Values Of Different Indicators Research Articles

- Prediction of Serum Neuritin and Neuron-specific Enolase for Prognosis in Patients with Traumatic Brain Injury combined with Spinal Cord Injury

Abstract: Serum neuritin and neuron-specific enolase (NSE) have predictive value for the prognosis of patients with combined traumatic brain injury (TBI) and spinal cord injury (SCI). Studying their predictive effects has positive value for disease control and treatment. Sixty patients with combined TBI and SCI were recruited and rolled into three groups according to prognosis: Group I (n=42, favorable prognosis), Group II (n=11, poor prognosis), and Group III (n=7, death). Clinical indicators were compared between the groups, and the predictive value of different indicators for prognosis was analyzed. NSE and serum neuritin levels were considerably higher in Groups II and III than in Group I (P<0.05). Multivariate logistic regression analysis showed that hypotension, NSE, serum neuritin, S100β, and combined injuries to other organs were associated with poor prognosis in patients with combined TBI and SCI. The combination of NSE and neuritin had considerably higher sensitivity (Sen), specificity (Spe), and accuracy (Acc) in predicting prognosis (P<0.05). Serum neuritin and NSE have favorable predictive value for the prognosis of patients with combined TBI and SCI. They can be actively promoted in clinical practice

Diagnostic value of cytokines in severe childhood Mycoplasma pneumoniae pneumonia combined with Adenovirus infection

BackgroundTo explore the alterations of inflammatory markers and immune-related cytokines in children infected with Mycoplasma pneumoniae (MP) combined with Adenovirus (ADV).MethodsThe study population consisted of 201 children with MPP, and they were grouped according to whether they were coinfected with ADV infection and critically ill. Additionally, comparative analyses were performed. The diagnostic value of different indicators and combined indicators for SMPP combined with ADV was assessed using ROC curves.ResultsThere was no difference between group A1 and group A2, group B1 and group B2 in terms of age, gender, duration of hospitalisation and fever. The levels of calcitoninogen(PCT), lactate dehydrogenase concentration(LDH), interleukin(IL)-6, IL-8, IL-10, IL-4, IL-12P70, and IFN-γ in group A were higher than group B. The severe group (A1, B1) was significantly higher than the mild group (A2, B2) in terms of D-dimer, CRP, PCT, LDH, IL-6, IL-8, IL-10, IL-17a and number of patients with pleural effusion, solid lung changes. Among the individual indexes of D-dimer, CRP, N%,LDH, and PCT, the AUC of the combined test was 0.977, which was higher than that of the individual indicators. Among IL-6, IL-8, IL-10, and IL-17a, the AUC of the combined assay was 0.802, which was higher than that of the individual indicators.ConclusionMP combined with ADV infection was associated with increased expression levels of IL-6, IL-8, IL-10, IL-4, IL-12P70, IFN-γ, and LDH. IL-6, IL-8, IL-10, IL-17a, LDH, PCT, CRP, and D-dimer could be used as predictors of SMPP and the combined test can improve the diagnostic value.

Early Predictive Value of Different Indicators for Persistent Organ Failure in Acute Pancreatitis: A Systematic Review and Network Meta-Analysis.

In this study, we conducted this network meta-analysis (based on the ANOVA model) to evaluate the predictive efficacy of each early predictor. Persistent organ failure (POF) is one of the determining factors in patients with acute pancreatitis (AP); however, the diagnosis of POF has a long-time lag (>48h). It is of great clinical significance for the early noninvasive prediction of POF. We conducted a comprehensive and systematic search in PubMed, Cochrane library, Embase, and Web of Science to identify relevant clinical trials, case-control studies, or cohort studies, extracted the early indicators of POF in studies, and summarized the predictive efficacy of each indicator through network meta-analysis. The diagnostic odds ratio (DOR) was used to rank the prediction efficiency of each indicator. We identified 23 studies in this network meta-analysis, including 10,393 patients with AP, of which 2014 patients had POF. A total of 10 early prediction indicators were extracted. The mean and 95% CI lower limit of each predictive indicator were greater than 1.0. Albumin had the largest diagnostic odds ratio, followed by high-density lipoprotein-cholesterol (HDL-C), Ranson Score, beside index for severity in acute pancreatitis Score, acute physiology and chronic health evaluation II, C-reactive protein (CRP), Interleukin 6 (IL-6), Interleukin 8 (IL-8), Systemic Inflammatory Response Syndrome (SIRS) and blood urea nitrogen. Albumin, high-density lipoprotein-cholesterol, Ranson Score, and beside index for severity in acute pancreatitis Score are effective in the early prediction of POF in patients with AP, which can provide evidence for developing effective prediction systems. However, due to the limitations of the extraction method of predictive indicators in this study, some effective indicators may not be included in this meta-analysis.

