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The environmental benefits of molecular oxygen as the oxidizing agent in oxidation reactions that synthesize fine chemicals cannot be overstated. Increased interest in developing robust photocatalysts is stimulated by the fact that the current photocatalytic transformation boom has made previously inaccessible synthetic approaches possible. Motivated by enzymatic catalysis, employing a reusable phenalenyl-based photocatalyst, we have successfully developed oxidative dehydrogenation utilizing molecular oxygen as a greener oxidant. Under photoinduced oxidative dehydrogenation conditions, different types of saturated N-heterocycles and alcohols were successfully dehydrogenated. The versatility of this bioinspired protocol is demonstrated by the fact that a wide variety of N-heteroaromatics, such as quinoline, carbazole, quinoxaline, acridine, and indole derivatives, as well as aldehydes and ketones, were successfully synthesized. Detailed mechanistic studies validate the proposed mechanism. Fluorescence lifetime and CV experiments revealed the crucial role of water on the efficiency of the reaction. The present protocol also provides chemoselectivity and scalability, leading to superior results and allowing for the functionalization of bioactive molecules at a late stage in a sustainable manner.

Technological innovation for re-identifying maritime vessels plays a crucial role in both smart shipping technologies and the pictorial observation tasks necessary for marine recon- naissance. Vessels are vulnerable to varying gradations of engaging in the marine environment, which is complicated and dynamic compared to the conditions on land. Fewer picture samples along with considerable similarity are characteristics of warships as a class of ship, making it more challenging to recover the identities of warships at sea. Consequently, a convolutional dynamic alignment network (CoDA-Net) re-identification framework is proposed in this research. To help the network understand the warships within the desired domain and increase its ability to identify warships, a variety of ships are employed as origin information. Simulating and testing the winning of war vessels at sea helps to increase the network’s ability to recognize complexity so that users can better handle the effects of challenging maritime environments. The impact of various types of ships as transfer items is also highlighted. The research results demonstrate that the enhanced algorithm increases the overall first hit rate (Rank1) by approximately 5.9%; it also increases the mean average accuracy (mAP) by approximately 10.7% and the correlation coefficient by 0.997%.

A highly regioselective photocatalytic method to access a variety of biaryl motifs under metal-free conditions has been developed. The organophotocatalyst is involved in π–π stacking interactions with the alkyne species, which promotes this photocatalytic process with thiophene. Mechanistic studies have shed light on these interactions and the overall process. Along with a broad functional-group tolerance and excellent regioselectivity, this protocol has been utilized in the late-stage functionalization of pharmaceuticals and other natural products.

Nowadays, the importance of continuous connectivity of wireless network has increased because of increase in the number of applications in handheld devices. For offering continuous connectivity, seamless mobility management is very much necessary. Many mobility management schemes have been proposed. The techniques can broadly classified as, tunnel based scheme and routing based scheme. The tunnel based techniques has the advantage of limited route management packet transmission. But disadvantage is that besides source and destination addresses each of the data packets carry an extra header for routing. In case of routing based scheme, advantage is that the data packets do not carry any extra header for routing. But the disadvantage is that during handoff lots of route management packets are transmitted. As the mesh client speed increases, these problems further increase. Hybrid mobility management technique has been proposed in this paper for minimizing the problems of both tunnel based scheme and routing based scheme. It has been designed to handle highly mobile mesh clients. The proposed scheme routes internet packets using routing table entries. On the other hand, it uses tunneling to route the intranet packets to the destination. The hybrid scheme and some of the existing schemes have been analyzed and compared. Performance comparison shows that the hybrid scheme outperforms these techniques.

Background and objective: Recent developments in deep learning have demonstrated impressive performance in accurately identifying individual diseases from chest X-rays (CXRs). However, multiple diseases, stability of the deep network, and class imbalance problems were not addressed with high accuracy for disease detection and classification. So, the main purpose of this work is to develop a fully automatic computer method to detect thirteen types of thoracic disease from CXRs with high accuracy. Methods: In this research, a CX-Ultranet has been proposed for the classification and detection of 13 different thoracic disorders from plain radiographic images. The baseline model employed is EfficientNet, and a multiclass cross-entropy loss function is utilized within a compound scaling structure. Channel shuffling is implemented at various stages of the network, creating reduction cells and incorporating more skip connections. The loss function algorithm and Adam optimizers work synergistically to stabilize the model and facilitate continuous learning from new data over time. Results: The CX-Ultranet demonstrates an average prediction accuracy of 88% when applied to diverse CXR datasets. In comparison to existing state-of-the-art techniques, the CX-Ultranet exhibits a remarkable improvement ranging from 5% to 15%. Additionally, it shows a reduction in operational time by approximately 30% compared to current cutting-edge models under similar environmental and data conditions. Conclusion: The proposed CX-Ultranet achieves superior overall accuracy and effectively addresses imbalanced classes within the dataset. Furthermore, it significantly reduces the duration of network training in relation to FLOPS, thereby establishing a novel benchmark in the field of CXR-based disease diagnosis.

Purpose: The purpose of this study is to propose an advanced methodology for automated diagnosis and classification of heart conditions using electrocardiography (ECG) in order to address the rising death rate from cardiovascular disease (CVD). Methods: Buffered ECG pulses from the MIT-BIH Arrhythmia dataset are integrated using a multi-modal fusion framework, refined using Gradient Descent optimization, and classified using the K-Means technique based on pulse magnitudes. Convolutional Neural Networks (CNNs) are used to detect anomalies. Results: The study achieves an average accuracy of 98%, outperforming current state-of-the-art methods. Sensitivity, specificity, and other metrics show significant improvements. The results also show the type of Cardiovascular disease detected using Confusion matrix plots. Conclusion: The proposed methodology demonstrates the utility of advanced machine learning, particularly deep learning, in the assessment of cardiovascular health. Based on the MIT-BIH Arrhythmia dataset, this study contributes to the development of accurate and efficient diagnostic tools for addressing urgent cardiac health challenges.

