bluuebunny/crossref_metadata_2025 huggingface dataset:
Working Memory in Navigational and Reaching Spaces in Typically Developing Children at Increasing School Stages
Authors: Bartonek Åsa , Guariglia Cecilia , Piccardi Laura
Published: October 2022

Background: Based on studies of children with motor disabilities on topographic working memory (TWM), no influence of age was reported. The only differences were in the degree of mobility and exploration of the environment. The more active a child was in exploring the environment, the less his/her TWM was poor. However, in typically developing children (TD), exploration of the environment increases with increasing age, and age-related effects have been described. Here, we aim at investigating TWM considering age in TD with the additional question of whether WM in the reaching space differed from that in the navigational space requiring body movements. We hypothesized that WM in both spaces would improve correspondingly with increasing age, assuming that the greater the autonomy in exploring the environment, the better TWM becomes. Method: 120 children (5–16 years old) performed the Corsi Block-Tapping test (CBT) and the Walking Corsi test (WalCT). Results: Statistical analyses evidenced significantly increasing WalCT and CBT spans between each school stage, except in the CBT span between middle stage (MS) and upper stage (US). CBT spans were significantly higher than in the WalCT in the pre-school, lower stage, and MS, with the CBT span increasing until MS, which is sufficient for using spatial orientation strategies effectively. Conclusions: When navigation is gradually controlled, a child may be able to pay increasingly more attention to wayfinding and behavior in traffic. Since the US group even presented as good in the WalCT as young adults living in metropolitan environments, assuming that children may gain spatial orientation from having opportunities to move in their surroundings, this is also relevant for children with motor disabilities.
Emotional Experience and Regulation in Juvenile Primary Headaches: A Cross-Sectional Pilot Study
Authors: Miscioscia Marina , Di Riso Daniela , Spaggiari Silvia , Poli Mikael , Gaiga Giacomo , Randazzo Giacomo , Pelizza Maria Federica , Galdiolo Laura , Raffagnato Alessia , Sartori Stefano , Toldo Irene
Published: October 2022

A headache is the most common neurological symptom in children. Its subtypes are migraine (MH) and tension-type headache (TTH). Internalizing rather than externalizing symptoms are more frequent in children with headaches, but little is known about the reasons why. We aim to: (a) examine the interplay between emotional experience, affective regulation, and internalizing symptoms in children suffering from primary headaches and their caregivers; (b) identify potential predictors of children with migraines’ internalizing symptoms. Fifty children and adolescents with a diagnosis of primary headaches and their caregivers were compared to a sample of fifty-one healthy peers and caregivers. Self-reports and parent-reports were administered. Results indicate higher negative affect and internalizing symptoms and lower bodily awareness of emotions in the clinical sample (n = 50; Mage = 11.66, SD = 2.25) compared to controls (n = 51; Mage = 11.73, SD = 2.32); mothers of TTH children self-reported lower emotional awareness and higher difficulties in engaging in goal-directed behavior; a higher frequency of headaches was associated with greater emotional regulation difficulties. Internalizing symptoms were predicted by higher self-reported negative affect and parent-reported internalizing symptoms, and lower self-reported ability in the verbal sharing of emotions. These findings suggest the importance of assessing the psychological features linked to children with primary headaches’ psychological well-being.
The Trap of the COVID-19 Pandemic: Italian Adolescents Fare Well at First, Maybe Thanks to Protective Trait Expression
Authors: Malerba Anna , Iannattone Sara , Casano Giorgia , Lauriola Marco , Bottesi Gioia
Published: October 2022

Abundant research indicates that the COVID-19 pandemic has been negatively affecting mental health in adolescence. Few works, however, benefit from data from the same sample before and after the onset of the pandemic. The present longitudinal study involved a non-clinical group of 136 Italian adolescents (Mage = 16.3 years ± 1.08, 67% girls) to investigate their psychological response to the first lockdown and explore the role of a protective trait (i.e., Positivity) in moderating the effect of Intolerance of Uncertainty (IU) on internalizing symptoms before and during the COVID-19 outbreak. Participants completed self-report questionnaires assessing psychopathological symptoms, psychological well-being, IU, and Positivity on three separate occasions: October 2019 (T1), January 2020 (T2), and April 2020 (T3). The results showed that internalizing and externalizing symptoms as well as psychological well-being did not vary significantly over time. Positivity was found to significantly moderate the relationship between IU and internalizing symptoms at T3 (i.e., during the COVID-19 lockdown) only. Overall, our findings suggest that the teenagers’ good adjustment to the initial phase of the pandemic might have been associated with the enhanced weight of the Positivity trait, which may have encouraged a positive attitude towards self, life, and the future.
An Investigation of CTP Use for Wireless Structural Health Monitoring of Infrastructure
Authors: Spyrou Evangelos D. , Kappatos Vassilios
Published: October 2022

