bluuebunny/crossref_metadata_2025 huggingface dataset:
Low-Temperature Diffusion of Au and Ag Nanolayers for Cu Bonding
Authors: Lee Sangmin , Park Sangwoo , Kim Sarah Eunkyung
Published: December 2023

With the recent rapid development of IT technology, the demand for multifunctional semiconductor devices capable of high performance has increased rapidly, and the miniaturization of such devices has also faced limitations. To overcome these limitations, various studies have investigated three-dimensional packaging methods of stacking devices, and among them, hybrid bonding is being actively conducted during the bonding process. studies of hybrid bonding during the bonding process are active. In this study, Cu bonding using a nano passivation layer was carried out for Cu/SiO2 hybrid bonding applications, with Au and Ag deposited on Cu at the nano level and used as a protective layer to prevent Cu oxidation and to achieve low-temperature Cu bonding. Au was deposited at about 12 nm, and Ag was deposited at about 15 nm, with Cu bonding carried out at 180 °C for 30 min, after which an annealing process was conducted at 200 °C for one hour. After bonding, the specimen was diced into a 1 cm × 1 cm chip, and the bonding interface was analyzed using SEM and TEM. Additionally, the 1 cm × 1 cm chip was diced into 2 mm × 2 mm specimens to measure the shear strength of the bonded chip, and the average shear strength of Au and Ag was found to be 5.4 and 6.6 MPa, respectively. The degree of diffusion between Au-Cu and Ag-Cu was then investigated; the diffusion activation energy when Au diffuses to Cu was 6369.52 J/mol, and the diffusion activation energy when Ag diffuses to Cu was 17,933.21 J/mol.
Influence of Internal Pressure on Hollow Section Steel Members in Fire
Authors: Kervalishvili Andrei , Talvik Ivar
Published: December 2023

Structural steel hollow section members are extensively utilized in civil engineering due to their excellent mechanical performance, favourable geometry for corrosion protection, and aesthetic appeal. Degradation in material properties of steel and thermal expansion at high temperatures must be regarded in designs for fire situations. The closed inner space of hollow sections presents challenges at elevated temperatures. The present study examines the effect of expanding air on the stress state in section walls of hermetically sealed circular and rectangular hollow sections. The effect of the gas pressure is calculated analytically and numerically. The pressure of the expanding air may substantially reduce the capacity of a tubular member. The influence on resistance depends on temperature, volume of the air in the tubular member, and geometry of the hollow section. The results of the study indicate that rectangular hollow sections with relatively large width-to-thickness ratios are more sensitive to internal pressure than circular hollow sections. The temperature range where the adverse effect of internal pressure occurs can include realistic critical temperatures in practical design and therefore deserve special attention to ensure the required safety.
Palmprint Recognition: Extensive Exploration of Databases, Methodologies, Comparative Assessment, and Future Directions
Authors: Amrouni Nadia , Benzaoui Amir , Zeroual Abdelhafid
Published: December 2023

This paper presents a comprehensive survey examining the prevailing feature extraction methodologies employed within biometric palmprint recognition models. It encompasses a critical analysis of extant datasets and a comparative study of algorithmic approaches. Specifically, this review delves into palmprint recognition systems, focusing on different feature extraction methodologies. As the dataset wields a profound impact within palmprint recognition, our study meticulously describes 20 extensively employed and recognized palmprint datasets. Furthermore, we classify these datasets into two distinct classes: contact-based datasets and contactless-based datasets. Additionally, we propose a novel taxonomy to categorize palmprint recognition feature extraction approaches into line-based approaches, texture descriptor-based approaches, subspace learning-based methods, local direction encoding-based approaches, and deep learning-based architecture approaches. Within each class, most foundational publications are reviewed, highlighting their core contributions, the datasets utilized, efficiency assessment metrics, and the best outcomes achieved. Finally, open challenges and emerging trends that deserve further attention are elucidated to push progress in future research.
Using Big Data Analytics and Heatmap Matrix Visualization to Enhance Cryptocurrency Trading Decisions
Authors: Ni Yensen , Chiang Pinhui , Day Min-Yuh , Chen Yuhsin
Published: December 2023

Using the Bollinger Bands trading strategy (BBTS), investors are advised to buy (and then sell) Bitcoin and Ethereum spot prices in response to BBTS’s oversold (overbought) signals. As a result of analyzing whether investors would profit from round-turn trading of these two spot prices, this study may reveal the following remarkable outcomes and investment strategies. This study first demonstrated that using our novel design with a heatmap matrix would result in multiple higher returns, all of which were greater than the highest return using the conventional design. We contend that such an impressive finding could be the result of big data analytics and the adaptability of BBTS in our new design. Second, because cryptocurrency spot prices are relatively volatile, such indices may experience a significant rebound from oversold to overbought BBTS signals, resulting in the potential for much higher returns. Third, if history repeats itself, our findings might enhance the profitability of trading these two spots. As such, this study extracts the diverse trading performance of multiple BB trading rules, uses big data analytics to observe and evaluate many outcomes via heatmap visualization, and applies such knowledge to investment practice, which may contribute to the literature. Consequently, this study may cast light on the significance of decision-making through the utilization of big data analytics and heatmap visualization.
Spatio-Temporal-Attention-Based Vehicle Trajectory Prediction Considering Multi-Vehicle Interaction in Mixed Traffic Flow
Authors: Zeng Jie , Ren Yue , Wang Kan , Hu Xiong , Li Jiufa
Published: December 2023

