bluuebunny/crossref_metadata_2025 huggingface dataset:
Wind-Driven Dynamics Around Building Clusters: Impact of Convex and Concave Curvilinear Morphologies and Central Angles
Authors: Gan Wei , Guo Han , Zhang Hongliang , Zhao Fuyun , Li Jinyu , Peng Shuqi , He Yi
Published: December 2024

Curvilinear building forms are increasingly common in modern architecture, yet their impact on wind dynamics remains understudied. This research examines the wind flow behavior around high-rise residential building rows configured with convex and concave curvilinear shapes, focusing on the influence of varying central angles. Using parametric models generated in Rhino’s Grasshopper, followed by experimentally validated CFD simulations, we analyze the aerodynamic effects of these configurations. The findings indicate contrasting impacts: convex curvilinear rows amplify wind flow as their central angles increase, while concave rows hinder airflow under similar conditions. These variations are linked to changes in the windward projective areas. This study enhances the understanding of wind behavior in urban settings and provides key insights for optimizing natural ventilation and designing sustainable curvilinear building clusters.
Measuring Speech Intelligibility with Romanian Synthetic Unpredictable Sentences in Normal Hearing
Authors: Astefanei Oana , Cozma Sebastian , Martu Cristian , Serban Roxana , Butnaru Corina , Moraru Petronela , Musat Gabriela , Radulescu Luminita
Published: December 2024

Background/Objectives: Understanding speech in background noise is a challenging task for listeners with normal hearing and even more so for individuals with hearing impairments. The primary objective of this study was to develop Romanian speech material in noise to assess speech perception in diverse auditory populations, including individuals with normal hearing and those with various types of hearing loss. The goal was to create a versatile tool that can be used in different configurations and expanded for future studies examining auditory performance across various populations and rehabilitation methods. Methods: This study outlines the development of Romanian speech material for speech-in-noise testing, initially presented to normal-hearing listeners to establish baseline data. The material consisted of unpredictable sentences, each with a fixed syntactic structure, generated using speech synthesis from all Romanian phonemes. A total of 50 words were selected and organized into 15 lists, each containing 10 sentences, with five words per sentence. Two evaluation methods were applied in two sessions to 20 normal-hearing volunteers. The first method was an adaptive speech-in-noise recognition test designed to assess the speech recognition threshold (SRT) by adjusting the signal-to-noise ratio (SNR) based on individual performance. The intelligibility of the lists was further assessed at the sentence level to evaluate the training effect. The second method was used to obtain normative data for the SRT, defined as the SNR at which a subject correctly recognizes 50% of the speech material, as well as for the slope, which refers to the steepness of the psychometric function derived from threshold recognition scores measured at three fixed SNRs (−10 dB, −7 dB, and −4 dB) during the measurement phase. Results: The adaptive method showed that the training effect was established after two lists and remained consistent across both sessions. During the measurement phase, the fixed SNR method yielded a mean SRT50 of −7.38 dB with a slope of 11.39%. These results provide reliable and comparable data, supporting the validity of the material for both general population testing and future clinical applications. Conclusions: This study demonstrates that the newly developed Romanian speech material is effective for evaluating speech recognition abilities in noise. The training phase successfully mitigated initial unfamiliarity with the material, ensuring that the results reflect realistic auditory performance. The obtained SRT and slope values provide valuable normative data for future auditory assessments. Due to its flexible design, the material can be further developed and extended to accommodate various auditory rehabilitation methods and diverse populations in future studies.
Integrating Drug Target Information in Deep Learning Models to Predict the Risk of Adverse Events in Patients with Comorbid Post-Traumatic Stress Disorder and Alcohol Use Disorder
Authors: Miranda Oshin , Qi Xiguang , Brannock M. Daniel , Whitworth Ryan , Kosten Thomas R. , Ryan Neal David , Haas Gretchen L. , Kirisci Levent , Wang Lirong
Published: December 2024

Background/Objectives: Comorbid post-traumatic stress disorder (PTSD) and alcohol use disorder (AUD) patients are at a significantly higher risk of adverse outcomes, including opioid use disorder, depression, suicidal behaviors, and death, yet limited treatment options exist for this population. This study aimed to build on previous research by incorporating drug target information into a novel deep learning model, T-DeepBiomarker, to predict adverse outcomes and identify potential therapeutic medications. Methods: We utilized electronic medical record (EMR) data from the University of Pittsburgh Medical Center (UPMC), analyzing 5565 PTSD + AUD patients. T-DeepBiomarker was developed by integrating multimodal data, including lab results, drug target information, comorbidities, neighborhood-level social determinants of health (SDoH), and individual-level SDoH (e.g., psychotherapy and veteran status). The model was trained to predict adverse events, including opioid use disorder, suicidal behaviors, depression, and death, within three months following any clinical encounter. Candidate medications targeting significant proteins were identified through literature reviews. Results: T-DeepBiomarker achieved high predictive performance with an AUROC of 0.94 for adverse outcomes in PTSD + AUD patients. Several medications, including OnabotulinumtoxinA, Dronabinol, Acamprosate, Celecoxib, Exenatide, Melatonin, and Semaglutide, were identified as potentially reducing the risk of adverse events by targeting significant proteins. Conclusions: T-DeepBiomarker demonstrates high accuracy in predicting adverse outcomes in PTSD + AUD patients and highlights candidate drugs with potential therapeutic effects. These findings advance pharmacotherapy for this high-risk population and identify medications that warrant further investigation.
Enhanced Foot Proprioception Through 3-Minute Walking Bouts with Ultra-Minimalist Shoes on Surfaces That Mimic Highly Rugged Natural Terrains
Authors: Biscarini Andrea , Calandra Andrea , Marcucci Alberto , Panichi Roberto , Belotti Angelo
Published: December 2024

