The dataset used in this paper is a large-scale query workload dataset, containing 5,153 unique queries with 81 unique templates from the TPC-DS Hive dataset.

Hydro‑Québec dataset on peak demand events in Québec, updated based on weather conditions and operating constraints, from December 1 to March 31. The dataset provides information on the periods during which customers who take part in savings offers during peak demand events are called upon to reduce the pressure on the power grid.

Learn more about the savings offers for residential customers during peak demand events Learn more about the savings offers for business customers during peak demand events

List of offers and description Residential customers CPC-D: Critical peak credit for Rate D

Winter Credit Option for residential customers

TPC-DPC: Critical peak rate for Rate D

Rate Flex D for residential customers

Business customers GDP-Affaires: Critical peak rate for rates DP, DM, G, G9, M, LG or H

Demand Response Option for business customers

CPC-G: Critical peak credit for rate G

Winter Credit Option for business customers

TPC-GPC: Critical peak rate for rates G

Rate Flex G for business customers

TPC-M: Critical peak rate for rates M, LG and G9.

Interruptible Electricity Options for medium-power business customers

TPC-L-Centre-C2, TPC-L-Centre-U, TPC-L-Sud-D1 et TPC-L-Sud-D2: Critical peak Rate L in sectors Centre (C2 and U) and Sud (D1 and D2)

Interruptible Electricity Options for large-power business customers

OEA: Additional electricity option

Additional Electricity Options for medium-power business customers of rates M and G9 Additional Electricity Option for large-power business customers of rates L and LG

Additional information Update frequency: depending on option and program Geographic coverage: Province of Québec, excluding the cities of Alma, Amos, Baie-Comeau, Coaticook, Joliette, Magog, Saguenay, Sherbrooke and Westmount (municipal systems), the territory served by the Coopérative régionale d’électricité de Saint-Jean-Baptiste-de-Rouville (cooperative system) and the regions supplied by off-grid systems Temporal coverage: December 1, 2024, to March 31, 2025 Initial distribution: 2024‑04‑11 Notices and conditions of use:

The information provided represents raw data. It is without a guarantee of quality and subject to change without notice.
In the event of any discrepancy between the information provided by this dataset and that received by the official e-mail notification, the latter shall prevail.

Predicted and observed values of optimized TPC-loaded SMEDDS formulation.

The global Thermoplastic Copolyester Elastomer (TPC-ET) market is experiencing robust growth, driven by increasing demand across diverse sectors like automotive, construction, and consumer goods. The market, valued at approximately $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 6% from 2025 to 2033. This expansion is fueled by several key factors. The automotive industry's adoption of TPC-ET for lightweighting components and improved fuel efficiency is a significant driver. Furthermore, the construction sector’s utilization of TPC-ET in durable and flexible applications is contributing to market growth. The rising demand for high-performance materials in consumer goods, such as durable electronics casings and appliance parts, further boosts market expansion. Different grades of TPC-ET, including industrial, pharmaceutical, and food grade, cater to the specific requirements of these industries, adding to market segmentation. Major players like DuPont, Celanese Corporation, and others are strategically investing in R&D and expanding their production capacities to meet the increasing global demand. Growth within regional markets reflects varying adoption rates. North America and Europe currently dominate the market due to established manufacturing infrastructure and high consumer spending. However, the Asia-Pacific region, particularly China and India, is demonstrating significant growth potential fueled by rapid industrialization and urbanization. While the market faces challenges such as fluctuating raw material prices and the potential for substitution by alternative materials, the overall outlook for TPC-ET remains positive, with projected continued growth over the forecast period driven by ongoing technological advancements and the increasing demand for sustainable and high-performance materials.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tutor's Turnaround: Can TPC Stock Recover?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This white paper describes LAr1-ND and the compelling physics it brings first in Phase 1 and next towards the full LAr1 program. In addition, LAr1-ND serves as a key step in the development toward large-scale LArTPC detectors. Its development goals will encompass testing existing and possibly innovative designs for LBNE while at the same time providing a training ground for teams working towards LBNE combining timely neutrino physics with experience in detector development.

