bluuebunny/crossref_metadata_2025 huggingface dataset:
What Are the Implications of COVID-19 on Breastfeeding? A Synthesis of Qualitative Evidence Studies
Authors: Antoniou Evangelia , Tzitiridou-Chatzopoulou Maria , Voyatzaki Chrysa , Iliadou Maria , Eskitzis Panagiotis , Dagla Maria , Palaska Ermioni , Orovou Eirini
Published: July 2023

Introduction: Exclusive breastfeeding until six months of life is the ideal way to feed infants. However, there is a significant number of infants who have never breastfed, despite the beneficial properties of breastfeeding. On the other hand, the coronavirus outbreak had significant effects on people’s health, both mentally and physically, and has also impacted the breastfeeding process. Aim: The aim of this study was to investigate the implication of COVID-19 on breastfeeding through qualitative data from databases. Methods: We searched online databases (PubMed, Google Scholar, PsycINFO) for studies published from 2019 to 2023. ‘Out of the 2598 papers we found, only 12 were included in the review’. More specifically, from the 1558 papers remaining from the title and abstract evaluation as well as duplicates, a further 1546 papers belonging to our exclusion criteria were removed (all types of reviews, letters to editors, and quantitative articles). Results: Our results covered three subjects: breastfeeding support during the pandemic, effects of social containment measures on breastfeeding, and additional outcomes regarding breastfeeding. Most voices found the effects of the pandemic on breastfeeding beneficial, with reduced professional support and a high degree of support from the environment. Additional negative factors were observed, as well as consequences of the pandemic in women’s lives. Conclusions: COVID-19 was the occasion to understand the power of the supportive environment of the woman, especially the partner, in establishing and maintaining breastfeeding. Therefore, policy makers and health professionals, especially midwives, should implement family-centered breastfeeding strategies that are more supportive of the partner role, providing problem counseling when and where deemed necessary.
Long- and Short-Term High-Intensity Interval Training on Lipid Profile and Cardiovascular Disorders in Obese Male Adolescents
Authors: Racil Ghazi , Chelly Mohamed-Souhaiel , Coquart Jeremy , Padulo Johnny , Teodor Dragos , Russo Luca
Published: July 2023

This study investigated the effects of short-term and long-term periods (8 and 16 weeks) of high-intensity interval training (HIIT) on cardiovascular components, blood lipids, and 6-min walking test performance in obese young boys (age = 16.2 ± 0.7) with >34% body fat. The participants were split into two groups: severe obesity (SOG; n = 17) and moderate obesity (MOG; n = 16). All participants performed on a cycle ergometer for 16 weeks (3 times per week) of HIIT at 100% peak power output at the ventilatory threshold and recovered at 50% of peak power. Except for BMI, both groups improved all body composition measures after 16 weeks, with a higher percentage of change (Δ) in SOG. The 6-min walking test increased in both groups (p < 0.001). Furthermore, cardiovascular variables, blood lactate concentration at rest and after 5-min post-exercise, blood lipids, and insulin concentrations improved significantly in both groups. After 16 weeks, MOG significantly improved in HRpeak, blood glucose concentration, and rating of perceived exertion (RPE), but the percentage of change (Δ) was higher in SOG for all the other variables. SOG showed a higher (Δ) waist-to-hip ratio, maximum heart rate, resting heart rate, systolic blood pressure, blood lactate at 5-min post-exercise, and triglyceride concentrations after 8 and 16 weeks of training. In conclusion, a long-term HIIT program appears to be an appropriate training approach for obese boys with extra body fat. However, considering the RPE values, short-duration training sessions should be planned.
Self-Compassion and Anxiety in Adolescents with and without Anxiety Disorder
Authors: Tali Edibe , Potharst Eva S. , de Bruin Esther I. , Utens Elisabeth M. W. J.
Published: July 2023

Previous studies have linked self-compassion to mental health, specifically anxiety, in non-clinical adolescents, suggesting that self-compassion can be a protective factor against anxiety. This study compared the overall level of self-compassion and (un)compassionate self-responding in adolescents with and without an anxiety disorder and assessed the association between self-compassion and anxiety. This cross-sectional study included adolescents (12–19 years) with an anxiety disorder (N = 23) and a reference group (N = 28). Participants completed the Self-Compassion Scale (SCS) and State Trait Anxiety Inventory (STAI). Results showed that overall self-compassion and uncompassionate self-responding were significantly lower and higher in the clinical than the reference group, respectively, while compassionate self-responding did not differ between groups. In the clinical group, only uncompassionate self-responding was significantly associated with higher anxiety. In the reference group, uncompassionate self-responding showed a significant positive association with anxiety, and compassionate self-responding showed a significant negative association with anxiety. Although the results suggest that low uncompassionate self-responding may buffer against anxiety, the role of compassionate and uncompassionate self-responding remains unclear. An alternative explanation is that the uncompassionate self-responding items measure the presence of psychopathology in adolescents with an anxiety disorder. More research on the construct validity of the SCS uncompassionate self-responding scale is needed.
High-Entropy Composite Coating Based on AlCrFeCoNi as an Anode Material for Li-Ion Batteries
Authors: Csík Dávid , Baranová Gabriela , Džunda Róbert , Zalka Dóra , Breitung Ben , Hagarová Mária , Saksl Karel
Published: July 2023

