Shri Ramswaroop Memorial College of Engineering and Management

The growing challenge of food waste management presents a critical opportunity for advancing the circular bioeconomy, aiming to transform waste into valuable resources. This paper explores innovative strategies for converting food wastes into renewable food resources, emphasizing the integration of sustainable technologies and zero-waste principles. The main objective is to demonstrate how these approaches can contribute to a more sustainable food system by reducing environmental impacts and enhancing resource efficiency. Novel contributions of this study include the development of bioproducts from various food waste streams, highlighting the potential of underutilized resources like bread and jackfruit waste. Through case studies and experimental findings, the paper illustrates the successful application of green techniques, such as microbial fermentation and bioprocessing, in valorizing food wastes. The implications of this research extend to policy frameworks, encouraging the adoption of circular bioeconomy models that not only address waste management challenges but also foster economic growth and sustainability. These findings underscore the potential for food waste to serve as a cornerstone in the transition to a circular, regenerative economy.

Since the impending warning of fossil fuel inadequacy, researchers’ focus has shifted to alternative fuel generation. This resulted in the use of a wide variety of renewable biomass sources for making biofuels. Biofuels made from biomass are seen as the most promising long-term strategy for addressing issues associated with conventional energy sources, atypical climate change, and greenhouse gas emissions. Hydrocarbons may be efficiently extracted from biomass, which contains a lot of sugars. Biofuels including bioethanol, biodiesel, biohydrogen, and biogas can be produced from biomass for widespread usage in transportation, industry, and households. In recent years, there have been numerous reports of breakthroughs in the manufacturing of biofuels and biogas. This paper examines the big picture of biogas generation, with an emphasis on the many forms of biomass utilization in both commercial and residential settings in rural areas.

Abstract Background Plant diseases significantly affect the crop, so their identification is very important. Correct identification of these diseases is crucial for establishing a good disease control strategy to avoid time and financial losses. In general, machines can greatly reduce the possibility of human error. In particular, computer vision techniques developed through deep learning have paved a way to detect and diagnose these plant diseases on the leaf. Methods In this work, the model AFD-Net was developed to detect and identify various leaf diseases in apple trees. The dataset is from Kaggle 2020 and 2021 and was financially supported by the Cornell Initiative for Digital Agriculture. An AFD-Net was proposed for leaf disease classification in apple trees and the results of the efficiency of the model are compared with other state-of-the-art deep learning approaches. Results The results of the experiments in the validation dataset show that the proposed AFD-Net model achieves the highest values of 98.7% accuracy for Plant Pathology 2020 and 92.6% for Plant Pathology 2021 compared to other deep learning models in the original and extended datasets. Discussion The results also indicate the efficiency of the proposed model in identifying leaf diseases on apple trees for major and minor classes, i.e., for multiple classification.

Importance of nanotechnology may be seen by penetration of its application in diverse areas including the food sector. With investigations and advancements in nanotechnology, based on feedback from these diverse areas, ease, and efficacy are also increasing. The food sector may use nanotechnology to encapsulate smart foods for increased health, wellness, illness prevention, and effective targeted delivery. Such nanoencapsulated targeted delivery systems may further add to the economic and nutritional properties of smart foods like stability, solubility, effectiveness, safeguard against disintegration, permeability, and bioavailability of smart/bioactive substances. But in the way of application, the fabrication of nanomaterials/nanostructures has several challenges which range from figuring out the optimal technique for obtaining them to determining the most suitable form of nanostructure for a bioactive molecule of interest. This review precisely addresses concepts, recent advances in fabrication techniques as well as current challenges/glitches of nanoencapsulation with special reference to smart foods/bioactive components. Since dealing with food materials also raises the quest for safety and regulatory norms a brief overview of the safety and regulatory aspects of nanomaterials/nanoencapsulation is also presented.

The transition from food waste to food security is a critical component of sustainability efforts. This approach focuses on repurposing organic waste products generated throughout the food supply chain into valuable resources. Food waste, encompassing everything from agricultural residues to post-consumer waste, represents a significant untapped potential that can be harnessed to enhance food security. By implementing strategies such as composting, bioconversion, and innovative recycling technologies, biowastes can be transformed into fertilizers, animal feed, and even new food products, thus closing the loop in the food system and aiding sustainable solutions for waste valorization. This transition not only addresses environmental concerns by reducing landfill waste and greenhouse gas emissions but also contributes to economic sustainability by creating new opportunities within the food production and waste management sectors. Ultimately, transforming food waste into a resource aligns with the broader goals of a circular economy, ensuring a sustainable, resilient, and food-secure future.

