Search Results (21)

Under the combined influence of climate change and human activities, the non-stationarity of reservoir runoff has significantly intensified, posing challenges for traditional statistical models to accurately capture its multi-scale abrupt changes. This study focuses on Qianping (QP) Reservoir and systematically integrates climate-driven mechanisms with machine learning approaches to uncover the patterns of runoff evolution and develop high-precision prediction models. The findings offer a novel paradigm for adaptive reservoir operation under non-stationary conditions. In this paper, we employ methods including extreme-point symmetric mode decomposition (ESMD), Bayesian ensemble time series decomposition (BETS), and cross-wavelet transform (XWT) to investigate the variation trends and mutation features of the annual runoff in QP Reservoir. Additionally, four models—ARIMA, LSTM, LSTM-RF, and LSTM-CNN—are utilized for runoff prediction and analysis. The results indicate that: (1) the annual runoff of QP Reservoir exhibits a quasi-8.25-year mid-short-term cycle and a quasi-13.20-year long-term cycle on an annual scale; (2) by using Bayesian estimators based on abrupt change year detection and trend variation algorithms, an abrupt change point with a probability of 79.1% was identified in 1985, with a confidence interval spanning 1984 to 1986; (3) cross-wavelet analysis indicates that the periodic associations between the annual runoff of QP Reservoir and climate-driving factors exhibit spatiotemporal heterogeneity: the AMO, AO, and PNA show multi-scale synergistic interactions; the DMI and ENSO display only phase-specific weak coupling; while solar sunspot activity modulates runoff over long-term cycles; and (4) The NSE of the ARIMA, LSTM, LSTM-RF, and LSTM-CNN models all exceed 0.945, the RMSE is below 0.477 × 10⁹ m³, and the MAE is below 0.297 × 10⁹ m³, Among them, the LSTM-RF model demonstrated the highest accuracy and the most stable predicted fluctuations, indicating that future annual runoff will continue to fluctuate but with a decreasing amplitude. Full article

There are few examples of covalent organic frameworks (COFs) based on phosphorus as the building element, probably because the structure of most phosphorus derivatives is pyramidal, which may prevent the stacking expected for classical 2-dimensional COFs. In addition, they are generally associated with linear difunctional derivatives. In this paper is reported the original association of a trifunctional 3-D compound with a trifunctional 2-D compound in an attempt to get a new COF. The condensation reaction between a thiophosphate derivative bearing three aldehydes and the trihydrazinotriazine has been carried out with the aim of obtaining either a COF or simply a porous organic polymer (POP), consisting in both cases of associated macrocycles, affording a new covalent triazine framework (CTF). The material resulting from this condensation has been characterized by multinuclear MAS NMR (³¹P, ¹H, and ¹³C), IR, and thermogravimetric analysis (TGA). All these data confirmed the condensation reactions. However, BET (Brunauer–Emmett–Teller) measurements indicated that the porosity of this material is low. Trapping dyes in solution, as a model of pollutants, by the insoluble porous material 3 has been attempted. Full article

Currently, several techniques based on probabilities and statistics, along with the rapid advancements in computational power, have deepened our understanding of a football match result, giving us the capability to estimate future matches’ results based on past performances. The ability to estimate the number of goals scored by each team in a football match has revolutionized the perspective of a match result for both betting market professionals and fans alike. The Poisson distribution has been widely used in a number of studies to model the number of goals a team is likely to score in a football match. Therefore, the match result can be estimated using a double Poisson regression model—one for each participating team. In this study, we propose an algorithm, which, by using Poisson distributions along with football teams’ historical performance, is able to predict future football matches’ results. This algorithm has been developed based on the Premier League’s—England’s top-flight football championship—results from the 2022–2023 season. Full article

Much work in the parimutuel betting literature has discussed estimating event outcome probabilities or developing optimal wagering strategies, particularly for horse race betting. Some betting pools, however, involve betting not just on a single event, but on a tuple of events. For example, pick six betting in horse racing, March Madness bracket challenges, and predicting a randomly drawn bitstring each involve making a series of individual forecasts. Although traditional optimal wagering strategies work well when the size of the tuple is very small (e.g., betting on the winner of a horse race), they are intractable for more general betting pools in higher dimensions (e.g., March Madness bracket challenges). Hence we pose the multi-brackets problem: supposing we wish to predict a tuple of events and that we know the true probabilities of each potential outcome of each event, what is the best way to tractably generate a set of n predicted tuples? The most general version of this problem is extremely difficult, so we begin with a simpler setting. In particular, we generate n independent predicted tuples according to a distribution having optimal entropy. This entropy-based approach is tractable, scalable, and performs well. Full article

