bluuebunny/crossref_metadata_2025 huggingface dataset:
Development-Associated Genes of the Epidermal Differentiation Complex (EDC)
Authors: Holthaus Karin Brigit , Eckhart Leopold
Published: January 2024

The epidermal differentiation complex (EDC) is a cluster of genes that encode protein components of the outermost layers of the epidermis in mammals, reptiles and birds. The development of the stratified epidermis from a single-layered ectoderm involves an embryo-specific superficial cell layer, the periderm. An additional layer, the subperiderm, develops in crocodilians and over scutate scales of birds. Here, we review the expression of EDC genes during embryonic development. Several EDC genes are expressed predominantly or exclusively in embryo-specific cell layers, whereas others are confined to the epidermal layers that are maintained in postnatal skin. The S100 fused-type proteins scaffoldin and trichohyalin are expressed in the avian and mammalian periderm, respectively. Scaffoldin forms the so-called periderm granules, which are histological markers of the periderm in birds. Epidermal differentiation cysteine-rich protein (EDCRP) and epidermal differentiation protein containing DPCC motifs (EDDM) are expressed in the avian subperiderm where they are supposed to undergo cross-linking via disulfide bonds. Furthermore, a histidine-rich epidermal differentiation protein and feather-type corneous beta-proteins, also known as beta-keratins, are expressed in the subperiderm. The accumulating evidence for roles of EDC genes in the development of the epidermis has implications on the evolutionary diversification of the skin in amniotes.
Decomposition Technique for Bio-Transmittance Imaging Based on Attenuation Coefficient Matrix Inverse
Authors: Priambodo Purnomo Sidi , Aminoto Toto , Basari Basari
Published: January 2024

Human body tissue disease diagnosis will become more accurate if transmittance images, such as X-ray images, are separated according to each constituent tissue. This research proposes a new image decomposition technique based on the matrix inverse method for biological tissue images. The fundamental idea of this research is based on the fact that when k different monochromatic lights penetrate a biological tissue, they will experience different attenuation coefficients. Furthermore, the same happens when monochromatic light penetrates k different biological tissues, as they will also experience different attenuation coefficients. The various attenuation coefficients are arranged into a unique k×k-dimensional square matrix. k-many images taken by k-many different monochromatic lights are then merged into an image vector entity; further, a matrix inverse operation is performed on the merged image, producing N-many tissue thickness images of the constituent tissues. This research demonstrates that the proposed method effectively decomposes images of biological objects into separate images, each showing the thickness distributions of different constituent tissues. In the future, this proposed new technique is expected to contribute to supporting medical imaging analysis.
Modeling Sequential Dependencies in Progressive Matrices: An Auto-Regressive Item Response Theory (AR-IRT) Approach
Authors: Myszkowski Nils , Storme Martin
Published: January 2024

Measurement models traditionally make the assumption that item responses are independent from one another, conditional upon the common factor. They typically explore for violations of this assumption using various methods, but rarely do they account for the possibility that an item predicts the next. Extending the development of auto-regressive models in the context of personality and judgment tests, we propose to extend binary item response models—using, as an example, the 2-parameter logistic (2PL) model—to include auto-regressive sequential dependencies. We motivate such models and illustrate them in the context of a publicly available progressive matrices dataset. We find an auto-regressive lag-1 2PL model to outperform a traditional 2PL model in fit as well as to provide more conservative discrimination parameters and standard errors. We conclude that sequential effects are likely overlooked in the context of cognitive ability testing in general and progressive matrices tests in particular. We discuss extensions, notably models with multiple lag effects and variable lag effects.
A Scalable Formal Framework for the Verification and Vulnerability Analysis of Redundancy-Based Error-Resilient Null Convention Logic Asynchronous Circuits
Authors: Mazumder Dipayan , Datta Mithun , Bodoh Alexander C. , Sakib Ashiq A.
Published: January 2024

The increasing demand for high-speed, energy-efficient, and miniaturized electronics has led to significant challenges and compromises in the domain of conventional clock-based digital designs, most notably reduced circuit reliability, particularly in mission-critical hardware. At scaled technology nodes, devices are vulnerable to transient or soft errors, such as Single Event Upset (SEU) and Single Event Latch-up (SEL). External radiation, internal electromagnetic interference (EMI), or noise are the primary sources of these errors, which can compromise the circuit functionality. In response to these challenges, the Quasi-Delay-Insensitive (QDI) Null Convention Logic (NCL) asynchronous design paradigm has emerged as a promising alternative, offering advantages such as ultra-low power performance, reduced noise and EMI, and resilience to process, voltage, and temperature variations. Moreover, its unique architecture and insensitivity to timing variations offers a degree of resistance against transient errors; however, it is not entirely resilient. Several resiliency schemes are available to detect and mitigate soft errors in QDI circuits, with approaches based on redundancy proving to be the most effective in ensuring complete resilience across all major QDI implementation paradigms, including NCL, Pre-charge/Weak-charge Half Buffers (PCHB/WCHB), and Sleep Convention Logic (SCL). This research focuses on one such redundancy-based resiliency scheme for QDI NCL circuits, known as the dual-modular redundancy-based NCL (DMR-NCL) architecture, and addresses the absence of formal methods for the verification and analysis of such circuits. A novel methodology has been proposed for formally verifying the correctness of DMR-NCL circuits synthesized from their synchronous counterparts, covering both safety (functional correctness) and liveness (the absence of deadlock). In addition, this research introduces a formal framework for the vulnerability analysis of DMR-NCL circuits against SEU/SEL. To demonstrate the framework’s efficacy and scalability, a prototype computer-aided support tool has been developed, which verifies and analyzes multiple DMR-NCL benchmark circuits of varying sizes and complexities.
Global Path Planning for Autonomous Ship Navigation Considering the Practical Characteristics of the Port of Ulsan
Authors: Yun Sang-Woong , Kim Dong-Ham , Kim Se-Won , Kim Dong-Jin , Kim Hye-Jin
Published: January 2024

