bluuebunny/crossref_metadata_2025 huggingface dataset:
The Evolution of Serological Assays during Two Years of the COVID-19 Pandemic: From an Easy-to-Use Screening Tool for Identifying Current Infections to Laboratory Algorithms for Discovering Immune Protection and Optimizing Vaccine Administration
Authors: Nicolai Eleonora , Tomassetti Flaminia , Pignalosa Stefano , Redi Serena , Marino Mariapaola , Basile Umberto , Ciotti Marco
Published: August 2024

The emergence of COVID-19 has evolved into a global pandemic, causing an unprecedented public health crisis marked by unprecedented levels of morbidity never seen in the recent past. Considerable research efforts have been made in the scientific community to establish an optimal method to identify severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infections and to understand the induced immune response. This review examined the development of serological tests during the COVID-19 pandemic, considering the factors affecting sensitivity and specificity, which are key to promote an efficient vaccination strategy for public health. The market has witnessed the introduction of various serological tests for the detection of SARS-CoV-2, such as the chemiluminescence immunoassay (CLIA), which emerged as a powerful and rapid tool to monitor the antibody response before and after vaccination or infection. Therefore, developing serological tests by studying antibody trends and persistence is essential for creating long-term strategies. Our analysis underscores the multifaceted applications of serological tests in pandemic management with a focus on the critical insights they provide into antibody dynamics that help in managing the ongoing pandemic and shaping future public health initiatives, providing a basis for optimizing the future response to viral threats.
Phenotypic and Genotypic Characterization of New Kabuli-Type Chickpea Lines in Australia for Resistance to Ascochyta Blight
Authors: Subedi Megha , Bhattarai Surya , Adorada Dante L.
Published: August 2024

Ascochyta blight (AB) is a major threat to Kabuli-type chickpea production worldwide. This study aimed to identify AB-resistant Kabuli-type chickpea lines through combined phenotypic and genotypic screening. Twenty-six Kabuli-type chickpea lines were phenotyped at the seedling stage using spray inoculation with conidial suspension. Genotyping employed marker-aided selection (MAS) with markers linked to quantitative trait loci (QTL) for AB resistance. The allele-specific marker, CaETR, closely linked to QTLAR1, and the sequence-tagged microsatellite (STMS) markers GAA47, TAA146, and TA194 linked to QTLAR1, QTLAR2, and QTLAR3 were used to assess their utility in distinguishing between resistant and susceptible chickpea lines. The study revealed that none of the lines tested were completely resistant (R) phenotypically. However, some lines, such as AVTCPK#6 and AVTCPK#14, were found to be moderately resistant (MR). Of the two MR lines identified phenotypically, only AVTCPK#6 was found to have bands linked to QTLs for adult plant resistance. The other MR line for AB showed the presence of bands in only one or two of the four markers used. These MR lines can be further utilized in chickpea breeding programs for the development of AB-resistant chickpea cultivars. It is recommended that these results be verified through repeat experiments, using more diverse isolates, and including additional chickpea lines as reference checks for resistance and susceptibility. The allele-specific marker, CaETR, closely linked to QTLAR1 and sequence-tagged microsatellite (STMS) markers GAA47, TAA146 and TA194 linked to QTLAR1, QTLAR2, and QTLAR3 were used to explore these markers’ utility in discriminating between resistant and susceptible chickpea lines. The study showed that phenotypically, none of the lines tested are completely resistant (R). However, some lines, namely AVTCPK#6 and AVTCPK#14, were found to be moderately resistant (MR). Of the two MR lines identified phenotypically, only AVTCPK#6 was identified to have bands linked to QTLs for adult plant resistance. The other MR line for AB showed the presence of bands in only one or two markers among the four markers used. These MR lines can be exploited further in chickpea breeding programs for the development of AB-resistant chickpea cultivars. It is recommended that these results are verified by repeat experiments, using more as well as diverse isolates alongside additional chickpea lines for resistant and susceptible reference checks.
Microstructural and Textural Evolution in Hexagonal Close-Packed Metals: The Case of Zirconium, Magnesium, and Titanium
Authors: Thool Khushahal , Yazar K. U. , Kavimani V. , Gupta Aman , Choi Shi-Hoon
Published: August 2024