In this study, a high entropy composite coating was synthesized by oxidizing a high entropy alloy, AlCrFeCoNi, at elevated temperatures in a pure oxygen atmosphere. X-Ray diffraction (XRD) analysis revealed that the prepared material was a dual-phase composite material consisting of a spinel-structured high entropy oxide and a metallic phase with a face-centered cubic structure. The metallic phase can improve the electrical conductivity of the oxide phase, resulting in improved electrochemical performance. Scanning electron microscopy with energy dispersive spectroscopy (SEM/EDS) analysis unveiled the compositional homogeneity of the composite material. The prepared material was utilized as an anode active material in lithium-ion batteries. Cyclic voltammetry (CV) revealed the oxidation and reduction regions, while the electrochemical impedance spectroscopy (EIS) measurements showed a decrease in the charge transfer resistance during the cycling process. A long-term rate capability test was conducted at various current densities: 100, 200, 500, 1000, and 2000 mA g−1. During this test, a notable phenomenon was observed in the regeneration process, where the capacity approached the initial discharge capacity. Remarkably, a high regeneration efficiency of 98% was achieved compared with the initial discharge capacity. This phenomenon is typically observed in composite nanomaterials. At a medium current density of 500 mA g−1, an incredible discharge capacity of 543 mAh g−1 was obtained after 1000 cycles. Based on the results, the prepared material shows great potential for use as an anode active material in lithium-ion batteries.
Thin 1,2,4-Triazole Films for the Inhibition of Carbon Steel Corrosion in Sulfuric Acid Solution
Authors: Avdeev Yaroslav G. , Nenasheva Tatyana A. , Luchkin Andrey Yu. , Marshakov Andrey I. , Kuznetsov Yurii I.
Published: July 2023

Etching of steel items in sulfuric acid solution is used in various human activities (oil and gas industry, metal production, utilities, transport, etc.). This operation is associated with significant material costs due to corrosion losses of the metal. It has been found that an efficient way to prevent corrosion of steel in sulfuric acid solution involves the formation of thin protective films consisting of corrosion inhibitor molecules of triazole class on its surface. It has been shown that the protection of steels with a 3-substituted 1,2,4-triazole (3ST) in H2SO4 solution is accompanied by the formation of a polymolecular layer up to 4 nm thick. The 3ST layer immediately adjacent to the steel surface is chemisorbed on it. The efficiency of this compound as an inhibitor of corrosion and hydrogen absorption by steel is determined by its ability to form a protective organic layer, as experimentally confirmed by XPS and AFM data. The kinetic constants of the main stages of hydrogen evolution and permeation into steel in the H2SO4 solution were determined. A significant decrease in both the reaction rate of cathodic hydrogen evolution and the rate of hydrogen permeation into steel by the triazole in question was noted. It has been shown that the preservation of the metal plasticity in the acid medium containing the triazole under study is due to a decrease in the hydrogen concentration in the metal bulk.
Optoelectronic Properties of Ferroelectric Composites of Bi3.25La0.75Ti3O12 (BLT) and Co-Doped BLT Thin Films Modified by FeCo-Doped BLT
Authors: Tang Rui , He Rui , Kim Sangmo , Bark Chung Wung
Published: July 2023

Driven by the growing demand for renewable and clean energy, the photovoltaic effect of various solar cells and materials was investigated for the conversion of light energy into electricity. We modified the Bi3.25La0.75Ti3O12 (BLT) and Co-doped BLT (Co–BLT) composites with Fe and Co-doped BLT (FeCo–BLT) films to narrow the bandgap and increase visible light absorption, thereby improving the efficiency of the photovoltaic reaction. In this study, BLT and Co–BLT thin films were fabricated by off-axis sputtering and then modified with FeCo–BLT thin films to produce dual-ferroelectric, thin-film composite materials that improved the photovoltaic power generation performance. Photoelectric test results showed that the modified double-ferroelectric, thin-film composites had superior optoelectronic properties. The current density was significantly enhanced by modifying the BLT films with doped Fe and Co. Therefore, this modification improved the efficiency of ferroelectric thin-film photovoltaic reactions.
A Software Verification Method for the Internet of Things and Cyber-Physical Systems
Authors: Manzhos Yuriy , Sokolova Yevheniia
Published: July 2023

With the proliferation of the Internet of Things devices and cyber-physical systems, there is a growing demand for highly functional and high-quality software. To address this demand, it is crucial to employ effective software verification methods. The proposed method is based on the use of physical quantities defined by the International System of Units, which have specific physical dimensions. Additionally, a transformation of the physical value orientation introduced by Siano is utilized. To evaluate the effectiveness of this method, specialized software defect models have been developed. These models are based on the statistical characteristics of the open-source C/C++ code used in drone applications. The advantages of the proposed method include early detection of software defects during compile-time, reduced testing duration, cost savings by identifying a significant portion of latent defects, improved software quality by enhancing reliability, robustness, and performance, as well as complementing existing verification techniques by focusing on latent defects based on software characteristics. By implementing this method, significant reductions in testing time and improvements in both reliability and software quality can be achieved. The method aims to detect 90% of incorrect uses of software variables and over 50% of incorrect uses of operations at both compile-time and run-time.
Tungsten Bronze-Type Ceramics for Temperature-Stable Energy Storage Properties: A Feasibility Study
Authors: Shi Xi , Khansur Neamul H.
Published: July 2023

