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Building on pecking order theory, this study seeks to understand the various financial factors that influence top management's decision regarding the company’s capital structure. The authors attempt to understand and analyse whether the capital structure of mid‐ and small‐cap firms is affected by cash surplus scaled to total assets. Along with other determinants of capital structure such as liquidity, profitability, tangibility, market capitalisation and age, this is considered one of the major factors. Cash surplus is calculated using data from the cash flow statement. It is defined as the difference in cash from operating activities and that from investing activities and is scaled to total assets. To the best of the authors’ knowledge, this is the first study to regress cash surplus scaled to total assets and other determinants over leverage to examine the impact on mid‐ and small‐cap firms. The pecking order theory was found to hold for firms earning cash surplus.

Data were collected from the CMIE Prowess database of all firms listed on the NIFTY Small cap 250 index and NIFTY Midcap 150 index. The data of non-financial firms belonging to the midcap and small-cap sector, listed on the National Stock Exchange of India from 2012 to 2019 were considered. After cleaning the data, an unbalanced panel of 171 companies totalling 1,362 observations for the NIFTY Small-cap 250 index and another panel of 96 companies with 761 observations for the NIFTY Midcap 150 index was created. Panel data regression analysis was used to determine the effect of cash surplus scaled to total assets on the firms' capital structure.

This study demonstrates how small- and midcap firms' behave differently in taking capital structure decisions. Pecking order theory was found to hold for firms earning cash surplus as a proportion of total assets (Surplusta).

The study was conducted through data available on secondary sources and database. The study can be better conducted by conducting a primary survey too. Further study may be conducted with a blend of secondary and questionnaire method. The results can be compared to check the similarity in findings.

Managers can benefit from the findings when making decisions on long- and short-term loans. This study can help managers in terms of the financial variables that have a role to play in the financial leverage of the company. The decision of the managers of midcap or small-cap firms would be different. Factors influencing short- and long-term borrowings are different. Academics can discuss whether there is any difference in the influence of capital structure variables of small- and midcap companies and the reasons for such differences. Judicious decisions on capital structure will create wealth for the shareholders as the right decision about leverage would result in a proper cost of capital. The findings also add to the existing literature on the Pecking order theory.

Academics can discuss whether there is any difference in the influence of capital structure variables of small- and midcap companies and the reasons for such differences.

The study extends the existing literature by demonstrating that the capital structure of mid and small-cap firms is affected by cash surplus scaled to total assets. The pecking order theory was found to hold for firms earning cash surplus. This study can inform the practitioners about the financial variables that have a role to play in the company's financial leverage. As the results and significance of the variables of the midcap or small-cap firms are different, the decisions of the managers of these firms would be separate for the capital structure of their firms. The study also infers that the factors influencing short and long-term borrowings are different. The study determines whether managers' decision-making in such companies is different in terms of raising short- and long-term loans. The study attempts to guide managers in considering the different variables that would influence their capital structure decisions, particularly the decision to include debt in the capital. Financial variables need not be of equal importance for managers belonging to small- and midcap companies.

Tactile sensation is an important sensory function for robots in contact with the external environment. To better acquire tactile information about objects, this paper aims to propose a three-layer structure of the interdigital flexible tactile sensor.

The sensor consists of a bottom electrode layer, a middle pressure-sensitive layer and a top indenter layer. First, the pressure sensitive material, structure design, fabrication process and circuit design of the sensor are introduced. Then, the calibration and performance test of the designed sensor is carried out. Four functions are used to fit and calibrate the relationship between the output voltage of the sensor and the contact force. Finally, the contact force sensing test of different weight objects and the flexible test of the sensor are carried out.

The performance test results show that the sensitivity of the sensor is 0.93 V/N when it is loaded with 0–3 N and 0.23 V/N when it is loaded with 3–5 N. It shows good repeatability, and the cross-interference between the sensing units is generally low. The contact force sensing test results of different weight objects show that the proposed sensor performs well in contact force. Each part of the sensor is a flexible material, allowing the sensor to achieve bending deformation, so that the sensor can better perceive the contact signs of the grasped object.

The sensor can paste the surface of the paper robot’s gripper to measure the contact force of the grasping object and estimate the contour of the object.

In this paper, a three-layer interdigital flexible tactile sensor is proposed, and the structural parameters of the interdigital electrode are designed to improve the sensitivity and response speed of the sensor. The indenter with three shapes of the prism, square cylinder and hemisphere is preliminarily designed and the prism indenter with better conduction force is selected through finite element analysis, which can concentrate the external force in the sensing area to improve the sensitivity. The sensor designed in this paper can realize the measurement of contact force, which provides a certain reference for the field of robot tactile.

