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Relative humidity (RH) significantly influences various aspects of human life, including agriculture, weather prediction, indoor air quality, and energy consumption. Its intricate non-linear behavior poses a significant challenge for accurate estimation. In the context of Indian climate change, precise forecasting of RH is vital for agriculture and weather prediction. This study proposes a new non-Gaussian approach, the Kumaraswamy seasonal autoregressive moving average (KSARMA) model, for RH prediction across India's five climatic zones (hot-dry, warm-humid, moderate, cold, and composite). Analyzing monthly RH data from 1981 to 2021, model selection was based on diagnostic analysis and Akaike's information criterion (AIC), with parameter estimation utilizing conditional maximum likelihood estimators (CMLE). The KSARMA model demonstrated superior predictive accuracy compared to other models (e.g., multilayer perceptron, SARIMA, Holt-Winter, and β SARMA) across all climatic zones, except the hot-dry zone, as supported by error metrics including RMSE (0.04-0.12), MAE (0.03-0.1), SMAPE (0.04-0.2), and MASE (0.5-.08)., Competing Interests: Declarations. Consent to participate: All authors have given their consent to participate in this manuscript. Consent for publication: All authors have given their consent to publish this manuscript. Conflict of interest: The authors declare no competing interests., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The present energy demand of the world is increasing but the fossil fuels are gradually depleting. As a result, the need for alternative fuels and energy sources is growing. Fuel cells could be one alternative to address the challenge. The fuel cell can convert CO 2 to value-added chemicals. The potential of bio-fuel cells, specifically enzymatic fuel cells and microbial fuel cells, and the importance of immobilization technology in bio-fuel cells are highlighted. The review paper also includes a detailed explanation of the microbial electrosynthesis system to reduce CO 2 and the value-added products during microbial electrosynthesis. Future research in bio-electrochemical synthesis for CO 2 conversion is expected to prioritize enhancing biocatalyst efficiency, refining reactor design, exploring novel electrode materials, understanding microbial interactions, integrating renewable energy sources, and investigating electrochemical processes for carbon capture and selective CO 2 reduction. The challenges and perspectives of bio-electrochemical systems in the application of CO 2 conversion are also discussed. Overall, this review paper provides valuable insights into the latest developments and criteria for effective research and implementation in bio-fuel cells, immobilization technology, and microbial electro-synthesis systems., (© 2024. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The excessive utilization of fossil fuels has worsened global warming and exacerbated the levels of air pollution in the environment, forcing us to consider alternative fuels for compression ignition engines. The current research aims to explore the possibilities of renewable fuels outperforming diesel fuel in terms of combustion, performance, and emission characteristics. Biodiesel is an environmentally friendly and renewable alternative fuel. The major drawback of biodiesel is the significant rise in nitrogen oxide (NOx) emissions. The main novelty and objective of this research is to investigate the performance and emission characteristics of variable compression ratio diesel engine using DPA antioxidant additive. For this investigation, diesel, Jatropha biodiesel (B30) and 100 ppm of phenolic antioxidant diphenylamine (DPA) blended with B30 have been used as fuel named B30+DPA100. From experimental outcomes, the inclusion of diphenylamine to B30 blend resulted in brake-specific fuel consumption (BSFC) and exhaust gas temperature (EGT) being reduced by 8.86% and 4.12%, respectively, compared to B30. Simultaneously, there was a 1.11% increase in brake thermal efficiency (BTHE). The B30+DPA100 fuel blend demonstrates effective control over NOx and other emissions. The emissions of NOx, carbon monoxide (CO), hydrocarbon (HC), and smoke from the B30+DPA100 blend have shown a reduction of 6.8%, 5.34%, 7.86%, and 15.67%, respectively, when compared to diesel. However, there has been an increase in carbon dioxide (CO 2 ) by 7.8%. One notable advantage of the B30+DPA100 blend is the significant decrease in NOx emissions. Additionally, the cylinder pressure for B30+DPA100 has been lowered by 4.93% compared to B30. On the other hand, the net heat release rate (NHR) has experienced a 1.72% increase. The particle size of different elements present in the crankcase oil has been calculated by Zetasizer Nano. The analysis revealed varying particle sizes for different elements in the crankcase oil: aluminum (2.724 μm), chromium (2.78 μm), iron (2.423 μm), and lead (2.587 μm)., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Still the world witnessed an upturn in environmental degradation in spite of commitment to climate change across the nations. However, this study attempts to examine linkages among environmental degradation, technological innovation, and electricity consumption in India from 1981 to 2018 using time series data. To examine the long-run equilibrium relationship among the studied variables, we used robust econometric methods such as the autoregressive distributed lag (ARDL), fully modified ordinary least square (FMOLS), and dynamic ordinary least square (DOLS) methods. Furthermore, Granger causality also investigates through the vector error correction model (VECM) model, to assess inter-connotation among the underlying variables. From our empirical findings, urbanization, financial development, and technological innovation have a negative impact on carbon emissions, indicating long-term improvements in environmental quality. While economic development and electricity consumption are deteriorating environmental quality in India. The study's findings suggest that policymakers should prioritize renewable energy, which decreases environmental damage without impeding economic growth., (© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The development of efficient nanomaterials with promising optical and surface properties for multifunctional applications has always been a subject of novel research. In this work, the study of highly efficient TiO 2 nanorods (NRs) and Ta-doped TiO 2 NRs (Ta-TiO 2 NRs) synthesized by alkaline hydrothermal treatment followed by soaking treatment has been reported. NRs were investigated for their potential applications as recyclable/reproducible visible light active photocatalysts and surface-enhanced Raman scattering (SERS) substrates in wastewater treatment. NRs were characterized by various microscopic (scanning and transmission electron microscopy), spectroscopic (X-ray diffraction, X-ray photoelectron, UV-visible, photoluminescence, and Raman spectroscopy), and surface (Brunauer-Emmett-Teller) techniques. The NRs exhibited promising optical properties with a band gap of 2.95 eV (TiO 2 NRs) and 2.58 eV (Ta-TiO 2 NRs) showing excellent photo-degradation activities for methylene blue (MB) dye molecules under natural sunlight. Particularly, Ta-TiO 2 NRs showed enhanced response as visible light active photocatalysts in normal sunlight and also as SERS substrate attributed to the additional defects introduced by Ta doping. It could be explained by the combined effect of doping-induced enhanced visible light absorption and charge transfer (CT) properties of Ta-TiO 2 NRs. Furthermore, Ta-TiO 2 NRs were investigated for their long-term stability, reproducibility of the data, and recyclability in view of their potential applications in water treatment., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

