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Law offenders take advantage of face masks to conceal their identities and in the present time of the COVID-19 pandemic wearing face masks is a new norm which makes it a daunting task for the investigation agencies to identify the offenders. To address the issue of detection of people wearing face masks using surveillance cameras, we propose a novel face mask vision system that is based on an improved tiny YOLO v4 object detector. The face masks detection network of the proposed vision system is developed by integrating tiny YOLO v4 with spatial pyramid pooling (SPP) module and additional YOLO detection layer and tested and validated on a self-created face masks detection dataset consisting of more than 50,000 images. The proposed tiny YOLO v4-SPP network achieved a mAP (mean average precision) value of 64.31% on the employed dataset which was 6.6% higher than tiny YOLO v4. Specifically, for detection of the presence of a small object like a face mask on the face region, the proposed tiny YOLO v4-SPP based vision system achieved an AP (average precision) of 84.42% which was 14.05% higher than the original tiny YOLO v4 thus, ensuring that the proposed network is capable of accurate detection of a mask on the face region in real-time surveillance applications where visibility of complete face area is a guideline.

The swift proliferation in traffic across computer networks has led to certain types of attacks and intrusions, raising a serious global concern of information security. Attack detection is possible by monitoring and observing occurrences in intrusion detection systems, however these systems tend to suffer from problem of curse of dimensionality, high false alarm rate, high time complexity and low detections. In order to overcome these limitations, we propose a feature reduced intrusion detection system employing optimized SVM as a classifier. Feature Reduction has been performed by fusing ranked features from information gain and chi square in a way that it has helped in retaining only important features and discarding the rest. The study further proposes an optimized version of SVM classifier using Big Bang Big Crunch (BBBC) optimization that simulates the big bang and big crunch theory of evolution of universe. BBBC has helped in finding an optimal set of SVM parameters quickly that are further used for classification. We also experimented with a number of fitness functions for gauging the performance of IDS and propose a new fitness function based on the weighted F1 score of various traffic classes. KDD-99 dataset has been used for experimentation and analysis. The paper further experiments the effects of under-sampling and oversampling of various traffic classes on the proposed IDS performance and recommends that maintaining a desired ratio for a mix of under-sampling and over-sampling of desired classes produces the best results.

The present study was conducted to evaluate the physico-chemical, nutritional and functional properties of pathernakh pear grown in Himachal Pradesh. The physical properties (weight, length, width and colour), chemical and nutritional properties of the pathernakh pear including moisture, ash, total sugar, reducing sugar and minerals of pathernakh pear were studied. Moreover, the pear variety was characterized for functional components with respect to total phenols, ascorbic acid and fiber analysis. The results indicated that the fruit is a good supplier of sugars, minerals, ascorbic acid and polyphenols. The edible portion of the fruit possess a high level of fiber content. Additionally, textural properties in terms of firmness of the fruit were also studied. The overall results concluded that pathernakh pear could be best for consumption owing to superior nutritional and functional properties that has many health benefits.

Staphylococcus aureus is a notorious human pathogen that causes superficial and invasive infections both in nosocomial and community-acquired settings. The prevalence of staphylococcal infections became more challenging after emerging resistance against topical antibiotics. S. aureus evolved resistance to β-lactam antibiotics due to modification and expression of penicillin-binding proteins (PBP), inactivation of drug by β-lactamase synthesis, limiting uptake of drug by biofilm formation, and reducing uptake by expression of efflux pump. The wave of resistance was first observed in penicillin by β-lactamase production and PBPs modification. The second wave of resistance emerged to methicillin by appearing methicillin-resistant S. aureus (MRSA) strains. Cephalosporin has long been used as the last resort for preventing MRSA infections, but resistant strains appeared during treatment. In progression to control MRSA or related infections, carbapenems have been used but strains developed resistance. S. aureus is among the high-priority resistance organisms that need renewed efforts for the research and development of new antibiotics and innovative preventive approaches. However, a lot of toiling is involved in devising an effective treatment against drug resistant S. aureus. This chapter aim is to retrospectively determine the progression of resistance in S. aureus, against different β-lactam antibiotics and their challenges of medication.

