bluuebunny/crossref_metadata_2025 huggingface dataset:
An Effective and Affordable Internet of Things (IoT) Scale System to Measure Crop Water Use
Authors: Payero José O.
Published: March 2024

Scales are widely used in many agricultural applications, ranging from weighing crops at harvest to determine crop yields to regularly weighing animals to determine growth rate. In agricultural research applications, there is a long history of measuring crop water use (evapotranspiration [ET]) using a particular type of scale called weighing lysimeters. Typically, weighing lysimeters require very accurate data logging systems that tend to be expensive. Recent developments in open-source technologies, such as micro-controllers and Internet of Things (IoT) platforms, have created opportunities for developing effective and affordable ways to monitor crop water use and transmit the data to the Internet in near real-time. Therefore, this study aimed to create an affordable Internet of Things (IoT) scale system to measure crop ET. A scale system to monitor crop ET was developed using an Arduino-compatible microcontroller with cell phone communication, electronic load cells, an Inter-Integrated Circuit (I2C) multiplexer, and analog-to-digital converters (ADCs). The system was powered by a LiPo battery, charged by a small (6 W) solar panel. The IoT scale system was programmed to collect data from the load cells at regular time intervals and send the data to the ThingSpeak IoT platform. The system performed successfully during indoor and outdoor experiments conducted in 2023 at the Clemson University Edisto Research and Education Center, Blackville, SC. Calibrations relating the measured output of the scale load cells to changes in mass resulted in excellent linear relationships during the indoor (r2 = 1.0) and outdoor experiments (r2 = 0.9994). The results of the outdoor experiments showed that the IoT scale system could accurately measure changes in lysimeter mass during several months (Feb to Jun) without failure in data collection or transmission. The changes in lysimeter mass measured during that period reflected the same trend as concurrent soil moisture data measured at a nearby weather station. The changes in lysimeter mass measured with the IoT scale system during the outdoor experiment were accurate enough to derive daily and hourly crop ET and even detect what appeared to be dew formation during the morning hours. The IoT scale system can be built using open-source, off-the-shelf electronic components which can be purchased online and easily replaced or substituted. The system can also be developed at a fraction of the cost of data logging, communication, and visualization systems typically used for lysimeter and scale applications.
Emission Characteristics and Potential Exposure Assessment of Aerosols and Ultrafine Particles at Two French Airports
Authors: Artous Sébastien , Zimmermann Eric , Philippot Cécile , Jacquinot Sébastien , Locatelli Dominique , Tarantini Adeline , Suehs Carey , Touri Léa , Clavaguera Simon
Published: March 2024

Airports are significant contributors of atmospheric pollutant aerosols, namely ultrafine particles (UFPs). This study characterizes the particle number concentration (PNC), the median particle size (dmn50), and the metallic composition of medium-haul area and engine aerosols at two French airports (Paris-CDG and Marseille). This study followed the standard operating procedures for characterizing aerosol emissions from 5 nm to 8 μm (OECD, 2015; EN 17058:2018). It allows determining which are the specific parameters directly related to the emission sources and their contribution to the overall aerosols measured at workplace in airports. The particulate emissions observed during aircraft engine start-up were ~19× higher than the average airborne concentration. The particle size distributions remained mostly <250 nm with dmn50 < 100 nm (showing a specificity for the medium-haul area with an average dmn50 of ~12 nm). The dmn50 can be used to distinguish emission peaks due to aircrafts (dmn50~15 nm) from those due to apron vehicle activities (dmn50 > 20 nm). Chemical elements (titanium and zinc) were identified as potential tracers of aircraft emissions and occurred mainly at the micrometric scale. For aircraft engine emissions, UFPs are mainly due to fuel combustion with the presence of carbon/oxygen. The study concludes with suggestions for future research to extend on the findings presented.
Fragility and Leakage Risk Assessment of Nuclear Containment Structure under Loss-of-Coolant Accident Conditions Considering Liner Corrosion
Authors: Li Xinbo , Gong Jinxin
Published: March 2024

The steel liner plays a key role in ensuring the leak-tightness of nuclear containment structures (NCSs). Once the liner is subjected to corrosion, its corrosion effects can severely impact the leak-tight function of the NCS, especially in severe accident scenarios. Therefore, evaluating the corrosion effects of the liner is essential to guaranteeing the safe operation of nuclear power plants. This paper presents a probabilistic safety analysis of the NCS under liner corrosion conditions. Firstly, the corrosion mechanism of the liner is elucidated, and a refined simulation method is developed to investigate the localized corrosion effects of the liner. Utilizing a probabilistic finite element method, the fragility of the NCS under loss-of-coolant accident (LOCA) conditions is evaluated. Finally, the leakage risk of the NCS at different degrees of liner corrosion is discussed. The results indicate that liner corrosion has a significant impact on the median pressure capacity and high-confidence–low-probability pressure capacity of the NCS. With the aggravation of liner corrosion, the safety margin of the NCS decreases, and the total probability of failure increases. Within the scope of this paper, the NCS can fulfill the probabilistic safety requirements.
Precision-Boosted Forest Fire Target Detection via Enhanced YOLOv8 Model
Authors: Yang Zhaoxu , Shao Yifan , Wei Ye , Li Jun
Published: March 2024

