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Surveillance is the emerging concept in the current technology, as it plays a vital role in monitoring keen activities at the nooks and corner of the world. Among which moving object identifying and tracking by means of computer vision techniques is the major part in surveillance. If we consider moving object detection in video analysis is the initial step among the various computer applications. The main drawbacks of the existing object tracking method is a time-consuming approach if the video contains a high volume of information. There arise certain issues in choosing the optimum tracking technique for this huge volume of data. Further, the situation becomes worse when the tracked object varies orientation over time and also it is difficult to predict multiple objects at the same time. In order to overcome these issues here, we have intended to propose an effective method for object detection and movement tracking. In this paper, we proposed robust video object detection and tracking technique. The proposed technique is divided into three phases namely detection phase, tracking phase and evaluation phase in which detection phase contains Foreground segmentation and Noise reduction. Mixture of Adaptive Gaussian (MoAG) model is proposed to achieve the efficient foreground segmentation. In addition to it the fuzzy morphological filter model is implemented for removing the noise present in the foreground segmented frames. Moving object tracking is achieved by the blob detection which comes under tracking phase. Finally, the evaluation phase has feature extraction and classification. Texture based and quality based features are extracted from the processed frames which is given for classification. For classification we are using J48 ie, decision tree based classifier. The performance of the proposed technique is analyzed with existing techniques k-NN and MLP in terms of precision, recall, f-measure and ROC.

The purpose of this article is to investigate on changes of the microbial load and the chemical and physical properties of date fruits stored for 6 months under two different temperatures.

A composite sample of 100 kg date fruits from the Khalas variety, season 2019, was collected from the local market in Al Ahsa Province, Saudi Arabia, packaged in 1 kg lots, stored at room and refrigerator temperatures and the microbial contamination and the chemical and physical properties monitored over a period of six months of storage. Total bacteria, lactic acid bacteria, Enterobacteriaceae, yeasts and molds were counted and representatives of yeast and mold contaminants were identified using morphological, physiological and molecular typing techniques. Changes in the color and texture of the samples were also monitored during storage.

The yeasts detected were two strains of each of Lachancea thermotolerans and Rhodosporidiobolus fluvialis and one strain of Cystofilobasidium lacus-mascardii. For molds, one strain of each of Aspergillus niger, Aspergillus flavus, Penicillium chrysogenum and Aspergillus caespitosus have been detected. No significant growth of these microorganisms was observed, but enough load persisted during storage that makes the samples not meeting the microbiological standards. There were significant changes in the color and texture of the fruits during storage.

These findings add important information that can help producers and processors to improve quality and promote marketing of date fruits, especially to international markets.

This work presents a system based on an ensemble of Convolutional Neural Networks (CNNs) and descriptors for bioimage classification that has been validated on different datasets of color images. The proposed system represents a very simple yet effective way of boosting the performance of trained CNNs by composing multiple CNNs into an ensemble and combining scores by sum rule. Several types of ensembles are considered, with different CNN topologies along with different learning parameter sets. The proposed system not only exhibits strong discriminative power but also generalizes well over multiple datasets thanks to the combination of multiple descriptors based on different feature types, both learned and handcrafted. Separate classifiers are trained for each descriptor, and the entire set of classifiers is combined by sum rule. Results show that the proposed system obtains state-of-the-art performance across four different bioimage and medical datasets. The MATLAB code of the descriptors will be available at https://github.com/LorisNanni.

This paper aims to investigate the feasibility of adding macro-textures to triangle meshes for additive manufacturing (AM) focusing on possible time and quality issues in both software processing and part fabrication.

A demonstrative software tool was developed to apply user-selected textures to existing meshes. The computational procedure is a three-dimensional extension of the solid texturing method used in computer graphics. The tool was tested for speed and quality of results, considering also the pre- and post-processing operations required. Some textured meshes were printed by different processes to test build speed and quality.

The tool can handle models with realistic complexity in acceptable computation times. Parts are built without difficulties or extra-costs achieving a good aesthetic yield of the texture.

The tool cannot reproduce sample patterns but requires the development of a generation algorithm for different type of textures. Mesh processing operations may take a long time when very fine textures are added to large parts.

Direct texturing can help obtain parts with aesthetic or functional textures without the need for surface post-treatments, which can be especially difficult and expensive for plastic parts.

The proposed method improves the uniformity and consistency of textures compared to existing approaches, and can support future systematic studies on the detail resolution of AM processes.