In recent years, analysis and interpretation of video sequences to detect and track objects of interest had become an active research field in computer vision and image processing. Detection and tracking includes extraction of moving object from frames and continuous tracking it thereafter forming persistent object trajectories over time. There are some really smart techniques proposed by researchers for efficient and robust detection or tracking of objects in videos. A comprehensive coverage of such innovative techniques for which solutions have been motivated by theories of soft computing approaches is proposed. The main objective of this research investigation is to study and highlight efforts of researchers who had conducted some brilliant work on soft computing based detection and tracking approaches in video sequence. The study is novel as it traces rise of soft computing methods in field of object detection and tracking in videos which has been neglected over the years. The survey is compilation of studies on neural network, deep learning, fuzzy logic, evolutionary algorithms, hybrid and recent innovative approaches that have been applied to field of detection and tracking. The paper also highlights benchmark datasets available to researchers for experimentation and validation of their own algorithms. Major research challenges in the field of detection and tracking along with some recommendations are also provided. The paper provides number of analyses to guide future directions of research and advocates for more applications of soft computing approaches for object detection and tracking approaches in videos. The paper is targeted at young researchers who will like to see it as platform for introduction to a mature and relatively complex field. The study will be helpful in appropriate use of an existing method for systematically designing a new approach or improving performance of existing approaches.

Staphylococcus aureus is an opportunistic pathogen, responsible for superficial and invasive infections both in nosocomial and community-acquired settings. The incidences of infection have become more problematic attributable to emerging drug resistance and biofilm formation. These challenges suggest the need for new antimicrobial agents against S. aureus. In present work, we purified a fungal xenobiotic (FI3) which elicits a potent antimicrobial activity against a list of tested microbes including methicillin sensitive (MSSA) and methicillin resistance (MRSA) S. aureus. The cell growth of MSSA and MRSA were completely ceased with the 1× minimum inhibitory concentration (MIC); 32 µg/mL and 128 µg/mL, respectively. The cell viability severely decreased within 90 min, due to disturbance of membrane homeostasis. This bactericidal effect was enhanced at lower pH (pH 4) with a speculation to retain positive charge. The FI3 potently disrupts biofilm adherence at 64 µg/mL and found to be a safe with no toxic effect on mammalian tissue. FI3 also leads to increase the potency of tested antibiotics. Taken together, we established that FI3 has a potent antimicrobial activity against tested microbes and safer to human tissue. It may be proven a leading molecule for the treatment of bacterial infections.

Chayote [Sechium edule (Jacq) Swartz] is an underutilized, nutritious vegetable, which is reported to contain a variety of phytochemicals. A process was designed to increase acceptance of C...

Face mask detection is a challenging research problem of computer vision because of the small sized area of face mask. The unavailability of proper datasets makes this problem even harder to crack. To address this bottleneck, we propose a novel face masks detection dataset consisting of 52,635 images with more than 50,000 tight bounding boxes and annotations for four different class labels namely, with masks, without masks, masks incorrectly, and mask area, which makes it a novel contribution for variety of face masks classification and detection tasks. Further, this dataset is tested with eight variants of the YOLO algorithm to determine its effectiveness. On the proposed dataset, original YOLO v4 achieved a mAP value of 71.69 % which was highest among all the original YOLO variants, tiny YOLO v4 achieved a mAP value of 57.71 % which was highest among all tiny variants. To propose new face masks detection algorithms that can perform with high accuracy in a limited computational resources environment, we selected four tiny variants of the YOLO algorithm and proposed new architectures modifications in their feature extraction networks that increased the overall performance and specifically, improved mAP by 4.12 % for tiny YOLO v3 and 2.54 % for tiny YOLO v4.

This paper proposes an Internet of Things (IoT) and deep learning-inspired multi-model system for detection, dissemination, and monitoring of Active Fire Locations(AFL) in agricultural activities. The IoT module of the proposed system works on the fusion of IoT sensors-based detectors and deep learning-based detectors. Fuzzy logic is used for the fusion of various sensors and providing real-time detection and location of AFL. The deep learning detector implements IP camera-based MobilenetV2 architecture for accurate and long-distance detections trained on a novel self-created dataset. The proposed framework also provides a software module for monitoring and tracking of various AFL. The software comes with several features like automatic extraction of fire locations from remote sensing sites, assigning active fire locations to multiple stakeholders, extracting farmers' names indulged in burning, automatic sending a notification to government agencies, and provisions for citizens centric participation. The results of the proposed framework are quite encouraging.

