This is a fast growing non profit service provider start-up mas Madhav predict and Training Institute . SIX mouth training program. In that training period we teach you how to reduce power loss or energy consumption by the varies parts of a machine. The time period of the training is 5 Days and 6 Hours. We also give the stiffen to our students.
We have proudly enrolled in our team professionals with dedication to the task on hand and with a track-record of field experience spread over a period of more than 29 years inMPTI Analysis and Condition Monitoring.
We at Vibrotech help the industries run without interruptions caused by factors known to the engineering mind, calling for urgent remedial measures. We offer the much needed helping hand in the form of consultancy services and training programmes ensuring unstoppable operation of rotating parts that serve as the back bone of industries.
Year 2015 had been a Great year for us where we celebrated the mark of 10th successful year completion. We are also proud to inform that our Founder/Director who has 31 years of rich experience in the field ofMPTI is listed under the International CertifiedMPTI Consultant by M/s.MPTI Institute, USA - the parent body forMPTI Engineers throughout the world.
We would like to thank all of our customers who continue their patronage & confidence in our team by repeating orders every year for the past 10 years.
We are also happy to announce that similar to Vibration analysis & In-situ Dynamic Balancing , we are also providing the following services on urgent, AMC & continuous monitoring services by our well trained & experienced engineers in ‐
Ground & BuildingMPTI Measurements
Motor Current Signature Analysis (MCSA).
Towards improving our Quality in rendering our services to all our valued customers, we are proud to announce that we are one of the fewMPTI consultant companies awarded with ISO 9001:2008 (Quality Standards), OHSAS 18001:2007 (Safety Standards) and ISO/IEC 17025: 2005 (Testing & Calibration).
Customer Base: Have increased manifold (more than 68%) with repeated calls from most of them indicating our high success rate of Analysis & Balancing.
Unique Assignment: Carrying outMPTI measurement & its effect on existing buildings for Metro Rail project, Solving failure and achieving required output in Coil laying Head for a major Steel Company.
Range of equipments analysed: From CNC machines to High speed / capacity Turbines & Compressors including heavy machineries like crushers & earth moving equipments.
Analysis carried out: Total 8235 equipments analysed (average 22 machines/day)
In-situ Dynamic Balancing: 495 rotors successfully balanced (success rate - 98.5%).
Timely Attending to customer requirements: 98%
Timely Emergency call attendance: 98% (less than 24 hours) - Rated very high as per industrial standards.
Customer Satisfaction: 98% (We serve almost all our customers repeatedly)
VIBROTECH TRAINERS & CONSULTANTS PVT. LTD., Chennai, wishes to Thank all our Valued Customers, Business Associates & Well Wishers for their continued confidence in our team.


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| DOSE: T+ 0:00 |
1 capsl |
oral |
MDMA |
(powder / crystals) |
| T+ 1:00 |
0.5 capsls |
insufflated |
MDMA |
(powder / crystals) |
I recently found myself in a new relationship, and I wasn't sure if I was ready for it. We had been seeing each other for about a month, and in that time, had been together everyday. I thoroughly enjoyed everything about Ryan. I had very strong feelings for him, but I have a really hard time opening up to people, whether it be good or bad.
Ryan has been in the rave scene for a couple of years. I never was, but have dropped about 4 times before. We decided that we would like to try it together. It took a lot of frenzied phone calls to secure three (large) gelcaps, containing light red/pink powder. We both swallowed one cap and sat on his bed watching a movie to wait for it to kick in.
It didn't take very long, perhaps 15 minutes. When I started feeling the first waves, I turned around and we started cuddling. In about five minutes, we were pretty fucked, but Ryan wanted to go outside and smoke a bowl. While we were in the garage, he said to me: 'I may say a lot of things tonight, but I want you to know that it isn't because of the drugs.'
