Snapshot Sampling Research Articles

Domestic and irrigation water quality on the southern slopes of Mount Kilimanjaro

This study assessed the water quality for drinking and irrigation purposes on the southern slopes of Mt. Kilimanjaro during the dry season under low flow conditions. Fifty-one samples covering eight different water sources (i.e., stream water from natural and anthropogenic impacted streams, domestic water, spring water, rainfall, groundwater, lake water and water from irrigation canals) were collected in a snapshot sampling campaign over 10 days in February 2023. First, physical, chemical and biological parameters were analysed and compared with Tanzanian and international drinking and irrigation water quality requirements. The samples were then ranked according to their suitability for drinking and/or irrigation using water quality indices (WQI). All drinking water quality parameters except for E. coli and turbidity were within the permissible limits. A generalised problem of faecal contamination was found in the study area, including in domestic water, highlighting the need to identify sources of contamination and remediate them before distribution. The drinking water quality index (DWQI) classified 89% of the samples as unsuitable and 11% as excellent for drinking. Irrigation water quality parameters were within the guidelines of restriction of use except for pH in 5 samples. In contrast to the DWQI, the vast majority of the water samples (88%) can be used for irrigation without restrictions according to the irrigation water quality index (IWQI). The suitability of water for irrigation was also assessed using three indices, i.e., Kelley’s Index, Soluble Sodium Percentage and Magnesium Ratio, which indicated potential problems with excess of sodium (about 30% of the samples) and magnesium (about 20%). Further studies combining suitability indices, soil characteristics and crop types are recommended to assess water quality for irrigation use.

A new chiller fault diagnosis method under the imbalanced data environment via combining an improved generative adversarial network with an enhanced deep extreme learning machine

The existing chiller fault diagnosis approaches often ignore the problem of data imbalance of chiller, which leads to low accuracy in diagnosing minority class fault samples. To conquer this issue, this paper proposes an improved generative adversarial network (IGAN) with an enhanced deep extreme learning machine (EDELM) method. Firstly, to better learn the latent structure of chiller fault data, the multi-head attention (MHA) mechanism is integrated into the traditional generative adversarial network (GAN) method to generate new samples that are more in line with the distribution of minority class fault samples for the purpose of obtaining a rebalanced dataset. Secondly, to fully handle the nonlinear features hidden in the massive chiller data, the deep extreme learning machine (DELM) basic classifier is trained on the rebalanced dataset. To enhance more attention to the misclassified samples, the adaptive boosting (AdaBoost) ensemble strategy is employed to train multiple DELM basic classifiers by updating the sample weights following the classification results through the iterative rounds. The voting weight of the current DELM basic classifier is given according to its fault diagnosis accuracy. Finally, multiple DELM basic classifiers are ensembled according to their voting weights to obtain the final ensemble classifier. The pattern of the snapshot sample is determined through the weighted voting strategy. Detailed experimental results based on the research project 1043 (RP-1043) conducted by the American society of heating, refrigeration, and air conditioning engineers (ASHRAE) confirm the effectiveness of the proposed IGAN-EDELM approach under imbalanced data environments.

Analisis Kualitas Air Sumur Gali Di Kelurahan Maubeli Kota Kefamenanu Kabupaten Timor Tengah Utara

The existance of dug well is important for the people of Maubeli from Kefamenanu City. The society consumed it since their adults been there. As quality as water consuming needed to be tested for sanitation and hyegene issues. The aims is to analyze dug water quality in Maubeli Village for physic parameters (temperature, odor, colour, TSS), chemistry (pH, COD, and microbiology (E.coli). Water sample from 5 locations and analized locally for temperature, odor, colour, pH and in Laboratorium Dinas Lingkungan Hidup dan Kehutanan from East Nusa Tenggara for TSS dan COD. Data compared to Peraturan Pemerintah No. 22/2021 and Permenkes No. 2/2023. The resulst showed that the temperature for 1st dan 2nd are 250C, 3rd to 5th: 270C, there’s good for odor and colour. TSS: 2,5 mgL-1, 3,5 mgL-1, 9 mgL-1, 4 mgL-1, and 5 mgL-1. pH: (8,1), (7,9), (8,2), (7,9), and (7,6). COD: 15,16 mgL-1, 17,02 mgL-1, 9,61 mgL-1, 12,05 mgL-1, and not found E. coli for all dug wells. Base on snapshot sampling that the water quality is good to be consumed but still need to boil it.

