Miracle-dz/UniFAR-FTUs huggingface dataset:
MIT Sloan School of Management

Automakers are now developing alternatives to internal combustion engines (ICE), including hydrogen fuel cells and ICE-electric hybrids. Adoption dynamics for alternative vehicles are complex due to the size and importance of the auto industry and vehicle installed base. Diffusion of alternative vehicles is both enabled and constrained by powerful positive feedbacks arising from scale and scope economies, R&D, learning by doing, driver experience, word of mouth, and complementary resources such as fueling infrastructure. We describe a dynamic model of the diffusion of and competition among alternative fuel vehicles, including coevolution of the fleet, technology, consumer behavior, and complementary resources. Here we focus on the generation of consumer awareness of alternatives through feedback from consumers’ experience, word of mouth and marketing, with a reduced form treatment of network effects and other positive feedbacks (which we treat in other papers). We demonstrate the existence of a critical threshold for sustained adoption of alternative technologies, and show how the threshold depends on economic and behavioral parameters. We show that word of mouth from those not driving an alternative vehicle is important in stimulating diffusion. Expanding the model boundary to include learning, technological spillovers and spatial coevolution of fueling infrastructure adds additional feedbacks that condition the diffusion of alternative vehicles. Results show scenarios for successful diffusion of alternative vehicles, but also suggest that marketing programs and subsidies for alternatives must remain in place for long periods for diffusion to become self-sustaining. *Corresponding author. Email: jjrs@mit.edu, jsterman@mit.edu
Molecular Transfer Model for pH eﬀects on Intrinsically Disordered Proteins: Theory and Applications

We present a theoretical method to study how changes in pH shape the heterogeneous conformational ensemble explored by intrinsically disordered proteins (IDPs). The theory is developed in the context of coarse-grained models, which enable a fast, accurate, and extensive exploration of conformational space at a given protonation state. In order to account for pH eﬀects, we generalize the Molecular Transfer Model (MTM), in which conformations are re-weighted using the transfer free energy, which is the free energy necessary for bringing to equilibrium in a new environment a “frozen” conformation of the system. Using the semi-grand ensemble, we derive an exact expression of the transfer free energy, which amounts to the appropriate summation over all the protonation states. Because the exact result is computationally too demanding to be useful for large polyelectrolytes or IDPs, we introduce a mean-ﬁeld (MF) approximation of the transfer free energy. Using a lattice model, we compare the exact and MF results for the transfer free energy and a variety of observables associated with the model IDP. We ﬁnd that the precise location of the charged groups (the sequence), and not merely the net charge, determines the structural properties. We demonstrate that some of
Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation

Tests of an Ensemble Kalman Filter for Mesoscale and Regional-Scale Data Assimilation. (May 2007) Zhiyong Meng, B.S., Beijing University; M.S., Chinese Academy of Meteorological Sciences Chair of Advisory Committee: Dr. Fuqing Zhang This dissertation examines the performance of an ensemble Kalman filter (EnKF) implemented in a mesoscale model in increasingly realistic contexts from under a perfect model assumption and in the presence of significant model error with synthetic observations to real-world data assimilation in comparison to the three-dimensional variational (3DVar) method via both case study and month-long experiments. The EnKF is shown to be promising for future application in operational data assimilation practice. The EnKF with synthetic observations, which is implemented in the mesoscale model MM5, is very effective in keeping the analysis close to the truth under the perfect model assumption. The EnKF is most effective in reducing larger-scale errors but less effective in reducing errors at smaller, marginally resolvable scales. In the presence of significant model errors from physical parameterization schemes, the EnKF performs reasonably well though sometimes it can be significantly degraded compared to its performance under the perfect model assumption. Using a combination of different physical parameterization schemes in the ensemble (the so-called “multi-scheme”
: empirical evidence for Brazilian industrial enterprises

