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This work aims to develop a novel fuzzy associator rule-based fuzzified deep convolutional neural network (FDCNN) architecture for the classification of smartphone sensor-based human activity recognition. This work mainly focuses on fusing the λ_max method for weight initialization, as a data normalization technique, to achieve high accuracy of classification.

The major contributions of this work are modeled as FDCNN architecture, which is initially fused with a fuzzy logic based data aggregator. This work significantly focuses on normalizing the University of California, Irvine data set’s statistical parameters before feeding that to convolutional neural network layers. This FDCNN model with λ_max method is instrumental in ensuring the faster convergence with improved performance accuracy in sensor based human activity recognition. Impact analysis is carried out to validate the appropriateness of the results with hyper-parameter tuning on the proposed FDCNN model with λ_max method.

The effectiveness of the proposed FDCNN model with λ_max method was outperformed than state-of-the-art models and attained with overall accuracy of 97.89% with overall F1 score as 0.9795.

The proposed fuzzy associate rule layer (FAL) layer is responsible for feature association based on fuzzy rules and regulates the uncertainty in the sensor data because of signal inferences and noises. Also, the normalized data is subjectively grouped based on the FAL kernel structure weights assigned with the λ_max method.

Contributed a novel FDCNN architecture that can support those who are keen in advancing human activity recognition (HAR) recognition.

A novel FDCNN architecture is implemented with appropriate FAL kernel structures.

The purpose of this paper is to investigate the inhibitive performance of Coreopsis tinctoria (C. tinctoria) plant extract for the corrosion of mild steel in 0.5 M H₂SO₄.

The inhibition efficiency was studied by weight loss, electrochemical measurements and the surface analysis was done by Raman, scanning electron microscopy/energy-dispersive X-ray spectroscopy (SEM-EDS) and atomic absorption spectroscopy (AAS) analysis.

Maximum inhibition efficiency of C. tinctoria in 0.5 M H₂SO₄ on mild steel is 80.62 per cent (500 ppm) at 303 ± 1K. The adsorption of the C. tinctoria on the mild steel surface in 0.5 M H₂SO₄ was found to obey Langmuir adsorption isotherm. Temperature studies were carried out and the significant parameters, such as change in enthalpy (ΔH^°), change in entropy (ΔS^°) and change in free energy (ΔG°_ads) and heat of adsorption (Qads), were calculated. The productive layer formed on the mild steel surface in 0.5 M H₂SO₄ were confirmed by the Raman spectral analysis.

This paper provides information on the inhibitive properties of C. tinctoria plant extract which is found to be a good corrosion inhibitor for mild steel in 0.5 M H₂SO₄.

This study examined the impact of the Input Credit Scheme (ICS) by the Integrated Water Management and Agriculture Development (IWAD) on the productivity and food security of smallholder rice farmers in Ghana.

Cross-sectional data from 250 rice farming households in the Mamprugu Moagduri district of the North East Region obtained from a multi-stage sampling technique were used for the study. Inverse Probability Weighted Regression Adjustment (IPWRA), Propensity Score Matching (PSM) and Kendall's coefficient of concordance were the methods of analysis employed.

Empirical results show that education, rice farming experience, dependency ratio, FBO membership, farm size and farm age were the significant factors influencing participation in the input credit scheme (ICS). Also, participants had an average rice productivity of 1,476.83 kg/ha, whereas non-participants had 1,131.81 kg/ha implying that participants increased their productivity by about 30%. In addition, the study revealed that participant households increased their household dietary diversity (HDDS) by 0.45 points amounting to about 8% diversity in their diets. High-interest rates associated with credit received, the short periods of credit repayment and the high cost of inputs provided under the scheme were the most challenging constraints associated with partaking in the ICS.

The available literature on agricultural interventions have predominantly emphasized input credit as a key factor for improving cropt productivity and food security of smallholders. This study provides compelling evidence that participation in ICSs can result in substantial benefits for agricultural development, as evidenced by increased productivity leading to improved food security. The significance of these findings is highlighted by the fact that, through participation in input credit schemes, smallholder rice farmers in many developing countries see substantial improvement in their capacity to access productive resources, thereby improving their productivity, while simultaneously reducing food insecurity.

Leveraging on the improved productivity of participants in the ICS, this study advocates that such input credit schemes should scale up to more food-insecure farming communities in Ghana.

The study uses a doubly robust econometric approach to evaluate the impact of ICS on smallholder rice farmers' productivity and food security in Ghana, making it the first of its kind. The findings offer a solid basis for future research and provide guidance for policymakers looking to boost agricultural development in Ghana.

