From Right to Left it Had No End: Inconceivable

 

In everyday conversation we use imprecise and implied terms like “it’s a hot day”, “it’s an early morning meeting”, and “there is only a short time allotted to each speaker.” Ordinarily, we understand intuitively the implied meanings of these terms even though each is individual-specific. Thus, for some people, early morning implies a time period before 9:30 a.m., while for others it may be 6.00 a.m. Yet we can communicate easily in such fuzzy terms that the best computer is unable to replicate. There are several reasons for this difference, but a significant characteristic is the capability of human beings to communicate in fuzzy terms. “The difference between human brain and the computers lies in the ability of the former to think and reason in imprecise, non-quantitative terms.” It is this proficiency that makes it possible for humans to decipher different scripts and handwriting, comprehend a variety of sounds, interpretmultiple meaning responses, and focus on information that is relevant in order to make decisions. Unlike the computer, human brain has the power of reasoning and thinking logically, but also of understanding things globally, peripherally, and holistically. Cognition is possible even though a term may be imprecise or have several shades of meanings. Unlike the computer that deals with dichotomous categorizations, human beings communicate in nuances that may have multiple interpretations. Thus, a human being can perceive a piece of information that is fuzzy in nature and respond to it in an unambiguous, clear manner by using a range of possible interpretations. At present, our analytical procedures generally follow the Boolean logic system, in which the law of the excluded middle is deeply entrenched. For this reason, we can deal with data that can have only two possible interpretations — it is either true or false, means yes or no, and so on. This system precludes any possibility of a situation falling in between, not true, but not false either. The Boolean logician would place this into an “impossible” category and thus reject its validity. Yet, from experience we know that there are situations in which it is not possible to take either of the extreme possibilities. For example, a police detective may narrow down the suspects by deciding that “he is not innocent but not guilty either” at some early stage of investigation. The planning, prioritization, and direction that flows from the criminal intelligence steering committee will enable the intelligence analyst to clearly understand the dynamics of the institutional situation and its implications for his/her role.

 

 

Although fuzzy logic is being used extensively in electronics and mathematical sciences, it has found little or no application in the social sciences, especially criminology. As a mathematical system, fuzzy logic generalizes the Boolean logic and can be a very useful tool for the social sciences, where concepts and terms involve shades of meanings. This paper outlines the essential mathematics behind this approach and develops a technique that could be useful in building offender profiles from fuzzy descriptions provided by witnesses. The paper also suggests several other possible areas of applications of this mathematical system.

 

 

Descriptions of suspects that police officers receive are often fuzzy in nature. Offenders are described as “tall”, “dark”, “young,” or even “rude”, terms that are imprecise and admit a range of possibilities. In fact, policing itself involves many issues that are fuzzy and difficult to measure exactly. For example, officer’s services are often evaluated as being “good” or “average,” while gang activity related areas are described as “dangerous” or “rowdy”. All these characteristics are essentially fuzzy, and therefore difficult to use with common statistical techniques. The concept of fuzzy variables will be introduced here to criminal justice practitioners, and a fuzzy logic-based mathematical procedure will be described that is capable of handling such variables. Although fuzzy logic has become a much talked about technique in mathematical and engineering literature, it has not yet found application in social science fields, though some researchers such as McDowell and recently Wu and Desai have mentioned the possible use of this mathematical system in criminological research work. 

 

Apart from the natural generalization to the concept of belongingness, another clear advantage of using such a theory of logic is that it allows the structuring of all that is separated by imprecise terminology. Uncertain situations, language, thoughts, expressions, feelings, and even perceptions can now be modeled by mathematical techniques based upon this system of logic. The system is essentially based upon the axiom that there exist “fuzzy sets or classes with unsharp boundaries in which the transition from membership to non-membership is gradual rather than abrupt.