This article introduces an innovative extension of the Kelly criterion, which has traditionally been used in gambling, sports wagering, and investment contexts. The Kelly criterion extension (KCE) refines the traditional capital growth function to better suit dynamic market conditions. The KCE improves the traditional approach to accommodate the complexities of financial markets, particularly in stock and commodity trading. This innovative method focuses on crafting strategies based on market conditions and player actions rather than direct asset investments, which enhances its practical application by minimizing risks associated with volatile investments. This paper is structured to first outline the foundational concepts of the Kelly criterion, followed by a detailed presentation of the KCE and its advantages in practical scenarios, including a case study on its application to blackjack strategy optimization. The mathematical framework and real-world applicability of the KCE are thoroughly discussed, demonstrating its potential to bridge the gap between theoretical finance and actual trading outcomes. Full article

Semi-hydrogenation of acetylene to ethylene over metal oxide-supported Au nanoparticles is an interesting topic. Here, a hydrotalcite-based MMgAlO_x (M=Cu, Ni, and Co) composite oxide was exploited by introducing different Cu, Ni, and Co dopants with unique properties, and then used as support to obtain Au/MMgAlO_x catalysts via a modified deposition–precipitation method. XRD, BET, ICP-OES, TEM, Raman, XPS, and TPD were employed to investigate their physic-chemical properties and catalytic performances for the semi-hydrogenation of acetylene to ethylene. Generally, the catalytic activity of the Cu-modified Au/CuMgAlO_x catalyst was higher than that of the other modified catalysts. The TOR for Au/CuMgAlO_x was 0.0598 h⁻¹, which was 30 times higher than that of Au/MgAl₂O₄. The SEM and XRD results showed no significant difference in structure or morphology after introducing the dopants. These dopants had an unfavorable effect on the Au particle size, as confirmed by the TEM studies. Accordingly, the effects on catalytic performance of the M dopant of the obtained Au/MMgAlO_x catalyst were improved. Results of Raman, NH₃-TPD, and CO₂-TPD confirmed that the Au/CuMgAlO_x catalyst had more basic sites, which is beneficial for less coking on the catalyst surface after the reaction. XPS analysis showed that gold nanoparticles exhibited a partially oxidized state at the edges and surfaces of CuMgAlO_x. Besides an increased proportion of basic sites on Au/CuMgAlO_x catalysts, the charge transfer from nanogold to the Cu-doped matrix support probably played a positive role in the selective hydrogenation of acetylene. The stability and deactivation of Au/CuMgAlO_x catalysts were also discussed and a possible reaction mechanism was proposed. Full article

The reduced burden of helminth parasites in industrialized countries is probably one of the reasons for the increased prevalence of autoimmune disorders such as multiple sclerosis (MS). The current study aimed to evaluate the potential preventive effects of hydatid cyst fluid (HCF) on the disease severity in an EAE mouse model of MS. EAE-induced mice were treated with HCF before and after EAE induction. An RT-PCR-based evaluation of IFN-γ, IL-1β, TNF, T-bet, IL-4, GATA3, IL-17, RoRγ, TGF-β, and FOXP3 expression levels in splenocytes and an ELISA-based analysis of IFN-γ and IL-4 levels in cell culture supernatant of splenocytes were performed. Histopathological examinations of mice during the study were also conducted. The expression levels of T-bet, IL-4, GATA3, TGF-β, and FOXP3 in EAE + HCF mice were significantly higher compared to EAE + PBS mice. In the EAE + HCF group, the expression levels of IFN-γ, IL-1β, and TNF were significantly lower than in the EAE + PBS group. The histopathological results showed significantly reduced inflammation and demyelination in EAE + HCF mice compared to EAE + PBS mice. Our study provides proof-of-concept in the EAE mouse model of MS that helminth-derived products such as HCF have a potential prophylactic effect on MS development and present a novel potential therapeutic strategy. Full article

The term allergy was coined in 1906 by the Austrian scientist and pediatrician Clemens Freiherr von Pirquet. In 1976, Dietrich Kraft became the head of the Allergy and Immunology Research Group at the Department of General and Experimental Pathology of the University of Vienna. In 1983, Kraft proposed to replace natural extracts used in allergy diagnostic tests and vaccines with recombinant allergen molecules and persuaded Michael Breitenbach to contribute his expertise in molecular cloning as one of the mentors of this project. Thus, the foundation for the Vienna School of Molecular Allergology was laid. With the recruitment of Heimo Breiteneder as a young molecular biology researcher, the work began in earnest, resulting in the publication of the cloning of the first plant allergen Bet v 1 in 1989. Bet v 1 has become the subject of a very large number of basic scientific as well as clinical studies. Bet v 1 is also the founding member of the large Bet v 1-like superfamily of proteins with members—based on the ancient conserved Bet v 1 fold—being present in all three domains of life, i.e., archaea, bacteria and eukaryotes. This suggests that the Bet v 1 fold most likely already existed in the last universal common ancestor. The biological function of this protein was probably related to lipid binding. However, during evolution, a functional diversity within the Bet v 1-like superfamily was established. The superfamily comprises 25 families, one of which is the Bet v 1 family, which in turn is composed of 11 subfamilies. One of these, the PR-10-like subfamily of proteins, contains almost all of the Bet v 1 homologous allergens from pollen and plant foods. Structural and functional comparisons of Bet v 1 and its non-allergenic homologs of the superfamily will pave the way for a deeper understanding of the allergic sensitization process. Full article

