Search results

The purpose of the study is to design and develop an pervasive and smart Internet of Things (IoT)-based sensor system to monitor he real-time intravenous (IV) fluid bag level.

This paper investigates such issue and performs several experiments to develop a non-invasive, semi-automatic system to monitor IoT-based IV fluid level in real-time.

The outcome of this study is a prototype hardware that includes an ESP8266 based embedded Web server to disseminate the fluid exhaust status flag to its connected users. Nurses can get the prompt intimation about the status of IV fluid bag whether it is about to get empty.

IoT is the backbone of the proposed system. Multi-master system need to be studied in future.

Non-invasive and real-time IoT-based novel technique is developed with power-efficient and cost-effective pervasive sensors.

This is applicable for pervasive and assistive e-health-care services by care givers and medical professionals.

The deployed system is controlled by ATtiny85 with help of LM35 temperature sensor. The results show a promising future of the proposed development in enhancing IoT-based smart health-care service in the coming days.

Background: Every so often, one experiences different physically unstable situations which may lead to possibilities of suffering through vicious physiological risks and extents. Dynamic physiological activities are such a key metric that they are perceived by means of measuring galvanic skin response (GSR). GSR represents impedance of human skin that frequently changes based on different human respiratory and physical instability. Existing solutions, paved in literature and market, focus on the direct measurement of GSR by two sensor-attached leads, which are then parameterized against the standard printed circuit board mechanism. This process is sometimes cumbersome to use, resulting in lower user experience provisioning and adaptability in livelihood activities. The purpose of this study is to validate the novel development of the cost-effective GSR sensing system for affective usage for smart e-healthcare.

This paper proposes to design and develop a flexible circuit strip, populated with essential circuitry assemblies, to assess and monitor the level of GSR. Ordinarily, this flexible system would be worn on the back palm of the hand where two leads would contact two sensor strips worn on the first finger.

The system was developed on top of Pyralux. Initial goals of this work are to design and validate a flexible film-based GSR system to detect an individual’s level of human physiological activities by acquiring, amplifying and processing GSR data. The measured GSR value is visualized “24 × 7” on a Bluetooth-enabled smartphone via a pre-incorporated application. Conclusion: The proposed sensor-system is capable of raising the qualities such as adaptability, user experience, portability and ubiquity for possible application of monitoring of human psychodynamics in a more cost-effective way, i.e. less than US$50.

Several novel attributes are envisaged in the development process of the GSR system that made it different from and unique as compared to the existing alternatives. The attributes are as follows: (i) use of reproductive sensor-system fabrication process, (ii) use of flexible-substrate for hosting the system as proof of concept, (iii) use of miniaturized microcontroller, i.e. ATTiny85, (iv) deployment of energy-efficient passive electrical circuitry for noise filtering, (v) possible use case scenario of using CR2032 coin battery for provisioning powering up the system, (vi) provision of incorporation of internet of things (IoT)-cloud integration in existing version while fixing related APIs and (vii) incorporation of heterogeneous software-based solutions to validate and monitor the GSR output such as MakerPlot, Arduino IDE, Fritzing and MIT App Inventor 2.

This paper is a revised version R1 of the earlier reviewed paper. The proposed paper provides novel knowledge about the flexible sensor system development for GSR monitoring under IoT-based environment for smart e-healthcare.

The purpose of this paper is a new thin-film based sensor proposed for sensitive and selective detection of mercury (Hg²⁺) ions in water. The thin-film platform is easy to use and quick for heavy metal ions (HMIs) detection in the picomolar range. Ion-selective self-assembled monolayer's (SAM) of thiol used for the detection of HMIs above the Au/Ti top surface.

