bluuebunny/crossref_metadata_2025 huggingface dataset:
Effective altruism and the dark side of entrepreneurship
Authors: Olumekor Michael , Mohiuddin Muhammad , Su Zhan
Published: October 2023

Purpose

Effective Altruism (EA) has become one of the most prominent socio-philosophical movements of recent years. EA is also facing intense scrutiny due to the business practices of some of its most prominent adherents. On the other hand, the dark triad traits of Machiavellianism, narcissism and psychopathy have been getting increasing attention in entrepreneurship research. There is growing evidence that these traits can motivate entrepreneurial intention. We therefore sought to investigate if there was a connection between the entrepreneurship discourse in EA and traits corresponding to dark triad behavior.

Design/methodology/approach

Using a discursive analytic method, we investigated the discursive threads on entrepreneurship in EA over a 10-year period.

Findings

While we believe EA brings a much-needed perspective to the overall debate on doing good, we found ample evidence that it might have promoted the sort of dark triad behavior which some evidence suggests can lead to financial success, but can equally lead to the type of morally bankrupt, unethical and even illegal practices of some entrepreneurs. We also discovered a somewhat temporal dimension in EA’s discourse on entrepreneurship, beginning with discourse encouraging some risk taking and entrepreneurship, before moving on to discourses on the benefits of having a smart and illicit character, and ending with a focus on aggressive risk taking.

Originality

The findings contribute to the still nascent debate on dark personality traits in entrepreneurship, and enriches the theoretical advancement of the field. However, our research differs from prior studies which were almost exclusively focused on the firm. Instead, we examine this phenomenon within a highly influential belief system/philosophical movement.
Exercise makes better mind: a data mining study on effect of physical activity on academic achievement of college students
Authors: Du Shuang , Hu Hang , Cheng Kaiwen , Li Huan
Published: October 2023

The effect of physical activity (PA) on academic achievement has long been a hot research issue in physical education, but few studies have been conducted using machine learning methods for analyzing activity behavior. In this paper, we collected the data on both physical activity and academic performance from 2,219 undergraduate students (Mean = 19 years) over a continuous period of 12 weeks within one academic semester. Based on students’ behavioral indicators transformed from a running APP interface and the average academic course scores, two models were constructed and processed by CHAID decision tree for regression analysis and significance detection. It was found that first, to attain higher academic performance, it is imperative for students to not only exhibit exceptional activity regularity, but also sustain a reduced average step frequency; second, the students completing running exercise with an average frequency of 1 time/week and the duration of 16–25 min excelled over approximately 88 percentage of other students on academic performance; third, the processing validity and reliability of physical observation data in complex systems can be improved by utilizing decision tree as a leveraging machine learning tool and statistical method. These findings provide insights for educational practitioners and policymakers who will seek to enhance college students’ academic performance through physical education programs, combined with data mining methods.
Trait mindfulness and personality characteristics in a microdosing ADHD sample: a naturalistic prospective survey study
Authors: Haijen Eline C. H. M. , Hurks Petra P. M. , Kuypers Kim P. C.
Published: October 2023

Background

Microdosing (MD), repeatedly taking psychedelics in small, non-hallucinogenic amounts, has been practiced by individuals to relieve attention deficit hyperactivity disorder (ADHD) symptoms. Generally, adults diagnosed with ADHD have lower levels of mindfulness and differ in personality structure from non-ADHD adults. How MD affects mindfulness and personality in adults with ADHD remains unexplored.

Aim

This study aimed to investigate the effects of 4 weeks of MD on mindfulness and personality traits in adults diagnosed with ADHD and those experiencing severe ADHD symptoms. It was expected that mindfulness and the personality traits conscientiousness, extraversion, agreeableness, and openness would increase and neuroticism would decrease after 4 weeks of MD compared to baseline. It was explored if using conventional ADHD medication alongside MD and/or having comorbidities influenced MD-induced effects.

Methods

An online prospective naturalistic design was used to measure participants before MD initiation and 2 and 4 weeks later. Validated self-report measures were used assessing mindfulness (15-item Five Facet Mindfulness Questionnaire) and personality traits (10-item version of the Big Five Inventory) at three time points.

Results

The sample included n = 233, n = 66, and n = 44 participants at the three time points, respectively. Trait mindfulness, specifically description and non-judging of inner experience, was increased, and neuroticism was decreased after 4 weeks of MD compared to baseline. The remaining personality traits remained unchanged. Using conventional medication and/or having comorbid diagnoses did not change the MD-induced effects on mindfulness and personality traits after 4 weeks.

Conclusion

MD induced changes in otherwise stable traits. Future placebo-controlled studies are warranted to confirm whether these changes occur in a controlled setting.
Profile and mental health characterization of childhood overprotection/overcontrol experiences among Chinese university students: a nationwide survey
Authors: Zhang Jiamei , Wu Zhipeng , Tao Haojuan , Chen Min , Yu Miaoyu , Zhou Liang , Sun Meng , Lv Dongsheng , Cui Guangcheng , Yi Qizhong , Tang Hong , An Cuixia , Liu Zhening , Huang Xiaojun , Long Yicheng
Published: October 2023

Introduction

The childhood experiences of being overprotected and overcontrolled by family members have been suggested to be potentially traumatic. However, the possible associated factors of these experiences among young people are still not well studied. This study aimed to partly fill such gaps by a relatively large, nationwide survey of Chinese university students.

