Fast-emerging Field Research Articles

Integrative Literature Review on People Analytics and Implications From the Perspective of Human Resource Development

Although people analytics is a fast-emerging field with importance to HRD, HRD researchers still lack a comprehensive understanding of it. This study examines the current body of knowledge in people analytics through the lens of human resource (HR) development by performing an integrative literature review of 91 articles. This study identifies five categories of knowledge: (1) definitions and boundaries of terms, (2) building blocks of people analytics in organizations, (3) types of analytics, (4) ethical issues related to people analytics, and (5) applications of people analytics to the HRD field. This study makes theoretical and practical contributions to the field of HR development by exploring the current state of people analytics and people analytics’ application to HR development practice. It also enhances scholars’ understanding of people analytics within HR development boundaries and provides insights for future research. Finally, this study provides practical guidance for HR development practitioners seeking to leverage people analytics in their work.

Achieving antimicrobial and superior mechanical properties in a scalable and cost-effective heterostructured stainless steel

Heterostructured materials (HSMs) can alleviate the strength-ductility trade-off of conventional materials. Microstructure modulation by low-cost and large-scalable thermo-mechanical treatments is a feasible route to produce HSMs. Using these thermo-mechanical treatments, we have developed new heterostructured and antimicrobial stainless steels (HS&ASSs) to reduce the infection rate of multiple diseases while increasing their service life. The HS&ASSs were designed to combine several known strengthening mechanisms, which significantly improved the yield strength compared to their homogeneous counterparts while retaining good ductility. Effective antibacterial property is also observed in the HS&ASSs. The microstructural evolution and processing-microstructure-properties relationship are thoroughly explained. This work demonstrates that the improved mechanical properties of HSMs can be successfully combined with multifunctional properties, thus opening a new window for this fast-emerging field.

Influence of wall thickness on mechanical properties and porosity of additive manufactured polymer-based porous structures for fibula bone regeneration

In the recent past, Bone tissue engineering (BTE) has become a potentially effective solution for repairing bone defects. In the meantime, various fields, such as material science, additive manufacturing, and clinical medicine, are interlinked in the fast-emerging field of bone tissue engineering. Additive Manufacturing (AM) is a unique technology that accumulates content rather than waste materials in the form of chips. AM techniques like selective laser sintering (SLS), commonly used to create complex polymer structures, allow for the direct fabrication of complex structural implant components for use in BTE. However, the wall thickness of the product limits the SLS systems. In detail, research into the influence of wall thickness on mechanical properties and scaffold porosity is required to develop such lightweight and complex structures. In this study, Polyamide12-based bone Scaffolds were fabricated using the SLS technique by varying the wall thickness ranging from 0.9 mm to 1.10 mm in steps of 0.05 mm. Compression tests, porosity, scanning electron microscopy, and differential scanning calorimetry were conducted on the fabricated specimens. The studies demonstrated that the higher wall thickness benefits in reduction of porosity. The sample fabricated with a wall thickness of 1.1 mm has about 76% less porosity than the sample fabricated with a wall thickness of 0.9 mm. In addition, the study also found that compression strength decreases as porosity increases. The compressive strength of the scaffolds has been measured to be between 45.62 and 90.67 MPa for a wall thickness between 0.90 and 1.10 mm. The maximum compressive strength of a porous polyamide specimen is 90.67 MPa at a wall thickness of 1.10 mm. The fabricated samples with wall thicknesses of 0.9 and 0.95 mm were not mechanically stable, as their compressive strength values were lower than the compressive strength of human fibula bone (68 N/mm2).

Bioactivity of Nanohybrid: Comprising of Metallosalen Incorporated into Lacunary Polyoxometalate and Encapsulated with Chitosan Biopolymer

Biocompatible polymer chitosan has note-worthy applications in biomedical science. Nanohybrids synthesized by using chitosan as an encapsulating unit for polyoxometalates (POMs) and study of its biomedical applications is a fast-emerging field. In this research work, a lacunary polyoxometalate (α-K8SiW11O39) has been synthesized and then alkylsilane has been inserted into its lacunary position. It has been then reacted with 2-hydroxy-1-naphtaldehyde followed by copper metalation and finally encapsulated into chitosan by using the ionotropic gelation technique, in which the chitosan and the POM served as cation and anion, respectively. The bands in UV-Vis spectra at characteristic wavelength indicate that the organosilane is successfully inserted into α-K8SiW11O39 and metalled with Cu. Then its encapsulation into chitosan to synthesize final nanohybrid has been confirmed by UV-Vis spectra. Further, chitosan, POM inserted with organosilane and nanohybrid have been characterized by FT-IR. EDX analysis reveals the presence of all the expected elements in their desired ratios. EDX study was further supported by Inductively Coupled Plasma (ICP) technique. Morphology of nanohybrid has been studied by Scanning Electron Microscope (SEM) imaging. The loading and entrapment efficiency of the prepared nanohybrid was exceptionally excellent and was calculated to be 81.2% and 59.1%, respectively. In vitro drug release study suggested sustainable and pH-modulated release behaviour of the nanohybrid. Further, it is the first time that an organosilane attached to lacunary POM, then metalated with Cu and finally encapsulated into CS has been investigated for drug delivery. The antibacterial activity of POM inserted with organosilane and nanohybrid have been tested against bacterial strains of B. subtilis gram (+)ve and E. coli gram (-)ve. The antibacterial activity of nanohybrid has been improved as compared to bare POM inserted with metallated organosilane and chitosan.

Understanding the Role of Architect in the Artificial Intelligence Era - “An Approach to AIA in Egypt”

AI is a fast-emerging field and is becoming an essential part of our future.Recently, most of used technologies are somewhat artificially intelligent, where itstill needs human involvement to carry out the main part of the task. AI has asignificant impact on multiple fields and industries, like IT, cybersecurity, medicine,and manufacturing since it provides them with countless creative opportunities.Compared to the mentioned fields, the development of AI in Architecture is stillnot commonly used. The dominant idea related to Architecture is that designerswill be replaced by smart robots to create art and design buildings. But on thecontrary, the open-ended creative results are still preserved for humans only. AIcan do endless tasks during Architecture design process requiring humanintelligence such as decision-making. Therefore, it is crucial to understand the role,advantages, and disadvantages of this technology within the design process fromthe viewpoint of professionals. Consequently, a Questionnaire will be held toseveral Professional Designers, Architects, and Researchers, then a summary oftheir responses will be reported in this article. The aim is to recognize how AItechniques can affect the Architect`s attitude to the design project.

Immunocapturing rare cells from blood: A simple and robust microsystem approach

Cell immunocapture microsystems are a fast-emerging field with several potential medical diagnostic applications. Isolation and quantification of circulating rare cells (CRCs) show great importance in the early stages of disease diagnostics and prognostics. Here, we present a simple and robust stop-flow microsystem (fabricated by a combination of glass microblasting and 3D printing) based on a planar antibody-coated surface that is effective in the immunocapture of the model as well as naturally occurring rare cells. A chip with a planar immunocapture channel working in the so-called stop-flow dynamic regime was designed to enable monitoring the efficiency of the cell capture by fluorescence microscopy. Up to 90% immunocapture efficiency of MCF-7 cells spiked into whole blood on CD326 antibody-coated planar surfaces was achieved. We discuss the role of the planar surface modifications, the influence of the set stop-flow dynamic conditions, and medium complexity on the efficiency of cell immunocapture. The presented results could be further employed in the design of microsystems for cell-size-independent isolation and identification of rare cells from blood.

Self-Supervised Video-Centralised Transformer for Video Face Clustering.

This article presents a novel method for face clustering in videos using a video-centralised transformer. Previous works often employed contrastive learning to learn frame-level representation and used average pooling to aggregate the features along the temporal dimension. This approach may not fully capture the complicated video dynamics. In addition, despite the recent progress in video-based contrastive learning, few have attempted to learn a self-supervised clustering-friendly face representation that benefits the video face clustering task. To overcome these limitations, our method employs a transformer to directly learn video-level representations that can better reflect the temporally-varying property of faces in videos, while we also propose a video-centralised self-supervised framework to train the transformer model. We also investigate face clustering in egocentric videos, a fast-emerging field that has not been studied yet in works related to face clustering. To this end, we present and release the first large-scale egocentric video face clustering dataset named EasyCom-Clustering. We evaluate our proposed method on both the widely used Big Bang Theory (BBT) dataset and the new EasyCom-Clustering dataset. Results show the performance of our video-centralised transformer has surpassed all previous state-of-the-art methods on both benchmarks, exhibiting a self-attentive understanding of face videos.