 

A set S is said to be fuzzy when an element can belong partially to it, rather than having to belong completely or not at all. Fuzzy set theory therefore begins with an assignment of grade of membership values which are not restricted to 0 (non-membership) or 1 (full membership). In classical set theory, membership is binary, since there are only two possible states, membership and non-membership. Conventionally, these are assigned the values 1 and 0, respectively. These two values comprise what can be called the valuation set, which is the set of possible membership values. However, a set is said to be fuzzy if the valuation set contains values between 0 and 1. In most versions of fuzzy set theory, the valuation set is the interval [0,1]. The higher the membership value, the more an element belongs to the concerned set S. The valuation set need not contain numerical values. Verbal membership values have also been utilized by Kempton17 in his anthropological studies of fuzzy linguist categories such as “absolutely not a”; “in some ways a”; “sort of a”; “primarily a”, “best example of a” etc. These membership values are merely an ordered set of verbal hedges, but they successfully elicit fuzzy judgments from respondents, as Nowakowska18 points out. Given the concept of degree of membership in the set S, the corresponding degree of membership in “not-S” (¬ S) called the negation of S is denoted as mS(x) = 1– mS(x) where mS(x) is membership value in S.

 

The unreliability of witnesses in identifying criminal suspects is well known to most police officers. Therefore, the focus on modeling the ways offenders perceive their likely targets appears a promising procedure to locate likely offenders. 

 

 

 

Acknowledgements:

www.aivd.nl    AIVD ©

Algemene Bestuursdienst – @Erik Akerboom ©

www.politie.nl  Politiekorpschef  @Janny Knol©

www.politie.nl WEB Politie - @Henk van Essen©

https://english.marechaussee.nl  Royal Netherlands Marechaussee    @Willem-Alexander King of the Netherlands©

 

 

Bibliography:

 

1.    Criminal Investigations – Crime Scene Investigation.2000

2.    Forensic Science.2006

3.    Techniques of Crime Scene Investigation.2012

4.    Forensics Pathology.2001

5.    Pathology.2005

6.    Forensic DNA Technology (Lewis Publishers,New York, 1991).

7.    The Examination and Typing of Bloodstains in the Crime Laboratory (U.S. Department of Justice, Washington, D.C., 1971).

8.   „A Short History of the Polymerase Chain Reaction". PCR Protocols. Methods in Molecular Biology.

9.    Molecular Cloning: A Laboratory Manual (3rd ed.). Cold Spring Harbor,N.Y.: Cold Spring Harbor Laboratory Press.2001

10.   "Antibodies as Thermolabile Switches: High Temperature Triggering for the Polymerase Chain Reaction". Bio/Technology.1994

11.   Forensic Science Handbook, vol. III (Regents/Prentice Hall, Englewood Cliffs, NJ, 1993).

12.   "Thermostable DNA Polymerases for a Wide Spectrum of Applications: Comparison of a Robust Hybrid TopoTaq to other enzymes". In Kieleczawa J. DNA Sequencing II: Optimizing Preparation and Cleanup. Jones and Bartlett. 2006

13.   Nielsen B, et al., Acute and adaptive responses in humans to exercise in a warm, humid environment, Eur J Physiol 1997

14.   Molnar GW, Survival of hypothermia by men immersed in the ocean. JAMA 1946

15.   Paton BC, Accidental hypothermia. Pharmacol Ther 1983

16.   Simpson K, Exposure to cold-starvation and neglect, in Simpson K (Ed): Modem Trends in Forensic Medicine. St Louis, MO, Mosby Co, 1953.

17.   Fitzgerald FT, Hypoglycemia and accidental hypothermia in an alcoholic population. West J Med 1980

18.   Stoner HB et al., Metabolic aspects of hypothermia in the elderly. Clin Sci 1980

19.   MacGregor DC et al., The effects of ether, ethanol, propanol and butanol on tolerance to deep hypothermia. Dis Chest 1966

20.   Cooper KE, Hunter AR, and Keatinge WR, Accidental hypothermia. Int Anesthesia Clin 1964

21.   Keatinge WR. The effects of subcutaneous fat and of previous exposure to cold on the body temperature, peripheral blood flow and metabolic rate of men in cold water. J Physiol 1960

22.   Sloan REG and Keatinge WR, Cooling rates of young people swimming in cold water. J Appl Physiol 1973

23.   Keatinge WR, Role of cold and immersion accidents. In Adam JM (Ed) Hypothermia – Ashore and Afloat. 1981, Chapter 4, Aberdeen Univ. Press, GB.

