Comprehensive Analysis Approach Research Articles

Integrating machine learning with mendelian randomization for unveiling causal gene networks in glioblastoma multiforme

BackgroundGlioblastoma multiforme (GBM) is a highly aggressive brain cancer with poor prognosis and limited treatment options. Despite advances in understanding its molecular mechanisms, effective therapeutic strategies remain elusive due to the tumor’s genetic complexity and heterogeneity.MethodsThis study employed a comprehensive analysis approach integrating 113 machine learning algorithms with Mendelian Randomization (MR) analysis to investigate the molecular underpinnings of GBM. Five publicly available gene expression datasets were analyzed to identify differentially expressed genes (DEGs) associated with GBM. Weighted Gene Co-expression Network Analysis (WGCNA) was used to identify GBM-related gene modules. Further, gene set enrichment and variation analyses were conducted to explore the biological pathways involved. The machine learning models were evaluated using Receiver Operating Characteristic (ROC) curves and confusion matrices to assess their predictive accuracy, with the best-performing model validated across external datasets. MR analysis was performed to establish causal relationships between genetically predicted gene expression levels and GBM outcomes.ResultsThe study identified 286 DEGs between GBM and adjacent normal tissues across five datasets. WGCNA highlighted the yellow module as the most relevant to GBM, containing key genes such as KLHL3, FOXO4, and MAP1A. Of the 113 machine learning models tested, Ridge regression achieved the highest area under the curve (AUC) of 0.92, demonstrating robust predictive accuracy. Validation using external datasets confirmed the model's reliability, with a classification accuracy of 89.5% in the training set and 85.3% in the validation sets. MR analysis provided strong evidence of a causal relationship between the expression levels of the identified genes and GBM risk.ConclusionsThis study demonstrates the power of combining machine learning and Mendelian Randomization to uncover novel genetic markers for GBM. The identified genes offer promising potential as biomarkers for GBM diagnosis and therapy, providing new avenues for personalized treatment strategies.

Comprehensive Approach for Sequential MALDI-MSI Analysis of Lipids, N-Glycans, and Peptides in Fresh-Frozen Rodent Brain Tissues.

Multiomics analysis of single tissue sections using matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI) provides comprehensive molecular insights. However, optimizing tissue sample preparation for MALDI-MSI to achieve high sensitivity and reproducibility for various biomolecules, such as lipids, N-glycans, and tryptic peptides, presents a significant challenge. This study introduces a robust and reproducible protocol for the comprehensive sequential analysis of the latter molecules using MALDI-MSI in fresh-frozen rodent brain tissue samples. The optimization process involved testing multiple organic solvents, which identified serial washing in ice-cold methanol, followed by chloroform as optimal for N-glycan analysis. Integrating this optimized protocol into MALDI-MSI workflows enabled comprehensive sequential analysis of lipids (in dual polarity mode), N-glycans, and tryptic peptides within the same tissue sections, enhancing both the efficiency and reliability. Validation across diverse rodent brain tissue samples confirmed the protocol's robustness and versatility. The optimized methodology was subsequently applied to a transgenic Alzheimer's disease (AD) mouse model (tgArcSwe) as a proof of concept. In the AD model, significant molecular alterations were observed in various sphingolipid and glycerophospholipid species, as well as in biantennary and GlcNAc-bisecting N-glycans, particularly in the cerebral cortex. These region-specific alterations are potentially associated with amyloid-beta (Aβ) plaque accumulation, which may contribute to cognitive and memory impairments. The proposed standardized methodology represents a significant advancement in neurobiological research, providing valuable insights into disease mechanisms and laying the foundation for potential preclinical applications. It could aid the development of diagnostic biomarkers and targeted therapies for AD and other neurodegenerative diseases, such as Parkinson's disease.