Structural Health Monitoring (SHM) is a very important research domain whereby civil infrastructure is monitored. Using wireless technologies can boost SHM by providing the level of autonomous operation that is essential for these tasks. Wireless routing, with its subset, geographic routing, is an important procedure that needs to be optimised, in order to lead packets to the basestation. Occasionally, routing is susceptible to interference and collisions due to a large number of connected devices. This fact led to cooperative transmission; cooperative networks are the ones that utilise relays to accomplish the transmission of packets; thus, resulting in link quality as well as throughput increase. In this paper, we investigate the Collection Tree Protocol (CTP) to show that it can be cooperative when used in an SHM for civil infrastructure monitoring applications giving a geographical essence to the routing protocol. We do that by exploiting the fact that the CTP’s mechanism uses its tree formation for a node to transmit to the best link quality parents. An example of a cooperative model to show that it may be applied to the protocol is given. Further, Indriya testbed results of direct and cooperative transmissions are given to strengthen the case of this work, with which a scenario where the CTP exhibits better link quality when using a relay is given. A practical addition is suggested, whereby an extra field in the packet struct is proposed, which will provide the CTP with further strength to changing conditions and direct communication loss.
Data Collection Theory in Healthcare Research: The Minimum Dataset in Quantitative Studies
Authors: Kwok Chun Shing , Muntean Elena-Andra , Mallen Christian D. , Borovac Josip Andelo
Published: October 2022

There is considerable interest in data analytics because of its value in informing decisions in healthcare. Data variables can be derived from routinely collected records or from primary studies. The level of detail for individual variables in quantitative studies is often disregarded. In this work, we aim to present the concept of a minimum dataset for any variable. The most basic level of data collection is the value of a variable. In addition, there may be an indicator of severity and a measure of duration or how long the value has been present. The time course defines how the values for a variable fluctuated over time. The validity or accuracy of the values for a variable is also important to avoid spurious findings. Finally, there may be additional modifiers which drastically change the impact of a variable. In conclusion, the minimum dataset is a framework which can be used for the purposes of study design and appraisal of studies. Not all data requires full consideration of the minimum dataset framework for each variable, but the framework may be important if more detailed results are desired.
Impact of Evening Light Exposures with Different Solid Angles on Circadian Melatonin Rhythms, Alertness, and Visual Comfort in an Automotive Setting
Authors: Weng Michael , Schöllhorn Isabel , Kazhura Maryia , Cardini Brian B. , Stefani Oliver
Published: October 2022

Future automotive interior lighting might have the potential to go beyond decorative purposes by influencing alertness, circadian physiology, and sleep. As the available space in the interior of an automobile for lighting applications is limited, understanding the impact of various luminous surface sizes on non-image-forming effects is fundamental in this field. In a laboratory study using a within-subject design, 18 participants were exposed to two bright light conditions with different solid angles and one dim light condition in a balanced, randomized order during the course of the evening. Our results demonstrate that both light conditions significantly increased subjective alertness and reduced salivary melatonin concentration but not cognitive performance compared to dim light. The solid angle of light exposure at constant corneal illuminance only affected visual comfort. While subjective alertness can be increased and melatonin can be attenuated with rather small luminaires, larger solid angles should be considered if visual comfort is a priority.
Natural Convection of Blood–Magnetic Iron Oxide Bio-nanofluid in the Context of Hyperthermia Treatment
Authors: Benos Lefteris , Ninos George , Polychronopoulos Nickolas D. , Exomanidou Maria-Aristea , Sarris Ioannis
Published: October 2022

Hyperthermia, an alternative medical approach aiming at locally increasing the temperature of a tumor, can cause the “death” of cancer cells or the sensitization of them to chemotherapeutic drugs and radiation. In contrast with the conventional treatments, hyperthermia provokes no injury to normal tissues. In particular, magnetic hyperthermia can utilize iron oxide nanoparticles, which can be administered intravenously to heat tumors under an alternating magnetic field. Currently, there is no theoretical model in the relative literature for the effective thermal conductivity of blood and magnetic nanoparticles. The scope of the present study is twofold: (a) development of a theoretical relationship, based on experimental findings and blood structure and (b) study of the laminar natural convection in a simplified rectangular porous enclosure, by using the asymptotic expansions method for deriving ordinary differential equations of the mass, momentum and energy balances, as a first approach of investigating heat transfer and providing theoretical guidelines. In short, the thermal conductivity of the resulting bio-nanofluid tends to increase by both increasing the concentration of the nanoparticles and the temperature. Furthermore, the heat transfer is enhanced for more intense internal heating (large Rayleigh numbers) and more permeable media (large Darcy numbers), while larger nanoparticle concentrations tend to suppress the flow.
Breakdown of Adiabatic Superconductivity in Ca-Doped h-BN Monolayer
Authors: Drzazga-Szczȩśniak Ewa A. , Szczȩśniak Dominik , Kaczmarek Adam Z. , Szczȩśniak Radosław
Published: October 2022