The use of minimalist shoes can lead to enhanced foot somatosensory activation and postural stability but can also increase the incidence of overuse injuries during high-impact or prolonged activities. Therefore, it appears useful to explore new strategies that employ minimalist shoes to effectively facilitate the somatosensory activation of the foot while minimizing acute and cumulative joint stress and risk of injury. To this purpose, this study introduces a novel exercise paradigm: walking for three minutes in ultra-minimalist shoes on artificial flat surfaces designed to mimic highly rugged natural terrains. The activity of foot muscles and lumbar multifidus, pain perception level, and stabilometric parameters were recorded and analyzed to characterize the novel exercise, comparing it to walking barefoot or in conventional shoes on the same rugged surface. Compared to being barefoot, ultra-minimalist shoes effectively filter nociceptive stimuli from the rugged surface, while compared to conventional shoes, they enhance the somatosensory input supporting static stability. Walking with ultra-minimalist and conventional shoes yielded higher gastrocnemius activity and lower tibialis anterior and multifidus activity compared to barefoot walking. This study highlights a practical and safe framework for enhancing foot somatosensory activation and postural stability. The new intervention is suitable for people of all ages, requires minimal time commitment, and can be performed in controlled environments such as homes, gyms, and healthcare facilities.
Impact of Cognitive VR vs. Traditional Training on Emotional Self-Efficacy and Cognitive Function in Patients with Multiple Sclerosis: A Retrospective Study Focusing on Gender Differences
Authors: Maggio Maria Grazia , Benenati Alessandra , Impellizzeri Federica , Rizzo Amelia , Barbera Martina , Cannavò Antonino , Gregoli Vera , Morone Giovanni , Chirico Francesco , Quartarone Angelo , Calabrò Rocco Salvatore
Published: December 2024

Background and aim: While conventional MS rehabilitation primarily addresses physical and cognitive symptoms, recent advances in VR technology offer immersive environments that facilitate both emotional and cognitive skill development. The purpose of this study is to evaluate the impact of VR-based training on emotional self-efficacy in multiple sclerosis (MS) patients and examine its association with cognitive function improvement. Additionally, this study aims to explore potential gender differences in these outcomes, hypothesizing that gender may influence the effectiveness of VR-based rehabilitation, which could inform more tailored approaches for emotional and cognitive rehabilitation in MS. Method: The present retrospective study analyzed data from 43 MS patients undergoing cognitive and behavioral rehabilitation at the IRCCS Centro Neurolesi “Bonino Pulejo” in Italy, comparing a VR intervention group (VR-G) and a control group receiving traditional rehabilitation. Emotional self-efficacy, depression, and anxiety were assessed, alongside cognitive function pre- and post-intervention. Results: Findings indicate that the VR-G showed significant improvements in managing negative emotions, reduced depressive and anxiety symptoms, and enhanced cognitive performance, particularly in verbal learning and working memory. Gender-based analysis revealed a trend suggesting that females in the VR-G may exhibit greater improvements in positive emotional self-efficacy, although this difference did not reach statistical significance. Spearman’s correlation highlighted associations between emotional self-efficacy and cognitive gains, supporting the potential of VR to foster both emotional and cognitive resilience. Conclusions: These findings suggest that VR training may provide a tailored approach for MS rehabilitation, enhancing therapeutic outcomes by integrating emotional and cognitive training in an immersive setting. Further research should investigate long-term effects and neurophysiological correlates of VR training to optimize MS rehabilitation.
Team Perspective Taking and Collective Thriving in College Students’ Innovation Teams
Authors: Zhao Hui , Han Mengjiao , Wang Zhenzhen , Liu Bangdan
Published: December 2024

Team perspective taking is a process of team member empathy, motivation for other people’s ideas and feelings, and the ability to understand objectively. It can have positive impacts on teams, but the question of whether team perspective taking positively affects the sense of collective thriving exhibited by the team has not been answered, and the intrinsic mechanism underlying this influence has not been revealed. To explore the impact of team perspective taking on the collective thriving of college student innovation teams, this study constructs a chain mediation model based on theories such as the socially embedded model of thriving. A questionnaire survey was conducted to investigate 225 college student innovation teams. The results show that (1) team perspective taking, team trust, and team reflexivity are positively correlated with collective thriving. (2) Team trust and team reflexivity play separate mediating roles in the influence of team perspective taking on collective thriving. Team trust also plays a chain mediating role, and its mediating path is team perspective taking → team trust → team reflexivity → collective thriving. Team perspective taking not only has a direct effect on the collective thriving of college students’ innovation teams but also has an indirect effect through the chain mediating path of team trust and team reflexivity. This study not only further enriches the antecedent literature on collective thriving but also verifies the promoting effect of various resource factors on collective thriving at the team level and provides a theoretical reference for the activation of collective thriving.
The Moderating Role of Emotional Intelligence on the Relationship Between Nurses’ Preparedness to Care for COVID-19 Patients and Their Quality of Work Life
Authors: Alkorashy Hanan A. , Basheer Aisha F. , Mohamed Hanem F.
Published: December 2024

