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Hepatitis C is one of the major health issues in both developed and developing countries. Hepatitis C virus (HCV) infection is more common in prisoners than in the general population. The purpose of this study was to determine the prevalence of HCV and its associated risk factors in Iranian male prisoners in Tehran.

In this cross-sectional study, the authors investigated the frequency and risk factors of hepatitis C infection among male prisoners in the Great Tehran Prison. Information on risk factors including the length of imprisonment, previous history of imprisonment, history of drug injection, history of tattooing, history of piercing, history of high-risk sex and family history of hepatitis C were extracted from patients’ records. To evaluate HCV status, blood samples were collected and tested.

In this study, 179 participants were included. Nine participants (5.0%, 95% CI, 2.3-9.3) were positive for hepatitis C. HCV infection was not significantly associated with age, marital status, education, previous history of imprisonment, length of imprisonment, piercing and high-risk sex; however, there was a significant association between a history of tattooing and a history of injecting drug use and Hepatitis C.

The prevalence of hepatitis C among male prisoners in Great Tehran Prison was 5% in this study, similar to recent studies on prisoners in Tehran. A history of drug injections as well as tattooing were the most important risk factors for hepatitis C in male prisoners.

The purpose of this paper is to investigate the surface properties, particle sizes and corrosion inhibition performance of sodium dodecyl sulfate (SDS) in the presence of imidazolium-based ionic liquid as an additive. Up to now, different properties of alone surfactants and ionic liquids have been studied. However, few studies have been devoted to mixed ionic liquid and surfactant. The significance and novelty of this research is the investigation of 1-methylimidazolium trinitrophenoxide ([MIm][TNP]) as ionic liquid effects on SDS corrosion behavior.

The inhibition effect of [MIm][TNP], SDS and their mixtures on mild steel surface in 2 M hydrochloric acid (HCl) solution was examined by electrochemical impedance spectroscopy, potentiodynamic polarization (PDP), scanning electron microscopy (SEM), atomic force microscopy and quantum chemical calculations as well as dynamic light scattering (DLS) and surface tension measurements to discuss surface properties of studied solutions.

Based on the results, ionic liquid/SDS mixtures significantly indicated better inhibition properties than pure surfactant solution. PDP curves indicated that the studied compounds act as mixed-type of inhibitors. The critical micelle concentration, surface properties and particle sizes were investigated from the surface tension measurements and DLS results.

Adsorption of the inhibitors on the steel surface obeyed the Villamil adsorption model. SEM was used for surface analysis and verified the inhibition efficiency of mixed IL/SDS system. Quantum chemical calculations were performed using density functional theory, and a good relationship between experimental and theoretical data has been obtained.

The purpose of this paper is to propose a semi‐analytical method for studying the interaction between reservoir and concrete gravity dams.

The reservoir is assumed to be unbounded at the far end and the solution is sought for incompressible and in‐viscid fluid. A concrete gravity dam is assumed to behave as a cantilever beam of variable section, and the inclination of the neutral axis is ignored.

It is shown that use of the differential quadrature method (DQM), with a few grid points in conjunction with the finite difference method (FDM), yields an acceptable convergence of results. Comparing the results of the proposed method with those of the literature shows the competency of the method.

DQM for space derivatives and FDM for time derivatives are used to discretize the partial differential equation of motion.

Inorganic oxide addition can be synergistically beneficial in organic coatings if it can impart anti-corrosion properties and also act as an additive to enhance physical and/or chemical properties. The aim of this study was to evaluate the anti-corrosion benefits of nano nickel zinc ferrite (NZF) in the polymer film.

The time-dependent anti-corrosion ability of NZF (0.12-1.0 per cent w/w NZF/binder), applied on API 5L X-80 carbon steel, was characterized by electrochemical techniques such as open circuit potential, electrochemical impedance spectroscopy, linear polarization resistance and potentiodynamic. Characterization of corrosion layer was done by removing coatings after 216 h of immersion in 3.5 per cent w/v NaCl. Optical microscopy, field emission scanning electron microscopy and X-ray diffraction techniques were used to characterize the corroded surface.

