Combined Methodology Research Articles

Using meta-heuristic algorithms with multi-layer perceptron for prediction of ultimate bearing capacity

ABSTRACT This study presents an innovative approach to predicting the Ultimate Bearing Capacity (Qu) in civil engineering, integrating a Multi-layer Perceptron (MLP) neural network using the Artificial Rabbits Optimization (ARO) and Improved Manta-Ray Foraging Optimizer (IMRFO) methods. The MLP effectively captures complex, non-linear relationships within geotechnical datasets, while the IMRFO and ARO algorithms fine-tune the MLP’s parameters for optimal performance. Validation results demonstrate the efficacy of this method, with the MLP+IMRFO (MLIM) model achieving an R2 value of 0.996 and a MARE of 0.113, highlighting its potential for enhancing predictive accuracy in civil engineering applications. This combined methodology offers a state-of-the-art solution for Qu forecasting, contributing to safer and more efficient construction practices.

The future of renewable energy in Iran's energy policy

Renewable energy has become a very important issue in the world. Access problems, high prices, and environmental issues of fossil fuels have caused the world to turn to renewable energy. Using the quantitative-qualitative combined methodology and the theoretical framework of energy security, this research has tried to answer the question of what the future of renewable energy in Iran is. The hypothesis of this research is that despite the adoption of programs and policies for the productivity and development of renewable energy in Iran, sanctions and investment problems make the development of renewable energy in Iran a structural problem. The findings of the research indicate that in the long term, solving structural challenges such as sanctions and investment and encouraging investment in renewable energy can provide a transition path by diversifying energy sources. So, the article tries to analyze the situation of renewable energy in terms of opportunities and challenges and show a vision of the future of this energy in Iran.

Machine Learning Using Template-Based-Predicted Structure of Haemagglutinin Predicts Pathogenicity of Avian Influenza.

Deep learning presents a promising approach to complex biological classifications, contingent upon the availability of well-curated datasets. This study addresses the challenge of analyzing three-dimensional protein structures by introducing a novel pipeline that utilizes open-source tools to convert protein structures into a format amenable to computational analysis. Applying a two-dimensional convolutional neural network (CNN) to a dataset of 12,143 avian influenza virus genomes from 64 countries, encompassing 119 hemagglutinin (HA) and neuraminidase (NA) types, we achieved significant classification accuracy. The pathogenicity was determined based on the presence of H5 or H7 subtypes, and our models, ranging from zero to six mid-layers, indicated that a four-layer model most effectively identified highly pathogenic strains, with accuracies over 0.9. To enhance our approach, we incorporated Principal Component Analysis (PCA) for dimensionality reduction and one-class SVM for abnormality detection, improving model robustness through bootstrapping. Furthermore, the K-nearest neighbor (K-NN) algorithm was fine-tuned via hyperparameter optimization to corroborate the findings. The PCA identified distinct clustering for pathogenic HA, yielding an AUC of up to 0.85. The optimized K-NN model demonstrated an impressive accuracy between 0.96 and 0.97. These combined methodologies underscore our deep learning framework's capacity for rapid and precise identification of pathogenic avian influenza strains, thus providing a critical tool for managing global avian influenza threats.

Combination of Variable Loads in Structural Design

This study delves into the intricacies of variable load combination factors (ψ) within structural codes under fundamental design scenarios, with Eurocodes serving as the primary reference. Currently, variable loads are combined by adding one load, the leading load with its full value, and the other load, the accompanying load, with a reduced value multiplied by a combination factor ψ. This approach employs an independent load combination methodology, utilizing hypothetical reference materials. In contrast, this paper advocates for a shift towards dependent load combination, anchored in the use of actual reference materials. Specifically, it is proposed that imposed loads be combined without the combination factor, i.e., ψ = 1. Given that combination factors are in approximate unity or pertain to infrequent load cases, this research recommends the elimination of ψ from codes altogether. This recommendation stems from the recognition that the current combination factor calculation excels in cases with approximately equal loads with a significant reliability gain, while more frequent unequal loads introduce a minor reliability gain and harmful unsafe errors. Despite the overall minor safety advantage of about 2–3% being negligible considering unavoidable safe errors of about 7% in codes, this simplification significantly reduces code complexity, enhances user-friendliness, and substantially decreases the workload associated with design processes.

