High Probability Research Articles

Hybrid Recommendation Algorithm based on Product Popularity and User Preference

In today's society, recommendation system is widely used to recommend users' preferred products and content. However, with the rapid development of the Internet, there are more and more contents on the Internet, and the algorithm complexity of recommending products to users through the recommendation system is getting higher and higher, and the recommendation algorithm is also more and more inclined to recommend popular products to users, which also leads to the decline in the novelty and diversity of the recommendation algorithm. In order to solve this problem, the study first adopts the method of inhibiting the recommendation ability of current popular products, and on this basis, introduces the influence of user rating on the recommendation ability of different products. At the same time, the product is classified, take the movie for example, can be classified as action film, comedy, children's film and so on. There is a very high probability that an adult will not like children's films, and a very high probability that a child will not like horror films. Impose a penalty on a product category that the user may not like, reducing the likelihood that the product will be recommended to the user. The proposed algorithm is compared with other excellent algorithms in this field on MovieLens movie score dataset, and the results of the experiments showed that the algorithm improved in accuracy, diversity, and novelty.

A new computational framework for log-concave density estimation

AbstractIn statistics, log-concave density estimation is a central problem within the field of nonparametric inference under shape constraints. Despite great progress in recent years on the statistical theory of the canonical estimator, namely the log-concave maximum likelihood estimator, adoption of this method has been hampered by the complexities of the non-smooth convex optimization problem that underpins its computation. We provide enhanced understanding of the structural properties of this optimization problem, which motivates the proposal of new algorithms, based on both randomized and Nesterov smoothing, combined with an appropriate integral discretization of increasing accuracy. We prove that these methods enjoy, both with high probability and in expectation, a convergence rate of order 1/T up to logarithmic factors on the objective function scale, where T denotes the number of iterations. The benefits of our new computational framework are demonstrated on both synthetic and real data, and our implementation is available in a github repository (Log-Concave Computation).

Differences in end-of-life care patterns between types of hospice used for cancer patients: a retrospective cohort study.

In response to the rapid aging population and increasing number of cancer patients, discussions on dignified end-of-life (EoL) decisions are active around the world. Therefore, this study aimed to identify the differences in EoL care patterns between types of hospice used for cancer patients. In this population-based cohort study, the Korean National Health Insurance Service cohort data containing all registered cancer patients who died between 2017 and 2021 were used. A total of 408,964 individuals were eligible for analysis. The variable of interest, the type of hospice used in the 6months before death, was classified as follows: (1) Non-hospice users; (2) Hospital-based hospice single users; (3) Home-based hospice single users; (4) