bluuebunny/crossref_metadata_2025 huggingface dataset:
Strain in the Midbrain: Impact of Traumatic Brain Injury on the Central Serotonin System
Authors: O’Connell Christopher J. , Brown Ryan S. , Peach Taylor M. , Traubert Owen D. , Schwierling Hana C. , Notorgiacomo Gabrielle A. , Robson Matthew J.
Published: January 2024

Traumatic brain injury (TBI) is a pervasive public health crisis that severely impacts the quality of life of affected individuals. Like peripheral forms of trauma, TBI results from extraordinarily heterogeneous environmental forces being imparted on the cranial space, resulting in heterogeneous disease pathologies. This has made therapies for TBI notoriously difficult to develop, and currently, there are no FDA-approved pharmacotherapies specifically for the acute or chronic treatment of TBI. TBI is associated with changes in cognition and can precipitate the onset of debilitating psychiatric disorders like major depressive disorder (MDD), generalized anxiety disorder (GAD), and post-traumatic stress disorder (PTSD). Complicating these effects of TBI, FDA-approved pharmacotherapies utilized to treat these disorders often fail to reach the desired level of efficacy in the context of neurotrauma. Although a complicated association, decades of work have linked central serotonin (5-HT) neurotransmission as being involved in the etiology of a myriad of neuropsychiatric disorders, including MDD and GAD. 5-HT is a biogenic monoamine neurotransmitter that is highly conserved across scales of biology. Though the majority of 5-HT is isolated to peripheral sites such as the gastrointestinal (GI) tract, 5-HT neurotransmission within the CNS exerts exquisite control over diverse biological functions, including sleep, appetite and respiration, while simultaneously establishing normal mood, perception, and attention. Although several key studies have begun to elucidate how various forms of neurotrauma impact central 5-HT neurotransmission, a full determination of precisely how TBI disrupts the highly regulated dynamics of 5-HT neuron function and/or 5-HT neurotransmission has yet to be conceptually or experimentally resolved. The purpose of the current review is, therefore, to integrate the disparate bodies of 5-HT and TBI research and synthesize insight into how new combinatorial research regarding 5-HT neurotransmission and TBI may offer an informed perspective into the nature of TBI-induced neuropsychiatric complications.
On the Cut-Off Value of the Anteroposterior Diameter of the Midbrain Atrophy in Spinocerebellar Ataxia Type 2 Patients
Authors: Álvarez-Cuesta José Alberto , Mora-Batista Camilo , Reyes-Carreto Ramón , Carrillo-Rodes Frank Jesus , Fitz Sergio J. Torralbaz , González-Zaldivar Yanetza , Vargas-De-León Cruz
Published: January 2024

(1) Background: Spinocerebellar ataxias (SCA) is a term that refers to a group of hereditary ataxias, which are neurological diseases characterized by degeneration of the cells that constitute the cerebellum. Studies suggest that magnetic resonance imaging (MRI) supports diagnoses of ataxias, and linear measurements of the aneteroposterior diameter of the midbrain (ADM) have been investigated using MRI. These measurements correspond to studies in spinocerebellar ataxia type 2 (SCA2) patients and in healthy subjects. Our goal was to obtain the cut-off value for ADM atrophy in SCA2 patients. (2) Methods: This study evaluated 99 participants (66 SCA2 patients and 33 healthy controls). The sample was divided into estimations (80%) and validation (20%) samples. Using the estimation sample, we fitted a logistic model using the ADM and obtained the cut-off value through the inverse of regression. (3) Results: The optimal cut-off value of ADM was found to be 18.21 mm. The area under the curve (AUC) of the atrophy risk score was 0.957 (95% CI: 0.895–0.991). Using this cut-off on the validation sample, we found a sensitivity of 100.00% (95% CI: 76.84%–100.00%) and a specificity of 85.71% (95% CI: 42.13%–99.64%). (4) Conclusions: We obtained a cut-off value that has an excellent discriminatory capacity to identify SCA2 patients.
Enhancing Organic Contaminant Removal from Wool Scouring Wastewater Using Chemically Modified Biochars
Authors: Li Simeng , Tasnady Desarae , Skelley Shannon , Calderon Blanca , Jiang Sherine
Published: January 2024

In recent times, biochar has emerged as a promising and sustainable solution for COD reduction in wastewater treatment. This study explores the potential of chemically modified biochars as efficient adsorbents for the removal of organic contaminants, specifically oils, fats, and grease (OFG), from wool scouring wastewater. Proximate analysis revealed distinct properties among the biochars, with KOH-treated biochar demonstrating the most promising characteristics, including lower volatile matter, higher fixed carbon content, and reduced ash content, indicating a stable and carbon-rich structure. A meticulous examination of the KOH-treated biochar’s surface characteristics revealed the presence of elevated carbon and nitrogen content, complemented by an expansive surface area measuring 724.4 m2/g. This surface area was at least twice as extensive as that observed in the other post-treated biochar samples. The kinetic adsorption of COD and soluble COD was well fitted by the pseudo-first-order model, with equilibrium achieved in approximately 200 min. The KOH-treated biochar exhibited the highest equilibrium adsorption capacities for both COD and soluble COD in both Dorset wool (Dorset) and Bluefaced Leicester (BFL) wastewater, highlighting its efficacy in OFG removal. Despite these promising results, further research is needed to explore biochar’s surface characteristics, pore structure, and performance under diverse conditions, as well as its integration with existing treatment processes and potential for regeneration and reuse. This study contributes to advancing sustainable wastewater treatment methods using chemically modified biochars.
Kinin Receptors and Kinin-Related Gene Expression in Astrocytic Brain Tumors
Authors: Stadnicka Izabela , Strzałka-Mrozik Barbara , Kimsa-Dudek Magdalena , Kaspera Wojciech , Plewka Andrzej , Szopa Wojciech , Stadnicki Antoni
Published: January 2024

