Extra Quality Research Articles

Sulfured FeMo carbides and nitrides catalysts upgrade extra heavy crude oil quality

In the context of the energy transition scenario, effective sulfur management is crucial. Enhancing the quality of extra heavy crude oil (EHCO) through catalytic processes, specifically hydrotreatment, is essential for reducing pollutant emissions like SOx into the atmosphere. Traditional hydrotreatment, utilizing MoS2-based catalysts typically on Al2O3 support, faces challenges with EHCO due to its elevated S and N content, which hampers catalyst efficiency. Metal carbides and nitrides exhibit promising electronic structures that confer resistance to deactivation in the presence of heteroatoms. This study compares the catalytic performances of Fe-promoted Mo sulfides, carbides, and nitrides (FeMoS(C,N)) in the thiophene hydrodesulfurization (HDS) reaction, serving as a model molecule for sulfur removal. Subsequently, we investigate the upgrading of a Venezuelan EHCO in terms of pollutant reduction, API gravity, and feedstock aromaticity. Catalysts were prepared from oxide precursors, varying the (Fe/(Fe+Mo)) atomic ratios (x = 0.00, 0.10, 0.33, 0.50, and 1.00), employing a temperature-programmed reaction protocol. Catalytic upgrading of EHCO was conducted in a stirred batch reactor, and the results were compared with a commercial CoMo-based catalyst. FeMoC(N) outperformed the commercial catalyst in sulfur removal. The elemental composition and nitrogen content of the feed remained constant; however, the sulfur content of asphaltenes decreased. Furthermore, the API gravity of crude oil increased when employing FeMoS and FeMoN catalysts, except with FeMoC, possibly linked to dealkylation reactions and the enrichment of lighter fractions with alkanes. FeMoN increased asphaltene aromaticity, while FeMoC decreased it. These results highlight the promise of FeMoC(N) as catalysts for HDS and upgrading heavy feedstocks.

The Relationship of Histamine Content in European Pilchard (Sardina pilchardus) with Freshness, Temperature, and Storage Duration

Histamine food poisoning, stemming from the consumption of certain histamine-rich fish species, such as tuna, mackerel, European pilchards, and herring, is one of the major public health issues worldwide. The present study aimed to evaluate the histamine content in fresh European pilchard (Sardina pilchardus Walbaum, 1792) of Mehdia, a coastal city in the north of Morocco. Three randomly selected batches of fresh European pilchards, each weighing 20kg, were obtained from different boats upon landing. The evolution of histamine production was monitored every 8 hours for 6 days, with one batch stored at 0°C and the other at 10°C. The organoleptic characteristics were examined considering sensory evaluation according to the rating system of European Council Regulation No. 2406/96 as common marketing standards for certain fishery products and the quality index method (QIM). The histamine content in European pilchard flesh was determined using the fluorometric method. The results indicated that the average histamine content did not exceed 5 ppm during storage at 0°C. The freshness ratings were highest during the first 3 days, corresponding to QIM values of 0 to 10 at 0°C. On days 4 and 5, the freshness ratings were on quality A, corresponding to QIM values of 11 and 12, and on the last day, they were on quality B, corresponding to a QIM value of 15 with preservation of the organoleptic quality. Statistically, a significant correlation was found between the European pilchard’s freshness and the storage duration. In contrast, this correlation between the histamine content and the storage duration was insignificant. At 10°C, the average histamine content exceeded the regulatory limit in force (100 ppm) after 32 hours of storage, and spoilage occurred on day 3. Statistical analysis revealed a strong correlation between the histamine content, storage temperature, the degree of freshness, and the duration of storage. The extra freshness quality index of European pilchard guarantees a very large margin of safety regarding histamine and can be consumed without risk. Keywords: Degree freshness, European pilchard, Histamine, Sardine, Storage, Temperature

Application of Three-Dimensional CFD Model to Determination of the Capacity of Existing Tyrolean Intake

