How Much Does the Recruitment Channel Really Matter: Recruiters’ and Applicants’ Behaviors in the South Asian Context

In this paper, we discern recruiters’ and applicants’ tendencies towards recruitment channels. By drawing on the contextual perspective of human resource management (HRM), we argue that the national institutional environment of a country greatly influences recruiters’ choices of recruitment channels and applicants’ level of attractiveness towards jobs. Using an experimental study (n = 200 graduate students) and in-depth interviews of 10 human resource managers, we find that a) although recruitment channels positively affect applicants’ perceptions of organizational attractiveness, they have no significant impact on applicants’ intentions to apply for the job and b) even though online recruitment channels are widely believed to have a greater impact on organizational attractiveness, recruiters in the south Asian context continue to prefer the paper-based recruitment channels. The study provides interesting insights for recruitment literature, by explicating that socio-cultural and economic context enormously shapes recruiters’ and applicants’ preferences of recruitment channels

A New Form of Interlocking Developing Technology for Level Crossings and Depots with International Applications

There are multiple large rail infrastructure projects planned or currently being undertaken within the United Kingdom. Many of these projects aim to reduce the continual issue of limited or overcapacity service. These projects involve an expansion of Rail lines, introducing faster lines, improved stations in towns and cities and better communication networks. Some major projects like Control Period 6 (CP6) are being managed by Network Rail; where projects are initiated throughout Great Britain. Many projects are managed outside Great Britain e.g., Trans-European Transport Network Program, which is planning for expansion of Rail lines (almost double) for High-Speed Rails (category I and II). These projects will increase the number of junctions and Level Crossings. A Level Crossing is where a Rail Line is crossed by a road or a walkway without the use of a tunnel or bridge. The misuse from the road users account for nearly 90% of the fatalities and near misses at Level Crossings. During 2016/2017, the Rail Network recorded 6 fatalities, about 400 near-misses and more than 77 incidents of shock and trauma. Accidents at Level Crossings represent 8% of the total accidents from the whole Rail Network. Office of Rail and Road (ORR) suggested that among these accidents at Level Crossings 90% of them are pedestrians. Such high numbers of accidents, fatalities and high risk have alarmed authorities. These authorities found it necessary to invest time and utilise given resources to improve the safety system at a Level Crossing using the safer and reliable interlocking system. The interlocking system is a feature of a control system that makes the state of two functions mutually independent. The primary function of Interlocking is to ensure that trains are safe from collision and derailment. Considering the risk associated with the Level Crossing system, the new proposed interlocking system should utilise the sensing system available at a Level Crossing to significantly reduce implementation cost and comply with the given standards and Risk Assessments. The new proposed interlocking system is designed to meet the “Safety Integrity Level- SIL” and possibly use the “2oo2” approach for its application at a Level Crossing, where the operational cycle is automated or train driver is alarmed for risk situations. Importantly, the new proposed system should detect and classify small objects and provide a reasonable solution to the current risk associated with Level Crossing, which was impossible with the traditional sensing systems. The present work discusses the sensors and algorithms used and has the potential to detect and classify objects within a Level Crossing area. The review of existing solutions e.g Inductive Loops and other major sensors allows the reader to understand why RADAR and Video Cameras are preferable choices of a sensing system for a Level Crossing. Video data provides sufficient information for the proposed algorithm to detect and classify objects at Level Crossings without the need of a manual “operator”. The RADAR sensing system can provide information using micro-Doppler signatures, which are generated from small regular movements of an obstacle. The two sensors will make the system a two-layer resilient system. The processed information from these two sensing systems is used as the “2oo2” logic system for Interlocking for automating the operational cycle or alarm the train drive using effective communication e.g., GSM-R. These two sensors provide sufficient information for the proposed algorithm, which will allow the system to automatically make an “intelligent decision” and proceed with a safe Level Crossing operational cycle. Many existing traditional algorithms depend on pixels values, which are compared with background pixels. This approach cannot detect complex textures, adapt to a dynamic background or avoid detection of unnecessary harmless objects. To avoid these problems, the proposed work utilises “Deep Learning” technology integrated with the proposed Vision and RADAR system. The Deep Learning technology can learn representations from labelled pixels; hence it does not depend on background pixels. The Deep 3 | P a g e Learning technology can classify, detect and localise objects at a Level Crossing area. It can classify and differentiate between a child and a small inanimate object, which was impossible with traditional algorithms. The system can detect an object regardless of its position, orientation and scale without any additional training because it learns representation from the data and does not rely on background pixels. The proposed system e.g., Deep Learning technology is integrated with the existing Vision System and RADAR installed at a Level Crossing, hence implementation cost is significantly reduced as well. The proposed work address two main aspects of training a model using Deep Learning technology; training from scratch and training using Transfer Learning techniques. Results are demonstrated for Image Classification, Object Detection and micro-Doppler signals from RADAR. An architecture of Convolutional Neural Network from scratch is trained consisting of Input Layer, Convolution, Pooling and Dropout Layer. The model achieves an accuracy of about 66.78%. Different notable models are trained using Transfer Learning techniques and their results are mentioned along with the MobileNet model, which achieves the highest accuracy of 91.9%. The difference between Image Classification and Object Detection is discussed and results for Object Detection are mentioned as well, where the Loss metrics are used to evaluate the performance of the Object Detector. MobileNet achieves the smallest loss metric of about 0.092. These results clearly show the effectiveness and preferability of these models for their applicability at Level Crossings. Another Convolutional Neural Network is trained using micro-Doppler signatures from the Radar system. The model trained using the micro-Doppler signature achieved an accuracy of 92%. The present work also addresses the Risk Assessment associated with the installation and maintenance of the system using Deep Learning technology. RAMS (Reliability, Availability, Maintainability and Safety) management system is used to address the General and Specific Risks associated with the sensing system integrated with the Deep Learning technology. Finally, the work is concluded with the preferred choice, its application, results and associated Risk Assessment. Deep Learning is an evolving field with new improvements being introduced constantly. Any new challenges and problems should be monitored regularly. Some future work is discussed as well. To further improve the model's accuracy, the dataset from the same distribution should be gathered with the cooperation of relevant Railway authorities. Also, the RADAR dataset could be generated rather than simulated to further include diversity and avoid any biases in the dataset during the training process. Also, the proposed system can be implemented and used in different applications within the Rail Industry e.g., passenger census and classification of passengers at the platform as discussed in the work

