Indian Institute of Technology Patna

Abstract This study was conducted with an objective to understand the role of green human resource management (GHRM) in fostering environmental performance of employee. Specifically, it examines the impact of GHRM practices on employee green performance behaviors (task related and voluntary) with organizational identification as a mediator and employee personal environmental values and gender as moderators. Three hundred one employee from automobile sector in India participated in the study. Using cross‐sectional research design, the proposed research model was tested with the help of hierarchical regression analysis. GHRM was found to significantly predict both task‐related and voluntary employee green behaviors. Organizational identification significantly mediated the effect, whereas gender and environmental values failed to moderate the relationship between GHRM and employee green behaviors. The study signifies the role of HRM in achieving environmental sustainability and emphasizes on the urgent need to embed sustainability dimension into HR systems to achieve sustainable development goals.

Information security and authentication are important challenges facing society. Recent attacks by hackers on the databases of large commercial and financial companies have demonstrated that more research and development of advanced approaches are necessary to deny unauthorized access to critical data. Free space optical technology has been investigated by many researchers in information security, encryption, and authentication. The main motivation for using optics and photonics for information security is that optical waveforms possess many complex degrees of freedom such as amplitude, phase, polarization, large bandwidth, nonlinear transformations, quantum properties of photons, and multiplexing that can be combined in many ways to make information encryption more secure and more difficult to attack. This roadmap article presents an overview of the potential, recent advances, and challenges of optical security and encryption using free space optics. The roadmap on optical security is comprised of six categories that together include 16 short sections written by authors who have made relevant contributions in this field. The first category of this roadmap describes novel encryption approaches, including secure optical sensing which summarizes double random phase encryption applications and flaws [Yamaguchi], the digital holographic encryption in free space optical technique which describes encryption using multidimensional digital holography [Nomura], simultaneous encryption of multiple signals [Prez-Cabr], asymmetric methods based on information truncation [Nishchal], and dynamic encryption of video sequences [Torroba]. Asymmetric and one-way cryptosystems are analyzed by Peng. The second category is on compression for encryption. In their respective contributions, Alfalou and Stern propose similar goals involving compressed data and compressive sensing encryption. The very important area of cryptanalysis is the topic of the third category with two sections: Sheridan reviews phase retrieval algorithms to perform different attacks, whereas Situ discusses nonlinear optical encryption techniques and the development of a rigorous optical information security theory. The fourth category with two contributions reports how encryption could be implemented at the nano-or micro-scale. Naruse discusses the use of nanostructures in security applications and Carnicer proposes encoding information in a tightly focused beam. In the fifth category, encryption based on ghost imaging using single-pixel detectors is also considered. In particular, the authors [Chen, Tajahuerce] emphasize the need for more specialized hardware and image processing algorithms. Finally, in the sixth category, Mosk and Javidi analyze in their corresponding papers how quantum imaging can benefit optical encryption systems. Sources that use few photons make encryption systems much more difficult to attack, providing a secure method for authentication. S Online supplementary data available from stacks.iop.org/JOPT/18/083001/mmedia (Some figures may appear in colour only in the online journal) Contents I. Encryption technologies 1. Secure optical sensing 4 2. Digital holographic encryption in free space optical technique 6 3. Simultaneous encryption and authentication of multiple signals 8 4. Amplitude-and phase-truncation based optical asymmetric cryptosystem 10 5. Optical security: dynamical processes and noise-free recovery 12

The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/.

Drought stress negatively affects crop performance and weakens global food security. It triggers the activation of downstream pathways, mainly through phytohormones homeostasis and their signaling networks, which further initiate the biosynthesis of secondary metabolites (SMs). Roots sense drought stress, the signal travels to the above-ground tissues to induce systemic phytohormones signaling. The systemic signals further trigger the biosynthesis of SMs and stomatal closure to prevent water loss. SMs primarily scavenge reactive oxygen species (ROS) to protect plants from lipid peroxidation and also perform additional defense-related functions. Moreover, drought-induced volatile SMs can alert the plant tissues to perform drought stress mitigating functions in plants. Other phytohormone-induced stress responses include cell wall and cuticle thickening, root and leaf morphology alteration, and anatomical changes of roots, stems, and leaves, which in turn minimize the oxidative stress, water loss, and other adverse effects of drought. Exogenous applications of phytohormones and genetic engineering of phytohormones signaling and biosynthesis pathways mitigate the drought stress effects. Direct modulation of the SMs biosynthetic pathway genes or indirect via phytohormones' regulation provides drought tolerance. Thus, phytohormones and SMs play key roles in plant development under the drought stress environment in crop plants.

