Data Distribution Research Articles

Implementasi Google Cloud Pub/Sub menggunakan Metode Subscription Pull dalam Pengiriman Data Promosi Toko di PT XYZ

As a company adopts a distributed business model, scalability issues often arise in the distribution of data from server to client. PT XYZ, as one of the companies facing this challenge, frequently encounters delays and request blocking when using synchronous communication models such as RESTful API. Additionally, there is resource and time wastage on the server side as it has to handle store requests one by one, resulting in losses for the store due to delayed access to required data for business processes. This research proposes a solution to address these issues by implementing a publish/subscribe communication model. Through the implementation of this architecture, clients and servers are separated by a broker, namely Google Cloud. Pub/sub technology is implemented using the Python programming language with the Waterfall development method. The results of this research include the design of a client-side application capable of receiving data from a previously deployed broker. Test results demonstrate that the client can receive data at any time without disrupting the data fetching process for other clients.

Sistem Informasi Pembelajaran Taman Pendidikan Al-Qur’an di Yayasan Al-Muttaqien Jadid (TPQ)

This research aims to determine the TPA academic system currently running at Al-Muttaqien Jadid. This research is descriptive research using qualitative methods. Data collection was carried out by interviews, observation and documentation, as well as using literature studies from various sources related to the questions asked in the research. It was found that there was a problem with the TPA Academic system, including regarding student registration, class division and scheduling, which still uses the bookkeeping method, it is difficult to get information regarding student data, class data, teacher data and subject data. Apart from that, the process of class division and scheduling cannot be completed quickly due to difficulties in finding data, the number of students being placed in excess of the quota often occurs, so mistakes or mistakes often occur in recording students' grades. So that registration, payment and distribution data can be processed quickly and accurately, a web-based application is needed. By implementing this application, it can help academic processing in preparing payment reports and student registration so that the management of TKA-TPA in the data collection process makes coordination and coaching easier. From the research results, it can be concluded that registration, payment, class division and scheduling using the bookkeeping method requires quite a long time in processing data and storing data.

2D signal estimation for sparse distributed target photon counting data

In this study, we explore the utilization of penalized likelihood estimation for the analysis of sparse photon counting data obtained from distributed target lidar systems. Specifically, we adapt the Poisson Total Variation processing technique to cater to this application. By assuming a Poisson noise model for the photon count observations, our approach yields denoised estimates of backscatter photon flux and related parameters. This facilitates the processing of raw photon counting signals with exceptionally high temporal and range resolutions (demonstrated here to 50 Hz and 75 cm resolutions), including data acquired through time-correlated single photon counting, without significant sacrifice of resolution. Through examination involving both simulated and real-world 2D atmospheric data, our method consistently demonstrates superior accuracy in signal recovery compared to the conventional histogram-based approach commonly employed in distributed target lidar applications.

Error detection for radiotherapy planning validation based on deep learning networks.

Quality assurance (QA) of patient-specific treatment plans for intensity-modulated radiation therapy (IMRT) and volumetric modulated arc therapy (VMAT) necessitates prior validation. However, the standard methodology exhibits deficiencies and lacks sensitivity in the analysis of positional dose distribution data, leading to difficulties in accurately identifying reasons for plan verification failure. This issue complicates and impedes the efficiency of QA tasks. The primary aim of this research is to utilize deep learning algorithms for the extraction of 3D dose distribution maps and the creation of a predictive model for error classification across multiple machine models, treatment methodologies, and tumor locations. We devised five categories of validation plans (normal, gantry error, collimator error, couch error, and dose error), conforming to tolerance limits of different accuracy levels and employing 3D dose distribution data from a sample of 94 tumor patients. A CNN model was then constructed to predict the diverse error types, with predictions compared against the gamma pass rate (GPR) standard employing distinct thresholds (3%, 3mm; 3%, 2mm; 2%, 2mm) to evaluate the model's performance. Furthermore, we appraised the model's robustness by assessing its functionality across diverse accelerators. The accuracy, precision, recall, and F1 scores of CNN model performance were 0.907, 0.925, 0.907, and 0.908, respectively. Meanwhile, the performance on another device is 0.900, 0.918, 0.900, and 0.898. In addition, compared to the GPR method, the CNN model achieved better results in predicting different types of errors. When juxtaposed with the GPR methodology, the CNN model exhibits superior predictive capability for classification in the validation of the radiation therapy plan on different devices. By using this model, the plan validation failures can be detected more rapidly and efficiently, minimizing the time required for QA tasks and serving as a valuable adjunct to overcome the constraints of the GPR method.

