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A distance metric known as non-Euclidean distance deviates from the laws of Euclidean geometry, which is the geometry that governs most physical spaces. It is utilized when Euclidean distance is inappropriate, for as when dealing with curved surfaces or spaces with complex topologies. The ability to apply deep learning techniques to non-Euclidean domains including graphs, manifolds, and point clouds is made possible by non-Euclidean deep learning. The use of non-Euclidean deep learning is rapidly expanding to study real-world datasets that are intrinsically non-Euclidean. Over the years, numerous novel techniques have been introduced, each with its benefits and drawbacks. This paper provides a categorized archive of non-Euclidean approaches used in computer vision up to this point. It starts by outlining the context, pertinent information, and the development of the field’s history. Modern state-of-the-art methods have been described briefly and categorized by application fields. It also highlights the model’s shortcomings in tables and graphs and shows different real-world applicability. Overall, this work contributes to a collective information and performance comparison that will help enhance non-Euclidean deep-learning research and development in the future.

Electrocardiogram (ECG) signal is informative as well as non-invasive clinical tool to diagnose cardiac diseases of human heart. However, the diagnosis requires professionals’ clarification and is also time-consuming. To make the diagnosis proficient, a novel convolutional neural network (CNN) has been proposed for automatic arrhythmia detection. In this work, the ECG data collected from the MIT-BIH database have been preprocessed, and segmented in short ECG segments of 60 s. Then, all these segments have been transformed into scalogram images obtained from time-frequency analysis using continuous wavelet transform (CWT). Finally, these scalogram images have been exploited as an input for our designed CNN classifier to classify cardiac arrhythmia. In this approach, the overall accuracy, sensitivity, and specificity are 99.39%, 98.79%, and 100% respectively. Proposed CNN model has significant advantages, and it can be used to differentiate the healthy and arrhythmic patients effectively.

Voltage sag compensation is provided on both sides of the Point of Common Coupling (PCC) by using FACTS Devices (STATCOM, APF, DSTATCOM, DVR, UPFC) and FCL. The FACTS dev ices provide compensation on input side and FCL provides compensation on output side. A sensitive load is considered at output side of PCC. This paper presents a component called Fault Current Limiter (FCL) in three phase lines. The main objective of the designed component is to protect the sensitive load from the shunt faults. The compensation is being provided at output side of PCC. Load voltage reduces upon the occurrence of shunt fault. The planned structure prevents voltage sag and counter balance the phase-angle of the PCC once fault prevalence. As a result, different feeders which are interlinked to the sub-station PCC can have attentive power quality. During this paper a high performance 3-phase fault current electrical model is planned. The analysis and design is carried out in MATLAB with SIMULINK.

Disposal of used lubricant oil in an ecosystem is a serious environmental threat. In this context, recycling waste lubricant oil into a valuable product has an immense commercial and environmental significance and is a challenging area of research. In this article, we have demonstrated the use of waste lubricant oil as a partial replacement of diesel oil of ammonium nitrate fuel oil (ANFO) explosives. Moreover, the effect on detonation properties and rock fragmentation patterns by the use of lubricant oil based ANFO in blasting operation has also been studied. The field trial experiments were carried out with lubricant oil based ANFO explosive to demonstrate the real applicability of the prepared explosive composition at Nimbeti Limestone open-pit mine of Shree Cement Limited, India. These explosive compositions revealed effective detonation performance. Importantly, air quality was analyzed during blasting operations to indicate that the emitted toxic fumes such as CO, NOx, and fine dust particles (PM2.5 & PM10) were found within the permissible limit at the mining area by the use of lubricant oil based explosives. Further, rock fragmentation analysis indicated that ANFO explosive compositions with 20 %, 30 %, and 40 % (w/w) lubricant oil in diesel oil revealed good rock fragmentation as compared with normal ANFO in the conducted experimental blast

Increasing the hydrophobicity of ammonium nitrate is of great significance for its application as commercial explosives. In this article, a surface functionalized ammonium nitrate with enhanced water‐resistance property was demonstrated using a new hydrophobic coating agent. Structural properties and thermal stability of the prepared samples were investigated by powder X‐ray diffraction, scanning electron microscopy and differential scanning calorimetric analysis. Fourier‐transform infrared spectroscopic results show that a uniform hydrophobic layer is successfully coated on the surface of ammonium nitrate prills. Surface coated ammonium nitrate shows excellent stability in aqueous environment with effective detonation property compared to commonly used ammonium nitrate fuel oil (ANFO) explosives. Thus, our surface modified ammonium nitrate could serve as a potential explosive for watery hole blasting operations.