Snapshot Sampling May Not Be Enough to Obtain Robust Estimates for Riverine Microplastic Loads.

Wastewater treatment plants (WWTPs) have been described as key contributors of microplastics (MPs) to aquatic systems, yet temporal fluctuations in MP concentrations and loads downstream are underexplored. This study investigated how different sampling frequencies (hourly, weekly, and monthly) affect MP estimates in a stream linked to a single WWTP. Utilizing fluorescence microscopy and Raman spectroscopy, considerable hourly variations in MP concentrations were discovered, while the polymer composition remained consistent. This temporal variability in MP loads was influenced by MP concentration, discharge rates, or a mix of both. These results show a high uncertainty, as relying on sparse snapshot samples combined with annual discharge data led to significant uncertainties in MP load estimates (over- and/or underestimation of emissions by 3.8 billion MPs annually at this site). Our findings stress the necessity of higher-frequency sampling for better comprehending the hydrodynamic factors influencing MP transport. This improved understanding enables a more accurate quantification of MP dynamics, crucial for downstream impact assessments. Therefore, preliminary reconnaissance campaigns are essential for designing extended, representative site-monitoring programs and ensuring more precise trend predictions on a larger scale.

Snapshot of cyanobacterial toxins in Pakistani freshwater bodies.

Cyanobacteria are known to produce diverse secondary metabolites that are toxic to aquatic ecosystems and human health. However, data about the cyanotoxins occurrence and cyanobacterial diversity in Pakistan's drinking water reservoirs is scarce. In this study, we first investigated the presence of microcystin, saxitoxin, and anatoxin in 12 water bodies using an enzyme-linked immunosorbent assay (ELISA). The observed cyanotoxin values for the risk quotient (RQ) determined by ELISA indicated a potential risk for aquatic life and human health. Based on this result, we made a more in-depth investigation with a subset of water bodies (served as major public water sources) to analyze the cyanotoxins dynamics and identify potential producers. We therefore quantified the distribution of 17 cyanotoxins, including 12 microcystin congeners using a high-performance liquid chromatography-high-resolution tandem mass spectrometry/mass spectrometry (HPLC-HRMS/MS). Our results revealed for the first time the co-occurrence of multiple cyanotoxins and the presence of cylindrospermopsin in an artificial reservoir (Rawal Lake) and a semi-saline lake (Kallar Kahar). We also quantified several microcystin congeners in a river (Panjnad) with MC-LR and MC-RR being the most prevalent and abundant. To identify potential cyanotoxin producers, the composition of the cyanobacterial community was characterized by shotgun metagenomics sequencing. Despite the noticeable presence of cyanotoxins, Cyanobacteria were not abundant. Synechococcus was the most abundant cyanobacterial genus found followed by a small amount of Anabaena, Cyanobium, Microcystis, and Dolichospermum. Moreover, when we looked at the cyanotoxins genes coverage, we never found a complete microcystin mcy operon. To our knowledge, this is the first snapshot sampling of water bodies in Pakistan. Our results would not only help to understand the geographical spread of cyanotoxin in Pakistan but would also help to improve cyanotoxin risk assessment strategies by screening a variety of cyanobacterial toxins and confirming that cyanotoxin quantification is not necessarily related to producer abundance.

