Search Results (7,670)

Thermal cameras are becoming popular in several applications of precision agriculture, including crop and soil monitoring, for efficient irrigation scheduling, crop maturity, and yield mapping. Nowadays, these sensors can be integrated as payloads on unmanned aerial vehicles, providing high spatial and temporal resolution, to deeply understand the variability of crop and soil conditions. However, few commercial software programs, such as PIX4D Mapper, can process thermal images, and their functionalities are very limited. This paper reports on the implementation of a custom MATLAB^® R2024a script to extract agronomic information from thermal orthomosaics obtained from images acquired by the DJI Mavic 3T drone. This approach enables us to evaluate the temperature at each point of an orthomosaic, create regions of interest, calculate basic statistics of spatial temperature distribution, and compute the Crop Water Stress Index. In the authors’ opinion, the reported approach can be easily replicated and can serve as a valuable tool for scientists who work with thermal images in the agricultural sector. Full article

Glaciers and glacierets of the Kuznetsky Alatau Mountains are distributed at altitudes of 1200–1500 m above sea level, which is not typical for continental areas. The main factor contributing to the persistence of glaciation here is abundant winter precipitation. According to ground surface temperature measurements, the negative annual values are typical for upper glacier boundaries only. Since intensive study during the compilation of the USSR Glacier Inventory (1965–1980), the glaciation of the region has undergone notable changes. To assess the current state of glaciation, Sentinel-2 satellite images were used; contours of the glaciers were traced on the basis of images from 2021 to 2023. In total, 78 glaciers and 57 glacierets were identified. UAV imagery and field inspection were used for validation. The total glaciated area has reduced from 8.5 to 3.1 km², which is 50–75% for selected river basins, with slope morphological types decreasing the most. According to our opinion, the morphological classification requires clarification due to absence of hanging glaciers, described previously. Full article

Existing approaches to social media sentiment analysis typically focus on static classification, offering limited foresight into how public opinion evolves. This study addresses that gap by introducing the Multi-Feature Sentiment-Driven Forecasting (MFSF) framework, a novel pipeline that enhances sentiment trend prediction by integrating rich contextual information from text. Using state-of-the-art transformer models on the Sentiment140 dataset, our framework extracts three concurrent signals from each tweet: sentiment polarity, aspect-based scores (e.g., ‘price’ and ‘service’), and topic embeddings. These features are aggregated into a daily multivariate time series. We then employ a SARIMAX model to forecast future sentiment, using the extracted aspect and topic data as predictive exogenous variables. Our results, validated on the historical Sentiment140 Twitter dataset, demonstrate the framework’s superior performance. The proposed multivariate model achieved a 26.6% improvement in forecasting accuracy (RMSE) over a traditional univariate ARIMA baseline. The analysis confirmed that conversational aspects like ‘service’ and ‘quality’ are statistically significant predictors of future sentiment. By leveraging the contextual drivers of conversation, the MFSF framework provides a more accurate and interpretable tool for businesses and policymakers to proactively monitor and anticipate shifts in public opinion. Full article

This study investigates the effectiveness of contact and online biology teaching by assessing student performance and gathering perceptions from students, teachers, and parents. Conducted in autumn 2021 with 3035 students, 124 biology teachers, and 719 parents, this study combined post-instruction assessments of student performance in knowledge reproduction and conceptual understanding with questionnaires examining perceptions of contact and online biology teaching effectiveness across students, teachers, and parents. To investigate how various teaching-related factors influence perceived understanding of biological content, we applied a CHAID-based decision tree model to questionnaire responses from students, teachers, and parents. Results indicated that students value engaging, flexible instruction, sufficient time to complete tasks and support for independent thinking. Teachers emphasized their satisfaction with teaching and efforts to support student understanding. In contact lessons, students preferred problem-solving, teacher guidance, and a stimulating environment. In online learning, they preferred low-stress, interesting lessons with room for independent work. Parents emphasized satisfaction with their child’s learning and the importance of a focused, stimulating environment. This comparative analysis highlights the need for student-centered, research-based biology teaching in both formats, supported by teachers and delivered in a motivating environment. The results offer practical insights for improving biology instruction in different teaching modalities. Full article

