Conventional Sampling Research Articles

Conventional net tow versus environmental DNA for metabarcoding-based analysis of plankton-environment interactions in polluted aquatic ecosystems

Human societal development has caused rapid degradation of freshwater ecosystems, resulting in significant biodiversity decline globally. The conservation of aquatic ecosystems, particularly the management of biodiversity in urban areas, has become an increasingly significant issue for ensuring the sustainability of these ecosystems. The restoration of degraded aquatic ecosystems relies on a comprehensive understanding of the interactions between biological communities and multiple stressors derived from human activities. Environmental DNA (eDNA)-based methods have emerged as effective tools for dissecting complex community-environment interactions. Nevertheless, potential causes for technical disparities and conclusions drawn from conventional sampling versus eDNA-based methods remain inadequately evaluated. In this study, we conducted a comprehensive survey of plankton communities along the environmental gradient of the North Canal River in Northern China to assess causes and consequences for any disparities between conventional plankton net tow and eDNA sampling for metabarcoding-based analysis of plankton-environment interactions. Our findings demonstrated that eDNA-metabarcoding was effective for characterizing both biodiversity and geographical distribution. Both methods exhibited general consistency in elucidating community structure and identifying the key environmental factors influencing geographical distribution of both zooplankton and phytoplankton. As we explored lower taxonomic ranks, we found inconsistent patterns at certain levels (e.g., phylum or order) within some taxa (e.g., Bacillariophyta and Rotifera), even substantial variability for several taxonomic groups (e.g., Euglenozoa and Apicomplexa). The varied environmental prevalence and persistence of eDNA, biological and ecological characteristics of diverse taxa in plankton, and sampling efficiency of both methods likely contributed to the observed disparities. Given its higher biodiversity recovery capacity, efficiency, sampling convenience, and capability to identify influential factors driving the geographical distribution of biodiversity, eDNA-based metabarcoding is recommended for studying plankton-environment interactions, particularly in studies involving a large number of samples at extended geographical scales.

Procedure to improve the surface fidelity of additive manufactured parts via direct slicing tessellation

Part appearance issues due to dimensional and shape inaccuracies (fidelity) hinder the use of additive manufacturing processes to obtain final products. Inaccuracies come from process parameter definition, post-processing operations, and the slicing process. Regarding the slicing procedure, the conventional approach is the indirect slicing of a tessellation that approximates the part model. Layer computation is straightforward in this method but introduces dimensional deviations for curvilinear surfaces. Direct slicing minimizes these deviations, but computation and layer representation are complex. This work blends both solutions, explaining how to build an improved tessellation from direct slices that can be introduced and processed in usual slicing programs. Two shapes , a simple hemisphere and a component of a retail product (a hair dryer nozzle), were printed by Fused Filament Fabrication (FFF), using conventional indirect slicing with different tessellation’s resolutions and the proposed Direct Slicing Tessellation (DST). The resulting samples were measured using a 3D scanner. Compared with a reference sample printed from a Computer-Aided Design (CAD) model discretized with a tolerance <6μm, maximum deviations were found on more areas of conventional indirect samples and with higher values (from 0.13 to 0.16 mm) than in the case of the DST samples (with maximum deviation of 0.1 mm at few points on the surface). Additionally, DST files are around ten times smaller than reference files. Finally, the deviation between the CAD model and the representation on the resulting surfaces of zebra stripes and mean curvature show that the proposed solution enhances the printed surface fidelity compared to the conventional indirect slicing.

Validation of the shortened 24-item multidimensional assessment of interoceptive awareness, version 2 (Brief MAIA-2)

