Current Metrics Research Articles

Targeting Sodium in Heart Failure.

A dominant event determining the course of heart failure (HF) includes the disruption of the delicate sodium (Na+) and water balance leading to (Na+) and water retention and edema formation. Although incomplete decongestion adversely affects outcomes, it is unknown whether interventions directly targeting (Na+), such as strict dietary (Na+) restriction, intravenous hypertonic saline, and diuretics, reverse this effect. As a result, it is imperative to implement (Na+)-targeting interventions in selected HF patients with established congestion on top of quadruple therapy with angiotensin receptor neprilysin inhibitor, β-adrenergic receptor blocker, mineralocorticoid receptor antagonist, and sodium glucose cotransporter 2 inhibitor, which dramatically improves outcomes. The limited effectiveness of (Na+)-targeting treatments may be partly due to the fact that the current metrics of HF severity have a limited capacity of foreseeing and averting episodes of congestion and guiding (Na+)-targeting treatments, which often leads to dysnatremias, adversely affecting outcomes. Recent evidence suggests that spot urinary sodium measurements may be used as a guide to monitor (Na+)-targeting interventions both in chronic and acute HF. Further, the classical (2)-compartment model of (Na+) storage has been displaced by the (3)-compartment model emphasizing the non-osmotic accumulation of (Na+), chiefly in the skin. 23(Na+) magnetic resonance imaging (MRI) enables the accurate and reliable quantification of tissue (Na+). Another promising approach enabling tissue (Na+) monitoring is based on wearable devices employing ion-selective electrodes for electrolyte detection, including (Na+) and (Cl-). Undoubtably, further studies using 23(Na+)-MRI technology and wearable sensors are required to learn more about the clinical significance of tissue (Na+) storage and (Na+)-related mechanisms of morbidity and mortality in HF.

Exploiting Contextual Embeddings in Hierarchical Topic Modeling and Investigating the Limits of the Current Evaluation Metrics

Abstract We investigate two essential challenges in the context of Hierarchical Topic Modeling (HTM) – (i) the impact of data representation and (ii) topic evaluation. The Data representation directly influences the performance of the topic generation, and the impact of new representations such as contextual embeddings in this task has been under-investigated. Topic evaluation, responsible for driving the advances in the field, assesses the overall quality of the topic generation process. HTM studies exploit the exact topic modeling (TM) evaluation metrics as traditional TM to measure the quality of topics. One significant result of our work is demonstrating that the HTM’s hierarchical nature demands novel ways of evaluating the quality of topics. As our main contribution, we propose two new topic quality metrics to assess the topical quality of the hierarchical structures. Uniqueness considers topic topological consistency, while the Semantic Hierarchical Structure (SHS) captures the semantic relatedness of the hierarchies. We also present an additional advance to the state-of-the-art by proposing the c-CluHTM. To the best of our knowledge, c-CluHTM is the first method that exploits contextual embeddings into NMF in HTM tasks. c-CluHTM enhances the topics’ semantics while preserving the hierarchical structure. We perform an experimental evaluation, and our results demonstrate the superiority of our proposal with gains between 12% and 21%, regarding NPMI and Coherence over the best baselines. Regarding the newly proposed metrics, our results reveal that Uniqueness and SHS can capture relevant information about the structure of the hierarchical topics that traditional metrics cannot.

Facile and Sustainable Electrostatic Integration of Eosin Y Dye for the Assay of the Atomoxetine Drug Through the Enhancement of the RRS Phenomenon.