Methods

A total of 5,823 university students across nine different provinces in China were included by the convenience sampling method in the data analyses. All participants completed the overprotection/overcontrol (OP/OC) subscale in a recently developed 33-item childhood trauma questionnaire (CTQ- 33). Data were also collected on all participants’ socio-demographic profiles and characterization of mental health. Binary logistic regression was conducted to investigate the associated socio-demographic and psychological factors of OP/ OC.

Results

The prevalence of childhood OP/OC was estimated as 15.63% (910/5,823) based on a cutoff OP/OC subscale score of  ≥ 13. Binary logistic regression suggested that being male, being a single child, having depression, having psychotic-like experiences, lower family functioning, and lower psychological resilience were independently associated with childhood OP/OC experiences (all corrected-p < 0.05). The OP/OC was also positively associated with all the other trauma subtypes (abuses and neglects) in the CTQ-33, while there are both shared and unique associated factors between the OP/OC and other trauma subtypes. Post-hoc analyses suggested that OP/OC experiences were associated with depression in only females and associated with anxiety in only males.

Discussion

Our results may provide initial evidence that childhood OP/OC experiences would have negative effects on young people’s mental health which merits further investigations, especially in clinical populations.
Determining surgical surge capacity with a hybrid simulation exercise
Authors: Blimark Magnus , Robinson Yohan , Jacobson Catharina , Lönroth Hans , Boffard Kenneth D. , Lennquist Montán Kristina , Laesser Ilja , Örtenwall Per
Published: October 2023

Background

To help test and improve surgical surge capacity, mass casualty incident (MCI) exercises generate valuable information. Both large scale table-top exercises (TTX) and full-scale exercises (FSX) have limitations if you want to test an organisation’s capability and structure. A hybrid exercise incorporating the advantages of TTX and FSX is a possible way forward, but is no standardised exercise method, yet. This study aims at evaluating the exercise results to determine the feasibility of a hybrid TTX/FSX exercise for an organization’s capability and structure.

Methods

A hybrid MCI simulation using moulaged figurants and simulation cards was designed, where the emergency department of a level 1 trauma centre receives 103 casualties over 4 h. After registration and triage, all casualties are expected to be resuscitated in real time and are transferred for further treatment inside the hospital (radiology, operating theatres, intensive care unit (ICU)/postop and wards). When reaching operation theatre, ICU or ward, figurants are replaced by simulation cards. Observers ensured that those procedures performed were adequate and adhered to realistic times. Use of resources (materials, drugs etc.) were registered. Primary endpoint was average time spent in the emergency department, from time of arrival, to transfer out. Secondary endpoints were related to patient flow and avoidable fatalities.

Results

The hospital managed to deal with the flow of patients without collapse of existing systems. Operating theatres as well as ICU and ward beds were available at the end of the exercise. Several details in the hospital response were observed that had not been noticed during previous TTX.

Conclusion

FSX have a valuable role in training, equipping, exercising, and evaluating MCI management. Hybrid simulations combining both FSX and TTX may optimise resource utilisation and allow more frequent exercises with similar organisational benefit.
Effects of air pollution on emergency visits for acute otitis media among children: a case-crossover study in Chongqing, China
Authors: Xiao Ling , Su Shuping , Chen Cheng , Yao Hongbing , Ding Ling
Published: October 2023

Background

Many epidemiological studies have demonstrated the short-term effects of air pollution on acute otitis media (AOM) in children, but few studies have explored the association between AOM and air pollution in Chinese children. This study aimed to analyze the effects of air pollution on emergency visits for AOM among children through a time-stratified case-crossover design in Chongqing, China.

Methods

The outpatient medical records of children from nine main urban districts who presented with AOM between December 22, 2018 and December 21, 2021 were collected from the Children’s Hospital of Chongqing Medical University. Data for air pollution variables, including the air quality index (AQI), particulate matter ≤ 10 μm (PM10), PM2.5, SO2, CO, NO2 and O3 from 17 monitoring sites were collected. Data for meteorological factors as confounding variables also were collected. Conditional logistic regression was used to analyze the data with single-pollutant models, multi-pollutant models, and stratified analyses.

Results

Increases in AQI, PM10, PM2.5, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in single-pollutant models and stratified analyses. Increases in PM10, SO2, CO and NO2 were positively associated with emergency visits for AOM among children in multi-pollutant models. NO2 had the most statistically significant OR values in all models, whereas significant effects of O3 were observed only in seasonal stratification. In single-pollutant models, we found that the best lag periods were lag 0–7 for air pollution variables except for O3 and the largest OR values were 1.185 (95%CI: 1.129–1.245) for SO2 in single-pollutant models. In stratified analyses, there were no difference between groups in these statistically significant OR values through gender and age stratification, while the differences between seasons in these OR values of PM10, SO2, CO, NO2 and O3 were statistically significant. Children aged 0 years and 3–5 years represented the most susceptible population, and among the seasons, susceptibility was greater during Winter and Spring.

