bluuebunny/crossref_metadata_2025 huggingface dataset:
Modified dindo-clavien system for registration of perioperative complications in children undergoing adenotonsillectomy
Authors: Arens Philipp , Hardt Juliane , Angrick Julie Charlotte , Olze Heidi , Coordes Annekatrin
Published: December 2022

Introduction

Surgical procedures in children are among the most commonly performed procedures in otolaryngology. Perioperative safety and documentation of complications are becoming increasingly important. This study investigates perioperative complications in a clinical cohort of children with adenotonsillar hyperplasia undergoing adenotonsillectomy using the standardized Dindo-Clavien reporting system.

Patients and Methods

Retrospective evaluation of 402 children who underwent adenotonsillectomy between 2009 and 2015. Patient parameters including all perioperative complications were investigated.

Results

In the study, 124 complications were found (106 mild, 16 severe). According to the Dindo-Clavien classification, 93 grade I, 15 grade II, 5 grade III, 11 grade IV and 0 grade V complications were documented. Complications were associated with additional diagnoses (p = 0.001), long-term medication intake (p = 0.003), duration of hospitalization (p < 0.001) and duration of surgery (p < 0.001), undergoing tonsillotomy (p = 0.022) or tonsillectomy (p < 0.001), differences in ASA score (p = 0.005) and differences in OSA-18 score (p = 0.011). Severe complications, classified as grade III and IV, were associated with premature birth (p = 0.026), additional diagnoses (p = 0.017), long-term medication intake (p < 0.001) and differences in ASA score (p =< 0.001).

Conclusion

The Dindo-Clavien classification is a standardized reporting system which can also be used for surgical procedures in children with adenotonsillar hyperplasia. The system shows associations with clinical parameters and thus can help to identify subgroups at risk of severe complications.
Short-term surgical outcomes in patients with unilateral complete cleft lip and palate after presurgical nasoalveolar molding therapy: A three-dimensional anthropometric study
Authors: Yin Jiayi , Zhang Shiming , Huang Ning , Shi Bing , Zheng Qian , Yang Chao
Published: December 2022

Objective

This brief research report aimed to evaluate the short-term efficacy of presurgical nasoalveolar molding (PNAM) therapy on the nasolabial morphology three dimensionally in patients with non-syndromic complete unilateral cleft lip and palate (UCLP).

Methods

Thirty-six patients with non-syndromic complete unilateral complete cleft lip and palate were enrolled retrospectively and categorized into 2 groups: 18 patients who had received PNAM treatment (PNAM group) and 18 age-matched patients who have not receive PNAM treatment (no PNAM group) from 2017 to 2021. The average starting age for PNAM therapy was 18.33 days, and the average PNAM treatment duration was 99.08 days. Twelve nasolabial parameters were measured to compare the postsurgical outcomes of two groups.

Results

In PNAM groups, cleft width, vertical distance between double Crista philtri and columellar deviation were reduced compared to that in no PNAM group. And nostril height was larger than that in no PNAM group. The differences between two groups were statistically significant (p < .05). There were no statistical differences in columellar length, nostril width and bi-alar width between two groups. However, the nostril width on cleft side in PNAM group was decreased by an average of 1.1 mm.

Conclusion

Our result indicated that PNAM therapy decreased cleft width and vertical distance between Crista philtri. It also increased nasal symmetry by decreasing columellar deviation, increasing nostril height.
How interaction molds semantics: The mood functions of Chinese “sum-up” adverbs
Authors: Song Jingyao
Published: December 2022

“Hezhe” (合着), “ganqing” (敢情), and “nao le bantian” (闹了半天) are common mood expressions in modern Chinese which have a common function of summarizing the information before (so called ‘sum-up’) as well as similar pragmatic functions. Study on these mood adverbs could reveal how the interactional mechanism molds the original semantic meanings of mood words and leads to new pragmatic functions. Five verbal corpora are applied to collect the real materials of usage containing the above three mood adverbs. Functional analysis and data statistics have been carried out to categorize the pragmatic functions of these words, calculate their distributions, and reconstruct their evolutional approach through an interactional perspective. We have found a core theoretical viewpoint that the similar functions of the three words emerged through the same pragmatic mechanism called “violation”. These functions are: (1) “unexpectation” from the violation of psychological expectation; (2) “criticism” from the violation of universal principles; (3) “humor” from the violation of communicative principles. A statistic work of several corpora showed that these functions of the above three words appear broadly in verbal materials, with differences in their proportions according to the communication types and genres. In Chinese teaching to speakers of other languages, more attention should be paid to these words stemming from dialects, especially during intermediate and advanced levels.
The Epic Game of Creating a Successful Gamified Co-Creation Strategy
Authors: Lopes João M. , Gomes Sofia , Santos Nélia , Cussina Hugo , Vieira Isabel , Escudeiro Maria , Maio Lissandra , Magalhães Yolanda
Published: December 2022

