scholarly journals Signs of Reincarnation: Exploring Beliefs, Cases, and Theory by James Matlock

2021 ◽  
Vol 35 (1) ◽  
pp. 166-182
Author(s):  
Peter Mulacz

At 1606:22, Clipper 759 informed the tower that it was ready for takeoff. At 1606:24, the local controller cleared the flight for takeoff, and at 1606:30, the first officer acknowledged the clearance. The acknowledgement was the last radio transmission received from Clipper 759. On July 8, 1982, Pan American World Airways Flight 759 (Clipper 759), a Boeing 727-235, N4737, was a regularly scheduled passenger flight from Miami, Florida, to Las Vegas, Nevada, with an en route stop at New Orleans, Louisiana. About 1607:57 central daylight time, Clipper 759, with 7 crewmembers, 1 nonrevenue passenger on the cockpit jumpseat, and 137 passengers on board, began its takeoff from runway 10 at the New Orleans International Airport, Kenner, Louisiana. At the time of flight 759’s takeoff, there were showers over the east end of the airport and to the east end of the airport along the airplane’s intended takeoff path. The winds at the time were gusty, variable, and swirling. Clipper 759 lifted off the runway, climbed to an altitude of between 95 feet to about 150 feet above the ground, and then began to descend. At 1608:57, the Ground Proximity Warning System (GPWS) activated and “Whoop whoop pull up whoop. . . .” was recorded. The airplane struck a line of trees about 2,376 feet beyond the departure end of runway 10 at an altitude of about 50 feet above the ground. The airplane continued on an eastward track for another 2,234 feet hitting trees and houses and then crashed into a residential area about 4,100 feet from the end of the runway. The airplane was destroyed during the impact, explosion, and subsequent ground fire. One hundred forty-five persons on board the airplane and eight persons on the ground were killed in the crash. Six houses were destroyed; five houses were damaged substantially.1,2 Moreover, nine people on the ground suffered severe injuries. The aircraft hit the ground with a considerable left bank angle, firstly hitting an oak tree with the left wing, cutting the power and the telephone lines mounted on poles, then destroying the houses of the Schultz family, the neighboring house, and a few others, and eventually cartwheeled and broke into pieces. Kerosene spilled from the ruptured tanks and ignited although there was a thunderstorm with heavy rain; three members of the Schultz family staying in their house were badly burned, one of them died in hospital. Among those killed on the ground—actually the first victim along the swathe of destruction caused by the crashing/impacting aircraft—was Jennifer Schultz, then eleven years of age, who was in the carport (perhaps was talking on the telephone, sitting on a swing there as she used to do) when disaster struck. On March 11th, 2008, in Bartlesville, Oklahoma, a girl, Rylann, was born to the O’Bannion family. Rylann appeared to be developing earlier than usual, but she showed some curious habits, e.g., for some time she kept sleepwalking. She started complaining that her hair touching her back hurt her back; she drew dramatic fits about putting on shirts. The clothing, she would complain, hurt her back, neck, and shoulders—it felt like her skin was burning. Referring to a photograph she mentioned time and again, she had been “bigger” than on that picture, a statement that didn’t make sense to her mother at that point in time. Eventually, at the age of three years and five months, again touching the topic of having been “bigger” before, she said: “Mommy, I died. I was in our backyard. It was raining. I was alone but I wasn’t scared. Then the rain shocked me. It was raining a lot. There was a loud noise, then the rain shocked me. I floated up to the sky then.” As the O’Bannion family subscribed to the Catholic faith, reincarnation was not a subject to consider. Over time, Rylann added new bits of memory; at the age of five she started talking about what happened to her “in heaven” after her death (meeting God and Jesus, and ‘Grandy Sally’ whom she never had met in reality), and that “you can choose to come back if you died before you were supposed to.” Once, out of the blue, she said “I remember the name of Jennifer.”

2020 ◽  
Vol 20 (2) ◽  
pp. 1
Author(s):  
Ni Putu Nonik Prianti ◽  
Roddialek Pollo ◽  
Judi K. Nasjoro ◽  
Sulton Kharisma

Radar is able to provide information about extreme weather observations in the form of heavy rain, so it is important to find the level of accuracy of the radar in providing extreme weather information. So that with accurate data disaster mitigation can be done by creating an early warning system using radar data in order to minimize the impact that will occur. Comparative analysis of the estimated rainfall events on the radar with surface observation data shows a good level of accuracy, but the blankness of the data on the radar due to damage thus influences the decision making of the forecasters when providing extreme weather information quickly to the public. By knowing the radar accuracy level is quite good in estimating rain events, BMKG can provide weather information in the form of appropriate early warning so that people can anticipate extreme weather events