In this paper, after introducing the notion of relative ideals in ordered semigroups, some important properties of these ideals are studied. Then notions of relatively prime and relatively weakly prime ideals are defined and some vital results have been proved. We also find some conditions under which relative ideals become left, right and two-sided ideals in any regular ordered semigroup. Finally, the notion of maximal and minimal relative ideals are introduced and some of their properties are studied.

In this paper, after introducing the notion of relative bi-ideals and relative quasi ideals in ordered semigroups, some important properties of these bi-ideals and quasi ideals are studied. Then relatively prime and relatively weakly semiprime bi-ideals are defined and some vital results have been proved. We also define relative regularity and relative intra-regularity of an ordered semigroup and prove some results based on the connection among intra-regularity of an ordered semigroup, relative quasi and relative bi-ideals of that ordered semigroup. Finally some important results connecting relative regularity, relatively prime bi-ideals and relatively weakly semiprime bi-ideals of an ordered semigroup have also been obtained.

[Image: see text] It is known that reactive oxygen (ROS) and nitrogen (RNS) species play a diverse role in various biological processes, such as inflammation, signal transduction, and neurodegenerative injury, apart from causing various diseases caused by oxidative and nitrosative stresses, respectively, by ROS and RNS. Thus, it is very important to quantify the concentration level of ROS and RNS in live cells, tissues, and organisms. Various small-molecule-based fluorescent/chemodosimetric probes are reported to quantify and map the effective distribution of ROS/RNS under in vitro/in vivo conditions with a great spatial and temporal resolution. Such reagents are now appreciated as an excellent tool for aiding breakthroughs in modern redox biology. This mini-review is a brief, but all-inclusive, account of such molecular probes that have been developed recently.

The usability of computer vision is everywhere, whereas deep learning revolutionized the concept of artificial intelligence including computer vision. This paper discusses the leverages of deep learning for computer vision. At first, the background details of computer vision and deep learning have been discussed. Important tasks of computer vision like image classification, object localization, object detection, segmentation are briefly explained. Various types of deep learning algorithms have been described. The architecture of the convolutional neural network$ has been explained briefly. Applications of deep learning in different fields of computer vision like image classifications, human activity recognition, scene text detection and recognition, object tracking, visual question answering, etc., have been discussed with the references of the recent state-of-the-art works.

The development of self-driving cars has always been an extensive research field for the automobile industry. To make a capable self-driving car, many challenges need to be resolved. Detection of the road condition is one of them. This paper focuses on a particular part-detection of speed bumps and potholes using a camera and analyzing the video feed with the help of artificial intelligence. To solve this problem a popular and lightweight algorithm, SSD (Single Shot Multibox Detector) is used. This is an optimal choice because of being lightweight and also accurate enough to run on mobile devices and to use in real-life situations. For detecting speed bumps and potholes, a dataset has been created based on the road structure of Bangladesh as the main priority of this system is to work on the local environment. Raspberry Pi has been used as the main processing unit because of being small but powerful. A warning system has been implemented so that it can warn the onboard driver about the upcoming pothole or speed bump. This system can also send a signal to the speed controller unit of the car to reduce the speed on detection to avoid accidents or damages to the car. The speed control unit is a microcontroller-based system that uses an ATmega328 microcontroller and L298 motor driver. This paper summarizes the combination of an artificial intelligence-based detection system injunction with a microcontroller-based speed control system in a cost-effective way that can be used in building self-driving cars.