CFD models of intakes in high-head hydropower systems are rare due to the lack of geometric data and cost of modeling. This study tests two different types of software to see how modeling can be performed in a cost-effective way with scarce input data and still have sufficient accuracy. The volume of fluid (VoF) model simulations are conducted using both ANSYS Fluent and OpenFOAM. The geometry is modelled from Google Earth satellite images, drone scanning data, and design drawings from the construction period and supported by field observations for extra quality control. From the model, both capacity parameters and flow pattern are calculated. For capacity, the Cd factor is calculated and compared with the literature. The simulations are conducted for a Tyrolean weir with rectangular bars (flat steel) in the rack. Simulated flow patterns through the rack with ANSYS Fluent and OpenFOAM are compared. OpenFOAM simulations yielded 15% to 20% higher water levels compared to the VOF model applied in Ansys Fluent. Also, when the flow rate was high, the water capture capacity calculated with ANSYS Fluent was 10% higher than that obtained with OpenFOAM. However, considering the total simulation times, modeling with OpenFOAM offered approximately 11% faster results.

Extraction methods and geographical variability influence on phenolic content, fatty acid composition and physicochemical quality, of Moroccan Picholine olive oils in the Fes-Meknes region

The study aims was to analyze the influence of extraction method and geographical origin on the quality of olive oils. Thirty samples of the "Moroccan Picholine" variety were collected from three types of crushers (hydraulic press, two-phase and three-phase) in four cities of the main Moroccan olive-growing region, Fes-Meknes, during the 2021 and 2022 harvest seasons. Oils from this region are generally of virgin and extra virgin olive oils quality, according to the International Olive Council, with high levels of oleic acid reaching up to 80.12 ± 1.94% for oils produced in Meknes, two-phase triturations. Two-phase extracted oils from Fez and Meknes show the highest MUFA/PUFA (monounsaturated fatty acids/ polyunsaturated fatty acids) ratios and lower quality parameters, indicating richness in monounsaturated fatty acids and improved oxidative stability. Polyphenol levels varied significantly between the cities of Fez, Meknes, Taounate and Taza, reaching 307.31 ± 17.44 mg/kg GAE for Fez, with notable differences (p < 0.05). In addition, chlorophyll and carotenoid levels differed significantly according to the type of extraction and the city of origin (p = 0.004 and p = 0.042, respectively). Principal component analysis identified three distinct groups based on extraction method and geographical location, highlighting the importance of these factors in obtaining olive oils with the specific characteristics sought.

Application of Raman Spectroscopy to Dynamic Binding Capacity Analysis.

Protein A affinity chromatography is a key step in isolation of biotherapeutics (BTs) containing fragment crystallizable regions, including monoclonal and bispecific antibodies. Dynamic binding capacity (DBC) analysis assesses how much BT will bind to a protein A column. DBC reduces with column usage, effectively reducing the amount of recovered product over time. Drug regulatory bodies mandate chromatography resin lifetime for BT isolation, through measurement of parameters including DBC, so this feature is carefully monitored in industrial purification pipelines. High-performance affinity chromatography (HPAC) is typically used to assess the concentration of BT, which when loaded to the column results in significant breakthrough of BT in the flowthrough. HPAC gives an accurate assessment of DBC and how this changes over time but only reports on protein concentration, requires calibration for each new BT analyzed, and can only be used offline. Here we utilized Raman spectroscopy and revealed that this approach is at least as effective as both HPAC and ultraviolet chromatogram methods at monitoring DBC of protein A resins. In addition to reporting on protein concentration, the chemical information in the Raman spectra provides information on aggregation status and protein structure, providing extra quality controls to industrial bioprocessing pipelines. In combination with partial least square (PLS) analysis, Raman spectroscopy can be used to determine the DBC of a BT without prior calibration. Here we performed Raman analysis offline in a 96-well plate format, however, it is feasible to perform this inline. This study demonstrates the power of Raman spectroscopy as a significantly improved approach to DBC monitoring in industrial pipelines.

