Miracle-dz/UniFAR-FTUs huggingface dataset:
Drone-Computer Communication Based Tomato Generative Organ Counting Model Using YOLO V5 and Deep-Sort

The growth and development of generative organs of the tomato plant are essential for yield estimation and higher productivity. Since the time-consuming manual counting methods are inaccurate and costly in a challenging environment, including leaf and branch obstruction and duplicate tomato counts, a fast and automated method is required. This research introduces a computer vision and AI-based drone system to detect and count tomato flowers and fruits, which is a crucial step for developing automated harvesting, which improves time efficiency for farmers and decreases the required workforce. The proposed method utilizes the drone footage of greenhouse tomatoes data set containing three classes (red tomato, green tomato, and flower) to train and test the counting model through YOLO V5 and Deep Sort cutting-edge deep learning algorithms. The best model for all classes is obtained at epoch 96 with an accuracy of 0.618 at mAP 0.5. Precision and recall values are determined as 1 and 0.85 at 0.923 and 0 confidence levels, respectively. The F1 scores of red tomato, green tomato, and flower classes are determined as 0.74, 0.56, and 0.61, respectively. The average F1 score for all classes is also obtained as 0.63. Through obtained detection and counting model, the tomato fruits and flowers are counted systematically from the greenhouse environment. The manual and AI-Drone counting results show that red tomato, green tomato, and flowers have 85%, 99%, and 50% accuracy, respectively.
Dynamic Weight Agnostic Neural Networks and Medical Microwave Radiometry (MWR) for Breast Cancer Diagnostics

Background and Objective: Medical microwave radiometry (MWR) is used to capture the thermal properties of internal tissues and has usages in breast cancer detection. Our goal in this paper is to improve classification performance and investigate automated neural architecture search methods. Methods: We investigated extending the weight agnostic neural network by optimizing the weights using the bi-population covariance matrix adaptation evolution strategy (BIPOP-CMA-ES) once the topology was found. We evaluated and compared the model based on the F1 score, accuracy, precision, recall, and the number of connections. Results: The experiments were conducted on a dataset of 4912 patients, classified as low or high risk for breast cancer. The weight agnostic BIPOP-CMA-ES model achieved the best average performance. It obtained an F1-score of 0.933, accuracy of 0.932, precision of 0.929, recall of 0.942, and 163 connections. Conclusions: The results of the model are an indication of the promising potential of MWR utilizing a neural network-based diagnostic tool for cancer detection. By separating the tasks of topology search and weight training, we can improve the overall performance.
Ultrasound accurately assesses depth of invasion in T1‐T2 oral tongue cancer

Depth of invasion (DOI) is important for the T‐classification of squamous cell carcinoma of the oral tongue (SCCOT) and incorporated in the TNM 8 classification of oral cavity cancer. To determine DOI clinical palpation is performed, but the preferred radiological modality remains controversial. The aim of this study was to investigate the assessment of DOI using ultrasound (US‐DOI).
Integrated Management of Late Blight Potato (Phytophthora infestans, (Mont) de Bary) Disease through Potato Varieties and Fungicides in Lay-Armachiho District, Ethiopia

Potato (Solanum tuberosum L.) is an important source of livelihood for smallholder farmers of north-western Ethiopia. However, its productivity is markedly low due to late blight disease caused by Phytophthora infestans. The objective of this study was to evaluate the integrated use of potato varieties and fungicides for the management of late blight disease. Field experiment was conducted in Lay-Armachiho district during the main potato cropping season of 2018. The experiment consisted of twelve treatments as factorial combinations of two synthetic fungicides (Ridomil and Mancozeb) and untreated check with four potato varieties (three improved varieties, namely, Jalene, Gudene, Belete, and Local variety) was laid out in RCBD design with three replications. Disease incidence, severity, AUDPC, yield, and yield components were analyzed using SAS software (version 9.1) at P < 0.05 to separate treatment means. Fungicide sprayed treatments significantly reduced late blight epidemics and increased potato tuber yield on the Belete variety. The lowest disease severity (38.50%) and AUDPC values (761.02% unit-days) were recorded on Belete variety when sprayed with Ridomil fungicide. Late blight disease severity, incidence, AUDPC, and disease progress rate were reduced more in Belete variety than in the other three varieties. The highest disease severity (91.40%) and lowest yield (10.63 ton·ha−1) were recorded from the local control plot with a clear significance difference as compared to other treated plots. This study revealed that the application of Ridomil fungicide with the combination of Belete variety was effective in controlling late blight potato disease with the highest cost-benefit advantage.
Use of Artificial Intelligence to Predict Intensive Care Admission or Death in Patients Hospitalised for COVID-19: The PREDICT-COVID Study