Emotional intelligence (EI) is increasingly recognized as a key factor in healthcare, where managing emotions is vital for job satisfaction, productivity, and interpersonal relationships. For nurses, particularly during the COVID-19 pandemic, EI plays a pivotal role in navigating emotional challenges and improving their quality of work life (QoWL). This study examined how EI moderates the relationship between nurses’ preparedness to care for COVID-19 patients and their QoWL. A cross-sectional, correlational design was used, involving 267 nurses from various healthcare settings. Data were collected through the Emotional Intelligence Scale, the Quality of Nursing Work Life survey, and demographic questionnaires. The sample was predominantly female (94.4%), with a mean age of 37.47 years (SD = 8.09) and an average of 8.43 years of experience (SD = 6.33). Most nurses (87.3%) attended COVID-19-related workshops, with 76.4% feeling prepared to care for COVID-19 patients. Emotional intelligence levels were high, with 93.6% of nurses reporting good personal competence and 85.4% reporting good social competence. Descriptive results showed that 71% of nurses perceived their QoWL as good, while 29% rated it as fair. Pearson correlation analysis revealed significant positive correlations between both personal competence (r = 0.33, p < 0.001) and social competence (r = 0.34, p < 0.001) with QoWL, but preparedness to care for COVID-19 patients did not correlate significantly with either EI or QoWL. Hierarchical regression analysis demonstrated that, although nurses’ preparedness alone did not predict QoWL (β = 0.034, p = 0.57), including emotional intelligence as a moderator explains 41% of the variance in QoWL. Both personal (β = 0.578, p < 0.001) and social competence (β = 0.665, p < 0.001) components of EI had significant buffering effects on the relationship between preparedness and QoWL. These findings suggest that fostering EI in nurses can enhance their resilience and improve their work life quality, particularly in high-stress healthcare environments like those experienced during the pandemic.
A Study of the Impacts of Different Opening Arrangements of Double-Skin Façades on the Indoor Temperatures of a Selected Building
Authors: Sun Qing , Song Junwei , Yu Ying , Ai Hongbo , Zhao Long
Published: December 2024

The aim of this study is to evaluate the indoor temperature of a double-skin façades (DSF) high-rise building in Xi’an under different window opening arrangements, and to assess their impact on the operating time of the air-conditioning system. Compared to conventional buildings, double-skin façade (DSF) buildings can reduce energy consumption. While current research trends focus primarily on heat transfer and materials, there is limited exploration of window opening arrangements. To address this gap, VENT engineering software 2018 was used to simulate indoor temperatures at various window opening angles and determine the optimal arrangement. Additionally, the extreme random tree (ET) algorithm was employed to develop a model for indoor temperature prediction. Climate data were sourced from an online database and processed using the Spearman correlation coefficient method. Window opening arrangements were designed using orthogonal tests, and the performance of the DSF was evaluated with computational fluid dynamics (CFD) software (Fluent) 2023R1. An analysis of temperature variation in the double-skin façade (DSF) curtain wall revealed that the ET algorithm predicted indoor temperatures with 93% accuracy at a 50° window opening angle. Optimal window opening arrangement 2 resulted in a 2.7% reduction in the average interior temperature, a 3.6% reduction at a height of 1.2 m, and a decrease in air-conditioning runtime by 1.33 h. The extreme random tree (ET) algorithm was found to be more accurate than other methods in predicting DSF performance. These findings provide insights for optimizing the control and application of double-skin façades and suggest potential synergies with other systems.
The Emotional Design of Street Furniture Based on Kano Modeling
Authors: Wang Zhaoxing , Han Chao , Yu Bing , Wei Kexin , Li Yiqing , Jin Sanfeng , Bai Panpan
Published: December 2024

With the acceleration of urbanization, the design of street furniture, as an indispensable part of urban public spaces, faces the challenge of neglecting the user experience. To address this situation, this study adopted a three-level theory of affective design and the Kano model to categorize and rank users’ affective needs. The results show that users prefer high-density street furniture that follows traditional urban space design and incorporates elements of traditional Chinese culture and they reject minimalist modern facilities and empty, “hard” plazas. Accordingly, this study proposes emotional design strategies to effectively address the existing problems. Furthermore, this study confirms that the Kano model has significant advantages in identifying the real emotional needs of users, providing data support, and guiding the emotional design of street furniture, which ensures the scientific validity and effectiveness of the design strategy. This study not only deepens the understanding of the emotional design of street facilities but also provides an important reference for future urban space design and planning.
Synchronized Contrast-Enhanced 4DCT Simulation for Target Volume Delineation in Abdominal SBRT
Authors: Faccenda Valeria , Panizza Denis , Niespolo Rita Marina , Colciago Riccardo Ray , Rossano Giulia , De Sanctis Lorenzo , Gandola Davide , Ippolito Davide , Arcangeli Stefano , De Ponti Elena
Published: December 2024

