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Effectiveness of integrated waste minimisation strategies in high-rise residential construction projects
Construction waste has become a significant sustainability concern in fast-growing Indian cities, especially in high-rise residential projects characterised by intensive material flows. This study conducted a comparative analysis of material waste across the various stages of eight high-rise residential projects in Bengaluru, India. Four of the projects followed the conventional method, while the remaining four used an efficient method to reduce material waste. The material usage and generation were recorded for seven phases, each lasting two months, both quantitatively and qualitatively, using data and observations. Additionally, Relative Reduction (RR) values were calculated to assess the effectiveness of the implemented interventions by comparing the projected values for the baseline scenarios of uncontrolled and controlled projects. Uncontrolled projects exhibited an average wastage growth of 23% and negative RR values (? 4.48% to ? 9.15%), indicating a deterioration in waste management performance. At the same time, the sites implementing waste control measures demonstrated waste stability or reduction, with RR values of 713%, due to improvements in site supervision, material storage, batch extraction accuracy, and control of material issues. Material-wise analysis further supported the reduction in waste under controlled conditions. The benchmarking system developed in this research will provide practical support for waste tracking and remedial actions. The study demonstrates, using data, that low-cost, straightforward process interventions can substantially increase the effectiveness of resource use in achieving SDG 11.6 and SDG 12.5. The Author(s) 2026. -
Construction Waste: Key Causes and Reduction Approaches
The construction industry is a significant contributor to global waste, with Construction and Demolition (C&D) waste comprising a substantial portion. This paper investigates the key causes and reduction approaches. Key sources of waste include material offcuts, packaging, and unused materials due to excess procurement or design changes. Factors that exacerbate waste include inadequate project planning, poor site management, and insufficient worker training. Economic factors often favor new materials over recycled ones due to cost and time concerns. Rapid urbanization and redevelopment further escalate C&D waste as older buildings are demolished for new construction. Technological advancements and innovative methods, such as prefabrication and modular construction, have the potential to reduce waste generation. However, traditional construction practices and resistance to change impede widespread adoption. This paper highlights the urgent need for integrated waste management strategies, emphasizing the roles of policy, education, and technology in reducing C&D waste. Sustainable practices, such as using recycled materials, improving on-site waste segregation, and adopting circular economy principles, are crucial for minimizing the environmental impact of construction activities. Addressing these factors is essential for achieving sustainability in the construction industry and supporting global environmental conservation efforts. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
An Overview of Material Waste Management in Construction Projects
Wastage of construction materials has long been a persistent issue within construction projects. The improper planning and management of construction materials during the performance of construction activities is a critical issue that negatively impacts the performance of construction projects. Encouraging sustainable waste management involves minimizing waste generation and promoting the reuse, recycling, and recovery of resources. This paper provides a broad overview of construction waste minimization and management, as well as mitigation factors for sustainable construction waste management. It integrates sustainability principles into waste management practices, including the adoption of a waste management hierarchy to advance environmental friendliness within the building industry. Also this delves into the significance of material waste, taking into account its environmental, economic, and social repercussions. It identifies various sources of material waste across the construction lifecycle, shedding light on the factors contributing to waste generation and inefficiencies. It evaluates existing practices and strategies utilized for waste minimization and management, encompassing approaches like reuse, recycling, and disposal. It emphasizes the crucial need to tackle material waste in construction projects to foster sustainability and optimize resource utilization in the built environment. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025. -
Evaluating social media content's effect on consumer engagement in the context of digital marketing
The advancement of social media platforms in promoting consumer participation in brand development and sustainable consumption has been substantial. Social media's popularity has increased significantly in the twenty-first century. To enhance sales performance, enterprises consistently seek novel strategies to integrate these platforms into their promotional initiatives. Social media functions as a platform for networking and communication; consequently, organizations must imbue their brands with personality to connect with consumers. Despite extensive academic research on corporate social media marketing techniques, the influence of these activities on consumer purchase choices remains largely unexplored. Organizations have recently embraced influencer marketing as a tactic to promote and publicize their content by leveraging the support of influential individuals. The growing frequency of product endorsements on social media highlights the importance of understanding the impact that these influencers have on customers. This research aims to analyze the influence of social media content and its characteristics on consumer engagement in the digital domain. Additionally, this study will serve as a foundation for future investigations in this area. The insights regarding the content elements of social media marketing that foster consumer engagement were contributed by seventy-five unique social media users. 2025 by the authors; licensee Learning Gate. -