The Multidimensional Assessment of Interoceptive Awareness (MAIA) was translated into many languages and frequently used in the last decade to assess self-reported interoceptive awareness. However, many studies demonstrated weaknesses regarding unstable factor structure and poor reliability of some scales. The 24-item Brief MAIA-2 questionnaire was developed, with only three items demonstrating the highest factor loadings in each of the eight scales of the MAIA-2. The cross-sectional online study used the 37-item MAIA-2 questionnaire in a non-clinical sample of 323 people aged between 16 and 75 (M = 26.17, SD = 9.12), including 177 women (54.80%). The sample comprised 156 athletes (48.30%) and 167 non-athletes (51.70%). The Confirmatory Factor Analysis showed adequate fit indices for a multidimensional model of the Brief MAIA-2, with the original eight scales: Noticing (awareness of subtle bodily sensations, such as the heartbeat, digestive sensations, or the breath), Not Distracting (ability to maintain attention to bodily sensations without being easily distracted by external stimuli), Not Worrying (tendency to not be overly concerned or anxious about bodily sensations or changes in the body), Attention Regulation (ability to regulate attention to bodily sensations and to shift attention between internal and external stimuli), Emotional Awareness (awareness and understanding of how emotions are associated with bodily sensations), Self Regulation (ability to regulate emotional responses and manage distress through an awareness of bodily sensations), Body Listening (tendency to listen to the body for insight and understanding), and Trusting (trust in bodily sensations as a source of information about one's feelings and needs). The hierarchical bi-factor (S·I – 1) model showed even better-fit indices. Therefore, the general factor of interoception was considered in further statistical tests. Confirmatory composite analysis showed high reliability and discriminant and convergent validity for most Brief MAIA-2 scales, except Noticing. Measurement invariance was confirmed across genders (Women, Men) and sports participation (Athletes, Non-athletes). However, group differences were also found for mean scores in particular scales of the Brief MAIA-2. Men scored significantly lower than women in Not Distracting but higher in Not Worrying, Attention Regulation, Self Regulation, Trusting, and the total score of interoceptive awareness. Gender discrepancies may be influenced by linguistic socialization, which tends to categorize shifts in internal states as either physiological or emotional. Athletes scored significantly lower than Non-athletes on the Not Distracting scale, but they showed higher scores in Noticing, Attention Regulation, Emotion Awareness, Self-Regulation, Body Listening, Trusting, and the global score, suggesting that physical training can improve most areas of interoception. Therefore, physical exercises and mindfulness training may be recommended to improve interoception, especially in women and people suffering from somatic and mental problems. The Brief MAIA-2 is a reliable and valid tool to measure multidimensional interoceptive sensibility in a non-clinical population. To improve well-being and athletic performance, Brief MAIA-2 can be used to assess the body's current perception of interoception and to detect its weak areas requiring improvement. However, the study has some limitations, such as a cross-sectional online self-report survey in a conventional non-clinical sample from Poland. Future cross-cultural studies should include representative samples for non-clinical and clinical populations from different countries and geographic regions to compare the Brief MAIA-2 with more objective psychophysiological methods of measuring interoception to reduce the limitations of these studies.

Assessment of App-Based Versus Conventional Survey Modalities for Reproductive Health Research in India, South Africa, and the United States: Comparative Cross-Sectional Study.

There is a widely acknowledged global need for more research on reproductive health (including contraception, menstrual health, sexuality, and maternal morbidities) and its impact on overall well-being. However, several factors-notably, high costs, considerable effort, and the sensitivity of these topics-impede the collection of the necessary data, especially in less accessible and lower-income populations. The burgeoning ownership of smartphones and growing use of menstrual tracking apps (MTAs) may present an opportunity to conduct reproductive health research with fewer impediments than those associated with conventional survey methods. The main objective was to ascertain the feasibility, potential usefulness, and limitations of conducting reproductive health research using a mainstream MTA. In each of the 3 countries, we evaluated questionnaire responses from (1) current users of an MTA (Clue) and (2) participants surveyed using conventional survey modalities (in-person interviews, SMS text messaging, and web-based questionnaires). We compared these responses with published data collected from large nationally representative benchmark samples (the United States Census and the Demographic and Health Surveys for South Africa and India). Given a sufficiently large user base, app-distributed surveys were able to quickly capture large samples on par with other methods and at low cost, with the additional advantage of being able to deploy remotely and simultaneously across countries. In each country, neither the app nor the conventional modality sample emerged as a consistently closer match to the distributions of the demographic attributes and the patterns of contraceptive use reported for the respective benchmark sample. Despite efforts to obtain representative samples, the conventional modality samples sometimes over- and other times underrepresented some subgroups (eg, underrepresentation of married persons in the United States and overrepresentation of rural residents in India). In all 3 countries, app users were younger, more educated, more likely to be urban residents, and more likely to use nonhormonal rather than hormonal contraceptive methods compared with the respective national benchmark. App users, compared with the conventional modality samples, consistently reported being more comfortable discussing their menstrual periods with other persons (eg, family, friends, and health care providers), suggesting that MTA users may be more likely to respond truthfully to questions on sensitive or taboo health topics. The app samples' consistency across countries regarding users' demographic profiles, contraceptive choices, and personal attitudes toward menstruation supports the validity of making cross-country comparisons of survey findings for a given app's users. MTAs such as Clue can provide a quick, scalable, and cost-effective method for collecting health data, including on sensitive topics, across a wide variety of settings and countries. With expanding global access to technology and the increasing use of these tools, consumer MTAs can be a viable survey modality to strengthen reproductive health research.