Kinins are a set of peptides present in tissues that are involved in the inflammatory response and cancer progression. However, studies showing the expression of kinin receptors in human glioma samples are still incomplete and contradictory. The aim of the present study was to ascertain the expression of BDKRB1 and BDKRB2 genes, as well as the level of B1R and B2R proteins in human gliomas, depending on the degree of malignancy. Additionally, representative kinin-dependent genes with altered expression were indicated. The expression profile of kinin-dependent genes was determined using oligonucleotide microarray technique. In addition, RT-qPCR was used to assess the expression level of selected differentiating genes. The location of kinin receptors in brain gliomas was assessed using immunohistochemical methods. The oligonucleotide microarray method was used to identify 12 mRNA IDs of kinin-related genes whose expression was upregulated or downregulated in gliomas of different grades. In immunohistochemically stained samples, the concentrations of BR1 and BR2 proteins, measured by optical density, were statistically significantly higher in grade G3 vs. G2 and G4 vs. G3. Increased expression of kinin receptors BDKRB1 and BDKRB2 in brain gliomas, depending on the degree of malignancy, suggests the involvement of kinins and their receptors in the disease’s pathogenesis. Quantitative assessment of mRNA BDKRB1, PRKAR1A, MAP2K, and EGFR in patients with brain tumors may hold diagnostic and therapeutic significance.
Skull Base Reconstruction by Subsite after Sinonasal Malignancy Resection
Authors: Kraimer Kristen , Geltzeiler Mathew
Published: January 2024

Reconstruction after the resection of sinonasal malignancies is complex and primarily dependent on the defect size and location. While the reconstructive paradigm for sellar mass resection is well delineated, the challenges associated with reconstruction after sinonasal malignancy resection are less well described. This narrative review will address the goals of reconstruction after both endonasal endoscopic and open sinonasal malignancy resection and reconstructive options specific to these subsites. The goals of reconstruction include repairing cerebrospinal fluid leaks, restoring sinonasal function, providing a nasal airway, and optimizing the patient’s quality of life. These goals are often complicated by the anatomic nuances of each involved sinus. In this review, we will discuss the methods of reconstruction specific to each sinonasal subsite and describe the factors that guide choosing the optimal reconstructive technique.
Systemic and Local Strategies for Primary Prevention of Breast Cancer
Authors: Zaluzec Erin K. , Sempere Lorenzo F.
Published: January 2024

One in eight women will develop breast cancer in the US. For women with moderate (15–20%) to average (12.5%) risk of breast cancer, there are few options available for risk reduction. For high-risk (>20%) women, such as BRCA mutation carriers, primary prevention strategies are limited to evidence-based surgical removal of breasts and/or ovaries and anti-estrogen treatment. Despite their effectiveness in risk reduction, not many high-risk individuals opt for surgical or hormonal interventions due to severe side effects and potentially life-changing outcomes as key deterrents. Thus, better communication about the benefits of existing strategies and the development of new strategies with minimal side effects are needed to offer women adequate risk-reducing interventions. We extensively review and discuss innovative investigational strategies for primary prevention. Most of these investigational strategies are at the pre-clinical stage, but some are already being evaluated in clinical trials and others are expected to lead to first-in-human clinical trials within 5 years. Likely, these strategies would be initially tested in high-risk individuals but may be applicable to lower-risk women, if shown to decrease risk at a similar rate to existing strategies, but with minimal side effects.
Assessment of Variables Related to the Risk of Severe Adverse Events in Cutaneous Melanoma Patients Treated with Immune Checkpoint Inhibitors
Authors: Trichkova Kremena Petrova , Görtler Franziska , Bjørge Line , Schuster Cornelia
Published: January 2024