An attention-based loss function and synthetic minority oversampling technique for alleviating class imbalance in predicting diabetes
Diabetes is a chronic disease due to higher blood sugar (or Glucose) levels in the blood. This study proposes a novel attention-based loss function and a lightweight artificial neural network (ANN) called Diabetic Lite (DB-Lite) for diabetes prediction in the Pima Indian Diabetes Dataset (PIDD). We show that the Pima dataset has many challenges. It is a small and imbalanced dataset; moreover, many features are non-linearly correlated in this dataset. The novelties of this research work are as follows: (i) A novel loss function of attention-based binary cross entropy (ABCE) is proposed for the first time to alleviate the statistical imbalance present within the Pima dataset. This ABCE loss function is incorporated in the DB-Lite model, which is trained from scratch. (ii) A Swish activation function is deployed in the hidden layer of DB-Lite instead of Rectified Linear Unit (ReLU) to deal with the non-linear dependency of features with the final outcome. (iii) The synthetic minority oversampling technique (SMOTE) is used as a pre-processing technique to mitigate the class imbalance problem from the Pima dataset. (iv) An adaptive learning rate is utilized while training the model to speed up the convergence of the DB-Lite model. Our final proposed framework has achieved 99.7% accuracy, 99.4% precision, 99.8% recall, and 99.6% F1 score in testing, which is the best result on this Pima dataset. The Welch t-testing (as a statistical hypothesis testing) and 10-fold cross-validation are utilized to prove the validity of the proposed loss function. 2025 -
Edge Attention Module for Object Classification
A novel edge attention-based Convolutional Neural Network (CNN) is proposed in this research for object classification task. With the advent of advanced computing technology, CNN models have achieved to remarkable success, particularly in computer vision applications. Nevertheless, the efficacy of the conventional CNN is often hindered due to class imbalance and inter-class similarity problems, which are particularly prominent in the computer vision field. In this research, we introduce for the first time an Edge Attention Module (EAM) consisting of a Max-Min pooling layer, followed by convolutional layers. This Max-Min pooling is entirely a novel pooling technique, specifically designed to capture only the edge information that is crucial for any object classification task. Therefore, by integrating this novel pooling technique into the attention module, the CNN network inherently prioritizes on essential edge features, thereby boosting the accuracy and F1-score of the model significantly. We have implemented our proposed EAM or 2EAMs on several standard pre-trained CNN models for Caltech-101, Caltech-256, CIFAR-100 and Tiny ImageNet-200 datasets. The extensive experiments reveal that our proposed framework (that is, EAM with CNN and 2EAMs with CNN), outperforms all pre-trained CNN models as well as recent trend models Pooling-based Vision Transformer (PiT), Convolutional Block Attention Module (CBAM), and ConvNext, by substantial margins. We have achieved the accuracy of 95.5% and 86% by the proposed framework on Caltech-101 and Caltech-256 datasets, respectively. So far, this is the best results on these datasets, to the best of our knowledge. All the codes along with graphs, and their classification reports are shared on an anonymous GitHub link: https://anonymous.4open.science/r/Object-Classification-7BE5. 2025 IEEE. -
Novel Pooling-Based VGG-Lite for Pneumonia and Covid-19 Detection From Imbalanced Chest X-Ray Datasets
This paper proposes a novel pooling-based VGG-Lite model in order to mitigate class imbalance issues in Chest X-Ray (CXR) datasets. Automatic Pneumonia detection from CXR images by deep learning model has emerged as a prominent and dynamic area of research, since the inception of the new Covid-19 variant in 2020. However, the standard Convolutional Neural Network (CNN) models encounter challenges associated with class imbalance, a prevalent issue found in many medical datasets. The innovations introduced in the proposed model architecture include: (I) A very lightweight CNN model, VGG-Lite, is proposed as a base model, inspired by VGG-16 and MobileNet-V2 architecture. (II) On top of this base model, we leverage an Edge Enhanced Module (EEM) through a parallel branch, consisting of a negative image layer, and a novel custom pooling layer 2Max-Min Pooling. This 2Max-Min Pooling layer is entirely novel in this investigation, providing more attention to edge components within pneumonia CXR images. Thus, it works as an efficient spatial attention module (SAM). We have implemented the proposed framework on two separate CXR datasets. The first dataset is obtained from a readily available source on the internet, and the second dataset is a more challenging CXR dataset, assembled by our research team from three different sources. Experimental results reveal that our proposed framework has outperformed pre-trained CNN models, and three recent trend existing models Vision Transformer, Pooling-based Vision Transformer (PiT) and PneuNet, by substantial margins on both datasets. The proposed framework VGG-Lite with EEM, has achieved a macro average of 95% accuracy, 97.1% precision, 96.1% recall, and 96.6% F1 score on the Pneumonia Imbalance CXR dataset, without employing any pre-processing technique. 2017 IEEE. -
