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This study investigated the identification and prevalence of fungal species in stored sorghum grains surveyed at Bodija and Oyo town markets in Oyo State and Ikire and Iwo town markets in Osun State, Western Nigeria using appropriate standard methods. Six distinct fungal species were isolated from the sorghum grains and identified through macroscopic and microscopic examination. These species included Aspergillus flavus, A. niger, A. tamarii, A. terreus, Fusarium sp., and Rhizopus stolonifer. The prevalence of fungal contamination was found to be higher in the markets of Oyo State compared to those in Osun State. Additionally, Aspergillus flavus and A. niger were the most prevalent fungal species, while Rhizopus stolonifer was the least prevalent. [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]

The identification and classification of pests in rice field are the prerequisites of early warning systems for pest dis-asters. Among these pests, the rice planthoppers cause the most serious damage. However, the existing deep learning models for rice planthopper recognition are characterized by large size and numerous parameters, which makes them unsuitable for the deployment on embedded devices with limited computational resources. To address this issue, a lightweight rice planthopper recognition model based on YOLOv5 is proposed in this paper. In the model, a lightweight convolutional network named GhostNet is employed as the backbone to reduce the operational parameters. Additionally, a convolutional attention module (CBAM) is integrated into the backbone network to effectively enhance the transmission of deep information, so as to improve the model's ability of recognizing rice planthopper images. The original CIoU loss function is replaced by the SIoU loss function to expedite model convergence. Experimental results demonstrate that the modified model achieves the mAP@0.5 as 82.8%, with the parameter count of 3.12x106 and the model size of 7.2MB. Compared to the original model, it is a reduction of 46.7% in size and 43.3% in parameters, with a minor accuracy loss of 0.1%. Clearly, the improved model can achieve lightweight characteristics and robust performance, and thus it provides a theoretical and practical foundation for early warning systems against rice planthopper infestations in rice fields. [ABSTRACT FROM AUTHOR]

Copyright of Revista Horizonte Médico is the property of Universidad de San Martin de Porres and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Collar use in cats is a controversial topic. Cat owners do have very different reasons for either deciding to use a collar on their cat or rejecting collar use. We conducted an online survey on cat management, use of and attitudes toward collars. Our survey participants were mainly women (88.8%), mostly came from Germany (88.2%), and owned 2 ± 2 cats. Collar use was reported by 32.9%. Overall, participants showed significantly higher agreement to the subscale risk perception than to the subscale benefit perception (Z = −56.997, P < 0.001, N = 4940). Participants (N = 318) who reported first-time collar fitting on their cat in the past 2 years used most often plastic breakaway buckles (rounded edges: 44.2%; round with edges inwards: 17.9%; rectangular: 10.4%), non-breakaway buckles were used by 14.4% (plastic: 9.4%; metal: 4.9%). The most common objects attached to the collar were ID tags/tubes (32.4%) and bells (22.1%) and tracker (19.7%). Based on lifetime experience, collar users were older, less often first-time owners, more often allowed their cats free roaming, and had less often professional experience with cats. Agreement to risks was lower than in non-users but still higher than agreement to benefits. In contrast, the perception of benefits outweighed perception of risks in recent collar users. Collar use in the past was strongly associated to risks from entrapment. This suggests that owners are willing to try collars but do not continue to use them over time as they may perceive more risks than benefits. • Collar use in cats is controversially discussed by cat owners and experts. • We explored owner attitudes most influential in the decision to use a collar. • Statements regarding risks received higher agreement than regarding benefits. • Recent collar users perception of benefits outweighed perception of risks. • In contrast, past collar users agreed strongly to risks, in particular entrapment. [ABSTRACT FROM AUTHOR]

Tattoos serve as a valuable tool for identification. In the forensic context, it is vital to establish a systematic approach for documenting tattoo-related information to facilitate efficient and fast comparisons, especially in postmortem cases. Despite some countries failing to recognize the potential significance of tattoos, this study presents a methodological framework for gathering comprehensive data on this form of body modification. This article presents the results of an investigation made in Mexico during 2019–2022. The proposed methodology introduces a systematic and distinct classification system tailored to the country in which it will be implemented. The proposal is accompanied by applying the methodology in a Forensic Medical Service (SEMEFO) in Mexico for a week to test its effectiveness and speed under high workloads and stressful conditions. The novelty of this article lies in emphasizing the need for established, replicable, and homologous methodologies for tattoo codification. Additionally, it presents a more in-depth codification, where the details of the tattoos to be classified are thoroughly analyzed. [ABSTRACT FROM AUTHOR]

DNA technology is the gold standard with respect to the identification of individuals from biological evidence. The technology offers the convenience of a universally similar approach and methodology for analysis across the globe. However, the technology has not realised its full potential in India due to the lack of a DNA database and lacunae in sample collection and preservation from the scene of crime and victims (especially those of sexual assault). Further, statistical interpretation of DNA results is non-existent in the majority of cases. Though the latest technologies and developments in the field of DNA analysis are being adopted and implemented, very little has been enacted practically to improve optimise sample collection and preservation. This article discusses current casework scenarios that highlight the pitfalls and ambiguous areas in the field of DNA analysis, especially with respect DNA databases, sampling, and statistical approaches to genetic data analysis. Possible solutions and mitigation measures are suggested. [ABSTRACT FROM AUTHOR]

Electromyographic (EMG) signals are biomedical signals that represent neuromuscular activities. The EMG signal is neither stationary nor periodic and exhibits complex interference patterns of several single motor unit action potentials (SMUAPs). This study aims to characterize EMG signals concerning firing patterns and other characteristics and to identify whether these MUAP firing patterns present short-range dependencies (SRD) or long-range dependencies (LRD). To do so, we characterized 208 EMG signals in terms of the number of phases, turns and combinations of phases. Then, we performed a statistical comparison of the (more efficient) Variance-time plot against the (less bias) Log-scale diagram for the estimation of the Hurst parameter and detection of LRD. Using these estimators, we managed to detect LRD in a sample taken with needle electrodes. In contrast, the tools used for the dependence identification on signals achieved with surface electrodes did not yield conclusive results on such dependence. [ABSTRACT FROM AUTHOR]