The suggested study follows specific protocols to guarantee that the atomoxetine drug analysis approach is environmentally friendly and sustainable. A number of recently created methods were used as prospective evidence for environmental sustainability and applicability, which is an essential point to emphasize. The current study introduces a new and very unique technology using ultrasensitive spectrofluorimetry to identify the atomoxetine (AXT) medication. A one-step, one-pot, direct spectrofluorimetric technique was used in this study's methodology, which was determined to be effective and environmentally sustainable for the drug's evaluation and validation. When AXT and EY reagent were mixed in an acidic setting, a highly resonant Rayleigh scattering product was promptly generated. The application of fluorimetric analysis was built on a novel theory that the augmentation in dye response subsequent to the addition of AXT was directly correlated to the resultant complex's molecular mass, as determined at a wavelength of 365nm. The complexation progression caused a considerable rise in the molecular mass, from 292.82 (AXT+) to 983.68 (AXT-EY) g mol-1. The quantum yield for the coupled product was measured. The linearity was determined to be between 30 and 1500 ng mL-1, while the sensitivity values were found to be between 9.2 and 28.1 ng mL-1. Analyzing AXT-EY complexes allowed us to determine the best values for all of the system's adjustable parameters. The system showed adherence to International Council for Harmonization (ICH) criteria without difficulties. The recommended procedure was then evaluated for environmental friendliness using many current environmental safety metrics. Fortunately, the current analytical technique is also recognized as a white one by the WAC standards, which integrate functional and ecological features using the Green/Red/Blue (RGB 12) design. A novel instrument called BAGI was used to assess the feasibility of the proposed approach in the field of analytical chemistry.

Psychoacoustic metrics and affective responses in indoor soundscapes: Implications for restorative soundscapes

This study investigates the links between psychoacoustic metrics and affective responses in indoor soundscapes, guided by the ISO/TS 12913 standard. Through the analysis of an indoor soundscapes dataset, we examine how various psychoacoustic metrics, namely sharpness (S), fluctuation strength (FS), and roughness (R), influence affective responses such as pleasant and eventful. Our research reveals that eventful and annoying soundscapes show higher values of (S), (FS), and (R). Conversely, pleasant soundscapes tend to show lower values in these metrics. The findings suggest that while these metrics can be effective in designing eventful soundscapes, they may be less useful for creating pleasant acoustic environments. This study contributes to the understanding of the applicability of current psychoacoustic metrics for the type of soundscape intervention and calls for the development of new psychoacoustic metrics to design restorative soundscapes that enhance well-being.

A comprehensive study on fidelity metrics for XAI

The use of eXplainable Artificial Intelligence (XAI) systems has introduced a set of challenges that need resolution. Herein, we focus on how to correctly select an XAI method, an open questions within the field. The inherent difficulty of this task is due to the lack of a ground truth. Several authors have proposed metrics to approximate the fidelity of different XAI methods. These metrics lack verification and have concerning disagreements. In this study, we proposed a novel methodology to verify fidelity metrics, using transparent models. These models allowed us to obtain explanations with perfect fidelity. Our proposal constitutes the first objective benchmark for these metrics, facilitating a comparison of existing proposals, and surpassing existing methods. We applied our benchmark to assess the existing fidelity metrics in two different experiments, each using public datasets comprising 52,000 images. The images from these datasets had a size a 128 by 128 pixels and were synthetic data that simplified the training process. We identified that two fidelity metrics, Faithfulness Estimate and Faithfulness Correlation, obtained the expected perfect results for linear models, showing their ability to approximate fidelity for this kind of methods. However, when present with non-linear models, as the ones most used in the state-of-the-art,all metric values, indicated a lack of fidelity, with the best one showing a 30% deviation from the expected values for perfect explanation. Our experimentation led us to conclude that the current fidelity metrics are not reliable enough to be used in real scenarios. From this finding, we deemed it necessary to development new metrics, to avoid the detected problems, and we recommend the usage of our proposal as a benchmark within the scientific community to address these limitations.

