precise prediction
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2022 ◽  
Author(s):  
Mohsen Farshad

The energy and entropy, expressed in free energy, determine the behavior of a system. Therefore, infinite knowledge of these two quantities leads to precise prediction of the system's trajectories. Here, we study how the energy and entropy affect the distribution of a two-component system in a box. First, using a model, we intuitively show that large particles prefer to position at contact with the wall as it accompanies an increase of the system's entropy. We intuitively show that this is a consequence of maximizing the accessible states for fluctuating degrees of freedom as a portion of excluded volumes reside outside of the box when they locate near the wall. Then we employ molecular dynamics simulations to extract the effect of entropy and energy on the binary mixture distribution and how they compete with each other to determine the system's configuration. While particle-particle and particle-wall attraction energies affect the distribution of particles, we show that the emergent entropic forces --- quasi-gravitational --- have a significant contribution to the configuration of the system. This system is realized clearly for a binary mixture of hard spheres in a box with reflective walls.


MAUSAM ◽  
2022 ◽  
Vol 64 (1) ◽  
pp. 193-202
Author(s):  
S.K. DUBE ◽  
JISMY POULOSE ◽  
A.D. ADRAO

tc Hkh m".kdfVca/kh; pØokr vkrk gS rc Hkkjr vkSj blds fudVorhZ {ks=ksa esa rwQkuh leqnzh rjaxksa dh vkinkvksa ds dkj.k tku vkSj eky dh Hkkjh gkfu] rVh; <k¡pksa dh {kfr vkSj —f"k dks gkfu igq¡prh gSA uoEcj 1970 esa caxykns’k ¼igys iwohZ ikfdLrku½ esa vk, ,d vR;ar iapaM pØokr dh otg ls yxHkx 3]00]000 yksxksa dh tkus xbZaA uoEcj 1977 esa vkU/kz esa vk, pØokr us Hkkjr ds iwohZ rV dks rgl ugl dj fn;k ftlesa yxHkx 10]000 yksxksa dh tkus xbZaA vDrwcj 1999 esa Hkkjr ds mM+hlk ds rV ij ,d izpaM pØokrh rwQku vk;k ftlls ml {ks= esa laifRr dh vR;kf/kd gkfu gksus ds vfrfjDr 15]000 ls Hkh vf/kd yksxksa dh tkus xbZaA gky gh esa ebZ 2008 esa vk, pØokr uxhZl ls E;kaekj esa yxHk.k 1]40]000 yksxksa dh tkusa xbZa vkSj laifRr dk vR;f/kd ek=k esa uqdlku gqvkA ;s fo’o dh lcls cM+h ekuoh; vkink;sa eq[;r% m".kdfVca/kh; pØokrksa ls lac) gaS o leqnzh rwQkuh rjaxksa ls izR;{k:i  ls tqMh gSA vr% ml {ks= esa laf{kIr iwokZuqeku vkSj leqnzh rwQkuh rjaxksa dh iwoZ psrkouh nsus dk izko/kku ml {ks= ds fgr esa gksrk gSA bl 'kks/k i= dk eq[; mÌs’; caxky dh [kkM+h vkSj vjc lkxj esa mBus okyh leqnzh rwQkuh rjaxksa dk iwokZuqeku djus ds fy, gky gh esa fodflr fd, x, ekWMyksa dks izdk’k esa ykuk gSA bl 'kks/k&i= esa o"kZ 2008 ls 2011 ds nkSjku caxky dh [kkM+h esa cus izpaM pØokrksa ls tqM+h leqnzh rjaxksa dk iwokZuqeku [email protected] djus esa fun’kZ ds fu"iknu dk Hkh mYys[k fd;k x;k gSA Storm surge disasters cause heavy loss of life and property, damage to the coastal structures and the losses of agriculture in India and its neighborhood whenever a tropical cyclone approaches. About 3,00,000 lives were lost in one of the most severe cyclone that hit Bangladesh (then East Pakistan) in November 1970. The Andhra Cyclone devastated the eastern coast of India, killing about 10,000 persons in November 1977. Orissa coast of India was struck by a severe cyclonic storm in October 1999, killing more than 15000 people besides enormous loss to the property in the region. More recently the Nargis cyclone of May 2008 killed about 1,40,000 people in Myanmar as well as caused enormous property damage. These and most of the world's greatest human disasters associated with the tropical cyclones have been directly attributed to storm surges. Thus, provision of precise prediction and warning of storm surges is of great interest in the region. The main objective of the present paper is to highlight the recent developments in storm surge prediction model for the Bay of Bengal and the Arabian Sea. Paper also describes the performance of the model in forecasting/simulating the surges associated with severe cyclones formed in the Bay of Bengal during 2008 to 2011.  


