The design of amino acid sensors have significant applications in biomarkers. Herein, we have developed an achiral Fe(III)-complex [FeIIIL(bzac)] (complex 1) (H2L = N,N′-dimethyl-N,N′-bi(2-hydroxy-3,5-dichlorobenzyl)-ethylenediamine (H2L) that shows highly selective sensing of histidine over others in an aqueous methanolic solution. The complex has been synthesized by a simple stirring method using H2L and benzylacetone (bzac) as the ancillary ligand and characterized by single crystal X-ray diffraction analysis along with other spectroscopic studies. Structural study shows that it is a simple mononuclear complex though the single molecule is chiral; however, the overall crystalline lattice is achiral in nature. In the complex, Fe(III) shows distorted octahedral geometry, and each discrete complex is connected by supramolecular hydrogen bonding interactions to form achiral 3D supramolecular structure. Both the absorption spectral analyses reveal that the complex has peaks at 484 nm due to ligand-to-metal charge transfer (LMCT) and emission spectra have a peak at 566 nm. In the presence of histidine, there is a red-shift of about 70 nm and peaks arise at about 564 nm, while the emission spectra shows that the peak at 566 nm disappears completely and a new peak at 603 nm appears. In the presence of other amino acids, there is no such distinct change in both the absorption and emission spectra of the complex. It also acts as an effective catalyst toward the oxidation of 3,5-di-tert-butylcatechol in methanol solvent.

SNU Management

New Journal of Chemistry

A new mononuclear Fe(iii) Mannich-base complex with selective histidine binding and catechol oxidase activity

This chapter summarizes the book's overall concept within a compact format wherein we have presented the succinct story of each chapter contributed to this volume. We describe the role of distinctly separate three poles and three polar regions (TPRs) in the planet's functioning as a system through various positive and negative feedback loops. Also we describe how the Earth as a system is connected through different pathways to its various spheres, such as geosphere, lithosphere, hydrosphere, atmosphere, biosphere, and the latest one, anthroposphere.

Center for Environmental Sciences & Engineering

WILEY

Advances in Remote Sensing in Relation to Spheres of the Planet Earth

With advances in remote-sensing technology and higher-quality products, the field of spectral indices has experienced a sizeable progressive evolution. These spectral indices computed by using different brands of remote-sensing products and applying additive, subtractive, or normalizing operations, have been and are being devised to detect diverse objects in various spheres of our planet. For instance, there are spectral indices that detect and delineate plant leaf moisture, that biome comes under biosphere, at distinctive spatial scales. There are other indices that are used to extract elements from the lithosphere like iron oxide content, or to show clay mineral content, etc. Similarly, there are spectral indices to detect various elements which constitute other spheres of our planet. In this work, we have used the Web of Science (WoS) database from 1999 to 2022 and searched for only English language research journals, book chapters, books, and scientific reports. We found 2227 documents in all the categories to perform a systematic, scientometric review regarding spectral indices used for the identification of elements constituting various objects of the five different spheres. The primary objective of this chapter is to present a systematic and scientometric review of spectral indices used for quantification of different elements of all the spheres of our planet relating to lithosphere, hydrosphere, atmosphere, biosphere, and anthroposphere, across remote-sensing platforms and sensors. The study also examines the rationale of spectral indices across the ever-advancing remote-sensing platform and sensors, and their future challenges, and investigates the challenges and prospects of this domain of study. This study will be useful for acquainting new researchers with the use spectral indices for their specific objectives.