This study introduces global path planning for autonomous ships in port environments, with a focus on the Port of Ulsan, where various environmental factors are modeled for analysis. Global path planning is considered to take place from departure to berth, specifically accounting for scenarios involving a need to navigate via anchorage areas as waypoints due to unexpected increases in port traffic or when direct access to the berth is obstructed. In this study, a navigable grid for autonomous ships was constructed using land, breakwater, and water depth data. The modeling of the Port of Ulsan’s traffic lanes and anchorage areas reflects the port’s essential maritime characteristics for global path planning. In this study, an improved A* algorithm, along with grid-based path planning, was utilized to determine a global path plan. We used smoothing algorithms to refine the global paths for practical navigation, and the validation of these paths was achieved through conducting ship maneuvering simulations from model tests, which approximate real-world navigation in navigational simulation. This approach lays the groundwork for enhanced route generation studies in complex port environments.
Research on the Heterogeneous Autonomous Underwater Vehicle Cluster Scheduling Problem Based on Underwater Docking Chambers
Authors: Wang Jia , Tao Tianyi , Lu Daohua , Wang Zhibin , Wang Rongtao
Published: January 2024

The onboard energy supply of Autonomous Underwater Vehicles (AUVs) is one of the main limiting factors for their development. The existing methods of deploying and retrieving AUVs from mother ships consume a significant amount of energy during submerging and surfacing, resulting in a small percentage of actual working time. Underwater docking chambers provide support to AUVs underwater, saving their precious energy and addressing this issue. When an AUV cluster is assigned multiple tasks, scheduling the cluster becomes essential, and task allocation and path planning are among the core problems in AUV cluster scheduling research. In this paper, based on the underwater docking chamber, an Improved Genetic Local Search Algorithm with Prior Knowledge (IGLSAPK) is proposed to simultaneously solve the task allocation and path planning problems. Under constraints such as onboard energy supply, AUV quantity, and AUV type, the algorithm groups AUVs, assigns tasks, and plans paths to accomplish tasks at different locations, aiming to achieve overall efficiency. The algorithm first generates an initial population using prior knowledge to improve its search efficiency. It then combines an improved local search algorithm to efficiently solve large-scale, complex, and highly coupled problems. The algorithm has been evaluated through simulation experiments and comparative experiments, and the results demonstrate that the proposed algorithm outperforms other algorithms in terms of speed and optimality. The algorithm presented in this paper addresses the grouping, task allocation, and path planning problems in heterogeneous AUV clusters. Its practical significance lies in its ability to handle tasks executed by a heterogeneous AUV group, making it more practical compared to previous algorithms.
Assessment of Shoreline Change from SAR Satellite Imagery in Three Tidally Controlled Coastal Environments
Authors: Savastano Salvatore , Gomes da Silva Paula , Sánchez Jara Martínez , Tort Arnau Garcia , Payo Andres , Pattle Mark E. , Garcia-Mondéjar Albert , Castillo Yeray , Monteys Xavier
Published: January 2024

Coasts are continually changing and remote sensing from satellites has the potential to both map and monitor coastal change at multiple scales. Unlike optical technology, synthetic aperture radar (SAR) is uninfluenced by darkness, clouds, and rain, potentially offering a higher revision period to map shoreline position and change, but this can only be feasible if we have a better interpretation of what shorelines as extracted from SAR imagery represent on the ground. This study aims to assess the application of shorelines extracted from SAR from publicly available satellite imagery to map and capture intra-annual to inter-annual shoreline variability. This is assessed in three tidally controlled coastal study areas that represent sand and gravel beaches with different backshore environments: low-lying dunes and marsh; steep, rocky cliff; and urban environments. We have found that SAR shorelines consistently corresponded to positions above the high-water mark across all three sites. We further discuss the influence of the scene geometry, meteorological and oceanographic conditions, and backshore environment and provide a conceptual interpretation of SAR-derived shorelines. In a low-lying coastal setting, the annual change rate derived through SAR presents a high degree of alignment with the known reference values. The present study contributes to our understanding of the poorly known aspect of using shorelines derived from publicly available SAR satellite missions. It outlines a quantitative approach to automatically assess their quality with a new automatic detection method that is transferable to shoreline evolution assessments worldwide.
Morphodynamic Response of Open and Embayed Beaches to Winter Conditions: Two Case Studies from the North Atlantic Iberian Coast
Authors: Fontán-Bouzas Ángela , Abreu Tiago , C. Ferreira Caroline , Silva Paulo A. , López-Olmedilla Laura , Guitián José , Bernabeu Ana M. , Alcántara-Carrió Javier
Published: January 2024