AIChE Journal ◽  
2021 ◽  
Author(s):  
Guang Yao ◽  
Chengang Yang ◽  
Dong Hu ◽  
Quan Zhu ◽  
Xiangyuan Li

2021 ◽  
Author(s):  
Nicholas J Audette ◽  
WenXi Zhou ◽  
David M Schneider

Many of the sensations experienced by an organism are caused by their own actions, and accurately anticipating both the sensory features and timing of self-generated stimuli is crucial to a variety of behaviors. In the auditory cortex, neural responses to self-generated sounds exhibit frequency-specific suppression, suggesting that movement-based predictions may be implemented early in sensory processing. Yet it remains unknown whether this modulation results from a behaviorally specific and temporally precise prediction, nor is it known whether corresponding expectation signals are present locally in the auditory cortex. To address these questions, we trained mice to expect the precisely timed acoustic outcome of a forelimb movement using a closed-loop sound-generating lever. Dense neuronal recordings in the auditory cortex revealed suppression of responses to self-generated sounds that was specific to the expected acoustic features, specific to a precise time within the movement, and specific to the movement that was coupled to sound during training. Predictive suppression was concentrated in L2/3 and L5, where deviations from expectation also recruited a population of prediction-error neurons that was otherwise unresponsive. Recording in the absence of sound revealed abundant movement signals in deep layers that were biased toward neurons tuned to the expected sound, as well as temporal expectation signals that were present throughout the cortex and peaked at the time of expected auditory feedback. Together, these findings reveal that predictive processing in the mouse auditory cortex is consistent with a learned internal model linking a specific action to its temporally precise acoustic outcome, while identifying distinct populations of neurons that anticipate expected stimuli and differentially process expected versus unexpected outcomes.


2021 ◽  
Vol 13 ◽  
Author(s):  
Joseph M. Gullett ◽  
Alejandro Albizu ◽  
Ruogu Fang ◽  
David A. Loewenstein ◽  
Ranjan Duara ◽  
...  

Background and Objectives: Prediction of decline to dementia using objective biomarkers in high-risk patients with amnestic mild cognitive impairment (aMCI) has immense utility. Our objective was to use multimodal MRI to (1) determine whether accurate and precise prediction of dementia conversion could be achieved using baseline data alone, and (2) generate a map of the brain regions implicated in longitudinal decline to dementia.Methods: Participants meeting criteria for aMCI at baseline (N = 55) were classified at follow-up as remaining stable/improved in their diagnosis (N = 41) or declined to dementia (N = 14). Baseline T1 structural MRI and resting-state fMRI (rsfMRI) were combined and a semi-supervised support vector machine (SVM) which separated stable participants from those who decline at follow-up with maximal margin. Cross-validated model performance metrics and MRI feature weights were calculated to include the strength of each brain voxel in its ability to distinguish the two groups.Results: Total model accuracy for predicting diagnostic change at follow-up was 92.7% using baseline T1 imaging alone, 83.5% using rsfMRI alone, and 94.5% when combining T1 and rsfMRI modalities. Feature weights that survived the p &lt; 0.01 threshold for separation of the two groups revealed the strongest margin in the combined structural and functional regions underlying the medial temporal lobes in the limbic system.Discussion: An MRI-driven SVM model demonstrates accurate and precise prediction of later dementia conversion in aMCI patients. The multi-modal regions driving this prediction were the strongest in the medial temporal regions of the limbic system, consistent with literature on the progression of Alzheimer’s disease.


Entropy ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. 1430
Author(s):  
Guisheng Chen ◽  
Zhanshan Li

Market basket prediction, which is the basis of product recommendation systems, is the concept of predicting what customers will buy in the next shopping basket based on analysis of their historical shopping records. Although product recommendation systems develop rapidly and have good performance in practice, state-of-the-art algorithms still have plenty of room for improvement. In this paper, we propose a new algorithm combining pattern prediction and preference prediction. In pattern prediction, sequential rules, periodic patterns and association rules are mined and probability models are established based on their statistical characteristics, e.g., the distribution of periods of a periodic pattern, to make a more precise prediction. Products that have a higher probability will have priority to be recommended. If the quantity of recommended products is insufficient, then we make a preference prediction to select more products. Preference prediction is based on the frequency and tendency of products that appear in customers’ individual shopping records, where tendency is a new concept to reflect the evolution of customers’ shopping preferences. Experiments show that our algorithm outperforms those of the baseline methods and state-of-the-art methods on three of four real-world transaction sequence datasets.


2021 ◽  
Author(s):  
Shinya IWASE ◽  
Taka-aki Nakada ◽  
Tadanaga Shimada ◽  
Takehiko Oami ◽  
Takashi Shimazui ◽  
...  

Abstract Background: Machine learning can predict outcomes and determine variables contributing to precise prediction, and can thus classify patients with different risk factors of outcomes. This study aimed to investigate the predictive accuracy for mortality and length of stay in intensive care unit (ICU) patients using machine learning, and to identify the variables contributing to the precise prediction or classification of patients.Methods: Patients (n=12,747) admitted to the ICU at Chiba University Hospital were randomly assigned to the training and test cohorts. After learning using the variables on admission in the training cohort, the area under the curve (AUC) was analyzed in the test cohort to evaluate the predictive accuracy of the supervised machine learning classifiers, including random forest (RF) for outcomes (primary outcome, mortality; secondary outcome, and length of ICU stay). The rank of the variables that contributed to the machine learning prediction was confirmed, and cluster analysis of the patients with risk factors of mortality was performed to identify the important variables associated with patient outcomes.Results: Machine learning using RF revealed a high predictive value for mortality, with an AUC of 0.945. In addition, RF showed high predictive value for short and long ICU stays, with AUCs of 0.881 and 0.889, respectively. Lactate dehydrogenase (LDH) was identified as a variable contributing to the precise prediction in machine learning for both mortality and length of ICU stay. LDH was also identified as a contributing variable to classify patients into sub-populations based on different risk factors of mortality.Conclusion: The machine learning algorithm could predict mortality and length of stay in ICU patients with high accuracy. LDH was identified as a contributing variable in mortality and length of ICU stay prediction and could be used to classify patients based on mortality risk.


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