Analysis Techniques Research Articles

Malware authorship attribution: Unmasking the culprits behind malicious software

With the digital age ushering in an unprecedented proliferation of malware, accurately attributing these malicious software variants to their original authors or affiliated groups has emerged as a crucial endeavor in cybersecurity. This study delves into the intricacies of malware authorship attribution by combining traditional analytical techniques with advanced machine learning methodologies. An integrated approach, encompassing static and dynamic analyses, yielded promising results in the challenging realm of malware attribution. Despite the encouraging outcomes, the research highlighted the multifaceted complexities involved, especially considering the sophisticated obfuscation techniques frequently employed by attackers. This paper emphasizes the merits of a holistic attribution model and underscores the importance of continuous innovation in the face of an ever-evolving threat landscape.

The Role of Mother Tongue and its Impact on Learning English Grammar at Elementary Level

A mother tongue plays a fundamental role in the use of other languages. The research was carried out to determine the influence of the Pashto language (L1) on the acquisition of English grammar at the elementary level in District Karak. This study employed the detailed procedure of thematic analysis technique and the Pretest-Post-test Equivalent-Groups Design for experimental design. The participants were grouped using convenient and random sampling techniques. It was found that a significant difference was observed between the scores of post-tests of the control group as well as the experimental group when the result of both groups was compared. Learners of the investigational group surpassed the learners of the control group in the domain of this particular component of grammar. It is recommended that the government should induct English teachers of the mother tongue of the learners so that they may utilize the mother tongue of the learners while teaching English grammar to their students.

Mediation of customer satisfaction: E-service quality, brand trust, and reuse intention

The purpose of this study is to explain the role of customer satisfaction in mediating e-service quality and brand trust on reuse intention in the Traveloka application. The theories used are the Techlonogy Acceptance Model (TAM) and Expectancy Disconfirmation Theory (EDT). This research is a type of quantitative research that is associative in nature. The population in this study are all Traveloka application users who have transacted at least three (3) times in one year. The sampling technique used purposive sampling with a total sample member of 205 respondents. Respondent data collection was carried out by distributing questionnaires. The data analysis technique in this study used PLS-based SEM. The results showed that all variables had a positive and significant effect. Customer satisfaction acts as a partial mediation in mediating the effect of e-service quality and brand trust on reuse intention on the Traveloka application. The results of this study can provide an empirical contribution about the relationship between the variables of e-service quality, brand trust, customer satisfaction, and reuse intention for the development of the Technology Acceptance Model (TAM) and Expectancy Disconfirmation Theory (EDT). Traveloka management can implement a marketing strategy that emphasizes service quality and information accuracy to increase customer intention to reuse the Traveloka application.

A comprehensive review on crack detection schemes and modelling techniques in high-speed rotor systems

ABSTRACT The combination of a rotor and bearings plays a pivotal role in various industrial applications, including gas turbines, compressors, turbochargers, aircraft engines, and wind generators. Ensuring the structural integrity of the rotor-bearing assembly during static and dynamic conditions is most important, as any failure could lead to unpredictable losses. Due to their continuous operation, high-speed rotors are more susceptible to faults such as misalignment, bow, eccentricity, and cracks. Among these faults, cracks are the most predominant fault that affects the system’s reliability. Despite significant advancements in rotor dynamics, cracks still threaten the integrity and performance of the rotor-bearing assembly. This review paper aims to thoroughly review the challenges and issues associated with crack formation in high-speed rotor systems. Various crack detection methods are also explored using vibration signals obtained from the rotor-bearing unit and their associated processing techniques. This study also gives an extensive overview of different crack modelling and analysis techniques. Furthermore, a novel rotor-bearing model is modelled using the finite element method, and the numerical results obtained from this model are validated with the existing literature model. This review paper will serve as a valuable resource for researchers that offers insights into the latest advancements and emphasises the importance of crack detection for ensuring the reliability of high-speed rotor systems.

Comparative efficiency of Chilean water utilities: a Bayesian stochastic frontier approach

ABSTRACT Assessing the potential relationship between ownership and performance of water utilities is relevant for water regulators. Incorporating technological heterogeneity among groups of water utilities could solve any incomparability issues and lead to more appropriate policy implications. This can be done with the use of the metafrontier framework which assumes that utilities operate under different technologies. The study applies this framework to compare the efficiency of private water utilities (PWU) and concessionary water utilities (CWU) in Chile using Bayesian Stochastic Frontier Analysis (SFA) techniques. The results indicated that PWU performed better than CWU. Nevertheless, both types of water utilities presented considerable cost inefficiency. PWU and CWU should considerably reduce costs by 34% and 48% using the industry technology as a whole, respectively. The methodological approach proposed in this study could be used to compare the performance of other groups of water utilities to support the decision making of water regulators.

