.&#x0D; Results and discussion. The variability budget was compiled based on the comparison of uncertainty estimates obtained from the requirements for maximum permissible variation in normal analytical practice (UNAP, bottom-up estimation) and experimental data (Uexp). This involved characterizing Metformin content in tablets using the Uniformity of Dosage Units (UDU) test as an independent method. The 100 % release of Metformin in the Dissolution test (infinity point) was proved by increasing the dissolution time. Having optimized Dissolution and UDU analytical procedures for variability budget compiling, we achieved insignificance of Uexp compared to the target uncertainty (Utg) for the Dissolution test in compliance testing. The differences in UDU and Dissolution mean results did not exceed UNAP for the release time of 45 and 60 min, i.e. uncertainty budget was proven. Uexp for the Dissolution test indicated the presence of an unknown statistically significant source of random variation, which, however, was less than Utg; therefore, the procedure is suitable for compliance testing.&#x0D; Conclusion. Experimental results confirmed the completeness of our knowledge about sources of variation (absence of bias) for the Dissolution test with 100 % release. An essential condition for compiling the budget was the optimization of uncertainty of analytical procedures. For UDU, all significant sources of variation were within the expected range. Yet, there is a need for additional research to identify and manage an unknown source of practically significant random variation for the Dissolution test
Exploration of the Utilization of Foreign Museum Resources in Junior High School World History Contextual Teaching – A Case Study on the Meiji Restoration

Addressing common issues in junior high school world history learning, such as unfamiliar content and weak spatial awareness, effectively using foreign museum resources in teaching through contextual methods proves to be a successful strategy. When selecting contextual teaching resources, it is crucial to adhere to principles based on student needs, teaching content, and the correct value orientation. Implementation should be based on students' experiences to achieve significant teaching results.
Report on English Vocabulary Teaching Practice in Primary School Based on Interactive Teaching Method

The famous German educator Zimmer proposed that the interactive teaching method has its roots in sociology. That is, people gain experience in the process of communicating for their own development. However, in the teaching process, English teaching focuses more on the communication between the teacher and the students. Through classroom interaction students constantly develop the ability to question and think creatively. In addition, this teaching method has become one of the more popular teaching methods as teachers design lesson plans with students’ interests in mind. Interactive teaching method has the qualities of variety, immortal, discipline, etc. This paper hopes that the practical study of interactive teaching methods will achieve the goal of increasing students’ interest as well as improving their learning through the integrated used of teaching methods. This study took the grade six of Chen guang Primary School, Fan cun Township, Kaifeng County, Henan Province as an example for classroom research. In the process of learning English, the interactive teaching method is more appropriate to elementary school students whose personalities are presently inception and advancement to investigate their true capacity. Eventually, the interactive teaching method continues to be advocated in the course of teaching practice.
An Analysis of Xia Nai's Museum Education Thought — A Study Centered on Xia Nai's Diary

Based on a systematic study of "Xia Nai's Diary", it was found that the famous archaeologist Xia Nai developed a systematic museum education philosophy based on his rich museum practice and mentor guidance. He believes that museums have important educational functions, serving as centers for professional knowledge learning, academic research materials, and patriotic sentiments. They should be fully utilized through the comparison of physical objects and literature. Based on this, he deeply realized the close relationship between museums and archaeology, providing valuable experience for guiding museum construction and archaeological development after returning to China.
Research on the Training Mode of Innovative Talents in Information and Computing Science from the Perspective of Scientific and Technological Innovation

This study aims to identify and analyze effective training modes for fostering innovative talents in information and computing science, with a focus on the perspective of scientific and technological innovation. The research method employed includes a comprehensive literature review, interviews with industry experts and academics, and empirical data analysis from existing training programs. The findings indicate that an integrated approach combining project-based learning, interdisciplinary collaboration, and practical experiences can enhance students' innovative capabilities. Recommendations from this research include the need for increased investment in educational facilities and resources, the formation of strategic partnerships between universities and industries, and the development of curricula responsive to the evolving needs of the job market. This research provides a significant contribution to the development of relevant and effective training strategies in response to societal demands for innovative talents in the field of information and computing science
The Neuropsychological Impact of Immersive Experiences in the Metaverse and Virtual and Augmented Reality

