Identification of characteristic aroma compounds in blanched garlic through multidimensional analysis and improvement of sensory quality via cysteine-assisted mild blanching

Effect of Lactiplantibacillus plantarum MX14 and Pichia membranifaciens PY33 co-fermentation on quality characteristics of red sour soup.

The multi-dimensional analysis is a promising approach to a new interpreting of environments by ground of the value-information and language-information on intellectual activities in various environment meanings to society. This paper presents a new analysis-system with semantic computing for environments in water-quality areas by integrating the fundamental important parameters of water-quality for creating the new meaning to society. The multi-water-parameter-analysis in a multi-dimensional space is important for current research issues in some water-quality research fields, which are based on the values and meanings of each parameter for obtaining the meaningful words in the category of agriculture, aquatic life, fish, drinking, industrial and irrigation. The multi-dimensional semantic space is significantly utilized for various interpretations related to the water-quality.

To assess the results of a quality improvement (QI) project designed to improve safety of emergency endotracheal intubation (EEI). Single center prospective observational. 16-bed intensive care unit. Nine pulmonary/critical care fellows. For 3 years, EEI performed by the medical intensive care unit team were analyzed to identify interventions that would improve quality of the procedure. By segmental process analysis, the procedure of EEI was subjected to iterative change. Major components of process improvement were development of a combined team approach, a mandatory checklist, use of crew resource management (CRM) tactics, and postevent debriefing. Quality analysis and improvement included training of fellows using scenario-based training (SBT) with computerized patient simulator (CPS) to improve mechanical skills of intubation and team leadership. Fellows received 15 sessions of SBT with CPS using a combined checklist and team approach before assuming team leadership position during real-life EEI. For a 10-month period, fellows carried digital voice recorders to EEI; which, when combined with recording of continuous oximetry and BP monitoring were used to assess the quality of EEI. 128 EEI were performed of which 101 had full data recorded. Complications were 14% severe hypoxemia (<80% saturation), 6% severe hypotension (SBP<70 mm Hg), 1% death, 20% difficult EEI (≥ 3 attempts), 11% esophageal intubations, 2% aspiration, and 1% dental injury; 62% EEI were successfully achieved on first attempt, 11% required >3 attempts. EEI may be performed by pulmonary/critical medicine (PCCM) fellows with safety comparable to that described in other studies on EEI. Important parts of the program included the use of formal iterative QI approach, the use of intensive SBT with CPS, basic CRM, a comprehensive checklist, and a combined team approach. A key benefit of the program was to make the process of EEI fully transparent for ongoing quality and safety improvement.

BackgroundVISION Max (Ortho-Clinical Diagnostics, Raritan, NJ, USA) is a newly introduced automated blood bank system. Cross-matching (XM) is an important test confirming safety by simulating reaction between packed Red Blood Cells (RBCs) and patient blood in vitro before transfusion. We assessed the benefits of VISION Max automated XM (A-XM) in comparison with those of manual XM (M-XM) by using multidimensional analysis (cost-effectiveness and quality improvement).Materials and methodsIn a total of 327 tests (130 patients), results from A-XM and M-XM were compared. We assessed the concordance rate, risk priority number (RPN), turnaround time, hands-on time, and the costs of both methods. We further simulated their annual effects based on 37,937 XM tests in 2018.ResultsThe concordance rate between A-XM and M-XM was 97.9% (320/327, kappa = 0.83), and the seven discordant results were incompatible for transfusion in A-XM, while compatible for transfusion in M-XM. None of the results was incompatible for transfusion in A-XM, while compatible for transfusion in M-XM, meaning A-XM detect agglutination more sensitively and consequently provides a more safe result than M-XM. A-XM was estimated to have a 6.3-fold lower risk (229 vs. 1,435 RPN), shorter turnaround time (19.1 vs. 23.3 min, P < 0.0001), shorter hands-on time (1.1 vs. 5.3 min, P < 0.0001), and lower costs per single test than M-XM (1.44 vs. 2.70 USD). A-XM permitted annual savings of 46 million RPN, 15.1 months of daytime workers’ labor, and 47,042 USD compared with M-XM.ConclusionThis is the first attempt to implement A-XM using VISION Max. VISION Max A-XM appears to be a safe, practical, and reliable alternative for pre-transfusion workflow with the potential to improve quality and cost-effectiveness in the blood bank.

