bluuebunny/crossref_metadata_2025 huggingface dataset:
Bone Cut Accuracy in Total Knee Arthroplasty: Do Conventional Cutting Guides Stay True to the Planned Coronal Orientation of the Components?
Authors: Leal-Blanquet Joan , Hinarejos Pedro , Gimenez-Valero Elisenda , Torres-Claramunt Raul , Sánchez-Soler Juan , Erquicia Juan , Gil-González Sergi , Zumel-Marne Angela , Monllau Juan Carlos
Published: March 2023

Background: Total knee arthroplasty (TKA) has become the gold standard for the definitive treatment of knee osteoarthritis. One crucial aspect in the implantation of a TKA is the precise orientation of the femoral and tibial components. The main purpose of the present study is to assess, in patients undergoing total knee replacement, whether the difference between the planned angulation in the distal femoral cut and the angulation obtained in the postoperative radiological control is low enough to consider the conventional bone-cutting guides reliable. Methods: A retrospective study was designed with a consecutive series of patients who had undergone primary total knee arthroplasty using conventional instrumentation over one year. The authors analysed the main variable (bone cuts) while considering different variables (age, gender, surgeon, prosthesis, laterality, constraint, body mass index and alignment) to identify different patient patterns that justify the results in the main variable. Descriptive variables were analysed using the Mann–Whitney U and Kruskal–Wallis tests. Additionally, the correlation between continuous variables was explored in accordance with the Spearman correlation. Results: A total of 340 patients with a mean age of 75 ± 9.16 years were finally included in the present study. The mean absolute error of the main variable for the femoral coronal bone cut was 1.89° (SD 1.53). For the tibial coronal bone cut, it was 1.31° (SD 2.54). These values correspond to what remains after subtracting the radiological angulation obtained in the postoperative period from the planned intraoperative angulation of the distal femoral cut. No associations were observed between the main variable (the angulation of the proximal tibial cut and distal femoral cut) and the rest of variables for either the femur or tibia. Conclusion: A discrepancy between the planned angulation and the final radiological measure on the coronal plane of the femur and tibia using conventional cutting guides has been demonstrated. The degree of deviation is low enough that it probably does not affect clinical outcomes. Therefore, the use of conventional cutting guides will continue to be an appropriate tool to perform bone cuts in knee replacement surgery.
An Optimization Strategy to Position CHIEF Points in Boundary-Element Acoustic Problems
Authors: Gonçalves Kleber de Almeida , Santurio Daniela Silva , Soares Delfim , Costa Pedro Alves , Godinho Luís
Published: March 2023

The use of boundary elements in the analysis of exterior acoustic problems poses challenges at specific frequencies, since fictitious eigenfrequencies may arise at the internal resonances of cavities, leading to inaccurate results or even unstable behavior. To filter out these fictitious eigenfrequencies, a scheme based on the combined Helmholtz integral equation formulation (CHIEF) can be used to prevent the so-called non-uniqueness problem, although it requires additional equations and points. The BEM formulation final accuracy will, however, depend on the correct choice of these points. Here, a strategy to help in defining good approximations for the position and number of such points is proposed, based on an optimization process which maximizes the system matrix’s smallest singular value. The accuracy of the method for exterior radiation problems is investigated using different examples. With low computational cost and simple implementation, the two proposed algorithms automatically circumvent the non-uniqueness problem, aiding the implementation of more stable BEM codes.
Effective Class-Imbalance Learning Based on SMOTE and Convolutional Neural Networks
Authors: Joloudari Javad Hassannataj , Marefat Abdolreza , Nematollahi Mohammad Ali , Oyelere Solomon Sunday , Hussain Sadiq
Published: March 2023

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models from achieving satisfactory results. ID is the occurrence of a situation where the quantity of the samples belonging to one class outnumbers that of the other by a wide margin, making such models’ learning process biased towards the majority class. In recent years, to address this issue, several solutions have been put forward, which opt for either synthetically generating new data for the minority class or reducing the number of majority classes to balance the data. Hence, in this paper, we investigate the effectiveness of methods based on Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs) mixed with a variety of well-known imbalanced data solutions meaning oversampling and undersampling. Then, we propose a CNN-based model in combination with SMOTE to effectively handle imbalanced data. To evaluate our methods, we have used KEEL, breast cancer, and Z-Alizadeh Sani datasets. In order to achieve reliable results, we conducted our experiments 100 times with randomly shuffled data distributions. The classification results demonstrate that the mixed Synthetic Minority Oversampling Technique (SMOTE)-Normalization-CNN outperforms different methodologies achieving 99.08% accuracy on the 24 imbalanced datasets. Therefore, the proposed mixed model can be applied to imbalanced binary classification problems on other real datasets.
Towards Delay Tolerant Networking for Connectivity Aware Routing Protocol for VANET-WSN Communications
Authors: Mohaisen Linda , Joiner Laurie
Published: March 2023