Recent advances in mental healthcare technology, especially in virtual reality (VR) and augmented reality (AR), have revolutionized psychiatry and psychology. This chapter critically examines these innovations' potential in treating various mental disorders, from anxiety and depression to PTSD and ADHD. It emphasizes the controlled and customizable nature of VR environments, which empower individuals to confront fears within personalized settings. Interdisciplinary collaboration is stressed, showcasing applications in psychology, from assessment to clinical treatment. The chapter explores neuropsychological impacts, detailing how VR modulates emotions, enhances neural plasticity in stroke rehabilitation, and alters neural networks. Despite promise, challenges like treatment standardization and ethical concerns persist, demanding careful consideration. The chapter advocates for ongoing research and global interdisciplinary cooperation to fully exploit these technologies' potential in mental healthcare.
Multimedia Text Extraction for Healthcare Applications

Image, audio, and video are rapidly evolving multimedia data, creating a platform for the creation, exchange, and storage of information in the modern era. Multiple techniques are available for content-based image, video, and audio retrieval using image processing methods to extract text features of the data. These methods produce similar text but not the same as per the user expectation. In this chapter, the authors offer an approach that uses edge map generation and Google tesseract-based text detection in image as well as in video. The accuracy of our method is 98% for speed video extraction, 100% for image/video/audio data.
Comparative Overview of Augmented Reality, Virtual Reality, and Mixed Reality

The virtual and augmented reality is a rapidly growing technology which focuses on visualization and recognition of 2D and 3D images using tools with bonded techniques (features). The virtual reality (VR) delivers only assumed digital world for viewers, but the expectations of viewers is real world with digital world. Thus, augmented reality (AR) has become popular to deliver enhanced view of VR. However, AR fulfilled the expectations of viewers but there is a gap between VR and AR. The intermediate gap is focused on enhanced visualization for controlling and manipulating VR and AR in parallel. In this chapter, the comparative study of VR, AR, and mixed reality (MR) are focused to expose different aspects of applications, performances, and tools requirements.
Preserving Meher and Woirata Corpus Languages using Neural Machine Translation

Research on languages, particularly regional languages, is extremely challenging to conduct because there is very little or no language corpus available, particularly for Indonesia's regional languages. This project seeks to construct a translation machine for Indonesian in Meher and Woirata languages, and vice versa. However, to be able to achieve this, a corpus of Meher and Woirata languages must first be developed. The production of this corpus was carried out through field studies, the researcher requested various speakers of this language to translate manually and then compared the results from several translators through focus group talks to identify the appropriate use of words. The outcomes of this translation process are then written in the form of a database of Indonesian-Meher and Indonesian-Woirata language pairings which will subsequently be utilized as a learning database for the translation machine that will be created. This research succeeded in collecting 714.000 words in the Meher language and 805.000 words in the Woirata language. These results were then employed as a machine translation learning corpus, the output of the translation carried out by this machine was then validated through direct assessment by speakers of the two languages. The results of this testing indicated an accuracy above 80% for both translation into the Meher language and translation into the Woirata language. From the research carried out, it can be concluded that the construction of the Meher language corpus and the Woirata language corpus which was carried out through field research was successful in gathering and establishing a language corpus for these two languages. Apart from that, the experimental results suggest that the employment of translation algorithms to convert Indonesian into regional languages and vice versa may be carried out and provide translations with acceptable accuracy. The contribution of this research is in the establishment of the Meher and Woirata language corpus so that it can be generally accessed by anyone who requires it.
Advancing the Shift-of-Strategy approach: Shifting suspects' strategies in extended interviews.

Interviewers often face the challenge of obtaining information from suspects who are willing to speak but are motivated to conceal incriminating information. The Shift-of-Strategy (SoS) approach is an interviewing technique designed to obtain new information from such suspects. This study provides a robust empirical test of the SoS approach using more complex crime events and longer interviews than previously tested as well as testing a new variation of the approach (SoS-Reinforcement) that included a strategic summary of the suspect's statement. We compared this new variation with a standard version of the approach (SoS-Standard) and an interviewing approach that involved no confrontation of discrepancies in the suspects' statements (Direct).We predicted that the two SoS versions would outperform the Direct condition in terms of participants' disclosure of previously unknown information. We also predicted that SoS-Reinforcement would outperform SoS-Standard. Finally, we expected that participants in the SoS conditions would not assess the interview or the interviewer more poorly than participants in Direct.A total of 300 participants completed an online mock crime procedure, and they were subsequently interviewed with one of the three interviewing techniques. Following the interview, participants provided assessments of their experiences being interviewed.Participants in both SoS-Standard (d = 0.49, 95% confidence interval [CI: 0.21, 0.78]) and SoS-Reinforcement (d = 0.59, 95% CI [0.30, 0.87]) disclosed more previously unknown information than participants in the Direct condition, but SoS-Reinforcement did not outperform SoS-Standard (d = 0.08, 95% CI [-0.20, 0.36]). Participants in SoS-Reinforcement assessed their experience more negatively than those in Direct. No such differences were observed in the remaining two-way comparisons.The study provides support for the effectiveness of eliciting new information through the SoS approach and illuminates possible experiential downsides with being subjected to the SoS-Reinforcement approach. (PsycInfo Database Record (c) 2024 APA, all rights reserved).
Nursing Team Training and Standardized Management in Hybrid OR.