A novel demodulation method for transmission using nitrogen–vacancy-based solid-state quantum sensor

Diamond based quantum sensing is a fast-emerging field with both scientific and technological significance. The nitrogen–vacancy (NV) center, a crystal defect in diamond, has become a unique object for microwave sensing applications due to its excellent stability, long spin coherence time, and optical properties at ambient condition. In this work, we use diamond NV center as atomic receiver to demodulate on–off keying (OOK) signal transmitted in broad frequency range (2 GHz–14 GHz in a portable benchtop setup). We proposed a unique algorithm of voltage discrimination and demonstrated audio signal transceiving with fidelity above 99%. This diamond receiver is attached to the end of a tapered fiber, having all optic nature, which will find important applications in data transmission tasks under extreme conditions such as strong electromagnetic interference, high temperatures, and high corrosion.

Optimized microwave sensing in broad frequency range by a fiber diamond probe

Diamond based quantum sensing is a fast-emerging field with both scientific and technological importance. The nitrogen-vacancy (NV) center, a crystal defect in diamond, is a unique model system for microwave sensing application due to its excellent photo-stability, long spin coherence time in ambient conditions. In this work, we systematically optimized the measurement parameters for microwave sensing. The system noise is analyzed, and 1/f noise is suppressed by introducing a differential algorithm. The gain of avalanche photodiode and the gating window of the pulsed fluorescence is optimized to further suppress the noise floor. The decoherence of spin is characterized by varying the duration of the laser and microwave. The minimal detectable power on a standard microstrip is characterized with sampling time down to 1 ms, showing flat frequency dependence. The results have important implications toward fast measurement of broadband microwave power, especially in the field of IC testing and radar signal processing under intense electromagnetic interference.

Longevity medicine: upskilling the physicians of tomorrow

Longevity medicine: upskilling the physicians of tomorrow

Plasmonic visible-NIR photodetector based on hot electrons extracted from nanostructured titanium nitride

The superior plasmonic characteristics of transition metal nitrides have been widely considered for enhancing the performance of light-harvesting devices. We explore this fast-emerging field further and report here the successful implementation of titanium nitride (TiN) nanoparticles prepared by reactive magnetron sputtering in a broadband photodetector device geometry. X-Ray Photoelectron Spectroscopy study reveals the presence of two different phases, viz., titanium nitride and titanium oxynitride in the prepared TiN sample. These two different nitride phases lead to two plasmon absorption bands, one in the visible region and the other in the Near Infrared (NIR) region of the electromagnetic spectrum, which is a fascinating finding of this study. The fabricated photodetector shows appreciable photoelectrical response in the visible as well as in the NIR region due to plasmon induced charge separation aided by the TiN nanoparticles. Very high photoresponsivities of 158 mA/W and 230 mA/W of the device at wavelengths of 450 nm and 950 nm, respectively, make it very promising as a broadband photodetector. The corresponding Internal Quantum Efficiencies are found to be 48% and 35%, respectively, at those two particular wavelengths. The charge transport mechanism of the device is explained with an energy level diagram, which shows that the hot electrons produced from the decay of plasmon are responsible for the generation of photocurrent.

Auto Detection of Number Plate of Person without Helmet

Automated Number Plate Recognition organization would greatly enhance the ability of police to detect criminal commotion that involves the use of motor vehicles. Automatic video investigation from traffic surveillance cameras is a fast-emerging field based on workstation vision techniques. It is a key technology to public safety, intelligent transport system (ITS) and for efficient administration of traffic without wearing helmet. In recent years, there has been an increased scope for involuntary analysis of traffic activity. It defines video analytics as computer-vision-based supervision algorithms and systems to extract contextual information from video. In traffic circumstancesnumeroussupervise objectives can be continue by the application of computer vision and pattern gratitude techniques, including the recognition of traffic violations (e.g., illegal turns and one-way streets) and the classification of road users (e.g., vehicles, motorbikes, and pedestrians). Currently most reliable approach is through the acknowledgment of number plates, i.e., automatic number plate recognition (ANPR).

Artificial intelligence in architecture: Generating conceptual design via deep learning

Artificial intelligence, and in particular machine learning, is a fast-emerging field. Research on artificial intelligence focuses mainly on image-, text- and voice-based applications, leading to breakthrough developments in self-driving cars, voice recognition algorithms and recommendation systems. In this article, we present the research of an alternative graph-based machine learning system that deals with three-dimensional space, which is more structured and combinatorial than images, text or voice. Specifically, we present a function-driven deep learning approach to generate conceptual design. We trained and used deep neural networks to evaluate existing designs encoded as graphs, extract significant building blocks as subgraphs and merge them into new compositions. Finally, we explored the application of generative adversarial networks to generate entirely new and unique designs.