Background subtraction (BS) is one of most commonly used methods for detection of moving objects in videos that works by subtracting current frame from a background frame. Effective background modeling and threshold plays a crucial role in BS and can govern accuracy and preciseness of object boundaries. This paper proposes a fuzzy entropy based approach modified BS algorithm for moving object detection with Kalman tracker. The standard BS method has been enhanced using concept of fuzzy 2-partition entropy and big bang big crunch optimization (BBBCO). BBBCO has been used to enhance standard BS algorithm for extracting various parameters required in BS algorithm by framing the problem of parameters detection as optimization problem which is solved using concept of fuzzy partition entropy. The proposed algorithm generates optimal threshold values along with various other measures for background modeling. The detected objects are further tracked using Kalman filter based tracker. The evaluation of proposed method has been done on videos from benchmark datasets and statistical parameters have been calculated. The method is also compared with standard BS and another recent study in the field. The results show promise of the proposed method.

Enhancement of hazy images or video is challenging task because of low contrast exhibited in them. Global contrast stretching methods have been successful in restoring contrasts but problems like overcompensation, truncation of pixel values amounting to loss of information tends to creep in. Artifacts may be introduced and images may loose its colorfulness. This paper presents an evolutionary enhancement method for restoring contrast in images or videos while preserving its colorfulness and brightness. The study proposes a novel histogram equalization method inspired by principles of water cycle algorithm. The proposed method first smoothes Y channel of YCbCr color space and divides input frame into two components using Otsu's 2D thresholding. A set of weighing constraints have been formulated and applied to both components individually in a controlled manner. Water cycle algorithm has been employed to exploit an optimal value of weighing factors for enforcement of constraints on individual components. A three dimension objective function has been designed to suitably perform equalization and control enhancement process. Experimental results show that proposed method is effective in removing haze like patterns in images and videos.

Background subtraction (BS) is one of the most commonly used methods for detecting moving objects in videos. In this task, moving objectpixels are extracted by subtracting the current frame from a background frame. The obtained difference is compared against a threshold value to classify pixels as belonging to the foreground or background regions. The threshold plays a crucial role in this categorization and can impact the accuracy and preciseness of the object boundaries obtained by the BS algorithm. This paper proposes an approach for enhancing and optimizing the performance of the standard BS algorithm. This approach uses the concept of fuzzy 2-partition entropy and Big Bang–Big Crunch Optimization (BBBCO). BBBCO is a recently proposed evolutionary optimization approach for providing solutions to problems operating on multiple variables within prescribed constraints. BBBCO enhances the standard BS algorithm by framing the problem of parameter detection for BS as an optimization problem, which is solved using the concept of fuzzy 2-partition entropy. The proposed method is evaluated using videos from benchmark datasets and a number of statistical metrics. The method is also compared with standard BS and another recently proposed method. The results show the promise of the proposed method.

Ginger is an important spice crop and India is one of the leading producer and exporter of ginger in the world. Ginger is widely used around the world in food as a spice both in fresh and dried form which adds flavour to the meal by creating spicy pungent taste. The chemical components of the ginger rhizome vary considerably depending on the location of cultivation and postharvest treatments. Ginger contains polyphenol compounds such as gingerol and its derivatives like zingiberone, bisabolene, camphene, geranial, linalool, borneol and oleoresin (combination of volatile oils and resin) that accounts for its characteristic aroma and therapeutic properties. Fresh ginger are perishable in nature and are spoiled due to improper handling, growth of spoilage microorganisms, susceptibility to rhizome rot, wilting and sprouting, action of naturally occurring enzymes, chemical reactions and structural changes during storage. Keeping in mind the low shelf-life of fresh ginger and inadequate facility for their modern storage leading to distress sale, value addition could be a viable alternative which will fetch remunerative price to the growers. The present scenario, nutritional importance, postharvest management, value added products of ginger have been discussed in detail in the review.

