bluuebunny/crossref_metadata_2025 huggingface dataset:
How do self-exempt beliefs affect intentions to quit smoking? An exploration of the mediating role of threat appraisal and coping appraisal
Authors: Zhang Dan , Liu Runhua , Li Xinchen , Yuan Yuanyuan , Zhou Geyao
Published: November 2023

Introduction

Numerous smokers are cognizant of the detrimental effects associated with this habit yet exhibit a persistent reluctance to cease their tobacco consumption. Self-exempt beliefs serve as an obstacle to the cessation of this addictive behavior. This research explored the impact of self-exempt beliefs on the readiness to quit smoking based on the Protection Motivation Theory (PMT) model and the mediating roles of threat appraisal and coping appraisal.

Methods

Self-exempt beliefs, PMT constructs, and the intention to quit smoking constituted the theoretical model. The questionnaires were collected from 488 Chinese adult male smokers based on snowball sampling. Exploratory Factor Analysis (EFA) was used to examine the underlying factor structure of the pre-designed self-exempt beliefs scale. The reliability, validity, path coefficients, and explanatory power of the model were calculated using Partial Least Squares Structural Equation Modeling (PLS-SEM).

Results and discussion

The results showed that : (1) three common factors (skeptic beliefs, bulletproof beliefs, and “worth it” beliefs) with a total of 11 items were retained after EFA; (2) skeptic beliefs and “worth it” beliefs had a significantly negative effect on both threat appraisal and coping appraisal, while bulletproof beliefs did not; (3) bulletproof beliefs had a significantly positive direct impact on intention to quit, “worth it” beliefs had a significantly negative direct impact on intention, while skeptic beliefs had no significantly direct impact on intention; (4) threat appraisal and coping appraisal positively and significantly predicted cessation intention; and (5) threat appraisal and coping appraisal, as two main cognitive processes, acted as full mediations between skeptic beliefs and the intention to quit, as complementary partial mediations between “worth it” beliefs and the intention, and as non-mediation between bulletproof beliefs and the intention. Our findings suggest that efforts to undermine or “prevent” these self-exempt beliefs, particularly “worth it” and skeptic beliefs, may be an effective tactic for health communication interventions for quitting smoking.
Healthcare bias and health inequalities towards displaced Syrians in Lebanon: a qualitative study
Authors: Khalifeh Riwa , D’Hoore William , Saliba Christiane , Salameh Pascale , Dauvrin Marie
Published: November 2023

Introduction

According to Lebanese official data, Lebanon hosts over 1.5 million displaced Syrians (DS). Research shows that migrants encounter barriers when accessing healthcare. The social determinants of health (SDOH) related to migration are an additional challenge for DS in Lebanon, though bias plays a significant factor in exacerbating health inequalities. This study aims to identify DS perception of healthcare biases in the Lebanese healthcare system, and its consequences on DS’ accessing and receiving quality healthcare in Lebanon.

Methods

A qualitative analysis using in-depth, semi-structured interviews was utilized. 28 semi-structured interviews were conducted with doctors (n = 12) and nurses (n = 16) in 2021. Six group interviews were conducted with DS (n = 22) in Lebanese healthcare facilities. The recruitment of participants relied on reasoned and targeted sampling. Thematic analysis was performed to identify common themes in participants’ experiences with DS accessing Lebanese healthcare.

Results

The findings indicated that there were barriers to accessing healthcare related to the SDOH, such as transportation and financial resources. The results also suggested that DS perceived health biases, including discriminatory behavior from Lebanese healthcare providers, stereotypes and racism leading to health inequalities.

Conclusion

Based on the perceptions and experiences reported by participants, the underlying causes of biases are due to the fragility of the Lebanese healthcare system when facing a humanitarian crisis as well as a collapsing infrastructure torn by past wars and the current socio-political and financial crises in the country. Global initiatives are required to provide the necessary resources needed for offering equitable health services. Such initiatives involve addressing biases, health inequities, discrimination, and the lack of a Lebanese infrastructure system for the provision of healthcare. Addressing health inequalities remains a major health objective in achieving health equity on the micro level (cultural awareness and competencies) and macro level (equitable distribution of resources, implementation of a universal health coverage) in order to guarantee quality healthcare services to DS.
Floods stress in lowland rice production: experiences of rice farmers in Kilombero and Lower-Rufiji floodplains, Tanzania
Authors: Michael Paulo Sulle , Mwakyusa Lupakisyo , Sanga Hilda G. , Shitindi Mawazo J. , Kwaslema Damiano R. , Herzog Max , Meliyo Joel L. , Massawe Boniface H. J.
Published: November 2023