Analyzing the impact of nursing workforce development, training and standardization on hybrid operating theatres.Thirty nurses in the mixed operating room of the First Affiliated Hospital of Nanchang University from January 2021 to December 2021 were selected as the control group to receive routine nursing management and training methods and another thirty nurses were selected as the experimental group to receive nursing team construction, training and standardized management based on conventional methods. Nurses' theoretical and operational scores, nurses' satisfaction, surgeon satisfaction with nurses, and nursing service quality scores were compared between two groups at baseline and after intervention.After the intervention, nurses in both groups had a significant improvement in theoretical and operational scores than those at baseline, and nurses in the experimental group had better scores than those in the control group, The difference was statistically significant (P = .002, P = .004). Nursing quality of surgical preparation, environmental management, surgical safety, and instrument management in the intervention group were significantly better than those at baseline and better than those in the control group. The difference was statistically significant (P = .001, P = .001, P = .001, P = .001). Satisfaction of nurses and doctors in the intervention group was significantly better than those at baseline and better than those in the control group. The difference was statistically significant (P = .002, P = .001).The effect of nursing team construction and training and standardized management of hybrid operating Room was ideal, which can improve nurses' theoretical knowledge and practical skills, and enhance the satisfaction of nurses and surgeons, providing patients with higher quality nursing services, which is worth adopting.
Executive summary

The digital transformation has heightened the need to implement effective cyber security measures across all European countries. The increasing reliance on digital technologies, accelerated by the rapid adoption of remote work in response to the COVID-19 pandemic, necessitates a strategic focus on cyber security and the development of a skilled workforce capable of addressing potential threats. Despite employment levels in cyber security being at an all-time high, analyses of the field show that there is still a substantial workforce gap, with Europe alone facing a shortage of over 300 000 professionals.
Key insights for building a skilled cyber security workforce in Europe

This chapter provides an overview of the report's objectives and rationale and summarises the main takeaways. It discusses the results from the analyses of the demand for cyber security professionals in France, Germany and Poland. It also summarises the main findings from the analysis of the landscape of cyber security education and training programmes in France. It concludes with actionable policy pointers.
FPGA/MATLAB Hardware in the Loop Testbed for Stochastic Artificial Neural Networks

Abstract Abstract—This paper presents a Hardware in the Loop (HWIL) testbed for evaluating a Stochastic Artificial Neural Network (SANN). The SANN is implemented in a Field Programmable Gate Array (FPGA), and this testbed allows test data to be generated on a computer using MATLAB, and sent to the FPGA SANN over a Universal Asynchronous Receiver/Transmitter (UART) interface. Initial hardware results are presented.
An REU/RET Project: IoT Platform and Network Data Visualization