Abstract Borophene, an elemental metallic Dirac material is predicted to have unprecedented mechanical and electronic character. Need of substrate and ultrahigh vacuum conditions for deposition of borophene restricts its large‐scale applications and significantly hampers the advancement of research on borophene. Herein, a facile and large‐scale synthesis of freestanding atomic sheets of borophene through a novel liquid‐phase exfoliation and the reduction of borophene oxide is demonstrated. Electron microscopy confirms the presence of β 12 , X 3 , and their intermediate phases of borophene; X‐ray photoelectron spectroscopy, and scanning tunneling microscopy, corroborated with density functional theory band structure calculations, validate the phase purity and the metallic nature. Borophene with excellent anchoring capabilities is used for sensing of light, gas, molecules, and strain. Hybrids of borophene as well as that of reduced borophene oxide with other 2D materials are synthesized, and the predicted superior performance in energy storage is explored. The specific capacity of borophene oxide is observed to be ≈4941 mAh g −1 , which significantly exceeds that of existing 2D materials and their hybrids. These freestanding borophene materials and their hybrids will create a huge breakthrough in the field of 2D materials and could help to develop future generations of devices and emerging applications.

Purpose Building on the theory of planned behavior (TPB), the purpose of this paper is to understand the green buying behavior of educated millennials in India. The study also attempts to extend the TPB by including two additional variables, environmental concern (EC) and willingness to pay premium, in the framework. Design/methodology/approach Data were collected from 202 students from various departments of an institute of higher education in India. The proposed model was tested with the help of structural equation modeling using bootstrapping procedures in SPSS AMOS 24. Findings Except for the direct association between subjective norm (SN) and purchase intention (PI), the study provided support for the TPB framework. EC was found to exert an indirect influence on green PI through its effect on attitude, SN and perceived behavioral control. Willingness to pay premium moderated the relationship of PI with green buying behavior. PIs were found to successfully translate into purchase behavior (PB). Practical implications This research by promoting an understanding on the factors affecting the green buying behavior of educated millennials in India will assist green marketers to tap the tremendous potential inherent in this market segment by formulating customized market plans and strategies. Originality/value The study extends the existing literature by validating and extending the TPB framework in a unique cultural context and advancing the understanding of underlying psychological mechanisms and boundary conditions of the relationship between PIs and PBs.

Abstract Nanocrystalline cobalt ferrite powder has been synthesised by citrate precursor and co-precipitation methods. Structural characterization of the samples has been carried out using powder X-ray diffraction, Fourier transform infrared spectroscopy (FT-IR) and field emission scanning electron microscope (FE-SEM). Distribution of cations among the two interstitial sites (tetrahedral and octahedral sites) has been estimated by analysing the powder X-ray diffraction patterns by employing Rietveld refinement technique, and the results reveal the existence of samples as a mixed type spinel with cubic structure. It is observed that the distribution of cations and structural parameters are strongly dependent on synthesis method and annealing temperature. The vibrational modes of the octahedral and tetrahedral metal complex in the sample have been examined using FT-IR in the wave number range of 390 to 750 cm −1 , and it shows an absorption band within this range, which confirms the spinel structure of the sample. The existence of constituents in the sample, i.e., Co, Fe and O has been authenticated using energy dispersive spectrum with the help of a FE-SEM.

Underwater robot designs inspired by the behavior, physiology, and anatomy of fishes can provide enhanced maneuverability, stealth, and energy efficiency. Over the last two decades, robotics researchers have developed and reported a large variety of fish-inspired robot designs. The purpose of this review is to report different types of fish-inspired robot designs based upon their intended locomotion patterns. We present a detailed comparison of various design features like sensing, actuation, autonomy, waterproofing, and morphological structure of fish-inspired robots reported in the past decade. We believe that by studying the existing robots, future designers will be able to create new designs by adopting features from the successful robots. The review also summarizes the open research issues that need to be taken up for the further advancement of the field and also for the deployment of fish-inspired robots in practice.

