Analysis Tools Research Articles

UNESCO, mining heritage and the scalar sustainability of tourism geographies at industrial World Heritage Sites

PurposeIndustrial heritage works within a world of contradictions, contentions and scalar liminality. Archaeologists and historians focus upon oral histories and discourses of tangible and intangible memory and heritage while planners and economists see industrial World Heritage, in particular, as a marketing ploy to redevelop deindustrialized spaces. Within this liminality, we explore the potential for geographical perspectives to solder such contradictions into transdisciplinary heritage assessments and tourism contexts. How might the spatial tools of landscape and scalar analyses expose alternative and sustainable futures within broader patterns of industrial heritage management and consumption?Design/methodology/approachUsing three comparative cases, interview and landscape methods and conducting discourse analysis within a spatial and scalar framework, we explore the increasing presence of industrial World Heritage.FindingsWe present both an institutional reflection upon the complexities of heritage discourse across complex spatial configurations and the intersectional historical, cultural, political, environmental and economic geographies that guide and emerge out of World Heritage Designations. Framed scalarly and spatially, we highlight common interpretation, tourism and heritage management styles and concerns found across industrial World Heritage. We point out trans-scalar considerations for future municipalities and regions looking to utilize their industrial landscapes and narratives.Originality/valueWe believe that more theoretical groundings in space and scale may lead to both the flexibility and the applicability needed to assess and, in turn, manage trans-scalar and trans-spatial complex heritage sites. These perspectives may be uniquely poised to assess the complex geographies of industrial, particularly mining, World Heritage Sites.

A Visual Environment for Data Driven Protein Modeling and Validation.

In structural biology, validation and verification of new atomic models are crucial and necessary steps which limit the production of reliable molecular models for publications and databases. An atomic model is the result of meticulous modeling and matching and is evaluated using a variety of metrics that provide clues to improve and refine the model so it fits our understanding of molecules and physical constraints. In cryo electron microscopy (cryo-EM) the validation is also part of an iterative modeling process in which there is a need to judge the quality of the model during the creation phase. A shortcoming is that the process and results of the validation are rarely communicated using visual metaphors. This work presents a visual framework for molecular validation. The framework was developed in close collaboration with domain experts in a participatory design process. Its core is a novel visual representation based on 2D heatmaps that shows all available validation metrics in a linear fashion, presenting a global overview of the atomic model and provide domain experts with interactive analysis tools. Additional information stemming from the underlying data, such as a variety of local quality measures, is used to guide the user's attention toward regions of higher relevance. Linked with the heatmap is a three-dimensional molecular visualization providing the spatial context of the structures and chosen metrics. Additional views of statistical properties of the structure are included in the visual framework. We demonstrate the utility of the framework and its visual guidance with examples from cryo-EM.

Output Regularization With Cluster-Based Soft Targets.

While supervised learning of over-parameterized neural networks achieved state-of-the-art performance in image classification, it tends to over-fit the labeled training samples to give inferior generalization ability. Output regularization deals with over-fitting by using soft targets as additional training signals. Although clustering is one of the most fundamental data analysis tools for discovering general-purpose and data-driven structures, it has been ignored in existing output regularization approaches. In this article, we leverage this underlying structural information by proposing Cluster-based soft targets for Output Regularization (CluOReg). This approach provides a unified way for simultaneous clustering in embedding space and neural classifier training with cluster-based soft targets via output regularization. By explicitly calculating a class relationship matrix in the cluster space, we obtain classwise soft targets shared by all samples in each class. Results of image classification experiments under various settings on a number of benchmark datasets are provided. Without resorting to external models or designed data augmentation, we get consistent and significant reductions in classification error compared with other approaches, demonstrating that cluster-based soft targets effectively complement the ground-truth label.

MediVizor: Visual Mediation Analysis of Nominal Variables.

