Variations In Orientation Research Articles

VPDet: Refined region CNN for hair follicle recognition in arbitrary angle

AbstractIn response to the growing demand for hair‐loss treatments, this study introduces the vector proposal detector (VPDet), a groundbreaking solution in hair transplant robotics. VPDet, distinct from traditional approaches, addresses the complex challenges of hair follicle detection, notably the variability in hair growth orientations and the intricacies of hair clustering. The method innovatively leverages the linear nature of hair, spanning a full 360‐degree orientation spectrum. The VPDet framework, a novel two‐stage object detection system, incorporates the vector proposal network and vector align blocks. These elements are crucial in transforming conventional anchor boxes into anchor vectors, thereby generating reference vectors across various scales and angles. The vector align block, a key innovation, uniquely combines vector and adjacent feature data to align features through shared maps. The extensive experiments, conducted on the FDU_HairFollicleDataset and an extended dataset, exhibit a remarkable enhancement in model performance, with a 51.3% increase in precision and a 20.8% boost in F1 score. The results not only demonstrate VPDet's superior capability in hair follicle recognition but also its potential in posture recognition for vector‐characteristic objects. This approach represents a significant advancement in both the field of hair transplant robotics and vector‐based object detection.

Assessing a multi-camera system to enhance fruit visibility for robotic harvesting in a V-trellised apple orchard

Accurate detection and localization of fruits within the canopy are crucial for various tasks, such as perception for robotic harvesters, vision-based yield estimation, and early disease detection. However, complex canopy structures render fruit detection challenging due to obstructions from leaves, branches, and other fruits. This study assesses the efficacy of utilizing multiple cameras at varying elevations and azimuthal angles to enhance fruit visibility. We used a V-trellised apple orchard as a case study, where four cameras were employed; the first was oriented normally to the fruit canopy, while three others maintained a fixed position relative to the first but with variable orientation. Adjusting elevation and azimuthal angles revealed that, in the region of interest of the canopy, a single camera whose optical axis was perpendicular to the canopy could detect up to 88.3% of the fruits detected by four cameras. Adding one more camera – for a total of two cameras – increased the detection rate up to 97.5% of the four-camera detection rate. Also, the experimental results showed that three cameras could detect between 96.0% to 99.7% of fruits compared to four cameras under the proposed camera configuration. Hence, the utility of adding a third or fourth camera would need to be considered carefully, given the added cost and complexity. The results of this study are a step toward exploring and optimizing multiple-camera perception systems for orchard operations, in particular, robotic harvesters.

On the infiltration of cellular solids by sheet molding compound: process simulation and experimental validation

This study examines a numerical method to simulate the production of novel multi-material metal-composite components, where an additive-manufactured cellular solid is infiltrated by a sheet molding compound (SMC) in a single-step compression molding operation. A single-fiber numerical approach is adopted to predict microstructural changes, such as fiber orientation, fiber-matrix separation, and fiber volume content variations during molding. The accuracy of the numerical predictions is confirmed by physical samples using micro-computed tomography and optical microscopy investigations at both the qualitative and quantitative scales. From optical microscopy observations, there emerged a positive correlation between experimental outcomes and simulation results, accurately capturing fiber swirling, wrinkling, and draping that occurred during molding. At a quantitative scale, a 0.6% mismatch was observed when void volume and unfilled areas were compared, as measured by micro-computed tomography and numerical simulation.

