Innovative Analysis Method Research Articles

Drought trend and variability based wavelet transform in Euphrates-Tigris Basin, Türkiye

This paper presents an analysis of trends in drought variables at 29 stations across the Euphrates-Tigris Basin over the period 1965–2022. Drought variables were calculated using the Standardized Precipitation Index (SPI) for 1-, 3-, 6- and 12-month scales. The drought values have been decomposed for these time scales through application of Discrete Wavelet Transform (DWT). The components are defined as short, medium and long periodicities. DWT-based trend analysis was computed using Mann-Kendall (MK) and Innovative Trend Analysis (ITA) methods. As the time scale and the periodicity of drought increase, a strong increase in drought occurs. It was revealed that drought increased significantly at all time scales in almost all stations, especially in the long periodicity, according to both MK and ITA. The droughts and the serious increasing trend of droughts in this basin constitute a significant threat to water resources.

Multi-physics coupling simulation and design of magnetic field-driven soft microrobots in liquid environments

Magnetic field-driven soft microrobots have widespread applications in biomedical science, microfluidic chips, nanoengineering, and various other domains. However, the existing methods for designing such magnetic-driven flexible robots largely rely on experimental trial and error or steady-state numerical simulated results, which fall short of meeting the intricate requirements for magnetic field editing and magnetic domain distribution. Addressing this issue, a multi-physics coupling numerical analysis method encompassing Magnetic-Fluid-Solid mechanics was developed. A complete process for transient numerical simulation resolution has been achieved. Utilizing this analysis method, the motion patterns of two typical prototypes of magnetic-driven miniature soft bionic robots, a bionic midge larvae robot, and a bionic jellyfish robot, have been analyzed. The central role of the magnetic field in driving these robotic designs—causing deformation in microrobots by designing magnetic domains and spinning the microrobots by controlling the magnetic field—has been revealed. We developed a theoretical model of a bionic fish tail fin robot driven solely by a rotating magnetic field and conducted comparative studies on the swimming efficiency, flexibility, stability, and relative advantages of the three types of robots. The impact of different magnetic domain distribution rules and different magnetic field driving methods on a robot's performance is analyzed, validating that higher swimming efficiency can be achieved by designing a robot's magnetic domains so that they undergo magnetic displacement under magnetic torque resulting in deformation. This innovative analysis method holds potential to provide valuable references for designing motion patterns of magnetically driven microrobots within liquid environments, thereby deepening our understanding toward various complex gait mechanisms involved in biological swimming.

Evaluation of Historic Trends for Monthly, Seasonal, and Annual Rainfall Series of Tenkasi, Tamil Nadu, India

Analyzing the variability and trends of rainfall plays a major role in water resource planning and management. Changes in rainfall patterns significantly influence the water availability in the irrigation structures and agronomical practices of crops. This study aimed to investigate the trends and estimate the magnitudes of monthly, seasonal, and annual rainfall series in Tenkasi, Tamil Nadu, India, using 49 years of rainfall data from 1971 to 2019. Sen’s Innovative trend analysis (ITA) method, Mann-Kendall (MK), and Simple Linear Regression (SLR) test were applied to assess the trends at 5 and 10 % significance levels. Trend magnitudes were estimated by Sen’s slope estimator (SSE) and SLR method. Changes in rainfall magnitude with mean values in percentage were estimated for all three slope estimation methods. The results revealed that the ITA method detected more significant trends of rainfall series over other methods. Significant downward trends were exhibited by October and north east monsoon (NEM) rainfall series had a trend magnitude of -0.43 mm/year and -0.04 mm/year. The percentage change in magnitude of the trend from mean values for October and the NEM series was -11.5% and -0.39%. Sub-trends within the October and NEM rainfall series showed that the low rainfall sub-series exhibited no trend, medium and high rainfall sub-series showed a downward trend. This study concluded that, in comparison to traditional trend analysis methods, the ITA method demonstrated a more rigorous investigation of trends. The significant decrease in rainfall during the NEM necessitates greater attention to water resources planning and management, serving as valuable scientific information for both crop planning and water resource management.

