On chip all-optical distinguishing of independently placed distinct types of single Rayleigh particle.

In order to determine whether a particle is plasmonic, dielectric, or chiral, different complex processes and chemicals are applied in lab setups and pharmaceutical industries. Sorting or categorizing a particle based on distinct optical forces can be a novel technique. When a beam of light interacts with a particle, it usually pushes the particle in the direction of the light's propagation. Counterintuitively, it can also pull the particle toward the light beam or move it toward a lateral direction. As far as we know, to date, no comprehensive report exists regarding a single optical arrangement capable of inducing entirely distinct behaviors of force for three disparate types of independently placed single Rayleigh particle. This study introduces an all-optical technique aimed at effectively sorting nanoscale Rayleigh-sized objects employing a plasmonic substrate, when each distinct type of single particle is placed over the substrate independently. Unfortunately, this proposed technique does not work for the cluster or mixture of distinct particles. In our proposed configuration, a simple linearly polarized plane wave is incident onto the plasmonic substrate, thereby engendering completely different responses from three different types of nanoparticles: Gold (plasmonic), SiO2 (dielectric), and Chiral particles. We conducted individual tests for our setup using linearly polarized plane waves at angles of 30-degree, 45-degree, and 60-degree individually. Consistent results were obtained across all angles. In each of the three distinct setups involving the aforementioned particle, a dielectric Rayleigh particle experiences an optical pulling force, a plasmonic Rayleigh particle experiences an optical pushing force, and a chiral Rayleigh particle encounters an optical lateral force. These distinctive force behaviors manifest as a result of the intricate interplay between the material properties of the nanoparticles and the characteristics of the plane-polarized beam, encompassing aspects such as plasmonic response, chirality, and refractive index. Moreover, this technique presents an environmentally sustainable and economically viable alternative to the utilization of expensive and potentially hazardous chemicals in nanoparticle sorting processes within industrial domains.

Quantum mechanical lateral force on an atom due to matter wave.

The area of trapping the atoms or molecules using light has advanced tremendously in the last few decades. In contrast, the idea of controlling (not only trapping) the movement of atomic-sized particles using matter waves is a completely new emerging area of particle manipulation. Though a single previous report has suggested the pulling of atoms based on matter-wave tractor beams, an attempt is yet to be made to produce a lateral force using this technique. This article demonstrates an asymmetric setup that engenders reversible lateral force on an atom due to the interaction energy of the matter wave in the presence of a metal surface. Several full-wave simulations and analytical calculations were performed on a particular set-up of Xenon scatterers placed near a Copper surface, with two counter-propagating plane matter waves of Helium impinging in the direction parallel to the surface. By solving the time-independent Schrödinger equation and using the solution, quantum mechanical stress tensor formalism is applied to compute the force acting on the particle. The simulation results are in excellent agreement with the analytical calculations. The results for the adsorbed scatterer case find this technique to be an efficient cleaning procedure similar to electron-stimulated desorption for futuristic applications.

Linear and nonlinear optical parameters of biodegradable chitosan/polyvinyl alcohol/sodium montmorillonite nanocomposite films for potential optoelectronic applications.

Innovations in sophisticated optoelectronic devices have increased interest in high-refractive index polymers. Herein, we report innovative nanocomposite films with high linear and nonlinear refractive indices prepared by casting chitosan (Cs) with polyvinyl alcohol (PVA) (50:50 wt%) along with different concentrations (10-50 wt%) of sodium montmorillonite (NaMMT) nanoclay. The refractive indices in addition to other optical parameters of homopolymers and hybrid materials were investigated by UV-Vis. spectroscopy and optical modeling to assess their potential applications in optics. Besides, the structure, morphology, and thermal stability of the prepared films were investigated by a multitude of experimental techniques including X-ray diffraction (XRD), attenuated total reflectance-Fourier transform infrared spectroscopy (ATR-FTIR), scanning electron microscopy (SEM), atomic force microscopy (AFM), and thermogravimetric analysis (TGA/DTG). The ATR-FTIR, XRD, SEM, and AFM measurements confirmed the complete exfoliation of NaMMT nanolayers in the Cs/PVA matrix. The TGA/DTG revealed an increase in the thermal stability of Cs/PVA film with increasing clay content. The UV-Vis. measurements revealed a decrease in the optical energy gap (Eg) and a substantial increase in the linear (nD) and nonlinear (n2) refractive indices as clay content increased. Additionally, the nanohybrids displayed low UV transmission and reflected about 80 % of UV rays, making them excellent candidates for UV protection. For the first time, the dissipation factor (tanδ) in the UV/Vis. region has been calculated and fitted with the Drude-Lorentz model to predict the plasma frequency (ωp), resonance frequency (ω0), and electron lifetime (τ) of pristine polymers and nanocomposites.