Bi₂MoO₆ was one of the important bismuth-based semiconductors with a narrow bandgap, and has been widely used in selective oxidation catalysts, supercapacitors, and energy-storage devices. A series of Bi₂MoO₆/ZnO composite photocatalysts with different mass ratios were synthesized by the hydrothermal method. The synthesized samples were characterized by XRD, PL, UV-Vis, SEM, TEM, XPS, and BET analysis techniques. Under visible light conditions, Methylene blue (MB) was used as the target degradation product to evaluate its photocatalytic performance. The results showed that the degradation rate constant of Bi₂MoO₆/ZnO (0.4-BZO) was about twice that of the traditional photocatalysis of ZnO. The Bi₂MoO₆/ZnO composite catalyst maintained stable performance after four consecutive runs. The high photocatalytic activity of Bi₂MoO₆/ZnO was attributed to the efficient electron transport of the heterojunction, which accelerates the separation of electron-hole pairs and reduces the probability of carrier recombination near the Bi₂MoO₆/ZnO heterojunction. Bi₂MoO₆/ZnO nanocomposites have potential applications in the field of photodegradation. Full article

Bimetallic MOFs have recently emerged as promising materials for wastewater treatment based on advanced oxidation processes. Herein, a new bimetallic MOF (FeCu-MOF) was fabricated by hydrothermal process. The structural, morphological, compositional and physicochemical properties of the as-synthesized bimetallic FeCu-MOF were characterized by XRD, FT-IR, SEM, TEM, BET, and XPS. TEM and XPS confirmed the homogeneous distribution of CuO₂ nanoparticles in the as-synthesized materials. The result of wastewater treatment indicated that 100% of MB was removed by 6.0 mM PMS activated with 0.6 g/L of FeCu-MOF in 30 min. The high catalytic performance of FeCu-MOF was probably due to the accelerated electron and mass transfer resulting from the existence of a homogeneous distribution of unsaturated metal sites and an abundant mesoporous structure. The obtained results from the competitive quenching tests demonstrated that sulfate radicals (SO₄^•−) were the major species responsible for MB oxidation. In addition, hydroxyl (·OH) and singlet oxygen (¹O₂) also had a nonnegligible role in the MB removal. Interestingly, the addition of acetate ion (CHCOO⁻) promoted the removal of MB while other anions (including NO₂⁻, H₂PO₄⁻, SO₄²⁻, HPO₄²⁻, and HCO₃⁻) inhibited the MB removal. Furthermore, a possible mechanism based on both heterogeneous and homogeneous activation of PMS was proposed, along with the MB degradation mechanism. Full article

Due to a colossal soccer market, soccer analysis has attracted considerable attention from industry and academia. In-game outcome prediction has great potential in various applications such as game broadcasting, tactical decision making, and betting. In some sports, the method of directly predicting in-game outcomes based on the ongoing game state is already being used as a statistical tool. However, soccer is a sport with low-scoring games and frequent draws, which makes in-game prediction challenging. Most existing studies focus on pre-game prediction instead. This paper, however, proposes a two-stage method for soccer in-game outcome prediction, namely in-game outcome prediction (IGSOP). When the full length of a soccer game is divided into sufficiently small time frames, the goal scored by each team in each time frame can be modeled as a random variable following the Bernoulli distribution. In the first stage, IGSOP adopts state-based machine learning to predict the probability of a scoring goal in each future time frame. In the second stage, IGSOP simulates the remainder of the game to estimate the outcome of a game. This two-stage approach effectively captures the dynamic situation after a goal and the uncertainty in the late phase of a game. Chinese Super League data have been used for algorithm training and evaluation, and the results demonstrate that IGSOP outperforms existing methods, especially in predicting draws and prediction during final moments of games. IGSOP provides a novel perspective to solve the problem of in-game outcome prediction in soccer, which has a potential ripple effect on related research. Full article