The Posterosuperior Tetralogy Scoring System as a Practical Tool to Predict Shoulder Function After Posterosuperior Large-to-Massive Rotator Cuff Repairs.

Fatty infiltration (FI) or atrophy alone has been found to be inaccurate in predicting shoulder function after repair of large-to-massive rotator cuff tears (L/MRCTs), especially when a diverse extent of FI and atrophy presents in multiple rotator cuff muscles. The Posterosuperior Tetralogy Scoring System (PS-Tetra Score), which integrates FI and atrophy, was proposed to predict shoulder function after surgery. It was hypothesized was that a PS-Tetra Score ≥3 would be a risk factor for poor shoulder function after repair of posterosuperior L/MRCTs and would possess greater diagnostic value than using isolated FI or atrophy of the supraspinatus (SSP) or infraspinatus (ISP). Case-control study; Level of evidence, 3. A total of 187 arthroscopic repairs of posterosuperior L/MRCTs were reviewed. Magnetic resonance imaging evaluations were performed of FI and atrophy of the SSP and ISP, teres minor hypertrophy, tendon retraction, and acromiohumeral distance. A postoperative American Shoulder and Elbow Surgeons (ASES) score of 70 was used to subgroup patients according to shoulder function. Univariate and multivariate analyses were performed to determine the risk factors of poor shoulder function (ASES ≤70). The diagnostic values of different indicators for predicting shoulder function were evaluated. In univariate analysis, female sex, higher Goutallier grade of the SSP and ISP, positive SSP tangent sign, and PS-Tetra Score ≥3 was significantly associated with ASES score ≤70, whereas in binary logistic regression analysis, a PS-Tetra Score ≥3 was the only significant risk factor for poor shoulder function. The occurrence rate of poor function in shoulders with a PS-Tetra Score of 0, 1, 2, 3, and 4 was 0% (0/52), 0% (0/52), 19.57% (9/46), 58.06% (18/31), and 83.33% (5/6), respectively. PS-Tetra Score ≥3 possessed higher crude agreement (87.70%), specificity (90.97%), positive predictive value (62.16%), and area under the receiver operating characteristic curve (0.814) than the other 3 indicators, with relatively high negative predictive value (94.00%) and moderate sensitivity (71.88%). PS-Tetra Score ≥3 was a risk factor of poor shoulder function after repair of posterosuperior L/MRCTs and possessed greater diagnostic value than using isolated FI or atrophy of SSP or ISP alone for predicting shoulder function.

An integrative and multi-indicator approach for wildlife health applied to an endangered caribou herd

Assessing wildlife health in remote regions requires a multi-faceted approach, which commonly involves convenient samplings and the need of identifying and targeting relevant and informative indicators. We applied a novel wildlife health framework and critically assessed the value of different indicators for understanding the health status and trends of an endangered tundra caribou population. Samples and data from the Dolphin and Union caribou herd were obtained between 2015 and 2021, from community-based surveillance programs and from captured animals. We documented and categorized indicators into health determinants (infectious diseases and trace elements), processes (cortisol, pathology), and health outcomes (pregnancy and body condition). During a recent period of steep population decline, our results indicated a relatively good body condition and pregnancy rates, and decreasing levels of stress, along with a low adult cow survival. We detected multiple factors as potential contributors to the reduced survival, including Brucella suis biovar 4, Erysipelothrix rhusiopathiae and lower hair trace minerals. These results remark the need of targeted studies to improve detection and investigations on caribou mortalities. We also identified differences in health indicators between captured and hunter sampled caribou, highlighting the importance of accounting for sampling biases. This integrative approach that drew on multiple data sources has provided unprecedented knowledge on the health in this herd and highlights the value of documenting individual animal health to understand causes of wildlife declines.

