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Managing workplace diversity: Issues and challenges
Sage Open pp.1-5 DoI No. 10.1177/2158244012444615 -
Managing workplace diversity: Issues and challenges
Diversity management is a process intended to create and maintain a positive work environment where the similarities and differences of individuals are valued. The literature on diversity management has mostly emphasized on organization culture; its impact on diversity openness; human resource management practices; institutional environments and organizational contexts to diversity-related pressures, expectations, requirements, and incentives; perceived practices and organizational outcomes related to managing employee diversity; and several other issues. The current study examines the potential barriers to workplace diversity and suggests strategies to enhance workplace diversity and inclusiveness. It is based on a survey of 300 IT employees. The study concludes that successfully managing diversity can lead to more committed, better satisfied, better performing employees and potentially better financial performance for an organization. The Author(s) 2012. -
Manganese telluride quantum dot decorated 3D printed structures for dye-degradation
The disastrous result of fast industrialization and uncontrolled industrial effluent discharge is the lack of fresh water. Scholars have endeavored to extract water from heavily contaminated industrial effluent by creating several materials capable of effective and environmental friendly treating of tainted water. In the subject of water treatment, three-dimensional (3D) printed complex architecture has shown to be an emerging technique. Recently, nanomaterials have reformed filter technology because of their improved morphological characteristics. The current study explores the uses of two-dimensional (2D) Manganese Telluride (MnTe2) quantum dots (QDs) to decorate the 3D printed architecture for wastewater treatment. The photocatalytic performance of the QDs decorated 3D printed structures was demonstrated through the degradation of organic dyes (methylene blue (MB) and methyl orange (MO) dye) in both dark and light exposure conditions. The coated structures exhibited the ability to adsorb the organic pollutant and clean the contaminated water. We observe ?78 % degradation efficiency for MB and ?48 % for MO in dye concentrations of 10 mg/100 ml. A colorimetric detection method was used for real-time detection of degradation efficacy. The obtained results indicated that QDs decorated 3D printed system can be a significant system for wastewater treatment. 2025 -
Mangrove area classification in Pichavaram using Hyperspectral Imaging and Optimized Channel-Level Residual CNN framework
The Pichavaram mangrove forest in Tamil Nadu is one of Indias most ecologically significant regions, supporting coastal health and local communities. However, effective mangrove area classification remains challenging due to field inaccessibility and inefficiency of traditional assessment methods, highlighting the demand for advanced solutions. As the existing remote sensing-based studies suffer from limited classification accuracy and high computational complexity, this study combined Hyperspectral Image (HSI) with an Optimized Channel Level Residual CNN (OC-LRCNN) model for improved results in mangrove-related research. The proposed model employs unsupervised feature extraction to capture essential patterns with minimal training data while channel-level residual connections enhance discriminative feature selection and reduce spectral redundancy. Utilizing the Pichavaram EO-1 Hyperion and AVIRIS-NG datasets, the proposed model is compared with traditional CNN, state-of-the-art deep learning architectures (VGG, ResNet, DenseNet) and machine learning methods like SVM and RF. The OC-LRCNN achieved classification accuracies of 98.2% and 99.0% for the Hyperion and AVIRIS-NG datasets with consistently high precision, recall, F1-score and kappa values. These findings demonstrate the models effectiveness in reliable mangrove classification and monitoring applications. The Author(s), under exclusive licence to Springer Nature B.V. 2026. -
Manipur: the British legacy
[No abstract available] -
Manipurs crisis of inclusion: why ignoring smaller tribes undermines peace
[No abstract available] -
Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection for Privacy Protection in Social Networks
Malicious activity detection is a vital component of ensuring privacy protection in social media networks. As users engage in online interactions, protecting their sensitive information becomes paramount. Social networks can proactively identify and mitigate malicious behaviors, such as cyberbullying, data breaches, and phishing attacks by applying advanced AI and machine learning (ML) technologies. This detection system analyzes user behavior patterns, content, and network traffic to flag suspicious activities, thus safeguarding user privacy and fostering a safer online environment. The incorporation of robust malicious activity detection mechanisms helps maintain trust in social networks and reinforces the commitment to preserving user privacy in an increasingly interconnected digital landscape. This article introduces a novel Manta Ray Foraging Optimizer with Deep Learning based Malicious Activity Detection (MRFODLMAD) technique for privacy protection in social networks. The drive of the MRFODL-MAD technique is to detect and classify malicious activities in the social network. To accomplish this, the MRFODL-MAD technique preprocesses the input data. For malicious activity detection, the MRFODL-MAD technique employs long short term memory (LSTM) system. The MRFO algorithm has been executed to hyperparameter tuning process to improve the performance of the LSTM network. The experimental outcomes of the MRFODL-MAD algorithm can be tested on social networking database and the results inferred the improved performance of the MRFODL-MAD algorithm under various different measures. 2023 IEEE. -