The morphological responses of two mesotidal beaches located in different coastal settings (embayed and open sandy beaches) on the northwestern Iberian coast were monitored during the winter of 2018/19. The offshore wave time series analysis is related to high-resolution topo-bathymetric measurements to explore spatial-temporal morphological variability at monthly to seasonal scales. Both locations are subjected to the North Atlantic wave climate which exhibits a pronounced seasonality. Throughout the last decade (2010–2020), significant wave heights reached values of up to Hs~9 m during winters and up to Hs~6 m during summers. On average, approximately 12 storms occurred annually in this region. The results clearly reveal divergent morphological responses and sediment transport behaviors at the upper beach and the intertidal zone during the winter for each location. In the embayed beach (Patos), sediment transport in the nearshore is governed by cross-shore processes between the beach berm and a submerged sandbar. In contrast, the open beach (Mira) showed dynamic sediment exchanges and three-dimensional morphologies alternating between accumulation and erosion zones. Overall, both beaches exhibited an erosional trend after the winter, particularly concerning berm erosion and the subaerial beach volume/shoreline retreat. This study highlights the contrasting morphodynamic response on open and embayed beaches to winter conditions, integrating both the subaerial and submerged zones. Local geological and environmental factors, as well as the coastal management strategies applied, will influence how the beach responds to winter wave events. Monitoring and understanding these responses are essential for effective coastal management and adaptation to changing climate.
Syncephalastrum massiliense sp. nov. and Syncephalastrum timoneanum sp. nov. Isolated from Clinical Samples
Authors: Kabtani Jihane , Boulanouar Fatima , Gaye Papa Mouhamadou , Militello Muriel , Ranque Stéphane
Published: January 2024

Mucormycosis is known to be a rare opportunistic infection caused by fungal organisms belonging to the Mucorales order, which includes the Syncephalastrum species. These moulds are rarely involved in clinical diseases and are generally seen as contaminants in clinical laboratories. However, in recent years, case reports of human infections due to Syncephalastrum have increased, especially in immunocompromised hosts. In this study, we described two new Syncephalastrum species, which were isolated from human nails and sputum samples from two different patients. We used several methods for genomic and phenotypic characterisation. The phenotypic analysis relied on the morphological features, analysed both by optical and scanning electron microscopy. We used matrix-assisted laser desorption–ionization time-of-flight mass spectrometry, energy-dispersive X-ray spectroscopy, and BiologTM technology to characterise the proteomic, chemical mapping, and carbon source assimilation profiles, respectively. The genomic analysis relied on a multilocus DNA sequence analysis of the rRNA internal transcribed spacers and D1/D2 large subunit domains, fragments of the translation elongation factor-1 alpha, and the β-tubulin genes. The two novel species in the genus Syncephalastrum, namely S. massiliense PMMF0073 and S. timoneanum PMMF0107, presented a similar morphology: irregular branched and aseptate hyphae with ribbon-like aspects and terminal vesicles at the apices all surrounded by cylindrical merosporangia. However, each species displayed distinct phenotypic and genotypic features. For example, S. timoneanum PMMF0107 was able to assimilate more carbon sources than S. massiliense PMMF0073, such as adonitol, α-methyl-D-glucoside, trehalose, turanose, succinic acid mono-methyl ester, and alaninamide. The polyphasic approach, combining the results of complementary phenotypic and genomic assays, was instrumental for describing and characterising these two new Syncephalastrum species.
Three New Species of Russulaceae (Russulales, Basidiomycota) from Southern China
Authors: Liu Sen , Zhu Mengjia , Keyhani Nemat O. , Wu Ziyi , Lv Huajun , Heng Zhiang , Chen Ruiya , Dang Yuxiao , Yang Chenjie , Chen Jinhui , Lai Pengyu , Zhang Weibin , Guan Xiayu , Huang Yanbin , Chen Yuxi , Su Hailan , Qiu Junzhi
Published: January 2024

The characterization of natural fungal diversity impacts our understanding of ecological and evolutionary processes and can lead to novel bioproduct discovery. Russula and Lactarius, both in the order Russulales, represent two large genera of ectomycorrhizal fungi that include edible as well as toxic varieties. Based on morphological and phylogenetic analyses, including nucleotide sequences of the internal transcribed spacer (ITS), the 28S large subunit of ribosomal RNA (LSU), the second largest subunit of RNA polymerase II (RPB2), the ribosomal mitochondrial small subunit (mtSSU), and the translation elongation factor 1-α (TEF1-α) gene sequences, we here describe and illustrate two new species of Russula and one new species of Lactarius from southern China. These three new species are: R. junzifengensis (R. subsect. Virescentinae), R. zonatus (R. subsect. Crassotunicatae), and L. jianyangensis (L. subsect. Zonarii).
Physiological and Transcriptome Responses of Pinus massoniana Seedlings Inoculated by Various Ecotypes of the Ectomycorrhizal Fungus Cenococcum geophilum during the Early Stage of Drought Stress
Authors: Zhang Xiaohui , Zhang Jinyan , He Juan , Li Mingtao , Matsushita Norihisa , Geng Qifang , Lian Chunlan , Zhang Shijie
Published: January 2024