Medical image segmentation method based on multi‐scale feature and U‐net network

AbstractIn medical image segmentation (MIS), better segmentation results can be obtained by training the deeper neural network. However, directly building too deep network will cause problems such as gradient disappearance, which will affect the segmentation effect. Therefore, a dilated inception U‐Net (DIU)‐net network is constructed by combining the multi‐scale feature fusion (MSFF) method and the concept of Inception in Google net based on U‐net, and its effectiveness is verified by experiments. The DIU‐net network's training accuracy has been improved in the lung computed tomography (CT) and fundus vascular CT image data sets. And the attenuation of the loss function is relatively stable, with the highest accuracy of 99.6%. In comparison of evaluation indicators, the values of different indicators of DIU‐net in the two data sets are higher than those of the comparison network. The DICE coefficient of DIU‐net in the lung CT image in the experiment is 0.986 on average, which is 0.2% higher than that of ResU‐net. SE value is 0.985, which is 1.9% higher than SegNet. Specificity value is slightly higher than the second segmentation effect. F1 score is 0.985, 0.6% higher than ResU‐net, area under curve value is 0.99, 0.7% higher than FCN‐8 s. In general, the DIU‐net network proposed in the study will not cause gradient disappearance and other problems in the experiment. At the same time, this method also shows high efficiency and has strong feasibility for the actual MIS.

Ecological Quality Status Evaluation of Port Sea Areas Based on EW-GRA-TOPSIS Model

It is of great significance to research a method to evaluate the ecological quality status of port sea areas objectively for the ecological environmental protection and sustainable development of sea areas. In this paper, a novel ecological quality status evaluation model is proposed based on the entropy weight method (EW), the gray relational analysis method (GRA), and the TOPSIS method. Firstly, a comprehensive evaluation indicator system is constructed from three aspects, namely seawater quality, sediments, and marine organisms. Secondly, the weight values of different indicators are obtained via the EW method, which can be obtained more objectively than via the subjective weighting method. Afterwards, the ecological quality status of port sea areas can be evaluated using the proposed method, which combines the advantages of the TOPSIS method and the GRA method. Finally, the effectiveness of the proposed EW-GRA-TOPSIS model is illustrated by a case study based on a port sea area of Tianjin. The results show that 66.7% of the monitoring stations in the port sea area are at a good level, 25% of them are at a fair level, only 8.3% are at a poor level, and none are at an excellent or bad level. Additionally, the evaluation results obtained using the proposed model are more consistent with the actual survey results than the compared typical TOPSIS method. It can objectively reflect the ecological quality status of monitoring stations, and consequently, it could be helpful for the marine department to make decisions on the sustainable development of port sea areas. In a further study, the influence of decision makers’ preferences on the evaluation results could be considered, and a more reasonable method to determine the evaluation grade standard should be researched to improve the proposed model.

Clinical characteristics and serum inflammatory markers of community-acquired mycoplasma pneumonia in children.

To compare the demographic and clinical features, laboratory and imaging findings in mycoplasma pneumoniae pneumonia (MPP) children with non-MPP (NMPP) children and general MPP (GMPP) children with refractory MPP (RMPP) children and analysis the relationship with the severity of disease. The study included 265 children with MPP and 230 children with NMPP in the Affiliated Changzhou No. 2 People's Hospital of Nanjing Medical University from 2020 to 2021. The children with MPP included RMPP (n = 85) and GMPP (n = 180). Demographic and clinical characteristics, laboratory and imaging findings of all children were measured as baseline data within 24 h after admission and the differences between MPP and NMPP, RMPP and GMPP patients were compared. ROC curves were used to evaluate the diagnostic and predictive value of different indicators for RMPP. Fever duration and hospital stay in children with MPP were longer than those with NMPP. The number of patients with imaging features of pleural effusion, lung consolidation and bronchopneumonia in MPP group was significantly higher than that in NMPP group. Compared with NMPP group, the levels of C-reactive protein (CRP), procalcitonin (PCT), serum amyloid A (SAA), erythrocyte sedimentation rate (ESR), lactic dehydrogenase (LDH), prothrombin time (PT), fibrinogen (FIB) and D-dimer and inflammatory cytokines (interleukin [IL]-6, IL-8, IL-10 and IL-1β) in MPP group were significantly higher (P < 0.05). The clinical symptoms and pulmonary imaging findings were more severe in RMPP group. The levels of white blood cell (WBC), CRP, PCT, SAA, ESR, alanine aminotransferase (ALT), LDH, ferritin, PT, FIB, D-dimer and inflammatory cytokines in RMPP group were higher than those in GMPP group. There was no significant difference in the level of lymphocyte subsets between the RMPP and GMPP group. IL-6, IL-10, LDH, PT, D-dimer and lung consolidation were independent risk factors for RMPP. IL-6 levels and LDH activity were good predictors of RMPP. In conclusion, there were differences in clinical characteristics and serum inflammatory markers between MPP group and NMPP group, RMPP group and GMPP group. IL-6, IL-10, LDH, PT and D-dimer can be used as predictive indicators for RMPP.