In the present paper, we report the breakdown of the adiabatic picture of superconductivity in a calcium-doped hexagonal boron nitride (Ca-h-BN) monolayer and discuss its implications for the selected properties of this phase. In particular, it is shown that the shallow conduction band of the Ca-h-BN superconductor potentially cause a violation of the adiabatic Migdal’s theorem. As a result, the pivotal parameters that describe the superconducting state in Ca-h-BN are found to be notably influenced by the non-adiabatic effects. This finding is described here within the vertex-corrected Eliashberg formalism that predicts a strong reduction of the order parameter, superconducting transition temperature and superconducting gap in comparison to the estimates obtained in the framework of the adiabatic theory. The observed trends are in agreement with the recent results on superconductivity in hexagonal monolayers and confirm that the non-adiabatic effects have to be taken into account during the design of such future low-dimensional superconductors.
Advanced Lung Cancer Patients’ Use of EGFR Tyrosine Kinase Inhibitors and Overall Survival: Real-World Evidence from Quebec, Canada
Authors: Qureshi Samia , Boily Gino , Boulanger Jim , Golo Kossi Thomas , Guédon Aude-Christine , Lehuédé Camille , Roussafi Ferdaous , Truchon Catherine , Strumpf Erin
Published: October 2022

EGFR tyrosine kinase inhibitors (EGFR-TKIs) are breakthrough palliative treatments for advanced lung cancer patients with tumors harboring mutations in the EGFR gene. Using healthcare administrative data, three cohorts were created to describe the use of three EGFR-TKIs that are publicly funded in Quebec for specific indications (i.e., 1st-line gefitinib, 1st-line afatinib, and post-EGFR-TKI osimertinib). The main objective was to compare overall survival (OS) among patients receiving these treatments to those in previous experimental and real-world studies. The patients who received EGFR-TKIs for indications of interest between 1 April 2001, and 31 March 2019 (or 31 March 2020, for post-EGFR-TKI osimertinib) were included to estimate the Kaplan-Meier-based median OS for each cohort. An extensive literature search was conducted to include comparable studies. For the gefitinib 1st-line (n = 457), the afatinib 1st-line (n = 80), and the post-EGFR-TKI osimertinib (n = 119) cohorts, we found a median OS (in months) of 18.9 (95%CI: 16.3–21.9), 26.6 (95%CI: 13.7-NE) and 19.9 (95%CI: 17.4-NE), respectively. Out of the 20 studies that we retained from the literature review and where comparisons were feasible, 17 (85%) had similar OS results, which further confirms the value of these breakthrough therapies in real-world clinical practice.
Dietary Habits Are Related to Phase Angle in Male Patients with Non-Small-Cell Lung Cancer
Authors: Detopoulou Paraskevi , Tsiouda Theodora , Pilikidou Maria , Palyvou Foteini , Mantzorou Maria , Perzirkianidou Persefoni , Kyrka Krystallia , Methenitis Spyridon , Kondyli Foivi S. , Voulgaridou Gavriela , Zarogoulidis Paul , Matthaios Dimitris , Oikonomidou Rena , Romanidou Maria , Giannakidis Dimitrios , Papadopoulou Sousana K.
Published: October 2022

Introduction: Lung cancer constitutes the most common cause of cancer death. Phase angle (PhA) has been related to lung cancer prognosis, which implies that the identification of dietary or other factors that could predict or modify PhA may have beneficial effects. Νutritional interventions have been linked with positive changes in PhA in certain types of cancer. Aim: The present study aimed to investigate the relationships between dietary habits/nutrition and PhA in NSCLC patients. Methods: The sample consisted of 82 male patients with non-small-cell lung cancer (NSCLC; stage IV) from the ‘Theageneio’ Cancer Hospital (Thessaloniki, Greece). Several parameters were assessed, such as body mass index (BMI), lean mass, PhA, Mediterranean diet score (MedDietScore), dietary patterns, smoking, resting metabolic rate, resting oxygen consumption (VO2), ventilation rate, and physical activity. Results: According to our results, a dietary pattern rich in potatoes and animal proteins (meat and poultry) was a significant determinant of PhA (B ± SE, p: 0.165 ± 0.08, p = 0.05) in multiple linear regression models after adjusting for age, smoking, lean tissue, and MedDietScore. Conclusion: In conclusion, dietary patterns may affect PhA, suggesting the crucial role of protein in cancer management and the prevention of sarcopenia.
Performance of Indocyanine Green Compared to 99mTc-Nanocolloids for Sentinel Lymph Node Detection in Early Vulvar Cancer
Authors: Benmoulay-Rigollot Camélia , Karpathiou Georgia , Prevot-Bitot Nathalie , Heinemann Mellie , Trombert-Paviot Beatrice , Barjat Tiphaine , Chauleur Céline
Published: October 2022

Study objective: The aim of this study was to evaluate the performance of indocyanine green (ICG) compared to that of the gold standard 99mtechnetium (99mTc-nanocolloids) in detecting sentinel lymph nodes (SLN) in early vulvar cancer. Material and Methods: A single-center retrospective cohort study comparing SLN detection by 99mTc-nanocolloids and ICG was performed in patients presenting early vulvar cancer (T1/2), with clinically negative nodes. All SLN showing a radioactive and/or fluorescent signal were resected. The primary endpoints were the sensitivity, positive predictive value (PPV) and false negative (FN) rate of ICG in detecting SLN compared to 99mTc-nanocolloids. Results: Thirty patients were included and 99 SLN were identified in 43 groins. Compared to 99mTc-nanocolloids, ICG had a sensitivity of 80.8% (95% CI [72.6; 88.6%]), a PPV of 96.2% (95% CI [91.8; 100%]) and a FN rate of 19.1% in detecting SLN. Seventeen (17.1%) infiltrated (positive) SLN were identified out of the 99 SLN detected. Compared to 99mTc-nanocolloids, ICG showed a sensitivity of 82.3% (95% CI [73.1; 91.5%]), a PPV of 100% and a FN rate of 17.6% (3/17) in detecting infiltrated SLN. Conclusion: Despite its many advantages, ICG cannot be used as the sole tracer for the detection of SLN in early vulvar cancer and should be employed in conjunction with 99mTc-nanocolloids.
Cardiovascular and Diabetes Diseases Classification Using Ensemble Stacking Classifiers with SVM as a Meta Classifier
Authors: Khan Asfandyar , Khan Abdullah , Khan Muhammad Muntazir , Farid Kamran , Alam Muhammad Mansoor , Su’ud Mazliham Bin Mohd
Published: October 2022

