Multiple Sections Research Articles

Human Capital and Economic Growth

This report thoroughly reviews existing literature in the field of human capital. A rationale has been presented along with seven questions the reviewed literature aims to answer by analytically comprehending an extensive range of relevant research papers and articles categorised into multiple sections followed by the summary and research gap. Since all existing work presents a strong argument in favour of the importance of maximizing the potential of human capital through various forms of investment, this paper seeks to understand whether there is a direct relationship shared by the economic growth and the quality of human capital in the economy. A multiple regression model has been deployed for this purpose and has been applied to data collected from World Bank.

Segmented Two-Dimensional Progressive Polynomial Calibration Method for Nonlinear Sensors.

Nonlinearity in sensor measurements reduces the sensor's accuracy. Therefore, accurate calibration is necessary for reliable sensor operation. This study proposes a segmented calibration method that divides the input range into multiple sections and calculates the optimized calibration functions for each one. This approach reduces the overall error rate and improves the calibration accuracy by isolating distinctive regions. The modified progressive polynomial calibration technique is used to calculate the calibration function. This algorithm addresses the computational complexity, allowing for reduced polynomial degrees and improving the accuracy. The segmented calibration method achieves a significantly lower error rate of 0.000006% compared to the original single calibration method, which has an error rate of 0.0823%, when using the same six calibration points and a fifth-degree polynomial function. This method maintains improved accuracy with fewer calibration points, and its ability to reduce the computational complexity and calculation time while using lower polynomial degrees is confirmed. Additionally, it can be extended to two dimensions to reduce the errors caused by cross-sensitivity. The results from a two-dimensional simulation show a reduction in the error rate ranging from 15.84% to 2.07% in an 8-bit signed fixed-point system. These results indicate that the segmented calibration method is an effective and scalable solution for various typical sensors.

Plastic Mechanism Models for use in DSM Localised Loading Design of Hat Sections

The Direct Strength Method (DSM) of design has been recently developed for the design of cold-formed steel members under localised loading. The method requires a yield load (Py) and an elastic buckling load (Pcr) as input variables to the DSM design equations. This paper summarises test results used to develop plastic mechanism models for calculating Py for hat sections subject to practical loading cases. All four loading cases, including Interior Two Flange (ITF), End Two Flange (ETF), Interior One Flange (IOF) and End One Flange (EOF) loading cases were investigated. The yield/plastic mechanism behaviour of multiple web sections is not yet fully understood, while several publications in the literature have discussed the mechanical behaviour of hat sections under localised loading. Hence, this paper explains and proposes a plastic mechanism model based on the experimental data for calculating the yield load (Py) to be used in the DSM for localised loading design of hat sections in conjunction with the elastic buckling load Pcr.

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25% to 99.13% across 10 test videos and an average accuracy of 97.88%. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance.

Seq-Scope: repurposing Illumina sequencing flow cells for high-resolution spatial transcriptomics.

Spatial transcriptomics technologies aim to advance gene expression studies by profiling the entire transcriptome with intact spatial information from a single histological slide. However, the application of spatial transcriptomics is limited by low resolution, limited transcript coverage, complex procedures, poor scalability and high costs of initial setup and/or individual experiments. Seq-Scope repurposes the Illumina sequencing platform for high-resolution, high-content spatial transcriptome analysis, overcoming these limitations. It offers submicrometer resolution, high capture efficiency, rapid turnaround time and precise annotation of histopathology at a much lower cost than commercial alternatives. This protocol details the implementation of Seq-Scope with an Illumina NovaSeq 6000 sequencing flow cell, allowing the profiling of multiple tissue sections in an area of 7 mm × 7 mm or larger. We describe the preparation of a fresh-frozen tissue section for both histological imaging and sequencing library preparation and provide a streamlined computational pipeline with comprehensive instructions to integrate histological and transcriptomic data for high-resolution spatial analysis. This includes the use of conventional software tools for single-cell and spatial analysis, as well as our recently developed segmentation-free method for analyzing spatial data at submicrometer resolution. Aside from array production and sequencing, which can be done in batches, tissue processing, library preparation and running the computational pipeline can be completed within 3 days by researchers with experience in molecular biology, histology and basic Unix skills. Given its adaptability across various biological tissues, Seq-Scope establishes itself as an invaluable tool for researchers in molecular biology and histology.