Spectral Indices Across Remote Sensing Platforms and Sensors Relating to the Three Poles: An Overview of Applications, Challenges, and Future Prospects

Advances in Remote Sensing Technology and the Three Poles is a multidisciplinary approach studying the lithosphere, hydrosphere (encompassing both limnosphere, and oceanosphere), atmosphere, biosphere, and anthroposphere, of the Arctic, the Antarctic and the Himalayas. The drastic effects of climate change on polar environments bring to the fore the often subtle links between climate change and processes in the hydrosphere, biosphere, and lithosphere, while unanswered questions of the polar regions will help plan and formulate future research projects.Sample topics covered in the work include:<ul><li>Terrestrial net primary production of the Arctic and modeling of Arctic landform evolution</li><li>Glaciers and glacial environments, including a geological, geophysical, and geospatial survey of Himalayan glaciers</li><li>Sea ice dynamics in the Antarctic region under a changing climate, the Quaternary geology and geomorphology of Antarctica</li><li>Continuous satellite missions, data availability, and the nature of future satellite missions, including scientific data sharing policies in different countries</li><li>Software, tools, models, and remote sensing technology for investigating polar and other environments</li></ul>For postgraduates and researchers working in remote sensing, photogrammetry, and landscape evolution modeling, Advances in Remote Sensing Technology and the Three Poles is a crucial resource for understanding current technological capabilities in the field along with the latest scientific research that has been conducted in polar areas.

Advances in Remote Sensing Technology and the Three Poles

World University root node

Recent Advancement and Role of Drones in Forest Monitoring: Research and Practices

The Billboard magazine is a world renowned music publication since 1984 and it releases a weekly ranking of the top 100 songs in various categories such as rock, pop, hip-hop, etc. Several studies have determined that it is possible to predict the approximate bucket of ranks that a song is likely to chart in using social and subjective indicators. The definitions of these indicators however can change over time, thus rendering the previous classifications erroneous. Here, we report successful results from our experiments in predicting the ranks and the number of weeks the songs are likely to stay on the charts, using objective and well-defined features, obtained from Spotify’s Web API. It extends existing research about classifying songs into rank buckets of Top-10 and Top-40 using these objective features, demonstrating that it is possible to predict exact ranks of the songs within a root-mean-squared-error of 28 ranks and the number of weeks of charting within a root-mean-squared-error of 7 weeks, demonstrating definitive trends between individual features and the ranks of the songs, demonstrating that objective metadata about the songs serve as good indicators about the trends in the Billboard charts, and can be used to predict a song’s performance on the charts within acceptable error rates.

Computer Science and Engineering

(SoE) School of Engineering

<a href="https://link.springer.com/chapter/10.1007/978-3-031-15512-3_19">Performance Prediction of Songs on Online Music Platforms</a>

Environmental sensing is becoming an important segment across the geographical globe even beyond. That can be important tool for primary alarming indicator for saving millions of lives in the world. Impact of sensing devices is playing key role in understanding; as a result, we stand a better chance of improving and keeping our impact to a minimum. The obvious benefits of environmental monitoring include pollution reduction, occupational disease reduction, and also minimizing the impact of human activities. Perhaps, the biggest advancement that environmental sensing will make in the future is that smart sensors will be implemented in order to monitor the natural gas infrastructure. Here, we will be discussing some of the most innovative ways in which smart sensors are used to reduce the strain on the environment.

Smart Nanostructure Materials and Sensor Technology

<a href="https://doi.org/10.1007/978-981-19-2685-3_6">Environmental Impact of Sensing Devices</a>

Mechanical Engineering

International Journal of Production Economics

<a href="https://www.sciencedirect.com/science/article/pii/S0925527322002390">Modelling and prediction of worker task performance using a knowledge-based system application</a>

