Moving Milk Bottle Cap Mysteries: Ghosts, Poltergeists, or the Ideal Gas Law?
Videos of moving bottle caps on milk containers appearing across social media have captured degrees of media interest
In some news publications, the phenomena had been sourced as having a paranormal origin, albeit a bit tongue in cheek. Other posts have also attributed the mystery movements to ghosts, poltergeists or haunted phenomena
However most posts, like the one featured, involve persons who are fascinated by the inexplicable movements in themselves. The milk containers in the videos are often nearly empty. This observation helps in pointing toward a normal versus paranormal explanation for the events at hand
The Ideal Gas Law conveys that the state of an amount of gas is determined by its pressure (P), volume (V), and temperature (T). The law is usually expressed in the equation:
PV = nRT
Where n is the number of moles (molecular mass), R is a the gas constant
When the milk container is removed from the refrigerator, the temperature (T) rises as the bottle warms up. The volume (V) of the container is unchanged but there is greater kinetic energy among the (n) gas molecules within it. The result is higher pressure (P) within the container
The Ideal gas Law equation requires adjustments for high pressures and temperatures, and there should be no other attractive forces among the molecules. But it is a good approximation for the conditions seen in the videos
Another example of the Ideal Gas Law in action can be seen in the jumping coin experiment
Crashcourse. (2013, May 7). The Ideal Gas Law: Crash Course Chemistry #12. YouTube
Delaney76. (2008, April 20). Moving Milk Bottle Cap. YouTube
Elearnin. (2013, May 7). Coin jump up trick revealed | Science experiment. YouTube
Keegan, N. (2015, Dec 24). Spooky moment ‘ghost’ lifts lid off bottle of semi-skimmed milk. The Sun
Mirror.co.uk. (2015, Dec 22). Watch terrifying moment ‘ghost’ lifts lid off bottle of semi-skimmed milk. The Mirror
Nave, C.R. (2017). Hyperphysics: Ideal Gas Law. Department of Physics and Astronomy. Georgia State University
Wikipedia. (2018, May 11). Ideal gas law
Exploring Nursing Ghost Stories through Machine Learning: Topic Discovery with Latent Dirichlet Allocation
NOTE: Click to open graphics for an expanded and clearer view of the findings they contain
As reported in earlier posts, the Allnurses.com web site hosts a long-running moderated discussion thread called “Nursing Ghost Stories” (NGS). The NGS collection spans over a decade (2005-2017) amounting to 199 pages as of the time of this writing. As a dataset NGS contains multiple first and second hand accounts and commentary on paranormal type experiences
The archive contains classic examples of hauntings and poltergeist phenomena. Patients were generally the percipients in ghost experiences. Sometimes the ghosts in question appeared to be former nurses in period dress, or former doctors and patients, or former area residents. However, these kinds of paranormal experiences did not dominate the collection
In actuality, the NGS archive conveys several varieties of psi and post-mortem survival phenomena. The archive contains several examples of extrasensory perception and presentiment in particular
There were also examples of after-death communication (ADC), which are sensed-presence or apparitional experiences involving deceased family members or friends. Unlike hauntings which are place-centered, ADC encounters are person-centered involving meaningful coincidences (or synchronicities) for the percipients
The archive contains several reports of near-death experiences (NDEs). However, the more representative encounters involved nearing death awareness (NDA) type experiences. In NDA situations, terminally-ill patients experiencing death-bed visions will have perceptions of welcoming apparitions of deceased relatives or loved ones
Provided below are examples of exchanges regarding NDA situations as characterized by nurses working in long-term care and palliative care settings
I’ve been a hospice nurse for 5 years. I have been with hundreds of people at the time of their death & I can tell you first hand that if the patient is alert enough to speak, you’ll hear them talking to loved ones that have already passed over
That is so true. I, too am a hospice nurse and when pts. start talking to their dead relatives, you know that they have about a week MAX before they are gone
From experience I’ve learned that when a pt tells you they’re going to die…they usually do…and if they start talking to dead family members…they usually die…it’s like the family members have come to take them…..