Hexagonal close-packed (HCP) metals, particularly Zirconium (Zr), Titanium (Ti), and Magnesium (Mg) alloys, have attracted significant attention due to their unique properties and wide-ranging applications in the aerospace, biomedical, and energy industries. This review paper provides a comprehensive analysis of the microstructural and textural evolution in these HCP materials under various conditions, including rolling, extrusion, drawing, and annealing. The focus of the present work lies on the deformed microstructure and texture development in HCP metals, thus elucidating the fundamental mechanisms that govern their response to mechanical stress. The interaction between dislocation movements, twinning, and slip systems is discussed in detail, illustrating how these factors contribute to the anisotropic behavior characteristic of low-symmetry HCP structures. Unlike high-symmetry metals, deformation in Zr alloys depends on the activation of various slips and twin deformation modes, which are sensitive to crystallographic orientation and strain. Like Zr, Ti alloys present a more complex deformation behavior, heavily influenced by their crystallographic orientation. The most common deformation textures in Ti alloys include split-transverse direction (split-TD), split-rolling direction (split-RD), and normal direction (ND) symmetric basal fiber textures. These textures emerge due to the activation of multiple slip systems and twinning, which are dependent on external factors such as temperature, strain rate, and alloy composition. For Mg alloys, the poor formability and brittleness associated with the dominance of the basal slip system under ambient conditions is a critical material development challenge. The activation of non-basal slip systems introduces complexities in controlling texture and microstructure. However, their activation is crucial for optimizing mechanical properties such as strength and fatigue resistance. The tendency for twinning in Mg alloys further complicates their deformation behavior, leading to challenges in ensuring uniform mechanical performance. Modifying the alloy composition, grain size, and texture can additionally influence the activation of these deformation mechanisms. This review further explores the roles of dynamic recrystallization and grain growth in tailoring mechanical properties, with a particular focus on microstructure and texture evolution during annealing. Through this detailed review, we aim to present a thorough understanding of the microstructural and textural evolution in HCP materials, thereby guiding future research and industrial applications.
Enhanced Thermoelectric Transport Properties of Electronegative-Element-Filled and (Ni, Te) Co-Doped Skutterudites through S Filling
Authors: Wang Boyu , Jiang Zhiyuan
Published: August 2024

Recently, there has been a growing interest in skutterudite (SKD) compounds containing electronegative elements such as Br, Cl, S, Se, and Te, owing to their increased diversity and the versatility of filler atoms. This study focused on the thermoelectric performance of a series of (Ni, Te) co-doped SKDs filled with the electronegative element S, denoted as SxNi0.4Co3.6Sb11.2Te0.8 (x = 0, 0.1, 0.2, and 0.3). These compounds were prepared using a combination of a solid-state reaction and spark plasma sintering techniques. The results showed that (Ni, Te) co-doping introduced excess electrons in the SKD lattice, while the incorporation of the element S into the SKD voids optimized carrier concentration. This led to a considerable increase in the absolute Seebeck coefficient to 110.6 μV K−1 at ambient temperatures. The presence of S fillers induced phonon resonance scattering and point scattering, which reduced lattice thermal conductivity and ultimately improved the thermoelectric figure of merit zT, which reached 0.93 for S0.3Ni0.4Co3.6Sb11.2Te0.8 at 823 K.
Fractal Dimension Distributions of Resting-State Electroencephalography (EEG) Improve Detection of Dementia and Alzheimer’s Disease Compared to Traditional Fractal Analysis
Authors: Yoder Keith J. , Brookshire Geoffrey , Glatt Ryan M. , Merrill David A. , Gerrol Spencer , Quirk Colin , Lucero Ché
Published: August 2024

Across many resting-state electroencephalography (EEG) studies, dementia is associated with changes to the power spectrum and fractal dimension. Here, we describe a novel method to examine changes in the fractal dimension over time and within frequency bands. This method, which we call fractal dimension distributions (FDD), combines spectral and complexity information. In this study, we illustrate this new method by applying it to resting-state EEG data recorded from patients with subjective cognitive impairment (SCI) or dementia. We compared the performance of FDD with the performance of standard fractal dimension metrics (Higuchi and Katz FD). FDD revealed larger group differences detectable at greater numbers of EEG recording sites. Moreover, linear models using FDD features had lower AIC and higher R2 than models using standard full time-course measures of the fractal dimension. FDD metrics also outperformed the full time-course metrics when comparing SCI with a subset of dementia patients diagnosed with Alzheimer’s disease. FDD offers unique information beyond traditional full time-course fractal analyses and may help to identify dementia caused by Alzheimer’s disease and dementia from other causes.
Analytical Validation of a DNA Methylation Biomarker Test for the Diagnosis of Barrett’s Esophagus and Esophageal Adenocarcinoma from Samples Collected Using EsoCheck®, a Non-Endoscopic Esophageal Cell Collection Device
Authors: Ghosal Abhisek , Verma Suman , Le Ivy T. , Lee Victoria T. , deGuzman Brian J. , Aklog Lishan
Published: August 2024

Barrett’s esophagus (BE) is a known precursor to esophageal adenocarcinoma (EAC). Guidelines recommend BE screening in populations with multiple risk factors, for which non-endoscopic esophageal cell collection with biomarker testing is considered as an acceptable alternative to esophagogastroduodenoscopy (EGD). The aim of this study was to evaluate analytical performance characteristics of EsoGuard® (EG), a DNA methylation biomarker assay, as a laboratory-developed test (LDT) in esophageal samples collected with the swallowable EsoCheck® (EC) device. EG is a next-generation sequencing (NGS) assay that evaluates methylated vimentin (VIM) and cyclin A1 (CCNA1), clinically validated biomarkers for the detection of BE and EAC. The studies were conducted according to standards of College of American Pathology (CAP), Clinical Laboratory Improvement Amendments (CLIA), and New York (NY) state requirements for the analytical validation of molecular assays. Comparison to Sanger sequencing showed that EG was 100% accurate at all 31 CpG sites evaluated by the assay. The analytical sensitivity, specificity, and accuracy of the assay were 89%, 100%, and 96%, respectively. Intra- and inter-assay precision was 100%. The limit of detection (LOD) was 1 in 400 methylated cells, and the reference range was 84%. In summary, EsoGuard demonstrates high analytical accuracy, repeatability, and reproducibility in samples collected using the EsoCheck device.
Computed Tomography of Contemporary Occupational Lung Disease: A Pictorial Review
Authors: Lee Jimin , Bambrick Marie , Lau Ambrose , Tarlo Susan M. , McInnis Micheal
Published: August 2024

