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Background: Microsporidia is an obligate intracellular eukaryote, which is capable of parasitizing vertebrates and invertebrates. Nosema ceranae, which can infect both Apis mellifera and Apis cerana, poses a serious threat and causes heavy losses to the worldwide apiculture. During infection, polar tube, a highly specialized invasion structure, ejected from the spore to deliver the sporoplasm into host cells to cause infection. Although seven different polar tube proteins (PTP1 ~ 7) have been reported from various microsporidia and showed key functions associated with spore invasion and proliferation, no systematic analysis on identification and characterization of polar tube proteins from N. ceranae was found. Methods and results: The polar tube proteins 2 (NcPTP2) was identified from the total polar tube proteins of N. ceranae by LC_MS/MS and the transcriptional profile was performed by RT-PCR. Sequence characterization analysis revealed that NcPTP2 was rich in lysine and had a signal peptide at the N-terminal. It had 3 potential O-glycosylation sites and 6 potential N-glycosylation sites. 25 phosphorylation sites were found on serine, tyrosine and threonine sites. Sequence alignment analysis revealed that NcPTP2 was homologous and had conserved cysteine residues with PTP2 proteins from other microsporidia. Indirect immunofuorescence analysis (IFA) and Immunoelectron Microscopy analysis (IEM) confirmed that NcPTP2 was localized on the polar tube of the germinated spores. The interaction between NcPTP2 and spore wall protein in N. ceranae indicated its potential function in anchoring and coiling of polar tube in spore. Conclusion: NcPTP2 was the first subcellular localized polar tube protein in N. ceranae and this work could provide an important basis for further analyzing the biological functions of polar tube proteins and uncovering the infection mechanism of N. ceranae to the host cells. [ABSTRACT FROM AUTHOR]

An efficient acidic natural deep eutectic solvent (choline chloride-acetic acid, Chcl-AA) was screened out and subsequently combined with ultrasound treatment to recover seabuckthorn leaf phenolics (SLP), and the Plackett–Burman and Box-Behnken designs were used to optimize the process parameters. The optimal parameters were water content of NADES 30%, solvent to solid ratio 32 mL/g, extraction time 14 min, extraction temperature 40°C, ultrasonic power 400 W, and ultrasonic duty cycle 75%. The values of total phenolic content (TPC) and total flavonoid content (TFC) under these conditions were 99.65 ± 2.49 milligram gallic acid equivalent per gram of dry weight (mg GAE/g DW) and 52.02 ± 1.21 milligram rutin equivalent per gram of dry weight (mg RE/g DW), respectively. Twenty-five phenolics were initially identified from SLP by ultra-performance liquid chromatography coupled with quadrupole-time of flight mass spectrometry (UPLC-Q-TOF/MS), among which hydrolyzable tannins, isorhamnetin derivatives, and quercetin derivatives are the main constituents. The SLP extract was further evaluated as to its ability to inhibit metabolic enzymes, i.e., α-glucosidase, α-amylase, pancreatic lipase, cholesterol esterase, xanthine oxidase, and angiotensin-converting enzyme. SLP exhibited significant inhibition of α-glucosidase (IC50 = 3.31 μg/mL), pancreatic lipase (IC50 = 57.62 μg/mL), and angiotensin-converting enzyme (IC50 = 63.13 μg/mL). Additionally, the compounds extracted using Chcl-AA exhibited superior bioactivity when compared to those using conventional green solvents, i.e., ethanol and water. These results provide a valuable technical buttress for the efficient extraction of SLP and their further application and research in functional foods. [ABSTRACT FROM AUTHOR]

In this study, one of the most frequently used polymeric materials in fused deposition modeling (FDM) acrylonitrile butadiene styrene (ABS) is reinforced with different amount of carbon nanotubes (CNTs). Thermogravimetric analysis and differential scanning calorimetry analysis are applied to examine thermal degradation behavior of produced nanocomposite filaments. Specimens are manufactured by fused deposition modeling by using produced nanocomposite filaments. Tensile, creep and viscoelastic-viscoplastic behaviors of FDM-printed nanocomposite samples are investigated by conducting tensile, creep and loading–unloading tests under different strain rates and strain levels. Morphology of 3D printed samples is examined through scanning electron microscopy. Void densities which plays important role in mechanical behavior of additively manufactured samples are determined via ImageJ and CNT reinforcement on void densities are investigated. Data obtained from tests are used in system identification process, and multi-input–single-output model structures are proposed for the prediction of tensile, creep and recovery behaviors of 3D printed nanocomposite materials. [ABSTRACT FROM AUTHOR]

Material testing and modeling is one of the cornerstones of virtual analysis of sheet metal forming processes. However, it is also becoming more and more relevant for incoming goods inspection, especially in view of the increasing amount of recycled material or frequent changes of suppliers, e.g. to provide workers, processes and/or process models with relevant information about a new batch of material. Efficient material testing and straight-forward test evaluation is essential for this. The flow curve and yield locus are central to describe the forming behavior of sheet metal materials. However, the parameters of the associated models are currently determined in various tests on different systems and with special sample geometries. The present work presents a methodology that allows the determination of a set of flow curve and yield locus parameters from three three-point bending tests only. The evaluation routine does not require finite element simulation and processes only the force-displacement information of the bending tests, which also places low demands on the measurement technology. The results were compared with a conventionally determined parameter set using a validation test, and the results are of reasonable quality, especially considering the minimal effort involved. [ABSTRACT FROM AUTHOR]

Kidney stones are the most common urinary system diseases, and early identification is of great significance. The purpose of this study was to use routine urine and blood detection indices to build a deep learning (DL) model to identify the presence of kidney stones in the early stage. A retrospective analysis was conducted on patients with kidney stones who were treated at West China Hospital of Sichuan University from January 2020 to June 2023. A total of 1130 individuals presenting with kidney stones and 1230 healthy subjects were enrolled. The first blood and urine laboratory data of participants at our hospital were collected, and the data were divided into a training dataset (80%) and a verification dataset (20%). Additionally, a long short-term memory (LSTM)-based adaptive feature weighting model was trained for the early identification of kidney stones, and the results were compared with those of other models. The performance of the model was evaluated by the area under the subject working characteristic curve (AUC). The important predictive factors are determined by ranking the characteristic importance of the predictive factors. A total of 17 variables were screened; among the top 4 characteristics according to the weight coefficient in this model, urine WBC, urine occult blood, qualitative urinary protein, and microcyte percentage had high predictive value for kidney stones in patients. The accuracy of the kidney stone (KS-LSTM) learning model was 89.5%, and the AUC was 0.95. Compared with other models, it has better performance. The results show that the KS-LSTM model based on routine urine and blood tests can accurately identify the presence of kidney stones. And provide valuable assistance for clinicians to identify kidney stones in the early stage. [ABSTRACT FROM AUTHOR]

