The document describes AFMM, a program for parametrizing molecular mechanics force fields. AFMM iteratively optimizes force field parameters to fit normal modes from quantum chemical calculations. It minimizes a merit function considering both vibrational frequencies and eigenvector projections. The program uses a Monte Carlo algorithm to refine parameters and improve the fit to reference quantum data, replacing manual parametrization.
This is a Powerpoint for basic understanding regarding Molecular dynamics and NAMD simulation to providing basic information, schematic representation, to understanding the mechanism or process of molecular dynamics ( MD), and NAMD simulation brief discussion.
A Research on Optimal Power Flow Solutions For Variable LoaIJERA Editor
This paper presents the optimal power flow solutions under variable load conditions. In this article we present the recent trend towards non-deterministic (random) search techniques and hybrid methods for OPF and give the conclusions. These methods have become popular because they have a theoretical advantage over the deterministic methods with respect to handling of non convexity, dynamics, and discrete variables. Present commercial OPF programs can solve very large and complex power systems optimization problems in a relatively less time. In recent years many different solution methods have been suggested to solve OPF problems. The paper contributes a comprehensive discussion of specific optimization techniques that can be applied to OPF Solution methodology.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Energy minimization methods - Molecular ModelingChandni Pathak
Methods to minimize the energy of molecules during drug designing - Computational chemistry. According to the PCI syllabus, B.Pharm 8th Sem - Computer-Aided Drug Design (CADD).
Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQ...journalBEEI
Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.
This is a Powerpoint for basic understanding regarding Molecular dynamics and NAMD simulation to providing basic information, schematic representation, to understanding the mechanism or process of molecular dynamics ( MD), and NAMD simulation brief discussion.
A Research on Optimal Power Flow Solutions For Variable LoaIJERA Editor
This paper presents the optimal power flow solutions under variable load conditions. In this article we present the recent trend towards non-deterministic (random) search techniques and hybrid methods for OPF and give the conclusions. These methods have become popular because they have a theoretical advantage over the deterministic methods with respect to handling of non convexity, dynamics, and discrete variables. Present commercial OPF programs can solve very large and complex power systems optimization problems in a relatively less time. In recent years many different solution methods have been suggested to solve OPF problems. The paper contributes a comprehensive discussion of specific optimization techniques that can be applied to OPF Solution methodology.
The peer-reviewed International Journal of Engineering Inventions (IJEI) is started with a mission to encourage contribution to research in Science and Technology. Encourage and motivate researchers in challenging areas of Sciences and Technology.
Energy minimization methods - Molecular ModelingChandni Pathak
Methods to minimize the energy of molecules during drug designing - Computational chemistry. According to the PCI syllabus, B.Pharm 8th Sem - Computer-Aided Drug Design (CADD).
Signature PSO: A novel inertia weight adjustment using fuzzy signature for LQ...journalBEEI
Particle swarm optimization (PSO) is an optimization algorithm that is simple and reliable to complete optimization. The balance between exploration and exploitation of PSO searching characteristics is maintained by inertia weight. Since this parameter has been introduced, there have been several different strategies to determine the inertia weight during a train of the run. This paper describes the method of adjusting the inertia weights using fuzzy signatures called signature PSO. Some parameters were used as a fuzzy signature variable to represent the particle situation in a run. The implementation to solve the tuning problem of linear quadratic regulator (LQR) control parameters is also presented in this paper. Another weight adjustment strategy is also used as a comparison in performance evaluation using an integral time absolute error (ITAE). Experimental results show that signature PSO was able to give a good approximation to the optimum control parameters of LQR in this case.
Techniques for Minimizing Power Consumption in DFT during Scan Test ActivityIJTET Journal
Lessening in test force is vital to enhance battery lifetime in versatile electronic gadgets utilizing intermittent individual test. It's to expand dependability of testing, and to lessen test expense. A conservative test set with exceedingly viable examples, every identifying different issues, is attractive for lower test expenses. Such examples build exchanging action amid dispatch and catch operations. In this paper, we exhibit a novel circuit strategy to essentially dispense with test force dissemination in combinational logic by veiling sign moves at the logic inputs amid sweep moving is exhibited. We execute the concealing impact by embeddings an additional supply gating transistor in the supply to ground way for the first-level doors at the yields of the output flip-flops. The gating transistor supply is killed in the output in mode, basically gating the supply. Further, DFT punishments are decreased by embracing specific trigger Scan structural planning. This building design diminishes exchanging action in the circuit-under-test (CUT) and builds the clock recurrence of the checking methodology. The assistant chain moves in the contrast between sequential test vectors and just the obliged moves (alluded to as trigger information) are connected. Power necessities are significantly decreased by the utilization of a two-stage heuristic technique. Utilizing ISCAS 89 benchmark circuits, this adequacy is to enhance SoC test measures (power, time, and information volume) is tentatively assessed and affirmed.
Maximum Loadability Enhancement with a Hybrid Optimization MethodjournalBEEI
Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
This paper recommends the use of grasshopper optimization algorithm (GOA), a nature-inspired optimization algorithm, for optimizing switching-angle applied to cascaded H-bridge multilevel inverter (CHBMLI). Switching angles are selected based on the minimum value of the objective function formulated using the concept of selective harmonic minimization pulse width modulation (SHMPWM) technique. MATLAB/Simulink-PSIM dynamic co-simulation conducted on a 3-phase 9-level CHBMLI shows that the CHBMLI controlled using GOA derived switching-angle is able to respond to varying modulation index demand and synthesize an AC staircase output voltage waveform with the desired fundamental harmonic and minimized selected low-order harmonics. Compared to Newton Raphson (NR) technique, GOA is able to find optimum switching-angle solutions over a wider modulation index range. Compared to Genetic Algorithm (GA), GOA is able to find global minima with higher probability. The simulation results validate the performance of GOA for switching-angle calculation based on the concept of SHMPWM.