Accurate identification of jamming patterns is a crucial decision-making basis for anti-jamming in wireless communication systems. Current works still face challenges in fully considering the substantial influence of environmental noise on identification performance. To address the issue, this paper proposes an automatic threshold denoising-based deep learning model. The proposed method aims to mitigate the impact of noise on recognition performance within the feature space. Considering the challenges posed by nonlinear transformations in deep denoising, a shallow denoising approach based on deep learning is proposed. By constructing a dataset of 12 jamming patterns under noisy conditions, the proposed method exhibits excellent recognition performance and maintains a low computational cost. [ABSTRACT FROM AUTHOR]

House construction projects, known for their critical importance and complexity, often face significant constraints related to cost and time. These constraints necessitate the use of robust project management techniques to ensure timely and within-budget completion. This study presents an analysis of key project management methodologies, specifically the critical path method (CPM) and program evaluation and review technique (PERT), in the context of a house construction project. The methodologies employed include the identification of the earliest start and latest finish times, along with forward and backward passes to determine the project's critical path. A comparative analysis of CPM and PERT was conducted, revealing a minor variance of just two days between the two methods in determining project completion. The probability of completing the house construction project within 105 days was calculated to be 74.54%, indicating that both CPM and PERT are effective in ensuring timely project completion. The findings underscore the efficacy and practical benefits of employing CPM and PERT techniques in managing construction projects. [ABSTRACT FROM AUTHOR]

Copyright of Journal of the Colombian Society of Psychoanalysis / Revista de la Sociedad Colombiana de Psicoanálisis is the property of Sociedad Colombiana de Psicoanalisis and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

We used a two-phased mixed methods study with an explanatory sequential design to understand how frequently paramedics attend patients who, on paramedic assessment with the Gold Standards Framework Proactive Identification Guidance, are end-of-life and have advance care planning. We subsequently explored paramedic views on paramedic screening of patients to assess if they are end-of-life and onward referral to their General Practitioner for advance care planning. Paramedics screened and recorded 14.9% of patients as end-of-life and 44.3% of these patients were assessed to have no advance care plan in place. When paramedics screened patients and they did have an advance care plan in place, 36.8% had only a Do Not Attempt Cardiopulmonary Resuscitation. Paramedics found using the Gold Standards Framework Proactive Identification Guidance to screen patients for end-of-life status useful and straightforward and considered themselves well-placed to complete this task. Future research is required to address the practicalities of implementing a paramedic screening and referral tool for end-of-life care that results in the intended outcome of supporting effective advance care planning. [ABSTRACT FROM AUTHOR]

Copyright of Studii de Ştiintă şi Cultură is the property of Studii de Stiinta si Cultura and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Ecosistemas y Recursos Agropecuarios is the property of Universidad Juarez Autonoma de Tabasco and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The genus Mycobacterium includes well-known bacteria such as M. tuberculosis causing tuberculosis and M. leprae causing leprosy. Additionally, various species collectively termed non-tuberculous mycobacteria (NTM) can cause infections in humans and animals, affecting individuals across all age groups and health conditions. However, information on NTM infection prevalence in Panama is limited. This study conducted a retrospective analysis of clinical records from 2017 to 2021, specifically focusing on patients with NTM isolates. Data were categorized by variables like sex, age, HIV status, and sample source. Among the 4430 clinical records analyzed, 698 were linked to patients with NTM isolates. Of these patients, 397 were male, and 301 were female. Most female patients with NTM isolates (n = 190) were aged >45 to 85 years, while most male patients (n = 334) fell in the >25 to 75 years age group. A noteworthy proportion of male patients (n = 65) were aged 25–35 years. A significant age difference between male (median [min-max] = 53 years [3–90]) and female (median [61 years [6–94]) patients was observed (p < 0.001). Regarding HIV status, 77 positive individuals were male, and 19 were female (p < 0.001). Most samples (n = 566) were sputum samples, with additional pulmonary-associated samples such as broncho-alveolar lavage, tracheal secretions, and pleural fluid samples. Among extrapulmonary isolates (n = 48), sources included catheter secretions, intracellular fluids, peritoneal fluid, blood cultures, cerebrospinal fluid, bone marrow samples, and capillary transplant lesions. Specifically, the analysis identified the pathogenic microorganisms responsible for mycobacteriosis in Panama during the specific period 2017–2021, as M. fortuitum (34.4%), M. intracellulare (20.06%), and M. abscessus (13.75%), respectively. This study highlights the growing public health concern of NTM infections in Panama. The research provides valuable insights into the prevalence and distribution of NTM species in the country, offering a foundation for the development and implementation of effective prevention and control strategies for NTM infections in Panama. [ABSTRACT FROM AUTHOR]