The study aims to address two objectives. First, to examine the socioeconomic and demographic factors contributing to financial literacy and second, to analyze if financial literacy affects investment choices.

The study uses financial inclusion insights (FII) survey data conducted by Intermedia, comprising 47,132 individuals in India. Further, instrument variable estimation has been used to analyze the relationship between financial literacy and individuals' investment choices.

The study finds that differences in financial literacy level can be attributed to various socioeconomic/demographic factors like age, gender, education levels, income, location of residence, sources of information, etc. Econometric analyses indicate that financial literacy influences investment decisions, mainly in businesses and traditional assets such as gold, property, etc.

The study contributes to the growing literature on financial literacy in the context of developing countries like India and highlights the role of financial literacy in how individuals make investment choices. Using a novel instrument, i.e. participation in the stock market by family or peers for advanced financial literacy, the results provide evidence that advanced financial literacy among individuals increases the probability of their stock market participation.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-12-2021-0764.

The paper reports an investigation into the use of aqueous extracts of rice bran as a green inhibitor for corrosion of carbon steel in hydrochloric acid (HCl) solution.

Extracts from the rice bran were used as the main component of an environmentally friendly corrosion inhibitor for use in HCl pickling processes. Inhibition behavior on carbon steel in HCl was investigated by means of mass‐loss tests, polarization curves, electrochemical impedance spectroscopy and atomic force microscopy.

The results show that the extract exhibited good inhibition performance in 1 M HCl. The inhibition efficiency increased with increase in the concentration of the inhibitor and was only moderately affected by temperature variations in the range 303‐363 K. The inhibitive action was due to adsorption on the A3 steel and the adsorption process was consistent with the Langmuir isotherm. The free energy of adsorption (ΔG_ads.) was −4.192 kJ/mol. The negative value of ΔG_ads. indicated spontaneous adsorption of the inhibitor occurred on the surface of A3 steel.

Rice bran extract is an effective inhibitor and can be used to protect carbon steel from corrosion in HCl solution.

The rice bran extracts are an effective green inhibitor and can be widely used in the pickling of metals.

The purpose of this paper is investigate the inhibition efficiency of three similar bi-cyclic organic compounds, namely, benzimidazole (BI), benzotriazole (BTAH) and benzothiazole (BTH) on carbon steel in 1 M hydrochloric acid (HCl) solution. Organic inhibitors are widely used to protect metals in acidic media. Among abundant suggestions for acid corrosion inhibitors, azole compounds have gained attention.

The inhibition efficiency of the three organic compounds was investigated using potentiodynamic polarization and electrochemical impedance spectroscopy (EIS).

Superiorities of BTH and BTAH corrosion inhibitors were shown by EIS data and polarization curves. Moreover, the results revealed that BTAH and BTH can function as effective mixed-type adsorptive inhibitors, whereas no inhibition behavior was observed for BI. Both BTAH and BTH obeyed Longmuir adsorption isotherm. The results obtained from this isotherm showed that both inhibitors adsorbed on the specimen surface physically and chemically. The difference in inhibition efficiencies of BTAH, BTH and BI was related to the presence of nitrogen and sulfur hetero atoms on their molecular structures.

This study evaluated inhibition efficiency of BI, BTAH and BTH using electrochemical methods. In addition, the study attempted to find inhibition mechanism of the inhibitors and to find modes of adsorption of the inhibitors, correlating effects of heteroatoms and inhibition efficiency.

This paper introduces a Bayesian two-sided group chain sampling plan (BT-SGChSP) by using binomial distribution to estimate the average proportion of defectives. In this Bayesian approach, beta distribution is used as a suitable prior of binomial distribution. The proposed plan considers both consumer's and producer's risks. Currently, group chain sampling plans only consider the consumer's risk and do not account for the producer's risk. All existing plans are used to estimate only a single point, but this plan gives a quality region for the pre-specified values of different design parameters. In other words, instead of point wise description for the designing of sampling plan based on a range of quality by involving a novel approach called quality region.

The methodology is based on five phases, which are (1) operating procedure, (2) derivation of the probability of lot acceptance, (3) constructing plans for given acceptable quality level (AQL) and limiting quality level (LQL), (4) construction of quality intervals for BT-SGChSP and (5) selection of the sampling plans.

The findings show that the operating characteristic (OC) curve of BT-SGChSP is more ideal than the existing Bayesian group chain sampling plan because the quality regions for BT-SGChSP give less proportion of defectives for same consumer's and producer's risks.