As a follow-on to the earlier wordcloud project, we wondered whether unsupervised machine learning, specifically topic generation models, could discover the abovementioned themes in the NGS archive
view documents as having a latent semantic structure of topics that can be inferred from co-occurrences of words in documents
Various packages and libraries for natural language processing within Python were used to include: the Natural Language ToolKit (NLTK) for processing the data set; scikit-learn to prepare and fit the LDA model; pyLDAvis to display the results and t-Distributed Stochastic Neighbor Embedding (t-SNE) to map topic distances
The project pipeline involved: data set processing; conversion of words and documents into a document-term matrix and vector space; fitting the LDA models; and displaying the results
Processing. The data set was decomposed into 199 documents from its constituent web pages. In contrast to the wordcloud project, the set of stopwords was enlarged to find meaningful insights in the NGS archive
Conversion. Vector transformations converted the data set into a document-term matrix for mathematical processing. The rows of the matrix correspond to documents with columns corresponding to the frequency of a term
Model Fit/Display. The LDA model was fitted using ten topics. Words within topics were sorted and ranked with respect to their frequency in and relevance within a topic
Results. Although topics produced from the model are unlabeled, words within topics usually can be woven into a coherent theme
The first four pyLDAvis graphs provide the top 30 words and bigrams in Topics 1 through 4 using Count vectorization
Topic 1 is the most representative of the body of stories in the thread and generated around 86% of the content. Words in Topic 1 included: “nurse” and “patient”; both nurses and patients were percipients and sometimes sources of “ghost” experiences. If apparitions represented unrecognized persons, patients had “asked” whom they “saw.” Many apparitional encounters involved patients who were “heard” “talking” to deceased “family” members or a “friend.“ These telepathic types of apparitions were often described as “sitting” near the bedsides of patients, or transiting their rooms or into an adjacent “hall” on their “floor.” Overall, this could be considered an apparitional experiences topic
Topic 4 is also derived from user commentary and seems reflective of general discussions on the paranormal, religious and exceptional experiences. Discussions included: “paranormal” television, “movie” and “radio” entertainment; synchronicities (meaningful coincidences) and “photo” and other evidence from paranormal investigations. Discussions also involved ghost stories outside a nursing context; some were urban legends and a few were probably larks. Overall, this could be considered a paranormal discussions topic and it generated around 3% of the content
The fifth pyLDAvis graph provides the top 30 words in Topic 1 using TF-IDF vectorization.
The findings were close to those encountered for Topic 1 with the Count Vectorization. However, it appears to be a combined apparitional experiences and extrasensory perception topic accounting for 94% of the content.
This consolidation arises from the fact that TF-IDF vectorization lowers the contribution weight of commonly used words
This project again demonstrates the usefulness of topic generation models for finding meaningful patterns in masses of unlabeled or unstructured data.
The LDA topic discovery method indicated several varieties of psi and survival experiences that went beyond ghost stories
Greater insights could be gained by structuring the NGS dataset and labeling the experiential elements within it. Follow-on research could employ semi-supervised methods to train models to classify types of psi and survival experiences and to find correlates within them
Specifically, deep learning models could be trained on the semantics around typologies of apparitions with tagged documents. Parapsychology categorizes apparitions along four lines: living agent; crisis; post-mortem; and haunting
Nonetheless, the apparitional experiences in NGS appear roughly consistent with survey results elsewhere. Apparitional experiences rarely occur in the general population, but when they do, the apparitions are likely to represent recognized persons, known to the individuals who are perceiving them
Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of machine Learning research, 3 (Jan), 993-1022.
Gauld, A., & Cornell, A. D. (1979). Poltergeists. Routledge Kegan & Paul.
Kircher, P. and Callanan, M. (2017, Dec 14). NDEs and Nearing Death Awareness in the Terminally Ill. International Association for Near Death Studies (IANDS).
Natural Language Toolkit: NLTK 3.2.5 documentation. (2017, Sep 24). NLTK Project.
Pearson, P. (2014). Opening Heaven’s Door: What the Dying May be Trying to Tell Us about where They’re Going. Random House Canada. Sponsored
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., … & Vanderplas, J. (2011). Scikit-learn: Machine learning in Python. Journal of machine learning research, 12(Oct), 2825-2830.
Sievert, C., & Shirley, K. (2014). LDAvis: A method for visualizing and interpreting topics. In Proceedings of the workshop on interactive language learning, visualization, and interfaces (pp. 63-70).
What’s Your Best Nursing Ghost Story? (2017, Oct 30). AllNurses.com
pyLDAvis Graph of Topic 1 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®. All rights reserved.
pyLDAvis Graph of Topic 2 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®. All rights reserved.
pyLDAvis Graph of Topic 3 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®. All rights reserved.
pyLDAvis Graph of Topic 4 (Count Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®. All rights reserved.
pyLDAvis Graph of Topic 1 (TF-IDF Vectorization) from Nursing Ghost Stories Corpus. (2018, Apr 08). © Maryland Paranormal Research ®. All rights reserved.