“Fever Sisters” in the artistic interpretation of Konstantin Balmont (based on the poems “The Spell of Twelve Girls” (1906) and “Thirteen Sisters” (1906))

The relevance of the topic is determined by the growing interest in studying the interaction between literature and folklore, as well as the importance of analyzing transformations of traditional folk motifs in the works of Russian poets of the 20th century. The image of fevers in Slavic mythology represents one of the most enigmatic phenomena of folk culture, making it an attractive subject for research. The work of Konstantin Balmont, known for his unique interpretations of folk motifs, allows us to uncover new aspects of this theme. The novelty of the study lies in the analysis of a little-studied aspect of Balmont's creativity – his appeal to the image of fevers in the poems “Spell from Twelve Maidens” and “Thirteen Sisters”. The work aims to identify the specifics of adapting folk motifs in the poet's works, as well as to determine the role of these images in shaping his artistic world. The purpose of the research is to analyze the features of the transformation of the traditional image of fevers in the Balmont's work and to reveal the mechanisms of his interaction with folk beliefs. To achieve this goal, it is necessary to solve the following tasks: to study the historical and cultural contexts in which ideas about fevers were formed; to consider the role of folk records by I. P. Sakharov and A. N. Afanasyev in Balmont's work; to conduct a comparative analysis of the two specified poems in terms of their use of folk motifs; to determine the extent of influence of folk incantations on the poet's work. The study was conducted using the method of comprehensive text analysis, including historical-cultural, comparative, and structural-semantic approaches. Materials from the works of renowned folklorists such as I. P. Sakharov, A. N. Afanasyev, T. A. Agapkina, A. L. Toporkov, M. I. Maslova, etc., were utilized. Special attention was paid to the analysis of the structure and content of Balmont's poems, as well as the identification of characteristic traits of his individual style. The main conclusions are as follows: Balmont deeply delved into the essence of folk beliefs and skillfully adapted traditional motifs to his creative world while preserving their original essence. He created original interpretations of folk images, giving them new life and actualizing them within the context of his contemporary era. The image of fevers in his poetry acquires symbolic significance, reflecting deep processes of human interaction with higher forces and one's own culture. Prospects for further research are associated with an in-depth analysis of the interrelationships between literature and folklore in Russian culture of the 20th century, focusing on the work of Balmont. It is also necessary to continue working on identifying the mechanisms of transformation of folk motifs in the works of other representatives of Russian literature, which will allow creating a more complete picture of the interaction between literature and folklore in the history of national culture.

Dynamic Response of Electromechanical Coupled Motor Gear System with Gear Tooth Crack

The motor gear system (MGS) is recognized for its potential in enhancing transmission efficiency and optimizing space utilization. However, the system is subjected to challenges, notably the occurrence of abnormal vibrations. These issues stem from the dynamic interaction between the motor and gears, the presence of nonlinear factors in gear system, and the impact of gear faults, all of which contribute to complex vibration patterns. Traditional dynamic models have been found to be inadequate in effectively addressing the complexities associated with electromechanical coupling problems in MGS. To address these limitations, a comprehensive analysis approach is proposed in this paper, which is grounded in the development of an electromechanical coupling model. This method involves establishing a coupled dynamic model of the motor and gear system, integrating numerical simulations, and experimental validations to thoroughly analyze the vibration characteristics of the system. Through this multifaceted methodology, a detailed analysis of the system’s vibration characteristics is conducted. The results indicate that internal excitations from tooth root cracks not only directly affect dynamic characteristics of the gear transmission system (GTS) but also indirectly influence dynamic behavior of the motor, which offers valuable insights into modeling integrated MGS and provides significant solutions for fault diagnosis within these systems.

Flow analysis of steady state forward flow of aortic valve prostheses using Particle Image Velocimetry

Abstract Transcatheter aortic valve replacement (TAVR) has become the standard therapy for aortic valve stenosis in patients with high surgical risk. Understanding the flow dynamics in TAVR is crucial for its evaluation and optimization. Experimental flow measurement by means of Particle Image Velocimetry (PIV) is increasingly applied alongside numerical analyses. This study introduces a novel test rig concept enabling the determination of velocity fields using Stereo-PIV under steady forward flow conditions through a TAVR during the peak systole matching the ISO 5840-1:2021 requirements. The experimental setup utilized an impeller pump to generate steady forward flow through a silicone aortic root model with implanted TAVR. A Stereo-PIV setup captured velocity fields in both ventricular inflow and aortic outflow regions. Test conditions were based on physiological flow rates determined from pulsatile measurements. Illumination of the added particles in the test fluid was achieved using an Nd:YAG laser. Fluorescent polystyrol particles (size: 50 μm) were used for flow visualization. Results showed characteristic flow patterns: a central jet flow entering the TAVR in the ventricular flow field. A jet flow directed towards the sinus side and a recirculation zone forming on the opposite side of the sinus could be detected in the aortic outflow. The width of the recirculation zone increases with distance from the TAVR. Maximum flow velocities were detected at 0.78 m/s for the ventricular flow field and 0.94 m/s for the aortic flow field. This study provides a comprehensive approach for fluid dynamic analysis of TAVR under steady flow conditions, offering insights into flow mechanics performance crucial for device optimization. Further investigations could enhance the PIV - measurement procedure by increasing both the spatial resolution and the tracer particle density within the acquired images for a comprehensive characterization of TAVR flow dynamics.

