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Translating artificial intelligence into socio-economic insight: a hybrid deep learning approach to employee financial well-being
This study aims to translate recent advancements in hybrid artificial intelligence (AI) modeling into a functional tool for assessing individual financial well-being. The objective is to develop a system that aids organizations in understanding employees financial stress, with broader implications for enhancing workplace productivity and societal economic resilience. A deep learning pipeline was developed to classify individuals into three financial well-being categories: Financially Secure, Moderately Stable, and Financially At-Risk. The approach utilizes a structured dataset of 20,000 Indian individuals and implements 15 advanced deep learning models, including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Gated Recurrent Units (GRU), Bidirectional Long Short-Term Memory (BiLSTM), and Wide & Deep networks. Model performance was assessed using standard evaluation metrics, including validation accuracy and ROC-AUC scores. Among the tested models, the hybrid Wide & Deep + CNN configuration yielded the highest performance, achieving a validation accuracy of 99.44% and a perfect ROC-AUC score of 1.0000. These results validate the models capacity for robust classification and real-world applicability to financial profiling. This study demonstrates a practical application of AI in financial decision support systems and contributes to organizational research by offering a scalable solution to assess and mitigate employee financial stress. The Author(s) 2026. -
Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems
Purpose This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024 Emerald Publishing Limited -
Machine learningbased approaches for enhancing human resource management using automated employee performance prediction systems
Purpose: This study focuses on enhancing the accuracy and efficiency of employee performance prediction to enhance decision making and improve organisational productivity. By introducing advance machine learning (ML) techniques, this study aims to create a more reliable and data-driven approach to evaluate employee performance. Design/methodology/approach: In this study, nine machine learning (ML) models were used for forecasting employee performance: Random Forest, AdaBoost, CatBoost, LGB Classifier, SVM, KNN, XGBoost, Decision Tree and one Hybrid model (SVM + XGBoost). Each ML model is trained on an HR data set covering various features such as employee demographics, job-related factors and past performance records, ensuring reliable performance predictions. Feature scaling techniques, namely, min-max scaling, Standard Scaler and PCA, have been used to enhance the effectiveness of employee performance prediction. The models are trained to classify data, predicting whether an employees performance meets expectations or needs improvement. Findings: All proposed models used in the study can correctly categorize data with an average accuracy of 94%. Notably, the Random Forest model demonstrates the highest accuracy across all three scaling techniques, achieving optimise accuracy, respectively. The results presented have significant implications for HR procedures, providing businesses with the opportunity to make data-driven decisions, improve personnel management and foster a more effective and productive workforce. Research limitations/implications: The scope of the used data set limits the study, despite our models delivering high accuracy. Further research could extend to different data sets or more diverse organisational settings to validate the models effectiveness across various contexts. Practical implications: The proposed ML models in the study provide essential tools for HR departments, enabling them to make more informed data driven decisions with regard to employee performance. This approach can enhance personnel management, improve workforce productivity and fostering a more effective organisational environment. Social implications: Although AI models have shown promising outcomes, it is crucial to recognise the constraints and difficulties involved in their use. To ensure the fair and responsible use of AI in employee performance prediction, ethical considerations, privacy problems and any biases in the data should be properly addressed. Future work will be required to improve and broaden the capabilities of AI models in predicting employee performance. Originality/value: This study introduces an exclusive combination of ML models for accurately predicting employee performance. By employing these advanced techniques, the study offers novel insight into how organisations might transition from a conventional evaluation method to a more advanced and objective, data-backed approach. 2024, Emerald Publishing Limited. -
Federated Multi-Task Learning Using Server-Side Normalized Loss-Based Weighting Method