The resilience of rice production to floods is a crucial aspect of agricultural development in flood-prone regions. However, little is known about the farming practices and challenges of rice farmers in two river basins in Tanzania: Lower-Rufiji and Kilombero, where rice is the main crop grown. This study aimed to investigate the experiences of farmers in these two rice-growing floodplains regarding the impact of floods on lowland rice production and farmer livelihoods, and the strategies they use to cope with floods. The study used a mixed-methods approach that involved household surveys, key informant interviews, and transect walks to collect data from 180 rice farmers and 14 experts and community leaders. The data analysis revealed that floods were the dominant stressor affecting rice production, occurring mostly during the long rainy season and experienced by all farmers in both locations. Floods not only caused significant crop losses, especially in Lower-Rufiji where farmers reported losing between 75 and 100% of their yield in cases of extreme floods, but also made it difficult to establish and manage crops, leading to crop land abandonment. Floods also affected the livelihoods of rice farmers by forcing them to abandon, rent, or sell their farms. Coexistence of floods with other biotic and abiotic stresses such as crop diseases and droughts also limited the use of short maturing high yielding rice varieties. Farmers have adopted on-farm strategies such as adjusting planting schedules, using different varieties of rice, changing to other crops or activities after the floods to cope with floods, but these practices face numerous challenges. The study recommends the development of early warning systems, breeding for flood-tolerant rice varieties, introgression of tolerance traits to multiple stresses and improving crop establishment methods to enhance the resilience of farmers to floods and other extreme climate events.
Effects of crude protein and non-essential amino acids on growth performance, blood profile, and intestinal health of weaned piglets
Authors: Correia Amanda Medeiros , Genova Jansller Luiz , Saraiva Alysson , Rocha Gabriel Cipriano
Published: November 2023

This study investigated the effect of crude protein (CP) and non-essential amino acid (NEAA) supplementation on the growth performance, blood profile, intestinal morphology, mRNA relative abundance of inflammatory and antioxidant markers, and tight junction proteins in piglets over the first 2 weeks after weaning. Ninety 21-day-old piglets (7.55 ± 0.72 kg) were assigned in a randomized block design to one of three dietary treatments: (1) high CP, a diet with 24% CP; (2) low CP, a diet with 18% CP; and (3) low CP + NEAA, a diet with 18% CP supplemented with 5 g/kg Arg (L-arginine; purity >99%) and 10 g/kg Glu + Gln (minimum 10% L-glutamine and minimum 10% L-glutamate). Piglets were fed with corn-soybean meal basal diets in a 14-day trial. There was an improvement (p < 0.05) in the feed conversion ratio of piglets fed the high-CP diet compared to treatments with low CP or low CP + NEAA. Serum urea nitrogen was higher (p < 0.05) in piglets fed high CP compared to other dietary treatments. In the duodenum, the villus height of animals fed the low-CP + NEAA diets was greater (p < 0.05) than those fed with the high- and low-CP diets. The goblet cell proportion of piglets fed low CP + NEAA or high CP was higher (p < 0.05) compared to low CP. In the jejunum, the crypt depth of the piglets with the high-CP dietary treatment was greater (p < 0.05) in comparison with low CP + NEAA. In the jejunum, IFN-γ mRNA expression was higher (p < 0.05) in animals fed the high-CP diets compared to other dietary treatments. However, superoxide dismutase and occludin mRNA expression were higher (p < 0.05) in animals fed low CP + NEAA than in piglets on the high-CP diets. In the ileum, the number of Peyer’s patches in piglets fed high CP was higher (p < 0.05) compared to other dietary treatments. In conclusion, the high-CP diet (24% CP) improves the feed conversion of piglets in the first 2 weeks after weaning compared to the low-CP diet (18% CP) supplemented or not with NEAA. However, the low-CP diet supplemented with NEAA (Arg, Gln, and Glu) improves intestinal health in piglets by promoting greater villus height and proportion of goblet cells in the duodenum, reducing jejunal crypt depth, and reducing Peyer’s number patches in the ileum. In addition, piglets that received the low-CP + NEAA diet showed an increase in superoxide dismutase and occludin and a lower expression of IFN-γ mRNA.
An Intelligent Autonomous Morphing Decision Approach for Hypersonic Boost-Glide Vehicles Based on DNNs
Authors: Hou Linfei , Liu Honglin , Yang Ting , An Shuaibin , Wang Rui
Published: November 2023

In addressing the morphing problem in vehicle flight, some scholars have primarily employed reinforcement learning methods to make morphing decisions based on task. However, they have not considered the constraints associated with the task process. The innovation of this article is that it proposes an intelligent morphing decision method based on deep neural networks (DNNs) for the autonomous morphing decision problem of hypersonic boost-glide morphing vehicles under process constraints. Firstly, we established a dynamic model of a hypersonic boost-glide morphing vehicle with a continuously variable sweep angle. Then, in order to address the decision optimality problem considering errors and the heat flux density constraint problem during the gliding process, interference was introduced to the datum trajectory in segments. Subsequently, re-optimization was performed to generate a trajectory sample library, which was used to train an intelligent decision-maker using a DNN. The simulation results demonstrated that, compared with the conventional programmatic morphing approach, the intelligent morphing decision maker could dynamically determine the sweep angle based on the current flight state, leading to improved range while still adhering to the heat flux density constraint. This validates the effectiveness and robustness of the proposed intelligent decision-maker.
Impact of Fusarium Crown Rot on Root System Area and Links to Genetic Variation within Commercial Wheat Varieties
Authors: Buster Mitchell , Simpfendorfer Steven , Guppy Christopher , Sissons Mike , Harden Steven , Flavel Richard J.
Published: November 2023

