Multiple Data Analysis Research Articles

Comparative analysis of multiple observation data during two typhoons affecting Hainan Island

Based on different observation data, the wind and rain processes of two typhoons that continuously affected Hainan Island in October 2021 were compared and analyzed. The analysis shows that the two typhoons are similar in weather conditions, water vapor transport conditions, cold air intrusion, sea surface temperature providing energy, and so on. The spatial distribution of rainfall and wind field exhibits an evidently asymmetrical structure. Before the typhoon landed, the gale area was located mainly in the northeast of the typhoon center. Furthermore, different detection data have advantages and disadvantages, which should be verified and used each other. For example, there are also differences between radar echo reflectivity, wind profile radar data and MTCSWA data. By means of analysis and research, the ability to analyze and articulate the typhoon impact process with high spatial-temporal resolution data is further enhanced, thereby aiding in the forecast and service work during the typhoon period.

Modeling of fluence-dependent hole-burned spectra and hole-growth kinetics using multiple two-level system model.

Numerical formalism is presented that perfectly describes resonant low-temperature hole-burned spectra (including zero-phonon holes, ZPHs) and spectral hole-growth dynamics of Al-phthalocyanine tetrasulphonate embedded in hyperquenched glassy water films over more than seven orders of fluence magnitude (0.4 µJ/cm2-5.9 J/cm2). Frequency changes during spectral hole-burning (HB) are traditionally explained with the help of a single extrinsic two-level-system (TLSext) associated with impurity molecules. The new multiple two-level system (n-TLSext) models and data analysis presented in this work show that each chromophore in an amorphous medium can couple with multiple independent TLSext, which maintain perfect photo-memory, allowing a full return of the photoproduct to the initial ("preburn") state. Modeling reveals that the experimentally observed narrow photoproduct peak at higher energies, in close vicinity of the zero-phonon hole (ZPH), reflects a dynamical feature of the HB process populating so-called "terminal" states (states that do not interact with laser excitation). Within the n-TLSext model, each chromophore possesses multiple possibilities to create a photoproduct when in interaction with the burning laser, i.e., chromophores can interact with burning laser-light multiple times until reaching the terminal states. Due to phonon-assisted absorption, terminal states are typically at higher energies than the ZPH, in agreement with the hole burned spectra reported for many molecules embedded in various amorphous solids. However, many HB systems reveal both blue- (high-energy) and red-shifted (low-energy) antiholes (i.e., photoproducts). We suggest that future modeling of resonant holes in various proteins using our n-TLSext model will provide more insight on the complexity of the protein energy landscape.

Optimalisasi Kinerja Karyawan Bagian Prodiksi PT. ABCD Melalui Stres Kerja dan Motivasi Kerja

As time progresses, science and technology in every organization is required to have competent human resources. From the data obtained, there is a decline in employee performance. From several factors that affect employee performance, namely work stress and work motivation, the research objective is to determine the influence of work stress and work motivation on the performance of production department employees. The sample used consists of all production department employees, totaling 33 employees. This research employs a quantitative method with multiple regression data analysis using SPSS version 27. Data collection techniques involve interviews, questionnaires, and documentation. The results of this study indicate that work stress has a negative partial effect on employee performance. Work motivation has a positive partial effect on employee performance. Work stress and work motivation simultaneously affect employee performance.

Pengaruh Perencanaan Pembelajaran dan Kreativitas Guru terhadap Hasil Belajar Mata Pelajaran SKI Siswa Kelas VIII MTs.Yasiba Kota Bogor

This research aims to test and find out empirical data regarding the influence of learning planning and teacher creativity on the learning outcomes of Islamic culture history subjects. In this study, the authors used a quantitative approach. The sample of this study was 81 respondents from class VIII MTs Yasiba students. The population in this study was all MTs students. Yasiba, which has 812 students in the even semester of the 2023-2024 academic year. Data collection is carried out using observation techniques, documentation and questionnaires. The data analysis technique in this research uses simple regression and multiple regression data analysis techniques with the help of SPSS version 17 software.Based on field data and analysis, this study found : (1) There was an influence of learning planning on SKI learning outcomes, obtaining an R square score of 0.331 or 33.1%. This means that the learning planning variable influences SKI learning outcomes by 33.1% and the remaining 66.9% is determined by other factors outside the regression model. (2) There is an influence of teacher creativity on SKI learning outcomes. In this determination test, an R square value of 0.379 or 37.9% was obtained%. This means that the variable teacher creativity in students' SKI learning outcomes is 37.9% and the remaining 62.1% is determined by other factors outside the regression model. (3) There are results from the determination test, a correlation value (R) of 0.673 is obtained. This shows that there is a correlation between learning planning and teacher creativity together in SKI learning outcomes of 0.673.

