Precise Data Research Articles

Intrinsic reactivity of stoichiometric IrO2(1 1 0) surface toward oxidative coupling of methane

This study investigates the oxidative coupling of methane on the IrO2(110) surface using TPRS simulations informed by DFT-derived data. We discover that the efficiency of the IrO2(110) surface in generating ethylene is strongly influenced by methane surface coverage. Our simulations reveal that the presence of surface hydroxyl group enhances the yield of C2+ species from methane oxidation, but this effect is counteracted at high methane coverages due to the accelerated formation of CH2OH. In addition, the study reveals that slight modifications in energy barriers at the branching point (C2H4 formation vs. CH2OH formation) significantly affect C2H4(g) production from the simulation, underscoring the importance of precise energetic data for accurate catalytic reaction predictions. The results have broader implications for reactions and catalysts where branching point selectivity determines high-value product yields. Thus, combining surface science with computational analysis is crucial for accurately determining energy profiles of key steps at the branching points.

Special Issue on Robotics and Mechatronics Technology for Aerial Robots

Aerial robot technologies, such as drones and unmanned aerial vehicles, are rapidly advancing in various domains. These robots now possess enhanced autonomous flying abilities and use sensor technology, Global Navigation Satellite System (GNSS), Light Detection and Ranging (LiDAR) system, and cameras to avoid obstacles, automatically land, and track flights. Evolving technologies in airspace management and route optimization ensure that flights are efficient and safe. Aerial robots have a wide range of applications, such as agriculture, disaster response, environmental monitoring, and inspection. These devices excel at precise data collection thanks to their high-resolution cameras and high-precision sensors. Swarm robotics, which allows collaboration between multiple aerial and ground robots, is also progressing. This enables quick coverage of large areas, which benefits disaster relief and land management. In addition, research and development of flying robots modeled after flying animals is underway. The evolution of aerial robots results in their efficient use in a variety of fields and new applications. This special issue includes the latest research papers and development reports on aerial robotic technology from the aforementioned perspectives. We hope that this special issue will pique researchers’ and engineers’ interest in aerial robots, thereby encouraging additional research and development. Finally, we would like to express our gratitude to all authors, all reviewers, the editorial board of the Journal of Robotics and Mechatronics, and Fuji Technology Press Ltd.

Remote Monitoring of Sympathovagal Imbalance During Sleep and Its Implications in Cardiovascular Risk Assessment: A Systematic Review.

Nocturnal sympathetic overdrive is an early indicator of cardiovascular (CV) disease, emphasizing the importance of reliable remote patient monitoring (RPM) for autonomic function during sleep. To be effective, RPM systems must be accurate, non-intrusive, and cost-effective. This review evaluates non-invasive technologies, metrics, and algorithms for tracking nocturnal autonomic nervous system (ANS) activity, assessing their CV relevance and feasibility for integration into RPM systems. A systematic search identified 18 relevant studies from an initial pool of 169 publications, with data extracted on study design, population characteristics, technology types, and CV implications. Modalities reviewed include electrodes (e.g., electroencephalography (EEG), electrocardiography (ECG), polysomnography (PSG)), optical sensors (e.g., photoplethysmography (PPG), peripheral arterial tone (PAT)), ballistocardiography (BCG), cameras, radars, and accelerometers. Heart rate variability (HRV) and blood pressure (BP) emerged as the most promising metrics for RPM, offering a comprehensive view of ANS function and vascular health during sleep. While electrodes provide precise HRV data, they remain intrusive, whereas optical sensors such as PPG demonstrate potential for multimodal monitoring, including HRV, SpO2, and estimates of arterial stiffness and BP. Non-intrusive methods like BCG and cameras are promising for heart and respiratory rate estimation, but less suitable for continuous HRV monitoring. In conclusion, HRV and BP are the most viable metrics for RPM, with PPG-based systems offering significant promise for non-intrusive, continuous monitoring of multiple modalities. Further research is needed to enhance accuracy, feasibility, and validation against direct measures of autonomic function, such as microneurography.

