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Influence of Consumers Self Perception on Devaluation of Ugly Produce Marketing Strategies to Reduce Food Waste in the Indian Context
Ugly produce refers to aesthetically imperfect fruits and vegetables and also fruits and vegetables with minor blemishes. Ugly produce does not refer to spoilt, rotten, or germ-infected fruits and vegetables. The basic premise of this study is from self-signaling and self-perception theories. The self-signaling theory states that when people make a choice, they disclose something of their character and personality not just to others, but also to themselves. Self-perception theory (SPT) developed by psychologist Daryl Bem asserts that people develop their attitudes by observing their own behavior and further concluding what attitudes must have caused it. Classically, consumers undervalue ugly produce because of altered self-perceptions; simply visualizing the consumption of imperfect produce acts as a self-indicative signal that negatively affects how consumers view themselves. Due to this, the unattractive produce, even though perfectly edible and with the same taste and nutritional value, is rejected by consumers merely based on shape or some other cosmetic blemish. We discussed the strategies adopted by Indian startups and organizations to reduce food waste. Deep discounting is the strategy followed by food retailers worldwide to sell ugly produce, however, this is not the best strategy as it leads to losses for both the retailers as well as the farmers. We suggested alternative strategies successfully followed by foreign retailers, such as spreading awareness, boosting self-confidence and esteem among consumers, attracting kids, etc., which can be followed by Indian food retailers for selling ugly fruits and vegetables. 2022, Associated Management Consultants Pvt. Ltd.. All rights reserved. -
Labor unrest at Wistron Corporation India plant What went wrong?
Learning outcomes: After completion of this case study, students/managers will be able to analyze reasons for the labor unrest at Wistron Corporations Indian manufacturing plant; examine the implementation of labor regulations applicable to the employment of contract workers by Wistron Corporation; infer the problems associated with rapid expansion in the workforce; analyze the labor regulatory challenges faced by Wistron Corporation; and demonstrate problem-solving skills. Case overview/synopsis: The focus of this case study was the crisis faced by Apples contract manufacturer Wistron Corporation due to labor unrest, riots and violence in its production facility located near Bangalore in India. This case study discussed the CEOs dilemma in resolving the crisis and regaining the confidence of stakeholders, namely, the contract employees, Apple Inc. and the State Government of Karnataka. To give the readers an overview of the crisis this case discussed in detail the underlying reasons for the labor unrest such as a rapid increase in manpower, unilateral increase in working hours without extra pay, unjustified pay cuts, understaffed and underqualified human resources (HR) department, ill-equipped attendance and payroll system. It also gave an overview of mistakes in labor management that could be avoided by a manufacturing firm. The case also discussed the pressure faced by the Wistron CEO due to probation and a new business freeze by Apple Inc. This case study is suitable for understanding the complexities of labor laws and the legal complications that can arise when a corporation disregards local labor laws while operating in foreign countries. Complexity academic level: The case is best suited for postgraduate and executive MBA students studying labor law, industrial psychology and HR management in commerce and business management streams. The authors suggest that the instructor should inform students to read the case study before attending the 90-min session. It can be executed in the classroom after discussing the theoretical concepts. Supplementary materials: Teaching notes are available for educators only. Subject code: CSS 6: Human Resource Management. 2024, Emerald Publishing Limited. -
Diet Coke faces negative publicity
Research methodology A secondary research method was used to collect data for this case. The authors have made use of newspaper articles and published articles written by journalists and experts, which are available in the public domain. The protagonists name has been masked. Case overview/synopsis This case study examine the controversy surrounding Diet Coke and the dilemma faced by the Coca-Cola vice president of marketing, Henry Kingston, over its product labeling. While Diet Coke has long been favored by health-conscious consumers seeking a low-calorie alternative, concerns over its use of aspartame, an artificial sweetener linked to potential health risks, had sparked consumer backlash, regulatory scrutiny and accusations that the company was misleading consumers with its labeling. The issue gained momentum when former US President Donald Trump publicly switched to regular Coke, citing health concerns over aspartame, further fueling media debates on artificial sweeteners. With increasing pressure from social media, consumer advocacy