Abstract Borophene, a 2D allotrope of boron and the lightest elemental Dirac material, is the latest very promising 2D material owing to its unique structural and electronic characteristics of the X 3 and β 12 phases. The high atomic density on ridgelines of the β 12 phase of borophene provides a substantial orbital overlap, which leads to an excellent electron density in the conduction level and thus to a highly metallic behavior. These unique structural characteristics and electronic properties of borophene attract significant scientific interest. Herein, approaches for crystal growth/synthesis of these unique nanostructures and their potential technological applications are discussed. Various substrate‐supported ultrahigh‐vacuum growth techniques for borophene, such as molecular beam epitaxy, atomic layer deposition, and chemical vapor deposition, along with their challenges, are also summarized. The sonochemical exfoliation and modified Hummer's technique for the synthesis of free‐standing borophene are also discussed. Solution‐phase exfoliation seems to address the scalability issues and expands the applications of these unique materials to various fields, including renewable energy devices and ultrafast sensors. Furthermore, the electronic, optical, thermal, and elastic properties of borophene are thoroughly discussed and are compared with those of graphene and its “cousins.” Numerous frontline applications are envisaged and an outlook is presented.

Signal processing techniques are an obvious choice for real-time analysis of electrocardiography (ECG) signals. However, classical signal processing techniques are unable to deal with the nonstationary nature of the ECG signal. In this context, this paper presents a new approach, i.e., discrete orthogonal stockwell transform using discrete cosine transform for efficient representation of the ECG signal in time-frequency space. These time-frequency features are further reduced in lower dimensional space using principal component analysis, representing the morphological characteristics of the ECG signal. In addition, the dynamic features (i.e., RR-interval information) are computed and concatenated to the morphological features to constitute the final feature set, which is utilized to classify the ECG signals using support vector machine (SVM). In order to improve the classification performance, particle swarm optimization technique is employed for gradually tuning the learning parameters of the SVM classifier. In this paper, ECG data exhibiting 16 classes of the most frequently occurring arrhythmic events are taken from the benchmark MIT-BIH arrhythmia database for the validation of the proposed methodology. The experimental results yielded an improved overall accuracy, sensitivity (Sp), and positive predictivity (Pp) of 98.82% in comparison with the existing approaches available in the literature.

Emotions and sentiments are subjective in nature. They differ on a case-to-case basis. However, predicting only the emotion and sentiment does not always convey complete information. The degree or level of emotions and sentiments often plays a crucial role in understanding the exact feeling within a single class (e.g., `good' versus `awesome'). In this paper, we propose a stacked ensemble method for predicting the degree of intensity for emotion and sentiment by combining the outputs obtained from several deep learning and classical feature-based models using a multi-layer perceptron network. We develop three deep learning models based on convolutional neural network, long short-term memory and gated recurrent unit and one classical supervised model based on support vector regression. We evaluate our proposed technique for two problems, i.e., emotion analysis in the generic domain and sentiment analysis in the financial domain. The proposed model shows impressive results for both the problems. Comparisons show that our proposed model achieves improved performance over the existing state-of-the-art systems.

Proteins are the essential biological macromolecules required to perform nearly all biological processes, and cellular functions. Proteins rarely carry out their tasks in isolation but interact with other proteins (known as protein-protein interaction) present in their surroundings to complete biological activities. The knowledge of protein-protein interactions (PPIs) unravels the cellular behavior and its functionality. The computational methods automate the prediction of PPI and are less expensive than experimental methods in terms of resources and time. So far, most of the works on PPI have mainly focused on sequence information. Here, we use graph convolutional network (GCN) and graph attention network (GAT) to predict the interaction between proteins by utilizing protein's structural information and sequence features. We build the graphs of proteins from their PDB files, which contain 3D coordinates of atoms. The protein graph represents the amino acid network, also known as residue contact network, where each node is a residue. Two nodes are connected if they have a pair of atoms (one from each node) within the threshold distance. To extract the node/residue features, we use the protein language model. The input to the language model is the protein sequence, and the output is the feature vector for each amino acid of the underlying sequence. We validate the predictive capability of the proposed graph-based approach on two PPI datasets: Human and S. cerevisiae. Obtained results demonstrate the effectiveness of the proposed approach as it outperforms the previous leading methods. The source code for training and data to train the model are available at https://github.com/JhaKanchan15/PPI_GNN.git .

Nonmonotonic variation with collision energy (sqrt[s_{NN}]) of the moments of the net-baryon number distribution in heavy-ion collisions, related to the correlation length and the susceptibilities of the system, is suggested as a signature for the quantum chromodynamics critical point. We report the first evidence of a nonmonotonic variation in the kurtosis times variance of the net-proton number (proxy for net-baryon number) distribution as a function of sqrt[s_{NN}] with 3.1 σ significance for head-on (central) gold-on-gold (Au+Au) collisions measured solenoidal tracker at Relativistic Heavy Ion Collider. Data in noncentral Au+Au collisions and models of heavy-ion collisions without a critical point show a monotonic variation as a function of sqrt[s_{NN}].