The detection of cardiac diseases is very important to prevent early cardiac death. The electrocardiogram (ECG) signal is an informative as well as non-invasive clinical tool to diagnose such diseases of the human heart. In this paper, a MATLAB based graphical user interface (GUI) has been developed, and scrupulous attention has been paid for cardiac signal processing to distinguish the subject with malignant ventricular ectopy (MVE) from one having normal sinus rhythm (NSR). Here, long-duration ECG segments of 60 seconds have been considered from the MIT-BIH database, and different analytical approaches have been conducted throughout the study. In heart rate variability (HRV) analysis, significant variations of heart rate have been observed for the subjects with MVE compared to NSR. Afterward, from linear analysis, the extracted time-domain and frequency-domain features of each subject provide divergent numerical values to differentiate these two classes effectively. Also, the numerical values of these features have been displayed in GUI which provides an overall picture of a subject’s heart condition in a single window. Moreover, time-frequency analysis of ECG signals has been carried out using continuous wavelet transform (CWT) that also categorizes particular heart diseases significantly. In addition, the incorporation of a convolutional neural network (CNN) in the GUI is a novel approach that facilitates the arrhythmia detection and classification process. Here, the CWT scalogram image of ECG signal of a particular subject has been used as input in CNN which identifies the subject suffering from MVE or not with 100% accuracy.

Fluorescent probes provide an unparalleled opportunity to visualize and quantify dynamic events. Here, we employ a medium-size, cysteine specific coumarin based switch-ON fluorescent probe ‘L’ to track protein unfolding profiles and accessibility of cysteine residues in proteins. It was established that ‘L’ is highly selective and exhibits no artifact due to interaction with other bystander species. ‘L’ is able to gauge subtle changes in protein microenvironment and proved to be effective in delineating early unfolding events that are difficult to otherwise discern by classic techniques such as circular dichroism. By solving the X-ray structure of TadA and probing the temperature dependent fluorescence-ON response with native TadA and its cysteine mutants, it was revealed that unfolding occurs in a stage-wise manner and the regions that are functionally important form compact sub-domains and unfold at later stages. Our results assert that probe ‘L’ serves as an efficient tool to monitor subtle changes in protein structure and can be employed as a generic dye to study processes such as protein unfolding.

In this research, a novel antenna array named Linearly arranged Concentric Circular Antenna Array (LCCAA) is proposed concerning lower beamwidth, lower sidelobe level, sharp ability to detect the false signal, and impressive SINR performance. The performance of the proposed LCCAA beamformer is compared with geometrically identical existing beamformers using the conventional technique where the LCCAA beamformer shows the lowest beamwidth and sidelobe level(SLL) of 12.50°and -15.17 dB in equal element accordingly. However, the performance gets degraded due to looking direction error, and robust techniques- fixed diagonal loading (FDL), optimal diagonal loading (ODL), and variable diagonal loading (VDL) are applied to all the potential arrays to minimize this problem. Furthermore, the LCCAA beamformer is further simulated to reduce the sidelobe applying tapering techniques where the hamming window shows the best performance having 17.097 dB less sidelobe level compared to the uniform window. The proposed structure is also analyzed under a robust tapered (VDL-hamming) method which reduces around 69.92 dB and 48.39 dB more sidelobe level compared to conventional and robust techniques. Analyzing all the performances, it is clear that the proposed LCCAA beamformer is superior and provides the best performance with the proposed robust tapered (VDL-hamming) technique.

Wind energy is a promising scheme in the power generation sector due to pollution-free power production and wind resources’ sufficiency worldwide. Installing wind turbines in all the possible extents can mitigate the rising energy demand. Built-up areas possess high potential for wind energy, including the rooftop of high-rise buildings, railway track, the region between or around multistoried buildings, and city roads. Harnessing wind energy from these areas is quite challenging since it has dramatic nature and turbulence for higher roughness on urban surfaces. This review paper endeavors to highlight the present status of urban wind farm technology and its commercial and environmental aspects. Observations and upcoming research trends have been presented based on up-to-the-minute information. It is concluded that further investigation of wind mapping and the suitable design of turbines is essential to make the urban wind farm a reliable and feasible option for decentralized power generation.

This paper presents a comparative study to recognize faces from a customized dataset of 10 identities of different celebrities using Convolutional Neural Network based models such as AlexNet, VGG16, VGG19 and MobileNet. These pre-trained models previously trained on ImageNet dataset are used with the application of Transfer Learning and Fine Tuning. For our experiment we used Keras API with TensorFlow backend written in Python. The performance analysis includes training, validation, and testing on different images created from original dataset. The validation accuracy of VGG19 model is found better than the other three but MobileNet model showed better test accuracy.