groups and regulatory bodies questioning whether the term diet misleads consumers into assuming the product is inherently healthier, Coca-Colas senior management team led by its CEO faces a critical dilemma whether to retain the strong brand recognition of Diet Coke or reposition the product with greater transparency. The case highlights the challenges of ethical marketing, corporate responsibility and consumer health awareness in the food and beverage industry, raising crucial questions about balancing brand loyalty with regulatory compliance and evolving consumer expectations. Complexity academic level This case study is suitable for under-graduate and post graduate students studying marketing strategy and brand management courses in business management and commerce streams can use this case. This case can also be used for marketing specialization courses at the undergraduate and postgraduate levels. 2025 Emerald Publishing Limited -
BYJUS Survival at Stake: A Founders Dilemma
The focus of this case is the survival challenges faced by BYJUS due to mounting losses and financial mismanagement. This case discusses the strategic mistakes committed by BYJUS, such as the acquisitions-based growth strategy and overspending on marketing. It also discusses operational mistakes such as low customer retention, security and privacy issues and the use of hard selling to fuel rapid growth. The primary focus of this case is the CEOs dilemma in resolving the crisis. BYJUS business model is discussed to provide students with an overview of the dynamics and challenges of the EdTech business and the companys initiatives to enhance the robustness of its business model. An overview of prominent EdTech companies competing with BYJUS in India is presented in the case to enable the students to understand the competitive scenario of the EdTech industry. 2025 Lahore University of Management Sciences -
Survey on Malicious URL Detection Techniques
Crimes in the cyberspace are increasing day by day. Recent cyber threat defense reports states that 80.7% of the systems are compromised at least once in 2020. Cyber criminals taking the pandemic situation as an opportunity for the mass attack through malicious URL circulated by email or text messages in social media. Performing cyber-attacks through malicious URLs is the handy method for the cyber criminals. Protecting from such attacks requires proper awareness and solid defense system. Some of the common approaches followed by the cybercriminals to deceive the victims are 1. Phishing URLs which is very similar to the legitimate URLs. 2. Redirecting URLs 3. Using JavaScript, redirects to the phishing URL when user interacts with webpage 4. Social engineering etc. As soon as the novice internet users clicks on the malicious URL link, cyber criminals can easily steal personal information or install malware on their device to get additional access. Recently malicious URLs are generated algorithmically and uses URL shortening service to evade the existing security setup such as firewall and web filters. In literature, the researchers have proposed several ways to detect the malicious URLs but, new attack vectors that are introduced by the cyber criminals can easily bypass the security system. The purpose of this paper is to provide an overview of various malicious URL detection techniques which includes blacklist based, rules based, machine learning and deep learning-based techniques. Most importantly, the paper discusses the common features used by the detection system from webpages to classify the URL as malicious or benign and various performance metrics. This will encourage the new researchers to bring out the innovative solutions. 2022 IEEE. -
Prominent label identification and multi-label classification for cancer prognosis prediction
Cancer prognosis prediction improves the quality of treatment and increases the survivability of the patients. Conventional methods of cancer prediction deal with single class by limiting the prognosis prediction to one response variable. The SEER Public Use cancer database has more prominent variables that support better prediction approach. The objective of this paper is to find the prominent labels from cancer databases and use them in a multi-class environment. The implementation consist of three phases namely, pre-processing, prominent label identification and multi-label classification. Breast, Colorectal and Respiratory Cancer Data sets have been used for the experimentation. Also random samples from all three data sets are generated to form a mixed cancer data. Patient survival, number of primaries and age at diagnosis are the prominent labels identified from others using the Decision tree, Nae Bayes and KNN algorithms. The three prominent labels have been tested using multi-label RAkEL algorithm to find the relations between them. The results of the empirical study are comparatively better than the traditional way of cancer prediction. 2012 IEEE. -
Efficient data mining techniques for medical data