White maize-based porridge is a staple food for about 80 per cent consumers in South Africa and in other sub-Saharan African countries contributing significantly to the diet of rural population in developing countries. White maize is deficient in some amino acids and over-dependency on its porridge may lead to high prevalence of malnutrition-related health conditions. Moringa oleifera (MO) and termite (Macrotermes falciger) are known to contain substantial amount of protein. The purpose of this study was to determine the effect of powders from MO leaves and termite on the nutritional and sensory properties of instant maize porridge.

Inclusion of MO and termite powder in instant maize porridge, using different treatments were considered using a completely randomised design. Factor levels were control (maize flour) cooked, blanched and uncooked MO samples. Data were analysed using SPSS version 23.

Protein content of fortified instant maize porridge (FMP) significantly increased from 10.0 to 21.2 per cent compared to unfortified porridge, and this could be attributed to the substitution effect, as fresh uncooked MO leaves are reported to be high in protein. Mineral content of FMP was higher in zinc, iron, calcium and magnesium. Calcium values of FMP were higher (276.8 mg/100 g) compared to unfortified porridge (7.1 mg/100 g). Upon the addition of MO leaves and termite powder, the zinc content increased from 3.4 mg/100to 7.6 mg/100 g. Higher iron values (27.9-36.9 mg/100 g) were observed among fortified samples. The sensory result showed that control sample had higher acceptance than the fortified samples (p = 0.02). Sensory analysis showed that among fortified porridges, blanched sample was rated high for colour and texture, cooked sample was higher in taste and uncooked sample was higher for aroma. Control sample had higher acceptance than fortified porridges for taste. The results of this study showed that the addition of MO leaves and termite powder to instant maize porridge has led to a substantial increase in the nutritional value of FMP.

This study was carried out to develop instant maize porridge fortified with MO leaves and termite powder suitable for infants, pregnant mothers and other maize consumers. The author aimed at improving the nutritional content of instant maize porridge by combining it with MO leaves and termite powders. The results showed that the addition of MO leaves and termite powders to instant maize porridge has led to a substantial increase in the nutritional value of FMP. Therefore, powder from MO leaves and termites could be used in complementary foods to increase protein and mineral contents.

The major objective of this research work was to evaluate various physico-chemical characteristics, such as, chemical composition, antioxidant capacity, objective color and texture profile analysis (TPA) of the wheat flour/chickpea flour (CF) blends, so that nutritious baked products could be consumed by the type-2 diabetic persons.

Wholegrain wheat flour (WGF) and white wheat flour (WWF) were substituted with CF at 0 to 40% levels. These wheat flour/CF blends were analyzed for proximate composition, the prepared dough and baked breads were tested for objective color, antioxidant capacity as trolox equivalent antioxidant capacity (TEAC), malondialdehyde (MDA) and total phenolic content (TPC) and TPA.

WGF had the highest TEAC (117.42 mM/100g) value, followed by WWF (73.98 mM/100g) and CF (60.67 mM/100g). TEAC, MDA and TPC values varied significantly among all the three flour samples.

Inclusion of whole chickpea (without dehulling) flour in such type of blends would be another interesting investigation during the future research studies.

These research findings have a great potential for the production of these baked products for human consumption on an industrial scale.

Production of breads using wheat flour and CF blends would benefits the consumers.

Production of Arabic and pan breads using wheat flour and CF blends would, therefore, combine the benefits of both the needed proteins of plant origin and the health-promoting bioactive compounds, in a most sustainable way for the consumers.

An accurate detection of overhead ground wire under open surroundings with varying illumination is the premise of reliable line grasping with the off-line arm when the inspection robot cross obstacle automatically. This paper aims to propose an improved approach which is called adaptive homomorphic filter and supervised learning (AHSL) for overhead ground wire detection.

First, to decrease the influence of the varying illumination caused by the open work environment of the inspection robot, the adaptive homomorphic filter is introduced to compensation the changing illumination. Second, to represent ground wire more effectively and to extract more powerful and discriminative information for building a binary classifier, the global and local features fusion method followed by supervised learning method support vector machine is proposed.

Experiment results on two self-built testing data sets A and B which contain relative older ground wires and relative newer ground wire and on the field ground wires show that the use of the adaptive homomorphic filter and global and local feature fusion method can improve the detection accuracy of the ground wire effectively. The result of the proposed method lays a solid foundation for inspection robot grasping the ground wire by visual servo.

