Innovative Analysis Method Research Articles

Seismic fragility analysis of nuclear containment structure with Bayesian cloud analysis framework

Nuclear containment structure plays a critical role in safe operation of nuclear power plants. Seismic safety of nuclear power plant structures has become a hot issue in nuclear engineering community following Fukushima nuclear accident. Seismic fragility analysis, as a key element in probabilistic safety assessment of nuclear power plants, is widely used in nuclear power plant structures. This study presents an innovative method for seismic fragility analysis of engineering structures. The proposed methodology combines conventional Cloud analysis method and Bayesian framework. The accuracy and efficiency of the proposed methodology were demonstrated by a single-degree-of-freedom system, heavy computational efforts but high accuracy seismic fragility analysis method was adopted for comparison. Seismic fragility analysis of a typical prestressed concrete nuclear containment structure was analyzed using the proposed method. Results indicated that method proposed in this study can improve the accuracy of seismic fragility analysis. Furthermore, with the enhance of performance level, the increasing ratios for median seismic capacity of the nuclear containment structure was decreasing.

PLIC-FSR-SYS: System reliability analysis based on parallel learning of influential components with filtered sample region

In practical engineering, system reliability analysis is highly concerned since many structures or products have multiple failure modes. Accordingly, this paper develops an innovative method for system reliability analysis by parallel learning of influential component limit-state functions with filtered sample region (PLIC-FSR-SYS) based on Kriging modeling. Different from the traditional adaptive learning methods that train only one component in each iteration when constructing the surrogate of the composite limit-state function, a new strategy is explored to adaptively identify several important components in one iteration so as to train them simultaneously. In the meanwhile, a filtering formula is explored to determine the fatal region so that the unimportant samples can be removed to further accelerate the training process. Based on the join forces of parallel learning of influential components and avoiding the training at unimportant samples, PLIC-FSR-SYS can achieve a fairly efficient system reliability analysis with multiple failure modes. Finally, four different case studies, including an engineering application to the ultra-voltage on-load tap-changer, are conducted to prove the effectiveness of the proposed method. The results indicate that compared to traditional adaptive learning methods, the proposed method makes a significant efficiency improvement for system reliability analysis with multiple failure modes.

Satellite measurement data-based assessment of spatiotemporal characteristics of ultraviolet index (UVI) over the state of Johor, Malaysia

The study examines the spatiotemporal and statistical characteristics of the daily ultraviolet index (UVI) over the Johor state of Malaysia. The datasets utilized in this study are the Ozone Monitoring Instrument (OMI)/Aura satellite’s daily UVI observations between October 2004 and March 2023. The innovative trend analysis (ITA) method is employed to identify the statistical trend of daily UVI. Basic statistics of daily, monthly, and seasonal UVI are also carried out to better characterize UVI in the study area. The daily UVI data collected for the analysis exhibit wide variability, with more than 80% of the daily UVI data falling above UVI 8, indicating the severity of UV radiation (UVR) reaching the study area. The monthly and seasonal UVI also display highly varying characteristics, as observed in the daily data. The pixel-based analysis of ITA results indicates a significant increasing and decreasing trend of daily UVI in the region, with values varying between − 9.42 × 10−6 and 3.79 × 10−5. However, for the study area as a whole, UVI shows a significant increasing trend of 4.50 × 10−5. The influence of parameters such as cloud optical thickness (COT), solar zenith angle (SZA), and O3 column on the strength of UVR reaching the study area is also investigated. Based on the results, the study area is characterized by low thickness, broken clouds, and sometimes cloudless conditions. Overall, the results show that UVI in the study area is increasing, and therefore, proper health guidelines and direct UVR exposure regulations need to be implemented to reduce serious health risks associated with UVR exposure.Graphical

Trend Stability Assessment for Hydrological Drought in Euphrates Basin (Türkiye) Using Triple Wilcoxon Test and Innovative Trend Analysis Methods