Urban Fire Risk Dynamics and Mitigation Strategies in Shanghai: Integrating Spatial Analysis and Game Theory

In recent decades, the increasing frequency of urban fires, driven by urban functional enhancements and climate change, has posed a growing threat to metropolitan sustainability. This study investigates the temporal and spatial characteristics of fire incidents in Shanghai from 2019 to 2023. Using satellite fire point data and official government records, kernel density analysis and wavelet analysis were employed to analyze the time series and spatial distribution of fire data. Subsequently, eleven primary factors influencing urban fire occurrence were identified, encompassing probability, regional characteristics, and hazard sources. A combined methodology of subjective and objective weights with game theory was used to generate a fire risk assessment at a 1 × 1 km2 grid scale. Furthermore, the spatial distribution characteristics of the assessments were analyzed. The results reveal that the downtown area exhibits the highest intensity of urban fires in terms of spatial domain, with a decreasing intensity towards the suburbs. Temporally, fire frequency demonstrates significant periodicity at an 18a time scale, while clear seasonal fluctuations and periodicity are observed at a 16-22a time scale, with higher occurrences in spring and winter. The study identifies typical aggregation patterns of urban fires, with high-risk centers in downtown Shanghai. Considering the impact of climate change and human activities, high-risk areas may gradually expand to adjacent urban suburbs, presenting a concerning future scenario. By examining the dual attributes of “combustibles and fireproof space” within urban greening systems, this research offers recommendations for the future strategies of disaster prevention and mitigation of green systems in Shanghai.

Climate litigation and financial markets: A disciplinary effect?

Climate litigation has witnessed a surge in recent decades, raising questions about its economic and financial implications. This paper investigates the disciplinary effect of climate litigation on corporations, focusing on stock price movements for targeted firms and their industry peers in response to announcements of lawsuit filings and negative court decisions. By using a combined event study and panel regression methodology for 77 lawsuit filings and 19 negative decisions that occurred between 2005 and 2021 in North America or Europe, we find that market reactions are quite limited. On average, lawsuit filings do not lead to stock price declines for targeted entities and their industry peers, indicating a lack of perceived financial threat by financial markets. Negative court rulings, while slightly affecting targets, do not result in spillovers to industry peers. However, nuances emerge, revealing that an increased frequency of lawsuits filings generates stronger stock declines for the industry around new filings. Regarding claims, legal actions seeking compensatory damages result in more negative stock returns when initiated. Also, lawsuits brought by individuals and NGOs, as opposed to government bodies, lead to stronger negative market reactions towards targets and peers around both filings and negative decisions. In the end, despite attracting significant media attention, our findings suggest that the disciplinary effect of climate litigation has been relatively weak thus far.

How Media Framing of Video Games Affects the Perception and Understanding of the Video Game Industry: The Opinions of Students at the University of Zagreb