DASR-Net: Land Cover Classification Methods for Hybrid Multiattention Multispectral High Spectral Resolution Remote Sensing Imagery

The prompt acquisition of precise land cover categorization data is indispensable for the strategic development of contemporary farming practices, especially within the realm of forestry oversight and preservation. Forests are complex ecosystems that require precise monitoring to assess their health, biodiversity, and response to environmental changes. The existing methods for classifying remotely sensed imagery often encounter challenges due to the intricate spacing of feature classes, intraclass diversity, and interclass similarity, which can lead to weak perceptual ability, insufficient feature expression, and a lack of distinction when classifying forested areas at various scales. In this study, we introduce the DASR-Net algorithm, which integrates a dual attention network (DAN) in parallel with the Residual Network (ResNet) to enhance land cover classification, specifically focusing on improving the classification of forested regions. The dual attention mechanism within DASR-Net is designed to address the complexities inherent in forested landscapes by effectively capturing multiscale semantic information. This is achieved through multiscale null attention, which allows for the detailed examination of forest structures across different scales, and channel attention, which assigns weights to each channel to enhance feature expression using an improved BSE-ResNet bilinear approach. The two-channel parallel architecture of DASR-Net is particularly adept at resolving structural differences within forested areas, thereby avoiding information loss and the excessive fusion of features that can occur with traditional methods. This results in a more discriminative classification of remote sensing imagery, which is essential for accurate forest monitoring and management. To assess the efficacy of DASR-Net, we carried out tests with 10m Sentinel-2 multispectral remote sensing images over the Heshan District, which is renowned for its varied forestry. The findings reveal that the DASR-Net algorithm attains an accuracy rate of 96.36%, outperforming classical neural network models and the transformer (ViT) model. This demonstrates the scientific robustness and promise of the DASR-Net model in assisting with automatic object recognition for precise forest classification. Furthermore, we emphasize the relevance of our proposed model to hyperspectral datasets, which are frequently utilized in agricultural and forest classification tasks. DASR-Net’s enhanced feature extraction and classification capabilities are particularly advantageous for hyperspectral data, where the rich spectral information can be effectively harnessed to differentiate between various forest types and conditions. By doing so, DASR-Net contributes to advancing remote sensing applications in forest monitoring, supporting sustainable forestry practices and environmental conservation efforts. The findings of this study have significant practical implications for urban forestry management. The DASR-Net algorithm can enhance the accuracy of forest cover classification, aiding urban planners in better understanding and monitoring the status of urban forests. This, in turn, facilitates the development of effective forest conservation and restoration strategies, promoting the sustainable development of the urban ecological environment.

Blockchain as Electronic Evidence Against Crypto Crimes in Indonesia

In the context of digital security, the utilization of blockchain technology as a means of evidence against crypto crimes has become an exceedingly crucial topic. This research elucidates whether the admissible evidence tool stipulated in Article 184 paragraph (1) of the criminal procedural law encompasses proof relating to crypto crimes and how the decentralized structure and transparent nature of blockchain can aid in furnishing accurate and credible evidence pertaining to crypto crimes. This study offers profound insights into the potential of blockchain concerning evidence provision and prevention of crypto crimes. The author employs normative research, a process aimed at uncovering legal rules, principles, and doctrines to address legal issues encountered. Based on the discussion, it can be concluded that blockchain can serve as an electronic evidence tool in crypto crimes. Aligned with the decentralized and transparent nature of blockchain, it can provide precise and permanent data.

Evaluating Medical Entity Recognition in Health Care: Entity Model Quantitative Study.