We report an organo-photocatalyzed carboacylation reaction that offers a springboard to create chemical complexity in a diversity-driven approach. The modular one-pot method uses feedstock aldehydes and alcohols as acyl surrogates and commercially available Eosin Y as the photoredox catalyst, making it simple and affordable to introduce structural diversity. Several biologically relevant skeletons have been easily synthesized under mild conditions in the presence of visible light irradiation by fostering a radical acylation/cyclization cascade. The proposed reaction mechanism was further illuminated by a number of spectroscopic studies. Furthermore, we applied this protocol for the late-stage functionalization of pharmaceuticals and blockbuster drugs.

Earth-abundant copper salts, in recent years, have attracted the attention of the scientific community with their ability to perform ligand-to-metal charge transfer (LMCT) upon light excitation, thereby eliminating the necessity of a high activation energy barrier required in thermal approaches. Herein, we have reported an application of photoinduced LMCT of Cu(II) species toward the synthesis of benzocoumarin derivatives from 2-aryl benzoic acids via intramolecular dehydrogenative coupling. Existence of light-induced LMCT is confirmed by using detailed mechanistic studies, which include lowering of the d–dtransition intensity upon photoexcitation (in UV–visible spectroscopic analysis) and drop-down of the EPR signal intensity on sequential irradiation. Moreover, X-ray photoelectron spectroscopy (XPS) studies reveal the photochemical conversion of Cu(II) to Cu(I) through LMCT. A large number of diverse functional groups could easily be tolerated under this newly developed protocol affording very good yield of the desired benzocoumarins. Additionally, the bulk-scale synthesis of a metabolite compound urolithin A further extends the applicability of this light-induced methodology.

By revealing the robust photooxidant properties of phenalenyl-based organic Lewis acid, we have introduced this moiety as an effective organophotocatalyst for the oxidative azolation of unactivated and feedstock arenes. In addition to its tolerance for various functional groups and scalability, this photocatalyst was shown to be promising for the defluorinative azolation of fluoroarenes.

We have presented a carbonate anion assisted photochemical protocol for the C–X bond activation. Anion−π interactions have been leveraged to generate aryl radicals from easily accessible aryl halides that are further utilized in C–P and C–B bond formation reactions with excellent reactivity and broad functional group tolerance. Spectroscopic investigations and DFT studies were conducted for mechanistic insights. This inexpensive method alleviates the use of a photocatalyst and the need of preactivation of the substrate for the light-induced activation of C–X bonds.

The integration of deep learning (DL) into co-clinical applications has generated substantial interest among researchers aiming to enhance clinical decision support systems for various aspects of disease management, including detection, prediction, diagnosis, treatment, and therapy. However, the inherent opacity of DL methods has raised concerns within the healthcare community, particularly in high-risk or complex medical domains. There exists a significant gap in research and understanding when it comes to elucidating and rendering transparent the inner workings of DL models applied to the analysis of medical images. While explainable artificial intelligence (XAI) has gained ground in diverse fields, including healthcare, numerous unexplored facets remain within the realm of medical imaging. To better understand the complexities of DL techniques, there is an urgent need for rapid advancement in the field of eXplainable DL (XDL) or eXplainable Artificial Intelligence (XAI). This would empower healthcare professionals to comprehend, assess, and contribute to decision-making processes before taking any actions. This viewpoint article conducts an extensive review of XAI and XDL, shedding light on methods for unveiling the “black-box” nature of DL. Additionally, it explores the adaptability of techniques originally designed for solving problems across diverse domains for addressing healthcare challenges. The article also delves into how physicians can interpret and comprehend data-driven technologies effectively. This comprehensive literature review serves as a valuable resource for scientists and medical practitioners, offering insights into both technical and clinical aspects. It assists in identifying methods to make XAI and XDL models more comprehensible, enabling wise model choices based on particular requirements and goals.

This paper proposes an effective system for indexing and retrieval of medical images, which uses steerable pyramid transform (SPT) and local neighborhood difference patterns (LNDP). In the first step of the proposed system, a medical image is subdivided into several sub-bands using SPT at different resolutions and orientations. Further, each sub-band is encoded using LNDP in a binary texture pattern to extract more complex texture features. Finally, LNDP histograms are computed for each SPT sub-band. Thereafter, all the sub-band histograms are combined to form a single histogram to produce the required feature vector. The proposed SPT-LNDP technique represents a medical image in an efficient way that contains many sources of information from various scales and orientations. Two open-source medical image datasets namely OASIS- MRI and VIA/I-ELCAP-CT images of human brain were considered for efficient evaluation of the proposed method. To validate the efficacy of the proposed method two parameters namely Average Retrieval Precision (ARP) and Average Retrieval Rate (ARR) were used. A detailed comparative study of the proposed method with other popular methods, which are described in this study, are also presented in this work. The results show improvement over other existing methods considered in this study.