Cardiovascular disease includes coronary artery diseases (CAD), which include angina and myocardial infarction (commonly known as a heart attack), and coronary heart diseases (CHD), which are marked by the buildup of a waxy material called plaque inside the coronary arteries. Heart attacks are still the main cause of death worldwide, and if not treated right they have the potential to cause major health problems, such as diabetes. If ignored, diabetes can result in a variety of health problems, including heart disease, stroke, blindness, and kidney failure. Machine learning methods can be used to identify and diagnose diabetes and other illnesses. Diabetes and cardiovascular disease both can be diagnosed using several classifier types. Naive Bayes, K-Nearest neighbor (KNN), linear regression, decision trees (DT), and support vector machines (SVM) were among the classifiers employed, although all of these models had poor accuracy. Therefore, due to a lack of significant effort and poor accuracy, new research is required to diagnose diabetes and cardiovascular disease. This study developed an ensemble approach called “Stacking Classifier” in order to improve the performance of integrated flexible individual classifiers and decrease the likelihood of misclassifying a single instance. Naive Bayes, KNN, Linear Discriminant Analysis (LDA), and Decision Tree (DT) are just a few of the classifiers used in this study. As a meta-classifier, Random Forest and SVM are used. The suggested stacking classifier obtains a superior accuracy of 0.9735 percent when compared to current models for diagnosing diabetes, such as Naive Bayes, KNN, DT, and LDA, which are 0.7646 percent, 0.7460 percent, 0.7857 percent, and 0.7735 percent, respectively. Furthermore, for cardiovascular disease, when compared to current models such as KNN, NB, DT, LDA, and SVM, which are 0.8377 percent, 0.8256 percent, 0.8426 percent, 0.8523 percent, and 0.8472 percent, respectively, the suggested stacking classifier performed better and obtained a higher accuracy of 0.8871 percent.
Evaluation of the Sealing Ability and Bond Strength of Two Endodontic Root Canal Sealers: An In Vitro Study
Authors: Marques Ferreira Manuel , Martinho José Pedro , Duarte Inês , Mendonça Diogo , Craveiro Ana Catarina , Botelho Maria Filomena , Carrilho Eunice , Miguel Marto Carlos , Coelho Ana , Paula Anabela , Paulo Siri , Chichorro Nuno , Abrantes Ana Margarida
Published: October 2022

Background: Obturation represents a critical step in endodontic treatment, which relies on a core material and a sealer. This study aims to evaluate the sealing ability and bond strength to the root canal walls of an epoxy resin-based sealer (AH-Plus®, Dentsply Sirona, Johnson City, TN, USA) and a bioceramic sealer (GuttaFlow Bioseal®, Coltène/Whaledent, GmbH + Co. KG, Langenau, Germany). Methods: Thirty-eight maxillary anterior teeth with single roots and identical round sections were separated into two experimental groups according to the root canal sealers used, namely, G1 = AH-Plus® and G2 = GuttaFlow Bioseal®, and two control groups, specifically, G3 = the negative control and G4 = the positive control. The sealing capacity was measured by the penetration of the radioactive isotope 99mTc. For the push-out test, the compressive force test was performed in a universal machine and the force was applied by exerting pressure on the surface of the material to be tested in the apical to the coronal direction and using three test points with different diameters. Results: GuttaFlow Bioseal® exhibited superior sealing ability compared to AH-Plus® (p = 0.003). Regarding the bond strength, AH-Plus® provided higher adhesion values than GuttaFlow Bioseal® in the three sections of the tooth root (p = 0.001). Conclusions: GuttaFlow Bioseal® had significantly better sealing ability than AH-Plus® but lower adhesion values in the three zones of the root canal, with statistically significant differences between the groups. However, it is important to note that for the action of endodontic sealers to be maximized, the root-filling technique must be most appropriate.
An Adaptive Control Framework for the Autonomous Aerobatic Maneuvers of Fixed-Wing Unmanned Aerial Vehicle
Authors: Cao Su , Yu Huangchao
Published: October 2022