Commercial chatbot monitoring: Approaches focused on automated conversation analysis

Purpose of the study: The purpose of this study is to review and analyze current automated techniques for monitoring chatbot conversations in the field of Conversational Artificial Intelligence. It aims to highlight the challenges and limitations of these techniques and provide insights into various metrics used to measure chatbot performance, with the goal of enhancing it. Methodology: The study employs a comprehensive literature review of existing automated techniques for monitoring chatbot conversations. Then, focusing on state-of-the-art approaches, the study introduces a division into numerical metrics (performance statistics and user engagement) and linguistic metrics (conversation analysis). Within conversation analysis, which is crucial for improving chatbot responses and accurately recognizing user intentions, the study identifies and presents three leading methods. Main Findings: The paper highlights that, while current chatbot numerical conversation metrics allow for continuous monitoring and enhancement of chatbot performance, there is still room for improvement in the automated linguistic analysis of chatbot conversations. Furthermore, monitoring chatbot conversations in an automatic way in order to implement adequate corrective actions, is an essential task for refining chatbot efficiency through continuous learning and adaptation. Applications of the study: The findings of this study have practical applications for businesses employing chatbots. By understanding the potential of current automated monitoring techniques and addressing their limitations, commercial chatbot systems can be improved for the benefit of customer satisfaction. Novelty/Originality of the study: The paper provides readers with the novel knowledge necessary to understand key metrics used to measure chatbot conversations from both numerical and linguistic perspectives. It adds value by guiding readers on how monitoring numerical metrics helps analyze chatbot interactions and explains how the automated linguistic analysis of chatbot conversation content is utilized in leading approaches.

Auditory Localization Performance in Cochlear Implant Recipients With Single-Sided Deafness: The Challenges and Limitations of Current Outcome Metrics.

Acoustic localization accuracy metrics currently employed in clinical literature both overestimate and underestimate performance benefit of cochlear implantation (CI) for single-sided deafness (SSD). Although localization in SSD with CI has been investigated, performance characterization has relied heavily on average error. Although attractively concise, this measure may misrepresent performance. Here, we characterize frequency-specific localization on a granular level in subjects with CI for SSD as a critical analysis of localization outcome metrics. Eight CI recipients with SSD were recruited. Stimuli of broadband (BBN) and narrowband noise (NBN) at low (500 Hz), mid (1000 Hz), and high (4000 Hz) frequencies were presented in a semianechoic chamber. Localization accuracy was quantified in mean angular error (MAE) and linear regression slope. Use of a CI for SSD subjects improved localization performance by slope for all stimuli ( p ≤ 0.0033) to a level that was equal to normal-hearing controls at 1 and 4 kHz ( p ≥ 0.2281). MAE was also significantly improved for SSD subjects using CI for BBN stimuli ( p ≪ 0.0001); however, no statistically significant improvement in MAE was seen for NBN ( p ≥ 0.5773) with CI use. Descriptive analysis of individual subject performance highlights the reasons for contradictory results. There is inherent challenge in characterizing localization benefit for individuals with CI for SSD. Our data demonstrate the limitations of utilization of average error as the sole metric for outcome benefit. We emphasize the importance of continued research investigating alternative outcome measures as we work toward a more refined understanding of the potential benefits and limitations of cochlear implantation for SSD.

Comprehensive Clinical Usability-Oriented Contour Quality Evaluation for Deep Learning Auto-segmentation: Combining Multiple Quantitative Metrics Through Machine Learning