[Display omitted] • ATR-FTIR spectroscopy was used to distinguish 3 wild cats from black guard hair. • The PLS-DA model successfully classified three species into distinct groups. • The PLS-DA model successfully differentiated human and non-human hair. • 100 % accuracy was achieved during the blind test. Hair is a commonly encountered trace evidence in wildlife crimes involving mammals and can be used for species identification which is essential for subsequent judicial proceedings. This proof of concept study aims, to distinguish the black guard hair of three wild cat species belonging to the genus Panthera i.e. Royal Bengal Tiger (Panthera tigris tigris), Indian Leopard (Panthera pardus fusca), and Snow Leopard (Panthera uncia) using a rapid and non-destructive ATR-FTIR spectroscopic technique in combination with chemometrics. A training dataset including 72 black guard hair samples of three species (24 samples from each species) was used to construct chemometric models. A PLS2-DA model successfully classified these three species into distinct classes with R-Square values of 0.9985 (calibration) and 0.8989 (validation). VIP score was also computed, and a new PLS2DA-V model was constructed using variables with a VIP score ≥ 1. External validation was performed using a validation dataset including 18 black guard hair samples (6 samples per species) to validate the constructed PLS2-DA model. It was observed that PLS2-DA model provides greater accuracy and precision compared to the PLS2DA-V model during cross-validation and external validation. The developed PLS2-DA model was also successful in differentiating human and non-human hair with R-Square values of 0.99 and 0.91 for calibration and validation, respectively. Apart from this, a blind test was also carried out using 10 unknown hair samples which were correctly classified into their respective classes providing 100 % accuracy. This study highlights the advantages of ATR-FTIR spectroscopy associated with PLS-DA for differentiation and identification of the Royal Bengal Tiger, Indian Leopard, and Snow Leopard hairs in a rapid, accurate, eco-friendly, and non-destructive way. [ABSTRACT FROM AUTHOR]

Accurate identification of the failure modes of Reinforced Concrete (RC) columns based on the design parameters of the structural members is critical for earthquake-resistant design and safety evaluation of existing structures. Existing identification methods have some problems, such as high cost, incomplete consideration of influencing factors, and low precision or recall in identifying shear or flexural-shear failure. In this paper, the main factors for the failure modes of RC columns are first analyzed and studied. Then, the problem of class imbalance in data samples is investigated. To identify the failure modes of RC columns, oversampling of data (BSB-FMC), model ensembling (RFB-FMC), cost-sensitive learning (CSB-FMC) and a fusion model of three strategies (BSFCB-FMC) are proposed. And finally, the SHapley Additive exPlanations (SHAP) method is used to provide a better interpretation of the designed model. The results show that the developed strategies can improve the accuracy of identifying the failure modes of RC columns compared to the models using a single Artificial Neural Network (ANN), a Support Vector Machine (SVM), a Random Forest (RF), and Adaptive Boosting (AdaBoost). The overall accuracy of the developed BSFCB-FMC model reaches 97%, and the precision and recall for the three failure modes are both above 90%. The designed model provides a solution for fast, accurate and cost-effective identification of the failure modes of RC columns. • We develop the models for identifying the failure modes of RC columns considering class imbalance problem. • The BSFCB-FMC model is developed by considering data oversampling, model ensembling, and cost-sensitive learning. • The designed model provides engineers with an interpretable, fast and stable model for identifying failure modes. [ABSTRACT FROM AUTHOR]

Accurately identifying the stage of the excavator working cycle is the prerequisite to achieve the staged energy-saving control. However, current identification methods often overlook the influence of hydraulic system latency on identification results and depend on a single model, resulting in poor generalization performance of the identification approaches. Moreover, expert calibration system remains a necessary factor for improving identification accuracy. Aiming at these issues, a hybrid multi-scale feature extractor and a decision-level data fusion classifier approach (HMSFE-DFC) is proposed to identify the working cycle stages of excavator. The input signal employs mixed signals from the main pump pressure and the control current of the proportional solenoid valve to reduce the response delay caused by the single main pump pressure signal. A hybrid multi-scale feature extractor is constructed using a convolutional neural network temporal self-attention feature extraction mechanism and one-dimensional ResNet-50 architecture to extract multiscale features. To prevent overfitting, a decision-level data fusion classifier is used to fuse the decisions information of numerous classifiers. The accuracy of stage identification for 10 consecutive working cycles reaches 95.21%, which verifies its effectiveness. • A hybrid multiscale feature extractor and decision level data fusion classifier approach is presented. • The main pump pressure and control current of the proportional solenoid valve are mixed to reduce the response delay. • The proposed approach can achieve an identification accuracy of 95.21% for 10 consecutive working cycles of an excavator. [ABSTRACT FROM AUTHOR]

The field of person re-identification (re-ID) encounters challenges such as inter-person similarity and occlusion. In this paper, we introduce an occluded pedestrian re-ID method based on Multiple Fusion and Semantic feature Mining (MFSM) to mitigate the adverse effects of occlusion on feature representation. Specifically, we devise a Multi-branch Strategic Enhancement Module (SEM) to bolster the robustness of data augmentation in addressing the issues posed by data memorization more consistently. This module emulates various real-world disturbances by implementing distinct and independent augmentation strategies. Furthermore, we propose the Triplet Cross Unit (TCU) to comprehensively exploit and consolidate both regional and relational visual cues, as well as multi-level features. The TCU facilitates transformers in acquiring an early understanding of translation invariance in images by transferring local patterns such as edges, textures, and colors from the lower CNN layers to the shallower transformer layers. Simultaneously, deeper transformer features offer more abstracted semantic visual representations that complement CNN's high-level semantic features. Lastly, we introduce the Global Squeeze-Excitation Fusion (GSEF) module to address the challenge of global feature segregation across different models in final outputs. The GSEF selectively merges features based on attention mechanisms, prioritizing the comprehensive utilization of valid global features. Extensive experiments demonstrate the state-of-the-art performance of our model in handling pedestrian occlusion, thus validating the efficacy of our method. • MFSM framework enables CNN-Transformer interaction, improving person re-identification accuracy, especially under occlusion. • Our strategic enhancement module expands data richness, suppressing noise and improving occlusion handling. • TCU combines CNN and Transformer features, improving re-ID performance without overemphasizing local features. • GSEF module consolidates diverse semantic features, enriching re-ID representation and improving accuracy. [ABSTRACT FROM AUTHOR]