As a link connecting the environmental perception system and the decision-making system, accurate obstacle trajectory prediction provides a reliable guarantee of correct decision-making by autonomous vehicles. Oriented toward a mixed human-driven and machine-driven traffic environment, a vehicle trajectory prediction algorithm based on an encoding–decoding framework composed of a multiple-attention mechanism is proposed. Firstly, a directed graph is used to describe vehicle–vehicle motion dependencies. Then, by calculating the repulsive force between vehicles using a priori edge information based on the artificial potential field theory, vehicle–vehicle interaction coefficients are extracted via a graph attention mechanism (GAT). Subsequently, after concatenating the vehicle–vehicle interaction feature with the encoded vehicle trajectory vectors, a spatio-temporal attention mechanism is applied to determine the coupling relationship of hidden vectors. Finally, the predicted trajectory is generated by a gated recurrent unit (GRU) decoder. The training and evaluation of the proposed model were conducted on the NGSIM public dataset. The test results demonstrated that compared with existing baseline models, our approach has fewer prediction errors and better robustness. In addition, introducing artificial potential fields into the attention mechanism causes the model to have better interpretability.
Study of the Kinetics of Adhesive Bond Formation Using the Ultrasonic Method
Authors: Kowalczyk Jakub , Jósko Marian , Wieczorek Daniel , Sędłak Kamil , Nowak Michał
Published: December 2023

Adhesive bonding is widely used in modern industry. It has many advantages—the main one being the reduction in production costs. It also has certain limitations. One of the limitations of adhesive bonds is the relatively long bonding time of the joints. The main objective of this research was to determine the possibility of studying the kinetics of adhesive bond formation using a non-destructive ultrasonic method. A research experiment was planned and carried out. Adhesive specimens were prepared, and their quality changes over time were evaluated. In addition, the change in ultrasonic measures during the testing of these bonds was evaluated, as well as the hardness of the adhesive. In this study, the choice of test apparatus was made, in particular ultrasonic probes for the adhesive used and the materials to be bonded. The choice of adhesive was also made, for one in which bonding phenomena occur uniformly throughout the volume. This work examined the changes in the mechanical strength and hardness with time. The tests showed that the greatest changes in mechanical strength occur within the first 24 h after the bond was made. With the mechanical strength reaching 12.6 Mpa after 216 h, the strength in the first 24 h was 10.36 (for bonded steel sheets). For bonded steel discs, the maximum tensile strength was 26.99 Mpa (after 216 h), with a hardness of 22.93 Mpa during the first 24 h. Also, significant changes were observed in the adhesive hardness during the first 24 h. The hardness of the adhesive after 216 h was 70.4 Shore’a on the D scale, while after 24 h it was 69.4 Shore’a on the D scale. Changes in the ultrasonic parameters of the adhesive bond quality were found to occur along with changes in the bond quality.
The Impact of Drilling Guide Length of a Surgical Guide on Accuracy of Pre-Drilling for Miniscrew Insertion
Authors: Hamanaka Ryo , Emori Toshiro , Ohama Mizuki , Yamamoto Kana , Horiguchi Yui , Yoshida Noriaki
Published: December 2023

Background: Temporary anchorage devices (TADs) are broadly used in orthodontic treatment. TADs must be placed accurately to avoid collision against tooth roots. To place miniscrews with a higher accuracy, a digitally designed surgical guide was proposed in previous studies. However, to our knowledge, there have been no articles that have assessed the minimal required length to achieve good drilling accuracy. The objective of the present study was to evaluate the accuracy of pre-drilling using computer-aided designed surgical guides with different lengths. Methods: A typodont model was scanned, and surgical guides of 3.0, 4.5, and 6.0 mm were designed. Duplicated typodonts and the surgical guides were printed with a 3D printer. Using these models and surgical guides, pre-drillings were performed. Freehand drilling was also conducted for the control. The drilled models were scanned with micro-computed tomography to evaluate the accuracy. Results: The mean errors at the tip of the drill were 0.44 mm, 0.61 mm, 0.41 mm, and 0.24 mm for the freehand drilling, and 3.0, 4.5, and 6.0 mm for the surgical guide, respectively. Conclusion: The results suggested that a longer surgical guide was recommended to achieve good insertion accuracy for a narrow interradicular space, and that 3.0 mm was enough when an error of 1.0 mm was acceptable for the insertion site.
Retrograde Study Using Radiographic Comparison of Bone Fill Observed after Guided Tissue Regeneration by the Type of Periodontal Bone Defect
Authors: Do Woo-Seok , Kim Yong-Gun , Suh Jo-Young , Kim Jin-Wook , Lee Jae-Mok
Published: December 2023