Cyclic alkyl(amino) carbene (cAAC)-anchored boryl- and stibanyl-phosphaalkenes with general formula cAAC = P–ER2[E = B, R = (NiPr2)2(3a-c); E = Sb, R = 2,4,6-triisopropylphenyl (5a-b)] have been synthesized and utilized as precursors for the bis-phosphaalkenyl dichlorogermane [(cAAC = P)2GeCl2] (6) and the first molecular example of a neutral polymeric mixed-valence AgI/AgIIphosphinidenide complex [(cAACP)2Ag4IAgIICl4]n(7). All compounds have been characterized by single-crystal X-ray diffraction and further investigated by nuclear magnetic resonance (NMR), mass spectrometric analysis, and UV–vis/fluorescence measurements. The paramagnetic complex 7has been characterized by ESR spectroscopy. Cyclic voltammetry studies of compounds 3/5have suggested possible one-electron quasi-reversible reductions, indicating their redox noninnocent behavior in solution. Quantum chemical studies revealed the electron-sharing nature of the P–B and P–Sb σ bonds in compounds 3and 5, and the polar CcAAC= P bonds in compounds 3, 5, and 6prevailing their phosphaalkene structures over phosphinidenes.

An atom economic method demonstrates the involvement of noncovalent interaction via hydrogen or halogen bonding interaction in triggering paired electrolysis for the group transfer reactions. Specifically, this method demonstrated the bromination of several aromatic and heteroaromatic compounds through the activation of the C(sp3)–Br bond of organic–bromo derivatives on demand. This electrochemical protocol is mild, and mostly no additional electrolyte is needed, which makes the workup process straightforward. Unlike the existing regioselective monobromination methods, this work utilizes a relatively small amount (1.2 equiv) of bromine surrogates that releases bromine on demand under the electrochemical condition and after completion of the reaction generates acetophenone as a useful byproduct. Green metrics indicate this protocol has a very good atom efficiency with an E-factor of 26.86 kg of waste/1 kg of product. In addition to the scale-up process, this strategy could be extended to the transfer of chlorine and thioaryl units. An extensive mechanistic study is accomplished to validate the hypothesis of noncovalent interaction using computational, spectroscopic, and cyclic voltammetry studies. Finally, the applicability of this newly developed nonbonding interaction to trigger paired electrolysis was extended to the chemoselective debromination of several dihalo organic compounds.

We report the dual role of cerium to promote the photoinduced ligand-to-metal charge transfer (LMCT) process for the generation of the alkyl radical and subsequent Lewis acid catalysis to construct stereodefined C–C bonds. This paradigm utilized ubiquitous carboxylic acids as alkyl radical surrogates and offers excellent diastereoselectivity for the formation of C-4 alkylated coumarins in good to excellent yield. UV–vis spectroscopy studies in combination with in situ Fourier transform infrared spectroscopy are consistent with the proposed mechanism, supporting the participation of the CeIV-carboxylate complex in photoinduced LMCT and its subsequent homolysis to generate the alkyl radial through the exclusion of CO2. Finally, the oxophilicity of cerium enables a two-point complexation with the in situ generated enolate intermediate and facilitates the diastereoselective protonation to form the desired product. Furthermore, this mild and atom-economical catalytic manifolds allow the late-stage modification of pharmaceuticals.

The development of stoichiometric oxidant-free regioselective annulation protocol is a challenging aspect in organic synthesis. Herein, we disclose electricity as a greener oxidant for the C–H/N–H annulation to construct cinnolines using rhodium(III) catalyst under mild conditions. A detailed mechanistic investigation revealed the possibility of both Rh(III/I) and Rh(III/IV) catalytic cycles for the formation of annulated product. Exclusive regioselectivity, diverse substrate scope, and commercially available cheap graphite electrodes are key features of this protocol.

Purpose: For radiologists, identifying and assessing thelung nodules of cancerous form from CT scans is a difficult and laborious task. As a result, early lung growing prediction is required for the investigation technique, and hence it increases the chances of a successful treatment. To ease this problem, computer-aided diagnostic (CAD) solutions have been deployed. The main purpose of the work is to detect the nodules are malignant or not and to provide the results with better accuracy. Methods: A neural network model that incorporates a feedback loop is the recurrent neural network. Evolutionary algorithms such as the Grey Wolf Optimization Algorithm and Recurrent Neural Network (RNN) Techniques are investigated utilising the Matlab Tool in this work. Statistical attributes are also produced and compared with other RNN with Genetic Algorithm (GA) and Particle Swarm Optimization (PSO)combinations for study. Results: The proposed method produced very high accuracy, sensitivity, specificity, and precision and compared with other state of art methods. Because of its simplicity and possible global search capabilities, evolutionary algorithms have shown tremendous promise in the area of feature selection in the latest years. Conclusion: The proposed techniques have demonstrated outstanding outcomes in various disciplines, outperforming classical methods. Early detection of lung nodules will aid in determining whether the nodules will become malignant or not.

Strategic religious engagement in relief & development efforts requires: a careful country assessment, establishing appropriate principles of engagement, and defining priority sectors and institutions (possibly including multireligious platforms equipped to serve as intermediaries). Important factors to take into account include: (a) a risk assessment; (b) review of information sources and gaps; (c) understanding religion-state relationships; (d) religious roles in civil society and with both formal and informal institutions; (e) involvement in humanitarian activities including prevention and response; and (f) possible sensitivities and conflicts involving religious actors including potential for engagement with a peacebuilding focus.

Strategic religious engagement (SRE) is important for five main reasons: (a) the direct and significant contributions of religious actors to humanitarian assistance and development, not least their high levels of trust and access, and their distinctive contributions to spiritual care; (b) The local presence and knowledge of faith-based organizations (FBOs) and religious communities that are critical to self-reliance; (c) the significant if often complex relationships that FBOs and religious communities have with governments and other civil society actors; (d) the dual capacity of religious communities to amplify/ameliorate suffering and conflict; and, (e) the central roles of religions in shaping humanitarian norms and law.