Malignant melanoma is a prevalent and aggressive cancer, with globally increasing incidences. While immune checkpoint inhibitors (ICIs) have prolonged the survival of patients with advanced melanoma over the last decade, this improvement comes with the risk of severe immune-related adverse events (irAEs). This systematic review investigates patient baseline characteristics (BCs) as predictive factors for developing severe gastrointestinal, hepatic, and pulmonary irAEs in patients treated with ipilimumab (anti-CTLA-4) and/or nivolumab/pembrolizumab (anti-PD-1). A systematic literature search was conducted in the Ovid databases MEDLINE and EMBASE on 22 April 2022, following the PRISMA guidelines. Out of 1694 articles, 13 were included in the final analysis. We analyzed BCs and the occurrence of severe colitis, hepatitis, and pneumonitis in 22 treatment arms and 3 treatment groups: anti-CTLA-4 (n = 2904), anti-PD-1 (n = 1301), or combination therapy (n = 822). However, missing data preclude a direct comparison of individual BCs and the association to specific irAEs between studies. Descriptive analysis did not identify any significant association between median age, gender distribution, or performance status and severe colitis, hepatitis, or pneumonitis for any of the three treatment groups. We call for greater transparency and standardization in the reporting of patient-specific irAEs.
Bayesian Methods for Information Borrowing in Basket Trials: An Overview
Authors: Zhou Tianjian , Ji Yuan
Published: January 2024

Basket trials allow simultaneous evaluation of a single therapy across multiple cancer types or subtypes of the same cancer. Since the same treatment is tested across all baskets, it may be desirable to borrow information across them to improve the statistical precision and power in estimating and detecting the treatment effects in different baskets. We review recent developments in Bayesian methods for the design and analysis of basket trials, focusing on the mechanism of information borrowing. We explain the common components of these methods, such as a prior model for the treatment effects that embodies an assumption of exchangeability. We also discuss the distinct features of these methods that lead to different degrees of borrowing. Through simulation studies, we demonstrate the impact of information borrowing on the operating characteristics of these methods and discuss its broader implications for drug development. Examples of basket trials are presented in both phase I and phase II settings.
Circulating Tumour Cells in the Prediction of Bone Metastasis
Authors: Choi Siu-Wai , Sun Aria Kaiyuan , Cheung Jason Pui-Yin , Ho Jemmi Ching-Ying
Published: January 2024

Bone is the most common organ for the development of metastases in many primary tumours, including those of the breast, prostate and lung. In most cases, bone metastasis is incurable, and treatment is predominantly palliative. Much research has focused on the role of Circulating Tumour Cells (CTCs) in the mechanism of metastasis to the bone, and methods have been developed to isolate and count CTCs from peripheral blood. Several methods are currently being used in the study of CTCs, but only one, the CellSearchTM system has been approved by the United States Food and Drug Administration for clinical use. This review summarises the advantages and disadvantages, and outlines which clinical studies have used these methods. Studies have found that CTC numbers are predictive of bone metastasis in breast, prostate and lung cancer. Further work is required to incorporate information on CTCs into current staging systems to guide treatment in the prevention of tumour progression into bone.
Closing the Loop: Enhancing Local Monitoring of Child Poverty to Leave No Child Behind
Authors: de la Rasilla Pablo , Stamos Iraklis , Proietti Paola , Siragusa Alice
Published: January 2024

Research on the Leave No One Behind principle of the Sustainable Development Goals (SDGs) within the context of the Agenda 2030 is currently prevalent; however, research on monitoring child poverty at the sub-national (local) level is still limited. This paper addresses this gap by examining indicators developed for monitoring the phenomenon at different territorial levels (global, European, and national) and assessing their territorial transposition locally, using the city of Cadiz, Spain, as a case study. Interviews with local stakeholders reveal that despite the availability and access to related indicators and data, relevant actors must enhance their efforts to utilize such indicators effectively. Based on desktop research and qualitative analysis, the paper delivers recommendations for improving local monitoring of child poverty in Europe and inducing policy changes. This knowledge can inform targeted interventions, policy formulation, and resource allocation to tackle child poverty and promote equitable and inclusive societies.
The Development of a CRISPR-FnCpf1 System for Large-Fragment Deletion and Multiplex Gene Editing in Acinetobacter baumannii
Authors: Wang Shuai , Ding Yue , Rong Hua , Wang Yu
Published: January 2024

Acinetobacter baumannii is a low-GC-content Gram-negative opportunistic pathogen that poses a serious global public health threat. Convenient and rapid genetic manipulation is beneficial for elucidating its pathogenic mechanisms and developing novel therapeutic methods. In this study, we report a new CRISPR-FnCpf1-based two-plasmid system for versatile and precise genome editing in A. baumannii. After identification, this new system prefers to recognize the 5′-TTN-3′ (N = A, T, C or G) and the 5′-CTV-3′ (V = A, C or G) protospacer-adjacent motif (PAM) sequence and utilize the spacer with lengths ranging from 19 to 25 nt. In direct comparison with the existing CRISPR-Cas9 system, it exhibits approximately four times the targetable range in A. baumannii. Moreover, by employing a tandem dual crRNA expression cassette, the new system can perform large-fragment deletion and simultaneous multiple gene editing, which is difficult to achieve via CRISPR-Cas9. Therefore, the new system is valuable and can greatly expand the genome editing toolbox of A. baumannii.
Match and Mismatch between Lived Experiences of Daytime Sleepiness and Diagnostic Instruments: A Qualitative Study amongst Patients with Sleep Disorders
Authors: Verhoef Vaida T. R. , Smolders Karin C. H. J. , Remmelswaal Lysanne , Peeters Geert , Overeem Sebastiaan , de Kort Yvonne A. W.
Published: January 2024