This paper aims to explore consumer engagement with online retailers through gamification, the use of game mechanics, and co-creation. A qualitative methodology was used through an exploratory case study approach. The interviews were conducted with 30 Portuguese consumers. It was found that the majority of respondents do not consider online shopping as a substitute for other entertainment activities. Respondents stressed that in online shopping, the important thing is personal satisfaction and their needs. According to the Hexad model, these characteristics fit the free spirit gamer, the Barttle model the explorer and the BrainHex model the seeker. As far as co-creation is concerned, the results indicate that most of the interviewees do not value the tools that exist on the retailers’ websites which allow them to create their own product. This study provides recommendations for retailers to implement a successful co-creation gamification strategy. Companies need to include gaming elements to enhance the retail experience and increase sales. In turn, this can increase competitive pressures among retailers by improving the services and products they offer. This paper provides empirical support for the current dearth of qualitative studies on the role of gamification and co-creation in the online retail experience. Many of the findings of this study are not in line with existing literature, allowing the conclusion that gamification alone is no longer enough to engage the online retail consumer with products and brands, requiring co-creation for the effective engagement of this consumer.
Smart Weather Data Management Based on Artificial Intelligence and Big Data Analytics for Precision Agriculture
Authors: Hachimi Chouaib El , Belaqziz Salwa , Khabba Saïd , Sebbar Badreddine , Dhiba Driss , Chehbouni Abdelghani
Published: December 2022

Smart management of weather data is an essential step toward implementing sustainability and precision in agriculture. It represents an important input for numerous tasks, such as crop growth, development, yield, and irrigation scheduling, to name a few. Advances in technology allow collecting this weather data from heterogeneous sources with high temporal resolution and at low cost. Generating and using these data in their raw form makes no sense, and therefore implementing adequate infrastructure and tools is necessary. For that purpose, this paper presents a smart weather data management system evaluated using data from a meteorological station installed in our study area covering the period from 2013 to 2020 at a half-hourly scale. The proposed system makes use of state-of-the-art statistical methods, machine learning, and deep learning models to derive actionable insights from these raw data. The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R2) and Root Mean Squared Error (RMSE) for this method were 0.96 and 0.04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET0) needed for efficient irrigation. To this, the platform uses the XGBoost model to achieve the precision of the Penman–Monteith method while using a limited number of meteorological variables (air temperature and global solar radiation). Results for this approach give R2 = 0.97 and RMSE = 0.07. This system represents the first incremental step toward implementing smart and sustainable agriculture in Morocco.
A Review of Botryosphaeria Stem Blight Disease of Blueberry from the Perspective of Plant Breeding
Authors: Ru Sushan , Ding Shunping , Oliver Jonathan E. , Amodu Ayodele
Published: December 2022

Stem blight of blueberry caused by fungal pathogens in the family Botryosphaeriaceae presents a major challenge to global blueberry production. Since its first documented outbreak in North Carolina, USA in the 1950s, Botryosphaeria stem blight has been reported in the blueberry production regions of more than nine countries across five continents. The lack of effective management strategies or resistant cultivars makes disease control especially challenging. With the goal of illuminating directions for future Botryosphaeria stem blight management, especially through resistant-cultivar development, this review summarizes the latest information on the distribution and causal pathogens of this disease, the pathogenicity of fungal species, disease resistance of blueberry cultivars, and currently recommended management practices. DNA sequencing techniques have revealed multiple fungal species that are associated with this disease. However, a lack of reliable methods to screen cultivars for stem blight resistance remains a major bottleneck for the development of resistant cultivars. Future studies should focus on at least four key areas: (1) the development and adoption of uniform and reliable screening protocols; (2) utilization of diverse and well-characterized Botryosphaeriaceae isolates for germplasm screens; (3) field evaluations of cultivar resistance and management practices; and (4) exploration of new tools for disease management and prevention.
Aggression in Group-Housed Male Mice: A Systematic Review
Authors: Weber Elin M. , Zidar Josefina , Ewaldsson Birgit , Askevik Kaisa , Udén Eva , Svensk Emma , Törnqvist Elin
Published: December 2022

Aggression among group-housed male mice is a major animal welfare concern often observed at animal facilities. Studies designed to understand the causes of male mice aggression have used different methodological approaches and have been heterogeneous, using different strains, environmental enrichments, housing conditions, group formations and durations. By conducting a systematic literature review based on 198 observed conclusions from 90 articles, we showed that the methodological approach used to study aggression was relevant for the outcome and suggested that home cage observations were better when studying home cage aggression than tests provoking aggression outside the home cage. The study further revealed that aggression is a complex problem; one solution will not be appropriate for all animal facilities and all research projects. Recommendations were provided on promising tools to minimize aggression, based on the results, which included what type of environmental enrichments could be appropriate and which strains of male mice were less likely to be aggressive.
Analysis of Ruggedness of 4H-SiC Power MOSFETs with Various Doping Parameters
Authors: Jang Min-Seok , Jeong Jee-Hun , Lee Ho-Jun
Published: December 2022

