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Background Healthcare information technology (HIT) applications are being ubiquitously adopted globally and have been indicated to have effects on certain dimensions of recruitment and retention of healthcare professionals. Retention of healthcare professionals is affected by their job satisfaction (JS), commitment to the organization and intention to stay (ITS) that are interlinked with each other and influenced by many factors related to job, personal, organization, etc. The objectives of the current study were to determine if HIT was one among the factors and, if so, propose a probable retention model that incorporates implementation and use of HIT as a strategy. Methods This was a cross-sectional survey study covering 20 hospitals from urban areas of India. The sample ( n  = 586) consisted of doctors, nurses, paramedics and hospital administrators. Data was collected through a structured questionnaire. Factors affecting job satisfaction were determined. Technology acceptance by the healthcare professionals was also determined. Interactions between the factors were predicted using a path analysis model. Results The overall satisfaction rate of the respondents was 51 %. Based on factor analysis method, 10 factors were identified for JS and 9 factors for ITS. Availability and use of information technology was one factor that affected JS. The need for implementing technology influenced ITS through work environment and career growth. Also, the study indicated that nearly 70 % of the respondents had awareness of HIT, but only 40 % used them. The importance of providing training for HIT applications was stressed by many respondents. Conclusion The results are in agreement with literature studies exploring job satisfaction and retention among healthcare professionals. Our study documented a relatively medium level of job satisfaction among the healthcare professionals in the urban area. Information technology was found to be one among the factors that can plausibly influence their job satisfaction and intention to stay. Based on the results of the study, a retention strategy has been suggested that utilizes implementation of HIT and provision of training to influence the retention of healthcare workers.

Beginning with the pioneering hybridoma technology developed in 1975, antibody generation methodologies have advanced substantially, culminating in today’s single-cell techniques. Each successive approach contributes unique applications, advantages, and drawbacks that reflect the field’s dynamic progress. We highlight the impact of integrating single-cell RNA sequencing (scRNA-seq) with display technologies. This holds potential for the healthcare industry by enabling efficient identification and development of diagnostic and therapeutic antibodies. Monoclonal antibodies (MAbs) produced via each major technology are discussed to illustrate practical outcomes. We have also explored the essential role of glycosylation in maintaining antibody stability and function. Furthermore, we discussed single-cell RNA sequencing (scRNA-seq) that enables high-resolution profiling of immune repertoires and tumour heterogeneity, facilitating the identification of antigen-specific antibodies and rare cell populations. Integration with microfluidics and computational analysis enhances biomarker discovery and cell-specific resolution. These advances support personalised therapies and accelerate next-generation antibody discovery. Finally, we address the emerging integration of machine learning and artificial intelligence in antibody discovery, emphasising recent advances in epitope mapping and predicting three-dimensional protein structures from primary amino acid sequences. Collectively, these developments are poised to revolutionise antibody engineering and expand its impact on therapeutic innovation.

The surface melt‐area time‐series (1979–2008) of the Greenland Ice Sheet (GrIS) shows large spatio‐temporal variability. The overall melt‐area time‐series is characterized by a step‐like increase in 1995. The melt‐area trend for the entire ice‐sheet between 1979–1994 is 7.64 × 103 ± 4.79 × 103km2/year, which is 8‐times higher than the period between 1995–2008 (9.64 × 102 ± 1.10 × 104km2/year). This step‐like increase of melt area in 1995 coincides well with mean summer air temperature patterns at 8 coastal sites. We find that the melt area and temperature change in 1995, both coincide to a general sign‐reversal in the North Atlantic Oscillation (NAO) index in 1995. We also find that the northerly sectors of the ice sheet do not clearly coincide with changes in the NAO suggesting the influence of NAO is being felt predominantly on the central‐eastern and central‐western sectors of the ice sheet.