Safety and risk analysis of total resection surgery for thoracic and lumbar spinal tumors: a decadal analysis of 103 cases

Study designRetrospective cohort study.PurposeTo explore the complications and risk factors for total resection (TR) of primary thoracic and lumbar spinal tumors over the past decade at our institution.MethodsPatients meeting inclusion criteria (primary spinal tumors, thoracic or lumbar location, TR at our center) were included. Demographic characteristics, surgical data, perioperative complications and management results were reviewed. Patients were stratified by tumor site, the number of excised segments, and recurrence status to elucidate distinctive characteristics.ResultsThe cohort comprised 103 patients, with a mean age of 35.8 years. On average, 1.83 vertebral segments were resected per patient. Perioperative complications were substantial, totaling 166 events, with 71 classified as major and 95 as minor, yielding an average of 1.61 complications per patient. No perioperative deaths occurred, but 79 patients (76.7%) experienced at least one complication. Multiple vertebral sections correlated with a higher complication rate (P = 0.031), and lumbar surgeries exhibited elevated risks of large vascular injury (P = 0.001), neurological deterioration, and cerebrospinal fluid leakage compared to thoracic cases. Conversely, thoracic spinal procedures showed a higher rate of pleural effusion (P = 0.004). Binary logistics stepwise regression identified multi-segmental resection as the independent risk factor for major perioperative complications.ConclusionsTR of primary spinal tumors is associated with a high perioperative complication rate, although most events have a favorable prognosis. Complication characteristics vary based on the surgical site, number of excised segments, and surgical history. A nuanced preoperative evaluating approach considering patient age, surgical segments, and extent of resection is crucial.

An Evidential Reasoning Method of Comprehensive Evaluation of Water Quality Based on Gaussian Distribution

This study provides an evidential reasoning method for water quality evaluation based on Gaussian distribution to handle the problem of comprehensive water quality evaluation for a region across a period (multiple sections and multiple time points). The method turns the collection of observed water quality indicator values into a probability distribution of water quality grades by using the Gaussian distribution to compute the confidence assessment of water quality grades over one period. It eliminates the subjectivity involved in determining confidence levels and the problem of information loss during data fusion that arises with conventional approaches. The probability distribution of each assessment grade is then determined by repeatedly synthesizing evidence of the same water quality grade using the improved evidential reasoning synthesis rule. To avoid the subjectivity included in experience-based weight settings, principal component analysis (PCA) is utilized to calculate the weights of water quality indicators based on contribution rates and load coefficients. In the end, utility theory is presented to modify the discrete probability distribution of precise numerical expressions, offering thorough results for the evaluation of water quality and facilitating the comparison of various water quality grades. Using the Xiangjiang River Basin as a case study, the proposed evaluation method is contrasted with popular techniques for assessing water quality, including the Single-Factor Evaluation Method, the Fuzzy Comprehensive Evaluation Method, and the Evidential Reasoning Comprehensive Evaluation Method. The findings suggest that the evidence reasoning approach for evaluating water quality that is based on Gaussian distribution is more rational, accurate, and scientific. Additionally, empirical studies on the annual water quality trends in various regions, the upstream, midstream, and downstream trends, and the water quality trends during wet and dry periods are conducted using this method to assess and analyze changes in water quality in the Xiangjiang River Basin during the “11th Five-Year Plan” and “12th Five-Year Plan” periods. The analysis findings demonstrate that, even if the rate of progress has slowed, the Xiangjiang River Basin’s overall water quality has been steadily improving since management and protection measures were put in place. This shows that the preventive and control efforts implemented in the “11th Five-Year Plan” and “12th Five-Year Plan” periods were successful; nevertheless, carrying out the current tactics might only have a limited impact. As a result, more advanced and creative approaches are required to encourage the ongoing enhancement of the water quality in the Xiangjiang River Basin.

Anatomical & Ayurvedic Changes in the Women's Body Resulting from Multiple Cesarean Sections

C-section is a common surgical delivery in cases where a vaginal delivery may pose a potential threat to the well-being of the child or mother. In most cases, C-section is normally safe, but undergoing several C-sections can cause enormous anatomical changes in a woman's body. These changes may affect the structure of the uterus, the abdominal wall, and tissues perforating in the area, thus presenting a possible health complication when pregnant.