Named entity recognition (NER) models are essential for extracting structured information from unstructured medical texts by identifying entities such as diseases, treatments, and conditions, enhancing clinical decision-making and research. Innovations in machine learning, particularly those involving Bidirectional Encoder Representations From Transformers (BERT)-based deep learning and large language models, have significantly advanced NER capabilities. However, their performance varies across medical datasets due to the complexity and diversity of medical terminology. Previous studies have often focused on overall performance, neglecting specific challenges in medical contexts and the impact of macrofactors like lexical composition on prediction accuracy. These gaps hinder the development of optimized NER models for medical applications. This study aims to meticulously evaluate the performance of various NER models in the context of medical text analysis, focusing on how complex medical terminology affects entity recognition accuracy. Additionally, we explored the influence of macrofactors on model performance, seeking to provide insights for refining NER models and enhancing their reliability for medical applications. This study comprehensively evaluated 7 NER models-hidden Markov models, conditional random fields, BERT for Biomedical Text Mining, Big Transformer Models for Efficient Long-Sequence Attention, Decoding-enhanced BERT with Disentangled Attention, Robustly Optimized BERT Pretraining Approach, and Gemma-across 3 medical datasets: Revised Joint Workshop on Natural Language Processing in Biomedicine and its Applications (JNLPBA), BioCreative V CDR, and Anatomical Entity Mention (AnatEM). The evaluation focused on prediction accuracy, resource use (eg, central processing unit and graphics processing unit use), and the impact of fine-tuning hyperparameters. The macrofactors affecting model performance were also screened using the multilevel factor elimination algorithm. The fine-tuned BERT for Biomedical Text Mining, with balanced resource use, generally achieved the highest prediction accuracy across the Revised JNLPBA and AnatEM datasets, with microaverage (AVG_MICRO) scores of 0.932 and 0.8494, respectively, highlighting its superior proficiency in identifying medical entities. Gemma, fine-tuned using the low-rank adaptation technique, achieved the highest accuracy on the BioCreative V CDR dataset with an AVG_MICRO score of 0.9962 but exhibited variability across the other datasets (AVG_MICRO scores of 0.9088 on the Revised JNLPBA and 0.8029 on AnatEM), indicating a need for further optimization. In addition, our analysis revealed that 2 macrofactors, entity phrase length and the number of entity words in each entity phrase, significantly influenced model performance. This study highlights the essential role of NER models in medical informatics, emphasizing the imperative for model optimization via precise data targeting and fine-tuning. The insights from this study will notably improve clinical decision-making and facilitate the creation of more sophisticated and effective medical NER models.

A standardised open science framework for sharing and re-analysing neural data acquired to continuous stimuli

Neurophysiology research has demonstrated that it is possible and valuable to investigate sensory processing in scenarios involving continuous sensory streams, such as speech and music. Over the past 10 years or so, novel analytic frameworks combined with the growing participation in data sharing has led to a surge of publicly available datasets involving continuous sensory experiments. However, open science efforts in this domain of research remain scattered, lacking a cohesive set of guidelines. This paper presents an end-to-end open science framework for the storage, analysis, sharing, and re-analysis of neural data recorded during continuous sensory experiments. We propose a data structure that builds on existing custom structures (Continuous-event Neural Data), providing a precise naming convention and data types, as well as providing a workflow for storing and loading data in the general-purpose BIDS structure. The framework has been designed to interface easily with existing toolboxes, such as EelBrain, NapLib, MNE, and the mTRF-Toolbox. We present guidelines by taking both the user view (how to rapidly re-analyse existing data) and the experimenter view (how to store, analyse, and share), making the process as straightforward and accessible. Additionally, we introduce a web-based data browser that enables the effortless replication of published results and data re-analysis.

Anatomical mapping of the 4th intercostal nerve's lateral cutaneous branch in both sexes: implications for advanced breast Reconstruction.

