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Evaluation of physicochemical and biological properties of SnO2 and Fe doped SnO2 nanoparticles
In recent decades, nanoparticle synthesis has been used for various physical and chemical methods. However, different toxic chemicals are used during this synthesis process to address these concerns, which has multiple effects on environmental toxicity and high cost. To avoid these problems, we need a cost-effective and environmentally friendly approach. In this study, green synthesis was used to make tin oxide (SnO2) and ferrous doped tin oxide (SFO) nanoparticles (NPs) from Morinda citrifolia leaf extracts. The X-ray diffraction patterns of SnO2 and SFO NPs reveal a tetragonal crystalline structure. From the FESEM image of synthesized SnO2 and SFO NPs, their spherical structure and chemical composition were identified by EDX spectrum. Through the DLS spectrum, the hydrodynamic size was observed at 66 and 61 nm for SnO2 and SFO NPs, respectively. In the FTIR spectrum, the OSnO stretching vibration peak arises at (606 & 509 cm?1 for SnO2 NPs) and (613 & 538 cm?1 for SFO NPs). Photoluminescence is used in materials to detect surface defects and impurity levels. The antibacterial activity of the SnO2, SFO NPs, and conventional antibiotics like amoxicillin NPs is effectively inhibited against S. aureus and E. coli bacterial strains. SFO NPs exhibit a higher antibacterial activity as compared to SnO2 and amoxicillin. The anticancer efficacy of increased SFO NPs compared to SnO2 NPs was tested against (MDA-MB-237) human breast cancer cells. These results suggest that Fe ions modified SnO2 NPs could be used in healthcare industrial applications to improve human health. 2022 Elsevier Ltd and Techna Group S.r.l. -
A facile one-step microwave synthesis of Pt deposited on N & P co-doped graphene intercalated carbon black - An efficient cathode electrocatalyst for PEM fuel cell
A facile, single step microwave assisted polyol route for simultaneously depositing platinum as well as co-doping graphene oxide, is herein proposed. However, low durability and full cell performance of Pt/NPG (platinum deposited on nitrogen phosphorous co-doped graphene) was observed due to restacking of graphene layers. This issue was addressed by intercalating CB into the graphene layers as spacers during the synthesis (in-situ addition of spacers - Pt/(NPG + S)). Moreover, to study the influence of spacers, external addition of spacers (ex-situ - Pt/(NPG) + S) were also examined. Results from our study indicate that in-situ addition of spacers- Pt/(NPG + S) enhanced the full cell performance (405 mW cm?2) and exhibited <40% ECSA loss (37.47%), thereby attaining DoE target. Thus, emerging as a durable cathode electrocatalyst (Pt/(NPG + S)) for PEM fuel cells. 2022 Hydrogen Energy Publications LLC -
Normalized Attention Neural Network with Adaptive Feature Recalibration for Detecting the Unusual Activities Using Video Surveillance Camera
Over the past few years, surveillance cameras have become common in many homes and businesses. Many businesses still employ a human monitor of their cameras, despite the fact that this individual is more probable to miss some anomalous occurrences in the video feeds owing to the inherent limitations of human perception. Numerous scholars have investigated surveillance data and offered several strategies for automatically identifying anomalous occurrences. Therefore, it is important to build a model for identifying unusual occurrences in the live stream from the security cameras. Recognizing potentially dangerous situations automatically so that appropriate action may be taken is crucial and can be of great assistance to law enforcement. In this research work, starting with an MRCNN for feature extraction and AFR for fine-tuning, this architecture has a number of key components (AFR). To increase the quality of the features extracted by the MRCNN, the AFR replicas the inter-dependencies among the features to enhance the quality of the low- and high-frequency features extracted. Then, a normalized attention network (NAN) is used to learn the relationships between channels, which used to identify the violence and speeds up the convergence process for training a perfect. Furthermore, the dataset took real-time security camera feeds from a variety of subjects and situations, as opposed to the hand-crafted datasets utilized in prior efforts. We also demonstrate the method's capability of assigning the correct category to each anomaly by classifying normal and abnormal occurrences. The method divided the information gathered into three primary groups: those in need of fire protection, those experiencing theft or violence, and everyone else. The study applied the proposed approach to the UCF-Crime dataset, where it outperformed other models on the same dataset. 2023 WITPress. All rights reserved. -