The aim of this paper is to carry out an empirical investigation into the relationship between innovation and the productive performance of Brazilian businesses measured by Work Productivity and Total Factor Productivity. Data taken from the Research of Innovation and estimated cross section models and panel data was used. The results suggest that innovation produces an incipient impact on competition in the national industry, reﬂected in the small magnitude of coefﬁcients associated with the diverse indicators of innovation
Best Arm Identiﬁcation in Rare Events

We consider the Best Arm Identiﬁcation (BAI) problem in the stochastic multi-armed bandit framework, where each arm has a small probability of realizing large rewards, while with over-whelming probability, the reward is zero. A key application of this framework is in online advertising, where click rates of advertisements could be a fraction of a single percent and ﬁnal conversion to sales, while highly proﬁtable, may again be a small fraction of the click rates. Lately, algorithms for BAI problems have been developed that minimise sample complexity while providing statistical guarantees on the correct arm selection. As we observe, these algorithms can be computationally prohibitive. We exploit the fact that the reward process for each arm is well approximated by a Compound Poisson process and arrive at algorithms that are faster, with a small increase in sample complexity. We analyze the problem in an asymptotic regime as rarity of reward occurrence reduces to zero, and reward amounts increase to inﬁnity. This helps illustrate the beneﬁts of the proposed algorithm. It also sheds light on the underlying structure of the optimal BAI algorithms in the rare event setting.
Pearson Hashing B-Tree With Self Adaptive Random Key Elgamal Cryptography For Secured Data Storage And Communication In Cloud

Communication security is a main issue to be resolved during cloud service provisioning to defend data from illegal access. The conventional techniques performance was not considered efficient. Since for storing the data securely they require more memory space and also communication overhead was higher. In order to address these drawbacks, Pearson B-Tree Hash-Based Self-Adaptive Random Key Elgamal Cryptography (PBH-SRKEC) Technique is proposed. The PBH-SRKEC technique develops a Pearson Hashing B-tree (PHB) in order to increase the data storage security in cloud with lower space complexity. The PHB creates B-tree and stores the cloud data hash value in its nodes in its place of storing data files. So, PBH-SRKEC technique minimizes space complexity of secured data storage in comparison to existing works. Further, PBH-SRKEC technique designs a Self-Adaptive Random Key Elgamal Cryptography (SARKEG) to advance communication security for data service provisioning in the cloud with minimal time. The SARKEG produces self-adaptive random public keys for each session and the key is disabled when the session time is completed. The results depict that the PBH-SRKEC technique enhance the cloud data security as well as reduce the communication overhead of cloud service provisioning when compared to recent existing works.
Soil Fertility Mapping: Comparison of Three Spatial Interpolation Techniques

Soil fertility mapping is essential when planning land use and developing crop fertilization strategies. Interpolation techniques are widely used for the mapping processes in varied fields of soil sciences to estimate the soil property values at unsampled sites. The aim of this work was to evaluate and compare the performances of three spatial interpolation methods (inverse distance weighting IDW; ordinary kriging OK; and spline) using the statistical criterion of root mean square error (RMSE) for cross validation and then generate a set of accurate soil property maps (pH, organic matter, phosphorus, and potassium). The study covers an area of 45 000 ha in the Loukkous irrigated district, Northwest of Morocco and includes 934 soil samples. These samples were collected from irregular cross-line nodes grids and were analyzed in the laboratory. Exploratory data analyses were first adopted to identify and remove all spatial outliers and to validate the normal distribution required for geostatistical analyses. In all cases, the distributions were found strongly skewed and needed to be transformed. BoxCox transformations were used; they performed well for all soil properties. Experimental variograms were fitted with the exponential, spherical and Gaussian models. With the use of the lowest RMSE approach, ordinary kriging model was selected as the best method compared to IDW and spline for interpolating with the exponential variogram for pH, organic matter and potassium, and the spherical model for phosphorus. A map was generated for each soil property. The maps indicated the low level of potassium and organic matter soil content throughout the study area. These maps could be used for optimizing crop fertilizing considering the different soil fertility levels. Keywords-: Soil Properties; geostatistics; spatial interpolation; soil maps; kiging; inverse distance weighting; spline,; prediction accuracy.
Deep Learning Approach using Patch-based Deep Belief Network for Road Extraction from Remote Sensing Imagery