Decomposer faecal food web and C sequestration in soil. Can near infrared spectroscopy describe transfers and transformations from fresh organic inputs to protected forms in soil aggregates?

This study sought to characterize the transfer of agricultural organic inputs by macrofauna to the biogenic aggregate compartments of the soil and test the existence of a faecal food web process based on coprophagic feeding behaviours. In an experimental plantain crop field of Colombia, we applied the agroecological FBO (Fertilisation Bio Organique ®) technology, a nucleation technique which consists in planting perennial plants in 1.0 x 0.4 × 0.4 m deep trenches where low- and high-quality organic materials are added in a specific design, and endogeic earthworms are inoculated. We evaluated the effectiveness of Near Infrared Spectroscopy (NIRS) to track this flow of organic matter through different natural stages of decomposition represented by soil macroaggregates produced by soil macroinvertebrates as faecal pellets and casts. After one year, great part of the organic inputs had been either mineralized or incorporated into macroaggregates of different sizes and origins. Using NIRS and physicochemical laboratory analyses, we assessed the quality and quantity of organic matter in the different types of aggregates separated manually according to their origin: large biogenic aggregates (LBA), medium sized biogenic aggregates (MBA) and small biogenic aggregates (SBA); physical aggregates (PA), root aggregates (RA) and residual soil (RS).Earlier studies had shown that these structures were presumably formed by the activity of macroinvertebrates of three different functional groups, Diplopoda and Isopoda litter transformers for SBA, epidendogeic earthworms for MBA and mesohumic endogeic earthworms for LBA, organized in a feeding succession.A Coinertia analysis between showed significant covariation among the two sets of physico chemical (22 variables) and NIR spectral (100 different wavelength absorbances) characterization of 135 samples representative of the three classes of biogenic macroaggregates, physical aggregates and residual non macroaggregated soil, (RV = 0.55; p < 0.001). This analysis clearly separated biogenic structures and ranked them according to their size, from small SBA to medium sized MBA and large LBA. Physical PA aggregates and RS residual soil were projected close to the LBAs in the coinertia factorial plan. Multiple combinatorial data analysis CDA, associated 5 specific wavelength absorbance ranges with aggregate types and residual soil. Along the sequence from small to large biogenic aggregates, residual soil and physical aggregates, wavelengths associated to easily decomposed substrates (in ranges 1708–1716 nm; 1796–1948 and 2164–2316 nm) had progressively decreasing absorbances. Substrates associated to slowly decomposing aromatic, alkane and phenolic substrates either increased (1420–1436) or decreased (1284–1380) along this sequence.These results are compatible with the hypothesis of a progressive transformation and transfer of organic residues first into small biogenic aggregates that are presumably faecal pellets of Isopoda and Diplopoda, then to medium sized biogenic aggregates identified as casts of epiendogeic earthworms and finally to large biogenic aggregates expected to be the casts of the large endogeic earthworms present in these soils. A definitive verification of the hypothesis and the involvement of specific macroinvertebrates will require direct experimentation in controlled laboratory conditions.

Foxg1 regulates translation of neocortical neuronal genes, including the main NMDA receptor subunit gene, Grin1

BackgroundMainly known as a transcription factor patterning the rostral brain and governing its histogenesis, FOXG1 has been also detected outside the nucleus; however, biological meaning of that has been only partially clarified.ResultsPrompted by FOXG1 expression in cytoplasm of pallial neurons, we investigated its implication in translational control. We documented the impact of FOXG1 on ribosomal recruitment of Grin1-mRNA, encoding for the main subunit of NMDA receptor. Next, we showed that FOXG1 increases GRIN1 protein level by enhancing the translation of its mRNA, while not increasing its stability. Molecular mechanisms underlying this activity included FOXG1 interaction with EIF4E and, possibly, Grin1-mRNA. Besides, we found that, within murine neocortical cultures, de novo synthesis of GRIN1 undergoes a prominent and reversible, homeostatic regulation and FOXG1 is instrumental to that. Finally, by integrated analysis of multiple omic data, we inferred that FOXG1 is implicated in translational control of hundreds of neuronal genes, modulating ribosome engagement and progression. In a few selected cases, we experimentally verified such inference.ConclusionsThese findings point to FOXG1 as a key effector, potentially crucial to multi-scale temporal tuning of neocortical pyramid activity, an issue with profound physiological and neuropathological implications.