Creating video games is a complex process that can be tracked in stages, requiring compliance with platform-specific requirements, content, visual elements, characters, and the existence of interaction among them. In this context, there is increasing talk about gamification to strengthen user engagement, motivation, and communication. The popularity of video games as a broader social phenomenon sparks divided public opinions, and media reporting has a significant impact on it. Framing a particular topic in a negative or positive tone contributes to the perception of events, individuals, or processes as threats or opportunities, potentially leading to calls for increased regulation. This paper aims to analyze media coverage of video games and to investigate how it affects public opinion and understanding of the game industry, using a combined methodology. Analysis of the content of media coverage on selected foreign and Croatian online portals that deal with technology, as well as Croatian newspaper portals that have special gaming sections, showed a difference in the approach to reporting. Foreign media portals have shown exceptional commitment to collecting information about upcoming games and monitoring economic, industry changes, and player opinions, while domestic ones are focused on innovations within the Croatian gaming scene. The emphasis is on promoting and showcasing local talent and progress in the country's gaming industry. At the same time, the results of the survey showed that the majority of participants are familiar with the gaming industry and actively participate in it. Furthermore, although they follow media reports and reviews about video games, it still does not significantly influence their decision to purchase a video game. Finally, most participants feel that politics and social agendas are not desirable in video games.

AUDIT TEKNOLOGI INFORMASI APLIKASI SISTEM PERSURATAN TERPADU (SIRANDU) DENGAN METODE TAM DAN WEBQUAL

In the rapidly evolving landscape of information technology, IT audits play a crucial role in ensuring that applications such as SIRANDU (Integrated Mailing System) adhere to quality standards and meet user acceptance criteria. This research investigates the determinants influencing users' overall evaluations of a website by employing a combined methodology of the Technology Acceptance Model (TAM) and Webqual. Data were gathered through online questionnaires administered via Google Forms to 100 users of the SIRANDU application. Structural Equation Modeling (SEM) analysis reveals significant positive impacts of usability, perceived usefulness, and attitude toward use on user intentions and actual system usage. Nevertheless, the overall assessment of the website remains inadequate. Recommendations include enhancing usability, improving information quality, and refining service interactions to augment user experience and satisfaction. Continuous evaluation and iterative enhancements are advocated to foster greater user satisfaction and broader adoption of the website. Keywords: SIRANDU application, TAM, Webqual, SEM

Data-Driven Smart Assessment for Enterprise Audit Risks Based on Radial Base Function Neural Network and Grey Correlation Analysis

In the era of big data, audit risk assessment is facing increasing business volume. Hence, it is important to develop automatic audit risk assessment methods for enterprises with the assistance of intelligent algorithms. As a consequence, this paper proposes data-driven smart assessment for enterprise audit risks-based radial basis function (RBF) neural networks and grey correlation analysis (GCA). First, evaluation data are collected and analyzed to understand the characteristics and influencing factors of enterprise audit risks. Secondly, RBF interpolation is introduced to establish the network structure for RBF neural networks. Then, GCA is integrated with RBF Interpolation (RBFNN) to formulate the automatic audit risk evaluation method, and the proposal is named RBF and GCA. Such a combined methodology can improve audit risk assessment efficiency. Finally, we make some experiments to evaluate the proposed TBF-GCA on a real-world dataset, and some evaluation indicators are specified based on the actual audit risk situation of the enterprises. The obtained results reveal that RBF–GCA has high accuracy in identity precision and can effectively identify audit risks for enterprises.

Fuel-air ratio effect on hydrogen-methane flames in a high pressure burner for gas turbines

This numerical simulation studied the effect of H2-CH4 flame equivalence ratio on turbulent premixed combustion at 50%-50% concentration (by volume). The equivalence ratio was varied from 0.45 to 1.0 in 0.5 increments for 126 kW operating power, matching a 3.3 bar inlet reactant pressure. Tests utilized a gas turbine combustor. The thermal field, flow field, and pollutant emissions (NOx and CO) underwent rigorous analysis. The modelling framework applied steady Reynolds-Averaged Navier-Stokes (RANS) equations coupled to a probability density function (PDF) approach for turbulence-chemistry interactions and a NOx formation model. Results showed increasing equivalence ratio from 0.45 to 1.0 elevated temperature approximately 900 K, significantly promoting NOx up to 1600 ppm and CO beyond 1900 ppm. However, equivalence ratio changes minimally impacted the overall flow field, maintaining stabilized flames. These findings provide new insight on thermochemical effects and flame stability in gas turbine (G.T) combustors across a range of equivalence ratios relevant for clean, high-pressure H2-CH4 combustion. The combined RANS-PDF methodology enables predictive simulation of turbulence, kinetics, emissions, and flame stability to guide optimal fuel-air ratio selection and low-emission combustor design.