Controlling the counterintuitive optical repulsive thrust of nano dimers with counter propagating type waves and background medium.

This work focuses on the utilization of counter-propagating plane waves for optical manipulation, which provides a unique approach to control the behavior of Rayleigh and Dipolar nanoparticles immersed in a homogeneous or heterogeneous medium. Our study presents an interesting finding of a repulsive force between plasmonic-chiral heterodimers where the particles move away from each other in both near and far field regions. Interestingly, this repulsive thrust supports the wave like nature of light for the case of homogeneous background but particle type nature of light for heterogenous background. At first, we have investigated the theory underlying the optical trapping of the chiral particle and the impact of this phenomenon on the overall repulsive behavior of the heterodimers placed in air (homogeneous) background. After that, our proposed set-up has further been investigated putting in air-water interface (heterogenous background) and by varying light angle only a little bit. Our observation for this interface case is suggesting the transfer of Minkowski momentum of photon to each optically pulled Rayleigh or dipolar particle of the dimer set, which ultimately causes a broad-band giant repulsive thrust of the dimers. However, in absence of the other particle in the cluster, a single half-immersed particle does not experience the pulling force for the broad-band spectrum. The 'common' reason of the observed repulsive thrust of the dimers for both the aforementioned cases has been attributed to "modified" longitudinal Optical Binding Force (OBF). Technically, this work may open a new way to control the repulsion and attraction between the nanoparticles both in near and far field regions by utilizing the background and the counter-propagating waves. We also believe that this work manifests a possible simple set-up, which will support to observe a background dependent wave 'or' particle nature of light experimentally.

Significant improvement of fidelity for encoded quantum bell pairs at long and short-distance communication along with generalized circuit.

Quantum entanglement is a unique criterion of the quantum realm and an essential tool to secure quantum communication. Ensuring high-fidelity entanglement has always been a challenging task owing to interaction with the hostile channel environment created due to quantum noise and decoherence. Though several methods have been proposed, correcting almost all arbitrary errors is still a gigantic task. As one of the main contributions of this work, a new model for 'large distance communication' has been proposed, which may correct all bit flip errors or other errors quite extensively if proper encoding and subspace measurements are used. To achieve this purpose, at the very first step, the idea of differentiating the 'long-distance communication' and 'short-distance applications' has been introduced. Short-distance is determined by the maximum range of applying unitary control gates by the qubit technology. How the error correcting ability of Quantum codes change for short and long-distance application is investigated in this work, which was not explored in previous literatures as far as we know. At the beginning, we have applied stabilizer formalism and Repetition Code for decoding to distinguish the error correcting ability in long and short distance communication. Particularly for short-distance communication, it has been demonstrated that a 'properly encoded' bell state can identify all the bit flip, or phase flip errors with 100% accuracy theoretically. In contrast, if the bell states are used in long-distance communication, the error-detecting and correcting ability reduces at huge amounts. To increase the fidelity significantly and correct the errors quite extensively for long-distance communication, a new model based on classical communication protocol has been suggested. All the required circuits in these processes have been generalized for arbitrary (even) numbers of ancilla qubits during encoding. Proposed analytical results have also been verified with the Simulation results of IBM QISKIT QASM.

Forecasting the United State Dollar(USD)/Bangladeshi Taka (BDT) exchange rate with deep learning models: Inclusion of macroeconomic factors influencing the currency exchange rates.