Appetized ginger plum leather was prepared by using different combinations of ginger and plum pulp with varying concentrations of appetizing mixture. The TSS of ginger and plum pulp were raised to 25oB by adding sugar and 1.0 to 2.5% appetizing mixture, followed by drying thin layers in dehydrator (55±2oC) to 12-14% moisture content. The standardization of most palatable recipe was done by evaluating sensory properties and highest score was obtained by ginger: plum (50:50) and 1.5% appetizing mixture. The appetized leather contained comparatively higher amount of ascorbic acid (13.16mg/100g), total phenols (55.89mg/100g) and antioxidant activity (72.94%). The leather was found most stable when packaged in laminated aluminium pouches during storage. The leather did not exhibit appreciable changes in titratable acidity, ascorbic acid, total sugars, phenols and antioxidant activity after 6 months. Thus the appetized ginger plum leather can be stored under ambient storage after packing in aluminium laminated pouches.

Background: The purpose of this review paper was to explore the components and their respective health effects and safety aspects regarding the consumption of diet drinks (DDs).Methods: A wide variety of the relevant publications (published before 2018) were identified through searching electronic databases (ScienceDirect, PubMed, SciELO, Google Scholar, Springer Link, and ResearchGate) on the basis of different keywords such as diet drink, market status, consumption pattern, composition, health effects, and regulations related to DDs. After the search of suitable literature, 139 papers were screened and reviewed.Results: Numerous brands of DDs have been introduced in the market along with a wide variety of modifications in order to attract the consumers of all age groups along with their respective professions. The major components of DDs are caffeine, taurine, ginseng, guarana, sodium, potassium, and sweeteners that contribute to the good taste, stimulating effect, increased performance and cognitive function, and reduced stress. However, ingestion of DDs over a prolonged course of time can pose multiple deleterious effects, i.e., intoxication, affective disorder, hypertension, reproductive toxicity, and neurological disorders. Consequently, different regulatory bodies of respective countries have formulated and enforced rules and regulations regarding the composition, consumption, labeling, distribution, and sales of DDs.Conclusions: An awareness regarding the components, consumption, and health effects is greatly required in the present era, and authors have tried to fill this gap.Key teaching pointsRise in socio-economic status, health awareness and change in dietary lifestyle are the major factors that fuel the demand of diet drinks; which has been witnessed by a drastic increase in the consumption pattern of the diet drinks among young to middle-age adults since last decade.A wide variety of the ingredients are used in the preparation of diet drinks including caffeine, ginseng (Panax ginseng), guarana (Paullinia cupana), taurine, artificial sweeteners, B Vitamins, sodium, potassium etc.Diet drinks are generally served cold and contain high levels of caffeine and other artificial sweeteners; which based on dosage, have been known to be beneficial as well as harmful.These ingredients possess numerous health benefits including anti-hypertensive, anti-oxidant, anti-cancer properties, improved metabolic functions, stress reduction and enhanced endurance, exercise and performance and increased cognitive function.A wide range of the adverse health effects such as nervousness, sleeplessness, behavioral changes, decreased appetite, heart palpitations, nausea, vomiting, dehydration etc. is being possessed by diet drinks upon the ingestion of these drinks over a prolonged course of time.Regulatory bodies should determine the safe limits of all ingredients for different age groups to get maximum health benefits.

A numerous types of tree flowers are present in nature and few of them such as Rhdodendron, Bauhinia, Mahua, Gulmohar, Palash, Sesbania, Woodfordia, Simbal, etc. are being utilized traditionally as food and medicine by the localities of India. These flowers are rich in phytochemical (flavonoids, anthocyanins, phenolic acids, carotenoids, tannins, saponins) and possessing numerous health benefits (antioxidant activity, anti-inflammation, anti-cancer activity, anti-diabetic activity, hepatoprotective activity). However, because of the low availability (i.e. short blooming period and at limited places) and poor post-harvest life, these flowers are commonly utilized by the local people as food and medicines during their respective flowering times only. A few attempts have been made toward the utilization of some tree flowers (Mahduca longifolia, Rhododendron arboretum), but others are still unexplored and need to be exploited to achieve food and nutritional security as well increase the opportunity of employment and improvement in the socio-economic status of the local tribes. Therefore, to achieve this, the present review was aimed to review and document the status of common edible tree flowers, their phytochemicals potential and, health benefits as well as their utilization as food and medicines.

