Reviewerly/exHarmony huggingface dataset:
Community Foundation Excellence Fundamentals Course

Nonprofit Business AdvisorVolume 2024, Issue 412 p. 4-4 Conferences & Events Community Foundation Excellence Fundamentals Course First published: 11 December 2023 https://doi.org/10.1002/nba.31614Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onEmailFacebookTwitterLinkedInRedditWechat No abstract is available for this article. Volume2024, Issue412January 2024Pages 4-4 RelatedInformation
FOE NET: Segmentation of Fetal in Ultrasound Images Using V-NET

Ultrasound is a non-invasive method to diagnose and treat medical conditions. It is becoming increasingly popular to use portable ultrasound scanning devices to reduce patient wait times and make healthcare more convenient for patients. By using ultrasound imaging, you will be able to obtain images with better quality and also gain information about soft tissues. The interference caused by tissues reflected in ultrasound waves resulted in intensified speckle sound, complicating imaging. In this paper, a novel Foe-Net has been proposed for segmenting the fetal in ultrasound images. Initially, the input US images are noise removal phase using two different filters Adaptive Gaussian Filter (AGF) and Adaptive Bilateral Filter (ABF) used to reduce the noise artifacts. Then, the US images are enhanced using CLAHE and MSR for smoothing to enhance the image quality. Finally, the denoised images are input to the V-net is used to segment the fetal in the US images. The experimental outcomes of the proposed Multi-Scale Retinex (MSR) is an image enhancement technique that improves image quality by adjusting its illumination and enhancing details. Foe-Net was measured by specific parameters such as specificity, precision, and accuracy. The proposed Foe-Net achieves an overall accuracy of 99.48%, specificity of 98.56 %, and precision of 96.82 % for segmented fetal in ultrasound images. The proposed Foe-Net attains better pre-processing outcomes at low error rates and, high SNR, high PSNR, and high SSIM values.
Strengthening Resilience to Safeguard Women from Social Engineering Attacks in Afghanistan

This study explores cybersecurity awareness and resilience among women at Women Online University in Afghanistan, focusing on social engineering threats. The introduction highlights the dynamic cybersecurity landscape, emphasizing the potent threat of social engineering attacks exploiting human vulnerabilities. Addressing a gap in understanding nuanced factors influencing women's vulnerability in academia, the research provides valuable insights for targeted interventions and policies. Using a robust quantitative methodology, the study involves 170 women from various faculties, employing a stratified sampling technique. Self-administered questionnaires with closed and open-ended inquiries capture participants' perspectives. The investigation meticulously identifies variables, categorizing them into independent, dependent, and control variables, using precise instruments like questionnaires for accuracy. Results depict diverse cybersecurity awareness, revealing variations in awareness levels and program effectiveness. ANOVA tests highlight significant differences, emphasizing the need for tailored program design. Regression analyses explore factors influencing vulnerability perception, emphasizing limited impact from personal information sharing on social media. The study uncovers notable differences in risk perception across categories, necessitating further exploration. In conclusion, this research provides nuanced insights into social engineering vulnerabilities among women in online education, emphasizing tailored interventions and considering socio-cultural nuances. Implications extend to informing policies, practices, and future research, aiming to enhance defense against social engineering threats for Women Online University in Afghanistan.
The Effects of Using Smartphones on the Cognitive Speed of Gifted Students

This study aimed to evaluate the effects of smartphone use on the cognitive speed of gifted students and its utilization in their education. To gather data for the study, a self-developed questionnaire was administered to 280 students in the region. The findings of the study revealed that smartphone usage may enhance intuitive thinking among gifted students but does not necessarily enhance their critical thinking and creativity. Additionally, the study highlighted that excessive use of smartphones could lead to poor concentration, depression, and anxiety, among other negative effects. While smartphones can be beneficial for gifted students to enhance their learning and improve academic performance, excessive and uncontrolled use of smartphones may have a detrimental impact on students' academic outcomes. The study concludes that the use of smartphones could positively influence the intuitive (emotional) thinking and social lives of gifted students. However, it is important to note that smartphones might negatively affect their critical and analytical thinking. Smartphones play a significant role in young people's lives and can impact education positively. Limiting their use in education may hinder access to knowledge in society.
Generalized sub-Poissonian states of two-beam fields

