Step In Analysis Research Articles

Rock-Cut Monuments at Macedonian Philippi – Taking Image Analysis to the Religioscape

Abstract The massive amount of rock-cut monuments along the slopes of the acropolis hill of Philippi in ancient Macedon has received scholarly attention since the first expeditions and following excavation and research campaigns conducted by the French School at Athens in the nineteenth century. The documentation of these peculiar monuments, comprising images in relief, etchings, inscriptions, and empty niches, consists of very few early drawings, photographs of single objects and sometimes of clusters of neighboring monuments, maps documenting the monuments’ location along the slopes of the acropolis rock (intra and extra muros) and brief descriptions leading to an iconographical categorization. Despite a variety of subjects, the depiction of a youthful huntress commonly denominated as Artemis/Diana is predominating. Based on the commonly accepted dating of these reliefs to the Roman era, the rock-cut monuments are generally interpreted as expressions of popular religion that went unregulated by the state; some even consider them as evidence for specifically female ritual practice. As part of the project “Rock-cut reliefs in Philippi: a microregional study on the religion of ancient Macedon,” a first on-site documentation campaign was carried out in March 2023. The presented case study of the rock-cut reliefs from Philippi aims at spatializing the religiously connotated depictions within the city-/landscape as a first step and applying the concept of religioscape. With this approach, it differs in the methodology used so far, namely the iconographical analysis of the objects as a first analytical step. This article at hand presents the method used to establish the first three-dimensional data sets of the reliefs for documenting as well as monitoring their condition and aims to demonstrate the immense value of these 3D models given the dangers natural corrosion and vandalism pose to the archaeological objects. Moreover, embracing the ability to capture the landscape setting in 3D allows us to visualize the three-dimensional embedding of the objects into the urban fabric of the city with its varied terrain profile. This allows for new analytical prospects, such as inter-visibility, reachability, or building economy.

The interaction of technological affordances and user preferences: A corpus-based study of graphic features across Twitter and Discord

This paper investigates the influence of communication device on linguistic variation across two levels of scale. Drawing on the notion of affordances (Hutchby, 2001), it explores how technological differences between the computer and smartphone may shape, without ultimately determining, the use of graphic features. To do so, the empirical study uses the methodological approach of ‘scaling down’ the analysis of two features across device types: first letter capitalisation and emoji. The first step of analysis is purely quantitative, focusing on linguistic variation in a corpus of a million messages from Twitter (now X). The second step of analysis uses quantitative and qualitative methods to investigate the same features within several thousand messages, by two known participants, across the platforms Twitter and Discord. Thus, the study integrates initial results from a large, anonymous corpus with a much smaller but more detailed one. Overall, device affordances are found to influence feature use; however, statistical tendencies regarding variation across device type do not necessarily hold true for each dimension of context or individual. The combination of different scales thus leads to a more thorough understanding of the influence of technology on linguistic variationand its limits in favour of contextual factors and individual preferences.

Comparison of K-Means and FCM Algorithms to Optimize Spatiotemporal Pore Pressure Prediction of Earth Dams

To identify the behavior and health monitoring of dams, it is necessary to correctly interpret the results of instrumentation in different phases of construction, impounding, and operation. Therefore, the approach based on spatiotemporal prediction is presented to improve the interpretation of pore pressure behavior of Eyvashan Earth Dam. In this research, using the results of other existing healthy piezometers, a spatiotemporal distribution model is proposed using panel data, which can be effective for predicting and reconstructing missing data. The optimal spatiotemporal clustering of pore pressure changes monitoring with K-Means and Fuzzy C-means (FCM) algorithms will enable the monitoring of points of the dam where instrumentations are not designed and installed or defective instrumentations. In predicting the pore pressure of dams, the input data is classified based on the pore pressure monitoring data, but with the use of clustering algorithms, the classification after the cluster analysis steps will lead to the proper resolution of the pore pressure clustering. According to the validation results of each of the clustering algorithms, the FCM clustering algorithm has more suitable results than the K-Means algorithm in determining the pore pressure clusters. In general, FCM clustering and K-Means algorithms are suitable and efficient tools in the field of more accurate monitoring of earth dams, and by using the proposed method, the detection of unusual areas of pore pressure and the related safety diagnosis is facilitated.