Algorithm bias refers to repetitive computer program errors that give some users more weight than others. The aim of this article is to provide a deeper insight of algorithm bias in AI-enabled ERP software customization. Although algorithmic bias in machine learning models has uneven, unfair and unjust impacts, research on it is mostly anecdotal and scattered.

As guided by the previous research (Akter et al., 2022), this study presents the possible design bias (model, data and method) one may experience with enterprise resource planning (ERP) software customization algorithm. This study then presents the artificial intelligence (AI) version of ERP customization algorithm using k-nearest neighbours algorithm.

This study illustrates the possible bias when the prioritized requirements customization estimation (PRCE) algorithm available in the ERP literature is executed without any AI. Then, the authors present their newly developed AI version of the PRCE algorithm that uses ML techniques. The authors then discuss its adjoining algorithmic bias with an illustration. Further, the authors also draw a roadmap for managing algorithmic bias during ERP customization in practice.

To the best of the authors’ knowledge, no prior research has attempted to understand the algorithmic bias that occurs during the execution of the ERP customization algorithm (with or without AI).

The purpose of this paper is to propose a generic framework based on the colour and the texture features for colour‐textured image segmentation. The framework can be applied to any real‐world applications for appropriate interpretation.

The framework derives the contributions of colour and texture in image segmentation. Local binary pattern and an unsupervised k‐means clustering are used to cluster pixels in the chrominance plane. An unsupervised segmentation method is adopted. A quantitative estimation of colour and texture performance in segmentation is presented. The proposed method is tested using different mosaic and natural images and other image database used in computer vision. The framework is applied to three different applications namely, Irish script on screen images, skin cancer images and sediment profile imagery to demonstrate the robustness of the framework.

The inclusion of colour and texture as distributions of regions provided a good discrimination of the colour and the texture. The results indicate that the incorporation of colour information enhanced the texture analysis techniques and the methodology proved effective and efficient.

The novelty lies in the development of a generic framework using both colour and texture features for image segmentation and the different applications from various fields.

This study seeks to explore the underlying benefits and constraints of travel among pregnant women. This study deploys a series of in-depth interviews involving 12 participants. The findings show that emotional refreshment and health promotion are the two benefits of travel expressed by pregnant travelers, while physical discomfort is an unavoidable constraint of travel. To lessen the obstacle of the trip, according to the constraint identified, this study suggests that service providers actively look into the particular needs of pregnant travelers, such as comfortable restrooms. Lastly, it gives a suggestion for future studies on the issues in connection with the examination of pregnant women's travel experiences in each trimester.

In this work, a numerical algorithm is presented for stability analysis of cold-formed steel (CFS) channel sections.

A nonlinear optimization problem is formulated using energy-based technique of idealized channel section subject shear, compression and biaxial bending. The total potential energy is minimized with respect to skew angle and half wavelength of the buckling mode. The optimization algorithm is updated sequentially using quadratic approximation until minimum buckling coefficient is attained. The developed algorithm is validated using other numerical techniques.

The described algorithm is computationally effective and can be utilized in the industry for analysis of CFS channels under any load combination.

The paper offers a new tool for engineers in practice to analyze channels subject to combined loadings.

Very limited literature dealt with the stability of channels under combined loading. A new numerical algorithm is provided to practitioners to utilize in the industry for analysis of channel sections under combined loading. Unlike finite element or finite strip methods, the channel is not discretized into subelements. Mathematical programming technique is used to find the buckling load. Parametric studies are then carried out to highlight influences of geometric interaction of the channel components and to provide useful guidance to the design of CFS channels.

The purpose of this study was to assess the nutritional behavior regarding the frequency of consumption of the main types of food by pregnant women attending antenatal classes in comparison to non-attendees.

A questionnaire survey was conducted in Poland among 200 women in the first pregnancy, 100 of whom were attending antenatal classes and 100 were non-attendees.

Women attending antenatal classes exhibited more favorable health-related nutritional behaviors in comparison with those not attending. Attendees eat more vegetables and milk products whereas non-attendees declared more processed meat and sweets consumption. Three-fourth of non-attendees and two-fourth of attendees considered their diet as good. It was also found that the level of physical activity of pregnant women attending antenatal classes was significantly higher than among those who did not attend such classes.