A thin-film based platform is suitable for the on-field experiments and testing of water samples. HMIs (antigen) and thiol-based SAM (antibody) interaction results change in surface morphology and topography. In this study, the authors have used different characterization techniques to check the selectivity of the proposed method. This change in the morphology and topography of thin-film sensor checked with Fourier-transform infrared spectroscopy, surface-enhanced Raman scattering spectroscopy, atomic force microscopy and scanning electron microscopy with energy dispersive x-ray analysis used for high-resolution images.

This thin-film based platform is straightforward to use and suitable for real-time detection of HMIs at the picomolar range. This thin-film based sensor platform capable of achieving a lower limit of detection (LOD) 27.42 ng/mL (136.56 pM) using SAM of Homocysteine-Pyridinedicarboxylic acid to detect Hg²⁺ ions.

A thin-film based technology is perfect for real-time testing and removal of HMIs, but the LOD is higher as compared to microcantilever-based devices.

The excessive use and commercialization of nanoparticle (NPs) are quickly expanding their toxic impact on health and the environment. The proposed method used the combination of thin-film and NPs, to overcome the limitation of NPs-based technique and have picomolar (136.56 pM) range of HMIs detection. The proposed thin-film-based sensor shows excellent repeatability and the method is highly reliable for toxic Hg²⁺ ions detection. The main advantage of the proposed thin-film sensor is its ability to selectively remove the Hg²⁺ ions from water samples just like a filter and a sensor for detection at picomolar range makes this method best among the other current-state of the art techniques.

Decision aids (DAs) in online retail stores ease consumers' information processing. However, online consumers do not use all decision aids in purchase decision-making. While the literature has documented the effects of individual decision aids or two decision aids at a time, no study has compared the efficacy of multiple decision aids simultaneously. Also, very few studies have looked at the use of decision aids for consumers with maximizing and satisficing tendencies. Hence, this study aims to understand the preferences of maximizers and satisficers towards online decision aids during the choice-making process.

This is an observational study with 60 individuals who were asked to purchase either a search-based or an experience-based product online. Participants' browsing actions and verbalizations during online shopping, were recorded and analysed using NVivo, and later the use of decision aids was mapped along their choice process.

Consumer's preference of decision aids varies across the two stages of the choice process (that is, consideration set formation and evaluation & choice). In their choice formation, maximizers use different decision aids in both stages, that is, filter tool and in-website search tool for search products, and collaborative filtering-based recommender systems and eWOM for experience products. Satisficers used more decision aids as compared to maximizers across the two stages for both product types.

This study is an exploratory attempt to understand how consumers use multiple decision aids present on e-commerce websites.

This study on spatial analysis of child labour in India is a macro level analysis on child labour using the census data, 2011 of Government of India. The population census which is conducted once in 10 years only provides district level data on work-force distribution. The study has spatial analysis of child labour in the age group of 5–14 years in India. To assess the magnitude of the children in the labour force, district level data of Census 2011 has been used in the study. The study has made an attempt to identify the districts where there is high level of children in the labour force. This paper aims to estimate the magnitude and trends of children’s workforce participation using the census data as it is the only data base, which is available at the district level since 1961 onwards. The study has made an attempt to identify the clustering of child labour across districts in India and how child labour is clustered by different background characteristics.

The study has used ArcGIS software package, GeoDa software and local indicator of spatial association test.

The findings of study reveal that the proportion of rural, total fertility rate (TFR) and poverty headcount ratio is positively associated, whereas female literacy and the pupil-teacher ratio are negatively associated with child labour. It suggests that in the hot-spot areas and areas where there is a high prevalence of child labour, there is need to increase the teacher's number at the school level to improve the teacher-pupil ratio and also suggested to promote the female education, promote family planning practices to reduce TFR in those areas for reducing the incidences of child labour.

The study also recommends that the incidences of child labour can be controlled by a comprehensive holistic action plan with the active participation of social workers.

The promulgation of effective legislation, active involvement of judiciary and police, political will, effective poverty alleviation and income generation programmes, sensitisation of parents, corporates and media can play effective role in mitigating the incidences of child labour in India. To achieve the sustainable development goals (SDGs) adopted by world leaders in 2015 to eradicate child labour in all its forms by 2025.