Healthy decision making for the well being is a challenge in the current era with abundant information everywhere. Data mining, machine newlinelearning and computational statistics are the leading fields of study that are supporting the empowered individual to take valuable decisions to optimize the outcome of any working domain. High demand for data newlinehandling exists in healthcare, as the rate of increase in patients is proportional to the rate of population growth and life style changes. Techniques for early diagnosis and prognosis prediction of diseases are the need of the hour to provide better treatment for the human community. Data mining techniques are a boon for building a quality and newlineefficient model for health prediction applications. As cancer explodes everywhere in recent years, the data sets from cancer newlineregistries have been focused as the medical data in this research. The main aim of thesis is to build a constructive and efficient classifier model for cancer prognosis prediction. Most of the existing system develops a diagnosis prediction models from the screening or survey data, as the data newlineset is widely available and are easy to collect due the insensitive nature of newlinethe factors involved in such research. Whereas the prognosis prediction requires a sensitive details of the patients those who are under treatment for a diagnosed disease. Hospitals and the community registries newlinemaintained by the government are the main source for data collection. Well maintained electronic hospital records with histopathology information is not public in India for the researchers. Hence cancer data newlinefrom a US based open access data center has been used in this research for all experimentation. This research work is a progressive model that gradually improves the newlineprediction accuracy by selecting appropriate data mining techniques in each phase. -
Defect Engineered Few Layered MoS2 for HumanMachine Interface
Ultrasensitive flexible devices have huge applications in many areas, like healthcare monitoring, humanmachine interaction, and wearable technology. However, improving the sensitivity of these devices is still challenging. In the current study, a flexible non-contact sensing system is designed with a humanmachine interface using defect-engineered, few-layered Molybdenum disulfide (MoS2). The fabricated sensors show high sensitivity when monitoring proximity, humidity, and in-plane applied strain. The output performance demonstrates the influence of surface defects, which greatly impact the average surface charge of the nanosheets. The experimental measurements and in-detail density functional theoretical (DFT) calculation further reveal surface charge variations on the basal planes that correlate with topographic defects and increase sensitivity. The electrical signals for different gestures of human hands are used to illustrate the identification of multidirectional bending and sliding events. These findings will contribute to understanding the effect of surface defects that play an important role in sensing applications with humanmachine interface. 2025 Wiley-VCH GmbH. -
Nature-Inspired Photoresponsive Bionic Robots Using the TelluriumMoS2Graphene Hybrid Structure
Motivated by biological natural living things, multifunctional soft robots have become an exciting system that can navigate by overcoming difficult situations. Photothermal self-excited actuators offer potential for self-driven soft robotics since they provide wireless power and control. However, it remains challenging to achieve photoresponsive actuation, which can serve as basic component in soft-bioelectronics. Tellurium (Te)-based nanostructures can be a promising candidate and offer greater infrared-photoresponsive properties. Therefore, in this work, we have systematically studied the effect of Te nanoparticles on the two-dimensional hybrid structure for advanced photoresponsive actuation under near-infrared (NIR) light exposure, which reaches ?85 C within ?5 s. This approach substantially improves the photothermal behavior including thermal conversion (? ? 12.7%), large bending (?5.74 cm1), and fast response (?250 ms), by increasing the internal temperature of the system. Leveraging this strategy, we have developed soft bionic Dragonfly, and it demonstrates multiple performances including controllable bending and wing movement at a maximum speed. The density functional theory (DFT) calculation and in situ Raman spectroscopy measurement reveal the photoactuation behavior of the system. This research proposes new idea of hybrid structure and exhibits substantial photothermal conversion efficiency with significant deformation for soft bionic applications. 2026 American Chemical Society -
An MoS2/PEDOT:PSS-based flexible NIR-responsive soft actuator
The development of sophisticated smart devices heavily relies on flexible soft actuators combined with near infrared (NIR) light responsive two-dimensional (2D) materials. Soft robots provide a number of benefits, such as flexibility, high sensitivity, compliance and security. Amidst many manufacturing and driving approaches, light has surfaced as a facilitator, aiding in the fabrication of soft actuators. Using few-layered molybdenum disulphide (MoS2) and poly(3,4-ethylenedioxythiophene)/poly(styrene sulfonate) (PEDOT:PSS), the current work aims to introduce a polymer nanocomposite film for soft actuator applications under NIR light exposure. The actuation behavior was impacted by PEDOT:PSS under NIR light exposure. In order to incorporate controllable deformation of the actuator, the photothermal properties of the composite film were investigated. In situ Raman spectroscopy and the density functional theory (DFT) calculation explain the structural change and energy optimization of PEDOT:PSS. A soft insect was further designed based on this photothermal property, which can deform under light exposure. Therefore, such flexible design has huge potential for soft robotics applications in modern technologies. 2025 RSC. -