The temperature-dependent energy storage properties of four tungsten bronze-type ceramics are studied together with an investigation of their structure and temperature-dependent permittivity response, i.e., Ba6Ti2Nb8O30 (BTN), Ba6Zr2Nb8O30 (BZN), Sr3TiNb4O15 (STN) and Sr3ZrNb4O15 (SZN) ceramics. With different cations at A and B sites, those four ceramics exhibit different crystal structures and show significantly different microstructure features and dielectric responses with changing temperatures. It was observed under SEM that BZN has smaller grains and a more porous structure than BTN. SZN shows the most porous structure among all samples, exhibiting a much lower permittivity response than other samples with no signs of phase transitions from room temperature to 400 °C. Though the energy storage response of those samples is generally quite low, they exhibit good temperature stability together with low dielectric loss. It was suggested that by obtaining a denser structure through chemical modification or other methods, those tungsten bronze ceramics with good temperature stability could be promising as energy storage devices when improved energy storage properties are achieved.
Superior Gluteal Nerve Anatomy and Its Injuries: Aiming for a More Secure Surgical Approach of the Pelvic Region
Authors: Pinho André R. , Leite Maria J. , Lixa João , Silva Miguel R. , Vieira Paula , Nery-Monterroso João , Bezerra Mariana C. , Alves Hélio , Madeira Maria Dulce , Pereira Pedro A.
Published: July 2023

Because most of the recognized causes of superior gluteal nerve (SGN) injury are iatrogenic, detailed knowledge of the anatomy of the SGN is crucial to prevent its injury associated with surgical procedures. This study aims to describe the precise location of SGN or its branches at the greater sciatic foramen, measure the distances of these neural structures to palpable bony landmarks, and evaluate the possible correlation between these parameters and pelvis size. Twenty human cadaveric hemipelvises were studied. After dissection to expose the SGN or its branches at the greater sciatic foramen, the distances from these neural structures to the greater trochanter (GT), to the anterior superior iliac spine (ASIS), to the posterior superior iliac spine (PSIS), to the ischial tuberosity (IT), and to the greater sciatic notch apex were measured. We found that at the greater sciatic foramen, the SGN emerges as a common trunk in 75% of hemipelvises, and already divided in its superior and inferior branches in 25% of hemipelvises. When the SGN exits the pelvis as a common trunk, it does so, in most cases, in contact with the bone at the apex of the greater sciatic notch or superior to the level of the apex. The median distance from the SGN at the greater sciatic notch to the PSIS, ASIS, GT and IT is 7.6 cm, 10.9 cm, 7.5 cm and 10.8 cm, respectively. We found a positive correlation between some of the analyzed parameters and the size of the pelvis. The anatomical data of this study may serve as pivotal guides during orthopedic pelvic surgery, contributing to minimize SNG iatrogenic lesions with significant implications in the patient’s quality of life.
Use Test of Automated Machine Learning in Cancer Diagnostics
Authors: Musigmann Manfred , Nacul Nabila Gala , Kasap Dilek N. , Heindel Walter , Mannil Manoj
Published: July 2023

Our aim is to investigate the added value of automated machine learning (AutoML) for potential future applications in cancer diagnostics. Using two important diagnostic questions, the non-invasive determination of IDH mutation status and ATRX status, we analyze whether it is possible to use AutoML to develop models that are comparable in performance to conventional machine learning models (ML) developed by experts. For this purpose, we develop AutoML models using different feature preselection methods and compare the results with previously developed conventional ML models. The cohort used for our study comprises T2-weighted MRI images of 124 patients with histologically confirmed gliomas. Using AutoML, we were able to develop sophisticated models in a very short time with only a few lines of computer code. In predicting IDH mutation status, we obtained a mean AUC of 0.7400 and a mean AUPRC of 0.8582. ATRX mutation status was predicted with very similar discriminatory power, with a mean AUC of 0.7810 and a mean AUPRC of 0.8511. In both cases, AutoML was even able to achieve a discriminatory power slightly above that of the respective conventionally developed models in a very short computing time, thus making such methods accessible to non-experts in the near future.
Analysis of Line and Tube Detection Performance of a Chest X-ray Deep Learning Model to Evaluate Hidden Stratification
Authors: Tang Cyril H. M. , Seah Jarrel C. Y. , Ahmad Hassan K. , Milne Michael R. , Wardman Jeffrey B. , Buchlak Quinlan D. , Esmaili Nazanin , Lambert John F. , Jones Catherine M.
Published: July 2023