Forecasting a currency exchange rate is one of the most challenging tasks nowadays. Due to government monetary policy and some uncertain factors, such as political stability, it becomes difficult to correctly forecast the currency exchange rate. Previously, many investigations have been done to forecast the exchange rate of the United State Dollar(USD)/Bangladeshi Taka(BDT) using statistical time series models, machine learning models, and neural network models. But none of the previous methods considered the underlying macroeconomic factors of the two countries, such as GDP, import/export, government revenue, etc., for forecasting the USD/BDT exchange rate. We have included various time-sensitive macroeconomic features directly impacting the USD/BDT exchange rate to address this issue. These features will create a new dimension for researchers to predict and forecast the USD/BDT exchange rate. We have used various types of models for predicting and forecasting the USD/BDT exchange rate and found that Among all our models, Time Distributed MLP provides the best performance with an RMSE of 0.1984. Finally, we have proposed a pipeline for forecasting the USD/BDT exchange rate, which reduced the RMSE of Time Distributed MLP to 0.1900 and has proven effective in reducing the error of all our models.

Quantifying imbalanced classification methods for leukemia detection.

Uncontrolled proliferation of B-lymphoblast cells is a common characterization of Acute Lymphoblastic Leukemia (ALL). B-lymphoblasts are found in large numbers in peripheral blood in malignant cases. Early detection of the cell in bone marrow is essential as the disease progresses rapidly if left untreated. However, automated classification of the cell is challenging, owing to its fine-grained variability with B-lymphoid precursor cells and imbalanced data points. Deep learning algorithms demonstrate potential for such fine-grained classification as well as suffer from the imbalanced class problem. In this paper, we explore different deep learning-based State-Of-The-Art (SOTA) approaches to tackle imbalanced classification problems. Our experiment includes input, GAN (Generative Adversarial Networks), and loss-based methods to mitigate the issue of imbalanced class on the challenging C-NMC and ALLIDB-2 dataset for leukemia detection. We have shown empirical evidence that loss-based methods outperform GAN-based and input-based methods in imbalanced classification scenarios.

Hydrogen peroxide pretreatment assisted phytoremediation of sodium dodecyl sulfate by Juncus acutus L.

BACKGROUND: Sodium Dodecyl Sulfate (SDS) an anionic surfactant pollutant has emerged as a serious hazard to the aquatic and terrestrial environment. Due to physical and chemical methodological difficulties for SDS removal, phytoremediation techniques are efficient alternative strategies to tackle such adversities. Juncus acutus L. (J. acutus) is a pioneer wetland species that has been recently exploited for phytoremediation purposes. To our knowledge, the role of exogenous hydrogen peroxide (H2O2), in improving the phytoextraction of SDS has not been examined yet. In this study, pretreatment foliar spray of H2O2 (15 mM) combined with two levels of SDS (50 and 100 ppm) in water culture was evaluated to remove SDS contamination and add value to the phytoremediation process. RESULTS: The outcomes revealed that J. acutus has considerable translocation and bioaccumulation abilities for SDS and can be utilized as an appropriate hyperaccumulator in SDS-contaminated sites. However, the involvement of H2O2 extended phytoremediation capacity and successive removal of SDS. H2O2 significantly assisted in increasing SDS remediation via more accumulation in J. acutus tissues by 29.9 and 112.4% and decreasing SDS concentration in culture media by 33.3 and 27.3% at 50 and 100 ppm SDS, respectively. Bioaccumulation factor (BCF) increased by 13.8 and 13.2%, while translocation factor (TCF) positively maximized by 82.4 and 76.2% by H2O2 application at 50 and 100 ppm SDS, respectively. H2O2 pretreatment could drive the decline in biochemical attributes in SDS-affected plants by modulating stress tolerance indices, pigments, water relations, proline content, enzymatic activities, and further, reduced oxidative stress in terms of electrolyte leakage, cellular H2O2, malondialdehyde (MDA) accumulation. CONCLUSIONS: H2O2 could play a potential role in maximizing phytoremediation capacity of SDS by J. acutus in polluted sites.