Background/Objectives: To present the technical aspects of contrast-enhanced 4DCT (ce4DCT) simulation for abdominal SBRT. Methods: Twenty-two patients underwent two sequential 4DCT scans: one baseline and one contrast-enhanced with personalized delay time (tdelay) calculated to capture the tumor in the desired contrast phase, based on diagnostic triple-phase CT. The internal target volume (ITV) was delineated on ten contrast phases, and a panel of three experts qualitatively evaluated tumor visibility. Aortic HU values were measured to refine the tdelay for subsequent patients. The commonly used approach of combining triple-phase CT with unenhanced 4DCT was simulated, and differences in target delineation were evaluated by volume, centroid shift, Dice and Jaccard indices, and mean distance agreement (MDA). The margins required to account for motion were calculated. Results: The ce4DCT acquisitions substantially improved tumor visibility over the entire breathing cycle in 20 patients, according to the experts’ unanimous evaluation. The median contrast peak time was 54.5 s, and the washout plateau was observed at 70.3 s, with mean peak and plateau HU values of 292 ± 59 and 169 ± 25. The volumes from the commonly used procedure (ITV2) were significantly smaller than the ce4DCT volumes (ITV1) (p = 0.045). The median centroid shift was 4.7 mm. The ITV1-ITV2 overlap was 69% (Dice index), 53% (Jaccard index), and 2.89 mm (MDA), with the liver volumes showing significantly lower indices compared to the pancreatic volumes (p ≤ 0.011). The margins required to better encompass ITV1 were highly variable, with mean values ≥ 4 mm in all directions except for the left–right axis. Conclusions: The ce4DCT simulation was feasible, resulting in optimal tumor enhancement with minimal resource investment, while significantly mitigating uncertainties in SBRT planning by addressing poor visibility and respiratory motion. Triple-phase 3DCT with unenhanced 4DCT led to high variability in target delineation, making the isotropic margins ineffective.
Retrospective-Prospective Observational Study of Italian Patients Treated in Melanoma Adjuvant Cohort MAP–MADAM (Maximing ADjuvAnt MAP): Interim Analysis
Authors: Consoli Francesca , Tucci Marco , Pigozzo Jacopo , Simeone Ester , Spagnolo Francesco , Troiani Teresa , Morgese Francesca , Del Vecchio Michele , Melotti Barbara , Tronconi Maria Chiara , Morelli Maria Francesca , Grosso Federica , Merelli Barbara , Marcon Ilaria , Valsecchi Diletta , Quaglino Pietro
Published: December 2024

Background/Objective: Dabrafenib and trametinib (D + T) have been approved for the treatment of stage III melanoma with BRAF V600E V600K mutations in an adjuvant setting, based on the results from the COMBI-AD trial. To provide early access to this combination therapy prior to its commercial availability in Italy, a Managed Access Program (MAP) was run in Italy from June 2018 to December 2019. Methods: The MADAM (Maximing ADjuvAnt MAP) study is an Italian retrospective–prospective observational study that included patients who received at least one dose of D + T through the MAP. The primary endpoints were relapse-free survival (RFS) and overall survival (OS). Results: This interim analysis presents findings after the first 24 months of follow-up. A total of 310 patients were included in the study; 240 completed the 12-month treatment with D + T, while 70 discontinued the combination. RFS rates were 93.2% at 12 months and 80.2% at 24 months. The median RFS was not reached for the overall population or any subgroups. Similarly, the median OS was not reached, with OS rates of 96.4% at 12 months and 92.5% at 24 months. Conclusions: D + T achieved an RFS benefit, with effects sustained beyond the treatment period, indicating positive outcomes in this patient population.
Recent Developments in Surgical Treatment of Spinal Deformity in Pediatric Patients: Experience from a Single-Center Series of 42 Neurofibromatosis Type 1 Patients
Authors: Mladenov Kiril V. , Stücker Ralf
Published: December 2024