This work investigates the effect of the doping concentration of SiC power metal-oxide–semiconductor field-effect transistors (MOSFETs) under an unclamped inductive switching (UIS) condition. Switching circuits such as inverters and motor-drive circuits often face unexpected operating conditions; therefore, a UIS test is performed to assess the avalanche ruggedness of the device, and design parameters such as the doping concentration should be considered to improve the UIS characteristics. Technology computer-aided design circuit simulation results, such as the current flows during failure and electrical changes, were obtained by changing the doping concentration of each region in the SiC power MOSFET.
Effect of Ti-B Grain Refiners on Wear and Corrosion of the A332 Alloy with Sr Modification
Authors: Arendarchuck Bruno E. , Mayer Andre R. , de Oliveira Willian R. , Pukasiewicz Anderson G. M. , Lourençato Luciano A. , Fals Hipolito D. C. , Martínez-Cámara Eduardo
Published: December 2022

Grain refiners play a critical role in changing characteristics and properties of casting aluminum alloys. The Al-Si alloy (A332) is one of the most popular hypoeutectic alloys with a large range of industrial applications. It has a varied phase and morphology; however, it features problems with acicular-shaped eutectic phase, and generally exhibits dendritic cast grain type. To change this situation, the Sr element acts as a modifier of eutectic, which, along with a grain refiner may increase mechanical properties. In this work, two different grain refiners (Al5Ti1B, Al5Ti2B) were applied to the A332 alloy modified with Sr, and analyzed in relation to grain size, hardness, corrosion resistance, and wear behavior. Corrosion tests in 3.5 wt.% NaCl solution, nanoindentations, and Heyn’s method to analyze grain size and microhardness as optical and SEM images were made to examine the changes caused by grain refiners. A reduction in grain size was achieved, and the influence in size and hardness of the β-Fe phase was verified in the wear and corrosion analyses.
Glycemic Management by a Digital Therapeutic Platform across Racial/Ethnic Groups: A Retrospective Cohort Study
Authors: Gershoni Tamar , Ritholz Marilyn D. , Horwitz David L. , Manejwala Omar , Donaldson-Pitter Trisha , Fundoiano-Hershcovitz Yifat
Published: December 2022

Significant racial/ethnic disparities in healthcare and diabetes technology use have been observed in Type 2 diabetes mellitus (T2DM), which are associated with nonengagement in diabetes self-management and out-of-range glycemia. This study aimed to assess whether there were differences in the blood glucose levels achieved by several racial/ethnic groups using the same digital tool. Study objectives were to determine whether engagement with the digital tool and blood glucose levels differ among ethnic groups, and to determine whether any differences in the in-target-glycemia are related to engagement levels. The retrospective real-world analysis followed a group of 1000 people with Type 2 diabetes who used the DarioTM digital therapeutic platform over 12 months. Participants included in the study had a blood glucose average > 180 mg/dL (hyperglycemia, high-risk) in their first month. The differences between/within the groups’ average blood glucose level (Avg.bg) and glycemic variability were evaluated. Furthermore, three general linear models were constructed to predict the Avg.bg by the number of blood glucose measurements (Bgm) in Model 1 (with the moderator White persons (WP)/people from racial and ethnic minority groups (REM)) and by the frequency of measurements by months (F.m) within REM and WP in Model 2 and Model 3, respectively. The Avg.bg was significantly reduced in each group over a year with no differences between REM/WP users. Blood glucose measurements in Model 1 and frequency of measurements by months in Model 2 and Model 3 predicted the Avg.bg (β1 = −0.20, p = 0.045; β2 = −4.38, p = 0.009; β3= −3.77, p < 0.001, respectively). Findings indicate a positive association between digital engagement and glycemia, with no differences between REM and WP participants.
Multi-Target Regression Based on Multi-Layer Sparse Structure and Its Application in Warships Scheduled Maintenance Cost Prediction
Authors: He Dubo , Sun Shengxiang , Xie Li
Published: December 2022

The scheduled maintenance cost of warships is the essential prerequisite and economic foundation to guarantee the effective implementation of maintenance, which directly influences the quality and efficiency of maintenance operations. This paper proposes a multi-target regression algorithm based on multi-layer sparse structure (MTR-MLS) algorithm, to achieve simultaneous prediction of the subentry costs of warship scheduled maintenance, and the total cost of the maintenance is estimated by summing the predicted values of the different subentry costs. In MTR-MLS, the kernel technique is employed to map the inputs to the higher dimensional space for decoupling the complex input–output nonlinear relationships. By deploying the structure matrix, MTR-MLS achieves a latent variable model which can explicitly encode the inter-target correlations via l2,1-norm-based sparse learning. Meanwhile, the noises are encoded to diminish the influence of noises while exploiting the correlations among targets. An alternating optimization algorithm is proposed to solve the objective function. Extensive experimental evaluation on real-world datasets and datasets of warships scheduled maintenance cost show that the proposed method consistently outperforms the state-of-the-art algorithms, which demonstrates its great effectiveness for cost prediction of warships scheduled maintenance.
The Effect of Bayer Red Mud Blending on the Mechanical Properties of Alkali-Activated Slag-Red Mud and the Mechanism
Authors: Li Juntong , Li Qing , Chen Ping , Yao Kai , Wang Penghuai , Ming Yang , Yi Jin , Zhi Lili
Published: December 2022

In this paper, Bayer red mud and ground granulated blast furnace slag were formed into three different ratios (7:3, 5:5 and 3:7) of alkali-activated cementitious materials (AASR) using a water glass solution as the alkali activator. By studying the compressive strength of different red mud content, the effect of Bayer red mud content on the mechanical properties of AASR was analyzed. The influence mechanism of red mud content on the mechanical properties of AASR was revealed by the variation of micro-porosity, connected porosity, Isothermal calorimetry, XRD, FTIR, and BSE/EDS testing techniques. The results show that the compressive strength of the specimens at the same age decreases with the increase of red mud content. On one hand, the addition of red mud makes the connected porosity in the AASR system increase, and the densification of the slurry structure decreases; on the other hand, the inert minerals in red mud participate in the hydration reaction to a lesser extent, the overall hydration reaction process becomes lower, and the generation of hydration products providing strength decreases.
A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute
Authors: Saleem Muhammad Imran , Siddiqui Atif , Noor Shaheena , Luque-Nieto Miguel-Angel , Otero Pablo
Published: December 2022

Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. The system is modular and can be enhanced by adding data to support more languages in the future. A supervised machine learning dataset is defined and created that provides automated multi-language communication between the DnM and NDnM people. It is expected that this system will support DnM people in communicating effectively with others and restoring a feeling of normalcy in their daily lives. The hand gesture detection accuracy of the system is more than 90% for most, while for certain scenarios, this is between 80% and 90% due to variations in hand gestures between DnM people. The system is validated and evaluated using a series of experiments.
A Self-Supervised Detail-Sensitive ViT-Based Model for COVID-19 X-ray Image Diagnosis: SDViT
Authors: An Kang , Zhang Yanping
Published: December 2022

COVID-19 has led to a severe impact on the society and healthcare system, with early diagnosis and effective treatment becoming critical. The Chest X-ray (CXR) is the most time-saving and cost-effective tool for diagnosing COVID-19. However, manual diagnosis through human eyes is time-consuming and tends to introduce errors. With the challenge of a large number of infections and a shortage of medical resources, a fast and accurate diagnosis technique is required. Manual detection is time-consuming, depends on individual experience, and tends to easily introduce errors. Deep learning methods can be used to develop automated detection and computer-aided diagnosis. However, they require a large amount of data, which is not practical due to the limited annotated CXR images. In this research, SDViT, an approach based on transformers, is proposed for COVID-19 diagnosis through image classification. We propose three innovations, namely, self-supervised learning, detail correction path (DCP), and domain transfer, then add them to the ViT Transformer architecture. Based on experimental results, our proposed method achieves an accuracy of 95.2381%, which is better performance compared to well-established methods on the X-ray Image dataset, along with the highest precision (0.952310), recall (0.963964), and F1-score (0.958102). Extensive experiments show that our model achieves the best performance on the synthetic-covid-cxr dataset as well. The experimental results demonstrate the advantages of our design for the classification task of COVID-19 X-ray images.
3D-Printed Materials Dentistry
Authors: Becker Kathrin
Published: December 2022

This editorial focuses on the Special Issue on 3D-printed materials in dentistry [...]
Special Issue: Smart Resilient Manufacturing
Authors: Lu Jinzhi , Zheng Xiaochen , Kiritsis Dimitris
Published: December 2022

During the past decades, the global manufacturing industries have been reshaped by the rapid development of advanced technologies, such as cyber-physical systems, Internet of Things, artificial intelligence (AI), machine learning, cloud/edge computing, smart sensing, advanced robotics, blockchain/distributed ledger technology, etc [...]
Robust Facial Expression Recognition Using an Evolutionary Algorithm with a Deep Learning Model
Authors: Arul Vinayakam Rajasimman Mayuri , Manoharan Ranjith Kumar , Subramani Neelakandan , Aridoss Manimaran , Galety Mohammad Gouse
Published: December 2022

