Partitioning Technique Research Articles

Detection of Pancreatic Cancer in CT Scan Images Using PSO SVM and Image Processing.

A diagnosis of pancreatic cancer is one of the worst cancers that may be received anywhere in the world; the five-year survival rate is very less. The majority of cases of this condition may be traced back to pancreatic cancer. Due to medical image scans, a significant number of cancer patients are able to identify abnormalities at an earlier stage. The expensive cost of the necessary gear and infrastructure makes it difficult to disseminate the technology, putting it out of the reach of a lot of people. This article presents detection of pancreatic cancer in CT scan images using machine PSO SVM and image processing. The Gaussian elimination filter is utilized during the image preprocessing stage of the removal of noise from images. The K means algorithm uses a partitioning technique to separate the image into its component parts. The process of identifying objects in an image and determining the regions of interest is aided by image segmentation. The PCA method is used to extract important information from digital photographs. PSO SVM, naive Bayes, and AdaBoost are the algorithms that are used to perform the classification. Accuracy, sensitivity, and specificity of the PSO SVM algorithm are better.

Width‐Based Discharge Partitioning in Distributary Networks: How Right We Are

AbstractRiver deltas are home to large populations and can be composed of complex channel networks which convey flows of matter to the shoreline. Knowledge of flow within individual channels is needed to quantify the distribution of discharge across the delta, and thus its sustainability over time. Due to a lack of field measurements at the local channel scale, researchers leverage remote sensing data to estimate the partitioning of flow. We compare data from 15 river deltas to discharge partitioning estimates based on channel network graphs derived from remote sensing imagery. We quantify errors in the common width‐based method and test alternative partitioning techniques to find that width‐based discharge partitioning is universally applicable, suggesting that absent any site‐specific information, discharge partitioning by average channel width is an appropriate approach. We also provide networks, streamflow measurements, and flux partitioning estimates for 28 delta networks as the Discharge In Distributary NeTworks (DIDNT) dataset.

Partition window assignment in hierarchically scheduled time-partitioned distributed real-time systems with multipath flows

Time and space partitioning techniques are implemented in the development of safety-critical applications to ensure isolation among components. A suitable arrangement of the execution of such partitions is a key challenge so that applications meet the timing requirements imposed to software. In this work, the effect of window sizes and context switch overheads in the partition window configuration is studied, with the aim of analyzing their impact when the response-time analysis and priority assignment techniques are applied. Then, a heuristic algorithm is proposed, in order to obtain a partition window configuration that enables the schedulability of partition-based safety critical systems. This algorithm is evaluated in synthetic test scenarios and it is also applied to a safety-critical use-case in the railway domain.

On Ray Shooting for Triangles in 3-Space and Related Problems

We consider several problems that involve lines in three dimensions, and present improved algorithms for solving them. The problems include (i) ray shooting amid triangles in $R^3$, (ii) reporting intersections between query lines (segments, or rays) and input triangles, as well as approximately counting the number of such intersections, (iii) computing the intersection of two nonconvex polyhedra, (iv) detecting, counting, or reporting intersections in a set of lines in $R^3$, and (v) output-sensitive construction of an arrangement of triangles in three dimensions. Our approach is based on the polynomial partitioning technique. For example, our ray-shooting algorithm processes a set of $n$ triangles in $R^3$ into a data structure for answering ray shooting queries amid the given triangles, which uses $O(n^{3/2+\varepsilon})$ storage and preprocessing, and answers a query in $O(n^{1/2+\varepsilon})$ time, for any $\varepsilon>0$. This is a significant improvement over known results, obtained more than 25 years ago, in which, with this amount of storage, the query time bound is roughly $n^{5/8}$. The algorithms for the other problems have similar performance bounds, with similar improvements over previous results. We also derive a nontrivial improved tradeoff between storage and query time. Using it, we obtain algorithms that answer $m$ queries on $n$ objects in \[ \max \left\{ O(m^{2/3}n^{5/6+\varepsilon} + n^{1+\varepsilon}),\; O(m^{5/6+\varepsilon}n^{2/3} + m^{1+\varepsilon}) \right\} \] time, for any $\varepsilon>0$, again an improvement over the earlier bounds.

