This paper presents a system for identification of wind features, such as gusts and wind shear. These are of particular interest in the context of energy-efficient navigation of Small Unmanned Aerial Systems (UAS). The proposed system generates real-time wind vector estimates and a novel algorithm to generate wind field predictions. Estimations are based on the integration of an off-the-shelf navigation system and airspeed readings in a so-called direct approach. Wind predictions use atmospheric models to characterize the wind field with different statistical analyses. During the prediction stage, the system is able to incorporate, in a big-data approach, wind measurements from previous flights in order to enhance the approximations. Wind estimates are classified and fitted into a Weibull probability density function. A Genetic Algorithm (GA) is utilized to determine the shaping and scale parameters of the distribution, which are employed to determine the most probable wind speed at a certain position. The system uses this information to characterize a wind shear or a discrete gust and also utilizes a Gaussian Process regression to characterize continuous gusts. The knowledge of the wind features is crucial for computing energy-efficient trajectories with low cost and payload. Therefore, the system provides a solution that does not require any additional sensors. The system architecture presents a modular decentralized approach, in which the main parts of the system are separated in modules and the exchange of information is managed by a communication handler to enhance upgradeability and maintainability. Validation is done providing preliminary results of both simulations and Software-In-The-Loop testing. Telemetry data collected from real flights, performed in the Seville Metropolitan Area in Andalusia (Spain), was used for testing. Results show that wind estimation and predictions can be calculated at 1 Hz and a wind map can be updated at 0.4 Hz. Predictions show a convergence time with a 95% confidence interval of approximately 30 s.

Performing optimisation and scale-up studies of crystallisation systems requires accurate and computationally efficient mathematical models. The assumption of the ideal mixing conditions in batch reactors typically produce inaccurate results while the computational expense of CFD models is still prohibitively high. Therefore, in this work, a new intermediary approach is proposed that takes into account the non-ideal mixing conditions in the reactor and requires less computational resources than full CFD simulations. Starting with the Danckwerts concept of the intensity of segregation, an analogy between its application to chemical reactions and the kinetics of the crystallisation phenomena (such as nucleation and growth) has been made. As a result, the modified kinetics expressions have been derived which incorporate the effect of non-idealities present in stirred reactors. This way, based on the experimental measurements of the mixing time using the Laser Induced Fluorescence (LIF) technique, computationally more efficient mathematical models can be developed in two ways: (1) the accurate semi-empirical correlations are available for standard mixing configurations with the most often used types of impellers, (2) CFD simulations can be utilised for estimation of the mixing time; in this case it is necessary to simulate only the mixing process. The benefits offered by the LIF experimental technique have been demonstrated and some frequent problems in its application analysed. The mixing time results for configurations with and without baffles for three types of impellers and four different rotational speeds have been presented. The false shorter mixing times in the non-baffled configurations have been observed and this phenomena explained by the existence of two segregated zones in the reactor and confirmed by additional experiments. The precise measurements in these cases have been shown as difficult using the LIF technique, particularly for higher rpms. The experimental data has been compared to the preliminary simulation results obtained from the Smoothed Particle Hydrodynamics method and the standard k-ε turbulence model with the modest success. The shortcomings of the SPH model have been recognized and the directions for the future work discussed.

As the most serious of the many worse new pathological changes caused by diabetes, there are many risk factors for the occurrence and development of DR. It mainly includes hyperglycemia, hypertension, hyperlipidemia and so on. Among them, hyperglycemia is the most critical cause, which plays a key role in the pathological changes of DR.High-sucrose diets(HSDs) lead to elevated blood glucose levels in vivo, which, through oxidative stress, inflammation, the production of AGEs and VEGF, causes plenty of pathological damages to the retina and ultimately bring about loss of vision. The existing therapies for DR primarily target the terminal stage of the disease, when irreversible visual impairment has appeared. Therefore, early prevention is particularly critical. Early prevention of DR-related vision loss requires adjustments to dietary habits, mainly by reducing sugar intake. This article primarily discusses the risk factors, pathophysiological processes, and molecular mechanisms associated with the development of DR caused by HSDs. It aims to raise awareness of the crucial role of diet in the occurrence and progression of DR, promote timely changes in dietary habits, prevent vision loss, and improve the quality of life. The aim is to make people aware of the importance of diet in the occurrence and progression of DR. According to the dietary modification strategies we give, patients can change their poor eating habits timely, avoid the retinopathy that can be avoided theoretically, and obtain a excellent outcome.

The second article in the series presents the application of the Smoothed Particle Hydrodynamics (SPH) method to modelling of batch crystallisation in stirred tanks. A methodology to integrate the population balance equations (PBE) in parallel and independently from the Navier-Stokes equations is demonstrated. The benefits of the proposed methodology in terms of computational requirements, accuracy and availability of the crystal size distribution are discussed. The specific formulation of the SPH equations where the resulting system of ordinary differential equations is solved using the weighted contributions rather than numerically by solving a linear system of equations allows for massive parallelisation and a very loose coupling of the population balance and the fluid dynamics. It has been demonstrated, that the population balance equations can be solved on a Shared Memory Architecture (SMA) system using the OpenMP interface while the fluid dynamics equations being computed independently on a General Purpose Graphics Processing Unit (GPGPU) using the NVidia CUDA technology. This way, a significant portion of the computational overhead due to the large number of additional transport equations resulting from the discretisation of the population balance was removed: the SPH simulation coupled with 200 population balance equations was only 40% slower compared to SPH-only simulation. Two methods for the solution of population balance equations that preserve full crystal size distribution were implemented: discretised population balance (DPB) and method of characteristics (MOCH). The DPB equations are solved using the high-resolution finite-volume method with flux limiter and the effect of a large number of different flux limiters have been investigated. Both methods were validated using the case studies from the literature where an analytical solution can be derived. The developed models were applied to a numerical solution of coupled computational fluid dynamics and population balance equations to model a batch crystallization process. The effect of the hydrodynamics on the local temperature/supersaturation and the resulting crystal size distribution was captured and compared to the ideal mixing case. The simulation results from the DPB and MOCH methods were compared in terms of computational requirements and accuracy and MOCH selected as computationally more efficient and accurate.

Background: Visual acuity (VA) assessments are crucial in ophthalmology but traditionally rely on in-clinic evaluations. The emergence of telemedicine has spurred interest in creating dependable self-administered VA tests for use beyond standard clinical environments. This study evaluates the practicality and validity of a self-administered Near VA card test against traditional Snellen and Rosenbaum Pocket Vision Screener (RPVS) methods for home monitoring and enhancing clinical workflow. Methods: In a cross-sectional study a near VA card (Hadassah Self-Visual Acuity Screener-HSVA) was developed with written and videotaped instructions for self-use. Patients with a minimal best corrected VA (BCVA) of 1.0 LogMAR in at least one eye were recruited from Ophthalmology and Optometry clinics. Outcomes included mean BCVA difference between the self-administered and those obtained by the examiner, and correlations among BCVA values obtained by Snellen, RPVS, HSVA and previous distance BCVA according to the patient electronic medical records. Results: A total of 275 participants (mean age: 42.5±19.4 years, range: 18-89 years, 47% female) were included. Test-retest reliability analysis for the HSVA demonstrated very good correlation and repeatability (n=38 patients.; Rs=1.0, P

The reasons for the circular sense of human color perception generated by two sorts of color opponent neurons and three cone types are not well understood. Here we use geometrical analysis to examine the hypothesis that opponency, the recursive nature of color perception, and trichromacy arise as the most efficient ways of distinguishing spectrally different points on a plane using a minimum of color classes and receptor types.

A time-sequential working, spatially multiplexed autostereoscopic 3D display design consisting of a fast switchable RGB-color filter array is presented. The wavelength-selective filter barrier emits the light from a display over a larger active area than common autostereoscopic barrier displays. An optical modelling of wavelength-selective barriers has been used for instance to calculate the light ray distribution properties of that arrangement. To find well working display designs, automated searches by simulation and computational evaluation has been proceeded. Wavelength-selective filter barrier arrangements exhibit characteristics different from common barrier displays with similar barrier pitch and ascent. In particular, these constructions show strong angular luminance dependency under barrier inclination specified by correspondent slant angle. In time-sequential implementation it is important to avoid that quick eye or eyelid movement lead to visible color artifacts. In the millisecond regime tunable liquid crystal Fabry-Pérot color filters for the colors red, green and blue are presented. They consist of a sub-micrometer thick nematic layer sandwiched between dielectric mirrors and ITO-electrodes. These cells shall switch narrow-banding light of red, green or blue. An array for a glasses-free 3D display has to be equipped with several thousand switchable filter elements having different color apertures. The newly introduced design is usable as a multi user display as well as a single user system with user adaptive control.

The paper delineates the developed vision system of the UR5 cobot and the operating algorithm of the robotic quality control station. The hardware-software architecture of the developed robotic station was shown, consisting of a UR5 cobot equipped with a web camera and a stationary industrial camera with a lighting system. Image processing and analysis algorithms were described, the method of control and communication between the station components was discussed, and two operating scenarios were presented as a single robotic station and a robotic line. Based on the results obtained, the level of measurement noise, accuracy, and repeatability of the developed vision system were estimated.

The ever-evolving ways attacker continues to im prove their phishing techniques to bypass existing state-of-the-art phishing detection methods pose a mountain of challenges to researchers in both industry and academia research due to the inability of current approaches to detect complex phishing attack. Thus, current anti-phishing methods remain vulnerable to complex phishing because of the increasingly sophistication tactics adopted by attacker coupled with the rate at which new tactics are being developed to evade detection. In this research, we proposed an adaptable framework that combines Deep learning and Randon Forest to read images, synthesize speech from deep-fake videos, and natural language processing at various predictions layered to significantly increase the performance of machine learning models for phishing attack detection.

Vision impairment may delay responses to hazards when driving. In a proof-of-concept driving simulator study, we evaluated a hazard warning device designed for vision impaired drivers. Three groups participated: 11 persons with central vision loss (CVL; median age 60 years), 12 with homonymous field loss (HFL; 52 years) and 11 with normal vision (NV; 60 years). CVL is characterised by impaired visual acuity and contrast sensitivity while HFL is a loss of vision in the same half of the visual field (blind side) in both eyes. Participants completed drives with and without the device which gave a directional vibro-tactile warning for an approaching pedestrian hazard when collision risk exceeded a pre-defined threshold. Warnings significantly (p< 0.001) reduced brake response times for CVL (∆= 0.54s) and HFL (∆= 0.30s), but not NV participants. For HFL participants, the reduction was greater (p = 0.02) for hazards on the blind (∆= 0.57s) than seeing side (∆= 0.10s). The warnings significantly reduced collision rates for CVL participants from 11 to 0% (all hazards) and for HFL participants from 30 to 1.7% (blind side hazards). Thus, hazard warnings may be beneficial in improving safety of vision impaired drivers; further research is warranted.

Vertical Indoor Farming (VIF) with hydroponics offers a promising perspective for sustainable food production. Intelligent control of VIF system components plays a key role in reducing operating costs and increasing crop yields. Modern machine vision (MV) systems use Deep Learning (DL) in combination with camera systems for various tasks in agriculture, such as disease and nutrient deficiency detection, and flower and fruit identification and classification for pollination and harvesting. This study presents the applicability of MV technology with DL modelling to detect the growth stages of chilli plants using YOLOv8 networks. The influence of different bird’s eye and side view datasets and different YOLOv8 architectures was analysed. To generate the image data for training and testing the YOLO models, chilli plants were grown in a hydroponic environment and imaged throughout their life cycle using four camera systems. The growth stages were divided into growing, flowering and fruiting classes. All trained YOLOv8 models showed reliable identification of growth stages with high accuracy. The results indicate that models trained with data from both views show better generalisation. YOLO’s middle architecture achieved the best performance.

(S)-Atenolol ((S)-2-(4-(2-Hydroxy-3-(isopropylamino)propoxy)phenyl)acetamide) has been synthesized in >99% ee with use of Candida antarctica lipase B from Syncozymes (Shanghai, China), in a kinetic resolution of the corresponding racemic chlorohydrin. Deprotonation of the starting phenol building block was base catalyzed. The enantiopurity of the chlorohydrin building block remained unchanged upon subsequent amination. All four steps in the synthesis protocol have been optimized compared to previously reported methods, which makes this new protocol more sustainable and in accordance with green chemistry principles compared to previously reported protocols.