STUDY OF TRANSIENT STABILITY BY TRANSIENT ENERGY FUNCTIONcscpconf
Stability analysis programs are a primary tool used by power system planning and operating engineers to predict the response of the system to various disturbances. Important onclusionsand decisions are made based on the results of stability studies. The conventional method of analyzing stability is to calculate the transient behaviour of generators due to a given disturbance. Direct methods of stability analysis identify whether or not the system will remain stable once the disturbance is removed by comparing it with a calculated threshold value.Direct methods not only avoid the time consuming solutions required in the conventional
method, but also provide a quantitative measure of the degree of system stability. Thisadditional information makes direct methods very attractive when the relative stability of
different plans must be compared or when stability limits must be calculated quickly. Directmethods of transient stability analysis of a multi machine power system, using a
function describing the system's transient energy, are discussed. By examining thetrajectory of the disturbed system, the following fundamental questions are dealt with:the concept of a controlling unstable equilibrium point (U.E.P), the manner in whichsome generators tend to lose synchronism, and identifying the energy directlyresponsible for system separation. Resolving this issue will substantially improve transient stability analysis by direct method.
Transistor mismatch effect on common-mode gain of cross-coupled amplifierTELKOMNIKA JOURNAL
In this paper, the analytical approach of MOS transistor mismatch effect on common-mode gain
of cross-coupled amplifier is presented. Transconductance (MOS transistor parameter) mismatch effect on
common-mode gain of cross-coupled amplifier was analyzed. This study was started with mathematical
derivation for representing the mismatch effect of transconductance between 2 differential pairs of crosscoupled
amplifier due to common-mode voltage. The derivation result was simulated based on Monte
Carlo simulation with random transconductance mismatch rate from 0.05% until 1%. The common-mode
gain increases 36.9 dB and average common-mode gain is -81.1 dB. The transconductance mismatch rate
increases followed by increase in common-mode gain. The results can be used by circuit designers to
design analog circuits, especially operational amplifier used for biosignals processing to minimize the
common-mode gain of their circuits. This research presents aid to circuit designers to improve their circuits
performance.
RADIAL BASIS FUNCTION PROCESS NEURAL NETWORK TRAINING BASED ON GENERALIZED FR...cseij
For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization
training method based on GA combined with SA is proposed in this paper. Through building generalized
Fréchet distance to measure similarity between time-varying function samples, the learning problem of
radial basis centre functions and connection weights is converted into the training on corresponding
discrete sequence coefficients. Network training objective function is constructed according to the least
square error criterion, and global optimization solving of network parameters is implemented in feasible
solution space by use of global optimization feature of GA and probabilistic jumping property of SA . The
experiment results illustrate that the training algorithm improves the network training efficiency and
stability.
Research on Transformer Core Vibration under DC Bias Based on Multi-field Cou...inventionjournals
The Mathematical models for DC bias vibration analysis of the transformer core are developed in this paper. The model is combined into multi-physical field coupling modeling for vibration analysis of the transformer. By applying the primary voltage as excitation and under different DC bias, vibrations of the transformer core is simulated and analyzed.
Techniques for Minimizing Power Consumption in DFT during Scan Test ActivityIJTET Journal
Lessening in test force is vital to enhance battery lifetime in versatile electronic gadgets utilizing intermittent individual test. It's to expand dependability of testing, and to lessen test expense. A conservative test set with exceedingly viable examples, every identifying different issues, is attractive for lower test expenses. Such examples build exchanging action amid dispatch and catch operations. In this paper, we exhibit a novel circuit strategy to essentially dispense with test force dissemination in combinational logic by veiling sign moves at the logic inputs amid sweep moving is exhibited. We execute the concealing impact by embeddings an additional supply gating transistor in the supply to ground way for the first-level doors at the yields of the output flip-flops. The gating transistor supply is killed in the output in mode, basically gating the supply. Further, DFT punishments are decreased by embracing specific trigger Scan structural planning. This building design diminishes exchanging action in the circuit-under-test (CUT) and builds the clock recurrence of the checking methodology. The assistant chain moves in the contrast between sequential test vectors and just the obliged moves (alluded to as trigger information) are connected. Power necessities are significantly decreased by the utilization of a two-stage heuristic technique. Utilizing ISCAS 89 benchmark circuits, this adequacy is to enhance SoC test measures (power, time, and information volume) is tentatively assessed and affirmed.
Maximum Loadability Enhancement with a Hybrid Optimization MethodjournalBEEI
Nowadays, a power system is operating in a stressed condition due to the increase in demand in addition to constraint in building new power plants. The economics and environmental constraints to build new power plants and transmission lines have led the system to operate very close to its stability limits. Hence, more researches are required to study the important requirements to maintain stable voltage condition and hence develop new techniques in order to address the voltage stability problem. As an action, most Reactive Power Planning (RPP) objective is to minimize the cost of new reactive resources while satisfying the voltage stability constraints and labeled as Secured Reactive Power Planning (SCRPP). The new alternative optimization technique called Adaptive Tumbling Bacterial Foraging (ATBFO) was introduced to solve the RPP problems in the IEEE 57 bus system. The comparison common optimization Meta-Heuristic Evolutionary Programming and original Bacterial Foraging techniques were chosen to verify the performance using the proposed ATBFO method. As a result, the ATBFO method is confirmed as the best suitable solution in solving the identified RPP objective functions.