The presence of others increases prosociality: examining the role of dating Partners accompany on donation
Research in the field of prosocial behavior has shown that the presence of others has a significant effect on individuals prosociality. However, no research has explored such an effect of romantic partners presence. Studies in evolutionary psychology have shown benevolence/prosociality as an important factor when choosing a romantic partner. Therefore, in the present study, we hypothesized that people will donate more in the presence of dating partners to maintain a positive impression on them. The research followed a mixed-method approach. The first study, a vignette-based experiment showed that people believed the presence of a dating partner significantly enhances the chances of donation. The second study was a between-subject experiment that confirmed the findings of study 1 from both donors and receivers perspectives. The third study was a qualitative investigation, where a semi-structured interview method was used to find out how and why the presence of a dating partner may influence donation. The interviews showed that the presence of dating partners increases prosociality mainly because donors want to make a good impression and project the right image of them in their partners eyes. The research overall suggests that the human need for self-presentation that projects them more socially likable shapes their willingness to extend a helping hand to others in the presence of their romantic partners. 2024 Taylor & Francis Group, LLC. -
Innovative approaches to renewable energy storage: Bridging the gap for a sustainable future
In this chapter, we reviewed works related to renewable energy storage technologies with a focus on innovative solutions that can increase the efficiency, scalability and sustainability of energy systems. This includes advanced materials such as solid- state and lithium- air batteries, hybrid storage systems combining multiple technologies, and decentralised storage supporting grid resilience and energy security. By storing energy generated from renewable sources such as the sun and wind, these innovations seek to reduce carbon footprints and enable a transition to a more sustainable energy future by solving the issue of the intermittency of renewables. The chapter examines the emerging trends and technological developments, highlighting the critical role that next- generation energy storage systems can play in driving the global shift towards renewables and transforming energy infrastructure into a cleaner, more resilient world. 2025, IGI Global Scientific Publishing. All rights reserved. -
Optimizing Machine Learning for Product Category Prediction in Digital Wallet Transactions: A Case Study of Feature-Driven Performance
The Digital Wallet transactions is one of the rapid phenomena in the application of technology. There were various studies which explored to application and sophistication of this digital wallet transactions. Based on the secondary data, the researcher developed a model for classifications using machine learning algorithms in Jupyter notebook (Python IDE). In the current study the performance of the machine learning model for classification is conducted on product categories in digital wallet transaction using many features such as product amount transaction fees cashback and encode categorical variables merchant name product name and payment methods. The test results of the classification model show and oral accuracy of the model at 92% with Precision recall and F1 scores averaging up to 0.92. It is noticeable that some of the features such as gas bill electricity bill showed weaker performance suggesting the need for further engineering and model tuning. This provide the deep understanding on how the transactions related features contribute to predicting the accuracy and highlights the potential for improving classification models for financial technology and its applications. The study also provides future directions and implications for the model refinement focusing on improving miss classification in categories. The Author(s), under exclusive license to Springer Nature Switzerland AG 2025. -
Analgesic and Anti-Inflammatory Potential of Indole Derivatives
Some indole analogues show a good analgesic activity but on the other hand, it has some serious side effects like gastric ulcer. Therefore, there is still a need to develop derivatives of non-steroidal anti-inflammatory drugs (NSAIDs) with fewer side effects. For this purpose, some indole derivatives were prepared with objectives to develop new derivatives with maximum efficacy and minimum side effects. 