Fusarium crown rot (FCR), caused by the fungal pathogen Fusarium pseudograminearum (Fp), is a major constraint to cereal production worldwide. The pathogen restricts the movement of solutes within the plant due to mycelial colonisation of vascular tissue. Yield loss and quality downgrades are exacerbated by this disease under water stress conditions. Plant root systems are adaptive and can alter their architecture to optimise production in response to changes in environment and plant health. This plasticity of root systems typically favours resource acquisition of primarily water and nutrients. This study examined the impact of FCR on the root system architecture of multiple commercial bread and durum wheat varieties. Root system growth was recorded in-crop in large transparent rhizoboxes allowing visualization of root architecture over time. Furthermore, electrical resistivity tomography was used to quantify spatial root activity vertically down the soil profile. Results demonstrated a significant reduction in the total root length and network area with the inoculation of FCR. Electrical resistivity measurements indicated that the spatial pattern of water use for each cultivar was influenced differently from infection with FCR over the growing season. Specifically temporal water use can be correlated with FCR tolerance of the varieties marking this investigation the first to link root architecture and water use as tolerance mechanisms to FCR infection. This research has implications for more targeted selection of FCR tolerance characteristics in breeding programs along with improved specific varietal management in-crop.
A Review of the Diverse Genes and Molecules Involved in Sucrose Metabolism and Innovative Approaches to Improve Sucrose Content in Sugarcane
Authors: Khan Qaisar , Qin Ying , Guo Dao-Jun , Yang Li-Tao , Song Xiu-Peng , Xing Yong-Xiu , Li Yang-Rui
Published: November 2023

Sugarcane (Saccharum spp. hybrid) is the chief source of sugar and biofuel globally and is prominent among cash crops. Sucrose is the main required product in sugarcane, and many studies have been performed to understand the phenomena of sucrose synthesis, metabolism, and accumulation in sugarcane. However, none of the studies concluded that a single gene is responsible for the sucrose content. Instead, a complex mechanism consisting of several genes, such as sucrose phosphate synthase genes (SPS1, SPS2, SPS4, SPS5), sucrose synthase genes (SuSy1, SuSy2, SuSy4), invertase genes (INV, CWIN, NIN1, CINV2), and phytohormone, trehalose, transcription factor (TF), protein kinase, and sugar transporter genes are working spatiotemporally in sugarcane. Currently, omics approaches like transcriptomics, proteomics, and metabolomics are also being used to explore the sugar metabolism in sugarcane, but integrated transcriptomic, proteomic, and metabolomic studies have been less reported. The results obtained from the integrated analysis of transcriptomics, proteomics, and metabolomics are more reliable because the strong gene expression, received in the form of abundant mRNA, does not guarantee the plentiful existence of associated proteins or their particular activity in the target cells or tissues, which discloses the restraint of single interpretation and stresses the significance of the integrated analysis of transcriptomics, proteomics, and metabolomics. This review highlights different genes and molecules contributing to sugar metabolism at different stages and the significance of omics approaches in explaining sucrose metabolism, especially sucrose accumulation in sugarcane. It is also a vital source of knowledge for sugarcane breeders, particularly associated with sucrose content improvement and bioethanol energy production.
A Review of Optimal Design for Large-Scale Micro-Irrigation Pipe Network Systems
Authors: Wang Yafei , Zhang Yangkai , Wang Wenjuan , Liu Zhengguang , Yu Xingjiao , Li Henan , Wang Wene , Hu Xiaotao
Published: November 2023

Micro-irrigation pipe network systems are commonly utilized for water transmission and distribution in agricultural irrigation. They effectively transport and distribute water to crops, aiming to achieve water and energy conservation, increased yield, and improved quality. This paper presents a model for the scaled micro-irrigation pipeline network system and provides a comprehensive review of the fundamental concepts and practical applications of optimization techniques in the field of pipeline network design. This paper is divided into four main sections: Firstly, it covers the background and theoretical foundations of optimal design for scaled micro-irrigation pipeline network systems. Secondly, the paper presents an optimal design model specifically tailored for scaled micro-irrigation pipeline networks. And then, it discusses various optimization solution techniques employed for addressing the design challenges of scaled micro-irrigation pipeline networks, along with real-world case studies. Finally, this paper concludes with an outlook on the ongoing research and development efforts in the field of scaled micro-irrigation pipeline network systems. In addition, this paper establishes a fundamental model for optimizing pipeline networks, to achieve minimum safe operation and total cost reduction. It considers constraints such as pipeline pressure-bearing capacity, maximum flow rate, and diameter. The decision-making variables include pipeline diameter, length, internal roughness, node pressure, future demand, and valve placement. Additionally, this paper provides an extensive overview of deterministic methods and heuristic algorithms utilized in the optimal design of micro-irrigation pipeline networks. Finally, this paper presents future research directions for pipeline network optimization and explores the potential for algorithmic improvements, integration of machine learning techniques, and wider adoption of EPANET 2.0 software. These endeavors aim to lay a strong foundation for effectively solving complex and challenging optimization problems in micro-irrigation pipeline network systems in the future.
Primary Care Antibiotic Prescribing and Infection-Related Hospitalisation
Authors: Menting Stein Gerrit Paul , Redican Enya , Murphy Jamie , Bucholc Magda
Published: November 2023