Sliced Wasserstein adversarial training for improving adversarial robustness

AbstractRecently, deep-learning-based models have achieved impressive performance on tasks that were previously considered to be extremely challenging. However, recent works have shown that various deep learning models are susceptible to adversarial data samples. In this paper, we propose the sliced Wasserstein adversarial training method to encourage the logit distributions of clean and adversarial data to be similar to each other. We capture the dissimilarity between two distributions using the Wasserstein metric and then align distributions using an end-to-end training process. We present the theoretical background of the motivation for our study by providing generalization error bounds for adversarial data samples. We performed experiments on three standard datasets and the results demonstrate that our method is more robust against white box attacks compared to previous methods.

A neural network copula function approach for solving joint basic probability assignment in structural reliability analysis

AbstractApplying evidence theory to structural reliability analysis under epistemic uncertainty, it is necessary to consider the correlation of evidence variables. Among them, solving the joint basic probability assignment (BPA) of the evidence variables is a crucial link. In this study, a solution method of joint BPA based on neural network copula function is proposed. This method is to automatically construct copula function through neural network, which avoids the process of selecting the optimal copula function. Firstly, the neural network copula function is constructed based on the sample set of evidence variables. Then, the expression for solving the joint BPA using the neural network copula function is derived through vectors. Furthermore, the expression is used to map the marginal BPA of evidence variables to joint BPA, thus realizing the solution of joint BPA. Finally, the effectiveness of this method is verified by three examples. The results show that the neural network copula function describes the data distribution better than the optimal copula function selected by the traditional method. In addition, there is actually an error in solving the reliability intervals using the traditional optimal copula function method, whereas the results of this paper's neural network copula function method are more accurate and better for decision making.

Updated geographic distributions for Texas reptiles

As is the case for many taxa in the Anthropocene, reptile conservation is challenged by an assortment of human-mediated factors. Distributional data for reptile species can be highly useful for informing conservation action. For example, species occurrence data can be used to model suitable habitat as well as quantify contraction, expansion, or shift in the distribution of a species. Texas, USA has a rich reptile fauna including one crocodilian, 32 testudine, 55 lacertilian, and 82 serpent species. However, literature pertaining to the distribution of these species has not been synthesized for over a decade. The goals of our study were to visually summarize all published distributional data for reptile species in the state with updated distribution maps; to tabulate all novel and historic distribution data that is absent from the last statewide synthesis; and to characterize any taxonomic, geographic, or temporal trends of distribution record reporting that have occurred in the state over the last decade. We discovered a total of 659 records that supplement the maps provided in the last statewide synthesis, 40% of which were found in published materials that predate that synthesis. Regarding distributional records published over the last decade, there was no apparent temporal trend – record reporting across years appeared to be stochastic. The number of records published for reptile families generally followed patterns of species richness, although several families had fewer or more records than would be expected. These results might be due to several factors including species biology, variable rates of potential range spread or contraction, and variable research attention (historically and contemporarily). Spatially, we found hot spots of record reporting in the southern, western, and northern portions of the state. We suspect these represent either a contemporary geographic bias of research attention, historically poor range characterization for reptile species occupying these regions, or a combination of both. We also found cold spots of record reporting in the northwestern portion of the Panhandle. These are likely due to natural patterns of reptile diversity as well as geographically biased research attention. Future work to be conducted on reptile distributions in Texas should include a thorough synthesis and revisiting of the voucher specimen data associated with these records.