Conclusion

Short-term exposure to air pollution can increase emergency visits for AOM among children in Chongqing, China.
TGF-β phospho antibody array identifies altered SMAD2, PI3K/AKT/SMAD, and RAC signaling contribute to the pathogenesis of myxomatous mitral valve disease
Authors: McNair Andrew J. , Markby Greg R. , Tang Qiyu , MacRae Vicky E. , Corcoran Brendan M.
Published: October 2023

Background

TGFβ signaling appears to contribute to the pathogenesis of myxomatous mitral valve disease (MMVD) in both dogs and humans. However, little is known about the extent of the downstream signaling changes that will then affect cell phenotype and function in both species.

Objective

Identify changes in downstream signals in the TGFβ pathway in canine MMVD and examine the effects of antagonism of one significant signal (SMAD2 was selected).

Materials and methods

Canine cultures of normal quiescent valve interstitial cells (qVICs) and disease-derived activated myofibroblasts (aVICs) (n= 6) were examined for TGFβ signaling protein expression using a commercial antibody array. Significant changes were confirmed, and additional proteins of interest downstream in the TGFβ signaling pathway and markers of cell phenotype were examined (PRAS40, S6K, elF4E IRS-1, αSMA, and VIM), using protein immunoblotting. RT-PCR examined expression of gene markers of VIC activation (ACTA2, TAGLN, andMYH10; encoding the proteins αSMA, SM22, and Smemb, respectively). Attenuation of pSMAD2 in aVICs was examined using a combination of RNA interference technology (siRNA) and the SMAD7 (antagonizes SMAD2) agonist asiaticoside.

Results

The antibody array identified significant changes (P< 0.05) in 19 proteins, of which six were phosphorylated (p). There was increased expression of pSMAD2 and pRAC1 and decreased expression of pmTOR, pERK1/2, and pAKT1. Expression of pPRAS40 and pIRS-1 was increased, as was the mTOR downstream transcription factor pS6K, with increased expression of peIF4E in aVICs, indicating negative feedback control of the PI3K/AKT/mTOR pathway. SMAD2 antagonism by siRNA and the SMAD7 agonist asiaticoside decreased detection of pSMAD by at least 50%, significantly decreased expression of the aVIC gene markersACTA2, TAGLN, andMYH10, and pαSMA, pAKT2, and pERK1, but had no effect on pS6K, pERK2, or pVIM expression in aVICs. SMAD2 antagonism transitioned diseased aVICs to normal qVICs, while maintaining a mesenchymal phenotype (VIM+) while concurrently affecting non-canonical TGFβ signaling.

Conclusion

MMVD is associated with changes in both the canonical and non-canonical TGFβ signaling pathway. Antagonism of SMAD2 transitions diseased-activated myofibroblasts back to a normal phenotype, providing data that will inform studies on developing novel therapeutics to treat MMVD in dogs and humans.
Preferred Islet Delivery Device Characteristics and Implantation Strategies of Patients With Type 1 Diabetes
Authors: Tol Maarten C. , de Bont Denise F. A. , Boon Wouter P. C. , de Koning Eelco J. P. , van Apeldoorn Aart A.
Published: October 2023

Islet delivery devices (IDDs) offer potential benefits for islet transplantation and stem cell-based replacement in type 1 diabetes. Little is known about patient preferences regarding islet delivery device characteristics and implantation strategies. Patient preferences for IDDs and implantation strategies remain understudied. We invited patients, parents and caregivers to fill in an online questionnaire regarding IDDs. An online survey gathered responses from 809 type 1 diabetes patients and 47 caregivers. We also assessed diabetes distress in a subgroup of 412 patients. A significant majority (97%) expressed willingness to receive an IDD. Preferred IDD attributes included a 3.5 cm diameter for 37.7% of respondents, while when provided with all options, 30.4% found dimensions unimportant. Respondents were open to approximately 4 implants, each with a 5 cm incision. Many favored a device functioning for 12 months (33.4%) or 24 months (24.8%). Younger participants (16–30) were more inclined to accept a 6 months functional duration (p < 0.001). Functional duration outweighed implant quantity and size (p < 0.001) in device importance. This emphasizes patients’ willingness to accommodate burdens related to IDD features and implantation methods, crucial for designing future beta cell replacement strategies.
Banff Digital Pathology Working Group: Image Bank, Artificial Intelligence Algorithm, and Challenge Trial Developments
Authors: Farris Alton B. , Alexander Mariam P. , Balis Ulysses G. J. , Barisoni Laura , Boor Peter , Bülow Roman D. , Cornell Lynn D. , Demetris Anthony J. , Farkash Evan , Hermsen Meyke , Hogan Julien , Kain Renate , Kers Jesper , Kong Jun , Levenson Richard M. , Loupy Alexandre , Naesens Maarten , Sarder Pinaki , Tomaszewski John E. , van der Laak Jeroen , van Midden Dominique , Yagi Yukako , Solez Kim
Published: October 2023