Extracurricular Management Information System

The extracurricular management information system aids educational institutions in efficiently managing extracurricular activities. These activities are vital for student development outside of the classroom. However, manual administration of extracurricular activities is time-consuming and challenging. In the digital age, extracurricular management information systems are necessary to enhance efficiency and effectiveness in managing these activities. This study aims to create and involve an extracurricular management information system that will enhance the efficiency and effectiveness of managing extracurricular activities in schools and educational institutions. This extracurricular management information system's construction employed a research technique encompassing needs analysis, system design, software development, implementation, and evaluation. During the needs analysis step, we conducted interviews with schools and educational institutions that have experience managing extracurricular activities. The interview results were used to design the system according to the user's needs. The findings of this analysis serve as a basis for developing suitable information systems. This research has developed software that utilizes cutting-edge technology to enable system access through various devices, such as PCs, cell phones, and tablets. In addition, we prioritize data security and user privacy during the development of this system. Implementing an extracurricular management information system has undergone testing in multiple schools and educational institutions. Evidence from the test results demonstrates that this method can enhance the effectiveness of overseeing extracurricular activities. The incorporation of cutting-edge technology enables users to access the system through various devices, including PCs, cellphones, and tablets, thereby enhancing user flexibility and accessibility. This system's implementation and testing have been limited to a small number of schools and educational institutions. An enormous scope is required to ensure the generalizability of the research outcomes.

Weakly-Supervised Hair SEM Microscope Image Segmentation Using a Priori Structure Information

The analysis of microscopic images of hair has a wide range of applications in the domains of cosmetics, healthcare and forensics. The segmentation of the hair represents the initial step of automatic large-scale quantitative analysis of microscopic images of hair. It ensures that subsequent quantitative measurements are performed in an appropriate area corresponding to the hair, avoiding artifacts near the boundaries. This process can be time-consuming, tedious and susceptible to subjective errors when conducted by a human operator. Deep learning methods represent a promising solution; however, obtaining pixel-level accurate masks is a costly process. This paper presents a novel weakly supervised pipeline for the segmentation of hair SEM (Scanning Electron Microscope) microscopic images, which requires only simple image-level annotations for training. The proposed method incorporates the Radon transform, the Sobel operator and a novel Boundary Discrimination Module (BD-module) for the estimation of the presence of boundaries. The proposed pipeline was evaluated on a recently collected hair SEM dataset (429 images). Furthermore, it is benchmarked with methods including Unet and Segment Anything Model (SAM). The results demonstrated a mean Hausdorff Distance improvement of over 30% and a standard deviation improvement of over 50% in comparison with the Unet and SAM. Moreover, we proposed additional refinement modules to address boundary nonlinear cases and conducted Grad-CAM analysis to enhance the interpretability of the BD-module. Additionally, we proposed a novel quality estimation metric based on gradient map for self-quality assessment. The SEM hair dataset is accessible to the research community in an open-source format.

Zakat in metaverse? Evidence from cyberspace

Purpose This study aims to explore the recent state of zakat metaverse innovation from unstructured data available in cyberspace, i.e. YouTube, Instagram, X (Twitter), Facebook, LinkedIn and Google. Design/methodology/approach This study used “zakat metaverse” keywords to harvest unstructured data and analysed using a mixed-method approach. First step of the analysis applied quantitative text analytics via machine learning tool, followed by the final step of qualitative inductive analysis. Findings Quantitative text analytics identified keywords related to zakat metaverse innovation, whereas qualitative analysis explored the critical insights behind those keywords, presented in thematic interpretation. Research limitations/implications This study only used unstructured internet data, in which other relevant information may not be covered. Practical implications Shariah evaluation of zakat obligations from virtual assets requires the relevantisation of fiqh (Islamic jurisprudence) zakat, which opens future debates. Social implications Many zakat institutions operate in emerging economies where digital poverty occurs, and such zakat metaverse innovation would potentially contribute to this digital divide. The relevance of such innovation becomes a major question regarding its inclusivity. Originality/value This study combines machine learning analytics and qualitative analysis to explore the recent state of metaverse innovation in zakat administration.

RHRVEasy: Heart rate variability made easy.