Vehicular Ad Hoc Networks (VANETs) are increasingly playing a fundamental role in improving driving safety. However, VANETs in a sparse environment may add risk to driving safety. The probability of a low density of vehicles in a rural area at midnight is very high. Consequently, the packet will be lost due to the lack of other vehicles, and the arrival of the following vehicles in the accident area is unavoidable. To overcome this problem, VANET is integrated with Wireless Sensor Network (WSN). The most challenging features of VANETs are their high mobility. This high mobility causes sensor nodes to consume most of their energy during communication with other nodes, leading to frequent network disconnectivity. With the evolution of VANET and WSN, the Store/Carry-Forward (SCF) paradigm has emerged as an exciting research area in the Delay Tolerant Networks (DTNs) to solve network disconnectivity. This paper proposes the Energy-Mobility-Connectivity aware routing protocol (EMCR) for a hybrid network of VANET-WSN. A comprehensive performance analysis that considers realistic propagation models and real city scenario traffic is performed in NS3. The simulation results show that the SCF mechanism is essential in the EMCR protocol to maximize the delivery ratio and minimize energy consumption and overhead.
Effect of Segregation on Deformation Behaviour of Nanoscale CoCrCuFeNi High-Entropy Alloy
Authors: Kazakov Arseny M. , Yakhin Azat V. , Karimov Elvir Z. , Babicheva Rita I. , Kistanov Andrey A. , Korznikova Elena A.
Published: March 2023

A molecular dynamics (MD) simulation method is used to investigate the effect of grain boundary (GB) segregation on the deformation behavior of bicrystals of equiatomic nanoscale CoCrCuFeNi high-entropy alloy (HEA). The deformation mechanisms during shear and tensile deformation at 300 K and 100 K are analyzed. It is revealed that upon tensile deformation, the stacking fault formation, and twinning are the main deformation mechanisms, while for the shear deformation, the main contribution to the plastic flow is realized through the GB migration. The presence of the segregation at GBs leads to the stabilization of GBs, while during the shear deformation of the nanoscale CoCrCuFeNi HEA without the segregation at GBs, GBs are subject to migration. It is found that the GB segregation can differently influence the plasticity of the nanoscale CoCrCuFeNi HEA, depending on the elemental composition of the segregation layer. In the case of copper and nickel segregations, an increase in the segregation layer size enhances the plasticity of the nanoscale CoCrCuFeNi HEA. However, an increase in the thickness of chromium segregations deteriorates the plasticity while enhancing maximum shear stress. The results obtained in this study shed light on the development of HEAs with enhanced mechanical properties via GB engineering.
Sensitivity Improvement of an Optical Fiber Sensor Based on Surface Plasmon Resonance with Pure Higher-Order Modes
Authors: Yang Chuanhao , Yan Bing , Wang Qi , Zhao Jing , Zhang Hongxia , Yu Hui , Fan Haojun , Jia Dagong
Published: March 2023

In this paper, we propose an approach to improve the sensitivity of an optical fiber surface plasmon resonance (SPR) sensor with a pure higher-order mode excited by a designed mode selective coupler (MSC). We calculate the proportion of the power of the higher-order mode in the cladding. Compared to the LP01 mode, the power proportion of the LP11 mode (LP21 mode) in the cladding theoretically improves by 100% (150%). To generate a relatively pure LP11 mode or LP21 mode, a mode selective coupler (MSC, 430–580 nm) is designed. The coupling efficiency of the LP01–LP11 mode coupler is over 80%, and that of the LP01–LP21 mode coupler is over 50%. The simulation results show that the sensitivity of the LP11  mode and the LP21 mode increases by approximately 330% and 360%, respectively, using the intensity modulation (n = 1.33–1.38, 430–580 nm); the resolution of the refractive indices of our sensor, using the LP11 mode (LP21 mode), is 2.6×10−4 RIU (2.4×10−4 RIU). The higher sensitivity and resolution of our presented fiber SPR sensor containing a visible MSC make it a promising candidate for the measurement of refractive indices.
Selection of Potential Regions for the Creation of Intelligent Transportation Systems Based on the Machine Learning Algorithm Random Forest
Authors: Shinkevich Aleksey I. , Malysheva Tatyana V. , Ershova Irina G.
Published: March 2023

The planning and management of traffic flow networks with multiple input data sources for decision-making generate the need for a mathematical approach. The program of measures for the development of the transport infrastructure of the Russian Federation provides for the selection of pilot regions for the creation of intelligent transportation systems. With extensive knowledge of theoretical and applied mathematics, it is important to select and adapt mathematical methods for solving problems. In this regard, the aim of the study is to develop and validate an algorithm for solving the problem of classifying objects according to the potential of creating intelligent transportation systems. The main mathematical apparatus for classification is the «random forest» machine learning algorithm method. A bagging machine learning meta-algorithm for high accuracy of the algorithm was used. This paper proposes the author’s method of sequential classification analysis for identifying objects with the potential to create intelligent transportation systems. The choice of using this method is justified by its best behavior under the large number of predictor variables required for an objective aggregate assessment of digital development and quality of territories. The proposed algorithm on the example of Russian regions was tested. A technique and algorithm for statistical data processing based on descriptive analytics tools have been developed. The quality of the classification analysis algorithm was assessed by the random forest method based on misclassification coefficients. The admissibility of retrained algorithms and formation of a «fine-grained» «random forest» model for solving classification problems under the condition of no prediction was proven to be successful. The most productive models with the highest probability of correct classification were «reached» and «finalized» on the basis of logistic regression analysis of relationships between predictors and categorical dependent variables. The regions of class 1 with «high potential for the creation of intelligent transportation systems» are most likely to be ready for the reorganization of infrastructure facilities; the introduction of digital technologies in the management of traffic flows was found.
Influencing Factors and Prewarning of Unsafe Status of Construction Workers Based on BP Neural Network
Authors: Liu Ningning , Xie Danfeng , Wang Changlong , Bai Yun
Published: March 2023