Background and Objectives: The current study aimed to compare the extent of bone growth achieved after guided tissue regeneration (GTR) based on the location and nature of the initial periodontal lesion. Materials and Methods: The study sample included 80 patients who underwent GTR at Kyungpook National University Dental Hospital since 2012 and were radiologically followed up for at least 2 years after treatment. The patients were divided into four groups based on the location (maxilla and mandible) and nature (furcation involvement and intrabony defects) of the lesion. Differences in bone levels before and after treatment were analyzed. Additionally, a comparison of outcomes based on the type of bone graft material used was conducted. Statistical analysis included a comparison of groups using a t-test, assuming equal variance. Results: The proportion of bone growth observed in the maxillary furcation, mandibular furcation, maxillary intrabony, and mandibular intrabony lesions was 26%, 22%, 31%, and 34%, respectively. No statistically significant differences in the amount of bone fill were observed between the groups. However, a significant positive correlation was found between the initial depth of the bone defect and the amount of bone fill observed after treatment. Conclusions: Contrary to previous evidence, the current study observed no significant differences in the amount of bone fill after GTR based on the location and nature of the lesion. Furthermore, the amount of bone regenerated was directly proportional to the initial depth of the alveolar bone defect.
Using Smart Traffic Lights to Reduce CO2 Emissions and Improve Traffic Flow at Intersections: Simulation of an Intersection in a Small Portuguese City
Authors: Santos Osvaldo , Ribeiro Fernando , Metrôlho José , Dionísio Rogério
Published: December 2023

Reducing CO2 emissions is currently a key policy in most developed countries. In this article, we evaluate whether smart traffic lights can have a relevant role in reducing CO2 emissions in small cities, considering their specific traffic profiles. The research method is a quantitative modelling approach tested by computational simulation. We propose a novel microscopic traffic simulation framework, designed to simulate realistic vehicle kinematics and driver behaviour, and accurately estimate CO2 emissions. We also propose and evaluate a routing algorithm for smart traffic lights, specially designed to optimize CO2 emissions at intersections. The simulations reveal that deploying smart traffic lights at a single intersection can reduce CO2 emissions by 32% to 40% in the vicinity of the intersection, depending on the traffic density. The simulations show other advantages for drivers: an increase in average speed of 60% to 101% and a reduction in waiting time of 53% to 95%. These findings can be useful for city-level decision makers who wish to adopt smart technologies to improve traffic flows and reduce CO2 emissions. This work also demonstrates that the simulator can play an important role as a tool to study the impact of smart traffic lights and foster the improvement in smart routing algorithms to reduce CO2 emissions.
Multi-Modal Ensemble Deep Learning in Head and Neck Cancer HPV Sub-Typing
Authors: Saikia Manob Jyoti , Kuanar Shiba , Mahapatra Dwarikanath , Faghani Shahriar
Published: December 2023

Oropharyngeal Squamous Cell Carcinoma (OPSCC) is one of the common forms of heterogeneity in head and neck cancer. Infection with human papillomavirus (HPV) has been identified as a major risk factor for OPSCC. Therefore, differentiating the HPV-positive and negative cases in OPSCC patients is an essential diagnostic factor influencing future treatment decisions. In this study, we investigated the accuracy of a deep learning-based method for image interpretation and automatically detected the HPV status of OPSCC in routinely acquired Computed Tomography (CT) and Positron Emission Tomography (PET) images. We introduce a 3D CNN-based multi-modal feature fusion architecture for HPV status prediction in primary tumor lesions. The architecture is composed of an ensemble of CNN networks and merges image features in a softmax classification layer. The pipeline separately learns the intensity, contrast variation, shape, texture heterogeneity, and metabolic assessment from CT and PET tumor volume regions and fuses those multi-modal features for final HPV status classification. The precision, recall, and AUC scores of the proposed method are computed, and the results are compared with other existing models. The experimental results demonstrate that the multi-modal ensemble model with soft voting outperformed single-modality PET/CT, with an AUC of 0.76 and F1 score of 0.746 on publicly available TCGA and MAASTRO datasets. In the MAASTRO dataset, our model achieved an AUC score of 0.74 over primary tumor volumes of interest (VOIs). In the future, more extensive cohort validation may suffice for better diagnostic accuracy and provide preliminary assessment before the biopsy.
RGGC-UNet: Accurate Deep Learning Framework for Signet Ring Cell Semantic Segmentation in Pathological Images
Authors: Zhao Tengfei , Fu Chong , Song Wei , Sham Chiu-Wing
Published: December 2023