This article proposes an adaptive flight framework that integrates a discrete-time incremental nonlinear dynamic inversion controller and a neural network (NN)-based observer for maneuvering flight. The framework is built on the feedback-inversion scheme in which the adaptive neural network augments a discrete-time disturbance observer in the loop. The effects of the modeling uncertainties and the exogenous perturbations are both taken into consideration and are alleviated by the observer. By utilizing the Lyapunov synthesis method, the updating rule of the NN’s weights is introduced, which guarantees the system’s stability with enhanced tracking performance. The efficiency of the proposed scheme is presented through numerical verification of a 6-DOF fixed-wing fighter performing several aggressive flight maneuvers. Extensive simulation results illustrate that this versatile controller is more practical for aerobatic flights compared with the discontinuous sliding mode (DSM) and the nonlinear dynamic inversion (NDI) methods. Given well-generated maneuver commands, the aircraft can accurately track the aggressive reference in the presence of modeling perturbations such as changes in aerodynamic coefficient, inertial parameters, and wind gusts.
Efficacy of Mapping Grassland Vegetation for Land Managers and Wildlife Researchers Using sUAS
Authors: O’Connell John R. , Glass Alex , Crawford Caleb S. , Eichholz Michael W.
Published: October 2022

The proliferation of small unmanned aerial systems (sUAS) is making very high-resolution imagery attainable for vegetation classifications, potentially allowing land managers to monitor vegetation in response to management or wildlife activities and offering researchers opportunities to further examine relationships among wildlife species and their habitats. The broad adoption of sUAS for remote sensing among these groups may be hampered by complex coding, expensive equipment, and time-consuming protocols. We used a consumer sUAS, semiautomated flight planning software, and graphical user interface GIS software to classify grassland vegetation with the aim of providing a user-friendly framework for managers and ecological researchers. We compared the overall accuracy from classifications using this sUAS imagery (89.22%) to classifications using freely available National Agriculture Imagery Program imagery (76.25%) to inform decisions about cost and accuracy. We also compared overall accuracy between manual classification (89.22%) and random forest classification (69.26%) to aid with similar decisions. Finally, we examined the impact of resolution and the addition of a canopy height model on classification accuracy, obtaining mixed results. Our findings can help new users make informed choices about imagery sources and methodologies, and our protocols can serve as a template for those groups wanting to perform similar vegetation classifications on grassland sites without the need for survey-grade equipment or coding. These should help more land managers and researchers obtain appropriate grassland vegetation classifications for their projects within their budgetary and logistical constraints.
Review of Biomimetic Approaches for Drones
Authors: Tanaka Saori , Asignacion Abner , Nakata Toshiyuki , Suzuki Satoshi , Liu Hao
Published: October 2022

The utilization of small unmanned aerial vehicles (SUAVs), commonly known as drones, has increased drastically in various industries in the past decade. Commercial drones face challenges in terms of safety, durability, flight performance, and environmental effects such as the risk of collision and damage. Biomimetics, which is inspired by the sophisticated flying mechanisms in aerial animals, characterized by robustness and intelligence in aerodynamic performance, flight stability, and low environmental impact, may provide feasible solutions and innovativeness to drone design. In this paper, we review the recent advances in biomimetic approaches for drone development. The studies were extracted from several databases and we categorized the challenges by their purposes—namely, flight stability, flight efficiency, collision avoidance, damage mitigation, and grasping during flight. Furthermore, for each category, we summarized the achievements of current biomimetic systems and then identified their limitations. We also discuss future tasks on the research and development associated with biomimetic drones in terms of innovative design, flight control technologies, and biodiversity conservation. This paper can be used to explore new possibilities for developing biomimetic drones in industry and as a reference for necessary policy making.
Algebraical Entropy and Arrow of Time
Authors: Gogberashvili Merab
Published: October 2022

Usually, it is supposed that irreversibility of time appears only in macrophysics. Here, we attempt to introduce the microphysical arrow of time assuming that at a fundamental level nature could be non-associative. Obtaining numerical results of a measurement, which requires at least three ingredients: object, device and observer, in the non-associative case depends on ordering of operations and is ambiguous. We show that use of octonions as a fundamental algebra, in any measurement, leads to generation of unavoidable 18.6 bit relative entropy of the probability density functions of the active and passive transformations, which correspond to the groups G2 and SO(7), respectively. This algebraical entropy can be used to determine the arrow of time, analogically as thermodynamic entropy does.
Adressing Energy Demand and Climate Change through the Second Law of Thermodynamics and LCA towards a Rational Use of Energy in Brazilian Households
Authors: Pereira Marina Torelli Reis Martins , Carvalho Monica , Mady Carlos Eduardo Keutenedjian
Published: October 2022

This study focuses on a typical Brazilian household through the lens of sustainable development, regarding energy demand and GHG emissions. The analysis encompasses both the direct and indirect energy, exergy consumption, and GHG emissions (quantified by life cycle assessment) associated with the usual routine of a household. The household is modeled as a thermodynamic system to evaluate inputs (food, electricity, fuels for transportation) and outputs (solid and liquid residues). The hypothesis is that each input and output contains CO2,eq emissions and exergy derived from its physical-chemical characteristics or production chains. Each household appliance is modeled and tested as a function of external parameters. The contribution of several industries was obtained to the total GHG emissions and exergy flows entering and exiting the household (e.g., fuels for transportation, food, gas, electricity, wastewater treatment, solid waste). It was verified that urban transportation was the flow with the highest GHG and exergy intensity, ranging between 1.49 and 7.53 kgCO2,eq/day and achieving 94.7 MJ/day, almost five times higher than the calculated exergy demand due to electricity. The second largest flow in GHG emissions was food due to the characteristics of the production chains, ranging from 1.6 to 4.75 kgCO2,eq/day, depending on the adopted diet. On the other hand, the electricity presented low GHG emissions due to the main energy sources used to generate electricity, only 0.52 kgCO2,eq/day. Moreover, the chemical exergy of the solid waste was 9.7 MJ/day, and is not irrelevant compared to the other flows, representing an interesting improvement opportunity as it is entirely wasted in the baseline scenario.
A Spiking Neural Network Based on Retinal Ganglion Cells for Automatic Burn Image Segmentation
Authors: Liang Jiakai , Li Ruixue , Wang Chao , Zhang Rulin , Yue Keqiang , Li Wenjun , Li Yilin
Published: October 2022