Two-beam states obtained by partial photon-number-resolving detection in one beam of a multi-mode twin beam are experimentally investigated using an intensified CCD camera. In these states, sub-Poissonian photon-number distributions in one beam are accompanied by sub-shot-noise fluctuations in the photon-number difference of both beams. Multi-mode character of the twin beam implying the beam nearly Poissonian statistics is critical for reaching sub-Poissonian photon-number distributions, which contrasts with the use of a two-mode squeezed vacuum state. Relative intensities of both nonclassical effects as they depend on the generation conditions are investigated both theoretically and experimentally using photon-number distributions of these fields. Fano factor, noise-reduction parameter, local and global nonclassicality depths, degree of photon-number coherence, mutual entropy as a non-Gaussianity quantifier, and negative quasi-distributions of integrated intensities are used to characterize these fields. Spatial photon-pair correlations as means for improving the field properties are employed. These states are appealing for quantum metrology and imaging including the virtual-state entangled-photon spectroscopy.
Restorasi Estetik dengan Strip Crown pada Gigi Anterior Desidui (Laporan Kasus)

Background: Anterior dental caries affect masticatory function and aesthetics. The most popular type of aesthetic crown for the restoration of deciduous incisors is the strip crown. Purpose: to report the case of restoration of an aesthetic restoration with a strip crown on anterior primary teeth. Case: A 5.5-year-old boy comes with his mother with complaints of tooth decay. He never hurts and has never been traumatized. A clinical examination revealed that the teeth 52, 51, 61, and 62 had extensive dentinal caries in all parts. Diagnosis based on ICDAS is caries class 5. Case Management: The teeth were subjected to crown-strip restoration in two visits. The first visit was for teeth 51 and 61, and the second visit was for teeth 52 and 62. The teeth were cleaned with carious tissue, lining, and application of flowable composite resin with strip crown, polishing, and finishing. Conclusion: Strip crown restoration can be an alternative choice for primary anterior teeth where caries extends throughout the crown and improves aesthetic results.
Metaverse Adaptability in Theory Context

This article aims to shed light on the usage possibility of the decomposed theory of planned behavior in the Metaverse to understand consumer behavioral intentions toward the metaverse better. The current research addresses the following questions. (1) How DTPB aids in metaverse adaptation. (2) Study scope and following research based on the findings. The current study questions the best determents of consumer attitudes to adapt Metaverse as a unidimensional or multidimensional. This opinion paper proposes directions for future studies that support the usage of decomposed constructs for a comprehensive understanding of consumer attitudes toward the adaption of Metaverse.
Deteksi Penyakit Pada Daun Tanaman Ubi Jalar Menggunakan Metode Convolutional Neural Network

Sweet potatoes are the world's third most important root crop and the fourth most popular staple food in developing countries, including Indonesia. Some diseases commonly found in sweet potato leaves are early blight (identified by leaves containing batataezim) and late blight (characterized by leaves that have chlorosis). These two diseases have different symptoms and require different treatments, but a slow identification process can lead to additional costs for plant care. This research will classify image data of sweet potato diseases using the Convolutional Neural Network (CNN) method. CNN is a derivative of the Multilayer Perceptron (MLP) designed to process image data with high network depth and is often used for classification tasks. The research uses a total of 750 images divided into 3 classes: images of healthy leaves, images of leaves with chlorosis, and images of leaves containing batataezim. Each leaf class will be labeled with 250 image data, and the labeled data will be further divided into training and testing sets. From these sets, prediction data will be obtained from the testing process during the CNN model training. The training accuracy resulted in a value of 98.17%, while the testing accuracy reached 98.67%. Additionally, the resulting loss values are remarkably low, at 0.04% for training and 0.03% for testing. The research findings will provide insights into the CNN method's ability to detect diseases in sweet potato plants, potentially impacting agricultural supervision, plant disease identification, and enabling more precise decisions regarding plant care actions.
Intensive reading strategies in EFL reading comprehension improvement