The Banff Digital Pathology Working Group (DPWG) was established with the goal to establish a digital pathology repository; develop, validate, and share models for image analysis; and foster collaborations using regular videoconferencing. During the calls, a variety of artificial intelligence (AI)-based support systems for transplantation pathology were presented. Potential collaborations in a competition/trial on AI applied to kidney transplant specimens, including the DIAGGRAFT challenge (staining of biopsies at multiple institutions, pathologists’ visual assessment, and development and validation of new and pre-existing Banff scoring algorithms), were also discussed. To determine the next steps, a survey was conducted, primarily focusing on the feasibility of establishing a digital pathology repository and identifying potential hosts. Sixteen of the 35 respondents (46%) had access to a server hosting a digital pathology repository, with 2 respondents that could serve as a potential host at no cost to the DPWG. The 16 digital pathology repositories collected specimens from various organs, with the largest constituent being kidney (n = 12,870 specimens). A DPWG pilot digital pathology repository was established, and there are plans for a competition/trial with the DIAGGRAFT project. Utilizing existing resources and previously established models, the Banff DPWG is establishing new resources for the Banff community.
Utilizing Artificial Neural Networks and Random Forests to Forecast the Dynamic Amplification Factors of Non-Structural Components
Authors: Vyshnavi P. , Challagulla S. P. , Adamu Musa , Vicencio Felipe , Jameel Mohammed , Ibrahim Yasser E. , Ahmed Omar Shabbir
Published: October 2023

Soft stories in buildings are well-known to present structural vulnerabilities during seismic events, and the failure of non-structural components (NSCs) has been evident in past earthquakes, along with structural damage. This study seeks to investigate how the presence of a soft story in a building affects the criteria for elastic floor acceleration. The soft story is assumed to be at the top, middle, and bottom levels of the structure. To comprehend the behavior of NSCs, the researchers analyze the floor response spectra (FRSs) and component acceleration amplification. Remarkably, the results reveal that the position of the soft story strongly influences the floor response spectra, with structures featuring a middle soft story showing the most significant amplification of component acceleration. In constructing the FRSs, the component dynamic amplification factors (CDAFs) play a vital role as they accurately illustrate how NSCs amplify floor vibrations. Consequently, the study delves into exploring machine learning (ML) models like artificial neural networks (ANNs) and random forest (RF) to map the intricate relationship between CDAFs, the dynamic characteristics of the building, and the behavior of NSCs. Upon comparison of the two models, the random forest model emerges as the superior method in predicting the CDAFs.
Applying the Artificial Neural Network and Response Surface Methodology to Optimize the Drilling Process of Plywood
Authors: Bedelean Bogdan , Ispas Mihai , Răcășan Sergiu
Published: October 2023

Plywood is a wood-based composite with many applications in construction, shipbuilding, and furniture production. One of the basic plywood processing and mandatory operations is drilling. Up to now, considerable and very diverse thematic research has been recently carried out on drilling, but little of that deals with modeling of the drilling process of plywood. Therefore, in this work, the artificial neural network modeling technique and response surface methodology were applied to model and optimize the drilling process of plywood. Two artificial neural network models were developed to predict the thrust force and the drilling torque based on drill tip angle, tooth bite, and drill type. The developed ANN models were used to complete the value of responses in the experimental design, which was requested by the response surface methodology. The trust force during the drilling of plywood is significantly influenced by the drill type (helical or flat). The most significant factor that affects the drilling torque during the drilling of plywood is the tooth bite. A helical drill assures a lower minimum thrust force and drilling torque than a flat drill. The proposed method could be used as an optimization tool during the design phase of the furniture manufacturing process.
A Study on Temperature and Pressure Characteristics in a Vessel as Charging Time of Helium Gas Changes
Authors: Park Jangwoo , Choi Junho , Kim Kwonse
Published: October 2023

The main propose in this research work is to investigate the temperature and pressure increase resulting from the variable valve of a mass flow controller during the charging and discharging of helium gas, which is being used as an alternative to hydrogen gas in a vessel. In the operation of this experiment, the high-pressure gas stored in the main tank is first reduced to low pressure using an electronic solenoid valve within a regulator to control the flow rate. Subsequently, the flow rate is precisely measured using an MFC (Mass Flow Controller) and supplied to the experimental tank. Throughout this process, temperature and pressure sensors detect changes in physical behavior, collect data using LabVIEW cDAQ, and repeat the process of analyzing and verifying reliable data according to the experiment’s conditions. The mass flow controller valve opening was set at 20%, 60%, and 100% while operating the LabVIEW programming. Also, this experiment was conducted at 20 °C ambient temperature and 0 bar gauge pressure. Both the temperature and pressure increase as the MFC valve opens further because the helium gas flow is accumulating during the valve opening time. Furthermore, in the case of helium temperature, it increases significantly when the gas is charged rapidly, compared to the pressure characteristics. Therefore, one can see that the vessel increases as the valve opening time increases, and the temperature changes; the temperature is more significant when the helium gas is charged rapidly during the valve opening time.
Software Operation Anomalies Diagnosis Method Based on a Multiple Time Windows Mixed Model
Authors: Shi Tao , Zou Zhuoliang , Ai Jun
Published: October 2023