Glioblastoma multiforme (GBM) is known as the primary malignant and most devastating form of tumor found in the central nervous system of the adult population. The active pharmaceutical component in current chemotherapy regimens is mostly hydrophobic and poorly water-soluble, which hampers clinical implications. Nanodrug formulations using nanocarriers loaded with such drugs assisted in water dispersibility, improved cellular permeability, and drug efficacy at a low dose, thus adding to the overall practical value. Here, we successfully developed a water-dispersible and biocompatible nanocargo (GO-PEG) based on covalently modified graphene oxide (GO) with a 6-armed poly(ethylene glycol) amine dendrimer for effective loading of the two hydrophobic anticancer drug molecules, CPI444 and vatalanib. These drug molecules target adenosine receptor&nbsp;(A2AR), vascular endothelial growth factor receptor (VEGFR), platelet-derived growth factor receptor (PDGFR), and type III stem cell receptor tyrosine kinase (c-KIT), which plays a crucial role in cancers. The effective cellular delivery of the drugs when loaded on GO-PEG is attributed to the increased permeability of the drug-nanoconjugate formulation. We observed that this combinatorial drug treatment with nanocargo resulted in a significant reduction in the overall cell survival as supported by reduced calcium levels and stem cell markers such as Oct4 and Nanog, which are two of the prime factors for GBM stem cell proliferation. Furthermore, reduced expression of CD24 upon treatment with nanoformulation impeded cellular migration. Cellular assays confirmed inhibition of cell proliferation, migration, and angiogenic potential of GBM treated with GO-PEG–Drug conjugates. Ultimately, GBM U87 cells assumed programmed cell death at a very low concentration due to nanocarrier-mediated drug delivery along with the chosen combination of drugs. Together, this study demonstrated the advantage of GO-PEG mediated combined delivery of CPI444 and vatalanib drugs with increased permeability, a three-pronged combinatorial strategy toward effective GBM treatment.

Chemistry

(SoNS) School of Natural Sciences

Frontiers in Oncology

Combinatorial therapy of CPI444 and Vatalanib loaded on PEGylated graphene oxide: Effective nanoformulation to target glioblastoma multiforme

The present study utilized earth observation Sentinel5P TROPOMI (TROPOspheric Monitoring Instrument) data for monitoring submarine volcanic activities. The world has seen a sequence of abrupt volcanic eruptions within one-month duration i.e., on 20th December 2021, 13th January 2022 and 15th January 2022. This caused the emissions of natural gaseous pollutants such as Sulphur Dioxide (SO2), Hydrogen Sulphide (H2S) and ash particles. Atmospheric SO2, AAI (Absorbing Aerosol Index) and Cloud fraction data are retrieved from the TROPOMI onboard the Copernicus Sentinel5P European Space Agency satellite. Results confirmed the increased concentration of SO2 column density after 20th December, and it was observed to be 0.002 mol/m2, the highest concentration during the period of study. Generally, SO2 concentration stays within 0.001 mol/m2 or less. Concentration was seen increasing after the first eruption and reached maximum after 15th January 2022 eruption to a level up to 0.002 mol/ m2. Similar outcomes were observed for AAI for the chosen site. AAI values increased to 25,000 fold (AAI values of 1.25) during the first eruption and increased to 29,000 fold on 15th January 2022 after eruptions (AAI values of 1.45) as compared AAI values of 0.005 or negative AAI values before eruptions. A cloud fraction of value 1 is observed during this time, along with the top most cloud height at 14,233.16 m (14.23 kms) above the crater of the volcano (maximum estimation limit by S5P). These values are higher as compared to isolated islands, as there are no other sources of emissions at surrounding environment. The outcomes support the role of Earth Observation datasets for disaster monitoring and management plans for mitigation. © 2022 Informa UK Limited, trading as Taylor &amp; Francis Group

Remote Sensing Letters

Highlighting the Role of Earth Observation Sentinel5P TROPOMI in monitoring volcanic eruptions: A Report on Hunga Tonga, a Submarine Volcano

The efficacy of Action Recognition methods depends upon relevance of the action (Verb) with respect to the subject (Noun). Existing methods overlook the suitability of Noun-Verb combination in defining an action. In this work, we propose an algorithm called Reduced Verb Set Generator (RVSGen) to reduce the number of possible verbs related to the actions, based upon the relevance of noun-verb combination. A dual modal fusion model for egocentric activity recognition is proposed here to combine the features extracted from the RGB channels and the Optical flow vectors for an egocentric video to recognize the human activity. Unlike state-of-the-art methods, where the key objects and temporal cues are extracted simultaneously, the proposed model first extracts the spatial features i.e., object information (Noun) from the RGB channels and then relates the Noun with the suitable action (Verb) obtained from motion information (Optical Flow). The verbs are predicted by an ConvLSTM architecture with the help of a modified softmax. The notion behind the modified softmax is to estimate the probability distribution with the reduced verb set obtained from RVSGen and the feature vector obtained from the ConvLSTM. With the help of an end-to-end trained architecture, the noun and verb are predicted which are then concatenated together constituting an action. The experiments are performed on a benchmark dataset. The results show the efficacy of the proposed method compared to the state-of-the-art. The codes related to this work can be found at: \url{https://github.com/mpLogics/EgoAR-RVSGen}.