Rapid Production Nasal Osteotomy Simulators With Multi-Modality Manufacturing: 3D Printing, Casting, and Molding.

To expand and improve upon previously described nasal osteotomy models with the goals of decreasing cost and production time while ensuring model fidelity. To assess change in participant confidence in their understanding of and ability to perform nasal osteotomies following completion of the simulation course. Prospective study. Simulation training course for otolaryngology residents at West Virginia University. A combined methodology of 3D printing, silicone molding, and resin casting was used to design a nasal osteotomy model to address material issues such as print delamination. Multiple models were then used in a simulation lab on performing nasal osteotomies. Model utility and impact on participant confidence was assessed at baseline, postlecture, and postsimulation lab. Using a combined manufacturing methodology, we achieved a production time reduction of 97.71% and a cost reduction of 82.02% for this polyurethane resin nasal osteotomy model relative to a previously described osteotomy model. Participants in the simulation course were noted to have a significant improvement in confidence in their understanding of and ability to perform nasal osteotomies from baseline and postlecture and also from postlecture and postsimulation lab (P < .05 for all). By incorporating multiple manufacturing modalities (molding and casting) in addition to 3D printing, this study achieved a large reduction in both production time and cost in fabrication of a nasal osteotomy simulator and addressed material limitations imposed by fused deposition modelingprinters. This design methodology serves as an example on how these barriers may be addressed in unrelated simulation projects. Model fidelity was improved with addition of a silicone soft tissue midface. Improvement in participant confidence was noted following completion of the simulation lab.

Ice-Enabled Transfer of Graphene on Copper Substrates Enhanced by Electric Field and Cu2O.

Graphene films grown by the chemical vapor deposition (CVD) method suffer from contamination and damage during transfer. Herein, an innovative ice-enabled transfer method under an applied electric field and in the presence of Cu2O (or Cu2O-Electric-field Ice Transfer, abbreviated as CEIT) is developed. Ice serves as a pollution-free transfer medium while water molecules under the electric field fully wet the graphene surface for a bolstered adhesion force between the ice and graphene. Cu2O is used to reduce the adhesion force between graphene and copper. The combined methodology in CEIT ensures complete separation and clean transfer of graphene, resulting in successfully transferred graphene to various substrates, including polydimethylsiloxane (PDMS), Teflon, and C4F8 without pollution. The graphene obtained via CEIT is utilized to fabricate field-effect transistors with electrical performances comparable to that of intrinsic graphene characterized by small Dirac points and high carrier mobility. The carrier mobility of the transferred graphene reaches 9090 cm2V-1s-1, demonstrating a superior carrier mobility over that from other dry transfer methods. In a nutshell, the proposed clean and efficient transfer method holds great potential for future applications of graphene.

Understanding Digital Transformation Village Credit Institutions Towards Sustainability Based on Local Genius

This study aims to understand the digitization transformation of village credit institutions towards sustainability based on local genius. Local geniuses are fundamental values for village credit institutions to enrich traditional local citizens. This research was conducted in village credit institutions and has since been transformed into digital. This research employed the combination methodology of Participatory Action Research in developing the application and Nusantara Paradigm as a view and the method using local values in implementing the application. Data were collected through in-depth interviews with stakeholders’ village credit institutions in Bali Province, the heads of village credit institutions, which is a digital transformation. This study realizes that window-dressing is a common practice that can be described using agency theory. Window dressings can be reduced through digital transformations. Sustainability exists as an enrichment of traditional local economics based on local genius with three harmonized relationships with God, humans, and the environment. Local genius not just the values written in regulation or standard operating procedures, but internalization in daily operational village credit institutions. Sustainability existence and growth measure with the comprehensive approach, harmonization with the God in our spiritual values, harmonization with the human through transparent report can be describe with development Humanism Agency Theory on Traditional Social Community Ownership. Harmonization in an environment with practical digital transformation to save our planet. The originality of this research is that it extends agency theory type 4 in accordance with social community ownership of a new type of conflict. The combined method of Participatory Action Research in developing the application and Nusantara Paradigm in implementing a new methodology approach reflecting digital process transformation based on local values.