Scholars have paid considerable attention to the factors that affect the safety states of construction workers. However, only a few studies have focused on the safety assessment and security alerts of individual workers. In this study, the term ‘frequency statistics’ refers to the factors considered by domestic and foreign experts and scholars. The statistical results were combined with the interpretation of these factors to determine 22 factors that negatively influence the safety status of construction workers, which were used as the research object. The initial weight of the research results was integrated into the BackPropagation neural network, using the improved analytic hierarchy process to establish an early warning model for the unsafe status of construction workers. The mean squared error meets the requirements of the model and the prediction accuracy meets the requirements of the sample. The model can effectively provide an early warning and correct the initial weighting of the results. The early warning model was then applied to a project that involved the construction of a primary school in Suzhou. The follow-up results show that the safety status of the workers significantly improved. These results show that the early warning model was successfully used in the safety assessment to provide security alerts to individual workers. The data obtained by comprehensively considering both workers and experts are universal, unlike those obtained by considering only one of these two groups. Among the indicators, safety awareness, protection measures, and team cohesion most strongly negatively affected the safety statuses of the construction workers. The results of the early warning model combined with the sensitivity analysis are targeted and applicable in the practice of safety monitoring.
Modelling the Wind Speed Using Exponentiated Weibull Distribution: Case Study of Poprad-Tatry, Slovakia
Authors: Pobočíková Ivana , Michalková Mária , Sedliačková Zuzana , Jurášová Daniela
Published: March 2023

In the paper, we statistically analysed data on the average hourly wind speed obtained from the meteorological station Poprad (located at the Poprad-Tatry airport, the Prešov region, Northern Slovakia) for the period 2005–2021. High altitude and rough mountainous terrain influence the weather conditions considerably and are a source of occasional weather risks. Finding an appropriate wind speed distribution for modelling the wind speed data is therefore important to determine the wind profile at this particular location. In addition to the commonly used two- and three-parameter Weibull distribution, a more flexible exponentiated Weibull (EW) distribution was applied to model the wind speed. Based on the results of the goodness-of-fit criteria (the Kolmogorov–Smirnov test, the Anderson–Darling test, Akaike’s and Bayesian information criteria, the root mean square error, and the coefficient of determination), the EW distribution obtained a significantly better fit to seasonal and monthly wind speed data, especially around the peaks of the data. The EW distribution also proved to be a good model for data with high positive skewness. Therefore, we can recommend the EW distribution as a flexible distribution for modelling a dataset with extremely strong winds or outliers in the direction of the right tail. Alongside the wind speed analysis, we also provided the wind direction analysis, finding out that the most prevailing direction was west (W)—with an occurrence rate of 34.99%, and a mean wind speed of 3.91 m/s, whereas the northern (N) direction featured the lowest occurrence rate of only 4.45% and the mean wind speed of 1.99 m/s.
Insight into the Gluten-Free Dough and Bread Properties Obtained from Extruded Rice Flour: Physicochemical, Mechanical, and Molecular Studies
Authors: Różańska Maria Barbara , Kokolus Patrycja , Królak Jakub , Jankowska Patrycja , Osoś Agata , Romanowska Magda , Szala Łukasz , Kowalczewski Przemysław Łukasz , Lewandowicz Jacek , Masewicz Łukasz , Baranowska Hanna Maria , Mildner-Szkudlarz Sylwia
Published: March 2023

The present study aimed to evaluate the effect of the extrusion process and particle size on the properties of rice flour (microstructure, pasting properties), gluten-free dough (rheological properties), and bread (texture, specific volume, water absorption capacity, low-field nuclear magnetic resonance (LF NMR) relaxometry). Rice flours were extruded at 80 and 120 °C with feed moisture (15 and 30%) and with the same particle size (<132 and >132–200 µm). Significant differences were observed between the pasting profiles of the flours before and after extrusion. The pasting profile of extruded flours confirmed that hydrothermal treatment partially gelatinized the starch, decreasing the viscosity during heating. The water binding properties increased with the extrusion temperature and moisture content and also with the particle size of the flour. The most important parameter influencing the mechanical properties of the dough was the moisture content of the flour and significant differences were observed between fine (<132 μm) and coarse flours (>132–200 μm). The molecular dynamics of particles containing protons in the bound and bulk fractions in each sample do not depend on the extruder parameters or granulation of the obtained fraction. LF NMR results confirmed that extrusion of rice flour led to a significant decrease in the T21 value compared to the control sample and an increase in the T22 value in breads made with flours with particle size <132 μm. A linear relationship was found between the spin-spin relaxation times (T1) changes and the equilibrium water activity (ar). The results showed that bread with extruded rice flour at the same die temperature resulted in a significantly higher bread volume (31%) and lower hardness (27%) compared to the control. The highest hardness was observed in the case of samples prepared with extruded flour with the addition of 15% moisture, regardless of temperature and particle size.
Optimized Unconventional Geometric Gates in Superconducting Circuits
Authors: Liu Yueheng , Zhang Xinding
Published: March 2023