IJCNN

First-person Activity Recognition by Modelling Subject-Action Relevance

Recent years have been witnessing significant developments in research in human action recognition based on skeleton data. The graphical representation of the human skeleton, available with the dataset, provides opportunity to apply Graph Convolutional Networks (GCN), to avail efficient analysis of deep spatial-temporal information from the joint and skeleton structure. Most of the current works in skeleton action recognition use the temporal aspect of the video in short-term sequences, ignoring the long-term information present in the evolving skeleton sequence. The proposed long-term Spatio-temporal Contrastive Learning framework for Skeleton Action Recognition uses an encoder-decoder module. The encoder collects deep global-level (long-term) information from the complete action sequence using efficient self-supervision. The proposed encoder combines knowledge from the temporal domain with high-level information of the relative joint and structure movements of the skeleton. The decoder serves two purposes: predicting the human activity and predicting skeleton structure in the future frames. The decoder primarily uses the high-level encodings from the encoder to anticipate the action. For predicting skeleton structure, we extract an even deeper correlation in the Spatio-temporal domain and merge it with the original frame of the video. We apply a contrastive framework in the frame prediction part so that similar actions have similar predicted skeleton structure. The use of the contrastive framework throughout the proposed model helps achieve exemplary performance while employing a self-supervised aspect to the model. We test our model on the NTU-RGB-D-60 dataset and achieve state-of-the-art performance. The codes related to this work are available at: \url{https://github.com/AnshulRustogi/Long-Term-Spatio-Temporal-Framework}.

Long-term Spatio-temporal Contrastive Learning framework for Skeleton Action Recognition

Single image dehazing is a popular research topic among the researchers in computer vision, machine learning, image processing, and graphics. Most of the recent methods for single image dehazing are based upon supervised learning set up. However, supervised methods require annotation of the data, which often makes the dehazing methods biased towards the manual annotation errors. Unsupervised methods are more likely to produce realistic, clear images. However, fewer efforts are found in the literature for single image dehazing in unsupervised set up. We propose an enhanced CycleGAN architecture for Unpaired single image dehazing, with an attention-based transformer architecture embedded in the generator. The proposed transformer comprises three components: 1) A Feature Attention (FA) block combining channel attention and pixel attention mechanism, 2) A Dynamic feature enhancement block for dynamically capturing the spatial structured features and 3) An adaptive mix-up module to preserve the flow of shallow features from downsampling. Experiments on the benchmark datasets show the efficacy of the proposed method. Codes for this work are available in the link: \url{https://github.com/rsjai47/Attention-Based-CycleDehaze}.