NOVEL PSO STRATEGY FOR TRANSMISSION CONGESTION MANAGEMENTelelijjournal
In post deregulated era of power system load characteristics become more erratic. Unplanned transactions
of electrical power through transmission lines of particular path may occur due to low cost offered by
generating companies. As a consequence those lines driven close to their operating limits and becomes
congested as the lines are originally designed for traditional vertically integrated structure of power
system. This congestion in transmission lines is unpredictable with deterministic load flow strategy.
Rescheduling active and reactive power output of generators is the promising way to manage congestion.
In this paper Particle Swarm Optimization (PSO) with varying inertia weight strategy, with two variants
e1-PSO and e-2 PSO is applied for optimal solution of active and reactive power rescheduling for
managing congestion. The generators sensitivity technique is opted for identifying participating generators
for managing congestion. Proposed algorithm is tested on IEEE 30 bus system. Comparison is made
between results obtained from proposed techniques to that of results reported in previous literature.
This paper recommends the use of grasshopper optimization algorithm (GOA), a nature-inspired optimization algorithm, for optimizing switching-angle applied to cascaded H-bridge multilevel inverter (CHBMLI). Switching angles are selected based on the minimum value of the objective function formulated using the concept of selective harmonic minimization pulse width modulation (SHMPWM) technique. MATLAB/Simulink-PSIM dynamic co-simulation conducted on a 3-phase 9-level CHBMLI shows that the CHBMLI controlled using GOA derived switching-angle is able to respond to varying modulation index demand and synthesize an AC staircase output voltage waveform with the desired fundamental harmonic and minimized selected low-order harmonics. Compared to Newton Raphson (NR) technique, GOA is able to find optimum switching-angle solutions over a wider modulation index range. Compared to Genetic Algorithm (GA), GOA is able to find global minima with higher probability. The simulation results validate the performance of GOA for switching-angle calculation based on the concept of SHMPWM.
STUDY OF TRANSIENT STABILITY BY TRANSIENT ENERGY FUNCTIONcscpconf
Stability analysis programs are a primary tool used by power system planning and operating engineers to predict the response of the system to various disturbances. Important onclusionsand decisions are made based on the results of stability studies. The conventional method of analyzing stability is to calculate the transient behaviour of generators due to a given disturbance. Direct methods of stability analysis identify whether or not the system will remain stable once the disturbance is removed by comparing it with a calculated threshold value.Direct methods not only avoid the time consuming solutions required in the conventional
method, but also provide a quantitative measure of the degree of system stability. Thisadditional information makes direct methods very attractive when the relative stability of
different plans must be compared or when stability limits must be calculated quickly. Directmethods of transient stability analysis of a multi machine power system, using a
function describing the system's transient energy, are discussed. By examining thetrajectory of the disturbed system, the following fundamental questions are dealt with:the concept of a controlling unstable equilibrium point (U.E.P), the manner in whichsome generators tend to lose synchronism, and identifying the energy directlyresponsible for system separation. Resolving this issue will substantially improve transient stability analysis by direct method.
Transistor mismatch effect on common-mode gain of cross-coupled amplifierTELKOMNIKA JOURNAL
In this paper, the analytical approach of MOS transistor mismatch effect on common-mode gain
of cross-coupled amplifier is presented. Transconductance (MOS transistor parameter) mismatch effect on
common-mode gain of cross-coupled amplifier was analyzed. This study was started with mathematical
derivation for representing the mismatch effect of transconductance between 2 differential pairs of crosscoupled
amplifier due to common-mode voltage. The derivation result was simulated based on Monte
Carlo simulation with random transconductance mismatch rate from 0.05% until 1%. The common-mode
gain increases 36.9 dB and average common-mode gain is -81.1 dB. The transconductance mismatch rate
increases followed by increase in common-mode gain. The results can be used by circuit designers to
design analog circuits, especially operational amplifier used for biosignals processing to minimize the
common-mode gain of their circuits. This research presents aid to circuit designers to improve their circuits
performance.
RADIAL BASIS FUNCTION PROCESS NEURAL NETWORK TRAINING BASED ON GENERALIZED FR...cseij
For learning problem of Radial Basis Function Process Neural Network (RBF-PNN), an optimization
training method based on GA combined with SA is proposed in this paper. Through building generalized
Fréchet distance to measure similarity between time-varying function samples, the learning problem of
radial basis centre functions and connection weights is converted into the training on corresponding
discrete sequence coefficients. Network training objective function is constructed according to the least
square error criterion, and global optimization solving of network parameters is implemented in feasible
solution space by use of global optimization feature of GA and probabilistic jumping property of SA . The
experiment results illustrate that the training algorithm improves the network training efficiency and
stability.
Research on Transformer Core Vibration under DC Bias Based on Multi-field Cou...inventionjournals
The Mathematical models for DC bias vibration analysis of the transformer core are developed in this paper. The model is combined into multi-physical field coupling modeling for vibration analysis of the transformer. By applying the primary voltage as excitation and under different DC bias, vibrations of the transformer core is simulated and analyzed.
Computational Chemistry aspects of Molecular Mechanics and Dynamics have been discussed in this presentation. Useful for the Undergraduate and Postgraduate students of Pharmacy, Drug Design and Computational Chemistry
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
Molecular Mechanics in Molecular ModelingAkshay Kank
In this slide you learn about the computational chemistry and its role in designing a drug molecule. Also learn concept about the molecular mechanics and its application to Computer Aided Drug Design. difference between the Quantum mechanics and Molecular Mechanics.