This essay introduces a special, open-access issue of this journal focused on the intersection of religion and relief & development. It describes the discussions—of terminology and tangibility, of perspective and practice—that shaped the research done for the “Evidence-Based Summit on Strategic Religious Engagement,” a conference organized and hosted October 5–8, 2020 by the US Agency for International Development (USAID). This special issue consists of four research papers produced for the Summit, along with four response essays. While the available empirical evidence, expertise, and experience varies across myriad issues involving the nexus of religion and relief & development, these papers do document the tremendous growth of this field in the last 20 years, as well as the enormous opportunity to contribute.

Religious involvement in humanitarian aid and development takes countless forms, varying by country, traditions, and sector. Research and operational evaluations have expanded in recent years, with some sectors far better documented than others, notably health and peacebuilding. There are, however, substantial gaps in evidence, most significantly regarding local and non-formal involvement. Pandemic preparedness and response are critical issues ready for further development. Family planning, a USAID priority, exemplifies sensitivities but also positive examples and potential. Experience of religious engagement on women’s empowerment including specific issues like gender-based violence (GBV) and child marriage, can be complex, but their importance warrants particular attention. Child protection poses similar issues.

The direct oxidative C(sp2)−H functionalization represents a step and atom economical approach for the utilization of aldehyde as promising acyl surrogates. A mild and practical copper catalyzed‐TBHP/DTBP promoted esterification of aldehydes in the presence of alkyl halides and alcohols has been developed. This strategy relies on naturally abundant aldehydes as acyl precursors, which can further couple with readily available alkyl halides/alcohols to transform into esters. Preliminary mechanistic investigations reveal that aldehyde in the presence of tert‐butoxy radical transforms into acyl radical. This protocol exhibits a broad substrate scope along with wide functional group compatibility and is applicable to a wide range of aldehydes, including challenging aliphatic aldehydes to undergo the C(sp2)−H bond scission to furnish the desired esters in good to excellent yields. A direct copper catalyzed‐TBHP/DTBP promoted oxidative esterificationof aldehydes for the synthesis of esters has been developed. This transformation is compatible with both alkyl halides and alcohols. This methodology displays a broad substrate scope and exhibits excellent functional group tolerance.

H2O dissociation is the rate-limiting step for the water–gas shift (WGS) reaction. Density functional theory (DFT) was used to study H2O and CO dissociation on a series of step (211) bimetallic alloy surfaces consisting of Cu and Ni metals. The water dissociation process was more facile on (211) step surfaces than on (111) flat surfaces. But CO dissociation on the (211) step surfaces was less reactive than that on its close-packed (111) counterparts. Depending on the initial-state structures, the transition states for H2O dissociation were located at different sites available on a (211) step surface. The introduction of Cu atoms on a bare Ni(211) surface (Ni-based alloy) decreased the reactivity of H2O dissociation, whereas the introduction of Ni atoms on a bare Cu(211) surface (Cu-based alloy) increased the reactivity. At a given molecular temperature, rate constant values were calculated. They showed that bare Ni(211) and Cu-based alloy surfaces exhibited a significantly higher rate constant for H2O dissociation than bare Cu(211) and Ni-based alloy surfaces. Different surface-based properties such as surface energy, work function, d-band center energy, and H adsorption energy were calculated and qualified as descriptors for H2O dissociation on the alloy surfaces. The linear relationship between the activation energy barrier and the reaction energy (Bronsted–Evans–Polanyi (BEP) relationship) held good for H2O and CO dissociation processes on all of the alloy surfaces. The transition state toward a given dissociation pathway is modified by the motion of top-layer lattice atom over which H2O gets adsorbed. The effect of surface temperature on the reactivity, which was calculated using semiclassical methods, showed that the reactivity increased with surface temperature on all of the alloy surfaces. C2dimer formation, the first step of coke formation on any catalyst surface, was less reactive for Cu-based alloy surfaces. Overall, considering all of the aspects of H2O and CO dissociation and C2dimer formation, Cu-based (211) step alloy surfaces showed improved performance as the WGS reaction catalyst.

Restitution offers the juvenile court a unique sentencing option in that it considers the victim's rights while it also punishes, rehabilitates, and deters the offender. However, there is some concern among practitioners and scholars as to whether restitutive sentencing can be incorporated into the current offender-oriented juvenile justice system without losing its original purpose--to restore victims to their original conditions prior to crimes. In the face of such concern, the State of Michigan, on June 1, 1988, enacted into law bills 4240 and 4558, bringing about changes in juvenile restitution. Author Sudipta Roy critically assesses the practicality of these two bills in achieving total justice for victims.

The era of continuous advancement in science and technology leads to an increase in global pollution in the aquatic environment. Due to the presence of emerging contaminants, water bodies pose a serious threat toward human beings as well as other aquatic animals. Among all the contaminants, there is a significant amount of fluoride reported in many literature studies. In this work, activated carbon was synthesized from saw dust. Saw dust was a low-cost material obtained as a byproduct of the various woodworking operations. In this investigation, we have developed a comparative study on Interval Type 2 Fuzzy Logic System (IT2FLS) and Response Surface Methodology (RSM) by which we can predict the removal of fluoride. Here, Absorbent dose, Contact time, and temperate are the input parameters in an imprecise data set. The comparative batch study was conducted for determining the influence of process parameters on adsorption. A comparative study on the adsorption model and the adsorption kinetics was also done. The mechanism of fluoridation adsorption is further analyzed from a thermodynamics point of view. For economical feasibility, the regeneration study was conducted with one of the samples where it showed the reduction in de-fluoridation efficiency by 28.39%in five cycles. Our objective is to apply different soft computing tools, IT2FLS and RSM approach, to predict the removal of fluoride from the wastewater treatment process. The prediction capability of both methods was compared on the basis of the coefficient of determination (R2). The R2value for IT2FLS was 0.993 and for RSM was 0.98. It can be concluded from the result that the IT2FLS has a more prominent prediction result than RSM.