Ocean current turbine power take-off design using fluid dynamics and towing tank experiments

This study investigates the performance and power generation capabilities of a small-scale hydrokinetic turbine by comparing numerical simulations with experimental measurements. The key difference between the two models comes from the initial numerical analysis which focused only on the permanent magnet DC motor (PMDC) motor’s parameters and did not account for the gear-head reduction that leads to discrepancies in current and torque predictions, especially at lower input voltages. In practice, friction losses within the gear-head increased the required current and torque, highlighting inefficiencies in the motor gear-head system. A modified experimental setup incorporated a magnetic coupling to address leakage issues and enhance system reliability. While the magnetic coupling resulted in a slight reduction in speed, current, and torque, it improved the overall integrity of the system which is essential for marine applications. The comparison between experimental results and Blade Element Momentum (BEM) simulations showed good agreement at lower speeds, but the simulations under-predicted power at higher speeds, likely due to the model’s limitations in capturing complex hydrodynamic phenomena. This shows the need for comprehensive analysis, integrating both numerical and experimental approaches to optimize turbine performance. Future research will focus on refining experimental methodologies and further improving turbine design and efficiency for hydrokinetic energy systems.

Ocean current turbine power take-off design using fluid dynamics and towing tank experiments

This study investigates the performance and power generation capabilities of a small-scale hydrokinetic turbine by comparing numerical simulations with experimental measurements. The key difference between the two models comes from the initial numerical analysis which focused only on the permanent magnet DC motor (PMDC) motor’s parameters and did not account for the gear-head reduction that leads to discrepancies in current and torque predictions, especially at lower input voltages. In practice, friction losses within the gear-head increased the required current and torque, highlighting inefficiencies in the motor gear-head system. A modified experimental setup incorporated a magnetic coupling to address leakage issues and enhance system reliability. While the magnetic coupling resulted in a slight reduction in speed, current, and torque, it improved the overall integrity of the system which is essential for marine applications. The comparison between experimental results and Blade Element Momentum (BEM) simulations showed good agreement at lower speeds, but the simulations under-predicted power at higher speeds, likely due to the model’s limitations in capturing complex hydrodynamic phenomena. This shows the need for comprehensive analysis, integrating both numerical and experimental approaches to optimize turbine performance. Future research will focus on refining experimental methodologies and further improving turbine design and efficiency for hydrokinetic energy systems.

GutMetaNet: an integrated database for exploring horizontal gene transfer and functional redundancy in the human gut microbiome.

Metagenomic studies have revealed the critical roles of complex microbial interactions, including horizontal gene transfer (HGT) and functional redundancy (FR), in shaping the gut microbiome's functional capacity and resilience. However, the lack of comprehensive data integration and systematic analysis approaches has limited the in-depth exploration of HGT and FR dynamics across large-scale gut microbiome datasets. To address this gap, we present GutMetaNet (https://gutmetanet.deepomics.org/), a first-of-its-kind database integrating extensive human gut microbiome data with comprehensive HGT and FR analyses. GutMetaNet contains 21567 human gut metagenome samples with whole-genome shotgun sequencing data related to various health conditions. Through systematic analysis, we have characterized the taxonomic profiles andFR profiles, and identified 14636 HGT events using a shared reference genome database across the collected samples. These HGT events have been curated into 8049 clusters, which are annotated with categorized mobile genetic elements, including transposons, prophages, integrative mobilizable elements, genomic islands, integrative conjugative elements and group II introns. Additionally, GutMetaNet incorporates automated analyses and visualizations for the HGT events and FR, serving as an efficient platform for in-depth exploration of the interactions among gut microbiome taxa and their implications for human health.