Thromboembolic conditions, such as stroke and myocardial infarction [MI], are the primary causes of mortality globally. High efficiency was demonstrated by fibrinolytic enzymes isolated from microbial sources in dissolving various blood clots. The study aimed to investigate and produce bacterial fibrinolytic enzymes from various Egyptian soil conditions using recombinant DNA technology. Fifty soil samples were examined to determine the production of fibrinolytic enzymes. The plasmin plate technique assessed the thrombolytic activities of the strong fibrinolytic enzyme-producing bacterial isolates. In addition to determining the molecular mass using a mass spectrometer and the western blot method, the activity and thermostability of the enzymes were characterized. An affinity chromatography approach was used to purify the fibrinolytic enzymes. However, bacterial recombinant DNA technology produced the pure bacterial fibrinolytic enzyme. Several dosage formulations for the formulation of fibrinolytic enzymes were tested. By using affinity chromatography and the SDS-PAGE test, the isolated bacterial fibrinolytic enzyme in this investigation was almost 83% pure. Bacillus cereus ATCC 4342 was identified as the predominant bacterial isolate secreting fibrinolytic enzymes. It was discovered that the fibrinolytic enzyme activity of fifty soil samples ranged from 0.167 U/ml to 1.853 U/ml. The extracellular enzyme extracted from Bacillus cereus culture supernatant demonstrated a relatively stable fibrinolytic activity over 18 hours at a pH range of 7.4–10, as well as stability with maximum productivity at 43℃ for over 60 minutes. In this investigation, the enzyme was referred to as Fibrin-proteinase[ FP]. The fibrin clots were directly thrombolyzed by the FP. The calculated values for the specific activity and molecular mass of the FP were approximately 40.71 units/mg of protein and approximately 30 KDa, respectively. It was observed that the presence of Fe+3 marginally boosted protease activity, but the presence of EDTA decreased it. On the other hand, adding monovalent cations like K+1 and Na+1 significantly increased the fibrinolytic activity of FP. The enzyme exhibited activity throughout the temperature range of 20 to 65℃, peaking at 55℃. The ideal range for the generation of protease was 35–43℃ and 8.0–10. The thermostable fibrinolytic enzyme FP, which was isolated from Bacillus cereus ATCC 4342 in various soil environments in Egypt during the current study, was a promising fibrinolytic enzyme because it demonstrated a more extensive and effective fibrinolytic activity and fewer side effects than the fibrinolytic enzymes that are currently available worldwide. It is advised that further research be done on optimizing the right formulation of bacterial fibrinolytic enzymes in the future to help dissolve different types of blood clots and, as a result, lower the high death rate that exists worldwide.

This study represents a significant stride in advancing furan chemistry, presenting the synthesis of high-molecular-weight polyfurfuryl alcohol (PFA) resin (Mw = 118,959 g/mol) from furfuryl alcohol (FA) using a co-catalytic system as against the conventional single-catalytic methods. The resulting PFA resin is fully characterised, and we conclude that it exhibits good shelf life and potential in diverse applications, serving as a standalone moulded material or contributing to composites, coatings, adhesives, and nanostructured carbons.

Microscopic vision plays an important role in automated micro-assembly. However, some uncertain factors in the assembly process, such as occlusion and stains can lead to the mistakes of feature extraction. Herein, to solve the problem, the deep learning techniques are introduced into the feature recognition tasks, focusing on the attention mechanism and visualizing CNNs for DL-based microscopic vision. The main contributions are summarized as follows: The CBAM attention mechanism is combined with the YOLOv5 algorithm to improve the accuracy and robustness of feature extraction. The micropart feature occlusion experiment results show that at 70% occlusion degree, YOLOV5-CBAM can reach 97.9% [email protected], which is 4.6% higher than the original one. Visualization analysis of DL-based model is conducted using Grad-CAM to make the decision result more transparent and avoid potential visual detection risks during assembly. The heatmap matching degree between GT area and high-light area is increased by 27.81% on average, which further verify the effectiveness of attention mechanism in micropart feature localization. Additionally, micropart surface stain and droplet quality classification models based on ResNet50 are trained to replace the manual sorting. The visual results are consistent with human eye discernment and judgement, confirming the reliability of parts and droplets sorting.

Transition-metal-free synthesis of 4-pyrones via TfOH-promoted nucleophilic addition/cyclization of diynones and water has been developed. This transformation is simple, atom economical and environmentally benign, providing rapid and efficient access to substituted 4-pyrones.

Non-isocyanate polyurethane synthesis by non-Sn catalysis is an essential challenge toward green polyurethane synthesis. Bismuth compounds are attractive candidates due to their low cost, low toxicity, and availability to urethane chemistry. We applied various Bi catalysts to the self-polycondensation of a bishydroxyurethane monomer and found BiCl3 to be an excellent catalyst through optimization. The catalytic activity and price of BiCl3 are comparable to those of Bu2SnO, while its toxicity is significantly low. BiCl3 is, therefore, a promising alternative to Sn-based catalysts in non-isocyanate polyurethane synthesis.

Some peptide-based drugs including oxytocin, vasopressin, ziconotide, pramlintide, nesiritide, and octreotide, contain one intramolecular disulfide bonds. A novel and reusable monodispersed silica nanosphere-supported Pt(IV) complex (SiO2@TPEA@Pt(IV)) was synthesized via a four-step procedure and used for formation of intramolecular disulfide bonds in peptides. TEM and chemical mapping results for the Pt(II) intermediates and for SiO2@TPEA@Pt(IV) show that the silica nanospheres possess a monodisperse spherical structure and contain uniformly distributed Si, O, C, N, Cl, and Pt. The valence state of Pt on the silica nanospheres was characterized by XPS. The Pt(IV) loaded on SiO2@TPEA@Pt(IV) was 0.15 mmol/g, as determined by UV-vis spectrometry. Formation of intramolecular disulfides in six dithiol-containing peptides of variable lengths by use of SiO2@TPEA@Pt(IV) was investigated and the relative oxidation yields were determined by HPLC. In addition, peptide 1 (Ac-CPFC-NH2) was utilized to study the reusability of SiO2@TPEA@Pt(IV). No significant decrease in the relative oxidation yield was observed after ten reaction cycles. Moreover, the structure of SiO2@TPEA@Pt(IV) after being used ten cycles was determined to be similar to its initial one, demonstrating the cycling stability of the complex.

Computational animation of spatiotemporal models for 3-D shape and motion is considered as core of dynamic real-time vision. This is based on sets of features evaluated from multi-focal image streams. Subjects are defined as special objects capable of sensing environmental parameters and of initiating own actions in combination with stored knowledge. Object/subject recognition and scene understanding are achieved on different levels. Multiple objects are tracked individually for perceiving their actual state (‘here and now’). Fast saccadic jumps in gaze direction allow flexible concentration on objects or parts of actual interest. By analyzing motion of relevant objects/subjects over a larger time scale on the level of state variables and documenting them in the ‘scene tree representation’ (computer graphics), the situation with respect to decision making is assessed. Various behavioral capabilities of subjects are represented on an abstract level for characterizing their potential behaviors. These are generated by stereotypical feed-forward and feedback control applications on a separate level close to the actuator hardware with corresponding methods. This dual representation on one level for decision making and one on the implementation level allows for flexibility and easy adaptation or extension. Results are shown for road vehicle guidance based on three cameras on a gaze-controlled platform.

Many accidents, such as those involving collisions or trips, appear to involve failures of vision; but the association between accident risk and vision as conventionally assessed, is weak or absent. We addressed this conundrum by embracing the distinction inspired by neuroscientific research, between vision for perception and vision for action. A dual-process perspective predicts that accident vulnerability will be associated more strongly with vision for action than vision for perception. Older and younger adults, with relatively high and relatively low self-reported accident vulnerability (Accident Proneness Questionnaire), completed three behavioural assessments targeting: vision for perception (Freiburg Visual Acuity Test); vision for action (Vision for Action Test - VAT); and the ability to perform physical actions involving balance, walking and standing (Short Physical Performance Battery). Accident vulnerability was not associated with visual acuity or with performance of physical actions; but was associated with VAT performance. VAT assesses the ability to link visual input with a specific action –launching a saccadic eye movement as rapidly as possible, in response to shapes presented in peripheral vision. The predictive relationship between VAT performance and accident vulnerability was independent of age, visual acuity and physical performance scores. Applied implications of these findings are considered.

Green synthesis of silver nanoparticles (AgNPs) using raw Whey has never been reported. Silver nanoparticle formation involves a reduction reaction of silver salt solution with Whey extract. Extract and metal salt concentrations, and temperature can control this reaction. In this work, incubation period, extract, AgNO3, and NaOH concentrations were used as the independent factors using central composite design under response surface methodology. The aim was to elucidate the influence these factors have on the measured UV-Vis absorption spectra fitted with a Voigt function, and to optimize the conditions for nanoparticle formation. The fitting parameters, peak wavelength (λ0), peak area (A), and Full Width at Half Maximum (FWHM), were the responses. Silver nanoparticle formation was only possible in an alkaline environment (pH 10). The peak wavelength and the FWHM are influenced mainly by extract and AgNO3 concentrations. In contrast, the peak area is influenced by a total of 33.54 % from the interaction terms of AgNO3 and extract concentrations with NaOH. Metallic AgNPs were formed for the following parameter ranges: a) Whey (0.4 – 1.6 % v/v), b) AgNO3 (0.15 – 0.6 mM), c) NaOH (10 – 50 mM), and d) Time (20 – 60 min). Silver oxide nanoparticles were also observed.

Neutral facial expression recognition is of great importance in various domains and applications. This study introduces a data-centric approach for neutral facial expression recognition, presenting a comprehensive study that explores different methodologies, techniques, and challenges in the field to foster a deeper understanding. The results show that data augmentation plays a crucial role in improving dataset performance. Additionally, the study investigates different model architectures and training techniques to identify the most effective approach, with the InceptionV3 model achieving the highest accuracy of 72%. Furthermore, the research examines the influence of preprocessing methods on the performance of both InceptionV3 and a simplified CNN model. Interestingly, the results indicate that preprocessing techniques positively affect the performance of the simpler CNN model but negatively impact the InceptionV3 model. The implemented system, used to evaluate the findings, demonstrates promising results, correctly classifying 77% of neutral expressions. However, there are still areas for improvement. Creating a specialized dataset that includes both neutral and non-neutral expressions would greatly enhance the accuracy of the system. By addressing limitations and implementing suggested improvements, neutral facial expression recognition can be significantly enhanced, leading to more effective and accurate results.

A study of curvature along the contour provides important sources of information about the shape of an object. Knowledge of contour curvature allows for the perception of and interaction with 3D objects. In this review, we will explore the value of contour curvature in the field of neurophysiology, psychophysics, computer vision, and psychology. The serial consolidation of disjoint edges, oriented in a particular manner, into composited curves allows building of identifiable shapes in occluded natural environments [Hess1999, Wertheimer1923, Zucker1989, Feldman2001]. Examples are our ability to pick objects, sort and manage shapes otherwise hidden or confounded by occlusion in messy natural environments. Contour curvature has been intensively studied in psychology as it forms the first steps in 2D shape building which eventually becomes 3D object recognition in the human brain.

Abstract. Results of research into four-membered phosphorus(V)-heterocycles, 2-halogen-1,2λ⁵-oxaphosphetanes are presented. The preparation of 2-halo-1,2λ⁵-oxaphosphetanes by reaction of P-haloylides with carbonyl compounds is described. The mechanism of asynchronous [2+2]-сycloaddition of ylides to aldehydes was proposed on the base of low-temperature NMR investigations. 2-Halogen-1,2λ⁵-oxaphosphetanes were isolated as individual compounds and their structures were confirmed by ¹Н, ¹³C, ¹⁹F and ³¹Р NMR spectra. These compounds are convenient reagents for preparing of various organic and organophosphorus compounds hardly available by other methods. Chemical and physical properties of the 2-halo-1,2λ⁵-oxaphosphetanes are reviewed. The 2-chloro-1,2λ⁵-oxaphosphetanes depending on substituents at the α-carbon atom rearrange with formation of 2-chloroalkylphosphonates or convert into trans-phosphorylated alkenes. Prospective synthetic applications of 2-halo-1,2λ⁵-oxaphosphetanes are analyzed. The 2-halo-1,2λ⁵-oxaphosphetanes may be easily converted to various alkenylphosphonates: allyl- or vinylphosphonates, phosphorus ketenes, thioketenes, ketenimines.

The application potential of cellulosic materials in natural composites and other fields needs to be explored to develop innovative, sustainable, lightweight, functional biomass materials that are also environmentally friendly. This study investigated Typha augostifolia (Typha sp.) as a potential new raw material for extracting cellulose nanocrystals (CNCs) for application in wastewater treatment composites. Alkaline treatments and bleaching were used to remove cellulose from the stem fibers. The CNCs were then isolated from the recovered cellulose using acid hydrolysis. The study showed a few distinct functional groups (O-H, -C-H, =C-H and C-O, and C-O-C) in the Fourier Transform Infrared (FTIR) spectra. Scanning electron microscope (SEM) revealed the smooth surface of CPC and CNCs that resulted from removing lignin and hemicellulose from powdered Typha augostifolia. Based on the crystalline index, the powdered Typha augostifolia, CPC, and CNCs were 42.86%, 66.94% and 77.41%. The loss of the amorphous section of the Typha sp. fiber resulted in a decrease in particle size. It may be inferred from the features of a Typha sp. CNC that CNCs may be employed as reinforcement in composites for wastewater treatment.