Culicoides, among the tiniest and most abundant hematophagous insects globally, serve as vectors for a variety of pathogens such as viruses, bacteria, parasites, protozoa, and nematodes. This study aimed to identify Culicoides species and assess their spatial distribution and seasonal occurrence in selected districts of the Central, South, and West Gondar zones, Northwest Ethiopia. A cross-sectional study was conducted between January to July 2023. A total of 44 UV light- onderstepoort traps were deployed in the study districts near specific areas. The traps were operational from dusk (6:00 PM) until dawn (6:00 AM) and were suspended at a height of 1.5 to 2 m above the ground. Poisson regression was used to assess associations, the Shannon diversity index to measure diversity, and QGIS 3.22.6 to create maps. In this study, 8,857 Culicoides were captured across the 44 trapping sites. Of the total flies captured flies, 8,838 were identified as belonging to 12 distinct species, while the classification of the remaining 19 flies remained unclear. Notably, C. kingi (54.01%) was the most prevalent species, followed by C. imicola (44.55%). The abundance of Culicoides observed from January to late April (3505) was significantly lower compared to the wet season (5355), with a marked increase in the capture of C. kingi (2499) from May to late July. A statistically significant association (p < 0.05) was observed between the occurrence of Culicoides and factors such as district, sampling point, and season. Spatial analysis revealed that C. kingi had a broader range of suitability than other Culicoides species, with high suitability observed in East Dembia. The diversity index analysis indicated that Culicoides species diversity was higher in samples from animal pens (H = 0.73) and during the wet season (H = 0.75). Additionally, this study documented the presence of eight Culicoides species namely C. corsicus, C. kibunensis, C. reioxi, C. kiouxi, C. saharienines, C. desertorum, C. reithi, and C. festivipennis, which have not been previously documented in Ethiopia. In conclusion, the study highlighted that the occurrence of Culicoides species was higher in East Dembia, with moderate presence in Wegera and West Armacho. Further research is needed to assess the impact of various Culicoides species on animal and human health, as well as their economic implications, and to develop corresponding control strategies based on these findings. [ABSTRACT FROM AUTHOR]

Zinnia elegans of Family Asteraceae is a flowering plant grown widely in gardens in Indonesia. The plant is also often integrated into pest management of different agricultural commodities as its colourful flowers may attract beneficial insects including natural enemies. A total of eight viral symptomatic and four non-symptomatic Z. elegans samples were collected from four districts in Kulon Progo and Sleman Regencies of Special Region of Yogyakarta. They were molecularly tested using two universal primer pairs for begomoviruses, and two specific primer pairs for cucumber mosaic virus (CMV, Cucumovirus) detections. The eight symptomatic samples were all positive for CMV but negative for begomoviruses infections. The four non-symptomatic samples were tested negative to begomoviruses and CMV. Partial RNA2 and RNA3 segments of four CMV isolates were sequenced to demonstrate that they are members of subgroup IB. 4Ze-Ngaglik isolate was shown to be mechanically transmitted to healthy Z. elegans and cucumber cv. Baresta (Cucumis sativus). To the best of our knowledge, this report confirmed the first CMV occurrence in Z. elegans in Indonesia. [ABSTRACT FROM AUTHOR]

A comprehensive survey of the octopus fauna around the Americas is presented to facilitate and accelerate the assessment of a full understanding of American octopus biodiversity. Brief accounts are provided summarizing research efforts on octopus species taxonomy, diversity and distribution in different regions of North and South America. Octopus americanus and O. insularis are compared and clearly distinguished from each other and from the closely-related European species, O. vulgaris. The use of genus names Paroctopus and Pinnoctopus is clarified. Included is a discussion of the recent application of genus name Paroctopus to warm-temperate and tropical species of small size, along with a cautionary note about species identifications in the light of past errors in misassigning large-to-giant cold-water species to genus Paroctopus. Related to problems with identifying species of Paroctopus, there is an appended note concerning misidentifications and the importance of thorough species descriptions to obtain species information at the levels of both phenotype and genotype. A lectotype is formally designated for Bathypolypus arcticus (Prosch, 1849); and the status of so-called 'Octopus giganteus' is reviewed briefly. A supplementary online database, AmeriCeph, provides basic information about all known octopus species of the Americas, including the institutional location of type material and the identification of voucher specimens and their depositories. DNA sequences registered in this database are not all fully compatible with barcoding standards. However, a subset of DNA sequences conforming to strict barcode identifications is provided in a second supplementary table, providing barcode sequences directly applicable also to improving standards of seafood traceability. This in turn contributes to building sustainability of exploited octopus fisheries stocks and identification of species suitable for aquaculture trials to meet the increasing commercial demand for octopus worldwide. [ABSTRACT FROM AUTHOR]

We address defining and identifying students with dyslexia within the context of multi-tier systems of support (MTSS). We review proposed definitions of dyslexia, evidence for proposed definitional attributes, and emphasize the role of instructional response in identifying students with dyslexia. We identify dyslexia as individuals with specific deficits in reading and spelling single words combined with inadequate response to evidence-based instruction. We propose a hybrid identification process in which assessment is utilized within school-wide MTSS allowing for integration of routinely collected progress monitoring data as well integrating with more formal diagnostic measures. This proposed "hybrid" method demonstrates strong evidence for valid decision-making and directly informs instruction. We close proposing a revised definition of dyslexia that incorporates these elements. [ABSTRACT FROM AUTHOR]

The humanitarian discourse has recently shifted towards bolstering the intervention capacities of affected countries and communities through "localization." However, the implementation of this approach has encountered challenges and disappointments, partly due to conventional short-term interventions driven by external organizations rather than beneficiaries. Empowering women in developing nations is vital for gender equality and women's rights, given the barriers they face such as unequal access to resources and underrepresentation in governance. Malawi"s context, marked by poverty, limited access to education and healthcare, and cultural norms posing barriers to gender equity, complicates efforts to address inequalities against women. This article explores how localization, particularly through local NGOs, can advance women's rights in Malawi by challenging gender norms. While recognizing its potential, caution is advised to avoid reinforcing past exclusions. This research investigates how aid localization, leveraging identification, affects gender equality and women's rights in disaster management and humanitarian efforts. Through field research in Malawi, including 33 interviews with NGO members and beneficiaries, it underscores the role of localization in promoting gender equality and women's rights, advocating for a more equitable and locally responsive approach to humanitarian action. [ABSTRACT FROM AUTHOR]