Enhanced electrical properties of CuO:CoO decorated with Sm2O3 nanostructure for high-performance supercapacitor
In the present investigation, we have synthesized samarium (Sm) nanoparticles (NPs) and anchored them onto the surface of CuO:CoO nanostructure (NS) by utilizing a simple chemical precipitation method. Nanostructures (NS) were characterized utilizing powdered X-ray diffraction (XRD), Fourier transform infrared spectroscopy (FT-IR), X-ray photoelectron spectroscopy (XPS), scanning electron spectroscopy (SEM), transmission electron spectroscopy (TEM), UVvisible spectroscopy (UVVis), and BrunauerEmmettTeller (BET) studies. Resulting Smx CuO: CoO (x = 1%, 5%, 10%, and 12%) NS were investigated for their anomalous electrical and supercapacitive behavior. NS energy storage performance was experimentally determined using cyclic voltammetry (CV), galvanostatic chargedischarge (GCD), and electrochemical impedance spectroscopy (EIS). Sm10%CuO:CoO exhibited better electrochemical response than other samples and showed a maximum specific capacitance of 283.6F/g at 0.25A/g in KOH electrolyte. However, contrary to our expectation, NS displayed rectifying nature in I-V, intercalative nature in C-V, and polaronic permittivity in all concentrations of Sm2O3 doping as compared with undoped CuO:CoO NS. The outstanding properties of Smx CuO:CoO NS are attributed to the synergy of high charge mobility of Sm NPs, leading to significant variation in dielectric permittivity, currentvoltage (I-V) response, capacitancevoltage (C-V) behavior, with the formation of Sm3+ ionic cluster. The clusters lead to a change in dipole moment creating a strong local electric field. Additionally, a CR2032 type symmetric supercapacitor cell was fabricated using Sm10%CuO:CoO, which exhibited a maximum specific capacitance of 67.4F/g at 0.1A/g. The cell was also subjected to 5000 GCD cycles where it retained 96.3% Coulombic efficiency. Graphical Abstract: [Figure not available: see fulltext.] 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
N-doped graphene quantum dots incorporated cobalt ferrite/graphitic carbon nitride ternary composite for electrochemical overall water splitting
Multicomponent electrocatalysts containing carbon supports play a crucial role in influencing the hydrogen and oxygen evolution reactions which enhance the total water splitting. Herein, we report a ternary composite with cobalt ferrite, graphitic carbon nitride, and N-doped graphene quantum dots prepared via hydrothermal technique. The purity of the samples is established by carrying out various characterization methods. The intrinsic characteristics of the obtained materials are investigated by employing electrocatalytic processes in an alkaline media toward hydrogen and oxygen evolution reactions. Cobalt ferrite/graphitic carbon nitride/N doped graphene quantum dots electrocatalyst demonstrates a very low overpotential towards hydrogen evolution reaction of 287 mV at a constant 10 mA cm?2 current density in 1.0 M KOH. Tafel slope and Rct values generated are 94 mV dec?1 and 0.86 cm2, respectively. Oxygen evolution reaction studies reveal an overpotential of 445 mV at 10 mA cm?2 with a Tafel slope of 69 mV dec?1. Finally, the cell potential needed for the cobalt ferrite/graphitic carbon nitride/N doped graphene quantum dots electrode to achieve 10 mA cm?2 in total water splitting is only 2.0 V while displaying long-term stability. 2022 Hydrogen Energy Publications LLC -
Geo-spatial crime density attribution using optimized machine learning algorithms
Law enforcement agencies use various crime analysis tools. A large amount of crime data has enabled crime analysis. In this paper, the proposed research methodology uses Kernel Density Estimation (KDE) in a Geographical Information System (GIS) to analyze crime-type data. Bangalore and India newsfeeds are considered for experimental purposes. The paper introduces an optimized KDE machine learning algorithm that detects hotspots, estimates a locations crime rate, and identifies point pattern lows and highs. As a result of the experiment, the proposed methodology identified that the bandwidth of the Geographical information system influences the visualization of crime density. The paper also aids in visually determining the appropriate bandwidth for the problem using an optimized KDE algorithm. We had identified a significant correlation between Newsfeed data and Official Government data, both overall Crime and by crime type. The proposed KDE model achieved a predictive performance of 77.49%. 2023, The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of Computer Applications and Management. -
Deep Learning Inspired Nonlinear Classification Methodology for Handwritten Digits Recognition Using DSR Encoder
