Miracle-dz/UniFAR-FTUs huggingface dataset:
Reimagining Post-Covid Relationships with Culturally and Linguistically Diverse Families: Reflecting with a Preschool Director

Abstract: Guided by the concept of “Pandemic as Portal” (Souto-Manning, 2021), this work utilizes ethnographic methods of inquiry and analysis to understand home–school relationships between Lisa, an African American preschool leader, and families at an early childhood center in the U.S. Midwest. Analysis of data from before and during the pandemic revealed continued themes of extended relationships between center leadership and families beyond preschool years, themes based in care over time, and political clarity of leadership. This political clarity drew on Lisa’s understanding of systemic racism and the school system that former students and older siblings would be entering. This paper also considers a disparity in the support and resources the center received, as it often had to rely on local problem-solving or established means and methods of communication to continue connecting with and supporting families throughout the pandemic, rather than turning to state or federal programs for support. Ultimately, the paper concludes that transformative and humanizing practices that developed before the pandemic helped guide the center through that time. The story of home-school relationships at this early childhood center provides examples of the potential to reimagine family engagement, avoiding a return to the “normalcy” of pre-pandemic home–school relationships across the U.S., which have historically been based in unequal power relationships that ignore systemic racism.
Method for detecting road pavement damage based on deep learning

A safe and healthy road condition plays a supporting role in the public travel and the national economy. Therefore, effective management and maintenance methods have become the key problems that the researchers and engineers are urgently solving, early damage detection and warning are also important for disaster emergency treatment, but some traditional road damage identification methods are often costly and need to be equipped with professional persons. Due to the complexity of pavement conditions, some existing defects datasets are not perfect, although the accuracy is high, they cannot be put into practical use. Based on the object detection technology of deep learning, the author introduced a novel method which is more effective and relatively cheap. In this paper, 5966 images with road damage of different angles and distances were collected, and the damage categories included Lateral Crack, Longitudinal Crack, Pothole and separation, Alligator Crack, and Damage around the well cover which had never been considered in the datasets in any researches. After training with GPU using convolutional neural network, the average precision can reach 96.3%.
Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis

Leishmania, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiting skin lesions to potentially fatal visceral complications. As such, the parasite has been the subject of much interest in the scientific community. In recent years, advances in diagnostic techniques such as flow cytometry, molecular biology, proteomics, and nanodiagnosis have contributed to progress in the diagnosis of this deadly disease. Additionally, the emergence of artificial intelligence (AI), including its subbranches such as machine learning and deep learning, has revolutionized the field of medicine. The high accuracy of AI and its potential to reduce human and laboratory errors make it an especially promising tool in diagnosis and treatment. Despite the promising potential of deep learning in the medical field, there has been no review study on the applications of this technology in the context of leishmaniasis. To address this gap, we provide a scoping review of deep learning methods in the diagnosis of the disease, drug discovery, and vaccine development. In conducting a thorough search of available literature, we analyzed articles in detail that used deep learning methods for various aspects of the disease, including diagnosis, drug discovery, vaccine development, and related proteins. Each study was individually analyzed, and the methodology and results were presented. As the first and only review study on this topic, this paper serves as a quick and comprehensive resource and guide for the future research in this field.
Interactive Visualization and Portable Image Blending of Massive Aerial Image Mosaics

Processing, managing and publishing the substantial volume of data collected through modern remote sensing technologies in a format that is easy for researchers - across broad skill levels and scientific domains - to view and use presents a formidable challenge. As a prime example, the massive scale of image mosaics produced by NEON’s Airborne Observation Platform (AOP), often several to hundreds of gigabytes in volume, demands efficient data management strategies. Additionally, these aerial mosaics frequently exhibit seams due to variations in lighting conditions during the data acquisition process. These seams undermine the integrity of subsequent scientific analyses, introducing distortions that hinder accurate interpretation of ecological patterns. Finally, one of NEON’s core objectives is to make these data broadly accessible to users, including those who are not yet versed in working with remote sensing data or who wish to view the datasets without needing to download and process them.In response to these challenges, we have developed a comprehensive data management pipeline that enables interactive access for analysis and visualization of NEON’s aerial mosaic collection. This pipeline automates data ingestion, conversion, and publication in a streamable format, facilitating seamless user interaction through web viewers and programming APIs. Moreover, we have implemented a portable blending algorithm aimed at eliminating these problematic seams from large aerial mosaics. This algorithm, grounded in the Conjugate Gradient (CG) method, has been implemented both in CUDA and using the modern SYCL programming model for enhanced portability across diverse computing platforms.Experimental results demonstrate scalable performance across both CPU and GPU architectures. This work not only addresses the challenges of large aerial data management and seam removal but also opens avenues for more accurate and comprehensive scientific investigations within the NEON ecosystem.
The International Planetary Data Alliance (IPDA): Overview of the Activities

