Genotyping of the kappa‐casein and beta‐lactoglobulin genes in Anatolian water buffalo by PCR‐RFLP

The objective of this study was to identify allele and genotype frequencies of the κ‐CN and β‐LG genes in Anatolian water buffalo. A total of 126 water buffalos from Turkey were genotyped using the PCR‐RFLP method. For gene κ‐CN, only B allele and BB genotype were observed. And for gene β‐LG, two types of alleles (A and B) and three types of genotypes were observed. The genotype frequencies of AA, AB and BB of β‐LG in Anatolian water buffalo were 0.254, 0.698 and 0.048, respectively. Surprisingly, the frequency of allele A was higher than that of allele B in contrast to world buffalo breeds.     

Polymorphism and expression of the porcine Tenascin C gene associated with meat and carcass quality

The research aimed to screen for polymorphism, expression of Tenascin C (TNC) and association with meat and carcass quality traits. Three single nucleotide polymorphisms were detected. In a Duroc×Pietrain F2 cross (DuPi) population, g.44488C>T was associated with meat color and ham weight; g.68794A>G was associated with pH at 24h post mortem in ham (pH24(H)) and muscle area but g.68841C>T was not statistically associated. Genotyping in a commercial Pietrain (Pi) population showed that g.44488C>T was associated with pH24(H), whereas g.68794A>G was associated with conductivity at 45 min post mortem in loin and backfat thickness. Diplotypes showed significant effects on pH24(H) in both populations. The expression was associated with pH at 45 min post mortem in loin and cooking loss. TNC was significantly higher in animals with higher muscle pH. Linkage analysis revealed four trans-regulated eQTL on four autosomes. These results suggest that TNC could be a potential candidate gene for meat quality traits in pigs.     

Fault Detection and Diagnosis in a Food Pasteurization Process with Hidden Markov Models

Hidden Markov Models (HMM) are used to detect abnormal operation of dynamic processes and diagnose sensor and actuator faults. The method is illustrated by monitoring the operation of a pasteurization plant and diagnosing causes of abnormal operation. Process data collected under the influence of faults of different magnitude and duration in sensors and actuators are used to illustrate the use of HMM in the detection and diagnosis of process faults. Case studies with experimental data from a high‐temperature‐short‐time pasteurization system showed that HMM can diagnose the faults with certain characteristics such as fault duration and magnitude.     

Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey

Energy production is considered as one of the key indicators for economic development. It is vital to improve the renewable energy production for global sustainability, while leveraging the national resources. This study is contributing to the demonstration of using genetic algorithms (GA) in the development of future energy scenarios as well as to the strategic energy studies in Turkey. The forecasting model developed in this study uses forward feeding back-propagation (BP) method improved by GA. The proposed model is applied in the Turkish case. The test errors are shown to emphasize the positive difference between the proposed model and the classical BP model. The results highlight that there is strong evidence indicating that the government should reconsider their current energy strategies.     

Interpreting ECG data by integrating statistical and artificialintelligence tools

The use of an automated system integrating data conditioning, statistical methods, and artificial intelligence tools to summarize and interpret high-frequency physiological data such as the electrocardiogram is investigated. The development of a methodology and its associated tools for real-time patient monitoring and diagnosis is accomplished by using the commercial programming environments MATLAB and G2, a real-time knowledge-based system (KBS) development shell. Data interpretation and classification is performed by integrating statistical classification methods and knowledge-based techniques with a graphical user interface that provides quick access to the analysis results as well as the original data. A KBS was developed that incorporates various statistical methods with a rule-based decision system to detect abnormal situations, provide preliminary interpretation and diagnosis, and to report these findings to the healthcare provider     

Control of grade transitions in distributed chemical reactor networks—An agent-based approach

Supervision of distributed manufacturing processes producing different grades of a product requires intelligent reconfiguration strategies during grade transition phases to minimize off-spec production. Agent-based approaches are ideal for such problems and they provide flexible, robust, and emergent solutions during dynamically changing process conditions. Three different multi-layered, multi-agent frameworks are proposed for the supervision of grade transitions in autocatalytic reactor networks. The first framework is the centralized framework and it is useful for small-scale grade transitions where only a small region of the network needs to be reconfigured. Alternatively, the other two frameworks use a decentralized approach. The first decentralized framework implements genetic algorithms and the second one uses self-organizing heuristics and auctions for large-scale grade transitions. The case studies demonstrate that as the complexity of the reconfiguration problem increases, decentralized solutions perform more efficiently.     