Twitter text is difficult to analyse due to the non-standard and unstructured data. Twitter does not accumulate user gender information as do other popular social media platforms. The demographic feature prediction and additional informative content are important for advertising, custom-made marketing and authorised investigation from the social medium. The proposed statistical method with real-time analysis using big data technologies is able to predict the gender of Twitter users. Gender prediction is performed using the naive Bayes classifier to address systemic issues, and Apache Hive is used to solve data cleaning, storage and processing issues. The proposed method is a speedy, easy-to-implement with pre-processing, close to state-of-the-art document text categorisation method using big data technologies.

A Schiff-base nickel(II)-phosphene-catalyzed chemodivergent C–H functionalization and cyclopropanation of aromatic heterocycles is reported in moderate to excellent yields and very good regioselectivity and diastereoselectivity. The weak, noncovalent interaction between the phosphene ligand and Ni center facilitates the ligand dissociation, generating the electronically and coordinatively unsaturated active catalyst. The proposed mechanisms for the reported reactions are in good accord with the experimental results and theoretical calculations, providing a suitable model of stereocontrol for the cyclopropanation reaction.

Dissociative chemisorption is one of the most significant steps in heterogeneous catalysis. The rate-limiting step for industrially important processes such as water gas-shift reaction and steam reforming of methane involves the dissociative chemisorption of water and methane, respectively. These reactions exhibit interesting mode-specificity and show a strong dependence on the surface temperature of the catalyst. The metals commonly used in industry as catalysts for these two processes have their own limitations. Certain bimetallic surfaces and subsurface alloys are suggested, which could be regarded as potential catalysts for these two industrial processes. How transition states are modified by the motion of the lattice atom during the reactions are shown using electronic structure calculations. In the present review, we have focused on the lattice atom distortion in the transition state, semi-classical tunnelling probability, and the influence of surface temperature on reactivity. Quantum dynamics study for HO dissociation on metal surface is explored using three-dimensional London-Eyring-Polanyi-Sato potential energy surface. A full quantum mechanical approach following reaction path Hamiltonian is also studied by including the effects of lattice motion and site averaging. The effects of initial vibrational mode on reactivity are reported. Vibrational efficacy is examined in terms of vibrational non-adiabatic couplings.

Objective: Amylases enzymes hydrolyze starch molecules to produce diverse products including dextrins, and progressively smaller polymers. These include glucose units linked through α-1- 1, α-1-4, α-1-6, glycosidic bonds. Methods: This enzyme carrying an (α /β) 8 or TIM barrel structure is also produced containing the catalytic site residues. These groups of enzymes possess four conserved regions in their primary sequence. In the Carbohydrate-Degrading Enzyme (CAZy) database, α-amylases are classified into different Glycoside Hydrolase Families (GHF) based on their amino acid sequence. The present objective was to study one such enzyme based on its molecular characterization after purification in our laboratory. Its main property of solid-natural starch degradation was extensively investigated for its pharmaceutical/ industrial applications. Results: Amylase producing bacteria Bacillus cereus sm-sr14 (Accession no. KM251578.1) was purified to homogeneity on a Seralose 6B-150 gel-matrix and gave a single peak during HPLC. MALDITOF mass-spectrometry with bioinformatics studies revealed its significant similarity to α/β hydrolase family. The enzyme showed an efficient application; favourable Km, Vmax and Kcat during the catalysis of different natural solid starch materials. Analysis for hydrolytic product showed that this enzyme can be classified as the exo-amylase asit produced a significant amount of glucose. Conclusion: Besides the purified enzyme, the present organism Bacillus cereus sm-sr14 could degrade natural solid starch materials like potato and rice up to the application level in the pharmaceutical/ industrial field for alcohol production.

Copy-move forgery (CMF), which copies a part of a picture and pastes it into another location, is one of the common strategies for digital image tampering. Due to the arrival of high-performance hardware and the compact use of image processing software, empowers creating image forgeries easy that are undetectable by the naked eye. For CMF detection, we suggest an efficient and vigorous method that could take care of numerous geometric ameliorations including rotation, scaling, and blurring. In the projected CMF detection system, we use Stockwell transform (S-transform) which hybrids the advantages of both scale invariant feature transform (SIFT) and wavelet transform (WT) to extract the key points and their descriptors from the overlapped image blocks. Furthermore, Euclidean distance (ED) between the overlapped blocks are measured to detect the similarities and to identify the tampered or forged region in the image. Besides, a novel fuzzy min-max neural network-based decision tree (FMMNN-DT) classifier is used to recognise the duplicated regions in the forgery image. The proposed system is tested and validated using MICC-F220 dataset and we present comparison among the proposed outcomes with some existing ones which ensure the significance of the proposed.