To improve the dissolution behavior of telmisartan (TMS), a poorly water-soluble angiotensin II receptor blocker, TMS-phospholipid complex (TPC) was prepared by solvent evaporation method and characterized by differential scanning calorimetry and powder X-ray diffractometry. The crystalline structure of TMS was transited into an amorphous state by TPC formation. The equilibrium solubility of TPC (1.3–6.1 mg/mL) in various vehicles was about 100 times higher than that of TMS (0.009–0.058 mg/mL). TPC-loaded self-microemulsifying drug delivery system (SMEDDS) formulation was optimized using the D-optimal mixture design with the composition of 14% Capryol 90 (oil; X1), 59.9% tween 80 (surfactant; X2), and 26.1% tetraglycol (cosurfactant; X3) as independent variables, which resulted in a droplet size of 22.17 nm (Y1), TMS solubilization of 4.06 mg/mL (Y2), and 99.4% drug release in 15 min (Y3) as response factors. The desirability function value was 0.854, indicating the reliability and accuracy of optimization; in addition, good agreement was found between the model prediction and experimental values of Y1, Y2, and Y3. Dissolution of raw TMS was poor and pH-dependent, where it had extremely low dissolution (< 1% for 2 h) in water, pH 4, and pH 6.8 media; however, it showed fast and high dissolution (> 90% in 5 min) in pH 1.2 medium. In contrast, the dissolution of the optimized TPC-loaded SMEDDS was pH-independent and reached over 90% within 5 min in all the media tested. Thus, we suggested that phospholipid complex formation and SMEDDS formulation using the experimental design method might be a promising approach to enhance the dissolution of poorly soluble drugs.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tutor Perini (TPC) Stock Forecast: Time to Get on Board the Construction Boom

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Mixture design for optimization of TPC-loaded SMEDDS formulation and the associated response data.

SLAC-PEP. TPC collaboration. Charged D* production in e+ e- annihilation at sqrt(s) = 29 Gev.

The v_3{TPC} as a function of transverse momentum for 0-5% centrality Au+Au collisions at sqrt(snn)=200 GEV.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

TPC Stock: In a Bubble?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

Check out Market Research Intellect's Thermoplastic Copolyester Elastomer (TPC-ET) Sales Market Report, valued at USD 1.2 billion in 2024, with a projected growth to USD 2.0 billion by 2033 at a CAGR of 7.5% (2026-2033).

Ensemble de données d’Hydro-Québec sur les événements de pointe au Québec, mis à jour en fonction des conditions météorologiques et des contraintes d’exploitation, du 1er décembre au 31 mars. L’ensemble de données fournit des renseignements sur les périodes pendant lesquelles la clientèle qui participe aux offres pour économiser lors des événements de pointe est sollicitée afin de réduire la congestion sur le réseau électrique.

            En savoir plus sur les offres
                pour économiser lors des événements de pointe pour la clientèle résidentielle

            En savoir plus sur les offres
                pour économiser lors des événements de pointe pour la clientèle d’affaires


          Liste des offres et description

            Clientèle résidentielle
              CPC-D : Crédit pointe critique pour le tarif D.

                Il s’agit de l’option de crédit hivernal pour la clientèle résidentielle et agricole.

              TPC-DPC : Tarification pointe critique pour le tarif D.

                Il s’agit du tarif Flex D pour la clientèle résidentielle et agricole.


            Clientèle affaires

              GDP-Affaires : option Tarification pointe critique pour les tarifs DP, DM, G, G9, M,
                LG ou H.


                Il s’agit de l’option
                    de gestion de la demande de puissance (GDP) pour la clientèle
                    d’affaires.

              CPC-G : Crédit pointe critique pour le tarif G.

                Il s’agit de 1'option
                    de crédit hivernal pour la clientèle d’affaires de petite puissance.


              TPC-GPC : Tarification pointe critique pour le tarif G.