 We both stripped (and we're both self conscious about our bodies), and pretty soon we were just laying there, me on top of him, and we just both started rambling about how amazing the other was. I easily said everything that I had wanted to say to him, but couldn't because of my lack of self confidence and fear of rejection. Everything came out perfect and right and carried the weight of what I was really feeling. I was also open to what he was saying (I usually don't respond well to compliments and will try to argue them away). It was very intense. It was also the first time I told him I loved him and vice versa - something we had both been wanting to say. I remember Ryan saying, in awe, 'this is what E was meant for.'
That was the most important part of the trip.
About an hour in, we both railed 1/2 of the last cap, and I started hallucinating hard (I was wondering why he had put his shirt back on, when he hadn't). I saw a cat, a clown, a melting man, and a woman in a wheel chair. We both saw the spinning stars on his ceiling. I had never hallucinated while on E before, and I was just mostly amused at the fact that I was hallucinating.
I remember looking in the mirror and realizing how beautiful I was. A lot of people have told me I was pretty, but I have always, always had a horrible self image. I think the E allowed me to stop focusing on my imperfections and allowed me to see myself as a whole person.
The best part of this is that is stuck. It still seems almost too good to be true, but I had more self confidence, and I was more open with Ryan. I still do and still am. I'm not always happy and euphoric, but the ratio of good days vs. bad has shifted dramatically (not all can be attributed to E, as I am with a wonderful person, but I believe the openess I experienced that night helped quite a bit).
Experience Reports are the writings and opinions of the individual authors who submit them.
Some of the activities described are dangerous and/or illegal and none are recommended by Erowid.


It is easy to generate and test solutions for
such tasks in Prolog. If this is done naively (either
in Prolog or with any other language), then it quickly leads to
infeasible programs, because there are typically too many
combinations to generate them all.
To efficiently solve combinatorial optimization tasks in many
cases of practical relevance, Prolog provides a declarative
solution called constraints. Importantly, constraints
can prune large parts of the search tree before the
search even begins, and also while the search is progressing. In
typical cases, this is vastly more efficient than naively
enumerating solutions. In addition, constraints retain the
generality we expect from relations, and so you can use
constraint-based Prolog programs
for generating, testing and completing
solutions of combinatorial tasks.
In principle, constraints can be provided by any programming
language. However, they blend in especially seamlessly
into logic programming languages like Prolog due
to their relational nature and built-in search and backtracking
mechanisms. For this reason, logic programming languages have
become the most important host languages for constraints, and all
widely used Prolog systems ship with several libraries or
built-in predicates for constraint solving.
CLP(X) stands for constraint logic
programming over the domainX.
Plain Prolog can be regarded as a CLP(H): Constraint
logic programming over Herbrand terms,
with (=)/2 and dif/2 as the most important
constraints that denote, respectively, equality and disequality
of terms.
There are dedicated libraries for several important domains.
Support of these libraries differs between Prolog systems, so
check your Prolog system's manual for more information.
For example, SICStus Prolog ships with:
CLP(FD) for integers
CLP(B) for Boolean variables
CLP(Q) for rational numbers
CLP(R) for floating point numbers
In Prolog,
declarative integer arithmetic
can thus be naturally used for solving combinatorial tasks.
Here is an example:
A chicken farmer also has some cows for a total
of 30 animals, and the animals
have 74 legs in all.
Note that no search was necessary at all. The constraint
solver has deduced the unique solution of the puzzle
via constraint propagation.
In industrial and academic use, the efficiency of a Prolog
system's constraint solvers is often an important factor when
choosing a system. This is because many commercial use cases
of Prolog also involve combinatorial optimization tasks.
Integers are the most relevant domain in practice, because
all finite domains can be mapped to finite subsets
of integers. Hence, all finite combinatorial
optimization tasks can be expressed via CLP(FD) constraints.
Example: Map Colouring
Let us consider map colouring, i.e., the task of assigning
colours to regions of a map in such a way that no
adjacent regions are assigned the same colour.
Video:
We can easily map this task to a combinatorial task
over integers, by using one variable for each region, and
one integer for each colour.