This study investigates the stability of hydrological drought trends in the Euphrates Basin from 1960 to 2020 using three-dimensional (3D) graphical representations based on innovative trend analysis (ITA) and triple Wilcoxon test (WT) methods. Unlike traditional ITA and WT, which are widely used for trend identification but do not inherently provide trend stability information, this study employs a novel approach to assess and visualize trend stability. The Triple WT method divides the data into three equal segments, examining differences without altering the time series. Drought indices are calculated for 3-month, 6-month, and 12-month time scales using historical streamflow data from five stations. The research identifies trends and their stabilities across three distinct periods: 1967–1984, 1985–2002, and 2003–2020. Results show that as the time scale increases, trend differences between extreme drought conditions diminish. One station consistently exhibits significantly decreasing trends, while three stations show unstable trends with notable variations in the standardized streamflow index (SSFI). The use of 3D-ITA and Triple WT effectively captures the dynamics and stability of drought trends, offering a deeper understanding of hydrological drought in the Euphrates Basin. These findings provide a reference for future studies on drought trend mechanisms in various climatic regions.

The use of accelerometers to assess upper limb function in patients with obstetric brachial plexus palsy

For half a century, the Mallet Scale (MS) has been utilized to assess upper limb function in patients with obstetric brachial plexus palsy (OBPP). However, the correct use of the MS requires trained personnel and the MS does not measure compensatory movements. For this reason, new methods are needed to compensate for these weaknesses. This study introduces an innovative method for objective functional motion analysis using accelerometers to measure upper limb movements in thirty patients with obstetric brachial plexus lesions. Five triaxial accelerometers were positioned on the chest and each upper limb. They recorded acceleration signals during repetitive everyday tasks: hand-to-mouth (HM), hand-to-neck (HN), and hand-to-spine (HS). From these signals, 54 features were extracted and subjected to linear correlation tests to identify 5 suitable features. An algorithm was then developed to categorize patients into five groups and compute an individual movement performance score (iMPScore) assessing the patient’s upper extremity function. By using the iMPScore more than 75% of all participants have been classified correctly with respect to their MS category. Identification of MS I category patients in general and assessing upper extremity function of MS I to III in HS tasks were most challenging. We conclude that the introduced approach is a valuable tool for gauging movement limitation of upper limbs in patients with obstetric brachial plexus palsy. Compared to other clinically established methods, it becomes possible to record and even quantify the extent of compensatory movements. In this way, an objective, user- and patient-friendly method is offered, which supports significantly physicians and therapists in their evaluation of OBPP.

An innovative evaluation method for clinical comparative analysis of occlusal contact regions obtained via intraoral scanning and conventional impression procedures: a clinical trial

ObjectivesTo compare the occlusal contact regions (OCRs) obtained through an intraoral scanning system and conventional impression procedures via an innovative evaluation method.Materials and methodsFifteen participants with complete dentitions and stable centric occlusion were included. Three groups were created based on the technique used to obtain the OCRs of quadrant posterior teeth at the maximal intercuspal position: 100 μm articulating paper (Control), an intraoral scanner (Test 1, T1) and conventional impression procedure (Test 2, T2). OCRs of control group were digitized by the intraoral scanner, while all conventional impressions were cast and digitized by an extraoral scanner. The virtual occlusal records of the 2 test groups were obtained by buccal bite registration. The OCRs within 100 μm in the 3 groups were three-dimensionally superimposed based on the tooth surfaces and the area of OCRs (SC, ST1, ST2) was calculated. The area of overlapping OCRs (SO) between the test groups and the control group was calculated. In the two test groups, the consistency rate of OCRs (SO/SC) and the positive rate of OCRs (SO/ST) were calculated and compared. For occlusal tightness evaluation, the mean occlusal clearances (OC) as well as minimum OC between the upper and lower models were calculated and compared.ResultsThe consistency rate of OCRs was 0.73 ± 0.17 for T1 group and 0.23 ± 0.13 for T2 group (p < 0.001). The positive rate of OCRs was 0.67 ± 0.15 for T1 group and 0.56 ± 0.23 for T2 group (p = 0.143). The mean OC was 51.32 ± 16.04 μm for T1 group and 68.20 ± 18.15 μm for T2 group (p = 0.024). The minimum OC was − 61.74 ± 35.38 μm for T1 group and 4.09 ± 27.15 μm for T2 group (p < 0.001).ConclusionsFor obtaining occlusal records in the quadrant posterior region, the tested intraoral scanning system was more reliable for recording occlusal contact regions and showed higher occlusal tightness compared with conventional impression procedures.Clinical relevance(1) The evaluation method can assist clinicians in making more objective analysis and comparisons among different sources of virtual occlusal records. (2) Occlusal tightness is a key and indispensable indicator in the evaluation of virtual occlusal records, and it can be quantified by measuring the occlusal clearance utilizing the current evaluation method.