Semantic segmentation of Signet Ring Cells (SRC) plays a pivotal role in the diagnosis of SRC carcinoma based on pathological images. Deep learning-based methods have demonstrated significant promise in computer-aided diagnosis over the past decade. However, many existing approaches rely heavily on stacking layers, leading to repetitive computational tasks and unnecessarily large neural networks. Moreover, the lack of available ground truth data for SRCs hampers the advancement of segmentation techniques for these cells. In response, this paper introduces an efficient and accurate deep learning framework (RGGC-UNet), which is a UNet framework including our proposed residual ghost block with ghost coordinate attention, featuring an encoder-decoder structure tailored for the semantic segmentation of SRCs. We designed a novel encoder using the residual ghost block with proposed ghost coordinate attention. Benefiting from the utilization of ghost block and ghost coordinate attention in the encoder, the computational overhead of our model is effectively minimized. For practical application in pathological diagnosis, we have enriched the DigestPath 2019 dataset with fully annotated mask labels of SRCs. Experimental outcomes underscore that our proposed model significantly surpasses other leading-edge models in segmentation accuracy while ensuring computational efficiency.
Synthetic Knee MRI T1p Maps as an Avenue for Clinical Translation of Quantitative Osteoarthritis Biomarkers
Authors: Tong Michelle W. , Tolpadi Aniket A. , Bhattacharjee Rupsa , Han Misung , Majumdar Sharmila , Pedoia Valentina
Published: December 2023

A 2D U-Net was trained to generate synthetic T1p maps from T2 maps for knee MRI to explore the feasibility of domain adaptation for enriching existing datasets and enabling rapid, reliable image reconstruction. The network was developed using 509 healthy contralateral and injured ipsilateral knee images from patients with ACL injuries and reconstruction surgeries acquired across three institutions. Network generalizability was evaluated on 343 knees acquired in a clinical setting and 46 knees from simultaneous bilateral acquisition in a research setting. The deep neural network synthesized high-fidelity reconstructions of T1p maps, preserving textures and local T1p elevation patterns in cartilage with a normalized mean square error of 2.4% and Pearson’s correlation coefficient of 0.93. Analysis of reconstructed T1p maps within cartilage compartments revealed minimal bias (−0.10 ms), tight limits of agreement, and quantification error (5.7%) below the threshold for clinically significant change (6.42%) associated with osteoarthritis. In an out-of-distribution external test set, synthetic maps preserved T1p textures, but exhibited increased bias and wider limits of agreement. This study demonstrates the capability of image synthesis to reduce acquisition time, derive meaningful information from existing datasets, and suggest a pathway for standardizing T1p as a quantitative biomarker for osteoarthritis.
Characteristics and Species Diversity of Semi-Natural Plant Communities on Langqi Island
Authors: Liu Yang , Huang Yunteng , Wang Yingxue , Wang Chunxiao , Xiao Zixin , Shen Siyuan , Zeng Jiyi , Deng Chuanyuan
Published: December 2023

(1) Background: Islands are relatively independent and fragile ecosystems with unique habitats. Studying the relatively stable semi-natural plant communities on islands can enrich island vegetation data and provide an understanding of the factors affecting species diversity and distribution. (2) Methods: The semi-natural plant communities on Langqi Island (LI) in Fujian Province were sampled, and the redundancy method was used to analyze the correlation between species diversity, community distribution, and environmental factors. (3) Results: (i) There were 432 species of vascular plants, with 306 genera and 110 families; (ii) the semi-natural plant communities consisted of three vegetation types, 10 formations, and 10 clusters; (iii) the species diversity indices of the semi-natural plant communities presented a trend of gradually decreasing along the shrub, tree, and herb layers. Different formations varied in terms of the species diversity index; and (iv) the soil total potassium and nitrogen contents significantly affected the species diversity of the LI semi-natural plant communities, whereas the pH, soil TN content, organic matter content, AK content, and altitude significantly affected the community distribution. (4) Conclusions: Selecting appropriate tree species for mixed forests based on plant characteristics and regional conditions, together with regulating the soil nutrient content, can improve the species diversity of semi-natural plant communities.
Mass Spectrometric ITEM-ONE and ITEM-TWO Analyses Confirm and Refine an Assembled Epitope of an Anti-Pertuzumab Affimer
Authors: Röwer Claudia , Olaleye Oladapo O. , Bischoff Rainer , Glocker Michael O.
Published: December 2023

Intact Transition Epitope Mapping—One-step Non-covalent force Exploitation (ITEM-ONE) analysis reveals an assembled epitope on the surface of Pertuzumab, which is recognized by the anti-Pertuzumab affimer 00557_709097. It encompasses amino acid residues NSGGSIYNQRFKGR, which are part of CDR2, as well as residues FTLSVDR, which are located on the variable region of Pertuzumab’s heavy chain and together form a surface area of 1381.46 Å2. Despite not being part of Pertuzumab’s CDR2, the partial sequence FTLSVDR marks a unique proteotypic Pertuzumab peptide. Binding between intact Pertuzumab and the anti-Pertuzumab affimer was further investigated using the Intact Transition Epitope Mapping—Thermodynamic Weak-force Order (ITEM-TWO) approach. Quantitative analysis of the complex dissociation reaction in the gas phase afforded a quasi-equilibrium constant (KD m0g#) of 3.07 × 10−12. The experimentally determined apparent enthalpy (ΔHm0g#) and apparent free energy (ΔGm0g#) of the complex dissociation reaction indicate that the opposite reaction—complex formation—is spontaneous at room temperature. Due to strong binding to Pertuzumab and because of recognizing Pertuzumab’s unique partial amino acid sequences, the anti-Pertuzumab affimer 00557_709097 is considered excellently suitable for implementation in Pertuzumab quantitation assays as well as for the accurate therapeutic drug monitoring of Pertuzumab in biological fluids.
Loss of the DYRK1A Protein Kinase Results in the Reduction in Ribosomal Protein Gene Expression, Ribosome Mass and Reduced Translation
Authors: Di Vona Chiara , Barba Laura , Ferrari Roberto , de la Luna Susana
Published: December 2023