Occupational lung disease remains one of the most common work-related illnesses and accounts for most deaths from occupational illness. Occupational lung diseases often have delayed manifestation over decades and nonspecific clinical presentations, making it challenging for clinicians to promptly identify the disease and implement preventive measures. Radiologists play a crucial role in identifying and diagnosing occupational lung diseases, allowing for removal of the exposure and early medical intervention. In this review, we share our clinical and radiologic approach to diagnosing occupational lung disease and its subtypes. A collection of sample cases of occupational lung diseases commonly encountered in the modern era at a large Canadian university hospital is included to facilitate understanding. This review will provide radiologists with valuable insights into recognizing and diagnosing occupational lung diseases.
Extraosseous Plasmacytomas: A Radiologist’s Perspective—A Narrative Review of the Literature
Authors: Stefanidis Konstantinos , Yusuf Gibran , Mulita Francesk , Tsalikidis Christos , Mitsala Athanasia , Konstantelou Elissavet , Kotsopoulou Maria , Koletsis Efstratios , Pitiakoudis Michail , Dimopoulos Platon
Published: August 2024

Extraosseous plasmacytomas (EPs) are rare neoplasms originating from plasma cells, often associated with multiple myeloma. EPs are classified into three subtypes: extramedullary myeloma, solitary extramedullary plasmacytoma (SEP), and multiple solitary plasmacytomas. They can manifest in various anatomical sites, including the lung, mediastinum, breast, liver, pancreas, stomach, mesentery, kidney, small and large bowel, testis, and soft tissue. Despite their rarity, EPs present a diagnostic challenge due to their non-specific imaging appearances, which can mimic other neoplastic and inflammatory conditions. This review aims to describe the radiographic features of EPs in the chest, abdomen, and pelvis based on a thorough analysis of the existing literature. While imaging plays a crucial role in the detection and characterization of EPs, histological confirmation is necessary to differentiate them from other neoplastic entities. The review underscores the importance of considering EPs in the differential diagnosis, particularly in patients with a history of multiple myeloma. Understanding the imaging characteristics of EPs is essential for accurate diagnosis and appropriate management. Early imaging is crucial in these patients to exclude the possibility of EP, as timely diagnosis can significantly impact patient outcomes.
Exploring the Multifactorial Landscape of Penile Cancer: A Comprehensive Analysis of Risk Factors
Authors: Amicuzi Ugo , Grillo Marco , Stizzo Marco , Olivetta Michelangelo , Tammaro Simone , Napolitano Luigi , Reccia Pasquale , De Luca Luigi , Rubinacci Andrea , Della Rosa Giampiero , Lecce Arturo , Coppola Paola , Papi Salvatore , Trama Francesco , Romano Lorenzo , Sciorio Carmine , Spirito Lorenzo , Crocetto Felice , Manfredi Celeste , Del Giudice Francesco , Ferro Matteo , Rocco Bernardo , Tataru Octavian Sabin , Balsamo Raffaele , Lucarelli Giuseppe , Del Biondo Dario , Barone Biagio
Published: August 2024

Penile cancer, while rare, is a critical public health issue due to its profound impact on patients and the complexities of its management. The disease’s multifactorial etiology includes risk factors such as HPV infection, poor hygiene, smoking, genetic predispositions, and socioeconomic determinants. This article provides a comprehensive review and analysis of these diverse risk factors, aiming to enhance understanding of the disease’s underlying causes. By elucidating these factors, the article seeks to inform and improve prevention strategies, early detection methods, and therapeutic interventions. A nuanced grasp of the multifactorial nature of penile cancer can enable healthcare professionals to develop more effective approaches to reducing incidence rates and improving patient outcomes.
The Usage of Different Hyaluronic-Acid-Containing Artificial Tears and the Treatment Outcome of Intense Pulsed Light Therapy for Dry Eye Disease: A Retrospective Cohort Study
Authors: Lee Chia-Yi , Yang Shun-Fa , Chen Yun-Chen , Chang Chao-Kai
Published: August 2024

In this study, we aimed to investigate the effect of different hyaluronic acid (HA)-containing artificial tears on dry eye disease (DED) treatment in combination with intense pulsed light (IPL) therapy. A retrospective cohort study was conducted, and the participants received IPL therapy and HA-containing artificial tears. There were 42 and 40 eyes in the 0.10% and 0.15% HA groups after selection, respectively. The main outcomes were the postoperative non-invasive tear break-up time (NITBUT), Schirmer II test results, ocular surface stain, and numbers of DED-related symptoms. A generalized linear model was utilized to produce the adjusted odds ratio (aOR) and 95% confidence interval (CI) of the main outcomes between groups. At the three-month follow-up, the NITBUT was significantly higher in the 0.15% HA group (p = 0.023), and the NITBUT recovery was also significantly better in the 0.15% HA group compared to the 0.10% HA group (p = 0.039). The multiple DED-related symptoms significantly correlated with no DED symptom improvement in both the 0.10% and 0.15% HA groups (both p < 0.05), while the low pre-treatment NITBUT was marginally related to no DED symptom improvement in the 0.10% HA group (p = 0.047). A low NITBUT and Schirmer II test result correlated with no DED symptom improvement in both the groups (all p < 0.05). In conclusion, the application of 0.10% and 0.15% HA-containing artificial tears revealed similar effects to IPL therapy for DED.
Computed Tomography Angiography as a Method for Diagnosing Intracavitary Coronary Arteries
Authors: Gać Paweł , Siudek Bartosz , Głuszczyk Agnieszka , Plizga Jakub , Grajnert Filip , Poręba Rafał
Published: August 2024