The overlapped handwritten digit classification is a global challenge and a significant measure to assess the network recognition ability ratio. Most efficient models have been designed based on convolutional neural networks (CNN) for effective image classification and digit identification. Subsequently, multiple CNN models have inadequate accuracy because of high degree parameter dimensions that lead to abnormal digit detection error rates and computation complexity. We propose a Deep Digit Recognition Network (DDRNet) based on Deep ConvNets to minimize the number of parameters and features to keep the model light while maximizing the accuracy with an adaptive voting (AV) scheme for digit recognition. The individual digit is identified by CNN, and uncertain digits or strings are identified by Deep Convolutional Network (DCN) with AV scheme through Voting-Weight Conditional Random Field (VWCRF) strategy. These methods originated with the YOLO algorithm. The simulations show that our DDRNet approach achieves an accuracy of 99.4% without error fluctuations, in a stable state with less than 15 epochs contrast with state-of-art approaches. Additionally, specific convolution techniques (SqueezeNet, batch normalization) and image augmentation techniques (dropout, back-propagation, and an optimum learning rate) were examined to assess the system performance based on MNIST dataset (available at: http://yann.lecun.com/exdb/mnist/). 2022, King Fahd University of Petroleum & Minerals. -
A Multi-Stimuli responsive organic luminogen with aggregation induced emission for the selective detection of Zn2+ ions in solution and solid state
Organic luminogens capable of excited state intramolecular electron transfer (ESIPT) have drawn prodigious attraction due to their enhanced emission in solid-state. A novel Schiff base molecule, 3,5-dibromo-2-hydroxybenzylidenenicotinohydrazide (DHN) exhibited stimuli-induced reversible fluorescence switching and selective binding propensity towards zinc in aqueous media, and the concentration-dependent studies showed a limit of detection of 9.135 nM. DHN was found to be weakly fluorescent in polar solvents with a quantum yield ranging between 0.0365 and 0.0789 but exhibited a very strong fluorescence in solid state (?exc = 370 nm) due to aggregation induced emission (AIE). The ESIPT fluorophore renders significant reversible halochromic properties in solution and solid-state. In addition, utilizing the solid-state fluorescence, we have prepared PVA-probe green-emitting composite films, which can be used for the on-site detection of Zn2+ in aqueous media. The practical applicability of DHN was proven by detecting Zn2+ in real drug samples. Finally, the ESIPT fluorophore was used for fluorescent imaging of intracellular zinc in the cells acquired from the nervous tissue of rats (N2a). The investigations carried out highlight the versatility of ESIPT Schiff bases used for the development of multi-responsive fluorescent materials for selective sensing of metal ions in both solid and solution states. 2022 Elsevier B.V. -
Balancing module in evolutionary optimization and Deep Reinforcement Learning for multi-path selection in Software Defined Networks
Software Defined Network (SDN) has been used in many organizations due to its efficiency in transmission. Machine learning techniques have been applied in SDN to improve its efficiency in resource scheduling. The existing models in SDN have limitations of overfitting, local optima trap and lower efficiency in path selection. This study applied Balancing Module (BM)-Spider Monkey Optimization (SMO)-Crow Search Algorithm (CSA) for multi path selection in SDN to improve its efficiency. The balancing module applies Gaussian distribution to balance between exploration and exploitation in the multi-path selection process. The Balancing module helps to escape local optima trap and increases the convergence rate. Deep Reinforcement learning is applied for resource scheduling in SDN. The Deep reinforcement learning technique uses the reward function to improve the learning performance, and the BM-SMO-CSA technique has 30 J energy consumption, where the existing models: DRL has 40 J energy consumption, and Graph-ACO has 62 J energy consumption. 2022 -
Fixed points in n-gonal graphical b-metric spaces under contractive conditions
In this paper, we will define a new metric space called n-gonal graphical b-metric space. We will also prove some fixed point theorems in said metric space and give suitable examples to illustrate our results. These results will help to solve many nonlinear convex models in machine learning and optimization by formulating them in fixed point schemes of optimization. Our paper opens the door for researchers to work in the intersecting area of machine learning and functional analysis in the frame work of n-gonal graphical b-metric space. 2023 World Scientific Publishing Company. -
Multigene Genetic Programming Based Prediction of Concrete Fracture Parameters of Unnotched Specimens