Secured Mechanism Towards Integrity of Digital Images Using DWT, DCT, LSB and Watermarking Integrations

"Watermarking" is one method in which digital information is buried in a carrier signal; the hidden information should be related to the carrier signal. There are many different types of digital watermarking, including traditional watermarking that uses visible media (such as snaps, images, or video), and a signal may be carrying many watermarks. Any signal that can tolerate noise, such as audio, video, or picture data, can have a digital watermark implanted in it. A digital watermark must be able to withstand changes that can be made to the carrier signal in order to protect copyright information in media files. The goal of digital watermarking is to ensure the integrity of data, whereas steganography focuses on making information undetectable to humans. Watermarking doesn't alter the original digital image, unlike public-key encryption, but rather creates a new one with embedded secured aspects for integrity. There are no residual effects of encryption on decrypted documents. This work focuses on strong digital image watermarking algorithms for copyright protection purposes. Watermarks of various sorts and uses were discussed, as well as a review of current watermarking techniques and assaults. The project shows how to watermark an image in the frequency domain using DCT and DWT, as well as in the spatial domain using the LSB approach. When it comes to noise and compression, frequency-domain approaches are far more resilient than LSB. All of these scenarios necessitate the use of the original picture to remove the watermark. Out of the three, the DWT approach has provided the best results. We can improve the resilience of our watermark while having little to no extra influence on image quality by embedding watermarks in these places.

Genome Size Variation across a Cypriot Fabeae Tribe Germplasm Collection.

DNA content is an important trait linked to the evolutionary routes of taxa and often connected to speciation. In the present study, we studied C-values variation across the Cypriot Fabeae gene pool. Several hundred plants (Vicia spp., Lens spp., Pisum spp.) were sampled across Cyprus. Accurate estimates were established by flow cytometry and propidium iodine staining for 155 discrete populations/accessions. A ten-fold variation was detected across lineages with 1C DNA content varying from 1.584 pg for V. cretica (ARI02420) to 13.983 pg for V. faba (ARI00187). In general, flow cytometry was precise for the characterization of species, even though there were instances of genome overlapping across taxa. Most analyses in the current work refer to species that have not been characterized before by flow cytometry (or any other DNA content estimation method). Still, a correlation to C-values previously reported in Kew Plant DNA C-values database was attempted. A high degree of correlation except for V. dalmatica was established. The evaluation of genome size trait in relation with the Fabeae phylogeny, revealed that Pisum and Lens genera were rather homogenous, but an astonishing fluctuation was shown for Vicia spp. Moreover, it was established that genome up- or down-scaling was not directly linked to speciation drivers. The genomic size measurements presented here could deliver extra quality control for the identification and characterization of taxa in germplasm collections, particularly in cases where species share morphological characters.

Sharing Is Saving? Building Costs Simulation of Collaborative and Mainstream Housing Designs

Building costs play a significant role in determining the affordability of a housing project, and these depend to a large extent on design choices. This paper is based on the premise that collaborative design processes, or co-design, used in collaborative housing (CH) in Europe reduce building costs and consequently increase the affordability of these housing projects. However, research remains scarce on the extent to which CH is an affordable solution from a design perspective compared to affordable mainstream housing (MH), in which no co-design is used. Therefore, this paper aims to fill this knowledge gap by assessing the impact of design choices on building costs in CH and MH. To this end, we developed a simulation model to compare the building costs of CH with MH based on their design choices. Findings indicate that CH represents a more affordable and space-efficient solution when compared to MH, if we look at the building costs per unit. This is because CH provides less expensive units while it includes larger common spaces and extra quality. These results help to refute existing claims about the unaffordability of CH design solutions.

