Miracle-dz/UniFAR-FTUs huggingface dataset:
Combined 18-FDG-PET/Contrast-Enhanced CT in the Diagnosis of Recurrent Colorectal Cancer: A Prospective Monocentric Pilot Study

Contrast-enhanced CT scan and 18DG-PET are used in the staging and surveillance of Colorectal Cancer (CRC). We here evaluate the feasibility and performances of systematically combining 18-FDG-PET and contrastenhanced CT whether as a stand-alone diagnostic examination, or as 2 separate examinations, for the early detection of recurrent colorectal cancer. 45 patients underwent 159 planned diagnostic follow-up sessions: 109 (68%) were performed as a combined modality during the same examination and 50 were performed as two separate examinations and acquired at different timepoints. Fourteen patients experienced recurrence during the study period. The sensitivity of 18-FDG-PET and contrast-enhanced CT scan in detecting a recurrent disease was 84.4% and 86.6 % respectively with a specificity of 96% and 97% respectively. 18-FDG-PET and contrast-enhanced CT findings’ were concordant in 79% of cases. The overall sensitivity and specificity in detecting CRC recurrence were 80% and 99% respectively in the combined procedure and 89% and 98% respectively in the two-time procedure. The positive predictive value of the procedure (combined or two-time procedure) for the detection of recurrent CRC was 100% with, however a shorter delay for decision with the combined [7.7 vs. 12.2 days] (p<0.05). The combined procedure is feasible in almost 2/3 of cases. When compared to the two-time procedure, it is performant in detecting recurrent CRC with shorter delay to therapy.
Knowledge, Attitude and Practice of Periodic Screening of Diabetes Mellitus among Teachers in Tikrit City in Iraq

Background: One of the most prevalent chronic diseases isDiabetes mellitus that have adverse effect on thehealth, and the goal today being placed on disease prevention, controlling and screening.Aim: To assess knowledge,attitude and Practice of periodic screening of DM among teachers in Tikrit city.Subject and Method: The study is a cross-sectional study that had conducted from 1st of November 2018until 30 of March 2019 on teachers in Tikrit city primary and secondary school using a questionnaire.Results: The knowledge regarding the diabetes and its screening a 24% were good and 46% were acceptedregarding the disease symptoms and about its control 84% mention that diet modification is the best way thenexercise and medication in lower percent while regarding complications 68% (much of them) knowledgewere bad, About the importance of periodic screening the great percentage was yes.Conclusion: There was 93.3% who have knowledge about diabetes mellitus screening among schoolteachers. Teacher’s method for controlling diabetes mostly are diet modifications 84% then exercise 42.5%.
Miniaturized Circularly Polarized Periodically Structured Surface Antenna for RFID Application Inspired by SRR

This article presents a novel miniaturized circularly polarized (CP) antenna based on periodically structured surfaces inspired by the vertical split-ring resonator (VSRR). The VSRR is considered as a magnetic dipole placed above the ground, which could excite strong boresight radiation with a reduced size. To enhance the aperture gain, multicapacitors and inductors (Multi-C&L) are loaded on the loop, which replace the single capacitive gap of the conventional split-ring resonator (SRR). Here an SRR-inspired periodic structure with a relatively uniform field distribution is achieved. The stepped impedance resonator (SIR) structure, as the unit cell of the SRR-inspired surface, is analyzed and investigated using the circuit models derived from the SRR. By cascading multiple high-value capacitors, a larger aperture with a uniform field distribution is obtained. Linearly polarized (LP) and CP antennas are studied. The proposed CP antenna is made of thin substrates and metallic screws, resulting in a negligible dielectric loss. It offers attractive features including a compact size, a low profile ( $0.191\lambda _{0} \times 0.191\lambda _{0} \times 0.032\lambda _{0}$ ), good radiation efficiency, novel working principle, flexible performance control, and a bandwidth well covering the 902–928 MHz UHF band, which is well situated for radio frequency identification (RFID) application.
Isotopic approaches to estimating the contribution of heterotrophic sources to Hawaiian corals