During the process of molecular structure elucidation the selection of the most probable structural hypothesis may be based on chemical shift prediction. The prediction is carried out using either empirical or quantum-mechanical (QM) methods. When QM methods are used, NMR prediction commonly utilizes the GIAO option of the DFT approximation. In this approach the structural hypotheses are expected to be investigated by scientist. In this article we hope to show that the most rational manner by which to create structural hypotheses is actually by the application of an expert system capable of deducing all potential structures consistent with the experimental spectral data and specifically using 2D NMR data. When an expert system is used the best structure(s) can be distinguished using chemical shift prediction, which is best performed either by an incremental or neural net algorithm. The time-consuming QM calculations can then be applied, if necessary, to one or more of the 'best' structures to confirm the suggested solution.
Tzitzikosta message for the world heritage monuments exhibitionAnax Fotopoulos
MESSAGE BY THE PRESIDENT OF THE HELLENIC NATIONAL COMMISSION FOR UNESCO EKATERINI TZITZIKOSTA OPENING OF THE PHOTOGRAPHIC EXHIBITION OF THE UNESCO WORLD HERITAGE SITES.
Architecture of the human regulatory network derived from encode dataAnax Fotopoulos
Transcription factors bind in a combinatorial fashion to specify the on-and-off states of genes; the ensemble of
these binding events forms a regulatory network, constituting the wiring diagram for a cell. To examine the
principles of the human transcriptional regulatory network, we determined the genomic binding information of
119 transcription-related factors in over 450 distinct experiments. We found the combinatorial, co-association of
transcription factors to be highly context specific: distinct combinations of factors bind at specific genomic locations.
In particular, there are significant differences in the binding proximal and distal to genes. We organized all the
transcription factor binding into a hierarchy and integrated it with other genomic information (for example,
microRNA regulation), forming a dense meta-network. Factors at different levels have different properties; for
instance, top-level transcription factors more strongly influence expression and middle-level ones co-regulate
targets to mitigate information-flow bottlenecks. Moreover, these co-regulations give rise to many enriched
network motifs (for example, noise-buffering feed-forward loops). Finally, more connected network components
are under stronger selection and exhibit a greater degree of allele-specific activity (that is, differential binding to the
two parental alleles). The regulatory information obtained in this study will be crucial for interpreting personal genome
sequences and understanding basic principles of human biology and disease.
The RET proto-oncogene encodes a receptor tyrosine kinase for members of the glial cell line-derived neurotrophic factor family of extracellular signalling molecules. RET loss of function mutations are associated with the development of Hirschsprung's disease, while gain of function mutations are associated with the development of various types of human cancer, including medullary thyroid carcinoma, multiple endocrine neoplasias type 2A and 2B, pheochromocytoma and parathyroid hyperplasia.
RET is an abbreviation for "rearranged during transfection", as the DNA sequence of this gene was originally found to be rearranged within a 3T3 fibroblast cell line following its transfection with DNA taken from human lymphoma cells. The human gene RET is localized to chromosome 10 (10q11.2) and contains 21 exons.
The natural alternative splicing of the RET gene results in the production of 3 different isoforms of the protein RET. RET51, RET43 and RET9 contain 51, 43 and 9 amino acids in their C-terminal tail respectively. The biological roles of isoforms RET51 and RET9 are the most well studied in-vivo as these are the most common isoforms in which RET occurs.
Common to each isoform is a domain structure. Each protein is divided into three domains: an N-terminal extracellular domain with four cadherin-like repeats and a cysteine-rich region, a hydrophobic transmembrane domain and a cytoplasmic tyrosine kinase domain, which is split by an insertion of 27 amino acids. Within the cytoplasmic tyrosine kinase domain, there are 16 tyrosines (Tyrs) in RET9 and 18 in RET51. Tyr1090 and Tyr1096 are present only in the RET51 isoform.
The extracellular domain of RET contains nine N-glycosylation sites. The fully glycosylated RET protein is reported to have a molecular weight of 170 kDa although it is not clear to which isoform this molecular weight relates.
From Smart Homes to Smart Cities: An approach based on Internet-of-ThingsAnax Fotopoulos
Several applications and services have been developed over the latest years for making houses smarter in terms of danger prevention, energy consumption, waste recycling, environmental monitoring and other life improvement implementations. Internet-of-Things (IoT) gave numerous possibilities decentralizing the control of smart homes. Numerous sensors and developed systems or services can all communicate via smart devices like smartphones. A continuously broaden interest arises from local and national authorities for the benefits of applying IoT strategies in whole cities. With main focus on energy and water consumption, cities can reduce significantly their costs and become environmentally and economically sustainable. In the transition from smart homes to smart cities serious challenges should be take into consideration including a human-centric approach and the beneficiary involvement of the citizens for local and national authorities. The design of an IoT strategy for smart cities is a multifaceted procedure which includes the study of economic, urban, demographic and geographical indicators. In this paper, empirical evidence from selected case studies are presented.
The social aspect of Smart Wearable Systems in the era of Internet-of-ThingsAnax Fotopoulos
Social networking (Web 2.0) changed the way of interaction and communication of humans-to-humans, companies-to-customers, universities-to-students and state-to-citizens. The movement from static web pages (Web 1.0) to social networking and the rapid growth of smart devices created a new need for more complex data-on-demand across multiple platforms and devices. Cloud computing, miniaturization of sensors and low energy wireless technologies offered adequate possibilities to measure and understand environmental, health, industrial and other indicators, delivered in smart devices or in the web. The skyrocketing proliferation of the bidirectional communication between sensors and smart devices created a new series of products bringing us to the era of Internet-of-Things (IoT). The ubiquitous computing (presumed as Web 3.0) states that computing will appear in any device and in any location. Smart Wearable Systems (SWS) constitute the latest effort of academia and industry to toward this direction, aiming to enhance the communication and the velocity between IoT applications, smart devices (smartphones, tablets & smart TVs) and social networks. In this paper an analysis over social aspects of SWS is performed. Recent reports show that IoT market will be bigger than the total market of smart devices and PCs combined, enlarging the overall interest.