There are four limitations in this study: first is the use of binomial distribution when deriving the probability of lot acceptance. Alternatively, it can be derived by using distributions such as Poisson, weighted Poisson and weighted binomial. The second is that beta distribution is used as prior distribution. Otherwise, different prior distributions can be used like: Rayleigh, exponential and generalized exponential. The third is that we adopt mean as a quality parameter, whereas median and other quintiles can be used. Forth, this paper considers probabilistic quality region (PQR) and indifference quality region (IQR).

The proposed plan is an alternative of traditional group chain sampling plans that are based on only current lot information. This plan considers current lot information with preceding and succeeding lot and also considers prior information of the product.

This paper first time uses a tight (three acceptance criteria) and introduces a BT-SGChSP to find quality regions for both producer's and consumer's risk.

Over the past decade, plant extracts are ultimate green candidatures to substitute the expensive and noxious synthetic corrosion inhibitors. In this regard, this study aims to focus on evaluating anti-corrosion properties of green inhibitor Cyperus rotundus (C. rotundus), a perennial herb found throughout India.

The biocompatible components present in C. rotundus extract was analyzed by gas chromatography–mass spectroscopy analysis. The corrosion inhibitory effect of C. rotundus was assessed by impedance, polarization and surface morphometric study [atomic force microscopy (AFM)]. Density functional theory (DFT) study was carried using DFT/B3LYP, and basis set used for calculations was 6-31G (d, p) using Gaussian 03 program package.

Predominant components such as octadecanoicacid, ethylester, n-hexadecanoic acid, pentanoicacid-4-oxoethyl ester, cyclotrisiloxane, hexamethyl, cyclotetrasiloxane and octamethyl were identified from the extract of C. rotundus. Impedance study demonstrated that the addition of inhibitor reduces the double-layer capacitance and increases the charge transfer resistance. Furthermore, polarization studies indicated that the extract of C. rotundus acted as a mixed-type inhibitor with decrease in corrosion current density with increase in concentration. AFM study evinced the formation of inhibitor film on mild steel surface. The donor–acceptor interactions of active sites of predominant phytoconstituents were substantiated by computational analysis (DFT).

This paper deals with the inhibition effect of extract of C. rotundus on mild steel in 0.5M H₂SO₄. C. rotundus has a capability to adsorb on the metal surface, thus hindering corrosion.

The purpose of this paper is to design an optimal reliability acceptance sampling plan (RASP) using the Type-I generalized hybrid censoring scheme (GHCS) for non-repairable products sold under the general rebate warranty. A cost function approach is proposed for products having Weibull distributed lifetimes incorporating relevant costs.

For Weibull distributed product lifetimes, acceptance criterion introduced by Lieberman and Resnikoff (1955) is derived for Type-I GHCS. A cost function is formulated using expected warranty cost and other relevant cost components incorporating the acceptance criterion. The cost function is optimized following a constrained optimization approach to arrive at the optimum RASP. The constraint ensures that the producer's and the consumer's risks are maintained at agreed-upon levels.

Optimal solution using the above approach is obtained for Type-I GHCS. As a special case of Type-I GHCS, the proposed approach is also used to arrive at the optimal design for Type-I hybrid censoring scheme as shown in Chakrabarty et al. (2019). Observations regarding the change in optimal design and computational times between the two censoring schemes are noted. An extensive simulation study is performed to validate the model for finite sample sizes and the results obtained are found to be in strong agreement. In order to analyze the sensitivity of the optimal solution due to misspecification of parameter values and cost components, a well-designed sensitivity analysis is carried out using a real-life failure data set from Lawless (2003). Interesting observations are made regarding the change in optimal cost due to change in parameter values, the impact of warranty cost in optimal design and change in optimal design due to change in lot sizes.

The research presents an approach for designing optimal RASPs using Type-I generalized hybrid censoring. The study formulates optimum life test sampling plans by minimizing the average aggregate costs involved, which makes it valuable in dealing with real-life problems pertaining to product quality management.

This study aims to reduce data bias during human activity and increase the accuracy of activity recognition.

A convolutional neural network and a bidirectional long short-term memory model are used to automatically capture feature information of time series from raw sensor data and use a self-attention mechanism to learn select potential relationships of essential time points. The proposed model has been evaluated on six publicly available data sets and verified that the performance is significantly improved by combining the self-attentive mechanism with deep convolutional networks and recursive layers.

The proposed method significantly improves accuracy over the state-of-the-art method between different data sets, demonstrating the superiority of the proposed method in intelligent sensor systems.

Using deep learning frameworks, especially activity recognition using self-attention mechanisms, greatly improves recognition accuracy.