Physiological Data for User Experience and Quality of Experience: A Systematic Review (2018–2022)

The evaluation of human responses in multimedia experiences using physiological data has a well-established presence in the academic literature. However, this field is currently undergoing transformative changes, driven by the accessibility of diverse and cost-effective devices, innovative software analysis methods, and the emergence of novel application domains such as Virtual and Augmented Reality and mulsemedia. To address the imperative of contextualizing these evolving trends in a contemporary context, this paper presents a systematic review with the objective of delineating the array of physiological data utilized in assessing Quality of Experience (QoE) and User Experience (UX) in multimedia studies. It also examines the devices employed for data collection and the analytical techniques applied to interpret the acquired data. While our review exposes both constraints and promising discoveries in these domains, it also emphasizes the escalating significance and practicality of leveraging physiological data in user assessments, especially as the boundaries between the physical and digital domains continue to blur.

Climatic Data Trend Analysis by Mann Kendal Test and Innovative Trend Analysis: A Case Study of Taloqan River at Takhar, Afghanistan.

Climate change is taking place due to increased population and anthropogenic activities. The impacts of climate change have increased the demand for sustainable management of water resources. The semi-arid climatic zone is widely affected by climate change. Afghanistan is vulnerable to climate change because of its climate and variable precipitation. This research has studied the trend analysis of climatic data in the Taloqan River, which is being utilized for drinking and irrigation. The recorded data of the Tangi-Farkhar hydro meteorological station from 2008 to 2021 is used in this work. The non-parametric Mann-Kendall (MK) trend test and the Innovative Trend Analysis (ITA) method are utilized in this investigation. This study has found an increasing trend in the mean value of temperature in May across the period of study but no trend in annual temperature data according to the MK test with a 95 percent confidence level. The ITA shows an increasing trend in the annual temperature data, a decreasing trend in precipitation, and a non-monotonic negative increasing trend in discharge. The MK test has presented no trend in the annual data of precipitation and discharge and showed an increasing trend in the mean discharge value in October and November across the study period.

Coupling effect between magnetic wires and its influence on high gradient magnetic separation performance

High gradient magnetic separation (HGMS) is effective for the separation of weakly magnetic minerals, and this method is achieved through the use of matrix, which is made of huge numbers of rod wires. So that the coupling effect of magnetic field and flow field between wires has a marked effect on the HGMS performance. In the investigation, the coupling effect between magnetic wires and its influence on high gradient magnetic separation performance were theoretically described and simulated using COMSOL Multiphysics. It is found that the magnetic field round a wire would be affected by the neighboring wires, and then a coupling effect of magnetic field between wires was produced, increasing the magnetic induction intensity on the upstream and downstream of wire surface. And the coupling effect of flow field could increase the slurry velocity at the regions of the wire surface with azimuth angles of 0° and 90°, which is beneficial for the selective capture of wire. These simulated results were basically validated with the experimental separation, using an innovative Magnetic Capture Analysis Method. It is found that the wire spacing has significant effect on the coupling effect of magnetic wires, and a critical spacing for wires could achieve an excellent coupling effect, which is beneficial for the improvement of HGMS performance. This investigation contributes to improve HGMS performance in concentrating fine weakly magnetic ores.

The effect of climate change on energy generated at hydroelectric power plants: A case of Sakarya river basin in Turkey

In this study, the effect of climate change on energy data produced in HEPPs in Sakarya River basin of Turkey was investigated. Data were analysis by using classical and modern analysis methods. Before applying these methods, serial correlation coefficients of the data were calculated and it was decided whether trend methods would be applied or not. As a result of the study, while a decreasing trend was observed in Mann-Kendall (50 % of data) and Innovative Trend Analysis (ITA) (90 % of data) methods in energy and streamflow data, unstable trends were observed in Three-Dimensional Innovative Trend Analysis (3D-ITA) method (70 % of data). However, a stable decreasing trend was observed in the middle region in energy and streamflow data of Pamukova HEPP. No trend was generally observed in all methods in precipitation data of Sakarya province. Stable trends were observed in the 3D-ITA method (In 67 % of the region). A generally increasing trend was observed in all methods in temperature data of Sakarya province. Stable trends were observed in the 3D-ITA method (In 67 % of the region). The beginning of the trend in Mann-Kendall Rank Correlation method in both energy-streamflow data and precipitation-temperature data showed the second half of 2016.