This study aims to investigate the efficacy of using intensive reading strategies in improving reading comprehension in the EFL classroom. Because of the children’s poor reading comprehension, classroom exercises are devised. It is a quantitative research method with a quasi-experimental design. A survey is administered to two groups of students to assess their reading comprehension assessments. Following that, both the control and experimental groups complete a pre-test. The experimental group then participated in a classroom intervention that focused on using intensive reading tactics to increase their reading comprehension. The target population is then given a post-test, and the findings were children examined to assess the improvement in students’ reading comprehension before and after treatment. It is determined that intensive reading tactics are beneficial in boosting reading comprehension; thus, they should be used to assist students in their English language learning journey.
Multi-Head CNN-based Software Development Risk Classification

Agile methodology for software development has been in vogue for a few decades, notably among small and medium enterprises. The omission of an explicit risk identification approach turns a blind eye to a range of perilous risks, thus dumping the management into strenuous situations and precipitating dreadful issues at the crucial stages of the project. To overcome this drawback a novel Agile Software Risk Identification using Deep learning (ASRI-DL) approach has been proposed that uses a deep learning technique along with the closed fishbowl strategy, thus assisting the team in finding the risks by molding them to think from diverse perspectives, enhancing wider areas of risk coverage. The proposed technique uses a multi-head Convolutional Neural Network (Multihead-CNN) method for classifying the risk into 11 classes such as over-doing, under-doing, mistakes, concept risks, changes, differences, difficulties, dependency, conflicts, issues, and challenges in terms of producing a higher number of risks concerning score, criticality, and uniqueness of the risk ideas. The descriptive statistics further demonstrate that the participation and risk coverage of the individuals in the proposed methodology exceeded the other two as a result of applying the closed fishbowl strategy and making use of the risk identification aid. The proposed method has been compared with existing techniques such as Support Vector Machine (SVM), Multi-Layer Perceptron (MLP), Generalized Linear Models (GLM), and CNN using specific parameters such as accuracy, specificity, and sensitivity. Experimental findings show that the proposed ASRI-DL technique achieves a classification accuracy of 99.16% with a small error rate with 50 training epochs respectively.
Serials Analysis Directions: The importance of Standard Numbers in Collaborative Collection Development for Serials

The Research Collections and Preservation Consortium (ReCAP) partners contracted the Center for Research Libraries (CRL) to do a volume level analysis of their combined print serial collections. This paper focuses on the decision to use the OCLC number as the primary match point and the starting point for the collection analysis, challenges encountered due to inconsistent OCLC number use across collections, the way those challenges informed the overall project, and the method CRL created to find OCLC numbers for records without them. Final results of the volume-level analysis, ongoing work to integrate Harvardâ€™s serials into the analysis, and the effects of ReCAP partnersâ€™ collaborative collection development initiatives are included.
Influence of Libraries in the Discovery Services Process

The National Information Standards Organization (NISO) Open Discovery Initiative (ODI) was undertaken to help ensure that all libraries, content providers, and discovery providers can enjoy mutual transparency into what metadata elements are provided and used for discovery throughout the process. This session explored the role of libraries to ensure that discovery services meet their users’ needs. The presenters promoted the ODI standing committee’s recently released conformance checklist, which outlines standardized actions for libraries to perform that benefit their user communities. Presenters discussed the benefits to all parties of completing the checklist and encouraged librarians to share feedback and descriptions of their needs with members of the NISO ODI committee.
Pengaruh Strategi Ekspositori Terhadap Hasil Belajar Baca Tulis Al-Qur’an (BTQ) Siswa Kelas Lima SD Negeri 4 Puding Besar