Adaptive HEVC Steganography Based on Steganographic Compression Efficiency Degradation Model

High Efficiency Video Coding (HEVC) places great emphasis on optimizing compression efficiency, where compression efficiency denotes file size ratio before and after compression. The current HEVC steganography is prone to cause degradation in compression efficiency. To analyze and avoid this problem, a Steganographic Compression Efficiency Degradation Model (SCEDM) is first proposed, which leverages the area ratio of different types of Coding Units (CU) as the distribution of block partitioning structure and combines with the K-L divergence to describe the compression efficiency degradation. By minimizing the output of the SCEDM, the degradation of compression efficiency caused by steganographies can be minimized. Besides, it is also proved that this minimizing process will not increase extra visual quality distortion. Based on this model, a novel adaptive steganography using HEVC intra block partitioning structure is proposed. This steganography consists of three parts: the CU Depth based Hierarchical Coding (CDHC) method, the structure merging strategy and the adaptive matching method. The CDHC method can convert secret binary bits to different block structures. The structure merging strategy improves the capacity, and the adaptive matching method minimizes the compression efficiency degradation according to the proposed SCEDM. The proposed steganography is further compared with state-of-the-art steganographies to confirm the effectiveness and advantages of the proposed model and steganography in compression efficiency, capacity, visual quality and resistance to video steganalysis.

Application of Artificial Neural Networks to the Analysis of Friction Behaviour in a Drawbead Profile in Sheet Metal Forming.

Drawbeads are used when forming drawpieces with complex shapes to equalise the flow resistance of a material around the perimeter of the drawpiece or to change the state of stress in certain regions of the drawpiece. This article presents a special drawbead simulator for determining the value of the coefficient of friction on the drawbead. The aim of this paper is the application of artificial neural networks (ANNs) to understand the effect of the most important parameters of the friction process (sample orientation in relation to the rolling direction of the steel sheets, surface roughness of the counter-samples and lubrication conditions) on the coefficient of friction. The intention was to build a database for training ANNs. The friction coefficient was determined for low-carbon steel sheets with various drawability indices: drawing quality DQ, deep-drawing quality DDQ and extra deep-drawing quality EDDQ. Equivalents of the sheets tested in EN standards are DC01 (DQ), DC03 (DDQ) and DC04 (EDDQ). The tests were carried out under the conditions of dry friction and the sheet surface was lubricated with machine oil LAN46 and hydraulic oil LHL32, commonly used in sheet metal forming. Moreover, various specimen orientations (0° and 90°) in relation to the rolling direction of the steel sheets were investigated. Moreover, a wide range of surface roughness values of the counter-samples (Ra = 0.32 μm, 0.63 μm, 1.25 μm and 2.5 μm) were also considered. In general, the value of the coefficient of friction increased with increasing surface roughness of the counter-samples. In the case of LAN46 machine oil, the effectiveness of lubrication decreased with increasing mean roughness of the counter-samples Ra = 0.32-1.25 μm. With increasing drawing quality of the sheet metal, the effectiveness of lubrication increased, but only in the range of surface roughness of the counter-samples in which Ra = 0.32-1.25 μm. This study investigated different transfer functions and training algorithms to develop the best artificial neural network structure. Backpropagation in an MLP structure was used to build the structure. In addition, the COF was calculated using a parameter-based analytical equation. Garson partitioning weight was used to calculate the relative importance (RI) effect on coefficient of friction. The Bayesian regularization backpropagation (BRB)-Trainbr training algorithm, together with the radial basis normalized-Radbasn transfer function, scored best in predicting the coefficient of friction with R2 values between 0.9318 and 0.9180 for the training and testing datasets, respectively.