A Surface Determination Technique for Dimensional and Geometrical Analysis in Industrial X-ray Computed Tomography

Industrial X-ray computed tomography (XCT) is a nondestructive technique that can measure workpieces with non-accessible internal features or multimaterial components and assess the dimensional properties of assemblies in assembled states. Surface determination is one of its most crucial steps, which consists of determining boundary surfaces between a solid material and the surrounding air or between different solid materials. It allows for extracting surface points and assessing different features of the object from the data acquired through XCT scans. This task is particularly complex because of challenges associated with material properties, artefacts and noise. The main objective of this work is to assess not just the dimensional but also the geometric characteristics of industrial parts, which requires a more accurate definition of surface points. Therefore, we propose a new surface determination technique (SDT) in XCT to achieve subvoxel accuracy in determining surface points. We demonstrated the effectiveness and stability of our method by comparing it with other SDTs documented in the literature or with results from commercial software. The validation involved measuring various attributes, such as diameter, cylindricity and flatness, of a multi-stepped aluminium part calibrated by a coordinate measuring machine.

Enhancing silicon spectral emission in LIBS using Tesla coil discharge

Abstract Laser-induced breakdown spectroscopy (LIBS) is a powerful technique for elemental analysis, offering rapid analysis, minimal sample preparation, wide elemental coverage, and portability. To enhance the detection sensitivity of LIBS, increasing the spectral emission intensity is crucial. This paper explores the use of Tesla coil (TC) discharge as an alternative to spark discharge in silicon LIBS. The study examines the influence of TC discharge on both time-integrated and time-resolved spectra, with and without TC discharge; the corresponding electron temperature and density are obtained. The results show that TC discharge significantly amplifies the spectral intensity, improving signal sensitivity in LIBS analysis. Specifically, in the laser energy range from 7.4 to 24.0 mJ, TC discharge increased the average spectral line intensities of Si (II) 385.60 nm and Si (I) 390.55 nm by factors of 8.4 and 5.1, respectively. Additionally, the average electron temperature and density were enhanced by approximately 3.2% and 4.2%, respectively, under TC discharge. The advantages of TC discharge include higher energy deposition, extended discharge duration, reduced electrode erosion, and enhanced safety. This research contributes to advancing LIBS technology and expanding its applications in various fields.

Simultaneous iodine and barium imaging with photon-counting CT

Photon-counting computed tomography (PCCT) could potentially offer significant improvements in image quality over conventional CT. This study leverages PCCT technology to achieve simultaneous multi-contrast imaging of barium (Z=56) and iodine (Z=53), addressing the limitations of conventional CT in differentiating materials with similar atomic numbers. Using a bench-top PCCT system with a cadmium zinc telluride (CZT) detector, experiments were conducted with various phantom setups and 1-5\% contrast agent concentrations and in a biological sample. Energy thresholds were adjusted to the K-edge absorption energies of barium (37.4 keV) and iodine (33.2 keV) to capture multi-energy CT images. K-edge decomposition was performed with K-edge subtraction and principal component analysis (PCA) techniques, demonstrating clear differentiation and accurate quantification of contrast agents in phantom combinations and the biological sample. The PCCT system successfully differentiated and quantified barium and iodine, with high correlations ($R^2 \approx 1$) between true and reconstructed concentrations. PCA outperformed K-edge subtraction by providing a superior capability to differentiate barium and iodine, particularly when calcium was present in the scanned object. These findings highlight the potential of PCCT for reliable, detailed imaging in clinical and research applications, particularly for contrast agents with close atomic numbers.

Uncovering the Complexity of Perinatal Polysubstance Use Disclosure Patterns on X: Mixed Methods Study