Excessive daytime sleepiness is a common symptom of sleep disorders. Despite its prevalence, it remains difficult to define, detect, and address. The difficulties surrounding sleepiness have been linked to an ambiguous conceptualization, a large variety of scales and measures, and the overlap with other constructs, such as fatigue. The present study aims to investigate patients’ descriptions of sleepiness-related daytime complaints and their phenomenology. We performed semi-directed interviews with patients diagnosed with obstructive sleep apnea (N = 15) or narcolepsy (N = 5). The interviewers took care of utilizing the participants’ terminology when describing daytime complaints related to their sleep disorder. Various aspects of the daytime complaints were investigated, such as their description and temporality. The transcribed content was thematically analyzed using an eclectic coding system, yielding five themes. The participants used different interchangeable descriptors (tired, sleepy, fatigued, exhausted) to express their daytime complaints. They enriched their description with indexes of magnitude (ranging from ‘not especially’ to ‘most gigantic, extreme’), oppositions to other states (using antipodes like energy, alertness, wakefulness, or rest), and indications of fluctuations over the day. Interestingly, the participants often used metaphors to express their experiences and their struggles. The lived experiences of the patients were found to not always align with common self-reported monitoring tools of sleepiness and to relate only in part with current conceptions. In practice, it is important to probe daytime complaints, such as daytime sleepiness, with a broader consideration, for example, by exploring antipodes, consequences, and time-of-day fluctuations.
Strengthening Partnerships to Safeguard the Future of Herbaria
Authors: Thiers Barbara M.
Published: January 2024

Herbaria remain the primary means of documenting plant life on earth, and the number of herbaria worldwide and the number of specimens they hold continues to grow. Digitization of herbarium specimens, though far from complete, has increased the discoverability of herbarium holdings and has increased the range of studies from which data from herbarium specimens can be used. The rather large number of herbaria about which no current information is available is a source of concern, as is herbarium consolidation and removal of herbaria to offsite storage facilities. Partnerships are key to the future health of herbaria. Benefits could accrue from the reimagining of the world’s herbaria as a global resource rather than a collection of independent, often competing institutions. Herbaria can extend the reach of their specimens by joining the nascent effort to link the species occurrence data they manage to other biological and environmental data sources to deepen our ability to understand the interrelationships of earth’s biota. To assure that data held by herbaria contribute to the range of conservation-related projects for which they are relevant, herbaria should embrace the tenets of Team Science and play a more proactive role in promoting their holdings for relevant research and conservation projects.
Thermal Threats to Freshwater Mussels: An Empirical Stream Assessment
Authors: Pandolfo Tamara J. , Cope W. Gregory , Weaver Daniel M. , Kwak Thomas J.
Published: January 2024

Freshwater mussels fulfill an important ecological role in aquatic ecosystems, but they currently face many threats, including thermal regime alteration. Thermal transformation of the aquatic environment is associated with climate change, land use alteration, and other pervasive anthropogenic global changes. To enhance our understanding of ecological thermal impacts, we combined extensive field measurements of temperature in the stream water column and substrate depths (5 and 15 cm) at sites where mussels occur, measures of abundance and species richness for mussels and fish, and thermal tolerance knowledge for mussels and fish to generate a comprehensive assessment of the potential threats mussels face as temperatures continue to rise as a result of global change. Mean summer (June–August 2010–2012) temperatures at mussel-occupied sites in the upper Tar River basin of North Carolina, USA, ranged from 16.2 to 34.7 °C. The mean temperature from the hottest 96 h at each site ranged from 23.5 to 31.5 °C. At 80% of sites, a period of moderate drought coincided with the hottest 96 h period. Temperature threshold exceedance durations indicated that chronic, combined chronic/acute, and acute freshwater mussel thermal tolerance thresholds (i.e., 28 °C, 30 °C, and 33 °C, respectively) based on laboratory exposures of glochidia (larvae) and juveniles were commonly exceeded. Water temperatures exceeded 28 °C for at least 24 h at 55% of sites and for at least 96 h at 35% of sites, and they exceeded 30 °C for at least 24 h at 15% of sites. We quantified a thermal buffering effect of the substrate that may be protective of mussels. There was a mean difference of 0.5 °C between the water column and the upper substrate (5 cm) and a mean difference of 0.9 °C between the water column and the lower substrate (15 cm). Maximum differences of up to 5.5 °C between the water column and the upper substrate and 11.5 °C between the water column and the lower substrate were observed. Our models estimating the relation between the water column and substrate temperatures more realistically characterize ambient temperature exposures and have widespread implications for mussel conservation and climate change risk assessment in similar streams. Freshwater mussels currently exist on the edge of their thermal limits, but their abundance and species richness cannot be explained by temperature patterns alone. Fish species richness was related to the thermal regime, indicating that species interactions may be an important driver of freshwater mussel responses to global change.
Interpretable Detection of Diabetic Retinopathy, Retinal Vein Occlusion, Age-Related Macular Degeneration, and Other Fundus Conditions
Authors: Li Wenlong , Bian Linbo , Ma Baikai , Sun Tong , Liu Yiyun , Sun Zhengze , Zhao Lin , Feng Kang , Yang Fan , Wang Xiaona , Chan Szyyann , Dou Hongliang , Qi Hong
Published: January 2024