This paper discusses the role of Transfer Learning (TL) and transformers in cancer detection based on image analysis. With the enormous evolution of cancer patients, the identification of cancer cells in a patient's body has emerged as a trend in the field of Artificial Intelligence (AI). This process involves analyzing medical images, such as Computed Tomography (CT) scans and Magnetic Resonance Imaging (MRIs), to identify abnormal growths that may help in cancer detection. Many techniques and methods have been realized to improve the quality and performance of cancer classification and detection, such as TL, which allows the transfer of knowledge from one task to another with the same task or domain. TL englobes many methods, particularly those used in image analysis, such as transformers and Convolutional Neural Network (CNN) models trained on the ImageNet dataset. This paper analyzes and criticizes each method of TL based on image analysis and compares the results of each method, showing that transformers have achieved the best results with an accuracy of 97.41% for colon cancer detection and 94.71% for Histopathological Lung cancer. Future directions for cancer detection based on image analysis are also discussed. [ABSTRACT FROM AUTHOR]

The early assessment of language and communication abilities is critical to meet the needs and challenges of inclusive societies. We developed a novel e-health approach whereby well-known assessment tools are available through online applications that allow a quick (computer, tablet, smartphone) on-screen assessment, making health monitoring and intervention in this field highly flexible and possible in a rich variety of contexts and users, including practitioners, educators, researchers, and caregivers. The app versions of the MacArthur-Bates Communicative Development Inventory Short forms (CDI) and the Communication and Symbolic Behavior Scales Developmental Profile (CSBS-DPTM) Infant-Toddler Checklist offer automatic scoring and percentile profiles for the individual infant and toddler. The easy, quick, decentralized access to health monitoring in the sensitive areas of cognitive and language development fosters the early identification of risk for language and communication impairments, early intervention and diagnosis, with individual, social and economic benefits. [ABSTRACT FROM AUTHOR]

The study aimed to investigate the attitudes of preschool teachers towards gifted children. A sample of 70 Slovenian preschool teachers was included in the study. The data was collected using a quantitative data collection technique and a questionnaire as a measurement tool. The results showed that preschool teachers believe gifted children’s characteristics are most easily recognised in the preschool years, especially in the second age group (3-6 years). Most respondents (82.9%) believe it is easier to recognise giftedness in children in the general intellectual domain. Most preschool teachers consider pedagogical differentiation and individualisation important when working with gifted preschool children. More than half of the respondents (57.1%) answered that they do not use tools for discovering gifted children, and the majority of them believe that they do not know the early signs of giftedness. In conclusion, gifted children should be identified in early childhood so that their development and potential can be adequately nurtured. [ABSTRACT FROM AUTHOR]

Mycobacteria of the Mycobacterium tuberculosis complex (MTC) are capable of infecting a wide variety of animals. Wild boar (Sus scrofa) has been recognized as an important wildlife reservoir for bovine tuberculosis. We screened wild boar in Slovenia for the presence of (1) Mycobacterium bovis in tissues and (2) antibodies to M. bovis in blood samples. In 2016 and 2017, 1284 tissue samples from 676 wild boar were subjected to cultivation. In 2018 and 2019, blood samples from 132 wild boar were examined using an ELISA kit. None of the MTC species were isolated from the tissue samples, and no antibodies to M. bovis were detected in the blood samples. Several nontuberculous mycobacteria (NTM), identified by 16S rRNA sequencing and/or matrix-assisted laser desorption ionization time-of-flight mass spectrometry, were found in the tissues of 9.8% of the wild boar: Mycobacterium nonchromogenicum, Mycobacterium peregrinum/Mycobacterium septicum, Mycobacterium avium, Mycobacterium engbaekii, Mycobacterium arupense, Mycobacterium algericum, Mycobacterium bohemicum, Mycobacterium confluentis, Mycobacterium flavescens, Mycobacterium fortuitum, Mycobacterium thermoresistibile, and Mycobacterium vaccae. Species-level identification was not possible for 21.2% of the isolates. At the time of the study, wild boar in Slovenia were not at risk from bTB; the significance of the presence of NTM in wild boar remains to be clarified and evaluated from a One Health perspective. [ABSTRACT FROM AUTHOR]

Copyright of Ethnologie Française is the property of Presses Universitaires de France and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Implementing processes for traceability is required in various industries to assure product quality during manufacturing, provide evidence on required processing conditions or facilitate product recalls. Commonly, radio-frequency identification (RFID) or code recognition techniques (e.g. Data Matrix) are applied to track the flow of workpieces through a manufacturing system and link processing data accordingly. Although the analysis of tracking data is well-examined, we still see a gap in the research on the trade-off between data acquisition, data analytics and data quality. Here, we present a framework to increase the value of existing data by enabling data analytics while addressing common pitfalls and reducing the costs of data management. [ABSTRACT FROM AUTHOR]

This essay aims to continue a conversation initiated in the 1980s about the contrasting terms "peer" and "tutor." This essay begins with a brief history of the terms, and applies Kenneth Burke's concept of identification to peer tutoring to attempt to explain these contradicting terms. Burke's theory of identification exemplifies the collaborative nature of peer tutoring wherein the peer tutor acts as a rhetor constructing new meaning through interaction and conversation with the tutee. Ultimately, peer tutoring's, and arguably Burke's, purpose is to continue helping people collaborate, arrive at new understanding, and overcome differences. [ABSTRACT FROM AUTHOR]

With widespread uses in areas as diverse as traffic analysis and medical imaging, picture segmentation is a basic problem in computer vision. Instance segmentation, which combines object recognition with segmentation, is a powerful tool for item identification and exact delineation. Using the Tomato Leaf disease dataset as an example, this research delves into the topic of segmentation training by capitalizing on the simplicity of enhanced YOLOv8-Seg models. Tomato leaf disease are the focus of this instancesegmentation dataset, which seeks to resolve the pressing problem of agricultural difficulties. One instance segmentation networks, YOLOv8n-Seg is presented and compared in this article for the purpose of Tomato leaf disease identification. The models are tested in difficult situations to see how well they can detect and separate garbage occurrences. Results show that enhanced YOLOv8-Seg is useful for agriculture by accurately segmenting instances of tomato leaf disease detection. [ABSTRACT FROM AUTHOR]