Attention-based Single Image Dehazing Using Improved CycleGAN

Nanodiamonds (NDs) are increasingly being assessed as potential candidates for drug delivery in cancer cells and they hold great promise in overcoming the side effects of traditional chemotherapeutics. In the current work, carboxylic acid functionalized nanodiamonds (ND-COOH) were covalently modified with poly(amidoamine) dendrimer (PAMAM) to form amine-terminated nanodiamonds (NP). Unlike ND-COOH, the chemically modified nanodiamond platform NP revealed a pH-independent aqueous dispersion stability, enhancing its potential as an effective carrier. Physical encapsulation of poorly water soluble cabazitaxel (CTX) drug on NP formed ND-PAMAM-CTX (NPC) nanoconjugates and substantially reduced the size of CTX from micrometer to nanometer. CTX was localized within the pores of nanoparticle aggregates and the cavities of the PAMAM dendrimer, thus facilitating the loaded drug’s controlled and sustained release. NPC’s cumulative CTX release efficiency was determined to be ∼95% at pH 4 after 96 h. A high cellular uptake of NPC both within the cytoplasm and nucleus of U87 cells is confirmed, accounting for a reduced IC50 value (1 nM). Both the cell cycle and Western blot analyses confirmed enhanced cell death and suppressed tubulin protein expression in NPC-treated cells. A significantly high inhibition to cell division with early apoptosis and reduced metastasis demonstrates the effective loading of CTX dosages on the nanocarrier. The present work highlights the potential of a newly designed nanocarrier NP as an efficient nanocargo for cellular delivery applications and may provide future insights to treat one of the most aggressive tumors in neuro-oncological research, glioblastoma multiforme (GBM).

ACS Applied Bio Materials

Dendrimer functionalized nanodiamonds as safe and efficient drug carrier for cancer therapy: Nucleus penetrating nanoparticles

IFAC PapersOnLine

A digital-twin based worker's work allocation framework for a collaborative assembly system

People often query various Points of Interest (POIs) to plan their city tour itineraries using location-based services that provide reviews and ratings of various attributes of a POI. Most traditional location-based recommendation systems focus on various attributes like rating, cost, and social similarities of either POIs, users, or both but do not focus on efficient retrieval of POIs. This paper proposes a spatial index structure uR-tree (utility-based R-tree) for efficiently processing a location-based query implied in a location-aware recommender system to select multiple POIs which a user can visit in a tour. The uR-tree is constructed considering a change in static attributes like availability of parking, basic amenities, etc., and dynamic attributes like ticket price, congestion, etc., of each POI with the aim to maximize a user’s experience. At first, the utility of each POI is calculated at different points of time based on its static and dynamic attributes. A uR-tree is constructed for the systematic indexing of the utility of each POI, and an algorithm is applied for the retrieval of top-k maximum utility POI in different time-intervals. Then, we propose two Utility-based trip scheduling algorithms for distance-based travel recommendations. The experiments were conducted on real-world location-based social network data sets and show that the proposed scheme has a lower response time, Disk I/Os, and a higher query success rate as compared to state-of-the-art, R-tree index structure.

Multimedia tools and Application

uR-tree: a spatial index structure for handling multiple point selection queries

<figure class="table"><table><thead><tr><th>Species distribution models (SDMs) have been used extensively in the field of landscape ecology and conservation biology since its origin in the late 1980s. But there is still a void for a universal modeling approach for SDMs. With recent advancements in satellite data and machine learning algorithms, the prediction of species occurrence is more accurate and realistic. Presently, four machine learning and regression-based algorithms, namely, generalized linear model, maximum entropy, boosted regression tree, and random forest (RF) are used to model the geographical distribution of Rhododendron arboreum, which is economically and medicinally important species found in the fragile ecosystem of Himalayas. To establish complex relation between the occurrence data and regional climatic and land use parameters, several satellite products, namely, MODIS, Sentinel-5p, GPM, ECOSTRESS, and shuttle radar topography mission (SRTM), are acquired and used as predictor variables to the different SDM algorithms. The performance evaluation has been conducted using the area under curve (AUC), which showed the best result for Maxent (AUC  =  0.871) and poor result was observed for RF (AUC  =  0.755) among all. The overall prediction confirmed the distribution of Rhododendron arboreum in the mid to high altitudes of central and southern parts of the Garhwal Division. We provide crucial evidence that combining multisatellite products using machine learning algorithms can provide a much better understanding of species distribution that can eventually help the researchers and policymakers to take the necessary step toward its conservation.</th></tr></thead></table></figure>

Journal of Applied Remote Sensing

Assessing the niche of Rhododendron arboreum using entropy and machine learning algorithms: role of atmospheric, ecological, and hydrological variables

Fantasy Sports are becoming more and more popular these days, hence the race to crack it is more trending than ever. In this paper, we focus on cricket (IPL) and Dream11. Using advanced statistical and graphical models, and new performance metrics for batting and bowling we aim to build a model that can predict the top performing 11 players out of the two teams. This involves predicting the player performance and selecting the best 11 while complying with league constraints. The proposed model on an average predicts 70% of the players from the Dream Team.