This study explores the fracture energy of notched and unnotched concrete specimens subjected to the classical three-point bend test, instantiating a gradational step in the continued development of concrete fracture mechanics. An experimental campaign involving 18 notched test specimens and nine unnotched specimens of three different grades of concrete, an examination of the existing literature models for unnotched specimens, and a novel Multigene Genetic programming (MGGP) based concrete fracture energy model for unnotched specimens are integral to this study. As a salient result, the multiple approaches to quasi-brittle materials adopted in the study, highlighted the criticality of the determination of fracture energy, tensile strength and characteristic length for the crack width study. The failure modes of notched and unnotched specimens were found to be similar. The reported literature has mainly focused on a limited number of fracture energy influencing parameters. Therefore, six impact parameters have been chosen and incorporated into the present study to provide a more acceptable explanation of concrete fracture behaviour. A sensitivity analysis of the parameters and an error analysis of the model undertaken have established the accuracy and robustness of the developed MGGP model. 2023 by the authors. Licensee C.E.J, Tehran, Iran. -
Insight into the effects of waste vegetable oil on self-healing behavior of bitumen binder
The application of waste vegetable oil (WVO) in bitumen has been the subject of research for years, however, the self-healing behavior of WVO modified bitumen (WMB) has not been adequately reported. In this research, molecular dynamics (MD) simulations and laboratory experiments were performed to reveal the effects of WVO on the self-healing behavior of bitumen. Models of base bitumen and WMB were constructed. Further, dynamic calculations were carried out for the self-healing models of base bitumen and WMB both with 10 microcracks. The energy properties, conformation and density of bitumen during the self-healing process were analyzed. Meanwhile, the effects of WVO on the fractional free volume (FFV) of bitumen, the distribution of bitumen components and the mobility of bitumen molecules were investigated. Finally, the modified fatigue-healing-fatigue (FHF) test was conducted to verify the effects of WVO on the self-healing efficiency of bitumen. Results show that Van der Waals forces drive the mobility of bitumen molecules. Along with the disappearance of the central microcrack, the density of the self-healing system gradually increases and finally reaches that of the bulk bitumen. WVO with superior mobility capacity increases the FFV of bitumen and converts asphaltene large aggregated structure into small aggregated structure, which facilitates the mobility of the bitumen during the self-healing process. Thus, the addition of WVO contributes to the self-healing efficiency of the bitumen. The modified FHF test also verified that the self-healing efficiency of bitumen is improved with the presence of WVO. These findings provide further insight into the self-healing behaviors of WMB. 2022 -
Open global shadow graph and its zero forcing number
Zero forcing number of a graph is the minimum cardinality of the zero forcing set. A zero forcing set is a set of black vertices of minimum cardinality that can colour the entire graph black using the colour change rule: each vertex of G is coloured either white or black, and vertex v is a black vertex and can force a white neighbour only if it has one white neighbour. In this paper we identify a class of graph where the zero forcing number is equal to the minimum rank of the graph and call it as a new class of graph that is open global shadow graph. Some of the basic properties of open global shadow graph are studied. The zero forcing number of open global shadow graph of a graph with upper and lower bound is obtained. Hence giving the upper and lower bound for the minimum rank of the graph. 2023, Proyecciones. All Rights Reserved. -
Source-load-variable voltage regulated cascaded DC/DC converter for a DC microgrid system
Solar energy is available abundantly, the utilization of solar energy is developing rapidly and the photovoltaic based direct current (DC) microgrid system design is under demand but the stability of the DC voltage is of most important issue, as the variation of the output DC voltage is a common problem when the load or source voltage varies, hence a regulated DC output voltage converter is proposed. This paper presents source-load-variable (SLV) voltage regulated cascaded DC/DC converter which is used to obtain regulated output voltage of 203.1 V DC at 0.4 duty ratio with 2% voltage fluctuations for the variation in the input source voltage and 1.5% voltage fluctuations for the variation in load resistance of the nominal value with lower output voltage ripple and without use of sub circuits. A simulation model of SLV voltage regulated cascaded DC/DC converter in LTspice XVII software environment for the assessment of converter performance at different input source voltages and load resistances are verified. 2023 Institute of Advanced Engineering and Science. All rights reserved. -