Federated multi-task learning is an approach where multiple clients collaboratively train related but distinct models on their local data without sharing it, thereby preserving privacy while leveraging collective knowledge. However, participating clients can have very different data distributions, sizes and quality, leading to statistical heterogeneity. This heterogeneity is a major challenge in federated learning, as noisy or inconsistent updates from some clients can slow down convergence or degrade the global model's performance. MOCHA is a seminal federated multi-task learning framework that explicitly models task relationships and optimizes clientspecific models, while addressing system challenges like communication costs, fault tolerance and client dropouts. In this work, we enhance MOCHA with a server-side normalized lossbased weighting technique that focuses on the quality of client updates. Each client in the federated multi-task setup computes its local training loss, which is sent to the server during communication rounds. The server normalizes these losses across clients and assigns adaptive aggregation weights, giving more influence to clients with lower normalized losses and down-weighting noisy or unreliable clients. This design simplifies client-side implementation because all weighting is performed at the server. Experiments on heterogeneous MNIST and CIFAR-10 tasks show that the proposed method achieves a slightly higher final-round average test accuracy (0.5108 vs. 0.5065), reduces average training loss by approximately 2.6% (from 1.1148 to 1.0858), and improves fairness by lowering the standard deviation of client accuracies by about 5% (from 0.3631 to 0.3450) compared to baseline MOCHA. These results indicate that server-side normalized loss-based weighting improves training stability, convergence behavior and crossclient fairness in federated multi-task learning under nonconvex optimization. 2025 IEEE. -
An Interpretable Federated Multi-Task Learning Framework for Smart Traffic Management with Hessian-Driven Optimization Insights
Smart traffic management faces challenges in balancing privacy, interpretability, and optimization robustness, particularly when using deep learning for vehicle detection and traffic prediction. Existing methods struggle to provide transparent feature attribution while preserving data confidentiality in decentralized settings. This study proposes a federated multi-task learning (FMTL) framework based on YOLOv10, trained on an original traffic dataset, to address these limitations. The framework simultaneously performs vehicle detection, traffic density analysis, and no-entry sign identification, while employing Grad-CAM to enhance interpretability and Hessian-based eigenvalue analysis to evaluate optimization complexity. Results demonstrate an average mean accuracy of 89.7% across three real-world locations, with Grad-CAM revealing meaningful focus on vehicle density and intersection geometry. Hessian analysis confirms the presence of mixed-sign eigenvalues, proving the non-convexity of the optimization surface and highlighting convergence challenges. These outcomes establish a privacypreserving, interpretable, and optimization-aware framework for real-world smart traffic management. 2025 IEEE. -
Comparison of Convergence Rates in Federated Learning and Federated Multi-Task Learning Using the CIFAR-10 Dataset
Many smart or cell phones have built-in distance, signal, and air pollution sensors. While collecting information, an acceleration registering device is a three-dimensional one and it can be applied in the gait analysis to address issues such as falls and health status determination. Indeed, the data is abundance in terms of quantity and some of the data may be of great concern in terms of privacy. In the time of Industry 4.0 the data has emerged as a key resource. Personal information/identity must not be maintained and hence cannot be stored at one place or all collected in a single place. AI models are moving to decentralized where a machine learning setting called Federated learning (FL) is being applied. FL has adversities such as statistical and systems heterogeneity. Actually, to better use shared information and build local models, Federated Multi-task learning (FMTL) has been devised. We also compare the number of iterations required to converge using CIFAR dataset of FL and FMTL. Several graphs illustrated in this paper show that convergence rates depend on the algorithm, number of communication rounds and number of clients or devices. Thus, it is clear that in some cases FL outperforms with FMTL in terms of convergence or conversely. However, it cannot be deduced that the type of FMTL always converges better than FL. The reliance on this graph is evident in this paper in order to as explain as prove the fact that, as the number of clients in FL rises, the rate of convergence declines. If ten communication rounds are employed with the use of the MOCHA algorithm, the model does not converge appropriately. The RMSE score declined from 1.14 to 1.02 throughout 20 epochs. 2025 IEEE. -
Modeling of Real Time Traffic Flow Monitoring System Using Deep Learning and Unmanned Aerial Vehicles