Ribosomal proteins (RPs) are evolutionary conserved proteins that are essential for protein translation. RP expression must be tightly regulated to ensure the appropriate assembly of ribosomes and to respond to the growth demands of cells. The elements regulating the transcription of RP genes (RPGs) have been characterized in yeast and Drosophila, yet how cells regulate the production of RPs in mammals is less well understood. Here, we show that a subset of RPG promoters is characterized by the presence of the palindromic TCTCGCGAGA motif and marked by the recruitment of the protein kinase DYRK1A. The presence of DYRK1A at these promoters is associated with the enhanced binding of the TATA-binding protein, TBP, and it is negatively correlated with the binding of the GABP transcription factor, establishing at least two clusters of RPGs that could be coordinately regulated. However, DYRK1A silencing leads to a global reduction in RPGs mRNAs, pointing at DYRK1A activities beyond those dependent on its chromatin association. Significantly, cells in which DYRK1A is depleted have reduced RP levels, fewer ribosomes, reduced global protein synthesis and a smaller size. We therefore propose a novel role for DYRK1A in coordinating the expression of genes encoding RPs, thereby controlling cell growth in mammals.
Engineered Electrochemiluminescence Biosensors for Monitoring Heavy Metal Ions: Current Status and Prospects
Authors: Chen Yuanyuan , Jiang Hui , Liu Xiaohui , Wang Xuemei
Published: December 2023

Metal ion contamination has serious impacts on environmental and biological health, so it is crucial to effectively monitor the levels of these metal ions. With the continuous progression of optoelectronic nanotechnology and biometrics, the emerging electrochemiluminescence (ECL) biosensing technology has not only proven its simplicity, but also showcased its utility and remarkable sensitivity in engineered monitoring of residual heavy metal contaminants. This comprehensive review begins by introducing the composition, advantages, and detection principles of ECL biosensors, and delving into the engineered aspects. Furthermore, it explores two signal amplification methods: biometric element-based strategies (e.g., HCR, RCA, EDC, and CRISPR/Cas) and nanomaterial (NM)-based amplification, including quantum dots, metal nanoclusters, carbon-based nanomaterials, and porous nanomaterials. Ultimately, this review envisions future research trends and engineered technological enhancements of ECL biosensors to meet the surging demand for metal ion monitoring.
Photoelectrochemical Enzyme Biosensor for Malate Using Quantum Dots on Indium Tin Oxide/Plastics as a Sensing Surface
Authors: Hlaváčová Tereza , Skládal Petr
Published: December 2023

A photoelectrochemical biosensor for malate was developed using an indium tin oxide (ITO) layer deposited on a poly(ethylene terephthalate) plastic sheet as a transparent electrode material for the immobilization of malate dehydrogenase together with CdTe quantum dots. Different approaches were compared for the construction of the bioactive layer; the highest response was achieved by depositing malate dehydrogenase together with CdTe nanoparticles and covering it with a Nafion/water (1:1) mixture. The amperometric signal of this biosensor was recorded during irradiation with a near-UV LED in the flow-through mode. The limit of detection was 0.28 mmol/L, which is adequate for analyzing malic acid levels in drinks such as white wines and fruit juices. The results confirm that the cheap ITO layer deposited on the plastic sheet after cutting into rectangular electrodes allows for the economic production of photoelectrochemical (bio)sensors. The combination of NAD+-dependent malate dehydrogenase with quantum dots was also compatible with such an ITO surface.
Soft Epidermal Paperfluidics for Sweat Analysis by Ratiometric Raman Spectroscopy
Authors: Golparvar Ata , Thenot Lucie , Boukhayma Assim , Carrara Sandro
Published: December 2023