Introduction to HMMER - A biosequence analysis tool with Hidden Markov Models Anax Fotopoulos
HMMER is used for searching sequence databases for homologs of protein sequences, and for making protein sequence alignments. It implements methods using probabilistic models called profile hidden Markov models (profile HMMs).
Compared to BLAST, FASTA, and other sequence alignment and database search tools based on older scoring methodology, HMMER aims to be significantly more accurate and more able to detect remote homologs because of the strength of its underlying mathematical models. In the past, this strength came at significant computational expense, but in the new HMMER3 project, HMMER is now essentially as fast as BLAST.
As part of this evolution in the HMMER software, we are committed to making the software available to as many scientists as possible. Earlier releases of HMMER were restricted to command line use. To make the software more accessible to the wide scientific community, we now provide servers that allow sequence searches to be performed interactively via the Web.
TIS prediction in human cDNAs with high accuracyAnax Fotopoulos
Correct identification of the Translation Initiation Start (TIS) in cDNA is an important issue for genome annotation. The aim of this work is to improve upon current methods and provide a performance guaranteed prediction.
THE IMPORTANCE OF MARTIAN ATMOSPHERE SAMPLE RETURN.Sérgio Sacani
The return of a sample of near-surface atmosphere from Mars would facilitate answers to several first-order science questions surrounding the formation and evolution of the planet. One of the important aspects of terrestrial planet formation in general is the role that primary atmospheres played in influencing the chemistry and structure of the planets and their antecedents. Studies of the martian atmosphere can be used to investigate the role of a primary atmosphere in its history. Atmosphere samples would also inform our understanding of the near-surface chemistry of the planet, and ultimately the prospects for life. High-precision isotopic analyses of constituent gases are needed to address these questions, requiring that the analyses are made on returned samples rather than in situ.
Multi-source connectivity as the driver of solar wind variability in the heli...Sérgio Sacani
The ambient solar wind that flls the heliosphere originates from multiple
sources in the solar corona and is highly structured. It is often described
as high-speed, relatively homogeneous, plasma streams from coronal
holes and slow-speed, highly variable, streams whose source regions are
under debate. A key goal of ESA/NASA’s Solar Orbiter mission is to identify
solar wind sources and understand what drives the complexity seen in the
heliosphere. By combining magnetic feld modelling and spectroscopic
techniques with high-resolution observations and measurements, we show
that the solar wind variability detected in situ by Solar Orbiter in March
2022 is driven by spatio-temporal changes in the magnetic connectivity to
multiple sources in the solar atmosphere. The magnetic feld footpoints
connected to the spacecraft moved from the boundaries of a coronal hole
to one active region (12961) and then across to another region (12957). This
is refected in the in situ measurements, which show the transition from fast
to highly Alfvénic then to slow solar wind that is disrupted by the arrival of
a coronal mass ejection. Our results describe solar wind variability at 0.5 au
but are applicable to near-Earth observatories.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
https://www.youtube.com/channel/UCAiarMZDNhe1A3Rnpr_WkzA/videos
Thanks...!
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Unveiling the Energy Potential of Marshmallow Deposits.pdf
AFMM Manual
1. AFMM: A Molecular Mechanics Force Field
Parametrization Program
A.C. Vaiana∗
, Z.Cournia, I.B. Costescu, J.C. Smith
Computational Molecular Biophysics, Interdisciplinary Center for Scientific Computing
(IWR), Im Neuenheimer Feld 368, Universit¨at Heidelberg, 69120 Heidelberg, Germany
∗
Present Address:Institut de Biologie Molculaire et Cellulaire CNRS (UPR 9002 - SMBMR) 15, rue
Ren Descartes - 67084 STRASBOURG Cedex
1
3. 1 Introduction
AFMM (Automated Frequency Matching Method) is a program package for CHARMM
[1] force field parametrization. The method includes fitting the molecular mechanics po-
tential to both vibrational frequencies and eigenvector projections derived from quantum
chemical calculations and is benchmarked on a series of already parametrized molecules
[2].
The program is written in Python and is provided as one Python source file (afmm.py).
Through a simple configuration file, the program is directed to import and then match
normal modes from a quantum mechanical (QM) program output file and a CHARMM
output file. The program optimizes the missing CHARMM parameters by iteratively
changing them in a random fashion in order to get the best fit with the reference set
(QM data). By implementing a Monte Carlo-like algorithm, the tedious task of manual
parametrization is replaced by an efficient automated procedure.
The method is best suited for optimization of small rigid molecules with a well-defined
energy minimum, for which the harmonic approximation to the energy surface is appro-
priate to describe their intra-molecular degrees of freedom. It is also of particular use in
deriving parameters for molecules for which experimental data are scarce.
Final testing of a parameter set should be performed against experimental or theoretical
data that are as far as possible independent of the data used in the optimization phase.
For example, molecular dynamics of the crystal structure (if present) using the set of
optimized parameters.
2 Theoretical Background
Molecular Dynamics (MD) aims to reproduce the time-dependent motional behavior,
structures and energies of molecular systems by integrating Newton’s equations of motion.
The potential energy of the system is described as a function of the atomic positions. This
function or “force field” describes how the energy changes when the system moves from one
conformation to another for example when bonds are rotated or atoms are displaced. The
functional form of the force field generally includes a set of empirical parameters which
are system dependent and must be tuned prior to performing simulations on a new system
or molecule. This tuning step is generally referred to as parametrization of the force field.
