Miracle-dz/UniFAR-FTUs huggingface dataset:
Improving the Performance of a Spectral Model to Estimate Total Nitrogen Content with Small Soil Samples Sizes

The application of near-infrared spectroscopy (NIRS) for rapid quantitative analysis of soil total nitrogen (STN) is of great significance to recycling nitrogen in the ecosystem and crops growth. However, collecting thousands of soil samples and chemical analysis are impracticable, more importantly a deviation from NIRS advantages of rapid, inexpensive and nondestructive. To more efficiently improve the estimation performance and reduce uncertainty of the model when working with small sample sizes (less than 100), solutions from soil particle size decomposition and model fusion were investigated. Elaborately, 123 Latosols samples were collected and decomposed them according to particle sizes to extend limited data at multiple scales. Based on all soil groups decomposed, a hyperspectral data recapture and model decision fusion method were implemented. The results demonstrated that the proposed method increased the scale of spectral data, extracted more STN-related spectral information, improved estimation accuracy, and reduced uncertainty. The fused model based on data from all decomposed groups yielded the best estimated results (root mean square error $$(RMSE) = 0.075g. kg^{-1}$$ ( R M S E ) = 0.075 g . k g - 1 , $$R^2 = 0.784$$ R 2 = 0.784 , and a ratio of performance to inter-quartile distance $$(RPIQ) = 3.787$$ ( R P I Q ) = 3.787 ) on the validation set. Through a tenfold cross-validation, the weighted fusion model with six groups of particle sizes data showed an improvement of 0.307 in $$R^2_cv$$ R c 2 v and an improved RPIQ of 1.015 compared to models constructed using conventional machine learning (ML) techniques and limited pristine data ($$R^2_cv = 0.442, RMSE = 0.119$$ R c 2 v = 0.442 , R M S E = 0.119 ). Therefore, when utilizing NIRS to build rapid and accurate STN predictive models, the proposed method demonstrates great potential in improving the reliability of soil spectral models under small sample sizes. Graphical Abstract
A Class-F Power Amplifier Design Using a Commercial Nonlinear Embedding Model

A 30 W Class-F Power Amplifier at 2 GHz is designed using a new commercial Angelov embedding model. The prototype Power Amplifier designed is verified in EM simulation using the normal Angelov model to provide an output peak power of 45 dBm with 71 percent drain efficiency. A prototype PA has been fabricated and measured.
Quali-quanti visual methods and political bots

Computational social science research on automated social media accounts, colloquially dubbed “bots”, has tended to rely on binary verification methods to detect bot operations on social media. Typically focused on textual data from Twitter (now rebranded as "X"), these inference-based methods are prone to finding false positives and failing to understand the subtler ways in which bots operate over time, through visual content and in particular contexts. This research brings methodological contributions to such studies, focusing on what it calls “bolsobots” in Brazilian social media. Named after former Brazilian President Jair Bolsonaro, the bolsobots refer to the extensive and skilful usage of partial or fully automated accounts by marketing teams, hackers, activists or campaign supporters. These accounts leverage online political culture to sway public opinion for or against public policies, opposition figures, or Bolsonaro himself. Drawing on empirical case studies, this paper implements quali-quanti visual methods to operationalise specific techniques for interpreting bot-associated image collections and textual content across Instagram, TikTok and Twitter/X. To unveil the modus operandi of bolsobots, we map the networks of users they follow (“following networks”), explore the visual-textual content they post, and observe the strategies they deploy to adapt to platform content moderation. Such analyses tackle methodological challenges inherent in bot studies by employing three key strategies: 1) designing context-sensitive queries and curating datasets with platforms’ interfaces and search engines to mitigate the limitations of bot scoring detectors, 2) engaging qualitatively with data visualisations to understand the vernaculars of bots, and 3) adopting a non-binary analysis framework that contextualises bots within their socio-technical environments. By acknowledging the intricate interplay between bots, user and platform cultures, this paper contributes to method innovation on bot studies and emerging quali-quanti visual methods literature.
Analysis of Bio-Inspired approaches and Social Media Analytics in Twitter datasets for Misinformation Detection

Bio-inspired algorithms, such as those with regard to collective intelligence and evolutionary algorithms have been increasingly used in social media analytics in recent years. These algorithms have been shown to be effective in many areas, such as sentiment analysis, community identification, and misinformation detection. This paper explains the underlying principles and features of the most recent bio-inspired techniques. The effectiveness of bio-inspired algorithms for the sentiment analysis categorization on Twitter datasets is studied. In this paper, several social media spam detection techniques that are currently in use are tabulated, and detailed study on the use of social media analytics and bio-inspired algorithms to detect misinformation in social media datasets is performed. The findings of the proposed analysis throws light into the efficiency of these algorithms and gaps for more research, which is inevitable to further improve their performance and robustness for misinformation detection.
Exploration- and Exploitation-Driven Deep Deterministic Policy Gradient for Active SLAM in Unknown Indoor Environments