Effect of local chemical disordering on magnetic properties in high entropy manganite of variable hole concentration

A series of high entropy manganite perovskites (La0.2Pr0.2Nd0.2Bi0·2Ba0.2)MnO3, (La1/6Pr1/6Gd1/6Bi1/6Sr1/6Ba1/6)MnO3, (La0.2Nd0.2Gd0.2Sr0·2Ba0.2)MnO3 and (La1/6Pr1/6Nd1/6Ca1/6Sr1/6Ba1/6)MnO3 with variable hole concentration at fixed A-site ionic radius (<rA> = 1.253 Å) have prepared by sol-gel technique. Single phase samples were characterized by the powder X-ray diffraction (PXRD), Field Emission Scanning Electron Microscopy (FE-SEM), Energy Dispersive X-ray (EDX) analysis and elemental mapping which confirm the phase purity of the samples, surface morphology, elemental ratio and chemical homogeneity. X-ray photoelectron spectroscopic (XPS) studies reveal the increase of Mn4+ ions systematically with the substitution of hole concentration at A-site. Magnetization study in this high entropy sample with variable hole concentration at fixed <rA> = 1.253 Å exhibits several contrasting behaviour compare to the conventional hole doped samples. In these high entropy manganites the variation in critical temperature (TC) does not follow expected trend neither with the hole concentration nor to the cationic size disorder parameter, σ2. Though the <rA> is fixed, there is a slight increase in tolerance factor (f) due to hole variation in B-site. Interestingly, TC variation with f shows similar trend to the hole concentration, which is expected to be increased linearly. The effect of f on TC is rather weaker in comparison to σ2. The large drop in TC for high entropy system with respect to the conventional manganite is related to the local structural distortion which severely hampers the Mn3+—O—Mn4+ double exchange interaction. The competing magnetic interactions lead to magnetic phase separation as well as Griffith like phase. Spin pinning is noticed for the samples containing six elements in A-site which is independent on hole concentration. Possible spin clustering is also realized above ferromagnetic TC. Careful analysis of the magnetization data suggests that observed anomaly in magnetic behaviour can be ascribed to the local chemical disordering apart from the size disorder parameter. The possible role on magnetic properties of the number of elements occupying in a crystallographic site is emphasized.

Active learning for medical image segmentation with stochastic batches.

The performance of learning-based algorithms improves with the amount of labelled data used for training. Yet, manually annotating data is particularly difficult for medical image segmentation tasks because of the limited expert availability and intensive manual effort required. To reduce manual labelling, active learning (AL) targets the most informative samples from the unlabelled set to annotate and add to the labelled training set. On the one hand, most active learning works have focused on the classification or limited segmentation of natural images, despite active learning being highly desirable in the difficult task of medical image segmentation. On the other hand, uncertainty-based AL approaches notoriously offer sub-optimal batch-query strategies, while diversity-based methods tend to be computationally expensive. Over and above methodological hurdles, random sampling has proven an extremely difficult baseline to outperform when varying learning and sampling conditions. This work aims to take advantage of the diversity and speed offered by random sampling to improve the selection of uncertainty-based AL methods for segmenting medical images. More specifically, we propose to compute uncertainty at the level of batches instead of samples through an original use of stochastic batches (SB) during sampling in AL. Stochastic batch querying is a simple and effective add-on that can be used on top of any uncertainty-based metric. Extensive experiments on two medical image segmentation datasets show that our strategy consistently improves conventional uncertainty-based sampling methods. Our method can hence act as a strong baseline for medical image segmentation. The code is available on: https://github.com/Minimel/StochasticBatchAL.git.