Heart Rate Variability (HRV) analysis aims to characterize the physiological state affecting heart rate, and identify potential markers of underlying pathologies. This typically involves calculating various HRV indices for each recording of two or more populations. Then, statistical tests are used to find differences. The normality of the indices, the number of groups being compared, and the correction of the significance level should be considered in this step. Especially for large studies, this process is tedious and error-prone. This paper presents RHRVEasy, an R open-source package that automates all the steps of HRV analysis. RHRVEasy takes as input a list of folders, each containing all the recordings of the same population. The package loads and preprocesses heart rate data, and computes up to 31 HRV time, frequency, and non-linear indices. Notably, it automates the computation of non-linear indices, which typically demands manual intervention. It then conducts hypothesis tests to find differences between the populations, adjusting significance levels if necessary. It also performs a post-hoc analysis to identify the differing groups if there are more than two populations. RHRVEasy was validated using a database of healthy subjects, and another of congestive heart failure patients. Significant differences in many HRV indices are expected between these groups. Two additional groups were constructed by random sampling of the original databases. Each of these groups should present no statistically significant differences with the group from which it was sampled, and it should present differences with the other two groups. All tests produced the expected results, demonstrating the software's capability in simplifying HRV analysis. Code is available on https://github.com/constantino-garcia/RHRVEasy.

Profitability, Leverage, Firm Value through Dividend Policy (Empirical Study on Coal and Lignite Mining Sub-Sector Companies on the IDX 2017-2022)

This study examines the influence of profitability and leverage on firm value as mediated by dividend policy. The research employs quantitative approaches, examining comparative cause-and-effect links utilizing secondary data from each company's financial records, obtained from either the company's official website or the IDX. This study used a non-probability, purposive sampling technique in coal and lignite subsector companies listed on the IDX from 2017 to 2022, using complete financial data. The research data is panel data examined using Eviews 12 with the subsequent steps of analysis: descriptive statistics, regression model estimation, classical assumptions, panel data regression, and path analysis using the sobel test. The analysis suggests that profitability and leverage have a significant positive effect on firm value, dividend policy has no effect on firm value, profitability has no influence on dividend policy, and leverage increases dividend policy. Consequently, dividend policy cannot mediate the effect of profitability but can mediate the effect of leverage on firm value.

Study on Multi-Scenario Rain-Flood Disturbance Simulation and Resilient Blue-Green Space Optimization in the Pearl River Delta

In the face of global climate change and rapid urbanization, the Pearl River Delta is confronted with frequent river floods and heavy rainfall, which leads to substantial economic losses and casualties. Enhancing the role of blue-green space in rain-flood resilience is crucial for mitigating such damages in this new era. Firstly, based on an analysis of the current status quo of blue-green space in the Pearl River Delta and the identification of potential areas at risk from rain and floods, this paper elucidates that resilient blue-green space in the Pearl River Delta should be guided by a systematic, bottom-line, and forward-looking orientation while considering spatial characteristics such as multi-scale network connectivity, redundancy and diversity/multi-functionality. Secondly, an optimization route is proposed based on steps of analysis of existing blue-green space, identification of inundated areas prone to rain and flood damage and optimization of blue-green spaces. Strategies for optimizing blue-green space are put forth including enhancing water corridor connectivity, optimizing ecological barriers and corridors, as well as constructing water gates to control hydrological flow direction. Simulation results demonstrate that under similar rain-flood disaster conditions, optimized blue-green space exhibits smaller sizes and lower depths of potential inundated areas compared to the original ones.

"Torsional vibration control in diesel engine-driven centrifugal pump: Validation through experimental results"

This study addresses the critical issue of torsional vibration in turbomachinery, which can lead to significant damage if not properly managed. The primary objective is to develop a practical design approach to enhance system resilience against torsional vibration problems. The research focuses on post-natural frequency analysis steps, including methods like Holzer's method for natural frequency determination and mode shape analysis. The study suggests that while identifying natural frequencies is manageable, accurately predicting torsional vibration problems remains challenging. The analysis recommends analyzing all interference points before considering design modifications, advocating for techniques like examining mode shapes or torque vs. speed curves to eliminate non-critical interference points. For remaining points, a damped forced vibration analysis is recommended, with guidelines provided for different machinery classes. Overall, this study provides a comprehensive, step-by-step analysis procedure applicable to most turbomachinery systems. By addressing the complexities of torsional vibration, this research aims to contribute to the development of safer and more reliable turbomachinery systems Specifically, conducting a torsional analysis on a pump package driven by a diesel engine to achieve similar behavior when it is driven by an electric motor. Replacing the electric motor with a diesel engine as a driver for the pump could cause a maximum to increase the vibration with 25 % while using the optimal damping for the diesel engine could lead to vibration deduction with 70 % when operate the pump without diesel engine damping.