Copyright of Soziale Welt is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

An accurate electromagnetic model is essential for an optimal controller tuning of the high-performance servo system. This paper proposes a fractional-order electromagnetic model of a permanent magnet synchronous motor (PMSM) servo system and an identification methodology of this model. The reason why the investigated electromagnetic model should be a fractional-order one is addressed with a detailed explanation. The influence of voltage source inverter nonlinearity, which may cause system identification error, is analyzed. An improved inverter nonlinearity model and compensation method are proposed to promote the accuracy of the model parameter identification. Compared with the existing typical electromagnetic models of the PMSM servo system, the current open-loop and closed-loop experiments prove that the proposed fractional-order electromagnetic model with time delay is more accurate for the actual physical system. The effectiveness of the proposed nonlinearity modeling and compensation scheme of the inverter is also verified on an experimental PMSM servo system. • A fractional-order electromagnetic model with time delay, considering the complete servo platform with PMSM, servo drive circuit, inverter nonlinearity, and digital delay, is proposed. • An identification methodology for fractional-order electromagnetic model is proposed. An improved inverter model based on a sigmoid function is used for voltage error compensation to improve the accuracy of parameter identification. • The experimental results on the PMSM current control platform demonstrate that the proposed model is more accurate than the traditional integer-order model. [ABSTRACT FROM AUTHOR]

Objective: Managers can modify river flow regimes using fish monitoring data to minimize impacts from water management infrastructure. For example, operation of the gate-controlled Delta Cross Channel (DCC) in California can negatively affect the endangered Sacramento River winter-run Chinook Salmon Oncorhynchus tshawytscha. Although guidelines have been developed for DCC operations by using real-time juvenile fish sampling count data, there is uncertainty about how environmental conditions influence fish occupancy and the extent to which those relationships are affected by sampling and identification error. Methods: We evaluated the effect of environmental conditions, imperfect detection, and misidentification error on salmon occupancy by analyzing data using hierarchical multistate occupancy models. A total of 14,147 trawl tows and beach seine hauls were conducted on 1058 sampling days between October and December from 1996 to 2019. During these surveys, 2803 juvenile winter-run Chinook Salmon were identified, and approximately 29% of the sampling days had at least one winter-run juvenile detected. Result: The probability of misidentifying an individual juvenile winter-run Chinook Salmon in the field was estimated to be 0.056 based on fish identification examinations and genetic sampling. Occupancy varied considerably and was related to flow characteristics, water clarity, weather, time of year, and whether occupancy was detected during the previous sampling day. However, these relationships and their significance changed considerably when accounting for imperfect detection and the probability of misidentifying individual juvenile salmon. Detection was <0.3 under average sampling conditions during a single sample and was influenced by flow, water clarity, site, and volume sampled. Conclusion: Our modeling results indicate that DCC gate closure decisions could occur on fewer days when imperfect detection and misidentification error are not accounted for. These findings demonstrate the need to account for identification and detection error while using monitoring data to assess factors influencing fish occupancy and inform future management decisions. [ABSTRACT FROM AUTHOR]

The benefit of digitalisation, including the use of information management systems and electronic databases, in improving the effectiveness of social protection programmes is acknowledged by researchers and practitioners. However, concerns about digital- and financial exclusion stemming from increased reliance on digital technologies have also surfaced in recent studies. This study uses official data from South Africa's General Household Survey and the Global Findex database to explore these concerns. The potential gains of expanding social protection programmes through digitalisation are tested with simulations and distinct levels of income shocks. Additionally, two logistic regressions are employed to analyse relevant demographic variables for digital and financial inclusion. In combination with the quantitative approach, qualitative interviews were conducted with social protection experts from the South African Social Security Agency to gain insights into the impact of the digitalisation of social protection programmes. The results suggest that digitalising identification and delivery mechanisms in social protection improves equality through increased income levels. Digital access is associated with education, income, gender, population group, family size, and participation in the formal or informal sector. Financial access is linked to education level and gender. The study also identified perceived opportunities, including enhanced verification and streamlined application processes, which can improve programme effectiveness. However, integration, coordination, limited digital infrastructure, and digital illiteracy challenges may increase both digital- and financial exclusion. [ABSTRACT FROM AUTHOR]

Gender identification from video is an emerging research field that aims to automatically classify the gender of individuals based on video data. Due to the numerous applications for this task, it has received a lot of attention, including surveillance, human-computer interaction, and targeted marketing. In this study, we propose a gender identification system that utilizes the Pelican optimizer algorithm in combination with a Support Vector Machine (SVM) classifier. The Pelican optimizer is a metaheuristic algorithm inspired by the hunting behaviour of pelicans and has shown promising results in solving optimization problems. Pelican optimizer algorithm (POA) is applied to optimize the SVM parameter selection process such as kernel function. The POA algorithm searches for an optimal subset of parameters that maximizes the classification performance of the SVM model after the application of preprocessing and feature extraction techniques such as Local Binary Pattern (LBP). Finally, the selected optimized parameters otherwise known as POA-SVM classifier learns a decision boundary based on the labeled training data. The POA-SVM model is trained to distinguish between male and female samples and generalize the classification to unseen video data. Experimental evaluations are conducted using a benchmark dataset consisting of video samples with labelled gender information. The effectiveness of the suggested system is contrasted with other cutting-edge gender identification techniques. The results demonstrate the effectiveness of the Pelican Optimization Algorithm-SVM system, showing improved accuracy of 95%, and sensitivity of 94.4% at a faster recognition rate in gender classification from video data. [ABSTRACT FROM AUTHOR]