Inappropriate prescribing of antibiotics has been widely recognised as a leading cause of antimicrobial resistance, which in turn has become one of the most significant threats to global health. Given that most antibiotic prescriptions are issued in primary care settings, investigating the associations between primary care prescribing of antibiotics and subsequent infection-related hospitalisations affords a valuable opportunity to understand the long-term health implications of primary care antibiotic intervention. A narrative review of the scientific literature studying associations between primary care antibiotic prescribing and subsequent infection-related hospitalisation was conducted. The Web of Science database was used to retrieve 252 potentially relevant studies, with 23 of these studies included in this review (stratified by patient age and infection type). The majority of studies (n = 18) were published in the United Kingdom, while the remainder were conducted in Germany, Spain, Denmark, New Zealand, and the United States. While some of the reviewed studies demonstrated that appropriate and timely antibiotic prescribing in primary care could help reduce the need for hospitalisation, excessive antibiotic prescribing can lead to antimicrobial resistance, subsequently increasing the risk of infection-related hospitalisation. Few studies reported no association between primary care antibiotic prescriptions and subsequent infection-related hospitalisation. Overall, the disparate results in the extant literature attest to the conflicting factors influencing the decision-making regarding antibiotic prescribing and highlight the necessity of adopting a more patient-focussed perspective in stewardship programmes and the need for increased use of rapid diagnostic testing in primary care.
Predicting Task Planning Ability for Learners Engaged in Searching as Learning Based on Tree-Structured Long Short-Term Memory Networks
Authors: Li Pengfei , Dong Shaoyu , Zhang Yin , Zhang Bin
Published: November 2023

The growing utilization of web-based search engines for learning purposes has led to increased studies on searching as learning (SAL). In order to achieve the desired learning outcomes, web learners have to carefully plan their learning objectives. Previous SAL research has proposed the significant influence of task planning quality on learning outcomes. Therefore, accurately predicting web-based learners’ task planning abilities, particularly in the context of SAL, is of paramount importance for both web-based search engines and recommendation systems. To solve this problem, this paper proposes a method for predicting the ability of task planning for web learners. Specifically, we first introduced a tree-based representation method to capture how learners plan their learning tasks. Subsequently, we proposed a method based on the deep learning technique to accurately predict the SAL task planning ability for web learners. Experimental results indicate that, compared to baseline approaches, our proposed method can provide a more effective representation of learners’ task planning and deliver more accurate predictions of learners’ task planning abilities in SAL.
Influence of Loess–Mudstone Strata Structure on Slope Seismic Stability of Loess Plateau in China
Authors: Zhang Zhaopeng , Qi Wenhao , Zhao Zhiyi , Zheng Tong
Published: November 2023

The widely distributed loess–mudstone strata structure in the Loess Plateau of China is one of the fundamental reasons for the susceptibility and group occurrence of seismic landslides in the area. This study took the loess landslides induced by the Haiyuan earthquake as a case study. Through extensive field investigations, the loess cover thickness, mudstone dip angle, slope height, and slope angle were chosen as the influencing factors. Together with mathematical statistics and geotechnical tests, two models, one of a loess–mudstone slope and the other of a pure loess slope, were constructed. Their seismic stabilities under different slope angles and slope heights were compared to analyze the influence of the underlying mudstone strata on the loess slope seismic stability. Meanwhile, a sensitivity analysis of these strata factors affecting the seismic stability of the loess–mudstone slope was carried out based on the orthogonal test. The results showed that ignoring the underlying mudstone strata would lead to smaller calculation results than the actual situation. The sensitivity influence of the factors on the loess–mudstone slope seismic stability comprised the slope angle, loess cover thickness, slope height, and mudstone dip angle from high to low.
CloudformerV3: Multi-Scale Adapter and Multi-Level Large Window Attention for Cloud Detection
Authors: Zhang Zheng , Tan Shuyang , Zhou Yongsheng
Published: November 2023