Impact of variation in gap spacing and orientation of split baffles on thermodynamics optimization in naturally convective flow in corrugated enclosure

The fundamental aim of this research work is to examine the effect of heated split baffles and the influence of different corrugation frequencies on steady-state natural convection within a sinusoidal corrugated square cavity. The configuration of the present model maintains a constant temperature of T c for the horizontal wavy boundaries and vertical walls while the inner split baffles are heated at a constant temperature of T h . The finite element method is applied to the discretized equations representing the fluid behavior within the enclosure, with the aim to investigate the inclination angles, variation of space between the baffles, corrugation frequencies, and the impact of Rayleigh number on the fluid motion and heat transfer. The variation in velocity and temperature profile is illustrated through the streamlines and isotherm contours. Moreover, the numerical results are carried out in terms of local and average Nusselt numbers of the heat transfer, which are analyzed for inside space of baffles and line dip-angles of the baffle ( θ = 0 ° , 45 ° , 90 ° ) , and ( φ = 0 ° , 30 ° , 60 ° , 90 ° ) . The key findings indicate that increasing the space l s between the baffles enhanced the convective heat transfer by 8.5 % , moreover, elevating the baffle angle to right angle ( θ = 90 ° ) amplify the heat transfer rate increase by 19.17 % . The augmentation in corrugation frequency leads 7.15 % reduction in heat transfer efficiency. The comparison of the present numerical simulations revealing a satisfactory and reliable match with the literature precedents.

Spectroscopic and SERS investigations of (E)-3-[(3-methoxybenzylidene) amino]rhodanine – Experimental, DFT and MD analyses in solvents

Studying the interfacial characteristics of nanoparticles and adsorption behavior of molecules on silver colloids requires application of surface enhanced Raman scattering (SERS) and density functional theory (DFT). In this research the effects of (E)-3-[(3-methoxybenzylidene)amino]-2-thioxothiazolidin-4-one (MAT) adsorption at various concentrations on silver colloids were investigated. It was shown that the MAT molecule interacted with silver through the oxygen, nitrogen and methyl groups. The computed wavenumbers for the MAT molecule were in agreement with experimental values, proving that DFT approach may be used to assign wavenumbers. It is important to note that when MAT is adsorbed on Ag and MAT is chemisorbed on the metal cluster, the electron density is reallocated and the charge is transferred from molecule to silver atoms. A variation in orientation of MAT molecule with regard to silver colloid changes the nature of adsorption as concentration changes as well. Higher fluctuation during molecular dynamics (MD) simulation gives molecular instability, while smaller fluctuation denotes a biological system that is noticeably stable. Solvent-accessible surface area (SASA) plots showed certain shrinkage for MAT and MAT-Ag6 complexes solvent accessible areas in methanol, while a certain increase of the solvent accessible regions was observed in water for both.

Importance of preoperative HRCT temporal bone in the orientation of the cochlea and its relation to intra-operative difficulties in cochlear implantation – predicting the grades of difficulty

Objective: This study emphasizes the role of preoperative high-resolution computed tomography (HRCT) temporal bone in evaluating the variation in cochlear orientation and proposes a grading system to determine the level of intraoperative difficulties encountered. Methods: Preoperative correlation of middle ear and inner ear structures along with the basal turn angle (BTA) was done to assess the orientation. Patients were divided into three groups depending on BTA and radiology findings. Results: Group A (BTA = 55°-60°) had statistically significant (p < 0.05) correlation between middle ear to inner ear structures on HRCT, whereas group B (BTA >60°) and C (BTA <55°) had variations in the alignment of middle ear structures to their corresponding inner ear structures. Conclusion: The association of BTA to the correlation between middle ear and inner ear structures can determine the orientation of the cochlea. This evaluation makes the surgeon aware of intraoperative challenges and helps in surgical planning.

Unveiling the entrepreneurial mindset: exploring orientation and intentions among students of prominent engineering disciplines

The advent of Industry 4.0 presents a spectrum of challenges for entrepreneurs in India, demanding specific skills and resources. Within this transformative landscape, engineering disciplines assume a pivotal role in navigating the complexities of the new industrial revolution yet pose challenges in instilling entrepreneurial skills. While universities are recognised for fostering entrepreneurial skills, a gap persists in comprehending students’ intentions to pursue entrepreneurship after completing their studies, especially within technical education systems. This emphasises the necessity for a comprehensive examination of variations in entrepreneurial orientation among diverse engineering disciplines. The study employed cross-sectional research and surveyed 370 final-year engineering students from leading engineering colleges in Karnataka, India. The data analysis included Exploratory Factor Analysis (EFA) and Structural Equation Modeling (SEM). The findings demonstrate a significant propensity for entrepreneurship among engineering students, highlighting attributes such as innovativeness, risk-taking, and proactiveness. However, contrary to expectations, the study does not discern distinct entrepreneurial orientations across different engineering disciplines. Importantly, it unveils that college education has minimal influence on students’ entrepreneurial intentions.