Corals obtain nutrition from the photosynthetic products of their algal endosymbionts and the ingestion of organic material and zooplankton from the water column. Here, we use stable carbon (δ13C) and nitrogen (δ15N) isotopes to assess the proportionate contribution of photoautotrophic and heterotrophic sources to seven Hawaiian coral species collected from six locations around the island of O‘ahu, Hawaiʻi. We analyzed the δ13C and δ15N of coral tissues and their algal endosymbionts, as well as that of dissolved inorganic matter, particulate organic matter, and zooplankton from each site. Estimates of heterotrophic contribution varied among coral species and sites. Bayesian mixing models revealed that heterotrophic sources (particulate organic material and zooplankton) contributed the most to Pocillopora acuta and Montipora patula coral tissues at 49.3% and 48.0%, respectively, and the least to Porites lobata at 28.7%, on average. Estimates of heterotrophic contribution based on the difference between δ13C of the host and algal endosymbiont (δ13Ch–e) and isotopic niche overlap often differed, while estimates based on δ15Nh–e were slightly more aligned with the estimates produced using Bayesian mixing models. These findings suggest that the utility of each approach may vary with coral health status, regions, and coral species. Overall, we find that the mean heterotrophic contribution to Hawaiian coral tissues ranges from 20% to 50%, suggesting a variety of trophic strategies. However, these findings did not always match past direct measurements of heterotrophic feeding, indicating that heterotrophically acquired nutrition does not necessarily get incorporated into tissues but can be respired or exuded in mucus.
Inelastic Dynamic Eccentricities in Pushover Analysis Procedure of Multi-Story RC Buildings

A documented pushover procedure on asymmetric, single-story, reinforced concrete (RC) buildings using inelastic dynamic eccentricities is extending in this paper on asymmetric multi-story RC buildings, aiming at the Near Collapse state. The floor lateral static forces of the pushover procedure are applied eccentric to the Mass Centers using appropriate inelastic dynamic or design eccentricities (dynamic plus accidental ones) to safely estimate the ductility demands of both the flexible and stiff sides of the building due to the coupled torsional/translational response. All eccentricities are applied with respect to the “Capable Near Collapse Principal System” of multi-story buildings, which is defined appropriately using the well-known methodology of the torsional optimum axis. Moreover, two patterns of lateral forces are used for performing the analysis, where in the second one an additional top-force is applied to consider the higher-mode effects. A six-story, asymmetric, torsionally-sensitive RC building is examined to verify the proposed pushover procedure relative to the results of non-linear dynamic analysis. The outcomes indicate that the proposed pushover procedure can safely predict the seismic ductility demands at the flexible and stiff sides, providing reliable estimates for the peak inter-story drift-ratios throughout the building as well as a good prediction of the plastic mechanism.
Cognitive Modeling of Task Switching in Discretionary Multitasking Based on the ACT-R Cognitive Architecture

Discretionary multitasking has emerged as a prevalent and important domain in research on human–computer interaction. Studies on modeling based on cognitive architectures such as ACT-R to gain insight into and predict human behavior in multitasking are critically important. However, studies on ACT-R modeling have mainly focused on concurrent and sequential multitasking, including scheduled task switching. Therefore, in this study, an ACT-R cognitive model of task switching in discretionary multitasking was developed to provide an integrated account of when and how humans decide on switching tasks. Our model contains a symbolic structure and subsymbolic equations that represent the cognitive process of task switching as self-interruption by the imposed demands and a decision to switch. To validate our model, it was applied to an illustrative dual task, including a memory game and a subitizing task, and the results were compared with human data. The results demonstrate that our model can provide a relatively accurate representation, in terms of task-switching percent just after the subtask, the number of task-switching during the subtask, and performance time depending on the task difficulty level; it exhibits enhanced performance in predicting human behavior in multitasking and demonstrates how ACT-R facilitates accounts of voluntary task switching.
Tomato Leaf Diseases Detection Using Deep Learning Technique

Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error-prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and also helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e. ResNet18, MobileNet, DenseNet201, and InceptionV3 on 18,162 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), six-class classification (healthy and various groups of diseased leaves), and ten-class classification (healthy and various types of unhealthy leaves) are also reported. InceptionV3 showed superior performance for the binary classification using plain leaf images with an accuracy of 99.2%. DenseNet201 also outperform for six-class classification with an accuracy of 97.99%. Finally, DenseNet201 achieved an accuracy of 98.05% for ten-class classification. It can be concluded that deep architectures performed better at classifying the diseases for the three experiments. The performance of each of the experimen-tal studies reported in this work outperforms the existing literature.
Aiding-Buoyancy Mixed Convection from a Pair of Side-by-Side Heated Circular Cylinders in Power-Law Fluids

Mixed-convection flow and heat transfer of non-Newtonian power-law fluids over a pair of side-by-side circular cylinders in aiding buoyancy are investigated numerically. The calculations are performed in an unconfined domain for the following range of physical control parameters: Reynolds number (Re) = 1–40, Richardson number (Ri) = 0–1, and power-law index (n) = 0.2–1 for a transverse gap ratio of 1.5 at a constant Prandtl number (Pr) of 50. Extensive numerical results are presented, such as viscous, pressure, and total drag coefficients, average Nusselt number, and transition from a steady to a time-periodic regime. The overall drag coefficient and its components decrease with the Reynolds number and increase with the Richardson number for all values of the power-law index considered. The average Nusselt number increases with an increase in both the Reynolds and Richardson numbers but decreases with an increase in the power-law index. It is observed that aiding buoyancy and shear-thinning tendency augme.
Study on the Flotation Behavior and Mechanism of Rhodochrosite and Calcite

The surface electrical behavior and flotation behavior of rhodochrosite and calcite were studied in the paper.The flotation tests,Zeta potential measurements and SEM-EDAX measurements show that Mn2+,which dissolved from rhodochrosite in artificial mixed mineral flotation experiments,can be absorbed on the calcite surface,and this makes the surface property of calcite transfer to rhodochrosite.According to the solution chemistry calculation,the amount of Mn2+ which dissolved from rhodochrosite is so great enough that it leads to the precipitation of MnCO3 on calcite surface.Therefore these two minerals'surface properties tend to be similar.This is the main reason why it is difficult to achieve the flotation separation of rhodochrosite and calcite.
Using network ecology to understand and mitigate long‐term insect declines

D I M I T R I O S P E T S O P O U L O S, 1 D AV I D H . L U N T, 2 J A M E S R . B E L L, 3 J A M E S J . N . K I T S O N, 1 L A R I S S A C O L L I N S, 4 N E I L B O O N H A M, 1 R A M I R O M O R A L E SH O J A S 3 and D A R R E N M . E VA N S 1 1School of Natural and Environmental Sciences, Newcastle University, Newcastle-upon-Tyne, U.K., 2Biological and Marine Sciences, Hardy Building, University of Hull, Hull, U.K., 3Rothamsted Insect Survey, Rothamsted Research, Harpenden, U.K. and 4Fera Science Ltd, York, U.K.
Cluster analysis with cellwise trimming and applications to robust clustering of curves

In this work, we propose a robust Cluster Analysis methodology based on cell trimming as an extension to a recently introduced robust version of Principal Component Analysis. This new approach allows for cellwise trimming in cluster analysis, which is more reasonable than traditional casewise trimming when the problem’s dimension is large. This type of trimming avoids an unnecessary loss of information when only a few cells of the entirely trimmed observations are atypical. An algorithm is proposed to apply this approach. This algorithm is particularized to the interesting case of functional cluster analysis. Simulations and applications to real data sets are given to illustrate the proposed methods.
Data‐driven modeling of bridge buffeting in the time domain using long short‐term memory network based on structural health monitoring