Mediation analysis is crucial for diagnosing indirect causal relations in many scientific fields. However, mediation analysis of nominal variables requires examining and comparing multiple total effects and their corresponding direct/indirect causal effects derived from mediation models. This process is tedious and challenging to achieve with classical analysis tools such as Excel tables. In this study, we worked closely with experts from two scientific domains to design MediVizor, a visualization system that enables experts to conduct visual mediation analysis of nominal variables. The visualization design allows users to browse and compare multiple total effects together with the direct/indirect effects that compose them. The design also allows users to examine to what extent the positive and negative direct/indirect effects contribute to and reduce the total effects, respectively. We conducted two case studies separately with the experts from the two domains, sports and communication science, and a user study with common users to evaluate the system and design. The positive feedback from experts and common users demonstrates the effectiveness and generalizability of the system.

MPNG: A MATPOWER-Based Tool for Optimal Power and Natural Gas Flow Analyses

MPNG is an open-source simulation package for solving Optimal Power and Natural Gas Flow (OPNGF) problems. It is coded in <small>Matlab</small> and developed using the widely-used tool for power systems research and education, <small>Matpower</small>. MPNG assembles a nonlinear steady state OPNGF formulation for analyzing the integration of electricity and natural gas networks with order-of-priority classes of consumers and gas nodal price-controlled storage facilities. This paper presents the details of the nonlinear optimization model behind MPNG, as well as several features that were designed into it for the purpose of achieving robustness. These include an approximation of the Weymouth equation for handling bi-directional gas flows in pipelines, a per-unit representation for the natural gas network, and penalized slack variables for power and gas balances. Simulation results using a test case and the Colombian power and natural gas networks are presented to highlight MPNG&#x0027;s capabilities to solve OPNGF problems.

Comparative Study and Evaluation of Hybrid Visualizations of Graphs.

Hybrid visualizations combine different metaphors into a single network layout, in order to help humans in finding the "right way" of displaying the different portions of the network, especially when it is globally sparse and locally dense. We investigate hybrid visualizations in two complementary directions: (i) On the one hand, we evaluate the effectiveness of different hybrid visualization models through a comparative user study; (ii) On the other hand, we estimate the usefulness of an interactive visualization that integrates all the considered hybrid models together. The results of our study provide some hints about the usefulness of the different hybrid visualizations for specific tasks of analysis and indicates that integrating different hybrid models into a single visualization may offer a valuable tool of analysis.

3D genomic analysis reveals novel enhancer-hijacking caused by complex structural alterations that drive oncogene overexpression.

Cancer genomes are composed of many complex structural alterations on chromosomes and extrachromosomal DNA (ecDNA), making it difficult to identify non-coding enhancer regions that are hijacked to activate oncogene expression. Here, we describe a 3D genomics-based analysis called HAPI (Highly Active Promoter Interactions) to characterize enhancer hijacking. HAPI analysis of HiChIP data from 34 cancer cell lines identified enhancer hijacking events that activate both known and potentially novel oncogenes such as MYC, CCND1 , ETV1 , CRKL , and ID4 . Furthermore, we found enhancer hijacking among multiple oncogenes from different chromosomes, often including MYC , on the same complex amplicons such as ecDNA. We characterized a MYC - ERBB2 chimeric ecDNA, in which ERBB2 heavily hijacks MYC 's enhancers. Notably, CRISPRi of the MYC promoter led to increased interaction of ERBB2 with MYC enhancers and elevated ERBB2 expression. Our HAPI analysis tool provides a robust strategy to detect enhancer hijacking and reveals novel insights into oncogene activation.

Application of the Маthematical Functions in the Economic Analysis of the Enterprise

Functions and their graphic presentation are some of the most widely used tools in various economic analyses of a business. After a graph is drawn, it is necessary to interpret it correctly in order to accurately assess the situation of the industrial company. The types of functions presented in the paper are viewed in terms of their application in economic analysis. A study of a particular firm has been made, the obtained data has been presented in table form and the graphic approach has been used to depict their functions. The authors have drawn conclusions and have made recommendations.