Developing a learners' attitude scale for the 'Diploma in Agri Inputs (DAI)' program in open and distance learning (ODL)

Attitude is a relatively enduring and general evaluation of an object, person, group, issue, or concept on a dimension ranging from negative to positive. It plays a vital role in the persuasion of one's behaviour concerning a particular psychological object. The present study aims to measure the attitude of learners towards the "Diploma in Agri Inputs (DAI)" programme offered through Open and Distance Learning (ODL) mode in Tamil Nadu Agricultural University (TNAU). To accomplish this, a standardized attitude scale was developed using Likert's summated raring approach, which followed the steps of item collection, relevancy test, item analysis, reliability test, and validity test. A total of 45 statements were collected, of which 25 were retained after editing. The statements were sent to Judges to judge its proper relevancy with a 5-point continuum. Finally, 14 statements were selected equal to or greater than the "t" value of 1.75. The reliability coefficient was found to be 0.82, which showed the scale's reliability. The developed attitude scale will be useful for researchers and extension functionaries in similar studies. Moreover, results revealed that most (70.86 percent) ODL learners had highly favourable attitudes towards the DAI programme. The reason might be the various attracting factors of the DAI programme, such as the teaching-learning process, Knowledge generation through Personal Contact Programme (PCP) classes, Affordable fees, Course duration, Practical exposure to diagnosing pests and diseases, Advanced study material, Student-centred learning, Success stories of agripreneurs and 'Fertilizer dealership license'.

DEVELOPMENT OF MICRO SMALL AND MEDIUM ENTERPRISES IN TOURIST DESTINATIONS AROUND THE NUSANTARA CAPITAL

The East Kalimantan region is a region with rapid development following the designation of the National Capital in one of its regions, namely North Penajam Paser Regency. Therefore, East Kalimantan Province needs to increase MSMEs in tourist destinations as a form of the government's role in optimizing the tourism sector in East Kalimantan. Assuming that the development of the area around IKN will occur rapidly due to the presence of IKN in East Kalimantan, it requires more effective policies to promote the tourism sector. The aim of this research is to collect data on the existing conditions of tourism (tourist destinations) around the IKN area as a forum for developing MSMEs, accommodating the problems of the tourism sector, MSMEs and problems that intersect between these sectors as well as identifying the strengths, weaknesses, opportunities and challenges of MSMEs in tourist destinations around the IKN area, to then provide policy recommendations. This research uses secondary data and primary data. The secondary data used is MSME data which is grouped based on the KBLI classification in the form of business scale; venture capital; business risk; and number of workers. Data collection of information on the existing conditions of MSMEs in tourist destinations around IKN is carried out at the Tourism Office in each Regency/City in East Kalimantan. Analysis of qualitative data was carried out using the SWOT technique. The initial step of SWOT analysis is carried out by determining the internal and external factor matrix. The IFE (Internal Factor Evaluation) and EFE (External Factor Evaluation) matrices consist of columns, weights, ratings and total values which are the result of multiplying the weights and ratings. Ranking of levels of importance is done with the help of the QDA application. The research results show that the condition of MSMEs in tourist destinations around IKN still needs to be improved. This condition is because MSMEs still do not have NIBs, product patents and trademarks, and there are limited human resources for developing digital marketing and financial management. The resulting recommendations are (1) Promotion and innovation of tourist destinations by highlighting superior natural and cultural tourism, (2) Controlling MSMEs related to business licensing, business clustering, and assistance in preparing business financial reports, (3) The government coordinates the use of funds. CSR and other assistance for the development of tourism facilities and MSMEs, as well as (4) Coordination of promotion of local products at tourist destinations as well as training assistance to increase product innovation.

A Tool for Reaction Monitoring in Real Time, the Development of a "Walk-Up Automated Reaction Profiling" System.