Cloud detection in remote sensing images is a crucial preprocessing step that efficiently identifies and extracts cloud-covered areas within the images, ensuring the precision and reliability of subsequent analyses and applications. Given the diversity of clouds and the intricacies of the surface, distinguishing the boundaries between thin clouds and the underlying surface is a major challenge in cloud detection. To address these challenges, an advanced cloud detection method, CloudformerV3, is presented in this paper. The proposed method employs a multi-scale adapter to incorporate dark and bright channel prior information into the model’s backbone, enhancing the model’s ability to capture prior information and multi-scale details from remote sensing images. Additionally, multi-level large window attention is utilized, enabling high-resolution feature maps and low-resolution feature maps to mutually focus and subsequently merge during the resolution recovery phase. This facilitates the establishment of connections between different levels of feature maps and offers comprehensive contextual information for the model’s decoder. The experimental results on the GF1_WHU dataset illustrate that the method proposed in this paper achieves MIoU of 92.89%, while achieving higher detection accuracy compared to state-of-the-art cloud detection models. Specifically, in comparison to Cloudformer, our method demonstrates a 1.11% improvement, while compared to CloudformerV2, there is a 0.37% increase. Furthermore, enhanced detection performance is achieved along cloud edges and concerning thin clouds, showcasing the efficacy of the proposed method.
Prediction of Short-Shot Defects in Injection Molding by Transfer Learning
Authors: Zhou Zhe-Wei , Yang Hui-Ya , Xu Bei-Xiu , Ting Yu-Hung , Chen Shia-Chung , Jong Wen-Ren
Published: November 2023

For a long time, the traditional injection molding industry has faced challenges in improving production efficiency and product quality. With advancements in Computer-Aided Engineering (CAE) technology, many factors that could lead to product defects have been eliminated, reducing the costs associated with trial runs during the manufacturing process. However, despite the progress made in CAE simulation results, there still exists a slight deviation from actual conditions. Therefore, relying solely on CAE simulations cannot entirely prevent product defects, and businesses still need to implement real-time quality checks during the production process. In this study, we developed a Back Propagation Neural Network (BPNN) model to predict the occurrence of short-shots defects in the injection molding process using various process states as inputs. We developed a Back Propagation Neural Network (BPNN) model that takes injection molding process states as input to predict the occurrence of short-shot defects during the injection molding process. Additionally, we investigated the effectiveness of two different transfer learning methods. The first method involved training the neural network model using CAE simulation data for products with length–thickness ratios (LT) of 60 and then applying transfer learning with real process data. The second method trained the neural network model using real process data for products with LT60 and then applied transfer learning with real process data from products with LT100. From the results, we have inferred that transfer learning, as compared to conventional neural network training methods, can prevent overfitting with the same amount of training data. The short-shot prediction models trained using transfer learning achieved accuracies of 90.2% and 94.4% on the validation datasets of products with LT60 and LT100, respectively. Through integration with the injection molding machine, this enables production personnel to determine whether a product will experience a short-shot before the mold is opened, thereby increasing troubleshooting time.
Image Generation with Global Photographic Aesthetic Based on Disentangled Generative Adversarial Network
Authors: Zhang Hua , Wang Muwei , Zhang Lingjun , Wu Yifan , Luo Yizhang
Published: November 2023

Global photographic aesthetic image generation aims to ensure that images generated by generative adversarial networks (GANs) contain semantic information and have global aesthetic feelings. Existing image aesthetic generation algorithms are still in the exploratory stage, and images screened or generated by a computer have not yet achieved relatively ideal aesthetic quality. In this study, we use an existing generative model, StyleGAN, to build the height of image content and put forward a new method based on the GAN disentangled representation of a global aesthetic image generation algorithm by mining GANs’ latent space, potential global aesthetic feeling, and aesthetic editing of the original image to realize the aesthetic feeling and content of high-quality global aesthetic image generation. In contrast with the traditional aesthetic image generation methods, our method does not need to retrain GANs. Using the existing StyleGAN generation model, by learning a prediction model to score the generated image and the score as a label to learn a support vector machine decision surface, we use the learned decision to edit the original image to obtain an image with a global aesthetic feeling. This method solves the problems of poor content construction effect and poor global beauty of the aesthetic images generated by the existing methods. Experimental results show that the proposed method greatly increases the aesthetic score of the generated images and makes the generated images more in line with people’s aesthetic.
Online Dynamic Network Visualization Based on SIPA Layout Algorithm
Authors: Wang Guijuan , Chen Huarong , Zhou Rui , Wu Yadong , Gao Wei , Liao Jing , Wang Fupan
Published: November 2023

Online dynamic network visualization is imperative for real-time network monitoring and analysis applications. It presents a significant research challenge for maintaining both layout stability and quality amid unpredictable temporal changes. While node-link diagrams are extensively utilized in online dynamic network visualization, previous node-link-diagram-based research primarily focused on stabilizing the layout by defining constraints on local node movement. However, these constraints often neglect the structural influence and its corresponding global impact, which may lead to that the representations of the network structure change significantly over time and a decrease in layout quality. To address this problem, we introduce the Structure-based Influence Propagation and Aging (SIPA) algorithm, a novel approach to preserve the stability of relative node positions and shapes of interconnected nodes (referred to as structures) between adjacent time steps. These stable structures serve as visual cues for users tracking the evolution of the network, thereby enhancing the overall layout stability. Additionally, we enhance dynamic network analysis by a highly interactive visualization system, enriching the layout result with multiple coordinated views of temporal trends, network features, animated graph diaries and snapshots. Our approach empowers users to interactively track and compare network evolution within a long-term temporal context and across multiple aspects. We demonstrate the effectiveness and performance of our approach through in-lab user studies and comparative experiments with three baseline dynamic network layout methods.
Experimental Investigation of Column Separation Using Rapid Closure of an Upstream Valve
Authors: Wang Huan , Zhou Ling , Liu Deyou
Published: November 2023