In the last two decades, the extensive literature that has measured agricultural productivity and growth rate remains controversial and provides few strategies about its main determinants. The present study aims to find out the key determinants of food grain yield (FGY) and examine the role of climate change and agricultural subsidy (SUB) in the context of India using annually data spanning from 1991 to 2018. The current study applied the ARDL modelling to investigate the impacts of climatic factors (average rainfall (RF), mean temperature (AT), and carbon emission (CO 2 ) and agricultural subsidy (SUB) on food grain yield (FGY) in the short-and long-term in India. The estimated outcomes indicate that climatic factors such as RF have a positive impact while AT and CO 2 have a negative effect on FGY. Similarly, non-climate variables such as gross capital formation (GCF) and fertilizer usage (FERT) positively contributed to FGY, while the area under crop (LUC), SUB, and employment (AL) negatively affected FGY in India. The results from Granger causality divulge that climatic and non-climatic elements are the main determinants of food grain yield, which have been playing play a significant role in enhancing food grain production and ensuring food security in India. Based on empirical outcomes and findings, some key policy implications emerged. Precisely, government and policy developers should focus on technological innovation and precision agriculture to increase agriculture production and productivity. Government should create funds to curb the climate change problem and promote eco-friendly renewable energy., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) causes novel coronavirus disease (COVID-19) outbreak in more than 200 countries around the world. The early diagnosis of infected patients is needed to discontinue this outbreak. The diagnosis of coronavirus infection from radiography images is the fastest method. In this paper, two different ensemble deep transfer learning models have been designed for COVID-19 diagnosis utilizing the chest X-rays. Both models have utilized pre-trained models for better performance. They are able to differentiate COVID-19, viral pneumonia, and bacterial pneumonia. Both models have been developed to improve the generalization capability of the classifier for binary and multi-class problems. The proposed models have been tested on two well-known datasets. Experimental results reveal that the proposed framework outperforms the existing techniques in terms of sensitivity, specificity, and accuracy., (© Springer-Verlag GmbH Germany, part of Springer Nature 2020.)