Chayote (Sechium edule) is a perennial climbing plant, belonging to the Cucurbitaceae family and is neglected or underutilized as a food or as a raw material in the food industry because of its bland taste. This bland taste can be overcome by blending with a suitable substrate such as sugarcane juice along with natural flavouring. However, this blending represents a preservation problem for the spiced juice, since both chayote juice and sugarcane juice are prone to microbial attack. Therefore, an attempt was made in the present study to preserve the spiced chayote juice by employing different hurdles i.e. pasteurization temperature (70 °C and 80 °C) and pasteurization time (10 and 20 min) followed by the addition of different concentrations of potassium metabisulphite (KMS) (200, 300 and 400 ppm). The samples so prepared were filled into glass bottles and stored under refrigeration for forty-five days. As the storage period proceeded there were significant decreases in pH, total protein content, total phenolic compounds, antioxidant activity, reducing power, metal chelation, tannin content, ascorbic acid content and overall acceptability, whereas there were significant increases in TSS (total soluble solids), titratable acidity, reducing sugars, total sugar, TPC (total plate count) and MIC (minimum inhibitory concentration). A cluster analysis of the data revealed that the pasteurization temperature exerted the most significant effect on the physicochemical and sensory characteristics of the spiced chayote juice as compared to the pasteurization time and KMS concentration, which failed to fall into the separate and respective clusters. In a nutshell, it was concluded that spiced chayote juice can be successfully preserved using hurdle technology (pasteurizing at 70 °C for 10 min followed by the addition of 400ppm KMS). Resumo Chuchu (Sechium edule) é uma trepadeira perene da família das Cucurbitaceae que está ignorada ou subutilizada como alimento ou como uma matéria-prima na indústria de alimentos, devido ao seu gosto brando. Este gosto brando pode ser superado fazendo misturas com um substrato apropriado, como caldo de cana e algum flavorizante natural. Contudo, esta mistura seria um problema para a preservação, desde que ambos − o suco de chuchu e o caldo de cana − são susceptíveis ao ataque microbiano. Portanto, neste estudo, foi feita uma tentativa de preservar o suco de chuchu com especiarias pelo emprego de diferentes barreiras, isto é, a temperatura de pasteurização (70 °C e 80 °C) e o tempo (10 e 20 min), seguidas pela adição de diferentes concentrações de metabissulfito de potássio (KMB) (200, 300 e 400 ppm). As amostras foram embaladas em vidros e armazenadas sob refrigeração para quarenta e cinco dias. Com o avanço do tempo de armazenamento, houve decréscimos significantes nos valores de pH, proteína total, compostos fenólicos totais, atividade antioxidante, poder redutor, quelação de metais, conteúdo de taninos, ácido ascórbico e na aceitação geral, e aumentos significantes em SST (sólidos solúveis totais), acidez titulável, açúcar redutor, açúcar total, contagem total em placas (TPC) e a concentração mínima inibitória. Uma análise de cluster dos dados revelou que a temperatura de pasteurização causou o efeito mais significante nas características físico-químicas e sensoriais do suco de chuchu com especiarias, quando comparada com o tempo de pasteurização e a concentração de KMB, que deixou de cair nos clusters separados e respectivos. Foi concluído que o suco de chuchu com especiarias podia ser preservado com sucesso através da tecnologia de barreiras, com pasteurização a 70 °C por 10 min seguida pela adição de 400 ppm de KMB.

Growth of mobile-based applications and traffic on web has made the role of schedulers in communication networks very challenging. The continuous pressure asserted by these applications is so large that even versatile WiMAX networks are facing stiff challenges in fair distribution of resources to these real- and non-real-time applications. Maintenance of fairness and flagrant quality of service levels among various applications is only possible with adaptive and intelligent scheduling mechanism. Design of such system is only possible if artificial intelligence is embedded in base station scheduler. This paper proposes mechanism for allocation of resources in WiMAX networks utilizing fuzzy logic principles. The proposed mechanism guarantees that latency and throughput requirements of real- and non-real-time traffic classes are met. Variations in incoming traffic have been exploited and used in decision making for offering bandwidth to maintain effective performance levels for all traffic classes. The proposed scheduler considers requests from subscribers and exploits powers of fuzzy logic to extract new weights for queues serving various traffic classes in a WiMAX network on the basis of most recent values of latency, throughput and share of traffic. The scheduling framework has been rigorously tested by performing versatile experiments for ensuring quality of service levels under hard hitting conditions. Fairness of scheduler has also been explored and results obtained are quite promising.