Marginal reduction in surface NO2 attributable to airport shutdown: A machine learning regression-based approach

Emissions from aviation and airport-related activities degrade surface air quality but received limited attention relative to regular transportation sectors like road traffic and waterborne vessels. Statistically, assessing the impact of airport-related emissions remains a challenge due to the fact that its signal in the air quality time series data is largely dwarfed by meteorology and other emissions. Flight-ban policy has been implemented in a number of cities in response to the COVID-19 spread since early 2020, which provides an unprecedented opportunity to examine the changes in air quality attributable to airport closure. It would also be interesting to know whether such an intervention produces extra marginal air quality benefits, in addition to road traffic. Here we investigated the impact of airport-related emissions from a civil airport on nearby NO2 air quality by applying machine learning predictive model to observational data collected from this unique quasi-natural experiment. The whole lockdown-attributable change in NO2 was 16.7 μg/m3, equals to a drop of 73% in NO2 with respect to the business-as-usual level. Meanwhile, the airport flight-ban aviation-attributable NO2 was 3.1 μg/m3, accounting for a marginal reduction of 18.6% of the overall NO2 change that driven by the whole lockdown effect. The airport-related emissions contributed up to 24% of the local ambient NO2 under normal conditions. Additionally, the average impact of airport-related emissions on the nearby air quality was ∼0.01 ± 0.001 μg/m3 NO2 per air-flight. Our results highlight that attention needs to be paid to such a considerable emission source in many places where regular air quality regulatory measures were insufficient to bring NO2 concentration into compliance with the health-based limit.

Quality of placements during the pandemic and the applicable sanitary regime as assessed by physiotherapy students from Poland

Introduction: The COVID-19 pandemic and the related sanitary rigour pose a great challenge for universities preparing young people to work in medical and medicine-related professions, including the profession of a physiotherapist. It is particularly difficult to conduct placements, during which both students and employees of the institution as well as patients will be provided with maximum safety, but at the same time high standards of education will be maintained. The aim of the study was to assess possible changes in the quality of placements that might have occurred during the COVID-19 pandemic and the applicable sanitary regime. Material and methods: The study was conducted in October 2021 among 67 students of physiotherapy at the University of Applied Sciences in Tarnow, Poland. The sample consisted of both women (n = 49) and men (n = 18) who had completed at least one full work placement prior to the outbreak of the pandemic and at least one full work placement during the pandemic. Students voluntarily filled in a purpose-built electronic questionnaire, in which they responded to 20 questions referring the conditions of placements, cooperation with staff, the atmosphere in the institution and the skills acquired there. They determined on a scale of 1 to 5 (1 – has definitely changed in favour, 5 – has definitely changed in an unfavourable way), how professional practices have changed due to the pandemic and the sanitary regime. Students could also add their comments in the form of recommendations aimed at improving the quality of placements. They were invited to do it with the following statement: Provide an example of a practical solution that would help improve the quality of placements. Results: According to the respondents, the most unfavourable changes in the quality of placements caused by the pandemic are: increased stress related to placements, worse conditions for learning how to diagnose and plan therapy, limited opportunities to learn about modern methods of therapy and limited opportunities to work directly with the patient. The respondents also noticed positive changes in the quality of placements. These were the opportunity to work in a small group, a better atmosphere in the institution, more help from the staff and better familiarization with the place of the placement. Conclusions: The pandemic has brought both positive and negative changes in the quality of placements. Monitoring the quality of placements and surveying students’ opinions is a very important tool in improving the quality of education. It is worthwhile to carry out extra quality control studies at times of change to be aware of possible changes in the quality of placements induced by such challenging situations. It was also interesting to notice that students expect some of the changes related to the sanitary regime to persist after the pandemic has ended.

Effect of Different Watering Regimes on Olive Oil Quality and Composition of Coratina Cultivar Olives Grown on Karst Soil in Croatia.

Croatian islands are olive growing areas characterized by poor conditions for olive trees because of karst soil and a precipitation deficiency. Under these conditions, irrigation is a very important factor for constant olive oil production. This paper aims to investigate the effects of different watering regimes on quantity, sensory and chemical quality and composition of Coratina cv. olive oil obtained from trees grown on ameliorate karst soil during two harvesting years. Olive trees were subjected to rainfed conditions and three different irrigation treatments (T1—deficit irrigation representing the usual producer’s practice, T2—regulated deficit irrigation in respect to phenological stages, T3—full irrigation). Irrigation treatments increased oil yield compared to rainfed conditions (T1 + 58%, T2 + 66% and T3 + 74%, representing average values for both studied years). All olive oil samples were of extra quality. Irrigation led to a decrease in carotenoids, volatiles, polyunsaturated fatty acids and linolenic acid contents, with no difference found among irrigation treatments. Total phenols and secoiridoids concentration was not affected by irrigation, indicating that similar oil quality could be achieved with less demand on the water supply. Obtained results could help producers to define a suitable irrigation management in particular conditions of ameliorate karst.