The forthcoming era of industrialization is marked by intelligent production, with the manufacturing industry and the Industrial Internet of Things (IIoT) driving the creation and innovation of new concepts and systems. One innovative solution addressing the demands of industrial settings is the time-slotted channel hopping (TSCH) protocol, known for its ultra-low power consumption and reliable communication. However, the scheduling mechanism at the core of TSCH requires substantial time for every node. In this study, we propose a sustainable scheduling method for Industrial IoT networks called Linguistic Interval Type 2 Fuzzy Logic System (LIT2FLS) based Priority Aware TSCH (LIT2FLPAS). The suggested method aims to promote service diversification to extend battery life and reduce delay. The significance of each node is determined by the LIT2FLS method, which allocates the required number of slots based on priority and the volume of generated traffic. We compare our proposed LIT2FLPAS algorithm with two state-of-the-art scheduling functions, Scheduling Function-Zero (SF0) and Low Latency Scheduling Function (LLSF). The results demonstrate that LIT2FLPAS exhibits superior adaptability compared to SF0 and LLSF. It provides collision-free communications and significantly enhances the duty cycle by 8%, battery life by 30.32%, and packet loss ratio by 95% while reducing latency by 75.69% and collisions by 91%. These findings underscore the effectiveness of the LIT2FLPAS algorithm in addressing the specific challenges of industrial IoT networks, offering improved performance metrics and robust communication in dynamic industrial environments. The proposed method presents a promising solution for achieving efficient and reliable communication in the evolving landscape of intelligent industrial production.

An Exigency Vehicle (EV) has to go more quickly to increase the likelihood that someone in danger would survive. Construction projects, strikes, and accidents may all be avoided with an effective vehicle routing solution. The Internet of Things (IoT) network simulation for Exigency Vehicle routing is proposed in this research utilizing a linguistic interval type 2 fuzzy logic system (LIT2FLS) based data fusion approach. The predicted effectiveness of our model for intelligent city applications, including emergency vehicle routing, is well demonstrated by the LIT2FLS correlation coefficients of 0.994%. This data fusion approach determines the exact level of congestion for a given place by combining sensory data with crowd reactions. In addition, OSRM employs a road communication system to detect real-time traffic variations and choose the least congested route. Furthermore, a sensor station for gathering the speeds and pollutants of moving automobiles on the road has also been established. An Android app has been developed to collect public information on blockages A driver of an Exigency Vehicle (EV) is directed towards a medical facility with the quickest, congestion-aware path by means of the Application software service. We have created an IT2FLS system that assists with decisions to calculate traffic congestion. Analyze the ability to scale and quickness of response associated with the suggested routing strategy.

Herein, we have developed a new class of organic photocatalysts that can mimic transition metals for several oxidative and reductive organic cross-coupling transformations. Due to its wide potential window in both the oxidation and reduction ranges, cinnoline exhibits dual catalytic activity under visible light illumination, acting as both a photoreductant and photooxidant.

This paper presents an efficient medical image indexing and retrieval method using two new proposed feature descriptors named as threshold local binary AND pattern (TLBAP) and local adjacent neighborhood average difference pattern (LANADP). In basic local binary pattern (LBP), every center pixel is considered as a threshold to generate the binary pattern, whereas in the proposed method a threshold value is calculated using the highest pixel intensity of the neighboring pixels to construct the threshold local binary pattern (TLBP). Thereafter, logical AND operation is performed between LBP and TLBP pattern to produce TLBAP pattern. The objective of the other feature descriptor named here as LANADP is to explore the relationship of neighboring pixels with its adjacent neighbors in vertical, horizontal and diagonal directions. In the proposed work, both TLBAP and LANADP features are concatenated in the form of the histograms to generate the final features vector and the performance of the system is evaluated. To test the effectiveness of the proposed method, three publicly available medical image databases, namely OASIS-MRI brain images, NEMA-CT images and VIA/ELCAP-CT images, are used. Two measures, viz. average retrieval precision and average retrieval rate, have been used to evaluate the performance of the method proposed which is further compared with some existing local pattern-based methods. The experimental results show that the proposed methods give better results as compared to the other existing methods considered in this study.

Methane-dissociative chemisorption is the rate-determining step in the industrially important steam reforming and dry reforming reactions of methane. Widely used industrial catalysts containing Ni as the active metal face the problems of carbon deposition and deactivation, whereas Pt surfaces with lower barrier are expensive to be used in the industrial scale. Using density functional theory calculations, a series of surface and subsurface Ni–Pt bimetallic surfaces were studied to understand the synergistic catalytic activity of alloying elements toward facilitating methane dissociation and in resisting carbon formation. Addition of Ni to Pt(111) decreased activation energy barriers, whereas a linear increase in barrier was found when Pt is added to Ni(111) surface. The observed reactivity trends were explained using surface-based descriptors like work function, surface energy, and d-band center and also using energy-based descriptors, namely, Bronsted–Evans–Polanyi and transition-state scaling relationships. Changes in barrier heights and locations of the barrier with lattice atom motion were calculated to include the effect of surface temperature on dissociation probabilities. Dissociation probabilities thus calculated at different surface temperatures using semiclassical methods showed that reactivity increased with surface temperature on all surface alloys. Overall, two surfaces, viz., Ni9/Pt(111) and sub-Pt9/Ni(111), showed improved behavior toward CH4dissociation, irrespective of the composition of underlying layers. C2formation on these two alloys also showed higher barriers compared to pure Ni(111) surface. However, considering all aspects like energy barriers to CH4dissociation and CH dissociation, carbon adsorption energy, and cost, the subsurface alloy, sub-Pt9/Ni(111), showed an enhanced overall performance as a reforming catalyst.