The reliability of a molecular mechanics calculation is dependent on both the functional
form of the force field and on the numerical values of the parameters implemented in
the force field itself. Thus, the first necessary step for reliable MD simulations is the
parametrization procedure.
AFMM is mainly intended for optimization of the force constants of the CHARMM
force field although optimization of all other CHARMM parameters is, in principle, pos-
sible. In CHARMM the empirical potential energy function is given by Eq.1:
3
4. V (rN
) =
bonds
Kb(b − b0)2
+
ub
Kub(s − s0)2
+
angles
Kθ(θ − θ0)2
+
dihedrals
Kχ(1 + cos(nχ − χ0)) +
impropers
Kψ(ψ − ψ0)2
+
nonbond
ǫij
Rmin
ij
rij
12
+
Rmin
ij
rij
6
+
qiqj
Drij
(1)
where Kb, Kub, Kθ, Kχ, Kφ are, respectively, the bond, Urey-Bradley, angle, dihedral
and improper dihedral constants. b, s, θ, χ, and φ represent, respectively, bond length,
Urey-Bradley 1-3 distance, bond angle, dihedral angle and improper torsion angle (the
subscript zero is used to represent the corresponding equilibrium value). Nonbonded in-
teractions between pairs of atoms (labeled i and j) at a relative distance rij are described
by the Lennard-Jones 6-12 (LJ) and Coulomb interaction terms. Rmin
ij and ǫij are, re-
spectively, the distance between atoms i and j at which the LJ potential is minimum and
the depth of the LJ potential well for the same pair of atoms. D is the effective dielectric
constant and qi the partial atomic charge on atom i.
General procedures for automated parametrization of molecular mechanics (MM) force
fields based on fitting to any given set of reference data involve the following main steps[3,
4]:
• Definition of a merit function based on the available reference data
• Choice of atom types to be used and definition of new atom types when needed
• Careful choice of initial parameter values
• Refinement of Parameters (optimization of the merit function)
• Testing and validation of the final parameter set
2.1 Computational Methods
The determination of the actual values of the various force constants in Eq.1 is a demand-
ing job. One major difficulty in the development of molecular force fields is that these
parameters cannot be directly determined from experiments. Nonetheless, they are more
directly related to quantities that are well defined quantum mechanically. Experimental
data pertaining to force field calculations, such as normal modes, infrared frequencies or
crystal lattice constants cannot be expressed as simple functions of the force field param-
eters. Furthermore, often availability of sufficient experimental data for parametrization
is rather scarce.
On the other hand, the second derivatives of the energy with respect to coordinates (i.e.
the Hessian matrix elements) are much more directly connected to the force constants of
the force field. The point charges of the system can also be readily computed. These
4
5. quantities are therefore available through ab initio calculations, which in this context are
invaluable.
AFMM provides an efficient automated way to generate intra-molecular force field
parameters using normal modes. The method can be, in principle, used with any atom-
based molecular mechanics program which has the facility of calculating normal modes
and the corresponding eigenvectors.
The basic principle behind the AFMM method is that it is iteratively tuning an initial
MM parameter set in order to reproduce the normal modes generated from a QM program.
The reference quantum mechanical normal modes can be calculated with various QM
programs (e.g. Gaussian 94/98[5, 6], NWChem[7], ADF[8]) and using various levels of
theory (e.g. Hartree-Fock, DFT). The choice of reference data upon which to base the
new parametrization is a critical step in the parametrization procedure. The reliability
and accuracy of the new parameters in reproducing various properties of the molecule
depend on the quality of the reference data.
Equilibrium values for bonds b0, angles, θ0 and dihedrals χ0 can be derived from the
quantum chemical ground state structure or from experimental X-ray or NMR structures.
A set of partial atomic charges can be computed from these packages using various meth-
ods as well. Use of AFMM for optimizing van der Waals parameters is not reccomended.
The van der Waals constants ǫij and Rmin
ij depend mostly on atomic properties and are
relatively insensitive to changes in the molecular environment. Therefore, they can be
copied from existing CHARMM values and should not be modified during refinement.
2.2 Description of the Method
Automated refinement methods are mostly based on optimizing a “merit function”, which
usually corresponds to minimizing a weighted sum of square deviations from a set of ref-
erence values. Refinement of parameter sets always involves exploring a high dimensional
space in search of an optimal set. As for any multidimensional search method, in param-
eter optimization there is always a substantial risk that the search will get stuck in a high
local minimum. Initializing the procedure from a good initial guess can help reducing this
risk.
2.2.1 Choice of atom types and definition of new atom types
A major requirement in MM force fields is the portability of the parameter set, that is, the
possibility to transfer large groups of parameters from one molecule to another. In this
respect, addition of new atom types to the force field when designing the new parameter
set should be limited only to specific cases in which existing types cannot be used.
2.2.2 Choice of initial parameter values
The initial guess has to be based on analogy to other existing CHARMM parameters
and on chemical intuition. Equilibrium values and hybridization of the atoms involved
should be carefully taken into account when designing a set of initial parameters. The
second term in Eq.1 is the Urey-Bradley term which is not present in most force fields
and within the CHARMM model its use is limited to a few cases. The initial parameter
5
6. set is then used for energy minimization and calculation of normal modes (frequencies
and eigenvectors) with CHARMM.
These can then be directly compared with the results of the “reference” normal modes
calculated by means of a quantum chemical program. Parameters are thus refined itera-
tively to fit the results of the quantum chemical normal mode calculation.
Another way to ensure the good choice of the initial parameter set involves checking
it by visual inspection of the motions involved in exchanged eigenvector modes, using the
Molden program[9] for example, and manually adjusting the parameters concerned. This
approach is particularly useful for critical torsion parameters. In some cases it is necessary
to derive initial parameters from rotational potential energy profiles (single point energy
calculations from QM programs) before achieving good optimization.