This study proposes a solution for Active Simultaneous Localization and Mapping (Active SLAM) of robots in unknown indoor environments using a combination of Deep Deterministic Policy Gradient (DDPG) path planning and the Cartographer algorithm. To enhance the convergence speed of the DDPG network and minimize collisions with obstacles, we devised a unique reward function that integrates exploration and exploitation strategies. The exploration strategy allows the robot to achieve the shortest running time and movement trajectory, enabling efficient traversal of unmapped environments. Moreover, the exploitation strategy introduces active closed loops to enhance map accuracy. We conducted experiments using the simulation platform Gazebo to validate our proposed model. The experimental results demonstrate that our model surpasses other Active SLAM methods in exploring and mapping unknown environments, achieving significant grid completeness of 98.7%.
Characteristics of Cancer Stem Cells and Their Potential Role in Endometrial Cancer

Simple Summary In this manuscript, the characteristics of cancer stem cells and their potential role in endometrial cancer are explored. An understanding of the mechanisms behind cancer stem cells in endometrial cancer is crucial because these cells potentially play a key role in how the cancer grows and spreads. The authors aim to review the most recent data concerning cancer stem cells in endometrial cancer, especially biomarkers which could enable identification and prognosis. The determination of signalling pathways crucial for the functioning of cancer stem cells could help with the design of effective treatment strategies. Abstract Endometrial cancer is one of most common types of gynaecological tumours in developing countries. It has been suggested that cancer stem cells play an important role in the development of endometrial cancer. These are a subset of highly tumorigenic cells with similar features to normal stem cells (unlimited proliferation, multi-potential differentiation, self-renewal, aggressiveness, invasion, recurrence, and chemo- and endocrine therapy resistance). Wnt/β-catenin, Hedghog, and Notch1 are the most frequently activated pathways in endometrial cancer stem cells. The presence of cancer stem cells is associated with the resistance to chemotherapy caused by different mechanisms. Various markers, including CD24, CD40, CD44, CD9, CD133, and CD 166, have been identified on the surface of these cells. A higher expression of such markers translates into enhanced tumorigenicity. However, there is no strong evidence showing that any of these identified markers can be used as the universal marker for endometrial cancer stem cells. Growing data from genomic and proteomic profiling shed some light on the understanding of the molecular basis of cancers in humans and the role of cancer stem cells. However, there is much left to discover. Therefore, more studies are needed to fully uncover their functional mechanisms in order to prevent the development and recurrence of cancer, as well as to enhance treatment effectiveness.
Need for Rethinking Modern Urban Planning Strategies Through Integration of ICTs

Recent progress in ICTs have paved the way for innovative services and interactive models and tools. Citizen participation and open innovation have become essential tools for urban planners. These concepts can be implemented through the crowdsourcing model, which is a people-centric approach to solve societal problems using Web 2.0 technologies. This has led to the collection and sharing of geocoded data through GIS. The large amount of data required is one of the drawbacks of GIS. However, collecting such data within short duration at minimum cost has now become possible through the development of web-based surveys coupled with use of DBMS. The effectiveness and importance of these three tools (crowdsourcing, GIS, and DBMS) in modern and future urban planning strategies cannot be undermined. In conclusion, the authors argue that integration of urban policies, modern technologies, and fundamental concepts of engineering will lead to discovery of new solutions to important age-old urban problems.
Factors that Influence AI Investment Decisions in Oman's Hydrocarbons Industry: A Review of the Theoretical Literature and Proposed Theoretical Mode

Artificial Intelligence technologies have exceptional capabilities that enhance the production efficiency of oil and gas companies by saving time and energy in terms of the technologies used and the workers involved, provided that the technology used is cost-effective for the field of concern. AI gradually penetrates various stages of the oil and gas sectors, such as intelligent drilling, intelligent development, intelligent pipelines, and intelligent refineries. This paper presents a comprehensive model for studying the elements that drive AI investment decisions in Oman’s hydrocarbons industry. This study combined theories of the technology acceptance model (TAM), innovation diffusion theory (IDT), business environment factors, and risk management with AI investment decisions. This study aims to discover the key factors that influence AI investment decisions by studying numerous elements that impact AI investment decisions in the sector. These drivers include AI's innovation attributes, the external and internal business environment, risk management, and the moderating of perceived usefulness and perceived ease of use. Gaining insight into these elements enables players in Oman’s hydrocarbons sector to make informed decisions about AI investment decisions. This, in turn, can lead to increased operational efficiency, cost savings, and better decision-making processes.
Exploring Women Entrepreneurs’ Knowledge and Participation in Entrepreneurial Schemes