Abstract Recently, modern power systems depend heavily on MicroGrids (MGs), which can accommodate Distributed Energy Resources (DERs) economically and with high flexibility. MGs integrated with DERs can assist in enhancing energy security, significant cost savings, and reduction in emission of greenhouse gases. In this paper, the assessment of operating performance of proposed MG system with DERs is employed to investigate the multi‐objective problems of cost optimization and economic scheduling. A grid‐connected Micro‐grid (MG) combined with solar photovoltaic (PV), wind turbine (WT), fuel cell (FC), and Battery Energy Storage System (BESS) is implemented to model the problem. This proposed model is considered as a test system for cost optimization and battery charging/discharging optimization. The developed framework is presented as multi‐objective function with constraints that can be tackled using an effective optimization technique. The above stochastic multi‐objective problem is optimized using various commonly used Physics based Meta‐heuristic techniques such as Simulated Annealing (SA), Harmony Search (HS), Slime Mold Algorithm (SMA), Gravitational Search Algorithm (GSA), Black Hole Optimization (BHO), Sine Cosine Algorithm (SCA), Multiverse optimization (MVO) and Lightning Search Algorithm (LSA). The assessment of the aforementioned physics‐based optimization techniques used on the proposed MG test system is compared using the results. According to the analysis, Black Hole Optimization (BHO) and Lightning Search Algorithm (LSA) both provide greater cost savings overall and for battery charging, respectively. The suggested optimization methods will take the BESS charging/discharging pattern and total cost savings into account.

Heterocyclic compounds containing 1,2,3-triazole and isatin as core structures have emerged as promising drug candidates due to their diverse biological activities such as anti-cancer, antifungal, antimicrobial, antitumor, anti-epileptic, antiviral, and more. The presence of 1,2,3-triazoles and isatin heterocycles in these hybrids, both individually known for their medicinal significance, has increasingly piqued the interest of drug discovery researchers, as they seek to delve deeper into their extensive pharmacological potential for enhancing therapeutic efficacy. Moreover, these hybrid compounds are synthetically accessible using readily available materials. Therefore, there is a pressing need to provide a comprehensive overview of the existing knowledge in this field, offering valuable insights to readers and paving the way for the discovery of novel 1,2,3-triazole-linked isatin hybrids with therapeutic potential.

When we attempt to trace a mobile robot relative to its surroundings, this phenomenon is delineated as Robot localization. Localization is regarded as most significant task for an independent robot on the basis of knowledge of the robot's location, decisions about future actions can be derived. In this paper we propose an approach to determine accurate position of mobile robotic devices by integrating data received from different kind of sensors. We use data from accelerometers, gyroscopes and low-cost encoders to get information about robot traffic.

Abstract A nine‐level inverter with quadratic boost ability is proposed in this paper. The presented topology produces quadruple boosted output voltage by utilizing 10 switches, one diode, two capacitors, and a single dc source. In the proposed topology a back‐end H‐bridge is not required to produce negative voltage levels, which in turn reduces the voltage stress across the switches to the maximum of twice the input voltage. The two switched capacitors (SC) are charged to the dc source voltage rating and twice the voltage rating of the dc source, respectively, to produce quadruple voltage boosting. The high‐voltage boosting capability makes it suitable for the integration of renewable energy sources to the grid. Nearest level control (NLC)‐based modulation technique is applied to produce gate driver signals for the inverter circuit. The proposed topology is compared with the recently published topologies in terms of used components such as switches, diodes, sources, and capacitors are made so as to show the advantages of the presented reduced component count topology. Finally, simulation and hardware results are obtained under certain loading conditions to validate the operability of the proposed topology. The total harmonic distortion (THD) of the proposed topology at the unity modulation index is 8.2%.