Identification of novel proteins in inflammatory bowel disease based on the gut-brain axis: a multi-omics integrated analysis

BackgroundThe gut-brain axis has garnered increasing attention, with observational studies suggesting its involvement in the disease activity and progression of inflammatory bowel disease (IBD), but the precise mechanisms remain unclear.Materials and methodsIn this study, we aimed to investigate “novel proteins” underlying IBD in the brain using a comprehensive multi-omics analysis approach. We performed integrated analyses of proteomics and transcriptomics in the human prefrontal cortex (PFC) tissue, coupled with genome-wide association studies (GWAS) of IBD, crohn’s disease (CD), and ulcerative colitis (UC). This included performing protein-wide association studies (PWAS), transcriptome-wide association studies (TWAS), Mendelian randomization (MR), and colocalization analysis to identify brain proteins associated with IBD and its subtypes.ResultsPWAS analyses identified and confirmation 9, 9, and 6 brain proteins strongly associated with IBD, CD, and UC, respectively. Subsequent MR analyses revealed that increased abundance of GPSM1, AUH, TYK2, SULT1A1, and FDPS, along with corresponding gene expression, led to decreased risk of IBD. For CD, increased abundance of FDPS, SULT1A1, and PDLIM4, along with corresponding gene expression, also decreased CD risk. Regarding UC, only increased abundance of AUH, along with corresponding gene expression, was significantly associated with decreased UC risk. Further TWAS and colocalization analyses at the transcriptome level supported strong associations of SULT1A1 and FDPS proteins with reduced risk of IBD and CD.ConclusionThe two “novel proteins,” SULT1A1 and FDPS, are strongly associated with IBD and CD, elucidating their causal relationship in reducing the risk of IBD and CD. This provides new clues for identifying the pathogenesis and potential therapeutic targets for IBD and CD.

A comprehensive propulsion system analysis: an integrated approach utilising engine simulation and free-running model experiments

Regulations concerning the environmental impact and energy efficiency of ships are becoming increasingly stringent. To meet these demanding criteria, new technologies are actively being developed to achieve emission reductions while maintaining efficient fuel energy conversion into propulsion power. However, the propulsion system behaviour in a dynamic environment of the actual sea is highly nonlinear and complex, and a profound understanding of the mutual interactions is still lacking. This paper proposes an integrated approach for comprehensive propulsion system analysis. The key feature of this technique is the utilisation of an intelligent propeller drive controlled by a marine diesel engine simulator (MDES) to drive the free-running model of a ship, providing real-like behaviour of the engine model in real-time in response to actual measured values. The core of the MDES consists of a continuous cycle-mean value dynamic engine model supplemented with a detailed combustion cycle evaluation. The developed fast calculation algorithm of crank-angle resolved thermodynamic equations of the gas state in the engine cylinder enables the complete engine model to be integrated into the real-time environment of a physical model ship. Thus, the propulsion system's performance can be analysed under conditions that closely mimic those of the actual sea environment.

Towards a sustainable decision analysis approach for moisture-safe building envelope design

Energy-efficient building designs, notably through adding insulation, can reduce energy consumption and greenhouse gas emissions by over 40 %, but may also increase risk of condensation. Therefore, a comprehensive decision analysis approach is essential for selecting optimal building envelope assemblies while considering their hygrothermal, economic, and environmental implications. This study introduces a novel decision analysis method based on the inutility decision analysis (IDA) concept, in which the building envelope with the lowest inutility is considered the optimal choice. Initially, different building envelope assemblies were assessed through probabilistic hygrothermal analysis combined with a machine learning metamodel. Subsequently, the environmental and economic consequences associated with each assembly were quantified by integrating moisture-related damage into these assessments. Combining these outputs yielded inutility indices for the case studies, guiding the selection of the most optimal alternative. The results indicated that an external wall, with a minor risk of moisture-related failure, may outperform other alternatives when considering economic and environmental metrics. However, the final decision on the acceptability of such risk should rest with the customers or owners. Balancing the trade-off between performance longevity, cost, and occupants’ health is crucial.

A comprehensive multivariate approach for GxE interaction analysis in early maturing rice varieties.