A facile, green synthesis of carbon quantum dots from Polyalthia longifolia and its application for the selective detection of cadmium
Carbon quantum dots (CQDs) has received world-wide recognition for their outstanding physicochemical properties that have the ability to substitute the semiconductor quantum dots. Herein, we have developed a strategy to determine the presence of Cd2+ using CQDs as a fluorescence probe. The CQDs were synthesized from the leaves of Polyalthia longifolia (a natural source) through a one-step hydrothermal method. The CQDs obtained from Polyalthia longifolia (p-CQDs) was characterized using XRD, TEM, FTIR, Raman Spectroscopy, XPS Studies, UVVisible spectroscopy and PL Spectroscopy. The p-CQDs displayed bright red fluorescence under the UV light, with good water solubility, and appreciable photostability and a quantum yield of 22%. The p-CQDs had a quasi-spherical morphology with an average particle size of 3.33 nm. The p-CQDs showed high selectivity and sensitivity for the detection of Cd2+ with a low limit of detection of 2.4 nM and a wide linear range of 7.3 nM12 ?M. The PL intensity of the p-CQDs showed a quenching effect in presence of Cd2+ and the mechanism of quenching was validated via fluorescence lifetime decay studies. We have also studied the effectiveness of the fluorescent probe developed for Cd2+ sensing in real samples of ground water and industrial effluents. 2022 Elsevier Ltd -
Student-managed investment funds (SMIFs) in India: the perspectives of student fund managers
Purpose: This paper aims to explore how the student fund managers perceive the benefits of being part of the fund. Furthermore, this paper examines the country-specific challenges of setting up and managing a student-managed investment fund (SMIF) in India. Design/methodology/approach: Qualitative content analysis technique is used to identify, compare and retrieve critical themes about the present state of SMIF clubs in India. The data collection method involved structured, in-depth online interviews with ten student fund managers from various higher educational institutions in India. Findings: Some of the studys key findings indicate that the existence of SMIFs as part of learning facilitates group decision-making and peer learning. Additionally, this study brings to light specific issues related to registration, incorporating real-world practices and integrating SMIF into the academic curriculum. Social implications: The outcomes of this study shall be of use to students and the teaching fraternity across Indian colleges and universities who aspire to set up SMIFs as part of experiential learning. This study will also help existing SMIF clubs in India understand how their counterparts work and can consequently improvise their organizational structure and functioning. Originality/value: To the best of the authors knowledge, this is the first interview-based evaluation of the present structure of SMIFs structured as clubs in India. 2022, Emerald Publishing Limited. -
An hybrid technique for optimized clustering of EHR using binary particle swarm and constrained optimization for better performance in prediction of cardiovascular diseases
The significant adoption of Electronic Health Records (EHR) in healthcare has furnished large new quantities of information for statistical machine gaining knowledge of researchers in their efforts to version and expects affected person health popularity, doubtlessly permitting novel advances in treatment. Unsupervised system learning is the project of studying styles in facts where no labels are present. In comparison to loads of optimization problems, an most beneficial clustering end result does not exist. One-of-a-kind algorithms with special parameters produce special clusters, and none can be proved to be the quality answer given that numerous good walls of the records might be found. In the previous work, a novel Two-fold clustering technique which uses the Long Short Term Memory (LSTM) technique (TFC: LSTM) for the prediction of Cardiovascular Disease (CVD) was proposed. The proposed model was fond to be experimentally efficient; however when applied to large EHR data, the model suffered from optimization issues on the number of clusters formed and time complexity. In order to overcome the drawbacks, this paper proposes a hybrid method of optimization using the Binary Particle Swarm (BPS) and Constrained Optimization (CO) for optimizing the number of clusters produced and to increase the efficiency in terms of decreasing the time complexity. 2022 The Authors -