DGSIST: Clustering spatial transcriptome data based on deep graph structure Infomax

Although spatial transcriptomics data provide valuable insights into gene expression profiles and the spatial structure of tissues, most studies rely solely on gene expression information, underutilizing the spatial data. To fully leverage the potential of spatial transcriptomics and graph neural networks, the DGSI (Deep Graph Structure Infomax) model is proposed. This innovative graph data processing model uses graph convolutional neural networks and employs an unsupervised learning approach. It maximizes the mutual information between graph-level and node-level representations, emphasizing flexible sampling and aggregation of nodes and their neighbors. This effectively captures and incorporates local information from nodes into the overall graph structure. Additionally, this paper developed the DGSIST framework, an unsupervised cell clustering method that integrates the DGSI model, SVD dimensionality reduction algorithm, and k-means++ clustering algorithm. This aims to identify cell types accurately. DGSIST fully uses spatial transcriptomics data and outperforms existing methods in accuracy. Demonstrations of DGSIST’s capability across various tissue types and technological platforms have shown its effectiveness in accurately identifying spatial domains in multiple tissue sections. Compared to other spatial clustering methods, DGSIST excels in cell clustering and effectively eliminates batch effects without needing batch correction. DGSIST excels in spatial clustering analysis, spatial variation identification, and differential gene expression detection and directly applies to graph analysis tasks, such as node classification, link prediction, or graph clustering. Anticipation lies in the contribution of the DGSIST framework to a deeper understanding of the spatial organizational structures of diseases such as cancer.

Interpretable spatially aware dimension reduction of spatial transcriptomics with STAMP

Spatial transcriptomics produces high-dimensional gene expression measurements with spatial context. Obtaining a biologically meaningful low-dimensional representation of such data is crucial for effective interpretation and downstream analysis. Here, we present Spatial Transcriptomics Analysis with topic Modeling to uncover spatial Patterns (STAMP), an interpretable spatially aware dimension reduction method built on a deep generative model that returns biologically relevant, low-dimensional spatial topics and associated gene modules. STAMP can analyze data ranging from a single section to multiple sections and from different technologies to time-series data, returning topics matching known biological domains and associated gene modules containing established markers highly ranked within. In a lung cancer sample, STAMP delineated cell states with supporting markers at a higher resolution than the original annotation and uncovered cancer-associated fibroblasts concentrated on the tumor edge’s exterior. In time-series data of mouse embryonic development, STAMP disentangled the erythro-myeloid hematopoiesis and hepatocytes developmental trajectories within the liver. STAMP is highly scalable and can handle more than 500,000 cells.

A Novel Bladeless-Spinal Wind Turbine Design for Efficient Energy Generation in Low wind Speeds

Wind energy is a widely used and renewable source for electricity generation. However, traditional wind turbines face challenges such as large footprints, noise, high costs, and efficiency limitations. To address these issues, bladeless wind turbines (BWTs) have gained popularity, and extensive research is being carried out to develop efficient designs. In this work, we propose a novel BWT design, inspired by the spine structure found in human bodies. The BWT architecture is modular, autonomous, and robust, making it suitable for small, portable, and small-scale applications. Our BWT design was inspired by the spine structure found in the human body and other vertebrates. The BSWT architecture is modular, autonomous, and robust. Detailed 3D models were created using Fusion 360 and SolidWorks software to refine and iterate the design. Our BWT features a complete electrical system with an energy harvesting base, a rectifier, converter, and a storage battery. The stored energy is then converted to AC and connected to the load. Our design utilizes oscillating rods divided into sections, enabling energy generation even at low wind speeds. The casing protects the rods from shear stresses and corrosion. While practical applicates involve a spine design with multiple sections, our experiments primarily focus on a single turbine.

A Mixed Methods Analysis of General Education Candidates’ Knowledge, Perceptions, and Application of Universal Design for Learning

Thirty general education candidates from elementary education, middle/secondary education, and arts education in multiple sections of one course engaged in a series of discussion posts and lesson plans related to creating inclusive general education lessons. Using candidate posts, rubric scores, feedback on lesson plans, and course evaluation ratings, a mixed methods approach was used to understand candidate knowledge, perceptions, and applications of Universal Design for Learning. The results from our research have important implications for policy, practice, and research. Policy makers must be aware of the lack of UDL in general education programs and IHEs need to do more than the cursory coverage of these principles. Additional research is needed for the implementation of lessons designed using UDL by general education candidates.

Early Aptian development and OAE 1a-linked demise of a carbonate platform in the Western Tethys: Lower Cretaceous of Sierra Mariola (South Iberian Paleomargin, SE Spain)

A carbonate platform succession of early Aptian age is described from the Sierra Mariola, which is located within the Prebetic Zone of the Betic External Zones (BEZ) of the Southern Iberian Palaeomargin, in SE Spain. The facies and stratigraphic architecture of the studied succession are described and analysed from multiple logged sections to characterize different depositional environments.The cyclic stacking of facies observed suggests a possible orbital forcing of climate transmitted to sedimentation. Superbundles are recognized describing two sequences of Regressive-Transgressive (R-T) evolution. Analysis of carbon-isotope data identifies both the interval before the hyperthermal event of the early Aptian Oceanic Anoxic Event (OAE 1a) and the onset of the latter event, with an abrupt change in sedimentation marking the demise of the carbonate platform. The field outcrop thus preserves a record of the transition from a greenhouse world to a hothouse event.