The genotype evaluation process requires analysis of GxE interactions to ascertain the responsiveness of a genotype to various environments, including the development of early maturing rice. However, the concept of interaction is relatively specific to grain yield. In contrast, grain yield is highly polygenic, so assessment should be carried out with multivariate approaches. Therefore, multivariate assessment in evaluating GxE interactions should be developed, especially for early maturing rice genotypes. The study aimed to develop a comprehensive multivariate approach to improve the comprehensiveness and responsiveness of GxE interaction analysis. The study was conducted in Bone and Soppeng districts, South Sulawesi, Indonesia, in two seasons. The study used a randomized complete block design, where replications were nested across two seasons and locations. Two check varieties and five early maturing varieties were replicated three times in each environment. Based on this study, a new approach to GxE interaction analysis based on multiple regression index analysis, BLUP analysis, factor analysis, and path analysis was considered adequate, especially for evaluating early maturing rice. This approach combined days to harvest, biological yield, and grain yield in multiple linear regression with weighting based on the combination of all analyses. The effectiveness of the GxE interaction assessment was reflected by high coefficient of determination (R2) and gradient (b) values above 0.8 and 0.9, respectively. Inpari 13 (R2=0.9; b=1.05), Cakrabuana (R2=0.98; b=0.99), and Padjajaran (R2=0.95; b=1.07) also have good grain yield with days to harvesting consideration, namely 7.83ton ha-1, 98.12 days; 7.37ton ha-1, 95.52 days; and 7.29ton ha-1, 97.23 days, respectively. Therefore, this index approach can be recommended in GxE interaction analysis to evaluate early maturing rice genotypes. Furthermore, Inpari 13, Cakrabuana, and Padjajaran are recommended as adaptive early maturing varieties.

Educational Research Methodology Trends of Southeast Asia English Major Undergraduate Theses: A Content Analysis

The present study thoroughly examines the predominant research methodology trends and topics applied in a specific time frame from 2019 to 2023 in educational research of the online searchable undergraduate theses in English major programs across Southeast Asia, utilizing a comprehensive content analysis approach. By employing keyword extraction, topic-specific modelling, and thematic evaluation, our objective is to ascertain the prevailing methodologies in English language teaching and the research interest themes from 347 undergraduate downloadable theses that have been investigated. The analysis explores the possible ramifications of these patterns on the caliber of undergraduate research, English language instruction, and the broader education domain within the given area. This study revealed a strong preference (54.5%) for quantitative approaches, highlighting the importance of empirical methodologies. Additionally common were qualitative approaches (24.8%), suggesting an increasing recognition of the importance of examining human factors. While action research (8.4%), particularly Classroom Action Research (CAR), was less prevalent than mixed-method approaches (12.8%), this suggests that action research needs to be recognized. The different ratios of research methods demonstrated a dedication to investigating various questions and advancing knowledge in language, literature, and education. The interesting research themes identified the complex and diverse nature of English language raising focus in Southeast Asia such as technological integration, teacher development, cultural studies, assessment, and language teaching methodologies. The study provides useful information for enhancing research mentorship and education, and it improves knowledge of research trends in this setting. Furthermore, the findings obtained from this study have the potential to contribute to the enhancement of curriculum design, instructional approaches, and distribution of resources in the English language education research area.

A Comprehensive Failure Analysis Approach Utilizing High-Resolution Targeted Patterns (HRTP) for ATPG Diagnosis Resolution Improvement, Test Coverage Resolution, and Further Fault Localization

A Comprehensive Failure Analysis Approach Utilizing High-Resolution Targeted Patterns (HRTP) for ATPG Diagnosis Resolution Improvement, Test Coverage Resolution, and Further Fault Localization

Reconceptualizing and Defining Exposomics within Environmental Health: Expanding the Scope of Health Research.