Optimization of chemical reactions requires a thorough analysis of reaction products and intermediates over a given time course. Chemical reactions are often analyzed by liquid chromatography-mass spectrometry (LC-MS), but generating LC-MS samples and data analysis is time-consuming and produces a significant amount of waste. We sought to remove the sample preparation and data analysis steps by implementing an iChemExplorer/Agilent LC-MS instrument as our reactor and analysis tool, coupled with an automated report generator of reaction progress over time. Herein, we show that our easy-to-use walk-up automated reaction profiling (WARP) system can sample chemical reactions multiple times to produce a data-rich report of reaction progress over time.

Machine Learning‐Based Estimation of Experimental Artifacts and Image Quality in Fluorescence Microscopy

Reliable characterization of image data is fundamental for imaging applications, FAIR data management, and an objective evaluation of image acquisition, processing, and analysis steps in an image‐based investigation of biological samples. Image quality assessment (IQA) often relies on human visual perception, which is not objective, or reference ground truth images, which are not often available. This study presents a method for a comprehensive IQA of microscopic images, which solves these issues by employing a set of reference‐free metrics that estimate the presence of experimental artifacts. The metrics are jointly validated on a semisynthetic dataset and are tested on experimental images. Finally, the metrics are employed in a machine learning model, demonstrating their effectiveness for automatic artifact classification through multimarker IQA. This work provides a reliable reference‐free method for IQA in optical microscopy, which can be integrated into the experimental workflow and tuned to address specific artifact detection tasks.

Consensus prediction of cell type labels in single-cell data with popV.

Cell-type classification is a crucial step in single-cell sequencing analysis. Various methods have been proposed for transferring a cell-type label from an annotated reference atlas to unannotated query datasets. Existing methods for transferring cell-type labels lack proper uncertainty estimation for the resulting annotations, limiting interpretability and usefulness. To address this, we propose popular Vote (popV), an ensemble of prediction models with an ontology-based voting scheme. PopV achieves accurate cell-type labeling and provides uncertainty scores. In multiple case studies, popV confidently annotates the majority of cells while highlighting cell populations that are challenging to annotate by label transfer. This additional step helps to reduce the load of manual inspection, which is often a necessary component of the annotation process, and enables one to focus on the most problematic parts of the annotation, streamlining the overall annotation process.

Chemical Speciation of Particulate Matter as a Tool in Air Pollution Forensics: A Case Study

Signature pollutant identification is an important step in the forensic analysis of environmental pollution cases. This makes the process of source identification easier while making legal responsibility allocation less challenging. Massive firework displays (FWD) associated with festivals, though are short duration events, have the potential to severely deteriorate the air quality due to the quantity and toxicity of the pollutants involved. In this study, the emissions during a FWD associated with a festival were analysed to identify the signature pollutants that help in attributing the emissions to the FWD with a certainty demanded by legal institutions. The strategy involved speciation of the particulate matter sampled from the site for metals, investigating all possible sources of the metals observed through literature study and field investigations, and comparing their concentrations in samples collected before and after FWD. An elimination procedure was adopted, where those metals that do not show an increase after FWD and those that have other possible sources are excluded from the possible list of signature pollutants. The signature pollutants obtained for the case investigated were As, B, Ba, Cd, Cr, Cu, Li, Mg, Mn, Pb, Sr, V, and Zn. The study also revealed serious violations, where metals legally banned for use in fireworks were observed in the emissions from FWD. The study presents a robust procedure for identifying signature pollutants that helps in attributing emissions to FWDs.

Development and Validation of Sensitivity for Needs of Elderly Scale Among Caregivers Based on Maslow’s Theory

The world currently lives in an aging society. Demand for elderly caregivers and services is rapidly rising. Elderly care encompasses not only medical care, but also the art of human care. Love and respect are usually considered the backbone of the modality of caregivers. This study aimed at developing a sensitivity for needs of elderly scale (SNES) among caregivers. Maslow’s theory of hierarchical needs, especially stage 3 (love and belongingness needs) and stage 4 (esteem needs), was used as a conceptual framework. This study consisted of four steps. The sample groups for the first two steps of item quality and exploratory factory analysis were 420 nursing students. A three-factor model emerged, which includes the sensitivity of needs for love and empathy, the sensitivity of needs for respect, and the sensitivity of needs for honor and dignity. The newly constructed measure consisted of 10 items, which explained the variance by 52.18%. The third step of confirmatory factor analysis, using a new group of 384 nursing students, indicated the model fit. In the final step, the scale was validated using 171 elderly caregivers. Moderate to strong absolute magnitudes of correlations between the new scale and psycho-moral strength, core self-evaluation, future orientation and self-control, moral disengagement, and caring behavior were found. Incremental validity was tested. Discussion and recommendations for further research and implications of the newly constructed measure were offered.