PurposeThe current commonly-used metrics for evaluating the quality of auto-segmented contours have limitations and do not always reflect the clinical usefulness of the contours. This work aims to develop a novel contour quality classification (CQC) method by combining multiple quantitative metrics for clinical usability-oriented contour quality evaluation for deep learning-based auto-segmentation (DLAS). MethodsThe CQC was designed to categorize contours on slices as acceptable, minor edit, or major edit based on the expected editing effort/time with supervised ensemble tree classification models using seven quantitative metrics. Organ-specific models were trained for five abdominal organs (pancreas, duodenum, stomach, small and large-bowels) using 50 MRI datasets. Twenty additional MRI and nine CT datasets were employed for testing. Inter-observer variation (IOV) was assessed among six observers and consensus labels were established through majority vote for evaluation. The CQC was also compared with a threshold-based baseline approach. ResultsFor the five organs, the average AUC was 0.982±0.01 and 0.979±0.01, the mean-accuracy was 95.8±1.7% and 94.3±2.1%, and the mean risk-rate was 0.8±0.4% and 0.7±0.5% for MRI and CT testing dataset, respectively. The CQC results closely matched the IOV results (mean-accuracy of 94.2±0.8% and 94.8±1.7%) and were significantly higher than those obtained using the threshold-based method (mean-accuracy of 80.0±4.7%, 83.8±5.2%, and 77.3±6.6% using one, two, and three metrics). ConclusionThe CQC models demonstrated high performance in classifying the quality of contour slices. This method can address the limitations of existing metrics and offers an intuitive and comprehensive solution for clinically oriented evaluation and comparison of DLAS systems.

Characterization of myocardial perfusion imaging systems - an extension of quality metrics

PurposeThe aim of this study was to evaluate the performance of myocardial perfusion imaging (MPI) systems in detecting perfusion defects (PDs). The defect perfusion index (DPI) was introduced to extend and further advance the current MPI quality metrics. MethodsAn anthropomorphic phantom simulating normal and pathological myocardial perfusion conditions was imaged by various NaI-crystal detector systems with and without corrections for scatter (SC) and attenuation (AC) (Symbia, Symbia + SC, Symbia IQ + SCAC, Symbia IQ), and cadmium-zinc-telluride detector systems without corrections (DSPECT, D530c). The extent of PD and the summed score (SS) were obtained by comparing polar maps with ad hoc normal databases created for each MPI system by using phantom polar maps with normal perfusion. The segmental uptake (SU) and the global uniformity (GU) were evaluated. The DPI was calculated on segments included in the PD to minimize attenuation artifacts outside the PD. The 17 segmental model was used. ResultsThe highest level of uniformity of polar map was obtained for Symbia IQ + SCAC. D530c showed the highest extent of PD and dependence of the extent on the PD position. It showed in general the lowest SU values and the highest GU due to attenuation artifacts. Nevertheless, D530c outperforms other MPI systems in terms of PD detection, showing the highest DPI value. DSPECT system showed the lowest SS value, and DPI values comparable to NaI-crystal detector systems. ConclusionThe DPI can be evaluated to investigate the intrinsic ability of MPI systems to detect PDs, whatever the quantitative post-processing software used.

AA5754 aluminium alloy springback reduction by post forming electro plastic effect (PFEPE)

Post Forming Electro Plastic Effect (PFEPE) has been proposed as a promising technology for mitigating forming forces and addressing springback challenges in the metal forming industry. However, several research gaps remain unaddressed for the industrialization of this technology. Firstly, there is a lack of experimental validation regarding the impact of stress reduction on springback. Secondly, the potential effect of the skin-effect on the current metrics of stress reduction needs to be evaluated. Additionally, a post-forming electrically assisted elastoplastic material model is necessary for further technology development in stamping processes. This study tackles these challenges by utilizing AA5754H22 as a reference material and integrating a comprehensive experimental campaign with finite element numerical models and empirical material model developments. Our findings confirm that PFEPE facilitates a significant reduction in springback, achieving approximately a 100% reduction. Although the skin-effect introduces non-uniform current flux density distribution, its impact at the macroscopic level is negligible for the studied thin samples. While the numerical results of springback fails to accurately replicate experimental results, the developed material model aligns well with experimental trends.

Concept and Implementation of a Novel Continuous Glucose Monitoring Solution With Glucose Predictions on Board.

The recently CE-marked continuous real-time glucose monitoring (rtCGM) solution Accu-Chek® (AC) SmartGuide Solution was developed to enable people with diabetes mellitus (DM) to proactively control their glucose levels using predictive technologies. The comprehensive solution consists of three components that harmonize well with each other. The CGM device is composed of a sensor applicator and a glucose sensor patch whose data are transferred to the connected smartphone by Bluetooth® Low Energy. The user interface of the CGM solution is powered by the AC SmartGuide app delivering current and past glucose metrics, and the AC SmartGuide Predict app providing a glucose prediction suite enabled by artificial intelligence (AI). This article describes the innovative CGM solution.