The detection of anomalies in software systems has become increasingly crucial in recent years due to their impact on overall software quality. However, existing integrated anomaly detectors usually combine the results of multiple detectors in a clustering manner and do not consider the changes in data anomalies in the time dimension. This paper investigates the limitations of existing anomaly detection methods and proposes an improved integrated anomaly detection approach based on time windows and a voting mechanism. By utilizing multiple time windows, the proposed method overcomes the challenges of cumulative anomalies and achieves enhanced performance in capturing anomalies that accumulate gradually over time. Additionally, two hybrid models are introduced, based on accuracy and sensitivity, respectively, to optimize performance metrics such as AUC, precision, recall, and F1-score. The proposed method demonstrates remarkable performance, achieving either the highest or only a marginal 3% lower performance compared to the optimal model.
Study on the Influence of Rock Shape on Rolling Distance
Authors: Liu Feng , Hu Ning , Sun Gangchen , Yang Bai
Published: October 2023

To explore the influence of rock shape on rolling distance and effectively mitigate rockfall hazards, the following research was conducted. Factors influencing the rolling distance of rocks were determined through model experiments, and the mechanics of rolling resistance were analyzed. The coefficient RF, reflecting the ease of rock rolling, and the shape parameter ψ, quantifying the rolling distance, were proposed. By incorporating the shape parameter into kinematic equations, formulas for calculating the rolling distance of rocks with varying shapes on diverse ground characteristics were derived. These formulas underwent validation using data from model experiments, revealing minimal disparities between the experimental and calculated values. Irregularities on the rolling surface, commonly referred to as “steps” due to the differences in surface hardness among various materials, as well as deviations and shifts in the rolling axis during rock movement, were identified as the primary factors influencing rolling distance and contributing to calculation errors. Combining these deviations for rocks of distinct shapes with theoretical formulas enabled the determination of the range of influence of rock rolling at various velocities, offering valuable insights for assessing areas prone to rockfall hazards.
Integrated Longitudinal and Lateral Control of Emergency Collision Avoidance for Intelligent Vehicles under Curved Road Conditions
Authors: Lai Fei , Yang Hui
Published: October 2023

The operation of the automatic emergency braking (AEB) system may lead to a significant increase in lateral offset of vehicles in curved road conditions, which can pose a potential risk of collisions with vehicles in adjacent lanes or road edges. In order to address this issue, this study proposes an integrated longitudinal and lateral control strategy for collision avoidance during emergency braking, which utilizes a control algorithm based on Time to Collision (TTC) for longitudinal control and a control algorithm based on yaw angle and preview point lateral deviation for lateral control. On one hand, the AEB system facilitates proactive longitudinal intervention to prevent collisions in the forward direction. On the other hand, the Lane Keeping Assist (LKA) system allows for lateral intervention, reducing the lateral offset of the vehicle during braking. To evaluate the effectiveness of this integrated control strategy, a collaborative simulation model involving Matlab/Simulink, PreScan, and CarSim is constructed. Under typical curved road conditions, comparative simulations are conducted among three different control systems: ➀ AEB control system alone; ➁ independent control system of AEB and LKA; and ➂ integrated control system of AEB and LKA. The results indicate that although all three control systems are effective in preventing longitudinal rear-end collisions, the integrated control system outperforms the other two control systems significantly in suppressing the vehicle’s lateral offset. In the scenario with a curve radius of 60 m and an initial vehicle speed of 60 km/h, System ➀ exhibits a lateral offset from the lane centerline reaching up to 1.72 m. In contrast, Systems ➁ and ➂ demonstrate significant improvements with lateral offsets of 0.29 m and 0.21 m, respectively.
Ergonomic Sports Mouthguards: A Narrative Literature Review and Future Perspectives
Authors: De Queiroz Talita Suelen , da Cruz Beatriz Serralheiro , Demachkia Amir Mohidin Mohamed , Borges Alexandre Luiz Souto , Tribst João Paulo Mendes , Paes Junior Tarcisio José de Arruda
Published: October 2023

Sports activities can increase participants’ risk of orofacial trauma. Therefore, it is crucial to have a comprehensive understanding of various types of sports mouthguards. This study aims to present a narrative literature review on ergonomic mouthguards, including their indications for use, classifications, materials, manufacturing methods, and the current status of additive manufacturing in their production. Research of the existing literature was performed in the Google Scholar, MEDLINE/PubMed, Web of Science, and ScienceDirect databases to identify the currently available publications on the topic of sports mouthguards from January 1951 to August 2023. The terms used were “sports mouthguard”, “mouthguards and orofacial traumas”, and “mouthguards and additive manufacturing”. A total of 920 articles were found, and 39 articles were selected and included in this review. While consensus exists regarding the significance of using sports mouthguards with optimal attributes, there is a need for standardization in the methodology for manufacturing custom-made mouthguards. These can be fashioned with conventional ethylene vinyl acetate (EVA) copolymer reinforcements. Such standardization would ensure uniform stress distribution and guarantee ample protection for oral tissues, allowing for universal reproducibility among dental practitioners. Additive manufacturing stands as an innovative method for fabricating mouthguards, displaying promising benefits. However, the materials and methodologies employed in this workflow still require refinement and characterization for a safe clinical integration.
Development of a Novel De-NOx Technology for the Aftertreatment of Ship Exhaust Gases
Authors: Savva Petros G. , Fessas Yiannis , Efstathiou Angelos M. , Costa Costas N.
Published: October 2023