Escalation of multimedia devices in recent times has prompted human appetite for affluent multimedia applications. The QoS demands of these applications can only be satisfied by more and more powerful networks. WiMAX (Worldwide Interoperability for Microwave Access) networks are best suitable to serve these real-time applications; however, quantity of non-real time and best-effort traffic also cannot be neglected. Fair and efficient distribution of resources is always challenging in these networks and schedulers need to be adaptive and passable in order to guarantee appreciable performance levels. This paper introduces a quality of service mechanism based on concepts of fuzzy logic for scheduling different traffic classes in WiMAX networks. The proposed framework simplifies fair allocation of resources to real as well as non-real-time traffic classes. The scheduling ploy is developed by modifying weighted fair queuing algorithm to work adaptively by changing weights of queues serving real- and non-real-time traffic using fuzzy logic inferencing capabilities. New weights are calculated adaptively and these weights decide amount of bandwidth allocated to a particular traffic class. The proposed system works on three parameters: amount of real- and non-real-time traffic in queues, throughput requirement for non-real-time flows, and latency requirement of real-time data. Efficiency and fairness of proposed method is evaluated by performing versatile experiments and comparisons with established practices. The results obtained as implication of proposed work are quite promising.

Staphylococcus aureus is an opportunistic pathogen, responsible for superficial and invasive infections both in nosocomial and community-acquired settings. The incidences of infection have become more problematic attributable to emerging drug resistance and biofilm formation. These challenges suggest the need for new antimicrobial agents against S. aureus. In present work, we purified a fungal xenobiotic (FI3) which elicits a potent antimicrobial activity against a list of tested microbes including methicillin sensitive (MSSA) and methicillin resistance (MRSA) S. aureus. The cell growth of MSSA and MRSA were completely ceased with the 1× minimum inhibitory concentration (MIC); 32 µg/mL and 128 µg/mL, respectively. The cell viability severely decreased within 90 min, due to disturbance of membrane homeostasis. This bactericidal effect was enhanced at lower pH (pH 4) with a speculation to retain positive charge. The FI3 potently disrupts biofilm adherence at 64 µg/mL and found to be a safe with no toxic effect on mammalian tissue. FI3 also leads to increase the potency of tested antibiotics. Taken together, we established that FI3 has a potent antimicrobial activity against tested microbes and safer to human tissue. It may be proven a leading molecule for the treatment of bacterial infections.

The fuzzy 2-partition entropy approach has been widely used to select threshold value for image segmenting. This approach used two parameterized fuzzy membership functions to form a fuzzy 2-partition of the image. The optimal threshold is selected by searching an optimal combination of parameters of the membership functions such that the entropy of fuzzy 2-partition is maximized. In this paper, a new fuzzy 2-partition entropy thresholding approach based on the technology of the Big Bang–Big Crunch Optimization (BBBCO) is proposed. The new proposed thresholding approach is called the BBBCO-based fuzzy 2-partition entropy thresholding algorithm. BBBCO is used to search an optimal combination of parameters of the membership functions for maximizing the entropy of fuzzy 2-partition. BBBCO is inspired by the theory of the evolution of the universe; namely the Big Bang and Big Crunch Theory. The proposed algorithm is tested on a number of standard test images. For comparison, three different algorithms included Genetic Algorithm (GA)-based, Biogeography-based Optimization (BBO)-based and recursive approaches are also implemented. From experimental results, it is observed that the performance of the proposed algorithm is more effective than GA-based, BBO-based and recursion-based approaches.

Ginger (Zingiber officinale Roscoe) is an important spice crop in India, which is also one of the leading producer and exporter of ginger in the world. During cultivation, the crop is severely infected by various diseases of them soft rot, yellows, Phyllosticta leaf spot, storage rot, bacterial wilt, mosaic, chlorotic fleck are important. These diseases reduce the potential yields drastically. The geographical distribution, losses, symptoms, causal organism, disease cycle, epidemiology and host resistance, cultural, biological, chemical and integrated management of above mentioned diseses have been discussed in the present paper.