Background: The management of spinal deformities in patients with NF-1 is challenging. The study aimed to assess the outcomes of the surgical treatment of spine deformities in children with neurofibromatosis type 1 with our treatment approach. Methods: A retrospective single-center study on pediatric patients with spinal deformities associated with NF-1 who received surgical treatment between 2006 and 2024. Results: The study group comprised 42 patients with a mean age at surgery of 9.8 years. Twenty-five patients (60%) were treated by means of growth-preserving techniques and 17 patients (40%) by means of definitive fusion. Preoperative halo-gravity traction was used in 14 (33%) cases. In the group treated with a growth-preserving technique, a 54.1% mean curve correction was observed at the latest follow-up, and growth of the thoracic spine was maintained at a physiological rate; however, 25 unplanned revision surgeries (mostly due to mechanical complications) were necessary. In the group treated by definitive fusion, a 66% curve correction was achieved at initial surgery, which remained unchanged at latest follow-up, and revision surgery was performed in three cases for augmentation of the fusion mass. There was one neurological complication (2%). Another patient developed a deep wound infection (2%). Conclusions: Good and sustainable surgical correction of spinal deformities can be achieved in pediatric patients with NF-1. Due to the bony dystrophic changes, surgical treatment is challenging and the complication rate is higher than in spinal deformities of other etiologies.
Elucidation of Ubiquitin-Related Functions via an Ubiquitin Overexpression Approach
Authors: Masuda Ryo , Yoshikawa Munetaka , Moriuchi Ryota , Oba Yumiko , Dohra Hideo , Kimura Yoko
Published: December 2024

To identify new ubiquitin-related functions using yeast, we searched for mutants conferring a temperature-sensitivity phenotype that could be rescued through ubiquitin overexpression. Screening of mutants using this overexpression strategy identified SPC2, which encodes a subunit of the endoplasmic reticulum (ER) signal peptidase complex (SPC). Ubiquitin overexpression rescued a high-temperature sensitivity of spc2 deletion mutant, suggesting that ubiquitin could compensate for Spc2 loss-of-function at high temperatures. The double mutant of Spc2 and Hrd1, an ER E3 ubiquitin ligase, showed a synergistic growth defect at higher temperatures. A weak genetic interaction was also observed between spc2Δ and cdc48-3 mutation. The results suggest a close functional relationship between SPC and the ubiquitin–proteasome system in yeast and further provide proof-of-principle for this ubiquitin overexpression approach to identify novel ubiquitin-related genes and associated cellular processes.
Climatology and Long-Term Trends in Population Exposure to Urban Heat Stress Considering Variable Demographic and Thermo–Physiological Attributes
Authors: Giannaros Christos , Galanaki Elissavet , Agathangelidis Ilias
Published: December 2024

Previous studies assessing population exposure to heat stress have focused primarily on environmental heat loads without accounting for variations in human thermo–physiological responses to heat. A novel 30-year (1991–2020) human thermal bioclimate dataset, consisting of hourly mPET (modified physiologically equivalent temperature) values for diverse populations, was employed in the present study to assist in addressing this gap. Focusing on the Athens urban area (AUA), Greece, the climatology and long-term trends in acclimatization-based strong heat stress (accliSHS) experienced by average male and female adult and senior individuals during the warm period of the year (April–October) were investigated. Results showed that an average adult (senior) in AUA experienced, on average, approximately 13 (18) additional days with at least 1 h accliSHS in 2020 compared with 1991. The increasing rates per year were particularly pronounced for days with ≥6 h accliSHS, indicating a rise in the daily duration of heat stress in AUA from 1991 to 2020. Combining the variations in climate and demographics in AUA during the examined 30-year period, the long-term trends in ≥1 h accliSHS exposure for the study population types were further examined. This analysis revealed that seniors’ exposure to ≥1 h accliSHS in AUA increased by up to +153,000 person-days × year−1 from 1991 to 2020. Increasing population aging was the main driver of this outcome, highlighting the urgent need for heat–health action planning in Greece.
Artificial Intelligence and Modern Technology in Dentistry: Attitudes, Knowledge, Use, and Barriers Among Dentists in Croatia—A Survey-Based Study
Authors: Ivanišević Ana , Tadin Antonija
Published: December 2024

Aim: This study aims to assess Croatian dentists’ knowledge, attitudes, and use of artificial intelligence (AI) and modern technology, while also identifying perceived barriers to AI and modern technology adoption and evaluating the need for further education and training. Materials and Methods: A cross-sectional survey was conducted in February 2024 among general dentists in Croatia using a self-structured questionnaire. A total of 200 respondents filled out the questionnaire. It included five sections: socio-demographic and professional information, self-assessment of AI and modern technology use, knowledge of AI in dentistry, current innovations and devices used in practice, and barriers to AI and modern technology integration in practice. Data were analyzed using descriptive statistics and a regression analysis to explore relationships between socio-demographic factors and AI knowledge. Results: The mean knowledge of AI systems was 3.62 ± 2.56 out of a possible score of 7, indicating relatively poor knowledge, with 47.5% demonstrating knowledge below the median. Most respondents (76.0%) did not use AI systems and modern technology in practice; however, prosthodontics (13.0%) and oral surgery (10.0%) were identified as the primary fields utilizing these technologies. Respondents rated their knowledge of modern technologies and AI as weak or moderate, with 60.5% engaged in continuous education. Despite 76.0% not using AI daily, 71.0% believed that these technologies could enhance patient care. Participants interested in further training showed significantly better knowledge of AI applications (p = 0.030). Major barriers included acquisition and maintenance costs (59.0%) and financial constraints (58.0%). Conclusions: The study revealed that most respondents had poor knowledge of AI systems. Despite this, there is a recognition of AI’s and modern technology potential in dentistry, emphasizing the need for enhanced education and training in this field.
Theil’s Index of Inequality: Computation of Value-Validity Correction
Authors: Kvålseth Tarald O.
Published: December 2024