Purpose: Propose a carefully developed prediction clinical tool to predict unfavourable outcome at admission of a SARS-CoV2–infected patient. Methods: This study is a post-hoc analysis of the NOSO-COR study, a multicentre prospective, observational study. All patients infected by SARS-CoV2 hospitalised in the Lyon-University hospitals from 8-March-2020 to 2-June-2020 were included. The database was split into a learning dataset (80%) and a validation dataset (20%). The primary composite outcome was the need for mechanical ventilation and/or transfer to intensive care unit and/or death within 21 days of admission. The PREDICT-COVID risk prediction tool was developed using a Bayesian network. Results: Data from 823 patients were analysed: age 70.6±16.9 years; BMI 26.7±5.4 kg/m2. Out of the 44 recorded variables, 11 that were the most linked to the primary outcome criteria were retained to develop the risk prediction tool. The primary composite endpoint was met by 36.5% of patients and 15.9% of patients died. The 5 most informative predictors were: C-Reactive-Protein, neutrophil-to-lymphocyte ratio, aspartate transaminase, shortness of breath, and prothrombin time. The final optimised models that used 11 variables had a mean±SD area under the receiver operating characteristic curve of 0.76±0.06, sensitivity of 55.5±7.0%, specificity of 78.6±4.6%, for the prediction of the primary outcome in patients hospitalised for COVID-19. The performance of the PREDICT-COVID prediction tool to predict the primary
A two‐fluid single‐column model of turbulent shallow convection. Part II: Single‐column model formulation and numerics

The two‐fluid single‐column model of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) is extended to include moisture and horizontal wind shear. Turbulent kinetic energy is introduced as a prognostic variable, dependence on a diagnosed boundary‐layer height is removed, and subfilter fluxes are approximated using a two‐fluid version of a Mellor–Yamada scheme. Three mechanisms for entrainment and detrainment processes are introduced, which represent entrainment of unstable air at the surface, forced detrainment of air at the top of the boundary/cloud layers, and turbulent mixing that relaxes the convective fluid to a reference profile. A semi‐implicit Eulerian discretization replaces the semi‐implicit semi‐Lagrangian implementation of Thuburn et al. (Quart. J. R. Meteorol. Soc., 2019, 145, 1535–1550) to improve numerical stability and conservation. The equations for the implicit time step are solved using a quasi‐Newton method, which is shown to perform well in numerical tests for conservation and convergence. The two‐fluid single‐column model presented in this article will be applied to simulations of shallow cumulus convection in Part III.
Causal Associations of Air Pollution With Cardiovascular Disease and Respiratory Diseases Among Elder Diabetic Patients