Forest fires present a significant challenge to ecosystems, particularly due to factors like tree cover that complicate fire detection tasks. While fire detection technologies, like YOLO, are widely used in forest protection, capturing diverse and complex flame features remains challenging. Therefore, we propose an enhanced YOLOv8 multiscale forest fire detection method. This involves adjusting the network structure and integrating Deformable Convolution and SCConv modules to better adapt to forest fire complexities. Additionally, we introduce the Coordinate Attention mechanism in the Detection module to more effectively capture feature information and enhance model accuracy. We adopt the WIoU v3 loss function and implement a dynamically non-monotonic mechanism to optimize gradient allocation strategies. Our experimental results demonstrate that our model achieves a mAP of 90.02%, approximately 5.9% higher than the baseline YOLOv8 network. This method significantly improves forest fire detection accuracy, reduces False Positive rates, and demonstrates excellent applicability in real forest fire scenarios.
Distributed Generation Cluster Division Method Considering Frequency Regulation Response Speed
Authors: Xu Yan , Hu Peng , Zhang Fengyang , Zhou Tao
Published: March 2024

With the large-scale integration of distributed generation (DG), it is difficult to realize distribution network planning and operation under specific requirements using the traditional cluster division method based on a single criterion. To reduce the complexity of frequency regulation control strategies, this paper proposes a cluster division method that synthesizes structural and functional indexes. First, the ability of DG within a cluster to provide flexibility to the system is analyzed. Then, a cluster response speed model is proposed to cope with the frequency regulation of demand flexibility on shorter time scales. Based on the above analysis, this paper proposes a distributed generation cluster (DGC) frequency regulation response speed index. The combined electrical distance based on the impedance–power reserve (I–PR) is defined by considering the power reserve of each node of the system. The I–PR is weighted to the structural indexes to improve the division. Meanwhile, in order to enhance the convergence speed of the algorithm for the division process, an adaptive genetic algorithm (GA) based on the encoding method of the weighted network adjacency matrix is used. Finally, distributed generation cluster division is performed on two systems to verify the validity of the proposed indexes in this paper.
New Simplified High-Order Schemes for Solving SDEs with Markovian Switching Driven by Pure Jumps
Authors: Li Yang , Xu Yingmei , Xu Qianhai , Zhang Yu
Published: March 2024

New high-order weak schemes are proposed and simplified to solve stochastic differential equations with Markovian switching driven by pure jumps (PJ-SDEwMs). Using Malliavin calculus theory, it is rigorously proven that the new numerical schemes can achieve a high-order convergence rate. Some numerical experiments are provided to show the efficiency and accuracy.
On Blow-Up Solutions for the Fourth-Order Nonlinear Schrödinger Equation with Mixed Dispersions
Authors: Niu Huiling , Youssouf Abdoulaye Ali , Feng Binhua
Published: March 2024

In this paper, we consider blow-up solutions for the fourth-order nonlinear Schrödinger equation with mixed dispersions. We study the dynamical properties of blow-up solutions for this equation, including the H˙γc-concentration and limiting profiles, which extend and improve the existing results in the literature.
Machine Learning Approaches for Predicting Risk of Cardiometabolic Disease among University Students
Authors: Musleh Dhiaa , Alkhwaja Ali , Alkhwaja Ibrahim , Alghamdi Mohammed , Abahussain Hussam , Albugami Mohammed , Alfawaz Faisal , El-Ashker Said , Al-Hariri Mohammed
Published: March 2024

Obesity is increasingly becoming a prevalent health concern among adolescents, leading to significant risks like cardiometabolic diseases (CMDs). The early discovery and diagnosis of CMD is essential for better outcomes. This study aims to build a reliable artificial intelligence model that can predict CMD using various machine learning techniques. Support vector machines (SVMs), K-Nearest neighbor (KNN), Logistic Regression (LR), Random Forest (RF), and Gradient Boosting are five robust classifiers that are compared in this study. A novel “risk level” feature, derived through fuzzy logic applied to the Conicity Index, as a novel feature, which was previously unused, is introduced to enhance the interpretability and discriminatory properties of the proposed models. As the Conicity Index scores indicate CMD risk, two separate models are developed to address each gender individually. The performance of the proposed models is assessed using two datasets obtained from 295 records of undergraduate students in Saudi Arabia. The dataset comprises 121 male and 174 female students with diverse risk levels. Notably, Logistic Regression emerges as the top performer among males, achieving an accuracy score of 91%, while Gradient Boosting lags with a score of 72%. Among females, both Support Vector Machine and Logistic Regression lead with an accuracy score of 87%, while Random Forest performs least optimally with a score of 80%.
Active Claw-Shaped Dry Electrodes for EEG Measurement in Hair Areas
Authors: Wang Zaihao , Ding Yuhao , Yuan Wei , Chen Hongyu , Chen Wei , Chen Chen
Published: March 2024