Abstract Internet of Things (IoT) is a connected network of devices that exchange data using different protocols. The application of IoT ranges from intelligent TVs and intelligent Refrigerators to smart Transportation. This research aims to provide students with hands-on training on how to develop an IoT platform that supports device management, connectivity, and data management. People tend to build interconnected devices without having a basic understanding of how the IoT platform backend function. Studying the Arm Pelion will help to understand how IoT devices operate under the hood. This past summer, Morgan State University has hosted undergraduate engineering students and high school STEM teachers to conduct IoT security research in the Cybersecurity Assurance & Policy (CAP) Center. The research project involved integrating various hardware sensor devices and real-time data monitoring using the Arm Pelion IoT development platform. Some of the student/teacher outcomes from the project include: 1) Learning about IoT Technology and security; 2) Programming an embedded system using Arm Mbed development board and IDE; 3 3) Developing a network of connected IoT devices using different protocols such as LWM2M, MQTT, CoAP; 4) Investigating the cybersecurity risks associated with the platform; and 5) Using data analysis and visualization to understand the network data and packet flow. First, the student/teacher must consider the IoT framework to understand how to address the security. The IoT framework describes the essential functions of an IoT network, breaking it down into separate layers. These layers include an application layer, middleware layer, and connectivity layer. The application layer allows the users to access the platform via a smartphone or any other dashboard. The Middleware layer represents the backend system that provides edge devices with data management, messaging, application services, and authentication. Finally, the connectivity layer includes devices that connect the user to the network, including Bluetooth or WiFi. The platform consists of several commercial IoT devices such as a smart camera, baby monitor, smart light, and other devices. We then create algorithms to classify the network data flow; to visualize the packets flow in the network and the structure of the packets data frame over time.
A rule-based machine learning model for financial fraud detection

Financial fraud is a growing problem that poses a significant threat to the banking industry, the government sector, and the public. In response, financial institutions must continuously improve their fraud detection systems. Although preventative and security precautions are implemented to reduce financial fraud, criminals are constantly adapting and devising new ways to evade fraud prevention systems. The classification of transactions as legitimate or fraudulent poses a significant challenge for existing classification models due to highly imbalanced datasets. This research aims to develop rules to detect fraud transactions that do not involve any resampling technique. The effectiveness of the rule-based model (RBM) is assessed using a variety of metrics such as accuracy, specificity, precision, recall, confusion matrix, Matthew’s correlation coefficient (MCC), and receiver operating characteristic (ROC) values. The proposed rule-based model is compared to several existing machine learning models such as random forest (RF), decision tree (DT), multi-layer perceptron (MLP), k-nearest neighbor (KNN), naive Bayes (NB), and logistic regression (LR) using two benchmark datasets. The results of the experiment show that the proposed rule-based model beat the other methods, reaching accuracy and precision of 0.99 and 0.99, respectively.
Enhancing healthcare services through cloud service: a systematic review

Although cloud-based healthcare services are booming, in-depth research has not yet been conducted in this field. This study aims to address the shortcomings of previous research by analyzing all journal articles from the last five years using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) systematic literature review methodology. The findings of this study highlight the benefits of cloud-based healthcare services for healthcare providers and patients, including enhanced healthcare services, data security, privacy issues, and innovative information technology (IT) service delivery models. However, this study also identifies challenges associated with using cloud services in healthcare, such as security and privacy concerns, and proposes solutions to address these issues. This study concludes by discussing future research directions and the need for a complete solution that addresses the conflicting requirements of the security, privacy, efficiency, and scalability of cloud technologies in healthcare.
Enhanced transformer long short-term memory framework for datastream prediction

In machine learning, datastream prediction is a challenging issue, particularly when dealing with enormous amounts of continuous data. The dynamic nature of data makes it difficult for traditional models to handle and sustain real-time prediction accuracy. This research uses a multi-processor long short-term memory (MPLSTM) architecture to present a unique framework for datastream regression. By employing several central processing units (CPUs) to divide the datastream into multiple parallel chunks, the MPLSTM framework illustrates the intrinsic parallelism of long short-term memory (LSTM) networks. The MPLSTM framework ensures accurate predictions by skillfully learning and adapting to changing data distributions. Extensive experimental assessments on real-world datasets have demonstrated the clear superiority of the MPLSTM architecture over previous methods. This study uses the transformer, the most recent deep learning breakthrough technology, to demonstrate how well it can handle challenging tasks and emphasizes its critical role as a cutting-edge approach to raising the bar for machine learning.
Unconventional photon blockade in the two-photon Jaynes–Cummings model with two-frequency cavity drivings and atom driving

In a two-frequency cavity driving and atom driving atom-cavity system, we find the photon blockade effect. In a truncated eigenstates space, we calculate the zero-delay second-order correlation function of the cavity mode analytically and obtain an optimal condition for the photon blockade. By including three transition pathways, we find that higher excitations of the cavity mode can be further suppressed and the zero-delay second-order correlation function can be reduced additionally. Based on the master equation, we simulate the system evolution and find that the analytical solutions match well with the numerical results. Our scheme is robust with small fluctuations of parameters and may be used as a new type of single photon source.