Refining ChatGPT-Generated Code: Characterizing and Mitigating Code Quality Issues

Since its introduction in November 2022, ChatGPT has rapidly gained popularity due to its remarkable ability in language understanding and human-like responses. ChatGPT, based on GPT-3.5 architecture, has shown great promise for revolutionizing various research fields, including code generation. However, the reliability and quality of code generated by ChatGPT remain unexplored, raising concerns about potential risks associated with the widespread use of ChatGPT-driven code generation. In this article, we systematically study the quality of 4,066 ChatGPT-generated programs of code implemented in two popular programming languages, i.e., Java and Python, for 2,033 programming tasks. The goal of this work is threefold. First, we analyze the correctness of ChatGPT on code generation tasks and uncover the factors that influence its effectiveness, including task difficulty, programming language, time that tasks are introduced, and program size. Second, we identify and characterize potential issues with the quality of ChatGPT-generated code. Last, we provide insights into how these issues can be mitigated. Experiments highlight that out of 4,066 programs generated by ChatGPT, 2,756 programs are deemed correct, 1,082 programs provide wrong outputs, and 177 programs contain compilation or runtime errors. Additionally, we further analyze other characteristics of the generated code through static analysis tools, such as code style and maintainability, and find that 1,930 ChatGPT-generated code snippets suffer from maintainability issues. Subsequently, we investigate ChatGPT’s self-repairing ability and its interaction with static analysis tools to fix the errors uncovered in the previous step. Experiments suggest that ChatGPT can partially address these challenges, improving code quality by more than 20%, but there are still limitations and opportunities for improvement. Overall, our study provides valuable insights into the current limitations of ChatGPT and offers a roadmap for future research and development efforts to enhance the code generation capabilities of artificial intelligence models such as ChatGPT.

Precisely Extracting Complex Variable Values from Android Apps

Millions of users nowadays rely on their smartphones to process sensitive data through apps from various vendors and sources. Therefore, it is vital to assess these apps for security vulnerabilities and privacy violations. Information such as to which server an app connects through which protocol, and which algorithm it applies for encryption, are usually encoded as variable values and arguments of API calls. However, extracting these values from an app is not trivial. The source code of an app is usually not available, and manual reverse engineering is cumbersome with binary sizes in the tens of megabytes. Current automated tools, however, cannot retrieve values that are computed at runtime through complex transformations. In this article, we present ValDroid , a novel static analysis tool for automatically extracting the set of possible values for a given variable at a given statement in the Dalvik byte code of an Android app. We evaluate ValDroid against existing approaches (JSA, Violist, DroidRA, Harvester, BlueSeal, StringHound, IC3, and COAL) on benchmarks and 794 real-world apps. ValDroid greatly outperforms existing tools. It provides an average F 1 score of more than 90%, while only requiring 0.1 s per value on average. For many data types including Network Connections and Dynamic Code Loading, its recall is more than twice the recall of the best existing approaches.

Phage-Displayed SH2 Domain Libraries: From Ultrasensitive Tyrosine Phosphoproteome Probes to Translational Research.

Tyrosine phosphorylation is a critical regulator of cell signaling. A large fraction of the tyrosine phosphoproteome, however, remains uncharacterized, largely due to a lack of robust and scalable methods. The Src homology 2 (SH2) domain, a structurally conserved protein domain present in many intracellular signal-transducing proteins, naturally binds phosphorylated tyrosine (pTyr) residues, providing an ideal scaffold for the development of sensitive pTyr probes. Its modest affinity, however, has greatly limited its application. Phage display is an in vitro technique used for identifying ligands for proteins and other macromolecules. Using this technique, researchers have been able to engineer SH2 domains to increase their affinity and customize their specificity. Indeed, highly diverse phage display libraries have enabled the engineering of SH2 domains as affinity-purification (AP) tools for proteomic analysis as well as probes for aberrant tyrosine signaling detection and rewiring, and represent a promising class of novel diagnostics and therapeutics. This review describes the unique structure-function characteristics of SH2 domains, highlights the fundamental contribution of phage display in the development of technologies for the dissection of the tyrosine phosphoproteome, and highlights prospective uses of SH2 domains in basic and translational research.