Biomass materials have received attention for energy storagebecaused of the advantage of low-cost, easy-to-prepare, and eco-friendliness. Three-dimensional carbon materialswith abundant pore structure gradually becomeresearch hotspot in high-performance energy storage. In this study, an easy-to-prepare, green, light and elastic activated carbon was present using the biomass Juncus effusus (JCE) via high-temperature pyrolysis, followed by activation in air. Compared with previously reported bio-carbons, the proposed air-activated bio-carbon contributes in the fabrication of pores to preserve the interconnected, reticular and tubular structure. Moreover, the interconnected porous material also inherits the excellent tenacity of the original JCE such as the material can be bended to below 90^o under an external force while maintaining structural integrity. The activated porous carbon material exhibits an enhanced electric double-layer capacitance (~210 F g^-1 at 1 A g^-1), with capacitance retention of ~78.62% at 10 A g^-1. The interconnected porous carbon microtubes electrode as a double-layer symmetric capacitor exhibits considerable capacitance retention (84%) after 2000 cycles at 1 A g^-1. The improved energy storage performance was proposed to be attributed to the shortened ionic diffusion distance and sufficient contact between the interface of the carbon electrode and electrolyte, which is resulted from the elastic, undamaged structure and types of pores. These results demonstrated that as-preparedcarbon materials have potentional application in symmetric capacitors.

This paper presents a novel CNN-based architecture, referred to as Q-Net, to learn local feature descriptors that are useful for matching image patches from two different spectral bands. Given correctly matched and non-matching cross-spectral image pairs, a quadruplet network is trained to map input image patches to a common Euclidean space, regardless of the input spectral band. Our approach is inspired by the recent success of triplet networks in the visible spectrum, but adapted for cross-spectral scenarios, where for each matching pair there are always two possible non-matching patches; one for each spectrum. Experimental evaluations on a public cross-spectral VIS-NIR dataset shows that the proposed approach improves the state-of-the-art. Moreover, the proposed technique can also be used in mono-spectral settings, obtaining a similar performance to triplet network descriptors, but requiring less training data.

A comprehensive kinetic model of the Fischer-Tropsch synthesis (FTS) is developed in a fixed bed reactor under operating conditions (temperature, 230–235°C, pressure, 20–25 bar, gas hourly space velocity, 4000–5000 cm³(STP)/h/g_catalyst ,H₂/CO feed molar ratio, 2.1) over a Co based catalyst. Reaction rate equations based on Eley-Rideal (ER) type model for initiation step and Langmuir-Hinshelwood-Hougen-Watson (LHHW) type model for propagation and termination steps of the FTS reactions have been considered and the readsorption of olefins were taken into account. The model that was reported in the literature was modified in order to explain many significant deviations from the ASF distribution. Optimum parameters have been obtained by Genetic Algorithms (GA). The calculated activation energies to produce n-paraffins and 1-olefins were in the range of 82.24 to 90.68 kJ/mol and 100.66 to 105.24 kJ/mol, respectively. The hydrocarbon distribution in FTS reactions was satisfactorily predicted particularly for paraffins.

The purpose of this study is to show an automated laser vision correction (LVC) with the transep-ithelial refractive keratectomy (TransPRK) surgery technique. Since more surgical steps have been automated over the years, we have standardized the TransPRK technique. We have been using it since 2010. This paper aims to illustrate the processes that are readily applicable to automation. To meet the patient's expectations and ensure that the patient has uncorrected visual acuity at least equal to the preoperative corrected vision with glasses, patient selection is crucial in refractive surgical procedures. One major benefit of automating the surgery is that it can be performed in a similar way by each surgeon or laser correction centre, so once the standard and results are good, these can be per-formed by different surgeons.

Three-dimensional V₂O₅ hollow structures have been prepared through a simple synthesis strategy combining solvothermal treatment and a subsequent thermal annealing. The V₂O₅ materials are composed of microspheres 2–3 μm in diameter and with a distinct hollow interior. The as-synthesized V₂O₅ hollow microspheres, when evaluated as a cathode material for lithium-ion batteries, can deliver a specific capacity as high as 273 mAh·g⁻¹ at 0.2 C. Benefiting from the hollow structures that afford fast electrolyte transport and volume accommodation, the V₂O₅ cathode also exhibits a superior rate capability and excellent cycling stability. The good Li-ion storage performance demonstrates the great potential of this unique V₂O₅ hollow material as a high-performance cathode for lithium-ion batteries.

For centuries, Foeniculi fructus (F. fructus) has been used as a traditional herbal medicine in China and Europe and is widely used as a natural therapy for digestive disorders, including indigestion, flatulence, and bloating. The mechanism by which F. fructus alleviates functional dyspepsia was analyzed through network pharmacology, and its therapeutic effect on an animal model of functional dyspepsia was investigated. The compounds, targets, and related diseases of F. fructus were studied utilizing the traditional Chinese medicine systems pharmacology (TCMSP) database. Information on the target genes was classified using the UniProt database. A network was built using Cytoscape 3.9.1, and functional dyspepsia-related genes were checked using the Cytoscape string application. The efficacy of F. fructus on functional dyspepsia, including gastroparesis and gastrointestinal motility, was confirmed by treatment with its extract in a mouse model of loperamide-induced functional dyspepsia. Seven compounds targeted twelve functional dyspepsia-associated genes. In a functional dyspepsia mouse model, F. fructus significantly suppressed the symptoms when compared to that in the control group. Our animal studies showed that the mechanism of action of F. fructus is closely related to gastrointestinal motility. Based on animal experimental results, the results showed that F. fructus provides a potential means to treat functional dyspepsia, suggesting that its medical mechanism for functional dyspepsia can be described by the relationship between seven key compounds of F. fructus, including oleic acid and β-sitosterol, and 12 functional dyspepsia-related genes.

The chemical investigation of the anti-yeast methanol extract from the stem bark of Terminalia mantaly led to the isolation of seven compounds: 3-O-methyl-4-O-α-rhamnopyranoside ellagic acid (1), 3-O-mehylellagic acid (2), arjungenin or 2,3,19,23-tetrahydroxyolean-12-en-28-oïc acid (3), arjunglucoside or 2,3,19,23-tetrahydroxyolean-12-en-28-oïc acid glucopyranoside (4), 2α,3α,24-trihydroxyolean-11,13(18)-dien-28-oïc acid (5), stigmasterol (6), stigmasterol 3-O-β-D-glucopyranoside (7). Their structures were established by means of spectroscopic analysis and comparison with published data. Compounds 1-5 were tested in vitro for activity against three pathogenic yeast isolates, Candida albicans, Candida parapsilosis and Candida krusei. The activity of compounds 1, 2 and 4 were comparable to that of the reference compound fluconazole (MIC values below 32 µg/ml) against the three tested yeast isolates. They were also tested for inhibitory properties against four enzymes of metabolic significance: Glucose-6-Phosphate Deshydrogenase (G6PD), human erythrocyte Carbonic anhydrase I and II (hCA I and hCA II), Glutathione S-transferase (GST). Compound 4 showed highly potent inhibitory property against the four tested enzymes with overall IC₅₀ values below 4 µM and inhibitory constant (Ki) <3 µM.

The TRPV3 calcium ion channel is vital for maintaining skin health and has been associated with various skin-related disorders. Since TRPV3 is involved in the development of skin inflammation, inhibiting TRPV3 could be a potential treatment strategy. Alpha-mangostin isolated from Garcinia mangostana L. extract exhibits diverse positive effects on skin health; however, the underlying mechanisms remain obscure. This study investigated the TRPV3-inhibitory properties of alpha-mangostin on TRPV3 hyperactive mutants associated with Olmsted syndrome and its impact on TRPV3-induced cytokine secretion and cell death. Our findings demonstrate that alpha-mangostin effectively inhibits TRPV3, with an IC50 of 0.077 ± 0.013 μM, showing inhibitory effects on both wild-type and mutant TRPV3. TRPV3 inhibition with alpha-mangostin decreased calcium influx and cytokine release, protecting cells from TRPV3-induced death. These results indicate that alpha-mangostin reduced inflammation in TRPV3-activated skin keratinocytes, suggesting that alpha-mangostin could be potentially used for improving inflammatory skin conditions such as dermatitis.

Solvent free copolymerization of epoxides derived from fatty acid esters of waste cooking oil with phthalic anhydride using (salen)CrIII Cl as catalyst and n-Bu4NCl/DMAP as co-catalyst was carried out for the first time under microwave irradiation, where reaction time was reduced from number of hours to minutes. The polyesters were obtained with molecular weight (Mw = 3084-6740 g/mol) and dispersity values (D = 1.18-1.92), when (salen)CrIII Cl/n-Bu4NCl was used as catalysts. While in case of DMAP as a co-catalyst, polyesters with improved molecular weight (Mw = 5537-6925 g/mol) and narrow dispersity values (D = 1.07-1.28) were obtained even at reduced concentrations of (salen)CrIII Cl and DMAP. The obtained products were characterized and evaluated by attenuated total reflection-fourier transform infrared spectroscopy (ATR-FTIR), proton nuclear magnetic resonance (1H-NMR) spectroscopy, gel permeation chromatography (GPC) and thermogravimetric analysis (TGA) Techniques.

A four-step synthesis of bifunctional, noncovalent organocatalysts based on the chiral (1R,2R)-cyclohexane-1,2-diamine scaffold containing a 1,2-benzenediamine H-bond donor was developed. Nucleophilic aromatic substitution of the 2-fluoronitrobenzene derivative with the commercial (1R,2R)-cyclohexane-1,2-diamine was followed by selective alkylation of the primary amino group, reduction of the aromatic nitro group and final derivatization of the primary aromatic amino group, i.e. acylation, sulfonation, reductive alkylation and arylation, leading to the four subtypes of organocatalysts. All new compounds were fully characterized. The prepared organocatalysts (32 examples) were tested in the Michael addition of acetylacetone to trans-β-nitrostyrene, yielding the addition product with incomplete conversions (up to 93%) and enantioselectivities of up to 41% ee.

Computer vision is a fast-expanding discipline focusing on analyzing, altering, and comprehending images at a high level. Its goal is to figure out what's going on in front of a camera and use that knowledge to manage a computer or robotic system or to show people new visuals that are more instructive or attractive than the original camera photos. Video surveillance, biometrics, automotive, photography, movie production, Web search, medicine, augmented reality gaming, new user interfaces, and many other applications are all possible with computer vision technologies. This paper aims to describe how computer vision will be used to play a winning game of blackjack.

During the last years there has been a high interest in the development of new purely-organic single-component conductors. Very recently, we have reported a new neutral radical conductor based on the perchlorotriphenylmethyl (PTM) radical moiety linked to a monopyrrolo-tetrathiafulvalene (MPTTF) unit by a -conjugated bridge (1). Interestingly, this system behaves as a semiconductor with high conductivity and small energy gap under high pressure. With the aim of developing a new material with improved conducting properties, we have designed and synthesized the radical dyad 2 which was functionalized with an ethylenedithio (EDT) group in order to improve the intermolecular interactions of the TTF subunits. The physical properties of the new radical dyad 2 were studied in detail in solution to further analyze its electronic structure as well as its potential use as radical conductor in the solid state.

Reactions of quinones with compounds containing an amino group can produce a wide variety of addition or substitution products that depend on reactivity of both quinone and amino derivative. 6,7-Dichloropyrido[1,2-a]benzimidazole-8,9-diones undergo selective nucleophilic substitution reaction with different benzohydrazides and α-hydroxy-p-quinone imine derivatives stabilized by strong intramolecular hydrogen bond were isolated. Synthesized compounds represent a combination of several structural motifs: benzimidazole core fused with α-hydroxy-p-quinone imine which contains a benzamido fragment. The protonation/deprotonation processes were investigated in a solution using UV-Vis spectroscopy and 1H NMR titration experiment. X-ray crystallography analysis revealed a set of weak non-covalent interactions such as intra- and intermolecular hydrogen bonds and π-π stacking. Additionally, the redox behavior of 6,7-dichloropyrido[1,2-a]benzimidazole-8,9-dione and its p-imino derivative was investigated in acidic and neutral environment using cyclic voltammetry measurements. Cathode material based on 6,7-dichloropyrido[1,2-a]benzimidazole-8,9-dione could act as potential effective active electrode in aqueous electrolyte batteries, however further optimization is required.

Computer vision is considered as an ally to solve business problems that require human intervention, intelligence and criteria. This topic of research has evolved in XXI century at faster peace, delivering various alternatives from open source until commercial platforms. With so many options and market growing, it result difficult to make a decision on which one to use, or even worse, realize it was not suited for different scenarios. In this paper we analyze five options selected arbitrarily and tested on a dataset of 755 images to detect persons in an image, using object detectors. We analyze elapsed time to process an image, error with observations by humans, number of persons detected, correlation of time and person density, object detected size and F1 Score, considering precision and recall. As we found there are score ties and similar behaviors among options available, we introduce a novel index that takes in consideration the number of persons and their pixel size, to propose the Vision Acuity Index of Computer Vision. The results demonstrate this is a good option to serve as indicator to make decisions. Also, this index proposed have a potential to be expanded for different business use cases, and to measure new proposed algorithms in the future along with the traditional metrics used previously.