Purpose:The Revised National Tuberculosis Control Programme (RNTCP) is now introducing daily fixed-dose regimen instead of Directly Observed Treatment, Short Course (DOTS) regimen for treatment of drug-sensitive tuberculosis (TB) in India. It would be beneficial to understand the drawbacks, barriers and advantages of the existing system for better implementation of new policy. Our study was aimed to evaluate the current microbiological status of new microbiologically confirmed pulmonary TB patients who have successfully completed intermittent DOTS regimen within last 2 years and also to find the economic barriers faced by beneficiaries to avail DOTS treatment. Materials and Methods:We included patients who had completed CAT 1 DOTS regimen within the last 2 years. The patients were interviewed. Sputum sample was collected for microscopy and cartridge-based nucleic acid amplification test. Results:All patients were adhered to intermittent DOTS therapy, and sputum conversion rate was 83%. Minor gastrointestinal side effects were experienced by 60% of cases and 87% consumed drugs under supervision. On microbiological examination, 10% of the study population was found to be positive for TB and they all were rifampicin sensitive. Those who had completed treatment within 1 year with no clinical symptoms re-appeared after treatment. Conclusion:Till date, RNTCP does not follow up the patients for any period of time after successful completion of treatment. Through the present study, we could find relapse cases in 10% of the previously treated non-symptomatic patients. These unnoticed relapse cases have potential to spread TB and increase disease burden of country. Thus, we can conclude that RNTCP has to follow up the patients after successful treatment to determine whether they relapse. It is needed for the success of programme and control of the disease in the country.

This article discusses the work on how power ultrasound can enhance agitation greatly, especially in constricted spaces. It also highlights the need of deeper understanding and elucidation of mass transfer phenomena under sonication, and thus the requirement for specialised electrochemical devices and methods to quantify mass transfer. It also points out that the use of imaging techniques can help in visualising cavitation bubbles and elucidating high-agitation mechanisms.

Plagiarism refers to the act of copying content without acknowledging the original source. Though there are several existing commercial tools for plagiarism detection, still plagiarism is tricky and challenging due to the rise in volume of online publications. Existing plagiarism detection methods use paraphrasing, sentence and key-word matching, but such techniques has not been very effective. In this work, a framework for fuzzy based plagiarism detection is proposed using a context-aware agglomerative clustering approach with an improved time complexity. The work aims in retrieving key concepts at word, sentence and paragraph level by integrating semantic features in a novel optimisation function to detect plagiarism effectively. The notion of fuzzy clustering has been applied to improve the robustness and consistency of results for clustering multi-disciplinary papers. The experimental analysis is supported by comparison with other contemporary techniques which indicate the superiority of proposed approach for plagiarism detection.

The tannase production ability by endophytic actinobacteria and the genetic identity of responsible tannase gene were determined. The studied strains were isolated from surface-sterilized leaf discs of Ailanthus excelsaRoxb. Four strains were found to hydrolyze tannic acid on solid media containing 0.4% tannic acid. The strain AL1L was found as tanBLPindicating production of tannase with diverse of substrate affinity. The tannase production from the potential strain AL1L was performed in liquid tannic acid broth (0.4%, w/v). The strain was later identified as Streptomycessp. AL1L on the basis of 16S rDNA homology. Highest enzyme activity was observed at 48 h of incubation at the exponential growth phase. The enzyme was purified by ammonium sulfate precipitation followed by dialysis (15 kD cut off). This enzyme, with molecular weight 180 kD shows highest catalytic activity at 35 °C, pH 6 with substrate concentration 0.1 g%. The purified enzyme possesses 1.4 × 10−3Kmand 11.15 U/ml as Vmax. The above study indicates high industrial prospective of endophytic actinobacteria as source of tannase of potential biotechnological applications.

With the rise in popularity of deep learning techniques, the use of semantic segmentation algorithms have become increasingly attractive to. In this paper, we studied the U-Net architecture for its performance and quality of predicted masks on the GLAS dataset. The dataset contains images of haematoxylin and eosin (HE) stained slides and their respective masks. A new architecture called as DenseRes-Net has been proposed, which is an improvement over the existing U-Net. In the proposed architecture, we combine the strength of the DenseNet and the Residual Network to enhance the quality of feature representation. The proposed DenseRes-Net achieves a 0.89% higher F1 score when compared with the U-Net. Additionally, we can observe a 2.69% increase in precision. These demonstrates the effectiveness of the proposed DenseRes-Net over the existing U-Net architecture.

We have developed an efficient visible light induced copper‐mediated protocol to synthesize conjugated 1,3‐diynes viadecarboxylative homocoupling of arylpropiolic acids. A variety of electron‐rich and electron‐deficient acids worked well in this methodology providing desired product upto 99% yield. Detailed mechanistic investigations including UV‐Vis spectroscopy and in‐situIR experiments have been carried out to elucidate the plausible reaction mechanism. Compared to previously reported thermal strategies, this new pathway occurs at room temperature, employs greener solvent and aerial oxygen as an oxidant. We have demonstrated a photoinduced copper mediated decarboxylative homocoupling of arylpropiolic acids to synthesize, 3‐diynes. A variety of arylpropiolic acids containing electron‐rich as well as electron‐deficient groups undergo conversion to afford the conjugated 1,3‐diynes. Detailed mechanistic studies were carried out to understand the mechanism. The synthesized molecule was successfully applied for post‐functionalization to afford compounds having utility in various fields.