Disappearance of mitral and tricuspid regurgitation in haemodialysis patients after ultrafiltration

We recovered an unusual bacterial strain from blood or sputum of three patients with septicemia, endocarditis, and/or respiratory failure. The three isolates were thin, curved, gram-negative, light brown, pigment-producing bacilli with variable catalase activity. They were asaccharolytic, oxidase-negative, nonmotile, and fastidious. Identification was not possible on the basis of these characteristics alone or in combination with cellular fatty acid profiles. Nucleic acid amplification and sequence analysis of the 16S rRNA gene revealed that all three isolates were identical and most closely related to the emerging pathogen Bordetella holmesii, diverging from the published sequence at three nucleotide positions (99.8% similarity). Isolation of a B. holmesii-like pathogen from sputum suggests that, in addition to producing septicemia, the organism may inhabit the respiratory tract like other Bordetella species.     

Vibrational stabilization of a chemical reactor: An experimental study

Vibrational control is a method for modification of dynamic properties of linear and nonlinear systems by introduction of fast, zero-average oscillations in a system's parameters. It has been previously shown that forced oscillations introduced in the reactant flow rates to a continuous stirred tank reactor (CSTR) cause a modification of theS- shaped steady-state curve. In the present note experimental evidence is given to show that this modification leads to the operation the CSTR at an asymptotically stable periodic regime located near an unstable steady-state of the reactor system having fixed flow rates. For a given average stabilized state, such a stabilizing mechanism results in a higher production rate or lower energy expenditure compared to a steady operation with shifted input conditions.     

Synthesis and investigation of bimetallic Ni-Co/Al2O3 nanocatalysts using the polyol process

ABSTRACT

Ni-Co/Al₂O₃ catalysts with different Ni:Co ratios by weight were prepared using a simple polyol process. The activities of the catalysts were evaluated for the catalytic partial oxidation of methane (CPOM) in the temperature range of 600–800°C. Numerous techniques such as x-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) surface area analysis, inductively coupled plasma-mass spectroscopy (ICP-MS), thermogravimetric analysis (TGA), high-resolution transmission electron microscopy analysis (HRTEM), scanning electron microscopy analysis (SEM-EDS) and temperature-programmed oxidation (TPO) were applied to characterize fresh and spent catalysts. The XRD analysis confirmed that the loaded particles were metals and showed possible bimetallic nano-alloy Ni-Co formation for Ni- and Co-containing catalysts. The highest metal dispersion was 15.7% for the Ni_2.8Co_2.6/Al₂O₃ catalyst. The catalytic test results showed no correlation between metal dispersion and the metal particle size, and the activity decreased in the order of Ni_7.7/Al₂O₃ > Ni_2.8Co_2.6/Al₂O₃ ≈ Ni_3.8Co_1.5/Al₂O₃ > Ni_2.0Co_3.8/Al₂O₃ >> Co_6.8/Al₂O₃ under a flow rate of 157,500 L kg^?1 h^?1 with CH₄/O₂ = 2 (using air as an oxidant) at 800°C. The obtained results also showed that when the actual atomic Ni/Co ratio was 1.07 in the Al₂O₃-supported catalyst, the dispersion of the active sites appeared to be promoted by Co addition, and the catalytic activity was stable over a reaction time of 10 h. Among all the tested catalysts, the Ni_2.8Co_2.6/Al₂O₃ catalyst exhibited acceptable activity (75%) without coking.