Synoptic sampling reveals spatial stability in flow and chemistry patterns despite large seasonal variability in a subarctic mountain catchment

AbstractSeasonality plays a critical role in cold mountain regions as variation in air temperature, ground thermal status, and precipitation phase alter the rate, timing and magnitude of hydrological and chemical transport. Additionally, cold mountain catchments can have highly variable topography, geology, permafrost, and landcover, which intrinsically add to this irregularity. Understanding how external and internal variability act to control mass fluxes requires sampling at a high spatial resolution over time, which rarely occurs in cold remote regions. In this work, we conduct five snapshot sampling surveys across 34 subcatchments during the ice‐free period in Wolf Creek Research Basin (a mesoscale montane subarctic catchment) and two additional winter surveys across a subset of sites to assess the drivers of variability in stream chemistry and discharge. We sampled for specific conductance (SpC), major ions, and dissolved organic carbon (DOC) and used statistical metrics and Bayesian mixing analysis to quantify patterns of flow and chemistry across space and time. Our results indicate patterns in both flow and chemistry remain largely consistent across seasons for all solutes. However, there was weaker correlation of chemistry between sites, suggesting asynchronous behaviour within the catchment. There was evidence of increasing production of ions and DOC along the stream network during high spring flows but not during low flows. Although concentrations and flows exhibit high seasonality in subarctic mountains, this seasonal variability does not alter spatial patterns that arise from highly variable catchment characteristics.

Efficient Low-Frequency Human Exposure Assessment with the Maximum Entropy Snapshot Sampling

Efficient Low-Frequency Human Exposure Assessment with the Maximum Entropy Snapshot Sampling

Adaptive restoration and reconstruction of incomplete flow fields based on unsupervised learning

Due to experimental limitations and data transmission constraints, we often encounter situations where we can only obtain incomplete flow field data. However, even with incomplete data, we can still extract valuable information about the main structural characteristics of the flow field. In this study, we propose a novel unsupervised learning reconstruction method to restore the incomplete flow field data. Our method comprises several key steps: First, based on the temporal evolution characteristics of the flow field, we employ the Autoregressive Integrated Moving Average model method to pre-reconstruct the incomplete data. Next, we introduce the Fuzzy Spatial Consistency Index (FSCI), which measures the spatial coherence among neighboring data variations. By utilizing FSCI as a guiding metric, we iteratively optimize and replace missing values using the Proper Orthogonal Decomposition method. Importantly, our reconstruction process does not rely on expensive high-fidelity data fusion or external intervention, making it highly efficient and cost-effective. We focus our research on the classic problem of flow around the hydrofoil and apply the unsupervised learning reconstruction method to restore incomplete flow fields with varying missing rates. We also investigate the impact of flow field stability, snapshot sampling frequency, and missing structure on reconstruction accuracy. Our results indicate that these factors significantly influence the reconstruction of velocity fields with a high missing rate compared with a lower missing rate. Additionally, we observe that the reconstruction performance of the streamwise velocity field is relatively inferior compared to the normal velocity field, and the reconstruction accuracy is higher for relatively stable flow fields. These findings provide valuable insights and further validate the effectiveness and applicability of the unsupervised learning reconstruction method for restoring incomplete flow fields.

Per- and polyfluoroalkyl substances and organofluorine in lakes and waterways of the northwestern Great Basin and Sierra Nevada