Recently, intelligent video surveillance technologies using unmanned aerial vehicles (UAVs) have been considerably increased in the transportation sector. Real time collection of traffic videos by the use of UAVs finds useful to monitor the traffic flow and road conditions. Since traffic jams have become common in urban areas, it is needed to design artificial intelligence (AI) based recognition techniques to attain effective traffic flow monitoring. Besides, the traffic flow monitoring system can assist the traffic managers to start efficient dispersal actions. Therefore, this study designs a real time traffic flow monitoring system using deep learning (DL) and UAVs, called RTTFM-DL. The proposed RTTFM-DL technique aims to detect vehicles, count vehicles, estimate speed and determine traffic flow. In addition, an efficient vehicle detection model is proposed by the use of Faster Regional Convolutional Neural Network (Faster RCNN) with Residual Network (ResNet). Also, a detection line based vehicle counting approach is designed, which is based on overlap ratio. Finally, traffic flow monitoring takes place based on the estimated vehicle count and vehicle speed. In order to guarantee the effectual performance of the RTTFM-DL technique, a series of experimental analyses take place and the results are examined under varying aspects. The experimental outcomes highlighted the betterment of the RTTFM-DL technique over the recent techniques. The RTTFM-DL technique has gained improved outcomes with a higher accuracy of 0.975. 2022 River Publishers. -
The subculture of gaming- An analysis of the culture and the behavioral patterns /
The aim of this study would be to reach out to people who identify themselves as gamers and then find out about the subculture that they represent. The study will be aimed at analyzing that culture and then identifying the behavioral and psychological influences it has on the “gamers”. Through this study the researcher has tried to break the myth on online gaming being addictive and inducing violence. -
Cotton-derived carbon fibers and MoS2 hybrids for efficient I3? reduction in bifacial dye-sensitized solar cells
In light of recent advancements, a novel platinum-free counter electrode for dye-sensitized solar cells (DSSCs) has been developed utilizing hierarchical MoS2 structures in conjunction with bio-derived carbon materials. Carbon fibers produced from cotton and molybdenum-doped carbon rods synthesized from melamine were fabricated through a straightforward hydrothermal process, which significantly enhanced both electrocatalytic activity and stability. The resulting counter electrodes exhibited notably low charge transfer resistances of 9.45 ? and 6.43 ?, thus facilitating efficient redox reactions. Consequently, DSSCs incorporating these materials achieved remarkable power conversion efficiencies of 7.04 % and 7.58 %, surpassing traditional platinum-based counter electrodes, which recorded an efficiency of 7.50 %. Furthermore, the high optical transmittance of these materials renders them suitable for bifacial DSSCs, broadening their potential applications. This research underscores the promise of bio-inspired carbon composites as sustainable and efficient alternatives in solar energy technologies, offering an environmentally friendly substitute for conventional noble metal electrodes. 2025 Elsevier Ltd -
Carbonized Molybdenum Disulfide-Decorated Carbon from Waste Papaya Straws as Counter Electrode for Bifacial Dye-Sensitized Solar Cells
Abstract: Ongoing research efforts are aimed at developing bifacial dye-sensitized solar cells (DSSCs) that are both economically viable and high-performance. In this investigation, molybdenum disulfide-decorated biomass-derived carbon from waste papaya straws (MoS2@PS) was synthesized via a hydrothermal technique, and then subsequently subjected to annealing at various temperatures, referred to as PS26, PS27, and PS28. Annealing MoS2 -decorated PS resulted in an increase in surface area which was confirmed using BrunauerEmmettTeller measurements, revealing type IV isotherms with an H3 hysteresis loop showing the mesoscopic nature of PS28. The maximum recorded photovoltaic conversion efficiency was approximately 6.85% for the PS28 composite counter electrode (CE), highlighting its potential as a platinum-free alternative. Moreover, cyclic voltammetry and Tafel polarization studies confirmed the superior electrocatalytic activity of the MoS2@PS CE in the reduction process of triiodide ions (I3?). Studies on transmittance were conducted to validate the bifacial characteristics of DSSCs. The results from electrochemical impedance spectroscopy indicate that the MoS2@PS CE-based DSSCs exhibit rapid charge transfer at the electrode/electrolyte interface, with a resistance of RCT = 24.27 ? for the PS28 counter electrode. The favourable attributes of optimal conversion efficiency, high transmittance, ease of preparation, rapid charge transfer, and affordability suggest that MoS2@PS counter electrodes hold significant potential for applications in DSSCs. The Minerals, Metals & Materials Society 2025. -
Transparent Cu-Pd decorated MoS2@functionalized carbon nanofiber composite counter electrodes: Efficient bifacial dye-sensitized solar cell