A step-by-step procedure for analysing the 16S rRNA-based microbiome diversity using QIIME 2 and comprehensive PICRUSt2 illustration for functional prediction.

Short-read sequencing technology has emerged as a preferred tool to analyse the bacterial composition of a niche by targeting hypervariable regions of the 16S rRNA gene. It targets the short hypervariable regions of the 16S rRNA gene and uncovers the taxonomic profile and their associated pathways. QIIME 2 is preferred and ready-to-use pipelines that perform stepwise analysis of massive short reads of 16S rRNA genes. This wrapper comprises several tools that include quality checking, denoising, taxonomic classification, alignment, and diversity analysis. However, it demands huge bioinformatic analysis practices which are quite challenging to many microbiologists working in the field of traditional microbiology. This paper, therefore, aimsto make microbiologists familiar with the steps of computational analysis for processing 16S rRNA-based sequences. Here, we are presenting stepwise processing of NGS sequences using the QIIME 2 platform along with their analyses, which include installing QIIME 2, importing and processing data, quality checks, taxonomy assignments, and diversity analysis. Besides, the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt2) has also been illustrated to understand the correlation between metabolic and physiological footprints of the different species observed during microbiome analysis. Therefore, this paper can be used as a handy toolkit for those researchers who are less familiar with its associated bioinformatic analysis.

EPCO-33. OMICON: A CLOUD PLATFORM FOR FAIR RESEARCH ON GENE COEXPRESSION NETWORKS IN NORMAL HUMAN BRAIN AND BRAIN TUMORS

Abstract Genome-wide coexpression analysis of bulk human brain samples is a powerful approach for identifying highly reproducible transcriptional signatures of cell types and cell states, since it can survey huge numbers of individuals, cells, and transcripts. However, it is difficult to optimize gene coexpression network construction, compare gene coexpression modules from independent datasets, or even locate gene expression datasets with similar attributes. To address these challenges, we have developed the OMICON platform (theomicon.ucsf.edu) to promote FAIR (Findable, Accessible, Interoperable, Reusable) research practices involving human brain gene coexpression networks. OMICON contains structured gene expression data for >17K bulk human brain samples (~10K normal and ~7K malignant glioma), which were collected from diverse public repositories and consortia (e.g., GEO, TCGA, CGGA, CPTAC, REMBRANDT, IvyGAP, GTEx, NABEC, the Allen Institute). To promote interoperability, we have standardized metadata for hundreds of dataset and sample attributes, including single-nucleotide and copy-number mutations. For each dataset, we have used the FindModules R function to construct dozens of gene coexpression networks by iterating over module detection parameters. These efforts have identified ~100K gene coexpression modules, which have been extensively characterized via enrichment analysis with thousands of curated gene sets. All datasets, metadata, gene coexpression networks, enrichment results, and analysis steps can be browsed with an interactive workflow visualization tool, which promotes accessibility, reusability, and reproducibility by maintaining complete data provenance with unique identifiers. To promote findability, we have built an advanced search engine to identify datasets, samples, modules, and more, by filtering standardized metadata (e.g., find gene coexpression modules in human glioblastoma datasets that are significantly enriched with markers of T-cells). A detailed description of OMICON’s functionality is described on our Help page: theomicon.ucsf.edu/home/help. Through this functionality, OMICON seeks to build community and focus therapeutic efforts around reproducible analyses of transcriptional variation in normal human brain and brain tumors.

Total RNA Extraction from Starchy Rice Endosperm.

RNA extraction is a critical step in gene expression analysis. While numerous RNA extraction kits are commercially available, most kits cannot be utilized for RNA extraction from rice endosperm that contains abundant starch. Here, I describe a three-step RNA extraction from rice starchy endosperm. The process involves phenol/chloroform extraction, ethanol precipitation, and lithium chloride precipitation. This method yields an abundant and high-quality total RNA even from a single starchy endosperm.