Diabetic retinopathy (DR), retinal vein occlusion (RVO), and age-related macular degeneration (AMD) pose significant global health challenges, often resulting in vision impairment and blindness. Automatic detection of these conditions is crucial, particularly in underserved rural areas with limited access to ophthalmic services. Despite remarkable advancements in artificial intelligence, especially convolutional neural networks (CNNs), their complexity can make interpretation difficult. In this study, we curated a dataset consisting of 15,089 color fundus photographs (CFPs) obtained from 8110 patients who underwent fundus fluorescein angiography (FFA) examination. The primary objective was to construct integrated models that merge CNNs with an attention mechanism. These models were designed for a hierarchical multilabel classification task, focusing on the detection of DR, RVO, AMD, and other fundus conditions. Furthermore, our approach extended to the detailed classification of DR, RVO, and AMD according to their respective subclasses. We employed a methodology that entails the translation of diagnostic information obtained from FFA results into CFPs. Our investigation focused on evaluating the models’ ability to achieve precise diagnoses solely based on CFPs. Remarkably, our models showcased improvements across diverse fundus conditions, with the ConvNeXt-base + attention model standing out for its exceptional performance. The ConvNeXt-base + attention model achieved remarkable metrics, including an area under the receiver operating characteristic curve (AUC) of 0.943, a referable F1 score of 0.870, and a Cohen’s kappa of 0.778 for DR detection. For RVO, it attained an AUC of 0.960, a referable F1 score of 0.854, and a Cohen’s kappa of 0.819. Furthermore, in AMD detection, the model achieved an AUC of 0.959, an F1 score of 0.727, and a Cohen’s kappa of 0.686. Impressively, the model demonstrated proficiency in subclassifying RVO and AMD, showcasing commendable sensitivity and specificity. Moreover, our models enhanced interpretability by visualizing attention weights on fundus images, aiding in the identification of disease findings. These outcomes underscore the substantial impact of our models in advancing the detection of DR, RVO, and AMD, offering the potential for improved patient outcomes and positively influencing the healthcare landscape.
The Emergence of the Normal Distribution in Deterministic Chaotic Maps
Authors: Zanette Damián H. , Samengo Inés
Published: January 2024

The central limit theorem states that, in the limits of a large number of terms, an appropriately scaled sum of independent random variables yields another random variable whose probability distribution tends to attain a stable distribution. The condition of independence, however, only holds in real systems as an approximation. To extend the theorem to more general situations, previous studies have derived a version of the central limit theorem that also holds for variables that are not independent. Here, we present numerical results that characterize how convergence is attained when the variables being summed are deterministically related to one another through the recurrent application of an ergodic mapping. In all the explored cases, the convergence to the limit distribution is slower than for random sampling. Yet, the speed at which convergence is attained varies substantially from system to system, and these variations imply differences in the way information about the deterministic nature of the dynamics is progressively lost as the number of summands increases. Some of the identified factors in shaping the convergence process are the strength of mixing induced by the mapping and the shape of the marginal distribution of each variable, most particularly, the presence of divergences or fat tails.
Is Monetary Policy a Driver of Cryptocurrencies? Evidence from a Structural Break GARCH-MIDAS Approach
Authors: Alam Md Samsul , Amendola Alessandra , Candila Vincenzo , Jabarabadi Shahram Dehghan
Published: January 2024

The introduction of Bitcoin as a distributed peer-to-peer digital cash in 2008 and its first recorded real transaction in 2010 served the function of a medium of exchange, transforming the financial landscape by offering a decentralized, peer-to-peer alternative to conventional monetary systems. This study investigates the intricate relationship between cryptocurrencies and monetary policy, with a particular focus on their long-term volatility dynamics. We enhance the GARCH-MIDAS (Mixed Data Sampling) through the adoption of the SB-GARCH-MIDAS (Structural Break Mixed Data Sampling) to analyze the daily returns of three prominent cryptocurrencies (Bitcoin, Binance Coin, and XRP) alongside monthly monetary policy data from the USA and South Africa with respect to potential presence of a structural break in the monetary policy, which provided us with two GARCH-MIDAS models. As of 30 June 2022, the most recent data observation for all samples are noted, although it is essential to acknowledge that the data sample time range varies due to differences in cryptocurrency data accessibility. Our research incorporates model confidence set (MCS) procedures and assesses model performance using various metrics, including AIC, BIC, MSE, and QLIKE, supplemented by comprehensive residual diagnostics. Notably, our analysis reveals that the SB-GARCH-MIDAS model outperforms others in forecasting cryptocurrency volatility. Furthermore, we uncover that, in contrast to their younger counterparts, the long-term volatility of older cryptocurrencies is sensitive to structural breaks in exogenous variables. Our study sheds light on the diversification within the cryptocurrency space, shaped by technological characteristics and temporal considerations, and provides practical insights, emphasizing the importance of incorporating monetary policy in assessing cryptocurrency volatility. The implications of our study extend to portfolio management with dynamic consideration, offering valuable insights for investors and decision-makers, which underscores the significance of considering both cryptocurrency types and the economic context of host countries.
“A World of Possibilities”: The Future of Technology in Higher Education, Insights from the COVID-19 Experience
Authors: McManus Ruth , Logan Anna , Wilders David , Pennycook Caitríona
Published: January 2024