Md Shad Akhtar, Dushyant Chauhan, Deepanway Ghosal, Soujanya Poria, Asif Ekbal, Pushpak Bhattacharyya. Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). 2019.

The chiral magnetic effect (CME) is predicted to occur as a consequence of a local violation of P and CP symmetries of the strong interaction amidst a strong electromagnetic field generated in relativistic heavy-ion collisions. Experimental manifestation of the CME involves a separation of positively and negatively charged hadrons along the direction of the magnetic field. Previous measurements of the CME-sensitive charge-separation observables remain inconclusive because of large background contributions. To better control the influence of signal and backgrounds, the STAR Collaboration performed a blind analysis of a large data sample of approximately 3.8 billion isobar collisions of 9644Ru+9644Ru and 9640Zr+9640Zr at √sNN=200 GeV. Prior to the blind analysis, the CME signatures are predefined as a significant excess of the CME-sensitive observables in Ru+Ru collisions over those in Zr+Zr collisions, owing to a larger magnetic field in the former. A precision down to 0.4% is achieved, as anticipated, in the relative magnitudes of the pertinent observables between the two isobar systems. Observed differences in the multiplicity and flow harmonics at the matching centrality indicate that the magnitude of the CME background is different between the two species. No CME signature that satisfies the predefined criteria has been observed in isobar collisions in this blind analysis.

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

Hexavalent chromium is a highly soluble environmental contaminant. It is a widespread anthropogenic chromium species that is 100 times more toxic than trivalent chromium. Leather, chrome plating, coal mining and paint industries are the major sources of hexavalent chromium in water. Hexavalent chromium is widely recognised as a carcinogen and mutagen in humans and other animals. It is also responsible for multiorgan damage, such as kidney damage, liver failure, heart failure, skin disease and lung dysfunction. The fate of the toxicity of hexavalent chromium depends on its oxidation state. The reduction of Cr (VI) to Cr (III) is responsible for the generation of reactive oxygen species (ROS) and chromium intermediate species, such as Cr (V) and Cr (IV). Reactive oxygen species (ROS) are responsible for oxidative tissue damage and the disruption of cell organelles, such as mitochondria, DNA, RNA and protein molecules. Cr (VI)-induced oxidative stress can be neutralised by the antioxidant system in human and animal cells. In this review, the authors summarise the Cr (VI) source, toxicity and antioxidant defence mechanism against Cr (VI)-induced reactive oxygen species (ROS).

Multi-modal sentiment analysis offers various challenges, one being the effective combination of different input modalities, namely text, visual and acoustic. In this paper, we propose a recurrent neural network based multi-modal attention framework that leverages the contextual information for utterance-level sentiment prediction. The proposed approach applies attention on multi-modal multi-utterance representations and tries to learn the contributing features amongst them. We evaluate our proposed approach on two multi-modal sentiment analysis benchmark datasets, viz. CMU Multi-modal Opinion-level Sentiment Intensity (CMU-MOSI) corpus and the recently released CMU Multi-modal Opinion Sentiment and Emotion Intensity (CMU-MOSEI) corpus. Evaluation results show the effectiveness of our proposed approach with the accuracies of 82.31% and 79.80% for the MOSI and MOSEI datasets, respectively. These are approximately 2 and 1 points performance improvement over the state-of-the-art models for the datasets.

A low cost, non-explosive process for the synthesis of graphene oxide (GO) is demonstrated. Using suitable choice of reaction parameters including temperature and time, this recipe does not require expensive membranes for filtration of carbonaceous and metallic residues. A pre-cooling protocol is introduced to control the explosive nature of the highly exothermic reactions during the oxidation process. This alleviates the requirement for expensive membranes and completely eliminates the explosive nature of intermediate reaction steps when compared to existing methods. High quality of the synthesized GO is corroborated using a host of characterization techniques including X-ray diffraction, optical spectroscopy, X-ray photoemission spectroscopy and current-voltage characteristics. Simple reduction protocol using ultra-violet light is demonstrated for potential application in the area of photovoltaics. Using different reduction protocols together with the proposed inexpensive method, reduced GO samples with tunable conductance over a wide range of values is demonstrated. Density functional theory is employed to understand the structure of GO. We anticipate that this scalable approach will catalyze large scale applications of GO.

Cassandra is an open source atomistic Monte Carlo software package that is effective in simulating the thermodynamic properties of fluids and solids. The different features and algorithms used in Cassandra are described, along with implementation details and theoretical underpinnings to various methods used. Benchmark and example calculations are shown, and information on how users can obtain the package and contribute to it are provided. © 2017 Wiley Periodicals, Inc.