Nonadiabatic Abelian geometric quantum computation has been extensively studied, due to its fast manipulation and inherent noise resistance. However, to obtain the pure geometric phase, the quantum state is required to evolve along some special paths to eliminate the dynamical phase. This leads to increasing evolution time and weakened gate robustness. The unconventional geometric quantum computation is an effective way to solve the above problems. Here, we propose a general approach to realize the unconventional geometric computation. Then, we discuss the effect of the ratio of geometric phase to dynamic phase on the performance of quantum gates. The results show that the selection of ratio corresponds to different quantum gate robustness. Therefore, we can optimize the ratio to get higher-fidelity quantum gates. At last, we construct the ratio-optimized quantum gates in a superconducting circuit and test its robustness. The fidelities of the T-gate, Hadamard H-gate, and controlled phase gate can be obtained as 99.98%, 99.95%, and 99.85%, respectively. Therefore, our scheme provides a promising way to realize large-scale fault-tolerant quantum computation in superconducting circuits.
Accuracy of the Sentence-BERT Semantic Search System for a Japanese Database of Closed Medical Malpractice Claims
Authors: Fujishiro Naofumi , Otaki Yasuhiro , Kawachi Shoji
Published: March 2023

In this study, we developed a similar text retrieval system using Sentence-BERT (SBERT) for our database of closed medical malpractice claims and investigated its retrieval accuracy. We assigned each case in the database a short Japanese summary of the accident as well as two labels: the category was classified as a hospital department mainly, and the process indicated a failed medical procedure. We evaluated the accuracies of a similar text retrieval system with the two labels using three different multilabel evaluation metrics. For the encoders of SBERT, we employed two pretrained BERT models, UTH-BERT and NICT-BERT, that were trained on huge Japanese corpora, and we performed iterative optimization to train the SBERTs. The accuracies of the similar text retrieval systems using the trained SBERTs were more than 15 points higher than those of the Okapi BM25 system and the pretrained SBERT system.
Combining Digital Image Correlation and Acoustic Emission to Characterize the Flexural Behavior of Flax Biocomposites
Authors: Habibi Mohamed , Laperrière Luc
Published: March 2023

Understanding the effect of staking sequences and identifying the damage occurring within a structure using a structural health monitoring system are the keys to an efficient design of composite-based parts. In this research, a combination of digital image correlation (DIC) and acoustic emission (AE) is used to locate and classify the type of damage depending on the stacking sequence of the laminate during flexural loading. As a first step, the results of the strain fields for unidirectional, cross-ply, and quasi-isotropic laminates were compared to discuss their global behavior and to correlate the different damage patterns with the possible failure mechanisms. The damage was then addressed using a comprehensive interpretation of the acoustic emission signatures and the K-means classification of the acoustic events. The development of each damage mechanism was correlated to the applied load and expressed as a function of the loading rate to highlight the effect of the stacking sequence. Finally, the results of DIC and AE were combined to improve the reliability of the damage investigation without limiting the failure mechanism to matrix cracking, interfacial failure, and fiber breakage, as expected by the unsupervised event clustering.
Fitting Power Spectrum of Scalar Perturbations for Primordial Black Hole Production during Inflation
Authors: Frolovsky Daniel , Ketov Sergei V.
Published: March 2023

A simple phenomenological fit for the power spectrum of scalar (curvature) perturbations during inflation is proposed to analytically describe slow roll of inflaton and formation of primordial black holes (PBH) in the early universe, in the framework of single-field models. The fit is given by a sum of the power spectrum of slow-roll inflation, needed for a viable description of the cosmic microwave background (CMB) radiation in agreement with Planck/BICEP/Keck measurements, and the log-normal (Gaussian) fit for the power spectrum enhancement (peak) needed for efficient PBH production, in the leading (model-independent) approximation. The T-type α-attractor models are used to get the simple CMB power spectrum depending upon the e-folds as the running variable. The location and height of the peak are chosen to yield the PBH masses in the asteroid-size window allowed for the whole (current) dark matter. We find the restrictions on the peak width.
Interpreting Infinite Numbers
Authors: Cuadras Carles M.
Published: March 2023

The mathematical concept of infinity, in the sense of Cantor, is rather far from applied mathematics and statistics. These fields can be linked. We comment on the properties of infinite numbers and relate them to some operations with random variables. The existence of statistical parametric models can be studied in terms of cardinal numbers. Some probabilistic interpretations of Gödel’s theorem, Turing’s halting problem, and the Banach-Tarski paradox are commented upon, as well as the axiom of choice and the continuum hypothesis. We use a basic but sufficient mathematical level.
Efficient Reallocation of BESS in Monopolar DC Networks for Annual Operating Costs Minimization: A Combinatorial-Convex Approach
Authors: Grisales-Noreña Luis Fernando , Montoya Oscar Danilo , Hernández Jesús C.
Published: March 2023