This research demonstrates a facile method for implementing bimetallic Cu-Pd-doped MoS2/Carbon fiber composites as a transparent counter electrode for bifacial dye-sensitised solar cell (DSSC) applications. The inert properties of the basal plane significantly limit the catalytic capabilities of MoS2. This limitation is alleviated through the incorporation of carbon fibers, owing to their excellent conductivity, catalytic activity, and stable structure. Cu and Pd nanoparticles were incorporated into MoS2, carbon fibers and a mixed MoS2/carbon fibers matrix via a one-step hydrothermal method. The structural, morphological, and catalytic properties were systematically investigated through microscopic studies, Cyclic Voltammetry, and Tafel analysis. Electrochemical Impedance Spectroscopy recorded charge transfer resistance RCT values for CuPdCNF, CuPdMS, and CuPdMSCNF are 16.50, 10.62, and 7.5 ?, respectively, attributed to the addition of metals that can alter both the geometric and electronic structures on the metal surface, which are closely associated with their catalytic efficiency. This approach has led to a significant enhancement in both short-circuit current density and overall efficiency with respect to bare MoS2 and Pt. The cells exhibited current densities of 16.00 mA/cm, 16.19 mA/cm, and 16.71 mA/cm, with corresponding efficiencies of 7.03 %, 6.86 %, and 7.77 %, respectively, under front illumination for CuPd-CNF, CuPd-MS, and CuPdMSCNF. Additionally, the introduction of these bimetallic NPs within the carbon and MoS2 matrix further increases the active site for catalytic reduction. The combination of significant efficiency and rear illumination adaptability underscores the strong potential for practical use in bifacial solar cell configurations. 2026 Elsevier B.V. -
Graph Neural Networks in Recommendation Systems for Superior User Experiences
Recommender systems are now an essential tool for traversing huge amounts of online information, especially as user tastes change in dynamic settings. They can be classified into different types based on their use, such as collaborative filtering and content-based recommendation. Graph Neural Networks (GNNs), however, are best suited to learn from graph-structured data and have become a revolutionary technology in building recommendation systems based on their capacity to capture intricate relationships and dependencies in graph-structured data. Unlike traditional methods, GNN-based recommendation systems capture both local and global connectivity patterns in user-item interaction graphs, enhancing prediction accuracy and robustness. This study looks at key types of Graph Neural Networks, known as GNNs, and includes Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), and GraphSAGE. Each type is good at tackling specific challenges like making personalized recommendations, handling cold-start problems, and managing large-scale data effectively. Big companies across different industries are using GNNs to enhance their services. For example, e-commerce giants like Amazon and Alibaba, and streaming platforms like Netflix and Spotify, use these networks to boost user engagement and satisfaction. The study also covers strategies for applying GNNs. This includes constructing user-item bipartite graphs, including domain-specific features, and employing contrastive learning methods to improve functionality. By combining theoretical concepts with real-world usage, we hope to offer an informed view of GNNs in recommendation systems. In addition, metrics like Precision, Recall, and Normalized Discounted Cumulative Gain (nDCG), providing implementable recommendations for future development in this fast-moving field, will be addressed. 2026 by John Wiley & Sons Inc. All rights reserved. -
The customer as co-creator of value consumer behavior on digital platforms: Empowering value co-creation in the digital era
This chapter will be based on the different combinations of literature reviews, online studies, and real-world case studies. It will analyze many fields like marketing, psychology, and information systems related to surveys which provide a holistic understanding of this topic. By focusing on customers and co-creators of value, digital media can unlock very new sources of innovation, engagement, and loyalty. This chapter will provide a whole framework for getting and leveraging these customer shifts. 2025, IGI Global Scientific Publishing. All rights reserved. -
Beyond the first bite: understanding how online experience shapes user loyalty in the mobile food app market