Extensive researches have linked air pollutants with cardiovascular disease (CVD) and respiratory diseases (RD), however, there is limited evidence on causal effects of air pollutants on morbidity of CVD or RD with comorbidities, particularly diabetes mellitus in elder patients. We included hospital admissions for CVD or RD among elder (≥65 years) diabetic patients between 2014 and 2019 in Beijing. A time‐stratified case‐crossover design based on negative‐control exposure was used to assess causal associations of short‐term exposure to air pollutants with CVD and RD among diabetic patients with the maximum lag of 7 days. A random forest regression model was used to calculate the contribution magnitude of air pollutants. A total of 493,046 hospital admissions were recorded. Per 10 μg/m3 uptick in PM1, PM2.5, PM10, SO2, NO2, O3, and 1 mg/m3 in CO was associated with 0.29 (0.05, 0.53), 0.14 (0.02, 0.26), 0.06 (0.00, 0.12), 0.36 (0.01, 0.70), 0.21 (0.02, 0.40), −0.08 (−0.25, 0.09), and 4.59 (0.56, 8.61) causal effect estimator for admission of CVD among diabetic patients, corresponding to 0.12 (0.05, 0.18), 0.09 (0.05, 0.13), 0.05, 0.23 (0.06, 0.41), 0.10 (0.02, 0.19), −0.04 (−0.06, −0.01), and 3.91(1.81, 6.01) causal effect estimator for RD among diabetic patients. The effect of gaseous pollutants was higher than particulate pollutants in random forest model. Short‐term exposure to air pollution was causally associated with increased admission of CVD and RD among elder diabetic patients. Gaseous pollutants had a greater contribution to CVD and RD among elder diabetic patients.
The impact of Artificial Intelligence on the marketing practices of Professional Services Firms

ABSTRACT Artificial intelligence (AI) is rapidly evolving, influencing service industries and Professional Services Firms (PSFs) embracing AI to improve margins. In this paper, we reveal how AI impacts the characteristics and marketing practices of PSFs. AI has the potential to provide considerable efficiency and organizational benefits, yet simultaneously changes service attributes, threatens the competitive advantage of deep client relationships, and directly influences the marketing practices of PSFs, including pricing strategy. Based on an extensive literature review, we present a conceptual model illustrating the changes impacting the business model, marketing practices, and client relationships of PSFs.
Impact of Charles Bonnet Syndrome on visually impaired older adults’ ability to engage in physical activity: A scoping review

Charles Bonnet syndrome (CBS) is a condition associated with sight loss, characterised by vivid, spontaneous visual hallucinations. Currently, it is unclear whether CBS presents challenges to participation in physical activities, in addition to barriers attributed to sight loss alone. The purpose of this scoping review was to establish the extent of the literature, and gaps in the knowledge base, concerning the impact of CBS on older adults’ engagement in physical activities. Review conduct was informed by Arksey and O’Malley and Levac et al.’s scoping review methodologies. Six academic databases were searched during May 2021, yielding 2709 results: eight articles met eligibility criteria. Two additional sources were located via a reference check of included papers and stakeholder consultation. Quantitative cross-sectional studies (n = 3) indicate that CBS may interfere with the ability to move around, while qualitative sources (case report/series n = 6; autobiography n = 1) show that sudden presentation of hallucinations in a person’s pathway, or threatening content, may jeopardise safety while walking. Moving to avoid a hallucination could present a fall risk if attention is diverted from environmental hazards. One case report stated that CBS did not affect personal care activities. Due to a limited evidence base, further empirical research is needed to achieve a comprehensive understanding about how CBS affects older adults’ participation in physical activities.
Genome-Based Analysis of Verticillium Polyketide Synthase Gene Clusters