PICTORIAL REVIEW OF EXTRAOSSEOUS EWING’S TUMOUR: A SINGLE CENTER EXPERIENCE

Purpose: Ewing’s family tumour is an extremely rare tumour, with annual incidence rates amongst Caucasian children <21 years being in the range of 2–3 cases per million in the U.S. There are mainly three subtypes including Ewing’s sarcoma (ES) of bone, extraosseous (EO) Ewing’s tumour and Peripheral primitive neuroectodermal tumour. Although extremely rare, this study represents a review of various types of cases and the significance of imaging including its baseline and post-treatment response radiological characteristics. There are a very few cases of EO ES in the current literature with variable spectrum of tumour site and their imaging characteristics.Materials and Methods: Electronic records were retrospectively reviewed from 1 May 2011 to 1 May 2016 with patients who were diagnosed as histologically proven ES. A number of patients, gender and base line computed tomography (CT)/magnetic resonance imaging findings for staging were reviewed.Results: A total of 568 patients with diagnosed ES were analysed, of which 15 patients had EO type of ES. Of these only 8 patients had baseline imaging available which included tumours arising from the occipital region, orbit, anterior mediastinum, anterior abdominal wall, mesentery, kidney, prostate gland and presacral region.Conclusion: EO ES is a rare entity and can involve a wide array of soft tissue organs. A cross-sectional imaging with CT and MR has a key role in pre- and post-treatment assessment.Key words: Computed tomography, Ewing’s sarcoma, extraosseous Ewing’s, magnetic resonance imaging, peripheral primitive neuroectodermal tumou

Characterization of cypermethrin degrading bacteria: A hidden micro flora for biogeochemical cycling of xenobiotics