Recent advancements in generative artificial intelligence have significantly progressed the field of text-to-music generation, enabling users to create music from natural language descriptions. Despite the success of various models, such as MusicLM, MusicGen, and AudioLDM, the current approaches struggle to capture fine-grained genre-specific characteristics, precisely control musical attributes, and handle underrepresented cultural data. This paper introduces a novel, lightweight fine-tuning method for the AudioLDM framework using low-rank adaptation (LoRA). By updating only selected attention and projection layers, the proposed method enables efficient adaptation to musical genres with limited data and computational cost. The proposed method enhances controllability over key musical parameters such as rhythm, emotion, and timbre. At the same time, it maintains the overall quality of music generation. This paper represents the first application of LoRA in AudioLDM, offering a scalable solution for fine-grained, genre-aware music generation and customization. The experimental results demonstrate that the proposed method improves the semantic alignment and statistical similarity compared with the baseline. The contrastive language–audio pretraining score increased by 0.0498, indicating enhanced text-music consistency. The kernel audio distance score decreased by 0.8349, reflecting improved similarity to real music distributions. The mean opinion score ranged from 3.5 to 3.8, confirming the perceptual quality of the generated music. Full article

As organizations continue to embrace digital transformation, the need for robust cybersecurity strategies has never been more critical. This paper explores the Zero Trust Architecture (ZTA) as a contemporary cybersecurity framework that addresses the challenges posed by increasingly interconnected systems. Zero Trust (ZT) operates under the principle of “never trust, always verify,” ensuring that every access request is thoroughly authenticated, regardless of the requester’s location within or outside the network. However, implementing ZT is a challenging task, requiring an adequate roadmap to prioritize the different initiatives in agreement with company culture, exposure and cyber posture. We apply multi-criteria decision analysis (MCDA) to evaluate the relative importance of various components within a ZT framework, using the Incomplete Analytic Hierarchy Process (IAHP). Expert opinions from professionals in cybersecurity and IT governance were gathered through structured questionnaires, leading to a prioritized ranking of the eight key ZT pillars, as defined by the Cybersecurity and Infrastructure Security Agency (CISA), Washington, DC, USA, along with a prioritization of the sub-elements within each pillar. The study provides actionable insights into the implementation of ZTA, helping organizations prioritize security efforts to mitigate risks effectively and build a resilient digital infrastructure. The evaluation results were used to create a prioritized framework, integrated into the ZEUS platform, developed with Teleconsys S.p.A., to enable detailed assessments of a firm’s cyber partner regarding ZT and identify improvement areas. The paper concludes by offering recommendations for future research and practical guidance for organizations transitioning to a ZT model. Full article

Simulated data created in silico using a previously reported method were sampled by bootstrapping to generate data sets for training multiple copies of an ensemble learner (i.e., a machine learning (ML) method). The posterior probabilities of class membership obtained by applying the ensemble of ML models to previously unseen validation data were fitted to a beta distribution. The shape parameters for the fitted distribution were used to calculate the subjective opinion of sample membership into one of two mutually exclusive classes. The subjective opinion consists of belief, disbelief and uncertainty masses. A subjective opinion for each validation sample allows identification of high-uncertainty predictions. The projected probabilities of the validation opinions were used to calculate log-likelihood ratio scores and generate receiver operating characteristic (ROC) curves from which an opinion-supported decision can be made. Three very different ML models, linear discriminant analysis (LDA), random forest (RF), and support vector machines (SVM) were applied to the two-state classification problem in the analysis of forensic fire debris samples. For each ML method, a set of 100 ML models was trained on data sets bootstrapped from 60,000 in silico samples. The impact of training data set size on opinion uncertainty and ROC area under the curve (AUC) were studied. The median uncertainty for the validation data was smallest for LDA ML and largest for the SVM ML. The median uncertainty continually decreased as the size of the training data set increased for all ML.The AUC for ROC curves based on projected probabilities was largest for the RF model and smallest for the LDA method. The ROC AUC was statistically unchanged for LDA at training data sets exceeding 200 samples; however, the AUC increased with increasing sample size for the RF and SVM methods. The SVM method, the slowest to train, was limited to a maximum of 20,000 training samples. All three ML methods showed increasing performance when the validation data was limited to higher ignitable liquid contributions. An ensemble of 100 RF ML models, each trained on 60,000 in silico samples, performed the best with a median uncertainty of 1.39x${10}^{-2}$ and ROC AUC of 0.849 for all validation samples. Full article