Crystal violet dye poses significant health risks to humans, including carcinogenic and mutagenic effects, as well as environmental hazards due to its persistence and toxicity in aquatic ecosystems. This study focuses on the efficient removal of crystal violet dye from aqueous media using novel Co3O4/Co3(BO3)2 nanostructures synthesized via the Pechini sol–gel approach. The nanostructures, which were abbreviated to EN600 and EN800, were fabricated at calcination temperatures of 600 and 800 °C, respectively. X-ray diffraction (XRD) analysis revealed that the synthesized samples have a cubic Co3O4 phase and an orthorhombic Co3(BO3)2 phase, with mean crystal sizes of 43.82 nm and 52.93 nm for EN600 and EN800 samples, respectively. The Brunauer–Emmett–Teller (BET) surface areas of EN600 and EN800 samples were 65.80 and 43.76 m2/g, respectively, indicating a significant surface area available for adsorption. Optimal removal of crystal violet dye was achieved at a temperature of 298 K, a contact time of 70 min, and a pH of 10. The maximum adsorption capacities were found to be 284.09 mg/g for EN600 and 256.41 mg/g for EN800, which are notably higher compared to many conventional adsorbents. The adsorption process followed the pseudo-second-order kinetic model and fitted well with the Langmuir isotherm. The adsorption was exothermic, spontaneous, and physical in nature. Moreover, the adsorbents exhibited excellent reusability, retaining high efficiency after multiple regeneration cycles using 6 mol/L hydrochloric acid. These findings highlight the potential of these Co3O4/Co3(BO3)2 nanostructures as effective and sustainable materials for water purification applications. [ABSTRACT FROM AUTHOR]

Spatial transcriptomics (ST) technologies allow for comprehensive characterization of gene expression patterns in the context of tissue microenvironment. However, accurately identifying domains with spatial coherence in both gene expression and histology in situ and effectively integrating data from multi-sample remains challenging. Here, we propose ResST, a graph self-supervised residual learning model based on graph neural network and Margin Disparity Discrepancy (MDD) theory. ResST aggregates gene expression, biological effects, spatial location, and morphological information to capture nonlinear relationships between a cell and surrounding cells for spatial domain identification. Also, ResST integrates multiple ST datasets and aligns latent embeddings based on MDD theory for correcting batch effects. Results show that ResST identifies continuous spatial domains at a finer scale in ten ST datasets acquired with different technologies. Moreover, ResST efficiently integrated data from multiple tissue sections vertically or horizontally while correcting batch effects. Overall, ResST demonstrates exceptional performance in analyzing ST datasets. A graph self-supervised residual learning framework (ResST) for natural domain identification and data integration of spatial transcriptomics. [ABSTRACT FROM AUTHOR]

Among patients with epilepsy, 30–40% experience recurrent seizures even after adequate antiseizure medications therapies, making them refractory. The early identification of refractory epilepsy is important to provide timely surgical treatment for these patients. In this study, we analyze interictal electroencephalography (EEG) data to predict drug refractoriness in patients with temporal lobe epilepsy (TLE) who were treated with monotherapy at the time of the first EEG acquisition. Various EEG features were extracted, including statistical measurements and interchannel coherence. Feature selection was performed to identify the optimal features, and classification was conducted using different classifiers. Functional connectivity and graph theory measurements were calculated to identify characteristics of refractory TLE. Among the 48 participants, 34 (70.8%) were responsive, while 14 (29.2%) were refractory over a mean follow-up duration of 38.5 months. Coherence feature within the gamma frequency band exhibited the most favorable performance. The light gradient boosting model, employing the mutual information filter-based feature selection method, demonstrated the highest performance (AUROC = 0.821). Compared to the responsive group, interchannel coherence displayed higher values in the refractory group. Interestingly, graph theory measurements using EEG coherence exhibited higher values in the refractory group than in the responsive group. Our study has demonstrated a promising method for the early identification of refractory TLE utilizing machine learning algorithms. [ABSTRACT FROM AUTHOR]

The use of machine learning techniques to identify problem gamblers has been widely established. However, existing methods often rely on self-reported labeling, such as temporary self-exclusion or account closure. In this study, we propose a novel approach that combines two documented methods. First we create labels for problem gamblers in an unsupervised manner. Subsequently, we develop prediction models to identify these users in real-time. The methods presented in this study offer useful insights that can be leveraged to implement interventions aimed at guiding or discouraging players from engaging in disordered gambling behaviors. This has potential implications for promoting responsible gambling and fostering healthier player habits. [ABSTRACT FROM AUTHOR]

Summary: This research conducts a comparative analysis and scoping review of 105 studies in the field of Fracture Liaison Service (FLS). The resulting two-dimensional framework represents a significant step toward FLS implementation. Purpose: The primary goal is to review interventions in real world settings in order to provide the FLS framework that specifies the essential elements of its implementation and offers different perspectives on that. Method: This study encompasses two phases: a comparative analysis of existing FLS models, including "Capture the Fracture," "5IQ," and "Ganda," and a scoping review from 2012 to 2022 in PubMed, Web of Science, Scopus, ProQuest, and IEEE databases limited to publications in English. Results: The resulting model of comparative analysis identifies patient identification, investigation, intervention and integration or continuity of care as the four main stages of FLS. Additionally, the elements of quality and information span across all stages. Following comparative analysis, the framework is designed to be used for content analysis of the included studies in the scoping review. The intersection of columns (Who, Where, When, What, How, Quality) with rows (Identification, Investigation, Intervention, and continuity of care) yields a set of questions, answered in tabular form based on the scoping review. Conclusion: The framework offers potential benefits in facilitating the adoption of effective approaches for FLS implementation. It is recommended to undertake an in-depth review of each of these components in order to uncover novel and innovative approaches for improving their implementation. [ABSTRACT FROM AUTHOR]

An array of analytical techniques (GC–MS, SPME–GC–MS, RP–HPLC–MS, FIA–MS, HPLC–UV, ICP–MS) was used to study the molecular composition of six ointment residues from the first third of the twentieth century. The objectives of the study were (i) to validate the applicability of a previously proposed strategy for the identification of lipids and active pharmaceutical ingredients in remains of historical pharmaceuticals and (ii) to shed light on early twentieth-century pharmaceutical practice. Although no further information on the composition of the samples studied was available (all were marked by a period pharmacist with the general label "Ointment" only), it was possible to identify the likely lipidic ointment bases as well as quantify the main likely active substances in all samples. This allowed us to partially reconstruct the composition of each ointment and to estimate its original purpose. [ABSTRACT FROM AUTHOR]

This paper discusses wave propagation in unbounded particle-based materials described by a second-gradient continuum model, recently introduced by the authors, to provide an identification technique. The term particle-based materials denotes materials modeled as assemblies of particles, disregarding typical granular material properties such as contact topology, granulometry, grain sizes, and shapes. This work introduces a center-symmetric second-gradient continuum resulting from pairwise interactions. The corresponding Euler-Lagrange equations (equilibrium equations) are derived using the least action principle. This approach unveils non-classical interactions within subdomains. A novel, symmetric, and positive-definite acoustic tensor is constructed, allowing for an exploration of wave propagation through perturbation techniques. The properties of this acoustic tensor enable the extension of an identification procedure from Cauchy (classical) elasticity to the proposed second-gradient continuum model. Potential applications concern polymers, composite materials, and liquid crystals. [ABSTRACT FROM AUTHOR]

We provide a framework for motivating and diagnosing the functional form in the structural part of nonlinear or linear structural equation models when the measurement model is a correctly specified linear confirmatory factor model. A mathematical population-based analysis provides asymptotic identification results for conditional expectations of a coordinate of an endogenous latent variable given exogenous and possibly other endogenous latent variables, and theoretically well-founded estimates of this conditional expectation are suggested. Simulation studies show that these estimators behave well compared to presently available alternatives. Practically, we recommend the estimator using Bartlett factor scores as input to classical non-parametric regression methods. [ABSTRACT FROM AUTHOR]