PLUME: Record, Replay, Analyze and Share User Behavior in 6DoF XR Experiences.

From education to medicine to entertainment, a wide range of industrial and academic fields now utilize eXtended Reality (XR) technologies. This diversity and growing use are boosting research and leading to an increasing number of XR experiments involving human subjects. The main aim of these studies is to understand the user experience in the broadest sense, such as the user cognitive and emotional states. Behavioral data collected during XR experiments, such as user movements, gaze, actions, and physiological signals constitute precious assets for analyzing and understanding the user experience. While they contribute to overcome the intrinsic flaws of explicit data such as post-experiment questionnaires, the required acquisition and analysis tools are costly and challenging to develop, especially for 6DoF (Degrees of Freedom) XR experiments. Moreover, there is no common format for XR behavioral data, which restrains data-sharing, and thus hinders wide usages across the community, replicability of studies, and the constitution of large datasets or meta-analysis. In this context, we present PLUME, an open-source software toolbox (PLUME Recorder, PLUME Viewer, PLUME Python) that allows for the exhaustive record of XR behavioral data (including synchronous physiological signals), their offline interactive replay and analysis (with a standalone application), and their easy sharing due to our compact and interoperable data format. We believe that PLUME can greatly benefit the scientific community by making the use of behavioral and physiological data available for the greatest, contributing to the reproducibility and replicability of XR user studies, enabling the creation of large datasets, and contributing to a deeper understanding of user experience.

A cognitive level evaluation method based on machine learning approach and Bloom of taxonomy for online assessments

Adaptive online learning can be realized through the evaluation of the learning process. Monitoring and supervising learners’ cognitive levels and adjusting learning strategies can increasingly improve the quality of online learning. This analysis is made possible by real-time measurement of learners’ cognitive levels during the online learning process. However, most of the currently used techniques for evaluating cognitive levels rely on labour-intensive and time-consuming manual coding. In this study, we explore the machine learning (ML) algorithms and taxonomy of Bloom’s cognitive levels to explore features that affect learner’s cognitive level in online assessments and the ability to automatically predict learner’s cognitive level and thus, come up with a recommendation or pedagogical intervention to improve learner’s acquisition. The analysis of 15,182 learners’ assessments of a specific learning concept affirms the effectiveness of our approach. We attain an accuracy of 82.21% using ML algorithms. These results are very encouraging and have implications for how automated cognitive-level analysis tools for online learning will be developed in the future.

Faktor yang Mempengaruhi Tingkat Kecurangan Karyawan PT. BUM Pabrik Perikanan Samudera Kendari

The purpose of this study is to provide empirical evidence of the influence of employee attitudes toward fraud on PT. BUM Kendari ocean fisheries, provide empirical evidence of the effect of pressure on the employee to fraud PT. BUM Kendari ocean fisheries, provide empirical evidence influence employees the opportunity to fraud at PT. BUM Kendari ocean fisheries, provide empirical evidence of the influence of employee capability to fraud at PT. BUM Kendari ocean fisheries, provide empirical evidence influence attitudes, the pressure occasion, and employee capability simultaneously to fraud on PT. BUM Kendari ocean fisheries. The population in this study were employees of PT. BUM Kendari ocean fisheries as many as 42 employees. The sampling technique using census method. Census method is sampling technique when all members of the population used as a sample. The analysis tool used is the Multiple Linear Regression Analysis. The results showed that There is a significant influence on the attitude (Rationalization) against fraud with a significance value of 0.045. There is a significant influence of pressure against fraud with a significance value of 0.008. There is a significant influence of opportunities against fraud with a significance value of 0.027. There is a significant influence on the capability of the fraud with a significance value of 0.002. There is a significant influence on the attitude (Rationalization), Pressure, opportunity and capability against fraud with a significance value of 0.009.