This article deals with the solution of a mixed-integer nonlinear programming (MINLP) problem related to the efficient reallocation of battery energy storage systems (BESS) in monopolar direct current (DC) grids through a master–slave optimization approach. The master stage solves the integer nature of the MINLP model, which is related to the nodes where the BESS will be located. In this stage, the discrete version of the vortex search algorithm is implemented. To determine the objective function value, a recursive convex approximation is implemented to solve the nonlinear component of the MINLP model (multi-period optimal power flow problem) in the slave stage. Two objective functions are considered performance indicators regarding the efficient reallocation of BESS in monopolar DC systems. The first objective function corresponds to the expected costs of the annual energy losses, and the second is associated with the annual expected energy generation costs. Numerical results for the DC version of the IEEE 33 bus grid confirm the effectiveness and robustness of the proposed master–slave optimization approach in comparison with the solution of the exact MINLP model in the General Algebraic Modeling System (GAMS) software. The proposed master–slave optimizer was programmed in the MATLAB software. The recursive convex solution of the multi-period optimal power flow problem was implemented in the convex discipline tool (CVX) with the SDPT3 and SEDUMI solvers. The numerical reductions achieved with respect to the benchmark case in terms of energy loss costs and energy purchasing costs were 7.2091% and 3.2105%, which surpassed the results reached by the GAMS software, with reductions of about 6.0316% and 1.5736%.
Image Translation of Breast Ultrasound to Pseudo Anatomical Display by CycleGAN
Authors: Barkat Lilach , Freiman Moti , Azhari Haim
Published: March 2023

Ultrasound imaging is cost effective, radiation-free, portable, and implemented routinely in clinical procedures. Nonetheless, image quality is characterized by a granulated appearance, a poor SNR, and speckle noise. Specific for breast tumors, the margins are commonly blurred and indistinct. Thus, there is a need for improving ultrasound image quality. We hypothesize that this can be achieved by translation into a more realistic display which mimics a pseudo anatomical cut through the tissue, using a cycle generative adversarial network (CycleGAN). In order to train CycleGAN for this translation, two datasets were used, “Breast Ultrasound Images” (BUSI) and a set of optical images of poultry breast tissues. The generated pseudo anatomical images provide improved visual discrimination of the lesions through clearer border definition and pronounced contrast. In order to evaluate the preservation of the anatomical features, the lesions in both datasets were segmented and compared. This comparison yielded median dice scores of 0.91 and 0.70; median center errors of 0.58% and 3.27%; and median area errors of 0.40% and 4.34% for the benign and malignancies, respectively. In conclusion, generated pseudo anatomical images provide a more intuitive display, enhance tissue anatomy, and preserve tumor geometry; and can potentially improve diagnoses and clinical outcomes.
Label-Free Saliva Test for Rapid Detection of Coronavirus Using Nanosensor-Enabled SERS
Authors: Ganesh Swarna , Dhinakaran Ashok Kumar , Premnath Priyatha , Venkatakrishnan Krishnan , Tan Bo
Published: March 2023

The recent COVID-19 pandemic has highlighted the inadequacies of existing diagnostic techniques and the need for rapid and accurate diagnostic systems. Although molecular tests such as RT-PCR are the gold standard, they cannot be employed as point-of-care testing systems. Hence, a rapid, noninvasive diagnostic technique such as Surface-enhanced Raman scattering (SERS) is a promising analytical technique for rapid molecular or viral diagnosis. Here, we have designed a SERS- based test to rapidly diagnose SARS-CoV-2 from saliva. Physical methods synthesized the nanostructured sensor. It significantly increased the detection specificity and sensitivity by ~ten copies/mL of viral RNA (~femtomolar concentration of nucleic acids). Our technique combines the multiplexing capability of SERS with the sensitivity of novel nanostructures to detect whole virus particles and infection-associated antibodies. We have demonstrated the feasibility of the test with saliva samples from individuals who tested positive for SARS-CoV-2 with a specificity of 95%. The SERS—based test provides a promising breakthrough in detecting potential mutations that may come up with time while also preparing the world to deal with other pandemics in the future with rapid response and very accurate results.
Assessment of the Genetic Diversity and Population Structure of Rhizophora mucronata along Coastal Areas in Thailand
Authors: Naktang Chaiwat , Khanbo Supaporn , Yundaeng Chutintorn , U-thoomporn Sonicha , Kongkachana Wasitthee , Jiumjamrassil Darunee , Maknual Chatree , Wanthongchai Poonsri , Tangphatsornruang Sithichoke , Pootakham Wirulda
Published: March 2023

