Fiscal Shocks, Budgetary Pressures, and Public Education Expenditure Stabilization

Fiscal shocks exert budgetary pressures on school districts and constrain their ability to provide public education. An emerging literature examines the role of fiscal reserves to mitigate expenditure cuts in school districts. In the U.S. context, this article provides evidence that Kentucky school districts from school years 2001–2002 to 2013–2014 drained fiscal reserves and cut expenditures in response to revenue decreases. Further, school districts drained fiscal reserves to stabilize non-instructional expenditures, which have fixed costs. Collectively, the findings presented in this article build evidence that school districts strategically respond to budgetary pressures

Volcano monitoring and early warning on Mt Etna, Italy, using volcanic tremor – Methods and technical aspects

Recent activity on Mt Etna was characterized by 25 lava fountains occurred on Mt Etna in 2011 and the first semester of 2012. In summer 2012 volcanic activity in a milder form was noticed within the Bocca Nuova crater, before it came to an essential halt in August 2012. Together with previous unrests (e. g., in 2007-08) these events offer rich material for testing automatic data processing and alert issue in the context of volcano monitoring. Our presentation focuses on the seismic background radiation – volcanic tremor – which has a key role in the surveillance of Mt Etna. From 2006 on a multi-station alert system exploiting STA/LTA ratios, has been established in the INGV operative centre of Catania. Besides, also the frequency content has been found to change correspondingly to the type of volcanic activity, and can thus be exploited for warning purposes. We apply Self Organizing Maps and Fuzzy Clustering which offer an efficient way to visualize signal characteristics and its development with time. These techniques allow to identify early stages of eruptive events and automatically flag a critical status before this becomes evident in conventional monitoring techniques. Changes of tremor characteristics are related to the position of the source of the signal. Given the dense seismic network we can base the location of the sources on distribution of the amplitudes across the network. The locations proved to be extremely useful for warning throughout both a flank eruption in 2008 as well as the 2011 lava fountains. During all these episodes a clear migration of tremor sources towards the eruptive centres was revealed in advance. The location of the sources completes the picture of an imminent volcanic unrest and corroborates early warnings flagged by the changes of signal characteristics. Automatic real time data processing poses high demands on computational efficiency, robustness of the methods and stability of data acquisition. The amplitude based multi-station approach is not sensitive to the failure of single stations and therefore offers a good stability. On the other hand, the single station approach, exploiting unsupervised classification techniques, limits logistic efforts, as only one or few key stations are necessary. A common characteristics of both strategies is their robustness to disturbances (undesired transients like earthquakes, noise, short gaps in the continuous data flow). False alarms were not encountered so far. A critical issue it the reliability of data storage and access. Therefore, a specific hardware cluster architecture has been proposed for failover protection, including a Storage Area Network system. We present concepts of the software architectures which allow easy data access following predefined user policies. We also envisage the integration of seismic data and those originating from other scientific fields (e. g., volcano imagery, geochemistry, deformation, gravity, magneto-telluric). This will facilitate cross-checking of evidences encountered from the single data streams, in particular allow their immediate verification with respect to ground truth

Monitoring System of Eastern Sicily (Italy) devised by a specialist team (UFSO) at the INGV- Catania Section, Italy.

Eastern Sicily in Italy is well-known as a high seismic and volcanic risk area. From a monitoring point of view, a team/unit of people has been created (UFSO) with the task of managing all the activities connected to the faultless operation of the Working Room that is the strategic centre during periods of routine operations or in the case of emergency. Among the primary activities of monitoring and surveillance, the management of the video camera network located on the main Sicilian active volcanoes represents a major goal. This task is achieved by means of permanent, visible and infrared cameras together with similar mobile systems, in order to observe each phenomenon related to the volcanic activity. The expert staff can therefore make decisions, in real time, from useful information in order to understand the phenomena in action. With the aim of maximizing the results and performance of all the networks, the UFSO is attentive to the planning and realization of hardware and software systems that are always available in the mobile van unit. In this context, the staff actively participates in national and European research projects dealing with the development and use of new systems with high technological content. Another aspect of the work, moreover, is represented by the development of supervisory control software, namely software providing automatic control of the working systems. Such algorithms allow to immediately and remotely signal to the duty-personnel states of alert of several modules, indicating, when possible, the probable failure causes