A dark and bright channel prior guided deep network for retinal image quality assessment

Background and objectiveRetinal image quality assessment is an essential task for the diagnosis of retinal diseases. Recently, there are emerging deep models to grade quality of retinal images. However, current models either directly transfer classification networks originally designed for natural images to quality classification of retinal images or introduce extra image quality priors via multiple CNN branches or independent CNNs. The purpose of this work is to address retinal image quality assessment by a simple deep model. MethodsWe propose a dark and bright channel prior guided deep network for retinal image quality assessment named GuidedNet. It introduces dark and bright channel priors into deep network without extra parameters increasing and allows for training end-to-end. In detail, the dark and bright channel priors are embedded into the start layer of a deep network to improve the discriminate ability of deep features. Moreover, we re-annotate a new retinal image quality dataset called RIQA-RFMiD for further validation. ResultsThe proposed method is evaluated on a public retinal image quality dataset Eye-Quality and our re-annotated dataset RIQA-RFMiD. We obtain the average F-score of 88.03% on Eye-Quality and 66.13% on RIQA-RFMiD, respectively. ConclusionsWe investigate the utility of the dark and bright channel priors for retinal image quality assessment. And we propose a GuidedNet by embedding the dark and bright channel priors into CNNs without much model burden. Moreover, to valid the GuidedNet, we re-create a new dataset RIQA-RFMiD. With the GuidedNet, we achieves state-of-the-art performances on a public dataset Eye-Quality and our re-annotated dataset RIQA-RFMiD.

Critical failure factors for continuous improvement methodologies in the Irish MedTech industry

PurposeThe main objective of this study is to investigate what are the critical success factors that exist for continuous improvement (CI) methodology deployment in the Irish medical technology (MedTech) industry. The research will, in particular, seek to establish if the highly regulated nature of the global MedTech industry is an additional critical failure factor (CFF) for the deployment of CI methodology. The study involves the analysis of the benefits, challenges, CFFs and tools most utilised for the application to the deployment of CI methodologies in the Irish medical device (MD) industry.Design/methodology/approachA quantitative survey was utilised in this study. The main participants were made up of senior quality professionals working in operational excellence, quality consultants, quality directors, quality engineers, quality managers and quality supervisors working in both manufacturing and service sectors from Irish MD companies. A total of 94 participants from the Irish MedTech industry responded to the survey.FindingsThe main finding of this study is that 42% of participants perceived that a highly regulated environment was a CFF to CI, whilst 79% of respondents utilised Lean Six Sigma in their organisations, and productivity and financial factors were found to be the highest reasons for CI deployment amongst the Irish MedTech industry. The top CFFs highlighted for CI in regulated industries were fear of extra validation activity, compliance versus quality culture and a regulatory culture of being “safe”. Another relevant finding presented in this paper is that just over 48% of participants felt that CI tools are very strongly integrated into the industries quality management systems (QMSs) such as the corrective and preventative action system, non-conformance and audit systems.Research limitations/implicationsAll data collected in the survey came from professionals working for Irish indigenous and multinational MedTech companies. It is important to highlight that n = 94 is a low sample size, which is enough for a preliminary survey but reinforcing the limitation in terms of generalisation of the results. A further study on a wider European and global scale as well as a comparison with the highly regulated pharma industry would be informative.Originality/valueThe authors understand that this is the very first research focussed on the CFFs for CI in the MedTech/MD manufacturing industry with a specific focus on the highly regulated nature of the industry as a potential CFF. The results of this study represent an important first step towards a full understanding of the applicability and use of CI in the medical-device-manufacturing industries on a global scale.

