The Structure of Expectations of the Weekly Money Supply Announcement

This paper examines the structure of expectations of the weekly money supply announcement in the late 1970s. The data used are from a weekly telephone survey of money market participants. The rationality and structure of expectations are explored with the data organized in three ways:the mean response to each weekly survey, the pooled sample of individual responses, and time series of responses by each individual in the survey.The effect of data aggregation on rationality tests is investigated. The structure of the expectations data are also examined and it is found that both strong regressive influences and adaptive learning characterize the data.

Inflation and the Consumer

macroeconomics, consumer, inflation

Potential Failure Mode Analysis for Mineral Extraction Near Reservoirs

This research was performed to develop a method of determining the potential hazard of underground mining near surface bodies of water. Underground mining creates a void within the subsurface that is eventually filled by overburden material. This strain movement perpetrates through the strata layers through fracturing and bedding plane separation, creating a depression above the mined seam (subsidence). The effect of subsidence may occur rapidly over the course of several weeks to months (longwall mining, pillar extraction) or may occur slowly (room and pillar mining); in some cases over one hundred years. These strain movements may affect the physical properties of the overlying rock layers, specifically hydraulic conductivity as it pertains to this research. Hydraulic conductivity is the ability for a medium to transmit water when submitted to a hydraulic gradient. The change in hydraulic conductivity may permanently alter the local groundwater table, or create a pathway from a surface body of water into the mine void. This may lead to erosion around the reservoir rim, potentially causing uncontrolled water loss within the reservoir.;This research was performed to develop a methodology to determine potential seepage failure modes due to changes in hydraulic conductivity in overburden, caused by underground coal mining. The research is separated into four tasks: 1) literature review of mine subsidence prediction and empirical assessment, 2) a method to develop potential seepage failure mode analysis of a mine site, 3) numerical analysis assessing seepage at a field site, and 4) semi-quantitative sensitivity analysis of risk based events for seepage mode failure near reservoirs.;Analysis shows that subsidence due to underground mining affects the hydraulic conductivity of the overlying medium, affecting the localized groundwater table and creating a cone of depression where hydraulic conductivity is increased. The extent of the cone of depression from the mine void is referred to as the Angle of Groundwater Influence. If this angle intersects with a reservoir pool level, seepage from the reservoir may cause uncontrolled drawdown or erosion.;Computer model analysis was performed on a field site to show how this methodology is applied. It was analyzed for three different lateral offset distances based on various pool levels. The numerical modeling results show that the reservoir pool has minimal impact if it lies beyond the affected overburden of the mine. However, if the reservoir rim intersects the impacted area, the increased flow rate may initiate erosion in the subsurface potentially leading to a failure mode for the reservoir. Within the subsurface, the controlling factor is the rock layer with the highest initial hydraulic conductivity located above the fractured zone. At the modeled field site, the changes in groundwater flow rate below the reservoir rim increased beyond one order of magnitude at Probable Maximum Flood (PMF) pool level. The findings developed within the sensitivity and field site analyses were used to develop practical application of the methodology to aid in determining the potential hazard from underground mining on surface bodies of water

How to feed the cities? Co-creating inclusive, healthy and sustainable city region food systems

People drive transitions. Current urban living conditions, specifically food systems, challenge the health, wellbeing and coherence of individuals and whole societies, and for effective change toward resilient communities, people need to reinvent the way they produce, distribute and consume food. Consequently, in their communities' people are creating foodscapes and governing the transition toward sustainable local food systems. Here, we introduce a conceptual framework to develop this transformation through empowering the urban multi-stakeholder society as the agent of this process. To do so, we reviewed scientific evidence and experiences from seven selected City Regions (Albacete, Baku, Dresden, Izmir, Ljubljana, Megara, and Valparaiso) as case studies and conducted a SWOT analysis to explore the capacity of food systems to enhance multi-functionality of urban landscapes, with special focus on social cohesion and quality of life. We grasp existing policies; hone them and leverage policies and strategies toward human-centered actions for future proofing food systems.Peer Reviewe

Blood test shows high accuracy in detecting stage I non-small cell lung cancer.

BACKGROUND: In a previous study (Goebel et. al, Cancer Genomics Proteomics 16:229-244, 2019), we identified 33 biomarkers for an early stage (I-II) Non-Small Cell Lung Cancer (NSCLC) test with 90% accuracy, 80.3% sensitivity, and 95.4% specificity. For the current study, we used a narrowed ensemble of 21 biomarkers while retaining similar accuracy in detecting early stage lung cancer. METHODS: A multiplex platform, 486 human plasma samples, and 21 biomarkers were used to develop and validate our algorithm which detects early stage NSCLC. The training set consisted of 258 human plasma with 79 Stage I-II NSCLC samples. The 21 biomarkers with the statistical model (Lung Cancer Detector Test 1, LCDT1) was then validated using 228 novel samples which included 55 Stage I NSCLC. RESULTS: The LCDT1 exhibited 95.6% accuracy, 89.1% sensitivity, and 97.7% specificity in detecting Stage I NSCLC on the blind set. When only NSCLC cancers were analyzed, the specificity increased to 99.1%. CONCLUSIONS: Compared to current approved clinical methods for diagnosing NSCLC, the LCDT1 greatly improves accuracy while being non-invasive; a simple, cost-effective, early diagnostic blood test should result in expanding access and increase survival rate

Diagnosis of Non-small Cell Lung Cancer for Early Stage Asymptomatic Patients.

BACKGROUND/AIM: In 2016 in the United States, 7 of 10 patients were estimated to die following lung cancer diagnosis. This is due to a lack of a reliable screening method that detects early-stage lung cancer. Our aim is to accurately detect early stage lung cancer using algorithms and protein biomarkers. PATIENTS AND METHODS: A total of 1,479 human plasma samples were processed using a multiplex immunoassay platform. 82 biomarkers and 6 algorithms were explored. There were 351 NSCLC samples (90.3% Stage I, 2.3% Stage II, and 7.4% Stage III/IV). RESULTS: We identified 33 protein biomarkers and developed a classifier using Random Forest. Our test detected early-stage Non-Small Cell Lung Cancer (NSCLC) with a 90% accuracy, 80% sensitivity, and 95% specificity in the validation set using the 33 markers. CONCLUSION: A specific, non-invasive, early-detection test, in combination with low-dose computed tomography, could increase survival rates and reduce false positives from screenings