Two interesting compounds such as quaternary ammonium lignin(QAL) and 2-ethylhexyl phosphoric acid mono-2-ethylhexyl ester (P507) were synthesized as ionic liquids(QP-ILs) for the extraction of metal ions. The extraction behavior of Co(II) and Ni(II) from chloride solution was compared between QP-ILs and P507. The results showed that the extraction rates of Co(II) and Ni(II) in the two solutions varied with the extraction conditions, but QP-ILs has stronger extractability for cobalt than P507, while Ni is on the contrary. Under the same conditions, the Co/Ni separation performance(βCo/Ni) in QP-ILs is superior to that in p507. At pH4.0, 55℃, concentration of 20%, extraction time of 10 minutes, the βCo/Ni of QP-ILs reached to 2703, while that of P507 was only 164. In conclusion, QP-ILs was more efficient than P507 for separating Co from mixed solutions.

Polypyrrole (PPy)-capped caped silver nanowire (Ag NW) nanomaterials (core–shell rod-shaped Ag NW@PPy) were synthesized suing a one–port suspension polymerization technique. The thickness of PPy layer on the 50-nm-thickness/15-μm-length Ag NW was effectively controlled to 10, 40, 50, and 60 nm. Thin films cast from one-dimensional conductive Ag NW@PPy formed a three-dimensional (3-D) conductive porous network structure and provided excellent electrochemical performance. The 3-D Ag NW@PPy network can significantly reduce the internal resistance of the electrode and maintain structural stability. As a result, a high specific capacitance of 625 F/g at a scan rate of 1 mV/s was obtained from the 3-D porous Ag NW@PPy composite film. The cycling performance over a long period exceeding 10,000 cycles was also evaluated. We expect that our core–shell-structured Ag NW@PPy composites and their 3-D porous structure network films can be applied as electrochemical materials for the design and manufacture of supercapacitors and other energy storage devices.

Multi-view stereo (MVS) employs multi-point photography for image point positioning and three-dimensional reconstruction technology. Recently, this technology has been introduced into the monitoring of road slopes due to advances in photography and computing technology. In general, the various phases of post-image processing procedures are applied to various photographic data. In this study a novel, automated image-monitoring system is proposed to improve the ability of automatic processing. First, an Internet of things (IoT)-based digital photography system architecture was constructed to provide automatic control of camera photography and real-time transmission of image data. In addition, a visual SfM-MVS 3D reconstruction technique was used to develop related software and hardware interfaces based on the built-in Python computing framework of Photoscan Pro. The software integrates fully automatic photography, image transmission, monitoring of data processing and product release programs. The experimental results show that the system architecture can be applied to fully automatic three-dimensional monitoring of road slopes.

In bone tissue engineering, ceramics have been the choice due to their excellent biological properties. But the paradigm changed with the discovery of bioactive glasses (BGs) in 1969 by Larry Hench and co-workers, due to their ability to bond to living tissues through the formation of an interfacial bone-like hydroxyapatite layer when the bioglass was put in contact with biological fluids in vivo. Among a number of tested compositions, the one exhibiting the highest bioactivity index is the well-known trademarked 45S5 Bioglass®. The topic received increasing attention particularly after 1985 when this material entered in the market of biomedical devices, inspiring many other investigations aiming at further exploring the in vitro and in vivo performances of this BG, or developing other related BG compositions. The research efforts gradually revealed a number of shortcomings of 45S5 Bioglass®, mostly derived from its high sodium content, initially intended to decrease the melting temperature and accelerating the degradation of the silicate network over time. But the extensive release of sodium from 45S5 Bioglass® in the biological fluids creates a high pH cytotoxic environment. Other serious drawbacks include a fast degradation rate, and a poor sintering ability, which hinders the reliable fabrication of porous scaffolds. Therefore, sol-gel was regarded as an attractive alternative to prepare alkali-free BG compositions. The process uses inorganic and/or organic precursors, which undergo hydrolysis and condensation at room-temperature, being less costly. When properly conducted, the sol-gel process might result in amorphous structures with all the components intimately mixed at the atomic scale. Moreover, developing new better performing materials for bone tissue engineering is a growing concern, as the ageing of the world’s population leads to lower bone density and osteoporosis. This work describes the sol-gel synthesis of a novel quaternary silicate-based BG with the composition 60 SiO2 – 34 CaO – 4 MgO – 2 P2O5 (mol%) was prepared using acidified distilled water as single solvent. By controlling the kinetics of the hydrolysis and condensation steps, an amorphous glass structure could be obtained. The results of XRD of samples calcined within the temperature range from 600-900 ºC demonstrated that amorphous nature was maintained until 800 ºC, followed by partial crystallization at 900 ºC. The specific surface area, an important factor in osteoconduction, was also evaluated over different temperatures, ranging from 160.6 ± 0.8 m2/g at 600 ºC down to 2.2 ± 0.1 m2/g at 900 ºC, being accompanied consistent changes in average pore size and agreeing pore size distribution. The immersion of the BG particles in simulated body fluid (SBF) led to the formation of an extensive apatite layer on its surface. These overall results indicate the proposed material is very promising for biomedical applications in bone regeneration and tissue engineering.

Peptides, both natural and synthetic, are well suited for a wide range of purposes and offer versa-tile applications in different fields such as biocatalysts, injectable hydrogels, tumor treatment, and drug delivery. The rising prominence of therapeutic peptides encompasses anticancer, antiviral, antimicrobial, and anti-neurodegenerative properties. The metals Na, K, Mg, Ca, Fe, Mn, Co, Cu, Zn, and Mo are ten of the twenty elements that are considered essential for life. Crucial for under-standing the biological role of metals is the exploration of metal-bound proteins and peptides. Aside from essential metals, there are other, non-essential metals that also interact biologically, exhibiting either therapeutic or toxic effects. Irregularities in metal binding contribute to diseases like Alzheimer’s, neurodegenerative disorders, Wilson's, and Menkes disease. Certain metal com-plexes have potential applications as radiopharmaceuticals. The examination of these complexes was done by preforming UV-Vis, IR, EPR, NMR spectroscopy, and X-ray analysis.

Candidiasis is considered an emerging public health concern because of the occurrence of drug-resistant Candida strains and the lack of available structurally diverse antifungal drug armamentariums. The indole alkaloid globospiramine from the anticandidal Philippine medicinal plant Voacanga globosa exhibits a variety of biological activities; however, its antifungal properties remain to be explored. In this study, we report the in vitro antifungal activities of globospiramine against Candida albicans and Candida tropicalis and explore its possible target proteins using in silico methods. The colony-forming unit (CFU) viability assay revealed time- and concentration-dependent anticandidal effects of the alkaloid by decreasing almost 50% of the viable CFUs 60 min after treatment. Results of the MIC and MFC assays indicated inhibitory and fungicidal effects of globospiramine against C. albicans (MIC = 8 µg/mL; MFC = 4 µg/mL) and potential fungistatic effects against C. tropicalis at lower concentrations (MIC = 4 µg/mL; MFC &gt; 64 µg/mL). The FAM-FLICA poly-caspase assay showed metacaspase activation in C. albicans cells at concentrations of 16 and 8 µg/mL, which agreed with the MIC and MFC values. Molecular docking and molecular dynamics simulation experiments indicated stable, strong binding of globospiramine with the target proteins 1,3-β-glucan synthase and Als3 adhesin enzymes, which are indirectly involved in apoptotic-driven candidal inhibition.

We describe a vision for a national-scale heavy mineral (HM) map generated through automated mineralogical identification and quantification of HMs contained in floodplain sediments from large catchments covering most of Australia. The composition of the sediments reflects the dominant rock types in each catchment, with the generally resistant HMs largely preserving the mineralogical fingerprint of their host protoliths through the weathering-transport-deposition cycle. Heavy mineral presence/absence, absolute and relative abundance, and co-occurrence are metrics useful to map, discover and interpret catchment lithotype(s), geodynamic setting, magmatism, metamorphic grade, alteration or mineralization. Underpinning this vision is a pilot project, focusing on a subset of the national sediment sample archive, which is used to demonstrate the feasibility of the larger, national-scale project. We preview a bespoke, cloud-based mineral network analysis (MNA) tool to visualize, explore and discover relationships between HMs as well as between them and geological setting or mineral deposits. We envisage that the Heavy Mineral Map of Australia and MNA tool will contribute significantly to mineral prospectivity analysis and modeling here, particularly for technology critical elements and their host minerals, which are central to the global economy transitioning to a more sustainable, decarbonized paradigm.

The objective of this work was to investigate the deacidification of bio-oils (BOs) from triglyceride-based biomass through the application of liquid-liquid extraction (LLE) in a simple stage, using methanol and water as a binary solvent and feed/solvent ratio of 1/1. For this, the effects of process parameters such as the water content present in the solvent, the content of carboxylic acids present in the bio-oil, and the extraction temperature on the deacidification process through the efficiency and distribution coefficient were evaluated, as well as the effects of such parameters on the quality of deacidified bio-oils through physical-chemical analyzes and GC-MS analysis. The results show that such process parameters significantly affect the quality of deacidified bio-oils. The ideal condition to have the highest acid removal, i.e., the highest deacidification efficiency (72.65%), is the one in which the deacidification process is performed with aqueous methanol (5% water) at 35 °C and for BOs that have a total acid number (TAN) equal to 24.38 mg KOH/g. Therefore, the process of deacidification by LLE using aqueous methanol is a promising alternative for removing carboxylic acids and other oxygenated compounds, contributing significantly to the upgrading or improving biofuels produced by catalytic thermal cracking.

Ultrasound (US) has become a widely used imaging modality in clinical practice, characterized by its rapidly evolving technology, advantages, and unique challenges such as low imaging quality and high variability. There is a critical need to develop advanced automatic US image analysis methods to enhance diagnostic accuracy and objectivity. Vision transformer, a recent innovation in machine learning, has demonstrated significant potential in various research fields, including general image analysis and computer vision, due to its capacity to process large datasets and learn complex patterns. Its suitability for automatic US image analysis tasks, such as classification, detection, and segmentation, has been recognized. This review provides an introduction to vision transformer and discusses its applications in specific US image analysis tasks, while also addressing the open challenges and potential future trends in its application in medical US image analysis. Vision transformer has shown promise in enhancing the accuracy and efficiency of ultrasound image analysis and is expected to play an increasingly important role in the diagnosis and treatment of medical conditions using ultrasound imaging as technology progresses.

This review paper provides a comprehensive overview of machine vision pose measurement algorithms. The paper focuses on the state-of-the-art algorithms and their applications. The paper is structured as follows: The introduction in Section 1 provides a brief overview of the field of machine vision pose measurement. Section 2 describes the commonly used algorithms for machine vision pose measurement. Section 3 discusses the factors that affect the accuracy and reliability of machine vision pose measurement algorithms. Section 4 presents the applications of machine vision pose measurement in various fields. The paper provides specific examples of how machine vision pose measurement is used in each of these fields. Finally, Section 5 summarizes the paper and provides future research directions. The paper highlights the need for more robust and accurate algorithms that can handle varying lighting conditions and occlusion. It also suggests that the integration of machine learning techniques may improve the performance of machine vision pose measurement algorithms. Overall, this review paper provides a comprehensive overview of machine vision pose measurement algorithms, their applications, and the factors that affect their accuracy and reliability. It provides a valuable resource for researchers and practitioners working in the field of computer vision.

The Avian retina is far less known than that of mammals such as mouse and macaque, and detailed study is overdue. The chicken (Gallus gallus) has potential as a model, in part because research can build on developmental studies of the eye and nervous system. One can expect differences between bird and mammal retinas simply because whereas most mammals have three types of visual photoreceptor birds normally have six. Spectral pathways and colour vision are of particular interest, because filtering by oil droplets narrows cone spectral sensitivities and birds are probably tetrachromatic. The number of receptor inputs is reflected in the retinal circuitry. The chicken probably has four types of horizontal cell, there are at least 11 types of bipolar cell, often with bi- or tri-stratified axon terminals, and there is a high density of ganglion cells, which make complex connections in the inner plexiform layer. In addition, there is likely to be retinal specialisation, for example chicken photoreceptors and ganglion cells have separate peaks of cell density in the central and dorsal retina, which probably serve different types of behaviour.