Effect of microstructural features on the fatigue behavior of the valve mechanism cam rolling contact

This study introduces a methodology that incorporates gradient structural features and crystal plasticity for assessing contact fatigue in engine valve cams. It explored the influence of grain characteristics and structural gradients on fatigue failure through the lens of Plastic Strain Energy Density (PSED). Findings indicate that variability in crystal orientation significantly impacts PSED and subsequent crack initiation. Additionally, shot peening induces a gradient in grain size, promoting orderly deformation, stress alleviation, and improved fatigue resistance. The study suggests that an optimal fatigue resistance in cam contacts is achievable with a gradient layer comprising 75% volume fraction, which strikes a balance between cost and technical feasibility.

Optimizing the viola-jones algorithm for robust face recognition in variable lighting and orientation conditions

Optimizing the viola-jones algorithm for robust face recognition in variable lighting and orientation conditions

Positive Orientation and Feelings of Loneliness among Polish Students during the COVID-19 Pandemic.

Background/Objectives: The COVID-19 pandemic was a time of limited direct contact with other people. The aim of this study was to determine the role of positive orientation and sociodemographic variables in the feelings of global, social and emotional loneliness and to seek predictors for loneliness among students during the COVID-19 pandemic. Methods: The study was conducted on a group of 798 students at the University of Warmia and Mazury in Olsztyn (Poland) between January and March 2022. The empirical data were gathered with the following research tools: the Positive Orientation Scale and the De Jong Gierveld Loneliness Scale-DJGLS. Results: The study found a correlation (r = -0.370; p < 0.001) between positive orientation and emotional loneliness. The level of loneliness was significantly higher in students who greatly reduced their social contact, compared to those who reduced them moderately (p < 0.001). The intensity of social loneliness among women was lower (p < 0.001) than among men. Those who lived with their families or with a close companion felt less intense emotional loneliness (p < 0.001) than those who lived alone. Conclusions: A positive orientation, which manifests itself in a favourable opinion about oneself and in attaching weight to positive aspects of life, was found to be the main determinant of the feelings of global and emotional loneliness. However, it did not prove to be a predictor of social loneliness in the group of students under study.

Molecular Mechanisms behind Circular Dichroism Spectral Variations between Channelrhodopsin and Heliorhodopsin Dimers.

Channelrhodopsin (ChR) and heliorhodopsin (HeR) are microbial rhodopsins with similar structures but different circular dichroism (CD) spectra: ChR shows biphasic negative and positive bands, whereas HeR shows a single positive band. We explored the physicochemical factors underlying these differences through computational methods. Using the exciton model based on first-principles computations, we obtained the CD spectra of ChR and HeR. The obtained spectra indicate that the protein dimer structures and the quantum mechanical treatment of the retinal chromophore and its interacting amino acids are crucial for accurately reproducing the experimental spectra. Further calculations revealed that the sign of the excitonic coupling was opposite between the ChR and HeR dimers, which was attributed to the contrasting second term of the orientation factor between the two retinal chromophores. These findings demonstrate that slight variations in the intermolecular orientation of the two chromophores can result in significant differences in the CD spectral shape.