Computer-driven formulation development of Ginsenoside Rh2 ternary solid dispersion.

(20S)-Ginsenoside Rh2 is a natural saponin derived from Panax ginseng Meyer (P. ginseng), which showed significantly potent anticancer properties. However, its low water solubility and bioavailability strongly restrict its pharmaceutical applications. The aim of current research is to develop a modified (20S)-Ginsenoside Rh2 formulation with high solubility, dissolution rate and bioavailability by combined computational and experimental methodology. The "PharmSD" model was employed to predict the optimal polymer for (20S)-Ginsenoside Rh2 solid dispersion formulations. The solubility of (20S)-Ginsenoside Rh2 in various polymers was assessed, and the optimal ternary solid dispersion was evaluated across different dissolution mediums. Characterization techniques included the Powder X-ray diffraction (PXRD) and Fourier transform infrared spectroscopy (FTIR). Molecular dynamics simulations were employed to elucidate the formation mechanism of the solid dispersion and the interactions among active pharmaceutical ingredient (API) and excipient molecules. Cell and animal experiments were conducted to evaluate the in vivo performance of the modified formulation. The "PharmSD" solid dispersion model identified Gelucire 44/14 as the most effective polymer for enhancing the dissolution rate of Rh2. Subsequent experiment also confirmed that Gelucire 44/14 outperformed the other selected polymers. Moreover, the addition of the third component, sodium dodecyl sulfate (SDS), in the ternary solid dispersion formulation significantly amplified dissolution rates than the binary systems. Characterization experiments revealed that the API existed in an amorphous state and interacted via hydrogen bonding with SDS and Gelucire. Moreover, molecular modeling results provided additional evidence of hydrogen bonding interactions between the API and excipient molecules within the optimal ternary solid dispersion. Cell experiments demonstrated efflux ratio (EfR) of Rh2 ternary solid dispersion was lower than that of pure Rh2. In vivo experiments revealed that the modified formulation substantially improved the absorption of Rh2 in rats. Our research successfully developed an optimal ternary solid dispersion for Rh2 with high solubility, dissolution rate and bioavailability by integrated computational and experimental tools. The combination of Artificial Intelligence (AI) technology and molecular dynamics simulation is a wise way to support the future formulation development.

Combined SERS-Raman screening of HER2-overexpressing or silenced breast cancer cell lines

BackgroundBreast cancer (BC) is a heterogeneous neoplasm characterized by several subtypes. One of the most aggressive with high metastasis rates presents overexpression of the human epidermal growth factor receptor 2 (HER2). A quantitative evaluation of HER2 levels is essential for a correct diagnosis, selection of the most appropriate therapeutic strategy and monitoring the response to therapy.ResultsIn this paper, we propose the synergistic use of SERS and Raman technologies for the identification of HER2 expressing cells and its accurate assessment. To this end, we selected SKBR3 and MDA-MB-468 breast cancer cell lines, which have the highest and lowest HER2 expression, respectively, and MCF10A, a non-tumorigenic cell line from normal breast epithelium for comparison. The combined approach provides a quantitative estimate of HER2 expression and visualization of its distribution on the membrane at single cell level, clearly identifying cancer cells. Moreover, it provides a more comprehensive picture of the investigated cells disclosing a metabolic signature represented by an elevated content of proteins and aromatic amino acids. We further support these data by silencing the HER2 gene in SKBR3 cells, using the RNA interference technology, generating stable clones further analysed with the same combined methodology. Significant changes in HER2 expression are detected at single cell level before and after HER2 silencing and the HER2 status correlates with variations of fatty acids and downstream signalling molecule contents in the context of the general metabolic rewiring occurring in cancer cells. Specifically, HER2 silencing does reduce the growth ability but not the lipid metabolism that, instead, increases, suggesting that higher fatty acids biosynthesis and metabolism can occur independently of the proliferating potential tied to HER2 overexpression.ConclusionsOur results clearly demonstrate the efficacy of the combined SERS and Raman approach to definitely pose a correct diagnosis, further supported by the data obtained by the HER2 gene silencing. Furthermore, they pave the way to a new approach to monitor the efficacy of pharmacologic treatments with the aim to tailor personalized therapies and optimize patients’ outcome.