In the competitive landscape of mobile food ordering applications (MFOA) in India, the primary focus is enhancing the customer experience to mirror or even exceed their offline meal acquisition experiences. Existing research underscores the pivotal role of a superior online experience in driving business success. Against the backdrop of a dearth of studies addressing online customer experience (OCE), our current research seeks to gain insight into its state and its implications for attitudes and intentions. Specifically, we investigate the impact of OCE on the continued usage intentions (CUI) of new MFOA users. This study not only sheds light on the relationship between OCE and CUI but also presents a fresh configuration of OCE, addressing its varied conceptualization. Furthermore, drawing on data collected from over 400 first-time users of MFOA, our findings reveal that e-satisfaction and e-trust act as full mediators in influencing CUI. Finally, the study also suggests that e-trust mediates the effect of e-satisfaction on the CUI of MFOA users. Our research contributes to our understanding of OCE by specifically highlighting the experiences and outcomes of first-time users of MFOAs. Practitioners should employ strategies including personalized orientation and data gathering, location-based services, in-app messaging, push notifications and gamification techniques to increase OCE and drive CUI. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2024. -
Phosphorus-doped molybdenum disulfide as counter electrode catalyst for efficient bifacial dye-sensitized solar cells
MoS2 is a promising counter electrode material for dye-sensitized solar cell owing to its optical and electrical properties and two-dimensional layered structure. However, it still suffers from minimal conductivity, poor charge transport and less active sites. The present study offers a promising method for enhancing catalytic and fast charge transfer in MoS2 through heteroatom doping of phosphorus. A facile one-step hydrothermal treatment was acquired to do the phosphorus doping. The spin-coated P-doped MoS2 (MSP2) counter electrode (CE) shows a superior power conversion efficiency of 7.93% for front illumination and 5.34% for rear illumination, outperforming Pt-based (7.41% and 5.75%) CE. Thus, phosphorous incorporation increases the number of active sites and improves the catalytic property of the material. The P-doped MoS2 (MSP2) CE film also shows high transmittance, making it a suitable choice for bifacial type of solar cell. 2023 Elsevier Ltd -
Exploring the Role of structurally modified Molybdenum disulfide composites with Prussian blue analogues as counter electrode catalysts for bifacial Dye-Sensitized solar cells
The present study aims to utilize Mn, Ni, and MnNi Prussian Blue Analogue (PBA) embedded MoS2 composites as Pt-free Counter Electrode (CE) in Dye Sensitized Solar Cells (DSSCs). Therefore, Ni-PBA, Mn-PBA, and MnNi-PBA were synthesized using a simple ageing procedure followed by a Hydrothermal method to prepare modified MoS2 composites. The crystalline structure, shape, surface area, and elemental oxidation state were analyzed using various studies. Also, the nanosheets formation around cubic structure further shows large numbers of active sites resulting in the high catalytic behaviour of the composites. Among the various composites, the Modified MoS2 based on MnNi-PBA, which was coated using a simple spin-coating procedure, exhibited the smallest ?EPP separation and the highest JRED value due to the rapid redox reaction at the CE/electrolyte interface and catalytic current. The maximum efficiency of 8.25 % was achieved for MnNi-PBA based composites, surpassing pristine MoS2 (6.72 %) and Pt (7.58 %) under front illumination (100 mW/cm2). Under rear illumination, the cell demonstrated a higher efficiency of 4.96 %, attributed to the high transmittance of the material-coated CE, making it suitable for bifacial applications. 2024 International Solar Energy Society -
Covid-19 and Quad's Soft Reorientation
Quadrilateral Security Dialogue comprises a group of countries the US, Japan, Australia, and India, that started maritime collaboration in the wake of the 2004 Indian Ocean Tsunami. The initiative lasted for a brief period before falling apart in 2008. The countries re-banded together in 2017 to consult on ensuring greater security and prosperity in a free and open Indo-Pacific region, and a rules-based order. During the Covid-19 pandemic, the group has been partnering on soft security aspects such as vaccine development and distribution. The paper suggests that this allows the group to become first movers in the areas of specific functional challenges. This paper looks at the role of health diplomacy in the region as a soft power tool. The theory is based on the works of Professor Joseph Nye who first coined the term 'soft power'. It focuses on the role of India in strategic altruism to enhance Quad's strategic influence in the region. Expanding global vaccine supply is an example of reaching out to low- and middle-income countries. The paper argues that enhancing such cooperative mechanisms will allow Quad to balance its cooperative and competitive outlook in the region, linking its security with prosperity and development objectives. 2021 -
The Belt and Road Initiative: Issues and Future Trends