One of the complicated issues of computer vision is the recognition of any sign languages. The deaf and the dumb people use these sign languages to communicate. In the area of deep learning at recent progress there are numerous applications that neural networks can have for interpreting sign languages. The recognition of American Sign Language static images employing a capable artificial intelligence tool, Convolutional Neural Network has been proposed in this paper. ASL dataset of 1815 images of 26 English alphabets has been used to train and validate our model. Validation accuracy has been found 94.34% which is better than many existing methods.

Objective of this work is focused on developing a system capable of identifying various diseases of paddy plant. Faster RCNN, a model of Convolutional Neural Network (CNN) has been executed for recognition. TensorFlow library has been used in Anaconda environment. Samples of diseased plants were collected from Bangladesh Rice Research Institute (BRRI), Rajshahi. Before initiating training procedure, the data images had to be processed. Then after completion of training program, the recognition was possible automatically. A design of a real time system has also been proposed.

BODIPY derivative, SF-1, exclusively shows a fluorescence ON response to HOCl and images endogenously generated HOCl in RAW 264.7 macrophages. Widefield and super resolution structured illumination microscopy images confirm localization in the Golgi complex and lysosomes, and hence specifically detects HOCl generated in these organelles. SF-1 is compatible with 3D-SIM imaging of individual cells.

Selective detection of nitroxyl (HNO), which has recently been identified as a reactive nitrogen species, is a challenging task. We report a BODIPY-based luminescence ON reagent for detection of HNO in aqueous solution and in live RAW 264.7 cells, based on the soft nucleophilicity of the phosphine oxide functionality toward HNO. The probe shows high selectivity to HNO over other reactive oxygen/nitrogen and sulfur species. Luminescence properties of the BODIPY-based chemodosimetric reagent make it an ideal candidate for use as a reagent for super-resolution structured illumination microscopy. The viability of the reagent for biological in vivo imaging application was also confirmed using Artemia as a model.

Reagents that allows detection and monitoring of crucial biomarkers with luminescence ON response have significance in clinical diagnostics. A new coumarin derivative is reported here, which could be used for specific and efficient chemodosimetric detection of cysteine, an important biomarker. The probe is successfully used for studying the biochemical transformation of N-acetylcysteine, a commonly prescribed Cys supplement drug to Cys by aminoacylase-1 (ACY-1), an important and endogenous mammalian enzyme. The possibility of using this reagent for quantification of ACY-1 in blood serum samples is also explored. Nontoxic nature and cell membrane permeability are key features of this probe and are ideally suited for imaging intracellular Cys in normal and cancerous cell lines. Our studies have also revealed that this reagent could be utilized as a redox switch to monitor the hydrogen-peroxide-induced oxidative stress in living SW480 cell lines. Peroxide-mediated cysteine oxidation has a special significance for understanding the cellular-signaling events.

In this paper, some properties of the ( k ∗ , k ) -lower part of ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy quasi-ideals are obtained. Then, we characterize regular ordered semigroups in terms of its ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy quasi-ideals, ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy generalized bi-ideals, ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy left ideals and ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy right ideals, and an equivalent condition for ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy left (resp. right) ideals is obtained. Finally, the existence theorems for an ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy quasi-ideal as well as for the minimality of an ( ∈ , ∈ ∨ ( k ∗ , q k ) ) -fuzzy quasi-ideal of an ordered semigroup are provided.

We report a new chemodosimetric reagent capable of detecting hydrazine in the presence of several other competing amine derivatives and ionic analytes of biological relevance. This reagent has been utilized for real time monitoring of in situ N2H4 release during the metabolism of a crucial tuberculosis drug, isoniazid, in live HepG2 cells. The fluorescence response of the reagent based on its specific reaction with N2H4 is used for developing an in vitro assay for aminoacylase-1.

A new reagent for quantification of CgS in plant extracts using a generalized methodology suitable for recognition of homocysteine (Hcy) with luminescence ON response.