EEG, which can provide brain alteration information via recording the electrical activity of neurons in the cerebral cortex, has been widely used in neurophysiology. However, conventional wet electrodes in EEG monitoring typically suffer from inherent limitations, including the requirement of skin pretreatment, the risk of superficial skin infections, and signal performance deterioration that may occur over time due to the air drying of the conductive gel. Although the emergence of dry electrodes has overcome these shortcomings, their electrode–skin contact impedance is significantly high and unstable, especially in hair-covered areas. To address the above problems, an active claw-shaped dry electrode is designed, moving from electrode morphological design, slurry preparation, and coating to active electrode circuit design. The active claw-shaped dry electrode, which consists of a claw-shaped electrode and active electrode circuit, is dedicated to offering a flexible solution for elevating electrode fittings on the scalp in hair-covered areas, reducing electrode–skin contact impedance and thus improving the quality of the acquired EEG signal. The performance of the proposed electrodes was verified by impedance, active electrode circuit, eyes open-closed, steady-state visually evoked potential (SSVEP), and anti-interference tests, based on EEG signal acquisition. Experimental results show that the proposed claw-shaped electrodes (without active circuit) can offer a better fit between the scalp and electrodes, with a low electrode–skin contact impedance (18.62 KΩ@1 Hz in the hairless region and 122.15 KΩ@1 Hz in the hair-covered region). In addition, with the active circuit, the signal-to-noise ratio (SNR) of the acquiring EEG signal was improved and power frequency interference was restrained, therefore, the proposed electrodes can yield an EEG signal quality comparable to wet electrodes.
Roles of Macrophages and Endothelial Cells and Their Crosstalk in Acute Lung Injury
Authors: Osorio-Valencia Sara , Zhou Bisheng
Published: March 2024

Acute lung injury (ALI) and its severe form, acute respiratory distress syndrome (ARDS), present life-threatening conditions characterized by inflammation and endothelial injury, leading to increased vascular permeability and lung edema. Key players in the pathogenesis and resolution of ALI are macrophages (Mφs) and endothelial cells (ECs). The crosstalk between these two cell types has emerged as a significant focus for potential therapeutic interventions in ALI. This review provides a brief overview of the roles of Mφs and ECs and their interplay in ALI/ARDS. Moreover, it highlights the significance of investigating perivascular macrophages (PVMs) and immunomodulatory endothelial cells (IMECs) as crucial participants in the Mφ–EC crosstalk. This sheds light on the pathogenesis of ALI and paves the way for innovative treatment approaches.
Neurotrauma—From Injury to Repair: Clinical Perspectives, Cellular Mechanisms and Promoting Regeneration of the Injured Brain and Spinal Cord
Authors: Stevens Andrew R. , Belli Antonio , Ahmed Zubair
Published: March 2024

Traumatic injury to the brain and spinal cord (neurotrauma) is a common event across populations and often causes profound and irreversible disability. Pathophysiological responses to trauma exacerbate the damage of an index injury, propagating the loss of function that the central nervous system (CNS) cannot repair after the initial event is resolved. The way in which function is lost after injury is the consequence of a complex array of mechanisms that continue in the chronic phase post-injury to prevent effective neural repair. This review summarises the events after traumatic brain injury (TBI) and spinal cord injury (SCI), comprising a description of current clinical management strategies, a summary of known cellular and molecular mechanisms of secondary damage and their role in the prevention of repair. A discussion of current and emerging approaches to promote neuroregeneration after CNS injury is presented. The barriers to promoting repair after neurotrauma are across pathways and cell types and occur on a molecular and system level. This presents a challenge to traditional molecular pharmacological approaches to targeting single molecular pathways. It is suggested that novel approaches targeting multiple mechanisms or using combinatorial therapies may yield the sought-after recovery for future patients.
Overall Survival Time Estimation for Epithelioid Peritoneal Mesothelioma Patients from Whole-Slide Images
Authors: Papadopoulos Kleanthis Marios , Barmpoutis Panagiotis , Stathaki Tania , Kepenekian Vahan , Dartigues Peggy , Valmary-Degano Séverine , Illac-Vauquelin Claire , Avérous Gerlinde , Chevallier Anne , Laverriere Marie-Hélène , Villeneuve Laurent , Glehen Olivier , Isaac Sylvie , Hommell-Fontaine Juliette , Ng Kee Kwong Francois , Benzerdjeb Nazim
Published: March 2024

Background: The advent of Deep Learning initiated a new era in which neural networks relying solely on Whole-Slide Images can estimate the survival time of cancer patients. Remarkably, despite deep learning’s potential in this domain, no prior research has been conducted on image-based survival analysis specifically for peritoneal mesothelioma. Prior studies performed statistical analysis to identify disease factors impacting patients’ survival time. Methods: Therefore, we introduce MPeMSupervisedSurv, a Convolutional Neural Network designed to predict the survival time of patients diagnosed with this disease. We subsequently perform patient stratification based on factors such as their Peritoneal Cancer Index and on whether patients received chemotherapy treatment. Results: MPeMSupervisedSurv demonstrates improvements over comparable methods. Using our proposed model, we performed patient stratification to assess the impact of clinical variables on survival time. Notably, the inclusion of information regarding adjuvant chemotherapy significantly enhances the model’s predictive prowess. Conversely, repeating the process for other factors did not yield significant performance improvements. Conclusions: Overall, MPeMSupervisedSurv is an effective neural network which can predict the survival time of peritoneal mesothelioma patients. Our findings also indicate that treatment by adjuvant chemotherapy could be a factor affecting survival time.
Development of Quasi-Passive Back-Support Exoskeleton with Compact Variable Gravity Compensation Module and Bio-Inspired Hip Joint Mechanism
Authors: Song Gijoon , Moon Junyoung , Kim Jehyeok , Lee Giuk
Published: March 2024