The expanding interest in digital biomarker analysis focused on non-invasive human bodily fluids, such as sweat, highlights the pressing need for easily manufactured and highly efficient soft lab-on-skin solutions. Here, we report, for the first time, the integration of microfluidic paper-based devices (μPAD) and non-enhanced Raman-scattering-enabled optical biochemical sensing (Raman biosensing). Their integration merges the enormous benefits of μPAD, with high potential for commercialization and use in resource-limited settings, with biorecognition-element-free (but highly selective) optical Raman biosensing. The introduced thin (0.36 mm), ultra-lightweight (0.19 g), and compact footprint (3 cm2) opto-paperfluidic sweat patch is flexible, stretchable, and conforms, irritation-free, to hairless or minimally haired body regions to enable swift sweat collection. As a great advantage, this new bio-chemical sensory system excels through its absence of onboard biorecognition elements (bioreceptor-free) and omission of plasmonic nanomaterials. The proposed easy fabrication process is adaptable to mass production by following a fully sustainable and cost-effective process utilizing only basic tools by avoiding typically employed printing or laser patterning. Furthermore, efficient collection and transportation of precise sweat volumes, driven exclusively by the wicking properties of porous materials, shows high efficiency in liquid transportation and reduces biosensing latency by a factor of 5 compared to state-of-the-art epidermal microfluidics. The proposed unit enables electronic chip-free and imaging-less visual sweat loss quantification as well as optical biochemical analysis when coupled with Raman spectroscopy. We investigated the multimodal quantification of sweat urea and lactate levels ex vivo (with syntactic sweat including +30 sweat analytes on porcine skin) and achieved a linear dynamic range from 0 to 100 mmol/L during fully dynamic continuous flow characterization.
The Relationship between Career Calling and Resilience among Rural-Oriented Pre-Service Teachers: The Chain Mediating Role of Career Adaptability and Decent Work
Authors: Wen Ya , Chen Huaruo , Liu Fei , Wei Xindong
Published: December 2023

From a Psychology of Working Theory (PWT) perspective, this study aims to explore how career adaptability and decent work mediate the effects of career calling on the resilience of rural-oriented pre-service teachers. The Career Calling Scale, Career Adaptability Scale, Decent Work Scale, and Resilience Scale were used to survey 393 rural-oriented pre-service teachers. The results found that career calling positively predicted the resilience of rural-oriented pre-service teachers; career adaptability mediated the relationship between career calling and resilience of rural-oriented pre-service teachers; decent work mediated the relationship between career calling and resilience of rural oriented pre-service teachers; and career adaptability and decent work play a chain mediating role between career calling and career adaptability of rural-oriented pre-service teachers. Therefore, this study concludes that career calling not only directly influences the resilience of rural-oriented pre-service teachers, but also indirectly influences the resilience of rural-oriented pre-service teachers through career adaptability and decent work.
Understanding the Factors and Consequences of Gas Deflagration Accident in Metro Shield Tunnel: Site Investigation and Numerical Analysis
Authors: Shen Yi , Sun Shuangchi , Sun Wei , Zhou Long , Huang Zhongkai
Published: December 2023

This study aims to investigate the factors and consequences of gas deflagration accidents in metro shield tunnels based on on-site investigation and numerical analysis. We built a numerical model and detection process for an underground shield tunnel subjected to an internal explosion from an actual accident. The tunnel geometry under consideration is the same as that used for the metro line. Concerning the limitations of research on the failure and recovery mechanism of shield segmental linings under the action of internal explosion load, an explosion accident of a shield segmental lining under construction caused by the shield tunneling machine destroying natural gas pipelines was discussed, in which the structure failure characteristics during the explosion and the structure repair method after the explosion were investigated. An interval repair scheme was proposed, which provides experience for the treatment of similar engineering accidents. To investigate the gas explosion within the tunnel during the accident, the finite element software Ansys LS-DYNA with the arbitrary Lagrangian–Eulerian (ALE) technique was employed to simulate the explosion scenario. Dynamic analyses were carried out to reproduce the blast scenario. The stress distribution within the segmental lining as well as the lining’s deformation were calculated. The potential applications of the treatment and planning of comparable engineering mishaps were discussed in the study.
Seismic Stability and Sustainable Performance of Diaphragm Walls Adjacent to Tunnels: Insights from 2D Numerical Modeling and Key Factors
Authors: Bigonah Nafiseh , Khaksar Reza Yeganeh , Fathollahi-Fard Amir M. , Gheibi Mohammad , Wacławek Stanisław , Moezzi Reza
Published: December 2023

The seismic performance of diaphragm walls adjacent to tunnels plays a pivotal role in ensuring the stability of underground infrastructure. This article presents an extensive investigation into the seismic behavior of such diaphragm walls through advanced two-dimensional (2D) numerical modeling. The primary objective is to establish the accuracy and reliability of the numerical model by comparing its results with a reference case history from Tianjin, China. Following successful validation, the study employs rigorous two-dimensional (2D) numerical analyses to examine the response of the diaphragm wall to seismic events while considering crucial factors. These factors encompass the dynamics of pore water pressure, the diverse acceleration histories of earthquakes, varying tunnel positions, and their combined influence on the horizontal displacement of the wall. From our findings, we can conclude that earthquake duration has a more substantial impact on displacement and wall deformation compared to peak ground acceleration (PGA). Longer earthquake durations are associated with greater displacement. In dynamic analyses, the presence of water diminishes soil displacement and concentrates plastic deformation points. The distance between the tunnel and the diaphragm wall significantly affects wall displacement and deformation. The effective distance is approximately 10 m. Our findings can inform better design and construction practices to enhance the stability of underground infrastructure in seismically active regions.
Inflammation-Associated Cytotoxic Agents in Tumorigenesis
Authors: Arnhold Jürgen
Published: December 2023