Burn is a common traumatic disease. After severe burn injury, the human body will increase catabolism, and burn wounds lead to a large amount of body fluid loss, with a high mortality rate. Therefore, in the early treatment for burn patients, it is essential to calculate the patient’s water requirement based on the percentage of the burn wound area in the total body surface area (TBSA%). However, burn wounds are so complex that there is observer variability by the clinicians, making it challenging to locate the burn wounds accurately. Therefore, an objective, accurate location method of burn wounds is very necessary and meaningful. Convolutional neural networks (CNNs) provide feasible means for this requirement. However, although the CNNs continue to improve the accuracy in the semantic segmentation task, they are often limited by the computing resources of edge hardware. For this purpose, a lightweight burn wounds segmentation model is required. In our work, we constructed a burn image dataset and proposed a U-type spiking neural networks (SNNs) based on retinal ganglion cells (RGC) for segmenting burn and non-burn areas. Moreover, a module with cross-layer skip concatenation structure was introduced. Experimental results showed that the pixel accuracy of the proposed reached 92.89%, and our network parameter only needed 16.6 Mbytes. The results showed our model achieved remarkable accuracy while achieving edge hardware affinity.
Multi-Focus Image Fusion Based on Hessian Matrix Decomposition and Salient Difference Focus Detection
Authors: Li Xilai , Wang Xiaopan , Cheng Xiaoqi , Tan Haishu , Li Xiaosong
Published: October 2022

Multi-focus image fusion integrates images from multiple focus regions of the same scene in focus to produce a fully focused image. However, the accurate retention of the focused pixels to the fusion result remains a major challenge. This study proposes a multi-focus image fusion algorithm based on Hessian matrix decomposition and salient difference focus detection, which can effectively retain the sharp pixels in the focus region of a source image. First, the source image was decomposed using a Hessian matrix to obtain the feature map containing the structural information. A focus difference analysis scheme based on the improved sum of a modified Laplacian was designed to effectively determine the focusing information at the corresponding positions of the structural feature map and source image. In the process of the decision-map optimization, considering the variability of image size, an adaptive multiscale consistency verification algorithm was designed, which helped the final fused image to effectively retain the focusing information of the source image. Experimental results showed that our method performed better than some state-of-the-art methods in both subjective and quantitative evaluation.
Theoretical and Experimental Study on the Thermal Insulation Performance of the Roof with Water-Retained Bricks
Authors: Han Rubing , Xu Zhimao , Long Enshen
Published: October 2022

In this paper, the thermal insulation performance of the roof with water-retained bricks was first analyzed theoretically with respect to the thermal inertia, attenuation and delay time of the roof with water-retained bricks. Then, the experimental rig was established to carry out the experimental research on the thermal insulation performance of the roof with and without water-retained bricks on the sunny, overcast and rainy days in the summer and on the sunny day in the winter. The results showed that: (1) the surface heat storage coefficient is affected by the evaporating heat transfer of the water layer; (2) the thermal inertness, attenuation and delay time of the roof with water-retained bricks are 2.575, 21 and 6.94 h, respectively, when the water depth is 2 cm; (3) on the sunny, overcast and rainy days in the summer, laying water-retained bricks can enhance the heat insulation performance of the roof, and can improve the thermal comfort of the loft; and (4) on the sunny day in the winter, after laying water-retained bricks, the average temperature of the loft in 24 h increases by 2.3 °C, and the temperature fluctuation of the loft decreases by 56.0%. Therefore, the thermal insulation effect is significantly improved after laying water-retained bricks on the roof from the results of both the theoretical and experimental study.
Correction: Hayashi et al. More Tolerant Reconstructed Networks Using Self-Healing against Attacks in Saving Resource. Entropy 2021, 23, 102
Authors: Hayashi Yukio , Tanaka Atsushi , Matsukubo Jun
Published: October 2022

The authors wish to make the following correction to this paper [...]
Image Clustering Algorithm Based on Predefined Evenly-Distributed Class Centroids and Composite Cosine Distance
Authors: Zhu Qiuyu , Hu Liheng , Wang Rui
Published: October 2022