During the emergency online pivot, two faculties (i.e., divisions) of a large Irish university aimed to document insights about teaching and learning in the context of the COVID-19 pandemic. An anonymous online survey of all teaching staff within each faculty was undertaken, some of the results of which are presented here. Key findings focus on the use of online technology in higher education, demonstrating the differential impacts on different disciplines and year groups, and pointing to levels of educator readiness and the need for focused professional development and training to prepare staff for integrating online learning to ensure satisfactory outcomes. The emergency online pivot led to significant progress in the use of technology in higher education, and greater academic staff recognition of the benefits and scope afforded by technology. Arising from their COVID-19 experiences, staff expressed a strong preference for blended learning as opposed to learning taking place either fully online or in-person. The potential role of hybrid learning in ongoing efforts towards sustainability was also noted. Many of our findings align with recent research, which is discussed throughout. We argue that a ‘one size fits all’ approach to online education is not appropriate, with greater nuance required and consideration of the rapidly changing educational landscape and issues of environmental sustainability.
Emotional and Mental Nuances and Technological Approaches: Optimising Fact-Check Dissemination through Cognitive Reinforcement Technique
Authors: Marcondes Francisco S. , Barbosa Maria Araújo , Gala Adelino de C. O. S. , Almeida José João , Novais Paulo
Published: January 2024

The issue of the dissemination of fake news has been widely addressed in the literature, but the issue of the dissemination of fact checks to debunk fake news has not received sufficient attention. Fake news is tailored to reach a wide audience, a concern that, as this paper shows, does not seem to be present in fact checking. As a result, fact checking, no matter how good it is, fails in its goal of debunking fake news for the general public. This paper addresses this problem with the aim of increasing the effectiveness of the fact checking of online social media posts through the use of cognitive tools, yet grounded in ethical principles. The paper consists of a profile of the prevalence of fact checking in online social media (both from the literature and from field data) and an assessment of the extent to which engagement can be increased by using simple cognitive enhancements in the text of the post. The focus is on Snopes and X (formerly Twitter).
Multiple Moving Vehicles Tracking Algorithm with Attention Mechanism and Motion Model
Authors: Gao Jiajun , Han Guangjie , Zhu Hongbo , Liao Lyuchao
Published: January 2024

With the acceleration of urbanization and the increasing demand for travel, current road traffic is experiencing rapid growth and more complex spatio-temporal logic. Vehicle tracking on roads presents several challenges, including complex scenes with frequent foreground–background transitions, fast and nonlinear vehicle movements, and the presence of numerous unavoidable low-score detection boxes. In this paper, we propose AM-Vehicle-Track, following the proven-effective paradigm of tracking by detection (TBD). At the detection stage, we introduce the lightweight channel block attention mechanism (LCBAM), facilitating the detector to concentrate more on foreground features with limited computational resources. At the tracking stage, we innovatively propose the noise-adaptive extended Kalman filter (NSA-EKF) module to extract vehicles’ motion information while considering the impact of detection confidence on observation noise when dealing with nonlinear motion. Additionally, we borrow the Byte data association method to address unavoidable low-score detection boxes, enabling secondary association to reduce ID switches. We achieve 42.2 MOTA, 51.2 IDF1, and 364 IDs on the test set of VisDrone-MOT with 72 FPS. The experimental results showcase our approach’s highly competitive performance, attaining SOTA tracking performance with a fast speed.
Improved Lv’s Distribution for Noisy Multicomponent LFM Signals Analysis
Authors: Yang Kai , Li Xueshi , Li Yang , Zheng Jibin
Published: January 2024

This paper presents the improved Lv’s distribution (ImLVD) for noisy multicomponent linear frequency-modulated (LFM) signals analysis, which is of significant importance in radar signal processing. Two goals of this paper are (i) to overcome drawbacks of the Lv’s distribution (LVD), and (ii) to study mechanisms of the constant delay introduction. Theoretical comparisons in cross-term suppression, resolution, peak-to-sidelobe level, anti-noise performance and implementation are performed for the maximum likelihood (ML) method, Wigner–Hough transform (WHT), LVD, parameterized centroid frequency–chirp rate distribution (PCFCRD) and ImLVD. Based on theoretical comparisons and illustrative examples, superiorities of the ImLVD are demonstrated and several unclear mechanisms of the introduced constant delay are interpreted. Finally, three numerical examples are given to illustrate that, because of the high cross-term suppression, resolution, peak-to-sidelobe level and anti-noise performance without the non-uniform integration variable, the ImLVD is more suitable for noisy multicomponent LFM signals analysis.
Evolutionary Game Dynamics between Distributed Energy Resources and Microgrid Operator: Balancing Act for Power Factor Improvement
Authors: Gautam Mukesh
Published: January 2024