Role of bond coat deposition method in the hot corrosion behavior of CSZ thermal barrier coatings

In this research, a NiCrAlY bond coat was applied using two methods – namely, conventional air plasma spraying (APS) and new spark plasma sintering (SPS) technique – on an Inconel-738 substrate. The ceria-stabilized zirconia (CSZ) topcoat was similar in both samples and deposited using the APS method. The hot corrosion performance of the fabricated thermal barrier coatings (TBCs) was studied at 950°C in a sodium sulfate (Na2SO4)–55 wt.% vanadium (V) oxide (V2O5) corrosive salt environment. The results showed that the hot corrosion resistance of the coating system with an SPS-processed NiCrAlY bond coat and a CSZ topcoat was much better than that of the conventional TBC sample because the adhesion of the bond coat to the substrate was very good in this sample, and the interface was free of any defects such as cracks and porosities. According to these results, no spallation was observed in the SPS-processed coating up to 38 h, while for the CSZ topcoat with an APS-processed bond coat, spallation occurred after 24 h.

Deep learning-based regional ECG diagnosis platform.

To enable the intelligent diagnosis of a variety of common Electrocardiogram (ECG), we investigate the deep learning-based ECG diagnosis system. From January 2015 to December 2019, four consecutive years of 100,120 conventional 12-lead ECG data were collected in our hospital. Utilizing this dataset, we constructed a deep learning model designed to intelligently diagnose prevalent ECG anomalies by employing a multi-task learning framework. The system performance was evaluated using various metrics, including sensitivity, specificity, negative predictive value, positive predictive value, and so forth. Additionally, we employed an ECG intelligent diagnostic platform for clinical application to undertake real-time online analysis of 2500 conventional 12-lead ECG samples in June 2020, aiming to validate our model. At this stage, we compared the performance of our model against the traditional manual identification method. The efficacy of the ECG intelligent diagnostic model was notably high for common and straightforward ECG patterns, such as sinus rhythm (F1=98.01%), sinus tachycardia (F1=96.26%), sinus bradycardia (F1=94.88%), and a normal electrocardiogram (F1=91.71%), as well as for Premature Ventricular Contractions (F1=91.62%). Nevertheless, when diagnosing rarer and more intricate ECG anomalies, the system requires an increased number of samples to refine the deep learning models. During the validation stage, our model exhibited better efficiency in terms of accuracy, labor time and labor cost when compared to the manual identification approach. Our deep learning-driven intelligent ECG diagnostic model clearly demonstrates significant clinical utility. The integrated artificial intelligence diagnosis system not only has the potential to augment physicians in their diagnostic processes but also offers a viable avenue to reduce associated labor costs.

Diagnostic accuracy and interobserver agreement of cholangioscopy for indeterminate biliary strictures: A single-center experience

Background and study aimsCharacterization of indeterminate biliary strictures (IDBS) still represents a major challenge. Digital single-operator cholangioscopy (DSOC) could potentially overcome limits of conventional biopsy and brush sampling. The aim of this study was to compare diagnostic accuracy of visual evaluation and DSOC-guided biopsies to conventional trans-papillary sampling techniques and to evaluate the inter-observer agreement (IOA) on visual diagnosis. Patients and methodsAll consecutive patients undergoing DSOC-guided biopsy after conventional sampling techniques for IDBS during a six—year period were retrospectively evaluated. Final diagnosis was based on histological evaluation of the surgical specimen if available or a clinical follow-up of at least 6 months. For IOA, 20-second DSOC clips were retrospectively reviewed by 6 experts and 6 trainees and classified according to the Monaco Classification. ResultsThirty-five patients underwent DSOC for IDBS in the study period; 14 patients (F = 9) with a median age of 64 years (range 53–76) met the study aim. After DSOC, strictures location was changed in three patients (additional yield of 21.4 %). Intraductal DSOC-guided biopsy were technically successful in all cases, with an adequacy of 92.8 %. No adverse events were recorded. Final diagnosis was benign disease in five cases and cholangiocarcinoma in the others. For IOA, 29 videos were evaluated with almost perfect agreement for final diagnosis (kappa 0.871; agreement 93.1, p <0.001), although overall accuracy of DSOC visual finding was 73.6 % and 64.4 % for experts and trainees, respectively. ConclusionsDSOC could improve diagnostic accuracy for IDBS, since it showed high sensitivity for visual finding and high specificity for DSOC guided-biopsy. Visual diagnosis seems reliable for diagnosis using the Monaco Classification.