Neural network enabled fringe projection through scattering media

The projection of fringes plays an essential role in many applications, such as fringe projection profilometry and structured illumination microscopy. However, these capabilities are significantly constrained in environments affected by optical scattering. Although recent developments in wavefront shaping have effectively generated high-fidelity focal points and relatively simple structured images amidst scattering, the ability to project fringes that cover half of the projection area has not yet been achieved. To address this limitation, this study presents a fringe projector enabled by a neural network, capable of projecting fringes with variable periodicities and orientation angles through scattering media. We tested this projector on two types of scattering media: ground glass diffusers and multimode fibers. For these scattering media, the average Pearson’s correlation coefficients between the projected fringes and their designed configurations are 86.9% and 79.7%, respectively. These results demonstrate the effectiveness of the proposed neural network enabled fringe projector. This advancement is expected to broaden the scope of fringe-based imaging techniques, making it feasible to employ them in conditions previously hindered by scattering effects.

Absorptive capacity, entrepreneurial orientation and organizational performance in state-owned companies

PurposeThe objective of this article is to analyze the relationship between absorptive capacity, entrepreneurial orientation, and performance within Brazilian federal state-owned companies.Design/methodology/approachPrimary data were obtained using survey research. Secondary data were obtained using the Federal State-owned Companies Bulletin and the website State-owned Companies Overview. Tested and validated scales were selected to reflect Absorptive Capacity (ACAP), Entrepreneurial Orientation (EO), and Organizational Performance (OP) variables applied to a sample of 119 managers and advisors of federal state-owned companies. Structural Equation Modeling techniques were applied to identify the statistical significance of the direct, indirect, and total effects. The final measurement and structural model presented convergent and discriminant validity with acceptable adjustment indexes.FindingsACAP was found to be directly related to the ability to identify, recognize, transform, and especially implement new knowledge. EO is related to the institution’s ability to take risks, be proactive, and innovate. Those are fundamental processes for fulfilling the mission of state-owned companies, for their survival, and for improving the quality of public services provided to society. These findings differ from studies carried out in private companies since the ACAP-exploration dimension in private businesses displays a relevant relationship between the factors composing EO, a feature not observed here.Originality/valueThe paper’s main contribution is to confirm the influence of ACAP on EO and EO on OP, considering the environment of Brazilian federal state-owned companies. Such results expand the scarce academic studies on absorptive capacity, entrepreneurial orientation, and performance in the public area, in addition to offering perspectives of specific training direction to expand the competencies of public managers.

Transmission Electron Microscopic and X-ray Diffraction Based Study of Crystallographic Bibliography Demonstrated on Silver, Copper and Titanium Nanocrystals: State of the Art Statical Review

This statistical review compares the crystallographic structures of functional nanocrystals composed of silver (Ag), copper (Cu) and titanium (Ti) using transmission electron microscopy (TEM) and X-ray diffraction (XRD) analyses. TEM provides high-resolution imaging to directly visualize individual nanoparticles' size, internal shape and crystallinity. Statistical analysis quantifies variations in lattice parameters, crystal structure, size distributions, phase compositions, lattice strains, preferred orientation and lattice volume of these three crystalline nanomaterials. The review highlights the complementary roles of TEM and XRD in comprehensive Ag, Cu and Ti nanocrystalline materials characterization. The crystallographic functional parameters of Ag were 2θ= 38.1° (111), 44.3° (200) and 64.4° (220); for Cu crystal 43.3° (111), 50.4° (200), 74.1° (220), 89.9° (311) and 95.1° (222) and 35.1° (100), 38.4° (002), 40.2° (101), 53.0° (102), 63.0° (103), 70.7° (110), 76.2° (112), 82.3° (201) demonstrated for Ti nanocrystals. The crystallographic predominant plane or Miller indices were also revealed by selected area electron diffraction (SAED) on TEM. The FCC structure of Ag and Cu is shown in larger lattice volumes compared to the HCP structure of Ti and prefer oriented. The degree of crystallinity of Ti, Cu and Ag nanocrystalline materials was observed at 90.0%, 98.0% and 100.0% respectively. This quantitative comparison provides valuable insights into the structural property relationships in these nanocrystals, enabling rational design strategies for optimizing their performance in various functional applications.