This retrospective case-control study evaluated the diagnostic performance of a commercially available chest radiography deep convolutional neural network (DCNN) in identifying the presence and position of central venous catheters, enteric tubes, and endotracheal tubes, in addition to a subgroup analysis of different types of lines/tubes. A held-out test dataset of 2568 studies was sourced from community radiology clinics and hospitals in Australia and the USA, and was then ground-truth labelled for the presence, position, and type of line or tube from the consensus of a thoracic specialist radiologist and an intensive care clinician. DCNN model performance for identifying and assessing the positioning of central venous catheters, enteric tubes, and endotracheal tubes over the entire dataset, as well as within each subgroup, was evaluated. The area under the receiver operating characteristic curve (AUC) was assessed. The DCNN algorithm displayed high performance in detecting the presence of lines and tubes in the test dataset with AUCs > 0.99, and good position classification performance over a subpopulation of ground truth positive cases with AUCs of 0.86–0.91. The subgroup analysis showed that model performance was robust across the various subtypes of lines or tubes, although position classification performance of peripherally inserted central catheters was relatively lower. Our findings indicated that the DCNN algorithm performed well in the detection and position classification of lines and tubes, supporting its use as an assistant for clinicians. Further work is required to evaluate performance in rarer scenarios, as well as in less common subgroups.
Effect of Cocoa Beverage and Dark Chocolate Intake on Lipid Profile in People Living with Normal and Elevated LDL Cholesterol: A Systematic Review and Meta-Analysis
Authors: Amoah Isaac , Lim Jia Jiet , Osei Emmanuel Ofori , Arthur Michael , Cobbinah Jesse Charles , Tawiah Phyllis
Published: July 2023

Cocoa beverage and dark chocolate are important dietary sources of polyphenol and have been hypothesised to improve the lipid profile. This systematic review and meta-analysis aimed to investigate the effect of cocoa beverage and dark chocolate intake on lipid profile in individuals living with normal and elevated LDL cholesterol. The question on whether cocoa beverage and chocolate differentially modify the lipid profile was also explored. A systematic literature search was conducted on PubMed and Cochrane Library on 26 February 2022 following the PRISMA guideline. Cocoa beverage and chocolate consumption had no significant effect on circulating concentrations of total cholesterol, LDL cholesterol, and triglycerides (p > 0.05, all), but favourably and significantly increased circulating concentration of HDL cholesterol by 0.05 (95% CI [0.02, 0.09]) mmol/L (p = 0.002). Changes in lipid profile were similar when comparing populations with normal vs. elevated LDL cholesterol (p > 0.05, all). When considering the food matrix, cocoa beverage intake significantly increased HDL cholesterol by 0.11 (95% CI [0.06, 0.17]) mmol/L (p < 0.001), but the improvement in HDL cholesterol was not significant when chocolate (p = 0.10) or a combination of cocoa beverage and chocolate (p = 0.19) (subgroup differences, p = 0.03) was administered. Cocoa consumption could be recommended as part of a healthy diet in the general population with normal and elevated LDL cholesterol.
Urban Forest Above-Ground Biomass Estimation Based on UAV 3D Real Scene
Authors: Zhao Yinyin , Zhou Lv , Chen Chao , Li Xuejian , Du Huaqiang , Yu Jiacong , Lv Lujin , Huang Lei , Song Meixuan
Published: July 2023

(1) Background: A three-dimensional (3D) real scene is a digital representation of the multidimensional dynamic real-world structure that enables the realistic and stereoscopic expression of actual scenarios, and is an important technological tool for urban refinement management. The above-ground biomass (AGB) of urban forests is an important indicator that reflects the urban ecological environment; therefore, the accurate estimation of AGB is of great significance for evaluating urban ecological functions. (2) Methods: In this study, multiangle aerial photographs of urban street trees were obtained via an unmanned aerial vehicle (UAV) single-lens five-way flight, from 0°, 0°, 90°, 180°, 270°, and five other directions. The multiple view stereo (MVS) algorithm was used to construct three-dimensional realistic models of two tree species: ginkgo and camphor. Then, structural parameters such as tree height, crown diameter, and crown volume were estimated from the 3D real-scene models. Lastly, single-tree AGB models were developed based on structural parameters. (3) Results: The results of this study indicated the following: (A) The UAV visible-light realistic 3D model had clear texture and truly reflected the structural characteristics of two tree species, ginkgo and camphor. (B) There was a significant correlation between the reference tree height, crown diameter and crown volume obtained from the realistic 3D model and the measured values; the R2 for ginkgo height was 0.90, the R2 for camphor crown diameter was 0.87, and the R2 for ginkgo crown volume was 0.89. (C) The accuracy of the AGB estimation models constructed with tree height and canopy volume as variables was generally higher than that of models with tree height and canopy diameter; the model with the highest accuracy of AGB estimation for ginkgo was the linear model with a validation accuracy R2 of 0.96 and RMSE of 8.21 kg, while the model with the highest accuracy of AGB estimation for camphor was the quadratic polynomial model with a validation accuracy R2 of 0.92 and RMSE of 27.74 kg. (4) Conclusions: This study demonstrated that the UAV 3D real-scene model can achieve high accuracy in estimating single-wood biomass in urban forests. In addition, for both tree species, there was no significant difference between the AGB estimates based on the UAV 3D real scene and LiDAR and the measured AGB. These results of urban single-wood AGB estimation based on the UAV 3D real-scene model were consistent with those of LiDAR and even with the measured AGB. Therefore, based on the UAV 3D real-scene model, the single-wood biomass can be estimated with high accuracy. This represents a new technical approach to urban forest resource monitoring and ecological environment function evaluation.
FBC-ANet: A Semantic Segmentation Model for UAV Forest Fire Images Combining Boundary Enhancement and Context Awareness
Authors: Zhang Lin , Wang Mingyang , Ding Yunhong , Wan Tingting , Qi Bo , Pang Yutian
Published: July 2023