Homogeneous liquid–liquid microextraction coupled with HPLC/DAD for determination of nirmatrelvir and ritonavir as COVID-19 combination therapy in human plasma

The study reports the development of a high-performance liquid chromatography/diode array detection method to measure the levels of nirmatrelvir and ritonavir in human plasma. These two antiviral medications are used for the treatment of COVID-19 and are marketed as Paxlovid®. The method employed sugaring-out induced homogeneous liquid–liquid microextraction to improve sensitivity. Optimization of the method was performed using the one variable at a time approach by adjusting several factors such as type of sugar, extractant, amount of sugar, volume of extractant, and pH of the aqueous sample to achieve the highest efficiency. The developed method was validated according to the Food and Drug Administration guidelines and demonstrated good linearity, accuracy, and precision. The range of linearity was from 1000 to 20,000 ng/mL for nirmatrelvir and 200 to 20,000 ng/mL for ritonavir with correlation coefficient values of 0.998 and 0.996, respectively. Selectivity studies revealed that no others peaks appeared in the retention times of the studied drugs. The stability of nirmatrelvir and ritonavir were also investigated through short term and three cycles of freeze–thaw, and both drugs were found stable. This analytical method could be useful for monitoring drug concentrations in patients undergoing treatment with these medications for COVID-19. In this work, for the first time, SULLME was used for the sensitive determination of nirmatrelvir and ritonavir in biological fluids. The developed method was able to determine both drugs in therapeutic levels with no need to sophisticated techniques like LC–MS. In addition to that, SULLME is considered a simple and green sample preparation in comparison with conventional sample preparation methods.

Study on mechanical characteristics and energy accumulation and release law of coal samples with different saturation and creep damage

ABSTRACT Under the long-term influence of overburden load and water infiltration, coal undergoes creep damage, and the disturbance caused by mining activities can lead to sudden instability, triggering geological disasters such as mine flooding. Therefore, this study prepared four types of coal samples with different saturations under creep damage conditions and conventional coal samples. A comparative study was conducted using uniaxial compression tests to examine the deterioration characteristics of physical and mechanical parameters, failure modes, and energy evolution patterns in these two types of coal samples. Additionally, a microscopic deterioration model of coal samples under the combined influence of water and creep damage was constructed to elucidate the mechanism of creep damage on the deterioration of mechanical properties in water-containing coal samples. The research findings indicate that coal samples affected by creep damage and conventional coal samples exhibit significant consistency in mechanical parameters and energy evolution patterns. Specifically, an increase in saturation is positively correlated with peak stress, elastic modulus, and dissipated energy. Conversely, peak strain, total input energy, and elastic strain energy are negatively correlated with rising saturation levels. Under the same saturation conditions, the change in mechanical parameters and energy characteristics is more pronounced in creep-damaged coal samples. Furthermore, the stress-strain curves of both coal sample types exhibit prolonged compaction stages, shortened elastic stages, and more significant yield stages. Under equivalent saturation levels, the stress-strain trends in creep-damaged coal samples show more substantial variations. Upon loading and failure, the conventional coal sample transitions from single inclined plane shear failure to tension-shear mixed failure as the saturation increases. Conversely, the creep-damaged coal sample shifts from “X”-type conjugate shear failure and tension-shear mixed failure to tension failure, signifying a tendency toward more complex failure modes. In cases where the coal sample has a water saturation of 41%, the deterioration of its mechanical parameters is primarily attributed to creep damage. However, when the water saturation reaches 41–100%, water plays a dominant role in the deterioration of the coal sample’s mechanical parameters. After experiencing creep damage, the internal micro-cracks of the coal sample are extensively developed, which is also the primary factor contributing to the deterioration of the coal sample’s mechanical properties and an increased proportion of dissipated energy.