The back support exoskeletons have garnered significant attention to alleviate musculoskeletal injuries, prevalent in industrial settings. In this paper, we propose AeBS, a quasi-passive back-support exoskeleton developed to provide variable assistive torque across the entire range of hip joint motion, for tasks with frequent load changes. AeBS can adjust the assistive torque levels while minimizing energy for the torque variation without constraining the range of motion of the hip joint. To match the requisite assistance levels for back support, a compact variable gravity compensation module with reinforced elastic elements is applied to AeBS. Additionally, we devised a bio-inspired hip joint mechanism that mimics the configuration of the human hip axis to ensure the free body motion of the wearer, significantly affecting assistive torque transmission and wearing comfort. Benchtop testing showed that AeBS has a variable assistive torque range of 5.81 Nm (ranging from 1.23 to 7.04 Nm) across a targeted hip flexion range of 135°. Furthermore, a questionnaire survey revealed that the bio-inspired hip joint mechanism effectively facilitates the transmission of the intended assistive torque while enhancing wearer comfort.
Attitudes Formation toward Minority Outgroups in Times of Global Crisis—The Role of Good and Bad Digital News Consumption
Authors: Kushnirovich Nonna , Lissitsa Sabina
Published: March 2024

This paper examines the relationships between the consumption of ‘bad’ or ‘good’ digital economic news and attitudes toward immigrant and ethnic minorities during the crisis that developed during the COVID-19 pandemic. The study considered attitudes toward two minority groups in Israel: immigrant citizens from English-speaking countries, and Israeli Palestinian citizens, an ethnic minority. The data were collected through an online survey of 866 respondents, who were members of the majority population group. The study found that, during the global crisis, exposure to bad digital news was associated with more positive attitudes toward both disadvantaged and non-disadvantaged minority groups. Moreover, in times of global crisis, people focused mostly on local rather than global digital news. In contrast to the idea of Intergroup Threat Theory, the study revealed that feelings of economic threat during the global crisis engendered higher cohesion between different population groups, and more positive attitudes toward minorities. In times of crisis, bad news for the economy brings good news for social solidarity—people tend to rally around the flag; this phenomenon even occurs between groups engaged in years-long, protracted conflict.
Mechanical Behavior of Precast Circular Semi-Continuous CFST Columns under a Uniaxial Eccentric Load
Authors: Cao Bing , Zhu Longfei , Qian Huashan , Pan Zhicheng
Published: March 2024

For this study, we conducted a detailed examination and comprehensive comparative analysis of the structural responses and mechanical behavior of bolted sleeve connections in precast circular semi-continuous steel tubular concrete (PCSCFST) columns. The research involved fourteen specimens, and we considered the impacts of various parameters, including eccentricity, external steel sleeve thickness, bolt diameter, and slenderness ratio. The findings revealed that the external steel sleeve significantly enhances the protection of the connection area, enabling the bolts to effectively withstand eccentric loads. However, sleeves that are too thick may lead to premature bolt failure, reducing their ultimate load-bearing capacity. Using bolts to transfer loads to the concrete significantly strengthens the restraining effect of the steel sleeve. Nonetheless, increasing the bolt diameter beyond a certain threshold may diminish this beneficial impact, potentially leading to connection failure and a decrease in ultimate load-bearing capacity. A new ‘cooperative value q’ measures component collaboration at ultimate capacity, showing that shorter columns offer less effective coordination than longer ones. Through regression analysis, we formulated a prediction for axial ultimate bearing capacity, closely aligning with the experimental data (Npre-a/Nu average value of 1.003, variance 0.00248). Three N–M curves, including the Eurocode 4 method, offered conservative predictions, with Eurocode 4 closely matching the experimental results. A refined prediction method following Eurocode 4 was developed, yielding an average Ppre-U/Pu value of 0.971 and a variance of 0.0107.
Psychological Distress and Work Engagement of Construction Workers during the COVID-19 Pandemic: A Differential Study by Sex
Authors: Gómez-Salgado Carlos , Camacho-Vega Juan Carlos , Allande-Cussó Regina , Ruiz-Frutos Carlos , Ortega-Moreno Mónica , Linares-Manrique Marta , García-Iglesias Juan Jesús , Fagundo-Rivera Javier , Rodríguez-Díaz Luciano , Vázquez-Lara Juana María , Gómez-Salgado Juan
Published: March 2024