The impact of drought stress on plant growth in arid regions is a critical concern, necessitating the exploration of strategies to enhance plant drought resistance, particularly during the early stages of drought stress. This study focuses on the ectomycorrhizal fungus Cenococcum geophilum, renowned for its extensive genetic diversity and broad host compatibility, making it a crucial ally for host plants facing external stresses. We utilized Pinus massoniana seedlings inoculated with different ecotypic strains of C. geophilum under drought stress. The results showed that the inoculation of most strains of C. geophilum enhanced the drought resistance of P. massoniana seedlings under the early stages of drought stress, by influencing the water content, photosynthesis, accumulation of osmotic adjustment substances, and antioxidant enzyme activities in both shoots and roots of seedlings. Transcriptome analysis showed that mycorrhizal seedlings mainly regulated energy metabolism and reduction–oxidation reaction to resist early drought stress. Notably, the level of drought resistance observed in mycorrhizal seedlings was irrespective of the level of drought tolerance of C. geophilum strains. This study contributes essential data for understanding the drought response mechanisms of mycorrhizal P. massoniana seedlings inoculated by distinct C. geophilum ecotypes and guidance on selecting candidate species of ectomycorrhizal fungi for mycorrhizal afforestation in drought areas.
Game Theory-Based Incentive Design for Mitigating Malicious Behavior in Blockchain Networks
Authors: Mssassi Souhail , Abou El Kalam Anas
Published: January 2024

This paper presents an innovative incentive model that utilizes graph and game theories to address the issue of node incentives in decentralized blockchain networks such as EVM blockchains. The lack of incentives for nodes within EVM networks gives rise to potential weaknesses that might be used for various purposes, such as broadcasting fake transactions or withholding blocks. This affects the overall trust and integrity of the network. To address this issue, the current study offers a network model that incorporates the concepts of graph theory and utilizes a matrix representation for reward and trust optimization. Furthermore, this study presents a game-theoretic framework that encourages cooperative conduct and discourages malicious actions, ultimately producing a state of equilibrium according to the Nash equilibrium. The simulations validated the model’s efficacy in addressing fraudulent transactions and emphasized its scalability, security, and fairness benefits. This study makes a valuable contribution to the field of blockchain technology by presenting an incentive model that effectively encourages the development of secure and trusted decentralized systems.
Sustainable Land Use Diagnosis Based on the Perspective of Coupling Socioeconomy and Ecology in the Xiongan New Area, China
Authors: Wang Xiaoxiao , Zhao Huafu , Qian Jiacheng , Li Xiao , Cao Congjie , Feng Zhe , Cui Yiqing
Published: January 2024

Analysis of land use sustainability (LUS) coupling socioeconomic and ecological systems has drawn the interest of researchers and decision-makers concerned with sustainable development. Exploring the relationships between land use systems can offer a coordinated route for localized and effective land governance strategies. We proposed a new theoretical framework for LUS considering coordinated development between socioeconomy and ecology. We applied it further to the Xiongan New Area, typical of a new region with government-led construction in eastern China, to explore in depth the coupling and coordination relationship among land use systems and their driving factors. Results revealed that: (1) ecological sustainability grew while socioeconomic sustainability declined in most areas of Xiongan New Area during 2010–2020 at the township level, (2) relationships among land use systems in 2010–2020 showed a trend of coordinated development, but generally, their coordination was still only at a primary and moderate level, with a shift from prominent ecological lag to prominent socioeconomic lag, (3) economic factors are critical to the coordinated development of land use systems, while the influence of ecological factors is also increasing, and (4) four development types were identified for the towns: coupling and developed, coupling and undeveloped, tradeoff and developed, and tradeoff and undeveloped. Policymakers can better appreciate the importance of government interventions in future land use planning in terms of sustainable development with the help of land use zoning optimization and sustainable land governance.
Spatial Planning and Land-Use Management
Authors: Gomes Eduardo , Costa Eduarda Marques da , Abrantes Patrícia
Published: January 2024

Preserving natural and semi-natural areas has become a crucial consideration for policymakers, with several drivers recognized as pivotal forces that shape landscapes globally [...]
The Triple-S Advantage of Endoscopic Management in Gastrointestinal Surgery Complications: Safe, Successful, and Savings-Driven
Authors: Mandarino Francesco Vito , Sinagra Emanuele , Barchi Alberto , Danese Silvio
Published: January 2024

Despite advances in gastrointestinal (GI) surgery, post-operative complications are not entirely avoidable [...]
Protein–Protein Interactions of Seryl-tRNA Synthetases with Emphasis on Human Counterparts and Their Connection to Health and Disease
Authors: Dulic Morana , Godinic-Mikulcic Vlatka , Kekez Mario , Evic Valentina , Rokov-Plavec Jasmina
Published: January 2024