A new physiologically benign and cell membrane permeable BODIPY based molecular probe, MB-Sn, specifically senses intracellular hydrogen polysulfides (H2Sn, n > 1) localized in the endoplasmic reticulum. This reagent is suitable for mapping the intracellular distribution of H2Sn by wide-field as well as super-resolution Structured Illumination Microscopy (SIM).

A rhodamine conjugate (L) with a pseudo Stokes shift of 165 nm is used for probing changes in solution pH under physiological conditions. This reagent is found to be nontoxic, and the luminescence response could be used for imaging changes in endogenous pH induced by dexamethanose (DMT) in the endoplasmic reticulum.

Two new uracil (U) and 5-flurouracil (5-FU) labeled ruthenium(II)-polypyridyl based cellular imaging reagents are reported. Confocal laser scanning microscopic images with live and paraformaldehyde (PFA) fixed MCF-7 cells are examined using these two low-cytotoxic reagents. Experimental results show that these two complexes, appropriately functionalized with U (1) and 5-FU (2), have specific affinity for the lipid dense regions like the endoplasmic reticulum, cell membrane, and cytoplasmic vacuoles in live MCF-7 cells, and dye internalization in these regions happened following an endocytosis pathway. Interestingly, these two complexes are found to be localized in the nucleus of the PFA fixed cells. For fixed cells, presumably the lipid layer disruption helped in the explicit localization of the complexes 1 and 2 in the cell nucleus through specific interaction with cellular DNA. Poor and non-specific internalization of an analogous model complex 3, without having a U or 5-FU moiety, reveals the definite influence of U or 5-FU as well as the role of lipophilicity of the respective complex 1 and 2 in the cellular internalization process. Apart from these, a large Stokes shift (∼160 nm) and an appreciably long lived 3MLCT excited state (∼320 ns) in aq. buffer medium (pH 7.4) are other key features for complexes 1 and 2. Unlike the common nuclear DNA staining reagents like DAPI, these low-cytotoxic reagents are found to be highly stable towards photo-bleaching upon irradiation with 455 nm at the MLCT band for these complexes.

A single gelator molecule (1) shows an ESIPT coupled AIEE process for generating multiple luminescent colors, including white-light, with varying aggregation as a function of the water content in a THF–water mixture. Luminescent property of 1 is retained in gel as well as in solid state.

A new coumarin-rhodamine conjugate is used as a specific probe for Pd(2+) ions and this could even delineate Pd(II) from Pd(0) or Pd(IV) in aqueous buffer medium (pH ∼ 7). Laser confocal microscopic studies reveal that efficient cellular internalization of this reagent helps in imaging the cellular uptake of Pd(2+) as low as 0.1 ppm in Hct 116 cells. This reagent could even be used for estimation of Pd(2+) in human urine samples.

Among various elements, mercury (Hg) and palladium (Pd) are featured in all lists of heavy metal toxins. Mercury is xenobiotics and toxic even at trace levels of exposure either in elemental (if aerosolized) or in its two ionic forms (viz. Hg(I) and Hg(II)). Importantly, the most common oxidation state of mercury is Hg(II) and this has an appreciably high value for enthalpy of solvation in aqueous media. This fact along with a high spin–orbit coupling constant value are not conducive for designing efficient luminescence ON-based receptors for Hg(II), which are essential for either detection or imaging of such toxins inside the cells or in specific organs. Such sensors have significance in evaluating the influence of such toxins in human physiology as well as in clinical diagnostics. Analogously, Palladium (in oxidation states of 0, II, and IV) is commonly used as a catalyst for the synthesis of various drugs or active pharmaceutical intermediates. Despite best efforts in purification and elimination of palladium used in such reactions, residual concentrations of Pd are often higher than the permissible level set by the World Health Organization. The affinity of Pd toward thiol-containing amino acids, proteins, DNA, or other macromolecules is known to cause physiological disorders. All these necessitate the design and development of efficient imaging reagents that are specific toward a certain cationic analytes and allow for the real-time monitoring for assessing the adverse influences on human physiology. Recent advances in such reagents primarily for Hg(II), Pd(0), and Pd(II) are discussed.