Flanched-Cross: An Efficient Cell Geometry for Beam Steering Metasurfaces with Orthogonal Polarisation Handling Capability

Geometry plays an important part in the characteristics of meta-cells used to design beam steering metasurfaces. One of the most desirable aspects of these cells is a large phase shift range that can be achieved with good transmission amplitude. However, the existing and most commonly used geometries for these cells are not able to produce a complete 360° phase range with an acceptable level of transmission amplitude. In this article, we present a new cell geometry, Flanched-Cross, that has superior transmission properties due to its unique shape and parametric variability than the commonly used geometries. The results are verified in simulation and further confirmed through prototyping and measurement. One- and two-dimensional steering are also performed for a dual-polarised base array to confirm the applicability of Flanched-Cross cell for beam steering purposes.

Flanched-Cross: An Efficient Cell Geometry for Beam Steering Metasurfaces with Orthogonal Polarisation Handling Capability

Geometry plays an important part in the characteristics of meta-cells used to design beam steering metasurfaces. One of the most desirable aspects of these cells is a large phase shift range that can be achieved with good transmission amplitude. However, the existing and most commonly used geometries for these cells are not able to produce a complete 360° phase range with an acceptable level of transmission amplitude. In this article, we present a new cell geometry, Flanched-Cross, that has superior transmission properties due to its unique shape and parametric variability than the commonly used geometries. The results are verified in simulation and further confirmed through prototyping and measurement. One- and two-dimensional steering are also performed for a dual-polarised base array to confirm the applicability of Flanched-Cross cell for beam steering purposes.

A new species (Begonia giganticaulis) of Begoniaceae from southern Xizang (Tibet) of China

Begonia giganticaulis, a huge new species in Begonia sect. Platycentrum of Begoniaceae from southern Xizang (Tibet) of China, is described. Morphologically, it is mostly similar to B. longifolia and B. acetosella, but clearly differs from the former mainly by its dioecious and taller plants, sparse hairs on abaxial veins, longer inflorescence, unique shape of fruits, and differs from the latter mainly by its late and longer flowering time, 6-tepals of female flower and 3-loculed ovary. The phylogenetic analyses also support the separation of the new species from other taxa. Based on the current data, its conservation status is assigned to Endangered (B2a) according to the IUCN Red List Categories and Criteria.

Perbandingan Naïve Bayes, SVM, dan k-NN untuk Analisis Sentimen Gadget Berbasis Aspek

The Samsung Galaxy Z Flip 3 is one of the gadgets that are currently popular among the public because of its unique shape and features. Youtube is one of the social media that can be accessed and enjoyed by the public, one of which is gadget review content on the GadgetIn channel. Youtube can provide information, whether people accept or are interested in this new gadget or not. This study aims to determine the sentiment of a gadget producer. Based on the results of the analysis and testing that has been carried out on the Youtube comments of the Samsung Galaxy Z Flip 3 gadget with a total of 9,597 comments, more users gave positive opinions in the design aspect and negative opinions on the price, specifications and brand image aspects. By using the CRISP-DM model and comparing the Naïve Bayes (NB), Support Vector Machine (SVM), and k-Nearest Neighbor (k-NN) classification methods, it is proven that the SVM classification model shows the best results. The average accuracy of SVM is 96.43% seen from four aspects, namely the design aspect of 94.40%, the price aspect of 97.44%, the specification aspect of 96.22%, and the brand image aspect of 97.63%.

Surprisingly inflexible: Statistically learned suppression of distractors generalizes across contexts

The present study investigates the flexibility of statistically learned distractor suppression between different contexts. Participants performed the additional singleton task searching for a unique shape, while ignoring a uniquely colored distractor. Crucially, we created two contexts within the experiments, and each context was assigned its own high-probability distractor location, so that the location where the distractor was most likely to appear depended on the context. Experiment 1 signified context through the color of the background. In Experiment 2, we aimed to more strongly differentiate between the contexts using an auditory or visual cue to indicate the upcoming context. In Experiment 3, context determined the appropriate response ensuring that participants engaged the context in order to be able to perform the task. Across all experiments, participants learned to suppress both high-probability locations, even if they were not aware of these spatial regularities. However, these suppression effects occurred independent of context, as the pattern of suppression reflected a de-prioritization of both high-probability locations which did not change with the context. We employed Bayesian analyses to statistically quantify the absence of context-dependent suppression effects. We conclude that statistically learned distractor suppression is robust and generalizes across contexts.