Seryl-tRNA synthetases (SerRSs), members of the aminoacyl-tRNA synthetase family, interact with diverse proteins, enabling SerRSs to enhance their role in the translation of the genetic message or to perform alternative functions in cellular processes beyond translation. Atypical archaeal SerRS interacts with arginyl-tRNA synthetase and proteins of the ribosomal P-stalk to optimize translation through tRNA channeling. The complex between yeast SerRS and peroxin Pex21p provides a connection between translation and peroxisome function. The partnership between Arabidopsis SerRS and BEN1 indicates a link between translation and brassinosteroid metabolism and may be relevant in plant stress response mechanisms. In Drosophila, the unusual heterodimeric mitochondrial SerRS coordinates mitochondrial translation and replication via interaction with LON protease. Evolutionarily conserved interactions of yeast and human SerRSs with m3C32 tRNA methyltransferases indicate coordination between tRNA modification and aminoacylation in the cytosol and mitochondria. Human cytosolic SerRS is a cellular hub protein connecting translation to vascular development, angiogenesis, lipogenesis, and telomere maintenance. When translocated to the nucleus, SerRS acts as a master negative regulator of VEGFA gene expression. SerRS alone or in complex with YY1 and SIRT2 competes with activating transcription factors NFκB1 and c-Myc, resulting in balanced VEGFA expression important for proper vascular development and angiogenesis. In hypoxia, SerRS phosphorylation diminishes its binding to the VEGFA promoter, while the lack of nutrients triggers SerRS glycosylation, reducing its nuclear localization. Additionally, SerRS binds telomeric DNA and cooperates with the shelterin protein POT1 to regulate telomere length and cellular senescence. As an antitumor and antiangiogenic factor, human cytosolic SerRS appears to be a promising drug target and therapeutic agent for treating cancer, cardiovascular diseases, and possibly obesity and aging.
Transapical Approach to Septal Myectomy for Hypertrophic Cardiomyopathy
Authors: Afanasyev Alexander , Bogachev-Prokophiev Alexander , Zheleznev Sergei , Ovcharov Mikhail , Zalesov Anton , Sharifulin Ravil , Demin Igor’ , Tsaroev Bashir , Nazarov Vladimir , Chernyavskiy Alexander
Published: January 2024

A 63-year-old symptomatic female with apical hypertrophic cardiomyopathy and diastolic disfunction was admitted to the hospital. What is the best way to manage this patient? This study is a literature review that was performed to answer this question. The following PubMed search strategy was used: ‘Hypertrophic obstructive cardiomyopathy’ [All Fields] OR ‘apical myectomy’ [All Fields], NOT ‘animal [mh]’ NOT ‘human [mh]’ NOT ‘comment [All Fields]’ OR ‘editorial [All Fields]’ OR ‘meta-analysis [All Fields]’ OR ‘practice-guideline [All Fields]’ OR ‘review [All Fields]’ OR ‘pediatrics [mh]’. The natural history of the disease has a benign prognosis; however, a watchful strategy was associated with the risk of adverse cardiovacular events. Contrastingly, transapical myectomy was associated with low surgical risk and acceptable outcomes. In our case, the patient underwent transapical myectomy with an unconventional post-operative period. Control echocardiography showed marked left ventricular (LV) cavity enlargement: LV end-diastolic volume, 74 mL; LV ejection fraction, 65%; and LV stroke volume index increased to 27 mL/m2. The patient was discharged 7 days after myectomy. At 6 months post-operation, the patient was NYHA Class I, with a 6 min walk test score of 420 m. Therefore, transapical myectomy may be considered as a feasible procedure in patients with apical hypertrophic cardiomyopathy and progressive heart failure.
Spatial Transcriptomic Profiling of Tetraspanins in Stage 4 Colon Cancer from Primary Tumor and Liver Metastasis
Authors: Suwatthanarak Thanawat , Thanormjit Kullanist , Suwatthanarak Tharathorn , Acharayothin Onchira , Methasate Asada , Chinswangwatanakul Vitoon , Tanjak Pariyada
Published: January 2024

Stage 4 colon cancer (CC) presents a significant global health challenge due to its poor prognosis and limited treatment options. Tetraspanins, the transmembrane proteins involved in crucial cancer processes, have recently gained attention as diagnostic markers and therapeutic targets. However, their spatial expression and potential roles in stage 4 CC tissues remain unknown. Using the GeoMx digital spatial profiler, we profiled all 33 human tetraspanin genes in 48 areas within stage 4 CC tissues, segmented into immune, fibroblast, and tumor compartments. Our results unveiled diverse gene expression patterns across different primary tumor sub-regions. CD53 exhibited distinct overexpression in the immune compartment, hinting at a potential role in immune modulation. TSPAN9 was specifically overexpressed in the fibroblast compartment, suggesting involvement in tumor invasion and metastasis. CD9, CD151, TSPAN1, TSPAN3, TSPAN8, and TSPAN13 displayed specific overexpression in the tumor compartment, indicating potential roles in tumor growth. Furthermore, our differential analysis revealed significant spatial changes in tetraspanin expression between patient-matched stage 4 primary CC and metastatic liver tissues. These findings provide spatially resolved insights into the expression and potential roles of tetraspanins in stage 4 CC progression, proposing their utility as diagnostic markers and therapeutic targets. Understanding this landscape is beneficial for tailoring therapeutic strategies to specific sub-tumor regions in the context of stage 4 CC and liver metastasis.
The Application of Ionic Liquids in the Lubrication Field: Their Design, Mechanisms, and Behaviors
Authors: Liu Mengmeng , Ni Jing , Zhang Caixia , Wang Ruishen , Cheng Qiang , Liang Weihao , Liu Zhifeng
Published: January 2024