PECULIARITIES OF 24 HOUR AMBULATORY BLOOD PRESSURE MONITORING INDICES IN HYPERTENSIVE PATIENTS OF DIFFERENT AGE GROUPS

Relevance. Until now, there is an ongoing debate about the value of different indicators of blood pressure (BP) − systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse blood pressure (PBP) as predictors of essential hypertension (EH). In this regard the issue of target BP levels in patients of different ages remains studied incompletely. Objective is to determine peculiarities of 24 hour ambulatory blood pressure monitoring indices in patients with essential hypertension, stage II of different age groups. Materials and methods. ABPM indicators were determined in 130 patients who were divided into 4 groups. 1st group (n=57) – middle-aged hypertensive patients (45-59 years old); 2nd group (n=43) – elderly hypertensive patients (60-74 years old); 3rd group (n=15) – middle-aged patients without EH – control for the 1st group; 4th group (n=15) − elderly patients without EH − control for the 2nd group. Indices of ABPM were determined using a portable recorder ABPM-04 (company "Meditech", Hungary). Measurements of BP were performed every 15 minutes during the day (from 6 to 22 hours) and every 30 minutes at night (from 22 to 6 hours). We determined the following indices: the average daily SBP (mm Hg), the average daly DBP (mm Hg), the average BP (mm Hg); the maximal daily SBP (mm Hg), the maximal daily DBP (mm Hg). The daily index (DI) was also calculated − the percentage of BP decrease at night compared to daytime BP calculated in % DI of SBP, DI of DBP, DI of average BP. Results. In group 1 the average and maximal DBP were lower than in group 2 by 7,4% (p&lt;0,001) and 13,1% (p&lt;0,05), respectively. In group 1, the average daily and maximal SBP decreased by 3,8% and 4,1%, respectively; average heart rate by 3,8%; DI of SBP by 7,8% and DI of average BP by 5,8% but the data were unreliably smaller compared to the data of middle-aged patients. In group 1, the maximal DBP, DI SBP and DBP were not significantly different from the corresponding indicators in the control group. The maximal DBP was higher by 29,3% and amounted to 115,6±21,0 mm Hg and DI SBP and DI DBP are lower by 11,1% and 17,9%, respectively; and were equal to 9,6±7,4 and 13,8±8,1, respectively. In the 2nd group the following parameters decreased unreliably (p&gt;0,05): heart rate by 8,6% and was 63,0±9,1/min., DI SBP by 17,6% and was 8,9±5,5%; DI DBP by 16,1% and was 14,1±7,1%; DI of average BP– by 20,4% and was equal to 11,3±6,1%. Conclusions. It is found that with the age DBP decreases with steadily increased SBP in hypertensive patients. The average daily and the maximal daily DBP are significantly less by 7,4% (p&lt;0,001) and 13,1% (p&lt;0,05) accordingly in elderly patients compared with the middle-aged.

Network synchronization deficits caused by dementia and Alzheimer's disease serve as topographical biomarkers: a pilot study.

Mild cognitive impairment (MCI) is known as an early stage of cognitive decline. Amnestic MCI (aMCI) is considered as the preliminary stage of dementia which may progress to Alzheimer’s disease (AD). While some aMCI patients may stay in this condition for years, others might develop dementia associated with AD. Early detection of MCI allows for potential treatments to prevent or decelerate the process of developing dementia. Standard methods of diagnosing MCI and AD employ structural (imaging), behavioral (cognitive tests), and genetic or molecular (blood or CSF tests) techniques. Our study proposes network-level neural synchronization parameters as topographical markers for diagnosing aMCI and AD. We conducted a pilot study based on EEG data recorded during an olfactory task from a group of elderly participants consisting of healthy individuals and patients of aMCI and AD to assess the value of different indicators of network-level phase and amplitude synchronization in differentiating the three groups. Significant differences were observed in the percent phase locking value, theta-gamma phase-amplitude coupling, and amplitude coherence between the groups, and classifiers were developed to differentiate the three groups based on these parameters. The observed differences in these indicators of network-level functionality of the brain can help explain the underlying processes involved in aMCI and AD.