Superhydrophobic ZIF-67 with exceptional hydrostability

Zeolitic Imidazolate Framework-67 (ZIF-67) has been used in a variety of applications including catalysis, separations, and energy storage. However, the weak hydrostability of ZIF-67, due to structural hydrolysis and degradation, dramatically limits their applicability after aqueous exposure. In this work, cosolvent-stabilized superhydrophobic, highly hydrostable ZIF-67 was synthesized at room temperature using a facile, one-pot hydrothermal synthesis route, and the effect of cosolvent concentration on ZIF-67 crystal structure properties and hydrostability was studied systematically. The underlying mechanism for the cosolvent-supported hydrostability improvement was also proposed. Furthermore, the influence of hydrotreatment on the resultant ZIF-67s' catalytic performance was studied in the ‘Sabatier reaction’ for CO2 to synthetic natural gas (CH4) conversion. The ZIF-67-derived calcined catalysts obtained from the hydrotreated samples of the cosolvent-stabilized ZIF-67 exhibited no prominent loss in catalytic performance and showed better CO2 conversion, higher CH4 selectivity, and less CO production, in comparison to the conventional ZIF-67 samples. Notably, the use of a lower ligand-to-metal ratio (∼8) in the current synthesis significantly reduced the overall chemical consumption, achieving highly economically and environmentally friendly manufacturing of exceptionally hydrostable ZIF-67.

Clinical Bridging Studies and Modeling Approach for Implementation of a Patient Centric Sampling Technique in Padsevonil Clinical Development

Volumetric absorptive microsampling (VAMS) techniques have gained popularity these last years as innovative tool for collection of blood pharmacokinetic (PK) samples in clinical trials as they offer many advantages over dried blood spot and conventional venous blood sampling. The use of Mitra®, a blood collection device based on volumetric absorptive microsampling (VAMS) technology, was implemented during clinical development of padsevonil (PSL), an anti-seizure medication (ASM) candidate. The present study describes the approach used to bridge plasma (obtained from conventional venous blood sampling) and blood exposures (obtained with Mitra®) to support the use of Mitra as sole blood PK sampling method in clinical trials. Paired blood (using Mitra®) and plasma samples (using conventional venous blood sampling) were collected in healthy volunteers as well as in patients with epilepsy. PSL concentration in plasma and blood were analyzed using different approaches which included evaluation of blood-to-plasma ratios (B/P) over time, linear regression, Bland-Altman analysis as well as development of a linear-mixed effect model based on clinical pharmacology studies. Results showed that the observed in vivo B/P and the measured bias between the 2 collection methods were consistent with the measured in vitro B/P. Graphical analysis demonstrated a clear time effect on the B/P which was confirmed in the linear mixed effect model with sampling time identified as significant covariate. Finally, the built-in model was validated using independent datasets and was shown to adequately predict plasma concentration based on blood concentration with a mean bias of less than 9% (predicted versus observed plasma concentration).Graphical

Effect of varying threading dislocation densities on the optical properties of InGaN/GaN quantum wells with intentionally created V-shaped pits

The effect of varying threading dislocation densities on the internal quantum efficiencies (IQEs) of InGaN quantum wells (QWs), with and without intentionally created “V-pits,” is reported here. InGaN QW samples grown on GaN-on-sapphire templates with threading dislocation densities of &amp;lt;1 × 108 and &amp;lt;1 × 109 cm−2 are compared, with and without GaN/InGaN superlattice (SL) layers incorporated to intentionally open up the threading dislocation cores and form large-size “V-pits.” The formation of “V-pits” is confirmed by cross-sectional transmission electron microscopy to initiate from threading dislocations in the SL layers. The densities of the pits are confirmed by plan-view SEM to agree with the substrate threading dislocation densities. The experimental room temperature IQEs of the “V-pit” QW samples are enhanced to 15% ± 1% compared to 6% ± 2% for conventional QW samples. Both conventional and “V-pit” samples show insensitivity to the magnitude of the dislocation densities with respect to IQE performance, while the “V-pit” samples show shifts in the peak emission wavelengths compared to the conventional samples, attributed to strain modulation. This study provides additional understanding of the causes of the observed insensitivity of the IQEs to different threading dislocation densities.