Unique and biodiverse, mangrove ecosystems provide humans with benefits and contribute to coastal protection. Rhizophora mucronata, a member of the Rhizophoraceae family, is prevalent in the mangrove forests of Thailand. R. mucronata’s population structure and genetic diversity have received scant attention. Here, we sequenced the entire genome of R. mucronata using 10× Genomics technology and obtained an assembly size of 219 Mb with the N50 length of 542,540 bases. Using 2857 single nucleotide polymorphism (SNP) markers, this study investigated the genetic diversity and population structure of 80 R. mucronata accessions obtained from the mangrove forests in Thailand. The genetic diversity of R. mucronata was moderate (I = 0.573, Ho = 0.619, He = 0.391). Two subpopulations were observed and confirmed from both population structure and principal component analysis (PCA). Analysis of molecular variance (AMOVA) showed that there was more variation within populations than between them. Mean pairwise genetic differentiation (FST = 0.09) showed that there was not much genetic difference between populations. Intriguingly, the predominant clustering pattern in the R. mucronata population did not correspond to the Gulf of Thailand and the Andaman Sea, which are separated by the Malay Peninsula. Several factors could have influenced the R. mucronata genetic pattern, such as hybridization and anthropogenic factors. This research will provide important information for the future conservation and management of R. mucronata in Thailand.
Surgical Management of Pterygium Colli with Significant Skin Laxity: A Case Report
Authors: Huttin Charline , Ringenbach Patrick , Durry Anastasia , Hogas Mihai , Popescu Ionut Raducu , Ciobica Alin , Nastasa Mihaela Elena
Published: March 2023

Pterygium Colli or “palmate neck” is a congenital malformation that is most often part of a polimalformative syndrome. This deformity is a source of aesthetic and social embarrassment. Its correction is surgical. We present the case of a pterygium colli in a patient with Noonan syndrome. He had a significant excess of skin with posterior skin laxity, causing significant social discomfort and imposing a vicious attitude, the head bent forward. We performed a posterolateral resection of this excess by resecting two posterior triangular flaps with a resulting t-shaped scar. The results were satisfactory; the excess skin was almost completely resorbed with minimal scarring. However, this technique did not correct the low lateral hairline implantation, and there were still two lateral flaps for which the patient did not wish to have a repeat surgery.
A Spring Compensation Method for a Low-Cost Biped Robot Based on Whole Body Control
Authors: Wang Zhen , Kou Lei , Ke Wende , Chen Yuhan , Bai Yan , Li Qingfeng , Lu Dongxin
Published: March 2023

At present, the research and application of biped robots is more and more popular. The popularity of biped robots can be better promoted by improving the motion performance of low-cost biped robots. In this paper, the method of the Linear Quadratic Regulator (LQR) is used to track a robot’s center of mass (COM). At the same time, the whole-body-control method and value function generated in the process of tracking COM are used to construct the quadratic programming (QP) model of a biped robot. Through the above method, the torque feedforward of the robot is obtained in the Drake simulation platform. The torque feedforward information of the robot is transformed into position feedforward information by spring compensation. In this paper, open loop control and spring compensation are used, respectively, to make the robot perform simple actions. Generally, after the compensation method of spring compensation is adopted, the roll angle and pitch angle of the upper body of the robot are closer to 0 after the robot performs an action. However, as the selected motion can introduce more forward and lateral motions, the robot needs more spring clearance compensation to improve performance. For improving the motion performance of a low-cost biped robot, the experimental results show that the spring compensation method is both reasonable and effective.
Recent Tissue Engineering Approaches to Mimicking the Extracellular Matrix Structure for Skin Regeneration
Authors: Hama Rikako , Reinhardt James W. , Ulziibayar Anudari , Watanabe Tatsuya , Kelly John , Shinoka Toshiharu
Published: March 2023

Inducing tissue regeneration in many skin defects, such as large traumatic wounds, burns, other physicochemical wounds, bedsores, and chronic diabetic ulcers, has become an important clinical issue in recent years. Cultured cell sheets and scaffolds containing growth factors are already in use but have yet to restore normal skin tissue structure and function. Many tissue engineering materials that focus on the regeneration process of living tissues have been developed for the more versatile and rapid initiation of treatment. Since the discovery that cells recognize the chemical–physical properties of their surrounding environment, there has been a great deal of work on mimicking the composition of the extracellular matrix (ECM) and its three-dimensional network structure. Approaches have used ECM constituent proteins as well as morphological processing methods, such as fiber sheets, sponges, and meshes. This review summarizes material design strategies in tissue engineering fields, ranging from the morphology of existing dressings and ECM structures to cellular-level microstructure mimicry, and explores directions for future approaches to precision skin tissue regeneration.
Fluorescent Nanocomposite Hydrogels Based on Conjugated Polymer Nanoparticles as Platforms for Alkaline Phosphatase Detection
Authors: Alacid Yolanda , Esquembre Rocío , Montilla Francisco , Martínez-Tomé María José , Mateo C. Reyes
Published: March 2023