Exposomics, the study of the exposome, is flourishing, but the field is not well defined. The term "exposome" refers to all environmental influences and associated biological responses throughout the lifespan. However, this definition is very similar to that of the term "environment"-the external elements and conditions that surround and affect the life and development of an organism. Consequently, the exposome seems to be nothing more than a synonym for the environment, and exposomics a synonym for environmental research. As a result, some have rebranded their "standard" environmental health research with the neologistic exposome term, whereas others ignore or seek to abandon the seemingly redundant concept of the exposome. We argue that exposomics needs to sharpen its mission focus to counteract this apparent redundancy. Exposomics should be defined as a research program in environmental health aimed at enabling a comprehensive and discovery-driven approach to identifying environmental determinants of human health. Similar to the aim of the Human Genome Project, exposomics aims to analyze the complete complexity of exposures and their corresponding biological responses. Exposomics' primary premise is that the existence of undiscovered, potentially interconnected, nongenetic (environmental) risk factors for health necessitates a comprehensive discovery-driven analysis approach. We argue that exposomics researchers should adopt our reconceptualization of exposomics and focus on the productiveness and integrity of their research program: its purpose and principles. We suggest that exposomics researchers should coordinate the writing of reviews that assess the program's productiveness and integrity, as well as provide a platform for exposomics researchers to define their vision for the field. https://doi.org/10.1289/EHP14509.

Risk Management and Failure Analysis in Diagnostic X-ray Equipment: A Comprehensive Analysis and Novel Approaches for Failure Prevention and System Reliability

Risk Management and Failure Analysis in Diagnostic X-ray Equipment: A Comprehensive Analysis and Novel Approaches for Failure Prevention and System Reliability

Modelling climate change-induced nonstationarity in rainfall extremes: A comprehensive approach for hydrological analysis

Climate change and global warming have induced a dynamic shift in extreme rainfall patterns, leading to nonstationary behaviour. This alteration in behaviour challenges conventional hydrologic design, which assumes stationarity and can yield misleading outcomes. This study aims to address nonstationarity by modelling distribution parameters with covariates. Utilizing a 70-year (1951–2020) high-resolution India Meteorological Department (IMD) gridded dataset, extreme annual rainfall across diverse Indian cities was extracted and modelled. Previous research and goodness-of-fit tests favour the Generalized Extreme Value (GEV) distribution for modelling extremes. This study incorporates indices like Nino3.4, dipole mode index, global and local temperature, CO2, and time to characterize nonstationarity in extreme annual rainfall, leveraging climate cycles and global warming. Performance assessment employs the Akaike information criterion, Bayesian information criterion and Likelihood ratio test, while quantile reliability is evaluated through confidence intervals (CIs). Findings reveal widespread nonstationary trends in most grid points, translating to wider CIs in estimated quantiles for chosen non-exceedance probability and covariates in fitted models. Generally, nonstationary conditions are associated with broader confidence bands in return levels, highlighting nonstationary model weaknesses compared to stationary models. However, the results showed that the rainfall extremes follow a nonstationary pattern. Hence, there is a strong need to develop nonstationary models of low uncertainty.

Risk culture and cost of capital – Insight from European banks

This study examines the impact of risk culture on the cost of capital (i.e., WACC, Cost of equity and cost of debt) in European banks. We measure risk culture using comprehensive textual analysis approach. Based on 134 banks from 2005 to 2022, findings reveal that risk culture is negatively associated with all cost of capital measures. This indicates that robust risk culture reduces information asymmetry and boosts investor confidence, leading to lower costs of capital. Our findings reflect the importance of aligning risk management practices with regulatory requirements and investor expectations.

Assessing the implications of imported electricity equipment in Indonesia: A comprehensive ECM approach for short-term and long-term analysis

This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

Detection of Protective Coatings Applied on Baroque Amber Artworks: Case Studies

Amber has been used to create decorative items for centuries, but its degradation presents challenges for conservators. This study identifies substances historically used to protect amber objects, especially those from 17th and 18th century Gdansk workshops. Despite their historical value, information on amber conservation is scarce. Traditional substances are noted, but their exact compositions and effects on amber remain unclear. Synthetic resins, introduced in the late 19th century, also degrade, complicating conservation due to their removal difficulty and interference with amber identification. This research aimed to develop methods for detecting and analyzing protective coatings on amber objects using macroscopic and microscopic techniques. Initial methods included analytical photography under visible and UV light and reflectance imaging spectroscopy (RIS) to assess the surface. Raman spectroscopy (RS) and X-ray fluorescence spectroscopy (XRF) were used for detailed analysis. RS provided precise layer-specific information but was sensitive to surface conditions, while XRF quickly identified inorganic compounds but not organic materials. Examining amber objects from Polish collections using this methodology revealed various protective substances, including synthetic resins and nitrocellulose varnishes. This research contributes to amber conservation by proposing a comprehensive material analysis approach, essential for developing effective conservation strategies for these historic objects.