The intracavitary coronary arteries (ICCA) course is a rare phenomenon, where the segments of the coronary artery go through the atria or ventricles of the heart. In the past, these changes were incidentally detected during invasive diagnostic procedures for other reasons, as well as during postmortem examinations. As the use of multidetector computed tomography angiography (CTA) becomes more widespread, it has emerged that the incidence of ICCA has been underestimated. We present images from two coronary computed tomography angiography cases, which document the existence of ICCA in patients with non-specific chest pain. In the first case, in a 66-year-old woman, in addition to confirming coronary artery disease without significant stenosis (CAD-RADS 2-category 2 in the coronary-artery-disease-reporting and data system), the course of the middle section of the right coronary artery (RCA) in the lumen of the right atrium was demonstrated. In the second case, in a 47-year-old man in whom the presence of atherosclerotic lesions in the coronary arteries was excluded (CAD-RADS 0), the course of the distal segment of the left anterior descending (LAD) was found in the lumen of the apical layers of the right ventricle. To sum up, it should be stated that coronary CTA is a non-invasive diagnostic method that allows for visualization of the ICCA. In coronary CTA performed for indications consistent with the guidelines of scientific societies, attention should also be paid to the possible intracavitary course of the coronary arteries. The identification of such a course of the coronary arteries may be useful when preparing the patient for potential future invasive procedures involving the cardiac cavities.
Exploring Conceptualizations of Disability Using Story-Completion Methods
Authors: Giouridis Nicole , Williams Toni Louise , McKenna Julia , Tomasone Jennifer Rose
Published: August 2024

This study explored conceptualizations of disability pertaining to peer relationships versus romantic relationships, as well as type of physical disability, using story-completion methods. Seventy-four graduate and undergraduate students from a Canadian university completed one of two versions of a story stem featuring an individual with a physical disability who was either a classmate or a potential romantic partner. Through the process of thematic analysis, three themes were generated as patterns across stories: (1) assumptions about disability present from first glance; (2) uncertainty in navigating negative assumptions of disability; and (3) from discomfort to acceptance of disability through social connection. Storylines differed depending on the type of relationship (i.e., peer or romantic) in both story length and outcome of the relationship. Findings suggest the usefulness of the relatively infrequently used method of story completion for assessing students’ narratives and discussion of meanings surrounding differing relationships with persons with a disability. This study further develops our understanding of cultural norms of disability, as well as highlights the importance of disability knowledge and interaction between persons with and without a disability, to foster positive change in representations and perceptions of disability.
Indole-3 Carbinol and Diindolylmethane Mitigated β-Amyloid-Induced Neurotoxicity and Acetylcholinesterase Enzyme Activity: In Silico, In Vitro, and Network Pharmacology Study
Authors: Ramakrishna Kakarla , Karuturi Praditha , Siakabinga Queen , T.A. Gajendra , Krishnamurthy Sairam , Singh Shreya , Kumari Sonia , Kumar G. Siva , Sobhia M. Elizabeth , Rai Sachchida Nand
Published: August 2024

Background: Alzheimer’s disease (AD) is a neurodegenerative disease characterized by beta-amyloid (Aβ) deposition and increased acetylcholinesterase (AchE) enzyme activities. Indole 3 carbinol (I3C) and diindolylmethane (DIM) are reported to have neuroprotective activities against various neurological diseases, including ischemic stroke, Parkinson’s disease, neonatal asphyxia, depression, stress, neuroinflammation, and excitotoxicity, except for AD. In the present study, we have investigated the anti-AD effects of I3C and DIM. Methods: Docking and molecular dynamic studies against AchE enzyme and network pharmacological studies were conducted for I3C and DIM. I3C and DIM’s neuroprotective effects against self and AchE-induced Aβ aggregation were investigated. The neuroprotective effects of I3C and DIM against Aβ-induced neurotoxicity were assessed in SH-S5Y5 cells by observing cell viability and ROS. Results: Docking studies against AchE enzyme with I3C and DIM show binding efficiency of −7.0 and −10.3, respectively, and molecular dynamics studies revealed a better interaction and stability between I3C and AchE and DIM and AchE. Network pharmacological studies indicated that I3C and DIM interacted with several proteins involved in the pathophysiology of AD. Further, I3C and DIM significantly inhibited the AchE (IC50: I3C (18.98 µM) and DIM (11.84 µM)) and self-induced Aβ aggregation. Both compounds enhanced the viability of SH-S5Y5 cells that are exposed to Aβ and reduced ROS. Further, I3C and DIM show equipotential neuroprotection when compared to donepezil. Conclusions: Our findings indicate that both I3C and DIM show anti-AD effects by inhibiting the Aβ induced neurotoxicity and AchE activities.
AI-Driven Patient Education in Chronic Kidney Disease: Evaluating Chatbot Responses against Clinical Guidelines
Authors: Acharya Prakrati C. , Alba Raul , Krisanapan Pajaree , Acharya Chirag M. , Suppadungsuk Supawadee , Csongradi Eva , Mao Michael A. , Craici Iasmina M. , Miao Jing , Thongprayoon Charat , Cheungpasitporn Wisit
Published: August 2024