Ionic liquids (ILs) are molten organic salts consisting of organic cations and weakly coordinating organic/inorganic anions at room temperature. ILs have excellent physical and chemical properties such as high thermal stability, high combustible temperature, high miscibility with organic compounds and so on, making them good candidates for high performance lubricants and lubricant additives. The functional designability of ILs makes them novel lubrication materials that can break through the bottleneck of the active control of friction and lubrication. This paper firstly briefly introduces how to design the physical and chemical properties of the ILs required for different friction conditions by bonding specific cations with anions. Then, the lubrication mechanisms of ILs as base lubricants and additives for oils and water are focused on. The correlation between the structure of ILs and the lubrication results are established, which can guide the structural design of ILs in different applications. The response behaviors of friction characteristics under external electric fields are analyzed, which can provide a theoretical basis for the intelligent control of friction based on ILs.
Exploiting Cross-Scale Attention Transformer and Progressive Edge Refinement for Retinal Vessel Segmentation
Authors: Yuan Yunyi , Zhang Yingkui , Zhu Lei , Cai Li , Qian Yinling
Published: January 2024

Accurate retinal vessel segmentation is a crucial step in the clinical diagnosis and treatment of fundus diseases. Although many efforts have been presented to address the task, the segmentation performance in challenging regions (e.g., collateral vessels) is still not satisfactory, due to their thin morphology or the low contrast between foreground and background. In this work, we observe that an intrinsic appearance exists in the retinal image: among the dendritic vessels there are generous similar structures, e.g., the main and collateral vessels are all curvilinear, but they have noticeable scale differences. Based on this observation, we propose a novel cross-scale attention transformer (CAT) to encourage the segmentation effects in challenging regions. Specifically, CAT consumes features with different scales to produce their shared attention matrix, and then fully integrates the beneficial information between them. Such new attention architecture could explore the multi-scale idea more efficiently, thus realizing mutual learning of similar structures. In addition, a progressive edge refinement module (ERM) is designed to refine the edges of foreground and background in the segmentation results. Through the idea of edge decoupling, ERM could suppress the background feature near the blood vessels while enhancing the foreground feature, so as to segment vessels accurately. We conduct extensive experiments and discussions on DRIVE and CHASE_DB1 datasets to verify the proposed framework. Experimental results show that our method has great advantages in the Se metric, which are 0.88–7.26% and 0.81–7.11% higher than the state-of-the-art methods on DRIVE and CHASE_DB1, respectively. In addition, the proposed method also outperforms other methods with 0.17–2.06% in terms of the Dice metric on DRIVE.
SVSeq2Seq: An Efficient Computational Method for State Vectors in Sequence-to-Sequence Architecture Forecasting
Authors: Sun Guoqiang , Qi Xiaoyan , Zhao Qiang , Wang Wei , Li Yujun
Published: January 2024

This study proposes an efficient method for computing State Vectors in Sequence-to-Sequence (SVSeq2Seq) architecture to improve the performance of sequence data forecasting, which associates each element with other elements instead of relying only on nearby elements. First, the dependency between two elements is adaptively captured by calculating the relative importance between hidden layers. Second, tensor train decomposition is used to address the issue of dimensionality catastrophe. Third, we further select seven instantiated baseline models for data prediction and compare them with our proposed model on six real-world datasets. The results show that the Mean Square Error (MSE) and Mean Absolute Error (MAE) of our SVSeq2Seq model exhibit significant advantages over the other seven baseline models in predicting the three datasets, i.e., weather, electricity, and PEMS, with MSE/MAE values as low as 0.259/0.260, 0.186/0.285 and 0.113/0.222, respectively. Furthermore, the ablation study demonstrates that the SVSeq2Seq model possesses distinct advantages in sequential forecasting tasks. It is observed that replacing SVSeq2Seq with LPRcode and NMTcode resulted in an increase under an MSE of 18.05 and 10.11 times, and an increase under an MAE of 16.54 and 9.8 times, respectively. In comparative experiments with support vector machines (SVM) and random forest (RF), the performance of the SVSeq2Seq model is improved by 56.88 times in the weather dataset and 73.78 times in the electricity dataset under the MSE metric, respectively. The above experimental results demonstrate both the exceptional rationality and versatility of the SVSeq2Seq model for data forecasting.
Fictitious Point Technique Based on Finite-Difference Method for 2.5D Direct-Current Resistivity Forward Problem
Authors: Tong Xiaozhong , Sun Ya
Published: January 2024