Background According to the Morbidity and Mortality Weekly Report, polysubstance use among pregnant women is prevalent, with 38.2% of those who consume alcohol also engaging in the use of one or more additional substances. However, the underlying mechanisms, contexts, and experiences of polysubstance use are unclear. Organic information is abundant on social media such as X (formerly Twitter). Traditional quantitative and qualitative methods, as well as natural language processing techniques, can be jointly used to derive insights into public opinions, sentiments, and clinical and public health policy implications. Objective Based on perinatal polysubstance use (PPU) data that we extracted on X from May 1, 2019, to October 31, 2021, we proposed two primary research questions: (1) What is the overall trend and sentiment of PPU discussions on X? (2) Are there any distinct patterns in the discussion trends of PPU-related tweets? If so, what are the implications for perinatal care and associated public health policies? Methods We used X’s application programming interface to extract >6 million raw tweets worldwide containing ≥2 prenatal health- and substance-related keywords provided by our clinical team. After removing all non–English-language tweets, non-US tweets, and US tweets without disclosed geolocations, we obtained 4848 PPU-related US tweets. We then evaluated them using a mixed methods approach. The quantitative analysis applied frequency, trend analysis, and several natural language processing techniques such as sentiment analysis to derive statistics to preview the corpus. To further understand semantics and clinical insights among these tweets, we conducted an in-depth thematic content analysis with a random sample of 500 PPU-related tweets with a satisfying κ score of 0.7748 for intercoder reliability. Results Our quantitative analysis indicates the overall trends, bigram and trigram patterns, and negative sentiments were more dominant in PPU tweets (2490/4848, 51.36%) than in the non-PPU sample (1323/4848, 27.29%). Paired polysubstance use (4134/4848, 85.27%) was the most common, with the combination alcohol and drugs identified as the most mentioned. From the qualitative analysis, we identified 3 main themes: nonsubstance, single substance, and polysubstance, and 4 subthemes to contextualize the rationale of underlying PPU behaviors: lifestyle, perceptions of others’ drug use, legal implications, and public health. Conclusions This study identified underexplored, emerging, and important topics related to perinatal PPU, with significant stigmas and legal ramifications discussed on X. Overall, public sentiments on PPU were mixed, encompassing negative (2490/4848, 51.36%), positive (1884/4848, 38.86%), and neutral (474/4848, 9.78%) sentiments. The leading substances in PPU were alcohol and drugs, and the normalization of PPU discussed on X is becoming more prevalent. Thus, this study provides valuable insights to further understand the complexity of PPU and its implications for public health practitioners and policy makers to provide proper access and support to individuals with PPU.

A Simplified Failure Assessment to Identify Crack Growth Behavior in the Gas Pipeline by Post-Hydrostatic Pressure

Hydrostatic pressure tests on pipelines have been found to be an inadequate means of critical integrity management due to the frequency of false negatives, which result from the inability of these tests to detect crack growth. It can be argued that focusing on pipe leakage does not guarantee future operability. A study presents a failure assessment methodology based on the failure assessment diagram (FAD), which aims to predict crack growth during hydrotesting. The calibrated pipe spool is validated by the application of data from hydrostatic tests and analytical techniques to ascertain the potential growth in circumferential surface cracks. A variety of factors, including pressure, material grades, flaw dimensions, and elliptical flaw angles, were examined in an effort to assess cracks. The results demonstrate that there is no pipeline leakage and minimal trapped air. Despite its location within the plastic zone, with a normalized pressure index of ≤1, the pipeline is deemed to be within acceptable limits according to the criteria established by the FAD. The assessment point was found to be predominantly influenced by the toughness ratio of the material grade. The crack propagated in the opposite direction with a maximum length a/c= 0.125 and a crack depth a/t= 0.2, which limited the toughness ratio. The load ratio indicates uniformity in elliptical angle flaw results. In this simplified failure assessment, the parameter describing the flaw size, which exhibits a strong correlation with the toughness ratio, plays a pivotal role. Further research and recommendations are also proposed.

Analyzing the Impact of Cost of Funds and Non-Performing Loans on Bank Profitability

The research was conducted to see how cost of funds (COC), and non-performing loans (NPL) on profitability is measured by return on assets (ROA). The research method used is descriptive and verification methods. Secondary data is sourced from Regional Development Banks of West Java and Banten Provinces in Indonesia quarterly financial reports for 2014 – 2022, while the data analysis technique uses multiple linear regression analysis. The results of the research are that COF and NPL has a negative and significant effect on ROA. Simultaneously, COF and NPL have a significant effect on ROA. The results of this research are useful for bank management in determining funding strategies with low cost of funds, and credit management strategies so that non-performing remains healthy.

Network Issue Agenda Setting on Facebook: Exploring the Interplay Between Polarized Campaigns and Party Supporters

This study undertook an analysis of network agenda setting during the 2020 U.S. Presidential campaign, focusing on the interactions between the campaigns and their respective supporters within the context of a polarized social media environment. By employing social network analysis techniques to examine issue agendas, the study revealed a relatively weak correlation between the agendas of the campaigns and their affiliated supporters on Facebook. Conversely, it found a notable association between entities sharing the same ideological orientation—party supporters displayed a higher degree of engagement with their own party’s campaign, and vice versa. The implications of these findings, from a theoretical, methodological, and practical standpoint, have been thoroughly discussed.