Chronic kidney disease (CKD) patients can benefit from personalized education on lifestyle and nutrition management strategies to enhance healthcare outcomes. The potential use of chatbots, introduced in 2022, as a tool for educating CKD patients has been explored. A set of 15 questions on lifestyle modification and nutrition, derived from a thorough review of three specific KDIGO guidelines, were developed and posed in various formats, including original, paraphrased with different adverbs, incomplete sentences, and misspellings. Four versions of AI were used to answer these questions: ChatGPT 3.5 (March and September 2023 versions), ChatGPT 4, and Bard AI. Additionally, 20 questions on lifestyle modification and nutrition were derived from the NKF KDOQI guidelines for nutrition in CKD (2020 Update) and answered by four versions of chatbots. Nephrologists reviewed all answers for accuracy. ChatGPT 3.5 produced largely accurate responses across the different question complexities, with occasional misleading statements from the March version. The September 2023 version frequently cited its last update as September 2021 and did not provide specific references, while the November 2023 version did not provide any misleading information. ChatGPT 4 presented answers similar to 3.5 but with improved reference citations, though not always directly relevant. Bard AI, while largely accurate with pictorial representation at times, occasionally produced misleading statements and had inconsistent reference quality, although an improvement was noted over time. Bing AI from November 2023 had short answers without detailed elaboration and sometimes just answered “YES”. Chatbots demonstrate potential as personalized educational tools for CKD that utilize layman’s terms, deliver timely and rapid responses in multiple languages, and offer a conversational pattern advantageous for patient engagement. Despite improvements observed from March to November 2023, some answers remained potentially misleading. ChatGPT 4 offers some advantages over 3.5, although the differences are limited. Collaboration between healthcare professionals and AI developers is essential to improve healthcare delivery and ensure the safe incorporation of chatbots into patient care.
Risk Assessment and Distribution Estimation for UAV Operations with Accurate Ground Feature Extraction Based on a Multi-Layer Method in Urban Areas
Authors: Zhou Suyu , Liu Yang , Zhang Xuejun , Dong Hailong , Zhang Weizheng , Wu Hua , Li Hao
Published: August 2024

In this paper, a quantitative ground risk assessment mechanism is proposed in which urban ground features are extracted based on high-resolution data in a satellite image when unmanned aerial vehicles (UAVs) operate in urban areas. Ground risk distributions are estimated and a risk map is constructed with a multi-layer method considering the comprehensive risk imposed by UAV operations. The urban ground feature extraction is first implemented by employing a K-Means clustering method to an actual satellite image. Five main categories of the ground features are classified, each of which is composed of several sub-categories. Three more layers are then obtained, which are a population density layer, a sheltering factor layer, and a ground obstacle layer. As a result, a three-dimensional (3D) risk map is formed with a high resolution of 1 m × 1 m × 5 m. For each unit in this risk map, three kinds of risk imposed by UAV operations are taken into account and calculated, which include the risk to pedestrians, risk to ground vehicles, and risk to ground properties. This paper also develops a method of the resolution conversion to accommodate different UAV operation requirements. Case study results indicate that the risk levels between the fifth and tenth layers of the generated 3D risk map are relatively low, making these altitudes quite suitable for UAV operations.
DFLM-YOLO: A Lightweight YOLO Model with Multiscale Feature Fusion Capabilities for Open Water Aerial Imagery
Authors: Sun Chen , Zhang Yihong , Ma Shuai
Published: August 2024

Object detection algorithms for open water aerial images present challenges such as small object size, unsatisfactory detection accuracy, numerous network parameters, and enormous computational demands. Current detection algorithms struggle to meet the accuracy and speed requirements while being deployable on small mobile devices. This paper proposes DFLM-YOLO, a lightweight small-object detection network based on the YOLOv8 algorithm with multiscale feature fusion. Firstly, to solve the class imbalance problem of the SeaDroneSee dataset, we propose a data augmentation algorithm called Small Object Multiplication (SOM). SOM enhances dataset balance by increasing the number of objects in specific categories, thereby improving model accuracy and generalization capabilities. Secondly, we optimize the backbone network structure by implementing Depthwise Separable Convolution (DSConv) and the newly designed FasterBlock-CGLU-C2f (FC-C2f), which reduces the model’s parameters and inference time. Finally, we design the Lightweight Multiscale Feature Fusion Network (LMFN) to address the challenges of multiscale variations by gradually fusing the four feature layers extracted from the backbone network in three stages. In addition, LMFN incorporates the Dilated Re-param Block structure to increase the effective receptive field and improve the model’s classification ability and detection accuracy. The experimental results on the SeaDroneSee dataset indicate that DFLM-YOLO improves the mean average precision (mAP) by 12.4% compared to the original YOLOv8s, while reducing parameters by 67.2%. This achievement provides a new solution for Unmanned Aerial Vehicles (UAVs) to conduct object detection missions in open water efficiently.
Optimizing AoI in IoT Networks: UAV-Assisted Data Processing Framework Integrating Cloud–Edge Computing
Authors: Ma Mingfang , Wang Zhengming
Published: August 2024