Salt stress is a major abiotic stressor that limits plant growth, development, and agricultural productivity, especially in regions with high soil salinity. With the increasing salinization of soils due to climate change, developing salt-tolerant crops has become essential for ensuring food security. This review consolidates recent advances in plant genetics, transcription factors (TFs), and next-generation sequencing (NGS) technologies that are pivotal for enhancing salt stress tolerance in crops. It highlights critical genes involved in ion homeostasis, osmotic adjustment, and stress signaling pathways, which contribute to plant resilience under saline conditions. Additionally, specific TF families, such as DREB, NAC (NAM, ATAF, and CUC), and WRKY, are explored for their roles in activating salt-responsive gene networks. By leveraging NGS technologies-including genome-wide association studies (GWASs) and RNA sequencing (RNA-seq)-this review provides insights into the complex genetic basis of salt tolerance, identifying novel genes and regulatory networks that underpin adaptive responses. Emphasizing the integration of genetic tools, TF research, and NGS, this review presents a comprehensive framework for accelerating the development of salt-tolerant crops, contributing to sustainable agriculture in saline-prone areas.

Abstract: Time series forecasting is a critical component in various fields such as finance, economics, meteorology, and engineering. Among the multitude of methods available for time series forecasting, the Autoregressive Integrated Moving Average (ARIMA) model stands out for its simplicity and effectiveness. This paper provides a comprehensive review of ARIMA models, focusing on their application in forecasting time series data. We begin with an overview of time series analysis and the theoretical foundations of ARIMA models. Subsequently, we delve into the process of building and fitting ARIMA models, discussing the steps involved and the considerations for model selection. Furthermore, we explore advanced topics such as seasonal ARIMA (SARIMA) models and discuss their relevance in handling seasonal data patterns. Additionally, we review recent advancements and extensions of ARIMA models, including hybrid models and machine learning-based approaches. Finally, we discuss the challenges and limitations associated with ARIMA modeling and provide recommendations for future research directions.

Visually Impaired Person Have to Face lot of Problems in their day to day life. They can not walk anywhere alone, there should be someone who can assist them. They also suffer from the problem of reading any Text and they could not identify any object around them. Smart glove described in this paper overcome all the Problems. This glove can detect any obstacle in the Path of blind person and can warn them. So that it ensures their safety. Also it work as a artificial eyes for them. This glove can extract text from any image which contains text and can convert text into speech. So that blinds can easily hear the text which they can not see. One more feature of this glove allows the blinds to identify the objects around them. The experimental results show good accuracy.

Investing in share market is a risky business. While some of the risks can be controlled, others can only be guarded against. To reduce the risk of an investment, many portfolio selection methods have been proposed. To generate an optimal stock portfolio, one has to select stocks and decide the proportion of the capital to be invested on each stock. In this paper, we present a genetic algorithm to generate stock portfolios. The proposed model uses the concept of graph theory to predict the prices of stock which are used as an input to the fitness function of the genetic algorithm to generate optimal portfolio. The proposed model gave good results when experimented on stocks of Bombay Stock Exchange.

Abstract A crack is a whole or partial split of either concrete or masonry into two or more portions caused by breaking or fracturing in concrete constructions. The bulk of fractures is caused by external forces higher than what the structure or its components can sustain acting on it. The most typical sign of degradation in concrete buildings is cracking. Once the fractured system has been assessed, an appropriate repair method that considers these reasons may be chosen. Selecting the best crack repair method may produce results that endure for a very long time while saving you a lot of time, money, and effort. The causes of cracks and several strategies for healing them are covered in this paper.

Contribution: The proposed work carries out the training and testing of the available data through an artificial neural network and develops a model to allocate the subject for maximum outcome. The system also provides percentagewise correlation among all the possible subjects of best fit to allocate among the faculty members. Background: Data mining and machine learning tools have amazed all professionals with their fast, accurate, precise, and feasible results. While their results cannot be directly superimposed on all education systems, they certainly provide ideas for improving teaching pedagogy based on the requirements and capabilities of the system. Intended Outcomes: The subject allocation among the faculty members in engineering studies plays a crucial role in teaching and training the students in the best possible way from the point of view of outcome-based education. The objective of this article is to present an effective model for subject allocation to faculty members based on various factors. Application Design: Faculty members have their diversified strengths because of their involvement in different institute activities. An appropriate subject allocation mechanism for any faculty accumulating the knowledge of an individual’s responsibilities and area of interest can support more significantly in achieving the course outcomes. Findings: 1) Subject allocation based on individuals’ involvement in academics, administrative, and research domains; 2) Subject allocation based on qualifications and experiences for engendering the outcome; and 3) A user-friendly model development for applying at an individual, department, or even at the institute level.