The shipping industry is the most fuel-efficient means of transporting goods, carrying more than 90% of the global freight task. Ships generally use low quality fuel to reduce costs and, as a result, the sulfur content in the exhaust gas stream is high. Emissions of sulfur oxides (SOx) and nitrogen oxides (NOx) from ships represent about 13% and 12%, respectively, of the global anthropogenic SOx and NOx emissions. In total, 95% of the total maritime NOx emissions are NO (nitric oxide) and 5% are NO2 (nitrogen dioxide). The present work focuses on the development and pilot operation of an advanced novel Selective Catalytic Reduction of NOx with H2 (H2-SCR) technology for the elimination of Nitrogen Oxides (NOx) emitted from ship exhaust gases. For the proper operation of the novel H2-SCR de-NOx unit, two additional conventional technologies were employed for the removal of SO2 and Particulate Matter (PM). In particular, the proposed novel H2-SCR de-NOx technology was combined with a Sea Water Absorption (SWA) unit and an oxidative catalytic system. A pilot unit has been successfully designed, assembled and implemented on a cruise ship for the abovementioned purposes. This effort is considered to be pioneering and is here attempted for the first time worldwide. It was proven, for the first time ever, that the Selective Catalytic Reduction of NOx with the use of H2 as a reducing agent in combination with a suitable catalyst can be considered a suitable NOx-pollution control technology for ships. In particular, it was found that more than 80% of NOx (to N2), 99.8% of SO2 and 72% of PM can be reduced by using the present combined SWA and H2-SCR technologies.
Near-Infrared Spectroscopy Coupled with a Neighborhood Rough Set Algorithm for Identifying the Storage Status of Paddy
Authors: Yang Dong , Zhou Yuxing , Li Qianqian , Jie Yu , Shi Tianyu
Published: October 2023

Rapid and non-destructive identification of the suitable storage status of paddy during storage is crucial for controlling the quality of stored grains, which can provide high-quality raw grains for rice processing. Near-infrared (NIR) spectroscopy combined with neighborhood rough set (NRS) and multiple classification methods were used to identify the different storage statuses of paddy. The NIR data were collected in the range of 1000–1800 nm, and three storage statuses from suitable storage to severely unsuitable storage were divided using the measured fatty acid value of paddy. The spectral features were selected using NRS, successive projection algorithm and variable combination population analysis methods. Random forest (RF), extreme learning machine, and soft independent modeling of class analogy classifiers coupled with spectral features were used to establish classification models to distinguish the different storage statuses of paddy. The comparison results indicated that the optimal wavelengths selected by NRS combined with the RF classifier to construct the NRS-RF series models led to satisfactory identification results, with high correct classification rates of 96.31% and 93.68% in the calibration and test sets, respectively; the indicators of sensitivity and specificity ranged from 0.93 to 0.99. Therefore, the combination of NIR technology with NRS and RF algorithms for identifying the storage status of paddy was feasible, as this would be more helpful for rapidly evaluating the changes of stored paddy quality. The proposed method from this study is expected to provide support for the development of non-destructive equipment for the accurate detection of the quality of stored paddy.
Enhancing Cyclone Intensity Prediction for Smart Cities Using a Deep-Learning Approach for Accurate Prediction
Authors: Jayaraman Senthil Kumar , Venkatachalam Venkataraman , Eid Marwa M. , Krithivasan Kannan , Raju Sekar Kidambi , Khafaga Doaa Sami , Karim Faten Khalid , Ahmed Ayman Em
Published: October 2023

Accurate cyclone intensity prediction is crucial for smart cities to effectively prepare and mitigate the potential devastation caused by these extreme weather events. Traditional meteorological models often face challenges in accurately forecasting cyclone intensity due to cyclonic systems’ complex and dynamic nature. Predicting the intensity of cyclones is a challenging task in meteorological research, as it requires expertise in extracting spatio-temporal features. To address this challenge, a new technique, called linear support vector regressive gradient descent Jaccardized deep multilayer perceptive classifier (LEGEMP), has been proposed to improve the accuracy of cyclone intensity prediction. This technique utilizes a dataset that contains various attributes. It employs the Herfindahl correlative linear support vector regression feature selection to identify the most important characteristics for enhancing cyclone intensity forecasting accuracy. The selected features are then used in conjunction with the Nesterov gradient descent jeopardized deep multilayer perceptive classifier to predict the intensity classes of cyclones, including depression, deep depression, cyclone, severe cyclone, very severe cyclone, and extremely severe cyclone. Experimental results have demonstrated that LEGEMP outperforms conventional methods in terms of cyclone intensity prediction accuracy, requiring minimum time, error rate, and memory consumption. By leveraging advanced techniques and feature selection, LEGEMP provides more reliable and precise predictions for cyclone intensity, enabling better preparedness and response strategies to mitigate the impact of these destructive storms. The LEGEMP technique offers an improved approach to cyclone intensity prediction, leveraging advanced classifiers and feature selection methods to enhance accuracy and reduce error rates. We demonstrate the effectiveness of our approach through rigorous evaluation and comparison with conventional prediction methods, showcasing significant improvements in prediction accuracy. Integrating our enhanced prediction model into smart city disaster management systems can substantially enhance preparedness and response strategies, ultimately contributing to the safety and resilience of communities in cyclone-prone regions.
Performance-Based Evaluation of CMIP5 and CMIP6 Global Climate Models and Their Multi-Model Ensembles to Simulate and Project Seasonal and Annual Climate Variables in the Chungcheong Region of South Korea
Authors: Adelodun Bashir , Ahmad Mirza Junaid , Odey Golden , Adeyi Qudus , Choi Kyung Sook
Published: October 2023