This study aims to map the point of emergence of the lateral cutaneous branch of the fourth intercostal nerve in both sexes, addressing gaps in the current literature. The findings are intended to support surgeons in restoring breast function by providing precise anatomical data, regardless of the specific surgical technique employed. An anatomical study was conducted using 50 sides from 25 formalin-preserved full-body donors, with equal representation of male and female subjects. Measurements were taken to determine the fascial point of emergence of the lateral cutaneous branch of the fourth intercostal nerve relative to the anterior axillary line and the transverse line. Multiple linear regression models were applied to evaluate the influence of variables such as sex, height, and side. Our findings indicate that the fascial point of emergence of the lateral cutaneous branch of the fourth intercostal nerve is consistently located within 20 to 24mm relative to the transverse line in 37 (74%) cases, and within 4 to 10mm relative to the anterior axillary line in 39 (78%) cases, regardless of sex, height, or side. The multiple linear regression analysis showed no significant correlation between these variables and the position of the point of emergence (p > 0.05). These results provide surgeons with a practical tool for nerve coaptation during breast reconstruction, showing that sex and height do not affect the anatomical localization of the point of emergence. Precise knowledge of this point can improve surgical accuracy and enhance sensory recovery, leading to better patient outcomes.

Hair-YOLO: a hair follicle detection model based on YOLOv8

Abstract Hair follicle detection technology has developed rapidly in recent years. Traditional manual detection methods are labor-intensive and inefficient. To address this problem, we propose a real-time hair follicle detection model called Hair-YOLO based on YOLOv8. This model focuses on accurately identifying the number of hairs within each follicle, providing precise data that help doctors assess hair density and follicle health of patients. First of all, we incorporate the re-parameterization Ghost module into the backbone, reducing the parameters and computational load. Then, the deformable convolution v3 operator is integrated into the neck network, enhancing adaptation to follicle shapes. Next, we propose a novel multi-scale feature perception separated and enhancement attention (Multi-SEAM) module, building upon the SEAM module, to address complex scalp scenarios. Furthermore, we enhance bounding box regression by replacing the standard complete intersection over union loss, with a modified point distance intersection over union loss. Finally, we construct a new hair follicle dataset and use it for a comparative analysis of Hair-YOLO and established models. Our model shows excellent performance, with a 14.26% increase in mAP@0.5:0.95, a 2.98% increase in Recall, and a 4.31% increase in Precision compared to the baseline.

3D Surface Velocity Field Inferred from SAR Interferometry: Cerro Prieto Step-Over, Mexico, Case Study

The Cerro Prieto basin, a tectonically active pull-apart basin, hosts significant geothermal resources currently being exploited in the Cerro Prieto Geothermal Field (CPGF). Consequently, natural tectonic processes and anthropogenic activities contribute to three-dimensional surface displacements in this pull-apart basin. Here, we obtained the Cerro Prieto Step-Over 3D surface velocity field (3DSVF) by accomplishing a weighted least square algorithm inversion from geometrically quasi-orthogonal airborne UAVSAR and RADARSAT-2, Sentinel 1A satellite Synthetic Aperture-Radar (SAR) imagery collected from 2012 to 2016. The 3DSVF results show a vertical rate of 150 mm/yr and 40 mm/yr for the horizontal rate, where for the first time, the north component displacement is achieved by using only the Interferometric SAR time series in the CPGF. Data integration and validation between the 3DSVF and ground-based measurements such as continuous GPS time series and precise leveling data were achieved. Correlating the findings with recent geothermal energy production revealed a subsidence rate slowdown that aligns with the CPGF’s annual vapor production.

Long-term climate-based sizing and economic assessment of air-water heat pumps for residential heating

This study presents a novel methodology for sizing air–water heat pumps (ASHP) for space heating and hot water supply in buildings. Utilizing long-term local climatic data, the developed tool determines the coefficient of performance (COP) and feasibility of ASHP. The sizing algorithm incorporates peak heat demand, unitary final energy demand, and seasonal heat demand across various detached house sizes. Annual local heating system operation time and key economic indicators are derived for each case across 3 European countries. A new approach is precise investigation of 10-year outdoor temperatures and apply it to heat pump (HP) modeling, which enabled a distinction between so-called warm and cold years. The results underscore substantial variations in net present value (NPV) between warm and cold years, illustrating the critical influence of precise climate data on the performance of HPs. The study revealed that for a 160 m2 house in Poland, annual electricity consumption by HPs ranged from 3736 kWh during warm years to 12,908 kWh during cold years, showing a significant variation in performance based on climate conditions. The comprehensive tool allows for precise adjustments of heating systems to actual local climate conditions, improving both economic and environmental outcomes. This comparative assessment aids in the optimal selection of HPs, ensuring energy efficiency and sustainability in residential heating systems. Building on studies from various climatic regions, this work addresses the knowledge gap in performance and explores the feasibility of HP in a changing climate using precise modeling techniques.