 Background: Cypermethrin is a Synthetic Pyrethroid (SP) having widespread applications in agriculture and industrial sector especially in sheep dip formulations and tanneries. Rhizoremediation offers a sustainable, environment-friendly and cost-effective means to carry out remediation of contaminated soils.Methods: Six bacterial strains were screened out and characterized at various doses of cypermethrin, heavy metal salts and antibiotics. The optimum growth conditions were determined for these bacterial isolates. The degradation of cypermethrin was confirmed through the growth of bacteria on minimal media (BHB) with cypermethrin and thin layer chromatographic analysis; retention factor values (Rf) were calculated and compared with standard Rf values.Results: Growth curve experiments revealed that three bacterial isolates were able to grow in the presence of cypermethrin. Tolerance to the high concentration of heavy metal salts (300µgmL-1) and resistance towards different antibiotics was observed in all three bacterial isolates indicating a positive correlation between pesticide degradation and tolerance to metals and antibiotics. Bacterial strains A-C1 and B-B2 were identified as Xanthomonas maltophilia and B-C2 as Acinetobacter sp. Cypermethrin degradation occurred concomitant with bacterial growth reaching an optical density (OD600) up to 0.869.Conclusion: Microbes present in rhizosphere have potential to mineralize the pesticides. A significant biodegradation of the cypermethrin was observed based on above mentioned lab parameters. These results paved the way for designing a multi-resistant bacterium that can be used to reverse the altered environment

A review of the technological developments for interlocking at level crossing

A Level Crossing remains as one of the highest risk assets within the railway system often depending on the unpredictable behaviour of road and footpath users. For this purpose, interlocking through automated safety systems remains a key area for investigation. Within Europe, 2015–2016, 469 accidents at crossings were recorded of which 288 lead to fatalities and 264 lead to injuries. The European Union’s Agency for Railways has reported that Level Crossing fatalities account for just under 28% of all railway fatalities. This paper identifies suitable obstacle detection technologies and their associated algorithms that can be used to support risk reduction and management of Level Crossings. Furthermore, assessment and decision methods are presented to support their application. Finally state of the art and synergistic opportunity of which a combination of obstacle detection sensors with intelligent decisions layers such as Deep Learning are discussed which can provide robust interlocking decisions for rail applications. The sensor fusion of video camera and RADAR is a promising solution for Level Crossings. By applying additional sensing techniques such as RADAR imaging, further capabilities are added to the system, which can lead to a more robust approach

A CPW fed quad-port MIMO DRA for sub-6 GHz 5G applications

The present work investigates a novel four-port, multiple-input multiple-output (MIMO), single element dielectric resonator antenna (DRA) for sub-6 GHz band. The DRA is designed and fabricated into a symmetric cross shape and fed using a coplanar waveguide (CPW) feed. A single radiator with four ports is rarely found in the literature. The -10 dB impedance bandwidth covered by the antenna is from 5.52 GHz to 6.2 GHz (11.6%) which covers fifth generation (5G) new radio (NR) bands N47 and wireless local area network (WLAN) IEEE 802.11a band. The isolation between orthogonal ports is about 15 dB while the isolation between opposite ports is 12 dB. The radiation pattern of the proposed antenna is bidirectional due to the absence of a ground plane below the DRA. The orthogonal modes excited in the DRA are [Formula: see text] and [Formula: see text] through the four symmetrical CPW feeds. The simulated and measured results of the proposed design show that MIMO characteristics are achieved by pattern diversity between the ports. Due to the perfect symmetry of the design, the proposed work could be extended to MIMO array applications as well

Green photosensitisers for the degradation of selected pesticides of high risk in most susceptible food: a safer approach

Pesticides are the leading defence against pests, but their unsafe use reciprocates the pesticide residues in highly susceptible food and is becoming a serious risk for human health. In this study, mint extract and riboflavin were tested as photosensitisers in combination with light irradiation of different frequencies, employed for various time intervals to improve the photo-degradation of deltamethrin (DM) and lambda cyhalothrin (λ-CHT) in cauliflower. Different source of light was studied, either in ultraviolet range (UV-C, 254 nm or UV-A, 320–380 nm) or sunlight simulator (> 380–800 nm). The degradation of the pesticides varied depending on the type of photosensitiser and light source. Photo-degradation of the DM and λ-CHT was enhanced by applying the mint extracts and riboflavin and a more significant degradation was achieved with UV-C than with either UV-A or sunlight, reaching a maximum decrement of the concentration by 67–76%. The light treatments did not significantly affect the in-vitro antioxidant activity of the natural antioxidants in cauliflower. A calculated dietary risk assessment revealed that obvious dietary health hazards of DM and λ-CHT pesticides when sprayed on cauliflower for pest control. The use of green chemical photosensitisers (mint extract and riboflavin) in combination with UV light irradiation represents a novel, sustainable, and safe approach to pesticide reduction in produce