Circulation of self-supplied water for significant energy recovery through heat integration

This study introduces an overhead heat-integrated distillation column (OHIDiC), a novel approach that maximizes heat recovery by multiple heat exchanges and product circulation. By utilizing the overhead vapor as a direct heat source, low-temperature feed and product water were used as cooling agents, thereby significantly reducing the condenser duty and reliance on cooling utilities. Additionally, the heated feed transfers heat to the column, leading to a substantial reduction of reboiler heat duty. Some of the heated product water is recycled back to the decanter for product cycling, while the rest is released as the final product. This circulation process ensures a continuous coolant supply, which contributes the reduction of condenser duty. Two processes were considered in this study, utilizing the water product from a non-reactive, and from a reactive separation within the system. When applied to the separation of a water-dodecanol mixture, OHIDiC reduced the condenser duty by 69.21% compared to a traditional distillation column, with a 31.46% reduction in the total utility consumption. When reactive distillation was incorporated into the OHIDiC, the higher overhead vapor temperature facilitated high heat transfer in the multiple heat exchange sections, thereby significantly reducing the total thermal load. This resulted in a reduction of up to 46.96% in total heat duty and a 36.06% decrease in CO2 emissions. These findings confirm that the OHIDiC achieves significant energy savings through the utilization of process-derived substances, with pronounced benefits when the temperature of the overhead vapor becomes higher.

The Effect of Reynolds Numbers on Flow-Induced Vibrations: A Numerical Study of a Cylinder on Elastic Supports

In the field of fluid dynamics, the Reynolds number is a key parameter that influences the flow characteristics around bluff bodies. While its impact on flow around stationary cylinders has been extensively studied, systematic research into flow-induced vibrations (FIVs) under these conditions remains limited. This study utilizes numerical simulations to explore the FIV characteristics of smooth cylinders and passive turbulence control (PTC) cylinders supported elastically within a Reynolds number range from 0.8 × 104 to 1.1 × 105. By comparing the vibration responses, lift coefficients, and wake structures of these cylinders across various Reynolds numbers, this paper aims to elucidate how Reynolds numbers affect the flow and vibration characteristics of these structures. The research employs images of instantaneous lift changes and vortex shedding across multiple sections to visually demonstrate the dynamic changes in flow states. The findings are expected to provide theoretical support for optimizing structural design and vibration control strategies in high-Reynolds-number environments, emphasizing the importance of considering Reynolds numbers in structural safety and design optimization.

An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.

The application of deep learning to spatial transcriptomics (ST) can reveal relationships between gene expression and tissue architecture. Prior work has demonstrated that inferring gene expression from tissue histomorphology can discern these spatial molecular markers to enable population scale studies, reducing the fiscal barriers associated with large-scale spatial profiling. However, while most improvements in algorithmic performance have focused on improving model architectures, little is known about how the quality of tissue preparation and imaging can affect deep learning model training for spatial inference from morphology and its potential for widespread clinical adoption. Prior studies for ST inference from histology typically utilize manually stained frozen sections with imaging on non-clinical grade scanners. Training such models on ST cohorts is also costly. We hypothesize that adopting tissue processing and imaging practices that mirror standards for clinical implementation (permanent sections, automated tissue staining, and clinical grade scanning) can significantly improve model performance. An enhanced specimen processing and imaging protocol was developed for deep learning-based ST inference from morphology. This protocol featured the Visium CytAssist assay to permit automated hematoxylin and eosin staining (e.g. Leica Bond), 40×-resolution imaging, and joining of multiple patients' tissue sections per capture area prior to ST profiling. Using a cohort of 13 pathologic T Stage-III stage colorectal cancer patients, we compared the performance of models trained on slide prepared using enhanced versus traditional (i.e. manual staining and low-resolution imaging) protocols. Leveraging Inceptionv3 neural networks, we predicted gene expression across serial, histologically-matched tissue sections using whole slide images (WSI) from both protocols. The data Shapley was used to quantify and compare marginal performance gains on a patient-by-patient basis attributed to using the enhanced protocol versus the actual costs of spatial profiling. Findings indicate that training and validating on WSI acquired through the enhanced protocol as opposed to the traditional method resulted in improved performance at lower fiscal cost. In the realm of ST, the enhancement of deep learning architectures frequently captures the spotlight; however, the significance of specimen processing and imaging is often understated. This research, informed through a game-theoretic lens, underscores the substantial impact that specimen preparation/imaging can have on spatial transcriptomic inference from morphology. It is essential to integrate such optimized processing protocols to facilitate the identification of prognostic markers at a larger scale.