Corrosion measurements confirm the electrochemical activity by metallic cations on the steel surface during corrosion process which results in improvement of anti-corrosion properties of steel. Moreover, surface techniques show compact corrosion layer coatings and presence of different metallic oxide phases for nanocomposite coatings.

The suggested protection mechanism was explained by the leaching and precipitation of metallic ion on the corroded surface which in turn slowed down the corrosion activity. Furthermore, improvement in barrier properties of rubber-based coatings was confirmed by the enhanced pore resistance. This work indicates that along with a wide range of applications of NZF, anti-corrosion properties can be taken as an addition.

This study aims to examine the value relevance of earnings in terms of predicting the value variables such as cash flow, capital investment (CI), dividend and stock return under the Indian institutional settings.

The study used panel Granger causality tests to examine causality relationships among variables and panel data regression models to check the statistical associations between earnings and value variables.

Based on a data set of 7,280 Bombay Stock Exchange-listed firm-years spanning over ten years from March 2009 to March 2018, the results show higher sensitivity of earnings toward cash flows, CI, divided and stock return and vice-versa. Further, the findings deduced from the empirical results demonstrate that earnings are positively related to value variables. Overall, the results established that earnings are value-relevant and have predictive ability to forecast the value variables that facilitate investors in portfolio valuation. The results are consistent with the predictive view of the value relevance of earnings. Several robustness checks confirm these results.

This study brings new empirical evidence from a distinct capital market, India, and provides a new facet to the value relevance debate in terms of its prediction view. The study is among earlier attempts that jointly measure the ability of earnings in forecasting different value variables by taking a uniform sample of firms at the same period. Hence, the study provides a comprehensive view of the predictive ability of reported earnings.

Rainfall–runoff relationship is one of the most complex hydrological phenomena. A conventional neural network (NN) with backpropagation algorithm has successfully modelled various non-linear hydrological processes in recent years. However, the convergence rate of the backpropagation NN is relatively slow, and solutions may trap at local minima. Therefore, a new metaheuristic algorithm named as cuckoo search optimisation was proposed to combine with the NN to model the daily rainfall–runoff relationship at Sungai Bedup Basin, Sarawak, Malaysia. Two-year rainfall–runoff data from 1997 to 1998 had been used for model training, while one-year data in 1999 was used for model validation. Input data used are current rainfall, antecedent rainfall and antecedent runoff, while the targeted output is current runoff. This novel NN model is evaluated with the coefficient of correlation (R) and the Nash–Sutcliffe coefficient (E²). Results show that cuckoo search optimisation neural network (CSONN) is able to yield R and E² to 0.99 and 0.94, respectively, for model validation with the optimal configuration of number of nests (n) = 20, initial discovery rate of alien eggs (painitial) = 0.6, hidden neuron (HN) = 100, iteration number (IN) = 1,000 and learning rate (LR) = 1 for CSONND4 model. The results revealed that the newly developed CSONN is able to simulate runoff accurately using only precipitation and runoff data.

This paper aims to examine the ways becoming information literate relates to the material objects in the Kente-weaving landscape.

An ethnographic research design was adopted wherein data was collected using participant observation and a semi-structured interview with 24 participants through their roles as either master weaver, junior weaver or novice weaver. Thematic analysis through a practice-based approach to information literacy frames the analysis of this study.

Information literacy relates to the material objects in terms of developing the know-how knowledge regarding the Kente-weaving tools used as well as what constitutes the quality of Kente fabrics.

Information literacy goes beyond having theoretical knowledge of the material objects of an information landscape. It is practical, not merely knowing the names of the material objects and what they are literary used for.

To the best of the authors’ knowledge, this is the first study that contributes to the understanding of how information literacy relates to material objects in the craft workplace.

The purpose of this study is to design and manufacture a new remote center of motion (RCM) mechanism for use in laparoscopic surgical operations. In addition, obtaining the forward and inverse kinematic equations of the RCM mechanism and performing real-time position control with the Proportional–Integral–Derivative (PID) control method.