2.2.3 The Merit Function
One of the major problems of parametrization methods that fit to vibrational frequencies
is identifying a calculated mode with the corresponding reference mode. It is possible to
obtain good matching of the frequencies for a molecule while exchanging the corresponding
eigenvectors. The resulting model would then reproduce well the vibrational frequencies
(and the energy) of the reference molecule. However, it may not reproduce the distribution
of energy among the inter-molecular modes, and thus the dynamical properties of the
molecule. It is therefore important to use a merit function that takes into account both
the frequencies and all the corresponding eigenvectors to avoid this problem.
The fitting method proposed here minimizes the above frequency exchange effect. In
the “ideal” case of a perfect molecular mechanics model, the vibrational properties of the
molecule, as calculated by molecular mechanics, should perfectly match those resulting
from the quantum ab initio calculation. For this to occur not only must the frequencies
coincide but also the two sets of eigenvectors (resulting from the two different calculations)
should coincide. Each eigenvector from the set calculated by molecular mechanics would
therefore be orthonormal to all but one (it s corresponding eigenvector) of the vectors
from the reference set (calculated using quantum chemical methods).
An efficient way to check simultaneously for both orthonormality and frequency match-
ing is to project each of the CHARMM eigenvectors χC
i (where the subscript i indi-
cates the normal mode number and the superscript C indicates that the modes are cal-
culated with CHARMM) onto the reference set of eigenvectors χQ
i (the superscript
Q indicates that these modes are calculated with a QM program, to find the frequency
νmax
j corresponding to the highest projection (j : χC
i ·χQ
i =max) and to plot this frequency
against the corresponding frequency, νi. In the ideal case mentioned above, this plot
would give a one-to-one relationship: νi = νmax
j and χC
i · χQ
i = δij where δij is the Kroe-
necker delta. Points that deviate from the ideal plot may indicate exchanged eigenvectors
or mismatched frequencies.
AFMM is based on minimizing the weighted sum-of-squares, Y 2
of the deviations from
6
7. the ideal situation as follows:
Y 2
=
3N−6
Ω2
i (νi − νmax
j )2
(2)
Ω
(1)
i =
1
maxj(χC
i · χQ
i )
(3)
Ω
(2)
i =
1
ωC
i
(4)
where N is the number of atoms in the molecule and there are 3N-6 independent
vibrational frequencies. In AFMM there are three possibilities to weigh the merit function:
1. The weights Ωi are chosen to be the inverse of the highest eigenvector projection.
This has the effect of biasing the merit function, even in the case of a good fre-
quency assignment, such that minimization of Y 2
leads to an improved eigenvector
projection distribution (Eq.3).
2. The weights Ωi are chosen to be the inverse of the MD frequency. This has the
effect of biasing the merit function towards better fitting of the lowest frequencies,
which are biologically more relevant (Eq.4).
3. No weights.
2.2.4 Parameter Refinement
For the automatic optimization of the chosen subset of parameters a standard Monte
Carlo (MC) scheme is used to minimize Y 2
. Although the subset of parameters to be
optimized can be chosen at wish by the user, it is advisable to perform optimizations
separately on bond, angle, and torsion constants. At each step i, all chosen parameters
are iteratively varied in the MC algorithm with a uniform distribution within a fixed
range, Y 2
i is evaluated, and, if Y 2
i < Y 2
i−1, the new parameter set is used in the next step,
i+1. The optimisation algorithm is illustrated in Fig.1
When comparing results for different molecules, normalization of Y 2
can be rather
tedious due to the different weights Ωi. For comparison purposes, then, after minimization
of Y 2
the root-mean-square deviation, σ from the reference case is calculated:
σ = 3N−6(νi − νmax
j )2
3N − 6
(5)
7
8. Starting
parameters
Run NMA in
CHARMM
Match MM and
QM NMA results
Y < Y
2 2
new old
Keep old
parameters
Keep new
parameters
Y
N
N
Y
STOP
Change
parameters
Check for
convergence
Calculate Y
2
Figure 1: Schematic representation of the optimization algorithm used in the AFMM method.
The method iteratively changes the parameters and matches both frequencies and eigenvector
projections from the molecular mechanics (CHARMM in this case) normal mode analysis (NMA)
with reference QM NMA.
3 Description of the Program
The current version of the program requires Python version 2 or newer and does not
require any non-standard Python modules. It contains definitions of two classes and the
normal modes import functions.
The param class contains information about the parameter to be optimized and a Monte
Carlo-like method to generate a new random value that is different from both the starting
and the current value.
The names of the normal modes import functions are composed of read followed by
the program name. For the QM output files, there are also functions that identify the
type of file, their name being composed of is followed by the program name. All the
normal modes import functions return lists of non-zero frequencies and corresponding
eigenvectors.
The afmm class is the core of the program and contains the following methods:
• ReadConfig - read the configuration file
• WriteNewParams - write new parameters in the CHARMM stream file
• WriteStreamedInput - write a new CHARMM input file
8
9. • RunMD - run MD program, checking for normal termination
• DotProduct - calculates the dot product between the eigenvectors
• TooLow - checks if a value is too close to zero (before division)
• Compute - matches the modes and computes the merit function
• Optimize - main routine that assigns new values to parameters and minimizes the
merit function
• OutputResults - writes out the minimum weighted sigma, the corresponding non-
weighted sigma, the optimized parameters and the frequency matching file
The main program consists of only 3 calls: reading the configuration file, calling Optimize
for computation then calling OutputResults.