Rotifer functionality as a potentially useful indicator of lake browning

Lake browning is considered a severe water quality problem in lake ecosystems, but it has received considerably less attention in water protection than eutrophication. Current metrics used in lake surveillance monitoring programmes, including the European Union’s Water Framework Directive, do not reflect browning sufficiently. The aims of the study were to explore the potential role of the functionality of rotifers as browning indicators and to improve understanding of the environmental parameters driving the functionality and diversity of rotifers. Seasonal data on rotifer communities and water quality from seven lakes with differing water colour and trophic conditions were analysed. The feeding guilds of rotifers enabled differentiation between lakes in terms of their ecological conditions, and, in particular, eutrophic and brown-water lakes were clearly distinguished from other lakes. The guild ratio of rotifers was positively affected by water colour, but inversely related to total phosphorus concentration. Our results suggest that zooplankton functionality provides a potential tool to assess ecosystem dynamics, particularly when assessing lake browning. Thus, our results suggest that application of the guild ratio of rotifers is a promising method to estimate the general browning status of lakes and may complement the metrics used in Water Framework Directive.

Prospects for the development of transport and logistic routes connecting the BRICS Countries

This article examines the current metrics and future prospects for the development of transportation and logistics routes connecting BRICS member countries (Brazil, Russia, India, China, South Africa, Egypt, Ethiopia, UAE, Saudi Arabia and Iran). It analyses railway transport indicators, the dynamics and structure of mutual trade, as well as the main destinations of Russian exports. A special focus is placed on infrastructure projects and trends in multimodal transportation. The study highlights the significance of investment in infrastructure and the optimization of logistics processes, emphasizing the potential for enhanced economic cooperation among BRICS members.

Energy Cost of Selected Physical Activities of Older Persons Living in Taguig City, the Philippines

The promotion of physical activity is important for fostering healthy aging. However, a substantial proportion of older adults remain sedentary. Current metrics assessing the energy cost of physical activity are primarily designed for the general adult population and inadequately account for distinct physiological changes in older individuals – encompassing variations in muscle mass, bone density, metabolism, and overall functional capacity. This study aimed to determine the energy cost of selected physical activities of Filipino older persons. This cross- sectional study conducted in Taguig City involved 19 older adults aged 60–79 yr using the COSMED Fitmate indirect calorimeter for resting metabolic rate determination, as well as the portable COSMED K5 indirect calorimeter for measuring energy cost of different physical activities – including treadmill walking, self-paced walking, a routine based on the DOST-FNRI manual, and multi-gym equipment. The outcomes of the study were presented in percentages and averages. Participants in the study were categorized based on their muscle strength and mass. Analysis revealed a consistent trend where older adults with lower appendicular skeletal muscle mass index and handgrip strength values demonstrated lower metabolic equivalent values across the different exercise routines. Notably, Routine 2 displayed an exception, with individuals above the 50th percentile of ASMI and HGS exhibiting lower METs than those below the 50th percentile. This study contributes insights into the energy cost of specific activities among older adults emphasizing the importance of physical activity in healthy aging and providing valuable information for tailored interventions and a nuanced understanding of energy expenditure in this age group.

GPA-Net:No-Reference Point Cloud Quality Assessment With Multi-Task Graph Convolutional Network.

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases.

Exploring the Continuous Glucose Monitoring in Pediatric Diabetes: Current Practices, Innovative Metrics, and Future Implications.