At the design stage, it is benefited from similar triangle rule. To obtain the kinematic equations in a simple way and facilitate control, two-fold displacement ratio is provided between the limbs where linear motion occurs. The rotation and displacement amounts required to move at the RCM point have been calculated by using the kinematic equations of the mechanism. Limb dimensions and motion limits are determined in the manner to avoid singularities and collisions. The x, y and z coordinates of the end effector have been defined as the reference point. Control of the mechanism was provided by PID control. To generate the user interface and control algorithm, MATLAB/Simulink real-time toolbox has been used. Four reference points were determined, control was performed and position error values were examined. MF634 Humusoft data acquisition card has been preferred to collect data from encoders.

A novel RCM mechanism has been designed and manufactured. Kinematic equations of this mechanism have been obtained. Position control of the cannula tip has been performed using PID control method for four different reference points. After settlement, maximum position error has been observed as 0.45 mm.

Structure of the designed mechanism is quite simple. Thus, costs are quite low. The operation area of the operator is widened by hanging the mechanism from the ceiling, so operational capability of health personnel is increasing. It helps to decrease the operation time and increase the success of the operation.

With this study, it is aimed to contribute to the literature by designing a new RCM mechanism. The rotation of the mechanism around the RCM point is provided by only one rotary motor, and the displacement of the RCM point in the vertical axis is provided by only one linear motor. The mechanism is also a surgical robot. The designed system is suitable for use in robot-assisted laparoscopic surgery in terms of maneuverability.

Demand planning (DP) is a key element of supply chain management (SCM) and is widely regarded as an important catalyst for improving supply chain performance. Regarding the availability of technology to process large amounts of data, artificial intelligence (AI) has received increasing attention in the DP literature in recent years, but there are no reviews of studies on the application of AI in supply chain DP. Given the importance and value of this research area, we aimed to review the current body of knowledge on the application of AI in DP to improve SCM performance.

Using a systematic literature review approach, we identified 141 peer-reviewed articles and conducted content analysis to examine the body of knowledge on AI in DP in the academic literature published from 2012 to 2023.

We found that AI in DP is still in its early stages of development. The literature is dominated by modelling studies. We identified three knowledge clusters for AI in DP: AI tools and techniques, AI applications for supply chain functions and the impact of AI on digital SCM. The three knowledge domains are conceptualised in a framework to demonstrate how AI can be deployed in DP to improve SCM performance. However, challenges remain. We identify gaps in the literature that make suggestions for further research in this area.

This study makes a theoretical contribution by identifying the key elements in applying AI in DP for SCM. The proposed conceptual framework can be used to help guide further empirical research and can help companies to implement AI in DP.

Making decisions in finance have been regarded as one of the biggest challenges in the modern economy today; especially, analysing and forecasting unstable data patterns with limited sample observations under the numerous economic policies and reforms. The purpose of this paper is to propose suitable forecasting approach based on grey methods in short-term predictions.

High volatile fluctuations with instability patterns are the common phenomenon in the Colombo Stock Exchange (CSE), Sri Lanka. As a subset of the literature, very few studies have been focused to find the short-term forecastings in CSE. So, the current study mainly attempted to understand the trends and suitable forecasting model in order to predict the future behaviours in CSE during the period from October 2014 to March 2015. As a result of non-stationary behavioural patterns over the period of time, the grey operational models namely GM(1,1), GM(2,1), grey Verhulst and non-linear grey Bernoulli model were used as a comparison purpose.

The results disclosed that, grey prediction models generate smaller forecasting errors than traditional time series approach for limited data forecastings.

Finally, the authors strongly believed that, it could be better to use the improved grey hybrid methodology algorithms in real world model approaches.

However, for the large sample of data forecasting under the normality assumptions, the traditional time series methodologies are more suitable than grey methodologies; especially GM(1,1) give some dramatically unsuccessful results than auto regressive intergrated moving average in model pre-post stage.