Layered natural oxide based soft actuators for controlling artificial motion by chemical stimulus
The chemical stimuli-based soft-actuators with complex actuation properties are of significant interest in the field of biomechanical and biomedical applications such as prosthetics. Soft actuators can manipulate and precisely control the fluid motion at the microscale and may play an important role in fluid transportation in many biological systems. Here, we have presented a two-dimensional (2D) material-based 3D printed system for the fabrication of porous soft actuators that display different actuations under the organic fluid stimulus. The few atomic layered thin chromite sheets (natural ores) show significant changes in their physical properties due to the strong interaction with organic molecules. The composite film is capable of showing controllable and sophisticated motions such as twisting, bending, rolling, and flipping in response to chemical stimuli. The introduction of porosity in the composite film dramatically increases the dynamic performances, detection range, and sensitivity. As a result, a high actuation (twisting angle) of ?540 5 and response time of ?0.9 s was achieved, which significantly enhanced the device performance. Finally, to offer further flexibility and controlled structural alterations, we designed a snail, leaf, and worm-like soft actuators that expand the practical applications. 2025 Elsevier Ltd -
A Framework for Integrating the Distributed Hash Table (DHT) with an Enhanced Blooms Filter in MANET
MANET, a self-organizing, infrastructure-less, wireless network is a fast-growing technology in day-to-day life. There is a rapid growth in the area of mobile computing due to the extent of economical and huge availability of wireless devices which leads to the extensive analysis of the mobile ad-hoc network. It consists of the collection of wireless dynamic nodes. Due to this dynamic nature, the routing of packets in the MANET is a complex one. The integration of distributed hash table (DHT) in MANET is performed to enhance the overlay of routing. The node status updating in the centralized hash table creates the storage overhead. The bloom filter is a data structure that is a space-effective randomized one but it allows the false-positive rates. However, this can be able to compensate for the issue of storage overhead in DHT (Distributed hash table). Hence, to overcome the storage overhead occurring in DHT, and reduce the false positives, the Bloom's filter is integrated with the DHT initially. Furthermore, the link stability is measured by the distance among mobile nodes. The optimal node selection should be done for the transmission of packets which is the lacking factor. If it fails to select the optimal path then the removal of malicious nodes may lead to the unwanted entry of nodes into the other clustering groups. Therefore, to solve this problem, the bloom's filter is modified for enhancing the link stability. The novelty of this proposed work is the integration of Bloom's filter with the Distributed Hash Table which provides good security on transmission data by removing false-positive errors and storage overhead 2022,International Journal of Advanced Computer Science and Applications.All Rights Reserved -
Link stability - based optimal routing path for efficient data communication in MANET
The paper delves into the complexities of Mobile Ad hoc Networks (MANETs), which consist of a diverse array of wireless nodes. In such networks, routing packets poses a significant challenge due to their dynamic nature. Despite the variety of techniques available for optimizing routing in MANETs, persistent issues like packet loss, routing overhead, and End-to-End Delay (EED) remain prevalent. In response to these challenges, the paper proposes a novel approach for efficient Data Communication (DC) by introducing a Link Stability (LS)-based optimal routing path. This approach leverages several advanced techniques, including Pearson Correlation Coefficient SWIFFT (PCC-SWIFFT), Galois-based Digital Signature Algorithm (G-DSA), and Entropy-based Gannet Optimization Algorithm (E-GOA). The proposed methodology involves a systematic process. Initially, the nodes in the MANET are initialized to establish the network infrastructure. Subsequently, the Canberra-based K Means (C-K Means) algorithm is employed to identify Neighboring Nodes (NNs), which are pivotal for creating communication links within the network. To ensure secure communication, secret keys (SK) are generated for both the Sender Node (SN) and the Receiver Node (RN) using Galois Theory. Following this, PCC-SWIFFT methodologies are utilized to generate hash codes, serving as unique