Managing perceptions on the linguistic terms of qualitative scales

In this paper, we consider the problem of how individuals perceive the ordinal proximities between the linguistic terms of an ordered qualitative scale. We address the challenge of accurately measuring these ordinal proximities in ordered qualitative scales used in surveys. To tackle this issue, we have devised a visual procedure, managed through sliders, and an appropriate software that generates the metrizable ordinal measure which represents the ordinal arrangement of the proximities between the linguistic terms of an ordered qualitative scale. The procedure has been applied to real-world survey data to validate its effectiveness. The results indicate that the new proposal significantly outperforms traditional techniques in terms of accuracy and reliability, providing a robust tool for researchers in fields requiring precise ordinal data analysis.

Microstructural Study of Medieval Crucible Steels from Archaeological Sites in Central and Northwest Asia: Identifying the Bulat

The microstructure of 9th–15th century artifacts made of crucible steel, found at sites in Central and Northwest Asia, is described. Metallographic study of items from settlements and burials with precise data on chronology, location, and accompanying artifacts is important for reconstructing the history of bulat steel and the technology of melting and processing ultra-high-carbon crucible steel. The study of the macro- and microstructure, and the chemical analysis of such items indicate an extremely high content of carbon—1.7–2.1 %. The characteristic feature of their microstructure is a dark matrix with white inclusions of ledeburite and iron carbides. The combination of structural components is reflected in the patterned structure of the metal. These properties suggest that such metal is identical to bulat steel. Findings of macrostructural analysis extend our knowledge of the varieties of this metal, its structural features, phase composition of separate groups of ultra-high-carbon crucible steel, smelting technology, plastic and thermal treatment, and physical properties.

Ensuring Trust: The Science and Technology Behind Food Authentication and Reliability

Food authentication of halal food, which includes the manufacturing technique, technical processing, identification of undeclared components, and species replacement in halal food items, has increased rapidly as a result of growing public awareness of food quality and safety. This highlights the need for in-depth investigation into analytical techniques to produce precise and trustworthy data for observing and managing the authenticity of halal food. This study offers a concise, objective summary of current research on the analytical methods applied to the evaluation of the authenticity of halal food.

Beyond the binary female/male sex classification: The impact of (trans)gender on the identification of human remains.

In cases of unidentified deceased persons, sex determination is a routine task in forensic medicine. However, the binary biological sex categories 'female' and 'male' may be challenged if it is not clear whether the information in the missing persons databases refers to the biological sex or the (felt and lived) gender. An umbrella term for people who do not identify with their birth sex (which usually is the biological, chromosomal sex) is 'transgender'. In recent decades, the legal and social situation of transgender people has changed in many countries making it easier to live their felt gender more openly. This development highlights the issue of potential challenges in the postmortem identification of transgender individuals. Serious problems in corresponding cases may be rare-but they must be considered and addressed in forensic practice to minimize the risk of delayed or failed identification. The impact of (trans)gender on the identification of human remains was examined by a narrative literature review under special consideration of the prevalences of transgender identities in general populations and in the group of unidentified deceased; possible actions to avoid problems in the postmortem identification of transgender persons in forensic practice are being proposed. One can assume that 1 of 200 people in the United States, the European Union and comparable societies is transgender with an opposite-sex identification, and 2 to 3 of 100 people live outside the typical female/male binary, with numbers increasing. If legally possible, an increasing number of transgender individuals will change their name and gender in civil registration. Transgender individuals are likely to be overrepresented in suicides and in victims of homicides. Although there are no precise data on the prevalence of transgender individuals in the group of unidentified deceased, the remarkably high reported prevalence in the general population and the over-representation of transgender individuals in suicides and homicides suggest that the topic is relevant to forensic practice. An autopsy does not always provide evidence of transgender identity, for example in skeletal remains. Particularly in unsolved cases, the possibility that an unidentified person may have been transgender should be considered. Knowledge and awareness of forensic practitioners on this topic should be strengthened by research and training. Databases and data reporting should be optimized. Recording in antemortem databases should clearly distinguish between 'biological sex' and 'apparent sex /lived gender identity'. When collecting postmortem data, a clear distinction should be made between "chromosomal sex" and "sex based on morphological findings". CLINICAL TRIAL NUMBER: Not applicable (review article).