Per- and polyfluoroalkyl substances (PFAS) are anthropogenic chemicals that occur ubiquitously in the environment and have been linked to numerous adverse health effects in humans and aquatic organisms. Although numerous environmental monitoring studies have been conducted, only one has evaluated PFAS in surface waters of the northwestern Great Basin, which features unique topography that results in dozens of endorheic basins and terminal lakes with no natural outlet, where PFAS may accumulate. To close this knowledge gap, we evaluated the occurrence of PFAS in grab samples from 15 lakes (headwater and terminal lakes) and 10 rivers in the Great Basin located in Nevada and California of the United States. PFAS and organofluorine were quantified by liquid chromatography tandem mass spectroscopy (LC-MS/MS) and combustion ion chromatography, respectively. The highest concentrations of PFAS occurred in samples taken near sites with known or suspected prior aqueous film forming foam (AFFF) application (~20 to 4754 ng/L). Samples near wastewater treatment plants and in urban areas also tended to have PFAS concentrations greater than those measured in remote, less anthropogenically influenced areas (~2 to 15 ng/L, <3 ng/L respectively). In limited snapshot sampling events PFAS appeared to accumulate in terminal lakes to some extent; in-lake concentrations were two to five times greater than those of their inflows. Fluorotelomer sulfonates were present downstream of a known AFFF application area likely to have had fluorotelomer-based foams applied to it, and the concentrations decayed in a predictable manner, suggesting they may be used as an indicator of PFAS transport away from an AFFF source. In all but two samples, organofluorine concentrations were greater than the sum of targeted PFAS (on a F basis) (median of 0.6 % of organofluorine identified via LC-MS/MS), although there was considerable variability in organofluorine measured in replicate samples.

Comparison of reduced basis construction methods for Model Order Reduction, with application to non-linear low frequency electromagnetics

Numerical simulation is more and more used during the design stage of a manufactured product in order to optimize its performances. However, it is often too time consuming, particularly when it is used to solve optimization problems, preventing an intensive usage. A-posteriori Model Order Reduction methods can be very effective to shorten the computational time. An approximated solution is then sought in a space of small dimension defined by a reduced basis. The accuracy of such methods is highly dependent on the choice of the reduced basis, extracted from preliminary numerical simulation. The method usually applied to construct such reduced basis is based on the Singular Value Decomposition (SVD), which can be time consuming, and is not adapted when a large collection of preliminary numerical simulations must be used to construct the basis. An alternative to this approach has been proposed recently with the Maximum Entropy Snapshot Sampling (MESS) method. In this paper, we propose to compare these methods with other approaches usually used for clustering or data classification based on vectors distance calculation, like the Centroidal Voronoi Tessellation (CVT), Density Based Spatial Clustering of Applications with Noise (DBSCAN), and Clustering Using Representatives (CURE). The methods are compared on a complex and realistic nonlinear problem in low frequency electromagnetics. The quality of the reduced bases obtained by the different methods are compared. Then, field distributions and global quantities, like eddy current losses and magnetic energy, are computed from the reconstructed results, to further analyze the quality of the reduced bases.

Imbalanced data based fault diagnosis of the chiller via integrating a new resampling technique with an improved ensemble extreme learning machine

Fault diagnosis of the chiller is essential to guarantee chiller's safe operation and reduce building energy consumption. However, the existing fault diagnosis methods rarely consider the chiller's imbalanced data conditions, which always leads to low diagnosis accuracy of the minority class samples. To figure out the issue of imbalanced fault pattern data during the chiller fault diagnosis, a hybrid resampling based improved extreme learning machine (HRIELM) is developed in our work. A new hybrid resampling technique (HRT) is first proposed to balance the majority and minority classes in the imbalanced fault pattern datasets. The HRT is then carried out repetitively to obtain multiple diverse rebalanced training datasets using different benchmark datasets. Subsequently, various primary extreme learning machine (ELM) models are established utilizing these rebalanced training datasets. Furthermore, an improved ELM model based on a novel selective ensemble learning strategy is presented to enhance the effectiveness of the chiller fault diagnosis. The basic ELM models with superior performance for diagnosing the neighbor subset constructed according to the fault snapshot sample are first picked out, and a weight factor is further defined to build the final selective ensemble ELM model. Finally, the fault pattern of the snapshot sample is identified according to the weighted voting mechanism. Detailed experimental results on the ASHRAE Research Project RP-1043 experimental datasets certify the effectiveness of the presented HRIELM scheme for the chiller fault diagnosis under the imbalanced data environments.