A new modality in targeted delivery of epirubicin for tumor theranosis based on PEGylated silver nanoparticles: Design, radiolabeling and bioevaluation.

This work highlights boosting the tumor targeting efficiency of epirubicin through loading on a new radionanosystem, based on the effective role of silver nanoparticles (AgNPs). Accordingly, PEGylated silver nanoparticles (PEG/AgNPs) were prepared in a size of 20.2 ± 0.1 nm. Additionally, epirubicin was loaded on PEG/AgNPs with a loading efficiency of 63 ± 3 %. Furthermore, both of PEG/AgNPs and EPI/PEG/AgNPs were radiolabeled with 131I isotope with radiolabeling yields of 85 ± 0.2 % and 90.3 ± 1 %, respectively. The in-vivo distribution of 131I-PEG/AgNPs and 131I-EPI/PEG/AgNPs were examined in healthy and tumor bearing mice models. Excitingly, 131I-EPI/PEG/AgNPs revealed a reticuloendothelial system (RES) avoidance and prolonged circulating time. In addition, 131I-EPI/PEG/AgNPs showed fast targeting of tumor site by 25.1 ± 0.1 %ID/g within 0.5 h after intravenous injection. Subsequently, the outcomes provided 131I-EPI/PEG/AgNPs as a new potential system for enhancement of tumor targeting and theranosis (therapy and/or imaging).

Optimization of mirtazapine loaded into mesoporous silica nanostructures via Box-Behnken design: in-vitro characterization and in-vivo assessment.

Employment of mesoporous silica nanostructures (MSNs) in the drug delivery field has shown a significant potential for improving the oral delivery of active pharmaceutical products with low solubility in water. Mirtazapine (MRT) is a tetracyclic antidepressant with poor water solubility (BCS Class II), which was recently approved as a potent drug used to treat severe depression. The principle of this research is to optimize the incorporation of Mirtazapine into MSNs to improve its aqueous solubility, loading efficiency, release performance, and subsequent bioavailability. The formulation was optimized by using of Box-Behnken Design, which allows simultaneous estimation of the impact of different types of silica (SBA-15, MCM-41, and Aluminate-MCM-41), a different drug to silica ratios (33.33%, 49.99%, and 66.66%), and different drug loading procedures (Incipient wetness, solvent evaporation, and solvent impregnation) on the MRT loading efficiency, aqueous solubility and dissolution rate. The optimized formula was achieved by loading MRT into SBA-15 at 33.33% drug ratio prepared by the incipient wetness method, which displayed a loading efficiency of 104.05%, water solubility of 0.2 mg/ml, and 100% dissolution rate after 30 min. The pharmacokinetic profile of the optimized formula was obtained by conducting the in-vivo study in rabbits which showed a marked improvement (2.14-fold) in oral bioavailability greater than plain MRT. The physicochemical parameters and morphology of the optimized formula were characterized by; gas adsorption manometry, scanning electron microscopy (SEM), polarized light microscopy (PLM), Fourier-transform infrared spectroscopy (FT-IR), differential scanning calorimetry (DSC), and X-ray powder diffraction (XRPD).

Isolation and Characterization of Bacteriophages Active against Pseudomonas aeruginosa Strains Isolated from Diabetic Foot Infections.