24.   Keatinge WR and Evans M, The respiratory and cardiovascular responses to immersion in cold and warm water. QJ Exp Physiol 1961

25.   Keatinge WR and Nadel JA, Immediate respiratory response to sudden cooling of the skin. J Appl Physiol 1965

26.   Golden F. St C. and Hurvey GR, The “After Drop” and death after rescue from immersion in cold water. In Adam JM (Ed). Hypothermia – Ashore and Afloat, Chapter 5, Aberdeen Univ. Press, GB 1981.

27.   Burton AC and Bazett HC, Study of average temperature of tissue, of exchange of heat and vasomotor responses in man by means of bath coloremeter. Am J Physiol 1936

28.   Adam JM, Cold Weather: Its characteristics, dangers and assessment, In Adam JM (Ed).Hypothermia – Ashore and Afloat, Aberdeen Univ. Press, GB1981.

29.   Modell JH and Davis JH, Electrolyte changes in human drowning victims.Anesthesiology 1969

30.   Bolte RG, et al., The use of extracorporeal rewarming in a child submerged for 66 minutes. JAMA 1988

31.   Ornato JP, The resuscitation of near-drowning victims. JAMA 1986

32.   Conn AW and Barker CA: Fresh water drowning and near-drowning — An update.1984;

33.   Reh H, On the early postmortem course of “washerwoman’s skin at the fingertips.” Z Rechtsmed 1984

34.   Gonzales TA, Vance M, Helpern M, Legal Medicine and Toxicology. New York, Appleton-Century Co, 1937.

35.   Peabody AJ, Diatoms and drowning – A review, Med Sci Law 1980

36.   Foged N, Diatoms and drowning — Once more.Forens Sci Int 1983

37.   "Microscale chaotic advection enables robust convective DNA replication.". Analytical Chemistry. 2013

38.   Sourcebook in Forensic Serology, Immunology, and Biochemistry (U.S. Department of Justice, National Institute of Justice, Washington, D.C.,1983).

39.   C. A. Villee et al., Biology (Saunders College Publishing, Philadelphia, 2nd ed.,1989).

40.   Molecular Biology of the Gene (Benjamin/Cummings Publishing Company, Menlo Park, CA, 4th ed., 1987).

41.   Molecular Evolutionary Genetics (Plenum Press, New York,1985).

42.   Human Physiology. An Integrate. 2016

43.   Dumas JL and Walker N, Bilateral scapular fractures secondary to electrical shock. Arch. Orthopaed & Trauma Surg, 1992; 111(5)

44.   Stueland DT, et al., Bilateral humeral fractures from electrically induced muscular spasm. J. of Emerg. Med. 1989

45.   Shaheen MA and Sabet NA, Bilateral simultaneous fracture of the femoral neck following electrical shock. Injury. 1984

46.   Rajam KH, et al., Fracture of vertebral bodies caused by accidental electric shock. J. Indian Med Assoc. 1976

47.   Wright RK, Broisz HG, and Shuman M, The investigation of electrical injuries and deaths. Presented at the meeting of the American Academy of Forensic Science, Reno, NV, February 2000

48. Broor SL, Kumar A, Chari ST, et al. 1989. Corrosive oesophageal strictures following acid ingestion: clinical profile and results of endoscopic dilatation.

49. Baud FJ, Barriot P, TOGS V, et al. 199 1. Elevated blood cyanide concentrations in victims of smoke inhalation.

50. Blackwell M, Robbins A. 1979. Arsine (arsenic hydride) poisoning in the workplace.