A solar collector is a simple and cheap device that converts solar radiation into valuable heat energy. The thermal performance of the solar collectors can be enhanced significantly with the suspension of nanoparticles in the base fluid. A novel design for a solar-assisted water heater (SWH) is proposed in the current study, and the effect of nanofluid has been investigated on the thermal efficiency of the SWH. The use of nanofluid is one of the prominent methods in comparison to other techniques for improving the performance of solar collectors. Therefore, the base working fluid, i.e., water is mixed with the alumina nanoparticles of average particle size of 30 nm, and they are assumed to be spherical. The flow and thermal characteristics of nanofluid through the solar water heater are simulated numerically with the help of the Eulerian-Eulerian two-phase model using the finite volume method (FVM). The commercial package ANSYS Fluent, is used for modeling the problem under transient conditions with a pressure-based solver. In comparison to a conventional flat plate collector, the proposed solar water heater consists of a corrugated absorber-plate and the effect of the radius of curvature has been investigated on the heat transfer and collector efficiency. With the proposed design, the heat transfer area available with the riser tubes increases remarkably and it leads to a 43% and 14% increase in heat transfer augmentation and collector efficiency, in comparison to the conventional solar water heater., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

The successful use of solar energy for cooking requires the systems adopted not only to have technical attributes that conveniently address specific cooking requirements but also are socially and economically acceptable to its end-users. When displacing cooking fuels used in developing countries, solar cooking can lead to (i) improved health in children and women, (ii) less local forest degradation, (iii) less local pollution, and (iv) lower contributions to global greenhouse gas emissions. The diverse range of thermal and photovoltaic solar cooking systems available, or under development, in different regions of the world is discussed in the present work. Particular attention is given to the social, cultural, and economic factors that have limited adoption of solar cookers. Technical developments that address these limitations are shown to only be effective when they facilitate traditional ways of cooking particular foods in and at desired times in specific climates., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Natural and anthropogenic pollution influence the general hydrochemistry of freshwater sources. Effective management strategies need an accurate evaluation of the water quality parameters, and inferences extracted from the data should be based on the most appropriate statistical methods. Conventional water quality indices (WQI) being related to a large number of water quality parameters results in significant variability and analytical costs. The focus of this study was to develop a remodeled water quality index (WQI min ) based on the localized trends in water quality and demonstrate it to understand water quality variations of Dal Lake (a freshwater lake in the Himalayan region). Spatio-temporal changes and trends of 14 water quality parameters were investigated that were arbitrated from the samples collected at 11 sampling locations during the water quality monitoring across the Dal Lake from September 2017 to August 2020. The results signify that the general mean WQI value was 81.9, and seasonal average WQI values ranges from 79.44 to 84.55. The water quality showed seasonal variance, with lowest values in summer, succeeded by autumn and winter, and highest in spring. Moreover, the results from stepwise multiple regression analysis indicated that the WQI min significantly correlates with six water quality parameters (ammonia, dissolved oxygen, chemical oxygen demand, temperature, turbidity, and nitrate) in Dal Lake. The WQI min model predicted the water quality of the Dal Lake with a coefficient of determination (R 2 ) value of 0.96, root mean square error (RMSE) value of 4.1, and percentage error (PE) of 5.3%. The developed WQI min model can be applied as a cost-effective and efficacious approach to determine the water quality of fresh surface water bodies., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