Forest fires are one of the most serious natural disasters that threaten forest resources. The early and accurate identification of forest fires is crucial for reducing losses. Compared with satellites and sensors, unmanned aerial vehicles (UAVs) are widely used in forest fire monitoring tasks due to their flexibility and wide coverage. The key to fire monitoring is to accurately segment the area where the fire is located in the image. However, for early forest fire monitoring, fires captured remotely by UAVs have the characteristics of a small area, irregular contour, and susceptibility to forest cover, making the accurate segmentation of fire areas from images a challenge. This article proposes an FBC-ANet network architecture that integrates boundary enhancement modules and context-aware modules into a lightweight encoder–decoder network. FBC-Anet can extract deep semantic features from images and enhance shallow edge features, thereby achieving an effective segmentation of forest fire areas in the image. The FBC-ANet model uses an Xception network as the backbone of an encoder to extract features of different scales from images. By transforming the extracted deep semantic features through the CIA module, the model’s feature learning ability for fire pixels is enhanced, making feature extraction more robust. FBC-ANet integrates the decoder into the BEM module to enhance the extraction of shallow edge features in images. The experimental results indicate that the FBC-ANet model has a better segmentation performance for small target forest fires compared to the baseline model. The segmentation accuracy on the dataset FLAME is 92.19%, the F1 score is 90.76%, and the IoU reaches 83.08%. This indicates that the FBC-ANet model can indeed extract more valuable features related to fire in the image, thereby better segmenting the fire area from the image.
The Design of a Contract Farming Model for Coffee Tree Replanting
Authors: Haryono Adi , Maarif Mohamad Syamsul , Suroso Arif Imam , Jahroh Siti
Published: July 2023

Coffee tree replanting is needed in Indonesia, yet a financing scheme is not available at the moment due to its economic feasibility and farmers’ reluctance to reinvest. This research aims to design contract farming for coffee tree replanting in Indonesia to support coffee farmer welfare. The method used in this study is Soft System Methodology (SSM), including interviews with several coffee cultivation respondents, with case studies in the Lampung region. The initial study indicates that the replanting program must integrate coffee farmers with banks or investors and coffee processing companies. The study of one farmer community successful in profiting from a new coffee production method enables the possibility of a new model of replanting through contract farming. Aside from banks, institutions that will have an essential role in the development of successful replanting are land owners and exporters or processing companies as offtakers. The implementation of the European Union Deforestation Regulation on traceability will impact Indonesia’s replanting effort, as exporters or importers will be accountable for the compliance of their supply chain. The five main factors that need to be evaluated consist of (1) financing as an initial investment, (2) farmer competencies as the farmer’s skill to implement the coffee tree replanting, (3) technology to ensure the successful implementation of coffee tree replanting, (4) production to maintain the income flow, and (5) synergy between actors to ensure the role in contract farming development.
Critical Immersive-Triggered Literacy as a Key Component for Inclusive Digital Education
Authors: Lazou Chrysoula , Tsinakos Avgoustos
Published: July 2023

The present empirical study investigates, analyzes, and discusses the affordances and limitations of an augmented reality (AR)-based educational intervention for the critical digital awareness of secondary school students through a sociocultural framework of instruction. The design of the educational intervention focuses on the empowerment of students’ critical digital skills in the new media produsage ecosystem in parallel with the development of target language skills through content and language integrated learning (CLIL) in a mobile augmented reality (MAR)-based transnational educational setting. This study focuses on examining the usefulness rather than the digital use of MAR from a socio-constructivism perspective with 77 participants from diverse socio-cultural backgrounds and educational settings. More specifically, an investigation of the potential of internalization of new knowledge through immersive-triggered inclusive educational practices is carried out, examining the effectiveness of the universal design for learning principles (UDL) as a pedagogical framework for AR-based instruction related to long-term memory retention, the synthesis of meaningful learning instances, and the creation of new knowledge. The study findings suggest that AR-based instruction, if incorporated into a robust pedagogical framework, can enhance attention and long-term memory retention, provide meaningful, inclusive learning opportunities, and facilitate digital well-being in the ever-evolving complex learning ecosystem. This study concludes by proposing the term of critical immersive-triggered literacy (CIT Literacy), defined as a skill development framework that triggers learners’ attention and facilitates digital well-being for meaningful learning instances via immersive technologies.
Teacher Education beyond the Pandemic in Spain
Authors: Gallifa Josep , Flores Carme , Montané Mireia
Published: July 2023