A limitation of the study was that it took place in Poland and voices from women of other European countries as well as diverse ethnicities were not represented. In addition, women were interviewed only during their pregnancy, and therefore it was not possible to investigate women's nutritional behaviors after delivery.

This research shows clearly that attending antenatal classes may involve with a beneficial effect on the nutritional behavior of pregnant women. It is expected that once established healthy nutritional habits should be practiced further.

This research is the indication of the important role of antenatal classes in developing more favorable health-related dietary behavior expressed by the participants. To our knowledge in Poland, it is the first time that the nutritional behavior of pregnant women attending and not attending antenatal classes were examined and compared. So far in Poland, the extent and type of nutritional education received by pregnant women across antenatal care has not been examined. This research can contribute to a broader recognition of nutritional knowledge and practice of pregnant women important for the health of mother and her baby.

The purpose of this paper is to design and simulate a piezoelectric micropump using microelectromechanical systems technology for drug delivery applications.

Two piezoelectric actuators are used to actuate and bend the diaphragms in the proposed structure. In this micropump, the liquid flow is rectified by two silicon check valves.

The use of two piezoelectric transducer (PZT) actuators in the parallel mod not only reduces dead volume but also increases stroke volume as well. In addition to increasing the flow rate, this phenomenon enhances the operation of the micropump to have self-priming as smoothly as possible.

This actuating method results in a 22% increase in flow rate and compression ratio, as well as a 15% reduction in function voltage. The fluid-solid interaction is simulated using COMSOL Multiphysics 5.3a.

With the proliferation of terrorist/extremist websites on the World Wide Web, it has become progressively more crucial to detect and analyze the content on these websites. Accordingly, the volume of previous research focused on identifying the techniques and activities of terrorist/extremist groups, as revealed by their sites on the so-called dark web, has also grown.

This study presents a review of the techniques used to detect and process the content of terrorist/extremist sites on the dark web. Forty of the most relevant data sources were examined, and various techniques were identified among them.

Based on this review, it was found that methods of feature selection and feature extraction can be used as topic modeling with content analysis and text clustering.

At the end of the review, present the current state-of-the- art and certain open issues associated with Arabic dark Web content analysis.

Agriculture is the backbone of a country, contributing more than half of the sector of economy throughout the world. The need for precision agriculture is essential in evaluating the conditions of the crops with the aim of determining the proper selection of pesticides. The conventional method of pest detection fails to be stable and provides limited accuracy in the prediction. This paper aims to propose an automatic pest detection module for the accurate detection of pests using the hybrid optimization controlled deep learning model.

The paper proposes an advanced pest detection strategy based on deep learning strategy through wireless sensor network (WSN) in the agricultural fields. Initially, the WSN consisting of number of nodes and a sink are clustered as number of clusters. Each cluster comprises a cluster head (CH) and a number of nodes, where the CH involves in the transfer of data to the sink node of the WSN and the CH is selected using the fractional ant bee colony optimization (FABC) algorithm. The routing process is executed using the protruder optimization algorithm that helps in the transfer of image data to the sink node through the optimal CH. The sink node acts as the data aggregator and the collection of image data thus obtained acts as the input database to be processed to find the type of pest in the agricultural field. The image data is pre-processed to remove the artifacts present in the image and the pre-processed image is then subjected to feature extraction process, through which the significant local directional pattern, local binary pattern, local optimal-oriented pattern (LOOP) and local ternary pattern (LTP) features are extracted. The extracted features are then fed to the deep-convolutional neural network (CNN) in such a way to detect the type of pests in the agricultural field. The weights of the deep-CNN are tuned optimally using the proposed MFGHO optimization algorithm that is developed with the combined characteristics of navigating search agents and the swarming search agents.

The analysis using insect identification from habitus image Database based on the performance metrics, such as accuracy, specificity and sensitivity, reveals the effectiveness of the proposed MFGHO-based deep-CNN in detecting the pests in crops. The analysis proves that the proposed classifier using the FABC+protruder optimization-based data aggregation strategy obtains an accuracy of 94.3482%, sensitivity of 93.3247% and the specificity of 94.5263%, which is high as compared to the existing methods.

The proposed MFGHO optimization-based deep-CNN is used for the detection of pest in the crop fields to ensure the better selection of proper cost-effective pesticides for the crop fields in such a way to increase the production. The proposed MFGHO algorithm is developed with the integrated characteristic features of navigating search agents and the swarming search agents in such a way to facilitate the optimal tuning of the hyperparameters in the deep-CNN classifier for the detection of pests in the crop fields.