An overview of activities of the IPDA is presented in the frame of the recently growing number of successful space experiments dedicated to planetary observation, with a significantly growing number of people involved in such activity and with significantly growing numbers of web services willing to share data and services in our research domain, but also, in close by domains such as astronomy, heliophysics and atmospheric sciences for the Earth. An overview of a number of space agencies and organizations is given. In total, IPDA consists of 13 national organizations: NASA (USA), CNES (France), ESA (Europe), STFC (UK), JAXA (Japan), ASI (Italy), ISRO (India), DLR (Germany), RKA (Russia), RCSA (China), FMI (Finland), ArSA (Armenia) and United Arab Emirates. Some projects of 2015 in frame of the IPDA activities are described.
THE IMPACT OF PUBLIC EXPENDITURE ON INFRASTRUCTURAL AND ECONOMIC DEVELOPMENT IN NIGERIA (1986-2022)

The paper examined the impact of public expenditure on infrastructural and economic development in Nigeria from 1986-2022). The study was a time series thereby employing Secondary data sourced from the Central Bank of Nigeria (CBN) Statistical bulletin 2022. The data were subjected to unit root diagnostic test to determine whether they are stationarity or otherwise. The data were integrated of order 1(0) and 1(1). Therefore, the Autoregressive Distributed Lag (ARDL) model was adopted, the Error Correction Model (ECM) technique was also adopted to check for the cointegration. The study reported along run relationship between public expenditure on infrastructural and economic development. The result shows that Public expenditures on health and construction infrastructures negatively affect economic development in Nigeria. While public expenditures on education, transportation and communication, and economic and services infrastructures positively affects economic development in Nigeria. The study concludes that pubic infrastructural expenditures influence economic development in Nigeria and the paper recommend that the financing options for closing Nigeria’s infrastructure gaps should focus on broadening the sources of finance and a better allocation of public resources. In this wise, the government should intensify the utilisation of the public-private-partnership (PPP) framework as government alone cannot finance infrastructural development in an emerging market economy like ours. Therefore, Nigeria needs to be more pragmatic in her infrastructural development, in order to create employment and reduce poverty.
Investigation of Endurance Characteristics in 3-D NAND Flash Memory With Trap Profile Analysis

In this article, the endurance characteristic of 3-D NAND Flash memory is investigated. The bitline (BL) current and the word-line (WL) current are measured for erase/write (EW) cycling up to 50k. Then, the trap profiles in the bandgap-engineered tunneling oxide (BE-TOX) layer are extracted through trap spectroscopy by charge injection and sensing (TSCIS) technique and technology computer-aided design (TCAD) simulation. The trap profiles show that the traps in the BE-TOX layer are generated toward the charge trap layer (CTL) side to the poly-Si channel side as EW cycling increases. Also, it shows that the deep traps below 2.0 eV start to be generated after 10k EW cycling and a double Gaussian distribution is fit well. Through the trap profile results, the degradation of both the BL current and the WL current according to EW cycling is completely explained.
Benthic Colonization on New Materials for Marine Ecosystem Restoration in Porto Cesareo, Italy

Suitable colonization materials are a pursued target in marine restoration programs. Known for making nutrients available while reducing pollutants and the risk of pathogens in terrestrial ecosystems, Biochar and Bioferment materials of organic origin were tested during a two-year experiment. We tested the efficacy of these materials for restoration purposes through experimental concrete tiles treated with Biochar (B) and Bioferment (F) and tiles made of concrete, which were used as controls (Ct) for the colonization of marine organisms in the marine protected area of Porto Cesareo, Southern Italy (20 m depth). Tiles were monitored for photographs from October 2019 to September 2021. Initially, Biochar treatment presented a higher percentage of total benthic cover (81.23 ± 2.76, median ± SE), differing from Bioferment treatment and control tiles (45.65 ± 5.43 and 47.95 ± 3.69, respectively). Significant interaction between treatments and times suggests changes in community structure related to Polychaeta cover increase in Bioferment and control materials from the second monitoring time. Furthermore, the underwater instability of Bioferment on the tiles could explain the similarity with control tiles in marine organisms’ covers. Hence, Biochar is shown to be a material with optimal stability in seawater, demonstrating greater capacity for marine organisms’ colonization in less time compared to the other two materials.
Spark-MLlib intrusion detection mechanism using machine learning models