To the knowledge of the subgenus Phrator Semenov, 1922 of the genus Omophron Latreille, 1802 (Coleoptera: Carabidae)

The species of the subgenus Phrator Semenov, 1922 are reviewed, with six species now recognized. One species is downgraded to subspecies: O. multiguttatum depressum Klug, 1853 stat. nov. Some new synonymies are proposed Omophron variegatum boiteli Alluaud, 1935 syn. n. = Omophron variegatum sardoum Reitter, 1907 syn. n. = Omophron variegatum seurati Alluaud, 1935 syn. n. = Omophron (Phrator) variegatum Olivier, 1812; Omophron (Phrator) ethiopiensis Valainis, 2016 syn. nov. = O. multiguttatum (s.str) Chaudoir, 1850; and Omophron (Phrator) alluaudi Dupuis, 1913 syn. n. = O. vittulatum Fairmaire, 1894. The variability of all the treated species is discussed. A key to the species, together with new distributional data, maps of distribution, illustrations of the habitus and of the most important features are provided.

Transformer based ensemble deep learning approach for remote sensing natural scene classification

ABSTRACT Very high resolution (VHR) remote sensing (RS) image classification is paramount for detailed Earth’s surface analysis. Feature extraction from VHR natural scenes is crucial, but it becomes a challenging task because of the overlapping edges present in images. Multiple open-source datasets exist in the literature to train robust models, and they have been benchmarked using deep learning models. However, different datasets contain different numbers of classes; a few of them could be absent in other datasets because they are independent of the other classes, and the classes are sometimes not mutually exclusive amongst the same dataset. Thus, it is very challenging to generalize a model trained on a single dataset to perform scene classification on unknown classes of multiple benchmark datasets and real-time images. Thus, this work introduces the Remote Sensing Natural Scenes 92 (RS_NS92) dataset, consisting of 36,785 images belonging to 92 classes, curated by selectively taking the union of all subclasses from five benchmark datasets. This class count is significantly higher than publicly available datasets and maintains a low-class imbalance and a comprehensive data distribution for robust model training. It also provides the remote sensing community with an extra platform to validate the performance on multiple benchmarks. Inspired by federated learning, an ensemble approach consisting of three feature extraction backbones: Vision Transformers, Swin Transformers, and ConvNeXt (termed the VSC_Ensemble model) is also introduced. This model can make extraordinary predictions across multiple datasets by finetuning weights using transfer learning. Experimental analysis with the proposed approach not only obtains a high test accuracy of 97.24% and an F1-score of 0.9587 for the 92 classes on a 90:10 split of the proposed benchmark dataset but also gets excellent results on unseen test images of other datasets, which are comparable to the state-of-the-art results.

ADIR: Advanced domain‐invariant representation via decoupling learning and information bottleneck

AbstractThe discrepancy in data distribution between training and testing scenarios, as well as the inductive bias of convolutional neural networks towards image styles, reduces the model's generalization ability. Many unsupervised domain generalization methods based on feature decoupling suffer from an initial neglect of explicit decoupling of content and style features, resulting in content features that still contain considerable redundant information, thereby restricting improvements in generalization capability. To tackle this problem, this paper optimizes the learning process of domain‐invariant (content) features into an information compression issue, minimizing redundancy in content features. Furthermore, to enhance decoupled learning, this paper introduces innovative cross‐domain loss functions and image reconstruction modules that explicitly decouple and merge content and style across different domains. Extensive experiments demonstrate the method's significant enhancements over recent cutting‐edge approaches.

OFIDA: Object-focused image data augmentation with attention-driven graph convolutional networks.

Image data augmentation plays a crucial role in data augmentation (DA) by increasing the quantity and diversity of labeled training data. However, existing methods have limitations. Notably, techniques like image manipulation, erasing, and mixing can distort images, compromising data quality. Accurate representation of objects without confusion is a challenge in methods like auto augment and feature augmentation. Preserving fine details and spatial relationships also proves difficult in certain techniques, as seen in deep generative models. To address these limitations, we propose OFIDA, an object-focused image data augmentation algorithm. OFIDA implements one-to-many enhancements that not only preserve essential target regions but also elevate the authenticity of simulating real-world settings and data distributions. Specifically, OFIDA utilizes a graph-based structure and object detection to streamline augmentation. Specifically, by leveraging graph properties like connectivity and hierarchy, it captures object essence and context for improved comprehension in real-world scenarios. Then, we introduce DynamicFocusNet, a novel object detection algorithm built on the graph framework. DynamicFocusNet merges dynamic graph convolutions and attention mechanisms to flexibly adjust receptive fields. Finally, the detected target images are extracted to facilitate one-to-many data augmentation. Experimental results validate the superiority of our OFIDA method over state-of-the-art methods across six benchmark datasets.