As a nation, Indian women are rising to the occasion and establishing themselves as potential business owners. It is critical for women to take the lead in non-traditional business fields if we want to dismantle the idea of gender-specific industry. The expansion of female-owned businesses is a key factor in the global economic recovery. The underrepresentation of women in business began to change as our understanding of the for-profit sector grew. Being careful, adaptive, and resistive to wastefulness is a key quality for women to have in business. Women are making waves in the business world, bringing fresh perspectives to SMBs. Entrepreneurial women are those who come up with a business idea, put it into words, plan and execute it, take calculated risks, reap the rewards of uncertainty, and aren’t afraid to fail. Due to difficulties in obtaining a suitable job and a need for social approval, many women choose to work remotely.
Crack Detection and Analysis of Concrete Structures Based on Neural Network and Clustering

Concrete is extensively used in the construction of infrastructure such as houses and bridges. However, the appearance of cracks in concrete structures over time can diminish their sealing and load-bearing capability, potentially leading to structural failures and disasters. The timely detection of cracks allows for repairs without the need to replace the entire structure, resulting in cost savings. Currently, manual inspection remains the predominant method for identifying concrete cracks. However, in today’s increasingly complex construction environments, subjective errors may arise due to human vision and perception. The purpose of this work is to investigate and design an autonomous convolutional neural network-based concrete detection system that can identify cracks automatically and use that information to calculate the crack proportion. The experiment’s findings show that the trained model can classify concrete cracks with an accuracy of 99.9%. Moreover, the clustering technique applied to crack images enables the clear identification of the percentage of cracks, which facilitates the development of concrete damage level detection over time.
Feedbacks Between Shallow Groundwater Dynamics and Surface Topography on Runoff Generation in Flat Fields

In winter, saturation excess (SE) ponding is observed regularly in temperate lowland regions. Surface runoff dynamics are controlled by small topographical features that are unaccounted for in hydrological models. To better understand storage and routing effects of small‐scale topography and their interaction with shallow groundwater under SE conditions, we developed a model of reduced complexity to investigate SE runoff generation, emphasizing feedbacks between shallow groundwater dynamics and mesotopography. The dynamic specific yield affected unsaturated zone water storage, causing rapid switches between negative and positive head and a flatter groundwater mound than predicted by analytical agrohydrological models. Accordingly, saturated areas were larger and local groundwater fluxes smaller than predicted, leading to surface runoff generation. Mesotopographic features routed water over larger distances, providing a feedback mechanism that amplified changes to the shape of the groundwater mound. This in turn enhanced runoff generation, but whether it also resulted in runoff events depended on the geometry and location of the depressions. Whereas conditions favorable to runoff generation may abound during winter, these feedbacks profoundly reduce the predictability of SE runoff: statistically identical rainfall series may result in completely different runoff generation. The model results indicate that waterlogged areas in any given rainfall event are larger than those predicted by current analytical groundwater models used for drainage design. This change in the groundwater mound extent has implications for crop growth and damage assessments.
Chains with Connections of Diffusion and Advective Types

The local dynamics of a system of oscillators with a large number of elements and with diffusive- and advective-type couplings containing a large delay are studied. Critical cases in the problem of the stability of the zero equilibrium state are singled out, and it is shown that all of them have infinite dimensions. Applying special methods of infinite normalization, we construct quasinormal forms, namely, nonlinear boundary value problems of the parabolic type, whose nonlocal dynamics determine the behavior of the solutions of the initial system in a small neighborhood of the equilibrium state. These quasinormal forms contain either two or three spatial variables, which emphasizes the complexity of the dynamical properties of the original problem.
A Pharmacist-Led Process to Monitor Discrepant Urine Drug Screen Results

Background: A urine drug screen (UDS) is a common risk-mitigation strategy tool for prescribing controlled substances, particularly opioids. Due to their complexity, UDS results can be misinterpreted and thereby have profound impacts on the patient-clinician relationship. From 2021 to 2022, a clinical dashboard to review potentially discrepant UDS results—based on a comparison of the results to the patient’s medication list— was made available by the Veterans Health Administration. Methods: This quality improvement project implemented a process for weekly clinical pharmacist reviews of the UDS dashboard. Significant discrepant UDS results were reviewed in depth. From June 2022 through September 2022, 700 UDSs were performed and 60 patients had significant discrepancies that warranted in-depth review. Results: Pharmacist interventions during the review included 39 collaborations with medication prescribers to discuss follow up (65%), 25 queries to a prescription drug monitoring program (42%), and 9 confirmatory UDS on the original sample (15%). In-depth reviews were required for about 4 patients weekly, with a mean length of 14 minutes. Conclusions: A pharmacist-led process to monitor discrepant UDS results led to opportunities for collaboration with prescribers and positively impacted confirmatory testing at a rural veterans affairs health system.
Evaluation of regulatory guidelines to minimize impacts to seagrasses from single family residential dock structures in Florida and Puerto Rico

