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Progress in bio-based biodegradable polymer as the effective replacement for the engineering applicators
The development of biopolymers has significantly touched each and every sphere of human life due to their eco friendliness and biodegradability. In recent decades, the production of biopolymers gained profound attention due to the serious environmental concerns and threat to the non-renewable resources. The increased use of synthetic plastic in biomedical and engineering applications stays as a major threat to environment when these xenobiotics enter the food chain and soil upon their careless discharge after use. The significant material properties of plastic has made it as an inevitable part in our day to day life, but the concern over the environment directs the research focus on searching and developing biopolymers and bio composites as sustainable alternatives for their synthetic counterparts. Biopolymers of commercial interest can be majorly produced intracellularly by microbes or can be extracted through chemical or biological methods from plant and animal based substrates. The potential candidates with high market value with specific reference to biomedical engineering and tissue engineering include as polyhydroxyalkanoates, cellulose, chitosan and chitin, hydroxyapatite, and pectin. Despite of having high degree of biocompatibility, the major hurdle that retracts their widespread use commercially is attributed to the cost of production. This can be tackled out by exploiting cheap raw materials like agro waste as substrate and by employing green approaches over solvent based conventional extraction methods. The reduction in the material properties of purified biopolymers restricts their widespread application especially in the fabrication of thermoplastic blends. This can be resolved by production of bio composites with improved properties than their parent biopolymers. The current review focuses on the recent developments in biopolymer science especially with regard to its application in engineering majorly biomedical and tissue engineering. This study throws light on the biosynthetic pathways, extraction methods and applications of commercially important biopolymers. Furthermore, the challenges, limitations, and future prospects in the production and commercialization of biopolymers is briefly discussed in this review. 2022 Elsevier Ltd -
Valorization of pineapple peel waste for fungal pigment production using Talaromyces albobiverticillius: Insights into antibacterial, antioxidant and textile dyeing properties
The present study explores natural pigments as sustainable alternatives to synthetic textile dyes. Due to their therapeutic applications and easy production, fungal pigments have gained attention. However, data on pigment production using solid-state fermentation and optimization is limited. Milk whey was used to grow Talaromyces sp., followed by an evaluation of pigment production in solid and liquid media. Pineapple peels were used as a cost-effective substrate for pigment production, and a one-factor-at-a-time approach was used to enhance pigment production. Pineapple peel-based media produced 0.523 0.231 mg/g of pigment after eight days of incubation. The crude pigment had promising antibacterial and significant antioxidant properties. The extraction fungal pigment's possible use as an eco-friendly textile dye was assessed through fabric dyeing experiments with different mordants. This work contributes to the valorization of agricultural waste and provides insight into using fungal pigments as sustainable alternatives to synthetic textile dyes. 2023 Elsevier Inc. -
A sustainable approach for fish waste valorization through polyhydroxyalkanoate production by Bacillus megaterium NCDC0679 and its optimization studies
Polyhydroxyalkanoates (PHAs) are considered as the only class of truly biodegradable and biocompatible polymers. Although extensive research has been carried out in producing them from a wide variety of organisms, their commercialization still faces hurdles majorly associated with the cost of production media. This research work exploits the use of discarded fish scale waste as a major media component for biopolymer production. The major novelty of the research work is the utilization of a Bacillus megaterium NCDC0679 for PHA production using fish scale waste that is not reported previously. Furthermore, a sequential and systematic statistical optimization strategy employing response surface methodology was used to trace out the level of the most significant variables and their interaction effects on PHA production add to the significant novelty of this work. The significance of the model developed was determined from the p values of ANOVA. Under optimized levels of glucose (50g/L), NaCl (0.125g/L), and fish scale hydrolysate concentration (62.5% v/v), maximum PHA yield of 6.33g/L was achieved in the shake flask culture system. This was found to be 5.50-fold higher than the unoptimized medium. The ANOVA results established the significance of the model (p < 0.05). The extracted polymer was characterized through Fourier-transform infrared (FTIR), nuclear magnetic resonance (NMR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Thus, the present investigation suggests an innovative method for valorization of fish scale waste for commercial production of PHA. 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature. -