— In this paper, an automated Deep Learning Architecture (DLA) called patch-based Deep Belief Neural Networks (P-DBN) is designed, implemented and experimentally evaluated for extracting semantic maps of roads in Remote Sensing (RS) images. Representative features are extracted by unsupervised pre-training of DBN and supervised fine-tuning phase. A Logistic Regression (LR) is added to the end of feature learning system to constitute a P-DBN-LR architecture. This LR classifier is employed to fine-tune the whole pre-trained network in a supervised way and classifies the patches from RS images. The features extracted from the image patches are fed to the architecture as input and it produces the class labels as a probability matrix as either a positive sample (road) or a negative sample (non-road). A math morphology algorithm is used to improve P-DBN performance during post processing. Experiments are conducted on a dataset of 970 RS scene images of urban and suburban areas to demonstrate the performance of the proposed network architecture. The proposed deep model resulted in an Overall Accuracy (OA) of 95.57% and F1-score of 0.9588. When compared to other state-of-the-art deep learning-based models such as U-Net, Cascaded CNN, Roadtracer, FCN and Salient features-SVM, our proposed P-DBN-LR model outperforms by 3.22% (0.9550 vs. 0.9242), 4.56% (0.9550 vs. 0.9114), 3.30% (0.9550 vs. 0.9233), 5.54% (0.9550 vs. 0.9020) and 7.40% (0.9550 vs. 0.8843), for the dataset . Experimental results demonstrate the effective performance of the proposed method for extracting roads from a complex scene.
Vowel and formant representation in human auditory speech cortex

Vowel sounds are a fundamental component of human speech across all languages. Vowels are cued acoustically by formants, the resonance frequencies determined by the shape of the vocal tract during speaking. An outstanding question in neurolinguistics is how the human brain processes vowel formants during speech perception. We used high-density intracranial recordings from the human speech cortex on the superior temporal gyrus (STG) while participants listened to natural continuous speech to address this question. We derived two-dimensional receptive fields based on the first and second formants to characterize tuning to vowel sounds. We found that neural activity at single STG sites was highly selective for particular zones in the formant space. Furthermore, this formant tuning shifted dynamically to adjust for speaker-specific spectral context. Despite this formant selectivity, local cortical responses were not sufficient to reliably discriminate between vowel categories. Instead, vowel category representations could be accurately decoded when using the entire population of formant encoding electrodes. Together, our results reveal that vowels are locally encoded in STG with complex acoustic tuning in two-dimensional formant space. As a population code this gives rise to phonological vowel perception.
Retcon: A Least-Commitment Story-World System

Retcon is an experimental interactive narrative engine that allows writers to author stories in which some aspects of the overall story, such as the tone, genre, and character relationships, can be chosen incrementally by the player. Stories are authored as a set of fragments (aka storylets) tagged with their story-world assumptions. The system tracks the active assumptions and removes from consideration any fragments that contradict the established story world. This allows the player some degree of co-authorship, and allows her to explore the possibility space of different stories afforded by story worlds.
Who Watches the Watchers: Attacking Glitch Detection Circuits

Over the last decades, fault injection attacks have been demonstrated to be an effective method for breaking the security of electronic devices. Some types of fault injection attacks, like clock and voltage glitching, require very few resources by the attacker and are practical and simple to execute. A cost-effective countermeasure against these attacks is the use of a detector circuit which detects timing violations - the underlying effect that glitch attacks rely on. In this paper, we take a closer look at three examples of such detectors that have been presented in the literature. We demonstrate four high-speed clock glitching attacks, which successfully inject faults in systems, where detectors have been implemented to protect. The attacks remain unnoticed by the glitch detectors. We verify our attacks with practical experiments on FPGA.
Canadian Network for Mood and Anxiety Treatments (CANMAT) 2016 Clinical Guidelines for the Management of Adults with Major Depressive Disorder