Electrocoagulation (EC) is an excellent and promising technology in wastewater treatment, as it combines the benefits of coagulation, flotation, and electrochemistry. During the last decade, extensive researches have focused on removal of emerging contaminants by using electrocoagualtion, due to its several advantages like compactness, cost-effectiveness, efficiency, low sludge production, and eco-friendness. Emerging contaminants (ECs) are micropollutants found in trace amounts that discharging into conventional wastewater treatment (WWT) plants entering surface waters and imposing a high threat to human and aquatic life. Various studies reveal that about 90% of emerging contaminants are disposed unscientifically into water bodies, creating problems to public health and environment. The studies on removal of emerging contaminants from wastewater are by global researchers are critically reviewed. The core findings proved that still more research required into optimization of parameters, system design, and economic feasibility to explore the potential of EC combined systems. This review has introduced an innovative collection of current knowledge on electro-coagulation for the removal of emerging contaminants., (© 2022. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Accurate prediction of the reference evapotranspiration (ET 0 ) is vital for estimating the crop water requirements precisely. In this study, we developed multi-layer perceptron artificial neural network (MLP-ANN) models considering different combinations of the meteorological data for predicting the ET 0 in the Beas-Sutlej basin of Himachal Pradesh (India). Four climatic locations in the basin namely, Kullu, Mandi, Bilaspur, and Chaba were selected. The meteorological dataset comprised air temperature (maximum, minimum and mean), relative humidity, solar radiation, and wind speed, recorded daily for a period of 35 years (1984-2019). The datasets from 1984 to 2012 and 2013 to 2019 were utilized for training and testing the models, respectively. The performance of the developed models was evaluated using several statistical indices. For each location, the best performed MLP-ANN model was the one with the complete combination of the meteorological data. The architecture of the best performing model for Kullu, Mandi, Bilaspur, and Chaba was (6-2-4-1), (6-5-4-1), (6-5-4-1), and (6-4-6-1), respectively. It was observed, however, that the performance of other models was also relatively good, given the limited meteorological data utilized in those models. Further, to appreciate the relative predictive ability of the developed models, a comparison was performed with four existing established empirical models. The approach adopted in this study can be effectively utilized by water users and field researchers for modelling and predicting ET 0 in data-scarce locations., (© 2022. The Author(s), under exclusive licence to Springer Nature Switzerland AG.)

COVID-19 is an acronym for coronavirus disease 2019. Initially, it was called 2019-nCoV, and later International Committee on Taxonomy of Viruses (ICTV) termed it SARS-CoV-2. On 30th January 2020, the World Health Organization (WHO) declared it a pandemic. With an increasing number of COVID-19 cases, the available medical infrastructure is essential to detect the suspected cases. Medical imaging techniques such as Computed Tomography (CT), chest radiography can play an important role in the early screening and detection of COVID-19 cases. It is important to identify and separate the cases to stop the further spread of the virus. Artificial Intelligence can play an important role in COVID-19 detection and decreases the workload on collapsing medical infrastructure. In this paper, a deep convolutional neural network-based architecture is proposed for the COVID-19 detection using chest radiographs. The dataset used to train and test the model is available on different public repositories. Despite having the high accuracy of the model, the decision on COVID-19 should be made in consultation with the trained medical clinician., Competing Interests: Conflict of interestAuthors do not have any conflict of interest., (© The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2021.)

In this study, the air pollution-related quality of life (AP-QOL) questionnaire was carried out in two geographically and economically different groups including New Delhi (Megacity) and Hamirpur, Himachal Pradesh (town), and APE scores were linked with respiratory and cardiovascular illness. The APE-Score was developed by AP-QOL questionnaire responses using Delphi technique and further analyzed using principal component analysis (PCA). For reliability of APE-Score and AP-QOL questionnaire, α-Cronbach's test and basic statistics were performed. The linear mixed-effect model and odds ratios were used to evaluate air pollution exposure and health outcomes. Overall, 720 academicians and 276 security guards were invited to participate in the questionnaire. Cronbach's α coefficients ranged from 0.70 to 0.84 indicated significant reliability in the AP-QOL questionnaire conducted in this study. Substantial variation in respiratory symptoms and their medical history were found - 76.9% ([95% confidential interval (CI)]: (- 83.8, - 66.9) (p < 0.05)) and - 28.6% (95% CI: (- 37.8, - 18.0) (p < 0.05)), respectively, with interquartile range (IQR) increase of APE score. The odds ratios (ORs) of respiratory medical history (MH Res.) showed a significant increase from 1.01 to 1.35 for low to high air pollution exposure in the academic group of IIT Delhi. Interestingly, for an academic group of NITH, the ORs for medical history of cardiovascular (MH Card.) showed an increase from 1.08 to 1.13 for low to high APE which was not the case for IIT Delhi academicians., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Epilepsy is a disease recognized as the chronic neurological dysfunction of the human brain which is described by the sudden and excessive electrical discharges of the brain cells. Electroencephalogram (EEG) is a prime tool applied for the diagnosis of epilepsy. In this study, a novel and effective approach is introduced to decompose the non-stationary EEG signals using the Fourier decomposition method. The concept of position, velocity, and acceleration has been employed on the EEG signals for feature extraction using [Formula: see text] norms computed from Fourier intrinsic band functions (FIBFs). The proposed scheme comprises three main sections. In the first section, the EEG signal is decomposed into a finite number of FIBFs. In the second stage, the features are extracted from FIBFs and relevant features are selected by using the Kruskal-Wallis test. In the last stage, the significant features are passed on to the support vector machine (SVM) classifier. By applying 10-fold cross-validation, the proposed method provides better results in comparison to the state-of-the-art methods discussed in the literature, with an average classification accuracy of 99.96% and 99.94% for classification of EEG signals from the BONN dataset and the CHB-MIT dataset, respectively. It can be implemented using the computationally efficient fast Fourier transform (FFT) algorithm.