Extreme climate change events are major causes of devastating impacts on socioeconomic well-being and ecosystem damage. Therefore, understanding the performance of appropriate climate models representing local climate characteristics is critical for future projections. Thus, this study analyses the performance of 24 GCMs from the Coupled Model Intercomparison Project Phases 5 and 6 (CMIP5 and 6) and their multi-model ensembles in simulating climate variables including average rainfall, maximum (Tmax), and minimum (Tmin) temperatures at annual and seasonal scales over the Chungcheong region of South Korea from 1975 to 2015. A trend analysis was conducted to estimate the future trends in climate variables in the 2060s (2021–2060) and 2080s (2061–2100). Inverse distance weighting and quantile delta mapping were applied to bias-correct the GCM data. Further, six major evaluating indices comprising temporal and spatial performance assessments were used, after which a comprehensive GCM ranking was applied. The results showed that CMIP6 models performed better in simulating rainfall, Tmax, and Tmin at both temporal and spatial scales. For CMIP5, the top three performing models were GISS, ACCESS1-3, and MRI-CGCM3 for rain; CanESM2, GISS, and MPI-ESM-L-R for Tmax; and GFDL, MRI-CGCM3, and CanESM2 for Tmin. However, the top three performing models in the CMIP6 were MRI-ESM2-0, BCC_CSM, and GFDL for rain; MIROC6, BCC_CSM, and MRI-ESM2-0 for Tmax, and GFDL, MPI_ESM_HR, and MRI-ESM2-0 for Tmin. The multi-model ensembles (an average of the top three GCMs) performed better in simulating rain and Tmin for both CMIP5 and CMIP6 compared with multi-model ensembles (an average of all the GCMs), which only performed slightly better in simulating Tmax. The trend analysis of future projection indicates an increase in rain, Tmax, and Tmin; however, with distinct changes under similar radiative forcing levels in both CMIP5 and CMIP6 models. The projections under RCP4.5 and RCP8.5 increase more than the SSP2-4.5 and SSP5-8.5 scenarios for most climate conditions but are more pronounced, especially for rain, under RCP8.5 than SSP5-8.5 in the far future (2080s). This study provides insightful findings on selecting appropriate GCMs to generate reliable climate projections for local climate conditions in the Chungcheong region of South Korea.
Study of Rotavirus Mathematical Model Using Stochastic and Piecewise Fractional Differential Operators
Authors: Alharthi Nadiyah Hussain , Jeelani Mdi Begum
Published: October 2023

This manuscript is related to undertaking a mathematical model (susceptible, vaccinated, infected, and recovered) of rotavirus. Some qualitative results are established for the mentioned challenging childhood disease epidemic model of rotavirus as it spreads across a population with a heterogeneous rate. The proposed model is investigated using a novel approach of fractal calculus. We compute the boundedness positivity of the solution of the proposed model. Additionally, the basic reproduction ratio and its sensitivity analysis are also performed. The global stability of the endemic equilibrium point is also confirmed graphically using some available values of initial conditions and parameters. Sufficient conditions are deduced for the existence theory, the Ulam–Hyers (UH) stability. Specifically, the numerical approximate solution of the rotavirus model is investigated using efficient numerical methods. Graphical presentations are presented corresponding to a different fractional order to understand the transmission dynamics of the mentioned disease. Furthermore, researchers have examined the impact of lowering the risk of infection on populations that are susceptible and have received vaccinations, producing some intriguing results. We also present a numerical illustration taking the stochastic derivative of the proposed model graphically. Researchers may find this research helpful as it offers insightful information about using numerical techniques to model infectious diseases.
Surface-Guided Radiotherapy: Can We Move on from the Era of Three-Point Markers to the New Era of Thousands of Points?
Authors: Psarras Michalis , Stasinou Despoina , Stroubinis Theodoros , Protopapa Maria , Zygogianni Anna , Kouloulias Vassilis , Platoni Kalliopi
Published: October 2023

The surface-guided radiotherapy (SGRT) technique improves patient positioning with submillimeter accuracy compared with the conventional positioning technique of lasers using three-point tattoos. SGRT provides solutions to considerations that arise from the conventional setup technique, such as variability in tattoo position and the psychological impact of the tattoos. Moreover, SGRT provides monitoring of intrafractional motion. Purpose: This literature review covers the basics of SGRT systems and examines whether SGRT can replace the traditional positioning technique. In addition, it investigates SGRT’s potential in reducing positioning times, factors affecting SGRT accuracy, the effectiveness of live monitoring, and the impact on patient dosage. Materials and Methods: This study focused on papers published from 2016 onward that compared SGRT with the traditional positioning technique and investigated factors affecting SGRT accuracy and effectiveness. Results/Conclusions: SGRT provides the same or better results regarding patient positioning. The implementation of SGRT can reduce overall treatment time. It is an effective technique for detecting intrafraction patient motion, improving treatment accuracy and precision, and creating a safe and comfortable environment for the patient during treatment.
Use of Stromal Intervention and Exogenous Neoantigen Vaccination to Boost Pancreatic Cancer Chemo-Immunotherapy by Nanocarriers
Authors: Chattopadhyay Saborni , Liao Yu-Pei , Wang Xiang , Nel André E.
Published: October 2023