Iterative local maximum synchrosqueezing-extracting transform

Recently, there has been significant interest in time-varying signal analysis. Multisynchrosqueezing transform (MSST) and improved multisynchrosqueezing transform (IMSST) have shown promise in overcoming the limitations of conventional methods. Although progress has been made, challenges still exist, such as the presence of non-reassigned time–frequency (TF) points and the presence of noise in the TF plane when processing noisy time-varying signals. In this paper, we introduce an innovative TF analysis method known as the iterative local maximum synchrosqueezing-extracting transform (ILMSSTSEO). First, the proposed method extracts the local maximum value of the instantaneous frequency (IF) during each iteration to eliminate non-reassigned TF points. Additionally, a synchroextracting operator is introduced unconventionally to enhance the concentration of TF energy and reduce noise in the TF representation. By doing so, the method effectively solves many challenges faced by existing methods and provides a new extraction framework for synchrosqueezing transform-based TF analysis methods.

An innovative rheology analysis method applies to the formulation optimization of Panax notoginseng total saponins ocular gel

Emphasizing the viscoelasticity of ophthalmic gels is crucial for understanding the residence time, structure, and stability of hydrogels. This study primarily aimed to propose an innovative rheology analysis method for ophthalmic gels, considering complex eye movements. This method was applied to select ophthalmic gels with favorable rheological characteristics. Additionally, the physical characteristics and in vitro release of the selected Panax notoginseng total saponins (PNS) gel were demonstrated. The selected PNS gel significantly increased the activities of SOD and decreased intracellular levels of MDA, TNF-α, and IL-1β in H2O2-treated ARPE-19 cells. Finally, the optimal formulation was selected as a suitable platform for ophthalmic delivery and was shown to significantly rescue ARPE-19 cells from oxidative cellular damage.

Cascaded Adaptive Dilated Temporal Convolution Network-Based Efficient Sentiment Analysis Model from Social Media Posts

Sentimental analysis is one of the most complicated tasks in text mining. It is the process of converting unstructured text to structural text to identify the meaningful pattern of data. Different kinds of sentiments in social media posts are classified into positive and negative. Natural Language Processing (NLP) plays an imperative task in examining sentiments on social media. Sentimental analysis techniques frequently use text data to help to monitor customer feedback about products and understand the needs of customers in the field of business. The text data are gathered from social media comments that lead to several drawbacks such as spelling errors, noise, and unstructured data, and some of the data are in abbreviation form in which abbreviations are hard to understand from the text data. Therefore, the innovative sentiment analysis method is implemented to identify the kinds of sentiments from the collected social media posts. At first, the text data are assembled from the social media posts. The collected text data are subjected to text preprocessing techniques to improve the data quality. NLP techniques such as a bag of n-grams, glove embedding, and Bidirectional Encoder Representations from Transformers (BERT) are applied to separate the features from the text data. The removed features from these techniques are given to the fusion of the optimal feature stage, where the weight optimization takes place via the Mutated Random Parameter-based Serval Optimization Algorithm (MRP-SOA). The attained optimal weighted fused features contain the most relevant information, and it is fed to the classifier for analyzing the sentiments. For analyzing the sentiments, the Cascaded Adaptive Dilated Temporal Convolutional Network (CADTCN) is utilized, and the parameters from ADTCN are optimized with the same to get higher classification outcomes. The analysis outcomes are compared with the traditional sentiment analysis models in terms of various metrics to show the success of the advanced design.

DNA Metabarcoding Approach as a Potential Tool for Supporting Official Food Control Programs: A Case Study.