Twenty-five years of Nipah outbreaks in Southeast Asia: A persistent threat to global health

ObjectivesNipah virus (NiV), a bat-borne zoonotic pathogen, poses persistent threats to global public health due to severe clinical manifestation and high case fatality rate (CFR). A critical examination of NiV outbreaks is essential for refining strategies and mitigating the impact of future infections. In this study, we provide a concise update on global NiV outbreaks that occurred during the past 25 years. MethodsIn this geospatial study, we conducted an in-depth examination of the epidemiological characteristics of human NiV cases and deaths from 1998 to 2024 through multiple analyses of public data and official reports. ResultsNiV emerged in 1998 in Malaysia during an outbreak among pig farmers. Since then, NiV outbreaks have been documented in five countries of South and Southeast Asia (Bangladesh, India, Malaysia, Philippines, and Singapore). As of May 2024, there have been 754 confirmed human NiV cases with 435 deaths (CFR: 58%) reported in these five countries. Bangladesh records the highest incidence (341 cases and 241 deaths; CFR: 71%) followed by Malaysia (283 cases and 109 deaths; CFR: 39%), India (102 cases and 74 deaths; CFR: 73%), the Philippines (17 cases and nine deaths; CFR: 53%), and Singapore (11 cases and one death; CFR: 9%). ConclusionsThe clinical outcomes of NiV have been underscoring constant global public health threats as no effective therapies and vaccines are available. Strong global understandings, with an eye on developing vaccines and therapeutics, are required to minimize clinical outcomes and future threats of NiV.

Probabilistic deconvolution of the distribution of relaxation times from multiple electrochemical impedance spectra

Electrochemical impedance spectroscopy (EIS) is widely used to study the properties of electrochemical materials and systems. However, analyzing EIS data remains challenging. Among various analysis methods, the distribution of relaxation times (DRT) has emerged as a novel non-parametric approach capable of providing timescale information. Among the various DRT inversion methods, those based on Gaussian processes (GP) are particularly promising because they provide uncertainty estimates for both EIS and DRT. However, current GP-based DRT implementations can only handle one spectrum at a time. This work extends these models to allow concurrent analysis of multiple impedance spectra as a function of experimental conditions. The new method, called the quasi-Gaussian process distribution of relaxation times, treats the DRT as a GP with respect to the experimental state and as a finite approximation of a positively constrained GP with respect to timescales. This new DRT inversion approach is validated against noise-corrupted artificial EIS data and applied to experimental data, allowing us to expedite EIS data analysis of multiple EIS data from a probabilistic perspective.

An integrated urban flooding risk analysis framework leveraging machine learning models: A case study of Xi'an, China

As global climate change intensifies and urbanization progresses, the frequency and extent of urban flooding have been increasing. This trend poses a significant threat to the safety and property of urban residents and severely impedes the advancement of sustainable development goals (SDGs). This study proposed an integrated urban flooding risk analysis framework to provide an essential basis for developing effective adaptation and mitigation measures. The kernel density estimation was adopted to analyze the distribution characteristics of urban flooding hotspots. The maximum entropy (MaxEnt) model was adopted for urban flooding risk probability assessment based on multi-source data. This study conducted an in-depth analysis from three perspectives including spatial analysis, risk planning and model comparison. The MaxEnt model and multiple exploratory spatial data analysis methods were combined to reveal spatial distribution patterns at grid cell scale. The MaxEnt and Zonation models were coupled to generate an urban flooding risk map. Further, six traditional machine learning models were compared with the MaxEnt model. The results indicate that rapid urbanization and intense human activities have significantly increased the number of buildings, leading to loss of previously existing blue and green spaces. These urban characteristics increase the vulnerability of cities to extreme rainfall. Compared to traditional machine learning models, the MaxEnt model has significant advantages. In the field of urban flooding risk, the MaxEnt model demonstrates significant potential for practical application. In summary, the framework empowers urban managers to comprehensively evaluate flooding risk in planning, supporting urban design and risk prevention.