Diabetic foot infection has become one of the most important public health concerns and is a growing problem. Pseudomonas aeruginosa is an important opportunistic multidrug-resistant bacterium in diabetic foot infections. In the absence of antibiotics active against MDR strains of P. aeruginosa, phage therapy becomes a key way to deal with P. aeruginosa infections. Out of 185 samples collected from diabetic foot ulcers, 50 (27.02%) isolates were identified as P. aeruginosa. The incidence increases with older ages, and males (n=34, 68%) predominated in all age groups. The tested isolates showed maximum susceptibility towards colistin (80%), imipenem (72%), amikacin (66%), and piperacillin/tazobactam (62%), while these isolates showed moderate susceptibility towards ceftazidime (58%), cefepime (52%) and gentamicin (46%). However, it showed complete resistance (100%) to ampicillin, cefaclor, and sulphamethoxazole/trimethoprim and highly resistance to clindamycin (90%) and amoxicillin/clavulanic acid (84%). Two bacteriophages (ϕPAE1 and ϕPAE2) isolated from sewage samples showed a broad host range against P. aeruginosaa clinical strains. ϕPAE2 infected 74% (37/50) and ϕPAE2 58% (29/50). Furthermore, both phages were host-specific, infecting only P. aeruginosa strains and could not infect other bacterial species in the cross-infectivity studies. Both phages were found to be relatively heat stable as over a period of 1 h, after exposure to a temperature range of 37-50°C, no significant loss in phage activity was observed. On the other hand, the lowest activity was observed at 70°C (39.15%) for ϕPAE1 whereas it was inactivated at 75°C while the lowest activity was observed at 75°C (38.01%) for ϕPAE2 whereas it was inactivated at 80°C. Isolated phages were able to survive and lyse host bacteria over a wide pH range. The optimum pH range for infection was from 6 to 8. Furthermore, ϕPAE1 lost its ability to lyses at pH 2, 3, 11 and 12, whereas; ϕPAE2 lost its infectivity at pH 2, 3 and 12. Chloroform was the most effective solvent that reduced the infectivity of ϕPAE1 and ϕPAE2 to 63.27% and 77.88%, respectively. On the other hand, petroleum ether showed the lowest effect on the infectivity of ϕPAE1 and ϕPAE2; it was reduced to 96.4% and 97.48%, respectively, followed by acetone and ethyl alcohol. The ability of P. aeruginosa phages to form plaques after different storage temperatures (4°C, 30°C, 37°C and 44°C) for a month was slightly affected. The storage of ϕPAE1 and ϕPAE2 at 4ºC showed the least effect on its infectivity, and the storage at 44ºC showed the highest reduction in its infectivity. Moreover, Phage counts were slightly decreased by increasing storage period and temperature.

Plasmonic octamer objects: reversal of near-field optical binding force without the aid of backgrounds.

In recent years, the near-field optical binding force has gained a lot of interest in the field of optical manipulation. The reversal of the near-field binding force, a new, to the best of our knowledge, kind of optical manipulation, has so far been investigated mostly between dimers and in a very few cases among tetramers by utilizing the help of suitable substrates or backgrounds. Until now, no known way to control the near-field optical binding force among octamer configurations has been found, to our knowledge. In this paper, we propose a plasmonic (silver) octamer configuration where we demonstrate the control and reversal (attraction and repulsion) of the near-field optical binding force of octamers by illuminating the system with a TM polarized Bessel beam. The control of the binding force and its reversal is explained based on the polarization and gradient forces created by the Bessel beam. As the aid of a background or substrate is not required, our proposed simplified approach has the potential to open up novel ways of manipulating multiple particles. Our investigation also implicitly suggests that for future research on controlling the reversal of the near-field optical binding force of multiple particles, Bessel beams can be the appropriate choice instead of plane waves.

Automated detection and grading of Invasive Ductal Carcinoma breast cancer using ensemble of deep learning models.

Invasive ductal carcinoma (IDC) breast cancer is a significant health concern for women all around the world and early detection of the disease may increase the survival rate in patients. Therefore, Computer-Aided Diagnosis (CAD) based systems can assist pathologists to detect the disease early. In this study, we present an ensemble model to detect IDC using DenseNet-121 and DenseNet-169 followed by test time augmentation (TTA). The model achieved a balanced accuracy of 92.70% and an F1-score of 95.70% outperforming the current state-of-the-art. Comparative analysis against various pre-trained deep learning models and preprocessing methods have been carried out. Qualitative analysis has also been conducted on the test dataset. After the detection of IDC breast cancer, it is important to grade it for further treatment. In our study, we also propose an ensemble model for the grading of IDC using the pre-trained DenseNet-121, DenseNet-201, ResNet-101v2, and ResNet-50 architectures. The model is inferred from two validation cohorts. For the patch-level classification, the model yielded an overall accuracy of 69.31%, 75.07%, 61.85%, and 60.50% on one validation cohort and 62.44%, 79.14%, 76.62%, and 71.05% on the second validation cohort for 4×, 10×, 20×, and 40× magnified images respectively. The same architecture is further validated using a different IDC dataset where it achieved an overall accuracy of 90.07%. The performance of the models on the detection and grading of IDC shows that they can be useful to help pathologists detect and grade the disease.