Since the beginning of the COVID-19 pandemic, a major impact on the mental health of the population has been observed, with women being one of the most affected groups. From the lockdown to “de-escalation” phases, sex differences have been recognised as significant determinants of mental health. Thus, equally ensuring physical and mental protection at work remains one of the challenges faced by industrial companies, especially in the construction sector, where the percentage of employed women has increased in recent years. This study aims to examine the impact of sex differences on psychological distress and work engagement in the productive construction sector, as well as related variables. For this, a cross-sectional descriptive study was performed. Descriptive statistical analyses were completed, and non-parametric Mann-Whitney U and Chi-squared tests were used to identify differences between men and women. This was followed by logistic regression analysis by sex. Psychological distress is more prevalent among women, even after controlling for most variables. Both sexes receive equal preventive measures and training from the companies, yet women still experience higher levels of psychological distress. At the beginning of the pandemic, women reported higher levels of anxiety and fear of COVID-19 and of perceived danger associated with the pandemic than men. However, these differences were not present by 2023. For men, work engagement appeared to be a determining factor for a stable mental health, while for women, health and physical status seemed to be more influential. In both sexes, psychological distress was found to be conditioned by mental and emotional well-being. In a sector where women are increasingly present, the differences observed in terms of how physical and mental health are affected across the two sexes justify the need to promote data analysis that acknowledges this reality.
A Review of Building Carbon Emission Accounting Methods under Low-Carbon Building Background
Authors: Xiong Lun , Wang Manqiu , Mao Jin , Huang Bo
Published: March 2024

With the continuous development of the global economy, carbon dioxide and other greenhouse gas emissions are persistently increasing, making global warming an indisputable fact. As a high-energy consuming industry, the building industry has gradually emerged as the primary source of greenhouse gas emissions during urban expansion. Consequently, countries are exploring sustainable development pathways for low-carbon buildings to minimize the detrimental impact caused by the construction industry. This paper summarizes the current status of low-carbon building development and, through literature analysis concerning carbon standard systems and carbon emission accountings, discusses the challenges and possible improvements for the future. Establishing a quantitative evaluation tool for carbon emissions and elucidating accounting methods in the construction field is fundamental and a prerequisite for comprehensively studying low-carbon buildings throughout their life cycle. The challenges of low-carbon building development are as follows: (1) lack of a set of carbon emission measurement standards that can be commonly used internationally, (2) lack of a deep and systematic study of the theory of carbon emission accountings and (3) difficulty in recognizing carbon emission boundaries and related data for existing carbon emission accounting methods. This paper provides a comprehensive analysis of the current progress in low-carbon building development, along with an examination and optimization of the application of carbon emission accounting methodologies within construction to address the challenges.
Steel Beam-to-Column Friction Joint under a Column Loss Scenario
Authors: Tartaglia Roberto , Carlevaris Roberto , D’Aniello Mario , Landolfo Raffaele
Published: March 2024

FREEDAM joints have been recently seismically prequalified for applications in European seismically prone countries. Despite their excellent seismic response, FREEDAM joints are not purposely conceived for exceptional loading conditions, such as in the case of a column loss scenario. Therefore, a comprehensive parametric numerical study has been carried out to investigate the robustness of this type of joint, varying the geometry of the beam–column assembly and the associated friction device. The results of the performed finite-element simulations allowed the identification of the critical components of the joints such as the upper T-stub connecting the upper beam flange to the column. This component is characterized by significant demand, due to the concentration of tensile and shear forces when catenary action develops in the beam. In order to enhance the ductility of the beam-to-column joint under large imposed rotations, the details of the upper T-stub connection were modified and numerically analyzed. The obtained results allowed for the verifying of the effectiveness of the amended details as well as characterizing the evolution of the tensile forces in the bolts.
Identification of the Beverage Sotol Adulterated with Ethylene Glycol Using UV-Vis Spectroscopy and Artificial Neural Networks
Authors: Gaxiola Fernando , Leal Jesús Javier , Manzo-Martínez Alain , Salmerón Iván , Linares-Morales José Rafael , Narro-García Roberto
Published: March 2024

Sotol is a traditional distilled alcoholic beverage produced in Mexico and the United States. Unfortunately, local authorities have detected that these beverages are sometimes adulterated with toxic substances such as ethylene glycol. This illegal practice of adulteration is dangerous and can cause serious health problems for the end consumers. In this work, an alternative, reliable, and rapid method is presented for identifying the presence of ethylene glycol in sotol samples using UV-Vis spectroscopy and neural networks with an accuracy of up to 100%.
Editorial for the Special Issue “Molecular Biology in Targeted Radionuclide Therapy Radiopharmaceutical Design”
Authors: Aprile Carlo , Geatti Onelio , Canziani Letizia , Lodola Lorenzo
Published: March 2024

Targeted radionuclide therapy (TRT) is gaining wide and rapid acceptance in clinical practice as it can deliver alpha or beta irradiation to a tumor-associated target which may be present in the tumor cell itself or in the microenvironment [...]
Evaluating Teachers’ Workplace Climate and Anxiety Response during the COVID-19 Pandemic: The Role of Information Seeking Platforms
Authors: Srem-Sai Medina , Quansah Frank , Agormedah Edmond Kwesi , Hagan John Elvis , Schack Thomas
Published: March 2024