In the framework of the mixed migration flows, the identification of the victims of trafficking in persons has become a delicate and challenging process, especially in the period 2015–2019, when the European Union experienced an unprecedented surge in migrant arrivals. Through a qualitative case study approach, the paper draws on interviews of street-level bureaucrats (Lipsky, 1980) responsible for the identification of potential or actual women victims of trafficking in persons within mixed migration flows2. The paper analyses how the European Union policies are implicated in the identification procedures adopted in Italy and to what extent a human rights-based and intersectional approach is applied in the Italian experience in 2015–2019. Issues related to the effective management of migration flows, including respect for the human rights of migrants and the prevention of discrimination for their treatment in terms of status, are closely intertwined with the process of early identification. The key findings were essentially threefold. First, a human rights-based approach – albeit with some critical issues – is applied during the identification procedures, as the multi-agency work is adopted as much as possible. Second, Italy did not completely implement some measures envisaged by the European Union policies, daunting early identification. Third, Italy seems to lack a comprehensive and coherent strategy, and policymaking is outsourced to a local policy network of agencies and institutions with their policy style. The paper sheds light on the need to change the approach used to identify victims of trafficking in persons. [ABSTRACT FROM AUTHOR]

Identification of human sex from cadaver examination is crucial for various fields like forensic odontology and archaeology. Sex recognition can be very challenging if the cadaver is exposed to extreme temperature and/or pressure. As the hardest part of the body, tooth remains can be useful in such circumstances. In this report, we demonstrate a proof-of-concept experiment for rapid recognition and classification of human sexuality from their tooth using laser-induced breakdown spectroscopy (LIBS) combined with machine learning algorithms. LIBS experiment was performed on the enamel part of the tooth samples of males and females. LIBS spectra of male and female teeth have shown a higher degree of similarity owing to the same chemical structure. Three machine learning algorithms including principal component analysis, artificial neural network, and logistic regression were used for their identification. The higher identification rates enable the strong possibility of human sex recognition from their teeth LIBS spectra. Further, a judicious feature selection approach was implemented to significantly reduce the data size to achieve maximum accuracy with minimal time. Finally, Student's t-test was applied with a 95% confidence level to both the datasets to identify the most relevant features. Our findings demonstrate that the LIBS coupled with machine learning can be used as a productive and handy tool for fast and accurate recognition of human sex/gender from their teeth samples in forensic odontology and archaeology practice. [ABSTRACT FROM AUTHOR]

Causal inference involves determining whether a treatment (e.g., an education program) causes a change in outcomes (e.g., academic achievement). It is well-known that causal effects are more challenging to estimate than associations. Over the past 50 years, the potential outcomes framework has become one of the most widely used approaches for defining, identifying, and estimating causal effects. In this paper, we review the potential outcomes framework with a focus on potential outcomes notation to define individual and average causal effects. We then show how three canonical assumptions, Unconfoundedness, Positivity, and Consistency, may be used to identify average causal effects. The identification results motivate methods for estimating causal effects in practice, which include model-based estimators, such as regression, inverse probability weighting, and doubly robust estimation, and procedures that target covariate balance, such as matching and stratification. Examples and discussion are grounded in the context of a running example of a study aimed at assessing the causal effect of receipt of special education services on 5th grade mathematics achievement in school-aged children. Practical considerations for education research are discussed. [ABSTRACT FROM AUTHOR]

The Southwest Vortex (SWV) is a mesoscale closed low-pressure system in Southwest China that significantly influences regional heavy rainfall. Understanding SWV characteristics and formation mechanisms is crucial for research in this field, with effective identification as the foundation. Currently, Chinese researchers lack a unified standard for SWV identification. Despite the availability of high spatiotemporal resolution reanalysis data, which provides a superior foundation for studying SWVs, existing methods struggle to fully exploit these datasets. Therefore, this study proposes PVSIA (Geopotential-Height-Field and Vector-Wind-Field based SWV Identification Algorithm), a method that integrates the geopotential height field and wind field characteristics. The PVSIA employs an innovative calculation method for wind vector rotation properties, enhancing identification speed and overcoming challenges posed by complex terrain. Furthermore, this study utilizes the ERA5 reanalysis dataset from 2019 to 2021 to systematically compare the performance of the PVSIA method with traditional geopotential height field methods in identifying SWVs, employing key evaluation metrics such as hit ratio, false alarm rate, and missing report rate. During this period, the PVSIA method achieved a hit rate of 94.25% in identifying SWVs, with a false alarm rate of 5.20% and a missing report rate of 5.74%. In contrast, the geopotential height field method, while achieving a higher hit rate of 98.85%, exhibited a significantly higher false alarm rate of 70.54%. In contrast, the PVSIA method maintains a high hit rate while significantly reducing the false alarm rate, demonstrating a more stable and reliable identification performance. [ABSTRACT FROM AUTHOR]

RNA silencing plays a direct functional role in gene expression and defense against pathogens during various developmental stages in plants. This silencing process is regulated by the RNA interference (RNAi) pathway, which relies on double-stranded RNA (dsRNA) generated from small RNAs (sRNAs). Three important protein families regulate the functions of sRNAs: Dicer-like (DCLs), Argonautes (AGOs), and RNA-dependent RNA polymerases (RDRs). These major components have not been identified and characterized in papaya. In this study, we identified the RNAi gene families and comprehensively characterized their regulatory functions through a bioinformatics approach in papaya. The papaya genome contains 3 CaDCL, 8 CaAGO and 4 CaRDR genes, which comprise diverse functional regulatory elements compared to their corresponding Arabidopsis AtDCL, AtAGO, and AtRDR genes, indicating their pivotal roles in the RNA silencing mechanism. Phylogenetic tree and multiple sequence analyses reveal that each CaDCL, CaAGO and CaRDR protein clusters and aligns with the corresponding RNAi genes in Arabidopsis. The results from conserved domain, motif, and gene structure analyses indicate a higher level of similarity within the same gene family with some exceptions. The chromosomal and sub-cellular localization of the predicted proteins shows a well distributed pattern. Furthermore, the gene ontology (GO) analysis demonstrates that the candidate RNAi genes are associated with the RNAi silencing mechanism and related pathways. The network and sub-network analyses reveal interactions between various transcription factors (TFs), such as ERF, Dof, MIKC_MADS, NAC, and BBR-BPC families, and the identified RNAi gene families. Additionally, an analysis of cis-acting regulatory elements uncovers light-responsive (LR), stress-responsive (SR), hormone-responsive (HR), and other activities (OT) functions in the candidate CaDCL, CaAGO, and CaRDR genes. Tissue-specific expression patterns of papaya RNAi genes showed differential expression in various tissues. RNA-seq analysis revealed five candidates CpDCL1, CpAGO1, CpAGO5, CpAGO10a, and CpRDR6 that demonstrated potential in response to anthracnose-resistant in papaya. Over all, our findings provide useful information for the genetic improvement of papaya cultivars in breeding programs to overcome stress responses and offer a better understanding of these gene families in papaya. [ABSTRACT FROM AUTHOR]