Despite the formidable treatment challenges of pancreatic ductal adenocarcinoma (PDAC), considerable progress has been made in improving drug delivery via pioneering nanocarriers. These innovations are geared towards overcoming the obstacles presented by dysplastic stroma and fostering anti-PDAC immune reactions. We are currently conducting research aimed at enhancing chemotherapy to stimulate anti-tumor immunity by inducing immunogenic cell death (ICD). This is accomplished using lipid bilayer-coated nanocarriers, which enable the attainment of synergistic results. Noteworthy examples include liposomes and lipid-coated mesoporous silica nanoparticles known as “silicasomes”. These nanocarriers facilitate remote chemotherapy loading, as well as the seamless integration of immunomodulators into the lipid bilayer. In this communication, we elucidate innovative ways for further improving chemo-immunotherapy. The first is the development of a liposome platform engineered by the remote loading of irinotecan while incorporating a pro-resolving lipoxin in the lipid bilayer. This carrier interfered in stromal collagen deposition, as well as boosting the irinotecan-induced ICD response. The second approach was to synthesize polymer nanoparticles for the delivery of mutated KRAS peptides in conjunction with a TLR7/8 agonist. The dual delivery vaccine particle boosted the generation of antigen-specific cytotoxic T-cells that are recruited to lymphoid structures at the cancer site, with a view to strengthening the endogenous vaccination response achieved by chemo-immunotherapy.
Bioinformatic Analysis Reveals the Role of Translation Elongation Efficiency Optimisation in the Evolution of Ralstonia Genus
Authors: Korenskaia Aleksandra Y. , Matushkin Yury G. , Mustafin Zakhar S. , Lashin Sergey A. , Klimenko Alexandra I.
Published: October 2023

Translation efficiency modulates gene expression in prokaryotes. The comparative analysis of translation elongation efficiency characteristics of Ralstonia genus bacteria genomes revealed that these characteristics diverge in accordance with the phylogeny of Ralstonia. The first branch of this genus is a group of bacteria commonly found in moist environments such as soil and water that includes the species R. mannitolilytica, R. insidiosa, and R. pickettii, which are also described as nosocomial infection pathogens. In contrast, the second branch is plant pathogenic bacteria consisting of R. solanacearum, R. pseudosolanacearum, and R. syzygii. We found that the soil Ralstonia have a significantly lower number and energy of potential secondary structures in mRNA and an increased role of codon usage bias in the optimization of highly expressed genes’ translation elongation efficiency, not only compared to phytopathogenic Ralstonia but also to Cupriavidus necator, which is closely related to the Ralstonia genus. The observed alterations in translation elongation efficiency of orthologous genes are also reflected in the difference of potentially highly expressed gene’ sets’ content among Ralstonia branches with different lifestyles. Analysis of translation elongation efficiency characteristics can be considered a promising approach for studying complex mechanisms that determine the evolution and adaptation of bacteria in various environments.
The Effectiveness of Cold Atmospheric Plasma (CAP) on Bacterial Reduction in Dental Implants: A Systematic Review
Authors: Alqutaibi Ahmed Yaseen , Aljohani Abdulbari , Alduri Abdullah , Masoudi Abdulmajid , Alsaedi Anas M. , Al-Sharani Hesham Mohammed , Farghal Ahmed E. , Alnazzawi Ahmad Abdulkareem , Aboalrejal Afaf Noman , Mohamed Abdel-Aleam H. , Zafar Muhammad Sohail
Published: October 2023

Background: The emergence of dental implants has revolutionized the management of tooth loss. However, the placement of clinical implants exposes them to complex oral environment and numerous microscopic entities, such as bacteria. Cold atmospheric plasma (CAP) is often used to treat the surfaces of dental implants, which alters morphological features and effectively reduces bacterial load. Purpose: This systematic review aims to assess the existing literature on the bactericidal properties of CAP when used on various kinds of dental implant surfaces. Review Method: An in-depth examination of MEDLINE/PubMed and EMBASE was performed to identify relevant studies, with the most recent search conducted in May 2023. Studies were selected based on their exploration of CAP’s effects on dental implants compared to control groups, focusing on CAP’s bactericidal efficacy. However, studies that lacked a control group or that failed to measure bactericidal effects were excluded. Results: After applying the selection criteria, 15 studies were ultimately included in the systematic review. The collected data suggest that CAP can effectively reduce bacterial loads on dental implant surfaces, including pathogens like Streptococcus mitis and Staphylococcus aureus. Furthermore, CAP appears to combat biofilms and plaques that are key contributors to periimplantitis. Conclusion: CAP emerges as a promising treatment option, exhibiting significant bactericidal activity on dental implant surfaces. CAP can decrease the rates of bacterial biofilm and plaque formation, leading to improved outcomes for dental implant patients.