Mapping Barriers to Net Zero in Quick Commerce A Fuzzy DEMATEL Approach
The fast-paced growth of q-commerce platforms has radically changed the shopping arena to deliver consumers unparalleled convenience. However, this speedy delivery poses significant challenges to achieving net-zero emissions, essentially due to inefficiencies in logistics and high energy usage. This research applies the Fuzzy DEMATEL approach to explain and analyze the barriers to sustainability in q-commerce by uncovering interconnections between factors. The findings showed that the primary logistical inefficiency is preceded by high energy usage and sustainable packaging as significant drivers. Other evaluated factors, though with lower scores, are regulatory challenges and consumer awareness. The mitigation of logistical inefficiencies can serve to greatly improve routing and resource management in such a way as to bring significant decreases in carbon footprint. Also, by augmenting consumer awareness for more sustainable practices, one creates an increasing demand for alternative choices, hence giving way to positive feedback that may help drive companies toward adopting even more sustainable approaches. From a policy perspective, the results indicate that regulatory frameworks should support investments in green infrastructure and technologies by engaging the different stakeholders, including businesses, consumers, and governmental entities, in a common strategy toward sustainability. While the present research supplies important insights into the challenges with which q-commerce is confronted while achieving net-zero emissions, it recognizes some constraints, such as potential biases due to expert judgments and the dynamic character of the business. The following studies would include more stakeholders and variables influencing sustainability and broaden the scope. Through addressing these barriers as a collective, the q-commerce industry can move toward achieving its net-zero dreams while advancing broader environmental goals for a greener world. 2026 selection and editorial matter, Siddhartha Roy, Soumya Sen, and Agostino Cortesi; individual chapters, the contributors. -
Mapping Cityscapes : Interrogating the Cultural Spaces in the Select Novels of Bapsi Sidhwa
Bapsi Sidhwa (1939) a well-known Pakistani Zoroastrian novelist in English offers the cityscapes of Lahore that provide the settings for her fictional works. The select newlinenovels for the study include The Crow Eaters (1978), The Pakistani Bride (1983), IceCandy Man (1988) and An American Brat (1993). Fascinated by the cityscapes of Lahore, the novelist personalizes the cityscapes and the personalized cityscapes are fictionalized. The novelist is aided by imagination. However, the imagined cityscapes in the select novels become illegible with a growing sense of alienation from the city. The cityscapes are cityspaces that are shape shifting. The metaphorical cityscapes in newlinethe select novels are woven with imagination, memory and nostalgia. The thesis examines the fictional representation of the cityscapes of Lahore and the relationship between the novelist and the imagined cityscapes. The study adopts the method of qualitative textual analysis in an attempt to examine the cityscapes. This illumines the in-between status of the cityscapes connecting the factual and fictional images of the city. The study unveils a layered construction of heterogeneous cityscapes which are selective and subjective. The urban cultural spaces are interrogated through the fictional characters who experience the city like fleurs and contribute to the making of the spatial stories. The acts of walking in the city offer knowledge of the city which enables the fictional characters to attain self-awareness. The awareness helps in achieving autonomy in the movements of the fictional characters. However, only a few fictional characters are perfect fleurs and the others view the city as voyeurs. Since the imagined cityscapes of Lahore are guided by the sense of place, the legibility of the cityscapes declines with the acts of alienation from the city. However, the novelist attempts to recover the palimpsest cityscapes from memory through cognitive mapping. -
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Mapping Cyclone and Flood Hazard Vulnerability in Puri District, Odisha, India, Using Geoinformatics