Biometric systems use the unique behavioral or physical characteristics of a user to verify their claimed identity. Due to the high probability of forgery or theft of traditional passwords and keys, there is a decreasing tendency to use them in security systems. By using biometric indicators, it becomes impossible to forge or steal them. Electroencephalogram (EEG) signals meet the basic requirements of biometric indicators, making them suitable for use in authentication and crypto-biometric systems. In this paper, the first step involves extracting features from recorded EEG signals using transformer-based models within an identification system. In the second step, the extracted features are imported into a key generation system. The proposed method maps the features of each user to different segments. The distributions of the segment indexes are then used to generate repeatable keys from EEG features in future sessions. The Transformer-based identification system achieved a mean accuracy of 99.8%, and the key generation system achieved a 0.1% mean Half Total Error Rate (HTER) using five different categories of visual stimulus. The high accuracy of the proposed identification system and the low error rate of the proposed key generation system indicate that features extracted by the Transformers are a good choice for visual stimulus EEG-based biometric systems. [ABSTRACT FROM AUTHOR]

Traditional methods of authentication, such as keys, passwords, and PIN codes, are increasingly being surpassed by the robustness and security offered by biometric systems. This study presents a system for human identification using electrocardiogram (ECG) signals, evaluated using two deep learning models: gated recurrent units (GRU) and long short-term memory (LSTM). The proposed methodology begins with signal denoising using a bandpass filter, followed by the decomposition of the signal into multiple intrinsic mode functions (IMFs) via a signal processing technique, empirical mode decomposition (EMD). The first two IMFs, after normalization and segmentation, serve as features for the deep learning models. The performance of these models was assessed on three distinct databases: NSRDB, MIT-BIH, and PTB. MIT-BIH contains a mixture of healthy and ill subjects, while the PTB and NSRDB databases contain only healthy subjects. The GRU model achieved accuracies of 98.57%, 98.26%, and 99.17% on these databases respectively, while the LSTM model achieved accuracies of 98.33%, 97.89%, and 98.27% respectively, demonstrating its reliability for biometric applications. While the proposed system was tested only on ECG, its applications can be extended to include other biomedical signals, indicating its potential for broader biometric use. [ABSTRACT FROM AUTHOR]

The identification of miRNA-disease associations is crucial for early disease prevention and treatment. However, it is still a computational challenge to accurately predict such associations due to improper information encoding. Previous methods characterize miRNA-disease associations only from single levels, causing the loss of multi-level association information. In this study, we propose TriFusion, a powerful and interpretable deep learning framework for miRNA–disease association prediction. It develops a tri-channel architecture to encode the association features of miRNAs and diseases from different levels and designs a feature fusion encoder to smoothly fuse these features. After training and testing, TriFusion outperforms other leading methods and offers strong interpretability through its learned representations. Furthermore, TriFusion is applied to three high-risk sexually associated cancers (ovarian, breast, and prostate cancers) and exhibits remarkable ability in the identification of miRNAs associated with the three diseases. TriFusion achieves accurate identification of associations between miRNAs and potential diseases by developing tri-channel graph neural network. [ABSTRACT FROM AUTHOR]

This paper proposed the additional fractional gradient descent identification algorithm based on the multi-innovation principle for autoregressive exogenous models. This algorithm incorporates an additional fractional order gradient to the integer order gradient. The two gradients are synchronously used to identify model parameters, thereby accelerating the convergence of the algorithm. Furthermore, to address the limitation of conventional gradient descent algorithms, which only use the information of the current moment to estimate the parameters of the next moment, resulting in low information utilisation, the multi-innovation principle is applied. Specifically, the integer-order gradient and additional fractional-order gradient are expanded into multi-innovation forms, and the parameters of the next moment are simultaneously estimated using multi-innovation matrices, thereby further enhancing the parameter estimation speed and identification accuracy. The convergence of the algorithm is demonstrated, and its effectiveness is verified through simulations and experiments involving the identification of a 3-DOF gyroscope system. [ABSTRACT FROM AUTHOR]

Clinical metagenomics (CMg) Nanopore sequencing can facilitate infectious disease diagnosis. In China, sub-lineages ST11-KL64 and ST11-KL47 Carbapenem-resistant Klebsiella pneumoniae (CRKP) are widely prevalent. We propose PathoTracker, a specially compiled database and arranged method for strain feature identification in CMg samples and CRKP traceability. A database targeting high-prevalence horizontal gene transfer in CRKP strains and a ST11-only database for distinguishing two sub-lineages in China were created. To make the database user-friendly, facilitate immediate downstream strain feature identification from raw Nanopore metagenomic data, and avoid the need for phylogenetic analysis from scratch, we developed data analysis methods. The methods included pre-performed phylogenetic analysis, gene-isolate-cluster index and multilevel pan-genome database and reduced storage space by 10-fold and random-access memory by 52-fold compared with normal methods. PathoTracker can provide accurate and fast strain-level analysis for CMg data after 1 h Nanopore sequencing, allowing early warning of outbreaks. A user-friendly page (http://PathoTracker.pku.edu.cn/) was developed to facilitate online analysis, including strain-level feature, species identifications and phylogenetic analyses. PathoTracker proposed in this study will aid in the downstream analysis of CMg. This study constructed a database and platform for the characterization of Klebsiellapneumoniae in clinical metagenomic Nanopore sequencing and developed a website for online analysis. [ABSTRACT FROM AUTHOR]

Royal Jelly (RJ) is a natural substance produced by honeybees, serving not only as nutrition for bee brood and queens but also as a functional food due to its health-promoting properties. Despite its well-known broad-spectrum antibacterial activity, the precise molecular mechanism underlying its antibacterial action has remained elusive. In this study, we investigated the impact of RJ on the bacteria model MG1655 at its half-maximal inhibitory concentration, employing LC–MS/MS to analyze proteomic changes. The differentially expressed proteins were found to primarily contribute to the suppression of gene expression processes, specifically transcription and translation, disrupting nutrition and energy metabolism, and inducing oxidative stress. Notably, RJ treatment led to a marked inhibition of superoxide dismutase and catalase activities, resulting in heightened oxidative damage and lipid peroxidation. Furthermore, through a protein–protein interaction network analysis using the STRING database, we identified CRP and IHF as crucial host regulators responsive to RJ. These regulators were found to play a pivotal role in suppressing essential hub genes associated with energy production and antioxidant capabilities. Our findings significantly contribute to the understanding of RJ's antibacterial mechanism, highlighting its potential as a natural alternative to conventional antibiotics. The identification of CRP and IHF as central players highlights the intricate regulatory networks involved in RJ's action, offering new targets for developing innovative antimicrobial strategies. [ABSTRACT FROM AUTHOR]