Food literacy as a resilience factor in response to health-related uncertainty

Purpose: During the Covid-19 pandemic, people were deprived of their freedom, unable to engage in physical and social activities, and worried about their health. Uncertainty, insecurity, and confinement are all factors that may induce stress, uneasiness, fear, and depression. In this context, this study aims to identify possible relationships of emotions caused by health risks and restrictions to outdoor activities with well-informed decisions about food consumption. Design/methodology/approach: The theoretical framework of this research draws on the stimulus-organism-response paradigm yielding six research hypotheses. An online survey was designated to test these hypotheses. A total of 1,298 responses were gathered from Italy, Greece, and the United Kingdom. Data analyses include demographic group comparisons, moderation, and multiple regression tests. Findings: The results showed that when people miss their usual activities (including freedom of movement, social contact, travelling, personal care services, leisure activities, and eating at restaurants) and worry about their health and the health of their families, they turn to safer food choices of higher quality, dedicating more of their time and resources to cooking and eating. Research limitations/implications: The findings showcase how risk-based thinking is critical for management and marketing strategies. Academics and practitioners may rely on these findings to include extreme conditions within their scope, understanding food literacy as a resilience factor to cope with health risks and stimulated emotions. Originality/value: This study identified food behavioural patterns under risk-laden conditions. A health risk acted as an opportunity to look at food consumption as a means of resilience

Data mining in the context of monitoring Mt Etna, Italy

The persistent volcanic activity of Mt Etna makes the continuous monitoring of multidisciplinary data a first-class issue. Indeed, the monitoring systems rapidly accumulate huge quantity of data, arising specific problems of an- dling and interpretation. In order to respond to these problems, the INGV staff has developed a number of software tools for data mining. These tools have the scope of identifying structures in the data that can be related to volcanic activity, furnishing criteria for the identification of precursory scenarios. In particular, we use methods of clustering and classification in which data are divided into groups according to a- priori-defined measures of similarity or distance. Data groups may assume various shapes, such as convex clouds or complex concave bodies.The “KKAnalysis” software package is a basket of clustering methods. Currently, it is one of the key techniques of the tremor-based automatic alarm systems of INGV Osservatorio Etneo. It exploits both Self-Organizing Maps and Fuzzy Clustering. Beside seismic data, the software has been applied to the geo- chemical composition of eruptive products as well as a combined analysis of gas-emission (radon) and seismic data. The “DBSCAN” package exploits a concept based on density-based clustering. This method allows discovering clusters with arbitrary shape. Clusters are defined as dense regions of objects in the data space separated by re- gions of low density. In DBSCAN a cluster grows as long as the density within a group of objects exceeds some threshold. In the context of volcano monitoring, the method is particularly promising in the recognition of ash par- ticles as they have a rather irregular shape. The “MOTIF” software allows us to identify typical waveforms in time series, outperforming methods like cross-correlation that entail a high computational effort. MOTIF can recognize the non-imilarity of two patterns on a small number of data points without going through the whole length of data vectors. All the developments aforementioned come along with modules for feature extraction and post-processing. Spe- cific attention is devoted to the obustness of the feature extraction to avoid misinterpretations due to the presence of disturbances from environmental noise or other undesired signals originating from the source, which are not relevant for the purpose of volcano surveillance