All imaging departments are expected to establish and maintain effective quality, safety, and performance improvement programs. Essential components of such programs include adherence to the basic principles of quality management and appropriate utilization of quality tools. The initial step is the gathering of relevant information, followed by the collection and analysis of quality and performance data; analysis and ranking of causes that likely contributed to a process failure, error, or adverse event; and prioritization and local implementation of solutions, with careful monitoring of newly implemented processes and wider dissemination of the tools when a process proves to be successful. Quality improvement requires a careful, dedicated, and continuously planned effort by a number of skilled and committed team members, with the goal being to do the right thing in a timely fashion in every case. This process can be sustained by offering rewards and celebrating successes, with all lessons learned disseminated throughout the department or organization.

Quality improvement in clinical laboratories is crucial to ensure accurate and reliable test results. With increasing awareness of the potential adverse effects of errors in laboratory practice on patient outcomes, the need for continual improvement of laboratory services cannot be overemphasized. A literature search was conducted on PubMed and a web of science core collection between October and February 2021 to evaluate the scientific literature quality of clinical laboratory quality improvement; only peer-reviewed articles written in English that met quality improvement criteria were included. A structured template was used to extract data, and the papers were rated on a scale of 0-16 using the Quality Improvement Minimum Quality Criteria Set (QI-MQCS). Out of 776 studies, 726 were evaluated for clinical laboratory literature quality analysis. Studies were analyzed according to the quality improvement and control methods and interventions, such as training, education, task force, and observation. Results showed that the average score of QI-MQCS for quality improvement papers from 1981-2000 was 2.5, while from 2001-2020, it was 6.8, indicating continuous high-quality improvement in the clinical laboratory sector. However, there is still room to establish a proper system to judge the quality of clinical laboratory literature and improve accreditation programs within the sector.

Discovery and induction on experiences and circumstances are the cornerstones of the development of a journal. As per a bibliometric perspective, the Journal Industrial Technology Innovation (ITI) is taken as an example to carry out multi-dimensional analysis by statistics and coupling of essential parameters and data in Issues 1~15, and implement strategic practices in Issues 16~18. Analysis show that, (1) Young authors of post-80s and post-90s are the main forces of paper publication, and their research papers need more revisions and refinements for the improvement of writing quality; (2) Coverage of the journal is increasingly broadened while urging expansion of more impacts; (3) Amount of funded research papers is decreasing, indicating requirement of deep understanding on the management structures and operation mechanisms of various kinds of affiliations. Practices show that, the development of the Journal ITI must be based on a long-term strategy, and following principles must be obeyed: (1) Giving priority to social benefits while unifying social and economic benefits; (2) Giving priority to the quality while unifying quality and quantity; (3) Avoiding academic discrimination while unifying expert attraction and new scholar encouragement; (4) Fostering academic editors as specialists of various fields for their project participation and discourse power strengthening.

This paper introduces the architecture and algorithms of TCMiner: a high performance data mining system for multi-dimensional data analysis of Traditional Chinese Medicine prescriptions. The system has the following competing advantages: (1) High Performance (2) Multi-dimensional Data Analysis Capability (3) High Flexibility (4) Powerful Interoperability (5) Special Optimization for TCM. This data mining system can work as a powerful assistant for TCM experts by conducting Traditional Chinese Medicine Data Mining such as Computer-Aided Medicine Pairing Analysis, Medicine Syndrome Correlation, Quality and Flavor Trend Analysis, and Principal Components Analysis and Prescriptions Reduction etc.KeywordsTraditional Chinese MedicineAssociation RuleMining Frequent PatternTask SchedulerData Mining SystemThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Quality of Voice in Patients With Partial Deafness Before and After Cochlear Implantation

Identification of characteristic aroma compounds in chicken meat and their metabolic mechanisms using gas chromatography–olfactometry, odor activity values, and metabolomics

PBF in Rwanda: what happened after the BTC-experience?

To extract potentially useful information from a large number of power quality monitoring data, this article carries out the analysis of power quality data by the use of multi-dimensional data analysis from multiple perspectives. Considering the data mining in data pretreatment, disturbance recognition related applications, then analyzes and builds a power quality cube. Through the cube analysis, the manager can have a comprehensive and detailed understanding of the trends of power quality data in order to operate in the power to make scientific decision-making.