Artificial insemination (AI) success in bovine reproduction is vital for the cattle industry's economic sustainability and for advancing the understanding of reproductive physiology. Identify high-fertile animals' fertility is a complex task due to multifactorial traits, including hormonal, age-related, and body condition factors. Early high-fertility identification is crucial for timely interventions and enhancing AI success. In this study, we present the potential use of Fourier-transform infrared (FTIR) spectroscopy on blood serum for early identification of high-fertile Nellore female cows for AI protocols. Blood serum FTIR spectra were obtained from Nellore female cows before AI. FTIR spectra underwent data analysis and the results demonstrated successful discrimination between animals that exhibit pregnant and non-pregnant diagnoses 30 days after AI. FTIR spectra revealed consistent vibrational modes, emphasizing Amide I and II bands. Principal Component Analysis (PCA) effectively segregated groups based on molecular information. Linear SVM with C = 10 and 4 PCs achieved 100% accuracy in the group classification. This innovative approach using FTIR spectroscopy and ML algorithms offers a promising means of high-fertile cow identification, potentially improving AI outcomes in Nellore cattle. The study presents valuable insights into advancements in reproductive management practices for this economically significant breed. [ABSTRACT FROM AUTHOR]

In this research, authors give a literature analysis of the methods used to detect and anticipate security risks in software testing by using a number of deep learning models. The purpose of this study is to conduct a literature review on the use of deep learning models in software testing for the purpose of detecting and predicting security issues. Moreover, the motive of this work is to analyze the exiting techniques related as software testing model and its application oriented performance. A thorough search of the available literature across several databases and resources forms the basis of this evaluation. There are a total of 69 publications found via the search, 34 of which are original research. Convolutional neural networks, long short-term memory networks, generative adversarial networks, and capsule networks are only some of the state-of-the-art methods for identifying and predicting security risks that are the topic of this research study. Datasets, performance indicators, and assessment criteria are all dissected for this examination. Finally, the benefits and drawbacks of using deep learning models to detect and anticipate software security flaws during testing are outlined in this article. Software testing experts and academics will benefit from this study since it will shed light on the present state of the art in the area and provide direction for the creation of innovative approaches to the detection and forecasting of security risks in software testing. [ABSTRACT FROM AUTHOR]

Globally, eye disorders have been a major issue, especially in developing nations where resources for technology and financing are limited. Due to its tremendous feature learning ability, CNN has obtained remarkable progress in the area of fundus images. Through appropriate analysis and examination of fundus images, computer-aided diagnosis may yield information with a standard value for experts in clinical diagnosis or screening. However, the majority of earlier investigations have focused on the identification of a particular fundus disease, and the accurate fast classification of multiple fundus diseases remains a major challenge. Extremely numerous retinal fundus images must be analyzed to achieve a categorization that is reliable, fast, and precise. As a result, this research aims to propose a novel classification model for eye illnesses based on four major steps: (a) Pre-processing, (b) segmentation of blood vessels, (c) Extracting features and (d) Multi-classification of eye disease. In order to accurately classify the images, it is necessary to extract the essential informational characteristics from the segmented blood vessels. The categorization of eye diseases is carried out using a hybrid classifier that integrates the SqueezeNet and Long-Term Recurrent Convolutional Network (LRCN). We trained and validated our models using tenfold cross-validation tests on a database of fundus images including five categories: Normal, Retinitis Pigmentosa, Pathological Myopia, Maculopathy and Glaucoma. The efficacy of the presented approach is evaluated based on specificity, F1-score, recall, precision and accuracy of 98%, 97.8%, 97.6%, 98.4% and 98% respectively. [ABSTRACT FROM AUTHOR]

During narrative reception, one psychological response audiences may experience toward story characters is identification, which involves a sense of merging between self and character. Given the lack of formally validated measures of this construct in the literature, the current paper introduces a new 12-item scale for measuring identification. Scale development and validation took place over three sequential studies. Exploratory factor analysis in Study 1 (N = 224) indicated four related factors: merging, perspective-taking, understanding, and emotional involvement. In Study 2 (N = 191), confirmatory factor analysis suggested that a second-order four-factor model provided a good fit to the data and a more parsimonious explanation of the scale's factor structure compared to a first-order model. In addition, the overall scale and subscales demonstrated adequate internal consistency and correlated in the expected directions with theoretically relevant and irrelevant constructs. Using a more demographically diverse sample, Study 3 (N = 290) established measurement invariance of the scale across two narratives in terms of configural, metric, and partial scalar invariance, and provided further support for its factor structure, reliability, and validity. [ABSTRACT FROM AUTHOR]

The use of targeted agents in the treatment of pediatric low-grade gliomas (pLGGs) relies on the determination of molecular status. It has been shown that genetic alterations in pLGG can be identified non-invasively using MRI-based radiomic features or convolutional neural networks (CNNs). We aimed to build and assess a combined radiomics and CNN non-invasive pLGG molecular status identification model. This retrospective study used the tumor regions, manually segmented from T2-FLAIR MR images, of 336 patients treated for pLGG between 1999 and 2018. We designed a CNN and Random Forest radiomics model, along with a model relying on a combination of CNN and radiomic features, to predict the genetic status of pLGG. Additionally, we investigated whether CNNs could predict radiomic feature values from MR images. The combined model (mean AUC: 0.824) outperformed the radiomics model (0.802) and CNN (0.764). The differences in model performance were statistically significant (p-values < 0.05). The CNN was able to learn predictive radiomic features such as surface-to-volume ratio (average correlation: 0.864), and difference matrix dependence non-uniformity normalized (0.924) well but was unable to learn others such as run-length matrix variance (− 0.017) and non-uniformity normalized (− 0.042). Our results show that a model relying on both CNN and radiomic-based features performs better than either approach separately in differentiating the genetic status of pLGGs, and that CNNs are unable to express all handcrafted features. [ABSTRACT FROM AUTHOR]

Emergency services rely heavily on Twitter for early detection of crisis tasks to enhance crisis management systems. However, employing state-of-the-art models often face data sparsity as well as their inadequacy to handle long-range dependencies between tweet tokens. Additionally, the authorities need to gain confidence in the model's prediction so that the detected task information can be better believed and prioritized. In this study, we present a generalized framework named explainable attentive model for crisis task identification (ExACT) to handle the above mentioned challenges, while identifying crisis task relevant tweets as well as provide the model explainability by utilizing a very small corpus of tweets. The novelty of ExACT is two-fold: (1) Data enrichment has been introduced by nondynamic contextual attributes derived from tweets to overcome the sparsity and improve data quality. (2) Feature enrichment has been incorporated using hierarchical attention at both local and global levels using residual self-attention and correlation attention to capture long-range dependencies. Additionally, LIME based explainability approach added to understand the task important tokens. Experiments reveal that ExACT has a competitive performance improvement over various state-of-the-art models in terms of F 1 -score ( 20 % and 14 % respectively) and accuracy ( 14 % and 16 % , respectively) across two different crisis tasks infrastructure damage and support signal identification. Consistent performance improvement for two different tasks considered from publicly available crisis event datasets depicts the model's generalizability. While, LIME supported explainable mechanism in ExACT can identify the important keywords but does not guarantee a high score in terms of plausibility and faithfulness metrics. [ABSTRACT FROM AUTHOR]