Bayesian estimation is applied to the analysis of backflow vortex instabilities in typical three- and four-bladed liquid propellant rocket engine inducers. The flow in the impeller eye is modeled as a set of equally intense and evenly spaced 2D axial vortices, located at the same radial distance from the axis and rotating at a fraction of the impeller speed. The circle theorem is used to predict the flow pressure in terms of the vortex number, intensity, rotational speed, and radial position. The theoretical spectra so obtained are frequency broadened to mimic the dispersion of the experimental results and parametrically fitted to the measured data by maximum likelihood estimation with equal and independent Gaussian errors. The method is applied to three inducers, tested in water at room temperature and different operating conditions. It successfully characterizes backflow instabilities using the signals of a single pressure transducer flush-mounted in the impeller eye, effectively bypassing the aliasing limitations and the data acquisition/reduction complexities of traditional multiple-sensor cross-correlation methods. The identification returns the estimates of the model parameters and their standard deviations, providing the information necessary for assessing the accuracy and statistical significance of the results. The flowrate is found to be the major factor affecting the backflow vortex instability, which, on the other hand, is rather insensitive to the occurrence of cavitation. The results are consistent with the data reported in the literature, as well as with those generated by the auxiliary models specifically developed for initializing the maximum likelihood searches and supporting the identification procedure. [ABSTRACT FROM AUTHOR]

Biometric identification systems are increasingly important today compared to traditional recognition/classification systems. Electromyography (EMG) signals and person identification/classification systems are preferred for high-security systems as they include physiological and behavioural movements. This study investigates biometric EMG signals based on convolutional neural networks (CNNs) and personal identification/classification systems. Bioelectric signals were recorded at six different wrist movements from five volunteer participants with a four-channel EMG device. To determine the spectrum characteristics of EMG signals, the frequency subbands of the signals were found using the discrete wavelet transform (DWT), empirical wavelet transform (EWT), and empirical mode decomposition (EMD) methods. In addition, statistical methods are used to improve the effectiveness of the feature vector. The CNN model was used to define or classify people. The performance of the developed system was evaluated using Accuracy, Precision, Sensitivity, Fscore parameters. As a result, a classification success of 95.66 % was achieved with the developed EMD-CNN method, 94.10 % with the DWT-CNN method, and 93.33 % with the EWT-CNN method. The artificial intelligence model presented in this study explains the effectiveness of EMG signals in person recognition or classification as a biometric identification system. Furthermore, the developed model shows promise for the development and design of future biometric recognition systems. [ABSTRACT FROM AUTHOR]

• FH screening in primary care practices are acceptable, appropriate, and feasible. • An implementation strategy package was co-developed with primary care stakeholders to improve screening for FH within their practices. • Demonstrating successful screening for FH in primary care practice will help increase the number of individuals identified with FH. • Generalization of the primary care implementation strategy to other settings will improve FH identification nationwide. We designed the Collaborative Approach to Reach Everyone with Familial Hypercholesterolemia (CARE-FH) clinical trial to improve FH screening in primary care and facilitate guideline-based care. The goal was to incorporate perspectives from end-users (healthcare system leaders, primary care clinicians, cardiologists, genetic counselors, nurses, and clinic staff) and improve translation of screening guidance into practice. We partnered with end-users to sequentially define the current state of FH screening, assess acceptability, feasibility, and appropriateness of implementing an FH screening program, and select clinically actionable strategies at the patient-, clinician-, and system-level to be deployed as a package in the CARE-FH clinical trial. Methods informed by implementation science and human centered design included: contextual inquiries, surveys, and deliberative engagement sessions. Screening for FH occurred rarely in primary care, and then only after a cardiovascular event or sometimes due to a family history of high cholesterol or early heart attack. Surveys suggested FH screening in primary care was acceptable, appropriate, and feasible. Reported and observed barriers to screening include insufficient time at patient encounters to screen, cost and convenience of testing for patients, and knowledge regarding causes of dyslipidemia. Facilitators included clear guidance on screening criteria and new therapies to treat FH. These results led to the development of multilevel strategies that were presented to end-users, modified, and then pilot tested in one primary care clinic. A refined implementation strategy package for FH screening was created with a goal of improving FH awareness, identification, and initiation of guideline-based care. https://clinicaltrials.gov/study/NCT05284513?id=NCT05284513&rank=1 Unique Identifier: NCT05284513 [Display omitted] [ABSTRACT FROM AUTHOR]

Existing feedforward, or FF, controllers are based on differentially flat models. For double-acting single-rod cylinders, more compact than the double-rod ones, flat models could be constructed either under an incompressible-flow assumption for low-pressure, slow-response applications or for a matched valve-cylinder combination. Contrasting the existing models tackling only a single nonlinear aspect like valve leakage, oil compressibility or friction, a novel FF controller, generalized for different types of valve-cylinder-pump combinations, has been developed here. It is based on the highest-order assumption of incompressible flow that sustains the piston motion. An FFPI controller has been implemented in a laboratory setup for displacement tracking against sinusoidal demands up to 9 Hz frequency. The FF parameters has been estimated by minimizing the deviation of simulation prediction from an offline experimental time-varying response. The PI gains have been selected through a stability analysis by extending Routh criteria for linearized time-variant error dynamics. Comparison against a PI-only controller with identical gains has established the energy-saving potential of the FFPI controller both working with the same variable-displacement pump. In comparison to existing nonlinear adaptive controllers, more precise and smoother responses have been obtained over wider frequency range at lower control expenses, admittedly with occasional marginal penalty in the phase variation. The FFPI controller has exhibited the shortest transient and the highest resilience against measurement noise. • Real-time energy-efficient Feedforward-PI control for electrohydraulic system. • Order-separated feedforward design for any double-acting cylinder, addressing multiple nonlinearities. • Estimated Feedforward parameter through GA-based identification. • Selected PI gains via stability analysis, extending Routh criteria for time-variant error dynamics. • Achieved precise and smooth tracking at lower control expense compared to existing nonlinear adaptive controllers. [ABSTRACT FROM AUTHOR]