Hyperspectral reflectance and machine learning for multi-site monitoring of cotton growth

Hyperspectral measurements can help with rapid decision-making and collecting data across multiple locations. However, there are multiple data processing methods (Savisky-Golay [SG], first derivative [FD], and normalization) and analyses (partial least squares regression [PLS], weighted k-nearest neighbor [KKNN], support vector machine [SVM], and random forest [RF]) that can be used to determine the best relationship between physical measurements and hyperspectral data. In the current study, FD was the best method for data processing and SVM was the best model for predicting average cotton (Gossypium spp. Malvaceae) height and nodes. However, the combination of FD and RF were best at predicting cotton leaf area index, canopy cover, and chlorophyll content across the growing season. Additionally, results from models developed by both SVM and RF were closely related to pseudo-CHIME satellite wavebands, where in-situ hyperspectral data were matched to the spectral resolutions of a future hyperspectral satellite. The information and results presented will aid producers and other members of the cotton industry to make rapid and meaningful decisions that could result in greater yield and sustainable intensification.

PENGARUH BRAND PERSONALITY, BRAND AWARENESS DAN KUALITAS PRODUK TERHADAP KEPUTUSAN PEMBELIAN BUKU DI GRAMEDIA BENGKULU

The title of this research is The Influence of Brand Personality, Brand Awareness and Product Quality on Book Purchasing Decisions at Gramedia Bengkulu. This research is based on the importance of the right system and strategy in conveying messages to consumers through marketing media which usually uses one or a combination of marketing variables. Many factors can influence purchasing decisions, including brand personality, brand awareness and product quality. This research aims to determine the influence of Brand Personality, Brand Awareness, product quality on product purchasing decisions at the Gramedia Store, Bengkulu City. This type of research is a quantitative approach. The method used in this research is descriptive method. The population in this study were all consumers of the Gramedia Store, Bengkulu City, Bengkulu. This research used an incidental sampling technique with a sample of 150 respondents. The data collection techniques include interviews, observation, literature study, and questionnaires. Based on the results of research using multiple regression data analysis techniques, it was found that brand personality, brand awareness and product quality together show an influence on consumer purchasing decisions at Gramedia Bengkulu Keywords: Brand Personality, Brand Awareness, Product Quality, Purchase Decision

Pengaruh Live Shopping, Online Review, Persepsi Harga dan Gratis Ongkir Terhadap Minat Beli Konsumen Muslim Kabupaten Pekalongan di Aplikasi Shopee

Live shopping, online reviews, price perception and shipping discounts are features released by the e-commerce platform Shopee. This feature aims to attract consumer buying interest. Ease of shopping will be one of the buying interests of consumers, especially Muslim consumers in Pekalongan district. This research aims to test the influence of Live shopping, online reviews, price perceptions and postage discounts on Muslim consumers' buying interest in the Shopee application, especially in Pekalongan Regency.This research is a type of quantitative research. The data collection method in this research is the questionnaire method using a sample of 96 respondents. The sampling technique uses the Accidental sampling method. This research uses multiple linear regression test data analysis with the help of SPSS 24.0. The research results show that live shopping, online reviews, price perceptions and postage discounts have a positive effect on Muslim consumers' buying interest in the Shopee application. Then simultaneously Live shopping, online reviews, price perceptions and postage discounts also influence Muslim consumers' buying interest in the Shopee application.

It is not a vacation of leisure: The effect of maternity-leave stigma on fertility intentions in China

In response to the public's puzzle about why maternity leave has unexpectedly failed to improve fertility problem in the Chinese context of a widespread extension of maternity leave, our study concentrates on a prevailing stigmatization phenomenon of maternity leave in the workplace, proposes the construct “maternity-leave stigma”, operationalizes it, and examines its probable detrimental effect on working individuals' fertility intentions drawing on conservation of resources theory, self-verification theory, and research on stigma and psychological contract violation. Conceptually, maternity-leave stigma is a kind of workplace stigma that primarily depicts the extent to which working individuals in the reproductive period view maternity leave or the event of taking maternity leave in a biased way. It mainly consists of four subdimensions called cognitive stigma, emotional stigma, moral stigma, and consequence stigma. Based on multiple analyses of the three-stage questionnaire survey data from working individuals of childbearing age in China, Study 1 (N1 = 296, N2 = 340) acquires a 12-item maternity-leave stigma scale with good reliability and validity and Study 2 (N2 = 340) substantiates that, working individuals' maternity-leave stigma tends to directly and indirectly inhibit their fertility intentions and their anticipatory psychological contract violation from organization is the crucial mediator. Moreover, working women are inclined to display a much stronger inhibiting effect of maternity-leave stigma on fertility intentions compared to working men. Our findings therefore resolve the public's puzzle, enrich workplace stigma, deepen the implementation effectiveness research of maternity leave policy, and are of practical implications for building a fertility-friendly society.