DDSC-SMOTE: an imbalanced data oversampling algorithm based on data distribution and spectral clustering

DDSC-SMOTE: an imbalanced data oversampling algorithm based on data distribution and spectral clustering

EML: Emotion-Aware Meta Learning for Cross-Event False Information Detection

Modern social media’s development has dramatically changed how people obtain information. However, the wide dissemination of various false information has severely detrimental effects. Accordingly, many deep learning-based methods have been proposed to detect false information and achieve promising results. However, these methods are unsuitable for new events due to the extremely limited labeled data and their discrepant data distribution to existing events. Domain adaptation methods have been proposed to mitigate these problems. However, their performance is suboptimal because they are not sensitive to new events due to they aim to align the domain information between existing events, and they hardly capture the fine-grained difference between real and fake claims by only using semantic information. Therefore, we propose a novel Emotion-aware Meta Learning (EML) approach for cross-event false information early detection, which deeply integrates emotions in meta learning to find event-sensitive initialization parameters that quickly adapt to new events. Emotion-aware meta learning is non-trivial and faces three challenges: 1) How to effectively model semantic and emotional features to capture fine-grained differences. 2) How to reduce the impact of noise in meta learning based on semantic and emotional features. 3) How to detect the false information in a zero-shot detection scenario, i.e., no labeled data for new events. To tackle these challenges, firstly, we construct the emotion-aware meta tasks by selecting claims with similar and opposite emotions to the target claim other than usually used random sampling. Secondly, we propose a task weighting method and event-adaptation meta tasks to further improve the model’s robustness and generalization ability for detecting new events. Finally, we propose a weak label annotation method to extend EML to zero-shot detection according to the calculated labels’ confidence. Extensive experiments on real-world datasets show that the EML achieves superior performances on false information detection for new events.

Abstract PO4-17-07: RELATE 2: A real-world observational study assessing feasibility, acceptability, perceived impacts of digital apps for patients receiving olaparib or trastuzumab deruxtecan (T-DXd) as part of routine clinical practice

Abstract Background: Safe and effective use of two new treatments, T-DXd or olaparib, in breast cancer (BC) requires awareness, education and co-ownership by patients and care teams. It is hypothesized that a patient-centered digital app that provides disease and treatment-specific guidance and symptom reporting functionality while collecting specific data could positively impact patients’ confidence, self-monitoring of symptoms and experience of care. This study will develop and assess two new treatment-specific versions of the accredited OWise app to evaluate how BC patients accept and use the app and how use in routine care appears to impact patients and their experience of care in relation to monitoring symptoms. As a novel element, this study will explore the feasibility of combining treatment-specific real-world data collection with digital health support to understand how treatment patterns and characteristics in the real-world setting. OWise is a specific BC app that has been endorsed by NHS digital and is widely available through NHS app library in UK and in Netherlands. It currently has over 18K registered users. Trial Design: The study involves two workstreams which include several sources for data collection. Workstream 1 involves collection of participants OWise data (including patient entered data as well as usage statistics) and a brief survey for participants to provide feedback on their experience using OWise every three months for up to 18 months. Survey questions are focused on their perceptions of the usefulness of the app for monitoring their health changes and communicating with their care team. Workstream 2, will launch approximately three months after the launch of Workstream 1 and will include qualitative interviews with two groups of people: 1) a small number of participants who are already enrolled in Workstream 1 and 2) clinicians (and multidisciplinary team/nurses) to further understand the perceived benefits of the app. The study’s duration will be 21 months Eligibility Criteria: The patients must be prescribed either T-DXd or Olaparib and be at least 18 years of age, be able to create an OWise account (e.g., mobile or web application) and have activated the unique study code for the treatment specific version of OWise and be able to provide online consent for study participation Aims: Primary objectives are to evaluate the feasibility, acceptability, and perceived impacts on patient experience, treatment use, and quality of care of two treatment specific OWise apps in routine BC care. Secondary objectives will focus on prescribing patterns, concomitant medicine use in UK practice, understanding strategies used in the real-world setting to manage AE and demographic and clinical characteristics of patients treated in the UK Statistical methods: Aggregated Participant-level quantitative data and usage data will be analyzed for response frequencies, means, standard deviations (SD), medians, ranges, and confidence intervals (CI). Descriptive statistics and advanced statistics will be used to identify core variables and trends, investigate data distribution links between data, test significant differences between datasets according to profile/status. Target Accrual: 300 patients T-DXd and 150 olaparib using the OWise app and being part of the real-world study. The prescription decision is independent of the study enrolment. Contact: Dr Sophie McGrath. sophie.mcgrath@rmh.nhs.uk Citation Format: Sophie McGrath, Peter Hall, Michael Acquadro, Soren Skovlund, Parveen Jayia, Stuart McIntosh, David Cameron. RELATE 2: A real-world observational study assessing feasibility, acceptability, perceived impacts of digital apps for patients receiving olaparib or trastuzumab deruxtecan (T-DXd) as part of routine clinical practice [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO4-17-07.