Vertebrate color vision is evolutionarily ancient. Jawless fish evolved four main spectral types of cone photoreceptor, almost certainly complemented by retinal circuits to process chromatic opponent signals. Subsequent evolution of photoreceptors and visual pigments are now documented for many vertebrate lineages and species, giving insight into evolutionary variation and ecological adaptation of color vision. We look at organization of the photoreceptor mosaic and the functions different types of cone in teleost fish, primates, and birds and reptiles. By comparison less is known about the underlying neural processing. Here we outline the diversity of vertebrate color vision and summarize our understanding of how spectral information picked up by animal photoreceptor arrays is adapted to natural signals. We then turn to the question of how spectral information is processed in the retina. Here, the quite well known and comparatively ‘simple’ system of mammals such as mice and primates reveals some evolutionarily conserved features such as the mammalian Blue^ON system which compares short and long wavelength receptors signals. We then survey our current understanding of the more complex circuits of fish, amphibians, birds and reptiles. Together, these clades make up more than 90% of vertebrate species, yet we know disturbingly little about their neural circuits for colour vision beyond the photoreceptors. Here, long-standing work on goldfish, freshwater turtles and other species is being complemented by new insights gained from the experimentally amendable retina of zebrafish. From this body of work, one thing is clear: The retinal basis of colour vision in non-mammalian vertebrates is substantially richer compared to mammals: Diverse and complex spectral tunings are established at the level of the cone output via horizontal cell feedforward circuits. From here, zebrafish use cone-selective wiring in bipolar cells to set-up color opponent synaptic layers in the inner retina, which in turn lead a large diversity of color-opponent channels for transmission to the brain. However, while we are starting to build an understanding of the richness of spectral properties in some of these species’ retinal neurons, little is known about inner retinal connectivity and cell-type identify. To gain an understanding of their actual circuits, and thus to build a more generalised understanding of the vertebrate retinal basis of color vision, it will be paramount to expand ongoing efforts in deciphering the retinal circuits of non-mammalian models.

Primate vision is an active process that constructs a stable internal representation of the 3D world based on 2D sensory inputs that are inherently unstable due to incessant eye movements. We present here a mathematical framework for processing visual information for a biologically-mediated active vision stereo system with asymmetric conformal cameras. This model utilizes the geometric analysis on the Riemann sphere developed in the group-theoretic framework of the conformal camera, thus far only applicable in modeling monocular vision. The asymmetric conformal camera model constructed here includes the fovea’s asymmetric displacement on the retina and the eye’s natural crystalline lens tilt and decentration, as observed in ophthalmological diagnostics. We extend the group-theoretic framework underlying the conformal camera to the stereo system with asymmetric conformal cameras. Our numerical simulation shows that the 1 theoretical horopter curves in this stereo system are conics that well approximate the empirical longitudinal horopters of the primate vision system.

The main inhibitory neurotransmitter in the mammalian brain is 4-aminobutyric acid (GABA). Phthalimide acts as a sodium channel antagonist, one of the finest targets for treating epilepsy is the sodium channel and JNK kinase activity is inhibited to treat epilepsy. a novel compound with multiple targets of action for the treatment of epilepsy also anti-oxidant activity has been designed. The compounds are designed in two series aldehydes and aniline. Docking of all the designed compounds has been done with the suitable protein GABA-AT, sodium channel, JNK protein, and anti-oxidant from protein bank. QSAR was done for constructing many linear regression models. In combination with the developed Docking and QSAR outcomes, ADMET has been performed for the active compounds. The result shows that 11 of the compounds have excellent binding energy in docking with all the receptors and also have good QSAR results and follow the ADMET. The structural understanding of human GABA-AT inhibition was obtained from molecular dynamics simulation studies for compound no. 11 at 5 nanoseconds. Synthesis and pharmacological activity also suggest that the designed compound no.11 be considered the best Novel Compound.

Time series forecasting has been a challenging area in the field of Artificial Intel-ligence. Various approaches such as linear Neural Networks, Recurrent Linear Neural Networks, Convolutional Neural Networks and recently Transformers have been attempted for the time series forecasting domain. Although, Trans-former-based architectures have been outstanding in the Natural Language Pro-cessing domain, especially in autoregressive language modeling, the initial at-tempts to use transformers in the time series arena have met with mixed success. One of the recent papers demonstrated that modeling the multi-variate time se-ries problem as independent channels on a transformer architecture produced lower mean square and mean absolute errors on benchmarks, however, another recent paper claimed that a simple linear neural network out-performs previous approaches including transformer-based designs. We investigate this paradox in detail comparing the linear Neural Network and Transformer based designs, provide insights to why a certain approach may be better for a particular type of problem. We also improve upon the recently proposed simple linear Neural Network based architecture by using dual pipelines with batch normalization and reversible instance normalization. Our enhanced architecture outperforms all existing architectures for time series forecasting on majority of the popular benchmarks.

Robotic odor source localization (OSL) is a technology that enables mobile robots or autonomous vehicles to find an odor source in unknown environments. An effective navigation algorithm that guides the robot to approach the odor source is the key to successfully locating the odor source. While traditional OSL approaches primarily utilize an Olfaction-only strategy, guiding robots to find the odor source by tracing emitted odor plumes, our work introduces a fusion navigation algorithm that combines both vision and olfaction-based techniques. This hybrid approach addresses challenges such as turbulent airflow, which disrupts olfaction sensing, and physical obstacles inside the search area, which may impede vision detection. In this work, we propose a hierarchical control mechanism that dynamically shifts the robot’s search behavior among four strategies: crosswind maneuver, Obstacle-avoid Navigation, Vision-based Navigation, and Olfaction-based Navigation. Our methodology includes a custom-trained deep-learning model for visual target detection and a moth-inspired algorithm for Olfaction-based navigation. To assess the effectiveness of our approach, we implemented the proposed algorithm on a mobile robot in a search environment with obstacles. Experimental results demonstrate that our Vision and Olfaction Fusion algorithm significantly outperforms Vision-only and Olfaction-only methods, reducing average search time by 54% and 30%, respectively.

Identification of plants is a challenging task which aims to identify the family, genus, and species level according to morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users to narrow down the possibilities. However, numerous morphological similarities between and within species make the classification difficult. In this paper, we tested a custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88K and 16K images for classifying plants at genus and species levels respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. The ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. ViT models also perform better for classifying plants at the species level compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success.

The quality of yarn is essential in the control of the fabrics processes. There is some commercial equipment that measures the quality of yarn based on sensors, of different types, used for collecting data about some yarn characteristic parameters. The existing equipment, for the above-mentioned purpose, is characterized by its high size and cost, and for allowing the analysis of only few yarn quality parameters. Thus, this paper presents the development and results obtained with the design of a mechatronic prototype integrating a computer vision system that allows, among other parameters, the analysis and classification, in real time, of the hairs of the yarn using artificial intelligence techniques. The system also determines other characteristics inherent to the yarn quality analysis, such as: linear mass, diameter, volume, twist orientation, twist step, average mass deviation, coefficient of variation, hairiness coefficient, average hairiness deviation and standard hairiness deviation, as well as performs spectral analysis. A comparison of the obtained results with the designed system and a commercial equipment was performed validating the undertaken methodology.

Practical applications of CsPbX3 nanocrystals (NCs) are limited by their poor stability. The formation of heterojunction between CsPbX3 NCs and oxides is an effective means to protect perovskite from polar solvents and other external factors. Significantly improving the stability and photocatalytic properties of the core/shell perovskite is very important for its application in photoelectric and photocatalytic technology. Here, we report the synthesis of asymmetrical CsPbBr3/TiO2 core-shell heterostructure NCs at single particle level by hot injection liquid phase synthesis and sol-gel method, where each CsPbBr3 NCs is partially covered by titanium dioxide. It is shown that the type II arrangement is generated at the heterogeneous interface, which greatly facilitates the separation of electron-hole pairs and increases the carrier transport efficiency. More crucial, due to the protection of titanium dioxide shell, the product has higher long-term stability in humid air compared with bare CsPbBr3 NCs. The asymmetrical core-shell heterostructure prepared in this study not only improves the stability of CsPbX3 NCs, but also provides some ideas for optoelectronic device applications and TiO2-based photocatalysts.

Image classification in Remote Sensing and Geographic Information Systems (GIS) containing various land-cover classes is essential for efficient and sustainable land-use estimation, and other tasks like object detection, localization and segmentation. Deep Learning (DL) techniques have shown a tremendous potential in the GIS domain. While Convolutional Neural Networks (CNN) have dominated most of the image analysis domain, a new architecture called transformers have proved to be a unifying solution for several AI-based processing pipelines. Vision Transformers (ViT), a variant of transformers can have comparable and in some cases better accuracy than a CNN. However, they suffer from a significant drawback associated with an excessive use of training parameters. In this research we explore several modifications in the vision transformer architectures, especially MobileViT that can be optimized while boosting accuracy. To verify our proposed approach these new architectures are trained on four land-cover datasets AID, EuroSAT, UC-Merced, and WHU-RS19. Experiments reveal that combination of lightweight convolutional layers including ShuffleNet along with depthwise separable convolutions and average pooling can reduce the trainable parameters by 17.85% and yet achieve higher accuracy than the base MobileViT. It is also observed that utilizing a combination of convolution layers along with multi-headed self attention layers in MobileViT variants provide better performance in capturing local and global features unlike the standalone ViT architecture that utilizes almost 95% more parameters than the proposed MobileViT variant.

People with Down syndrome have more visual problems than general population. They experience a premature aging, and it would be expected they will also have an acceleration in worsening visual function. A prospective observational study which includes visual acuity, refractive error, accommodation, binocular and colour vision was performed to young adults with (n=69) and without (n=65) Down syndrome and to a senior group (n=55) without Down syndrome. Results showed significant differences in visual acuity between groups (p&lt;0.001) and it can be improved with a new prescription in 40% of the participants with Down syndrome. Regarding to accommodative state, no significant differences were found between groups of young people. Concerning binocular vision, 64.7% of strabismus was observed in the group with Down syndrome (p&lt;0.001). Visual abnormalities are significant in young adults with Down syndrome and are different from those of older people without Down syndrome, some of which can be improved by providing the optimal prescription as well as regular eye examinations.

Potholes on roads pose a major threat to motorists and autonomous vehicles. Driving over a pothole has the potential to cause serious damage to a vehicle, which in turn may result in fatal accidents. Currently, many pothole detection methods exist. However, these methods do not utilize deep learning techniques to detect a pothole in real-time, determine the location thereof and display its location on a map. The success of determining an effective pothole detection method, which includes the aforementioned deep learning techniques, is dependent on acquiring a large amount of data, including images of potholes. Once adequate data had been gathered, the images were processed and annotated. The next step was to determine which deep learning algorithms could be utilized. Three different models, including Faster R-CNN, SSD and YOLOv3 were trained on the custom dataset containing images of potholes to determine which network produces the best results for real-time detection. It was revealed that YOLOv3 produced the most accurate results and performed the best in real-time, with an average detection time of only 0.836s per image. The final results revealed that a real-time pothole detection system, integrated with a cloud and maps service, can be created to allow drivers to avoid potholes.

Introduction Color vision deficiency (CVD) constitutes one of the frequently observed eye disorders in all human populations. Color is a prominent sign utilized in the medical profession to study and identify histopathological specimens, lab instruments, and patient examination. Color deficiency affects the medical skills of students resulting in poor clinical examination and color appreciation. There is no effective screening of CVD at any level of the medical profession. Hence, this study was aimed to determine the prevalence of CVD among medical students. Materials and methods This was a cross-sectional study conducted from September 2019 to February 2020 over a period of six months in Karachi, Pakistan. All medical students aged 18-21 years of either gender enrolled in the first and second years of medical college were included in this study. The examination was performed during daylight. Ishihara plates were placed at a distance of 75 cm from the subject and tilted so that the plane of the paper lies perpendicular to the line of vision. Students were given five seconds to read the plate and one examiner was instructed to mark the checklist. A score of less than 12 out of 14 red/green test plates (not including the demonstration plate) was considered as a CVD. All statistical analysis was performed using Statistical Package for Social Sciences version 20.0 (Armonk, NY: IBM Corp). Results The mean age of the medical students was 19.61± 1.22 years. There were (n=123) 53.0% females and (n=111) 47.0% males. Most of the medical students (n=131, 56.0%) belonged to the upper-middle-class socioeconomic group. CVD was observed in (n=13) 6.0%of medical students. Age (p=0.001) and socioeconomic status (p=0.001) were the only demographic factors significantly associated with color deficiency. Conclusions Color deficiency, although an unnoticed concern, is fairly common among medical students. Medical students must be screened for CVD as this will enable them to be aware of their limitations in their future observational skills as a doctor and devise ways of overcoming them in clinical practice.

Autonomous aerial refueling technology is an effective solution to extend flight duration of unmanned aerial vehicles, and also a great challenge due to its high risk. For autonomous probe-and-drogue refueling tasks, relative navigation to provide relative position between the receiver aircraft and the refueling drogue is the first and essential step, and vision-based method is the most frequently used. A new monocular vision navigation sensor with image processing strategy consisting of the drogue detection method and the tracking method is developed for autonomous aerial refueling in this paper. In the drogue detection method, thresholding and mathematical morphology method are adopted to eliminate image interference, and contours extraction method is applied to obtain all contours, which are then subsequently checked to achieve target contour of drogue. In the tracking method, a rectangle of interest (ROI) of current frame image is determined by positioning results of last frame, and then processed by the previous drogue detection method. Finally, the proposed image processing strategy in monocular vision navigation sensor is validated using real flight images, which are captured from an autonomous aerial refueling testbed using a micro six-rotor aircraft as receiver aircraft.