Assessing the effectiveness of alternative landslide partitioning in machine learning methods for landslide prediction in the complex Himalayan terrain

Several devastating landslides have occurred in the NW Himalayas, which has prompted several researchers to strive for improvement in landslide susceptibility modelling (LSM) methodologies. This research analyzes the effectiveness of alternative landslide partitioning techniques on LSM in the landslide-prone district, Muzaffarabad, Pakistan. We developed a landslide inventory of 961 landslides and then traditionally divided it into training (672; 70%) and testing (289; 30%) samples. These training samples (672) are processed by the Average Nearest Neighbour Index (ANNI) method to estimate the spatial pattern of landslides in nature. The results provide an ANNI ratio of 0.672 confirming that the landslides distribution pattern is cluster in the complex Himalayan terrain of Muzaffarabad. Among 672, the majority of landslides (529; 79%) depict cluster behaviour, while 189 landslides (21%) depict random behaviour. To evaluate the effectiveness of landslide cluster samples in prediction, five machine learning algorithms (MLAs), that is, K-Nearest Neighbour (KNN), Naïve Bayes (NB), Random Forest (RF), Extreme Gradient Boosting (XGBoost) and Logistic Regression (LR) using proposed cluster (529) and traditional (672) random training samples along with 17 geo-environmental factors are executed. However, testing samples (289; 30%) separated at the initial stage remained the same to check the model’s effectiveness. The areas under the curve (AUC-ROC), sensitivity, specificity, Kappa index and accuracy (ACC) have been used to evaluate the MLA’s performances. An alternative partitioning technique (cluster) shows the highest predictive power with AUC-ROC values ranging from 0.96 to 0.86, Kappa index ranges from 0.76 to 0.60 and ACC ranges from 0.90 to 0.83. Conversely, the random partitioning approach performs less well with AUC-ROC values ranging from 0.95 to 0.83, Kappa index ranges from 0.70 to 0.49 and ACC ranges from 0.87 to 0.80. In comparison, the RF cluster sampling-based model outperforms the other models and their counterparts. The RF model achieved the highest accuracy (0.902), highest AUC (0.962) and highest Kappa index (0.755) followed by XGboost having ACC (0.885), AUC (0.95) and Kappa index (0.724) employing proposed cluster training samples. However, traditional random training samples yield comparatively low ACC of RF (0.868) and XGboost (0.862). These results confirm that cluster training sampling performs well in obtaining reliable and precise LSMs for complex Himalayan terrain. Although random landslide partitioning for training datasets is seldom utilized in LSM, this study highlights that cluster partitioning for landslide training datasets might be a realistic and reliable approach.

A Machine Learning Approach to Identify Previously Unconsidered Causes for Complications in Aesthetic Breast Augmentation.

Primary breast augmentation is one of the most commonly requested aesthetic procedures. Considering the large number of procedures performed in connection with a high demand, it is crucial to prevent complications. For this reason, finding and avoiding possible sources of complications is decisive. Between January 2010 and December 2021, 1625 female patients underwent an aesthetic breast augmentation performed by a single surgeon. The data collected were analyzed through a machine learning technique for binary recursive partitioning. This made it possible to detect unknown sources of a complication and determine a vertex for the various features. When analyzing the data, for most features a high importance score with low entropy was achieved, concluding a high significance. In addition, reproducibility was demonstrated through detailed testing and training accuracies in the algorithm. With this procedure, in addition to known risks such as a high BMI and round implant shape, a larger than A preoperative bra-cup size (OR: 2.7) and a taller body could also be identified as most significant influencing factors for complications. Preoperative breast size plays an exceptionally important role in the occurrence of complications and should be a factor held in a surgeon's considerations. In addition, this study shows ways to transfer artificial intelligence into plastic surgery to increase medical quality. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72.

Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer’s disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic frontotemporal degeneration mutation carriers using hierarchical spectral clustering and compared the outcome to that obtained with a more conventional voxel-wise tensor- and voxel-based morphometric analysis. In the symptomatic groups, the hierarchical spectral clustering-based method yielded results that were largely in line with those obtained with the voxel-wise approach. In asymptomatic C9orf72 expansion carriers, spectral clustering detected changes in size in medial temporal cortex that voxel-wise methods could only detect in the symptomatic phase. Furthermore, in the asymptomatic and the symptomatic phases, the spectral clustering approach detected changes in shape in the premotor cortex in C9orf72. In summary, the present study shows the merit of hierarchical spectral clustering for data-driven segmentation and detection of structural changes in the symptomatic and asymptomatic stages of monogenic frontotemporal degeneration.

Penerapan Teknik Partisi Langkah Kuda Papan Catur pada Pelabelan Super (a,d)-P_2 (▷) ̇ H-Antimagic Total Covering Sebarang Dua Graf dan Aplikasinya

Let be a finite collection of simple, nontrivial and undirected graphs. A graph is as antimagic total covering if there is bijectif function for every subgraph in which isomorfic to and the total weight, form arithmetic sequence , in which a,b are integers and n is a number of graph cover of which the result of total comb product operation. A antimagic total covering is as "super" if smallest label is used for vertex labelling. The way for labelling a graph this time, using a knight move partition techniques application. The graph use total comb product operation . Take a copy of and a number of , then put the copy of -sequence in graph vertex to -sequence vertex of and put the copy of -sequence in graft edge to -sequence edge of is definition of total comb product. In this article, will be investigated about Knight Move Partition Techniques Application in Labelling Super Antimagic Total Covering for Any Two Graphs and Its Application (in Constructing Ciphertext).

New Strategies To Improve Differential-Linear Attacks With Applications To Chaskey

Abstract Differential-linear cryptanalysis, as the combination of differential and linear cryptanalysis, is an efficient way to attack many kinds of ciphers. Recently, various refinements to this cryptanalytic technique have been proposed, especially with good effects on ARX ciphers. In the current framework of a differential-linear attack, a cipher $E$ is often divided into three parts: a differential part $E_1$, a linear part $E_2$ and a connective part $E_m$. It is a challenging problem to deal with the connective part when building a differential-linear distinguisher, and for ARX ciphers, estimating the correlation of $ E_m $ experimentally under given input difference $\Delta _m$ and output linear mask $\Gamma _m$ is the main approach so far. In this paper, we discuss the effects of $ \Delta _{m} $ and $ \Gamma _{m} $ on the correlation of $ E_m $ for the first time. As a result, we propose a new strategy to find $\Delta _m$ and $\Gamma _m$ to build differential-linear distinguishers with high correlations for ARX ciphers based on algebraic equations derived from their round functions. For the key recovery parts of differential-linear attacks, we also find a new partitioning technique which will reduce the time complexity. Based on our new methods, we improve the differential-linear attack on 7-round Chaskey.

Capturing Spatial Relationship Mapping Patterns between GPS Coordinates and Road Network Using Machine Learning and Partitioning Techniques

Map matching is a technique used to identify which path a vehicle is travelling on in a road map. Since it is a crucial fundamental step for a wide range of transportation applications, many map-matching algorithms have been devised ranging from simple geometric calculation methods to more sophisticated methods. However, the study of spatial relationship patterns between GPS coordinates and road segments mapped with map-matching has not received enough attention from researchers. This paper presents a framework, called Proxy Map Matching (PMM), to learn such patterns using machine learning techniques. However, we find that solely employing machine learning techniques on such data is not sufficient to capture the patterns. Solving this problem that way results in an inaccurate proxy model. In PMM, we construct several proxy map matchers and assign them to each group of data based on their spatial proximity, thereby achieving accuracy improvement. An experiment on real-world data shows that the framework achieves above 85% accuracy with integration of machine learning techniques, and outperforms the methods which solely employ machine learning techniques significantly. Moreover, the proposed proxy model can perform very fast matching. For 14,177 GPS coordinate pairs per second, we achieve 88.4% accuracy.