Barriers and Enablers for Continuous Improvement Methodologies within the Irish Pharmaceutical Industry

This study aims to investigate the barriers that exist when implementing continuous improvement methodologies, such as Lean Six Sigma (LSS), within the Irish Pharma industry. The main finding of this study is that 45% of participants perceived that a highly regulated environment could be a barrier to continuous improvement implementation, while 97% of respondents utilised Continuous improvement (CI) methods, such as Lean, Six Sigma, and LSS, within their organisations. While the International Conference of Harmonisation integrates CI into its Pharmaceutical Quality Systems (PQS) regulations, the highest motivation for CI implementation amongst the Irish Pharma industry is to improve Productivity and Quality. The main obstacles highlighted for CI implementation in Pharma attributed to stringent regulatory regimes were fear of extra validation activity, a compliance versus quality culture, and a regulatory culture of being “safe”. Another relevant finding presented in this paper is that participants CI LSS tools are very strongly integrated into the pharma industries corrective and preventative action system, deviations, and internal audit systems. Limitations of the research are that all the data collected in the survey came from professionals working for multinational Pharmaceutical companies based in Ireland. The authors understand that this is the first research focused on the barriers and status of CI initiatives in the pharmaceutical industry. The results of this study represent an important step towards understanding the enablers and obstacles for the use of continuous improvement methodologies in pharmaceutical manufacturing industries on a global scale.

To Study the Consumer Satisfaction in Consuming the Fast Food Products With Special Reference To Domino’S

This research aims at providing information about fast food industry, its trend, reason for its emergence and several other factors that are responsible for its growth.5 This report provides extensive research and rational analysis on the Indian fast food industry and about the changing trends in market. The research study looks into the market condition and future forecasts, and outlines of current trends and analyses. Domino’s pizza is an American restaurant chain and international franchise pizza delivery. This project will be focusing the customers of Domino’s pizza outlets in Chhattisgarh.9 The purpose of this project is to view the customer satisfaction in domino’s pizza outlets. It tells about the consumer perception and consumer preference about the Domino’s products. It will explore the level of satisfaction of customers in pricing factors and in delivery of a products and it will give the suggestions to the Domino’s outlet to improve some extra qualities such adding some products reduction in price etc.7 This research is based on consumer perception of domino’s quality of service. The fast food industry of India and is one the country. Further, on the back of changing and busy lifestyle, middle class population and the industry will continue to grow at a pace in coming years. In the developed countries and continues to expand there and in many other industrial countries in the coming years.10 They are most trends developed in the youth areas. Youth like most fast food then the elder ones. In Chhattisgarh region the dominos trends are in DURG Region and RAIPUR.12 To explain customer satisfaction better it may be important to look additional factors to seek better measures of the constructs. Customer satisfactions are different. Full service restaurants should focus on three elements- service quality, price and food quality, value.5

Proposal for Integrated Management of Verticillium Wilt Disease in Avocado Cultivar Hass Crops

The area planted with avocado crops in Colombia has been growing rapidly in recent years, especially for export varieties such as Hass. The increase in planted area coincided with increased phytosanitary problems, where pathogens such as fungi of the genus Verticillium spp. are becoming of economic importance. The objective of this study was to evaluate different control strategies for avocado wilt disease caused by Verticillium spp., under in vitro, net house, and field conditions. Strategies tested included fungicides (benomyl, azoxystrobin, captan, and carbendazim), beneficial and antagonistic microorganisms (Trichoderma sp., and Rhizoglomus fasciculatum), and physical and cultural practices such as solarization, drainage and removal of diseased tissues. Treatments T7fi (pruning-solarization-Trichoderma-mycorrhiza-sucrose-organic matter-drainage) and T8fi (fungicide-pruning-solarization-Trichoderma-mycorrhiza-sucrose-organic matter-drainage), showed the greatest reduction in the area under disease progress curve and Verticillium dahliae inoculum in soil and plant tissues under field conditions. Fruit with extra quality increased 120.8% with T7fi and 108% with T8fi, compared to the control with diseased trees. The highest costs were identified for T7fi and T8fi; however, these treatments also showed the best cost/benefit relationship. Integrated approaches as in T7fi and T8fi showed the best results for Verticillium wilt control. As no fungicides of chemical synthesis are included in T7fi (pruning-solarisation-Trichoderma-mycorrhiza-sucrose-organic matter-drainage), it should be preferred to T8fi, which does include them, to avoid their negative impacts on avocado production.