A bold new purpose for an old method: Using invertebrate kick‐netting to improve monitoring of microplastic pollution in running waters

AbstractFreshwaters are impacted by many pollutants. Scientists and resource managers need to reliably detect and monitor these pollutants by employing appropriate sampling techniques to quantify and mitigate their impacts. Emerging freshwater contaminants such as microplastics are difficult to monitor as effective sampling techniques have not been fully developed and lack standardization. Here, we propose a novel method, adapted from stream invertebrate kick‐net sampling, which can be employed for long‐term, standardized microplastic monitoring. We hypothesized that invertebrate kick‐net sampling would be effective at collecting microplastics, capturing higher microplastic concentrations than standard microplastic sampling, due to collecting suspended and benthic microplastics simultaneously. We sampled 28 streams for microplastics using standard drift and sediment microplastic collection methods and invertebrate kick‐netting. As predicted, kick‐netting captured microplastics at higher concentrations than conventional sampling, regardless of whether values were expressed per volume of water (as in drift samples), per kg of sediment or per area sampled (as in benthic samples). Consequently, kick‐net sampling has the potential to be a time‐ and cost‐efficient tool for monitoring microplastic pollution. We recommend the employment of invertebrate kick‐netting as a new, standardized means to investigate and routinely monitor microplastic concentrations worldwide. This would generate a more robust dataset of global freshwater microplastic pollution, making it possible to answer unresolved questions pertaining to changes in microplastic pollution profiles. Standardized, long‐term records of microplastic concentrations in freshwaters would also allow a more accurate assessment of the ecological risks of microplastic pollution. Finally, long‐term microplastic data could be used to inform much‐needed regulatory decisions pertaining to microplastic pollution.

Developing the water-energy nexus performance of direct evaporative coolers in a hot and dry climate: Toward a green space cooling

Direct Evaporative Coolers (DECs) have several advantages such as a user-friendly, cost-effective, small, and easy installation, therefore, they are frequently used for space cooling in hot and dry climates. DEC is a cost-effective system applied in residential or medium-sized buildings such as private offices and commercials; however, the gap of the system is higher water and energy consumption that are addressed in this paper. Water in a dry climate is vital and means life and greenhouse gases threaten lives on the earth. This research is an experimental method conducted for more than 2 years to develop a conventional DEC into a green-efficient system. The research aims to discover and reduce the water as well as energy consumption of DECs in a hot and dry climate. Energy transitions from fossil to solar is another aim of this research. Compared with the conventional sample in the developed prototype the water consumption was reduced by 23.8 liters/day (about 56%), and the operating hours of the system were also decreased by 55%. Currently, there are approximately 2.1 million DEC systems in operation in the case study city, so by applying the developed system, up to 6.2 million m3 of water can be saved in each warm period. The reduction of operating hours caused a reduction of 67.5 W in electrical energy consumption per hour. In addition, by energy transition from fossil to solar electricity the in-operation CO2 emission of the developed system is zero. The implementation of this green cooling model will greatly reduce water and energy consumption and the developed model can be repeated and imitated in similar climates.

Comparing eDNA metabarcoding and conventional pelagic netting to inform biodiversity monitoring in deep ocean environments

Abstract The performance of environmental DNA (eDNA) metabarcoding has rarely been evaluated against conventional sampling methods in deep ocean mesopelagic environments. We assessed the biodiversity patterns generated with eDNA and two co-located conventional methods, oblique midwater trawls and vertical multinets, to compare regional and sample-level diversity. We then assessed the concordance of ecological patterns across water column habitats and evaluated how DNA markers and the level of sampling effort influenced the inferred community. We found eDNA metabarcoding characterized regional diversity well, detecting more taxa while identifying similar ecological patterns as conventional samples. Within sampling locations, eDNA metabarcoding rarely detected taxa across more than one replicate. While more taxa were found in eDNA than oblique midwater trawls within sample stations, fewer were found compared to vertical multinets. Our simulations show greater eDNA sampling effort would improve concordance with conventional methods. We also observed that using taxonomic data from multiple markers generated ecological patterns most similar to those observed with conventional methods. Patterns observed with Exact Sequence Variants were more stable across markers suggesting they are more powerful for detecting change. eDNA metabarcoding is a valuable tool for identifying and monitoring biological hotspots but some methodological adjustments are recommended for deep ocean environments.