A data‐driven approach for modeling bridge buffeting in the time domain is proposed based on the structural health monitoring (SHM) system. The long short‐term memory (LSTM) network is applied to model the bridge aerodynamic system with the potential fluid memory effect which is characterized by an uncertain time lag between inflow wind and the structural response. SHM is incorporated into this data‐driven approach due to the advantages of prototype measurements such as the ability to consider the high Reynolds number effects and the real natural winds with nonuniformity and nonstationarity. The cell state in the LSTM module is applied to carry the potential fluid memory effects for predicting the aerodynamic response. We compare the obtained data‐driven model and the conventional finite element model in the buffeting response prediction. The data‐driven model shows higher accuracy than the conventional model, indicating that the proposed data‐driven approach has promising potential in modeling bridge aerodynamics. The incorporation of the proposed LSTM‐based bridge aerodynamic model and the field monitoring enables us to move buffeting predictions from lab theory to practical engineering.
Analysis of Sales Predictions from the Point of View of the Increase in Daily Newspaper Sales

This study aims at Analysis of Sales Predictions from the Point of View of the Increase in Daily Newspaper Sales. The methods of this research is gather evidence through a variety of approaches, including library analysis and field research. This paper makes use of both qualitative and quantitative evidence. Primary data sources, namely data collected from respondents or direct interviews with business executives, specifically about marketing strategies, especially those relating to promotion. Secondary evidence. Promotional operations are carried out optimally, which is facilitated by sufficient costs, especially in potential areas. Apart from repeat buyers, the organization offers its goods in store, which enables the company to monitor its output figures accurately and seamlessly.
Adicción a las redes sociales en estudiantes mexicanos: percepciones de discentes y docentes

The expansion of the internet and mobile devices has contributed to a shift in communication. Currently, most youths and adolescents are registered and prioritize communication through social networks, which, in many cases, involves an abusive and addictive use of them. The main objective of this study is to analyze the perceptions that Mexican students and professors have regarding their level of addiction to social networks. The sample is made up of 17,600 students and 1,480 teachers in the Mexican high school system and the instrument used has been the Social Media Addiction Scale-Student Form (SMAS-SF) translated, adapted and validated to the Mexican context. Among the main results, it can be highlighted that there are differences between the perceptions of both groups regarding the addiction level of students to social networks as well as the frequency and time of social networking and navigating the internet
Identification of Passenger Demand in Public Transport Using Machine Learning

An essential aspect of the transport system is public passenger transport and the Public Transport (PT) movement prediction is significant issues faced in the transport planning area because of its operational importance. In recent years, Intelligent Transportation Systems (ITS) have received a growing amount of interest. There are many advances and innovative applications that have been introduced for a safer, highly efficient, and even congenial environment from PT. A reliable and efficient system of traffic flow prediction is required for accomplishing these applications that build an event with the application of ITS implementations to resolve the potential road situation in advance. However, the PT network efficiency plays the main role for all urban authority areas in which the advancement of both communication and location devices are randomly increasing the data availability generated over the operational platform. In order to recognize trends useful for improving the Schedule Plan, adequate Machine Learning (ML) approaches need to be implemented. Therefore, this paper focused in heterogeneous data that affect the prediction value which is utilized for predicting the demand transport required in the particular route and arrival time of public transport using Density-Based Spatial Clustering of Applications with Noise (DBSCAN) with Seasonal Autoregressive Integrated Moving Average (SARIMA) algorithm to analyze the forecasting of the real-time passenger demand dynamically endorsed the growth of the dynamic bus management and scheduling. Moreover, the accuracy of proposed SARIMA Model is compared with traditional hybrid model such as Gaussian Mixture Model (GMM) with ARIMA model for providing an efficient and robust prediction of PT based on passenger demand.
A typology of urban speciality shops selling rural provenance food products – a contribution from Portugal