This method AHSL has achieved 80.8 per cent detection accuracy on data set A which contains relative older ground wires and 85.3 per cent detection accuracy on data set B which contains relative newer ground wires, and the field experiment shows that the robot can detect the ground wire accurately. The performance achieved by proposed method is the state of the art under open environment with varying illumination.

This paper aims to examine the geochemical change experienced by laterites in Kerala, India, subjected to tropical monsoonal climate. These sediments are underlain by hard rock. The source rock characteristics have a major stake on the ultimate composition of sediments, as also the climatic conditions which an area experience.

Core samples have been obtained from several locations in a lateritic plateau. The upper portions of the borehole cores are composed of the lateritic hard cap, followed by lateritic soils. The soil samples were subjected to sediment texture analysis and XRF analysis (Bruker S4 Pioneer Sequential Wavelength-Dispersive XRF) for the determination of major elements ((in oxide form).

Major element geochemistry has revealed the following order of relative proportions of elements (in oxide form) SiO₂ > Al₂O₃ > Fe₂O₃ > TiO₂ >> Na₂O > P₂O₅ > CaO > K₂O > MgO > MnO. Even though the concentrations of SiO₂, Al₂O₃ and Fe₂O₃ contribute 90% of major element chemistry, there is no significant correlation found for these elements within themselves or with others.

Microscale movement of elements could not be characterised in this study. This requires access to an electron probe micro analyzer.

The practical implication of tropical weathering is that enhanced chemical leaching leads to movement of most elements out of the system, except for Al, leading to the possible formation of bauxite, or aluminous laterite.

The weathered products in this study provide livelihood sustenance for many of the local households, through manual production of laterite bricks, which are used in construction.

The indices of the intensity of chemical alteration/weathering like chemical index of alteration (CIA), chemical index of weathering (CIW) and weathering index of parker (WIP) reveal that the sediments indicate intense weathering of the source area prior to being deposited in the present location. This indicates enhanced monsoonal activity in the provenance areas, than that obtained today.

Unconventional machining processes, particularly ultrasonic vibration cutting (UVC), can overcome such technical bottlenecks. However, the precise mechanism through which UVC affects the in-service functional performance of advanced aerospace materials remains obscure. This limits their industrial application and requires a deeper understanding.

The surface integrity and in-service functional performance of advanced aerospace materials are important guarantees for safety and stability in the aerospace industry. For advanced aerospace materials, which are difficult-to-machine, conventional machining processes cannot meet the requirements of high in-service functional performance owing to rapid tool wear, low processing efficiency and high cutting forces and temperatures in the cutting area during machining.

To address this literature gap, this study is focused on the quantitative evaluation of the in-service functional performance (fatigue performance, wear resistance and corrosion resistance) of advanced aerospace materials. First, the characteristics and usage background of advanced aerospace materials are elaborated in detail. Second, the improved effect of UVC on in-service functional performance is summarized. We have also explored the unique advantages of UVC during the processing of advanced aerospace materials. Finally, in response to some of the limitations of UVC, future development directions are proposed, including improvements in ultrasound systems, upgrades in ultrasound processing objects and theoretical breakthroughs in in-service functional performance.

This study provides insights into the optimization of machining processes to improve the in-service functional performance of advanced aviation materials, particularly the use of UVC and its unique process advantages.

This study aims to analyze the lean supply chain management (LSCM) strategy's role as a mechanism to address technology uncertainty and provide organizations with competitive advantage.

An empirical study was conducted of 276 Spanish focal firms in industrial sectors that occupy an intermediate position in the supply chain to investigate the influence of technology uncertainty on LSCM implementation and the latter's effect on operational performance and competitiveness. A covariance-based structural equation model (CB-SEM) was used to test three hypotheses.

Technological uncertainty encourages progress in the implementation of lean throughout the supply chain and so is a mechanism that not only brings stability to the focal company but also improves its performance and puts it in a better competitive position.

Managers are suggested to consider the strategic integration with supply chain partners and the establishment of long-term relationships based on trust and commitment advocated by LSCM to enhance organizations' capabilities and effectively and flexibly respond to technological changes.

This study focuses on the effects of environmental uncertainty on the supply chain. The past literature has focused on the behavior of individual firms to deal with uncertainty, but this work shifts the level of analysis to the supply chain. Therefore, the strategic change to deal with what is happening in the environment is now switched to the supply chain level.