Continuous glucose monitoring (CGM) systems, including real-time CGM and intermittently scanned CGM, have revolutionized diabetes management, particularly in children and adolescents with type 1 diabetes (T1D). These systems provide detailed insights into glucose variability and detect asymptomatic and nocturnal hypoglycemia, addressing limitations of traditional self-monitoring blood glucose methods. CGM devices measure interstitial glucose concentrations constantly, enabling proactive therapeutic decisions and optimization of glycemic control through stored data analysis. CGM metrics such as time in range, time below range, and coefficient of variation are crucial for managing T1D, with emerging metrics like time in tight range and glycemia risk index showing potential for enhanced glycemic assessment. Recent advancements suggest the utility of CGM systems in monitoring the early stages of T1D and individuals with obesity complicated by pre-diabetes, highlighting its therapeutic versatility. This review discusses the current CGM systems for T1D during the pediatric age, established and emerging metrics, and future applications, emphasizing the critical role of CGM devices in improving glycemic control and clinical outcomes in children and adolescents with diabetes.

Impact of Psychological Factors on Rehabilitation After Anterior Cruciate Ligament Reconstruction: A Systematic Review.

Anterior cruciate ligament (ACL) tears are a common orthopaedic injury, and the incidence of ACL reconstruction (ACLR) continues to increase. Current clinical practice guidelines (CPGs) recognize the role of psychological factors in rehabilitation, but patient-reported outcome measures (PROs) and psychological readiness are rarely incorporated into rehabilitation. The purpose of this review was to highlight the importance of psychological health after ACL injury, understand the current metrics used to monitor psychological recovery, and outline how psychological recovery can be better incorporated in current CPGs. A systematic review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines (PRISMA); 63 studies were identified with a PubMed search using the term "ACL Injuries/psychology". Exclusion criteria included lack of consideration of psychological effects or studies validating PROs after ACLR. Studies were reviewed by multiple reviewers, and a total of 38 studies were included after applying exclusion criteria. Systematic review. Level 3b. Two independent reviewers analyzed the included articles to extract sample size, psychological readiness scale or other measures used, and key results. Psychological outcomes, especially kinesiophobia and fear of reinjury, are seen commonly after ACLR. Psychological factors were shown to impede return to sport (RTS), alter measurable knee biomechanics, and potentially increase the risk for re-rupture. Targeted interventions such as kinesiotaping, knee bracing, and imagery training can help improve psychological and functional testing after ACLR. ACLR is often complicated by psychological factors. Psychological readiness is a crucial yet often unincorporated part of rehabilitation. Patients with higher levels of kinesiophobia and lower psychological readiness to RTS specifically should be identified to allow for administration of interventions, such as imagery training, knee bracing, or kinesiotaping, that can mitigate the negative effects of psychological outcomes and improve recovery.

Recall Laterality and Bilaterality: Possible New Screening Mammography Quality Metrics

Purpose: Current screening mammography quality metrics are important and helpful, but do not address all quality concerns. An individual screening mammography reader may be systematically insensitive to findings present in the breast of one side, laterality bias, evidenced by left versus right difference in advised immediate recalls. Current metrics are not designed to detect laterality bias. Whether a reader exhibits laterality bias, or what an appropriate ratio/range of bilateral versus unilateral recalls should be, have never been discussed in medical literature. Methods: As a trainee quality project, five attending (‘consultant’ in Europe) radiologists’ screening mammography reports over 2 years at an academically affiliated, public hospital were tallied with regard to laterality of recommended recall, and with respect to unilateral versus bilateral recalls advised. The chi-square (χ²) statistic was applied to reports advising unilateral recall. Findings: No group laterality bias was discovered. One radiologist (the most experienced) evidenced a consistent laterality bias over 2 years (p=0.07) against left-breast findings. Of reports recommending recall, the radiologists’ single-year range for recall regarding both breasts was 10.2–23.3%; for both years combined, the individual radiologists ranged from 13.6–17.9%. The group, 2-year mean recommending bilateral recall was 16.5%. Conclusion: A radiologist may exhibit laterality bias, favoring detection of findings in one breast over the other, a concern never before considered. Audit to discern such bias leads simultaneously to assessment of bilateral recall bias. Possible causes of these biases are discussed, and research regarding them as possible quality metrics is encouraged.