From masks to muscles: Mapping facial structure of Nycticebus.

Facial musculature in mammals underlies mastication and nonverbal communicative facial displays. Our understanding of primate facial expression comes primarily from haplorrhines (monkeys and apes), while our understanding of strepsirrhine (lemurs and lorises) facial expression remains incomplete. We examined the facial muscles of six specimens from three Nycticebus species (Nycticebus coucang, Nycticebus javanicus, and Nycticebus menagensis) using traditional dissection methodology and novel three-dimensional facial scanning to produce a detailed facial muscle map, and compared these results to another nocturnal strepsirrhine genus, the greater bushbaby (Otolemur spp.). We observed 19 muscles with no differences among Nycticebus specimens. A total of 17 muscles were observed in both Nycticebus and Otolemur, with little difference in attachment and function but some difference in directionality of movement. In the oral region, we note the presence of the depressor anguli oris, which has been reported in other primate species but is absent in Otolemur. The remaining muscle is a previously undescribed constrictor nasalis muscle located on the lateral nasal alar region, likely responsible for constriction of the nares. We propose this newly described muscle may relate to vomeronasal organ functioning and the importance of the use of nasal musculature in olfactory communication. We discuss how this combined methodology enabled imaging of small complex muscles. We further discuss how the facial anatomy of Nycticebus spp. relates to their unique physiology and behavioral ecology.

Bitcoin’s bubbly behaviors: does it resemble other financial bubbles of the past?

A number of financial bubbles have occurred throughout history. The objective of this study was to identify the main similarities between Bitcoin price behavior during bubble periods and a number of historical bubbles. Once this had been carried out, we aimed to determine whether the solutions adopted in the past would be effective in the present to reduce investors’ risk in this digital asset. This study brings a new approach, as studies have previously been conducted analyzing the similarity of Bitcoin bubbles to other bubbles individually, but these were not conducted in such a broad manner, addressing different types of bubbles, and over such a broad time period. Starting from a dataset with 9967 records, a combined methodology was used. This consisted of an analysis of the standard deviations, the growth rates of the prices of the assets involved, the percentage increase in asset prices from the origin of the bubble to its peak and its fundamental value, and, finally, the bubble index. Lastly, correlation statistical analysis was performed. The results obtained from the combination of the above methods reveal the existence of certain similarities between the Bitcoin bubbles (2011, 2013, 2017, and 2021) and the tulip bubble (1634–1637) and the Mississippi bubble (1719–1720). We find that the vast majority of the measures taken to avoid past bubbles will not be effective now; this is due to the digital and decentralized nature of Bitcoin. A limitation of the study is the difficulty in making a comparison between bubbles that occurred at different historical points in time. However, the results obtained shed light and provide guidance on the actions to be taken by regulators to ensure the protection of investors in this digital asset.