India is vulnerable to many natural and human-made disasters due to its unique geo-climatic and socio-economic conditions. This paper focuses on natural disasters; such as cyclones and floods in the Puri district in the Indian state of Odisha. In this study, a number of floods and cyclones that occurred in the district were identified. The thematic maps of the influencing factors such as soil type, flood and cyclone vulnerability, elevation, and 2020 land cover were created using ArcGIS 10.3. Thereafter, the weighted overlay method was adopted based on analytical hierarchy process (AHP) to map the overall vulnerability of the district. The results derived from this study exhibited that the district is highly vulnerable to floods and cyclones. Finally, strategies were recommended for hazard risk reduction covering enhancing awareness towards hazards, improving early warning systems, establishing better communication between various stakeholders, and strengthening environmental protection and disaster risk reduction. Furthermore, measures for mitigation such as creating shelters, post-disaster rehabilitation, better and improved health facilities, incorporating green infrastructure at critical locations, relying on nature-based measures, execution of mangrove plantation along the coastal belt of the district, creating barriers or dykes to prevent water tides, and plummeting leachate due to improper waste disposal near the coast are suggested. The analysis and mapping of hazard vulnerability can act as a reference for urban planners and policymakers to promote Sustainable Development Goal (SDG) number 11 which is sustainable cities and communities. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024. -
Mapping extinction using GALEX and SDSS photometric observations
The primary objective of this work is to create an all sky extinction map of the Milky Way galaxy. We have cross-matched the Sloan Digital Sky Survey (SDSS data release 8) photometric observations with that of Galaxy Evolution Explorer (GALEX data release 6). This provides a wide range of wavelength coverage from Far Ultra-Violet through the optical spectrum and gives one unique SDSS source for every GALEX source. We discuss a sample of ?32000 objects in the north galactic pole (?75 latitude) from this combined database. The Castelli and Kurucz Atlas was fit to the photometric observations of each star, best fit being determined using a chi-square test. Best fit parameters provide the spectral type and extinction towards each of the objects. The shift in magnitude obtained during the best-fit can be used to determine the distance to each of the stars. With this data, a comprehensive extinction map can be made for the high-latitude objects and later extended to all-sky. 2013 AIP Publishing LLC. -
Mapping Fire, Earthquake and Bio-hazard in Delhi: A Micro-level Study
Delhi, being Indias capital territory, is a massive metropolitan area that is extremely vulnerable to various types of disasters because of the widely spread built-up area that houses the population from all over the country. Delhi lies in Seismic Zone IV14, which makes the area sensitive to disasters. Another major problem that Delhi is currently facing is of proper garbage disposal, since the density of the population is high, tons of waste is generated. A fair share of the waste generated also includes biomedical waste. Delhi generates more biomedical waste than it can process. The area chosen for the present study is Chirag Delhi and Sheikh Sarai, located in south Delhi. This area is urbanized, and a home to a large number of people. The area is populated, poorly managed and highly vulnerable to disasters. The study area also has two colleges situated near the residential area because of which the area is subjected to a lot of traffic jam. The purpose of choosing this area for this study is its vulnerability to disasters like fire, earthquake and biohazard. The study area has pockets with high rise buildings or ill-designed high-risk areas without specific consideration for earthquake resistance. Moreover, the area lacks proper waste management. It has been identified that the area is a highly vulnerable place when it comes to hazards like fire, earthquake and biohazards. The people living there are in a constant threat for their lives. One of the major problems is that the community lacks dedication and determination, which has been tested through a schedule and observation method, to change their circumstances and bring about a change in the area that would benefit them and their families. The Editor(s)(ifapplicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG 2024. -
Mapping location and identity in the works of indian english novelists:
This thesis examines the context of location in relation to constructs of identity in Salman Rushdie s MC, Vikram Seth s ASB and Amitav Ghosh s TSL. It is contended that articulation of selfhood is achieved through its interaction with narrative constructions of space and these depictions serve to map representations of nation. Writers migrant experiences are shown to have a bearing on the aesthetics and geopolitics of these representations. Even though these texts challenge the reductive processes of homogenization at work in the formation of nationalcultural identities, it is contended that they foreground transnational lifestyles and identities.Some of the questions that the thesis asks are: Does the cultural-geographical location of the writer shape the aesthetics of the work? If so, to what extent? In what ways does the diasporic newlineexperience influence the (re)presentation of mediated and inter-connected spaces? How is a newlinecharacter, who does not share the author s diasporic location and experience, depicted? Do the works cater to a Western readership by presenting a palatable version that is only purportedly transnational? Or, are the writers lapsing into a master narrative of universalism? newlineThe creative paradigm allows for the unfolding of the enigma of identity by the interplay of the questions surrounding place - Where am I and what is my place in the world, which reveals who I am. There are real geographies of social action, as well as metaphorical spaces and sites of power that have to be understood in their own right and in the context of shared loci that come together to construct identity. Thus, a comparative study of the novels is conducted on various registers such as dynamics of space, negotiation of borders and boundaries, delineation of multiple identities and representation of nation via language and history. The thesis argues for newlineaesthetic negotiation of borders across locations that maybe geographic and psychic; in order to grapple with and empower subjectivities. -