In this study, the phenomenon of column separation that occurs in the downstream pipeline of a rapid closure valve is experimentally investigated. Special attention is paid to the dynamic behavior of the formation, growth, and collapse processes of cavities, which are observed using a high-speed camera. Synchronized images of cavity patterns and measured pressure histories are analyzed to elucidate the process of water column separation, the mechanism of column separation events, and the influence of parameters on the transient flow. Experimental results indicate that during the collapse process of vapor cavities, a superposition phenomenon involving a positive pressure wave and collapse wave occurs, resulting in a nearly three times rise of Joukowsky pressure. In all test cases, the maximum pressure of the pipeline exceeded 150 times the reservoir static pressure. A new classification for a water hammer combined with cavitation (four types of pressure oscillation patterns) is proposed based on whether the duration of column separation decreases sequentially and the maximum pipeline pressure follows the first collapse of cavities at the valve. As the initial flow velocity increases, there is generally an increase in maximum pressure; however, this trend may be scattered under certain operation conditions.
Experimental Evaluation of Surface Roughness, Burr Formation, and Tool Wear during Micro-Milling of Titanium Grade 9 (Ti-3Al-2.5V) Using Statistical Evaluation Methods
Authors: Khan Muhammad Ayyaz , Khan Muhammad Ali , Aziz Shahid , Faraz Muhammad Iftikhar , Tahir Abdul Malik , Jaffery Syed Husain Imran , Jung Dong-Won
Published: November 2023

Titanium grade 9 (Ti-3Al-2.5V) stands out as a preferred material in various industrial applications because of its suitable properties. Its applications span diverse sectors, including precision manufacturing, where it is utilized to produce honeycomb structures for advanced aeronautics, as well as for certain biomedical components. In parallel, micro-milling has gained widespread utilization across medical, aerospace, and electronic industries due to the increasing demand for miniature products in these domains. This current research study aims to explore the impact of various micro-milling process parameters—specifically, feed rate, cutting speed, and depth of cut—on the surface quality, burr formation, and tool flank wear of titanium grade 9. Research findings reveal that the feed rate plays a major role in influencing surface roughness (contribution ratio (CR): 62.96%) and burr formation (CR: 55.20%). Similarly, cutting speed and depth of cut significantly affect surface roughness, contributing 20.32% and 9.27%, respectively, but are insignificant factors for burr width. Tool flank wear is primarily influenced by cutting speed (CR: 54.02%), with feed rate contributing 33.18%. Additionally, the feed rate and cutting speed are significant factors in determining the length of the burr, with contribution ratios of 77.70% and 7.77%, respectively. Confirmatory tests conducted at optimum parameters selected from the main effects plot validated the experimental results.
BODIPY-Based Molecules for Biomedical Applications
Authors: Das Sarasija , Dey Sudipto , Patra Sanujit , Bera Arindam , Ghosh Totan , Prasad Bibin , Sayala Kapil Dev , Maji Krishnendu , Bedi Anjan , Debnath Sashi
Published: November 2023

BODIPY (4,4-difluoro-4-bora-3a,4a-diaza-s-indacene) derivatives have attracted attention as probes in applications like imaging and sensing due to their unique properties like (1) strong absorption and emission in the visible and near-infrared regions of the electromagnetic spectrum, (2) strong fluorescence and (3) supreme photostability. They have also been employed in areas like photodynamic therapy. Over the last decade, BODIPY-based molecules have even emerged as candidates for cancer treatments. Cancer remains a significant health issue world-wide, necessitating a continuing search for novel therapeutic options. BODIPY is a flexible fluorophore with distinct photophysical characteristics and is a fascinating drug development platform. This review provides a comprehensive overview of the most recent breakthroughs in BODIPY-based small molecules for cancer or disease detection and therapy, including their functional potential.
Biological Evaluation of the Osteoinductive Potential of Dry Teeth after Chemical Demineralization Treatment Using the Tooth Transformer Device
Authors: Franceschelli Sara , Lagioia Rosalba , De Cecco Federica , Minetti Elio , Ballini Andrea , Panella Valeria , Speranza Lorenza , Grilli Alfredo , Mastrangelo Filiberto
Published: November 2023