The WHO announced coronavirus disease a Public Health Emergency on 30 January 2020, and it spreads across the whole planet. Aftermath of outbreak of this disease at the global level is more frightening and panicking than anyone's worst nightmare. With more than 23 mln positive coronavirus cases and more than 800,000 causalities all over the world, the potential of this virus cannot be undermined. This pandemic has victimized all human beings residing on 209 countries and territories of the world. It emerged as an unbeatable global challenge that the world has never witnessed before. Consequently, the affected countries have sealed their borders and made populations reside in their homes until the pandemic is over. Thus, the victims of coronavirus are not only the ones who are exposed to it but also the ones who are affected by the lockdown imposed by the governments. The paper aims to evaluate the effect of COVID-19 on air pollution of various countries. Papers indicating the relationship between air pollution levels and lockdown measures are analyzed. The dramatic U-turn from environmental degradation is definitely a silver lining in these black clouds. This paper reviews the repercussions of the pandemic in some nations, while war-like preparations continue to fight it. COVID-19 has dramatically improved the quality of air. Also, it has greatly affected the economy of various countries due to lockdown., Competing Interests: Conflict of InterestThe authors declare that they have no conflict of interest., (© Springer Nature B.V. 2020.)

COVID-19 is an infectious disease caused by a newly discovered corona virus SARS-COV-2. It is the most dangerous epidemic existing currently all over the world. To date, there is no licensed vaccine and not any particular efficient therapeutic agent available to prevent or cure the disease. So development of an effective vaccine is the urgent need of the time. The proposed study aims to identify potential vaccine candidates by screening the complete proteome of SARS-COV-2 using the computational approach. From 14 protein entries in UniProtKB, 4 proteins were screened for epitope prediction based on consensus antigenicity predictions and various physico-chemical criteria like transmembrane domain, allergenicity, GRAVY value, toxicity, stability index. Comprehensive analysis of these 4 antigens revealed that spike protein (P0DTC2) and nucleoprotein (P0DTC9) show the greatest potential for experimental immunogenicity analysis. These 2 proteins have several potential CD4+ and CD8+ T-cell epitopes, as well as high probability of B-cell epitope regions as compared to well-characterized antigen the matrix protein 1 [Influenza A virus (H5N1)]. In addition, the epitope SIIAYTMSL predicted from spike protein (P0DTC2) and epitope SPRWYFYYL predicted from nucleoprotein (P0DTC9) exhibited more than 60% population coverage in the target populations Europe, North America, South Asia, Northeast Asia taken in this study. These epitopes have also been found to exhibit highly significant TCR-pMHC interactions having a joint Z value of 4.51 and 4.37 respectively. Therefore, this analysis suggests that the predicted epitopes might be suitable vaccine candidates and should be subjected to further in-vivo and in-vitro studies., Competing Interests: Conflict of interestAuthors declare that they have no conflict of interest., (© The Author(s), under exclusive licence to Springer Nature B.V. 2021.)