Food authentication significantly impacts consumer health and the credibility of Food Business Operators (FBOs). As European regulations mandate the verification of food authenticity and supply chain integrity, competent authorities require access to innovative analytical methods to identify and prevent food fraud. This study utilizes the DNA metabarcoding approach on meat preparations, sampled during an official control activity. It assesses animal and plant composition by amplifying DNA fragments of the 12S rRNA and trnL (UAA) genes, respectively. The results not only confirmed the declared species but also revealed undeclared and unexpected taxa in products labelled as containing a single animal species and various unspecified plant species. Notable findings such as the presence of Murinae, Sus scrofa, Ovis aries, and Pisum sativum could raise public health concerns, compromise consumer choices made for ethical or religious reasons, and reflect the hygienic conditions of the processing plant. This study demonstrates that the DNA metabarcoding approach looks to be a promising support tool for official control authorities to ensure food authenticity and safety, and to develop risk profiles along the supply chain.

Trend analysis on CO2 emissions and their implications: a comparative study between India and China.

This research paper aims to provide a comparative trend analysis of CO2 emissions from the two largest emitters, India and China. The analysis focuses on the main sources of CO2 emissions-coal, oil, cement, and gas and their annual data and global share percentages from 1960 to 2019. The study uses non-parametric trend analysis methods, which do not rely on assumptions of normality, outliers, or data length. Pettitt's test, a well-established non-parametric method, was used to detect sudden shifts in the data. The Mann-Kendall (MK) test and Sen's slope estimator were then applied to identify the presence or absence of monotonic linear trends and assess the magnitude of the slopes. In addition, the innovative trend analysis (ITA) method was used, which is particularly effective in detecting and visualizing monotonic, non-monotonic, and sub-trends. The ITA method has the advantage of presenting these trends in a graphical format. According to the results of Pettitt's test, an abrupt change was detected in India in 1989 for all sources of CO2 emissions. In China, however, an abrupt change was detected in 1989 for coal and gas-related sources, while other sources showed a change point in 1990. The results of the MK test and the ITA method showed that all sources show only monotonic increasing trends. Based on the results of Sen's slope estimator, the average rate of change of CO2 emissions is significantly higher in China than in India in all categories after the detection of the abrupt change point. Policymakers should promote the adoption of renewable energy sources, such as solar, wind, hydro, and geothermal, and also implement strategies to control deforestation to counteract the abnormal increase in CO2 emissions. Finally, this research lays a solid foundation for future studies on CO2 emissions.

Bar adsorptive microextraction and liquid chromatography-diode array detection of synthetic cannabinoids in oral fluid.

In recent years, synthetic cannabinoids (SCs) have become a major public health issue. For this reason, there is a need for innovative analytical methods that allow its monitoring in biological matrices. In this work, we propose a novel methodology to screen eight SCs (AM-694, cumyl-5F-PINACA, MAM-2201, 5F-UR-144, JWH-018, JWH-122, UR-144 and APINACA) in oral fluids. A bar adsorptive microextraction method followed by microliquid desorption combined with high-performance liquid chromatography with diode array detection (BAµE-µLD/HPLC-DAD) was developed to monitor the target SCs. The main factors affecting the BAµE technology were fully optimized for oral fluid analysis. Under optimized experimental conditions, the proposed methodology showed good linear dynamic ranges from 20.0 to 2000.0 µg L-1 (r2 > 0.99, relative residuals < 15%), limits of detection between 2.0 and 5.0 µg L-1 and suitable average recovery yields (87.9-100.5%) for the eight studied SCs. The intra- and interday accuracies (bias ≤ ± 14.7%) and precisions (RSD ≤ 14.9%) were also evaluated at three spiking levels. The validated methodology was then assayed to oral fluid samples collected from several volunteers. The proposed analytical approach showed remarkable performance and could be an effective alternative for routine monitoring of the target compounds in oral fluid.

Implementing Triage-Bot: Supporting the Current Practice for Triage Nurses.