Several studies have already demonstrated the biocompatibility of a tooth as a grafting material in the regeneration of bone tissue, showing its osteoconductive potential, while no studies have verified whether the osteoinductive potential of a tooth remains constant or is altered after its treatment with the Tooth Transformer (TT) device. The aim of the study was to demonstrate that the treatment with the TT device did not alter the osteoinductivity of an extracted tooth that was stored dry. Twelve extracted human teeth were collected from real patients. Caries, tartar and filling materials were removed from each tooth; each tooth was coarsely cut and stored at room temperature (RT) until use. Each sample was shredded, demineralized and disinfected, using the TT device. Protein extraction was carried out for each sample, and Western Blot analysis was performed to test the presence of mineralization protein LIM-1 and transforming growth factor-β. The presence of the human Bone Morphogenetic Protein 2 (BMP-2) and human collagen Type I (COL-I) was found in dry tooth samples processed with the TT device and subjected to Enzyme-Linked Immunosorbent Assay (ELISA) testing. The treatment of chemical demineralization using the TT device does not alter the osteoinductive potential of a dry tooth.
Fluorescent-Based Neurotransmitter Sensors: Present and Future Perspectives
Authors: Govindaraju Rajapriya , Govindaraju Saravanan , Yun Kyusik , Kim Jongsung
Published: November 2023

Neurotransmitters (NTs) are endogenous low-molecular-weight chemical compounds that transmit synaptic signals in the central nervous system. These NTs play a crucial role in facilitating signal communication, motor control, and processes related to memory and learning. Abnormalities in the levels of NTs lead to chronic mental health disorders and heart diseases. Therefore, detecting imbalances in the levels of NTs is important for diagnosing early stages of diseases associated with NTs. Sensing technologies detect NTs rapidly, specifically, and selectively, overcoming the limitations of conventional diagnostic methods. In this review, we focus on the fluorescence-based biosensors that use nanomaterials such as metal clusters, carbon dots, and quantum dots. Additionally, we review biomaterial-based, including aptamer- and enzyme-based, and genetically encoded biosensors. Furthermore, we elaborate on the fluorescence mechanisms, including fluorescence resonance energy transfer, photon-induced electron transfer, intramolecular charge transfer, and excited-state intramolecular proton transfer, in the context of their applications for the detection of NTs. We also discuss the significance of NTs in human physiological functions, address the current challenges in designing fluorescence-based biosensors for the detection of NTs, and explore their future development.
Avoiding Academic Burnout: Academic Factors That Enhance University Student Engagement
Authors: Reyes-de-Cózar Salvador , Merino-Cajaraville Alba , Salguero-Pazos María Rosa
Published: November 2023

Burnout is one of the major problems in higher education and is linked to a decline in students’ academic performance and achievement. Burnout, when prolonged over time and added to stress and high workloads, promotes the intention to drop out of studies, which translates into negative consequences for individuals and groups. Academic engagement is proposed as an effective alternative to offer solutions to improve the quality of education and counteract current negative trends. This study is based on a correlational–descriptive research design. It aimed to find out to what extent students feel engaged in their university studies and to identify and analyze possible correlations between engagement and specific classroom variables. To this end, a sample of 764 college students was studied. The result showed that students feel connected to and interested in their studies and that the area of knowledge impacts student engagement. They also indicate how learning strategies used in the classroom positively impact academic engagement.
Response Displacement Method for Seismic Calculation of Subway Station Complex Structure of TOD Mode
Authors: An Junhai , Feng Fuqiang , Zhang Yanhua , Li Ming
Published: November 2023

The TOD mode with rail transit stations as the center has become an important direction of future central city construction. However, at present, there is still a lack of simplified calculation methods for the seismic design of a subway station complex structure of TOD mode. Based on the load-structure model of the classic response displacement method and the seismic deformation characteristics of the underground complex structure, an improved response displacement method for seismic calculation of the subway station complex structure considering the influence of the upper frame structure was proposed in this paper. Then, based on the actual engineering case, the applicability of the improved reaction displacement method was verified through investigating the influencing factors such as seismic wave type, ground motion intensity, building position, structure form, structure stiffness, and stratum stiffness. Furthermore, a series of numerical simulation experiments were conducted to verify the simplified method and further evaluate its computational accuracy. The result shows that the error in calculating the internal force and deformation response of a station complex structure by using the improved reaction displacement method can be controlled at about 10%. The improved response deformation method is proved to be a highly practical pseudo-static method.
Vision-Based Guiding System for Autonomous Robotic Corner Cleaning of Window Frames
Authors: Tung Tzu-Jan , Al-Hussein Mohamed , Martinez Pablo
Published: November 2023