Automatic segmentation of blood cells from microscopic slides: A comparative analysis.

With the recent developments in deep learning, automatic cell segmentation from images of microscopic examination slides seems to be a solved problem as recent methods have achieved comparable results on existing benchmark datasets. However, most of the existing cell segmentation benchmark datasets either contain a single cell type, few instances of the cells, not publicly available. Therefore, it is unclear whether the performance improvements can generalize on more diverse datasets. In this paper, we present a large and diverse cell segmentation dataset BBBC041Seg1, which consists both of uninfected cells (i.e., red blood cells/RBCs, leukocytes) and infected cells (i.e., gametocytes, rings, trophozoites, and schizonts). Additionally, all cell types do not have equal instances, which encourages researchers to develop algorithms for learning from imbalanced classes in a few shot learning paradigm. Furthermore, we conduct a comparative study using both classical rule-based and recent deep learning state-of-the-art (SOTA) methods for automatic cell segmentation and provide them as strong baselines. We believe the introduction of BBBC041Seg will promote future research towards clinically applicable cell segmentation methods from microscopic examinations, which can be later used for downstream tasks such as detecting hematological diseases (i.e., malaria).

3C-GAN: class-consistent CycleGAN for malaria domain adaptation model.

Unpaired domain translation models with distribution matching loss such as CycleGAN are now widely being used to shift domain in medical images. However, synthesizing medical images using CycleGAN can lead to misdiagnosis of a medical condition as it might hallucinate unwanted features, especially if theres a data bias. This can potentially change the original class of the input image, which is a very serious problem. In this paper, we have introduced a modified distribution matching loss for CycleGAN to eliminate feature hallucination on the malaria dataset. In the context of the malaria dataset, unintentional feature hallucination may introduce a facet that resembles a parasite or remove the parasite after the translation. Our proposed approach has enabled us to shift the domain of the malaria dataset without the risk of changing their corresponding class. We have presented experimental evidence that our modified loss significantly reduced feature hallucination by preserving original class labels. The experimental results are better in comparison to the baseline (classic CycleGAN) that targets the translating domain. We believe that our approach will expedite the process of developing unsupervised unpaired GAN that is safe for clinical use.

A comparative analysis of deep learning architectures on high variation malaria parasite classification dataset.

Malaria, one of the leading causes of death in underdeveloped countries, is primarily diagnosed using microscopy. Computer-aided diagnosis of malaria is a challenging task owing to the fine-grained variability in the appearance of some uninfected and infected class. In this paper, we transform a malaria parasite object detection dataset into a classification dataset, making it the largest malaria classification dataset (63,645 cells), and evaluate the performance of several state-of-the-art deep neural network architectures pretrained on both natural and medical images on this new dataset. We provide detailed insights into the variation of the dataset and qualitative analysis of the results produced by the best models. We also evaluate the models using an independent test set to demonstrate the model's ability to generalize in different domains. Finally, we demonstrate the effect of conditional image synthesis on malaria parasite detection. We provide detailed insights into the influence of synthetic images for the class imbalance problem in the malaria diagnosis context.

H-Shaped Copolymer of Polyethylene and Poly(ethylene oxide) under Severe Confinement: Phase State and Dynamics.