Nursing Ghost Stories as told through Natural Language Processing
Language comes natural and easy to humans but is difficult for machines because of its structure and ambiguity. Ghost stories perhaps showcase that ambiguity as paranormal experiences are difficult to reconcile or explain within known scientific principles or inferences
Natural Language Processing (NLP) is a field of computing that enables computers to analyze, understand and communicate human language. Today natural language processing powers several technologies such as: speech recognition assistants; language translation; sentiment analysis; entity and relationship recognition; as well as text summation, parsing and analysis
The Natural Language Toolkit (NLTK) is a platform of libraries and programs for natural language processing written in the Python programming language. NLTK was developed at the University of Pennsylvania and first released in 2001
Word clouds are visual representations of a text, where the sizing of words displayed reflects their prominence or emphasis within the text. The word cloud application used here was developed with NLTK and other Python modules
Word clouds provide high-level analysis of themes associated with a corpora (body) of text. In business, they can be used to highlight pain points from customer feedback. For this effort, word clouds were applied to a collection of ghost experiences as told by nurses
Allnurses.com hosts a long-running discussion thread called “Nursing Ghost Stories” (NGS). The NGS collection spans over a decade (2005-2017) amounting to 199 pages as of the time of this writing. The NGS archive contains a mixture of first and second hand accounts along with commentary
Two corpora were developed from the NGS collection. One corpus contained plain text and another corpus was tokenized (tagged) by sentences, words and parts of speech. A word cloud was subsequently generated from the plain text corpus that is displayed above. Common stop words, for example prepositions were filtered out prior to the generation of the display
The word cloud is interesting for what it does and does not emphasize. For example, the words ghosts and hauntings along with their roots or extensions are not prominent in the display. Hauntings involve recurrent paranormal experiences commonly experienced in the form of ‘imitative noises” and in more elevated forms through apparitions. Many NGS discussions appear to involve sensed presence experiences
There also appears to be substantive emphasis on nearing death awareness (NDA) type experiences as said or told by patients, many of whom were in long-term care or hospice settings, to nurses under their supervision. In their final phases, terminally-ill patients often perceive welcoming apparitions or visitations from deceased relatives or loved ones. In NDA experiences, patients often appear to hold conversations with persons who are not physically present
As would be expected, terms of reference associated with medical profession are most prominent in the word cloud, however these terms could be filtered from future word clouds to potentially obtain deeper insights on the experiences
In the near-term, NGS corpora can be used to develop sentiment analysis. Also worth exploring are word pairings and conditional frequencies connected with them. A longer term effort would mix natural language processing with machine learning to characterize types of encounters within the NGS collection
Bird, S., Klein, E., & Loper, E. (2009). Natural language processing with Python: analyzing text with the natural language toolkit. “ O’Reilly Media, Inc.”.
Kircher, P. and Callanan, M. (2017, Dec 14). NDEs and Nearing Death Awareness in the Terminally Ill. International Studies for Near Death Studies (IANDS).
Natural Language Toolkit: NLTK 3.2.5 documentation. (2017, Sep 24). NLTK Project
Pearson, P. (2014). Opening Heaven’s Door: What the Dying May be Trying to Tell Us about where They’re Going. Random House Canada. Sponsored
What’s Your Best Nursing Ghost Story? (2017, Oct 30). AllNurses.com
Wordcloud from NGS Corpus. (2018, Feb 19). © Maryland Paranormal Research ®. All rights reserved.
Located in Kentucky is a greasy gritty dive bar known as
Bobby Mackeys Music World. While guests can come and enjoy riding a mechanical
bull or busting a move on the line dancing floor, they may also experience
The building was originally used as a slaughterhouse in the
1850s. During this time the owners would take any leftover body parts and
simply dump them into the well in the basement, resulting in a grisly and
reeking pile of remains. Eventually the slaughterhouse closed in the 1890s and
a local Satanic cult moved in. The Satanic cult was thought to have used the
well as a site for rituals. As a result the well has become what many believe
to be a portal to Hell. It is still thought to be a portal to Hell today.
In the 1896 a young
woman named Pearl Bryan met a grisly fate on the property. Pearl Bryan was the
daughter of a wealthy local farmer who fell in love with a man named Scott
Jackson. Jackson was a dental student who was alleged to be a member of the
Satanic cult that resided in the closed down slaughterhouse. After Pearl became
pregnant, Jackson and his friend, fellow dental student Alonzo Walling, decided
to perform their own abortion. Pearl was heavily sedated on cocaine, and the procedure
went on for hours before the two students realized that they had completely messed
up the job. Upon realizing their mistake, they murdered Pearl and chopped off
her head so that police could not recognize her. Her body was dumped in a field
on the property, and her head was supposedly used in a Satanic ritual. Now
visitors to the premises can sometimes spot the headless figure of Pearl roaming
Eventually the building was restored in the 1920s and became
a popular speakeasy for mobsters. In the 1950’s the building became another popular
nightclub named the Latin Quarter. During this time there was a dancer known as
Johanna. Johanna was one of the most popular dancers until she became pregnant with
local singer, Robert Randall’s, child. When Johanna’s father found out about
the baby, he had some of his shadier connections kill Randall. Johanna was
distraught and hung herself in a dressing room. Johanna is now one of the most
active ghosts on site. Visitors can spot her apparition hanging around her
dressing room area. Others have felt her touching them or have heard a
disembodied voice in the area.