The most important component that can express a person’s mental condition is facial expressions. A human can communicate around 55% of information non-verbally and the remaining 45% audibly. Automatic facial expression recognition (FER) has now become a challenging task in the surveying of computers. Applications of FER include understanding the behavior of humans and monitoring moods and psychological states. It even penetrates other domains—namely, robotics, criminology, smart healthcare systems, entertainment, security systems, holographic images, stress detection, and education. This study introduces a novel Robust Facial Expression Recognition using an Evolutionary Algorithm with Deep Learning (RFER-EADL) model. RFER-EADL aims to determine various kinds of emotions using computer vision and DL models. Primarily, RFER-EADL performs histogram equalization to normalize the intensity and contrast levels of the images of identical persons and expressions. Next, the deep convolutional neural network-based densely connected network (DenseNet-169) model is exploited with the chimp optimization algorithm (COA) as a hyperparameter-tuning approach. Finally, teaching and learning-based optimization (TLBO) with a long short-term memory (LSTM) model is employed for expression recognition and classification. The designs of COA and TLBO algorithms aided in the optimal parameter selection of the DenseNet and LSTM models, respectively. A brief simulation analysis of the benchmark dataset portrays the greater performance of the RFER-EADL model compared to other approaches.
A Proposal for Decentralized and Secured Data Collection from Unmanned Aerial Vehicles in Livestock Monitoring with Blockchain and IPFS
Authors: Ortega Julio César Úbeda , Rodríguez-Molina Jesús , Martínez-Núñez Margarita , Garbajosa Juan
Published: December 2022

Livestock monitoring often requires human supervision to guide farm animals to a specific point and the displacement of workers to the places where these animals are, which is likely to be several kilometers away, thus resulting in a repetitive task that requires a significant amount of time and demands the usage of land vehicles capable of moving swiftly through the countryside. In addition to that, data collection about animal behaviour with such procedures is often insufficient and cannot be shared in a secure enough manner. This paper describes how Using Unmanned Aerial Vehicles (UAVs) tailored for this kind of task, when combined with other protocols and software technologies, can provide a useful to mitigate these issues. To prove this end, a functional prototype has been designed, built and tested, offering the operator accurate monitoring of farm facilities and animals. Additionally, security has been conceived as a cornerstone of the presented system from the very beginning. Not only the communication protocols used for this purpose have built-in security layers, but also InterPlanetary File System (IPFS) and blockchain have been used as the technologies that enhance data storage among peers in a network.
Surgical Complications of Orthognathic Surgery
Authors: Joachim Michael V. , Brosh Yair , Rivera Camron M. , Troulis Maria J. , AbdelRaziq Murad , Abu El-Naaj Imad
Published: December 2022

Objectives: Orthognathic surgery is popular and provides patients with improved aesthetics and functionality. The procedure is considered safe and predictable. Possible complications do exist and can be life-threatening. The aim of this study is to assess the prevalence of intra- and post-operative complications, and to define possible correlations between diagnoses. Study Design: Medical records of 103 patients who underwent orthognathic surgery in a 4.5-year period (2013–2017), at the Baruch Padeh “Tzafon” Medical Center in Poriya (PMC), Israel, were retrospectively collected. The data were analyzed for descriptive statistics and non-parametrical tests. Results: In total, there were 56 complications in 45 patients (43.7% probability of complication per patient). Major complication occurred in only one case (1%), moderate complications appeared in 8.7% of the cases, while the most prevalent minor complications were temporary nerve injury and hardware issues (17.5% and 15.5%, respectively). Statistical analysis revealed that bi-max surgery has significantly higher (OR 1.34, CI 99% 1.05–1.69, p = 0.019) prevalence of complications than one-jaw surgery, as did skeletal class II patients (OR 2.75, CI 99% 2.25–3.35, p = 0.022), as compared to skeletal class III patients. Conclusions: Serious complications seem to be rare in orthognathic surgery at PMC.
Comparison of Dual-Combustion Ramjet and Scramjet Performances Considering Combustion Efficiency
Authors: Wu Xianju , Wei Zhijun
Published: December 2022

The performances of a dual-combustion ramjet (DCR) and a scramjet were compared via computational fluid dynamics numerical simulation to provide theoretical guidance for engine selection for a hypersonic vehicle. Kerosene, C12H23, with an equivalence ratio of 0.8, was employed as the fuel, and the reactive flow was modeled using six-species and four-step chemistry. The results show that the DCR has a central combustion mode, which has a smaller temperature gradient and more uniform heat release, resulting in higher combustion efficiency, compared to the near-wall combustion mode of the scramjet. The total pressure recovery coefficient of scramjet is 0.9% lower than that of DCR under the Ma6 condition, but 5.6% higher than that of DCR under the Ma7 condition. The combustion efficiency of DCR is 35.6% and 25.4% higher than that of the scramjet under Ma6 and Ma7 conditions, respectively. The decrease in the combustion efficiency of the DCR is caused by the increase in the dissociation rate of CO2 into CO with the increase in temperature. The performance of DCR is better than that of scramjet under both conditions. However, the performance advantage of DCR decreases as the Mach number increases. Specifically, under the conditions of Ma6 and Ma7, the specific impulse or specific thrust of DCR was 2.67 times and 1.51 times that of scramjet, respectively.
The Pathology of the First Permanent Molar during the Mixed Dentition Stage—Review
Authors: Stoica Stephanie Nicole , Nimigean Victor , Vîrlan Maria Justina Roxana , Nimigean Vanda Roxana
Published: December 2022