Biodegradation studies of polyhydroxyalkanoates extracted from Bacillus subtilis NCDC 0671
The major characteristic feature that distinguishes polyhydroxyalkanoates (PHAs) from its synthetic counterparts is its biodegradability. PHAs are the only class of biopolymers reported to be 100% degradable under both aerobic and anaerobic conditions without production of any toxic residues. The biodegradability of PHAs is influenced by several factors like moisture, temperature, pH, surface area and molecular weight of the polymer. The rate of biodegradation varies greatly depending on the environment. Biodegradation studies were carried out using plating method and direct inoculation method using selected Bacillus strains. Fungal degradation of PHA sheets was assessed using Penicillium chrysogenum. Biodegradation of PHA sheets in different soil types like river valley, agricultural land and garden soil was investigated. The degree of PHA degradation in aqueous environment was studied by incubating the sheets in distilled water, sea water, fish tank water and pond water. The highest degradation rate was observed with agriculture land soil (35.47 0.13%) and fish tank soil (36.93 0.13%). The non-toxic nature of the soil incubated with PHA sheets was ensured using plant growth test. 2019, World Research Association. All rights reserved. -
Valorization of pineapple peels through single cell protein production using saccharomyces cerevisiae NCDC 364
Background and objective: Pineapple peels contain significant quantities of carbohydrates, which can be used as cheap raw materials for production of commercially important products through fermentation. The aim of this study was to use this feed stock for the cultivation of Saccharomyces cerevisiae NCDC 364 and its use as single cell protein. Material and methods: The single cell protein was produced using discarded pineapple peels and Saccharomyces cerevisiae NCDC 364. Optimization of bioprocess variables (temperature, pH, incubation period, carbon source and nitrogen source) affecting single cell protein production was carried out using classical "one factor at a time" approach. The harvested cells from optimized media were screened for amino acid content using high-performance thin-layer chromatography. Results and conclusion: The Saccharomyces cerevisiae NCDC 364 produced maximum single cell protein in pineapple peel based media, compared to non-optimized media. The "one factor at a time" approach showed that the maximum biomass production was achieved at optimized levels of temperature of 25C, pH of 5, incubation period of 120 h, carbon source of 1% sucrose and nitrogen source of 0.5% beef extract. The amino acid profiling of the harvested biomass using high-performance thin-layer chromatography analysis revealed that tryptophan included a comparatively higher concentration of 6.52%, followed by threonine (3.25%). Results of this study suggest that easily available raw materials such as fruit peels offer cost-effective substrates for production of commercially important microbial proteins for alarming global issues linked to protein malnutrition. Conflict of interest: The authors declare no conflict of interest. 2019 National Nutrition and Food Technology Research Institute. -
Universal Electrical Motor Acoustic Noise Reduction based on Rotor Surface Modification
Electromagnetic noise is referred to the audible sound which is produced by materials vibrating due to electromagnetic force. In the present day circumstances, a greater attention is being given to the electromagnetic acoustic noise produced by electrical machines. It is found to annoy human beings and other living organisms due to its tonal sound. The current work aims at designing a rotor for a universal motor with the objective to decrease the acoustic noise by minimization of forced density harmonics. The design consists of some irregularities in the rotor surface to decrease the acoustic noise by internally modifying the air gap permeance. Simulation shall be carried out based on FEM. A lot of research is being carried out on the methods of reducing the noise from electrical machines. The results of the current work significantly help in reducing a lot of noise pollution. The change in the rotor surface will reduce the electromagnetic acoustic noise from the electrical machine. It will also affect the torque parameters positively as studied from earlier research work. 2019 IEEE. -
Food Waste and Fermentation
Food waste (FW) generation and disposal is an unavoidable part of the day-to-day life of every human being. Irrespective of the geographical area, the problems associated with food waste and its valorisation pose a great threat to all countries. Taking into consideration the nutritive value of the discarded food, there are several strategies developed for its effective valorisation rather than careless discharge that may lead to several environmental hazards. The concept of food-based biorefineries for the development of value-added products has gained much momentum in recent decades. Food waste based on its composition can be utilised for producing a variety of products through fermentation approach. The major products of fermentation of food waste include industrially important enzymes, biopolymers, biofuels, single-cell proteins (SCPs) and organic acids. Each of these products has very high commercial value and a diverse scope for applications. As the modern world goes through the principles of sustainable development and a closed-loop system ensuring zero wastage, the fermentation of food waste for product development can prove worthwhile in solving the issues associated with food waste generation and disposal. 2025 The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG. -