High-frequency soluble reactive phosphorus in-situ analysis in lakes

High-frequency in-situ monitoring of nutrients in lakes is increasingly becoming relevant to understand event-driven nutrient pulses better. These pulses are essential for phytoplankton development but are usually not captured during conventional sampling programs. In-situ monitoring of nutrients in lakes has been lagging despite the role of nutrients in eutrophication. We evaluated an in-situ wet chemistry soluble reactive phosphorus (SRP) analyzer to establish a methodological basis for high-frequency measurements of SRP. The device (based on the molybdate blue method) was evaluated in laboratory and field tests by comparing it with conventional laboratory analysis. The results of the in-situ analyser during laboratory were similar to the conventional analysis (R2 > 0.995, NSE = 0.989, PBIAS = 8.4 %). Repeated measurements by the in-situ analyzer in a 0.1 mg L-1 test solution had a mean and standard deviation of 0.1005 ± 0.0047 and a MAPE of 3.39, indicating the stability of the measurements. During field tests in a hypertrophic lake with anoxic hypolimnion, the in-situ analyzer SRP concentrations were lower and divergent from the laboratory analysis; due to potential interference of the in-situ analyzer's reagents by sulfides. Under aerobic conditions during field tests, the in-situ P results of a eutrophic, mixed shallow lake were similar to those from water samples analyzed in the laboratory (R2 = 0.562 – 0.812, NSE = 0.397 – 0.707, PBIAS = -8.5 – 24.6 %). There was no difference in SRP concentration among the unfiltered, 0.2 µm, and 10 µm pore size filtered samples; filter pore size did not play a role in the measured SRP concentrations. The in-situ SRP analyzer revealed previously unobserved phenomena, e.g., multiple strongly discontinuous SRP peaks reaching almost 0.1 mg L-1; it therefore, offers a solid methodological basis for high-frequency monitoring of SRP. Incorporating high-frequency in-situ nutrient analysis in lake monitoring provides insights into lakes' short-term nutrient dynamics. Such information is essential for understanding the effects of extreme events like droughts, storms, and heat waves.

Ultrasonic Thickness Measurement Method and System Implementation Based on Sampling Reconstruction and Phase Feature Extraction.

The existing ultrasonic thickness measurement systems require high sampling frequencies for echo signal acquisition, leading to complex circuit designs and high costs. Moreover, extracting the characteristics of ultrasonic echo signals for accurate thickness measurement poses significant challenges. To address these issues, this paper proposes a method that utilizes conventional sampling frequencies to acquire high-frequency ultrasonic echo signals, overcoming the limitations of high-frequency data acquisition imposed by the Nyquist-Shannon sampling theorem. By employing an improved sampling reconstruction technique, the multi-cycle sampling signals are reconstructed and rearranged within a single cycle, effectively increasing the equivalent sampling frequency. Additionally, a combination of coarse estimation using fast Fourier transform (FFT) and precise phase extraction using the moving sine fitting algorithm is proposed for accurate thickness measurement, resolving the limitations of common thickness measurement methods such as peak detection, envelope detection, and Hilbert autocorrelation in terms of low measurement accuracy. Experimental results obtained from thickness measurements on 45 steel ultrasonic test blocks within the range of 3 mm to 20 mm indicate a measurement error of ±0.01 mm, while for thicknesses ranging from 1 mm to 50 mm, the measurement error is ±0.05 mm.

Random average sampling and reconstruction in a reproducing kernel space

Suppose that signals of interest reside in a reproducing kernel space defined on a metric measure space. We consider the scenario that the sampling positions are distributed on a bounded domain of a metric measure space, and the sampling data are local averages of the original signals in a reproducing kernel space. For signals concentrated on in that reproducing kernel space, we study the stability of this sampling procedure by establishing a weighted sampling inequality of bi‐Lipschitz type. This type of stability implies a weak version of conventional sampling inequality. We propose an iterative algorithm that reconstruct these concentrated signals from finite sampling data. The reconstruction error is characterized through the concentration ratio and the Hausdorff distance between the set of sampling positions and . We also consider the random sampling scheme where the sampling positions are i.i.d. randomly drawn on , and the sampling data are local averages of concentrated signals. We demonstrate that these concentrated signals can be approximated from the random sampling data with high probability when the sampling size is at least of the order with being the measure of .