In Canada, emergency departments (ED) have 15.1 million unscheduled visits every year; this has been suggested to indicate that patients rely on ED to address the gaps experienced by 6.5 million Canadians who lack a primary care provider. When this large number of visits is coupled with a predicted shortage of 100,000 nurses in Canada by 2030, ED can be expected to face resource limitations, which highlights the importance of triage systems as a source of immediate support. Technology that incorporates innovative analytical methods, automation of routine, and efficient processing can be leveraged to enhance patient outcomes, streamline clinical processes, and improve the overall quality and efficiency of healthcare delivery. This paper aims to highlight how the Triage-Bot, a proposed AI system, can assist ED nurses when triaging patients. The Triage-Bot system is based on the Canadian Triage and Acuity Scale (CTAS), which currently serves as a standardized and highly effective tool for prioritizing patient care in emergency departments across the country. Pre-set and open-ended questions are asked using voice and video, allowing patients to describe their health concerns and conditions. Triage-Bot automatically measures the following vital signs: heart rate (HR), heart rate variability (HRV), oxygen saturation (SpO2), respiratory rate (RR), blood pressure (BP), blood glucose (BG), and stress. The system uses artificial intelligence models, particularly those with a deep learning approach that simultaneously analyzes both the user's facial expression and voice tone. Implementation: A systematic review addressed the implications of AI in nursing and concluded that it could contribute to patient care by providing personalized instructions and/or remotely monitoring patients. The Triage-Bot system can be implemented in healthcare facilities, such as emergency department waiting rooms. The information it collects can then be added to a patient's health records to support nurses in assessing the severity of each patient's condition. Limitations: If the system is accessed without a nurse's guidance, it is imperative that the user receives information regarding when to visit a healthcare provider or ED. Continuous improvements in Triage-Bot's accessibility for patients with varying abilities are required to ensure that the system remains user-friendly during times of illness. The voice and text interaction can also be influenced by a user's understanding of language, culture, and age-related factors.

Streamflow trends and flood frequency analysis: a regional study of the UK.

In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann-Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches.

Collaborative-driven reservoir formation pressure prediction using GAN-ML models and well logging data

Formation pressure is the foundation and prerequisite for achieving efficient drilling and risk control in complex geological formations. However, intelligent prediction of formation pressure faces challenges such as insufficient sample sizes, poor prediction performance, and low prediction accuracy, which severely restrict the realization of safe and efficient drilling. To address this, this paper proposes a new method based on Generative Adversarial Networks (GAN) and Machine Learning (ML), aiming to improve adaptation to complex geological conditions and effectively capture the nonlinear relationship between logging data and formation pressure for intelligent prediction of formation pressure. The research results show that the GAN technology successfully amplified the 125 cable-measured formation pressure data by 204.8 times. Compared to machine learning predictions without using GAN technology, the constructed GAN-ML model reduced the average root mean square error by 0.59 MPa and the average absolute error by 0.71 MPa in the training set. The GAN-ML model predicted the formation pressure of adjacent wells with an average root mean square error of 1.59 MPa and an average absolute error of 1.96 MPa compared to the cable-measured formation pressure values. This achievement significantly enhances the robustness and generalization performance of intelligent models, demonstrating the potential of GAN-ML in addressing small sample data problems. It opens a new technical path for data-driven intelligent prediction and provides innovative data analysis and prediction methods for the field of geological exploration.

Analytical Methods for the Determination of Pharmaceuticals and Personal Care Products in Solid and Liquid Environmental Matrices: A Review.

Among the various compounds regarded as emerging contaminants (ECs), pharmaceuticals and personal care products (PPCPs) are of particular concern. Their continuous release into the environment has a negative global impact on human life. This review summarizes the sources, occurrence, persistence, consequences of exposure, and toxicity of PPCPs, and evaluates the various analytical methods used in the identification and quantification of PPCPs in a variety of solid and liquid environmental matrices. The current techniques of choice for the analysis of PPCPs are state-of-the-art liquid chromatography coupled to mass spectrometry (LC-MS) or tandem mass spectrometry (LC-MS2). However, the complexity of the environmental matrices and the trace levels of micropollutants necessitate the use of advanced sample treatments before these instrumental analyses. Solid-phase extraction (SPE) with different sorbents is now the predominant method used for the extraction of PPCPs from environmental samples. This review also addresses the ongoing analytical method challenges, including sample clean-up and matrix effects, focusing on the occurrence, sample preparation, and analytical methods presently available for the determination of environmental residues of PPCPs. Continuous development of innovative analytical methods is essential for overcoming existing limitations and ensuring the consistency and diversity of analytical methods used in investigations of environmental multi-class compounds.

Development of an accelerated ageing protocol for the study of phytocannabinoid stability in Cannabis sativa L.