Microbial Pigments in Textile and Dyeing Industries
[No abstract available] -
Polyhydroxyalkanoates as potential tools for denitrification of wastewater
Denitrification is the main process in water and wastewater treatment which plays the critical role in mitigating nitrate contamination and ensuring environmental and public health safety. Introduction of polyhydroxyalkanoates (PHA) in water treatment systems enables widening of its application in denitrification processes. Emphasising the importance of denitrification in water treatment helps to prevent the surge of nitrate concentrations, thereby hindering eutrophication and maintaining the balance of aquatic ecosystems. Therefore, there is an increasing urgency for the development of sustainable and efficient denitrification techniques to address contemporary challenges in water quality management. PHAs are produced by a variety of microorganisms under limited nutritional conditions and present themselves as promising candidate for denitrification due to their favourable characteristics which include biocompatibility, biodegradability and ability to store carbon and energy. The current chapter delves with the mechanisms governing denitrification employing PHA along with the intricate biochemical pathways and metabolic processes involved. PHA serves a dual role as both a carbon source and an electron donor in denitrifying bacteria, aiding in the conversion of nitrate to nitrogen gas in anaerobic environments. The chapter furthermore addresses various factors such as substrate availability, microbial community composition, and environmental parameters which are affecting the efficiency of PHA-mediated denitrification. Despite the potential advantages, challenges impede the widespread adoption of PHA in denitrification processes. Technical constraints such as substrate availability, yield of PHA and reactor design, coupled with economic factors such as production costs, present significant barriers. Future research endeavours should prioritise optimising processes, surmounting technical and economic hurdles, and comprehending the ecological ramifications for water and wastewater treatment systems using PHA. Springer Nature Singapore Pte Ltd. 2025. All rights reserved. -
Feedstocks for production of polyhydroxyalkanoates: Sugar-and starch-rich waste as fermentation substrates
Effective management of food and agricultural waste faces a crucial challenge in today's world, primarily due to the necessity to sustainably feed the ever-growing global population. A signifcant portion of this waste generated is plant waste, including both sugar-rich and starch-rich materials. These waste materials are often considered as non-product leftovers due to their perceived lack of economic value compared to the costs associated with their collection, storage, and recovery for reuse. However, by employing appropriate technological methods, such wastes can be utilized as feedstocks for the production of value-added products such as polyhydroxyalkanoates (PHA). PHAs are biodegradable polymers with a wide range of applications, offering an environmentally friendly potential substitute to the conventional plastics. Recycling plant wastes holds immense potential for application across various industries, ultimately leading to a reduction in the adverse impacts caused by their accumulation in the environment. This chapter delves into the utilization of plant wastes (sugar-rich and starch-rich wastes including beet molasses, sugarcane molasses, corn steep liquor, and starchy wastewater) for the production of PHAs. It discusses PHA recovery methods and characterization techniques crucial for evaluating the properties of PHA, thereby laying the groundwork for understanding the material quality and suitability for various applications. Additionally, diverse applications of PHAs, ranging from packaging materials to biomedical devices, are explored by highlighting the potential of utilizing plant wastes to contribute to a circular economy. Springer Nature Singapore Pte Ltd. 2025. All rights reserved. -
A sustainable approach for fish waste valorization through polyhydroxyalkanoate production by Bacillus megaterium NCDC0679 and its optimization studies
Polyhydroxyalkanoates (PHAs) are considered as the only class of truly biodegradable and biocompatible polymers. Although extensive research has been carried out in producing them from a wide variety of organisms, their commercialization still faces hurdles majorly associated with the cost of production media. This research work exploits the use of discarded fish scale waste as a major media component for biopolymer production. The major novelty of the research work is the utilization of a Bacillus megaterium NCDC0679 for PHA production using fish scale waste that is not reported previously. Furthermore, a sequential and systematic statistical optimization strategy employing response surface methodology was used to trace out the level of the most significant variables and their interaction effects on PHA production add to the significant novelty of this work. The significance of the model developed was determined from the p values of ANOVA. Under optimized levels of glucose (50g/L), NaCl (0.125g/L), and fish scale hydrolysate concentration (62.5% v/v), maximum PHA yield of 6.33g/L was achieved in the shake flask culture system. This was found to be 5.50-fold higher than the unoptimized medium. The ANOVA results established the significance of the model (p < 0.05). The extracted polymer was characterized through Fourier-transform infrared (FTIR), nuclear magnetic resonance (NMR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), and thermogravimetric analysis (TGA). Thus, the present investigation suggests an innovative method for valorization of fish scale waste for commercial production of PHA. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2022. -