identifiers for data packets or routing information. Signatures are created and verified at the SN and RN using the G-DSA. Verified nodes are subsequently added to the routing entry table, facilitating the establishment of multiple paths within the network. The Optimal Path (OP) is selected using the E-GOA, considering factors such as link stability and network congestion. Finally, Data Communication (DC) is initiated, continuously monitoring LS to ensure optimal routing performance. Comparative analysis with existing methodologies demonstrates the superior performance of the proposed model. In summary, the proposed approach offers a comprehensive solution to enhance routing efficiency in MANETs by addressing critical issues and leveraging advanced algorithms for key generation, signature verification, and path optimization. 2024, Universitas Ahmad Dahlan. All rights reserved. -
Segmentation of overlapping leukemic cells in histopathological images using HSV- based watershed transformation
Accurate segmentation of white blood cells (WBCs) is essential for computer-aided diagnosis, as overlapping and densely clustered cells often present significant challenges. This work introduces a hybrid framework for segmentation that proposes a fusion of hue and saturation in the Hue Saturation Value (HSV) domain. Gaussian smoothing, Otsu thresholding, and Morphological refinement is employed to enhance cell contrast and eliminate noise. A marker-based watershed algorithm is subsequently applied for accurate separation of overlapping WBCs. Evaluation on the ALL-IDB2 dataset confirms the methods capability through achieving a Dice Similarity Coefficient(DSC) of 0.8929 and an Intersection over Union (IoU) of 0.8099 to produce well-defined cellular boundaries. The novelty of this study lies in the integrated hue-saturation fusion and marker-based watershed strategy, offering improved boundary localization and reliable segmentation of overlapping WBCs. Bharati Vidyapeeth's Institute of Computer Applications and Management 2025. -
A Spatio-temporal Model for the Analysis and Classification of Soil Using the IoT
The Internet of Things (IoT) is an evolving trend in the field of computer applications where various hardware and software are connected together to address a specific problem. With the help of the IoT, the world has become smart and enabled itself to connect various objects (e.g., cars, computers, mobile phones, and smart appliances) with distinctive Internet protocol addresses, which allows them to interact with one another, thus accomplishing various procedures. Applications of the IoT include but are not restricted to smart cities, healthcare, industry, and robotics. Amongst a huge list of applications furnished by the IoT, agricultural IoT is the theme of this chapter. The IoT in agriculture transforms entities such as crops, soils, and livestock in a smart way by utilizing underlying technologies such as embedded systems, pervasive computing, sensor networks, ubiquitous computing, ad hoc networks, various wireless communication technologies, Internet protocols and other advanced technologies. The research here focuses on the most important agriculture entity soil. It is the soil that determines the yield of a crop. The more fertile the soil, more qualitative is the yield. The main idea behind the research is to identify the soil most suitable for agriculture. Using a spatio-temporal model, the soil samples collected from various parts of the country are classified into agricultural soil and non-agricultural soil. This classification is done by the aid of features such as the pH of the soil, and its humidity, moisture, and temperature collected from IoT sensors. The chapter begins with an introduction to the usage of IoT technology in different areas of agriculture followed by an account of the proposed state-of-the-art model, and its results, analysis, and a conclusion. 2022 selection and editorial matter, Vikram Bali, Vishal Bhatnagar, Deepti Aggarwal, Shivani Bali, and Mario JosDiv; individual chapters, the contributors. -
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Revolutionizing Road Traffic Management and Enforcement: Harnessing AI, ML, and Geospatial Techniques
This study investigates the synergistic application of Artificial Intelligence (AI), Machine Learning (ML), and Geospatial Technologies in optimizing traffic management systems. Through a mixed-methods research design, it evaluates the potential of these technologies to enhance urban traffic flow and reduce congestion. The research emphasizes the critical importance of data quality, ethical considerations, and the selection of appropriate technological solutions based on specific urban traffic scenarios. Findings highlight the significant role of integrated AI and geospatial analyses in improving traffic predictions and operational efficiency. Future work will focus on developing more sophisticated models that ensure privacy, equity, and adaptability to new transportation trends. 2024 IEEE. -
Enhancing Traffic Incident Management and Regulatory Compliance Using IoT and Itms: A Mumbai Traffic Police Case Study