Small-size sensor nodes are used as the basic component for collecting and sending the data or information in the ad hoc mode in wireless sensor network (WSN). This network is generally used to collect and process data from different regions where the movement of human is very rare. The sensor nodes are deployed in such a region for collecting data using ad hoc network where, at any time, the unusual situation may happen or there is no fixed network that can work positively and provide any transmission procedure. The location may be very remote or some disaster-prone area. In disaster-prone zone, after disaster, most often no fixed network remains alive. In that scenario, the ad hoc sensor network is one of the reliable sources for collecting and transmitting the data from that region. In this type of situation, sensor network can also be helpful for geo-informatic system. WSN can be used to handle the disaster management manually as well as through an automated system. The main problem for any activity using sensor node is that the nodes are very much battery hunger. An efficient power utilization is required for enhancing the network lifetime by reducing data traffic in the WSN. For this reason, some efficient intelligent software and hardware techniques are required to make the most efficient use of limited resources in terms of energy, computation and storage. One of the most suitable approaches is data aggregation protocol which can reduce the communication cost by extending the lifetime of sensor networks. The techniques can be implemented in different efficient manners, but all are not useful in same application scenarios. More specifically, data can be collected by dynamic approach using rendezvous point (RP), and for that purpose, intelligent neural network-based cluster formation techniques can be used and for fixing the targeted base station, the ant colony optimization algorithm can be used. In this work, we have made a comprehensive study of such energy efficient integrated sensor-based system in order to achieve energy efficiency and to prolong network lifetime.

The use of online social media for post-disaster situation analysis has recently become popular. However, utilizing information posted on social media has some potential hazards, one of which is rumor. For instance, on Twitter, thousands of verified and non-verified users post tweets to convey information, and not all information posted on Twitter is genuine. Some of them contain fraudulent and unverified information about different facts/incidents - such information are termed as rumors. Identification of such rumor tweets at early stage in the aftermath of a disaster is the main focus of the current work. To this end, a probabilistic model is adopted by combining prominent features of rumor propagation. Each feature has been coded individually in order to extract tweets that have at least one rumor propagation feature. In addition, content-based analysis has been performed to ensure the contribution of the extracted tweets in terms of probability of being a rumor. The proposed model has been tested over a large set of tweets posted during the 2015 Chennai Floods. The proposed model and other four popular baseline rumor detection techniques have been compared with human annotated real rumor data, to check the efficiency of the models in terms of (i) detection of belief rumors and (ii) accuracy at early stage. It has been observed that around 70% of the total endorsed belief rumors have been detected by proposed model, which is superior to other techniques. Finally, in terms of accuracy, the proposed technique also achieved 0.9904 for the considered disaster scenario, which is better than the other methods.

Coronavirus disease 2019 (Covid-19) is a contiguous disease which affected a large volume of population with a high mortality rate across the globe. For dealing with the recent spread of COVID-19, one of the prime measures was to vaccinate people in full extent. People across the globe have diverse opinion regarding the vaccination process, its side effect and effectiveness. Such opinions get located into different micro-blogging sites including twitter. Opinion mining through analyzing public sentiments of such micro-blogs is a common method for detection of public responses. This paper focuses on classifying the public opinions expressed related to COVID-19 vaccination at sub topic level. The procedure tries to find out different keywords regarding positive, negative and neutral sentences. From those keywords, different related query set was constructed using Rocchio query expansion algorithm for positive, negative and neutral sentiments. Later Extended query set is used to form subtopic using LDA algorithm to identify the nature of the tweets. The proposed LDA model came across with 0.56 coherence score with twenty subtopics, which is fair enough to classify the tweets in different classes. This trained model is finally used to classify the tweets in real time with Apache Kafka framework regarding different subtopic based on positive, negative or neutral sentiment.

Radio frequency identification (RFID) technology can automatically and inexpensively track items as they are moved through the supply chain. This can automate the whole updating and management system, thereby making the system work with a much smaller workforce and reducing the error that can occur because of interference by human beings. One of the major advantages RFID provides is that it does not require direct physical contact with the objects and also does not require the object to be placed in its ‘Line-of-Sight’. This has given it an edge over other auto-identification systems, like bar-codes. The recent proliferation of RFID tags and readers would require dedicated and very efficient middleware solutions that manage readers and process the vast amount of captured data according to the need of various applications. RFID middleware is the software sitting in between various RFID readers and the enterprise applications. Extracting meaningful information out of huge amount of scan data is a challenging task. In this paper we like to analyze the requirements and propose a design for such an RFID middleware. This paper demonstrates how to enable the middleware to handle a large amount of RFID scan data and execute business rules in real-time. The conventional existing middleware solutions show dramatic degradation in their performance when the number of simultaneously working readers increases. Our proposed solution tries to recover from that situation also. One of the major issues for large scale deployment of RFID systems is the design of a robust and flexible middleware system to interface various applications to the RFID readers. Most of the existing RFID middleware systems are costly, bulky, non-portable and heavily dependent on the support software. Our work also provides flexibility for easy addition and removal of applications and hardware.