Mapping of built-up area and change detection in bengaluru using semi-automatic classification
Built-up areas are ever-increasing in nature to cater to the growing population's needs due to the migration of people to urban areas. Indian cities are under stress due to unplanned developmental activities. Land use and the land cover pattern are critical to maintaining the balance of various resources. In this study, Spatio-temporal changes have been mapped from 1989 to 2022 for the Bengaluru urban region. Geospatial techniques have been adopted to map land use, land cover changes and urban growth. Passive remote sensing data sets, which are freely available, were used in this study. QGIS and ESRI's ArcGIS software packages analysed the satellite images. Vegetation indices such as the Normalised vegetation index (NDVI), Normalised Difference Water index (NDWI), and Normalised difference Built-up index (NDBI) have been used along with supervised and unsupervised classification techniques. Images were classified into water bodies, vegetation, built-up area and others. It has been observed that there is an increase in the built-up area decrease in vegetation and water bodies. As per this study, policymakers and society need to consider the conservation of natural resources and developmental activities for sustainable development. 2023 Author(s). -
Mapping of groundwater availability in dry areas of rural and urban regions in India using IOT assisted deep learning classification model
Groundwater is a crucial resource for fulfilling the water requirements of India's rural and urban areas. The heterogeneous nature of geological, hydrological, and climatic factors results in substantial variability in the accessibility of groundwater across disparate regions. The present investigation centers on the cartography of groundwater accessibility in arid zones of rural and urban Indian areas using a Deep Learning Classification Model (DL-GWCM) supported by the Internet of Things (IoT). The introductory section underscores the importance of groundwater in India, where groundwater sources cater to around 80% of rural and 50% of urban water demands. The text highlights statistical data derived from surveys that indicate a notable decrease in groundwater levels. This underscores the pressing necessity for implementing effective monitoring and management strategies. The DL-GWCM is a proposed solution that aims to enhance the precision and effectiveness of groundwater availability mapping by incorporating IoT technology and Deep Learning Classification. The DL-GWCM comprises multiple constituent elements, such as Groundwater Prediction, Water Quality Index, and Conventional Neural Network- Bidirectional Long Short-Term Memory (CNNBi LSTM) classification. The process of Groundwater Prediction involves the utilization of past data and environmental factors to make precise forecasts of groundwater levels. The Water Quality Index evaluates the quality of subsurface water resources, guaranteeing their secure and enduring utilization. The Deep Learning Classification Model with IoT technology was implemented for groundwater accessibility mapping in Indian arid zones. It integrates Groundwater Prediction, Water Quality Index, and CNNBi LSTM classification. The model makes precise forecasts using past data and environmental factors, ensuring secure water quality. Using the CNNBi LSTM classification model improves the precision of groundwater availability mapping due to its resilient classification capabilities. These findings suggest that the DL-GWCM outperforms conventional approaches. The mean values of all five metrics for the proposed method are presented as follows: The performance metrics of the model are as follows: Root Mean Square Error (RMSE) of 0.77%, Mean Absolute Error (MAE) of 2.13%, Relative Absolute Error (RAE) of 8.72%, Root Relative Squared Error (RRSE) of 0.92%, and Correlation Coefficient (CC) of 0.92. The results of the proposed methodology facilitate the discernment of regions with abundant or scarce groundwater accessibility, thereby supporting the sustainable management and planning of groundwater resources. 2024 Elsevier B.V. -
Mapping Road Traffic Injury in India: Causes, Prevention, Economic Impact, and Role of Public Health Governance