For concreteness, let us colour the following map:
We shall use the integers 0, 1, 2, ..., to represent
suitable colours. Moreover, we know from
the Four
Colour Theorem that at most 4 colours suffice.
regions(Rs) :-
Rs = [A,B,C,D,E,F],
Rs ins 0..3,
A #\= B, A #\= C, A #\= D, A #\= F,
B #\= C, B #\= D,
C #\= D, C #\= E,
D #\= E, D #\= F,
E #\= F.
Disequality constraints ((#\=)/2) are used to state that
pairs of integers that correspond to adjacent regions must
be different.
To obtain concrete solutions, we use labeling:
availability of global constraints tailored for specific applications.
Graphs
Graphs are an extremely important concept in mathematics
and computer science, and many combinatorial tasks can be
formulated as problems involving graphs. For example, the
preceding map colouring task is
an instance of a more general task
called graph colouring.
We distinguish between directed and undirected
graphs.
Directed graphs
A directed graph consists of:
a set V of vertices
a set A of arcs, which are directed edges of
the form v→w (v, w
∈ V).
In Prolog, there are different ways to represent
directed graphs. One way to do it is to write down the arcs
as Prolog facts:
Such a representation is called temporal, because solutions
are iteratively reported on backtracking. This
representation is very well suited for making large amounts
of data efficiently accessible. An example use case is a
set of public transport routes between different
locations.
A typical way to describe paths with such a representation is:
?- bagof(To, arc_from_to(From, To), Arcs).
From = a, Arcs = "bc"
; From = b, Arcs = "c"
; From = c, Arcs = "a".
With these predicates, we can obtain a representation of the graph
in the form of an adjacency list, where we store which
vertices are reachable from any given vertex:
?- findall(From-Ls, bagof(To, arc_from_to(From, To), Ls), Is).
Is = [a-"bc",b-"c",c-"a"].
In Prolog, this is often a good representation for graphs. We
can easily turn this into
an association list of the
form V→Vs to obtain logarithmic worst
case (and expected) access time when fetching the
vertices Vs that are reachable from any given
vertex V. For utmost efficiency, if your Prolog system
supports it, you can represent each vertex as a
Prolog variable, and attach information (including arcs) in
the form of
variable attributes.
This even allows destructive modifications in O(1). With this
method, you can for example efficiently compute the strongly
connected components of a directed graph.
See scc.pl for more
information.
Note that isolated vertices do not participate in
any arc, and so—at least in general—we also
need to represent the vertices separately:
vertices([a,b,c,d]).
In the adjacency list representation, the isolated
vertex d can be represented as d-[].
Undirected graphs
An undirected graph consists of:
a set N of nodes
a set E of undirected edges of the
form x−y (x, y
∈ N).
For example, let us reconsider the map colouring task from
a graph theoretic perspective. The CLP(FD) formulation
can be regarded as implicitly describing a graph that
is induced by the constraints that are stated between the
variables. We now make that graph available explicitly as a
Prolog term that represents an undirected graph:
?- bagof(E, edge(N, E), Es).
Es = "bcdf", N = a
; Es = "cda", N = b
; Es = "deab", N = c
; Es = "efabc", N = d
; Es = "fcd", N = e
; Es = "ade", N = f.
An explicit representation is well suited for further analysis of
the graph.
Trees
In graph theory, a tree is an undirected graph in which any
two nodes are connected by exactly one path.
In Prolog, trees are of special significance because
Prolog terms naturally correspond
to trees.
For example, we can fairly enumerate binary trees as follows:
?- length(Ls, _), phrase(tree(T), Ls).
Ls = [], T = nil
; Ls = [_A], T = node(_B,nil,nil)
; Ls = [_A,_B], T = node(_C,nil,node(_D,nil,nil))
; Ls = [_A,_B], T = node(_C,node(_D,nil,nil),nil)
; Ls = [_A,_B,_C], T = node(_D,nil,node(_E,nil,node(_F,nil,nil)))
; ... .
Further reading
Here are several applications that give you an impression of what
you can do with a CLP(FD) system:
Other constraint solvers and libraries also have important
applications. For example, see
logic puzzles for several use
cases of CLP(B), and a delayed
column generation example with library(simplex).