Active Learning from an Imbalanced Dataset: A Study Conducted on the Depression, Anxiety, and Stress Dataset
The proposed chapter deals with psychological data related to depression, anxiety, and stress to study how the classification and analysis is carried out on imbalanced data. The proposed study not only contributes on providing practical information about the balancing techniques such as synthetic minority oversampling technique but also reveals the strategy for dealing with the working of many existing classification algorithms such as the support vector machine, random forest, XGBoost, etc. on the imbalanced dataset. The present use of evaluation metrics that are solely implied for the imbalanced data classification is also illustrated. It was observed that the ordinary model assessment techniques do not precisely quantify model execution when gone up against imbalanced datasets and that the common techniques such as the logistic regression and decision tree have a predisposition toward classes that have many observations. The attributes of the minority class are treated low and are routinely overlooked. Henceforth, there is a high likelihood of misclassification of the minority class when compared to the majority class. A confusion matrix which contains data about the real and predicted class is used as an assessment standard to check the exhibition of grouping calculation. Rather than going for accuracy, F-score and the area under the curve are considered as the measures to evaluate the classification model. 2022 selection and editorial matter, Vishal Jain, Sapna Juneja, Abhinav Juneja, and Ramani Kannan. -
An Empirical Study ofSignal Transformation Techniques onEpileptic Seizures Using EEG Data
Signal processing may be a mathematical approach to manipulate the signals for varied applications. A mathematical relation that changes the signal from one kind to a different is named a transformation technique in the signal process. Digital processing of electroencephalography (EEG) signals plays a significant role in a highly multiple application, e.g., seizure detection, prediction, and classification. In these applications, the transformation techniques play an essential role. Signal transformation techniques are acquainted with improved transmission, storage potency, and subjective quality and collectively emphasize or discover components of interest in an extremely measured EEG signal.The transformed signals result in better classification. This article provides a study on some of the important techniques used for transformation of EEG data. During this work, we have studied six signal transformation techniques like linear regression, logistic regression, discrete wavelet transform, wavelet transform, fast Fourier transform, and principal component analysis with Eigen vector to envision their impact on the classification of epileptic seizures. Linear regression, logistic regression, and discrete wavelet transform provides high accuracy of 100%, and wavelet transform produced an accuracy of 96.35%. The proposed work is an empirical study whose main aim is to discuss some typical EEG signal transformation methods, examine their performances for epileptic seizure prediction, and eventually recommend the foremost acceptable technique for signal transformation supported by the performance. This work also highlights the advantages and disadvantages of all seven transformation techniques providing a precise comparative analysis in conjunction with the accuracy. 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Classification Algorithms Used in the Study of EEG-Based Epileptic Seizure Detection
Epilepsy is a neurological illness that has become more frequent around the world. Nearly 80% of epileptic seizure sufferers live in low- and middle-income nations. In persons with encephalopathy, the risk of dying prematurely is three times higher than in the general population. Three-quarters of people with brain illnesses in low-income countries do not receive the treatment they require. Recurrent seizures are a symptom of epilepsy, characterized by strange bursts of excess energy in mind. Experts agree that most people diagnosed with epilepsy may be managed successfully, provided the episodes are discovered early on. As a result, machine learning plays an essential role in seizure detection and diagnosis. Support Vector Machine(SVM), Extreme Gradient Boosting(Xgboost), Decision Tree Classifier, Linear Discriminant Analysis(LDA), Perceptron, Naive Bayes Classifier, k-Nearest Neighbor(k-NN), and Logistic Regression are eight of the most widely used machine learning classification algorithms used to classify EEG based mostly Epileptic Seizures. Almost all classifiers, according to the study, give an efficient process. Despite this, the results show that SVM is the most effective method for detecting epileptic seizures, with a 96.84% accuracy rate. For diagnosing Epileptic Seizures using EEG signals, the perceptron model has a lower accuracy of 76.21% percent. 2021 IEEE. -