Innovative mixed finite element method for bending analysis of functionally graded beams: modelling, validation, and applications

In this research, we consider solutions for the Euler-Bernoulli theory (CBT) and the theory of Timoshenko (TBT). It presents an innovative mixed finite element method tailored for analysing beams with functionally graded properties, focusing on their behaviour under bending loads. The method employs an isoparametric formulation in natural coordinates, enabling precise modelling of complex geometries common in structural engineering and materials science. A significant contribution is extending the mixed finite element approach to assess the bending behaviour of functionally graded beams. Hence, it is vital to understand material responses to external forces. To illustrate its effectiveness and versatility, we provide two numerical examples involving cantilever and simply-supported isotropic beams with property variations along the material’s thickness, following power-law distributions. Additionally, a third example features a cantilever made from isotropic functionally graded material with quadratic property changes through the thickness. The method’s robustness and credibility are established through rigorous validation against numerical and analytical solutions found in existing literature. This validation confirms the accuracy and reliability of the mixed finite element method for assessing functionally graded materials under bending conditions, enhancing its utility in structural analysis. This research introduces a potent numerical tool for investigating the behaviour of functionally graded materials subjected to bending, providing valuable implications for engineering and materials science applications.

Bitemarks and 3D scanner: An objective comparison for bitemarks. A pilot study

The marks left by the bite of a subject (bitemark) represent an unequivocal character, being useful in identifying the possible perpetrator of a crime. To date, the analysis of bitemarks is mainly based on the direct vision of photographic finds and on the visual comparison with the teeth of the hypothetically responsible person. However, the bitemark also retains three-dimensional characteristics detectable with a 3D scanner. In this pilot study, an innovative method of bitemark analysis, utilizing a three-dimensional scanner and some software, will be introduced, enabling a quantitative comparison of bitemarks and their corresponding human dentitions. For this purpose, 10 complete plaster models (human dentitions) of 10 adult subjects were used to make 20 experimental bitemarks on dentistry wax. All materials were individually scanned with the iTero® 3D scanner and reworked with MeshMixer software. A visual analysis of the characteristics and a computerized analysis with the CloudCompare software were also performed. Study showed a different trend of the distribution curves of the points which were obtained comparing the human dentition and coincident and non-coincident bitemarks. Current results support that the intraoral 3D scanner allows the fast record and the preservation of the three-dimensional characteristics of the bitemarks, and it allows computerized analyses to be carried out.

Discourse factor of semantic uncertainty in reverse machine translation of media texts

The article is devoted to the study of semantic uncertainty in the texts of legal and media discourses using online programs. The object of the study is conflict factors caused by semantic uncertainty in problem areas. An assessment and description of problem areas in machine translation of media texts was carried out, taking into account lexical ambiguity. The prerequisites for possible conflict situations when translating texts on legal and environmental topics are described. In achieving this goal, both traditional linguistic and innovative methods of analysis (use of artificial intelligence technologies) were used. The characteristics of the philosophical category of uncertainty are given according to the following methodology: 1) assessment of the semantic uncertainty of reverse machine translation texts, taking into account the discursive factor; 2) comparison of legislative and media texts for their similarity to the original after machine translation. The comparative analysis took into account the following parameters: the total number of words in the texts; similarity of texts in percentage terms; presence of various fragments. It is shown that discursive and linguistic means of realizing semantic uncertainty have a significant impact on the reader’s perception of the text. A comparison of two discourses of the same thematic focus on environmental management revealed the influence of the context (discourse) and the intermediary language on the degree of translatability of the text. It has been established that semantic uncertainty and tension zones as conflict-generating factors depend on the quality of translation programs. The study took into account possible errors associated with the limited capabilities of the online programs used. Prospects for studying problem areas in reverse machine translation include expanding sources of empirical material, involving other computer-assisted translation systems, and the possibility of applying research results in an expanded context of “potential conflict”.