State of Health Estimation for Lithium-Ion Battery Based on Sample Transfer Learning under Current Pulse Test

Considering the diversity of battery data under dynamic test conditions, the stability of battery working data is affected due to the diversity of charge and discharge rates, variability of operating temperature, and randomness of the current state of charge, and the data types are multi-sourced, which increases the difficulty of estimating battery SOH based on data-driven methods. In this paper, a lithium-ion battery state of health estimation method with sample transfer learning under dynamic test conditions is proposed. Through the Tradaboost.R2 method, the weight of the source domain sample data is adjusted to complete the update of the sample data distribution. At the same time, considering the division methods of the six auxiliary and the source domain data set, aging features from different state of charge ranges are selected. It is verified that while the aging feature dimension and the demand for target domain label data are reduced, the estimation accuracy of the lithium-ion battery state of health is not affected by the initial value of the state of charge. By considering the mean absolute error, mean square error and root mean square error, the estimated error results do not exceed 1.2% on the experiment battery data, which highlights the advantages of the proposed methods.

Temperature field reconstruction based on femtosecond laser prepared of multiwavelength fibre bragg grating array

Temperature field measurements are of great significance in industrial production, scientific research, and other fields. Contact temperature measurements usually achieve high spatial resolution by increasing the layout density of the sensors. However, in practical applications, achieving high-density sensor placement is often difficult. Therefore, when the number of sensors is limited, it is necessary to perform function fitting or interpolation of the temperature of the sampling points to reconstruct the temperature field distribution. This study proposed a temperature field reconstruction method based on femtosecond laser prepared multiwavelength fibre Bragg grating (FBG) array. A multiwavelength FBG array was prepared using the femtosecond laser phase mask method combined with stress stretching, and applied for measuring the temperature field distribution in a tubular high-temperature furnace. Cubic spline interpolation and backpropagation (BP) neural networks were used to construct two-dimensional temperature field models for the temperature field distribution data measured by the FBGs, and the prediction accuracies of the two models were compared. The test results show that the root mean square error of the temperature field distribution constructed using the BP neural network is 0.7333 °C, which is approximately 23.18% of the predicted results of the cubic spline interpolation model, indicating that it is a high-precision temperature field reconstruction method.