Simple Summary Fungi can produce many types of secondary metabolites, including mycotoxins. Poisonous mushrooms and mycotoxins that cause food spoilage have been known for a very long time. For example, Aspergillus flavus, which can grow on grains and nuts, produces highly toxic substances called Aflatoxins. Despite their menace to other living organisms, mycotoxins can be used for medicinal purposes, i.e., as antibiotics, growth-promoting compounds, and other kinds of drugs. These and other secondary metabolites produced by plant-pathogenic fungi may cause host plants to display disease symptoms and may play a substantial role in disease progression. Therefore, the identification and characterization of the genes involved in their biosynthesis are essential for understanding the molecular mechanism involved in their biosynthetic pathways and further promoting sustainable knowledge-based crop production. Abstract Polyketides are structurally diverse and physiologically active secondary metabolites produced by many organisms, including fungi. The biosynthesis of polyketides from acyl-CoA thioesters is catalyzed by polyketide synthases, PKSs. Polyketides play roles including in cell protection against oxidative stress, non-constitutive (toxic) roles in cell membranes, and promoting the survival of the host organisms. The genus Verticillium comprises many species that affect a wide range of organisms including plants, insects, and other fungi. Many are known as causal agents of Verticillium wilt diseases in plants. In this study, a comparative genomics approach involving several Verticillium species led us to evaluate the potential of Verticillium species for producing polyketides and to identify putative polyketide biosynthesis gene clusters. The next step was to characterize them and predict the types of polyketide compounds they might produce. We used publicly available sequences from ten species of Verticillium including V. dahliae, V. longisporum, V. nonalfalfae, V. alfalfae, V. nubilum, V. zaregamsianum, V. klebahnii, V. tricorpus, V. isaacii, and V. albo-atrum to identify and characterize PKS gene clusters by utilizing a range of bioinformatic and phylogenetic approaches. We found 32 putative PKS genes and possible clusters in the genomes of Verticillium species. All the clusters appear to be complete and functional. In addition, at least five clusters including putative DHN-melanin-, cytochalasin-, fusarielien-, fujikurin-, and lijiquinone-like compounds may belong to the active PKS repertoire of Verticillium. These results will pave the way for further functional studies to understand the role of these clusters.
Employees’ Perceived Risk of Discussing COVID-19 Concerns in
 the Workplace: The Impact of Perceived Organizational Support and Policy
 Communication on Voicing Health Concerns in Times of Crisis

Objectives: The coronavirus disease 2019 (COVID-19) pandemic transformed the nature of work across the globe and forced organizations to grapple with the challenge of protecting their workers, while maintaining operations. Part of the challenges associated with the pandemic included instituting new COVID-19 workplace policies (e.g., social distancing, masking) designed to protect employees from spread of the virus. The present study is designed to understand employee perceptions of the risks of discussing their COVID-19 concerns in the workplace and how this is related to perceived organizational support and COVID-19 policy communication. Methods: Two-hundred and fifty-two individuals working during the COVID-19 pandemic completed an online survey adminis-tered via Amazon’s Mechanical Turk. Participants’ COVID-19 policy communication within their organization, their perceptions of organizational support, and their perceived risk associated with discussing COVID-19 concerns in the workplace were analyzed with Pearson correlations and hierarchical regression models using SPSS 27. Results: COVID-19 policy communication was negatively correlated to employees’ levels of perceived organizational support ( r = –.148, p = .018). The regression models indicated that COVID-19 policy communication and perceived organizational support explain a significant amount of employees’ perceived risks of discussing COVID-19 concerns in the workplace. Conclusions: Organizations need to be aware of whether employees feel supported by organizational policies and whether they can reveal their concerns about health-related issues and/or organizational crises in their workplace. The implications of the study findings are discussed in terms of the risks of discussing health concerns in the workplace and the nature of work in times of crisis.
Microstructure-based modelling prediction of elasticity in hydrating cement paste

This work was financially supported by project POCI-01-0145-FEDER-007457 – (CONSTRUCT - Institute of R&D in Structures and Construction) and by project POCI-01-0145-FEDER-007633 (ISISE) funded by FEDER funds through COMPETE2020 - Programa Operacional Competitividade e Internacionalizacao (POCI) – and by national funds through FCT - Fundacao para a Ciencia e a Tecnologia. FCT and FEDER (COMPETE2020) are acknowledged for the funding of the research project PTDC/ECM-EST/1056/2014 (POCI-01-0145-FEDER-016841). The first author also thanks the individual grant with reference number of SFRH/BPD/114754/2016 funded by FCT
The Effect of Destination Image and Social Influence on Visiting Intentions and Visiting Decisions at Museum