2012 ◽  
Vol 82 (3) ◽  
pp. 216-222 ◽  
Author(s):  
Venkatesh Iyengar ◽  
Ibrahim Elmadfa

The food safety security (FSS) concept is perceived as an early warning system for minimizing food safety (FS) breaches, and it functions in conjunction with existing FS measures. Essentially, the function of FS and FSS measures can be visualized in two parts: (i) the FS preventive measures as actions taken at the stem level, and (ii) the FSS interventions as actions taken at the root level, to enhance the impact of the implemented safety steps. In practice, along with FS, FSS also draws its support from (i) legislative directives and regulatory measures for enforcing verifiable, timely, and effective compliance; (ii) measurement systems in place for sustained quality assurance; and (iii) shared responsibility to ensure cohesion among all the stakeholders namely, policy makers, regulators, food producers, processors and distributors, and consumers. However, the functional framework of FSS differs from that of FS by way of: (i) retooling the vulnerable segments of the preventive features of existing FS measures; (ii) fine-tuning response systems to efficiently preempt the FS breaches; (iii) building a long-term nutrient and toxicant surveillance network based on validated measurement systems functioning in real time; (iv) focusing on crisp, clear, and correct communication that resonates among all the stakeholders; and (v) developing inter-disciplinary human resources to meet ever-increasing FS challenges. Important determinants of FSS include: (i) strengthening international dialogue for refining regulatory reforms and addressing emerging risks; (ii) developing innovative and strategic action points for intervention {in addition to Hazard Analysis and Critical Control Points (HACCP) procedures]; and (iii) introducing additional science-based tools such as metrology-based measurement systems.


2021 ◽  
Vol 54 (1) ◽  
pp. 163-185
Author(s):  
Sung Min Han ◽  
Mi Jeong Shin

AbstractIn this article, we argue that rising housing prices increase voter approval of incumbent governments because such a rise increases personal wealth, which leads to greater voter satisfaction. This effect is strongest under right-wing governments because those who benefit from rising prices—homeowners—are more likely to be right-leaning. Non-homeowners, who are more likely to vote for left-leaning parties, will view rising housing prices as a disadvantage and therefore feel the government does not serve them well, which will mitigate the advantage to left-wing governments. We find support for our arguments using both macro-level data (housing prices and government approval ratings in 16 industrialized countries between 1960 and 2017) and micro-level data (housing prices and individuals’ vote choices in the United Kingdom using the British Household Panel Survey). The findings imply that housing booms benefit incumbent governments generally and right-wing ones in particular.


Author(s):  
Arthur C. Trembanis ◽  
Enrico Duo ◽  
Stephanie Dohner ◽  
Edoardo Grottoli ◽  
Paolo Ciavola

Abstract. Developing and implementing a quick response post-storm survey protocol has the potential to improve impact assessments of coastal storms. Pre- and post-event surveys are essential to properly quantify the storm impacts on the coast. In this study, a combination of traditional RTK GPS and Unmanned Aerial Vehicle drone platform was utilized as part of a coordinated storm response workflow. The comprehensive approach employed in this pilot case study was conducted on the Emilia-Romagna coast (Italy), in the immediate aftermath of an extreme storm event that impacted the shoreline on the 5th–6th February 2015 called the Saint Agatha Storm. The activities were supported by timing information on the approaching storm provided by the regional early warning system. We collected aerial photos from a commercial off-the-shelf drone immediately after the Saint Agatha Storm and generated both orthomosaic and digital elevation models utilizing structure-from-motion photogrammetry techniques. The drone- based survey approach allowed us to quickly survey an area of 0.25 km2 within a 10-minute flight resulting in a ground sampling distance of 2.5 cm/pixel. Flooding and erosion impacts are analyzed and presented for the target study area. Limitations and possible applications for coastal management of the quick response post-storm surveying protocol are highlighted.


2013 ◽  
Vol 61 (10) ◽  
pp. E12
Author(s):  
John Moscona ◽  
Sumit Tiwari ◽  
Kevin DeAndrade ◽  
Henry Quevedo ◽  
Matthew Peters ◽  
...  

Author(s):  
Bernhard Kienesberger ◽  
Christoph Arneitz ◽  
Vanessa Wolfschluckner ◽  
Christina Flucher ◽  
Peter Spitzer ◽  
...  