Dasydorylas Skevington, 2001 (Pipunculidae, Tomosvaryellini) of Colombia, with description of three new species and a key to Neotropical males

The cosmopolitan pipunculid genus Dasydorylas Skevington, 2001 includes just a few species from the Neotropical Region and is completely unknown to Colombia. Three new species of Dasydorylas are described from protected areas and conflict territories of limited access in Colombia, namely Dasydorylas colombiensis sp. nov. (type locality: Santuario de Fauna y Flora Iguaque, Boyacá), D. gibber sp. nov. (type locality: Santuario de Fauna y Flora Iguaque, Boyacá), and D. santainesensis sp. nov. (type locality: Páramo de Santa Inés, Belmira, Antioquia). Diagnoses, illustrations and distributional data of the new species are presented. Dasydorylas nigellus (Rafael, 1991) is recorded for the first time from Colombia and its amended diagnosis is provided. An identification key to males of all Neotropical species is presented. With this paper, the number of Neotropical species of Dasydorylas is increased from six to nine.

Edge Caching Data Distribution Strategy with Minimum Energy Consumption

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O(n2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution.

Zoogeographical regions in the Atlantic Forest: patterns and potential drivers

AbstractAimTo delineate present‐day zoogeographical regions of terrestrial vertebrates (frogs, lizards, snakes, birds and non‐volant mammals) in the Atlantic Forest. Within each taxonomic group, we examine the relative importance of abrupt climatic transitions, orographic barriers, past climate change and rivers in shaping zoogeographical boundaries.LocationSouth America's Atlantic Forest.MethodsWe applied a network‐based method to delineate zoogeographical regions, using distribution data (range maps) for 455 species of frogs, 103 lizards, 220 snakes, 917 birds and 202 non‐volant mammals, in 50 × 50 km grid cells. We used hierarchical generalized linear mixed‐effects models to test environmental predictors associated with zoogeographical boundaries. Finally, we intersected the bioregion maps delineated for each group to identify general patterns across all vertebrates.ResultsWe identified four zoogeographical regions for birds and snakes, and five for frogs, lizards and non‐volant mammals. Depending on the group, contemporary and past climate conditions, elevation variation and/or rivers were associated with zoogeographical boundaries. The combined maps indicate that the Atlantic Forest retains four spatially cohesive zoogeographical regions based on present‐day distribution of vertebrates.Main conclusionsCross‐taxon congruence indicates that the geographical and environmental characteristics of the Atlantic Forest have a strong influence on the location of zoogeographical regions for vertebrates. In contrast, transition zones appear to be associated with the spatial distribution of life history traits of each group, potentially explaining the observed differences in the number of bioregions across groups and the position of zoogeographical boundaries. This work paves the way for further research into the evolutionary assembly of the Atlantic Forest's zoogeographical regions and may help inform conservation priorities for maintaining their distinctive faunas.

Lifting the veil off treatment effect heterogeneity

Clinicians often suspect that a treatment effect can vary across individuals. However, they usually lack “evidence-based” guidance regarding potential heterogeneity of treatment effects (HTE). Potentially actionable HTE is rarely discovered in clinical trials and is widely believed (or rationalized) by researchers to be rare. Conventional statistical methods to test for possible HTE are extremely conservative and tend to reinforce this belief. In truth, though, there is no realistic way to know whether a common, or average, effect estimated from a clinical trial is relevant for all, or even most, patients. This absence of evidence, misinterpreted as evidence of absence, may be resulting in sub-optimal treatment for many individuals. We first summarize the historical context in which current statistical methods for randomized controlled trials (RCTs) were developed, focusing on the conceptual and technical limitations that shaped, and restricted, these methods. In particular, we explain how the common-effect assumption came to be virtually unchallenged. Second, we propose a simple graphical method for exploratory data analysis that can provide useful visual evidence of possible HTE. The basic approach is to display the complete distribution of outcome data rather than relying uncritically on simple summary statistics. Modern graphical methods, unavailable when statistical methods were initially formulated a century ago, now render fine-grained interrogation of the data feasible. We propose comparing observed treatment-group data to “pseudo data” engineered to mimic that which would be expected under a particular HTE model, such as the common-effect model. A clear discrepancy between the distributions of the common-effect pseudo data and the actual treatment-effect data provides prima facie evidence of HTE to motivate additional confirmatory investigation. Artificial data are used to illustrate implications of ignoring heterogeneity in practice and how the graphical method can be useful.