Due to the swift development of the Internet of Things (IoT), massive advanced terminals such as sensor nodes have been deployed across diverse applications to sense and acquire surrounding data. Given their limited onboard capabilities, these terminals tend to offload data to servers for further processing. However, terminals cannot transmit data directly in regions with restricted communication infrastructure. With the increasing proliferation of unmanned aerial vehicles (UAVs), they have become instrumental in collecting and transmitting data from the region to servers. Nevertheless, because of the energy constraints and time-consuming nature of data processing by UAVs, it becomes imperative not only to utilize multiple UAVs to traverse a large-scale region and collect data, but also to overcome the substantial challenge posed by the time sensitivity of data information. Therefore, this paper introduces the important indicator Age of Information (AoI) that measures data freshness, and develops an intelligent AoI optimization data processing approach named AODP in a hierarchical cloud–edge architecture. In the proposed AODP, we design a management mechanism through the formation of clusters by terminals and the service associations between terminals and hovering positions (HPs). To further improve collection efficiency of UAVs, an HP clustering strategy is developed to construct the UAV-HP association. Finally, under the consideration of energy supply, time tolerance, and flexible computing modes, a gray wolf optimization algorithm-based multi-objective path planning scheme is proposed, achieving both average and peak AoI minimization. Simulation results demonstrate that the AODP can converge well, guarantee reliable AoI, and exhibit superior performance compared to existing solutions in multiple scenarios.
Unveiling Potential Industry Analytics Provided by Unmanned Aircraft System Remote Identification: A Case Study Using Aeroscope
Authors: Wallace Ryan J. , Rice Stephen , Lee Sang-A , Winter Scott R.
Published: August 2024

The rapid proliferation of unmanned aircraft systems (UAS), commonly known as drones, across various industries, government applications, and recreational use necessitates a deeper understanding of their utilization and market trends. This research leverages UAS detection technology—specifically DJI’s Aeroscope—to track serial numbers and predict product usage, market penetration, and population estimation. By analyzing three years of data from Aeroscope sensors deployed around a major airport in the Southern United States, this study provides valuable insights into UAS operational patterns and platform lifespans. The findings reveal trends in platform utilization, the impact of new product releases, and the decline in legacy platform use. This offers critical data for informed decision-making in market trends, product development, and resource allocation.
Meta Surface-Based Multiband MIMO Antenna for UAV Communications at mm-Wave and Sub-THz Bands
Authors: Saeidi Tale , Saleh Sahar , Timmons Nick , Al-Gburi Ahmed Jamal Abdullah , Karamzadeh Saeid , Althuwayb Ayman A. , Rashid Nasr , Kaaniche Khaled , Ben Atitallah Ahmed , Elhamrawy Osama I.
Published: August 2024

Unmanned aerial vehicles (UAVs) need high data rate connectivity, which is achievable through mm-waves and sub-THz bands. The proposed two-port leaky wave MIMO antenna, employing a coplanar proximity technique that combines capacitive and inductive loading, addresses this need. Featuring mesh-like slots and a vertical slot to mitigate open-stopband (OSB) issues, the antenna radiates broadside and bidirectionally. H-shaped slots on a strip enhance port isolation, and a coffee bean metasurface (MTS) boosts radiation efficiency and gain. Simulations and experiments considering various realistic scenarios, each at varying vertical and horizontal distances, show steered beam patterns, circular polarization (CP), and high-gain properties, with a maximum gain of 13.8 dBi, an axial ratio (AR) <2.9, a diversity gain (DG) >9.98 dB, and an envelope correlation coefficient (ECC) <0.003. This design supports drones-to-ground (D2G), drone-to-drone (D2D), and drone-to-satellite (D2S) communications.
Efficient First-Order Algorithms for Large-Scale, Non-Smooth Maximum Entropy Models with Application to Wildfire Science
Authors: Provencher Langlois Gabriel , Buch Jatan , Darbon Jérôme
Published: August 2024

Maximum entropy (MaxEnt) models are a class of statistical models that use the maximum entropy principle to estimate probability distributions from data. Due to the size of modern data sets, MaxEnt models need efficient optimization algorithms to scale well for big data applications. State-of-the-art algorithms for MaxEnt models, however, were not originally designed to handle big data sets; these algorithms either rely on technical devices that may yield unreliable numerical results, scale poorly, or require smoothness assumptions that many practical MaxEnt models lack. In this paper, we present novel optimization algorithms that overcome the shortcomings of state-of-the-art algorithms for training large-scale, non-smooth MaxEnt models. Our proposed first-order algorithms leverage the Kullback–Leibler divergence to train large-scale and non-smooth MaxEnt models efficiently. For MaxEnt models with discrete probability distribution of n elements built from samples, each containing m features, the stepsize parameter estimation and iterations in our algorithms scale on the order of O(mn) operations and can be trivially parallelized. Moreover, the strong ℓ1 convexity of the Kullback–Leibler divergence allows for larger stepsize parameters, thereby speeding up the convergence rate of our algorithms. To illustrate the efficiency of our novel algorithms, we consider the problem of estimating probabilities of fire occurrences as a function of ecological features in the Western US MTBS-Interagency wildfire data set. Our numerical results show that our algorithms outperform the state of the art by one order of magnitude and yield results that agree with physical models of wildfire occurrence and previous statistical analyses of wildfire drivers.
Bridging Teacher Knowledge and Practice: Exploring Authentic Assessment across Educational Levels
Authors: Hains-Wesson Rachael , le Roux Sanri
Published: August 2024