                Il s’agit du tarif
                    Flex G pour la clientèle d’affaires.

              TPC-M : Tarif pointe critique pour les tarifs M, LG et G9.

                Il s’agit de l'option
                    d’électricité interruptible pour la clientèle d’affaires de moyenne
                    puissance.


              TPC-L-Centre-C2, TPC-L-Centre-U, TPC-L-Sud-D1 et TPC-L-Sud-D2: Tarif pointe critique pour le tarif L destiné aux
                secteurs
                Centre (C2 et U) et Sud (D1 et D2).

                Il s’agit de l'option
                    d’électricité interruptible pour la clientèle d’affaires de grande
                    puissance.


              OEA: option d’électricité additionnelle.

                Il s’agit de l’option
                    d’électricité additionnelle pour la clientèle de moyenne puissance pour les tarifs M et G9 et de l’option
                    d’électricité additionnelle pour la clientèle de grande puissance pour les tarifs L et LG.






          Renseignements additionnels

            Fréquence de mise à jour : en fonction de l'option et du programme.
            Couverture géographique : la province de Québec, à l’exception des villes d’Alma,
              d’Amos, de Baie-Comeau, de Coaticook, de Joliette, de Magog, de Saguenay, de Sherbrooke
              et de Westmount (réseaux municipaux), du territoire desservi par la Coopérative
              régionale d’électricité de Saint-Jean-Baptiste-de-Rouville (réseau coopératif) ainsi que
              des régions alimentées par des réseaux autonomes.
            Couverture temporelle : du 1er décembre 2024 au 31 mars 2025
            Licence : Creative
                Commons CC‑BY‑NC 4.0 (sans utilisation commerciale)

            Besoin d’utiliser ce signal dans un contexte commercial ? Communiquez
                avec nous !
            Notices et conditions d’utilisation :

                les données fournies sont brutes, sans garantie de qualité, et peuvent changer sans préavis,
                en cas de disparité entre les informations fournies par ce jeu de données et celles reçues par l’avis officiel envoyé par courriel, ce dernier a priorité.

Variables and responses used in the D-optimal mixture design.

Ratio of the p_{T} distribution in the 60% small-q_{2}^{TPC} sample to the unbiased sample of prompt D0 mesons in Pb-Pb...

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

Tutor Perini's (TPC) Stock Outlook: Mixed Signals Emerge

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

By varying the fluorescent tags of resorcin[4]arene-based tetracarboxylic acids from phenyl to naphthyl, two highly luminescent metal–organic frameworks (MOFs), namely, [Zn2(TPC4A)(DMF)(H2O)4]·3H2O (1) and [(CH3)2NH2]2[Zn(TNC4A)]·4H2O (2), were successfully achieved (TPC4A = 2,8,14,20-tetra-phenyl-6,12,18,24-tetra-methoxy-4,10,16,22-tetra-carboxy-methoxy-resorcin[4]arene and TNC4A = 2,8,14,20-tetra-1-naphthal-6,12,18,24-tetra- methoxy-4,10,16,22-tetra-carboxy-methoxy-resorcin[4]arene). Compound 1 features a unique 2D network, while 2 exhibits a fascinating 3D framework. The highly selective detection of small organic molecules as well as Fe2+ and Fe3+ was performed for 1 and 2 as fluorescent sensors. Remarkably, luminescent 1 and 2 were used as sensory materials for the sensing of various amine vapors with high selectivity and rapid response. Most strikingly, clear fluorescence “on–off” switch-functions toward small organic molecules as well as amine vapors were also explored for luminescent 1 and 2.

Ratio of v_{2}{EP} in the 20% large-q_{2}^{TPC} sample to the unbiased sample vs. p_{T} of average prompt D0, D+ mesons...

Tutor Perini Les Charges D'Exploitation - Les valeurs actuelles, des données historiques, des prévisions, des statistiques, des tableaux et le calendrier économique - Jun 2025.Data for Tutor Perini | Les Charges D'Exploitation including historical, tables and charts were last updated by Trading Economics this last June in 2025.