The self-assembly and the dynamics of an H-shaped copolymer composed of a polyethylene midblock and four poly(ethylene oxide) arms (PE-b-4PEO) are investigated in the bulk and under severe confinement into nanometer-spaced LAPONITE clay particles by means of small- and wide-angle X-ray diffraction (SAXS, WAXS), differential scanning calorimetry (DSC), polarizing optical microscopy (POM), rheology, and dielectric spectroscopy (DS). Because of the H-shaped architecture, the PE midblock is topologically frustrated and thus unable to crystallize. The superstructure formation in the bulk is dictated solely by the PEO arms as inferred by the crystallization/melting temperature relative to the PEO homopolymer. Confinement produced remarkable changes in the interlayer distance and PEO crystallinity but left the local segmental dynamics unaltered. To reconcile all structural, thermodynamic, and dynamic effects, a novel morphological picture is proposed with interest in emulsions. Key parameters that stabilize the final morphology are the severe chain confinement with the associated entropy loss and the presence of interactions (hydrophobic/hydrophilic) between the LAPONITE and the PEO/PE blocks.

Solving visual pollution with deep learning: A new nexus in environmental management.

Visual pollution is a relatively new concern amidst the existing plethora of mainstream environmental pollution, recommending the necessity for research to conceptualize, formalize, quantify and assess it from different dimensions. The purpose of this study is to create a new field of automated visual pollutant classification, harnessing the technological prowess of the 21st century for applications in environmental management. From the wide range of visual pollutants, four categories have been considered viz. (i) billboards and signage, (ii) telephone and communication wires, (iii) network and communication towers and (iv) street litter. The deep learning model used in this study simulates the human learning experience in the context of image recognition for visual pollutant classification by training and testing a convolutional neural network with several layers of artificial neurons. Data augmentation using image processing techniques and a train-test split ratio of 80:20 have been used. Training accuracy of 95% and validation accuracy of 85% have been achieved by the deep learning model. The results indicate that the upper limit of accuracy i.e. the asymptote, depends on the dataset size for this type of task. This study has several applications in environmental management. For example, the deployment of the trained model for processing of video/live footage from smartphone applications, closed-circuit television and drones/unmanned aerial vehicles can be applied for both the removal and management of visual pollutants in the natural and built environment. Furthermore, generating the 'visual pollution score/index' of urban regions such as towns and cities will create a new 'metric/indicator' in the field of urban environmental management.

pH and Salt Response of Mixed Brushes Made of Oppositely Charged Polyelectrolytes Studied by in Situ AFM Force Measurements and Imaging.

The response of mixed brushes made of poly(acrylic acid) and poly(2-vinyl pyridine) with a mixing ratio of about 60:40 was studied using atomic force microscopy (AFM) force measurements with colloidal probes and AFM imaging with a sharp tip in the pH range between 2.5 and 8 and at varying KCl concentrations up to 1 M. It was found that under all conditions a dense polyelectrolyte complex layer coexists with excess polyelectrolyte chains in varying swelling states depending on pH and salt concentration. The mixed brush thus combines typical features of polyelectrolyte brushes and complexes. So, the increase of the salt concentration not only led to a transition from osmotic to salted brush regime but also to salt-induced softening or partial decomposition of the complex layer. Attractive forces at high salt concentrations indicated the presence of P2VP chains in the swollen layer even at high pH values.

Plasmonic Spherical Heterodimers: Reversal of Optical Binding Force Based on the Forced Breaking of Symmetry.

The stimulating connection between the reversal of near-field plasmonic binding force and the role of symmetry-breaking has not been investigated comprehensively in the literature. In this work, the symmetry of spherical plasmonic heterodimer-setup is broken forcefully by shining the light from a specific side of the set-up instead of impinging it from the top. We demonstrate that for the forced symmetry-broken spherical heterodimer-configurations: reversal of lateral and longitudinal near-field binding force follow completely distinct mechanisms. Interestingly, the reversal of longitudinal binding force can be easily controlled either by changing the direction of light propagation or by varying their relative orientation. This simple process of controlling binding force may open a novel generic way of optical manipulation even with the heterodimers of other shapes. Though it is commonly believed that the reversal of near-field plasmonic binding force should naturally occur for the presence of bonding and anti-bonding modes or at least for the Fano resonance (and plasmonic forces mostly arise from the surface force), our study based on Lorentz-force dynamics suggests notably opposite proposals for the aforementioned cases. Observations in this article can be very useful for improved sensors, particle clustering and aggregation.