Typically, a single method is employed in machine learning (ML) based intrusion detection to identify intrusion information. However, this approach lacks flexibility, has a low detection rate, and struggles to handle high-dimensional data. Consequently, it is not efficient in addressing these challenges. This study proposes a new intrusion detection architecture that utilizes Spark and ensures resilient data dissemination across the platform to improve its effectiveness. It consists of preprocessing module, a label encoder module, a feature extraction module, a classification module and a database module. The preprocessing module compresses information by utilizing the module for label encoding. This generates a lower-dimensional reconstruction and classification characteristic. The database module has the capability to store the compressed characteristics of all traffic. This enables the classifier to be tested and then returns these features back into the original traffic, facilitating retraining. In order to evaluate the efficacy of the framework, simulations were conducted using the CICIDS 2017 dataset to accurately replicate the network traffic. Based on the test findings, the accuracy of both multiclass and binary classification surpasses that of earlier studies. High precision was achieved for the traffic that was restored. The possible application of the proposed architecture for edge/fog networks is discussed in the conclusion.
Phenotypic Plasticity, Growth, Biochemical Composition, Total Phenolic Content, and AntioxidanActivity of Microalga Desmodesmus multivariabilis Under Different Nitrogen and Phosphorus Concentrations

The use of microalgae in biotechnological studies is increasing day by day because of their ability to produce and store secondary metabolites under extreme conditions. When the culture conditions such as light, nutrients, pH, and temperature are modified, the synthesis and storage amount of these secondary metabolites change. In this study, the effects of different nitrogen and phosphorus concentrations on the morphological changes, growth, biochemical composition, total phenolic content, and antioxidant activ­ ity of Desmodesmus multivariabilis were investigated. The study results show that changing of the nitrogen and phosphorus concentration in the microalgae culture medium is an effective strategy for changing growth, biochemical composition, total phenolic content, and antioxidant activity parameters of microalgae. All culture conditions should be detected, depending on the substance desired to be ob­ tained from microalga and its biotechnological application. Therefore, this study is substan­ tial as it is the first study on this concept with D. multivariabilis, and for obtaining eco­ nomically important bioactive compounds of this species, this paper will play a key role.
Association between mortality risk levels and AIFELL triage scores for suspected COVID-19 patients

BACKGROUND: It is crucial to examine and classify patients as soon as possible to save their lives when they display Coronavirus Disease of 2019 (COVID-19) symptoms. The Altered sense of smell/taste, Inflammation, Fever, Elevated Lactate dehydrogenase, and Lymphocytopenia (AIFELL) evaluation tool is quick, easy, and simple for medical professionals. OBJECTIVES: Determine the relationship between the COVID-19 patient confirmation and the AIFELL score. Examine any relationships between the AIFELL score and the degree of mortality. MATERIALS AND METHODS: A retrospective study was conducted on 970 hospitalized (18 years or older) with a COVID-19 diagnosis in 2021. Patients admitted to the intensive care unit (ICU) as critical cases and moderate cases. The Chi-square test was utilized. RESULTS: The scores of the AIFELL tool ranged from 0 to 6 points; the AIFELL score for COVID-19 symptoms with a high score (4–6) made up 41.5% of the patients. More than half of the patients (58.7%) were men; the oldest age group ranged from 40 to 50 years. A very high risk of dying due to a positive COVID-19 virus exists in more than a fifth of patients (21.5%). The ICU (37.5%) received around a third of the patients. The findings showed significant associations between levels of mortality risk and gender and age. There were significant associations between AIFELL scores and mortality risk levels. AIFELL scores, mortality risk levels, and patient admissions to the critical care unit were strongly associated. CONCLUSION: The AIFELL scores were excellent for predicting COVID-19 mortality risk levels and ICU admission.
Polyharmonic Representation of the Electromagnetic Field Generated by an Oscillating Particle near a Dispersive Bulk

This study introduces a polyharmonic framework for analyzing the electromagnetic (EM) field generated by an oscillating point charge near a dispersive bulk of size comparable to the wavelength under study. We critically evaluate traditional approaches such as Liénard-Wiechert, Landau, and Raimond, and propose a Fourier representation of sources that simplifies numerical implementation and enhances analytical clarity. Our method effectively addresses the limitations of conventional models and is applicable to both relativistic and non-relativistic scenarios. It includes the oscillating point dipole fields, providing a comprehensive understanding of the EM field behavior. The Finite Element Method (FEM) is employed for numerical analysis, demonstrating the method’s adaptability to complex geometries. While offering significant insights, this study acknowledges certain limitations and outlines directions for future research.
Integrated School Teachers’ Experiences on Implementing Inclusive Education Policy of Nepal