Arthropods are considered the most successful animals on earth. They are an essential part of the aquatic food chain and efficient bioindicators depicting the biotic community structure and water quality. This study aimed to generate baseline data on the arthropod community structure of Jaja Creek and downstream sections of Imo River in Uta Ewa village, Ikot-Abasi Local Government Area, Akwa-Ibom State, Nigeria. A variety of sampling techniques, including the scoop net method at low tides in the littoral zone, square lift net anchored on a paddling boat, sweep net, and locally made crab traps were adopted for the sampling of the arthropods. Forty-six arthropod species were identified and classified into three classes: Arachnida, Crustaceans, and Insecta, with nine orders and twenty-six families. Sesarma alberti, Aratus pisonii, Sesarma elegans, Armases sp, Neosarmatium meinerti, Nematopalaemon sp, and Macrobrachium caledonicum were among the various arthropod species identified in this study. The class Crustaceans had the highest individual abundance of 135,809 (94.74%), followed by the class Insecta, which had a total numerical individual abundance of 7,339 (5.12%), and the order Arachnida (206; 0.14%). For the first time, members of the class Insecta and the families Sesarmidae, Pilumnidae, and Penaeidae (Penaeus sp.) were collected and identified in this portion of the Niger Delta Creek and Imo River. Given the importance of aquatic arthropod species to the catchment region and the country, a comprehensive conservation strategy should be developed to conserve and defend their survival. [ABSTRACT FROM AUTHOR]

Implantable medical devices (IMDs) play a crucial role in improving individuals' well-being and ensuring their safety by providing real-time health data monitoring for recovery. The use of energy harvesting (EH) technology has become increasingly popular among researchers because it offers the potential to extend the battery life of IMDs and reduce their weight. This study successfully examined the expansion of EH in the field of IMDs, the distribution of publications across different countries, and the identification of the most influential authors for potential research collaborations. A bibliometric analysis was conducted to evaluate two metrics: performance and science mapping. Data was collected from the Scopus database from the initial publications until October 2023, encompassing 250 articles published in Englishlanguage journals. The titles, keywords, and abstracts of these publications were analyzed and interpreted using VOS Viewer (version 1.6.19). Furthermore, network analysis using VOS Viewer enabled the identification of key research clusters. The findings reveal a continuous increase in EH for research on infectious and parasitic diseases over the 15-year period from 2008 to 2023. The United States and the University of Bern are recognized as the leading contributors to this field, based on their country and institutional contributions, respectively. The author with the most published papers and citations hails from China. Additionally, this study identifies several opportunities for collaboration with countries, institutions, authors, and research hotspots in EH for IMDs that benefit the reader. [ABSTRACT FROM AUTHOR]

Gender identification from video is an emerging research field that aims to automatically classify the gender of individuals based on video data. Due to the numerous applications for this task, it has received a lot of attention, including surveillance, human-computer interaction, and targeted marketing. In this study, we propose a gender identification system that utilizes the Pelican optimizer algorithm in combination with a Support Vector Machine (SVM) classifier. The Pelican optimizer is a metaheuristic algorithm inspired by the hunting behaviour of pelicans and has shown promising results in solving optimization problems. Pelican optimizer algorithm (POA) is applied to optimize the SVM parameter selection process such as kernel function. The POA algorithm searches for an optimal subset of parameters that maximizes the classification performance of the SVM model after the application of preprocessing and feature extraction techniques such as Local Binary Pattern (LBP). Finally, the selected optimized parameters otherwise known as POA-SVM classifier learns a decision boundary based on the labeled training data. The POA-SVM model is trained to distinguish between male and female samples and generalize the classification to unseen video data. Experimental evaluations are conducted using a benchmark dataset consisting of video samples with labelled gender information. The effectiveness of the suggested system is contrasted with other cutting-edge gender identification techniques. The results demonstrate the effectiveness of the Pelican Optimization Algorithm-SVM system, showing improved accuracy of 95%, and sensitivity of 94.4% at a faster recognition rate in gender classification from video data. [ABSTRACT FROM AUTHOR]

In this contribution, order reduction of tenth-order single-machine-infinite-bus (SMIB) system is proposed with the help of matching of time moments (TMs) and Markov parameters (MPs) utilizing Routh array (RA) approximation. The unknown denominator coefficients of desired reduced-order model (ROM) are obtained by RA approximation method. The RA approximation method is simple in application. Moreover, stability of desired ROM can be ensured directly using RA approximation. The determination of numerator coefficients is done by matching of TMs and MPs of system and desired ROM. To show the effectiveness of proposed method, comparative analysis is performed. The ROMs already obtained by researchers for SMIB system using different methods are compared on the basis of time-domain specifications (TDSs). TDSs considered are rise time, settling time, undershoot, overshoot, peak and peak time. The comparative analysis considering performance error criteria (PEC) such as integral of absolute error, integral of time absolute error, integral of time-squared absolute error (IT 2 AE), integral of squared-error, integral of time-squared-error (IT 2 SE) and integral of time-squared square error (IT 2 SE) is also provided. The tabulated values of TDSs and PEC along with plots prove applicability of proposed method for order reduction of SMIB system. [ABSTRACT FROM AUTHOR]

Normal wound healing follows four distinct phases: haemostasis, inflammation, prolifération and finally, maturation. If any barriers to healing occur within these four phases, the healing process will be delayed or may even stall (Mitchell, 2021). One of the common barriers to healing is hyper or overgranulation, or 'proud flesh'. Hypergranulated wounds can cause concern to both patients and healthcare professionals, and, although common in wound care, there is a limited evidence base and currently no guidelines for management. This article discusses the causes of hypergranulation, with suggestions on how it can be managed. [ABSTRACT FROM AUTHOR]