PurposeThe purpose of this paper is to explore and typify the characteristics and diverse features of urban speciality stores selling rural provenance food, taking the case of three cities in Portugal.Design/methodology/approachThe study was based on hierarchical cluster analysis, performed upon data collected from a survey to 113 shops, located in Aveiro (n = 15), Lisbon (n = 56) and Porto (n = 42). FindingsThe study identified three clusters of shops according to the type of rural provenance products sold, services provided and clientele characteristics: the wine focused, the rural provenance focused and the generalist. The study confirms that in Portugal, small food retail outlets, with different rural provenance patterns and degrees of specialization have considerably grown in large cities over the last decade.Research limitations/implicationsThe study contributes to typifying urban speciality food stores selling rural provenance products and to addressing critical research gaps on this topic. The study highlights the dynamism of small food retail outlets and their significance, mediating and responding to changing patterns of food consumption in urban spaces.Originality/valueThis study contributes to a better understanding and characterization of food speciality shops in urban settings and their links with rural territories of provenance, an under-researched topic on the food retail literature.
Capacity Reservation and Wholesale Price Contracts under Forecast Sharing: A Behavioral Assessment

W e study a supply chain setup in which a buyer has private end customer demand information that she can share with the supplier. The demand information is relevant to the supplier’s capacity decision. We address the question of whether the supplier beneﬁts from installing nonlinear capacity reservation contracts rather than wholesale price contracts. We contribute to the literature by providing the ﬁrst internally valid comparison of both contracts with human decision makers. We setup an experimental study with four treatments (both contracts as well as different supplier mar-gins). From a supplier’s perspective, we observe that the capacity reservation contract signiﬁcantly outperforms the wholesale price contract; however, the supplier’s beneﬁt from using capacity reservation is much higher under low margins than under high margins. Regarding supply chain performance, the positive effect for the supplier exceeds the negative effect for the buyer in the low margin setting, while the two effects neutralize each other in the high margin setting. We identify behavioral factors explaining deviations from the theoretical predictions. In particular, we observe naı¨ve anchoring and trust as strong behavioral drivers common to both contract types. Even though the complexity of the nonlinear contract results in weaker performance than that predicted by theory, our study reveals that suppliers can still beneﬁt from installing them; thus, providing important managerial implications for the choice of the contract type.
Importance of interaction rewiring in determining spatial and temporal turnover of tritrophic (Piper‐caterpillar‐parasitoid) metanetworks in the Yucatán Península, México

Natural history studies documenting spatial and temporal variation of species assemblages and their interactions are critical for understanding biodiversity and community ecology. We characterized caterpillar–parasitoid assemblages on shrubs in the genus Piper across remnants of semi‐evergreen forest in the Yucatán Península during the rainy and rainy–dry seasons. We collected caterpillars feeding on Piper leaves and reared them to adults or parasitoids to: (i) describe tritrophic interactions between Piper, caterpillars, and parasitoids, (ii) compare empirical metanetworks among sites and seasons, and (iii) investigate patterns in species and interaction turnover across spatial and temporal scales to understand the contribution of species composition and interaction rewiring to overall interaction turnover. We found six Piper species supporting 79 species of caterpillars, which in turn hosted 20 species of parasitoids. In total, there were 116 realized trophic interactions. Species and interactions exhibited substantial turnover at temporal and spatial scales. Total interaction turnover was more pronounced across seasons in all sites (>93%), than it was between sites (<91%). We also found that interaction rewiring contributed more to overall interaction turnover than species turnover. The spatial and temporal variation in metanetworks documented here contribute to understanding fine‐scale temporal and spatial turnover in tropical species and interactions and raise important questions about the lability of consumer specialization and the short‐term effects of interaction rewiring on the stability of biotic communities. Our results highlight the importance of tropical food web studies that are based on natural history using consistent field methods to document bi‐ and tripartite interactions.