A snapshot sample on how COVID-19 impacted and holds up a mirror to European water education

Abstract. COVID-19 caused many disruptions, not only in society, but also in university education, including in hydrology and water-related sciences. Taking part in an academic teaching training course at Uppsala University during COVID-19, we got curious about how COVID-19 might have impacted European water education. Consequently, we chose to investigate this aspect in the mandatory project of the course by conducting an online survey. In this paper, we communicate the results of the survey and reflect (hold up a mirror to water education) on how the teaching of hydrology and water-related sciences changed due to COVID-19. The answers of 28 respondents, working in the field of hydrology at different universities across Europe, showed that in the pre-COVID-19 classroom lectures, laboratory work and fieldwork were commonly used teaching formats in courses with 10 to more than 40 students. These results agreed with those found in the literature. The occurrence of COVID-19 forced hydrological education to suddenly move from classroom to online teaching, which was possible thanks to the available digital tools and technical infrastructure. The practiced online teaching format remained lectures. Most of the respondents (&gt;40 %) reported not using classroom assessment techniques to gauge the students' performances. In addition, a loss of human interaction in the online environment was noticeable. Hence, whether students reached their learning outcomes during distance teaching was largely unknown. The most affected learning activities were the ones that could not be moved to online teaching, such as laboratory work and fieldwork. As a result, comprehensive hydrological knowledge might be missing for at least several cohorts of hydrologists. In this way, COVID-19 caused a secondary effect on society which needs skills in solving future challenges such as water management in a changing climate. Next to negative aspects, we observed positive COVID-19 aspects; for example, the hydrology community explored novel teaching formats and shared teaching material and experiences online. COVID-19 forced hydrology teachers to explore, improvise, and be creative to continue teaching. Hydrology can use this experience to learn from and modernize hydrology education by developing a lesson design suited for the online environment, including best practices and making practical and “exotic” non-traditional teaching formats accessible to all hydrology and water students.

A multifaceted index of population health to detect risk-prone populations and underlying stressors in wildlife

Local declines of wild populations represent the most visible part of biodiversity loss, and their detection often relies on long-term surveys. An alternative to identify risk-prone populations is to use indicators informing on their general health (i.e., their general fitness and ability to cope with changing environment) based on simple and complementary parameters estimated from snapshot sampling. However, most studies on wildlife population health focus on one or only a few parameters, yielding potentially biased conclusions for conservation. Here, we developed a multifaceted index of population health by combining 3 complementary indicators, namely pathological, ecological, and genetic indicators, based on an integrative approach traditionally used to assess ecosystem multifunctionality. We investigated their complementarity and relevance for detecting brown trout (Salmo trutta) risk-prone populations at a large spatial scale, and the underlying environmental stressors. The multifaceted health index properly represented the individual indicators' complementary information. It identified a cluster of moderately risk-prone populations and raised the alarm for one population. Each indicator was individually associated with distinct environmental stressors relevant for brown trout requirements. The multifaceted health index highlighted surrounding agricultural land and oxygen concentration as the most impacting environmental factors for the general health and sustainability of brown trout populations. The implementation of such integrative index can be transferred to a wide range of species and contexts. This index therefore provides to environmental managers and conservationists a snapshot and easily operated tool to identify risk-prone populations and areas to restore or conserve.

Reduced order multirate schemes in industrial circuit simulation

In this paper the industrial application of Reduced Order Multirate (ROMR) schemes is presented. This paper contains the mathematical foundations of the ROMR schemes and elaborates on the construction of these schemes using specific Model Order Reduction (MOR) techniques. Especially the Maximum Entropy Snapshot Sampling method for generating a reduced basis and reduction by Gauß–Newton with Approximated Tensors (GNAT). This basis generation method is also used for generating the basis for the gappy hyper-reduction method used for nonlinear function evaluation. For the multirate integration part, a Backward Differentation Formula approach to integration is used in conjunction with a coupled-slowest-first multirate approach. After introducing the numerical approach to industrial circuit simulation validation experiments are performed. First a simple academic model is used, and then an industrial test case is simulated as presented by STMicroelectronics. A significant speedup in simulation time is achieved whilst accuracy and convergence is kept.