As teachers, we are living and working in times of abundant challenge and change. These challenges transpire across different education levels and sectors, including K–12, vocational, tertiary, and adult learning. Within this vast education ecosystem, a major challenge for all teachers is to allocate time, effort, and resources to ensure that their students receive a quality education with real-world implications, influencing soft-skill attainment, such as teamwork, communication, and critical thinking skills. In this article, the authors discuss, through a theoretical lens, the value of considering a national and universal approach to self- and peer-evaluations of authentic assessment tasks to improve teacher practice in Australia. Currently, there is modest opportunity amongst K–12 and tertiary teachers to comprehensively learn together, limiting cross-fertilisation of practice and interconnectedness, and as a national community of practice. The authors argue in this paper that offering an avenue to share knowledge and practice in authentic assessment design could potentially assist in addressing this challenge. Therefore, the article is dedicated to exploring the barriers and opportunities to advance a national and universal approach to transferable professional development in authentic assessment practice within the Australian education ecosystem.
Development of the Checklist for Imitation, Exploration and Imagination Behaviour for Play Observation in Preschool Age
Authors: Gastaldi Francesca Giovanna Maria , Longobardi Claudio , Fabris Matteo Angelo , Mastrokoukou Sofia
Published: August 2024

The purpose of this paper is to offer a new coding system to capture preschool playful behaviours based on the imitative, exploratory and imaginative aspects underlying playful behaviours (CIEIPO checklist: Imitation, Exploration, Imagination, for Play Observation). A series of focus group sessions (four in total) involving experts in the field were conducted to identify the most typical preschool play behaviours. In addition, a thorough literature review allowed for the operational definition of the items. The number of participants consisted of 280 preschool-aged children, aged 3 to 5 years (Mage = 4.5, SDage = 1.8), 130 of whom were girls. The most frequently observed behaviours were exploratory (150 times) and imaginative (136 times), while imitative behaviours occurred 58 times. The index of agreement between raters was 0.89 (Cohen’s Kappa). The results show important implications for promoting play skills in preschool children, with particular relevance to the developmental functions involved (exploratory, symbolic and imitative skills).
Augmented Feature Diffusion on Sparsely Sampled Subgraph
Authors: Wu Xinyue , Chen Huilin
Published: August 2024

Link prediction is a fundamental problem in graphs. Currently, SubGraph Representation Learning (SGRL) methods provide state-of-the-art solutions for link prediction by transforming the task into a graph classification problem. However, existing SGRL solutions suffer from high computational costs and lack scalability. In this paper, we propose a novel SGRL framework called Augmented Feature Diffusion on Sparsely Sampled Subgraph (AFD3S). The AFD3S first uses a conditional variational autoencoder to augment the local features of the input graph, effectively improving the expressive ability of downstream Graph Neural Networks. Then, based on a random walk strategy, sparsely sampled subgraphs are obtained from the target node pairs, reducing computational and storage overhead. Graph diffusion is then performed on the sampled subgraph to achieve specific weighting. Finally, the diffusion matrix of the subgraph and its augmented feature matrix are used for feature diffusion to obtain operator-level node representations as inputs for the SGRL-based link prediction. Feature diffusion effectively simulates the message-passing process, simplifying subgraph representation learning, thus accelerating the training and inference speed of subgraph learning. Our proposed AFD3S achieves optimal prediction performance on several benchmark datasets, with significantly reduced storage and computational costs.
Aggregators Used in Fuzzy Control—A Review
Authors: Kozielski Mirosław , Prokopowicz Piotr , Mikołajewski Dariusz
Published: August 2024

An important group of decision-making problems is decision-making under uncertainty, including with incomplete or linguistically described data. Command and control systems, fitting into the multi-sensor paradigm of Industry 4.0/5.0, are becoming increasingly multifactorial. This trend will intensify, requiring uncertainty and incompleteness to be considered and mathematical description and data-processing systems better adapted to them. Aggregators are a group of tools used in solving the aforementioned decision problems, including within fuzzy systems. Aggregating functions are a useful tool mainly in those artificial intelligence systems with problems arising from incomplete data. The aim of this article is to review and describe existing aggregators used in fuzzy control in terms of their usefulness and limitations of their use. Particular attention is paid to the criteria for matching a suitable aggregator to a particular computational problem. This represents an important step towards the further use of this group of technologies in electronic devices and IT systems.
An Autonomous Monitoring System with Microwatt Technology for Exploring the Lives of Arctic Subnivean Animals
Authors: Kalhor Davood , Poirier Mathilde , Gauthier Gilles , Ibarra-Castanedo Clemente , Maldague Xavier
Published: August 2024