A meta-heuristic based hybrid predictive model for sensor network data
Many prediction algorithms and techniques are used in data mining to predict the outcome of the response variable with respect to the values of input variables. However from literature, it is confirmed that a hybrid approach is always better in performance than a single algorithm. This is because the hybridization leads to combine all the advantages of the individual approaches, leading to the production of more effective and much improved results. Thus, making the model a productive one, which is far better than model proposed using individual techniques or algorithms. The purpose behind this chapter is to provide information to the users on how to build and investigate a hybrid Feed-forward Neural Network (FNN) using nature inspired meta heuristic algorithms such as the Gravitational Search Algorithm (GSA), Binary Bat Algorithm (BBAT), and hybrid BBATGSA algorithm for the prediction of sensor network data. Here, FNN is trained using a hybrid BBATGSA algorithm for predicting temperature data in sensor network. The data is collected using 54 sensors in a controlled environment of Intel Berkeley Research lab. The developed predictive model is evaluated by comparing it with existing two meta heuristic models such as FNNGSA and FNNBBAT. Each model is tested with three different V-shaped transfer functions. The experimental results and comparative study reveal that the developed FNNBBATGSA shows best performance in terms of accuracy. The FNNBBATGSA under three different V-shaped transfer functions produced an accuracy of 91.1, 98.5, and 91.2%. 2019, Springer-Verlag GmbH Germany, part of Springer Nature. -
Clustering Faculty Members fortheBetterment ofResearch Outcomes: A Fuzzy Multi-criteria Decision-Making Approach inTeam Formation
From a talent-pool of people, choosing an efficient team is tough. Faculty members of a higher education institution constitute the talent-pool. Teams have to be formed from them so that research output of each team is maximum. Amongst numerous research skills, thirteen are identified as most desirable skills. The level of these thirteen skills, viz., concept articulation, formatting according to templates/style sheets, identifying the relevant literature, initiative, logical reasoning, patience, problem formulation/problem finding, proof reading skills/identifying mistakes in written communication, searching/browsing skills/quick search techniques, sense of positive criticism, statistical knowledge, the ability to stay calm, and written communication skills, varies from person to person. Historical ranking of these skills and self-evaluation of the level of acquisition of these skills is used along with the years of experience, educational qualification, gender, marital status, etc., to rank individual faculty members. The fuzzy ranking of the faculty members thus obtained is used to cluster them into teams that are efficient in complementary skills. Each team thus formed is involved in collaborative research leading to research publication. The model is successfully implemented in a university department with 40 faculty members. 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. -
Constraint Governed Association Rule Mining for Identification of Strong SNPs to Classify Autism Data
Autism is a heterogeneous neuro developmental disorder found among all age groups. Nowadays more patients are detected with autism but very less awareness is prevailing in the society related to it. This paved a way for many researchers to carry out serious study on autism and its characteristics. Studying behavior and characteristics of Autistic patients is very important for diagnosing the level of autism. Classifying the association of different characteristic in autistic patients at gene level using machine learning techniques can give an important insight to the doctors and the care takers of the patients. Research is being carried out to identify the genes responsible for autism. The changes in gene sequence may lead to different characteristics in different people. Thus genotypic research is found to reveal well defined insight about various characteristics in autistic patients and their associations with genes. Single Nucleotide Polymorphism (SNP) being high in features indicate human genome variability and is associated with identification of traits for many human diseases including autism. The main aim of the proposed work is to identify SNP sequences which are responsible for carrying the autistic traits. This paper explore the application of Constraint Governed Association Rule Mining (CGARM) technique on SNP data for dimensionality reduction and thereby selecting the strong predominant SNP features which are relevant enough to accomplish classification with high accuracy. The research work incorporates the application of CGARM and is carried out in two stages. In the first stage CGARM was used to choose significant SNP features resulting in dimensionality reduction. In the second stage classification was carried out by subjecting the selected features to Artificial Neural Network (ANN) algorithm. The main advantage of