In the wake of the pandemic, there has been a clear need to understand the “new normal” contexts of teaching and learning and how they affect teacher education. In order to rethink and reimagine teacher education, a priority should be to have a firm grasp on the traits that young people bring with them when they enroll in Education degree programs. In other words, we must understand their distinctive generational traits and how the various crises of recent years have affected them. This paper has three main aims, each of which is addressed in a corresponding section of the article. The first objective is to gain a better understanding of the educational needs and learning styles of this new generation of students and to examine how they are influenced by present-day realities. A second section strives to identify the most prominent challenges that are reshaping teacher education programs on a global scale. The third and final part explores the bidirectional interaction between these two previous variables by examining the educational approaches and methodologies that have been implemented recently in Spain and discussing the extent to which they have been able to meet students’ evolving needs. Given that these changes, transformations and pedagogical concerns are observable worldwide, these analyses and reflections are potentially relevant beyond the Spanish context.
A Network Clustering Algorithm for Protein Complex Detection Fused with Power-Law Distribution Characteristic
Authors: Wang Jie , Jia Ying , Sangaiah Arun Kumar , Song Yunsheng
Published: July 2023

Network clustering for mining protein complexes from protein–protein interaction (PPI) networks has emerged as a prominent research area in data mining and bioinformatics. Accurately identifying complexes plays a crucial role in comprehending cellular organization and functionality. Network characteristics are often useful in enhancing the performance of protein complex detection methods. Many protein complex detection algorithms have been proposed, primarily focusing on local micro-topological structure metrics while overlooking the potential power-law distribution characteristic of community sizes at the macro global level. The effective use of this distribution characteristic information may be beneficial for mining protein complexes. This paper proposes a network clustering algorithm for protein complex detection fused with power-law distribution characteristic. The clustering algorithm constructs a cluster generation model based on scale-free power-law distribution to generate a cluster with a dense center and relatively sparse periphery. Following the cluster generation model, a candidate cluster is obtained. From a global perspective, the number distribution of clusters of varying sizes is taken into account. If the candidate cluster aligns with the constraints defined by the power-law distribution function of community sizes, it is designated as the final cluster; otherwise, it is discarded. To assess the prediction performance of the proposed algorithm, the gold standard complex sets CYC2008 and MIPS are employed as benchmarks. The algorithm is compared to DPClus, IPCA, SEGC, Core, SR-MCL, and ELF-DPC in terms of F-measure and Accuracy on several widely used protein–protein interaction networks. The experimental results show that the algorithm can effectively detect protein complexes and is superior to other comparative algorithms. This study further enriches the connection between analyzing complex network topology features and mining network function modules, thereby significantly contributing to the improvement of protein complex detection performance.
IoTFuzzBench: A Pragmatic Benchmarking Framework for Evaluating IoT Black-Box Protocol Fuzzers
Authors: Cheng Yixuan , Chen Wenxin , Fan Wenqing , Huang Wei , Yu Gaoqing , Liu Wen
Published: July 2023

High scalability and low operating cost make black-box protocol fuzzing a vital tool for discovering vulnerabilities in the firmware of IoT smart devices. However, it is still challenging to compare black-box protocol fuzzers due to the lack of unified benchmark firmware images, complete fuzzing mutation seeds, comprehensive performance metrics, and a standardized evaluation framework. In this paper, we design and implement IoTFuzzBench, a scalable, modular, metric-driven automation framework for evaluating black-box protocol fuzzers for IoT smart devices comprehensively and quantitatively. Specifically, IoTFuzzBench has so far included 14 real-world benchmark firmware images, 30 verified real-world benchmark vulnerabilities, complete fuzzing seeds for each vulnerability, 7 popular fuzzers, and 5 categories of complementary performance metrics. We deployed IoTFuzzBench and evaluated 7 popular black-box protocol fuzzers on all benchmark firmware images and benchmark vulnerabilities. The experimental results show that IoTFuzzBench can not only provide fast, reliable, and reproducible experiments, but also effectively evaluate the ability of each fuzzer to find vulnerabilities and the differential performance on different performance metrics. The fuzzers found a total of 13 vulnerabilities out of 30. None of these fuzzers can outperform the others on all metrics. This result demonstrates the importance of comprehensive metrics. We hope our findings ease the burden of fuzzing evaluation in IoT scenarios, advancing more pragmatic and reproducible fuzzer benchmarking efforts.
Test Structures for the Characterization of the Gate Resistance in 16 nm FinFET RF Transistors
Authors: Lauritano Mario , Baumgartner Peter , Çağri Ulusoy Ahmet
Published: July 2023

The gate resistance is a parasitic element in transistors for RF and millimeter-wave circuits that can negatively impact power gain and noise figure. To develop accurate device models, a reliable measurement methodology is crucial. This article reviews the standard measurement methodology used in the literature and proposes also an additional method, which is evaluated using suitable test structures in a 16 nm FinFET process. The advantages and disadvantages of the two approaches are discussed along with their respective application scenarios.
A Hardware Non-Invasive Mapping Method for Condition Bits in Binary Translation
Authors: Li Chunqiang , Liu Zhiwei , Shang Yunhai , He Lenian , Yan Xiaolang
Published: July 2023