This study reports on the development of bio-based hydrophobic coatings for packaging papers through deposition of polyhydroxybutyrate (PHB) particles in combination with nanofibrillated cellulose (NFC) and plant wax. In a first approach, PHB particles in micrometer range (PHB-MP) were prepared through a phase-separation technique providing internally nanosized structures. The particles were transferred as a coating by dip-coating filter papers in the particle suspension, followed by sizing with a carnauba wax solution. This approach allowed partial to almost full surface coverage of PHB-MP over the paper surface resulting in static water contact angles of 105° to 122° and 129° to 144° after additional wax coating. In a second approach, PHB particles with submicron sizes (PHB-SP) were synthesized by an oil-in-emulsion (o/w) solvent evaporation method, and mixed in aqueous suspensions with 0 to 7 wt% NFC. After dip-coating filter papers in PHB-SP/NFC suspensions and sizing with a carnauba wax solution, static water contact angles of 112° to 152° were obtained. The intrinsic properties of the particles were analyzed by scanning electron microscopy, thermal analysis and infrared spectroscopy, indicating higher crystallinity for PHB-SP than PHB-MP. The chemical interactions between the more amorphous PHB-MP particles and paper fibers were identified as an esterification reaction, while the morphology of the NFC fibrillar network was playing a key role as binding agent in the retention of more crystalline PHB-SP at the paper surface, hence contributing to higher hydrophobicity.

UAV network operation enables gathering and fusion from disparate information sources for flight control in both manned and unmanned platforms. In this investigation, a novel procedure for detecting runways and horizons as well as enhancing surrounding terrain is introduced based on fusion of enhanced vision system (EVS) and synthetic vision system (SVS) images. EVS and SVS image fusion has yet to be implemented real-world situations due to signal misalignment. We address this through a registration step to align the EVS and SVS images. Four fusion rules combining discrete wavelet transform (DWT) sub-bands are formulated, implemented and evaluated. The resulting procedure is tested on real EVS-SVS image pairs and pairs containing simulated turbulence. Evaluations reveal that runways and horizons can be detected accurately even in poor visibility. Furthermore, it is demonstrated that different aspects of the EVS and SVS images can be emphasized by using different DWT fusion rules. The procedure is autonomous throughout landing, irrespective of weather. We believe the fusion architecture developed holds promise for incorporation into head-up displays (HUDs) and UAV remote displays to assist pilots landing aircraft in poor lighting and varying weather. The algorithm also provided a basis rule selection in other signal fusion applications.

With the emergence of autonomous functions in road vehicles, there has been an increased use of Advanced Driver Assistance Systems comprising various sensors to perform automated tasks. Light Detection and Ranging (LiDAR) is one of the most important types of optical sensor that detects the positions of obstacles, representing them as clusters of points in 3-dimensional space. LiDAR performance degrades significantly when driving in rain as raindrops adhere to the outer surface of the sensor assembly. The performance degradation behaviors include missing points and reduced reflectivity of the points. It was found that the extent of degradations is highly dependent on the interface material properties, which subsequently affects the shapes of the adherent droplets, causing different perturbations to the optical rays. A fundamental investigation is performed on the protective polycarbonate cover of the LiDAR assembly coated with four classes of materials – hydrophilic, almost-hydrophobic, hydrophobic, and superhydrophobic. Water droplets are controllably dispensed onto the cover to quantify the signal alteration due to each droplet of various sizes and shapes. To further understand the effects of droplet motion on LiDAR signals, sliding droplet conditions are simulated using numerical analysis and validated with physical optical tests using a 905 nm laser source and receiver to mimic the LiDAR detection mechanism. Comprehensive explanations are presented on LiDAR performance degradation in rain from both material and optical perspectives. These can aid component selection and the development of signal enhancing strategies for integrating LiDARs in vehicle designs to minimize the impact of rain.

Blood cell analysis is a crucial diagnostic process in medical practice. In particular, detecting white blood cells (WBCs) is essential for diagnosing of many diseases. The manual screening of blood films is a time-consuming and subjective process, which can lead to inconsistencies and errors. Therefore, automated detection of blood cells can improve the accuracy and efficiency of the screening process. In this study, an explainable Vision Transformer (ViT) model was proposed for the automatic detection of WBCs from blood films. The proposed model utilizes the self-attention mechanism to extract relevant features from the input images and leverages transfer learning by incorporating pre-trained model weights to improve its performance. The proposed model achieved a classification accuracy of 99.40% for five distinct types of WBCs and exhibited potential in reducing the time required for manual screening of blood films by pathologists. Upon examination of the misclassified test samples, it was observed that incorrect predictions were correlated with the presence or absence of granules in the cell samples. To validate this observation, the dataset was divided into two classes, namely Granulocytes and Agranulocytes, and a secondary training process was conducted. The resulting ViT model trained for binary classification achieved an accuracy of 99.70%, recall of 99.54%, precision of 99.32%, and F-1 score of 99.43% during the test phase. To ensure the reliability of the ViT model's multi-class classification of WBCs, the pixel areas that the model focuses on in its predictions are visualized through the Score-CAM algorithm.

The influence of the film thickness and the substrate’s refractive index on the surface mode at the superstrate is an important study step that may help clearing some of the misunderstandings surrounding their propagation mechanism. A single sub-wavelength slit perforating a thin metallic film is among the simplest nanostructure capable of launching Surface Plasmon Polaritons on its surrounding surface when excited by an incident field. Here, the impact of the substrate and the film thickness on surface waves is investigated. When the thickness of the film is comparable to its skin depth, SPP waves from the substrate penetrate the film and emerge from the superstrate, creating a superposition of two SPP waves, that leads to a beat interference envelope with well-defined loci which are the function of both the drive frequency and the dielectric constant of the substrate/superstrate. As the film thickness is reduced to the SPP’s penetration depth, surface waves from optically denser dielectric/metal interface would dominate, leading to volume plasmons that propagate inside the film at optical frequencies. Interference of periodic volume charge density with the incident field over the film creates charge bundles that are periodic in space and time.

Hand gestures may play an important role in medical applications for health care of elderly people, where providing a natural interaction for different requests can be executed by making specific gestures. In this study we explored three different scenarios using a Microsoft Kinect V2 depth sensor then evaluated the effectiveness of the outcomes. The first scenario utilized the default system embedded in the Kinect V2 sensor, which depth metadata gives 11 parameters related to the tracked body with five gestures for each hand. The second scenario used joint tracking provided by Kinect depth metadata and depth threshold together to enhance hand segmentation and efficiently recognize the number of fingers extended. The third scenario used a simple convolutional neural network with joint tracking by depth metadata to recognize five categories of gestures. In this study, deaf-mute elderly people execute five different hand gestures to indicate a specific request, such as needing water, meal, toilet, help and medicine. Then, the requests were sent to the care provider’s smartphone because elderly people could not execute any activity independently. The system transferred these requests as a message through the global system for mobile communication (GSM) using a microcontroller.

Cobalt oxide nanoparticles are promising candidates for various kinds of practical application. The interest of researchers and scientists in these nanoparticles is due to their unique physical and chemical properties such as density, melting point, magnetic characteristics and band gap. The need to develop high performance and low-cost technologies for obtaining Co3O4 nanoparticles is a major reason in research in this area. The widespread commercial use of Co3O4 nanoparticles will open doors for numerous novel cost effective technologies, which are mostly dependent on the type of synthesis method and raw materials. A possible solution to this problem is to use Cobalt nitrates as a cost-effective precursor using the solution combustion method. Solution combustion synthesis method is one of the best candidate methods that have the potential for high performance and is also cost-effective. This review examines recent progress in the synthesis of Co3O4 nanoparticles by solution combustion method and underscores their significance across various fields, highlighting the need for further research and exploration of their multifaceted applications. In addition, the most recent advancements in the applications of Co3O4 nanoparticles in sensitive sensing, catalysis, supercapacitors/batteries and water treatment/pollutants removal are reviewed and described, focusing on current trends, challenges, and future outlooks.

The successful 2020 launch and 2021 landing of the National Aeronautics and Space Administration’s (NASA) Perseverance Mars rover initiated the first phase of the NASA and European Space Agency (ESA) Mars Sample Return (MSR) campaign. The goal of the MSR campaign is to collect scientifically interesting samples from the Martian surface and return them to Earth for further study in terrestrial laboratories. The MSR campaign consists of three major spacecraft components to accomplish this objective: the Perseverance Mars rover, the Sample Retrieval Lander (SRL) and the Earth Return Orbiter (ERO). Onboard the ERO spacecraft is the Capture, Containment and Return System (CCRS). CCRS will capture, process and return to Earth the samples that have been collected after they are launched into Mars orbit by the Mars Ascent Vehicle (MAV), which is delivered to Mars onboard the SRL. To facilitate the processing of the orbiting sample (OS) by CCRS we have designed and developed a vision system to determine the OS capture orientation. The vision system is comprised of two cameras sensitive to the visible portion of the electromagnetic spectrum and two illumination modules constructed from broadband light emitting diodes (LED). Vision system laboratory tests and physics-based optical simulations predict CCRS ground processing will be able to correctly identify the OS post-capture orientation using only a single vision system image that is transmitted to Earth from Mars orbit.

As education continues to evolve in response to technological advancements, the concept of Education 5.0 has gained prominence. However, it is crucial to critically analyze the limitations and challenges of this model. This article presents a comprehensive examination of Education 5.0, identifies its drawbacks, and proposes a vision for Education 6.0. Through insightful case studies and discussions, this article aims to provide a nuanced perspective on the future of education.

Humankind has entered a new era wherein a main characteristic is the convergence of various technologies providing services exerting major impact upon all aspects of human activity, be it social interactions as well as interactions with the natural environment. Fixed networks are about to play a major role in this convergence, since they form, along with mobile networks, the backbone that provides access to a broad gamut of services, accessible from any point of the globe. It is for this reason that we introduce a forward-looking approach for fixed networks, particularly focused on Fixed 6th Generation (F6G) networks. First, we adopt a novel classification scheme for the main F6G services, comprising six categories. This classification is based on the key service requirements, namely latency, capacity and connectivity. F6G networks differ from those of previous generations (F1G-F5G) in that they concurrently support multiple key requirements. We then propose concrete steps towards transforming the main elements of fixed networks, such as optical transceivers, optical switches etc. such that they satisfy the new F6G service requirements. Our study categorizes the main networking paradigm of optical switching into two categories namely ultra-fast and ultra-high capacity switching, tailored to different service categories. With regard to the transceiver physical layer, we propose: a) use of all-optical processing to mitigate performance barriers of analog to digital and digital to analog converters (ADC/DAC); b) exploitation of optical multi-band transmission, space division-multiplexing and adoption of more efficient modulation formats.

Maze navigation using one or more robots has become a recurring challenge in scientific literature and real life practice, with fleets having to find faster and better ways to navigate environments such as a travel hub (e.g. airports) or to evacuate a disaster zone. Many methods have been used to solve this issue, including the implementation of a variety of sensors and other signal receiving systems. Most interestingly, camera-based techniques have increasingly become more popular in this kind of applications, given their robustness and scalability. In this paper, we have implemented an end-to-end strategy to address this scenario, allowing a robot to solve a maze in an autonomous way, by using computer vision and path planning. In addition, this robot shares the generated knowledge to another by means of communication protocols, having to adapt its mechanical characteristics to be able to solve the same challenge. The paper presents experimental validation of the four components of this solution, namely camera calibration, maze mapping, path planning and robot communication. Finally, we present the integration and functionality of these methods applied in a pair of NAO robots.

Esterquats constitute a unique group of quaternary ammonium salts (QASs) that contain an ester bond in the structure of the cation. Despite the numerous advantages of this class of compounds, only two mini-reviews discuss the subject of esterquats: the first one (2007) briefly summarizes their types, synthesis, and structural elements required for a beneficial environmental profile and only briefly covers their applications whereas the second one only reviews the stability of selected betaine-type esterquats in aqueous solutions. The rationale for writing this review is to critically reevaluate the relevant literature and provide others with a “state-of-the-art” snapshot of choline-type esterquats and betaine-type esterquats. Hence, the first part of this survey thoroughly summarizes the most important scientific reports demonstrating effective synthesis routes leading to the formation of both types of esterquats. In the second section, the susceptibility of esterquats to hydrolysis is explained, and the influence of various factors, such as the pH, the degree of salinity or the temperature of the solution, was subjected to thorough analysis that includes quantitative components. The next two sections refer to various aspects associated with the ecotoxicity of esterquats. Consequently, their biodegradation and toxic effects on microorganisms are extensively analyzed as crucial factors that can affect their commercialization. Then, the reported applications of esterquats are briefly discussed, including functionalization of macromolecules, such as cotton fabric as well as their successful utilization on a commercial scale. The last section demonstrates the most essential conclusions and reported drawbacks that allow us to elucidate future recommendations regarding the development of these promising chemicals.