This study entitled “Integrated School Teachers’ Experiences on Implementing Inclusive Education Policy of Nepal” was carried out to investigate the experiences of integrated school teachers in implementing inclusive education policy of Nepal. The study had been carried out by selecting ten integrated schools of Kathmandu valley by convenience sampling method. Ten special education teachers and ten physical education teachers from each schools were selected by purposive sampling method. Semi-structured interview schedule was prepared and administered to the participants through telephonic interview. The interview questions were reviewed by a physical education and special education experts to maintain its reliability. The study found that teachers have been experiencing low self-awareness about educating students with disabilities. They also experienced serious lacking in their knowledge and skill in them and also their perceptions towards inclusion was immature. They want their school administration to be more responsible and active in developing inclusive culture inside the school and also urged to provide them the opportunities of their professional development through training and exposures.
A Hybrid Cross Layer with Fuzzy Chaotic Whale Wolf Optimization-Based Efficient Routing for Wireless Sensor Networks

An efficient clustering and routing of Wireless Sensor Networks (WSNs) is a critical challenge, with a significant impact on Quality of Service (QoS) performance. In this research, a Cross-Layer-based Fuzzy Chaotic Whale Wolf Optimisation (CL-FCWWO) is proposed to improve the performance in WSNs. The K-medoids with Improved Artificial-Bee-Colony (K-IABC)-based energy-efficient clustering and the routing protocol is utilized to address the power asymmetry issue in WSNs. Cross-layer routing is suggested to reduce network transmission latency and power consumption. The MATLAB is used to construct this approach and compared the results to other techniques. The suggested CL-FCWWO outperforms in terms of energy consumption, throughput, and network lifetime. These results are validated by comparisons with existing routing algorithms such as Cross-layer based Opportunistic Routing Protocol (CORP), Cross-Layer Harris-Hawks-Optimization-algorithm (CL-HHO) and Adaptive Sailfish Optimisation (ASFO). From the results, it showed that proposed CL-FCWWO algorithm performed better than CORP, ASFO and CL-HHO methods, showing its effectiveness in improving WSNs performance.
Research on Control Algorithm Theory and Visual Recognition Algorithm of Network Devices

Our society’s production and living practices have undergone a significant change as a result of the quick advancement of computer science and technology and their ever-expanding uses. Algorithms for network graph computation have emerged as a key resource for handling the increasing volume of web data. This is especially important when dealing with large amounts of network data, including data from social media, the Internet and the Internet of Things. By combining the theory of control algorithms with network graph computation algorithms, it provides a strong support for building smarter and more efficient automated systems as well as more reliable network infrastructures. The main content of this paper will focus on the control algorithm theory and network graph computing algorithm to briefly explain and analyze, and based on artificial intelligence algorithms for visual identification of network equipment in computer labs, mainly for computer lab operation and maintenance situation for supervision and equipment identification. When the laboratory equipment terminal fails, the server side of the system can obtain the current operating condition of the equipment terminal, collect the interface image information of the current equipment terminal through the server-side data acquisition system, carry out intelligent identification of the acquired data information. The paper will also delve into the analysis and processing of identification results. It will detail how these results can be effectively processed and interpreted. Additionally, it will discuss methods of analyzing the results and potential solutions to market to end users. Ultimately, the paper aims to provide users with reasonable and feasible solutions to equipment anomalies. The recognition algorithm has high timeliness and short response time, which basically meets the daily operation and maintenance needs of the laboratory, and provides solutions to the user’s problem solving, aiming to provide valuable contributions to the academic and practical application fields.
Rare earth elements from heavy mineral sands: Assessing the potential of a forgotten resource

Heavy mineral (HM) sands are usually exploited for titanium and zirconium minerals such as rutile, ilmenite and zircon, but also contain a variety of possible co/by-products, including critical metals such as rare earth elements (REE) in minerals such as monazite and xenotime. The global HM sands industry remains dominated by Australia, which until the mid-1990s included the export of monazite concentrates for REE extraction and provided ∼15–30% of world REE production. The lack of recognition for HM sands as a potential REE resource adds to confusion about potential REE supplies. This study presents a comprehensive assessment of Australian HM sand resources, including an assessment of likely contained monazite, showing that these REE can potentially double the value of a HM sand project. As such, there remains significant opportunities for HM sands projects to increase their economic value by also extracting monazite and xenotime for their REE, with further research needed to assess environmental and radioactivity issues.