Multi-Resolution Discrete Cosine Transform Fusion Technique Face Recognition Model

This work presents a Multi-Resolution Discrete Cosine Transform (MDCT) fusion technique Fusion Feature-Level Face Recognition Model (FFLFRM) comprising face detection, feature extraction, feature fusion, and face classification. It detects core facial characteristics as well as local and global features utilizing Local Binary Pattern (LBP) and Principal Component Analysis (PCA) extraction. MDCT fusion technique was applied, followed by Artificial Neural Network (ANN) classification. Model testing used 10,000 faces derived from the Olivetti Research Laboratory (ORL) library. Model performance was evaluated in comparison with three state-of-the-art models depending on Frequency Partition (FP), Laplacian Pyramid (LP) and Covariance Intersection (CI) fusion techniques, in terms of image features (low-resolution issues and occlusion) and facial characteristics (pose, and expression per se and in relation to illumination). The MDCT-based model yielded promising recognition results, with a 97.70% accuracy demonstrating effectiveness and robustness for challenges. Furthermore, this work proved that the MDCT method used by the proposed FFLFRM is simpler, faster, and more accurate than the Discrete Fourier Transform (DFT), Fast Fourier Transform (FFT) and Discrete Wavelet Transform (DWT). As well as that it is an effective method for facial real-life applications.

Assessing layered HIV prevention programming: optimizing outcomes for adolescent girls and young women.

To assess how exposure to multiple, layered interventions predicts HIV-related outcomes among adolescent girls (15-19 years) and young women (20-24 years) in Kenya. Survey data from adolescent girls and young women (n = 736) with 14-16 months of engagement with DREAMS, a comprehensive HIV prevention program that provides a range of health education, life skills, social protection, and social and behaviour change interventions. Nonparametric recursive partitioning technique - classification and regression tree (CART) - to identify the best predictors (DREAMS interventions) for achieving the desired HIV-related outcomes (consistent condom use and no transactional sex or sexual violence). Among adolescent girls, schooling support reduced the likelihood of engaging in transactional sex, whereas schooling support and exposure to parenting program reduced the likelihood experiencing sexual violence. Likelihood of consistent condom use increased among adolescent girls with exposure to preexposure prophylaxis (PrEP), schooling support, and the violence prevention programming. Among young women, multiple pathways reduced the likelihood of engaging in transactional sex: exposure to the male sexual partner program; exposure to the youth fund program; exposure to the violence prevention program; or exposure/engagement with schooling support, parenting programming, and the youth fund program. For young women, consistent condom use increased with schooling support and male partner engagement. Additionally, engagement in violence prevention program and male partner engagement increased the likelihood of not experiencing sexual violence among young women. Exposure to a combination of DREAMS interventions predicted outcomes that can reduce HIV risk among AGYW, though the pathways differed by outcome and age group.

OD-Based Partition Technique to Improve Arterial Signal Coordination Using Connected Vehicle Data

Maximizing two-way arterial bands often plays a critical role in signal coordination along urban arterials. With an increasing number of signals involved, the arterial bands tend to shrink to some extent resulting in inefficient arterial signal coordination, and not all vehicles fully take advantage of the arterial bands to travel through the entire corridor. In response to that, system partition is a technique for handling arterials with many signals. Rather than designing end-to-end signal coordination, efficient arterial signal coordination is highly reliant on traffic origin and destination (OD) patterns on the arterial, which have been difficult to obtain using conventional data collection methods. The emerging big data sources, such as connected vehicles, provide great potential to gather such invaluable OD information for improving arterial signal coordination. This research proposes an easy-to-implement OD-based partition technique to improve arterial signal coordination by utilizing vehicle trajectory data automatically collected from connected vehicles. The proposed signal timing technique was tested using an arterial with 17 signalized intersections in Orange County, California. The results demonstrated that the OD-based partition technique improved the arterial average travel speed by 2.7% and 12.1% for the eastbound and westbound directions, respectively. At the same time, the proposed technique shortened the arterial average travel time by 2.6% and 11.1% for the eastbound and westbound directions, respectively. The total travel time was shortened both for the main-street and side-street major traffic flows.