Pengaruh Financial Literacy, Risk Tolerance, dan Religiusitas terhadap Keputusan Investasi di Pasar Modal Syariah (Studi pada Mahasiswa FEBI UIN K.H. Abdurrahman Wahid Pekalongan)

The phenomenon of investment in the capital market is increasingly developing, especially the sharia capital market. Establishing a sharia capital market is the right decision considering that Indonesia is the country with the largest number of Muslims. The sharia capital market should be helped by the presence of a large Muslim community. However, in reality the level of public interest in the Islamic capital market has decreased. Irwan Abdullah as Head of the IDX Sharia Capital Market Division said that the market share of the sharia capital market decreased after the Covid-19 pandemic. Determining investment decisions is related to several things, such as financial literacy, risk tolerance, and religiosity. The aim of this research is to analyze the influence of financial literacy, risk tolerance and religiosity on the decision to invest in the sharia capital market among FEBI UIN K.H Abdurrahman Wahid Pekalongan students. This type of research is field research or field research with a quantitative approach. The population in this study was 2569 FEBI students. The data collection method is a questionnaire that has been tested as valid and reliable using a sample of 100 respondents. This research uses a multiple linear regression test data analysis method with the help of SPSS 21. The results of this research show that financial literacy and religiosity influence investment decisions. Meanwhile, risk tolerance does not influence investment decisions. Then simultaneously financial literacy, risk tolerance and religiosity influence investment decisions.

Network-based integrative multi-omics approach reveals biosignatures specific to COVID-19 disease phases.

COVID-19 disease is characterized by a spectrum of disease phases (mild, moderate, and severe). Each disease phase is marked by changes in omics profiles with corresponding changes in the expression of features (biosignatures). However, integrative analysis of multiple omics data from different experiments across studies to investigate biosignatures at various disease phases is limited. Exploring an integrative multi-omics profile analysis through a network approach could be used to determine biosignatures associated with specific disease phases and enable the examination of the relationships between the biosignatures. To identify and characterize biosignatures underlying various COVID-19 disease phases in an integrative multi-omics data analysis. We leveraged a multi-omics network-based approach to integrate transcriptomics, metabolomics, proteomics, and lipidomics data. The World Health Organization Ordinal Scale WHO Ordinal Scale was used as a disease severity reference to harmonize COVID-19 patient metadata across two studies with independent data. A unified COVID-19 knowledge graph was constructed by assembling a disease-specific interactome from the literature and databases. Disease-state specific omics-graphs were constructed by integrating multi-omics data with the unified COVID-19 knowledge graph. We expanded on the network layers of multiXrank, a random walk with restart on multilayer network algorithm, to explore disease state omics-specific graphs and perform enrichment analysis. Network analysis revealed the biosignatures involved in inducing chemokines and inflammatory responses as hubs in the severe and moderate disease phases. We observed distinct biosignatures between severe and moderate disease phases as compared to mild-moderate and mild-severe disease phases. Mild COVID-19 cases were characterized by a unique biosignature comprising C-C Motif Chemokine Ligand 4 (CCL4), and Interferon Regulatory Factor 1 (IRF1). Hepatocyte Growth Factor (HGF), Matrix Metallopeptidase 12 (MMP12), Interleukin 10 (IL10), Nuclear Factor Kappa B Subunit 1 (NFKB1), and suberoylcarnitine form hubs in the omics network that characterizes the moderate disease state. The severe cases were marked by biosignatures such as Signal Transducer and Activator of Transcription 1 (STAT1), Superoxide Dismutase 2 (SOD2), HGF, taurine, lysophosphatidylcholine, diacylglycerol, triglycerides, and sphingomyelin that characterize the disease state. This study identified both biosignatures of different omics types enriched in disease-related pathways and their associated interactions (such as protein-protein, protein-transcript, protein-metabolite, transcript-metabolite, and lipid-lipid interactions) that are unique to mild, moderate, and severe COVID-19 disease states. These biosignatures include molecular features that underlie the observed clinical heterogeneity of COVID-19 and emphasize the need for disease-phase-specific treatment strategies. The approach implemented here can be used to find associations between transcripts, proteins, lipids, and metabolites in other diseases.