Other incidents that happen on at the bar include furniture
moving on its own, banging, screaming, and Jukeboxes turning on and off by itself.
In honor of the Super Bowl, here are several stadiums which are home to more than just their respective living football teams.
In the Detroit Lions stadium it is believed that the ghost of Jimmy Hoffa can still be heard cheering on his favorite team. Jimmy Hoffa, the leader of the International Brotherhood of the Teamsters Union, was a major Detroit Lions fan up until his disappearance from a restaurant parking lot. Although no one can say what happened to him for sure, many believe that he was murdered. The former Giants stadium was also believed to be linked to the mysterious Hoffa disappearance. It was speculated that Hoffa’s body was buried underneath concrete under one of the end zones. However, there has never been any evidence to support this claim, and the stadium has since been demolished.
Memorial Stadium at Indiana University is haunted by a spirit known as “Mr. Plume.” “Mr Plume”, who’s real name was Michael Plume, was a student at the university in the 1960s. During this time, the Memorial Stadium was just being built. Only a few months away from its completion, the body of the 19 year old student was found hanging from the rafters of an incomplete section. Although he was in a construction site filled with dirt and dust, his shoes remained completely clean. Despite this odd detail, Plumes death was ruled a suicide. Now people who visit the stadium can sometimes still see the ghostly apparition of his body swinging from a rope in the spot that he died.
The Wisconsin Badger’s stadium is home to several ghostly specters. Long before it was turned into a sports field, it was the site of a training site for Union Soldiers during the Civil War. Nearby was also a prison camp for Confederate soldiers. Visitors to the area can sometimes still catch a glimpse of long gone soldiers wandering around.
Many prisons built in the 1800s and early 1900s were
massive foreboding structures which offered only the most basic needs to the
inmates they housed. The antiquated concept of reform was designed to break the
spirits and force conformity. The methods used were viewed even then as being
cruel and barbaric.
Many who study paranormal activity believe these prisons,
each with its own history of immense pain and suffering, attract spirits who
are caught between worlds.
They believe some of these spirits were too evil to move
on, others have old scores to settle and some wander the prison’s cell blocks
looking for the way out.
Years after “The Rock” was closed as a prison,
stories persist that Alcatraz is haunted. Ghost hunters have said they feel
parts of the island and areas of the prison evoke a certain
“strangeness,” but it was mostly employees, working in areas of the
prison alone, who have reported most of the unexplained events that haunt the
dark corridors of Alcatraz. Reports of areas that are suddenly cold,
unexplained clanging sounds, sobbing coming from empty corridors and reports
of Al Capone playing his banjo in the shower room.
Charles Dickens visited the prison in the 1840s and found
the conditions appalling. He described the inmates at Eastern Penn as being
“buried alive…” and wrote about the psychological torture the
inmates suffered at the hands of their captors.
Today, many claim to have met some of those tortured
souls, while walking through the deserted halls of Eastern State Penitentiary.
From weeping, giggling, whispering and paralyzing forces, this penitentiary
keeps paranormal investigators busy.
Also known as the Ohio State Reformatory, Mansfield
Reformatory is believed to hold the spirits of many of its worst criminals,
some of them being the prison employees, locked away forever.
Since its closure, rumors of the spirits of tortured
inmates who died in the prison fill the halls with restless energy, unable to
escape the prison’s bars. Guilty guards and tales or brutal prison officials
also contribute to the stories of the haunted facility, as they remained
trapped in the gruesome nightmare they created for the prisoners locked inside.
The ghosts are Mansfield do not seem shy however, since many a visitor
photographing the old prison has manage to capture images of orbs in their photographs.
In the late 1800s, Moundsville took over all executions
for the state. But the executions were only a small part of the violent past at
Moundsville. Suicide, murder and torturous and violent punishments
contributed to the death of hundreds of inmates. Today, some visitors and
employees claim to see evidence that many spirits still inhabit the old
penitentiary. Paranormal experts say the prison experiences residual haunting,
which they describe as tragic events from the past that are repetitively
replayed forever. The penitentiary also seems unable to keep prisoners
from coming in, although they are only heard and never seen, as they push the
circular entrance gate that guides them inside the prison walls.