An efficient labeled memory system for learned indexes

The appearance and wide use of memory hardware bring significant changes to the conventional vertical memory hierarchy that fails to handle contentions for shared hardware resources and expensive data movements. To deal with these problems, existing schemes have to rely on inefficient scheduling strategies that also cause extra temporal, spatial and bandwidth overheads. Based on the insights that the shared hardware resources trend to be uniformly and hierarchically offered to the requests for co-located applications in memory systems, we present an efficient abstraction of memory hierarchies, called Label, which is used to establish the connection between the application layer and underlying hardware layer. Based on labels, our paper proposes LaMem, a labeled, resource-isolated and cross-tiered memory system by leveraging the way-based partitioning technique for shared resources to guarantee QoS demands of applications, while supporting fast and low-overhead cache repartitioning technique. Besides, we customize LaMem for the learned index that fundamentally replaces storage structures with computation models as a case study to verify the applicability of LaMem. Experimental results demonstrate the efficiency and efficacy of LaMem.

An empirical study of various candidate selection and partitioning techniques in the DIRECT framework

Over the last three decades, many attempts have been made to improve the DIRECT (DIviding RECTangles) algorithm’s efficiency. Various novel ideas and extensions have been suggested. The main two steps of DIRECT-type algorithms are selecting and partitioning potentially optimal rectangles. However, the most efficient combination of these two steps is an area that has not been investigated so far. This paper presents a study covering an extensive examination of various candidate selection and partitioning techniques within the same DIRECT algorithmic framework. Twelve DIRECT-type algorithmic variations are compared on 800 randomly generated GKLS-type test problems and 96 box-constrained global optimization problems from DIRECTGOLib v1.1 with varying complexity. Based on these studies, we have identified the most efficient selection and partitioning combinations leading to new, more efficient, DIRECT-type algorithms. All these algorithms are included in the latest version of DIRECTGO v1.1.0 and are publicly available.

Improving Recognition of Overlapping Activities with Less Interclass Variations in Smart Homes through Clustering-Based Classification.

The systems of sensing technology along with machine learning techniques provide a robust solution in a smart home due to which health monitoring, elderly care, and independent living take advantage. This study addresses the overlapping problem in activities performed by the smart home resident and improves the recognition performance of overlapping activities. The overlapping problem occurs due to less interclass variations (i.e., similar sensors used in more than one activity and the same location of performed activities). The proposed approach overlapping activity recognition using cluster-based classification (OAR-CbC) that makes a generic model for this problem is to use a soft partitioning technique to separate the homogeneous activities from nonhomogeneous activities on a coarse-grained level. Then, the activities within each cluster are balanced and the classifier is trained to correctly recognize the activities within each cluster independently on a fine-grained level. We examine four partitioning and classification techniques with the same hierarchy for a fair comparison. The OAR-CbC evaluates on smart home datasets Aruba and Milan using threefold and leave-one-day-out cross-validation. We used evaluation metrics: precision, recall, F score, accuracy, and confusion matrices to ensure the model's reliability. The OAR-CbC shows promising results on both datasets, notably boosting the recognition rate of all overlapping activities more than the state-of-the-art studies.

Efficient spatial data partitioning for distributed kNN joins

Parallel processing of large spatial datasets over distributed systems has become a core part of modern data analytic systems like Apache Hadoop and Apache Spark. The general-purpose design of these systems does not natively account for the data’s spatial attributes and results in poor scalability, accuracy, or prolonged runtimes. Spatial extensions remedy the problem and introduce spatial data recognition and operations. At the core of a spatial extension, a locality-preserving spatial partitioner determines how to spatially group the dataset’s objects into smaller chunks using the distributed system’s available resources. Existing spatial extensions rely on data sampling and often mismanage non-spatial data by either overlooking their memory requirements or excluding them entirely. This work discusses the various challenges that face spatial data partitioning and proposes a novel spatial partitioner for effectively processing spatial queries over large spatial datasets. For evaluation, the proposed partitioner is integrated with the well-known k-Nearest Neighbor (kNN) spatial join query. Several experiments evaluate the proposal using real-world datasets. Our approach differs from existing proposals by (1) accounting for the dataset’s unique spatial traits without sampling, (2) considering the computational overhead required to handle non-spatial data, (3) minimizing partition shuffles, (4) computing the optimal utilization of the available resources, and (5) achieving accurate results. This contributes to the problem of spatial data partitioning through (1) providing a comprehensive discussion of the problems facing spatial data partitioning and processing, (2) the development of a novel spatial partitioning technique for in-memory distributed processing, (3) an effective, built-in, load-balancing methodology that reduces spatial query skews, and (4) a Spark-based implementation of the proposed work with an accurate kNN spatial join query. Experimental tests show up to 1.48 times improvement in runtime as well as the accuracy of results.

