Effect of Steam-Assisted Hybrid Cooking on Textural Quality Characteristics,Cooking Loss,and Free Moisture Content of Beef

Semitendinosus muscles were cooked in a steam-assisted hybrid oven and also convection ovens at three different oven temperatures (180, 210, and 240°C) until three different end point temperatures [65°C (medium-rare), 72°C (medium), 80°C (medium-well)] were reached. Textural properties of cooked beef were investigated by the Warner Bratzler shear test and texture profile analysis. Cooking loss and free moisture content of muscle tissue was determined for each cooking condition. In addition, sensory analysis was carried out in order to compare with the instrumental results and correlations between instrumental texture parameters and sensory results. Steam-assisted hybrid oven cooking of beef resulted in a tougher texture, higher cooking loss, and lower free moisture content than convection cooking. High correlation coefficients (r² > 0.70) were observed between instrumental texture measurements and sensory results for all ovens, especially in terms of tenderness. The free moisture content and adhesiveness values were also correlated well with juiciness (r² > 0.70) for all oven types.     

Comparison of a competitive ELISA with an HPLC method for the determination of aflatoxin M1 in Turkish White, Kasar and Tulum cheeses

AFM1 is a hydroxylated metabolite produced when ruminants ingest contaminated feed with AFB1 and transferred to dairy products such as cheese, which represents an important risk factor for consumers. Turkish White, Kashar and Tulum cheeses are traditional cheese types in Turkey. This study was planned to compare the performance of a competitive ELISA for the determination of AFM1 in Turkish White, Kasar and Tulum cheeses against a new HPLC method. For this purpose, different AFM1 concentrations (50–400 ng/kg) were added to the cheese samples and the toxin levels were determined by ELISA and HPLC. Also, both methods were performed in 24 real samples obtained from different markets. In conclusion, the results obtained by ELISA in this study were related to those by HPLC for AFM1.     

Is urinary bladder pressure a sensitive indicator of intra-abdominal pressure?

Contribution of one case of right paratesticular rhabdomyosarcoma in a 3-year and 4-month old male patient. Following radical orchiectomy and clinical staging, grading is IRS Group I (fully resected localized disease). Subsequently, the patient received 7 polychemotherapy courses and was found to be asymptomatic 4 years after treatment.     

Preparation of polyaniline montmorillonite clay composites for the removal of diethyl hexyl phthalate from aqueous solutions

The work presents the synthesis of polyaniline functionalized montmorillonite (PANI/MMT) clay composites and evaluation of their performance as an adsorbent for the mitigation of toxic diethyl hexyl phthalate (DEHP) from water. The formation of composite was confirmed by different analytical techniques such as FTIR, zeta potential analysis, XRD, SEM, and AFM. The adsorption capacity of PANI/MMT was superior to MMT. DEHP followed partition mechanism on PANI/MMT whereas MMT favored surface adsorption mechanism. The performance of PANI/MMT for DEHP removal was unaffected by the change in pH of solution, change in salinity, and hardness contents in aqueous system and consecutive adsorption desorption cycles compared to MMT. Furthermore, both PANI/MMT and MMT exhibited greater adsorption capacities as compared to the previously published work and showed satisfactory performance in real water samples.     

Partial slip problem for two semi-infinite strips in contact

Two semi-infinite strips brought into frictional contact, end-to-end, by a normal pressure are then subjected to a monotonically increasing or oscillating tangential force remote from the interface. The full stress tensor for the partial slip regime is found.     