Enhancing Planning for Autonomous Driving via an Iterative Optimization Framework Incorporating Safety-Critical Trajectory Generation

Ensuring the safety of autonomous vehicles (AVs) in complex and high-risk traffic scenarios remains a critical unresolved challenge. Current AV planning methods exhibit limitations in generating robust driving trajectories that effectively avoid collisions, highlighting the urgent need for improved planning strategies to address these issues. This paper introduces a novel iterative optimization framework that incorporates safety-critical trajectory generation to enhance AV planning. The use of the HighD dataset, which is collected using the wide-area remote sensing capabilities of unmanned aerial vehicles (UAVs), is fundamental to the framework. Remote sensing enables large-scale real-time observation of traffic conditions, providing precise data on vehicle dynamics, road structures, and surrounding environments. To generate safety-critical trajectories, the decoder within the conditional variational auto-encoder (CVAE) is innovatively designed through a data-mechanism integration method, ensuring that these trajectories strictly adhere to vehicle kinematic constraints. Furthermore, two parallel CVAEs (Dual-CVAE) are trained collaboratively by a shared objective function to implicitly model the multi-vehicle interactions. Inspired by the concept of “learning to collide”, adversarial optimization is integrated into the Dual-CVAE (Adv. Dual-CVAE), facilitating efficient generation from normal to safety-critical trajectories. Building upon this, these generated trajectories are then incorporated into an iterative optimization framework, significantly enhancing the AV’s planning ability to avoid collisions. This framework decomposes the optimization process into stages, initially addressing normal trajectories and progressively tackling more safety-critical and collision trajectories. Finally, comparative case studies of enhancing AV planning are conducted and the simulation results demonstrate that the proposed method can efficiently enhance AV planning by generating safety-critical trajectories.

Autonomous Underwater Vehicle Navigation Enhancement by Optimized Side-Scan Sonar Registration and Improved Post-Processing Model Based on Factor Graph Optimization

Autonomous Underwater Vehicles (AUVs) equipped with Side-Scan Sonar (SSS) play a critical role in seabed mapping, where precise navigation data are essential for mosaicking sonar images to delineate the seafloor’s topography and feature locations. However, the accuracy of AUV navigation, based on Strapdown Inertial Navigation System (SINS)/Doppler Velocity Log (DVL) systems, tends to degrade over long-term mapping, which compromises the quality of sonar image mosaics. This study addresses the challenge by introducing a post-processing navigation method for AUV SSS surveys, utilizing Factor Graph Optimization (FGO). Specifically, the method utilizes an improved Fourier-based image registration algorithm to generate more robust relative position measurements. Then, through the integration of these measurements with data from SINS, DVL, and surface Global Navigation Satellite System (GNSS) within the FGO framework, the approach notably enhances the accuracy of the complete trajectory for AUV missions. Finally, the proposed method has been validated through both the simulation and AUV marine experiments.

Diagnostic accuracy of rapid antigen tests for detection of Delta and Omicron variants of severe acute respiratory syndrome coronavirus 2: Direct evidence from prospective clinical settings.