Human Genetics of Cardiac Arrhythmias.

Inherited forms of cardiac arrhythmias mostly are rare diseases (prevalence <1:2000) and considered to be either "primary electrical heart disorders" due to the absence of structural heart abnormalities or "cardiac ion channel disorders" due to the myocellular structures involved. Precise knowledge of the electrocardiographic features of these diseases and their genetic classification will enable early disease recognition and prevention of cardiac events including sudden cardiac death.The genetic background of these diseases is complex and heterogeneous. In addition to the predominant "private character" of a mutation in each family, locus heterogeneity involving many ion channel genes for the same familial arrhythmia syndrome is typical. Founder pathogenic variants or mutational hot spots are uncommon. Moreover, phenotypes may vary and overlap even within the same family and mutation carriers. For the majority of arrhythmias, the clinical phenotype of an ion channel mutation is restricted to cardiac tissue, and therefore, the disease is nonsyndromic.Recent and innovative methods of parallel DNA analysis (so-called next-generation sequencing, NGS) will enhance further mutation and other variant detection as well as arrhythmia gene identification.

Research on fruit shape database mining to support fruit class classification using the shuffled frog leaping optimization (SFLO) technique

&lt;abstract&gt;&lt;p&gt;Association rule mining (ARM) is a technique for discovering meaningful associations within databases, typically handling discrete and categorical data. Recent advancements in ARM have concentrated on refining calculations to reveal connections among various databases. The integration of shuffled frog leaping optimization (SFLO) processes has played a crucial role in this pursuit. This paper introduces an innovative SFLO-based method for performance analysis. To generate association rules, we utilize the apriori algorithm and incorporate frog encoding within the SFLO method. A key advantage of this approach is its one-time database filtering, significantly boosting efficiency in terms of CPU time and memory usage. Furthermore, we enhance the optimization process's efficacy and precision by employing multiple measures with the modified SFLO techniques for mining such information.The proposed approach, implemented using MongoDB, underscores that our performance analysis yields notably superior outcomes compared to alternative methods. This research holds implications for fruit shape database mining, providing robust support for fruit class classification.&lt;/p&gt;&lt;/abstract&gt;

Innovative Polygon Trend Analysis Method: A Case Study of the South Gujarat Region

The unpredictability of precipitation can have dramatic impacts on agriculture, ecosystems, and water resource manage¬ment. It is needed to study the change in trends of precipitation due to climate change. The present aims to assess the impact of climate change, trend analysis using the Innovative polygon trend analysis (IPTA) method considering monthly precipitation data of 120 years for seven districts of South Gujarat. Further comparison is carried out with the trends predicted by the well accepted Mann-Kendall (M-K) method. IPTA method shows increasing trends during the monsoon season (June and August) but decreasing trends for the monthly precipitation of July for all seven districts. It can be said that over the period of years, less precipitation occurs in the month of July. De- creasing trends are observed for the post-monsoon and pre-monsoon seasons. No trend is observed for the monthly precipitation of Febru- ary which demands preparedness in the water Management system. Interestingly, monthly variation of trends is observed for Dang Dis- trict for both monsoon and post-monsoon seasons. However, a variation in trends in the monsoon season leads to difficulties in the water management system, and sometimes flood events occur. Trends predicted by the Mann-Kendall method show decreasing trends in the post-monsoon season and increasing trends for the monsoon season. Whereas Tapi and Bharuch districts show decreasing trends in the month of June. No trend is predicted in the pre-monsoon season. Comparative analysis of trends predicted by IPTA and M-K method shows similar results for Post Monsoon. IPTA predicts decreasing trends in pre-monsoon season whereas the M-K test shows no trends in pre-monsoon season. The monthly prediction by IPTA may bring useful information for water utility sectors and decision-makers in the study area.