This work describes the development and characterization of fluorescent nanocomposite hydrogels, with high swelling and absorption capacity, and prepared using a green protocol. These fluorescent materials are obtained by incorporating, for the first time, polyfluorenes-based nanoparticles with different emission bands—poly[9,9-dioctylfluorenyl-2,7-diyl] (PFO) and poly[(9,9-di-n-octylfluorenyl-2,7-diyl)-alt-(1,4-benzo-{2,1,3}-thiadiazole)] (F8BT)—into a three-dimensional polymeric network based on polyacrylamide. To this end, two strategies were explored: incorporation of the nanoparticles during the polymerization process (in situ) and embedment after the hydrogel formation (ex situ). The results show that the combination of PFO nanoparticles introduced by the ex situ method provided materials with good storage stability, homogeneity and reproducibility properties, allowing their preservation in the form of xerogel. The fluorescent nanocomposite hydrogels have been tested as a transportable and user-friendly sensing platform. In particular, the ability of these materials to specifically detect the enzyme alkaline phosphatase (ALP) has been evaluated as a proof-of-concept. The sensor was able to quantify the presence of the enzyme in an aqueous sample with a response time of 10 min and LOD of 21 nM. Given these results, we consider that this device shows great potential for quantifying physiological ALP levels as well as enzyme activity in environmental samples.
Highly Stretchable and Robust Electrochemical Sensor Based on 3D Graphene Oxide–CNT Composite for Detecting Ammonium in Sweat
Authors: Hua Yunzhi , Guan Mingxiang , Xia Linzhong , Chen Yu , Mai Junhao , Zhao Cong , Liao Changrui
Published: March 2023

Wearable electrochemical sensors have attracted tremendous attention and have been experiencing rapid growth in recent years. Sweat, one of the most suitable biological fluids for non-invasive monitoring, contains various chemical elements relating abundant information about human health conditions. In this work, a new type of non-invasive and highly stretchable potentiometric sweat sensor was developed based on all-solid-state ion-selective electrode (ISE) coupled with poly(dimethylsiloxane; PDMS) and polyurethane (PU). This highly stretchable composite of PDMS-PU allows the sensor to be robust, with the PDMS providing a flexible backbone and the PU enhancing the adhesion between the electrodes and the substrate. In addition, graphene–carbon nanotube (CNT) network 3D nanomaterials were introduced to modify the ion selective membrane (ISM) in order to increase the charge transfer activity of the ISEs, which also could minimize the formation of water layers on the electrode surface, as such nanomaterials are highly hydrophobic. As a result, the sensor demonstrated a wide detection range of NH4+ from 10−6 M to 10−1 M with high stability and sensitivity—showing a high sensitivity of 59.6 ± 1.5 mV/log [NH4+] and an LOD lower than 10−6 M. Under a strain of 40%, the sensor still showed a sensitivity of 42.7 ± 3.1 mV/log [NH4+]. The proposed highly stretchable and robust electrochemical sweat sensor provides a new choice for wearable-device-based personal daily healthcare management beyond hospital-centric healthcare monitoring.
Battling Unawareness of One’s Test Performance: Do Practice, Self-Efficacy, and Emotional Intelligence Matter?
Authors: Pilotti Maura A. E. , El Alaoui Khadija , Waked Arifi N.
Published: March 2023

The “Dunning–Kruger effect” refers to the tendency of poor performers to overestimate test outcomes. Although a widespread phenomenon, questions exist regarding its source and sensitivity to countermeasures. The present field study aimed to (a) examine whether practice with tests used in previous classes can enhance students’ ability to estimate test outcomes, (b) determine the main source of the effect (i.e., is it unawareness of one’s readiness or wishful thinking?), and (c) assess the extent to which particular individual differences can be used as predictors of test performance. In this study, participants practiced with old tests and then completed the final exam. Before and after the exam, they predicted their grades and indicated their subjective confidence in the predictions made. Furthermore, participants’ emotional intelligence and self-efficacy about their academic abilities were surveyed. Results suggested that poor performers were not unaware of their test preparation, but rather engaged in wishful thinking. In fact, although they overestimated their test grades, their estimates not only improved after completing the final test but also were regarded with little confidence. Overall, estimation bias was a good predictor of students’ final test performance, whereas subjective confidence and emotional intelligence only weakly predicted such performance. Thus, if proactive interventions are to be developed for at-risk students, performance-estimation tasks may offer valuable information regarding such students’ future performance in a course much more than emotional intelligence and self-efficacy measures.
Many Facets of Eating Disorders: Profiling Key Psychological Features of Anorexia Nervosa and Binge Eating Disorder
Authors: Rossi Alessandro Alberto , Pietrabissa Giada , Tagliagambe Andrea , Scuderi Anna , Montecchiani Lorenzo , Castelnuovo Gianluca , Mannarini Stefania , Dalla Ragione Laura
Published: March 2023

Objective. The present study employs a profile analysis to identify and compare psychological features and core eating disorder (ED) symptoms in clinical samples of patients with anorexia nervosa (AN) and binge eating disorder (BED) and the general population (GP). Methods. A sample comprising 421 participants (142 patients with AN; 139 patients with BED; and 140 participants from the GP) was surveyed with the Eating Disorder Inventory-3 (EDI-3). Individuals with AN and BED were recruited and tested during their first week of a multidisciplinary inpatient program for weight loss and rehabilitation at the ‘Rete DCA USL Umbria 1′ (Eating Disorders Services), Italy. Results. The findings suggest distinct patterns of symptom presentation between the three samples across all the EDI-3 dimensions—with both the AN and BED groups scoring significantly higher than the GP. Patients with AN registered greater scores in all the psychological trait scales and the drive for thinness ED-specific dimension of the EDI-3 compared with their BED counterpart—which, instead, scored higher in the bulimia and body dissatisfaction subscales. These data support the transdiagnostic nature of the main risk factors for the onset and maintenance of EDs—which would vary in severity levels—and the existence of disease-specific pathways giving rise to AN and BED. Conclusion. This study for the first time compares patients with AN and BED with a non-clinical sample on main ED psychological features. This might inform classification approaches and could have important implications for the development of prevention and early intervention programs.
Topology Optimization of Stiffened Steel Plate Shear Wall Based on the Bidirectional Progressive Structural Optimization Method
Authors: He Jianian , Li Xuhao , Chen Shizhe , Xian Huasheng
Published: March 2023