1-(1H-indol-1-yl)-2-(sstituephenoxy)-ethan-1-one derivatives (M1M4) were analyzed further by thin-layer chromatorgarphy (TLC), melting point, IR, and 1H-NMR. The synthesized compounds then underwent oral toxicity studies that include hematological, biochemical, and histopathological findings. The compound was then evaluated for invivo anti-inflammatory and analgesic activities on carrageenan-induced rat paw edema and acetic acid-induced writhing methods. As a result of the biological activities, promising results were obtained in the compound M2 (2-(2-aminophenoxy)-1-(1H-indol-1-yl)ethanone) and it was subjected to further studies. It was found that compound M2 was practically nontoxic, and no clinical abnormalities were found in hematology and biochemistry, correlated with histopathological observation. It also showed significant anti-inflammatory and analgesic activities at its oral high dose (400 mg/kg). The study suggested that compound M2 was found to have significant anti-inflammatory and analgesic activities. The possible mechanism of M2 might suggest being act as a central anti-nociceptive agent and peripheral inhibitor of painful inflammation. The possible mechanism of action of the compounds whose biological activity was evaluated was explained by molecular docking study against COX-1 and COX-2, and the most active compound M2 formed ?9.3 and ?8.3 binding energies against COX-1 and COX-2. In addition, molecular dynamics (MD) simulation of both M2s complexes with COX-1 and COX-2 was performed to examine the stability and behavior of the molecular docking pose, and the MM-PBSA binding free energies were measured as ?153.820 11.782 and ?172.604 9.591, respectively. Based on computational ADME studies, compounds comply with the limiting guidelines. 2022 Taylor & Francis Group, LLC. -
Experimental investigation and influence of filling ratio on heat transfer performance of a pulsating heat pipe
Experimental investigation of the two-phase system of a pulsating heat pipe taken into account useful heat transfer In the field of thermal management, many new prospective concepts and techniques have been developed, one of which is the pulsating heat pipe, a classic heat transfer technique. The PHP is constructed from 8 turns of copper tubes with inner diameters of 2 mm, wall widths of 1 mm, and a total length of 5324 mm. The CLPHP uses ethylene glycol as the functioning liquid at different fill proportions of 45 %, 55 %, 65 %, 75 %, and 85 % of its amount. The evaporator section is heated electrically by a plate heater ranging from 120 W to 600 W, and the condenser section is cooled by a continuous flow of cooling water. The results thermal resistance decreases gradually with an increase in heat transfer rate. It is apparent that a lower rate of thermal resistance is by a fill ratio of 55 %. The evaporator temperature is 181.57 C and the condenser temperature is 41.06 C for ethylene glycol measured for calculating heat transfer performance at 600 W, thermal resistance is 0.136 C/W, heat transfer coefficient is 526.45 W/m2-C, and enhanced heat transfer is thus good, exhibiting good improvement at a full percentage of 55 % and when compared with CFD results. 2023 Elsevier Ltd -
Induction of radio frequency transmission in indian railway for smooth running of traffic during fog
Our railway system drives whole sole based on its electrical signaling but due to poor visibility it becomes impossible to run the traffic smoothly We are suggesting to use radio wave communication technology for running of train when conventional signaling cant be followed due to poor visibility. During winter season, due to heavy fog especially in North India and East India it becomes almost impossible to drive the train on time. Our idea can remove this problem permanently. A dedicated radio frequency band will be used by railway service and a specific frequency will be assigned to all tracks running to a specific direction. All trains will be equipped with a transmitter and a receiver. Train drivers will get notification of received radio frequency within a certain circumference (5 km). So if it receives the same frequency which it is transmitting then the driver will understand another train is there on the same track so signaling room and the driver will also be aware of the fact. Then the control room or the driver can take action considering speed and distance between this two accordingly. If another train will be running on the next track then also it will receive signal but in that case it will run at as usual speed. 2017 Taylor & Francis Group, London. -
A Study towards constructing a reproductive health account as sub-account of health at sub-district level of india
Reproductive health is a state where everyone of the reproductive age cohort can make newlineinformed choices based on their reproductive health needs and reach a state of bliss and newlinewell-being. Informed choices are a possibility only if there is awareness regarding options of healthcare available. Awareness further indicates capacity to measure the worth of the options available in hand. One major aspect of measuring this worth is dominated by the financial aspect of awareness. In other words, expenditures incurred on reproductive health should be an information for all stakeholders to understand, analyse and arrive at informed newlinechoices. It has been unanimously felt that this domain of health needs more sustained efforts in terms of research into the specific components of expenditures. One such instrument which has been suggested is to construct a system of reproductive health accounts which TOVCI can track the fund movements among the different actors operating in the sector of reproductive health. Reproductive health accounts at local and contextual levels, has to conform to the existing national framework of health accounting so as to lend itself to inter and intra-regional comparisons. It consists of a group of matrices which capture origin of funds from financial sources to the destination where funds will ultimately be used on health functions, based on accounting boundaries of space, activity and time. This study attempted to construct a reproductive health account at sub-district levels in the district of Ramanagara in the state of Karnataka, India. Two sub-districts, Ramanagara and Channapatna were chosen for this purpose based on their health and reproductive health indicators. Primary data was collected from a household survey based on probability proportional to size sampling method. Questionnaires for data collection were borrowed from World Health Organization Guide to producing Reproductive Health Account. -