Currently, there is a tenacious need to find new ways to purify water by eliminating bacterial biofilms, textile dyes, and toxic water pollutants. These contaminants pose significant risks to both human health and the environment. To address this issue, in this study, we have developed an eco-friendly approach that involves synthesizing Cobalt-doped Cerium Iron Oxide (CCIO) nanocomposite (NC) using an aqueous extract of Gossypium arboreum L. stalks. The resulting nanoparticles can be used to effectively purify water and tackle the challenges associated with these harmful pollutants. Nanoparticles excel in water pollutant removal by providing high surface area for efficient adsorption, versatile design for simultaneous removal of multiple contaminants, catalytic properties for organic pollutant degradation, and magnetic features for easy separation, offering cost-effective and sustainable water treatment solutions. CCIO nanocomposite has been synthesized by green co-precipitation method due to biomolecules and co-enzymes present in the aqueous extract of Gossypium arboreum L. stalk; a single-step procedure completed in 5 h reaction time. Further, the synthesis of nanocomposites was confirmed by various characterization techniques such as Fourier-transform infrared (FT-IR) spectroscopy, X-ray diffraction (XRD), Field emission scanning electron microscopy (FE-SEM), Transmission electron microscopy (TEM), Thermogravimetric analysis (TGA), Dynamic light scattering (DLS), and Energy Dispersive X-Ray (EDX). CCIO NCs were discovered to have a spherical shape and an average size of 40 nm. Based on DLS zeta potential analysis, CCIO NCs were found to be anionic. Also, CCIO NCs showed significant antimicrobial and antioxidant activity. Overall, considering the physical and chemical properties, the application of CCIO NCs in the adsorption of various dyes (~91 %) and water pollutants (chromium = ~ 60 %) has been considered since they exhibit great adsorption capacity owing to their microporous structure which can be a step forward in water purification.

3,4-Dihydroquinolin-2(1H)-ones represent a class of valuable bioactive compounds with six-membered nitrogen-containing heterocyclic structures. The development of simple, mild and efficient synthetic methods has been widely concerned by synthetic chemists. In this review, we have summarized a series of different synthetic strategies for the synthesis of dihydroquinoline-2(1H)-ones via catalytic annulation of α,β-unsaturated N-arylamides in the past decade, including covering electrophilic cyclization, radical initiated cyclization and photochemical cyclization reactions. Besides, the substrate scope and mechanistic details are also discussed. This paper will provide a useful reference for the development of diverse synthesis methodologies of 3,4-dihydroquinolin-2(1H)-ones.

A human eye has about 120 million rod cells and 6 million cone cells. This huge number of light sensing cells inside a human eye will continuously produce a huge quantity of visual signals which flow into a human brain for daily processing. However, the real-time processing of these visual signals does not cause any fatigue to a human brain. This fact tells us the truth which is to say that human-like vision processes do not rely on complicated formulas to compute depth, displacement, and colors, etc. On the other hand, a human eye is like a PTZ camera. Here, PTZ stands for pan, tilt and zoom. We all know that in computer vision, each set of PTZ parameters (i.e., coefficients of pan, tilt and zoom) requires a dedicated calibration to determine a camera’s projection matrix. Since there is an infinite number of PTZ parameters which could be produced by a human eye, it is unlikely that a human brain stores an infinite number of calibration matrices for each human eye. Therefore, it is an interesting question for us to answer, which is to say whether simpler formulas of computing depth and displacement exist or not. Moreover, these formulas must be calibration friendly (i.e., easy process on the fly or on the go). In this paper, we disclose an important discovery of a new solution to 3D projection in a human-like binocular vision system. The purpose of doing 3D projection in binocular vision is to undertake forward and inverse transformations (or mappings) between coordinates in 2D digital images and coordinates in a 3D analogue scene. The formulas underlying the new solution are accurate, easily computable, easily tunable (i.e., to be calibrated on the fly or on the go) and could be easily implemented by a neural system (i.e., a network of neurons). Experimental results have validated the discovered formulas.

In recent years, vision-centric perception has played a crucial role in autonomous driving tasks, encompassing functions such as 3D detection, map construction, and motion forecasting. However, the deployment of vision-centric approaches in practical scenarios is hindered by substantial latency, often deviating significantly from the outcomes achieved through offline training. This disparity arises from the fact that conventional benchmarks for autonomous driving perception predominantly conduct offline evaluations, thereby largely overlooking the latency concerns prevalent in real-world deployment. While a few benchmarks have been proposed to address this limitation by introducing effective evaluation methods for online perception, they do not adequately consider the intricacies introduced by the complexity of input information streams. To address this gap, we propose the Autonomous-driving Streaming I/O (ASIO) benchmark, aiming to assess the streaming inputs characteristics and online performance of vision-centric perception in autonomous driving. To facilitate this evaluation across diverse streaming inputs, we initially establish a dataset based on the CARLA Leaderboard. In alignment with real-world deployment considerations, we further develop evaluation metrics based on information complexity specifically tailored for streaming inputs and streaming performance. Experimental results indicate significant variations in model performance and ranking under different major camera deployments, underscoring the necessity of thoroughly accounting for the influences of model latency and streaming inputs characteristics during real-world deployment. To enhance streaming performance consistently across distinct streaming inputs features, we introduce a backbone switcher based on the identified streaming inputs characteristics. Experimental validation demonstrates its efficacy in perpetually improving streaming performance across varying streaming inputs features.

Due to the influence of background conditions, lighting conditions, occlusion issues and the image resolution, how to extract robust person features is one of the difficulties in ReID research. Vision in Transformers (ViT) has achieved significant results in the field of computer vision. However, the existing problems still limit its application in ReID due to slow extraction of person features and difficulty in utilizing local features of people. To solve the mentioned problems, we utilize Pyramid Vision Transformer (PVT) as the backbone of feature extraction and propose a PVT-based ReID method in conjunction with other studies. Firstly, some improvements suitable for ReID are used on the PVT backbone, and we establish a basic model by using powerful methods verified on CNN-based ReID. Secondly, in an effort to further promote the robustness of the person features extracted by the PVT backbone, two new modules are designed. (1) The local feature clustering (LFC) is recommend to enhance the robustness of person features by calculating the distance between local features and global feature to select the most discrete local features and clustering them. (2) The side information embeddings (SIE) are used to encode non-visual information and send it into the network for training to reduce its impact on person features. Finally, the experiments show that PVTReID has achieved excellent results in ReID datasets and are 20% faster on average than CNN-based ReID methods.

Regular inspections during construction work verify that the work completed is consistent with the plans and specifications and ensure that it is within the planned time and budget. This requires frequent physical site observations to independently measure and verify the completion percentage of the construction progress performed over periods of time. The current computer vision-based (CV) techniques for the measurement of as-built elements, predominantly use 3D laser scanning or 3D Photogrammetry modelling to determine the geometrical properties of as-built elements on construction sites. Both techniques require data acquisition from several positions and angles to generate sufficient information about the element’s coordinates making the deployment of these techniques on dynamic construction project sites a challenging task. In this paper, we propose a pipeline for automating the measurement of as-built components using artificial intelligence (AI) and computer vision (CV) techniques. The pipeline requires a single image obtained with a stereo-camera system to measure the size of selected objects or as-built components. We demonstrate our approach by measuring the size of concrete walls and columns. The novelty of this work is attributed to the fully automated CV-based method for measuring any given element using a single image only. The proposed solution is suitable for use in measuring the sizes of as-built components of built assets. It has the potential to be further developed and integrated with BIM models for use on construction projects for progress monitoring.

This paper presents a two-dimensional mathematical model of compound eye vision. Such a model is useful for solving navigation issues for autonomous mobile robots on the ground plane. The model is inspired by the insect compound eye that consists of ommatidia, which are tiny independent photoreception units, each of which combines a cornea, lens, and rhabdom. The model describes the planar binocular compound eye vision, focusing on measuring distance and azimuth to a circular feature with an arbitrary size. The model provides a necessary and sufficient condition for the visibility of a circular feature by each ommatidium. On this basis, an algorithm is built for generating a training data set to create two deep neural networks (DNN): the first detects the distance, and the second detects the azimuth to a circular feature. The hyperparameter tuning and the configurations of both networks are described. Experimental results showed that the proposed method could effectively and accurately detect the distance and azimuth to objects.

Centimeter-level localization and rotation angle estimation of flatbed trucks are major problems in unmanned forklift automated loading scenes. To solve it, this study proposes a method for high-precision positioning and rotation angle estimation of flatbed trucks based on BeiDou and vision. First, an unmanned forklift equipped with a ToF camera and double antenna mobile receiver for BeiDou positioning collects depth images and localization data near the front and rear endpoints of the flatbed. Then DDRNet-23-slim was used to segment the flatbed from the depth image and extract the straight lines at the edges of the flatbed using the Hough transform and compute the set of intersection points of the lines. A neighborhood feature vector was designed to locate the endpoint of a flatbed from a set of intersection points by feature screening. Finally, the relative coordinates of the endpoints were converted to a customized forklift navigation coordinate system by BeiDou positioning, and rotation angle estimation was performed by the front and rear endpoints. Experiments showed that the endpoint positioning error was less than 3 cm and the rotation angle estimation error was less than 0.1°. The precision of the method meets the demand for automated flatbed truck loading by unmanned forklifts.

Modern organic synthesis is majorly focused on developing environmentally benign synthetic protocols by employing green chemistry principles. Accordingly, in our recent research work, we herein report the use of modified supramolecular host cyclodextrin as an effective solid based green catalyst for accessing structurally diverse and medicinally relevant pyrimidone architectures. The catalyst and the synthesized compounds 4 (a-r) were characterized using FT-IR, NMR and GC-Mass spectroscopy. Major highlights of the reported work include: atom economical process, extremely milder reaction conditions, operational simplicity, high isolated yields, and excellent catalyst turnover number. The molecular docking studies suggest that the compound 4n has the hydrogen bonding, hydrophobic and π-pair interactions with the active site of active sites of CXT M 15 receptor.

The market for shark fin soup, a fashionable, high-end Chinese dish, has resulted in intensive shark fishing across all oceans. At the same time, 90% of teleost fish stocks are over-exploited, making sharks the most lucrative target: fisheries that have not previously hunted them are now. As top and middle predators, sharks have high ecological value and poor capacity to withstand fishing mortality, but though their numbers are plummeting, the secretive nature of the fin trade, along with the difficulties of obtaining relevant data, obscure their true status. In consumer countries, shark fin is a luxury item and rich consumers are willing to pay high prices. There is little interest in sustainability or legal trade. Thus market demand will continue to fuel the search for sharks and those accessible to fishing fleets are increasingly endangered. Current legal protections are not working, as is clearly seen in the case of the shortfin mako. Claims that sharks can withstand such targeted, industrial hunting and be sustainably fished under these circumstances are shown to be misguided. In the interests of averting a catastrophic collapse across the planet’s aquatic ecosystems, sharks must be given effective protection. We recommend that all sharks, chimaeras, manta rays, devil rays, and rhino rays be protected from international trade through an immediate CITES Appendix I listing. However, a binding international agreement for protection, not only of sharks, but of the loss of biodiversity in general, is what is most needed to allow marine and fresh water ecosystems to recover.

Deep Learning (DL) has made significant advances in computer vision with the advent of Vision Transformers (ViT). Unlike Convolutional Neural Networks (CNNs), ViTs use self-attention to extract both local and global features from image data, and then use residual connections to feed these features directly into a fully networked multilayer perceptron head. In hospitals, hematologists prepare peripheral blood smears (PBSs) and read them under a medical microscope to detect abnormalities in blood counts such as leukemia. However, this task is time-consuming and prone to human error. This study investigates the transfer learning process of Google ViT and ImageNet CNNs to automate the reading of PBSs. The study used two online PBS datasets, PBC and BCCD, and transferred them into balanced datasets to investigate the influence of data amount and noise immunity on both neural networks. The PBC results show that Google ViT is an excellent DL neural solution for data scarcity. The BCCD results show that Google ViT is superior to ImageNet CNNs in dealing with unclean, noisy image data because it is able to extract both global and local features and use residual connections, despite the additional time and computational overhead.

Dams are a critical infrastructure system for many communities, but they are also one of the most challenging to inspect. Dams are typically very large and complex structures, and the result is that inspections are often time-intensive and require expensive, specialized equipment and training to provide inspectors with comprehensive access to the structure. The scale and nature of dam inspections also introduces additional safety risks to the inspectors. Unmanned aerial vehicles (UAV) have the potential to address many of these challenges, particularly when used as a data acquisition platform for photogrammetric three-dimensional (3D) reconstruction and analysis, though the nature of both UAV and modern photogrammetric methods necessitates careful planning and coordination for integration. This paper presents a case study on one such integration at the Brighton Dam, a large-scale concrete gravity dam in Maryland, USA. A combination of multiple UAV platforms and multi-scale photogrammetry was used to create two comprehensive and high-resolution 3D point clouds of the dam and surrounding environment at intervals. These models were then assessed for their overall quality, as well as their ability to resolve flaws and defects that were artificially applied to the structure between inspection intervals. The results indicate that the integrated process is capable of generating models that accurately render a variety of defect types with sub-millimeter accuracy. Recommendations for mission planning and imaging specifications are provided as well.