Background/Objectives: There is a growing need for autism spectrum disorder (ASD) assessment tools that are diagnostically aligned, clinically usable, and accessible across diverse service contexts. The Diagnostic Autism Spectrum Interview—Version 2 (DASI-2) is a freely available, semi-structured clinical interview mapped directly to DSM-5 and ICD-11 criteria. This pilot study aimed to adapt DASI-2 into Hungarian and explore the (1) acceptability of DASI-2 administration, (2) agreement with prior clinical ASD diagnoses, and (3) relationship between DASI-2 observational ratings and ADOS-2 classifications. Methods: Following a multistep translation procedure, DASI-2 was administered to seven children previously assessed for ASD in a multidisciplinary Hungarian clinical setting. The assessment included a parent interview, direct assessment with the child or young person, and completion of the DASI observational record (OR1–OR4). DASI diagnostic outcomes were compared with prior clinical decisions, and OR scores were analyzed in relation to ADOS-2 classifications. Results: All participants completed the DASI-2 interview in full. Agreement with prior clinical diagnosis was found in six of seven cases (κ = 0.70, indicating substantial agreement). When exploring the one non-aligned case, the divergence in diagnostic outcome was due to broader contextual information considered by the initial clinical team which influenced clinical opinion. The five participants diagnosed with ASD showed substantially higher DASI observational scores (mean = 15.26) than the two who were not diagnosed (mean = 1.57), mirroring ADOS-2 severity classifications. Conclusions: These findings support the acceptability and preliminary validity of DASI-2. Its inclusive structured observational record may provide a practical complement to resource-intensive tools such as the ADOS-2; however, further validation in larger and more diverse samples is needed. Full article

Public opinion on public healthcare rationing regarding gender identity is crucial for democratic policymaking because of public concern regarding sexual orientation, gender identity, and gender expression (SOGIE). Based on rationality theory, rationally equitable rationing depends on equity orientations and prioritizing public interest over self-interest. Specifically, equity orientations include those toward equality, need, personal contribution, and social contribution. To project public preference for public healthcare rationing, this study involved 744 Chinese people in a web survey. These participants indicated their preferences for public healthcare rationing and self-interest, public interest, and equity orientations, including those based on contribution, equality, and need. Regression analysis based on the rationality framework showed that public healthcare rationing that was equal across SOGIE identities was predominantly preferable, based on rational equity. In contrast, public healthcare rationing without considering SOGIE was less preferable, and rationing unequally across gender identities was not preferred. These results imply that affirmative and egalitarian rationing is the most rationally equitable approach. Full article

The modified Rankin Scale (mRS) is a widely used outcome measure for assessing disability in stroke care; however, its administration is often affected by subjectivity and variability, leading to poor inter-rater reliability and inconsistent scoring. Originally designed for hospital discharge evaluations, the mRS has evolved into an outcome tool for disability assessment and clinical decision-making. Inconsistencies persist due to a lack of standardization and cognitive biases during its use. This paper presents design principles for creating a standardized clinical assessment tool (CAT) for the mRS, grounded in human–computer interaction (HCI) and cognitive engineering principles. Design principles were informed in part by an anonymous online survey conducted with clinicians across Canada to gain insights into current administration practices, opinions, and challenges of the mRS. The proposed design principles aim to reduce cognitive load, improve inter-rater reliability, and streamline the administration process of the mRS. By focusing on usability and standardization, the design principles seek to enhance scoring consistency and improve the overall reliability of clinical outcomes in stroke care and research. Developing a standardized CAT for the mRS represents a significant step toward improving the accuracy and consistency of stroke disability assessments. Future work will focus on real-world validation with healthcare stakeholders and exploring self-completed mRS assessments to further refine the tool. Full article

The use of informal language on social media and the sheer volume of information make it difficult for a computer system to analyse it automatically. The aim of this work is to design a new group decision-making method that applies two new consensus methods based on sentiment analysis. This method is designed for application in the analysis of texts on social media. To test the method, we will use posts from the so called social network X. The proposed model differs from previous work in this field by defining a new degree of subjectivity and a new degree of reliability associated with user opinions. This work also presents two new consensus measures, one focused on measuring the number of words classified as positive and negative and the other on analysing the percentage of occurrence of those words. Our method allows us to automatically extract preferences from the transcription of the texts used in the debate, avoiding the need for users to explicitly indicate their preferences. The application to a real case of public investment demonstrates the effectiveness of the approach in collaborative contexts that used natural language. Full article