This article investigates the intricate dynamics between Distributed Energy Resources (DERs) and the Microgrid Operator (MGO) within a microgrid interconnected with the main grid. Employing an evolutionary game framework, the study scrutinizes the strategic evolution of DERs’ decision-making processes in their interactions with the MGO. Modeled as an evolutionary game, these interactions encapsulate the strategies adopted by DERs, resulting in stable equilibrium strategies over time. Motivated by direct benefits linked to increased active power production, DERs strive to sell all available power, while the MGO focuses on optimizing the microgrid’s overall performance. The study assesses the microgrid’s performance in terms of its power factor, emphasizing the strategic balance DERs must achieve in their active power generation to avoid penalization. This penalization results in decreased individual utility for DERs due to the overall power factor decrease resulting from their prioritization of active power generation. Additionally, the diminished overall power factor implies a decrease in MGO utility. The individual utility of each DER is further influenced by the strategies adopted by other DERs, impacting the penalization factor. Leveraging a modified IEEE 13-node distribution microgrid consisting of three DERs, the study presents case studies encompassing both cooperative and non-cooperative evolutionary game scenarios. These case studies illuminate the intricacies of interactions and the resulting equilibrium outcomes.
Deep Learning-Based Detection Algorithm for the Multi-User MIMO-NOMA System
Authors: Wang Qixing , Zhou Ting , Zhang Hanzhong , Hu Honglin , Pignaton de Freitas Edison , Feng Songlin
Published: January 2024

Recently, non-orthogonal multiple access (NOMA) has become prevalent in 5G communication. However, the traditional successive interference cancellation (SIC) receivers for NOMA still encounter challenges. The near-far effect between the users and the base stations (BS) results in a higher bit error rate (BER) for the SIC receiver. Additionally, the linear detection algorithm used in each SIC stage fails to eliminate the interference and is susceptible to error propagation. Consequently, designing a high-performance NOMA system receiver is a crucial challenge in NOMA research and particularly in signal detection. Focusing on the signal detection of the receiver in the NOMA system, the main work is as follows. (1) This thesis leverages the strengths of deep neural networks (DNNs) for nonlinear detection and incorporates the low computational complexity of the successive interference cancellation (SIC) structure. The proposed solution introduces a feedback deep neural network (FDNN) receiver to replace the SIC in signal detection. By employing a deep neural network for nonlinear detection at each stage, the receiver mitigates error propagation, lowers the BER in NOMA systems, and enhances resistance against inter-user interference (IUI). (2) We describe its algorithm flow and provide simulation results comparing FDNN and SIC receivers under MIMO-NOMA scenarios. The simulations clearly demonstrate that FDNN receivers outperform SIC receivers in terms of BER for MIMO-NOMA systems.
GaitMGL: Multi-Scale Temporal Dimension and Global–Local Feature Fusion for Gait Recognition
Authors: Zhang Zhipeng , Wei Siwei , Xi Liya , Wang Chunzhi
Published: January 2024

Gait recognition has received widespread attention due to its non-intrusive recognition mechanism. Currently, most gait recognition methods use appearance-based recognition methods, and such methods are easily affected by occlusions when facing complex environments, which in turn affects the recognition accuracy. With the maturity of pose estimation techniques, model-based gait recognition methods have received more and more attention due to their robustness in complex environments. However, the current model-based gait recognition methods mainly focus on modeling the global feature information in the spatial dimension, ignoring the importance of local features and their influence on recognition accuracy. Meanwhile, in the temporal dimension, these methods usually use single-scale temporal information extraction, which does not take into account the inconsistency of the motion cycles of the limbs when a human body is walking (e.g., arm swing and leg pace), leading to the loss of some limb temporal information. To solve these problems, we propose a gait recognition network based on a Global–Local Graph Convolutional Network, called GaitMGL. Specifically, we introduce a new spatio-temporal feature extraction module, MGL (Multi-scale Temporal and Global–Local Spatial Extraction Module), which consists of GLGCN (Global–Local Graph Convolutional Network) and MTCN (Multi-scale Temporal Convolutional Network). GLGCN models both global and local features, and extracts global–local motion information. MTCN, on the other hand, takes into account the inconsistency of local limb motion cycles, and facilitates multi-scale temporal convolution to capture the temporal information of limb motion. In short, our GaitMGL solves the problems of loss of local information and loss of temporal information at a single scale that exist in existing model-based gait recognition networks. We evaluated our method on three publicly available datasets, CASIA-B, Gait3D, and GREW, and the experimental results show that our method demonstrates surprising performance and achieves an accuracy of 63.12% in the dataset GREW, exceeding all existing model-based gait recognition networks.
Fake News Detection Based on Knowledge-Guided Semantic Analysis
Authors: Zhao Wenbin , He Peisong , Zeng Zhixin , Xu Xiong
Published: January 2024