A Visual Tracking Algorithm Based on Estimation of Regression Probability Distribution

During target tracking, the target is often interfered by uncertainties like occlusion and motion blur. The interference leads to inaccurate tracking and even loss of the target. To solve the problem, this paper designs a target tracking algorithm based on the estimation of regression probability distribution (RPDE). Specifically, the uncertainty degree of the tracking frame was estimated by learning the statistical properties of regression parameters, and the quality of that frame was evaluated by fusing the predicted regression probability scores with classification scores. Next, an anchor-free regression mechanism was introduced to improve the computing speed. During network training, a simple and efficient strategy was presented for joint prediction, which jointly expresses classification scores and regression scores to eliminate the extra quality estimation branches in training and prediction. After that, the performance of our algorithm was tested on several public benchmarks, namely, OTB2015, VOT2016, GOT10k, and UAV123, and contrasted with several state-of-the-art algorithms. The results show that the proposed algorithm, named SiamRPDE for short, performed excellently on several benchmarks, and achieved the speed of 125 frames per second (FPS).

Analysis of acoustic and voice quality features for the classification of infant and mother vocalizations

Classification of infant and parent vocalizations, particularly emotional vocalizations, is critical to understanding how infants learn to regulate emotions in social dyadic processes. This work is an experimental study of classifiers, features, and data augmentation strategies applied to the task of classifying infant and parent vocalization types. Our data were recorded both in the home and in the laboratory. Infant vocalizations were manually labeled as cry, fus (fuss), lau (laugh), bab (babble) or scr (screech), while parent (mostly mother) vocalizations were labeled as ids (infant-directed speech), ads (adult-directed speech), pla (playful), rhy (rhythmic speech or singing), lau (laugh) or whi (whisper). Linear discriminant analysis (LDA) was selected as a baseline classifier, because it gave the highest accuracy in a previously published study covering part of this corpus. LDA was compared to two neural network architectures: a two-layer fully-connected network (FCN), and a convolutional neural network with self-attention (CNSA). Baseline features extracted using the OpenSMILE toolkit were augmented by extra voice quality, phonetic, and prosodic features, each targeting perceptual features of one or more of the vocalization types. Three web data augmentation and transfer learning methods were tested: pre-training of network weights for a related task (adult emotion classification), augmentation of under-represented classes using data uniformly sampled from other corpora, and augmentation of under-represented classes using data selected by a minimum cross-corpus information difference criterion. Feature selection using Fisher scores and experiments of using weighted and unweighted samplers were also tested. Two datasets were evaluated: a benchmark dataset (CRIED) and our own corpus. In terms of unweighted-average recall of CRIED dataset, the CNSA achieved the best UAR compared with previous studies. In terms of classification accuracy, weighted F1, and macro F1 of our own dataset, the neural networks both significantly outperformed LDA; the FCN slightly (but not significantly) outperformed the CNSA. Cross-examining features selected by different feature selection algorithms permits a type of post-hoc feature analysis, in which the most important acoustic features for each binary type discrimination are listed. Examples of each vocalization type of overlapped features were selected, and their spectrograms are presented, and discussed with respect to the type-discriminative acoustic features selected by various algorithms. MFCC, log Mel Frequency Band Energy, LSP frequency, and F1 are found to be the most important spectral envelope features; F0 is found to be the most important prosodic feature.