4 Needed files
AFMM needs the following files to start the parametrization:
4.1 CHARMM Input File
A CHARMM input file whose name has to have an extension “.inp” is required. The
CHARMM input file should be organized as follows:
• Read in the topology/parameter/structure files. When defining files you have to use
the absolute paths or run AFMM in the directory where these files are to be found.
In the parameter file you have to replace the parameter you want to optimize (force
constant) with a variable e.g. “@p1” for the first, “@p2” for the second and so on.
• Minimize the structure e.g. mini abnr nstep 50000 tolgrd 0.00001
• Run the normal modes calculation using the “vibran” module. You shouldn’t output
the mass-weighted eigevectors which is the default in CHARMM, so you have to use
the nomass option within the vibran module. You can find an example input file
within the distribution (opt.inp). The output CHARMM file will be automatically
created with extension .out (opt.out).
4.2 QM Output File
Currently 3 types of output files are supported for optimization in AFMM: NWChem 4.5
and older, Gaussian 94/98 and Molden format. In principle, any normal mode output
can be transformed in the Molden format which contains the frequencies, coordinates and
eigenvectors. A number of people have created scripts or programs that output Molden
format files starting from other programs output files.
See: http://www.cmbi.kun.nl/∼schaft/molden/others.html.
Important: In order for the parametrization algorithm to be able to compare the
same normal modes and eigenvectors two things should be taken care of:
9
10. • Firstly, the order of the atoms in the coordinate set used in CHARMM and QM
programs respectively should be the same. We suggest that a minimization of
the molecule (using initial guesses for the parameters) should be run first with
CHARMM and the output coordinates to be given to the QM program for further
optimization and normal mode analysis.
• Second, the orientation of the two molecules should be the same when comparing the
normal modes. We suggest that when the QM calculation is finished, the optimized
coordinates should be converted in a CHARMM .pdb or .crd file and give it as input
to CHARMM before the parametrization starts.
5 Structure of the AFMM configuration file
The AFMM configuration file has to be called “afmm.cfg” and to be in the current direc-
tory. An example configuration file is distributed with the program.
The configuration file is composed of two sections. The first section is called [parameters]
and contains information on the parameters that you want to optimize. The general syn-
tax is:
parameter name = min value max value start value
The AFMM parameter names (parameter name) should correspond to the variable names
(e.g. p1) that you defined in the CHARMM parameter file. The first number is the min-
imum value that the parameter can take during the optimization. The second number
is the maximum value that the parameter can take during the optimization. The third
number is the starting value (the initial guess). The numbers can be specified as integers
or reals. The items can be separated by any number of whitespaces or tabs. In the fol-
lowing example:
[parameters]
p1 = 200.0 1000.0 500
the parameter p1 will be optimized in the range of 200.0-1000.0 with a starting value
of 500.0.
The second section is called [general] and contains settings that control the flow of
the program. The settings that are recognized by the current version are listed below;
the values given are just an example:
• maxsteps=10000
The maximum number of optimization steps for each parameter.
• maxsigmasteps = 2000
The maximum number of steps after which, if sigma remains constant, you want
the optimization to stop (Convergence Criterion).
10
11. • mdexec = /usr/local/charmm/c28b1/cmf
Path of the CHARMM executable.
• mdinp = /home/cournia/propene/propene.inp
Path of the CHARMM input file you want to run.
• qmout = /home/cournia/propene/propene freq.out
Path of the output QM file that contains the frequencies and the eigenvectors.
• qmfactor = 0.89
Empirical scaling factor for the QM frequencies[10].
• afmmfile = freq.dat
AFMM frequency matching output file to be written at the end of the optimization.
• weighting = frequency
Choose between frequency, projection or none for the way you want your parametriza-
tion to be weighted (see section 2).
6 Running the program
The program can be run as follows:
python afmm.py
If any errors occur in the configuration file, they will be printed. Errors in the [parameters]
section lead to ignoring the parameter for which they occured. Errors in the [general]
section lead to the termination of the program.
While running, the program will print on standard output the values of weighted sigma
that result during the optimization. When the program exits after the convergence cri-
terion is fulfilled or maximum number of optimization steps is reached, the minimum
weighted sigma, the corresponding non-weighted sigma and the final parameter set is
printed on standard output and the frequency matching file is created. The frequency
matching file will contain 2 columns, the first containing the scaled QM frequencies (if a
scaling factor was given to the program) and the second containing corresponding MM
frequency values.
For comparison between different molecules or optimizations with different weights, the
non-weighted sigma should be used.
Based on previous benchmark studies on test molecules, a good optimization is reached
when the value of σ is within 0-100cm−1
. This is normally achieved with a maxsteps value
of 100000 optimization cycles and a convergence criterion (maxsigmasteps) of 10000 cy-
cles for each parameter.
11
12. 6.1 Citing the Program
Please use the following citations when publishing results obtained with AFMM:
• A.C. Vaiana, A.Schulz, J. Wolfrum, M. Sauer and J.C. Smith, “Molecular Mechan-
ics Force Field Parameterization of the Fluorescent Probe Rhodamine 6G Using
Automated Frequency Matching”, J Comput Chem 24: 632-639, 2003
• A.C. Vaiana, Z. Cournia, I.B. Costescu and J.C. Smith, “AFMM: A Molecular
MEchanics Force Field Vibrational Parametrization Program”, Computer Physics
Communications, accepted for publication
For additional applications see:
• Z. Cournia, A.C. Vaiana, G.M. Ullmann and J.C. Smith, “Derivation of a Molecular
Mechanics Force Field for Cholesterol”, Pure and Applied Chemistry, 76(1):189-196,
2004
12
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14