Quantitatively solitary wave tuning strategies based on one-dimensional cylindrical granular chains

One-dimensional (1D) granular systems support solitary wave propagation; however, solitary wave tuning, which serves as an effective strategy in stress wave control to achieve highly intelligent and efficient wave attenuation, still lacks. Herein, we design a 1D cylindrical granular system and comprehensively investigate solitary wave tuning strategies based on the system through mass, modulus, and thickness mismatch. We establish the experiment setups by using a vertical sliding track to observe the basic solitary wave traveling behavior as well as to validate the numerical simulation model. By considering the three governing wave tuning strategies, i.e., mass, modulus, and thickness mismatch, we parametrically explore the relations between the compression ratio and the mismatch values of different indicators. Based on the simple single-layer 1D chain, we further design a multi-layered 1D chain where the middle layers serve as tuning layers such that both “strong–weak” and “weak–strong” interfaces are created. A full tuning range is obtained by coupling different strategies based on the multilayer granular chains. Particularly, for a single layer granular chain, a mechanism map is established based on a normalized Ashby plot and numerical results of compression ratios, providing insights into precisely designing new attenuation systems. Results unlock the unique solitary wave tuning mechanism and provide design guidance for next-generation signal measurement, detection, and monitoring system.

MON-217 New Methods for Primary Aldosteronism Screening by Exploring the Values of Different Indicators and in Combination with Predictive Model

Background: Primary Aldosteronism (PA) is the most common cause of secondary hypertension. The endocrine society guidelines recommend case detection of PA by determining the aldosterone-renin ratio (ARR) under standard conditions, but the cut-off value of upright ARR varies greatly. In addition, as with all other biochemical tests, there are false positives and false negatives for upright ARR. In order to avoid false negative, some researchers have proposed plasma renin activity (PRA) < 1.0 ng/ml/h as a screening indicator. In order to avoid false positive caused by low PRA, Professor Young of the Mayo Clinic have proposed that the ARR combined with the plasma aldosterone concentration (PAC) > 15 ng / dL for PA screening. In 2019, Professor Young proposed PA screening positive when PAC≥10ng/dL and PRA<1.0 ng/ml/h. In addition, angiotensin II (AT-II) directly stimulates the synthesis of aldosterone. Compared with renin, the decrease of AT-II level and the increase of aldosterone to AT-II ratio (AA2R) may reflect the aldosterone autonomy secretion. However, whether these screening indicators are superior to upright ARR remains to be further verified. Therefore, either exploring the diagnostic efficacy of PAC, PRA, or AA2R as a screening indicator or even better developing a clinical prediction model combined with multiple indicators may provide a more accurate method for PA screening. Objective: To explore the value of different indicators and the logistic regression model (nomogram) for primary aldosteronism screening. Methods: The clinical data of 499 patients with PA and 479 patients with essential hypertension (EH) diagnosed by West China Hospital from 2009 to 2018 were retrospectively analyzed. The diagnostic performance of different screening methods was compared by plotting receiver operating characteristic curves (ROC). Results: The proportion of low potassium in PA group was higher than that in the EH group (86.20% vs. 36.50% P<0.001). The area under the ROC curve (AUC) of upright ARR was greater than that of upright AA2R, upright PRA, upright PAC, supine ARR, and lowest blood potassium (P<0.05); The AUC of logistic regression model (nomogram) [which consists of the upright PAC, the upright PRA, and the lowest blood potassium] was greater than that of upright ARR (96.3% vs. 94.6%, P<0.05). Conclusion: The upright ARR is the best single screening indicator; AA2R (radioimmunoassay) is not recommended for PA screening. Ultimately, the logistic regression model (nomogram) is superior to the upright ARR.