Data Mining in the Context of Monitoring Mt Etna, Italy

The persistent volcanic activity of Mt Etna makes the continuous monitoring of multidisciplinary data a first-class issue. Indeed, the monitoring systems rapidly accumulate huge quantity of data, arising specific problems of andling and interpretation. In order to respond to these problems, the INGV staff has developed a number of software tools for data mining. These tools have the scope of identifying structures in the data that can be related to volcanic activity, furnishing criteria for the identification of precursory scenarios. In particular, we use methods of clustering and classification in which data are divided into groups according to apriori- defined measures of similarity or distance. Data groups may assume various shapes, such as convex clouds or complex concave bodies.The “KKAnalysis” software package is a basket of clustering methods. Currently, it is one of the key techniques of the tremor-based automatic alarm systems of INGV Osservatorio Etneo. It exploits both Self-Organizing Maps and Fuzzy Clustering. Beside seismic data, the software has been applied to the geochemical composition of eruptive products as well as a combined analysis of gas-emission (radon) and seismic data. The “DBSCAN” package exploits a concept based on density-based clustering. This method allows discovering clusters with arbitrary shape. Clusters are defined as dense regions of objects in the data space separated by regions of low density. In DBSCAN a cluster grows as long as the density within a group of objects exceeds some threshold. In the context of volcano monitoring, the method is particularly promising in the recognition of ash particles as they have a rather irregular shape. The “MOTIF” software allows us to identify typical waveforms in time series, outperforming methods like cross-correlation that entail a high computational effort. MOTIF can recognize the non-imilarity of two patterns on a small number of data points without going through the whole length of data vectors. All the developments aforementioned come along with modules for feature extraction and post-processing. Specific attention is devoted to the obustness of the feature extraction to avoid misinterpretations due to the presence of disturbances from environmental noise or other undesired signals originating from the source, which are not relevant for the purpose of volcano surveillance

Predictors of mortality following emergency open colectomy for ischemic colitis: A single-center experience

Background: Ischemic colitis (IC) is a severe emergency in gastrointestinal surgery. The aim of the present study was to identify the predictors of postoperative mortality after emergent open colectomy for IC treatment. Additionally, we compared postoperative outcomes of patients undergoing emergent colectomy due to aortic surgery-related IC (AS-IC group) vs. other IC etiologies (Other-IC group). Methods: We analyzed records of consecutive patients who underwent emergency open colectomy for IC between 2008 and 2019. Logistic regression analysis was performed to identify clinical and operative parameters associated with postoperative mortality. The AS-IC and Other-IC groups were compared for mortality, morbidity, ICU stay, hospital stay, and survival. Results: During the study period, 94 patients (mean age, 67.4 ± 13.7 years) underwent emergent open colectomy for IC. In the majority of cases, IC involved the entire colon (53.2%) and vasopressor agents were required preoperatively (63.8%) and/or intraoperatively (78.8%). Thirty-four patients underwent surgery due to AS-IC, whereas 60 due to Other-IC causes. In the AS-IC group, 9 patients had undergone endovascular aortic repair and 25 open aortic surgery; 61.8% of patients needed aortic surgery for ruptured abdominal aortic aneurism (AAA). Overall, 66 patients (70.2%) died within 90 days from surgery. The AS-IC and Other-IC groups showed similar operative outcomes and postoperative complication rates. However, the duration of the ICU stay (19 days vs. 11 days; p = 0.003) and of the total hospital stay (22 days vs. 16 days; p = 0.016) was significantly longer for the AS-IC group than for the Other-IC group. The rate of intestinal continuity restoration at 1 year after surgery was higher for the Other-IC group than for the AS-IC group (58.8% vs. 22.2%; p = 0.05). In the multivariate model, preoperative increased lactate levels, a delay between signs/symptoms' onset and surgery > 12 h, and the occurrence of postoperative acute kidney injury were statistically associated with postoperative mortality. Neither IC etiology (aortic surgery vs. other etiology) nor ruptured AAA was associated with postoperative mortality. Conclusion: Emergency open colectomy for IC is associated with high postoperative mortality, which appears to be unrelated to the IC etiology. Preoperative lactate levels, > 12-h delay to surgery, and postoperative acute kidney injury are independent predictors of postoperative mortality