Representasi Premanisme dalam Film Pendek Loz Jogjakartoz (Analisis Semiotika Roland Barthes)

This research was conducted with the aim of knowing and explaining the representation of thuggery shown in the Loz Jogjakartoz short movie. Thuggery is one of the most familiar issues in Indonesia. This research is a qualitative research using analysis technique Roland Barthes' semiotic theory. The results of this research are the analysis denotative, connotative, and mythological meanings. In addition to a thug, thuggis behavior is also demonstrated by detective and liquor seller. The representation of thuggery in the Loz Jogjakartoz short movie depicts thugs who do not look exactly like the stereotypical image of thugs understood by society, which is that thugs must have tattoos, piercing, long hair, behave rudely, get drunk, and wear leather jackets. However in this movie, the representation of thuggery is only shown through aspects of having piercing, wear leather jacket, and behaving rudely.

Intricate DG and EV Planning Impact Assessment with Seasonal Variation in a Three-Phase Distribution System

Modern power systems present opportunities and challenges when integrating distributed generation and electric vehicle charging stations into unbalanced distribution networks. The performance and efficiency of both Distributed Generation (DG) and Electric Vehicle (EV) infrastructure are significantly affected by global temperature variation characteristics, which are taken into consideration in this study as it investigates the effects of these integrations. This scenario is further complicated by the unbalanced structure of distribution networks, which introduces inequalities that can enhance complexity and adverse effects. This paper analyzes the manner in which temperature changes influence the network operational voltage profile, power quality, energy losses, greenhouse harmful emissions, cost factor, and active and reactive power losses using analytical and heuristic techniques in the IEEE 69 bus network in both three-phase balance and modified unbalanced load conditions. In order to maximize adaptability and efficiency while minimizing the adverse impacts on the unbalanced distribution system, the findings demonstrate significant variables to take into account while locating the optimal location and size of DG and EV charging stations. To figure out the objective, three-phase distribution load flow is utilized by the particle swarm optimization technique. Greenhouse gas emissions dropped by 61.4%, 64.5%, and 60.98% in each of the three temperature case circumstances, while in the modified unbalanced condition, they dropped by 57.55%, 60.39%, and 62.79%. In balanced conditions, energy loss costs are reduced by 95.96%, 96.01%, and 96.05%, but in unbalanced conditions, they are reduced by 91.79%, 92.06%, and 92.46%. The outcomes provide valuable facts that electricity companies, decision-makers, along with other energy sector stakeholders may utilize to formulate strategies that adapt to the fluctuating patterns of electricity distribution during fluctuations in global temperature under balanced and unbalanced conditions of network.

Comparison of RNN-LSTM, TFDF and stacking model approach for weather forecasting in Bangladesh using historical data from 1963 to 2022

Forecasting the weather in an area characterized by erratic weather patterns and unpredictable climate change is a challenging endeavour. The weather is classified as a non-linear system since it is influenced by various factors that contribute to climate change, such as humidity, average temperature, sea level pressure, and rainfall. A reliable forecasting system is crucial in several industries, including transportation, agriculture, tourism, & development. This study showcases the effectiveness of data mining, meteorological analysis, and machine learning techniques such as RNN-LSTM, TensorFlow Decision Forest (TFDF), and model stacking (including ElasticNet, GradientBoost, KRR, and Lasso) in improving the precision and dependability of weather forecasting. The stacking model strategy entails aggregating multiple base models into a meta-model to address issues of overfitting and underfitting, hence improving the accuracy of the prediction model. To carry out the study, a comprehensive 60-year meteorological record from Bangladesh was gathered, encompassing data on rainfall, humidity, average temperature, and sea level pressure. The results of this study suggest that the stacking average model outperforms the TFDF and RNN-LSTM models in predicting average temperature. The stacking average model achieves an RMSLE of 1.3002, which is a 10.906% improvement compared to the TFDF model. It is worth noting that the TFDF model had previously outperformed the RNN-LSTM model. The performance of the individual stacking model is not as impressive as that of the average model, with the validation results being better in TFDF.

Multimodel regional frequency analysis of CMIP extreme precipitation

Abstract A recurrent question in climate risk analysis is determining how climate change will affect
heavy precipitation patterns.
Dividing the globe into homogeneous sub-regions should improve the modeling of heavy precipitation by inferring common regional distributional parameters.
In addition, biases due to model errors in global climate models (GCMs) should be considered to understand the climate response to diffrent forcing effects.
Within this context, we propose an efficient clustering algorithm that, compared to classical regional frequency analysis (RFA) techniques, is covariate-free and accounts for dependence.
It is based on a new non-parametric dissimilarity that combines both the RFA constraint and the pairwise dependence.
We derive asymptotic properties of our dissimilarity estimator, and we interpret it for generalized extreme value distributed pairs.