Evaluating the response of current biotic indices and functional metrics to natural and anthropogenic predictors in disconnected pools of temporary rivers

Temporary rivers, forming the majority of river networks worldwide, are key biodiversity hotspots. Despite their great value for maintaining biodiversity and ecosystem functioning, they are often neglected in biomonitoring programs due to several challenges, such as their variable hydromorphology and the difficulty of establishing reference conditions given their dynamic nature, resulting in highly variable communities. Disconnected pools often form in temporary rivers when flow ceases, providing refuge for aquatic taxa. Given their importance for biodiversity conservation, revising and adapting biotic indices are needed. Here, we evaluate the performance of current biological indices designed for perennial rivers (macroinvertebrates, diatoms) and functional metrics (macroinvertebrates) in assessing biological quality of disconnected pools. We sampled 55 disconnected pools in Catalonia, NE Spain, covering local (e.g., physico-chemical variables, water chemistry) and regional (e.g., human influence, hydrological variables at the water body level) natural and anthropogenic gradients. Only a few macroinvertebrate biotic indices (e.g., family richness, EPT/EPT + OCH and OCH) showed strong responses to anthropogenic predictors and were unaffected by natural predictors at both local and regional scales, making them suitable for biomonitoring. Of the newly adopted functional metrics of macroinvertebrate communities tested, only two (i.e., functional redundancy of predators and response diversity based on the total community) responded strongly to anthropogenic predictors. The rest showed varying responses to the interactive effect of anthropogenic and natural predictors, requiring calibration efforts. Models assessing these metrics explained <40 % of the total variation, likely due to the interplay of colonization/extinction dynamics and density-dependent trophic interactions governing community assemblages in disconnected pools. Although some existing biological metrics could potentially be used to monitor the ecological status of disconnected pools, we call for further development of biomonitoring tools specifically designed for these habitats since they will become more widespread with global change.

Signal processing analysis for detection of anomalies in numerical series

It might be instinctively assumed that the occurrence of the first digit of a randomly selected number is uniformly distributed among 1 to 9. However, the Newcomb–Benford law (NBL), also known as the first-digit law or Benford’s law, reveals a completely different fact: the distributions follows a logarithm-type law. This bias in the frequency of occurrence of the leading digits of numbers is evident in everything from the lengths of rivers, the areas of countries, stock prices, and population numbers. Omnipresence of NBL emerges in mathematics (the Fibonacci sequence, the distribution of prime numbers, etc.) and also in the field of physics (i.e. the half-lives of unstable nuclei). From the earliest years, a large bibliography has been developed and applied to many fields such as fraud detection in finances, international trade and election results analysis. Usually, the diagnosis of such works is reduced to a qualitative binary assessment (conformity or non-conformity). The present paper aims to establish a supervised methodology to address existing gaps and enhance the reliability of final decisions. These gaps include uncertainty about the minimum data volume required to validate Benford’s Law, the low sensitivity of current metrics (Euclidean distance or Mean Absolute Difference) to detect minor levels of adulteration, and the need to quantify both the level and nature of adulteration. Two new procedures have been established coming from Signal Analysis and Processing theory. The first relates the deconvolution between the empirical leading digit distribution and the Benford one. The second procedure consists of obtaining the generating functions (via Hilbert and Fourier transforms) to increase the contrast between both functions: the empirical under research and Benford’s as the reference. Based on calibrated datasets, we are able to quantify the magnitude of accidental errors or malicious fraud adulteration and make the discrimination between both. Finally, as benchmark, fifteen real-world datasets were studied. This study shows that most of datasets are suspicious of Fraud. Notably, in the field of human health, several datasets demonstrate significant fraudulent deviations, defying conventional patterns of fraud and error. This phenomenon has been termed Berserker Fraud.