The Theil index is one of the most popular indices of economic inequality, one reason for which is no doubt due to its convenient additive decomposition property. One of its weaknesses, however, is its lack of any intuitively meaningful interpretations. Another, and more serious, limitation of Theil’s index, as argued in this paper, is its lack of the value-validity property. That is, this index does not meet a particular condition based on metric distances between income-share distributions required in order for the range of potential index values to provide true, realistic, and valid representations of the economic inequality characteristic. After outlining the value-validity condition, this paper derives a simple transformation of Theil’s index that meets this condition to a high degree of approximation. Randomly generated income-share distributions are used to demonstrate and verify the validity of the corrected index. The new index formulation, which is simply a power function of Theil’s index, can then be used to make appropriate and reliable representations of absolute and relative difference comparisons of economic inequalities.
Symptom Burden and Dietary Changes Among Older Adults with Cancer: A Cross-Sectional Study
Authors: Büthe Lea , Westhofen Gina , Hille Andrea , Büntzel Judith
Published: December 2024

Background: Malnutrition has a direct impact on both the toxicities of cancer therapy and the overall survival of oncological patients. However, its prevalence amongst vulnerable groups such as older patients (age ≥ 65 years) is often underestimated. Screening tools recognizing patients at risk are well established, yet they do not take into account that cancer therapy may lead to changes in dietary habits or that therapy’s side effects may negatively influence nutritional status. Methods: To close this gap, we combined the validated Nutritional Risk Score 2002 (NRS-2002) and G8 screening tools with short questionnaires addressing diet changes and symptom load and screened 300 cancer inpatients between 12/2022 and 12/2023. Descriptive statistics (Fisher’s exact, Student’s t-test) as well as heat mapping were applied for data analysis. Results: Overall, two in three inpatients ≥65 years were at risk for malnutrition, and the majority of patients (87.67%) scored ≤14 points on the G8 and were considered frail. Surprisingly, the symptom complex of oral discomfort was most often mentioned by patients (xerostomia—178/300 patients, loss of appetite: 122/300 patients, dysgeusia: 93/300 patients). Diet changes were also common, with patients mainly avoiding certain foods (122/300 patients) or using dietary supplements (106/300 patients). Conclusions: Taken together, older cancer inpatients are frail and have a high risk of malnutrition. Screening should not only consider energy intake but also symptom burden and dietary changes to optimize supportive care.
The Impact of Cancer Status on Anxiety in Prostate Cancer Patients: A Network Analysis
Authors: Sharpley Christopher F. , Vessey Kirstan A. , Bitsika Vicki , Arnold Wayne M. , Christie David R. H.
Published: December 2024

Prostate cancer (PCa) patients often also suffer from comorbid anxiety, which can impede treatment efficacy as well as be intrinsically unpleasant. Identification of the associations between particular symptoms of anxiety that are most likely to occur at different points in the PCa diagnosis–treatment journey can inform anxiety treatment choices and potentially influence their overall treatment outcomes. Although simple correlational analyses and ANOVA models of data analysis have been used to address this issue, the possibility of confounds due to the inter-relationships between other anxiety symptoms argues for the use of network analysis, which calculates each symptom–symptom connection while also taking into account the entire range of symptom relationships. Responses to the GAD-10 self-report scale for Generalised Anxiety Disorder were collected from 415 PCa patients who were grouped according to whether (1) their PCa was just diagnosed and undergoing initial treatment; (2) their cancer was in remission; or (3) their cancer was recurring after initial treatment. The results of the network analysis indicated several areas where clinically relevant differences were present between the three PCa groups, but caution was applied to the results of statistical tests due to unequal sample sizes. Individual GAD symptom–symptom association differences are discussed in terms of their implications for directed and individualised anxiety-management treatment models.
Exploring the Contribution of a Generalist Citizen Science Project for Alien Species Detection and Monitoring in Coastal Areas. A Case Study on the Adriatic of Central Italy
Authors: Compagnone Federica , Varricchione Marco , Stanisci Angela , Matteucci Giorgio , Carranza Maria Laura
Published: December 2024

Coastal areas are biodiversity hotspots, providing essential ecosystem services, yet they are among the most threatened systems, particularly by alien species invasion. The European regulation on invasive alien species (IAS) highlights early detection as a key prerequisite for effective containment or eradication strategies. Traditional monitoring methods are costly and time-consuming, and Citizen Science (CS) may be a promising alternative. We assessed the contribution of the generalist CS project “Wild Coast Adriatic” (WCA) developed on the iNaturalist platform to the detection of alien species (AS) along the Central Adriatic coast. Using WCA, we extracted alien occurrences and explored AS seasonal patterns, geographic origins, dangers (EU regulation), and distributions inside protected areas (Natura 2000 and LTER sites). Between 2020 and 2023, WCA gathered 2194 research-grade observations of 687 species, including 139 records of 50 AS, five of which are of European concern. Asteraceae and Fabaceae (plants) as well as insects and mollusks (fauna) were the most abundant aliens. The observations increased over time, with more records concentrated in autumn and summer. Most AS come from the Americas and occurred outside the protected areas. Our results underline the contribution of CS data for detecting AS in coastal ecosystems, offering a valid support for early warning, monitoring, and management strategies.
Characteristics and Influencing Factors of Ecological Stoichiometry of Shrub Fine Roots in the Alpine Region of Northwest China
Authors: Ma Jian , Feng Qi , Liu Wei , Chen Bin , Zhu Meng , Zhang Chengqi , Ta Feng , Tian Xiaoping , Zhan Yufang , Li Xiaopeng
Published: December 2024