The clustering algorithms based on deep neural network perform clustering by obtaining the optimal feature representation. However, in the face of complex natural images, the cluster accuracy of existing clustering algorithms is still relatively low. This paper presents an image clustering algorithm based on predefined evenly-distributed class centroids (PEDCC) and composite cosine distance. Compared with the current popular auto-encoder structure, we design an encoder-only network structure with normalized latent features, and two effective loss functions in latent feature space by replacing the Euclidean distance with a composite cosine distance. We find that (1) contrastive learning plays a key role in the clustering algorithm and greatly improves the quality of learning latent features; (2) compared with the Euclidean distance, the composite cosine distance can be more suitable for the normalized latent features and PEDCC-based Maximum Mean Discrepancy (MMD) loss function; and (3) for complex natural images, a self-supervised pretrained model can be used to effectively improve clustering performance. Several experiments have been carried out on six common data sets, MNIST, Fashion-MNIST, COIL20, CIFAR-10, STL-10 and ImageNet-10. Experimental results show that our method achieves the best clustering effect compared with other latest clustering algorithms.
A Proposed Interpretation of the Wave–Particle Duality
Authors: Jung Kurt
Published: October 2022

Within the framework of quantum mechanics, the wave function squared describes the probability density of particles. In this article, another description of the wave function is given which embeds quantum mechanics into the traditional fields of physics, thus making new interpretations dispensable. The new concept is based on the idea that each microscopic particle with non-vanishing rest mass is accompanied by a matter wave, which is formed by adjusting the phases of the vacuum fluctuations in the vicinity of the vibrating particle. The vibrations of the particle and wave are phase-coupled. Particles move on continuous approximately classical trajectories. By the phase coupling mechanism, the particle transfers the information on its kinematics and thus also on the external potential to the wave. The space dependence of the escorting wave turns out to be equal to the wave function. The new concept fundamentally differs from the pilot wave concept of Bohmian mechanics.
Research on Joint Resource Allocation for Multibeam Satellite Based on Metaheuristic Algorithms
Authors: Gao Wei , Wang Lei , Qu Lianzheng
Published: October 2022

With the rapid growth of satellite communication demand and the continuous development of high-throughput satellite systems, the satellite resource allocation problem—also called the dynamic resources management (DRM) problem—has become increasingly complex in recent years. The use of metaheuristic algorithms to obtain acceptable optimal solutions has become a hot topic in research and has the potential to be explored further. In particular, the treatment of invalid solutions is the key to algorithm performance. At present, the unused bandwidth allocation (UBA) method is commonly used to address the bandwidth constraint in the DRM problem. However, this method reduces the algorithm’s flexibility in the solution space, diminishes the quality of the optimized solution, and increases the computational complexity. In this paper, we propose a bandwidth constraint handling approach based on the non-dominated beam coding (NDBC) method, which can eliminate the bandwidth overlap constraint in the algorithm’s population evolution and achieve complete bandwidth flexibility in order to increase the quality of the optimal solution while decreasing the computational complexity. We develop a generic application architecture for metaheuristic algorithms using the NDBC method and successfully apply it to four typical algorithms. The results indicate that NDBC can enhance the quality of the optimized solution by 9–33% while simultaneously reducing computational complexity by 9–21%.
Quantum Mechanics: Statistical Balance Prompts Caution in Assessing Conceptual Implications
Authors: Drummond Brian
Published: October 2022

Throughout quantum mechanics there is statistical balance, in the collective response of an ensemble of systems to differing measurement types. Statistical balance is a core feature of quantum mechanics, underlying quantum mechanical states, and not yet explained. The concept of “statistical balance” is here explored, comparing its meaning since 2019 with its original meaning in 2001. Statistical balance now refers to a feature of contexts in which: (a) there is a prescribed probability other than 0 or 1 for the collective response of an ensemble to one measurement type; and (b) the collective response of the same ensemble to another measurement type demonstrates that no well-defined value can be attributed, for the property relevant to the original measurement type, to individual members of the ensemble. In some unexplained way, the outcomes of single runs of a measurement of the original type “balance” each other to give an overall result in line with the prescribed probability. Unexplained statistical balance prompts caution in assessing the conceptual implications of entanglement, measurement, uncertainty, and two-slit and Bell-type analyses. Physicists have a responsibility to the wider population to be conceptually precise about quantum mechanics, and to make clear that many possible conceptual implications are uncertain.
Correction: Umer et al. High Order Split Operators for the Time-Dependent Wavepacket method of Triatomic Reactive Scattering in Hyperspherical Coordinates. Entropy 2019, 21, 979
Authors: Umer Umair , Zhao Hailin , Usman Syed Kazim , Sun Zhigang
Published: October 2022

The authors would like to update the affiliations for Umair Umer and Syed Kazim Usman in the published publication [...]
Statistical Degree Distribution Design for Using Fountain Codes to Control the Peak-To-Average Power Ratio in an OFDM System
Authors: Bi Cheng , Xiang Zheng , Ren Peng , Yin Ting , Zhang Yang
Published: October 2022