The study aims to achieve the SDGs adopted by world leaders in 2015 to eradicate child labour in all its forms by 2025.

The study is purely original and there are no such studies in Indian context by using the latest software.

Artificial intelligence (AI) has sparked interest in various areas, including marketing. However, this exhilaration is being tempered by growing concerns about the moral and legal implications of using AI in marketing. Although previous research has revealed various ethical and legal issues, such as algorithmic discrimination and data privacy, there are no definitive answers. This paper aims to fill this gap by investigating AI’s ethical and legal concerns in marketing and suggesting feasible solutions.

The paper synthesises information from academic articles, industry reports, case studies and legal documents through a thematic literature review. A qualitative analysis approach categorises and interprets ethical and legal challenges and proposes potential solutions.

The findings of this paper raise concerns about ethical and legal challenges related to AI in the marketing area. Ethical concerns related to discrimination, bias, manipulation, job displacement, absence of social interaction, cybersecurity, unintended consequences, environmental impact, privacy and legal issues such as consumer security, responsibility, liability, brand protection, competition law, agreements, data protection, consumer protection and intellectual property rights are discussed in the paper, and their potential solutions are discussed.

Notwithstanding the interesting insights gathered from this investigation of the ethical and legal consequences of AI in marketing, it is important to recognise the limits of this research. Initially, the focus of this study is confined to a review of the most important ethical and legal issues pertaining to AI in marketing. Additional possible repercussions, such as those associated with intellectual property, contracts and licencing, should be investigated more deeply in future studies. Despite the fact that this study gives various answers and best practices for tackling the stated ethical and legal concerns, the viability and efficacy of these solutions may differ depending on the context and industry. Thus, more research and case studies are required to evaluate the applicability and efficacy of these solutions in other circumstances. This research is mostly based on a literature review and may not represent the experiences or opinions of all stakeholders engaged in AI-powered marketing. Further study might involve interviews or surveys with marketing professionals, customers and other key stakeholders to offer a full knowledge of the practical difficulties and solutions. Because of the rapid speed of technical progress, AI’s ethical and regulatory ramifications in marketing are continually increasing. Consequently, this work should be a springboard for more research and continuing conversations on this subject.

This study’s findings have several practical implications for marketing professionals. Emphasising openness and explainability: Marketing professionals should prioritise transparency in their use of AI, ensuring that customers are fully informed about data collection and utilisation for targeted advertising. By promoting openness and explainability, marketers can foster customer trust and avoid the negative consequences of a lack of transparency. Establishing ethical guidelines: Marketing professionals need to develop ethical rules for the creation and implementation of AI-powered marketing strategies. Adhering to ethical principles ensures compliance with legal norms and aligns with the organisation’s values and ideals. Investing in bias detection tools and privacy-enhancing technology: To mitigate risks associated with AI in marketing, marketers should allocate resources to develop and implement bias detection tools and privacy-enhancing technology. These tools can identify and address biases in AI algorithms, safeguard consumer privacy and extract valuable insights from consumer data.

This study’s social implications emphasise the need for a comprehensive approach to address the ethical and legal challenges of AI in marketing. This includes adopting a responsible innovation framework, promoting ethical leadership, using ethical decision-making frameworks and conducting multidisciplinary research. By incorporating these approaches, marketers can navigate the complexities of AI in marketing responsibly, foster an ethical organisational culture, make informed ethical decisions and develop effective solutions. Such practices promote public trust, ensure equitable distribution of benefits and risk, and mitigate potential negative social consequences associated with AI in marketing.

To the best of the authors’ knowledge, this paper is among the first to explore potential solutions comprehensively. This paper provides a nuanced understanding of the challenges by using a multidisciplinary framework and synthesising various sources. It contributes valuable insights for academia and industry.