Network architecture based on opportunistic Delay Tolerant Network (DTN) is best applicable for post-disaster scenarios, where the controlling point of relief work is any fixed point like a local school building or a hospital, whose location is known to everyone. In this work, 4-tier network architecture for post-disaster relief and situation analysis is proposed. The disaster struck area has been divided into clusters known as Shelter Points (SP). The architecture consists of mobile Relief Workers (RW) at tier 1, Throw boxes (TB) at tier 2 placed at fixed locations within SPs. Data Mules (DM) like vehicles, boats, etc. operate at tier 3 that provide inter-SP connectivity. Master Control Station (MCS) is placed at tier 4. The RWs are provided with smart-phones that act as mobile nodes. The mobile nodes collect information from the disaster incident area and send that information to the TB of its SP, using DTN as the communication technology. The messages are then forwarded to the MCS via the DMs. Based on this architecture, a novel DTN routing protocol is proposed. The routing strategy works by tracking recent direction of movement of mobile nodes by measuring their consecutive distances from the destination at two different instants. If any node moves away from the destination, then it is very unlikely to carry its messages towards the destination. For a node, the fittest node among all its neighbours is selected as the next hop. The fittest node is selected using parameters like past history of successful delivery and delivery latency, current direction of movement and node’s recent proximity to the destination. Issues related to routing such as fitness of a node for message delivery, buffer management, packet drop and node energy have been considered. The routing protocol has been implemented in the Opportunistic Networks Environment (ONE) simulator with customized mobility models. It is compared with existing standard DTN routing protocols for efficiency. It is found to reduce message delivery latency and improve message delivery ratio by incurring a small overhead .

Purpose - The purpose of this paper is to make a strong case for investing in information and communication technologies (ICT) for building up of quality human resource capital for economic upliftment of India. An attempt has been made to explore the possibilities of online learning (OL) e-learning towards building up of quality human resources in higher education for a developing nation like India. A comprehensive environmental scanning of various e-learning experiments, tools, projects to facilitate e-learning or various institutional level efforts has been carried out. The paper also seeks to highlight the options available with traditional institutes for deploying ICT and for implementing e-learning.Design methodology approach - The paper is a descriptive account of the contemporary situation in India with regard to education especially e-learning and draws on a variety of secondary sources both published and unpublished.Findings - Argues that the development of e-learning has been limited and reasons out why. The challenges of traditional face-to-face education vis-à-vis e-learning in India are enlisted and suggestions for management of the e-learning process by institutes which intend to venture into e-learning are enumerated. The paper advocates the urgency for the traditional institutions to put an impetus on investment in ICT for providing e-instruction for delivery of knowledge by riding the information super highway.Research limitations implications - Presents a review of literature developed from secondary sources.Practical implications - Models of e-learning that exclude any face-to-face contact may have limited prospects, but blended learning offers significant potential both on and off campus and should be pursued if the benefits of e-learning are to be fully realized.Originality value - This paper provides a useful overview of a scenario of OL e-learning in India's higher education; and, from this summary of the present situation, goes on to suggest possible ways to transform the \"digital divide\" into \"digital opportunities\".

Due to high chances of loss in connectivity, a Delay Tolerant Network (DTN) can be used to communicate between nodes without having any fixed connection between the source and destination. A Post Disaster Scenario presents a very challenging environment to communicate in and to analyze those situations is even a harder task to accomplish. It necessitates very efficient co-ordination to successfully accomplish situation analysis and resource management. Efficient co-ordination between relief and rescue teams in such situations can be achieved through multicasting, since it allows sending single packet to multiple destinations. Though multicasting in MANET has been studied extensively, but the implementation of efficient multicasting in DTN is a very challenging task due to its frequent partitioning characteristic. In this work a Collaborative Time-Stamp based Multicast Routing (CTMR) Protocol has been proposed. The messages have been implemented using customized bundles, where the destination of a multicast bundle consists of a group of nodes. Node grouping mechanism has been adopted to suit a post disaster condition. Collaborative bundle creation and selection mechanism has been utilized so that localized redundant information flow is minimized. Suitable time and space limits have been selected to further reduce redundancy. The group forwarding strategy is based on probabilistic measures calculated using historical encounter records, and on a multiple parameter priority queue. This protocol has been implemented in the ONE simulator and is compared with other existing unicast and multicast routing protocols on important routing parameters like delivery ratio and delivery delay. Comparison results show that CTMR can be a novel efficient solution to multicasting in a DTN.