Background: The Canadian Network for Mood and Anxiety Treatments (CANMAT) has revised its 2009 guidelines for the management of major depressive disorder (MDD) in adults by updating the evidence and recommendations. The target audiences for these 2016 guidelines are psychiatrists and other mental health professionals. Methods: Using the question-answer format, we conducted a systematic literature search focusing on systematic reviews and meta-analyses. Evidence was graded using CANMAT-defined criteria for level of evidence. Recommendations for lines of treatment were based on the quality of evidence and clinical expert consensus. “Psychological Treatments” is the second of six sections of the 2016 guidelines. Results: Evidence-informed responses were developed for 25 questions under 5 broad categories: 1) patient characteristics relevant to using psychological interventions; 2) therapist and health system characteristics associated with optimizing outcomes; 3) descriptions of major psychotherapies and their efficacy; 4) additional psychological interventions, such as peer interventions and computer- and technology-delivered interventions; and 5) combining and/or sequencing psychological and pharmacological interventions. Conclusions: First-line psychological treatment recommendations for acute MDD include cognitive-behavioural therapy (CBT), interpersonal therapy (IPT), and behavioural activation (BA). Second-line recommendations include computer-based and telephone-delivered psychotherapy. Where feasible, combining psychological treatment (CBT or IPT) with antidepressant treatment is recommended because combined treatment is superior to either treatment alone. First-line psychological treatments for maintenance include CBT and mindfulness-based cognitive therapy (MBCT). Patient preference, in combination with evidence-based treatments and clinician/system capacity, will yield the optimal treatment strategies for improving individual outcomes in MDD.
Shape ﬂuctuations in a Fermi system with nonlinear dissipativity

The contribution of thermal ﬂuctuations to the widths of isoscalar giant multipole resonances (GMR) in heated nuclei is studied. Starting from the collisional kinetic equation, it is shown that an additional contribution to the nuclear friction and the corresponding GMR widths arises due to the nonlinear dissipativity eﬀect. It is also shown that the magnitude of the contributions of the thermal ﬂuctuations to the nuclear friction coeﬃcient and the GMR widths do not exceed ∼ 20%
Validation of Biomarker-Based ABCD Score in Atrial Fibrillation Patients with a Non-Gender CHA2DS2-VASc Score 0-1 A

ABCD score [age (≥60 years), B-type natriuretic peptide (BNP) or N-terminal pro-BNP (≥300 pg/mL), creatinine clearance (<50 mL/min/1.73 m 2 ), and dimension of the left atrium (≥45 mm)] for stroke risk stratification in non-gender CHA 2 DS 2 -VASc score 0–1.
Splay fault slip during the Mw 8.8 2010 Maule Chile earthquake

Splay faults are thrusts that emerge from the plate boundaries of subduction zones. Such structures have been mapped at several convergent margins and their activity commonly ascribed to large megathrust earthquakes. However, the behavior of splay faults during the earthquake cycle is poorly constrained because typically these structures are located offshore and are difficult to access. Here we use geologic mapping combined with space and land geodesy, as well as offshore sonar data, to document surface-fault ruptures and coastal uplift at Isla Santa Maria in south-central Chile (37°S) caused by the 27 February 2010 Maule earthquake ( M w 8.8). During the earthquake, the island was tilted parallel to the margin, and normal faults ruptured the surface and adjacent ocean bottom. We associate tilt and crestal normal faulting with growth of an anticline above a blind reverse fault rooted in the Nazca–South America plate boundary, which slipped during the Maule earthquake. The splay fault system has formed in an area of reduced coseismic plate-boundary slip, suggesting that anelastic deformation in the upper plate may have restrained the 2010 megathrust rupture. Surface fault breaks were accompanied by prominent discharge of fluids. Our field observations support the notion that splay faulting may frequently complement and influence the rupture of subduction-zone earthquakes.
A simple capillary tube method for the determination of the specific gravity of 25 and 50 micro l quantities of urine.