Electromagnetic noise characteristics analysis and noise traceability in induction motor

Abstract To investigate the electromagnetic vibration and noise issues in induction motors, this study employs a combined methodology of experimental, theoretical, and simulation approaches to analyze the characteristics of electromagnetic noise. Initially, a theoretical analysis of the radial electromagnetic force of the motor is conducted, followed by a simulation of the motor’s electromagnetic vibration and noise at a working condition of 3000 rpm, incorporating a Multiphysics coupled analysis of electromagnetic-vibration-noise. Vibration testing on a specific compressor motor reveals that the primary vibration noise characteristic frequency is centered around 2000 Hz. A comparison between simulation and experimental results shows a high degree of congruence, indicating the reliability of the simulation method. Subsequently, an acoustic camera kit, Brüel &amp; Kjær PULSE Reflex, based on a beamforming method, is employed for noise source identification and acoustic holography testing at 3000 rpm, which successfully localizes the noise sources. The analysis and experiments provided herein can serve as a reference for the development of electromagnetic vibration noise suppression techniques in induction motors.

Novel Approach for Predicting Vertical Electron Attachment Energies in Bulk-Solvated Molecules.

When low-energy electrons interact with molecules, they can give rise to transient anion states commonly known as resonances. These states are formed through vertical electron attachment processes and have the potential to induce various forms of DNA lesions, including base damage, single- and double-strand breaks, cross-links, and clustered lesions that are challenging to repair. So far, most experimental and theoretical studies have investigated the formation of resonances of (bio)molecules in the gas phase or in microsolvated environments. Since cellular environments are mainly composed of water molecules, it is crucial to understand how bulk water affects the resonances of (bio)molecules. Given the existing gap in studies on resonances of bulk-solvated molecules, we propose a novel theoretical-computational approach to address this void. Our approach combines the multibasis-set (time-dependent-)density functional theory and self-consistent sequential quantum mechanics/molecular mechanics polarizable electrostatic embedding methods. We apply this combined methodology to predict the vertical electron attachment energies of 1-methyl-5-nitroimidazole (1M5NI), a well-known radiosensitizer model, in bulk water. In addition, we analyze the rapid mutual polarization between the resonances (both shape- and core-excited) of 1M5NI and the surrounding bulk water environment. For comparison, we also studied the isolated and microsolvated 1M5NI. Overall, while the polarization of the environment is clearly sensitive to the solute charge, causing a significant impact on the vertical electron affinity and consequently on the attachment electron energies, it does not have a significant impact on the excitation energies of the anion.

Evaluation of Density Functional Theory-Generated Data for Infrared Spectroscopy of Novel Psychoactive Substances Using Unsupervised Learning

Novel psychoactive substances (NPSs) are compounds plotted to modify the chemical structures of prohibited substances, offering alternatives for consumption and evading legislation. The prompt emergence of these substances presents challenges in health concerns and forensic assessment because of the lack of analytical standards. A viable alternative for establishing these standards involves leveraging in silico methods to acquire spectroscopic data. This study assesses the efficacy of utilizing infrared spectroscopy (IRS) data derived from density functional theory (DFT) for analyzing NPSs. Various functionals were employed to generate infrared spectra for five distinct NPS categories including the following: amphetamines, benzodiazepines, synthetic cannabinoids, cathinones, and fentanyls. PRISMA software was conceived to rationalize data management. Unsupervised learning techniques, including Hierarchical Cluster Analysis (HCA), Principal Component Analysis (PCA), and t-distributed stochastic neighbor embedding (t-SNE), were utilized to refine the assessment process. Our findings reveal no significant disparities among the different functionals used to generate infrared spectra data. Additionally, the application of unsupervised learning demonstrated adequate segregation of NPSs within their respective groups. In conclusion, integrating theoretical data and dimension reduction techniques proves to be a powerful strategy for evaluating the spectroscopic characteristics of NPSs. This underscores the potential of this combined methodology as a diagnostic tool for distinguishing IR spectra across various NPS groups, facilitating the evaluation of newly unknown compounds.