The reclamation of graywater for non-potable purposes has attained utmost importance, particularly in developing nations. The present research aimed to evaluate the optimal condition of electro-coagulation system in treatment of graywater and its reuse. Moreover, the study also evaluates the impact of major operating parameters on pollutant removal and anode dissolution. To achieve this, two-factor (voltage potential and time) and 5-level (- 1, - 0.5, 0, + 0.5, and + 1) full factorial design, based on response surface methodology (RSM) has been executed for the actual design. The data were acquired after conducting 20 experiments, as suggested by RSM (response surface methodology). Design Expert 12.0.8.0 software has been used to design mathematical model to obtain optimum condition (14 V and 47 min) at pH of 7.35, which provides experimental removal efficiency (75.6% chemical oxygen demand, 78.7% total dissolved solids, 93.4% turbidity, and 63.2% chloride) with minimal electrode consumption of 1.38 mg L -1 . Adequacy of the model developed has been verified by ANOVA. The operating cost of treating graywater at the optimized condition obtained as 0.7 US$/kg COD.

Early classification of 2019 novel coronavirus disease (COVID-19) is essential for disease cure and control. Compared with reverse-transcription polymerase chain reaction (RT-PCR), chest computed tomography (CT) imaging may be a significantly more trustworthy, useful, and rapid technique to classify and evaluate COVID-19, specifically in the epidemic region. Almost all hospitals have CT imaging machines; therefore, the chest CT images can be utilized for early classification of COVID-19 patients. However, the chest CT-based COVID-19 classification involves a radiology expert and considerable time, which is valuable when COVID-19 infection is growing at rapid rate. Therefore, an automated analysis of chest CT images is desirable to save the medical professionals' precious time. In this paper, a convolutional neural networks (CNN) is used to classify the COVID-19-infected patients as infected (+ve) or not (-ve). Additionally, the initial parameters of CNN are tuned using multi-objective differential evolution (MODE). Extensive experiments are performed by considering the proposed and the competitive machine learning techniques on the chest CT images. Extensive analysis shows that the proposed model can classify the chest CT images at a good accuracy rate.

The crystallins (α, β and γ), major constituent proteins of eye lens fiber cells play their critical role in maintaining the transparency and refractive index of the lens. Under different stress factors and with aging, β- and γ-crystallins start to unfold partially leading to their aggregation. Protein aggregation in lens basically enhances light scattering and causes the vision problem, commonly known as cataract. α-crystallin as a molecular chaperone forms complexes with its substrates (β- and γ-crystallins) to prevent such aggregation. In this chapter, the structural features of β- and γ-crystallins have been discussed. Detailed structural information linked with the high stability of γC-, γD- and γS-crystallins have been incorporated. The nature of homologous and heterologous interactions among crystallins has been deciphered, which are involved in their molecular association and complex formation.

Protein aggregation, due to the imbalance in the concentration of Cu 2+ and Zn 2+ ions is found to be allied with various physiological disorders. Copper is known to promote the oxidative damage of β/γ-crystallins in aged eye lens and causes their aggregation leading to cataract. Therefore, synthesis of a small-molecule 'chelator' for Cu 2+ with complementary antioxidant effect will find potential applications against aggregation of β/γ-crystallins. In this paper, we have reported the synthesis of different Schiff bases and studied their Cu 2+ complexation ability (using UV-Vis, FT-IR and ESI-MS) and antioxidant activity. Further based on their copper complexation efficiency, Schiff bases were used to inhibit Cu 2+ -mediated aggregation of recombinant human γD-crystallin (HGD) and β/γ-crystallins (isolated from cataractous human eye lens). Among these synthesized molecules, compound 8 at a concentration of 100 μM had shown ~95% inhibition of copper (100 μM)-induced aggregation. Compound 8 also showed a positive cooperative effect at a concentration of 5-15 μM on the inhibitory activity of human αA-crystallin (HAA) during Cu 2+ -induced aggregation of HGD. It eventually inhibited the aggregation process by additional ~20%. However, ~50% inhibition of copper-mediated aggregation of β/γ-crystallins (isolated from cataractous human eye lens) was recorded by compound 8 (100 μM). Although the reductive aminated products of the imines showed better antioxidant activity due to their lower copper complexing ability, they were found to be non-effective against Cu 2+ -mediated aggregation of HGD.