BayeSMART: Bayesian clustering of multi-sample spatially resolved transcriptomics data.

The field of spatially resolved transcriptomics (SRT) has greatly advanced our understanding of cellular microenvironments by integrating spatial information with molecular data collected from multiple tissue sections or individuals. However, methods for multi-sample spatial clustering are lacking, and existing methods primarily rely on molecular information alone. This paper introduces BayeSMART, a Bayesian statistical method designed to identify spatial domains across multiple samples. BayeSMART leverages artificial intelligence (AI)-reconstructed single-cell level information from the paired histology images of multi-sample SRT datasets while simultaneously considering the spatial context of gene expression. The AI integration enables BayeSMART to effectively interpret the spatial domains. We conducted case studies using four datasets from various tissue types and SRT platforms, and compared BayeSMART with alternative multi-sample spatial clustering approaches and a number of state-of-the-art methods for single-sample SRT analysis, demonstrating that it surpasses existing methods in terms of clustering accuracy, interpretability, and computational efficiency. BayeSMART offers new insights into the spatial organization of cells in multi-sample SRT data.

Detachment of leading-edge vortex enhances wake capture force production

During stroke reversals, insect wings interact with their own wake flow from the preceding half-stroke, resulting in an unsteady aerodynamic mechanism known as ‘wing–wake interaction’ or ‘wake capture’. To better elucidate this mechanism, we numerically solved the incompressible Navier–Stokes equations at Reynolds numbers $10^2$ and $10^3$ . Simulations were conducted for wing planforms defined using the beta function distribution with varying aspect ratios ( $AR=2\unicode{x2013}6$ ) and radial centroid locations ( $\hat {r}_1=0.4\unicode{x2013}0.6$ ), whilst employing representative normal hovering kinematics. The wake development from the considered flapping wing planforms was investigated, and the wake capture contribution to aerodynamic force production was quantified by comparing the force generation between the fifth and first stroke cycles at multiple sections along the wingspan. Our results revealed that on the inboard wing region experiencing an attached leading-edge vortex (LEV) structure, wing–wake interaction is dominated by an unsteady downwash effect, resulting in a reduction in local force production. However, in regions closer to the wingtip experiencing detachment of the LEV, wing–wake interaction is dominated by an unsteady upwash effect, leading to an increase in local force production. Consequently, the global wake capture force production is controlled by the extent of LEV detachment, which primarily increases with the increase of wing aspect ratio. This suggests that for normal hovering flapping wings, the typical loss in translational force production due to wingtip stall is partially mitigated by wake capture effects.

A graph self-supervised residual learning framework for domain identification and data integration of spatial transcriptomics

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets.

An approach of 2D convolutional neural network–based seismic data fault interpretation with linear annotation and pixel thinking

AbstractThis article introduces an improved method for geological fault interpretation utilizing a 2D convolutional neural network approach with a focus on coalbed horizons, dealing with the problem as an image classification task. At the beginning, linear annotations, reflecting geological fault features, are applied to multiple seismic sections. By considering texture differences between fault and non‐fault areas, we construct samples that represent these distinct zones for training deep neural networks. Initially, fault annotations are transformed into single dots to facilitate pixel‐based processing. To depict a specific dot's geological structure, we employ a matrix clipped around the point, determined by a combination of range and step parameters. Convolutional layers generate filters equivalent to seismic data transformation, streamlining the need for analysis and selection of seismic attributes. The article discusses enhancing the efficiency of 2D convolutional neural network–based fault interpretation by optimizing sample selection, data extraction and model construction procedures. Through the incorporation of data from two mining areas (totalling 27.09 km2) in sample creation, the overall accuracy exceeds 0.99. Recognition extends seamlessly to unlabelled sections, showcasing the innovative technical route and methodology of fault interpretation with linear annotation and pixel‐based thinking. This study presents a method that integrates planar and raster thinking, transitioning from vision‐oriented geological structure annotation to algorithm‐oriented pixel location. The proposed 2D convolutional neural network–based matrix‐oriented fault/non‐fault binary classification demonstrates feasibility and reproducibility, offering a new automated approach for fault detection in coalbeds through convolutional neural network algorithms.