This paper aims to present findings from a study of non-profit organizations (NPOs), including a model of knowledge needs that can be applied by practitioners and scholars to further develop the NPO sector.

A survey was conducted with NPOs operating in Canada and Australia. An analysis of survey responses identified the different types of knowledge essential for each organization. Respondents identified the importance of three pre-determined themes (quantitative data) related to knowledge needs, as well as a fourth option, which was a free text box (qualitative data). The quantitative and qualitative data were analyzed using descriptive statistical analyses and a grounded theory approach, respectively.

Analysis of the quantitative data indicates that NPOs ' needs are comparable in both countries. Analysis of qualitative data identified five major categories and multiple sub-categories representing the types of knowledge needs of NPOs. Major categories are knowledge about management and organizational practices, knowledge about resources, community knowledge, sectoral knowledge and situated knowledge. The paper discusses the results using semantic proximity and presents an emergent, evidence-based knowledge management (KM)-NPO model.

The findings contribute to the growing body of literature in the KM domain, and in the understudied research domain related to the knowledge needs and experiences of NPOs. NPOs will find the identified categories and sub-categories useful to undertake KM initiatives within their individual organizations. The study is also unique, as it includes data from two countries, Canada and Australia.

The purpose of the paper is to design a nonlinear dynamic inversion (NDI) based robust fault-tolerant control (FTC) for aircraft longitudinal dynamics subject to system nonlinearities, aerodynamic parametric variations, external wind disturbances and fault/failure in actuator.

An uncertainty and disturbance estimator (UDE) technique is used to provide estimate of total disturbance enabling its rejection and thereby achieving robustness to the proposed NDI controller. As needed in the NDI design, the successive derivatives of the output are obtained through an UDE robustified observer making the design implementable. Further, a control allocation scheme consigns control command from primary actuator to the secondary one in the event of fault/failure in the primary actuator.

The robustness is achieved against the perturbations mentioned above in the presence of actuator fault/failure.

Lyapunov analysis proves practical stability of the controller–observer structure. The efficacy and superiority of the proposed design has been demonstrated through Monte-Carlo simulation.

Unlike in many FTC designs, robustness is provided against system nonlinearities, aerodynamic parametric variations, external wind disturbances and sinusoidal input disturbance using a single control law which caters for fault-free, as well as faulty actuator scenario.

A study on convective aspects was carried out on a Couette flow in an irregular channel by applying a constant uniform magnetic field parallel to the channel flow.

The dynamic study of such a flow resulted in highly nonlinear coupled partial differential equations. To solve these partial differential equations analytically, regular perturbation method was invoked for velocity, temperature and concentration with a combined parameter of Soret and Forchheimer. The numerical computational results have been extracted for various nondimensional parameters with regard to fluid and particle flow as well as for temperature and solute concentration.

The current article presents a novel approach to assess the effects of drag force as well as the diffusion-based interactions between the velocity, temperature and concentrations with the aid of Soret and Dufour on two-dimensional MHD mixed with a dusty viscoelastic fluid.

The results found are in good agreement with the earlier studies in the absence of nonlinear effect of Forchheimer model.

The purpose of this paper is to identify factors related to rural healthcare services and establish a hierarchical model for the effective rural healthcare management in India.

A questionnaire survey identified and correlated numerous factors related to the Uttarakhand rural healthcare systems. Experts opinion were translated into a reachability matrix and an interpretive structural model. A fuzzy matriced impacts croises-multiplication applique and classment (FMICMAC) analysis arranged the factors as hierarchical stages using their driving power.

The interpretive structural and FMICMAC hierarchical models suggest four key driving factors: diseases, climatic conditions, population growth and political pressure.

Despite numerous issues, rural healthcare services can be improved by considering key driving factors that could be used as a prediction tool for policy makers.

Results demonstrate that population control, coordinating services with local bodies and rural health center annual maintenance can be game changers toward better healthcare services.