Despite widespread application during the coronavirus disease-19 pandemic, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection using patient-performed rapid antigen tests (RATs) is limited, especially regarding the Delta and Omicron variants. Therefore, in this study, we evaluated the performance of RATs in identifying Delta and Omicron infections in self-test settings. In this multicenter clinical performance study conducted in Korea between November 2021 and February 2022, we included participants without prior diagnostic device experience. Using 2 RAT types, we compared the results with real-time reverse transcriptase-polymerase chain reaction testing, focusing on clinical sensitivity and specificity. Reverse transcriptase-polymerase chain reaction helped confirm 77 SARS-CoV-2 infections among 280 participants. RATs exhibited high positive agreement for Omicron detection but lower rates for Delta, especially among partially vaccinated individuals. This study provides direct evidence that RATs, originally developed for ancestral strains of SARS-CoV-2, effectively detect major variants such as Delta and Omicron in real patient/clinical settings. By confirming variant presence through sequencing, our research offers significant and novel insights into the performance of RATs, particularly in the context of breakthrough infections postvaccination, with precise data on vaccination status and timing obtained from government records.

Chemical Composition and Spectral Variation in Gem-Quality Blue Iron-Bearing Tourmaline from Brazil

This study, conducted a spectroscopic analysis of 10 gem-quality blue tourmaline samples from Minas Gerais, Brazil, focused on detailed variations in their infrared, Raman, and UV-VIS spectra. Conventional gemological tests, electron-probe microanalysis, infrared spectroscopy (mid- and near-infrared), Raman spectroscopy, and UV-visible spectroscopy were used to systematically analyze the chemical composition and spectral characteristics of the samples. The infrared spectra revealed vibrations of [YO6], [TO4], [BO3], [OH], and H2O groups, indicating different bonding profiles, with the [OH] vibrational frequency showing a direct correlation with FeO and MnO content. The Raman spectra primarily reflected the stretching vibrations of metal–oxygen bonds and hydroxyl groups, indicating the complexity of the local environment in the crystal structure. The UV-VIS spectra showed that the broad absorption band around 725 nm was due to intermetallic charge transfer between Fe2+ and Fe3+. This work provides new insights into the local bonding environment within the crystal structure by providing precise spectral data of natural blue tourmaline, and a more accurate classification and evaluation of blue tourmaline through fine spectral change characteristics related to crystal chemistry has important implications for both academic research and the gemstone industry.

A hybrid-fuzzy-decision-making framework for digital technologies selection

In the era of Industry X.0, manufacturers are increasingly adopting digital technologies such as robotics, big data, mobile communications, and information technology to enhance productivity and profitability. However, navigating this digital revolution presents significant challenges, particularly in selecting the most suitable technologies amidst uncertainties and the lack of precise quantitative data. Fuzzy logic, initially introduced to manage vagueness and imprecision, has proven to be an essential tool in various domains for handling these uncertainties. However, as systems and environments become more complex, traditional fuzzy logic exhibits limitations in effectively representing and managing deeper levels of uncertainty. This realization has driven the development of several extensions to fuzzy logic, designed to address challenges associated with uncertainty representation. Yet, a central question remains: Which fuzzy logic extension is the most relevant for better dealing with uncertainty? This study introduces a hybrid-fuzzy decision-making framework for technology selection, which allows for more than one representation of fuzzy information. Using a multi-expert, multi-criteria decision-making (MCDM) strategy, the study seeks to support organizations in selecting the most suitable digital technology for their operations, particularly when precise quantitative data is unavailable, and decisions must rely on expert opinions. The suggested framework is designed to handle decision-makers' hesitancy, subjectivity, and ambiguity in the decision-making process. To showcase the practical application of the proposed methodology, a case study was conducted to select appropriate technologies in the Moroccan automotive sector. The results demonstrate that, regardless of the fuzzy logic extension used, digital automation and automatic identification consistently rank among the top technologies, underscoring their importance according to most experts. Our proposed approach is particularly effective in resolving conflicts between different fuzzy logic extensions, thereby facilitating the ranking of the remaining technologies. Notably, additive manufacturing ranked third, followed by the other technologies.