Optimizing the Performance and Governance of MSMEs in the Special Region of Yogyakarta Through Digitalization

A significant feature of technology is its capacity to change the routines and behavior of business players, especially MSMEs actors. MSMEs players in the Special Area have been impacted by the Covid-19 outbreak and are now looking to the MSMEs sector for business opportunities. It is necessary to overcome the difficulty of attaining the digitization of MSMEs. The fact that the MSMEs actors are not fundamentally and mentally equipped is another problem. The study's objective is to evaluate the effectiveness of using digital literacy, e-commerce, and skills as a tactic to enhance MSMEs governance and performance. The main sources of data for the study were imaginative MSMEs Daerah Istimewa Yogyakarta actors. The analytical unit of the study is the manager or owner of MSMEs in Daerah Istimewa Yogyakarta. In this study, primary data are gathered by a questionnaire. The sample size was established using a simple proportionate random sampling approach. The AMOS SEM analysis tool and a sample of 20% of all MSMEs in each district and city of the Daerah Istimewa Yogyakarta are used to achieve this. According to the study's findings, E-Commerce affects the MSMEs Governance, Digital Expertise affects MSMEs Governance and Performance, MSMEs Governance the performance of MSMEs. However, E-Commerce has no effect on the performance of MSMEs.

An improved IPA approach driven by big data and its application to customer satisfaction research of energy-saving appliance

Against the backdrop of increasing global demand for reducing greenhouse gas emissions, promoting the use of energy-saving and environmentally friendly products has become a crucial aspect of low-carbon economic development. Customer satisfaction plays a vital role in the promotion of these products. To address the challenges of dealing with big data in the conventional customer satisfaction analysis tool, Importance Performance Analysis (IPA), a machine learning-based method is proposed to improve IPA. Firstly, the Latent Dirichlet Allocation (LDA) model is used to capture users’ opinions on different product topics. Then, the Support Vector Machine (SVM) and Random Forest (RF) algorithms are employed respectively to assess the satisfaction and importance of product attributes, enabling an objective measurement of customer satisfaction and adapting to the current trend of big data. The proposed method is applied to the analysis of water heater satisfaction on the JD platform, obtaining satisfaction levels for 10 topics. The research findings demonstrate that the improved IPA method based on SVM-RF effectively explores customer satisfaction and can provide some improvement strategies for platform managers and manufacturers.

Linearized Models of the Coupled Rotorcraft Flight Dynamics and Acoustics for Real-Time Noise Prediction

This article demonstrates the linearization of the coupled rotorcraft flight dynamics and aeroacoustics to provide realtime acoustic predictions in generalized maneuvering flight. To demonstrate the methodology, the study makes use of a nonlinear simulation model of a generic utility helicopter (PSUHeloSim) that is coupled with an aeroacoustic solver based on a marching cubes algorithm. A periodic equilibrium of the coupled rotorcraft flight dynamics and acoustics is first found at a desired flight condition using a modified harmonic balance trim solution method. Next, the nonlinear time-periodic dynamics are linearized about that periodic equilibrium and transformed into an equivalent higher order linear time-invariant system in harmonic decomposition form. Composite aeroacoustic measures are included as an output of this system. To speed up runtime and make control design tractable, the order of these harmonic decomposition models is reduced via residualization to an 8-state model where the states are representative of the rigid-body dynamics of the aircraft. This 8-state model is shown to provide accurate acoustic response predictions for small-amplitude pilot inputs and to abate runtime by a factor of approximately 104, thus enabling acoustic predictions in generalized maneuvering flight that are significantly faster than real time. The 8-state model is subsequently used to demonstrate the use of linear system tools for the dynamic analysis of the coupled rotorcraft flight dynamics and acoustics.