PERCEPTRON: an open-source GPU-accelerated proteoform identification pipeline for top-down proteomics

PERCEPTRON is a next-generation freely available web-based proteoform identification and characterization platform for top-down proteomics (TDP). PERCEPTRON search pipeline brings together algorithms for (i) intact protein mass tuning, (ii) de novo sequence tags-based filtering, (iii) characterization of terminal as well as post-translational modifications, (iv) identification of truncated proteoforms, (v) in silico spectral comparison, and (vi) weight-based candidate protein scoring. High-throughput performance is achieved through the execution of optimized code via multiple threads in parallel, on graphics processing units (GPUs) using NVidia Compute Unified Device Architecture (CUDA) framework. An intuitive graphical web interface allows for setting up of search parameters as well as for visualization of results. The accuracy and performance of the tool have been validated on several TDP datasets and against available TDP software. Specifically, results obtained from searching two published TDP datasets demonstrate that PERCEPTRON outperforms all other tools by up to 135% in terms of reported proteins and 10-fold in terms of runtime. In conclusion, the proposed tool significantly enhances the state-of-the-art in TDP search software and is publicly available at https://perceptron.lums.edu.pk. Users can also create in-house deployments of the tool by building code available on the GitHub repository (http://github.com/BIRL/Perceptron)

HybridEval: An Improved Novel Hybrid Metric for Evaluation of Text Summarization

The present work re-evaluates the evaluation method for text summarization tasks. Two state-of-the-art assessment measures e.g., Recall-Oriented Understudy for Gisting Evaluation (ROUGE) and Bilingual Evaluation Understudy (BLEU) are discussed along with their limitations before presenting a novel evaluation metric. The evaluation scores are significantly different because of the length and vocabulary of the sentences, this suggests that the primary restriction is its inability to preserve the semantics and meaning of the sentences and consistent weight distribution over the whole sentence. To address this, the present work organizes the phrases into six different groups and to evaluate “text summarization” problems, a new hybrid approach (HybridEval) is proposed. Our approach uses a weighted sum of cosine scores from InferSent’s SentEval algorithms combined with original scores, achieving high accuracy. HybridEval outperforms existing state-of-the-art models by 10-15% in evaluation scores

TIAToolbox as an end-to-end library for advanced tissue image analytics

Background: Computational pathology has seen rapid growth in recent years, driven by advanced deep-learning algorithms. Due to the sheer size and complexity of multi-gigapixel whole-slide images, to the best of our knowledge, there is no open-source software library providing a generic end-to-end API for pathology image analysis using best practices. Most researchers have designed custom pipelines from the bottom up, restricting the development of advanced algorithms to specialist users. To help overcome this bottleneck, we present TIAToolbox, a Python toolbox designed to make computational pathology accessible to computational, biomedical, and clinical researchers. Methods: By creating modular and configurable components, we enable the implementation of computational pathology algorithms in a way that is easy to use, flexible and extensible. We consider common sub-tasks including reading whole slide image data, patch extraction, stain normalization and augmentation, model inference, and visualization. For each of these steps, we provide a user-friendly application programming interface for commonly used methods and models. Results: We demonstrate the use of the interface to construct a full computational pathology deep-learning pipeline. We show, with the help of examples, how state-of-the-art deep-learning algorithms can be reimplemented in a streamlined manner using our library with minimal effort. Conclusions: We provide a usable and adaptable library with efficient, cutting-edge, and unit-tested tools for data loading, pre-processing, model inference, post-processing, and visualization. This enables a range of users to easily build upon recent deep-learning developments in the computational pathology literature