Abstract γ‐Polyglutamic acid (γ‐PGA) microgel, produced by Bacillus spp., represents a promising biomaterial with diverse industrial applications due to its biodegradability, biocompatibility, and nontoxic nature. This review explores the current methodologies in the industrial production of γ‐PGA microgel, emphasizing the optimization of fermentation conditions, genetic engineering of Bacillus strains, and advances in downstream processing techniques. Key applications in pharmaceuticals, agriculture, and environmental management are discussed, highlighting its role in drug delivery systems, as a biocontrol agent, and in wastewater treatment. Future perspectives include enhancing production efficiency through synthetic biology, expanding its application scope, and addressing economic and regulatory challenges to facilitate broader adoption. The integration of innovative technologies and multidisciplinary approaches is crucial for the sustainable development and commercial success of γ‐PGA microgel.

This paper focuses on short-term load forecasting for the day ahead using an Ensemble learning-based Random Forest method. The study uses real-time hourly load data and meteorological data from Bengaluru city, Karnataka, India, to predict the load. The inputs considered for the load forecasting are load profile data, dry bulb temperature, dew point temperature, and humidity data for 31 days from January 1, 2021, to January 31, 2021. The results obtained from the Random Forest model are compared with those obtained from the Ensemble learning-based Bootstrap Aggregation model to evaluate the effectiveness of the proposed method. The study uses statistical parameters such as Maximum Absolute Percent Error (MAPE), Maximum Absolute Error (MAE), and Root Mean Square Error (RMSE) to analyze the predicted load. The findings indicate that the proposed Random Forest model yields better results, with a Mean Absolute Percentage Error (MAPE) of 2.75% compared to the other Ensemble learning-based Bootstrap Aggregation method.

Surface texture plays a role in enhancing the performance of hydrodynamic journal bearings by reducing friction coefficients and increasing load-carrying capacity. However, its impact on the dynamic performance of the bearings remains largely unexplored. This study aims to fill this gap by investigating the optimized surface texture of twin-grooved two-lobe hydrodynamic journal bearings using genetic algorithms. Through the optimization of surface texture, significant enhancements in the dynamic performance of the bearings, including improvements in fluid film damping, stiffness, and omega threshold speed, are achieved. Utilizing GA optimization, textured bearings demonstrate a remarkable enhancement in dynamic performance, with an impressive increase of 195.55% in omega threshold speed. These findings provide valuable insights for enhancing bearing designs and stability, thereby contributing to advancements in tribological engineering.

This introduction explores the transformative potential of AI technologies in addressing complex challenges facing drinking water systems, while also examining the ethical considerations and technical hurdles that must be navigated for responsible and effective deployment. Drinking water is a fundamental resource essential for human health, economic prosperity, and ecosystem integrity. However, managing water quality and distribution systems presents significant challenges, exacerbated by population growth, urbanization, climate change impacts, aging infrastructure, and emerging contaminants. Traditional methods of water quality monitoring and management rely on periodic sampling, laboratory analysis, and manual intervention, which are often time-consuming, resource-intensive, and may not provide real-time insights needed to prevent waterborne diseases or respond swiftly to contamination events. Firstly, AI enables real-time detection of water quality deviations and potential contaminants through advanced sensor networks and predictive analytics.

Being a socio-economic and political hub in India, Delhi-National Capital Region is emerging as a magnet of attraction for people at a revolutionary pace. Despite the extensive network of Indian railways and road transport, the inadequacy of public transport has its undeniable aftermaths such as rising accidental statistics, pollution concentration, commuting time delay, and energy wastage. This study focuses on the analytical assessment of regional rapid transit system as an alternative to existing public transport to reduce the dependency of commuters on personalized vehicles, provide seamless connectivity in regional transportation and highlight the issues & perspectives of urbanization, traffic congestion, road safety, pollution and parking. In this research study, an analytical survey on the public opinion is performed in the following aspects: socio-economic, travel behavior, walkable access, transportation mode, preference of modal share, interchange, and safety with comfort. The result reveals that commuters’ opinion toward regional rapid transit has appeared to be 46% positive, 35% positive with amenities and 19% negative.