In the rapidly urbanizing landscape of Mumbai, a megacity confronted with significant traffic management and law enforcement challenges, the deployment of an advanced city surveillance system represents a transformative approach to urban governance. This paper examines the integration of over 11,000 CCTV cameras into the Mumbai Traffic Police's operational framework, covering an area of 438 square kilometers encompassing 41 traffic divisions and 94 police stations. Since its inception in 2016, the system has been pivotal in enhancing safety, order, and mobility within the city, especially amid obstacles such as ongoing infrastructure projects, traffic congestion, accidents, and natural disasters. Central to this study is the analysis of the Mumbai City Surveillance System Project (MCSP), which leverages CCTV technology to generate and classify Incident Reports (IR) based on severity, ranging from minor disruptions to significant emergencies. The period from October 2021 to 2023 saw a marked increase in IR generation, from 742 reports in 2021 to 10,392 in 2022 and 9,639 in 2023, indicating the system's growing efficacy in real-time traffic management and incident response.This paper further explores the cutting-edge integration of Artificial Intelligence (AI) and Internet of Things (IoT) technologies within the MCSP framework, highlighting the role of computational intelligence in enhancing the capabilities of Intelligent Transportation Systems (ITS). By employing AI-driven predictive analytics, the system effectively anticipates traffic conditions based on diverse variables such as traffic flow, vehicle speed, and weather, thereby optimizing traffic management strategies.The findings underscore the significant impact of AI and IoT technologies in redefining urban transportation networks, demonstrating improved efficiency, safety, and resilience in the face of Mumbai's complex transportation challenges. This study contributes to the discourse on smart city initiatives, offering insights into the role of advanced computational technologies in facilitating intelligent transportation solutions and shaping the future of urban living. 2024 IEEE. -
Impact of human resource practice on work engagement and turnover intention in information technology companies
Orientation: The information technology (IT) sector, a global economic driver, faces high employee turnover because of low work engagement. This study examines the relationship between human resource management (HRM) practices and their impact on work engagement and turnover intention (TI) in IT companies. Research purpose: The primary purpose of this research article is to investigate how HRM practices influence employee work engagement and TI in the IT sector. Motivation for the study: This study is motivated by the need to address this critical issue by exploring the role of HRM practices in shaping employee engagement and TI. Research approach/design and method: The research data came from 10 IT organisations in Pune IT parks. Non-probability convenience sampling was used to collect data. Data were analysed using Structural Equation Modelling (SEM), Statistical Package for Social Science (SPSS) and Moment Structure Analysis to evaluate the hypotheses. Main findings: The study found that HRM practices such as effective communication (EC), training satisfaction (TS), performance appraisal satisfaction (PAS), pay satisfaction (PS) and opportunities for development (OFD) positively influence work engagement among IT employees. Addressing these HRM practices can enhance employee retention and engagement in the IT sector. Practical/managerial implications: Implementing these strategies can lead to a more committed and productive workforce, improving overall organisational performance and retention. Contribution/value-add: This research offers actionable recommendations for IT companies to improve employee retention and engagement, filling a gap in existing literature by focussing exclusively on the unique challenges and dynamics of the IT industry. 2024. The Authors. -
Analyzing Job Satisfaction, Job Performance, and Attrition in International Business Machines Corporation through Python
Since workers significantly impact the firm's operation, businesses invest heavily in them. They must deliver better and more excellent performance to compete with the increasing competition. Employee performance is becoming more and more important for business success and staying ahead of the competition, so companies are putting more money into things like training, growth centers, and careers. The target audience was the employees working in International Business Machines Corporation. The data was analyzed through the process of Exploratory Data Analysis using Python. There is a 0.002297 link between Job Satisfaction and Performance Rating, and a 0.002572 correlation between Work Life Balance and Performance Rating. The relationship between work-life balance and job involvement is -0.01462, indicating a negative impact on work-life balance for people who are heavily interested in their occupations. The study would help Human Resources Managers formulate their policies and understand the employees better in the current environment. Here, Job Satisfaction and Performance Rating served as mediators, and the findings show that their influence on Attrition is minimal at this firm. 2024 IEEE.