Understanding the relationships between nutrient content in plant roots and ecological stoichiometry is crucial for elucidating nutrient utilization strategies and material cycling in alpine plant communities. However, data characterizing the stoichiometric characteristics of plant roots in this region remain limited. In this study, we collected fine-root and soil samples from five common alpine shrub species—Salix gilashanica, Potentilla fruticosa, Caragana jubata, Caragana tangutica, and Berberis diaphana—to investigate the carbon (C), nitrogen (N), and phosphorus (P) stoichiometric characteristics of their fine roots and examine the potential nutrient control strategies based on the soil properties. Our analysis revealed that the mean C (541.38 g kg−1) and P (1.10 g kg−1) contents in the shrub fine roots exceeded the average levels of the plant roots in China. However, the mean N content (8.61 g kg−1) was lower than the global average. Notably, the mean C:N ratio (71.3) in these fine roots was significantly higher than the global average, whereas both the mean C:P ratio (527.61) and N:P ratio (8.11) were considerably lower. The N:P ratios in the fine roots of the five shrub species were below 14, indicating nitrogen limitation for growth in the degraded alpine shrub communities. Our findings indicate that soil available phosphorus (33.2%) and pH (20.5%) are the primary factors influencing the eco-stoichiometric characteristics of shrub fine roots in the Qilian Mountains. These findings provide valuable data and theoretical support for a better understanding of the role of shrub roots in nutrient cycling within alpine ecosystems.
Advancing Interstitial Cystitis/Bladder Pain Syndrome (IC/BPS) Diagnosis: A Comparative Analysis of Machine Learning Methodologies
Authors: Janicki Joseph J. , Zwaans Bernadette M. M. , Bartolone Sarah N. , Ward Elijah P. , Chancellor Michael B.
Published: December 2024

Background/Objectives. This study aimed to improve machine learning models for diagnosing interstitial cystitis/bladder pain syndrome (IC/BPS) by comparing classical machine learning methods with newer AutoML approaches, utilizing biomarker data and patient-reported outcomes as features. Methods. We applied various machine learning techniques to biomarker data from the previous IP4IC and ICRS studies to predict the presence of IC/BPS, a disorder impacting the urinary bladder. Data were sourced from two nationwide, crowd-sourced collections of urine samples involving 2009 participants. The models utilized included logistic regression, support vector machines, random forests, k-nearest neighbors, and AutoGluon. Results. Expanding the dataset for model training and evaluation resulted in improved performance metrics compared to previously published findings. The implementation of AutoML methods yielded enhancements in model accuracy over classical techniques. The top-performing models achieved a receiver-operating characteristic area under the curve (ROC-AUC) of up to 0.96. Conclusions. This research demonstrates an improvement in model performance relative to earlier studies, with the top model for binary classification incorporating objective urinary biomarker levels. These advancements represent a significant step toward developing a reliable classification model for the diagnosis of IC/BPS.
A Robust Tuberculosis Diagnosis Using Chest X-Rays Based on a Hybrid Vision Transformer and Principal Component Analysis
Authors: El-Ghany Sameh Abd , Elmogy Mohammed , A. Mahmood Mahmood , Abd El-Aziz A. A.
Published: December 2024

Background: Tuberculosis (TB) is a bacterial disease that mainly affects the lungs, but it can also impact other parts of the body, such as the brain, bones, and kidneys. The disease is caused by a bacterium called Mycobacterium tuberculosis and spreads through the air when an infected person coughs or sneezes. TB can be inactive or active; in its active state, noticeable symptoms appear, and it can be transmitted to others. There are ongoing challenges in fighting TB, including resistance to medications, co-infections, and limited resources in areas heavily affected by the disease. These issues make it challenging to eradicate TB. Objective: Timely and precise diagnosis is essential for effective control, especially since TB often goes undetected and untreated, particularly in remote and under-resourced locations. Chest X-ray (CXR) images are commonly used to diagnose TB. However, difficulties can arise due to unusual findings on X-rays and a shortage of radiologists in high-infection areas. Method: To address these challenges, a computer-aided diagnosis (CAD) system that uses the vision transformer (ViT) technique has been developed to accurately identify TB in CXR images. This innovative hybrid CAD approach combines ViT with Principal Component Analysis (PCA) and machine learning (ML) techniques for TB classification, introducing a new method in this field. In the hybrid CAD system, ViT is used for deep feature extraction as a base model, PCA is used to reduce feature dimensions, and various ML methods are used to classify TB. This system allows for quickly identifying TB, enabling timely medical action and improving patient outcomes. Additionally, it streamlines the diagnostic process, reducing time and costs for patients and lessening the workload on healthcare professionals. The TB chest X-ray dataset was utilized to train and evaluate the proposed CAD system, which underwent pre-processing techniques like resizing, scaling, and noise removal to improve diagnostic accuracy. Results: The performance of our CAD model was assessed against existing models, yielding excellent results. The model achieved remarkable metrics: an average precision of 99.90%, recall of 99.52%, F1-score of 99.71%, accuracy of 99.84%, false negative rate (FNR) of 0.48%, specificity of 99.52%, and negative predictive value (NPV) of 99.90%. Conclusions: This evaluation highlights the superior performance of our model compared to the latest available classifiers.
Quality Assessment of MRI-Radiomics-Based Machine Learning Methods in Classification of Brain Tumors: Systematic Review
Authors: Nayak Shailesh S. , Pendem Saikiran , Menon Girish R. , Sampathila Niranjana , Koteshwar Prakashini
Published: December 2024