Detection of annual rainfall trends using innovative trend analysis method in Benin

Detection of annual rainfall trends using innovative trend analysis method in Benin

Detection of annual rainfall Trends Using Innovative Trend Analysis Method in Benin.

Detection of annual rainfall Trends Using Innovative Trend Analysis Method in Benin.

Consumer Behaviour: Analysing Marketing Campaigns through Recommender Systems and Statistical Techniques

This article examines consumer behaviour’s impact on marketing campaigns' effectiveness using a recommender system and statistical analysis methods. Understanding consumer behaviour is essential in today's fiercely competitive and constantly evolving market. Our study aims to highlight the significant impact of consumer behaviour on marketing data through the innovative application of recommender systems supported by state-of-the-art machine learning and data analysis techniques. This approach addresses the formidable challenges of accurately predicting consumer behaviour. We provide a detailed introduction to recommendation systems, emphasizing their vital role in the modern marketing landscape. We then outline our theories, laying the groundwork for a deeper understanding of the relationship between marketing data and consumer behaviour. Additionally, we present a rigorous data analysis process that begins with data cleaning and progresses through univariate and bivariate analysis, culminating in advanced techniques such as the Apriori algorithm to discover association rules and thoroughly explore this symbiotic relationship. Our findings demonstrate the applicability and effectiveness of our methodology for interpreting the complex interplay between consumer behaviour and marketing data. Our conclusions highlight essential trends and offer practical recommendations for enhancing marketing strategies significantly. By elucidating the dynamic relationships between consumer behaviour and marketing outcomes, our study contributes to a more sophisticated understanding of consumer dynamics in the contemporary business environment. Furthermore, this paper underscores the importance of understanding consumer behaviour and the benefits of employing innovative data analysis methods. By decoding consumption trends, businesses can optimize their marketing strategies and improve customer satisfaction, strengthening their competitive edge in a constantly shifting market. Finally, incorporating recommender systems with artificial intelligence and machine learning tools for collaborative filtering can further refine these strategies, substantially boosting marketing efficacy.

Defect engineering of CH3NH3PbI3 towards enhanced carrier lifetime: combined detailed balanced study and NAMD-TDDFT

An innovative defect analysis method enhances a novel understanding of perovskite solar cells, linking the growth environment, defect characteristics and device performance.

Identification of ferroptosis-related diagnostic markers in primary Sjögren's syndrome based on machine learning.

Primary Sjogren's syndrome (pSS) is a common autoimmune disorder that affects up to 0.3-3% of the global population. Ferroptosis has recently been identified to play a significant role in autoimmune diseases. However, the molecular mechanisms of ferroptosis in the initiation and progression of pSS remains unclear. To investigate the molecular mechanisms underlying the occurrence and progression of pSS, we utilized a comprehensive approach by integrating data obtained from the Gene Expression Omnibus (GEO) database with data from the FerrDb database to identify the ferroptosis-related differentially expressed genes (DEGs). Furthermore, we implemented an innovative transcriptomic analysis method utilizing a computer-aided algorithm to establish a network between hub genes associated with ferroptosis and the immune microenvironment in pSS patients. Our results revealed significant differences in the gene expression profiles of pSS samples compared to normal tissues, with 1,830 significantly up-regulated genes and 1,310 significantly down-regulated genes. In addition, our results showed a significant increase in the proportions of B cells and CD4+ T cells in pSS samples compared to normal tissues. AND then, our analysis revealed that a combination of six ferroptosis-related genes, including TBK1, SLC1A4, PIK3CA, ENO3, EGR1, and ATG5, could serve as optimal markers for the diagnosis of pSS. The combined analysis of these six genes accurately diagnosed the occurrence of pSS. This study offers valuable insights into the pathogenesis of pSS and highlights the importance of targeting ferroptosis-related DEGs, which suggests a novel treatment strategy for pSS.