Comparative Study Analysis on News Articles Categorization using LSA and NMF Approaches
Due to exponentially growing news articles every day, most of their important data goes unnoticed. It is important to come up with the ability to automatically analyse these articles and segregate them based on the context and related to their particular domain. This paper applies topic modelling which is one of the most growing unsupervised machine learning fields on a million headlines articles in order to produce topics to describe the context of the news article. There are various generative models but we specifically focusing on the non-negative matrix factorization (NMF) and Latent Semantic Analysis (LSA) for implementing and evaluating news dataset. Furthermore, the findings reveal that both NMF and LSA are useful topic modelling tools and classification frameworks, but based on the experimental results the LSA model performed well to identify the hidden data with better mean coherence values and also consumes lesser execution time than NMF. 2022 IEEE. -
An equal split triple-band wilkinson power divider employing extended cross shaped microstrip line /
Microwave and Optical Technology Letters, Vol.60, Issue 10, pp.2488-2492. -
Monitoring nyiragongo volcano using a federated cloud-based wireless sensor network
Current Nyiragongo Volcano observatory systems yield poor monitoring quality due to unpredictable dynamics of volcanic activities and limited sensing capability of existing sensors (seismometers, acoustic microphones, GPS, tilt-meter, optical thermal, and gas flux). The sensor node has limited processing capacity and memory. So if some tasks from the sensor nodes can be uploaded to the server of cloud computing then the battery life of the sensor nodes can be extended. The cloud computing can be used both for processing of aggregate query and storage of data. The two principal merits of this paper are the clear demonstration that the Cloud Computing model is a good fit with the dynamic computational requirements of Nyiragongo volcano monitoring and the novel optimization algorithm for seismic data routing. The proposed new model has been evaluated using Arduino-Atmega328 as hardware platform, Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud connected to some famous public clouds such as Amazon EC2, ThingSpeak, SensorCloud and Pachube. 2017 IEEE. -
P-phase picker using virtual cloud-based Wireless Sensor Networks
Wireless Sensor Networks, mainly regarded as numerous resource-limited nodes linked via low bandwidth, have been intensively deployed for active volcano monitoring during the few past years. This paper studies the problem of primary waves received by seismic wireless sensors suffering from limited bandwidth, processing capacity, battery life and memory. To address these challenges, a new P-phase picking approach where sensors are virtualized using cloud computing architecture followed by a novel in-network signal processing algorithm, is proposed. The two principal merits of this paper are the clear demonstration that the Cloud Computing model is a good fit with the dynamic computational requirements of volcano monitoring and the novel signal processing algorithm for accurate P-phases picking. The proposed new model has been evaluated on Mount Nyiragongo using Eucalyptus/Open Stack with Orchestra-Juju for Private Sensor Cloud then to some famous public clouds such as Amozon EC2, ThingSpeak, SensorCloud and Pachube. The testing has been successful at 75%. The recommendation for future work would be to improve the effectiveness of virtual sensors by applying optimization techniques and other methods. 2015 IEEE. -
Effectiveness of adjuvant psychological therapy on alexithymia fatigue and affective dimensions among women with breast cancer
Psychological aspects in women with breast cancer are many. Among them are those related to what patients go through at different phases of treatment such as newlinediagnosis, pre and post-surgery, chemotherapy and radiotherapy. Women with breast cancer experience psychological repercussions which are specific to them. Some of them are poor body image, self-depreciation, weight changes and hair loss can be distressing to women with breast cancer. newlineThe underlying cause could be a deficit in emotional processing and affect regulation. This could lead to an inability in verbalising and identifying feelings newlinewhich is known as alexithymia. Closely related is the concept of fatigue which is newlinesubjective and tiredness which could last beyond treatments related to cancer. An newlineoverriding concept which could explain and understand these concepts is affect and newlinemood. Towards this end the objective of the study was to examine the efficacy of adjuvant psychological therapy in breast cancer in terms of alexithymia, fatigue, newlinedepression, anxiety, stress and positive and negative affect. newlineThe study also explored if there was an association among alexithymia, fatigue, depression, anxiety, stress and negative affect. The study consisted of 20 patients in the intervention and control groups each. newlineThey were administered the following scales namely, Toronto Alexithymia Scale (TAS-20), Checklist of Individual Strength, Positive and Negative Affect Scale and Depression, Anxiety and Stress Scale. Towards the end of the sessions, they were administered Revised Sessions Reactions Scale. Adjuvant Psychological Therapy is a therapy tailor made for those with cancer which includes both cognitive and behavioral techniques. The results indicated that among the subscales and total alexithymia scores, newlinethere were statistically significant differences across three time-frames in the newlineintervention group.