To reduce the loss of seagrasses due to dock construction and shading, dock construction guidelines for areas containing seagrass beds were developed in 2001. This study indentifies 68 docks located in five regions throughout Florida and Puerto Rico that have been constructed since 2001. The docks are evaluated for compliance with the Seagrass Guidelines developed in 2001, compliance with permit conditions, and presence of seagrass cover. Statistical analyses are used to compare seagrass cover and irradiance in shaded areas beneath the docks with adjacent unshaded areas and examine the relationships between dock structural parameters such as height and width, seagrass cover, and light. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR. ERDC/EL TR-08-41 iii
Hepatitis C Virus Infected Human Megakaryocytes and Platelets: Intra-and Extracellular Evaluation

This study aimed to determine whether the hepatitis C virus (HCV) infects human megakaryocytes and platelets and to measure the expression of receptors involved in virus-cell interaction. Platelets from healthy donors were infected with HCV in vitro and analyzed for viral expression and the receptors claudin-1 and cluster of differentiation 81 (CD81). HCV was detected on the surface and cytoplasm of both cells; cytoplasmic expression was higher compared to the surface. Platelets presented a claudin-1+/CD81-phenotype, and megakaryocytes showed aclaudin-1+/CD81+ phenotype. We conclude that megakaryocytes and platelets are susceptible to HCV infection, regardless of CD81 expression, and megakaryocytes may serve as possible sites of viral replication.
Spatial Correlations between Nitrogen Budgets and Surface Water and Groundwater Quality in Watersheds with Varied Land Covers

Anthropogenic nitrogen (N) inputs can have detrimental environmental effects, necessitating a comprehensive understanding of the nitrogen budget (NB) and its spatial correlation with the water quality. This study, utilizing a 2016 dataset, scrutinized 850 subwatersheds with diverse land covers across the Republic of Korea (ROK). Employing Geographically Weighted Regression (GWR), it examined the spatial correlations between the NBs and the quality of the groundwater and river water at the watershed scale. Robust correlations (R2 = 0.87) were observed between the groundwater quality and NBs, surpassing those of the surface water (R2 = 0.48). Sensitivity analyses highlighted the importance of high-resolution spatial data in capturing nuances within complex land covers. The integration of such data led to increases in the spatial correlations between the groundwater and river water quality of approximately 0.6–0.9 and 0.3–0.5, respectively. Notably, when the agricultural land cover exceeded 10%, significant enhancements in the spatial correlations were observed, emphasizing the pivotal role of agriculture in nutrient and water quality. At a 10% cropland ratio, the spatial correlations between the watershed-scale NBs and river/groundwater quality increased by approximately 76% and 501%, respectively. This study provides novel insights into the spatial relationships among NBs, water quality, and land use, highlighting the significance of high-resolution data and the impact of agricultural practices on watershed management. These findings contribute valuable information for developing strategies to mitigate nitrogen pollution.
Are We Talking about the Same Thing? Modeling Semantic Similarity between Common and Specialized Lexica in WordNet

Specialized languages can activate different sets of semantic features when compared to general language or express concepts through different words according to the domain. The specialized lexicon, i.e., lexical units that denote more specific concepts and knowledge emerging from specific domains, however, co-exists with the common lexicon, i.e., the set of lexical units that denote concepts and knowledge shared by the average speakers, regardless of their specific training or expertise. Communication between specialists and non-specialists can show a big gap between language(s), and therefore lexical units, used by the two groups. However, quite often, semantic and conceptual overlapping between specialized and common lexical units occurs and, in many cases, the specialized and common units refer to close concepts or even point to the same reality. Considering the modeling of meaning in functional lexical resources, this paper puts forth a solution that links common and specialized lexica within the WordNet model framework. We propose a new relation expressing semantic proximity between common and specialized units and define the conditions for its establishment. Besides contributing to the observation and understanding of the process of knowledge specialization and its reflex on the lexicon, the proposed relation allows for the integration of specialized and non-specialized lexicons into a single database, contributing directly to improving communication in specialist/non-specialist contexts, such as teaching–learning situations or health professional-patient interactions, among many others, where code-switching is frequent and necessary.