Embracing uncertainty in cerebrospinal fluid dynamics: A Bayesian approach to analysing infusion studies

IntroductionCerebrospinal fluid (CSF) infusion test analysis allows recognizing and appropriately evaluating CSF dynamics in the context of normal pressure hydrocephalus (NPH), which is crucial for effective diagnosis and treatment. However, existing methodology possesses drawbacks that may compromise the precision and interpretation of CSF dynamics parameters. Research questionThis study aims to circumvent these constraints by introducing an innovative analysis method grounded in Bayesian inference. Material and methodsA single-centre retrospective cohort study was conducted on 858 patients who underwent a computerized CSF infusion test between 2004 and 2020. We developed a Bayesian framework-based method for parameter estimation and compared the results to the current, gradient descent-based approach. We evaluated the accuracy and reliability of both methods by analysing erroneous prediction rates and curve fitting errors. ResultsThe Bayesian method surpasses the gradient descent approach, reflected in reduced inaccurate prediction rates and an improved goodness of model fit. On whole cohort level both techniques produced comparable results. However, the Bayesian method holds an added advantage by providing uncertainty intervals for each parameter. Sensitivity analysis revealed significance of the CSF production rate parameter and its interplay with other variables. The resistance to CSF outflow demonstrated excellent robustness. Discussion and conclusionThe proposed Bayesian approach offers a promising solution for improving robustness of CSF dynamics assessment in NPH, based on CSF infusion tests. Additional provision of the uncertainty measure for each diagnostic metric may perhaps help to explain occasional poor diagnostic performance of the test, offering a robust framework for improved understanding and management of NPH.

Single-Impact Electrochemistry for Bacterial Sensing

The main objective in the detection and analysis of bio-entities such as bacteria is a low detection limit, a short assay time and a high selectivity in identification, ideally combined with easy-handling and cheap instrumentation. In this way, the development of innovative analytical methods in terms of high sensitivity and spatial-temporal resolution allowing both qualitative and quantitative analysis at single-cell and subcellular levels is becoming a technological requirement. The main advantage of electrochemistry in biosensing applications is to combine high sensitivity and ease of use with the possibility of miniaturization at low cost. Especially, new electrochemical techniques and various types of electrodes have been recently developed offering the feasibility of analysis at the single-cell level with high sensitivity and selectivity. Nano-electrochemistry has been widely extended to various applications such as biosensing thanks to the continuous improvement of the instrumentation sensitivity, especially for individual entity electrochemical detection. Single-impact electrochemistry provides a low limit of detection (in principle, one single species) inherent to this electro-analytical method and the ability to study single entities (cells, viruses, bacteria, nanoparticles...) in real-time through a dynamic measurement [1]. The electrochemical nano-impacts method or discrete collisions technique (stochastic events) consists in detecting single impacts of various entities such as nanoparticles, cells, bacteria, vesicles, viruses, proteins in solution at a polarized ultramicroelectrode (UME) [2]. For each collision event, a specific signal is recorded in the chronoamperometry curve (current as a function of time) corresponding to an “impact” of the entity onto the UME surface. Crucial information can be reached from the analysis of the electrochemical impact event in the amperometry measurement, such as the concentration and the size of the colliding entity.Electrochemical nano-impacts (or collisions) of single bacteria are usually detected by recording a chronoamperometry measurement (i–t curve) at a UME biased at a redox potential of an electroactive species in an aqueous electrolyte containing between 106 and 109 bacteria per milliliter [1, 3]. Since 2015, most of the reports in this area concern Escherichia coli (E. coli) as a model bacterium but this sensitive electro-analytical technique has not proved to be efficient for selective detection yet, except for the discrimination between Gram-positive and Gram-negative bacteria. In order to develop single-impact electrochemistry toward the identification of bacterial cells, several studies deal with the use of redox mediators for probing the electrochemical activity of the bacterium. Also, an original and recent approach is to detect the virulence factors released by pathogenic bacteria rather than the cells themselves, based on redox liposomes single-impact electrochemistry [2, 4].