the proposed work is its ability to reduce the dimensions without compromising the quality i.e. using CGARM strong SNPs were selected by applying various constraints like Syntactical constraints, Semantical constraints and Dimensionality Constraints resulting in higher accuracy. The CGARM technique is applied on Autism data collected from National Center for Biotechnology Information (NCBI) repository. The data is divided into a set of 118 features, out of 118 features CGARM contributed in identifying 22 predominant SNPs. Further by applying forward selection method top 17 features were selected and were given as input to ANN. The 10 fold cross validation resulted in 76.9% accuracy which was found to be 50% more than that of original features. The proposed work contributed in reducing the dimension by 85% and provided 76.9% accuracy with the help of only 15% features. 2020 IEEE. -
A decade survey on internet of things in agriculture
The Internet of Things (IoT) is a united system comprising of physical devices, mechanical and digital machines, and different hardware components like sensors, actuators, cameras etc., monitored and operated by the software. The combination of devices and systems connected over the internet opens the pathway for development of various applications beneficial in terms of economic growth of a nation. IoT has evolved as a potentially emerging computer technology solving various real-life problems and issues. IoT covers vast group of applications, from warfare to surveillance, from habitat monitoring to energy harnessing, predictive analytics and personalized health care, and so on. Among various fields, agriculture is one important field having maximum scope of implementation and investment. The main aim of this book chapter is to furnish all the details related to applications of IoT in the field of agriculture. This includes the details related to data collection, types of sensors used, deployment details, data access through cloud. It also covers details related to various communication technologies used in IoT such as Bluetooth, LoRaWAN, LTE, 6LowPAN, NFC, RFID etc. And above all, the chapter focuses on the significance of IoT on agronomics, agricultural engineering, crop production and livestock production. This chapter is a decade survey conducted to study the contribution of IoT in the field of agriculture. Around 40 research papers for the duration 2008-2018 are collected from peer reviewed journals and conferences. The collected articles are analyzed to provide relevant information required for the various end users. Springer Nature Switzerland AG 2020. -
COLPOUSIT: A Hybrid Model for Tourist Place Recommendation based on Machine Learning Algorithms
Tourism is an important sector for a country's economic growth. The travel recommendations should be made focused on better growth and attract more travelers. There is a huge amount of travel information and ideas available on the web that allows the users to make poor travel decisions. This paper focuses on building a hybrid travel recommender system by implementing collaborative-based, popularity-based, and nearby place weighted recommender system. The proposed system recommends the travel spots to the users based upon their interests and other criteria specified. In order to implement these methods, we applied a comparative study on different machine learning algorithms for collaborative-based approach and have performed weighted hybridization. These methods provide a personalized and customized list of similar places with respect to places of interest to the users. Thus, a hybrid system built using these methods provides a better recommendation of places with the advantages of these methods. The obtained results confirm that the hybrid method better than other recommender approaches when used separately. 2021 IEEE. -
Seismic Performance Assessment of Reinforced Concrete Frames: Insights from Pushover Analysis
This paper offers a comprehensive exploration of the seismic response of Reinforced Concrete (RC) frames examined through pushover analysis. The frames analyzed are designed as per IS 13920 and IS 456 for different levels of earthquake intensities and different levels of axial loads. Nonlinear analysis techniques have gained prominence in assessing the response of RC frames, especially when subjected to extreme loading events or when accurate predictions of structural behavior are required beyond the linear elastic range. The study aims to delve into the structural behavior of RC frames under seismic influences, employing pushover analysis as the principal analytical tool. With a focus on assessing the effectiveness and reliability of pushover analysis, the research endeavors to elucidate the seismic performance of RC frames while considering their response to different seismic zones and axial loading scenarios. The methodology involves conducting a series of pushover analyses on RC frames using advanced structural analysis software. The results obtained are meticulously analyzed to discern the shear capacities and ultimate displacements of the frames, by investigating the displacement versus shear capacity relationship across varying seismic zones and axial loading scenarios. Through this comprehensive investigation, the paper aims to enhance our understanding of the seismic behavior of RC frames and will provide valuable insights for seismic design. The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.