The COVID-19 disease affected the school workplace climate for teachers and led to psychological consequences. However, it is not clear how the workplace climate affected the anxiety levels of teachers. This study assessed the connection between workplace climate and COVID-19-related anxiety among senior high school (SHS) teachers during the COVID-19 pandemic. This study further examined the moderating role of professional and social media platform use on the relationship between workplace climate and COVID-19-related anxiety among teachers. Through a cross-sectional survey design, 395 high school teachers were conveniently sampled from various schools in the Central Region of Ghana. A questionnaire was used to survey participants, and the obtained data were analysed using descriptive statistics as well as simple linear regression and moderation analyses with Hayes’ PROCESS. This study revealed a negative association between workplace climate and anxiety. The relationship between workplace climate and anxiety was contingent on social media use but not professional platform use. Therefore, the consumption of unscrutinised COVID-19-related information on social media heightened fear and anxiety among teachers, even in the midst of a safe workplace environment. An effective strategy against teachers’ COVID-19-related anxiety required the provision of accurate science-driven information about the virus. School counselling psychologists, school welfare officers, and school health coordinators are encouraged to collaborate towards designed interventions that promote a safe working environment and the mental health of teachers.
Assessment of Cervicovaginal Smear and HPV DNA Co-Test for Cervical Cancer Screening: Implications for Diagnosis and Follow-Up Strategies
Authors: Kaya Terzi Neslihan , Yulek Ozden
Published: March 2024

Objective: Cervical cancer is a major cause of cancer-related mortality, necessitating effective screening and diagnostic methods. This study aimed to assess the performance of cervicovaginal smear (CVS) and human papillomavirus (HPV)-DNA co-test. Study design: The pathology results of 225 female patients who underwent HPV-DNA testing with CVS between 2014 and 2022 and were subsequently diagnosed by colposcopic cervical biopsy or second CVS were retrospectively analyzed. Results: CVS samples showed atypical squamous cells of undetermined significance (ASCUS), low-grade squamous intraepithelial lesion (LSIL), high-grade squamous intraepithelial lesion (HSIL), and cervical cancer. Concordance between the first and second diagnoses demonstrated moderate agreement for LSIL. ASCUS cases exhibited a significant correlation with HPV-DNA positivity and higher-grade cervical lesions. In biopsy, sensitivity and specificity for CIN1/LSIL were 87.5% and 53.3, respectively, while for CIN2-3/HSIL, they were 83.87% and 58.49%. HPV testing showed significant correlation with histopathologic results. In women over 40 years, more intraepithelial lesions were diagnosed compared to younger women (p < 0.005). The conventional smear technique proved reliable in detecting high-grade lesions. Conclusions: Despite the limitations of our study, our results emphasize the value of HPV-DNA testing to avoid unnecessary interventions and to establish appropriate follow-up strategies.
Species-Level Classification of Peatland Vegetation Using Ultra-High-Resolution UAV Imagery
Authors: Simpson Gillian , Nichol Caroline J. , Wade Tom , Helfter Carole , Hamilton Alistair , Gibson-Poole Simon
Published: March 2024

Peatland restoration projects are being employed worldwide as a form of climate change mitigation due to their potential for long-term carbon sequestration. Monitoring these environments (e.g., cover of keystone species) is therefore essential to evaluate success. However, existing studies have rarely examined peatland vegetation at fine scales due to its strong spatial heterogeneity and seasonal canopy development. The present study collected centimetre-scale multispectral Uncrewed Aerial Vehicle (UAV) imagery with a Parrot Sequoia camera (2.8 cm resolution; Parrot Drones SAS, Paris, France) in a temperate peatland over a complete growing season. Supervised classification algorithms were used to map the vegetation at the single-species level, and the Maximum Likelihood classifier was found to perform best at the site level (69% overall accuracy). The classification accuracy increased with the spatial resolution of the input data, and a large reduction in accuracy was observed when employing imagery of >11 cm resolution. Finally, the most accurate classifications were produced using imagery collected during the peak (July–August) or early growing season (start of May). These findings suggest that despite the strong heterogeneity of peatlands, these environments can be mapped at the species level using UAVs. Such an approach would benefit studies estimating peatland carbon emissions or using the cover of keystone species to evaluate restoration projects.
Inquiry and Mathematical Modelling with Real-Archaeological Objects in Secondary Education
Authors: Falcó-Solsona Pere Joan , Ledezma Carlos , Sala-Sebastià Gemma , Font Vicenç
Published: March 2024

Inquiry and modelling are relevant processes of mathematical activity, and both are considered as important competencies for problem solving, since their subprocesses have a family resemblance that makes them mutually complementary and enriched. In this line, we report an investigation focused on the study of the synergies and relationships established between the inquiry and modelling processes emerging in a learning situation, based on a realistic and authentic archaeological context. To this end, we considered one theoretical cycle to explain each process, and then designed a learning situation contextualised in a realistic and authentic archaeological context which promoted the development of inquiry and modelling skills for its solving. Methodologically, we conducted a case study with students in a secondary school of Badalona (Catalonia, Spain), who solved the learning situation grouped into working teams. Through a thematic analysis, we identified the steps of both processes followed by the students when solving the learning situation, and thus we delved into the synergies and relationships established between inquiry and modelling processes. Finally, we reflect on how both processes mutually enrich when dealing with a problem-situation and propose an integrative cycle for the development of inquiry and modelling skills in the classroom.
Multi-Scale Residual Spectral–Spatial Attention Combined with Improved Transformer for Hyperspectral Image Classification
Authors: Wang Aili , Zhang Kang , Wu Haibin , Iwahori Yuji , Chen Haisong
Published: March 2024