Research Challenges in Information Science

Team Selection Using Statistical and Graphical Approaches for Cricket Fantasy Leagues

A square-planar Cu(II) complex [Cu(L)Cl], 1, with sterically constrained tridentate phenol-based ligand (HL= N,N,N′-trimethyl-N′-(2-hydroxy-3,5-di-tert-butylbenzyl)-ethylenediamine) with N, N, O donor sites has been synthesized. The complex is characterized by single crystal X-ray diffraction study as well as other spectroscopic techniques. The reported complex crystallizes in monoclinic space group C2/c with a = 30.248(6), b = 13.750(3) and c = 11.410(2) Å with β = 110.232(2)°. The Cu(II) ion adopts a square planar environment in this complex. Electrochemical study of the complex 1 gives quasi-reversible reductive response at E1/2 ≈ −0.5 V due to the reduction of the Cu(II) center along with a reversible oxidation peak at E1/2 ≈ 0.75 V. The oxidation peak arises due to the ligand-based oxidation of phenolate group to phenoxyl radical in the complex. The Cu(II) complex exhibits catechol oxidase activity in methanol as observed by the UV–vis spectroscopy of the aerial oxidation of 3,5-DTBC to 3,5-DTBQ and the reaction proceeds via the formation of ligand phenoxyl radical. The turnover number for complex 1 is 2560 h−1.

Fulltext

Crystals

Synthesis, Structure and Catechol Oxidase Activity of Mono Nuclear Cu(II) Complex with Phenol-Based Chelating Agent with N, N, O Donor Sites

We propose a novel video sampling scheme for human action recognition in videos, using Gaussian Weighing Function. Traditionally in deep learning-based human activity recognition approaches, either a few random frames or every $k^{th}$ frame of the video is considered for training the 3D CNN, where $k$ is a small positive integer, like $4$, $5$, or $6$. This kind of sampling reduces the volume of the input data, which speeds-up the training network and also avoids overfitting to some extent, thus enhancing the performance of the 3D CNN model. In the proposed video sampling technique, consecutive $k$ frames of a video are aggregated into a single frame by computing a Gaussian-weighted summation of the $k$ frames. The resulting frame preserves the information in a better way than the conventional approaches and experimentally shown to perform better. In this paper, a 3-Dimensional deep CNN is proposed to extract the spatio-temporal features and follows Long Short-Term Memory (LSTM) to recognize human actions. The proposed 3D CNN architecture is capable of handling the videos where the camera is placed at a distance from the performer. Experiments are performed with KTH, WEIZMANN, and CASIA-B Human Activity and Gait datasets, whereby it is shown to outperform state-of-the-art deep learning based techniques. We achieve 95.78%, 95.27%, and 95.27% over the KTH, WEIZMANN, and CASIA-B human action and gait recognition datasets, respectively. &nbsp;&nbsp;

Multimedia Tools and Applications

An Information-rich Sampling Technique over Spatio-Temporal CNN for Classification of Human Actions in Videos

Shiv Nadar University is a comprehensive, multidisciplinary, research-focused, and student-centric University offering a full range of academic programs at the undergraduate, postgraduate and doctoral level. With state-of-the-art infrastructure, the University comprises of academic wings, sophisticated labs, international standard sports facilities, amphitheaters, auditorium, conference rooms and smart classrooms. The University&rsquo;s goal is to become internationally recognized for the quality of its research and creative endeavors and their applicability to improving quality of life, generating new insights and expanding the boundaries of human knowledge creativity. Committed to excellence in teaching, research and service, the University aims to serve the higher education needs of India and the world beyond.