LightGyro: A Batteryless Orientation Measuring Scheme Based on Light Reflection

In industrial production, the orientation of facility components can indicate whether the facility is on a regular operating track. For example, when a component gets loose, the orientation variation of the component would exceed the normal range. A common approach for orientation measurement is to attach an inertial measurement unit (IMU) to the target device. However, the IMU requires additional power maintenance. This article presents LightGyro, a cheap and efficient batteryless scheme to measure the orientation, in which we attach a reflective film to the target device and use a camera to capture the light spot on the reflective film. The basic idea of LightGyro is to extract the light spots in the captured frame and use their pixel coordinates to infer the orientation. It is difficult to recognize a single light spot, because the spot lacks distinctive features. To solve the problem, we switch light sources on and off to regulate the appearance of light spots and utilize frame subtraction to extract light spots. The depth of field of light spot is lost in the process of camera projection, which is necessary for the orientation measurement. To address the issue, we propose a light array-based reflection model to extract the depth of field from the relative positions of multiple light spots. To the best of our knowledge, this is the first work to utilize reflection to measure orientation. Experiment results show that the orientation error of LightGyro decreases with the increasing length of the reflection route and the orientation error can achieve less than 1 ˆ .

Detection of objects in satellite and aerial imagery using channel and spatially attentive YOLO-CSL for surveillance

A novel lightweight Rotational Object Detection algorithm is proposed to overcome the shortcomings of conventional computer-vision-aided object detection methods used in Remote Sensing and Surveillance that overlook the variability in size and orientation of objects in satellite and aerial images. This advanced algorithm integrates a branch dedicated to angle prediction and employs the circular smooth label (CSL) method for angle classification. This approach is suitable for scenarios that require detection in rotational boxes. Our work is further distinguished by the introduction of a novel Channel and Spatial Attention (CSA) module, which is seamlessly integrated into the YOLOv5-CSL framework via the C3CS module. This module accentuates pertinent features through both the channel and spatial attention mechanisms. In addition, bicubic interpolation and the GELU activation function were incorporated into the YOLOv5-CSLA model. Our model achieved 57.86 mAP on the challenging DOTA v2 dataset surpassing the second-best method by 0.20 points and simultaneously consuming 11 million fewer parameters and 103 fewer GFLOPs (our model consumes 25 M Params and 54 GFLOPs), justifying its suitability for deployment on a large majority of platforms, as the compute required is a challenge in real-time deployment scenarios.

Preliminary study on the mechanical behavior of clay in one-dimensional compression with DEM simulations

The mechanical behavior and physical properties of clay are closely associated with its microstructure. Current research on the macroscopic mechanics and physical properties of clay is comprehensive and systematic. However, the microstructural variations underlying these characteristics have been predominantly examined under mercury intrusion porosimetry and scanning electron microscopy, while quantitative and systematic studies are notably limited. This research employs the discrete element method (DEM) simulation to investigate the microscopic responses of clay, simulating one-dimensional compression tests on two clay types: card-house and book-house structures. The analysis of pore size and morphology was conducted using virtual mercury intrusion porosimetry and the pore segmentation method. The clay microstructure was quantitatively characterized by parameters such as pore aspect ratio, orientation distribution, and cumulative pore volume curve. The findings demonstrate that DEM effectively replicates the fundamental mechanical behavior of clay, revealing and explaining the evolution pattern of clay pore structure during one-dimensional compression in terms of platelet alignment adjustment. This macroscopic compression is characterized by variations in pore size distribution, orientation, and aspect ratio.