The purpose of this study was to analyze and prove the effect of destination image and social influence on visiting intentions and visiting decisions on visitors to the Mulawarman museum in Kutai Kartanegara Regency. This study used 120 respondents and this study used a survey method with a sample in Kutai Kartanegara Regency with the Accidental Sampling Method sampling technique. Respondents were only interviewed once when they met (cross section). Data from respondents (Primary Data) was then tabulated and tested using statistical methods. Data were collected using research tools (questionnaires). Based on the results of the analysis and discussion, it can be concluded that the image of the destination has a significant effect on the visiting intentions of visitors. So the better the image of the destination, the higher the intention to visit. Social has no significant effect on visitors' intention to visit. So the better the social level, the higher the intention to visit. The image of the destination has no significant effect on the decision to visit the visitors. So the high or low level of the image of the destination does not affect the decision to visit. Social influence has a significant effect on the decision to visit. So the better the social level, the higher the decision to visit. Intention to visit has a significant effect on the decision to visit. So the higher the intention to visit, the higher the decision to visit.
Work-family conflict as antecedent to workplace deviance: a study among bankers

Hinged on the Job Demand-Resource model and Source Attribution Theory, this study assessed the relationship between work-family conflict (family-towork and work-to-family conflicts) and workplace deviant behaviours (organizational and interpersonal deviance) among bank employees in Ghana. The quantitative two-wave longitudinal design was used, and data collected from 301 respondents. Unique self-generated codes were used to match the responses from each respondent in both times. Work-to-family conflict predicted interpersonal deviant behaviour in both Time 1 and Time 2, and organizational deviant behaviour in Time 2 but not in Time 1. Also, family-to-work conflict predicted interpersonal deviant behaviour in both Time 1 and Time 2 and also predicted organizational deviant behaviour in Time 1 only. This is one of the few empirical studies assessing the relationship between work-family conflict and workplace deviance, and in particular, among bankers. Assessing such relationships in this study over time has shown that variations in nature of the hypothesized relationships could be a result of extraneous variables (dynamic employee behaviour or environmental changes) not anticipated. Banks must pay particular attention to the job demands imposed on employees and again take interest in the out-of-job experiences of staff that could affect their job performance and involvement in deviant behaviours. Keywords: Work-family conflict; workplace deviance; bankers; Ghana; antecedents
The Causal Relationship between Learner Motivation and Language Achnievement: New Dynamic Perspective

It is well known that successful second language (L2) learners are motivated individuals. Accordingly, L2 researchers have tested the predictive power of different motivational constructs on language learning outcomes. However, this perspective appears to neglect the assessment of achievement as a predictor of future motivation. To assess this possibility, we first employed the latent growth curve model (LGCM) to evaluate the initial values and growth rates of the two variables. We further applied a newly developed statistical method, the random-intercept cross-lagged panel model (RI-CLPM), to study the causal relationship. A total of 226 language students were monitored for 17 weeks at three time points. The analysis showed an increasing trend in the association between the growth levels of both variables. However, students’ autonomous motivation at Time 1 appears to affect achievement at Time 2. Further, the second wave of the RI-CLPM illustrated that achievement at Time 2 impacted autonomous motivation at Time 3, while motivation failed to predict scores on achievement at Time 3.
High performance voltage output filter realizations using second generation voltage conveyor

In this article, new voltage‐output filter realizations based on second generation voltage conveyor (VCII) as basic building block are presented. The results of this study show that VCII is a suitable candidate for applications requiring a voltage as filter output signal, considering that the availability of a low impedance voltage output terminal in VCII makes unnecessary the use of extra voltage buffers at the filter output, resulting in filter implementations with simpler structure, reduced power consumption, and chip area. New configurations of VCII based filters with first order low‐pass (LP), second order LP, and second order bandpass (BP) transfer functions are shown. The proposed circuits enjoy a very simple structure employing only one active element and four passive components. Simulation results for a VCII transistor implementation in a 0.35 μm standard CMOS technology and a supply voltage of ±1.65 V approve the presented theory.