Background: The eruption of the first permanent molar (FPM) marks the beginning of the mixed dentition, which is one of the most dynamic periods in the development and growth of the stomatognathic system. Aim: The purpose of the study was to investigate the pathology of the first permanent molar (FPM) during the transition stage between the primary and permanent dentition. Materials and Methods: A literature search was performed in Clarivate Web of Science, Google Scholar, and PubMed databases. Cohort and cross-sectional studies, reviews, case reports, background information, and expert opinion were included. Results: Ectopic eruption is one of the most frequent pathologies during the mixed dentition period, and ectopic eruption of the maxillary FPM is the most encountered. Hypomineralization is currently the most prevalent pathology of the hard dental tissue in FPM, often in association with the same type of damage on the incisors in the context of the so-called molar incisor hypomineralization (MIH). FPM particular vulnerability to caries is due to its posteruptive enamel maturation, which is accomplished during the mixed dentition stage. Conclusions: Proper clinical management of FPM pathology is a demanding task for the pediatric dentist and is of great importance for the development of a healthy permanent dentition and stable occlusion.
Special Issue “Advances in Aerial, Space, and Underwater Robotics”
Authors: Cocuzza Silvio , Doria Alberto , Allotta Benedetto
Published: December 2022

Free-base robotic systems are extensively used in underwater, air, and space environments [...]
Assessment and Retrofitting of Building Structures: Experimental Testing and Modelling—Editorial
Authors: Furtado André
Published: December 2022

Collapse of, or severe damage to, existing buildings during strong earthquakes has resulted in significant economic losses, severe injuries, and casualties. Progress made over the last few decades has had a considerable impact on the seismic safety of modern buildings designed according to new standards. However, the majority of existing buildings in southern European countries do not meet the safety requirements set by the Eurocodes. Thus, the assessment of existing buildings not designed with modern codes and the development of effective retrofitting techniques are currently of paramount importance to society. The use of accurate modelling strategies and appropriate seismic assessment methodologies is crucial to understand the behaviour of existing buildings and to develop efficient and proper mitigation measures, thus, preventing future damage, casualties, and economic losses. The effect of non-structural elements should not be neglected, since they could play a vital role in buildings’ structural performance. Another major challenge is to ensure the sustainability of renovation schemes in terms of both the environmental burden (i.e., CO2) and economic investment in seismic regions. The sustainable renovation of existing buildings typically focuses on reducing operational energy consumption and using low-carbon materials in the refurbishment process, without accounting for structural deficiencies that could leave the building exceptionally unsafe and hamper the refurbishment investment, particularly in areas prone to seismic activity. This Special Issue focuses on innovations in the context of assessment and retrofitting of building structures: experimental testing and modelling.
Synthesis of Hydrophobic Poly(γ-Glutamic Acid) Derivatives by Enzymatic Grafting of Partially 2-Deoxygenated Amyloses
Authors: Anai Tomoya , Abe Shogo , Shobu Kousei , Kadokawa Jun-ichi
Published: December 2022

We have previously found that a partially 2-deoxygenated (P2D)-amylose, produced by glucan phosphorylase (GP)-catalyzed enzymatic copolymerization, shows hydrophobic nature. Based on this finding, the present study demonstrates hydrophobization of a strong hydrophilic polypeptide, i.e., poly(γ-glutamic acid) (PGA), by grafting of the P2D-amylose chains via GP-catalyzed enzymatic approach. After maltooligosaccharide primers for the enzymatic reaction were modified on the PGA chain, we performed GP-catalyzed copolymerization of d-glucan with α-d-glucose 1-phosphate as comonomers in different feed ratios from the primers to produce P2D-amylose-grafted PGAs. We analyzed the structures (chemical and crystalline) of the products, precipitated from reaction mixtures, by 1H NMR and powder X-ray diffraction measurements, respectively. The values of the water contact angle of the cast films, prepared from DMSO solutions of the products with different 2-deoxyglucose/glucose unit ratios, were greater than 100°, indicating efficient hydrophobization of the hydrophilic polypeptide by the present approach.
Detection of Fungal Infections on the Wood Surface Using LTM Imaging
Authors: Sioma Andrzej , Lenty Bartosz
Published: December 2022

Wood used in production processes can be infected by various fungi growing on its surface. The presence of fungi on the wood surface results from the method of storage, handling and transport of the wood. However, the presence of fungi on wood carries a high risk to the health of production operators and users. At the same time, it has a negative impact on the quality and durability of manufactured products. Because of the risks indicated, an attempt was made to develop an industrial, automated system for detecting fungal infections. This paper presents a vision method for detecting fungal infections on the wood surface. A description of the vision system using the laser triangulation method (LTM) to build a three-dimensional surface image is shown. The paper consists of an analysis of the imaging resolution and a description of the concept of using laser illuminator power selection for identifying fungal-infested surfaces. Imaging results for the selected wavelength of electromagnetic radiation are presented. Measurements and parameters describing the identified areas are shown. It was found that it is possible to choose imaging method parameters and laser illumination power allowing identification under industrial conditions of a fungus-infected region on a wood surface while using the image to determine product measurement parameters.
Convolution-Transformer Adaptive Fusion Network for Hyperspectral Image Classification
Authors: Li Jiaju , Xing Hanfa , Ao Zurui , Wang Hefeng , Liu Wenkai , Zhang Anbing
Published: December 2022