Road Traffic Injuries (RTIs) represent the main reason for fatalities globally and are acknowledged as a significant national health problem. RTIs affect the victims and also have a profound impact on the family and relations. The socio-economic conditions of families, and consequently society and the nation, are negatively influenced by these incidents. A significant number of deaths on the roads involve cyclists, motorcyclists, and pedestrians. The prevalence of RTIs is particularly high in African and other middle-income countries, while developed nations experience comparatively fewer incidents. Each year, RTIs result in approximately 1.2 million deaths worldwide (WHO, 2018), marking them as a primary, preventable cause of mortality. Global attention has shifted towards the critical need for road safety, particularly with the endorsement of the 2030 Agenda for Sustainable Development Goals (SDGs). Implementing robust legal enforcement could lead to behavioural changes among road users. India ranks among the highest in the world for road accident-related fatalities. Ad-hering to safety measures such as using helmets, wearing seat belts, maintaining appropriate speeds, and following traffic regulations would significantly reduce Road Traffic Injuries. Stakeholders in road safety must be made aware of the economic costs, Disability Adjusted Life Year (DALY), and human losses associated with RTIs and their repercussions. This paper aims to outline the existing RTI situation, its causes, preventive strategies, magnitude of economic burden, costs involved in RTI, catastrophic health expenditure, Return on Investment (RoI) in Trauma Care Systems, Financing mechanisms, Governance, and the Health sector's role in addressing RTIs. The findings indicate that road accidents are the predominant reason for mortality in India (with many incidents being underreported or undocumented), and the state must take a proactive approach to tackle this issue by fostering strong connections among various stakeholders, while the health sector should implement a multifaceted strategy to manage RTIs. Authors. -
Mapping the AI landscape in healthcare quality: A bibliometrics analysis
This study explores the application of Artificial Intelligence (AI) in healthcare quality improvement through a bibliometric analysis of 222 documents retrieved from the Scopus database using the keywords "healthcare," "quality," and "AI." By examining bibliographic coupling, citations, co-citations, author keywords, and co-occurrence networks, the research unveils the key themes, prominent authors, and emerging trends in this field. The analysis reveals a focus on areas like machine learning for disease prediction, clinical decision support systems, and patient safety improvement. Leading authors and research groups are identified, and promising future directions such as explainable AI and integration with electronic health records are highlighted. This study contributes to understanding the current landscape of AI in healthcare quality improvement and guiding future research for maximizing its impact. 2024, IGI Global. All rights reserved. -
Mapping the Field of Research; Computational Intelligence and Innovation
This paper measures and maps the past studies in the field of Computational Intelligence and Innovation and further understand the application of Computational Intelligence in the field of study of innovation related to businesses. The bibliometric analysis shows the associations of various sub themes of research that was done between the period 2000 to Aug 2022. Scopus database is used to collect relevant documents of the field of study where 115 documents are sourced. The descriptive nature of the field of studies is analyzed in detail and further using VOS Viewer, the network analysis study is conducted to understand the association of authors, author country publication, themes and publication pattern, in detail. Further, an in-depth review analysis is done to understand the application of Computational Intelligence in the fields of Business Management and Social Science with aids innovation in the respective fields. Recent studies focus on machine learning, neural network, digital transformation, internet of things and other upcoming areas. The growth in these sub themes exhibit the multidisciplinary research happening in this field. This is paving way for future researchers to use the already found computing intelligence techniques to varied subject areas like medicine, management, economics etc., to foster innovation. 2022 IEEE. -
Mapping the Landscape of Business Intelligence Research: A Bibliometric Approach
The integration of Business Intelligence (BI) is an essential element in contemporary enterprises, facilitating the conversion of voluminous data into valuable insights to support informed decision-making. Consequently, a considerable body of literature has been devoted to investigating the utilization of Business Intelligence (BI) in enhancing company efficiency and competitiveness. The present investigation employs bibliometric methods as a means to examine the research pertaining to Business Intelligence (BI). This includes an examination of the main writers and universities, publication patterns, and the intellectual framework of the domain. This investigation centers on the timeframe spanning from 2000 to 2022 and scrutinizes a corpus of 3729 Scopus articles pertaining to business intelligence. The findings suggest that the domain of Business Intelligence (BI) has experienced a substantial expansion recently. The study's results reveal significant contributors, establishments, nations, and references in the discipline, along with developing research patterns and prospects for further investigation. In general, this research emphasizes the significance of bibliometric evaluation as a means of comprehending the present status of BI research and discovering approaches to enhance the utilization of BI in contemporary organizational decision-making procedures. This study has the potential to provide valuable insights into the present state of research within the field, pinpoint significant trends and themes, and highlight potential avenues for future research. 2023 IEEE.