The focus of this research concerns the influence of expository strategy learning on the results of reading and writing the Al-Qur'an (BTQ) of fifth grade students at SD Negeri 4 Puding Besar. The purpose of this research is to determine the effect of expository strategy learning on the results of reading and writing the Al-Qur'an (BTQ) of fifth grade students at SD Negeri 4 Puding Besar. This type of research is quantitative research, using a simple linear regression method. The population of this research was all fifth grade students at SD Negeri 4 Puding Besar. And the total sample for this study was 32. The data collection technique for this research uses a questionnaire technique. The research instrument technique uses validity tests and data reliability tests. The results of this research show that in the Expository Strategy Learning variable (X) there were 32 students who filled out the questionnaire. Obtained results for Mean 51.53, Median 52.50, Mode 40.00, Standard Deviation 12.750, Variance 162.580, Range 47.00, Minimum 29.00, Maximum 76.00, Summary 1649.00. Meanwhile, for the variable Al-Qur'an Reading and Writing Results (BTQ) (Y), there were 32 students who filled out the questionnaire. Obtained results for Mean 58.71, Median 59.00, Mode 52.50, Standard Deviation 11.562, Variance 133.693, Range 42.00, Minimum 38.00, Maximum 80.00, Sum 1879.00. So it can be said that the influence of the Expository Strategy Learning variable (X) on the Al-Qur'an Reading and Writing (BTQ) Result variable (Y) is 48.8% while the remaining 51.2% is influenced by other factors. Based on the results of the Fisher test, the value of Sig0.00&lt;0.05 means that Ha is accepted so that there is a significant influence between Expository Strategy Learning on Al-Qur'an Reading and Writing (BTQ) Results.
Computer Network Design Using the Simple Queue Method in Maximising Network Performance in Companies

Computer Network Design Using the Simple Queue Method at CV. Ide Maju Berkarya is made to maximise the performance of existing networks in the company. This design is to provide network capacity for users evenly using the Simple Queue method and can monitor the existing network in the company so that there can be no more problems in getting network capacity where previously the capacity that each user got was uneven. This computer network design is based on a case study at CV. Ide Maju Berkarya which lasted for 4 (four) months. The object of activity carried out in the form of direct observation to the field to see the circumstances and situations that occur, how the performance of the existing network at CV. Ide Maju Berkarya. From the conditions that occur in the field, it can be seen that the distribution of nets for each employee is not evenly distributed, there is no network that can monitor the network in the company so that problems often occur when using the internet becomes slow and disconnected. Conclusions and suggestions on the design of this computer network are to be able to improve the performance of existing networks in the company by sharing the capacity of each user evenly when using internet access in the company and can monitor traffic and users connected to the network.
KAJIAN PAVING BLOCK CAMPURAN LIMBAH PLASTIK PET TERHADAP KUAT TEKAN DENGAN STANDARD SNI 03-0691-1996

Currently the estimation of each person goes through 170 plastic bags every year. More than 17 billion plastic bags are distributed for free by supermarkets or markets around the world. This research aims to reduce PET plastic waste by reusing it, plastic bottles can be used as a mixture in making paving blocks. The compressive strength test was carried out based on variations in the curing of PET plastic waste paving blocks, namely when the PET plastic waste paving blocks were 28 days old. PET plastic waste paving blocks have the best compressive strength value at Pb 5 of 8.8 MPa and meet SNI 03-0691-1996 which is classified into quality D
Rancang Bangun Sistem Informasi Terpadu Salon Rumah Cantik Nadia Berbasis Macro Microsoft Excel