Clinical Audits – A Quality Improvement Tool in Transfusion Medicine

A clinical audit is a quality improvement process that seeks to improve services and outcomes through a systematic review of care against explicit criteria. It is now a recognized tool for improving clinical quality of care. However, in transfusion medicine, they have been utilized in a limited manner. This review intends to discuss the methodology of carrying out a clinical audit systematically. Clinical audit is not research and focuses on the improvement of ordinary and routine practices. Various methodologies for selecting and prioritizing a good audit topic can be employed. Based on the Donabedian model, process-based audits from donor selection to the actual bedside transfusion could be audited to bring in improvement. Likewise, transfusion processes can be audited based on the dimensions of quality in the context of timeliness, effectiveness, and appropriateness. Prioritization of a topic can be done through a quality impact assessment. The objectives help quantify the aim of the audit, while the standards include criteria that are quantified against targets, based on the current evidence for the best possible care. A snapshot sample (20–50 cases) is sufficient to carry out a clinical audit. Where targets are not met, root-cause analysis and quality improvement tools guide the implementation of changes in transfusion practices. To ensure that change is implemented and internalized, it is necessary to have the entire team and the management on board; communication with all stakeholders is key. Re-audit, after the change has been internalized, is an essential part of all clinical audits. Meticulously planning and proper implementation ensure improvement of the care that transfused patients receive.

Adaptive Diagonal Loading Technique to Improve Direction of Arrival Estimation Accuracy for Linear Antenna Array Sensors

Diagonal loading is one of the most widely used and effective methods to improve the robustness of both adaptive beamformers and Direction of Arrival (DOA) estimation due to the involvement of the sensor received covariance matrix. In addition, subspace-based DOA estimation techniques rely on multiple snapshots to achieve high estimation accuracies. This paper presents the study of a modified diagonally loaded sample covariance matrix for accurate DOA estimation in adverse scenarios. The proposed and novel technique deciphers poor DOA estimation in a low SNR environment by computationally changing the received sample covariance matrix. Our method is computationally simple as it does not require peak searching and does not depend on the coherency of the signal. The efficacy of the proposed method is examined via computer simulation for various sensor array sizes and the number of snapshot samples. Based on our numerical simulation results, our proposed method generally outperforms most state-of-the-art DOA estimators. In a finite number of snapshots and a single signal source, our proposed method performs 9.5% better than the state-of-the-art DOA estimation technique, 2.8% in multiple signal sources, and 8.5% in a single snapshot, single signal source environment of gained DOA estimation performance.

A Covariance Matrix Reconstruction Approach for Single Snapshot Direction of Arrival Estimation.

Achieving accurate single snapshot direction of arrival (DOA) information significantly improves communication performance. This paper investigates an accurate and high-resolution DOA estimation technique by enabling single snapshot data collection and enhancing DOA estimation results compared to multiple snapshot methods. This is carried out by manipulating the incoming signal covariance matrix while suppressing undesired additive white Gaussian noise (AWGN) by actively updating and estimating the antenna array manifold vector. We demonstrated the estimation performance in simulation that our proposed technique supersedes the estimation performance of existing state-of-the-art techniques in various signal-to-noise ratio (SNR) scenarios and single snapshot sampling environments. Our proposed covariance-based single snapshot (CbSS) technique yields the lowest root-mean-squared error (RMSE) against the true DOA compared to root-MUSIC and the partial relaxation (PR) approach for multiple snapshots and a single signal source environment. In addition, our proposed technique presents the lowest DOA estimation performance degradation in a multiple uncorrelated and coherent signal source environment by up to 25.5% with nearly negligible bias. Lastly, our proposed CbSS technique presents the best DOA estimation results for a single snapshot and single-source scenario with an RMSE of against the true DOA compared to root-MUSIC and the PR approach with nearly negligible bias as well. A potential application for CbSS would be in a scenario where accurate DOA estimation with a small antenna array form factor is a limitation, such as in the intelligent transportation system industry and wireless communication.