Hyperspectral image (HSI) classification is an important but challenging topic in the field of remote sensing and earth observation. By coupling the advantages of convolutional neural network (CNN) and Transformer model, the CNN–Transformer hybrid model can extract local and global features simultaneously and has achieved outstanding performance in HSI classification. However, most of the existing CNN–Transformer hybrid models use artificially specified hybrid strategies, which have poor generalization ability and are difficult to meet the requirements of recognizing fine-grained objects in HSI of complex scenes. To overcome this problem, we proposed a convolution–Transformer adaptive fusion network (CTAFNet) for pixel-wise HSI classification. A local–global fusion feature extraction unit, called the convolution–Transformer adaptive fusion kernel, was designed and integrated into the CTAFNet. The kernel captures the local high-frequency features using a convolution module and extracts the global and sequential low-frequency information using a Transformer module. We developed an adaptive feature fusion strategy to fuse the local high-frequency and global low-frequency features to obtain a robust and discriminative representation of the HSI data. An encoder–decoder structure was adopted in the CTAFNet to improve the flow of fused local–global information between different stages, thus ensuring the generalization ability of the model. Experimental results conducted on three large-scale and challenging HSI datasets demonstrate that the proposed network is superior to nine state-of-the-art approaches. We highlighted the effectiveness of adaptive CNN–Transformer hybrid strategy in HSI classification.
Effects on Respiratory Pressures, Spirometry Biomarkers, and Sports Performance after Inspiratory Muscle Training in a Physically Active Population by Powerbreath®: A Systematic Review and Meta-Analysis
Authors: Fernández-Lázaro Diego , Corchete Luis A. , García Juan F. , Jerves Donoso David , Lantarón-Caeiro Eva , Cobreros Mielgo Raúl , Mielgo-Ayuso Juan , Gallego-Gallego David , Seco-Calvo Jesús
Published: December 2022

Sports performance in athletes can be limited by respiratory factors, so it is understandable to propose that inspiratory muscle training (IMT) can improve respiratory function and exercise performance. Power-Breathe® (PwB) is a sectorized respiratory muscle training tool that uses a resistive load to train IMT. There is currently a growing interest in respiratory muscle training, so we set out to systematically assess the effects of IMT with PwB on respiratory parameters and athletic performance in physically active, healthy adults. Based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guideline, the Cochrane and PEDro scales to assess methodological quality, effect size using the Rosenthal formula, and the Cochrane tool for estimation of risk of bias, studies searchable in Medline, Web of Science, and Cochrane. In addition, for the performance of the meta-analysis, the documentation and quantification of the heterogeneity in each meta-analysis were directed through the Cochran’s Q test and the I2 statistic; in addition, a publication bias analysis was performed using funnel plots. Of the total of 241 studies identified in the search, 11 studies for the systematic review and nine for the meta-analysis met the exclusion and/or inclusion criteria. IMT, with PwB, showed significant improvements in maximal inspiratory pressure (MIP) and substantial improvements in forced vital capacity (FVC) in the meta-analysis results. Also, sports performance was significantly increased by IMT with PwB. In conclusion, the use of PwB is an IMT tool that improves respiratory and sports performance.
Successional Development of the Phototrophic Community in Biological Soil Crusts on Coastal and Inland Dunes
Authors: Kammann Sandra , Schiefelbein Ulf , Dolnik Christian , Mikhailyuk Tatiana , Demchenko Eduard , Karsten Ulf , Glaser Karin
Published: December 2022

(1) Biological soil crusts (biocrusts) are microecosystems consisting of prokaryotic and eukaryotic microorganisms growing on the topsoil. This study aims to characterize changes in the community structure of biocrust phototrophic organisms along a dune chronosequence in the Baltic Sea compared to an inland dune in northern Germany. (2) A vegetation survey followed by species determination and sediment analyses were conducted. (3) The results highlight a varying phototrophic community composition within the biocrusts regarding the different successional stages of the dunes. At both study sites, a shift from algae-dominated to lichen- and moss-dominated biocrusts in later successional dune types was observed. The algae community of both study sites shared 50% of the identified species while the moss and lichen community shared less than 15%. This indicates a more generalized occurrence of the algal taxa along both chronosequences. The mosses and lichens showed a habitat-specific species community. Moreover, an increase in the organic matter and moisture content with advanced biocrust development was detected. The enrichment of carbon, nitrogen, and phosphorus in the different biocrust types showed a similar relationship. (4) This relation can be explained by biomass growth and potential nutrient mobilization by the microorganisms. Hence, the observed biocrust development potentially enhanced soil formation and contributed to nutrient accumulation.
Elevated Plasma Immunoglobulin Levels Prior to Heart Transplantation Are Associated with Poor Post-Transplantation Survival
Authors: van den Hoogen Patricia , Huibers Manon M. H. , van den Dolder Floor W. , de Weger Roel , Siera-de Koning Erica , Oerlemans Marish I. F. , de Jonge Nicolaas , van Laake Linda W. , Doevendans Pieter A. , Sluijter Joost. P. G. , Vink Aryan , de Jager Saskia C. A.
Published: December 2022