Background: The ears have increasingly been recognized as one of the supportive tools in forensics, based on the identification of landmark variations of ear biometrics in living persons. However, no studies on the reliability of such comparisons have been done on the deceased. Methods: The study aimed to investigate the correlation between ear biometrics and the age, sex, and stature of the deceased. The study was conducted on 181 deceased persons, aged between 18 and 70 years old on cases received by the Forensic Unit of Universiti Kebangsaan Malaysia Medical Centre. Documentation of age, sex, race, and height was recorded, and photographs of bilateral ears were taken. Measurements of twelve ear biometrics based on the Iannarelli method and ear length and ear width were taken from the photographs. Results: Results showed that there was a significant difference between males and females in six ear biometrics. There was also a significant correlation between ear biometrics, that is, ear length and ear width with the age and height of an individual. Conclusions: In brief, there exists a significant difference between males and females in ear biometrics with good correlations between ear biometrics and the height and age of an individual. Hence, the ear can be used for personal identification in the forensic field. [ABSTRACT FROM AUTHOR]

The realization of a project requires the application of stages, which are often prone to the appearance of risks. They can affect, negatively or positively, the evolution of a project, from all points of view. A good identification and framing of risks can lead to their effective management, throughout the life of a project, in order to implement an effective project. Through the present research, we consider the global framing of risks, to support their effective management, for the benefit of projects. [ABSTRACT FROM AUTHOR]

Within the realm of machine learning, kernel methods stand out as a prominent class of algorithms with widespread applications, including but not limited to classification, regression, and identification tasks. Our paper addresses the challenging problem of identifying the finite impulse response (FIR) of single-input single-output nonlinear systems under the influence of perturbations and binary-valued measurements. To overcome this challenge, we exploit two algorithms that leverage the framework of reproducing kernel Hilbert spaces (RKHS) to accurately identify the impulse response of the Proakis C channel. Additionally, we introduce the application of these kernel methods for estimating binary output data of nonlinear systems. We showcase the effectiveness of kernel adaptive filters in identifying nonlinear systems with binary output measurements, as demonstrated through the experimental results presented in this study. [ABSTRACT FROM AUTHOR]

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Studies designed to estimate the effect of an action in a randomized or observational setting often do not represent a random sample of the desired target population. Instead, estimates from that study can be transported to the target population. However, transportability methods generally rely on a positivity assumption, such that all relevant covariate patterns in the target population are also observed in the study sample. Strict eligibility criteria, particularly in the context of randomized trials, may lead to violations of this assumption. Two common approaches to address positivity violations are restricting the target population and restricting the relevant covariate set. As neither of these restrictions is ideal, we instead propose a synthesis of statistical and simulation models to address positivity violations. We propose corresponding g-computation and inverse probability weighting estimators. The restriction and synthesis approaches to addressing positivity violations are contrasted with a simulation experiment and an illustrative example in the context of sexually transmitted infection testing uptake. In both cases, the proposed synthesis approach accurately addressed the original research question when paired with a thoughtfully selected simulation model. Neither of the restriction approaches was able to accurately address the motivating question. As public health decisions must often be made with imperfect target population information, model synthesis is a viable approach given a combination of empirical data and external information based on the best available knowledge. [ABSTRACT FROM AUTHOR]

This study presents a novel machine learning approach for predicting the anisotropic parameters of the Yld2000–2d non-quadratic yield function using a hole expansion test. Heterogeneous stress-strain fields during the test substitute for multiple experiments required in the conventional parameter identification approach. An artificial neural network model for the parameter prediction is developed using a virtually generated training dataset composed of strains from hole expansion simulations, performed using randomly selected anisotropic parameters. The developed model predicts the Yld2000–2d parameters for AA6022-T4 based on the measured strain field from a hole expansion experiment, and the parameter results are evaluated by comparing anisotropy in uniaxial tension tests, the yield locus, and thinning variation in hole expansion test. [ABSTRACT FROM AUTHOR]

Nowadays, the growing complexities of manufacturing processes and systems make it difficult to identify the root causes of critical deviations in performance. Conventional methods often fall short in capturing the multifaceted nature of these challenges, despite a wealth of diverse untapped manufacturing data. To harness the full potential of diverse data sets and transform them into a valuable asset to guide root cause exploration, this paper presents an innovative approach that combines multimodal predictive analysis and explainable artificial intelligence (XAI) to uncover insights into system dynamics. This work contributes to a paradigm shift in industrial decision-making regarding manufacturing diagnostics. [ABSTRACT FROM AUTHOR]

Vietnamese ginseng (Panax vietnamensis Ha & Grushv.) is one of the most precious herbs in Vietnam because it contains many valuable bioactive compounds, especially ginsenosides. The protopanaxadiol synthase (PPDS) and protopanaxatriol synthase (PPTS) are considered the key enzymes for the ginsenoside biosynthesis pathway in Panax species. This study is the first report on the PPDS and PPTS genes and their enzymes in Vietnamese ginseng (named PvH_PPDS and PvH_PPTS, respectively). Two PvH_PPDS and PvH_PPTS genes contain 345 aa and 736 aa, respectively, with a common structure of 3 exons and 2 introns. The domain CYP450, which is conserved and essential for catalytic activity in the cytochrome P450 family, was detected in both of these genes. Their 3D protein structures were homology-modeled and annotated by bioinformatics tools. The phylogenetic tree shows that the PvH_PPDS and PvH_PPTS genes differ in their level of similarity with the corresponding genes of other Panax species. The elicitation of vanadium compounds improved the expression level of PvH_PPDS and PvH_PPTS genes as well as the accumulation of dammarane-type major ginsenosides in adventitious roots of Vietnamese ginseng. The present work is the first attempt to further understand two key enzymes in the biosynthesis of ginsenosides in this medicinal herb [ABSTRACT FROM AUTHOR]