Chronic inflammatory processes are related to all stages of tumorigenesis. As inflammation is closely associated with the activation and release of different cytotoxic agents, the interplay between cytotoxic agents and antagonizing principles is highlighted in this review to address the question of how tumor cells overcome the enhanced values of cytotoxic agents in tumors. In tumor cells, the enhanced formation of mitochondrial-derived reactive species and elevated values of iron ions and free heme are antagonized by an overexpression of enzymes and proteins, contributing to the antioxidative defense and maintenance of redox homeostasis. Through these mechanisms, tumor cells can even survive additional stress caused by radio- and chemotherapy. Through the secretion of active agents from tumor cells, immune cells are suppressed in the tumor microenvironment and an enhanced formation of extracellular matrix components is induced. Different oxidant- and protease-based cytotoxic agents are involved in tumor-mediated immunosuppression, tumor growth, tumor cell invasion, and metastasis. Considering the special metabolic conditions in tumors, the main focus here was directed on the disturbed balance between the cytotoxic agents and protective mechanisms in late-stage tumors. This knowledge is mandatory for the implementation of novel anti-cancerous therapeutic approaches.
Novel HER-2 Targeted Therapies in Breast Cancer
Authors: Fernandes Catarina Lopes , Silva Diogo J. , Mesquita Alexandra
Published: December 2023

Human epidermal growth factor 2 (HER-2)-positive breast cancer represents 15–20% of all breast cancer subtypes and has an aggressive biological behavior with worse prognosis. The development of HER-2-targeted therapies has changed the disease’s course, having a direct impact on survival rates and quality of life. Drug development of HER-2-targeting therapies is a prolific field, with numerous new therapeutic strategies showing survival benefits and gaining regulatory approval in recent years. Furthermore, the acknowledgement of the survival impact of HER-2-directed therapies on HER-2-low breast cancer has contributed even more to advances in the field. The present review aims to summarize the newly approved therapeutic strategies for HER-2-positive breast cancer and review the new and exploratory HER-2-targeted therapies currently under development.
Study of the Composition and Properties of Bivalve Mollusk Shells as Promising Bio-Indifferent Materials for Photocatalytic Applications (Example of Practical Use)
Authors: Zaitsev Aleksey V. , Astapov Ivan A.
Published: December 2023

This paper studies the composition and properties of shells of bivalve mollusks (Crenomytilus grayanus, Callista brevisiphonata, and Mizuhopecten yessoensis) from coastal discharges with a view to the possibility of their use in photocatalytic water treatment systems. The clam shells are considered in terms of application in the form of a powder material as a precursor for creating photocatalysts, and also as a carrier of photocatalytic coatings. It was shown that the main phase composing the shell material was calcium carbonate in two crystallographic modifications—calcite and aragonite. The presence of inorganic impurities in all studied clam shells did not exceed one mass percent. The main share was made up of elements included in the composition of calcium carbonate, which confirmed the high bio-indifference of the materials under study. Depending on the physiological and environmental features of the structure of clam shells, different contents of the organic component in their composition were observed. The granulometric characteristics of crushed clam shells (average diameter, specific surface area, and distribution modality) were studied. It was shown that the maximum values of bending strength of 5 MPa and compressive strength of 2 MPa are characterized by Mizuhopecten yessoensis shells with the lowest porosity of 2.91%. The features of sorption and photosorption processes of both whole and crushed shells in relation to four organic dyes at different temperatures and degrees of illumination were studied. Based on crushed shells of Mizuhopecten Yessoensis and titanium dioxide, functional materials (CaxTiyOz) were obtained, and their morphology and photocatalytic properties were studied. An example of the practical use of clam shells as a carrier of a photocatalytic coating is given.
The “DrownSafe” Project: Assessing the Feasibility of a Puppet Show in Teaching Drowning Prevention to Children and Parents
Authors: Peixoto-Pino Lucía , Barcala-Furelos Roberto , Paz-García Begoña , Varela-Casal Cristina , Lorenzo-Martínez Miguel , Gómez-Silva Adrián , Rico-Díaz Javier , Rodríguez-Núñez Antonio
Published: December 2023

Drowning remains a prominent global pediatric health concern, necessitating preventive measures such as educational initiatives for children and caregivers. In this study, we aimed to assess the feasibility and educational effectiveness of an interactive puppet show centered on teaching water safety to children and parents. A 30 min original theater performance, featuring two actors and three puppets (a girl, a crab, and a lifeguard), was conducted. Subsequently, 185 children (aged 4 to 8) and their 160 parents (134 mothers and 26 fathers) participated in this quasi-experimental study. Pre- and post-show tests were administered to evaluate knowledge and behaviors regarding aquatic environments. Prior to the puppet show, 78% of the children exhibited basic aquatic competency. Only 33% considered swimming alone risky. Following the intervention, 81.6% of the children changed their perception of the risks of solo beach activities, showing improved knowledge regarding contacting an emergency number (from 63.2% to 98.9%, p < 0.001). The intervention increased parents’ intention to visit lifeguard-patrolled beaches and improved their CPR knowledge with regard to drowning victims by 58.8%. In conclusion, a drowning prevention puppet show positively impacted children and parents, potentially enhancing safety behaviors during water-related leisure activities, warranting its consideration part of comprehensive drowning prevention strategies.
Multi-Network Latency Prediction for IoT and WSNs
Authors: Balota Josiah E. , Kor Ah-Lian , Shobande Olatunji A.
Published: December 2023