Model of discharge coefficient of crest gate rubber weir at fully closed condition

Crest Gate Rubber Weir is a modification of rubber weir with adding a metal plate or crest gate on the upstream side. The rubber in this weir functioning as a support while the crest gate serves on elevating water. Although many have been implemented, this weir’s discharge coefficient needs be researched considering its unique shape. This study looks for discharge coefficient to determine the discharge that passes through weirs at fully closed conditions. The research was conducted with a hydraulic model resembling part of Tirtonadi Weir in Surakarta with a hydraulic model. The model is made in an angle 53°, which represents the prototype at fully closed condition. Laboratory experiment shows that the discharge coefficient of this weir is greater than the Ogee Weir and Sluice Gate for the same height because of less flow resistance from this weir structural form.

Internet of Things as an element of the frost protection system in orchards

Frosts cause serious damage to fruit and vegetable crops. In Poland, temperature decreases, and the occurrence of inflow (advective) frosts most often fall during the flowering period, i.e., in a phase extremely important for the development of the plant. For orchards, this causes the inflorescences of early flowering trees (cherries, plums and certain varieties of apple and pear trees) to freeze. A modern idea for reducing frost losses in orchards is to heat the air with mobile heaters. Protection by these machines consists of passing using an agricultural tractor in rows of trees or shrubs and heating and mixing the air. The problem that farmers may encounter during frosts is the awareness of when exactly such a weather condition occurs in their orchard. Weather forecasts are not detailed and usually apply to the entire region. Dangerous temperature declines below the critical minimum can occur locally and are also conditioned by geographical location and terrain diversity. The aim of the article is to present a measuring system that allows the construction of an individualised temperature model taking into account the unique shape of the orchard surface. The system is made in Internet of Things technology using long-range radio communication protocol LoRaWAN. Data from distributed measurement sensors are processed on a network server and displayed as a final application. The task of the system is to monitor the current situation in the orchard and to notify the farmer of the need to initiate a protective procedure. The operation of the system also supports the efficient use of mobile heating machines. The system facilitates the location of the areas in the orchard with the lowest temperature, as well as provides feedback on temperature changes inside the treetops caused by the passage of the mobile heater.

Spatial suppression due to statistical regularities in a visual detection task

Increasing evidence demonstrates that observers can learn the likely location of salient singleton distractors during visual search. To date, the reduced attentional capture at high-probability distractor locations has typically been examined using so called compound search, in which by design a target is always present. Here, we explored whether statistical distractor learning can also be observed in a visual detection task, in which participants respond target present if the singleton target is present and respond target absent when the singleton target is absent. If so, this allows us to examine suppression of the location that is likely to contain a distractor both in the presence, but critically also in the absence, of a priority signal generated by the target singleton. In an online variant of the additional singleton paradigm, observers had to indicate whether a unique shape was present or absent, while ignoring a colored singleton, which appeared with a higher probability in one specific location. We show that attentional capture was reduced, but not absent, at high-probability distractor locations, irrespective of whether the display contained a target or not. By contrast, target processing at the high-probability distractor location was selectively impaired on distractor-present displays. Moreover, all suppressive effects were characterized by a gradient such that suppression scaled with the distance to the high-probability distractor location. We conclude that statistical distractor learning can be examined in visual detection tasks, and discuss the implications for attentional suppression due to statistical learning.

Real-Time Human Ear Detection Based on the Joint of Yolo and RetinaFace

Biometric traits gradually proved their importance in real-life applications, especially in identification field. Among the available biometric traits, the unique shape of the human ear has also received loads of attention from scientists through the years. Hence, numerous ear-based approaches have been proposed with promising performance. With these methods, plenty problems can be solve by the distinctiveness of ear features, such as recognizing human with mask or diagnose ear-related diseases. As a complete identification system requires an effective detector for real-time application, and the current richness and variety of ear detection algorithms are poor due to the small and complex shape of human ears. In this paper, we introduce a new human ear detection pipeline based on the YOLOv3 detector. A well-known face detector named RetinaFace is also added in the detection system to narrow the regions of interest and enhance the accuracy. The proposed method is evaluated on an unconstrained dataset, which shows its effectiveness.