Understanding subnivean life is crucial, particularly due to the major role in food webs played by small animals inhabiting this poorly known habitat. However, challenges such as remoteness and prolonged, harsh winters in the Arctic have hampered our understanding of subnivean ecology in this region. To address this problem, we present an improved autonomous, low-power system for monitoring small mammals under the snow in the Arctic. It comprises a compact camera paired with a single-board computer for video acquisition, a low-power-microcontroller-based circuit to regulate video acquisition timing, and motion detection circuits. We also introduce a novel low-power method of gathering complementary information on animal activities using passive infrared sensors. Meticulously designed to withstand extreme cold, prolonged operation periods, and the limited energy provided by batteries, the system’s efficacy is demonstrated through laboratory tests and field trials in the Canadian Arctic. Notably, our system achieves a standby power consumption of approximately 60 µW, representing a seventy-fold reduction compared to previous equipment. The system recorded unique videos of animal life under the snow in the High Arctic. This system equips ecologists with enhanced capabilities to study subnivean life in the Arctic, potentially providing insights to address longstanding questions in ecology.
Facial Age Estimation Using Multi-Stage Deep Neural Networks
Authors: Bekhouche Salah Eddine , Benlamoudi Azeddine , Dornaika Fadi , Telli Hichem , Bounab Yazid
Published: August 2024

Over the last decade, the world has witnessed many breakthroughs in artificial intelligence, largely due to advances in deep learning technology. Notably, computer vision solutions have significantly contributed to these achievements. Human face analysis, a core area of computer vision, has gained considerable attention due to its wide applicability in fields such as law enforcement, social media, and marketing. However, existing methods for facial age estimation often struggle with accuracy due to limited feature extraction capabilities and inefficiencies in learning hierarchical representations. This paper introduces a novel framework to address these issues by proposing a Multi-Stage Deep Neural Network (MSDNN) architecture. The MSDNN architecture divides each CNN backbone into multiple stages, enabling more comprehensive feature extraction, thereby improving the accuracy of age predictions from facial images. Our framework demonstrates a significant performance improvement over traditional solutions, with its effectiveness validated through comparisons with the EfficientNet and MobileNetV3 architectures. The proposed MSDNN architecture achieves a notable decrease in Mean Absolute Error (MAE) across three widely used public datasets (MORPH2, CACD, and AFAD) while maintaining a virtually identical parameter count compared to the initial backbone architectures. These results underscore the effectiveness and feasibility of our methodology in advancing the field of age estimation, showcasing it as a robust solution for enhancing the accuracy of age prediction algorithms.
Modeling Snap-Off during Gas–Liquid Flow by Using Lattice Boltzmann Method
Authors: Zhang Ke , Ji Yuan , Zhang Tao , Zhao Tianyi
Published: August 2024

Understanding the mechanisms of snap-off during gas–liquid immiscible displacement is of great significance in the petroleum industry to enhance oil and gas recovery. In this work, based on the original pseudo-potential lattice Boltzmann method, we improved the fluid–fluid force and fluid–solid force scheme. Additionally, we integrated the Redlich–Kwong equation of state into the lattice Boltzmann model and employed the exact difference method to incorporate external forces within the lattice Boltzmann framework. Based on this model, a pore–throat–pore system was built, enabling gas–liquid to flow through it to investigate the snap-off phenomenon. The results showed the following: (1) The snap-off phenomenon is related to three key factors: the displacement pressure, the pore–throat length ratio, and the pore–throat width ratio. (2) The snap-off phenomenon occurs only when the displacement pressure is within a certain range. When the displacement pressure is larger than the upper limit, the snap-off will be inhibited, and when the pressure is less than the lower limit, the gas–liquid interface cannot overcome the pore–throat and results in a “pinning” effect. (3) The snap-off phenomenon is controlled using the pore–throat structures: e.g., length ratio and the width ratio between pore and throat. It is found that the snap-off phenomenon could easily occur in a “long-narrow” pore–throat system, and yet hardly in a “short-wide” pore–throat system.
The Impact of Air Renewal with Heat-Recovery Technologies on Energy Consumption for Different Types of Environments in Brazilian Buildings
Authors: Castillo Santiago York , Busanello Daiane , Santos Alexandre F. , Venturini Osvaldo J. , Sphaier Leandro A.
Published: August 2024

This work evaluates the impact of air renewal on energy consumption for indoor environments. For this purpose, an analysis of the problem of air renewal at a Brazilian level was carried out, as well as research into the energy impact of air renewal without energy recovery and the different existing technologies for recovering energy from renewed air. On the other hand, the influence of heat-recovery systems was analyzed in three Brazilian cities (Manaus, São Paulo, and Brasília) for different environments, where a classroom in Manaus has an approximately 50% external air factor and a 42% sensible heat factor. However, classrooms in São Paulo and Brasília have a lower external air factor (27% and 8%, respectively) and a higher sensible heat factor (61% and 78%, respectively). Considering a system with heat recovery, the external air factor decreases to 23%, 10%, and 3% for Manaus, São Paulo, and Brasília, respectively. This allows us to understand the influence of heat-recovery systems, which reduce the external air factor and increase the sensible heat factor.