Aiming to solve the problems of different spectral bands and spatial pixels contributing differently to hyperspectral image (HSI) classification, and sparse connectivity restricting the convolutional neural network to a globally dependent capture, we propose a HSI classification model combined with multi-scale residual spectral–spatial attention and an improved transformer in this paper. First, in order to efficiently highlight discriminative spectral–spatial information, we propose a multi-scale residual spectral–spatial feature extraction module that preserves the multi-scale information in a two-layer cascade structure, and the spectral–spatial features are refined by residual spectral–spatial attention for the feature-learning stage. In addition, to further capture the sequential spectral relationships, we combine the advantages of Cross-Attention and Re-Attention to alleviate computational burden and attention collapse issues, and propose the Cross-Re-Attention mechanism to achieve an improved transformer, which can efficiently alleviate the heavy memory footprint and huge computational burden of the model. The experimental results show that the overall accuracy of the proposed model in this paper can reach 98.71%, 99.33%, and 99.72% for Indiana Pines, Kennedy Space Center, and XuZhou datasets, respectively. The proposed method was verified to have high accuracy and effectiveness compared to the state-of-the-art models, which shows that the concept of the hybrid architecture opens a new window for HSI classification.
Design and Implementation of SSS-Based AUV Autonomous Online Object Detection System
Authors: Wang Shihao , Liu Xiaoyu , Yu Siquan , Zhu Xinghua , Chen Bingbing , Sun Xiaoyu
Published: March 2024

Underwater object detection is an important task in marine exploration. The existing autonomous underwater vehicle (AUV) designs typically lack an integrated object detection module and are constrained by communication limitations in underwater environments. This results in a situation where AUV, when tasked with object detection missions, require real-time transmission of underwater sensing data to shore-based stations but are unable to do so. Consequently, the task is divided into two discontinuous phases: AUV acquisition of underwater data and shore-based object detection, leading to limited autonomy and intelligence for the AUV. In this paper, we propose a novel autonomous online underwater object detection system for AUV based on side-scan sonar (SSS). This system encompasses both hardware and software components and enables AUV to perform simultaneous data acquisition and object detection for underwater objects, thereby providing guidance for coherent AUV underwater operations. Firstly, this paper outlines the hardware design and layout of a portable integrated AUV for reconnaissance and strike missions, achieving online object detection through the integration of an acoustic processing computer. Subsequently, a modular design for the software architecture and a multi-threaded parallel design for the software workflow are developed, along with the integration of the YOLOv7 intelligent detection model, addressing three key technological challenges: real-time data processing, autonomous object detection, and intelligent online detection. Finally, lake experiments show that the system can meet the autonomy and real-time requirements of predefined object detection on AUV, and the average positioning error is better than 5 m, which verifies the feasibility and effectiveness of the system. This provides a new solution for underwater object detection in AUV.
Calibration of Crushable Foam Models for the Jellyroll of Cylindrical Lithium-Ion Batteries
Authors: Ahn Young Ju
Published: March 2024

Crushable foam plasticity models are employed to simulate material response under essentially monotonic loading. For the plastic part of the behavior, the default crushable foam model in Abaqus/Explicit is the volumetric hardening model, where the yield surface evolves by the volumetric compacting plastic strain, and the other available model is the isotropic hardening model, where the yield curve is centrally located at the origin in the pressure—the Mises stress plane. In this study, the characteristic of two models was examined by applying them to a simple 18650 lithium-ion cylindrical cell. The computation cell model consists of the shell casing and the homogenized jelly roll which represents the electrode assembly. Both crushable foam models were calibrated to represent the homogenized mechanical properties of the jellyroll, and the load–displacement relations were compared with the experimental results. Then, we examined the deformation characteristic of jellyroll for each crushable foam model.
A Multi-Criteria Decision-Making Framework for Zero Emission Vehicle Fleet Renewal Considering Lifecycle and Scenario Uncertainty
Authors: Aiello Giuseppe , Quaranta Salvatore , Inguanta Rosalinda , Certa Antonella , Venticinque Mario
Published: March 2024

In the last decade, with the increased concerns about the global environment, attempts have been made to promote the replacement of fossil fuels with sustainable sources. For transport, which accounts for around a quarter of total greenhouse gas emissions, meeting climate neutrality goals will require replacing existing fleets with electric or hydrogen-propelled vehicles. However, the lack of adequate decision support approach makes the introduction of new propulsion technologies in the transportation sector a complex strategic decision problem where distorted non-optimal decisions may easily result in long-term negative effects on the performance of logistic operators. This research addresses the problem of transport fleet renewal by proposing a multi-criteria decision-making approach and takes into account the multiple propulsion technologies currently available and the objectives of the EU Green Deal, as well as the inherent scenario uncertainty. The proposed approach, based on the TOPSIS model, involves a novel decision framework referred to as a generalized life cycle evaluation of the environmental and cost objectives, which is necessary when comparing green and traditional propulsion systems in a long-term perspective to avoid distorted decisions. Since the objective of the study is to provide a practical methodology to support strategic decisions, the framework proposed has been validated against a practical case referred to the strategic fleet renewal decision process. The results obtained demonstrate how the decision maker’s perception of the technological evolution of the propulsion technologies influences the decision process, thus leading to different optimal choices.
Critical Egress Parameters Governing Assisted Evacuation in Hospital Buildings
Authors: Kodur Venkatesh , Jha Ankush , Lajnef Nizar
Published: March 2024