The analysis of node order structure in dynamic temporal networks is significant for network propagation control. To further accurately characterize the inter-layer coupling relationship of dynamic temporal networks, this paper firstly defines the node neighborhood structure homogeneity rate and node neighborhood location heterogeneity rate based on the node neighborhood structure evolution feature information and node neighborhood location evolution feature information, and integrates the influence of the change in both neighborhood structure and neighborhood location on the node importance in the process of node temporal evolution. Secondly, a Supra-Adjacency Matrix based on Neighborhood Structure (NSAM) temporal network node importance order structure modeling method is proposed by combining the local structure and overall structure evolution information of nodes during the temporal evolution process. Finally, the node importance order structure of the dynamic temporal network is obtained by combining the eigenvector centrality to represent the node importance attribute value. Simulations show that compared with the classical hierarchical temporal network model, the NSAM model can improve the identification accuracy by 38.2% and 7.8%, respectively, and can identify the important nodes in the dynamic temporal network more effectively. [ABSTRACT FROM AUTHOR]

Malware detection has become a critical aspect of ensuring the security and integrity of computer systems. With the ever-evolving landscape of malicious software, developing effective detection methods is of utmost importance. This study focuses on the identification of important features for malware detection methods, aiming to enhance the accuracy and efficiency of such systems. In this work, we propose an ensemble approach called FRAMC to identify the key features that contribute significantly to the detection of malware. The effectiveness of FRAMC is assessed using different types of classifiers on a number of real-world malware datasets. The outcomes of our analysis demonstrate that the proposed approach excels in terms of performance when compared to other methods. [ABSTRACT FROM AUTHOR]

Microplastics are emerging contaminants that undergo progressive aging under environmental conditions such as sunlight irradiation, mechanical forces, temperature variations, and the presence of biological organisms. Since aging modifies microplastic properties, such as their own toxicity and the toxicity of trapped pollutants, advanced methods to analyze microplastics are required. Here we review methods to analyze microplastic aging with focus on the aging process, qualitative identification, quantitative characterization, and chemometrics. Qualitative identification is done by mechanical techniques, thermal techniques, e.g., thermal degradation and gas chromatography–mass spectrometry, and spectral techniques, e.g., infrared, Raman, fluorescent, and laser techniques. Quantitative characterization is done by microscopy and mass spectrometry. Microplastic aging results in a series of surface physical changes, biofilm formation, chemical oxidation, thermal alternation, and mechanical deterioration. Changes in mechanical and thermal properties allow to differentiate aged microplastics. Infrared and Raman spectroscopy are rapid and sensitive for chemical identification of microplastics in complex environmental samples. Combining two techniques is preferable for accurate detection and categorization. [ABSTRACT FROM AUTHOR]

Especially in species with complex social systems, the relatedness between individuals is important information. Visual phenotypic cues present one way to identify closely related conspecifics. Humans are capable of recognizing such visual cues in the faces of their own as well as several primate species, but to which degree this applies to non-primate species is largely unexplored. Here we demonstrate the capability of 110 test persons to recognize faces of 47 male Asian elephants based on 186 photographs. The human examiners were not only able to recognize an individual based on its face after several years, but also to identify, at decreasing accuracy, full brother pairs and paternal kinship. People regularly working with elephants were more successful than laypersons. However, even laypersons recognized 73.3% of the same individuals. The identification of individual elephants by a look at their faces presents a simple approach which can be a valuable tool for in situ research. [ABSTRACT FROM AUTHOR]

Reading screenings are an essential element of a preventative model of education. Early language and literacy screenings can identify students at risk of later reading difficulties. This pilot study investigated the feasibility and impact of a community-based organization providing free language and literacy screenings using an application based screening with largely automated scoring. The community organization paired screening results with parent education on language and literacy acquisition and evidence-based instructional practices tailored to the students' identified risks. The mixed methods utilized survey data from parents/caregivers (n = 19) and volunteer screeners (n = 8) and interviews of community partners (n = 2), volunteers (n = 2), and parents (n = 2). Results of the pilot met the feasibility and impact goals. Community partners felt it was important to provide access to screening, and volunteers found the screening application easy to administer. Volunteer screeners reported the screening application was easy to administer, and children were engaged throughout the screening. Parents reported that the screening results and parent education significantly impacted their decision-making for their child(ren). [ABSTRACT FROM AUTHOR]

This paper proposed a three-loop physical parameter identification method considering physical feasibility and nonlinear friction model. The full physical parameters can be obtained with physical feasibility by constructing an optimization problem. And the nonlinear friction model which considered Stribeck effect is employed to improve identification accuracy. In the first loop, the physical parameters are identified with a regression model. In the second loop, the nonlinear friction model is identified with a nonlinear optimization method. And in the third loop, the obtained friction parameters are updated and the identification results are to be further optimized. Different from traditional methods like the least squares (LS), weight least squares (WLS) and other optimization methods which can only get base parameters and do not consider Stribeck effect, the proposed scheme can get physical parameters with physical constraints. It is useful in many robotic applications, like model-based control. The Stribeck effect is also employed to improve identification accuracy. The experimental results verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Alighting bus stops inferring is of great significance for origin–destination estimation. Cellular signaling data (CSD), a kind of individual trajectory generated by mobile phones, provides a new idea for alighting stop identification. To explore the capacity of CSD in this field, this study proposes a method of inferring alighting bus stops by integrating smart card data, bus GPS data, and CSD. Firstly, a correspondence table is generated by individual matching, which correspondingly links mobile phone users in CSD and bus passengers in smart card data. Secondly, the inferred alighting bus stops are determined by the radii of the circumscribed circles of triangles consisting of directly projective points, piecewise projective points, and CSD points. The proposed method is verified by an experimental dataset from a behavioral simulation experiment of 10 volunteers in Foshan, China. The results show that the recognition rate is 92.94% and the inference accuracy is 65.82%, or 93.67% under a one-stop error. In the case of a real dataset in Foshan, the proposed method with a recognition rate of 53.02% highly outperforms the trip-chain-based method. The difference in the recognition rate between the two datasets is due to that the real dataset is more likely to be incomplete than the experimental data, which indicates that the performance and effectiveness of the proposed method are sensitive to the data quality and completeness of CSD and bus GPS data. Having said that, the proposed method can infer both alighting stops of linked bus trips and single unlinked bus trips. [ABSTRACT FROM AUTHOR]