ABSTRACTThe paper proposes a robust and blind watermarking scheme based on visual cryptography for copyright protection of digital images. It shuffles the pixel locations of the binary watermark using Arnold cat map transform to realize the effect of encryption for better security and it divides the image into blocks of equal size. Two secret shares are generated from the watermark and the image based on visual cryptography by comparing the global mean value of the pixels in the image with the mean values of all pixels of randomly selected blocks. As documented in the experimental results, the proposed scheme is more robust in comparison with other visual cryptography-based watermarking schemes. It can resist several attacks such as JPEG compression, gamma correction, impulse noise, Gaussian noise, blurring, median filter, rotation, scaling, and sharpening attacks on different types of images. The computational time for generating secret shares and detection of the watermark is comparatively small.

ABSTRACTIn this paper, an iterative implement of level set methodology has been proposed for the precise segmentation of normal and abnormal tissues in magnetic resonance imaging (MRI) brain images. In this segmentation, the normal tissues such as WM (white matter), GM (grey matter), and CSF (cerebrospinal fluid) with other regions of human head such as skull, marrow, and muscles skin are segmented and abnormal tissues such as haemorrhage, oedema, and tumour can be segmented if any. The segmentation is done by using iterative three region level set method based on the condition sharp peak greater than three. The iterative segmented component generates a hierarchical structure for correct segmentation. The performance of the segmentation method is estimated by different metrics such as accuracy, similarity index, and relative error. The performance of segmentation method is examined using a defined set of MRI brain.

This paper propose a computerized method of magnetic resonance imaging (MRI) of brain binarization for the uses of preprocessing of features extraction and brain abnormality identification. One of the main problems of MRI binarization is that many pixels of brain part cannot be correctly binarized due to extensive black background or large variation in contrast between background and foreground of MRI. We have proposed a binarization that uses mean, variance, standard deviation and entropy to determine a threshold value followed by a non-gamut enhancement which can overcome the binarization problem of brain component. The proposed binarization technique is extensively tested with a variety of MRI and generates good binarization with improved accuracy and reduced error. A comparison is carried out among the obtained outcome with this innovative method with respect to other well-known methods.

This study investigated the effect of water on the physical and electrochemical properties of a choline chloride deep eutectic solvent (DES). Physical and electrochemical measurements were performed on the DES containing varying amounts of water. When the water content was increased from 1% to 15 wt%, the viscosity and density declined while the conductivity increased significantly. Similarly, the limiting current for Cu reduction at 15 wt% of water was also found to be three times greater than the limiting current at 1 wt% of water. This latter effect could be mostly attributed to viscosity changes, but there was evidence of deviations from ideal Stokes-Einstein behaviour. These experimental results show that inclusion of water facilitates ion mobility, and generally results in improved electrochemical characteristics.

The effect of pulse parameters on the deposition of copper from a chloride-based deep eutectic solvent (DES) was investigated. While satisfactory deposits could be obtained for some pulse conditions, at small duty cycles or long off-times the current efficiencies obtained were very low. These findings were consistent with a corrosion process occurring during the off-time, most likely the comproportionation reaction: Cu + CuIICl42- -> 2CuICl2-. Initial experimental results were consistent with the corrosion rate being controlled by the transport of the CuIICl42- species to the electrode surface. With this assumption, a simple corrosion model was developed to account for the observed loss in current efficiency as a function of the pulse conditions. While this model broadly predicts the observed results some discrepancy between the observed and measured were noted.

The paper proposes a shot boundary detection system using Gist and local descriptor. Gist provides the perceptual and conceptual information of a scene. The proposed system can be roughly divided into three steps. The first step consists of forming of groups of frames by calculating the correlation of the Gist features between consecutive frames of the video. Secondly, abrupt transitions are found out using the group (G), MSER and a threshold (for abrupt separately, $$th_{cut}$$ thcut ). And lastly, gradual transitions of the video are found using triangular pattern matching. We have performed the experiment on TRECVid 2001 and 2007 dataset. The novel contribution of this paper is that the proposed system shows an activity-based shot boundary detection where only the possible transition regions of a video are considered for shot detection. This approach reduces the computational complexity by processing the transition regions only. We have achieved better results in terms of F1, precisionand recall, when compared to previously published approaches.

A novel contrast in palladium and copper catalysis is revealed to form products of different chemotypes resulting in a phenazine to azoarene twist through an altered mechanistic pathway (from non-radical C–H activation mode of C–N coupling to radical N–N coupling) during the oxidative self-coupling of anilines catalysed by Pd–Ag and Cu–Ag nanoclusters.

Fetal brain segmentation and gestational age prediction have been under active research in the field of medical image processing for a long time. However, both these tasks are challenging due to factors like difficulty in acquiring a proper fetal brain image owing to the fetal movement during the scan. With the recent advancements in deep learning, many models have been proposed for performing both the tasks, individually, with good accuracy. In this paper, we present Multi-Tasking Single Encoder U-Net, MTSE U-Net, a deep learning architecture for performing three tasks on fetal brain images. The first task is the segmentation of the fetal brain into its seven components: intracranial space and extra-axial cerebrospinal fluid spaces, gray matter, white matter, ventricles, cerebellum, deep gray matter, and brainstem, and spinal cord. The second task is the prediction of the type of the fetal brain (pathological or neurotypical). The third task is the prediction of the gestational age of the fetus from its brain. All of this will be performed by a single model. The fetal brain images can be obtained by segmenting it from the fetal magnetic resonance images using any of the previous works on fetal brain segmentation, thus showing our work as an extension of the already existing segmentation works. The Jaccard similarity and Dice score for the segmentation task by this model are 77 and 82%, respectively, accuracy for the type of prediction task is 89% and the mean absolute error for the gestational age task is 0.83 weeks. The salient region identification by the model is also tested and these results show that a single model can perform multiple, but related, tasks simultaneously with good accuracy, thus eliminating the need to use separate models for each task.