Developing a composite indicator to prioritize tree planting and protection locations

Trees provide numerous ecosystem services to benefit human health, and many cities have launched tree planting and management programs to increase tree populations and optimize tree locations through diverse tree priority schemes. Existing tree priority schemes are typically local-specific, expert-driven, and tree-planting-focused. In this study, a framework that captures interactions among the environment, tree and human demographic information is built. This framework provides a composite indicator, namely a tree priority planting or priority protection index (PPI), that can be integrated within a decision support system such as i-Tree Landscape to provide nationally consistent and locally relevant ways to strategically optimize tree planting and management locations across the entire United States. Three scenarios with the human health concerns are tested in a case study of New York City. The analyses are conducted at the census block group scale that is the finest-level scale available at i-Tree Landscape. The resulting PPI maps are analyzed using spatial statistics and compared against each other to investigate the impacts of alternative investments of limited public resources. The results show that: (1) tree priority patterns change greatly with alternative objectives; (2) adding more indicators to build PPIs lead to more diverse tree priority patterns as high (or low) values of different indicators are often not geographically coincident; (3) incorporating more indicators may not necessarily provide more useful information because the influences of individual indicators may be reduced and diluted by a higher level of aggregation; and (4) disaggregating PPIs may reveal corresponding contributions of individual indicators. Applying the proposed framework to build PPIs has important implications for tree priority effort, scientific exploration, education, and public engagement.

New Globalization as an Exogenous Factor in the Formation of the Foreign Trade Policy of the Republic of Korea

The article reveals the content of the "new globalization" concept and characterizes the readiness of the Republic of Korea (RK) for a new technological order. Based on the analysis of the values of different indicators (KOF Index of Globalization, Digital Evolution Index, Digital Adoption Index and a number of others), the conclusion about the high dynamics of Korean society integration into the new global economy is made. South Korea is among the leading countries in terms of digital implementation in business and public administration. The country has a well-established system of e-government, which, among other things, allows it to export the system to other countries of the world. The procedure of customs clearance of goods has been optimized, the "single window" technology has been introduced, public procurement is carried out only through electronic bidding. The traditional close relationship between the state and the chaebols makes it possible to successfully transfer the policy of digitalization into business practices. As a result, digitalization has a positive impact on the development of Korean foreign trade: on the one hand, it allows to increase exports due to the growth of goods and services of the IT sector and related industries, and on the other hand - to reduce the time and money spent on logistics, customs clearance and on the interaction between government agencies.

Effect of the number of non-conforming samples on the Kappa indicator values

Evaluate the quality of this measurement system is possible by using multiple methods, which are described in the methodology guides for evaluation of the measurement system quality. Within these methods, values of different indicators are evaluated. This paper deals with the cross tabulation method and foremost Kappa indicator. More specifically is examined the effect of the number of used nonconforming samples on explanatory power of this indicator. The effect on the kappa values is examined from several aspects that must be taken into account during evaluation of the quality of measurement system for attributive quality characteristics.

Diagnostic Value of CD64+ Index of Neutrophils for Patients with Leukemia Combined with Early Infection

To analyze the diagnostic value of CD64+ index of neutrophils for patients with leukemia combined with early infection. From June 2014 to June 2015, 100 AL patients admitted in Third People's Hospital of Qinghai province were chosen as AL group, among them 50 AL patients were in infection group and other 50 patients were in non-infection group; at the same time, 200 healthy people were chosen as control group. The sensitivity, specificity and Youden's index of PCT, CRP, Neu% and CD64+ index in 300 subjects were calculated, and the diagnostic value of different indicators were compared. In 100 AL patients, the sensitivitys of CD64+, Neu%, PCT and CRP were respectively 88.00%, 82.00%, 74.00% and 70.00%; their spesitivitys were respectively 80.00%, 78.00%, 82.00% and 84.00%; their Youden's indexex of were respectively 0.68, 0.60, 0.56 and 0.54. CD64+ index of neutrophils has diagnostic value for patients with leukemia combined with early infection.