Binary translation, as an important bridge for application compatibility between different instruction set architectures (ISAs), has attracted much attention in the industry. However, due to hardware resource limitations of the target ISA, the translation efficiency and the practicability are poor. Recently, Apple has made it possible to run x86 programs on ARM through a translation technology called Rosetta based on software-hardware collaboration. In this paper, we proposed a hardware non-invasive mapping method for condition bits (HNIMCB) in binary translation, which innovatively implements the setting and referencing operations of the condition bits without changing the original instruction encoding and function of the target processor. This method is applicable for binary translation from source architectures with condition bit operations to target architectures without condition bit operations. It eliminates the difference of conditional bit resources between the source and target ISAs, reduces the computational instructions and memory access operations after translation from the source to the target ISA, and dramatically improves the translation efficiency. We conducted this experiment on a functional simulation level using the QEMU binary translator from ARM to RISC-V. A series of benchmark tests revealed that the total number of instructions decreased by 41%, while the number of memory access instructions decreased by 37% after the translation applying with the HNIMCB.
Distribution of CO2 Concentration and Its Spatial Influencing Indices in Urban Park Green Space
Authors: Jiang Yunfang , Liu Yangqi , Sun Yingchao , Li Xianghua
Published: July 2023

Park green spaces (PGSs) are an important component of urban natural carbon sinks, while their spatial morphological patterns can affect the carbon sequestration capacity themselves. This study selected six typical urban parks in the central district of Shanghai and analyzed the correlation between spatial morphological indices and CO2 concentration distribution of the PGSs based on ENVI-met and BRT models. It further explored the interaction mechanisms in the carbon cycling process of urban PGSs under the influence of different spatial morphology. The results shows that urban PGSs play the role of carbon sink in diurnal period, and the difference of CO2 concentration distribution in green space is the largest at 11 a.m. The green biomass (Gb) and arboreal area ratio (Ar) are the most important indices affecting the distribution of carbon concentration. The two indices describing spatial patterns, namely, Cohesion (Co) and canopy density (CanopyD) also significantly impact CO2 concentration. These indices have a positive impact on carbon sinks. The parkway area ratio (Pr) is a disturbing index that also has an obvious negative impact on the distribution of CO2 concentration. The moderate herbs area ratio (Hr) and open land area ratio (Or) are conducive to regulating the microclimate environment and enhancing carbon sink capacity. There is an interactive relationship between spatial indices and microclimate environment indices, as well as physical and physiological indices in the carbon sink process of green areas. The study suggested that in green space management aiming at carbon reduction, combined with the influence threshold of Gb on carbon reduction and paying attention to the importance of green amount on carbon sequestration, the vegetation density and allocation ratio should be rationally distributed to form a park green space landscape with efficient carbon fixation.
Effects of Cellulase and Xylanase Addition on Fermentation Quality, Aerobic Stability, and Bacteria Composition of Low Water-Soluble Carbohydrates Oat Silage
Authors: Liu Wei , Si Qiang , Sun Lin , Wang Zhijun , Liu Mingjian , Du Shuai , Ge Gentu , Jia Yushan
Published: July 2023

Most oat forage has low water-soluble carbohydrates (WSC), which may be the main limited factor for silage fermentation safely, but oat is rich in cellulose and hemicellulose; therefore, we assume that xylanase and cellulase as additives can reduce the content of cellulose and xylan in oat silage, increase the microbial fermentable sugar content, and improve the fermentation quality of the silage. After wilting, oats were treated as follows: (i) distributed water (CK); (ii) silages inoculated with xylanase (X); (iii) silages inoculated with cellulase (C), ensiling for 3 days (early stage of silage) and 60 days (late stage of silage), respectively, after ensiling 60 days for a 5-day aerobic exposure study. The pH, neutral detergent fiber (NDF), and acid detergent fiber (ADF) were significantly reduced by xylanase and cellulase treatment during the late stage of silage, and the concentration of lactic acid, acetic acid, and ammonia nitrogen increased remarkably. The WSC content reached its peak with xylanase treatment during the late stage of silage. The content of crude protein (CP) was not affected by additives but by the silage period; CP and ether extract (EE) significantly increased during the late stage of silage compared to the early stage. After ensiling, the bacterial community showed that xylanase and cellulase treatment increased the relative abundance of lactic acid bacteria. Lactobacillus has a higher relative abundance with cellulase treatment after 60 days of ensiling; this can effectively reduce the pH of silage and ensure long-term, stable storage of silage. Cellulase and xylanase increased bacterial diversity during aerobic exposure and improved the aerobic stability of silage significantly. This study indicated that different additives and silage periods had significant effects on chemical compositions, fermentation quality, and bacterial community; meanwhile, both additives improved the aerobic stability of silage. In summary, when the WSC of oat is low, cellulase and xylanase have good effects as silage additives, and the comprehensive effect of cellulase is more prominent.
Techno-Economic Analysis of the Production of Lactic Acid from Lignocellulosic Biomass
Authors: Manandhar Ashish , Shah Ajay
Published: July 2023