Cardiac allograft vasculopathy (CAV) and antibody-mediated rejection are immune-mediated, long-term complications that jeopardize graft survival after heart transplantation (HTx). Interestingly, increased plasma levels of immunoglobulins have been found in end-stage heart failure (HF) patients prior to HTx. In this study, we aimed to determine whether increased circulating immunoglobulin levels prior to transplantation are associated with poor post-HTx survival. Pre-and post-HTx plasma samples of 36 cardiac transplant recipient patients were used to determine circulating immunoglobulin levels. In addition, epicardial tissue was collected to determine immunoglobulin deposition in cardiac tissue and assess signs and severity of graft rejection. High levels of IgG1 and IgG2 prior to HTx were associated with a shorter survival post-HTx. Immunoglobulin deposition in cardiac tissue was significantly elevated in patients with a survival of less than 3 years. Patients with high plasma IgG levels pre-HTx also had significantly higher plasma levels after HTx. Furthermore, high pre-HTX levels of IgG1 and IgG2 levels were also significantly increased in patients with inflammatory infiltrate in CAV lesions. Altogether the results of this proof-of-concept study suggest that an activated immune response prior to transplantation negatively affects graft survival.
Carrying Temoporfin with Human Serum Albumin: A New Perspective for Photodynamic Application in Head and Neck Cancer
Authors: Mattioli Edoardo Jun , Ulfo Luca , Marconi Alessia , Pellicioni Valentina , Costantini Paolo Emidio , Marforio Tainah Dorina , Di Giosia Matteo , Danielli Alberto , Fimognari Carmela , Turrini Eleonora , Calvaresi Matteo
Published: December 2022

Temoporfin (mTHPC) is approved in Europe for the photodynamic treatment of head and neck squamous cell carcinoma (HNSCC). Although it has a promising profile, its lipophilic character hampers the full exploitation of its potential due to high tendency of aggregation and a reduced ROS generation that compromise photodynamic therapy (PDT) efficacy. Moreover, for its clinical administration, mTHPC requires the presence of ethanol and propylene glycol as solvents, often causing adverse effects in the site of injection. In this paper we explored the efficiency of a new mTHPC formulation that uses human serum albumin (HSA) to disperse the photosensitizer in solution (mTHPC@HSA), investigating its anticancer potential in two HNSCC cell lines. Through a comprehensive characterization, we demonstrated that mTHPC@HSA is stable in physiological environment, does not aggregate, and is extremely efficient in PDT performance, due to its high singlet oxygen generation and the high dispersion as monomolecular form in HSA. This is supported by the computational identification of the specific binding pocket of mTHPC in HSA. Moreover, mTHPC@HSA-PDT induces cytotoxicity in both HNSCC cell lines, increasing intracellular ROS generation and the number of γ-H2AX foci, a cellular event involved in the global response to cellular stress. Taken together these results highlight the promising phototoxic profile of the complex, prompting further studies to assess its clinical potential.
An Efficient Lightweight Hybrid Model with Attention Mechanism for Enhancer Sequence Recognition
Authors: Aladhadh Suliman , Almatroodi Saleh A. , Habib Shabana , Alabdulatif Abdulatif , Khattak Saeed Ullah , Islam Muhammad
Published: December 2022

Enhancers are sequences with short motifs that exhibit high positional variability and free scattering properties. Identification of these noncoding DNA fragments and their strength are extremely important because they play a key role in controlling gene regulation on a cellular basis. The identification of enhancers is more complex than that of other factors in the genome because they are freely scattered, and their location varies widely. In recent years, bioinformatics tools have enabled significant improvement in identifying this biological difficulty. Cell line-specific screening is not possible using these existing computational methods based solely on DNA sequences. DNA segment chromatin accessibility may provide useful information about its potential function in regulation, thereby identifying regulatory elements based on its chromatin accessibility. In chromatin, the entanglement structure allows positions far apart in the sequence to encounter each other, regardless of their proximity to the gene to be acted upon. Thus, identifying enhancers and assessing their strength is difficult and time-consuming. The goal of our work was to overcome these limitations by presenting a convolutional neural network (CNN) with attention-gated recurrent units (AttGRU) based on Deep Learning. It used a CNN and one-hot coding to build models, primarily to identify enhancers and secondarily to classify their strength. To test the performance of the proposed model, parallels were drawn between enhancer-CNNAttGRU and existing state-of-the-art methods to enable comparisons. The proposed model performed the best for predicting stage one and stage two enhancer sequences, as well as their strengths, in a cross-species analysis, achieving best accuracy values of 87.39% and 84.46%, respectively. Overall, the results showed that the proposed model provided comparable results to state-of-the-art models, highlighting its usefulness.