With the widespread application of the direct-current resistivity method, searching for accurate and fast-forward algorithms has become the focus of research for geophysicists and engineers. Three-dimensional forward modeling can be the best way to identify geo-electrical anomalies but are hampered by computational limitations because of the large amount of data. A practical compromise, or even alternative, is represented by 2.5D modeling characterized using a 3D source in a 2D medium. Thus, we develop a 2.5D direct-current resistivity forward modeling algorithm. The algorithm incorporates the finite-difference approximation and fictitious point technique that can improve the efficiency and accuracy of numerical simulation. Firstly, from the boundary value problem of the electric potential generated by the point source, the discrete expressions of the governing equation are derived from the finite-difference approach. The numerical solutions of the discrete electric potential are calculated after the approximate treatment of the boundary conditions with a finite-difference method based on a fictitious point scheme. Secondly, through the simulation of a homogeneous half-space model and a one-dimensional model, and compared with the analytical results, the correctness and stability of the finite-difference forward algorithm are verified. Lastly, through the numerical simulation for a two-dimensional model, 2.5D direct-current sounding responses are summarized, which can provide a qualitative interpretation of field data.
RNDLP: A Distributed Framework for Supporting Continuous k-Similarity Trajectories Search over Road Network
Authors: Jiang Hong , Tong Sainan , Zhu Rui , Wei Baoze
Published: January 2024

Continuous k-similarity trajectories search over a data stream is an important problem in the domain of spatio-temporal databases. Given a set of trajectories T and a query trajectory Tq over road network G, the system monitors trajectories within T, reporting k trajectories that are the most similar to Tq whenever one time unit is passed. Some existing works study k-similarity trajectories search over trajectory data, but they cannot work in a road network environment, especially when the trajectory set scale is large. In this paper, we propose a novel framework named RNDLP (Road Network-based Distance Lower-bound-based Prediction) to support CKTRN over trajectory data. It is a distributed framework based on the following observation. That is, given a trajectory Ti and the query trajectory Tq, when we have knowledge of D(Ti), we can compute the lower-bound and upper-bound distances between Tq and Ti, which enables us to predict the scores of trajectories in T and employ these predictions to assess the significance of trajectories within T. Accordingly, we can form a mathematical model to evaluate the excepted running cost of each trajectory we should spend. Based on the model, we propose a partition algorithm to partition trajectories into a group of servers so as to guarantee that the workload of each server is as the same as possible. In each server, we propose a pair-based algorithm to predict the earliest time Ti could become a query result, and use the predicted result to organize these trajectories. Our proposed algorithm helps us support query processing via accessing a few points of a small number of trajectories whenever trajectories are updated. Finally, we conduct extensive performance studies on large, real, and synthetic datasets, which demonstrate that our new framework could efficiently support CKST over a data stream.
The Equilibrium Solutions for a Nonlinear Separable Population Model
Authors: Covei Dragos-Patru , Pirvu Traian A. , Sterbeti Catalin
Published: January 2024

The paper investigates a nonlinear model that describes population dynamics with an age structure. The fertility rate, which varies with age, follows a nonconstant pattern. The model exhibits a multiplicative structure for both fertility and mortality rates. Remarkably, this multiplicative structure renders the model separable. In this setting, it is shown that the number of births in unit time can be expressed using a system of nonlinear ordinary differential equations. The asymptotic behavior of solutions to this system has been established for a specific case. This result is significant because it provides a mathematical framework for understanding the dynamics of birth rates in certain settings. Furthermore, this paper explicitly identifies the steady-state solution and the equilibrium solution. As in any research paper, new directions of study remain open.
How Chinese Undergraduate Students’ Perceptions of Assessment for Learning Influence Their Responsibility for First-Year Mathematics Courses
Authors: Wang Bo , Peng Yangui , Cao Zhenxi
Published: January 2024

Assessment for learning (AFL) has been associated with curriculum and teaching reform for the past three decades. However, studies on undergraduate students’ perceptions of their mathematics teachers’ AFL practices are still very limited in the Chinese higher education context. This quantitative study investigated three independent variables—teacher formal feedback and support, interactive dialog and peer collaboration, and learning-oriented assessment—that influence undergraduate students’ ability to take responsibility for their learning through the mediation of the factor of active engagement with subject matter in first-year mathematics courses. One hundred and sixty-eight students from a Chinese “double-first-class” university were recruited to provide valid questionnaire data using the convenience sampling method. Partial least-squares structural equation modeling (PLS-SEM) was used to analyze the data. The results showed that interactive dialog and peer collaboration, as well as learning-oriented assessment, have a direct effect on students’ active engagement with the subject matter and an indirect effect on undergraduate students taking responsibility for their learning in first-year mathematics courses. In addition, learning-oriented assessment was the biggest factor influencing undergraduate students’ ability to take responsibility for their learning in first-year mathematics courses. This study contributes by developing a conceptual model and providing new insights into Chinese higher education sectors on factors that can improve undergraduate students’ ability to take responsibility for their learning.
Predator–Prey Dynamics and Ideal Free Distribution in a Heterogeneous Environment
Authors: Tsybulin Vyacheslav , Zelenchuk Pavel
Published: January 2024