The Belt and Road Initiative (BRI) is a China-led plan that involves infrastructure and construction projects in more than 140 countries, out of which 65 countries account for 30% of the worlds gross domestic product, 35% of the worlds trade, 39% of the global land, 64% of the worlds population, 54% of the worlds CO2 emissions and 50% of the worlds energy consumption (Du & Zhang, 2018, China Economic Review, 47, 189205). The project announced in 2013 is often considered Chinese Premier Xi Jinpings dream. It quickly grew in sectoral and geographical complexity from the Arctic to deep oceans, to Latin American countries, Africa and even collaborations in maritime and outer space. Nine years into the making, the project suffered disruption in the wake of the COVID-19 pandemic. Travel restrictions and lockdowns led to suspension and slowdown in the project. However, the Chinese leadership continues to remain optimistic regarding the BRI and is opting for digital, health and sustainability models to keep the initiative running. The article analyses the strategic and economic significance of the BRI from its inception to now. It focuses on the impact of the pandemic on the BRI and stakeholders responses to the project, and looks into attempts by China to make it a success in the post-pandemic world. 2023 Indian Council of World Affairs(ICWA). -
Nature of music engagement and its relation to resilient coping, optimism and fear of COVID-19
The COVID-19 pandemic has resulted in unprecedented lockdowns, a work from home culture, social distancing and other measures which badly affected the world populace.Individuals over the globe reported experiencing several psychosocial and psychosomatic problems.Nevertheless, this pandemic allowed us to be with ourselves, to understand the importance of healthy lifestyles and to devote time to our passions and hobbies when we were socially isolated.Against this background, the present study was undertaken to explore the nature of peoples everyday musical engagement and to examine how the experience and functions of music were related to resilient coping, life orientation and fear from COVID-19.In an online survey, a total of 197 participants responded to a questionnaire designed to assess the nature of musical engagement (level of musical training, functional niche of music, listening habits and involvement in musical activities), functions of music (FMS), resilient coping (BRCS), life orientation (LOT-R), and fear of COVID-19 (FCV-19S).Results indicate that for most of the respondents, music listening was a preferred activity during the pandemic which resulted in positive effects on their mood, heart rate and respiratory rates.More than 80 per cent of respondents reported music as a source of pleasure and enjoyment and claimed that it helped to calm them, release their stress, and help them relax.Significant positive correlations were found between the functions of music (memory-based and mood-based), optimism and resilient coping and mood-based functions of music and optimism were found to predict resilient coping among individuals.These results suggest that meaningful and active music engagement may lead to optimism which may result in effective resilient coping during the crisis.Moreover, reflecting upon our everyday musical engagements can promote music as a coping skill. 2025 selection and editorial matter, Asma Parveen and Rajesh Verma; individual chapters, the contributors. -
Yulu: Moving Towards a Sustainable Future
The rapidly rising rate of urbanization, which is closely linked to economic growth, has exposed the world to several challenges such as inequality, environmental degradation, traffic congestion, infrastructural concerns and social conflicts. Therefore, urban sustainability has emerged as one of the most debatable discussions across the world. The existing network of transportation can no longer keep up with the growing demand in metropolitan cities. Short distance travel has become an unresolved issue for daily commuters. The case presents how MMVs have emerged as an alternative mode of transport for resolving issues of daily commuters regarding the first-mile connectivity, last-mile connectivity and short distance travel to reach their final destination. MMVs are basically light-weight vehicles which occupy less space on road. These vehicles include bicycles, e-bikes, skateboards, hoverboards and other battery-operated vehicles. The case narrates the journey of Yulu, a dockless bike-sharing venture which promoted the concept of green consumerism among the daily commuters at affordable rates. The venture initially started in the IT city of Bangalore and later expanded its operations to other cities such as Pune, Navi Mumbai, Gurugram and Bhubaneswar. The speciality of this venture is that it offers a sustainable solution to ever-increasing problems of traffic congestion and aggravating air pollution issues in metropolitan cities. Dilemma: How to offer a sustainable solution to the ever-increasing problem of traffic congestion and aggravating air pollution due to rising vehicular traffic? How to make short distance travel affordable and more convenient for daily commuters? Theory: Three pillars of sustainable development. Type of Case: Problem solving applied case. Protagonist: Present. Discussion and Case Questions: What strategies should be employed by the start-up to make it a more popular form of commute? How can the increasing rate of damage to the vehicles be brought down? How does organization structure and cluster management practices of Yulu help it to become more sustainable? How can the regulatory bodies and government promote and adopt such start-ups in their urban planning projects? 2020 SAGE Publications.