Electrochemical boriding of titanium for improved mechanical properties

In this study, we introduce a rapid boriding technique that can produce very thick titanium diboride (TiB₂) layers on titanium substrates. We also discuss the effects of process duration on boride layer thickness, chemistry and, morphology. In our experiments, the boriding of commercial purity titanium (CP-Ti) substrates was carried out in an electrochemical cell in which we used a mixture of sodium tetraborate and sodium carbonate as the base ingredients of molten electrolyte at 950 °C and at current density of 300 mA/cm². The titanium test pieces were attached to the cathode holder of the electrochemical cell while a graphite crucible served as the anode. Both TiB and TiB₂ phases were detected by X-ray diffraction method even after 5 min of treatment. Scanning electron microscopy (SEM) images verified that the production of 4.5 µm thick TiB₂ layers was feasible after boriding for an hour. The boride layers consisted of a homogeneous TiB₂ phase on the top and TiB whiskers toward the substrate. The micro-indentation studies indicated that the layer on top has hardness values as high as 40 GPa. The main advantages of this technique are its ability to produce very thick and hard TiB₂ quickly and to create no gaseous emissions or solid wastes during or after the treatment.     

Sorption of remazol brilliant blue R onto polyurethane‐type foam prepared from peanut shell

The powders of peanut shell is first liquefied with the mixture of polyethylene glycol‐400 and monoethylene glycol (MEG) using sulfuric acid at 160°C for 2 h. Polyurethane (PU)‐type rigid foam is prepared from the reaction between peanut shell liquefied with the MEG and diphenylmethane diisocyanate. Then the PU foam is used as a sorbent for the removal of the remazol brilliant blue R from aqueous solution. The sorption of the dye increases with increasing initial concentration, temperature, and ionic strength, while decreasing with increasing pH. From the isotherm and kinetic studies, it is seen that the sorption process is in the best agreement with the Langmuir isotherm and the pseudo second‐order kinetic model. Desorption and FTIR studies show that a chemisorption process occurs between dye and foam, probably indicating electrostatic interactions. The thermodynamic parameters (i.e., standard free energy, standard enthalpy, and standard entropy) are determined, and the results obtained are discussed in detail. © 2012 Wiley Periodicals, Inc. J. Appl. Polym. Sci, 2013     

Automated Deep Learning Based Melanoma Detection and Classification Using Biomedical Dermoscopic Images

Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at the initial stage. Besides, the k-means clustering technique is applied for the image segmentation process. In addition, Adagrad optimizer based Capsule Network (CapsNet) model is derived for effective feature extraction process. Lastly, crow search optimization (CSO) algorithm with sparse autoencoder (SAE) model is utilized for the melanoma classification process. The exploitation of the Adagrad and CSO algorithm helps to properly accomplish improved performance. A wide range of simulation analyses is carried out on benchmark datasets and the results are inspected under several aspects. The simulation results reported the enhanced performance of the ADL-MDC technique over the recent approaches.     

Artificial Intelligence Based Sentiment Analysis for Health Crisis Management in Smart Cities

Smart city promotes the unification of conventional urban infrastructure and information technology (IT) to improve the quality of living and sustainable urban services in the city. To accomplish this, smart cities necessitate collaboration among the public as well as private sectors to install IT platforms to collect and examine massive quantities of data. At the same time, it is essential to design effective artificial intelligence (AI) based tools to handle healthcare crisis situations in smart cities. To offer proficient services to people during healthcare crisis time, the authorities need to look closer towards them. Sentiment analysis (SA) in social networking can provide valuable information regarding public opinion towards government actions. With this motivation, this paper presents a new AI based SA tool for healthcare crisis management (AISA-HCM) in smart cities. The AISA-HCM technique aims to determine the emotions of the people during the healthcare crisis time, such as COVID-19. The proposed AISA-HCM technique involves distinct operations such as pre-processing, feature extraction, and classification. Besides, brain storm optimization (BSO) with deep belief network (DBN), called BSO-DBN model is employed for feature extraction. Moreover, beetle antenna search with extreme learning machine (BAS-ELM) method was utilized for classifying the sentiments as to various classes. The use of BSO and BAS algorithms helps to effectively modify the parameters involved in the DBN and ELM models respectively. The performance validation of the AISA-HCM technique takes place using Twitter data and the outcomes are examined with respect to various measures. The experimental outcomes highlighted the enhanced performance of the AISA-HCM technique over the recent state of art SA approaches with the maximum precision of 0.89, recall of 0.88, F-measure of 0.89, and accuracy of 0.94.