AbstractThis study focuses on the impact of a prevention program regarding dog bites in children. As a consequence of our previous investigation in 2005, we have initiated a child safety program for primary school children starting January 2008 until present to teach children how to avoid dog attacks and how to behave in case of an attack. In our retrospective study, we analyzed all patients younger than 15 years presenting with dog-related injuries between 2014 and 2018. As the main indicator for success of the prevention measures taken, we have defined the severity of injury in comparison to our previous study. Out of 296 children with dog-related injuries, 212 (71.6%) had sustained a dog bite. In the vast majority (n = 195; 92%), these patients presented with minor injuries; the extremities were most commonly affected (n = 100; 47%). Injuries to the head (n = 95; 45%) and trunk (n = 18; 8%) were less frequent. The proportion of severe injuries (8%) was significantly lower compared to our previous study, where 26% of children presented with severe injuries necessitating surgical intervention, while the number of patients requiring in-hospital treatment declined from 27.5% in the period 1994–2003 to 9.0% in the period between 2014 and 2018 (p < 0.05).Conclusion: Teaching of primary school children may effectively reduce the injury severity of dog bites. What is Known:• Dog bites are a substantial healthcare problem especially in children. What is New:• This study shows that a broad-based prevention program for primary school children can effectively decrease the severity but not the frequency of dog bite injuries in children.


2021 ◽  
Vol 10 (8) ◽  
pp. 557
Author(s):  
Qiuping Li ◽  
Haowen Luo ◽  
Xuechen Luan

Heavy rain causes the highest drop in travel speeds compared with light and moderate rain because it can easily induce flooding on road surfaces, which can continue to hinder urban transportation even after the rainfall is over. However, very few studies have specialized in researching the multistage impacts of the heavy rain process on urban roads, and the cumulative effects of heavy rain in road networks are often overlooked. In this study, the heavy rain process is divided into three consecutive stages, i.e., prepeak, peak, and postpeak. The impact of heavy rain on a road is represented by a three-dimensional traffic speed change ratio vector. Then, the k-means clustering method is implemented to reveal the distinct patterns of speed change ratio vectors. Finally, the characteristics of the links in each cluster are analyzed. An empirical study of Shenzhen, China suggests that there are three major impact patterns in links. The differences among links associated with the three impact patterns are related to the road category, travel speeds in no rain days, and the number of transportation facilities. The findings in this research can contribute to a more in-depth understanding of the relationship between the heavy rain process and the travel speeds of urban roads and provide valuable information for traffic management and personal travel in heavy rain weather.


2010 ◽  
Vol 15 (13) ◽  
Author(s):  
P J Nogueira ◽  
A Machado ◽  
E Rodrigues ◽  
B Nunes ◽  
L Sousa ◽  
...  

The experience reported in an earlier Eurosurveillance issue on a fast method to evaluate the impact of the 2003 heatwave on mortality in Portugal, generated a daily mortality surveillance system (VDM) that has been operating ever since jointly with the Portuguese Heat Health Watch Warning System. This work describes the VDM system and how it evolved to become an automated system operating year-round, and shows briefly its potential using mortality data from January 2006 to June 2009 collected by the system itself. The new system has important advantages such as: rapid information acquisition, completeness (the entire population is included), lightness (very little information is exchanged, date of death, age, sex, place of death registration). It allows rapid detection of impacts (within five days) and allows a quick preliminary quantification of impacts that usually took several years to be done. These characteristics make this system a powerful tool for public health action. The VDM system also represents an example of inter-institutional cooperation, bringing together organisations from two different ministries, Health and Justice, aiming at improving knowledge about the mortality in the population.


Flood is one of the most devastating natural calamities affecting parts of the state from past few years. The recurring calamity necessitates an efficient early warning system since anticipation and preparedness play a key role in mitigating the impact. Though heavy and erratic rainfall has been marked as one of the main reasons for flood in several places, flood witnessed by various regions of Kerala was the result of sudden opening of reservoirs indicating poor dam management. The unforeseen flow of water often provided less time for evacuation. Prediction thus plays key role in avoiding loss of life and property, followed by such calamities. The vast benefits and potentials offered by Machine Learning makes it the most promising approach. The developed system is a model by taking Malampuzha Dam as reference. Support Vector Machine (SVM) is used as machine learning method for prediction and is programmed in python. The idea has been to create early flood prediction and warning system by monitoring different weather parameters and dam-related data. The feature vectors include current live storage, current reservoir level, rainfall and relative humidity from the period 2016-2019. Based on the analysis of these parameters, the open/closure of shutters of the dam is predicted. Release of shutters has varied impacts in the nearby regions and is measured by succeeding prediction, by mapping regions on grounds of level warning to be issued. Warning is issued through Flask-based server, by identifying vulnerable areas based on flood hazard reference for regions. The dam status prediction model delivered highest prediction accuracy of 99.14% and associated levels of warning has been generated in the development server, thus preventing unexpected release.


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