Abstract : This study aims to design and build an integrated or computerized information technology system, making it easier for salon employees to enter data, search data, and make monthly and annual reports, thereby reducing errors that occurred before. The type of data used in this study uses qualitative and quantitative research data types using primary and secondary data sources. The data collection techniques used were observation, interviews, documentation, and literature study. This study used data flow diagrams system modeling and entity relationship diagrams. The analysis tool used was macros in microsoft excel, which can facilitate data processing properly. The result of this study was the application of an integrated information system that was more effective and more efficient than the system previously applied to Rumah Cantik Nadia Salon.&#x0D;
The out-of-plane optical constant of a two-dimensional crystal: experimental observation of an elusive quantity

The observation of the out-of-plane optical constants of a monolayer two-dimensional crystal is a challenging task. I illustrate an experimental procedure able to observe these quantities.
Analisis Kapasitas Penampang Drainase Jalan Perkotaan Akibat Peluapan Debit Banjir Maksimum (Studi Kasus Pada Jalan Jhoni Anwar Kota Padang)

The problem of flooding or inundation can arise from several factors, one of which is the incapacity of the existing drainage system to accommodate the increased volume of water flowing into it. For example, the drainage system on Jhoni Anwar Street in Padang City has become a frequent cause of flooding. This street should ideally be flood-free, especially as it serves as a major connecting road in Padang City. Therefore, the author intends to conduct research on the drainage system's capacity on this street. The data analysis required for this study includes hydrological analysis, including rainfall data, average rainfall, and flood discharge. Additionally, hydraulic analysis is also necessary, such as analyzing the existing channels, backwater analysis, planning the dimensions of the drainage system, and determining the flood points for each channel. It is known that the drainage system on Jhoni Anwar Street can only accommodate a water discharge of 0.24 m3/second, while the peak discharge during a 5-year flood period reaches 3.11 m3/second. Therefore, new dimensions for the drainage system are needed. The planned dimensions include a wet cross-sectional height of 140 cm or 1.40 m, a total cross-sectional height (h) of 160 cm or 1.60 m, and a channel width (b) of 1.42 cm or 1.42 m. With these dimensions, the channel will be able to accommodate a water discharge of 3.36 m3/second, in accordance with the criteria for peak flood discharge
Toward Textual Transform Coding*

Inspired by recent work on compression with and for humans, the success of transform-based approaches to information processing, and the rise of powerful language-based AI, we propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">textual transform coding</i> . It shares some of its key properties with traditional transform-based coding underlying much of our current multimedia compression technologies. It can form the basis for compression at bit rates until recently considered uselessly low, and for boosting human satisfaction from reconstructions at more traditional bit rates.
FedSarah: A Novel Low-Latency Federated Learning Algorithm for Consumer-Centric Personalised Recommendation Systems

Data heterogeneity, insufficient scalability, and data privacy protection are the technological challenges of personalized recommendations. This study proposes a new federated learning algorithm (FedSarah) to address low scalability caused by data heterogeneity and uneven computing power in consumer-centric personalized recommendation systems while protecting data privacy of consumers. The algorithm updates the stochastic gradient estimates using a recursive framework on consumer clients. The outer loop calculates the entire gradient for updating global model, and the inner loop calculates the stochastic gradient based on the accumulated stochastic information for updating local models. To increase the stability of convergence, the inner loop modifies intrinsic parameters to change the number of training rounds and the direction of model update on consumer clients. The detailed mathematical analysis and experiments demonstrate that FedSarah has good convergence. In addition, it’s shown that the algorithm can achieve a performance improvement of nearly 5% in terms of accuracy compared to the traditional FedAvg and FedProx algorithms under the condition of heterogeneous data. Furthermore, under the condition of effective privacy protection on consumers’ data, the new algorithm can significantly lessen the impact of data heterogeneity on the real-time service of consumer-centric personalized recommendation systems with low communication latency. The code is available at https://github.com/DashingJ-82/FedSarah.git.