Raspberry (Rubus idaeus L.) is a crop with increasing commercial value due to its nutritional properties and demand in national and international markets. Throughout the growing process, there have been reports of the presence of galls on roots and stems, which reduce plant size and yields. The current study used phylogenetic reconstruction to identify bacteria associated with these symptoms in raspberry plantations in the Mexican states of Jalisco and Michoacán. A total of 69 representative strains selected according to their colony morphotype were identified based on partial sequencing of the 16S ribosomal gene. Forty-nine of them were tested for pathogenicity, including of which three strains induced gall symptoms in raspberry and tomato plants. A set of 28 strains identified within the Agrobacterium and Rhizobium genera were chosen for amplification of the atpD, glnA, gyrB, and rpoB genes. Strain CPO 2.419 was selected for whole genome sequencing, with a total length of 5,679,921 base pairs assembled into 46 contigs. This strain was identified as A. tumefaciens (GenBank JAVIYJ000000000.1) using phylogenetic reconstruction. According to the OrthoANIu and dDDH percentage values, the CPO 2.419 strain belongs to the A. tumefaciens complex, along with the closely related strains of A. tumefaciens LMG 232 (99.4, 89.1), A. tumefaciens CNPSo 675 (98.5, 84.8), and A. tumefaciens ATCC 4720T (98.1, 78), respectively. This study contributes to the understanding of the genomic Agrobacterium diversity in raspberry-producing areas in Mexico and highlights the relevance of genomics for accurate bacterial identification, with important implications for agriculture. [ABSTRACT FROM AUTHOR]

Abstract—The results of comprehensive studies of the properties of imported syringe gland materials are presented. The studied materials were identified and their domestic analogs were found. [ABSTRACT FROM AUTHOR]

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Purpose: The implementation of sustainability and circular economy (CE) models in agri-food production can promote resource efficiency, reduce environmental burdens, and ensure improved and socially responsible systems. In this context, indicators for the measurement of sustainability play a crucial role. Indicators can measure CE strategies aimed to preserve functions, products, components, materials, or embodied energy. Although there is broad literature describing sustainability and CE indicators, no study offers such a comprehensive framework of indicators for measuring sustainability and CE in the agri-food sector. Methods: Starting from this central research gap, a systematic literature review has been developed to measure the sustainability in the agri-food sector and, based on these findings, to understand how indicators are used and for which specific purposes. Results: The analysis of the results allowed us to classify the sample of articles in three main clusters ("Assessment-LCA," "Best practice," and "Decision-making") and has shown increasing attention to the three pillars of sustainability (triple bottom line). In this context, an integrated approach of indicators (environmental, social, and economic) offers the best solution to ensure an easier transition to sustainability. Conclusions: The sample analysis facilitated the identification of new categories of impact that deserve attention, such as the cooperation among stakeholders in the supply chain and eco-innovation. [ABSTRACT FROM AUTHOR]

Telemedicine and video-based diagnosis have raised significant concerns regarding the protection of facial privacy. Effective de-identification methods require the preservation of diagnostic information related to normal and pathological facial movements, which play a crucial role in the diagnosis of various movement, neurological, and psychiatric disorders. In this work, we have developed FaceMotionPreserve , a deep generative model-based approach that transforms patients' facial identities while preserving facial dynamics with a novel face dynamic similarity module to enhance facial landmark consistency. We collected test videos from patients with Parkinson's disease recruited via telemedicine for evaluation of model performance and clinical applicability. The performance of FaceMotionPreserve was quantitatively evaluated based on neurologist diagnostic consistency, critical facial behavior fidelity, and correlation of general facial dynamics. In addition, we further validated the robustness and advancements of our model in preserving medical information with clinical examination videos from a different cohort of patients. FaceMotionPreserve is applicable to real-time integration, safeguarding facial privacy while retaining crucial medical information associated with facial movements to address concerns in telemedicine, and facilitating safer and more collaborative medical data sharing. [ABSTRACT FROM AUTHOR]

In this study, we propose a novel method for identifying lithology using an attention mechanism-enhanced graph convolutional neural network (AGCN). The aim of this method is to address the limitations of traditional approaches that evaluate unbalanced lithology by improving the identification of thin layers and small samples, while providing reliable data support for reservoir evaluation. To achieve this goal, we begin by using Principal Component Analysis (PCA) with maximum and minimum distance clustering (Max-min-distance) to correct the logging curves, which compensates for the low resolution of thin layers and enhances the accuracy of stratigraphic representation. Subsequently, we transform the logging data into graph-structured data by connecting distance similarity points and feature similarity points of the logging samples. We then use the graph convolutional network (GCN) to identify lithology, leveraging both labeled and unlabeled data to enhance the ability to identify lithology in small sample datasets. Additionally, our model incorporates a channel and spatial attention mechanism that assigns weights to the graph structure during lithology identification, improving the model's capability to discern differences across samples. To evaluate the performance of our model, we constructed a lithology dataset comprising five wells and conducted experiments. The results indicate that our approach achieves a maximum accuracy of 97.67%, surpassing the performance of a singlestructure model in lithology identification. In conclusion, our proposed method provides a promising and effective approach for unbalanced lithology identification, significantly improving accuracy levels. [ABSTRACT FROM AUTHOR]

Seafloor geodetic observation technology is a pivotal method for capturing plate boundary earthquakes and related phenomena. Currently, this task is performed by Global Navigation Satellite System-Acoustic ranging (GNSS-A), a technique that integrates GNSS observations with acoustic ranging. This method involves measuring the round-trip travel time of an acoustic signal between a surface platform and a preinstalled seafloor station, which is then converted into a distance for positioning. While the horizontal positioning accuracy can reach the centimeter level, the vertical accuracy tends to be loser. Recent research has revealed that equipment-dependent distortion of sound waves causes problems for accurate travel time identification, leading to a decrease in vertical component accuracy. In this paper, we conducted an analysis of distortion tendencies using water tank test data and quantitatively identified differences in distortion magnitude related to instruments, angle dependence, and directional dependence attributed to the sea surface platform. We proposed a new identification algorithm, Acoustic Ambiguity Reduction (AAR) method, using a catalog of distortions for each device, and improved the observational capabilities of the vertical component. [ABSTRACT FROM AUTHOR]

Aiming at the three-body contact problem of mechanical rough surface containing wet coal dust interface, the three-body contact model of rough surface containing wet coal dust interface is constructed by comprehensively considering the contact deformation of rough surface and contact characteristics of wet coal dust, and based on the crushing theory. By analysing the contact force, load-bearing particle size and adjacent contact angle thresholds of the wet coal dust layer, the force chain identification criterion is formulated. Finally, quantitative calculations of the force chain characteristics are performed to reveal the effect of different initial porosities on the three-body contact stiffness, which is verified experimentally. The results of the study show that the average contact force of the wet coal dust layer can be used as the force chain contact force threshold, the average particle size can be used as the force chain particle size threshold, and the force chain angle threshold is determined by the particle coordination number. As the initial porosity decreases, the number, length and stiffness of force chains in the wet coal dust layer increase significantly, and the stiffness reaches a maximum value of 2.007 × 108 pa/m at the moment of downward pressure to stabilisation, while the trend of force chain bending varies in the opposite direction, and its minimum bending degree decreases to 20°. The maximum relative error between the simulation and experimental results of three-body contact stiffness is 9.64%, which proves the accuracy of the force chain identification criterion and the quantitative calculation of three-body contact stiffness by force chain. [ABSTRACT FROM AUTHOR]