In vitro approach was adopted to study the effect of salinity on survival and growth of rough lemon (Citrus jambhiri Lush.) seeds. North-West (Punjab) part of India has been facing a major problem of soil salinity for citrus orchards. Therefore, it is logical to study the salinity tolerance of common citrus rootstock, rough lemon (Citrus jambhiri Lush.), grown in the region. The seeds were treated with nine different doses of sodium salt. In all the treatments, leaves of rough lemon seedlings showed severe injury symptoms of chlorosis and necrosis while the seeds cultured in control did not show any injury. There was a significant decrease in seed germination, seedling height, internodal length, and subsequently plant weight with increasing concentration of salt. In contrast to the above characteristics, the length of primary roots increased proportionally with the increase in salt concentration in the culture media. As under stress conditions, the in vitro grown seedlings tend to increase the root length for its survival. In comparison to the control, salt treatments showed increased level of Na+ and Cl- ions in the seedlings and also resulted in a decrease of K+/Na+ ratio. Tolerance index was found minimum (100) in control and maximum in 154mM NaCl treatments after 4 and 8 weeks.Key words: Citrus, salt, sensitivity, chlorosis, sodium chloride.

Technology for utilization of seabuckthorn berries for preparation of fruit leather/bar was optimized by modifying the foam mat drying technique. The conversion of seabuckthorn juice/pulp into foam was standardized by whipping the pulp after addition of CMC @ 0-3% at 5 °C and drying the resultant foam in dehydrator (55 ± 2 °C) to a moisture content of about 12-14%. The fruit bar prepared from sulphited juice/pulp wrapped in a butter paper followed by packing in polyethylene pouches (20 g) and stored at ambient temperature (14.6-26.1 °C) experienced least changes in quality attributes like ascorbic acid (1045.7 mg/100 g vs 997.5 mg/100 g) and carotenoids (80.4 mg/100 g vs 72.3 mg/100 g) as compared to the leather made from the unsulphited pulp. Storage studies indicate that fruit bars are mildly hygroscopic (0.46-0.65) and can be stored within the RH of 46-65% at room temperature.

Studies were conducted to develop a novel product-osmotically dried and appetizing flakes from fresh ginger rhizomes keeping in view its nutritional significance. Ginger contains ascorbic acid (8.48 mg/100 g), antioxidant activity (57.45%), crude protein (2.73%) and total phenols (10.18 mg/100 g) that accounts for its characteristic aroma and therapeutic properties. Mechanically peeled rhizomes were sliced into suitable size of 3.5×0.4×0.4 cm3 followed by blanching in 1.0% lemon juice or citric acid for 15 minutes for the preparation of osmotically dried and appetized ginger flakes. The cost of production of osmotically dried ginger flakes was calculated as ` 22.64 to 22.97/200g for glass jars and ` 15.65 to ` 15.72/200g for aluminium laminated pouches for citric acid and lime juice blanching, respectively. The commercial adoption of this technology seems to be a profitable venture for efficient utilization of fresh ginger rhizome as well as enhancing the income of growers.

Pre-treatments and methods of drying for producing good quality dried bell pepper powder for use in the ready-to-eat (RTE) food products were optimized. Out of various pre-treatments used (blanching in boiling water, KMS, CA and combination of KMS + CA at different concentrations), soaking of bell pepper shreds in KMS@ 0.20 % + CA@ 0.50 % after blanching fasten the drying process (19.75 h) compared to control (22.60 h), when dried in mechanical dehydrator at 58 ± 2 °C. Blanching prior to drying improved the rate of drying and produced product with lower acidity (1.25 %). The samples (T7) treated with KMS@ 0.20 % + CA@ 0.50 % significantly (p

Soft Computing is a multifaceted technique comprising of Fuzzy Logic, Neural Network, Genetic algorithms and other Evolutionary computation. These paradigms have found wide variety of applications in the field of image processing. One of the most vital applications of image segmentation is edge detection where edge refers to the boundary between two consistent regions and edge detection is the process of detecting and finding abrupt discontinuities in an image. This paper summarizes hybrid and non-hybrid approaches for edge detection. The objective of this paper is to survey the core issues for soft computing based approaches for edge detection.

In the present study, pre-treatments have been standardized for the preparation of intermediate moisture products from papaya viz. osmotically dried slices and candy. Out of various treatments tried, the pretreatment consisting of blanching of fruit slices for 1 minute with 0.2% citric acid followed by dipping in 0.2% KMS for 20 minutes was standardized.