Occurrence and Behaviour of Shorebirds Depend on Food Availability and Distance of Beaches from Urban Settlements

Coastal ecosystems provide important feeding opportunities for shorebirds, depending on their prey availability, hydrodynamic conditions and human pressure. We aimed to evaluate short-term responses (occurrence and minimum approach distance) of shorebirds to urbanization and natural drivers on an extensive beach arc adjacent to the largest hypersaline coastal lagoon of South America. The presence of the migrant Sanderling Calidris alba and resident Collared Plover Charadrius collaris was geo-coordinated and for each record and an equivalent number of random points we measured the distance from urban settlements, swash width and invertebrate abundance (food availability) in a snapshot sampling. The distance at which each shorebird flock escaped from humans was determined (minimum approach distance). The occurrence of shorebirds was predicted by food availability, and higher crustacean density was found in areas of shorebird occurrence (37 ± 19 individuals/m2) compared to random points (10 ± 10 individuals/m2). This result highlights the importance of the beach as a feeding area and the need for conservation of shorebirds' prey. The resident Collared Plover delayed their escape from humans in areas closer to urban areas, suggesting a higher tolerance to humans on disturbed beaches, where they can prioritize the food intake rather than vigilance. The larger flocks let the researcher get closer, corroborating the risk-dilution theory stating that flocking behaviour during foraging provides protection of birds from predators. In conclusion, our results showed that monitoring of sandy beaches based on shorebirds' presence and behaviour can be a reliable tool, especially close to coastal lagoons that constitute foraging sites for these charismatic species.

Female Surgeons for Obesity Treatment: a Snapshot Sampling in Academic Productivity : The Role of Women in Obesity Surgery.

The proportion of females choosing a career in surgery is lower than that of males. Through the Obesity Surgery and SOARD journals of 2018-2020, the number of articles with female first/senior authors was identified and their characteristics were assessed. Almost 40% of the published papers were written by females, being mostly original and from University Hospitals, although the difference in the numbers between journals was prominent (p = 0.011). Articles with a female as first author had a female or male as senior, at a ratio 1:2, while less than 10% of female senior authors had a male as first. The number of females as corresponding authors in SOARD was significantly higher (p < 0.001). The findings of the present study underline the existing gender inequity in bariatric surgery.

Maximum entropy snapshot sampling for reduced basis modelling

PurposeThe maximum entropy snapshot sampling (MESS) method aims to reduce the computational cost required for obtaining the reduced basis for the purpose of model reduction. Hence, it can significantly reduce the original system dimension whilst maintaining an adequate level of accuracy. The purpose of this paper is to show how these beneficial results are obtained.Design/methodology/approachThe so-called MESS method is used for reducing two nonlinear circuit models. The MESS directly reduces the number of snapshots by recursively identifying and selecting the snapshots that strictly increase an estimate of the correlation entropy of the considered systems. Reduced bases are then obtained with the orthogonal-triangular decomposition.FindingsTwo case studies have been used for validating the reduction performance of the MESS. These numerical experiments verify the performance of the advocated approach, in terms of computational costs and accuracy, relative to gappy proper orthogonal decomposition.Originality/valueThe novel MESS has been successfully used for reducing two nonlinear circuits: in particular, a diode chain model and a thermal-electric coupled system. In both cases, the MESS removed unnecessary data, and hence, it reduced the snapshot matrix, before calling the QR basis generation routine. As a result, the QR-decomposition has been called on a reduced snapshot matrix, and the offline stage has been significantly scaled down, in terms of central processing unit time.