This study reports the results of an evaluation of the techno-economic feasibility of a biorefinery with an annual lactic acid production capacity of 100,000 metric tons using lignocellulosic biomass. Corn stover and miscanthus were considered as model feedstocks, and three different fermentation pathways involving bacteria, fungi, and yeast were compared with respect to their ability to convert biomass feedstocks to lactic acid. Equipment, raw materials, utilities and labor requirements, and lactic acid production costs were estimated. The minimum selling price (at a 10% internal rate of return) per metric ton of lactic acid produced from different feedstocks for lactic acid bacteria, fungi, and yeast-based pathways were in the range of USD 1243–1390, USD 1250–1392, and USD 993–1123, respectively, with lower costs for miscanthus. Lactic acid production using genetically engineered yeast strains can eliminate the need for the simultaneous neutralization and recovery of lactic acid, resulting in lower equipment, chemical, and utility requirements and lower lactic acid production costs. Lactic acid production costs were highly sensitive to the conversion rates of sugars into lactic acid, feedstock cost, production plant size, operation hours, and acid hydrolysis reactor costs. Improvements in process conditions and efficiencies and lower costs of equipment and consumables are necessary to utilize lignocellulosic biomass for lactic acid production at lower costs while remaining cost-competitive with respect to first-generation and petroleum-based feedstocks.
Hybrid Deep Modeling of a GS115 (Mut+) Pichia pastoris Culture with State–Space Reduction
Authors: Pinto José , Ramos João R. C. , Costa Rafael S. , Oliveira Rui
Published: July 2023

Hybrid modeling workflows combining machine learning with mechanistic process descriptions are becoming essential tools for bioprocess digitalization. In this study, a hybrid deep modeling method with state–space reduction was developed and showcased with a P. pastoris GS115 Mut+ strain expressing a single-chain antibody fragment (scFv). Deep feedforward neural networks (FFNN) with varying depths were connected in series with bioreactor macroscopic material balance equations. The hybrid model structure was trained with a deep learning technique based on the adaptive moment estimation method (ADAM), semidirect sensitivity equations and stochastic regularization. A state–space reduction method was investigated based on a principal component analysis (PCA) of the cumulative reacted amount. Data of nine fed-batch P. pastoris 50 L cultivations served to validate the method. Hybrid deep models were developed describing process dynamics as a function of critical process parameters (CPPs). The state–space reduction method succeeded to decrease the hybrid model complexity by 60% and to improve the predictive power by 18.5% in relation to the nonreduced version. An exploratory design space analysis showed that the optimization of the feed of methanol and of inorganic elements has the potential to increase the scFv endpoint titer by 30% and 80%, respectively, in relation to the reference condition.
Biofilm Inhibition, Antibacterial and Antiadhesive Properties of a Novel Biosurfactant from Lactobacillus paracasei N2 against Multi-Antibiotics-Resistant Pathogens Isolated from Braised Fish
Authors: Mouafo Hippolyte Tene , Sokamte Alphonse Tegang , Manet Linda , Mbarga Arsene Joseph Manga , Nadezdha Sachivkina , Devappa Somashekhar , Mbawala Augustin
Published: July 2023

This study aimed to assess the antibiotic susceptibility and biofilm formation ability of pathogens isolated from braised fish as well as characterize and evaluate the antibacterial, antiadhesive, and antibiofilm activities of the biosurfactant from Lactobacillus paracasei subsp. tolerans N2 against these pathogens. The susceptibility of six pathogens isolated from braised fish (Escherichia coli EM2, Staphylococcus aureus SA1, Salmonella enteritidis PE1, Pseudomonas aeruginosa CT3, Yersinia enterolitica MH5, Proteus mirabilis MR2, and Klebsiella pneumoniae AG5) to 16 antibiotics revealed multiple resistances with an MAR index greater than 0.3. These pathogens were able to form biofilms with S. aureus SA1, which showed the highest ability. Using biochemical and elemental analyses, FTIR, GC-MS, 1H NMR and high-resolution mass spectrometry, the biosurfactant was characterized as a novel glycolipoprotein made of two congeners of mass 482.28 and 507.27 m/z, respectively. They showed bactericidal and antiadhesive activities against all pathogens. The biosurfactant inhibited biofilm formation by these pathogens and eradicated mature biofilms.
A Real-Time Pre-Response Experiment System for High-Rise Building Fires Based on the Internet of Things
Authors: Zhao Haoyou , Yu Zhaoyang , Zhu Jinpeng
Published: July 2023

The primary objective of the current fire protection system in high-rise buildings is to extinguish fires in close proximity to the detectors. However, in the event of rapidly spreading fires, it is more effective to limit the transmission of fire and smoke. This study aims to develop an IoT-based real-time pre-response system for high-rise building fires that is capable of limiting the spread of fire and smoke. The proposed system collects fire data from sensors and transmits them to a cloud computer for real-time analysis. Based on the analysis results, the cloud computer controls the actions of alarm devices, ventilation equipment, and fine water mist nozzles. The system can dynamically adjust the entire system’s behavior in real time by adopting pre-response measures to extinguish fires and limit the spread of fires and smoke. The system was tested on a simulation platform similar to actual high-rise buildings to evaluate its impact on fires and smoke. The results demonstrate the system’s effectiveness in extinguishing fires and suppressing the spread of fires and smoke.