Antimicrobial graphene materials: the interplay of complex materials characteristics and competing mechanisms.

Graphene materials (GMs) exhibit attractive antimicrobial activities promising for biomedical and environmental applications. However, we still lack full control over their behaviour and performance mainly due to the complications arising from the coexistence and interplay of multiple factors. Therefore, in this minireview, we attempt to illustrate the structure-property-activity relationships of GMs' antimicrobial activity. We first examine the chemical/physical complexity of GMs focusing on five aspects of their materials characteristics: (i) chemical composition, (ii) impurities and imperfections, (iii) lateral dimension, (iv) self-association (e.g., restacking), and (v) composite/hybrid formation. Next, we briefly summarise the current understanding of their antimicrobial mechanisms. Then, we assign the outlined materials characteristics of GMs to the proposed antimicrobial mechanisms. Lastly, we share our vision regarding the future of research and development in this fast-emerging field.

Towards Comparative Research in Strategic Communication: A Systematic Analysis of Cross-National Studies and Future Directions

ABSTRACTComparative cross-national research permits scholars to understand how strategic communication is impacted by environmental conditions in different countries. Although the field has burgeoned enormously over the past two decades, there has been little discussion about what comprises an excellent comparative study, and little is known about the state-of-the-art of comparative research in the field of strategic communication. This article suggests a set of general scientific criteria for solid comparative research and applies these to assess the quality of comparative studies in strategic communication. Based on an analysis of 75 studies located through a systematic search in 16 journals, this article sketches the contours of a fast-emerging research field, which is increasingly driven by European and Asian scholars. The results identify common deficiencies and indicate that the potential of comparative research to contextualize and explain strategic communication in different countries has not yet been fully exploited. Future directions for engaging in more advanced comparative strategic communication research are given.

Current advancements of magnetic nanoparticles in adsorption and degradation of organic pollutants.

Nanotechnology is a fast-emerging field and has received applications in almost every field of life. Exploration of new synthetic technologies for size and shape control of nanomaterials is getting immense consideration owing to their exceptional properties and applications. Magnetic nanoparticles (MNPs) are among the most important group of nanoparticles thanks to their diverse applications in medical, electronic, environmental, and industrial sectors. There have been numerous synthetic routes of MNPs including thermal decomposition, co-precipitation, microemulsion, microwave assisted, chemical vapor deposition, combustion synthesis, and laser pyrolysis synthesis. The synthesized MNPs have been successfully applied in medical fields for therapy, bioimaging, drug delivery, and so on. Among environmental aspects, there has been great intimidation of organic pollutants in air and water. Utilization of various wastes as adsorbents has removed 80 to 99.9% of pollutants from contaminated water. MNPs as adsorbents compared to coarse-grained counterparts have seven times higher capacity in removing water pollutants and degrading organic contaminants. This study is focused to introduce and compile various routes of MNP synthesis together with their significant role in water purifications and degradation of organic compounds. The review has compiled recent investigation, and we hope it will find the interest of researchers dealing with nanoparticles and environmental research. Graphical abstract Synthesis and applications of magnetic nanoparticles.

Insight into the antiferromagnetic structure manipulated by electronic reconstruction

Antiferromagnetic (AFM) materials, with robust rigidity to magnetic field perturbations and ultrafast spin dynamics, show great advantages in information storage and have developed into a fast-emerging field of AFM spintronics. However, a direct characterization of spin alignments in AFM films has been challenging, and their manipulation by lattice distortion and magnetic proximity is inevitably accompanied by ``ferromagnetic'' features within the AFM matrix. Here we resolve the $G$-type AFM structure of $\mathrm{SrCo}{\mathrm{O}}_{2.5}$ and find that the interfacial AFM structure could be modulated intrinsically from in plane to out of plane with a canted angle of ${60}^{\ensuremath{\circ}}$ by the charge transfer and orbital reconstruction in $\mathrm{SrCo}{\mathrm{O}}_{2.5}/\mathrm{L}{\mathrm{a}}_{2/3}\mathrm{S}{\mathrm{r}}_{1/3}\mathrm{Mn}{\mathrm{O}}_{3}$ heterostructures both experimentally and theoretically. Such an interfacial AFM reconfiguration caused by electronic reconstruction does not cause the ferromagnetic feature and changes the magnetization switching process of $\mathrm{L}{\mathrm{a}}_{2/3}\mathrm{S}{\mathrm{r}}_{1/3}\mathrm{Mn}{\mathrm{O}}_{3}$ from in plane to perpendicular to the plane, in turn. Our study not only reveals the coupling between charge, orbital, and AFM structure, but also provides a unique approach to manipulating AFM structure.