Excited-state intramolecular proton transfer (ESIPT) salicylaldehyde Schiff bases: ratiometric sensing of ammonia and biologically relevant ions in solution and solid state
The intricate molecular architecture of ESIPT salicylaldehyde Schiff bases facilitates dynamic processes, inducing tunable photoluminescent properties. Notably, their halochromic nature, exhibiting colour changes in response to external stimuli, adds a vibrant dimension to their molecular repertoire. This sensitivity extends to environmental factors, making them valuable indicators for alterations in surroundings. The compound (E)-N-(3,5-dibromo-2-hydroxybenzylidene)-4-methylbenzohydrazide (PTBH) demonstrates exceptional sensitivity to ammonia, enabling real-time detection (LOD = 0.14 nM) in both solution (ratiometric) and the solid state. Moreover, their metal chelation capability allows simultaneous sensing of Mg2+ and Fe2+ ions, addressing environmental hazards. Exploiting molecular recognition, the fluorescent probe serves as sensors for amino acids, opening new avenues in biomedical diagnostics. The study introduces a novel solid-state emissive Schiff base, highlighting its stimuli-responsive photoluminescent properties and diverse applications, emphasising its potential in intelligent fluorescent materials for analytical and sensing technologies. 2024 Informa UK Limited, trading as Taylor & Francis Group. -
Broad-band mHz QPOs and spectral study of LMC X-4 with AstroSat
We report the results of broad-band timing and spectral analysis of data from an AstroSat observation of the high-mass X-ray binary LMC X-4. The Large Area X-ray Proportional Counter (LAXPC) and Soft X-ray Telescope (SXT) instruments onboard the AstroSat observed the source in 2016 August. A complete X-ray eclipse was detected with the LAXPC. The 340 keV power density spectrum showed the presence of coherent pulsations along with a ?26 mHz quasi-periodic oscillation feature. The spectral properties of LMC X-4 were derived from a joint analysis of the SXT and LAXPC spectral data. The 0.525 keV persistent spectrum comprised of an absorbed high-energy cut-off power law with photon index of ? ? 0.8 and cut-off at ?16 keV, a soft thermal component with kTBB ? 0.14 keV, and Gaussian components corresponding to Fe K?, Ne IX, and Ne X emission lines. Assuming a source distance of 50 kpc, we determined 0.525 keV luminosity to be ?2 1038 erg s?1 2022 The Author(s) Published by Oxford University Press on behalf of Royal Astronomical Society. -
Evaluation of tea (Camellia sinensis L.) phytochemicals as multi-disease modulators, a multidimensional in silico strategy with the combinations of network pharmacology, pharmacophore analysis, statistics and molecular docking
Tea (Camellia sinensis L.) is considered as to be one of the most consumed beverages globally and a reservoir of phytochemicals with immense health benefits. Despite numerous advantages, tea compounds lack a robust multi-disease target study. In this work, we presented a unique in silico approach consisting of molecular docking, multivariate statistics, pharmacophore analysis, and network pharmacology approaches. Eight tea phytochemicals were identified through literature mining, namely gallic acid, catechin, epigallocatechin gallate, epicatechin, epicatechin gallate (ECG), quercetin, kaempferol, and ellagic acid, based on their richness in tea leaves. Further, exploration of databases revealed 30target proteins related to the pharmacological properties of tea compounds and multiple associated diseases. Molecular docking experiment with eight tea compounds and all 30proteins revealed that except gallic acid all other seven phytochemicals had potential inhibitory activities against these targets. The docking experiment was validated by comparing the binding affinities (Kcalmol?1) of the compounds with known drug molecules for the respective proteins. Further, with the aid of the application of statistical tools (principal component analysis and clustering), we identified two major clusters of phytochemicals based on their chemical properties and docking scores (Kcalmol?1). Pharmacophore analysis of these clusters revealed the functional descriptors of phytochemicals, related to the ligandprotein docking interactions. Tripartite network was constructed based on the docking scores, and it consisted of seven tea phytochemicals (gallic acid was excluded) targeting five proteins and ten associated diseases. Epicatechin gallate (ECG)-hepatocyte growth factor receptor (PDB id 1FYR) complex was found to be highest in docking performance (10kcalmol?1). Finally, molecular dynamic simulation showed that ECG-1FYR could make a stable complex in the near-native physiological condition. Graphical abstract: [Figure not available: see fulltext.]. 2022, The Author(s), under exclusive licence to Springer Nature Switzerland AG.