Corner cleaning is the most important manufacturing step of window framing to ensure aesthetic quality. After the welding process, the current methods to clean the welding seams lack quality control and adaptability. This increases rework, cost, and the waste produced in manufacturing and is largely due to the use of CNC cutting machines, as well as the reliance on manual inspection and weld seam cleaning. Dealing with manufacturing imperfections becomes a challenging task, as CNC machines rely on predetermined cleaning paths and frame information. To tackle such challenges using Industry 4.0 approaches and automation technology, such as robots and sensors, in this paper, a novel intelligent system is proposed to increase the process capacity to adapt to variability in weld cleaning conditions while ensuring quality through a combined approach of robot arms and machine vision that replaces the existing manual-based methods. Using edge detection to identify the window position and its orientation, artificial intelligence image processing techniques (Mask R-CNN model) are used to detect the window weld seam and to guide the robot manipulator in its cleaning process. The framework is divided into several modules, beginning with the estimation of a rough position for the purpose of guiding the robot toward the window target, followed by an image processing and detection module used in conjunction with instance segmentation techniques to segment the target area of the weld seam, and, finally, the generation of cleaning paths for further robot manipulation. The proposed robotic system is validated two-fold: first, in a simulated environment and then, in a real-world scenario, with the results obtained demonstrating the effectiveness and adaptability of the proposed system. The evaluation of the proposed framework shows that the trained Mask R-CNN can locate and quantify weld seams with 95% mean average precision (less than 1 cm).
Basosquamous Carcinoma: Comprehensive Clinical and Histopathological Aspects, Novel Imaging Tools, and Therapeutic Approaches
Authors: Murgia Giulia , Denaro Nerina , Boggio Francesca , Nazzaro Gianluca , Benzecry Valentina , Bortoluzzi Paolo , Passoni Emanuela , Garrone Ornella , Marzano Angelo
Published: November 2023

Basosquamous carcinoma (BSC), an uncommon and aggressive nonmelanoma skin cancer exhibiting characteristics ranging from basal cell carcinoma (BCC) to squamous cell carcinoma (SCC), is a subject of controversy in terms of its classification, pathogenesis, histologic morphology, biologic behavior, prognosis, and management. This narrative review is based on an electronic search of English-language articles in PubMed that included the terms “basosquamous carcinoma” and/or “metatypical carcinoma of the skin” in their titles. The review aims to succinctly present and assess current data on the epidemiology, clinical presentation, dermoscopic, LC-OCT, and histopathologic characteristics, as well as the genetics and management of BSC, providing insight into this intriguing entity. As a conclusion, dermoscopy, deep incisional biopsies, and immunohistologic techniques should be applied in clinically suspicious lesions to achieve an early diagnosis and better prognosis of this tumor. Surgical treatments, including wide excision and Mohs’ micrographic surgery, remain the treatment of choice. Finally, Hedgehog pathway inhibitors and checkpoint inhibitors, must be thoroughly investigated with large controlled trials, since they may offer an alternative solution to irresectable or difficult-to-treat locally advanced cases of basosquamous carcinoma.
Unraveling the Intricacies of Autophagy and Mitophagy: Implications in Cancer Biology
Authors: Lee Sunmi , Son Ji-Yoon , Lee Jinkyung , Cheong Heesun
Published: November 2023

Autophagy is an essential lysosome-mediated degradation pathway that maintains cellular homeostasis and viability in response to various intra- and extracellular stresses. Mitophagy is a type of autophagy that is involved in the intricate removal of dysfunctional mitochondria during conditions of metabolic stress. In this review, we describe the multifaceted roles of autophagy and mitophagy in normal physiology and the field of cancer biology. Autophagy and mitophagy exhibit dual context-dependent roles in cancer development, acting as tumor suppressors and promoters. We also discuss the important role of autophagy and mitophagy within the cancer microenvironment and how autophagy and mitophagy influence tumor host–cell interactions to overcome metabolic deficiencies and sustain the activity of cancer-associated fibroblasts (CAFs) in a stromal environment. Finally, we explore the dynamic interplay between autophagy and the immune response in tumors, indicating their potential as immunomodulatory targets in cancer therapy. As the field of autophagy and mitophagy continues to evolve, this comprehensive review provides insights into their important roles in cancer and cancer microenvironment.
CAR NK Cell Therapy for the Treatment of Metastatic Melanoma: Potential & Prospects
Authors: Hibler Winston , Merlino Glenn , Yu Yanlin
Published: November 2023

Melanoma is among the most lethal forms of cancer, accounting for 80% of deaths despite comprising just 5% of skin cancer cases. Treatment options remain limited due to the genetic and epigenetic mechanisms associated with melanoma heterogeneity that underlie the rapid development of secondary drug resistance. For this reason, the development of novel treatments remains paramount to the improvement of patient outcomes. Although the advent of chimeric antigen receptor-expressing T (CAR-T) cell immunotherapies has led to many clinical successes for hematological malignancies, these treatments are limited in their utility by their immune-induced side effects and a high risk of systemic toxicities. CAR natural killer (CAR-NK) cell immunotherapies are a particularly promising alternative to CAR-T cell immunotherapies, as they offer a more favorable safety profile and have the capacity for fine-tuned cytotoxic activity. In this review, the discussion of the prospects and potential of CAR-NK cell immunotherapies touches upon the clinical contexts of melanoma, the immunobiology of NK cells, the immunosuppressive barriers preventing endogenous immune cells from eliminating tumors, and the structure and design of chimeric antigen receptors, then finishes with a series of proposed design innovations that could improve the efficacy CAR-NK cell immunotherapies in future studies.