The human nose has been used as a detector in gas chromatography analysis to evaluate odoriferous compounds related to aroma and quality of wine. Several olfactometric techniques are available to access the description, intensity, and/or duration of the odor of each compound. Olfactometry can be associated with one-dimensional gas chromatography or multidimensional gas chromatography, including heart-cut gas chromatography and comprehensive two-dimensional gas chromatography. Multidimensional gas chromatography may help to resolve coeluted compounds and detect important trace components for the aroma. The identification of odor-active compounds may help to differentiate wines according to terroir, grapes cultivars used in winemaking or types of aging, understand the role of fungal infection of grapes for wine quality, find the best management practices in vineyard and vinification to obtain the greatest quality. In addition, when the instrumental techniques are combined with sensory analysis, even more accurate information may be obtained regarding the overall wine aroma. This review discloses the state of the art of olfactometric methods and the analytical techniques used to investigate odor-active compounds such as one-dimensional gas chromatography, multidimensional gas chromatography, and comprehensive two-dimensional gas chromatography. The advances in knowledge of wine aroma achieved with the use of these techniques in the target and profiling approaches were also discussed.

In view of the lack of teaching resources and the impossibility of real-time sharing and application of teaching resources in English teaching, this paper proposes a multidimensional analysis and application of English teaching quality based on an artificial intelligence model. This paper analyzes the basic framework and application of the Internet of Things technology and puts forward the corresponding hierarchical classification and teaching quality monitoring mode. Secondly, the monitoring framework of the Internet of Things to achieve the quality of English teaching and then the basic theory of the Internet of Things is analyzed. Finally, the experiment shows that the average score of English has been greatly improved by comparing the traditional teaching mode and the new classroom quality monitoring mode under the Internet of Things technology. The main dimensions of teaching quality, such as teachers’ quality, teaching attitude, teaching content, and teaching methods, are analyzed in depth, and the coefficient of most sample data is more than 0.7, which has a good application effect. Whether running on the test set or the mixed test set, the accuracy of the new classroom quality monitoring model proposed in this paper is the highest among the three models. The correct rate on the test set can reach 99.71%, the correct rate on the mixed test set can reach 98.01%, and the correct rate can reach 98.67%, which shows the superiority of the performance of the new classroom quality monitoring model.

With the intensification of global climate change and environmental degradation, the goals of carbon peaking and carbon neutrality have become crucial strategies for promoting sustainable development in various countries. However, most studies on ecological environment quality (EEQ) focus on urban areas, with limited attention to county-level analyses, particularly regarding the complex interactions between climate, topography, and human activities. This study aims to address this gap by investigating the spatiotemporal evolution and multidimensional driving factors of EEQ in 107 counties of Shaanxi Province, China. Using the Google Earth Engine (GEE) platform and MODIS imagery, along with methods such as the Remote Sensing Ecological Index (RSEI), Hurst exponent, and GeoDetector, this study analyzed the evolutionary characteristics and driving mechanisms of county-level EEQ, and explored improvement and management strategies for different types of county EEQ within the framework of dual carbon goals. The results indicate that: 1) From 2000 to 2020, the overall EEQ in Shaanxi Province showed a fluctuating upward trend, improving from a moderate level to a good level, although some counties experienced slight degradation from 2010 to 2020. 2) The spatial distribution of county-level EEQ displayed a “low-high-low-high” gradient from north to south, indicating superior ecological conditions in the southern and central-northern counties, while northern regions faced significant ecological challenges. 3) The future trend of EEQ in Shaanxi Province is expected to be one of continuous improvement, although attention must be paid to the ongoing degradation risks in highly urbanized areas. 4) The spatial differentiation of county-level EEQ is primarily driven by climate factors and influenced by the synergistic effects of multiple factors. For counties with varying levels of EEQ, it is essential to comprehensively consider the interactions between climate, topography, and human factors, and to implement tailored carbon sequestration enhancement strategies. The results not only propose targeted approaches to reinforce carbon storage but also offer valuable policy guidance, thereby making a significant contribution to achieving the dual carbon goals at the county level.