Sliding-window cross-correlation and mutual information methods in the analysis of solar wind measurements

Context. When describing the relationships between two data sets, four crucial aspects must be considered, namely: timescales, intrinsic lags, linear relationships, and non-linear relationships. We present a tool that combines these four aspects and visualizes the underlying structure where two data sets are highly related. The basic mathematical methods used here are cross-correlation and mutual information (MI) analyses. As an example, we applied these methods to a set of two-month’s worth of solar wind density and total magnetic field strength data. Aims. Two neighboring solar wind parcels may have undergone different heating and acceleration processes and may even originate from different source regions. However, they may share very similar properties, which would effectively “hide” their different origins. When this hidden information is mixed with noise, describing the relationships between two solar wind parameters becomes challenging. Time lag effects and non-linear relationships between solar wind parameters are often overlooked, while simple time-lag-free linear relationships are sometimes insufficient to describe the complex processes in space physics. Thus, we propose this tool to analyze the monotonic (or linear) and non-monotonic (or non-linear) relationships between a pair of solar wind parameters within a certain time period, taking into consideration the effects of different timescales and possible time lags. Methods. Our tool consists of two parts: the sliding-window cross-correlation (SWCC) method and sliding-window mutual information (SWMI) method. As their names suggest, both parts involve a set of sliding windows. By independently sliding these windows along the time axis of the two time series, this technique can assess the correlation coefficient (and mutual information) between any two windowed data sets with any time lags. Visualizing the obtained results enables us to identify structures where two time series are highly correlated, while providing information on the relevant timescales and time lags. Results. We applied our proposed tool to solar wind density and total magnetic field strength data. Structures with distinct timescales were identified. Our tool also detected the presence of short-term anti-correlations coexisting with long-term positive correlations between solar wind density and magnetic field strength. Some non-monotonic relationships were also found. Conclusions. The visual products of our tool (the SWCC+SWMI maps) represent an innovative extension of traditional numerical methods, offering users a more intuitive perspective on the data. The SWCC and SWMI methods can be used to identify time periods where one parameter has a strong influence on the other. Of course, they can also be applied to other data, such as multi-wavelength photometric and spectroscopic time series, thus providing a new tool for solar physics analyses.

Learning Aligned Vertex Convolutional Networks for Graph Classification.

Graph convolutional networks (GCNs) are powerful tools for graph structure data analysis. One main drawback arising in most existing GCN models is that of the oversmoothing problem, i.e., the vertex features abstracted from the existing graph convolution operation have previously tended to be indistinguishable if the GCN model has many convolutional layers (e.g., more than two layers). To address this problem, in this article, we propose a family of aligned vertex convolutional network (AVCN) models that focus on learning multiscale features from local-level vertices for graph classification. This is done by adopting a transitive vertex alignment algorithm to transform arbitrary-sized graphs into fixed-size grid structures. Furthermore, we define a new aligned vertex convolution operation that can effectively learn multiscale vertex characteristics by gradually aggregating local-level neighboring aligned vertices residing on the original grid structures into a new packed aligned vertex. With the new vertex convolution operation to hand, we propose two architectures for the AVCN models to extract different hierarchical multiscale vertex feature representations for graph classification. We show that the proposed models can avoid iteratively propagating redundant information between specific neighboring vertices, restricting the notorious oversmoothing problem arising in most spatial-based GCN models. Experimental evaluations on benchmark datasets demonstrate the effectiveness.

Agile fusion: developing 'Eat at Right Place' sentiment analysis tool

This study presents the development and validation of the "Eat at Right Place Initiative," a sentiment analysis tool for restaurant reviews. Combining a user-centric approach with the Scrum framework, the mHealth agile development and evaluation framework was implemented, deviating from the initially considered Scrum framework. A multidisciplinary team navigated three phases, aligning sprints, goals, and backlogs. Phase 1 focused on product identification through interviews and surveys. Phase 2 involved development and alpha testing using a bidirectional encoder representation from transformers (BERT) rule-based sentiment analysis model. The final phase, beta testing, incorporated user feedback for usability enhancements. Ethical considerations were prioritized, ensuring participant consent and confidentiality. The study culminated in a robust aspect-based sentiment analysis model, effective in capturing nuanced insights from diverse restaurant aspects. Beta testing revealed actionable insights, marking the tool as fit for release. This sentiment analysis tool addresses consumer and owner needs, with iterative development and real-world testing laying the groundwork for future enhancements.