There are contexts where TCP/IP is not suitable for performing data transmission due to long delays, timeouts, network partitioning, and interruptions. In these scenarios, mobile opportunistic networks (MONs) are a valid option, providing asynchronous transmissions in dynamic topologies. These architectures exploit physical encounters and persistent storage to communicate nodes that lack a continuous end-to-end path. In recent years, many routing algorithms have been based on social interactions. Smartphones and wearables are in vogue, applying social information to optimize paths between nodes. This work proposes Refine Social Broadcast (RSB), a social routing algorithm. RSB uses social behavior and node interests to refine the message broadcast in the network, improving the delivery probability while reducing redundant data duplication. The proposal combines the identification of the most influential nodes to carry the information toward the destination with interest-based routing. To evaluate the performance, RSB is applied to a simulated case of use based on a realistic loneliness detection methodology in elderly adults. The obtained delivery probability, latency, overhead, and hops are compared with the most popular social-based routers, namely, EpSoc, SimBet, and BubbleRap. RSB manifests a successful delivery probability, exceeding the second-best result (SimBet) by 17% and reducing the highest overhead (EpSoc) by 97%. Full article

BET inhibitors (BETi) including OTX015 (MK-8628) and JQ1 demonstrated antileukemic activity including NPM1c AML cells. Nevertheless, the biological consequences of BETi in NPM1c AML were not fully investigated. Even if of better prognosis AML patients with NPM1c may relapse and treatment remains difficult. Differentiation-based therapy by all trans retinoic acid (ATRA) combined with arsenic trioxide (ATO) demonstrated activity in NPM1c AML. We found that BETi, similar to ATO + ATRA, induced differentiation and apoptosis which was TP53 independent in the NPM1c cell line OCI-AML3 and primary cells. Furthermore, BETi induced proteasome-dependent degradation of NPM1c. BETi degraded NPM1c in the cytosol while BRD4 is degraded in the nucleus which suggests that restoration of the NPM1/BRD4 equilibrium in the nucleus of NPM1c cells is essential for the efficacy of BETi. While ATO + ATRA had significant biological activity in NPM1c IMS-M2 cell line, those cells were resistant to BETi. Gene profiling revealed that IMS-M2 cells probably resist to BETi by upregulation of LSC pathways independently of the downregulation of a core BET-responsive transcriptional program. ATO + ATRA downregulated a NPM1c specific HOX gene signature while anti-leukemic effects of BETi appear HOX gene independent. Our preclinical results encourage clinical testing of BETi in NPM1c AML patients. Full article

The interdisciplinary nature of sports and the presence of various systemic and non-systemic factors introduce challenges in predicting sports match outcomes using a single disciplinary approach. In contrast to previous studies that use sports performance metrics and statistical models, this study is the first to apply a deep learning approach in financial time series modeling to predict sports match outcomes. The proposed approach has two main components: a convolutional neural network (CNN) classifier for implicit pattern recognition and a logistic regression model for match outcome judgment. First, the raw data used in the prediction are derived from the betting market odds and actual scores of each game, which are transformed into sports candlesticks. Second, CNN is used to classify the candlesticks time series on a graphical basis. To this end, the original 1D time series are encoded into 2D matrix images using Gramian angular field and are then fed into the CNN classifier. In this way, the winning probability of each matchup team can be derived based on historically implied behavioral patterns. Third, to further consider the differences between strong and weak teams, the CNN classifier adjusts the probability of winning the match by using the logistic regression model and then makes a final judgment regarding the match outcome. We empirically test this approach using 18,944 National Football League game data spanning 32 years and find that using the individual historical data of each team in the CNN classifier for pattern recognition is better than using the data of all teams. The CNN in conjunction with the logistic regression judgment model outperforms the CNN in conjunction with SVM, Naïve Bayes, Adaboost, J48, and random forest, and its accuracy surpasses that of betting market prediction. Full article

The prediction of sport event results has always drawn attention from a vast variety of different groups of people, such as club managers, coaches, betting companies, and the general population. The specific nature of each sport has an important role in the adaption of various predictive techniques founded on different mathematical and statistical models. In this paper, a common approach of modeling sports with a strongly defined structure and a rigid scoring system that relies on an assumption of independent and identical point distributions is challenged. It is demonstrated that such models can be improved by introducing dynamics into the match models in the form of sport momentums. Formal mathematical models for implementing these momentums based on conditional probability and empirical Bayes estimation are proposed, which are ultimately combined through a unifying hybrid approach based on the Monte Carlo simulation. Finally, the method is applied to real-life volleyball data demonstrating noticeable improvements over the previous approaches when it comes to predicting match outcomes. The method can be implemented into an expert system to obtain insight into the performance of players at different stages of the match or to study field scenarios that may arise under different circumstances. Full article