This paper presents the critical egress parameters that influence emergency evacuation in a typical hospital building. A parametric study of a 20-story hospital building is conducted using a computer model “Pathfinder” to simulate the evacuation efficiency and assess the influencing parameters. The main egress parameters that influence the evacuation efficiency, including the location of stairways, number of stairways, location of the fire, exit width, and number of low-speed occupants are varied. Two scenarios are simulated: one being the regular (practice) evacuation drill and the other is the actual fire drill. The result shows that the location of stairways significantly affects the total evacuation time with the optimal stairway arrangement consisting of one stairway outside the core of the building. Similarly, the story level at which the fire occurs is another key parameter with fires at lower levels being critical to dictating the evacuation time in a hospital building. The total evacuation time when the fire occurs between the third and sixth floor is found to be 170 min which is 36% and 15% higher than fires at the top story levels (15–18th floor) and the intermediate story levels (9–12th floor), respectively.
Dispersive Solid-Phase Extraction and Ultra-Performance Liquid Chromatography–Tandem Mass Spectrometry—A Rapid and Accurate Method for Detecting 10 Macrolide Residues in Aquatic Products
Authors: Chen Jinyu , Mei Guangming , Zhang Xiaojun , Huang Daoxiang , He Pengfei , Xu Dan
Published: March 2024

The amount of macrolide (MAL) residues in aquatic products, including oleandomycin (OLD), erythromycin (ERM), clarithromycin (CLA), azithromycin (AZI), kitasamycin (KIT), josamycin (JOS), spiramycin (SPI), tilmicosin (TIL), tylosin (TYL), and roxithromycin (ROX), was determined using solid-phase extraction and ultra-performance liquid chromatography–tandem mass spectrometry (UPLC-MS/MS). The residues were extracted with 1% ammonia acetonitrile solution and purified by neutral alumina adsorption. Chromatographic separation was completed on an ACQUITY UPLC BEH C18 column with acetonitrile–0.1% formic acid aqueous solution as the mobile phase, and mass spectrometry detection was performed by multiple reaction monitoring scanning with the positive mode in an electrospray ion source (ESI+). Five isotopically labeled compounds were used as internal standards for quality control purposes. The findings indicated that across the mass concentration span of 1.0–100 μg/L, there was a strong linear correlation (R2 > 0.99) between the concentration and instrumental response for the 10 MALs. The limit of detection of UPLC-MS/MS was 0.25–0.50 μg/kg, and the limit of quantitation was 0.5–1.0 μg/kg. The added recovery of blank matrix samples at standard gradient levels (1.0, 5.0, and 50.0 μg/kg) was 83.1–116.6%, and the intra-day precision and inter-day precisions were 3.7 and 13.8%, respectively. The method is simple and fast, with high accuracy and good repeatability, in line with the requirements for accurate qualitative and quantitative analysis of the residues for 10 MALs in aquatic products.
Enrichment of Flavonoids in Short-Germinated Black Soybeans (Glycine max L.) Induced by Slight Acid Treatment
Authors: Huang Caimei , Quan Xiaolan , Yin Yongqi , Ding Xiangli , Yang Zhengfei , Zhu Jiangyu , Fang Weiming
Published: March 2024

Exogenous abiotic stimulant treatments are a straightforward and effective method for enhancing secondary metabolites in plants. In this study, the response surface optimization method was used to optimize the conditions for enriching flavonoids in short-germinated black soybeans under a slight acid treatment, and the mechanism of flavonoid accumulation during black soybean germination was explored. The results show that the use of a 126.2 mM citric acid–sodium citrate buffer (pH 5.10) as a slight acid treatment resulted in the highest flavonoid content when the black soybeans were germinated for 24 h. Under these conditions, the isoflavonoid (glycitin, daidzein, and genistein) increased significantly, and the flavonoid content reached 2.32 mg/g FW. The microacidified germination treatment significantly increased the activities and relative gene expression levels of key enzymes involved in flavonoid metabolism (4-coumarate-CoA ligase and cinnamic acid 4-hydroxylase, etc.). However, the slight acid treatment inhibited the growth of the black soybeans and caused damage to their cells. This was evidenced by significantly higher levels of malondialdehyde, superoxide anion, and hydrogen peroxide compared to the control group. Furthermore, the antioxidant system in the short-germinated soybeans was activated by the slight acid treatment, leading to a significant increase in the activities and relative gene expression levels of catalase and peroxidase. The results above show that a slight acid treatment was beneficial in inducing the accumulation of flavonoids during the growth of black soybean sprouts. This lays a technical foundation for producing black soybean products that are rich in flavonoids.