The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.
Robust Face Mask Detection by a Socially Assistive Robot Using Deep Learning
Authors: Zhang Yuan , Effati Meysam , Tan Aaron Hao , Nejat Goldie
Published: December 2023

Wearing masks in indoor and outdoor public places has been mandatory in a number of countries during the COVID-19 pandemic. Correctly wearing a face mask can reduce the transmission of the virus through respiratory droplets. In this paper, a novel two-step deep learning (DL) method based on our extended ResNet-50 is presented. It can detect and classify whether face masks are missing, are worn correctly or incorrectly, or the face is covered by other means (e.g., a hand or hair). Our DL method utilizes transfer learning with pretrained ResNet-50 weights to reduce training time and increase detection accuracy. Training and validation are achieved using the MaskedFace-Net, MAsked FAces (MAFA), and CelebA datasets. The trained model has been incorporated onto a socially assistive robot for robust and autonomous detection by a robot using lower-resolution images from the onboard camera. The results show a classification accuracy of 84.13% for the classification of no mask, correctly masked, and incorrectly masked faces in various real-world poses and occlusion scenarios using the robot.
Impact of Crushed Natural and Recycled Fine Aggregates on Fresh and Hardened Mortar Properties
Authors: Burgmann Sophie , Breit Wolfgang
Published: December 2023

Increasing the amount of crushed natural and recycled fine aggregates in mortar and concrete can help to reduce depletion of resources and increase the recycling rate of construction and demolition waste. Differences in particle morphology influence fresh and hardened mortar and concrete properties. The quantitative assignment of this impact to specific characteristics, such as shape or angularity in differentiation to other mix design parameters, is currently scarcely known. Therefore, a multiple linear regression analysis was performed to investigate the impact of crushed natural and recycled fine particles on rheological and strength properties of mortar. The emphasis lies on the impact of differences in shape and angularity, which were quantified by the three-dimensional particle representation obtained from micro-computed tomography. A total of 160 mortar mixtures containing 5 sands of different origins and varying water-to-cement ratios, binder-to-aggregate ratios, and shapes of grading curves were produced. The results indicate that the particle shape and angularity of the crushed natural and recycled fine aggregates had a complex impact on fresh and hardened mortar properties and interacted with other mix design parameters. Careful composition of the aggregate fraction with respect to shape and angularity and their interaction with mix design parameters is necessary to maintain sufficient mortar properties.
A Nomogram and Risk Classification System Predicting the Prognosis of Patients with De Novo Metastatic Breast Cancer Undergoing Immediate Breast Reconstruction: A Surveillance, Epidemiology, and End Results Population-Based Study
Authors: Zhao Jingjing , Bian Shichang , Di Xu , Xiao Chunhua
Published: December 2023

Background The lifespan of patients diagnosed with de novo metastatic breast cancer (dnMBC) has been prolonged. Nonetheless, there remains substantial debate regarding immediate breast reconstruction (IBR) for this particular subgroup of patients. The aim of this study was to construct a nomogram predicting the breast cancer-specific survival (BCSS) of dnMBC patients who underwent IBR. Methods A total of 682 patients initially diagnosed with metastatic breast cancer (MBC) between 2010 and 2018 in the Surveillance, Epidemiology, and End Results (SEER) database were included in this study. All patients were randomly allocated into training and validation groups at a ratio of 7:3. Univariate Cox hazard regression, least absolute shrinkage and selection operator (LASSO), and best subset regression (BSR) were used for initial variable selection, followed by a backward stepwise multivariate Cox regression to identify prognostic factors and construct a nomogram. Following the validation of the nomogram with concordance indexes (C-index), receiver operating characteristic (ROC) curves, calibration curves, and decision curve analyses (DCAs), risk stratifications were established. Results Age, marital status, T stage, N stage, breast subtype, bone metastasis, brain metastasis, liver metastasis, lung metastasis, radiotherapy, and chemotherapy were independent prognostic factors for BCSS. The C-indexes were 0.707 [95% confidence interval (CI), 0.666–0.748] in the training group and 0.702 (95% CI, 0.639–0.765) in the validation group. In the training group, the AUCs for BCSS were 0.857 (95% CI, 0.770–0.943), 0.747 (95% CI, 0.689–0.804), and 0.700 (95% CI, 0.643–0.757) at 1 year, 3 years, and 5 years, respectively, while in the validation group, the AUCs were 0.840 (95% CI, 0.733–0.947), 0.763 (95% CI, 0.677–0.849), and 0.709 (95% CI, 0.623–0.795) for the same time points. The calibration curves for BCSS probability prediction demonstrated excellent consistency. The DCA curves exhibited strong discrimination power and yielded substantial net benefits. Conclusions The nomogram, constructed based on prognostic risk factors, has the ability to provide personalized predictions for BCSS in dnMBC patients undergoing IBR and serve as a valuable reference for clinical decision making.