Cannabis sativa L. is a plant belonging to the Cannabaceae family known primarily for its recreational use due to the psychoactive properties of Δ9-tetrahydrocannabinol (THC). Despite this, several compounds belonging to the category of phytocannabinoids have shown in recent years a number of potentially promising therapeutic effects that have increased the interest in the pharmaceutical field towards this plant. However, the content of these compounds is very variable and influenced by different factors, such as growing conditions and time of the year. An indication of the status and age of Cannabis samples is provided by the content of CBN, a minor phytocannabinoid and degradation product of other phytocannabinoids, including THC. In this research work an innovative, solid state analytical approach has been developed to observe and evaluate the variations in the content of two phytocannabinoids (CBN and CBD) in Cannabis-derived products over time. In order to simulate the ageing of the Cannabis samples, an artificially accelerated ageing procedure has been developed and optimised by using high temperatures. The analyses were carried out using an innovative ATR-FTIR method for solid state analysis, enabling direct analysis of a solid sample without any pretreatment phase. This study has allowed the development of an innovative analytical approach for the evaluation of the age and state of conservation of Cannabis samples and may be a useful tool both in the industrial, pharmaceutical and forensic fields.

White matter tract crossing and bottleneck regions in the fetal brain.

There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.

Trends of meteorological and hydrological droughts and associated parameters using innovative approaches

Climate change and drought have profound effects on hydro-meteorological series. In addition to spatial, these effects could be on annual, seasonal, monthly, or daily temporal scales. In the literature, seasonal Mann-Kendall and Kendall Tau, Standardized Precipitation Index (SPI), Standardized Precipitation Evapotranspiration Index (SPEI), and Standardized Runoff Index are mostly used to detect seasonal effects (autumn, winter, spring, and summer), despite some restrictive assumption. Innovative Polygon Trend Analysis (IPTA) method developed from Innovative Trend Analysis (ITA) analyzes monthly effects on hydro-meteorological variables without restrictive assumptions. In this study, the IPTA method is revised and developed as Periodic Innovative Polygon Trend analysis (P-IPTA) to analyze hydro-meteorological series in periods of 1, 3, 6, 9, and 12 months instead of one-month duration. The method turns to the IPTA for one-month evaluations. Also, ITA method is improved by adding the frequencies for each drought classification (F-ITA). For the precipitation and water availability (Istanbul, Türkiye) and stream flow (Danube River, Romania) series, the P-IPTA method has been used in addition to the SPI, SPEI, and SDI methods to detect the trends in meteorological and hydrological droughts, and their associated parameters. There are generally decreasing trends, increasing drought frequencies, and decreasing wet event frequencies in the study areas. As the period lengths of the hydro-meteorological series increase, drought becomes more evident. Unlike these methods, the method results are consistent with the F-ITA SPI, SPEI, and SDI graphs and can give drought and wet periods. Similarly, the new P-IPTA method will enable researchers to investigate seasonal effects not only on hydro-meteorological series but also on any variable.

An Innovative Method for Deterministic Multifactor Analysis Based on Chain Substitution Averaging

The aims of this paper are to present the methodology, derived mathematical expressions for determining the individual factor influences and the adaptation for the conditions of dynamic deterministic factor analysis and the results of the application of the developed new method for deterministic factor analysis, called the averaged chain substitution method. After formulating the concept of the considered approach, all mathematical expressions used to create models containing up to four factor variables are presented and summarized. The scientific novelty of the study is in the obtained new equations for determining the individual factor influences by the method of averaged chain substitution and the method of analogy for five-factor additive or difference-multiplicative and for five-factor additive or difference-multifactor models with an additive or different part in the numerator of the factor model. The presented mathematical expressions accurately and unambiguously quantify the impact of individual factor influences for all types of factor models and thus significantly expand the applicability of the averaged chain substitution method in the theory and practice of financial-economic analysis. The proposed formalization and algorithmization of the evaluation process makes the method easy to apply by all economic and financial analysts for the purposes of deterministic factor analysis. The methodology was applied to perform a dynamic deterministic factor analysis of the total liquidity of Monbat AD and ELHIM-ISKRA AD for the period 2017–2021, based on the consolidated annual financial statements of the companies, available on the website of the Bulgarian Stock Exchange.