Figure 1 shows typical electroimmunodiffusion quantitations of immunoglobulins on Cellogel. The technique is also satisfactory for estimating other serum proteins, eg, :-lipoproteins. It has the advantages of speed and economy of antiserum: the whole procedure from preparing the immunostrip to estimating the antigen can be carried out within five hours and it consumes only very small measured amounts of antiserum. It appears to be especially applicable to measurement of 'minor' serum components and immunoglobulins present at low dilutions in other biological fluids. Cellogel also achieves economy in the Mancini technique, since a single immunostrip will accommodate up to 59 determinations.
RESEARCH ARTICLE Matrix Metalloproteinase-9 -1562T Allele and its Combination with MMP-2 -735 C Allele are Risk Factors for Breast Cancer

Background: Expression of matrix metalloproteinases (MMPs) is up-regulated in human cancers. The aim of present study was to investigate the role of MMP-9 C-1562T polymorphism and its interaction with MMP-2 C-735T polymorphism in susceptibility to breast cancer in a population from Western Iran with Kurdish ethnic background. Materials and Methods: The study sample of 205 individuals consisted of 101 breast cancer patients and 104 healthy subjects. MMP-9 C-1562T and MMP-2 C-735T variants were identified using the polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) method. Results: Among 67.4% of studied patients the breast cancer developed in the third and forth decades of the life. The frequency of MMP-9 T allele was 17.3% in patients and 10.1% in controls. The presence of T allele significantly increased the risk of breast cancer by 1.87-fold [OR=1.87 (95% CI 1.05-3.33, p=0.035)]. The frequency of MMP-9 CT+TT genotype tended to be higher in those patients with a family history of cancer in first degree-relatives (36.8%) than those without a family history (28.3%, p=0.37). We observed an interaction between the MMP-9 -1562 T allele with MMP-2 -735 C allele that significantly increased the risk of breast cancer [OR=1.42 (95% CI 1.02-1.98, p=0.036)]. Conclusions: The present study demonstrated that MMP-9 C-1562T polymorphism alone and in combination with MMP-2 C-735T polymorphism increased the risk of breast cancer that might be a useful biomarker in identifying women at risk of developing breast cancer. Also, this study revealed that in most women from Western Iran breast cancer presents in third and fourth decades of life.
Chromatographic study of sitagliptin and ertugliflozin under quality-by-design paradigm

The present study entails the systematic development and validation of a stability-indicating RP-HPLC method for the analysis of sitagliptin and ertugliflozin in a fixed-dose combination. Analytical quality by design (AQbD) concepts were used to define critical method variables, employing Pareto risk assessment and a Placket-Burman screening design, preceded by a Box-Behnken design with response surface analysis to optimise critical method parameters such as % acetonitrile (X1), buffer pH (X2) and column oven temperature (X3). Multiple response optimisation (Derringer’s desirability) of variables was accomplished by studying critical analytical attributes, such as resolution, retention time and theoretical plates. The title analytes were separated effectively on a PRONTOSIL C18 column at 37 °C using a mobile phase of acetonitrile:acetate buffer, pH 4.4 (36:64 percent v/v), pumped at a flow rate of 1 mL/min, and UV detection at 225 nm. Linearity was observed over a concentration range of 25-150 µg/mL and 3.75-22.5 µg/mL at retention times of 2.82 and 3.92 min for sitagliptin and ertugliflozin, respectively. The method obeyed all validation parameters of the ICH Q2(R1) guidelines. The proposed robust method allows the study of the selected drugs in pharmaceutical dosage forms as well as in drug stability studies under various stress conditions.
Strive or thrive

Partial resistance in plants generally exerts a low selective pressure on pathogens, and thus ensuring their durability in agrosystems. However, little is known about the effect of partial resistance on the molecular mechanisms of pathogenicity, a knowledge that could advance plant breeding for sustainable plant health. Here we investigate the gene expression of Phytophthora capsici during infection of pepper ( Capsicum annuum L.)