THE INFLUENCE OF COMPENSATION AND WORK DISCIPLINE ON EMPLOYEE PRODUCTIVITY IN PT. WAILALA TRANS IN BAUBAU CITY

This research starts from the framework of thinking that Compensation and Work Discipline greatly influence employee productivity. This research aims to determine the influence of compensation and work discipline on productivity employees PT. Wailala Trans in Baubau City. The population in this study were all employees of PT. Wailala Trans has 34 employees. This type of research uses a quantitative approach. The data sources used are primary data and secondary data, where the primary data comes from observations and documentation at the company PT. Wailala Trans, and secondary data was obtained by distributing questionnaires to 34 employees of the company PT. Wailala Trans. The data analysis technique used is multiple regression data analysis by combining statistical calculations using the Data Analysis Tools program in Windows Excel version 16 and the Statistical Package for the Social Science (SPPS) application version 23. The results of this research show that the compensation variable (X1) has no effect on employee productivity, the work discipline variable (X2) has an effect on employee productivity, meanwhile the compensation and work discipline variables together have a significant and simultaneous effect on the work productivity of employees PT. Wailala Trans, can be seen from the significance value of Sig 0.002 &lt; 0.05, and the Fcount value of 7.560 &gt; Ftable value of 3.304.

Tour Guide App

Abstract— The motive of this android application, is to manage a tour for the users through mobile computing with complete requirements and needs in location. about each and every tourist location in that particular city or place with tailored description. It also enables user to search a particular location efficiently. App module has a tailored information and facility through mobile phones on user to plan any journey and to provide traveler awareness of the current COVID “Hill stations” and “Adventure sports” category. multiple set data analysis and compared with the complete search technique of identical service quality performance. Keywords: Tourism , Android , Covid-19 I

Pengaruh Pelatihan dan Pengembangan Karir Terhadap Kinerja Karyawan pada Toko Plastik di Kabupaten Sleman

The aim of the research is to determine the effect of simultaneous and partial training and career development on employee performance at Plastic Shops in Sleman Regency. Efforts to realize optimal performance from an employee, of course, require training and career development programs so that there are no significant gaps in goals, measures and assessments. This research uses multiple linear data analysis methods using primary data obtained from questionnaires. The sample and research amounted to 21 people. The research results show that, with a t-value of 2.975 and a significant t-test value of 0.008 &lt;0.05, training has an effect on employee performance. In the simultaneous test the F-count value was 20.740 with a significance of 0.000. Because the significance is less than 0.05, it can be concluded that training and development has a positive and simultaneous effect on employee performance at Plastic Shops in Sleman Regency.

A multi-modal joint attention network for vibro-acoustic fusion diagnosis of engines

Abstract Deep learning has proven to be effective in diagnosing faults in power machinery and its diagnosis performance relies on a sufficient data set. In practice, a well-labeled data set with sufficient samples is very rare, especially for those machinery running in varying loading cases. The situation is particularly pronounced for multi-cylinder internal combustion engines, where the excitations from cylinders interact with significant background noise, and different data distributions are complicated. To tackle these issues, we propose a novelty multi-modal joint attention network (MJA-Net) for fusing the vibration and acoustic signals for diagnosing multiple faults. In MJA-Net, feature maps from both modalities are input separately into the convolutional module to learn independent features, and joint attention module (JAM) is utilized to enhance the vibro-acoustic information interaction and distribution consistency across modalities. The analysis of multiple loads vibro-acoustic experimental data shows that MJA-Net has a superior classification performance in limited sample tasks, compared to the single-modal methods. Furthermore, MJA-Net outperforms other fusion methods on average accuracy at 97.65%, as well as feature representativeness, and vibro-acoustic feature consistency across loads. JAM has superior diagnosis performance to other alternative modules. The class activation maps (CAM) generated by the Layer CAM highlight the key impact components related to the engine working mechanisms, providing valuable insight into MJA-Net’s interpretation for multi-fault recognition.

The Moderating Role Of Intellectual Capital In The Relationship Between Tax Avoidance And Firm Value

This research examines the relationship between tax avoidance and firm value by placing intellectual capital as a moderating variable. The research was conducted on companies listed on the Indonesia Stock Exchange (BEI) operating in the primary consumer goods sector, especially those in the food and beverage industry for 2019-2022. The method uses multiple linear regression panel data analysis to test the hypothesis. There were 68 samples used in testing after purposive sampling was carried out. The research results show that tax avoidance does not affect firm value. The presence of intellectual capital also cannot moderate the relationship between these two variables. It is hoped that this research can add to the literature for further research regarding the relationship between tax avoidance and firm value by including intellectual capital as a moderating factor. This research also contributes to investors making investment decisions related to tax planning policies in companies in the primary consumer sector, especially those in the food and beverage industry.