Eu speciation in apatite at 1 bar: An experimental study of valence-state partitioning by XANES, lattice strain, and Eu/Eu* in basaltic systems

Abstract Partition coefficients for rare earth elements (REEs) between apatite and basaltic melt were determined as a function of oxygen fugacity (fO2; iron-wüstite to hematite-magnetite buffers) at 1 bar and between 1110 and 1175 °C. Apatite-melt partitioning data for REE3+ (La, Sm, Gd, Lu) show near constant values at all experimental conditions, while bulk Eu becomes more incompatible (with an increasing negative anomaly) with decreasing fO2. Experiments define three apatite calibrations that can theoretically be used as redox sensors. The first, a XANES calibration that directly measures Eu valence in apatite, requires saturation at similar temperature-composition conditions to experiments and is defined by: ( E u 3 + ∑ E u ) Apatite = 1 1 + 10 - 0.10 ± 0.01 × l o g ⁡ ( f o 2 ) - 1.63 ± 0.16 . The second technique involves analysis of Sm, Eu, and Gd in both apatite and coexisting basaltic melt (glass), and is defined by: ( Eu E u * ) D Sm × Gd = 1 1 + 10 - 0.15 ± 0.03 × l o g ⁡ ( f o 2 ) - 2.46 ± 0.41 . The third technique is based on the lattice strain model and also requires analysis of REE in both apatite and basalt. This calibration is defined by ( Eu E u * ) D lattice strain = 1 1 + 10 - 0.20 ± 0.03 × l o g ⁡ ( f o 2 ) - 3.03 ± 0.42 . The Eu valence-state partitioning techniques based on (Sm×Gd) and lattice strain are virtually indistinguishable, such that either methodology is valid. Application of any of these calibrations is best carried out in systems where both apatite and coexisting glass are present and in direct contact with one another. In holocrystalline rocks, whole rock analyses can be used as a guide to melt composition, but considerations and corrections must be made to either the lattice strain or Sm×Gd techniques to ensure that the effect of plagioclase crystallization either prior to or during apatite growth can be removed. Similarly, if the melt source has an inherited either a positive or negative Eu anomaly, appropriate corrections must also be made to lattice strain or Sm×Gd techniques that are based on whole rock analyses. This being the case, if apatite is primary and saturates from the parent melt early during the crystallization sequence, these corrections may be minimal. The partition coefficients for the REE between apatite and melt range from a maximum DEu3+ = 1.67 ± 0.25 (as determined by lattice strain) to DLu3+ = 0.69 ± 0.10. The REE partition coefficient pattern, as observed in the Onuma diagram, is in a fortuitous situation where the most compatible REE (Eu3+) is also the polyvalent element used to monitor fO2. These experiments provide a quantitative means of assessing Eu anomalies in apatite and how they be used to constrain the oxygen fugacity of silicate melts.

A restriction estimate in R3 using brooms

If f is a function supported on the truncated paraboloid in R3 and E is the corresponding extension operator, then we prove that for all p>3+3∕13, ‖Ef‖Lp(R3)≤C‖f‖L∞. The proof combines Wolff’s two ends argument with polynomial partitioning techniques. We also observe some geometric structures in the wave packets.

Pengujian Aplikasi Sistem Informasi Absensi Karyawan PT Prakarsa Mitra Andalan Menggunakan Metode Black Box

Technological developments that gave birth to many quality software products. Quality software is software that has gone through testing. In software testing there are several things that must be tested, namely the software must be in accordance with what the user wants or needs, the software must work well when used so that there are no errors, the software must have a good response time when run. Software must be a cost and time efficiency solution. In software testing, one of the methods used is by using the blackbox method. The blackbox method has its own technique, one of which is the equivalence partitioning technique. The equivalence partitioning technique is a technique of dividing several parts or partitions to check each part or partition by testing the software.