SMOS prototype algorithm for detecting autumn soil freezing

A prototype algorithm for hemispheric scale detection of autumn soil freezing using space-borne L-band passive microwave observations is presented. The methodology is based on earlier empirical and theoretical studies of L-band emission properties of freezing and thawing soils. We expand a method originally developed for soil freeze–thaw (F/T) state detection from L-band tower based observations to satellite scale, applying observations from the European Space Agency's Soil Moisture and Ocean Salinity (SMOS) mission. The developed algorithm is based on first establishing spatially variable thresholds for L-band brightness temperatures representing frozen and thawed states of soil, and comparing these to current values of different indicators of soil freezing, calculated based on observed brightness temperature at different polarizations and incidence angles. An exponential relation between the freezing indicators and the depth of soil frost is developed based on a large amount of manual soil frost tube observations across Finland. An additional processing filter based on observed physical temperature and snow cover information is used to flag obvious F/T detection errors. The estimated soil F/T-states provided in this study are limited to the autumn freezing period, as melting snow in spring effectively prevents acquisition of information from the soil surface using microwaves for large areas in Northern latitudes. The F/T estimate is produced as daily information and provided in the equal-area scalable Earth (EASE) grid. Soil F/T-state is categorized into three discrete levels: ‘frozen’, ‘partially frozen’, and ‘thawed’, and accompanied with a quality data matrix estimating the data reliability for each freezing season separately. Comparisons to in situ data were conducted at 10 different locations in Finland, Northern America and Siberia. These comparison results indicate that the onset of autumn soil freezing can be estimated from SMOS observations to within 1 to 14 days, depending on the freezing indicator applied and the in situ data used in comparison. Although the initial results are encouraging, more comprehensive assessment of SMOS based soil F/T estimates still requires further comparison to other reference sites, particularly to sites with measurements available for all locally representative land cover types, as well as other satellite-based soil freezing products.

RAIDShield

Modern storage systems orchestrate a group of disks to achieve their performance and reliability goals. Even though such systems are designed to withstand the failure of individual disks, failure of multiple disks poses a unique set of challenges. We empirically investigate disk failure data from a large number of production systems, specifically focusing on the impact of disk failures on RAID storage systems. Our data covers about one million SATA disks from six disk models for periods up to 5 years. We show how observed disk failures weaken the protection provided by RAID. The count of reallocated sectors correlates strongly with impending failures. With these findings we designed RAIDS hield , which consists of two components. First, we have built and evaluated an active defense mechanism that monitors the health of each disk and replaces those that are predicted to fail imminently. This proactive protection has been incorporated into our product and is observed to eliminate 88% of triple disk errors, which are 80% of all RAID failures. Second, we have designed and simulated a method of using the joint failure probability to quantify and predict how likely a RAID group is to face multiple simultaneous disk failures, which can identify disks that collectively represent a risk of failure even when no individual disk is flagged in isolation. We find in simulation that RAID-level analysis can effectively identify most vulnerable RAID-6 systems, improving the coverage to 98% of triple errors. We conclude with discussions of operational considerations in deploying RAIDS hield more broadly and new directions in the analysis of disk errors. One interesting approach is to combine multiple metrics, allowing the values of different indicators to be used for predictions. Using newer field data that reports an additional metric, medium errors , we find that the relative efficacy of reallocated sectors and medium errors varies across disk models, offering an additional way to predict failures.

A METHOD FOR THE STATE EVALUATION OF DISTRIBUTED INFORMATION SYSTEM

Information system has many geographically distributed objects and a administrator can’t evaluate its current state or he must process too many indicators of state and this is not always possible. Article describes the method of evaluation a state the distributed information system that has realized with elements of the artificial intelligence. Artificial neural networks may be used for clustering and generalization of data and as a associative memory. This enables to minimize the time required for computing and search the solutions to emerging problems. This method is realized as the model that uses the processed data received from controlled objects of the distributed information system. The model calculates generalized indicator that determines the state of distributed information system as one value that is easy ranked. Because of territorial width and large numbers of objects, the administrator does not always have the opportunity to assess the current state of the information system. Processing a large number of values of different indicators is necessary for such an evaluation. The article describes a way to assess the current state of the distributed information system that is implemented using elements of artificial intelligence. The use of artificial neural networks for clustering and aggregation of the data, as well as associative memory, can minimize the time required for the calculations and searching solutions for emerging problems. The proposed method is implemented as a model, which has the source data that are processed by the data collected from sites of the distributed information system. The model calculates the common ratio that defines the current state of the distributed information system in a single and easy calculated value.

Defining and measuring urban regions: A sensitivity analysis

AbstractThis paper evaluates the impact of alternative city boundary definitions on economic performance. First we discuss the theoretical background and motivate the empirical work. Then we present the methodological concept of the sensitivity analysis, which will be applied to a variety of data of Zürich and Bern (the financial and the administrative centres of Switzerland) in order to see how the values of different indicators vary depending on the definition adopted. Finally we will show whether the empirical patterns found are statistically significant. The analysis shows, that the delimitation of a city or city region indeed matters.