Background: Brain tumors present a complex challenge in clinical oncology, where precise diagnosis and classification are pivotal for effective treatment planning. Radiomics, a burgeoning field in neuro-oncology, involves extracting and analyzing numerous quantitative features from medical images. This approach captures subtle spatial and textural information imperceptible to the human eye. However, implementation in clinical practice is still distant, and concerns have been raised regarding the methodological quality of radiomic studies. Methodology: A systematic literature search was performed to identify original articles focused on the use of radiomics for brain tumors from 2015 based on the inclusion and exclusion criteria. The radiomic features train machine learning models for glioma classification, and data are split into training and testing subsets to validate the model accuracy, reliability, and generalizability. The present study systematically reviews the status of radiomic studies concerning brain tumors, also using the radiomics quality score (RQS) to assess the quality of the methodology used in each study. Results: A systematic search of PubMed identified 300 articles, with 18 studies meeting the inclusion criteria for qualitative synthesis. These studies collectively demonstrate the potential of radiomics-based machine learning models in accurately distinguishing between glioma subtypes and grades. Various imaging modalities, including MRI, PET/CT, and advanced techniques like ASL and DTI, were utilized to extract radiomic features for analysis. Machine learning algorithms such as deep learning networks, support vector machines, random forests, and logistic regression were applied to develop predictive models. Conclusions: The present study indicates high accuracies in glioma classification, outperforming traditional imaging methods and inexperienced radiologists in some cases. Further validation and standardization efforts are warranted to facilitate the clinical integration of radiomics into routine practice, ultimately enhancing glioma management and patient outcomes. Open science practices: Machine learning using MRI radiomic features provides a simple, noninvasive, and cost-effective method for glioma classification, enhancing transparency, reproducibility, and collaboration within the scientific community.
Parametric Design Approach for Wireless Power Transfer System: UAV Applications
Authors: Terrah Mohammed , Smail Mostafa-Kamel , Pichon Lionel , Bensetti Mohamed
Published: December 2024

Adopting Wireless Power Transfer (WPT) technology to an Unmanned Aerial Vehicle (UAV) involves adding extra components, which may impact the drone’s overall weight and performance. This paper aims to enhance UAV performance by designing a lightweight WPT system through a parametric design approach. This method explores novel perspectives by identifying the most suitable combination of parameters in terms of efficiency, weight, and feasibility. Various parameters such as the compensation topology, number of turns of coils, and frequency were studied. The system was analyzed through a coupled simulation approach, where electromagnetic modeling of the coupler using the finite element method (FEM) was combined with electrical circuit simulations, providing a more accurate assessment of the overall system efficiency and behavior considering variations in the coupling factor due to misalignment. A prototype of the resulting configuration was designed and tested experimentally versus misalignment at reduced power using a specific test bench. The results show a 70% efficiency level with SP compensation that was improved to 80% with SS compensation.
Electro-Thermal Co-Optimization Design of GaN MMIC PA
Authors: Chen Xinhuang , Li Bin , Mao Fengyuan , Wu Zhaohui
Published: December 2024

A method of electro-thermal co-optimization design for the Gallium nitride (GaN) monolithic microwave integrated circuit (MMIC) power amplifier (PA) is introduced in this paper. Due to the self-heating effect of the GaN high electron mobility transistor (HEMT), it is necessary to pay attention to the influence of thermal resistance change on circuit performance when designing a high-power RF PA. For this purpose, a three-dimensional finite element analysis model of GaN multi-gate HEMT is developed. The thermal resistance and junction temperature of the device under a RF dynamic current are extracted by heat transfer simulation and can be substituted into the temperature node of the transistor model for PA circuit simulation design. To verify the proposed method, a Class AB MMIC PA was designed and tested using a 0.15-μm GaN-on-SiC process. Through the application of the above methods, the designed PA performance is optimized and achieves the performance of over 60% power-added efficiency (PAE) and 38 dBm saturation power (Psat) within a compact area of 1.6 mm × 2.2 mm. It is demonstrated that the proposed method can effectively improve the consistency of simulation results and measurement results, which can be a valuable reference for high-power MMIC PA design.