The concept of an ideal free distribution (IFD) is extended to a predator–prey system in a heterogeneous environment. We consider reaction–diffusion–advection equations which describe the evolution of spatial distributions of predators and prey under directed migration. Modification of local interaction terms is introduced, if some coefficients depend on resource. Depending on coefficients of local interaction, the different scenarios of predator distribution are possible. We pick out three cases: proportionality to prey (and respectively to resource), indifferent distribution and inversely proportional to the prey. These scenarios apply in the case of nonzero diffusion and taxis under additional conditions on diffusion and migration rates. We examine migration functions for which there are explicit stationary solutions with nonzero densities of both species. To analyze solutions with violation of the IFD conditions, we apply asymptotic expansions and a numerical approach with staggered grids. The results for a two-dimensional domain with no-flux boundary conditions are presented.
Approximate Analytical Solutions for Strongly Coupled Systems of Singularly Perturbed Convection–Diffusion Problems
Authors: El-Zahar Essam R. , Al-Boqami Ghaliah F. , Al-Juaydi Haifa S.
Published: January 2024

This work presents a reliable algorithm to obtain approximate analytical solutions for a strongly coupled system of singularly perturbed convection–diffusion problems, which exhibit a boundary layer at one end. The proposed method involves constructing a zero-order asymptotic approximate solution for the original system. This approximation results in the formation of two systems: a boundary layer system with a known analytical solution and a reduced terminal value system, which is solved analytically using an improved residual power series approach. This approach combines the residual power series method with Padé approximation and Laplace transformation, resulting in an approximate analytical solution with higher accuracy compared to the conventional residual power series method. In addition, error estimates are extracted, and illustrative examples are provided to demonstrate the accuracy and effectiveness of the method.
A ∂¯-Dressing Method for the Kundu-Nonlinear Schrödinger Equation
Authors: Hu Jiawei , Zhang Ning
Published: January 2024

In this paper, we employed the ∂¯-dressing method to investigate the Kundu-nonlinear Schrödinger equation based on the local 2 × 2 matrix ∂¯ problem. The Lax spectrum problem is used to derive a singular spectral problem of time and space associated with a Kundu-NLS equation. The N-solitions of the Kundu-NLS equation were obtained based on the ∂¯ equation by choosing a special spectral transformation matrix, and a gradual analysis of the long-duration behavior of the equation was acquired. Subsequently, the one- and two-soliton solutions of Kundu-NLS equations were obtained explicitly. In optical fiber, due to the wide application of telecommunication and flow control routing systems, people are very interested in the propagation of femtosecond optical pulses, and a high-order, nonlinear Schrödinger equation is needed to build a model. In plasma physics, the soliton equation can predict the modulation instability of light waves in different media.
A Novel Approach to Enhance DIRECT-Type Algorithms for Hyper-Rectangle Identification
Authors: Belkacem Nazih-Eddine , Chiter Lakhdar , Louaked Mohammed
Published: January 2024

This paper introduces novel enhancements to the most recent versions of DIRECT-type algorithms, especially when dealing with solutions located at the hyper-rectangle vertices. The BIRECT algorithm encounters difficulties in efficiently sampling points at the boundaries of the feasible region, leading to potential slowdowns in convergence. This issue is particularly pronounced when the optimal solution resides near the boundary. Our research explores diverse approaches, with a primary focus on incorporating a grouping strategy for hyper-rectangles of similar sizes. This categorization into different classes, constrained by a predefined threshold, aims to enhance computational efficiency, particularly involving a substantial number of hyper-rectangles of varying sizes. To further improve the new algorithm’s efficiency, we implemented a mechanism to prevent oversampling and mitigate redundancy in sampling at shared vertices within descendant sub-regions. Comparisons with several DIRECT-type algorithms highlight the promising nature of the proposed algorithms as a global optimization solution. Statistical analyses, including Friedman and Wilcoxon tests, demonstrated the effectiveness of the improvements introduced in this new algorithm.
Trends and Extremes in Time Series Based on Fuzzy Logic
Authors: Agayan Sergey , Bogoutdinov Shamil , Kamaev Dmitriy , Dzeboev Boris , Dobrovolsky Michael
Published: January 2024

The authors develop the theory of discrete differentiation and, on its basis, solve the problem of detecting trends in records, using the idea of the connection between trends and derivatives in classical analysis but implementing it using fuzzy logic methods. The solution to this problem is carried out by constructing fuzzy measures of the trend and extremum for a recording. The theoretical justification of the regression approach to classical differentiation in the continuous case given in this work provides an answer to the question of what discrete differentiation is, which is used in constructing fuzzy measures of the trend and extremum. The detection of trends using trend and extremum measures is more stable and of higher quality than using traditional data analysis methods, which consist in studying the intervals of constant sign of the derivative for a piecewise smooth approximation of the original record. The approach proposed by the authors, due to its implementation within the framework of fuzzy logic, is largely focused on the researcher analyzing the record and at the same time uses the idea of multiscale. The latter circumstance provides a more complete and in-depth understanding of the process behind the recording.