Revealed preferences on meat substitute consumption and political attitudes - Testing the left-right and environmental concerns framework

The promotion of meat substitutes to reduce meat intake is a promising way to reduce the environmental and public health externalities of meat consumption while preserving the important role of taste and texture in meat products. However, the market for meat substitutes is developing more slowly than expected. Therefore, we analyze the factors associated with the heterogeneity in meat substitute consumption in Germany, a country where meat traditionally plays an important role. We use revealed preference data on meat substitute sales from 1025 individual retailers, sociodemographic data, and election results from 92 regions in Germany over the period 2017–2021, to analyze whether differences in meat substitute consumption are associated with consumers' political orientation (liberal/left or conservative/right) and socio-demographic variables. We also investigate whether election results for parties with stronger climate protection goals are associated with meat substitute consumption. Our results show that meat substitute consumption varies significantly across Germany and that this is related to differences in socio-demographic characteristics and voting behavior across regions. Voting for the Green Party and parties with strong climate protection ambitions is positively related to the market share of meat substitutes. In contrast, voting for Germany's most conservative party, which has the lowest ambitions in terms of climate protection targets, is associated with lower meat substitute consumption. Therefore, manufacturers could develop tailored marketing strategies that specifically target these voter groups in order to increase the market share of meat substitutes as alternatives to meat products.

Object Detection Using CNN

Object detection system using Convolutional Neural Network(CNN) that can accurately identify and classify objects in videos. The purpose of object detection using CNN to enhance technology such as security cameras, smart devices by enabling them to identify and understand objects in videos. Object detection using CNN is a fascinating filed in computer vision. Detection can be difficult since there are all kinds of variations in orientation, lighting, background that can result in completely different videos of the very same object. Now with the advance of deep learning and neural network, we can finally tackle such problems without coming up with various heuristics real-time. The project “Object detection using CNN for video streaming” detects objects efficiently based on CNN algorithm and apply the algorithm on image or video data. In this project, we develop a technique to identify an object considering the deep learning pre-trained model MobileNet for Single Shot Multi-Box Detector (SSD). This algorithm is used for real-time detection and for webcam, which detects the objects in a video stream. Therefore, we use an object detection module that can detect what is in the video stream. In order to implement the module, we combine the MobileNet and the SSD framework for a fast and efficient deep learning-based method of object detection. The main purpose of our research is to elaborate the accuracy of an object detection method SSD and the importance of pre-trained deep learning model MobileNet. The experimental results show that the Average Precision (AP) of the algorithm to detect different classes as car, person and chair is 99.76%, 97.76% and 71.07%, respectively. The main objective of our project is to make clear the object detecting accuracy. The existing methods are Region Based Convolutional Neural Network(R-CNN) and You Only Look Once(YOLO).R-CNN could not pushed real time speed though its system is updated and new versions of it are deployed and YOLO network is popular but YOLO is to struggle to detect objects grouped close together, especially smaller ones. To avoid the drawbacks of these methods we proposed this model which included single shot multi-box detector (SSD), this algorithm is used for real time detection and Mobile-Net architecture.

Human Activity Recognition in a Free-Living Environment Using an Ear-Worn Motion Sensor.

Human activity recognition (HAR) technology enables continuous behavior monitoring, which is particularly valuable in healthcare. This study investigates the viability of using an ear-worn motion sensor for classifying daily activities, including lying, sitting/standing, walking, ascending stairs, descending stairs, and running. Fifty healthy participants (between 20 and 47 years old) engaged in these activities while under monitoring. Various machine learning algorithms, ranging from interpretable shallow models to state-of-the-art deep learning approaches designed for HAR (i.e., DeepConvLSTM and ConvTransformer), were employed for classification. The results demonstrate the ear sensor's efficacy, with deep learning models achieving a 98% accuracy rate of classification. The obtained classification models are agnostic regarding which ear the sensor is worn and robust against moderate variations in sensor orientation (e.g., due to differences in auricle anatomy), meaning no initial calibration of the sensor orientation is required. The study underscores the ear's efficacy as a suitable site for monitoring human daily activity and suggests its potential for combining HAR with in-ear vital sign monitoring. This approach offers a practical method for comprehensive health monitoring by integrating sensors in a single anatomical location. This integration facilitates individualized health assessments, with potential applications in tele-monitoring, personalized health insights, and optimizing athletic training regimes.