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Abstract: Environmental pollution is an aggressive change in the surroundings mainly because of heavy metals, dyes and organic pollutants, which are responsible for crucial natural and health issues. The undiscerning industrial activities make the water resources inappropriate for utilization and the accumulation of these pollutants in water is accountable for harmful effects on aquatic plants and animals. An eco-benign approach was explored for the synthesis of silica nanoparticles (KW-NS) using waste aquatic weeds. In the present study, waste aquatic Kariba weedswere utilized to synthesize and characterize silica nanoparticles and to investigate their applicability in the removal of Cr(VI) ions from aqueous as well as industry effluent. The silica nanoparticles using Kariba weeds(KW-NS) were prepared using sol-gel method and characterized using SEM, FTIR and BET analysis. Various parameters such as pH, adsorbent dosage, initial metal concentration and contact time on the adsorption of Cr(VI) were investigated and optimized. Adsorption isotherm, kinetics, thermodynamics, and regeneration mechanism were studied to determine the efficacy of KW-NS. The adsorption equilibrium for Cr(VI) was achieved at an optimum pH of 4.3 with 4 g L–1adsorbent dosage at 60 min with 10 mg L–1metal ion concentration and maximum adsorption capacity reached up to 96.54 mg g–1. The applicability of the synthesized KW-NS was checked with the tannery and electroplating industry effluent and posed 72 and 60% removal efficiency, respectively. Results obtained from this study indicate that the waste aquatic Kariba weedhas the potential to synthesize silica nanoparticles in a more efficient and economical way than the conventional synthesis. These nanoparticles can be effectively applied as an adsorbent to treat industrial effluents for heavy metal removal.

Micro, small and medium enterprises (MSMEs) play a crucial role in the economic development of any country. Therefore, survival of these MSMEs becomes very imperative. The objective of this study is to build machine learning models to predict the survival of MSMEs and identify the factors that influence the survival. The data for the study was extracted from Fourth All India Census of MSMEs conducted by Ministry of MSMEs, Government of India. Three machine learning algorithms such as logistic regression, decision tree and random forest are used to build models. Random forest algorithm provided the highest accuracy. Also the study identified outstanding loan, market value, purchase value, owner's social category, nature of activity, bank account, cluster type, power source, quality, and organisation type as the variables that significantly influence the firm survival. MSMEs can monitor those factors and frame appropriate policies that would help MSMEs to survive and sustain.

Sustainability symbolises an equitable world with responsible governments, businesses and people striving to protect the natural environment and social equity, at the same time not compromising their quest for economic growth. This empirical study on the sustainability scenario in SMEs is undertaken in Coimbatore district, Tamil Nadu, South India. The sample for the study consisted of 220 SMEs drawn from the members of CODISSIA. This pioneering empirical research has identified the mutual influences amongst the drivers of sustainability. These variables were identified based on a literature review and a survey conducted among small and medium enterprises. The interpretive structural modelling analysis has been used to identify the interrelationships amongst the identified variables. Further, a MICMAC analysis was used to classify the variables as driver variables, linkage variables, autonomous variables and dependent variables. The results of the study provide insights on the appropriate factors that drive sustainability practices.

IoT infrastructures and web services have primarily been deployed in sustainable smart cities and societies. Users are highly attracted to the personalized web services by the IoT infrastructure due to the improved quality of service (QoS) it offers. To acquire service personalization, the complex distribution systems (smart cities) collect users' sensitive data leading to different privacy issues such as unauthorized user access. The heavy security schemes designed for complex architectures cannot be directly implemented in resource‐constrained IoT devices. The existing models designed cannot offer customization to user privacy where the user has little or no control over their data. However, there are other issues in such a system, including centralization, security, privacy, scalability, and communication latency. To overcome these issues, in this paper, we proposed a web service‐based adaptive optimal lightweight CNN (AOLCNN) for a secure smart city. The use of a lightweight convolutional neural network (LCNN) combined with the mayfly optimization (MO) method improves the production process in the smart industry. The MO algorithm not only optimizes the LCNN weights but also enhances the model's performance by selecting the appropriate web service parameters for the user to get rid of irrelevant web services, resulting in increased smart industrial productivity. Blockchain technology is incorporated to enhance the security, privacy, and transparency of IoT systems and also help them to create and verify web services. The built‐in key store system particularly intended for Android smartphones is used in the proposed security‐based web service application. The NIST Curve P‐256 is used to generate key pairs and verify them using the elliptic curve digital signature algorithm (ECDSA). The performance evaluation of the smart web service‐based AOLCNN model in terms of security, privacy, latency, and memory usage shows our proposed model offers significant performance when compared to the conventional techniques. The experimental findings suggest that the AOLCNN satisfies the needs of scalable IoT applications and the security scheme incorporated also helps the device to safeguard it from malicious entities and unauthorized user access. To propose a novel adaptive optimal lightweight convolutional neural network (AOLCNN) based IoT sustainable smart city environment. A software‐designed network (SDN) controller is responsible for manageability, stability as well as observability thereby providing a programming interface to the network infrastructure. The proposed security‐based web service application makes use of an Android‐specific key store system that employs the elliptic curve digital signature algorithm (ECDSA). The security application described in this paper prevents an attacker from obtaining the key and using it to access the web service application.

Lead is considered as one of the deadliest heavy metals which causes severe impact on the environment. In the present work,the experimental study was carried out by using eco-friendly nano-adsor- bent material ONK (organo-nanokaoline) and ONB (organo-nanobentonite) for the effective removal of Pb(II) ions from battery synthetic effluent. The organically modified adsorbents were characterized by Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), scanning electron microscope (SEM), energy dispersive X-ray (EDX), thermogravimetric analysis (TGA). Operating parameters such as pH, adsorbent dosage and contact time were experimentally optimized for the maximum removal of ions. The optimum conditions were found to be at pH of 4, a dosage of 4 g/L, 30 min equilibrium time with 400 mg/L initial concentration for ONK. The adsorption data was fitted with isotherm models such as Langmuir and Freundlich. The monolayer adsorption capacity of ONK and ONB for Pb(II) ions was reported to be about 47 mg/g (ONK), 32 mg/g (ONB), respectively. Adsorption kinetics and mechanism were analyzed using pseudo-first order, pseudo-second order, intra particle diffusion model, and Boyd model. The pseudo second order kinetic model provided the best fit for absorption of ions.

CuInS2 (CIS) is studied widely as a promising absorber material for high efficient and low cost thin film solar cells. CIS thin films are prepared on soda lime glass substrates using Successive Ionic Layer Adsorption and Reaction (SILAR) technique at different deposition temperatures (40 to 70 °C). The structural, compositional and optical properties are studied with x-ray diffractometer, energy dispersive x-ray analyzer and spectrophotometer. The influence of the deposition temperature on the properties of CIS thin films is discussed in this paper in detail.

CuInS2(CIS) is studied widely as a promising absorber material for high efficient and low cost thin film solar cells. CIS thin films are prepared on soda lime glass substrates using Successive Ionic Layer Adsorption and Reaction (SILAR) technique at different deposition temperatures (40 to 70 °C). The structural, compositional and optical properties are studied with x-ray diffractometer, energy dispersive x-ray analyzer and spectrophotometer. The influence of the deposition temperature on the properties of CIS thin films is discussed in this paper in detail.