Utilizing fountain codes to control the peak-to-average power ratio (PAPR) is a classic scheme in Orthogonal Frequency Division Multiplexing (OFDM) wireless communication systems. However, because the robust soliton distribution (RSD) produces large-degree values, the decoding performance is severely reduced. In this paper, we design statistical degree distribution (SD) under a scenario that utilizes fountain codes to control the PAPR. The probability of the PAPR produced is combined with RSD to design PRSD, which enhances the smaller degree value produced. Subsequently, a particle swarm optimization (PSO) algorithm is used to search the optimal degree value between the binary exponential distribution (BED) and PRSD distribution according to the minimum average degree principle. Simulation results demonstrate that the proposed method outperforms other relevant degree distributions in the same controlled PAPR threshold, and the average degree value and decoding efficiency are remarkably improved.
Communicative Efficiency or Iconic Learning: Do Acquisition and Communicative Pressures Interact to Shape Colour- Naming Systems?
Authors: Gyevnar Balint , Dagan Gautier , Haley Coleman , Guo Shangmin , Mollica Frank
Published: October 2022

Language evolution is driven by pressures for simplicity and informativity; however, the timescale on which these pressures operate is debated. Over several generations, learners’ biases for simple and informative systems can guide language evolution. Over repeated instances of dyadic communication, the principle of least effort dictates that speakers should bias systems towards simplicity and listeners towards informativity, similarly guiding language evolution. At the same time, it has been argued that learners only provide a bias for simplicity and, thus, language users must provide a bias for informativity. To what extent do languages evolve during acquisition versus use? We address this question by formally defining and investigating the communicative efficiency of acquisition trajectories. We illustrate our approach using colour-naming systems, replicating a communicative efficiency model based on the information bottleneck problem, and an acquisition model based on self-organising maps. We find that to the extent that language is iconic, learning alone is sufficient to shape language evolution. Regarding colour-naming systems specifically, we find that incorporating learning biases into communicative efficiency accounts might explain how speakers and listeners trade off communicative effort.
Gender-, Age- and Educational Attainment Level-Specific Output–Employment Relationship and Its Dependence on Foreign Direct Investment
Authors: Dargenyte-Kacileviciene Laura , Butkus Mindaugas , Matuzeviciute Kristina
Published: October 2022

This paper analyses the gender-, age- and educational attainment level-specific output–employment relationship and its dependence on foreign direct investment (FDI). The unbalanced panel covers 25 European Union countries’ data from 2000 to 2020. Empirical estimations are made using the pooled OLS estimator. The impact of FDI on gender-, age- and educational attainment level-specific output–employment elasticities is estimated by including the multiplicative terms between gross domestic product (GDP) and FDI in regression models. The main results indicate the positive impact of economic growth on employment, with the highest output–employment elasticities for males and youth regardless of gender. The estimation results also indicate limited abilities of economic growth to increase the employment of highly educated people and females older than 25 years regardless of their educational attainment level. Our results suggest that higher FDI level in the host countries is mostly associated with the decreasing employment reaction to economic growth. Although FDI is an important factor affecting the output–employment relationship, it does not help to solve the problem of unemployment in the EU, especially for youth.
It Flows Both Ways: Relationships between Families and Educators during the COVID-19 Pandemic
Authors: Haines Shana Jackson , Strolin-Goltzman Jessica , Ura Sarah K. , Conforti Andrew , Manga Abijah
Published: October 2022

This is a critical moment of extraordinary rates of teacher burnout exacerbated by the COVID-19 pandemic. In order to implement effective intervention and policy, we must identify the drivers of educators’ stress and factors that may act as protective mechanisms. The complexity of this problem calls for a social–ecological approach. In this study, we investigated the impact of individual, organizational, and relational factors on educators’ pandemic-related stress. Using hierarchical multiple regression, we found that secondary traumatic stress and family, school, student, and community relationships significantly predicted pandemic-related stress after controlling for race, age, and gender. Family, school, and community collaboration is well-documented to benefit students; however, our results suggest that these relationships also benefit teachers, and may have been a protective factor on pandemic-related stress. That is, the positive influence of family, school, and community relationships flows both ways—to students and their families as well as to educators. Our findings highlight the importance of more research on secondary traumatic stress and home–school–community collaborations to mitigate teacher stress and burnout, as well as the implication for practice to increase emphasis on these issues in teacher preparation and ongoing professional development.
“A Lot Takes Place Digitally Now, So It Can Be Good to Train on It’’: A Large-Scale Repeated Cross-Sectional Study on Recording Live-Streamed Educational Activities among Health, Social, and Education Students
Authors: Almendingen Kari , Skotheim Torhild , Magnus Ellen Merethe
Published: October 2022

Audio recording is used in language and music education as an active learning resource to help students reflect on their performance and improve their accuracy. Recordings may be used to provide feedback on both verbal and nonverbal skills and to prepare professional candidates for talking to vulnerable users or other professionals. Despite its potential, recording among health, social, and education candidates to help them improve their digital relations skills is usually not part of pre-service training. Therefore, the objective was to assess the extent to which professional students were affected by recordings in live-stream settings and to explore their perspectives through qualitative elaboration. The design was a repeated cross-sectional mixed-methods study conducted in 2020–2021 (n = 1040 and n = 2238, response rate: 46% and 45%, respectively). Although participation was reduced, active online participation was low, regardless of recording practice. Educational background and age were determinants of active participation, regardless of the recording practice. Active students were the least affected by recording. Students wanted more recordings. Their perspectives revolved around different practices, privacy issues, and the recording used for traditional knowledge transfer instead of active learning. Although the General Data Protection Regulation must be met, we suggest that recording is an underused learning resource.