As an application, we cluster annual daily precipitation maxima of 16 GCMs from the coupled model intercomparison project.
We combine the climatologically consistent subregions identified for all GCMs.
This improves the spatial clusters coherence and outperforms methods either based on margins or on dependence.
Finally, by comparing the natural forcings partition with the one with all forcings, we assess the impact of anthropogenic forcing on precipitation extreme patterns.

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

Implementing innovation in project-based learning in electro-mechanical engineering education

This study investigates the implementation of project-based learning (PBL) in electro-mechanical engineering education, emphasizing the modeling and application of an innovation process to enhance student engagement, motivation, and employability. Initially, the innovation process was systematically modeled using Structured Analysis and Design Technique (SADT) to manage and organize skills, knowledge, and resources effectively. This model was subsequently applied in an educational experience focused on highly technical aspects such as energy conversion modeling, mechanical improvements, material science, and efficiency analysis. These aspects were primarily applied in the context of permanent magnet synchronous machines employed in electric vehicle (EV) axles. The SADT modeling allowed for a clear delineation of the innovation phases, ensuring a structured approach to project execution. Statistical analysis of the PBL approach indicated significant improvements in student performance, with a 25% increase in problem-solving skills and a 30% enhancement in critical thinking abilities. The case study demonstrated that industry-oriented projects significantly boost student satisfaction, particularly in problem definition and solution identification, leading to a more engaging and effective learning experience.

Analysis of Exhaled Human Breaths using Graphene Gas Sensor Based on the Male Gender

Breath analysis is an intriguing method that has the potential to significantly improve non-implantable physical health management via the use of flexible sensors to monitor breathing behaviours. In comparison to other analytical techniques for detecting breath elements, non-invasive breathing diagnostics based on chemical sensors can provide numerous benefits, including nanotechnology, power efficiency, simplicity of structure, and cost-effectiveness, contributing to the flexibility of experimental studies. This paper presents the graphene-based human breath sensor. Graphene was prepared in a paste form by mixing the graphene powder with two types of the binder. Two binders were prepared to compare their performance to human breath. The graphene paste was deposited using screen-printing on different substrates, Kapton film, and glass. The sensing film was thermally annealed at 200°C for 30 minutes and characterized using SEM and FTIR. All graphene gas sensor samples responded well to collected male human breath samples: Bumiputera (Sarawak), Malay, Chinese, and Indian. The best graphene gas sensor for males was OG-T(G) and OG-T(K) with sensitivity, S=1.10415 and S=1.01629. Based on the comparison results of male human breath, the fastest response time and recovery time were 0.52s and 13s, respectively.

Comprehensive Analysis of Cetuximab Critical Quality Attributes: Impact of Handling on Antigen-Antibody Binding

Background/Objectives: Cetuximab, formulated in Erbitux® (5 mg/mL), is a therapeutic monoclonal antibody (mAb) widely used in several cancer treatments. Currently, there is insufficient knowledge about the behavior of cetuximab with regard to the risk associated with its routine handling or unintentional mishandling in hospitals. Forced degradation studies can simulate these conditions and provide insights into the biophysical and biochemical properties of mAbs. Methods: In this study, we conducted a deep physicochemical and functional characterization of the critical quality attributes of cetuximab in control samples and under controlled degraded conditions, including freeze–thaw cycles, heat, agitation, and light exposure. To achieve this purpose, we used a set of proper analytical techniques, including CD, IT-FS, DLS, SE/UHPLC-UV, UHPLC-MS/MS, and ELISA, to check functionality based on antigen–antibody binding. Results: The results revealed that light exposure was the stress stimuli with the greatest impact on the drug product, leading to the formation of non-natural oligomers, fragmentation, and oxidation of methionine residues. Additionally, cetuximab (Erbitux®, 5 mg/mL) showed a tendency to aggregate when submitted to 60 °C for 1 h. In terms of functionality, cetuximab (Erbitux®, 5 mg/mL) samples were found to be affected when subjected to freeze–thaw cycles, 60 °C (1 h), and when exposed to light (daylight with room temperature excursion and accelerated light exposure). Conclusions: Thus, we suggest that Erbitux® (5 mg/mL) should be shielded from these environmental conditions, as they compromise both the safety and efficacy of the drug product.