The structure of arylpiperazine moieties as an important pharmacophore could generate wide pharmacological activities．Arylpiperazine derivatives referred in this paper possessed antitumor activity. The title compounds were crystallized by slow evaporation. 2-(4-(2-(4-Phenylpiperazin-1-yl)ethyl)benzyl)isoindoline-1,3-dione(1), 2-(4-(2-(4-(4-Bromophenyl)piperazin-1-yl)ethyl)benzyl)isoindoline-1,3-dione (2) and 2-(4-(2-(4-(4-Chlorophenyl)piperazin-1-yl)ethyl)benzyl)isoindoline-1,3-dione (3) crystallizes in the triclinic space group P-1 with two molecules in the unit cell. The unit cell parameters for 1 are a = 6.9159(14) Å, b = 9.999(2) Å, c = 16.925(3) Å, α = 88.25(3)°, β = 85.14(3) °, γ = 79.22(3) °. The unit cell parameters for 2 are a = 6.9995(14) Å, b = 9.919(2) Å, c = 17.671(4) Å, α= 97.55(3)°, β = 92.19(3) °, γ = 102.23(3) °. The unit cell parameters for 1 are a = 6.9872(14) Å, b = 9.863(2) Å, c = 17.557(4) Å, α=96.81(3)°, β = 91.89(3) °, γ = 101.74(3) °. Pi-pi interactions were observed between molecules, and compound 2 and 3 showed halogen interactions between molecules nearby, which was different from compound 1.

We have developed a family of N-alkyl-nitrobenzamides that exhibit promising antitubercular ac-tivities. These compounds are a simplification of the structure of known inhibitors of DprE1, an essential Mtb enzyme and an emergent anti-TB target. The compounds were obtained by a simple synthetic methodology and their antitubercular, cyto-toxicity and stability were studied. The most active compounds were the 3,5-dinitro and the 3-nitro-5-trifluoromethyl derivatives. Lipophilicity was shown to be a main parameter modulating activity within each family. The most active compound presented MIC of 16 ng/mL and had in-termediate lipophilicities. The activity was also studied in a macrophage model of infection. In this model the compounds with chain lengths of 6 and 12 carbon atoms presented the best results, ex-hibiting activity profiles comparable to isoniazid. Docking studies performed suggested that the fit of our highly active compounds to the DprE1 binding pocket was very similar to the literature compounds. Furthermore, the metrics used in the analysis were able to distinguish between the families of the highly and least active compounds. Assessment of susceptibility over multiple species and computational together with the structure of the compounds are complementary information that supports DprE1 as a likely target of action for this type of compounds.

Currently, obesity is a critical global public health burden. Numerous studies have demonstrated the regulation of the pathogenesis of obesity and metabolic abnormalities by the gut microbiota and microbial factors; however, their involvement in the various degrees of obesity is not yet understood well. Previously, obesity has been shown to be associated with decreased levels of vitamin B12. Considering exclusive microbial production of vitamin B12, we hypothesised that a decrease in cobalamin levels in obese individuals may be at least partially caused by its depleted production in the intestinal tract by the commensal microbiota. In the present study, our aim was to estimate the abundance of enzymes and metabolic pathways for vitamin B12 synthesis in the gut microbiota of mouse models of alimentary and genetically determined obesity, to evaluate the contribution of the obesogenic microbiome to vitamin B12 synthesis in the gut. We have defined a significantly lower representation of enzymes and metabolic pathways for vitamin B12 biosynthesis in obese mice compared to non-obese mice, wherein enzyme depletion was more pronounced in lepr(-/-) (db/db) mice, which developed severe obesity. The representation of enzymes involved in cobalamin synthesis is strongly correlated with representation of several microbes in high-fat diet fed mice, whereas there were almost no correlations in db/db mice. Therefore, the degree of obesity and the composition of the correspondent microbiota are the main contributors to the representation of genes and pathways for cobalamin biosynthesis in the mouse gut.

In order to solve the labor-intensive and time-consuming problem in the process of measuring yak body ruler and weight in yak breeding industry in Qinghai Province, a non-contact method for measuring yak body ruler and weight was proposed in this experiment, and key technologies based on semantic segmentation, binocular ranging and neural network algorithm were studied to boost the development of yak breeding industry in Qinghai Province. Main conclusions: (1) Study yak foreground image extraction, and implement yak foreground image extraction model based on U-net algorithm; select 2263 yak images for experiment, and verify that the accuracy of the model in yak image extraction is over 97%. (2) Develop an algorithm for estimating yak body ruler based on binocular vision, and use the extraction algorithm of yak body ruler related measurement points combined with depth image to estimate yak body ruler. The final test shows that the average estimation error of body height and body oblique length is 2.6%, and the average estimation error of chest depth is 5.94%. (3) Study the yak weight prediction model; select the body height, body oblique length and chest depth obtained by binocular vision to estimate the yak weight; use two algorithms to establish the yak weight prediction model, and verify that the average estimation error of the model for yak weight is 10.7% and 13.01% respectively.

Это исследование сосредоточено на разработке системы искусственного зрения для гибкого дельта-робота-манипулятора и ее интеграции с межмашинной связью для оптимизации взаимодействия устройств в реальном времени. Эта интеграция направлена ​​на увеличение скорости роботизированной системы и улучшение ее общей производительности. Предлагаемое сочетание системы искусственного зрения с межмашинной связью позволяет обнаруживать и распознавать цели с высокой точностью в режиме реального времени в ограниченном пространстве, предназначенном для позиционирования, дальнейшей локализации и выполнения производственных процессов, таких как сборка или сортировка деталей. . В данном исследовании RGB-изображения используются в качестве входных данных для алгоритма MASK-R-CNN, а результаты обрабатываются в соответствии с особенностями прототипа дельта-манипулятора робота. Данные, полученные от MASK-R-CNN, адаптированы для использования в системе управления дельта-роботом с учетом ее уникальных характеристик и требований к позиционированию. Технология M2M позволяет манипулятору робота быстро реагировать на изменения, например, на перемещение объектов или изменение их положения, что имеет решающее значение для задач сортировки и упаковки. Система была протестирована в условиях, близких к реальным, чтобы оценить ее производительность и надежность.

Deep learning heavily relies on statistical correlations to drive artificial intelligence (AI) innovations, particularly in computer vision applications like autonomous driving and robotics. However, despite providing a solid foundation for deep learning, these statistical correlations can be vulnerable to unforeseen and uncontrolled factors. The lack of prior knowledge guidance can result in spurious correlations, introducing confounding factors and affecting the model's robustness. To address this challenge, recent research efforts have focused on integrating causal theory into deep learning methodologies. By modelling the inherent and unbiased causal structure, causal theory can potentially mitigate the impact of spurious correlations effectively. Hence, this paper explores the basics of causal methodologies in image classification.

Artificial intelligence’s rapid advancement has enabled various applications, including intelligent video surveillance systems, assisted living, and human-computer interaction. These applications often require one core task: video-based human action recognition. Research in human video-based human action recognition is vast and ongoing, making it difficult to assess the full scope of available methods and current trends. This survey provides an in-depth exploration of the vision-based human action recognition field, comprehensively offering the available techniques and their evolution, highlighting the cutting-edge ideas driving its development. We also analyze the most used keywords in research papers over the past years to identify trends and predict possible future directions. Hence, this concise survey helps researchers understand the breadth of existing approaches, evaluate current research trends, and stay up-to-date on potential developments.

The primary distribution systems are comprised of power lines delivering power to utility feeders from substations. The inspection and maintenance of damaged and broken power system insulators are of paramount importance for continuous power supply and public safety. hence, to identify any faults and defects in advance a periodic inspection of power line insulators and other components be ensured beforehand. To automate the process and reduce operational cost and risk Unmanned Aerial Vehicles (UAVs) are being extensively utilized. As they present a safer and efficient way to examine the power system insulators and their components without closing the power distribution system ensuring continuous supply to the end-users. To achieve these objectives in this work a novel dataset is designed by capturing real images using UAVs and manually generated images collected to overcome the data insufficiency problem. Deep Laplacian pyramids based super-resolution network is implemented to reconstruct high-resolution training images. To improve the visibility of low light images a low light image enhancement technique is used for the robust exposure correction of the training images. Using computer vision-based object detection techniques to identify faults and classify them according to classes they belong. A different fine-tuning strategy is implemented for fine-tuning the object detection model to increase detection accuracy for the specific faulty insulators. To improve the faults detection several flight path strategies are proposed for efficient inspection. Such strategies overcome the shuttering effect of insulators along with providing a less complex, time, and energy-efficient approach for capturing video stream of the power system components. Performance of different object detection models is presented for selecting the suitable one for fine-tuning on the specific faulty insulator dataset. Our proposed approach gives a less complex and more efficient flight strategy along with better results. For defect detection, our proposed method achieved an F1-score of 0.81 and 0.77 on two different datasets. Our approach is based on real aerial inspection of in-service porcelain insulators by extensive evaluation of several video sequences showing robust faults recognition and diagnostic capabilities. Our approach is demonstrated on data acquired by a drone in Swat Pakistan.

This work presents the development and implementation of a distributed navigation system based on computer vision. The autonomous system consists of a wheeled mobile robot with an integrated colour camera. The robot navigates through a laboratory scenario where the track and several traffic signals must be detected and recognized by using the images acquired with its on-board camera. The images are sent to a computer server that processes them and calculates the corresponding speeds of the robot using a cascade of trained classifiers. These speeds are sent back to the robot, which acts to carry out the corresponding manoeuvre. The classifier cascade should be trained before experimentation with two sets of positive and negative images. The number of images in these sets should be considered to limit the training stage time and avoid overtraining the system.

Salamanders have been habitual residents of research laboratories for more than a century, and their history in science is tightly interwoven with vision research. Nevertheless, many vision scientists – even those working with salamanders – may be unaware of how much our knowledge about vision, and particularly the retina, has been shaped by studying salamanders. In this review, we take a tour through the salamander history in vision science, highlighting the main contributions of salamanders to our understanding of the vertebrate retina. We further point out specificities of the salamander visual system and discuss the perspectives of this animal system for future vision research.

This paper presents a system that merges computer vision and surface electromyography techniques to carry out grasping tasks. To perform this, the vision-driven system is used to compute pre-grasping poses of the robotic system based on the analysis of tridimensional object features. Then, the human operator can correct the pre-grasping pose of the robot using surface electromyographic signals from the forearm during wrist flexion and extension. Weak wrist flexions and extensions allow a fine adjustment of the robotic system to grasp the object and finally, when the operator considers that the grasping position is optimal, a strong flexion is performed to initiate the grasping of the object. The system has been tested with several subjects to check its performance showing a grasping accuracy of around 95% of the attempted grasps which increases by around a 9% the grasping accuracy of previous experiments in which electromyographic control was not implemented.

Some traditional robots are based on offline programming reciprocal motion, and with the continuous upgrading of vision technology, more and more tasks are replaced by machine vision. For the current problem of insufficient accuracy of robot target localization based on binocular vision, and an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance For the current problem of insufficient accuracy of robot target localization based on binocular vision, an improved random sampling consistency algorithm is proposed to complete parallel robot target localization and grasping under the guidance of multi Firstly, the RANSAC algorithm is improved based on the SURF algorithm; then the parallax gradient method is applied to iterate the matched point pairs several times to further optimize the data; then the 3D reconstruction is completed by the program technique; finally the obtained data is finally the obtained data is input into the robot arm and the camera internal and external parameters are obtained by the calibration method so that the robot can accurately locate and The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi- The experiments show that the improved algorithm has advantages in recognition accuracy and grasping success rate under multi-vision system.

The use of visual signals in horticulture has attracted significant attention and encompassed a wide range of data types such as 2D images, videos, hyperspectral images, and 3D point clouds. These visual signals have proven to be valuable in developing cutting-edge computer vision systems for various applications in horticulture, enabling plant growth monitoring, pest and disease detection, quality and yield estimation, and automated harvesting. However, unlike other sectors, developing deep learning computer vision systems for horticulture encounters unique challenges due to the limited availability of high-quality training and evaluation datasets necessary for deep learning models. This paper investigates the current status of vision systems and available data in order to identify the high-quality data requirements specific to horticultural applications. We analyse the impact of the quality of visual signals on the information content and features that can be extracted from these signals. To address the identified data quality requirements, we explore the usage of a deep learning-based super-resolution model for generative quality enhancement of visual signals. Furthermore, we discuss how these can be applied to meet the growing requirements around data quality for learning-based vision systems. We also present a detailed analysis of the competitive quality generated by the proposed solution compared to cost-intensive hardware-based alternatives. This work aims to guide the development of efficient computer vision models in horticulture by overcoming existing data challenges and paving a pathway forward for contemporary data acquisition.