Recently, fake news, such as low-quality news with intentionally false information, has threatened the authenticity of news information. However, existing detection methods are inefficient in modeling complicated data and leveraging external knowledge. To address these limitations, we propose a fake news detection framework based on knowledge-guided semantic analysis, which compares the news to external knowledge through triplets for fake news detection. Considering that equivalent elements of triplets may be presented in different forms, a triplet alignment method is designed to construct the bridge between news documents and knowledge graphs. Then, a dual-branch network is developed to conduct interaction and comparison between text and knowledge embeddings. Specifically, text semantics is analyzed with the guidance generated by a triplet aggregation module to capture the inconsistency between news content and external knowledge. In addition, a triplet scoring module is designed to measure rationality in view of general knowledge as a complementary clue. Finally, an interaction module is proposed to fuse rationality scores in aspects of text semantics and external knowledge to obtain detection results. Extensive experiments are conducted on publicly available datasets and several state-of-the-art methods are considered for comparison. The results verify the superiority of the proposed method in achieving more reliable detection results of fake news.
Enzymatic In Situ Interesterification of Rapeseed Oil with Methyl Formate in Diesel Fuel Medium
Authors: Makareviciene Violeta , Kazancev Kiril , Sendzikiene Egle , Gumbyte Milda
Published: January 2024

The purpose of this research was to evaluate the process of enzymatic biodiesel synthesis by directly using rapeseed as a raw material, extracting the oil contained within and interesterifying with a mixture of methyl formate and mineral diesel, choosing the amount of mineral diesel so that the ratio between it and the rapeseed oil in the seeds was 9:1. As the final product of the interesterification process, a mixture of mineral diesel and biodiesel was obtained directly, which is conventionally produced by mixing the mineral diesel and biodiesel. The tests were performed using enzymatic catalysis using the lipase Lipozyme TL TIM. Process optimization was performed using the response surface methodology. A model describing the interaction of three independent variables and their influence on the yield of rapeseed oil methyl esters was developed. The physical and chemical indicators of the product obtained under optimal interesterification conditions were evaluated.
Effects of Exposure to Urban Atmospheric Particulate Matter Suspended in Seawater on the Mussel Mytilus galloprovincialis
Authors: Rodrigues Inês , Ferreira Inês João , Duarte Regina M. B. O. , Diniz Mário
Published: January 2024

Atmospheric particulate matter (PM) contains numerous constituents, including organic molecules, inorganic ions, and metals, with some of them possessing hazardous properties. Although mainly associated with air pollution, PM can rapidly be transferred from air and land to aquatic ecosystems, and consequently poses a risk to marine biota. The aim of this work was to evaluate how urban atmospheric PM (a standard reference mixture of urban PM, known to contain various organic and inorganic contaminants), suspended in seawater, may cause toxicity in marine organisms. To this purpose, mussels (Mytilus galloprovincialis) were exposed to two concentrations of suspended PM: 5.7 and 11.4 mg/L. After 7, 14, and 21 days, the animals were collected and the gills and digestive gland were analysed for stress biomarkers (CAT, SOD, GPX, GST, MDA, and Ubi). In general, the results show that exposure to different concentrations of PM caused an increase in GST, UBI, and GPx activities compared to their respective controls. The average activities of GST (87.65 ± 30.23 nmol/min/mg of total protein) in the gills of the animals exposed to 11.4 mg/L of PM increased after 21 days of exposure, and the activity of GPx (8.04 ± 3.09 nmol/min/mg of total protein) in the gills increased after 14 days in the animals exposed to 5.7 mg/L of PM. MDA results also provided information on cellular damage, with the most pronounced effects being found in the gills of exposed mussels. This study confirms that mussels are useful as “early warning” indicators of environmental contamination and provides important information on the effects of PM on marine biota.
Assessment of the Analytic Burned Area Index for Forest Fire Severity Detection Using Sentinel and Landsat Data
Authors: Guo Rentao , Yan Jilin , Zheng He , Wu Bo
Published: January 2024

The quantitative assessment of forest fire severity is significant for understanding the changes in ecological processes caused by fire disturbances. As a novel spectral index derived from the multi-objective optimization algorithm, the Analytic Burned Area Index (ABAI) was originally designed for mapping burned areas. However, the performance of the ABAI in detecting forest fire severity has not been addressed. To fill this gap, this study utilizes a ground-based dataset of fire severity (the composite burn index, CBI) to validate the effectiveness of the ABAI in detecting fire severity. First, the effectiveness of the ABAI regarding forest fire severity was validated using uni-temporal images from Sentinel-2 and Landsat 8 OLI. Second, fire severity accuracy derived from the ABAI with bi-temporal images from both sensors was evaluated. Finally, the performance of the ABAI was tested with different sensors and compared with representative spectral indices. The results show that (1) the ABAI demonstrates significant advantages in terms of accuracy and stability in assessing fire severity, particularly in areas with large numbers of terrain shadows and severe burn regions; (2) the ABAI also shows great advantages in assessing regional forest fire severity when using only uni-temporal remotely sensed data, and it performed almost as well as the dNBR in bi-temporal images. (3) The ABAI outperforms commonly used indices with both Sentinel-2 and Landsat 8 data, indicating that the ABAI is normally more generalizable and powerful and provides an optional spectral index for fire severity evaluation.