Many studies on structural topology optimization of steel plate shear walls have been conducted. However, research on topology optimization using the bidirectional evolutionary structural optimization method is limited. Accordingly, this study optimized the topology of the stiffening effect of steel plate shear walls (SPSWs) based on this method. A finite element model of the SPSW was established using Abaqus software through the “sandwich” modeling method. An optimization region was expanded into two optimization regions. As the optimization targets, SPSWs with different aspect ratios were selected. Elastoplastic optimization of a single-layer SPSW was performed through the horizontal displacement cyclic loading, and the distribution law of the stiffening effect was obtained. The stiffeners on the SPSW were arranged according to the SPSW-A075 scheme. Monotonic and reciprocating loading simulation tests were performed on the unstiffened SPSW and common transverse and longitudinal stiffeners to analyze their mechanical properties. The results show that the optimized layout of the stiffened SPSW demonstrated better seismic performance and energy dissipation capacity. The buckling bearing capacity increased by 2.17–2.61 times, and the stiffness and initial stiffness improved significantly.
Measuring Comprehensive Production Efficiency of the Chinese Construction Industry: A Bootstrap-DEA-Malmquist Approach
Authors: Yue Aobo , Yin Xupeng
Published: March 2023

Production efficiency is a critical research topic in the field of construction economics and management. It reflects the developmental potential and competitiveness of the economy or an economic system. An objective and reasonable assessment framework of the production efficiency in the construction industry is essential to promote the industry’s high-quality development. This study aims to propose a scientific and holistic framework to examine the production efficiency in the construction industry and to investigate evolution patterns from a macroeconomic perspective. Input and output indicators were identified through the value-added and the fuzzy Delphi methods. In addition, the production efficiency in the construction industry was examined via the bootstrap-DEA and Malmquist exponential decomposition models. A case study in China was conducted at the end of this research. The panel data of 31 provinces from 2010 to 2020 were applied in the case study. The results reveal the following: (1) The bootstrap-DEA model results show that the trends of production efficiency before and after rectification are similar, but the difference is largest at the peak. Moreover, the production efficiency value after correction is evidently lower than that which is obtained by the traditional DEA model. (2) The Malmquist index decomposition results show that the change trend of technical efficiency in the construction industry is contrary to that of the scale efficiency. In addition, the improvement of scale efficiency cannot bring a melioration of management efficiency or the accumulation of production experience. (3) There is no direct correlation between production efficiency and economic development. High-value areas and median areas are contiguous, and they are mainly distributed in the central and eastern provinces. The findings accurately reflect construction industry productivity, providing practical data for developing policy recommendations for bridging regional construction development gaps.
Frequency of Peripheral CD8+ T Cells Expressing Chemo-Attractant Receptors CCR1, 4 and 5 Increases in NPC Patients with EBV Clearance upon Radiotherapy
Authors: Mahajan Shweta , Balcioglu Hayri E. , Oostvogels Astrid , Dik Willem A. , Chan K. C. Allen , Lo Kwok-Wai , Hui Edwin P. , Tsang Anna , Tong Joanna , Lam Wai Kei Jacky , Wong Kenneth , Chan Anthony T. C. , Ma Brigette B. Y. , Debets Reno
Published: March 2023

Radiotherapy (RT) is the standard-of-care for Epstein–Barr virus (EBV)-associated nasopharyngeal carcinoma (NPC), where the post-RT clearance of plasma EBV DNA is prognostic. Currently, it is not known whether the post-RT clearance of plasma EBV DNA is related to the presence of circulating T-cell subsets. Blood samples from NPC patients were used to assess the frequency of T-cell subsets relating to differentiation, co-signaling and chemotaxis. Patients with undetectable versus detectable plasma EBV DNA levels post-RT were categorized as clearers vs. non-clearers. Clearers had a lower frequency of PD1+CD8+ T cells as well as CXCR3+CD8+ T cells during RT compared to non-clearers. Clearers exclusively showed a temporal increase in chemo-attractant receptors CCR1, 4 and/or 5, expressing CD8+ T cells upon RT. The increase in CCR-expressing CD8+ T cells was accompanied by a drop in naïve CD8+ T cells and an increase in OX40+CD8+ T cells. Upon stratifying these patients based on clinical outcome, the dynamics of CCR-expressing CD8+ T cells were in concordance with the non-recurrence of NPC. In a second cohort, non-recurrence associated with higher quantities of circulating CCL14 and CCL15. Collectively, our findings relate plasma EBV DNA clearance post-RT to T-cell chemotaxis, which requires validation in larger cohorts.