This study examines the development of the digital economy and society in the Republic of Kazakhstan by combining international benchmarking with a context-specific national framework. It highlights the limitations of existing global indices such as DESI, NRI, and EGDI in capturing the structural and institutional dimensions of digital transformation in emerging economies. To address this gap, the study introduces a novel composite metric, the Index of Digital Economy Development (IDED), which integrates five sub-indices: infrastructure, usage, human capital, economic digitization, and transformation effectiveness. The methodology involves comparative index analysis, the construction of the IDED, and statistical validation through a public opinion survey and regression modeling. Key findings indicate that cybersecurity is a critical yet under-represented component of digital development, showing strong empirical correlations with DESI scores in benchmark countries. The results also highlight Kazakhstan’s strengths in digital public services and internet access, contrasted with weaknesses in business digitization and innovation. The proposed IDED offers a more comprehensive and policy-relevant tool for assessing digital progress in transitional economies. This study contributes to the literature by proposing a replicable index structure and providing empirical evidence for the inclusion of cybersecurity in national digital economy assessments. The aim of the study is to assess Kazakhstan’s digital economy development by addressing limitations in global measurement frameworks. Methodologically, it combines comparative index analysis, the construction of a national composite index (IDED), and statistical validation using a regional survey and regression analysis. The findings reveal both strengths and gaps in Kazakhstan’s digital landscape, particularly in cybersecurity and SME digitalization. The IDED introduces an innovative, context-sensitive framework that enhances the measurement of digital transformation in transitional economies. Full article

Nanozymes, as a new generation of artificial enzymes, have attracted increasing attention in the field of biomedicine due to their multiple enzymatic characteristics, multi-functionality, low cost, and high stability. Among them, carbon dot nanozymes (CDzymes) possess excellent enzymatic-like catalytic activity and biocompatibility and have been developed for various diagnostic and therapeutic studies of diseases. Here, we briefly review the representative research on CDzymes in recent years, including their synthesis, modification, and applications, especially in orthopedic diseases, including osteoarthritis, osteoporosis, osteomyelitis, intervertebral disc degenerative diseases, bone tumors, and bone injury repair and periodontitis. Additionally, we briefly discuss the potential future applications and opportunities and challenges of CDzymes. We hope this review can provide some reference opinions for CDzymes and offer insights for promoting their application strategies in the treatment of orthopedic disease. Full article

Road traffic accidents are a leading cause of injury and death among adolescents, making road safety education crucial. This study assesses the performance of and users’ opinions on the Route 2 School (R2S) traffic safety education program, designed for secondary school students (13–17 years) in Belgium. The program incorporates gamified e-learning modules containing, among others, podcasts, interactive 360° visuals, and virtual reality (VR), to enhance traffic knowledge, situation awareness, risk detection, and risk management. This study was conducted across several cities and municipalities within Belgium. More than 600 students from school years 3 to 6 completed the platform and of these more than 200 students filled in a comprehensive questionnaire providing detailed feedback on platform usability, preferences, and behavioral risk assessments. The results revealed shortcomings in traffic knowledge and skills, particularly among older students. Gender-based analysis indicated no significant performance differences overall, though females performed better in risk management and males in risk detection. Furthermore, students from cities outperformed those from municipalities. Feedback on the R2S platform indicated high usability and engagement, with VR-based simulations receiving the most positive reception. In addition, it was highlighted that secondary school students are high-risk groups for distraction and red-light violations as cyclists and pedestrians. This study demonstrates the importance of gamified, technology-enhanced road safety education while underscoring the need for module-specific improvements and regional customization. The findings support the broader application of e-learning methodologies for sustainable, behavior-oriented traffic safety education targeting adolescents. Full article

Background: Veno-arterial extracorporeal membrane oxygenation (V-A ECMO) is increasingly used to support patients with refractory cardiogenic shock or cardiac arrest. While femoral artery cannulation remains the most common arterial access, axillary artery cannulation has emerged as a valuable alternative in selected cases. Objective: This narrative review aims to synthesize current evidence and expert opinion on axillary artery cannulation in V-A ECMO, focusing on its technical feasibility, physiologic implications, and clinical outcomes. Methods: A comprehensive literature search was performed in PubMed and Scopus using relevant keywords related to ECMO, axillary artery, cannulation techniques, and outcomes. Emphasis was placed on prospective and retrospective clinical studies, expert consensus statements, and technical reports published over the past two decades. Results: Axillary cannulation provides antegrade aortic flow, potentially reducing the risk of differential hypoxia and improving upper body perfusion. However, the technique presents unique technical challenges and may carry risks such as hyperperfusion syndrome or arterial complications. Emerging data suggest favorable outcomes in selected patient populations when performed in experienced centers. Conclusions: Axillary cannulation represents a promising arterial access route in V-A ECMO, particularly in cases with contraindications to femoral cannulation or when upper-body perfusion is a concern. Further prospective studies are needed to better define patient selection criteria and long-term outcomes. Full article