Social impacts and life cycle assessment: proposals for methodological development for SMEs in the European food and drink sector

Small and medium enterprises (SMEs) account for 99 % of companies operating in the European food and drink industry and, often, are part of highly fragmented and complex food chains. The article focuses on the development of a social impact assessment methodology for SMEs in selected food and drink products as part of the EU-FP7 SENSE research project. The proposed methodology employs a top-down and bottom-up approach and focuses on labour rights/working conditions along the product supply chain as the key social impact indicator, limiting key stakeholder classification to workers/employees and local communities impacted by the production process. Problems related to this emerging field are discussed, and questions for further research are expounded. The article reviews both academic and ‘grey’ literature on life cycle assessment (LCA) and its relationship to social LCA (S-LCA) and SMEs at the beginning of 2013 and includes case study evidence from the food sector. A pilot questionnaire survey sent to European food and drink sector SMEs and trade associations (as partners in the research project) about their knowledge, experience and engagement with social impacts is presented. Proposals are elaborated for a social impact assessment methodology that identifies the key data for SMEs to collect. The literature reveals the complexity of the S-LCA approach as it aims to unite disparate and often conflicting interests. Findings from the pilot questionnaire are discussed. Using a top-down and bottom-up approach, the proposed methodology assesses data from SMEs along the supply chain in order to gauge social improvements in the management of labour-related issues for different product sectors. Issues relating to the ‘attributional’ choice of a social impact indicator and key stakeholder categories are discussed. How ‘scoring’ is interpreted and reported and what the intended effect of its use will be are also elaborated upon. Whilst recognising the difficulty of devising a robust social impact assessment for SMEs in the food and drink sector, it is argued that the proposed methodology makes a useful contribution in this fast-emerging field.

Behavioral Finance and Economics Back to the Basics: Adam Smith, the Behavioralist!

This draft presentation shares with the readers a critical and historical truth about the fast-emerging field of Behavioral Finance & Economics. The fact that the roots of Behavioral Finance & Economics can be easily traced back to the founding of Capitalism and Free Enterprise System and specifically to Adam Smith and his “other book”, The Theory of Moral Sentiments (TMS). Sentiments, which was first published in 1759, went through six revisions in Smith’s lifetime; including the last revision in 1790, the year of his death. For comparison, Wealth of Nations was published in 1776. I am certainly not the first to make such a claim as a 2010 JBF&E article by Vernon Smith and a 2005 JPE writing by Ashraf, Camerer, and Loewenstein made similar observations and Smith went even further and developed some testable propositions.

REVIEW ON VEHICULAR SPEED, DENSITY ESTIMATION AND CLASSIFICATION USING ACOUSTIC SIGNAL

Traffic monitoring and parameters estimation from urban to non urban (battlefield environment) traffic is fast-emerging field based on acoustic signals. We present here a comprehensive review of the state-of-the-art acoustic signal for vehicular speed estimation, density estimation and classification, critical analysis and an outlook to future research directions. This field is of increasing relevance for intelligent transport systems (ITSs). In recent years video monitoring and surveillance systems has been widely used in traffic management and hence traffic parameters can be achieved using such systems, but installation, operational and maintenance cost associated with these approaches are relatively high compared to the use of acoustic signal which is having very low installation and maintenance cost. The classification process includes sensing unit, class definition, feature extraction, classifier application and system evaluation. The acoustic classification system is part of a multi sensor real time environment for traffic surveillance and monitoring. Classification accuracy achieved by various studied algorithms shows very good performance for the 'Heavy Weight' class of vehicles as compared to the other category Light Weight. Also a slight performance degrades as vehicle speed increases. Vehicular speed estimation corresponds to average speed and traffic density measurement, and can be substantially used for traffic signal timings optimization.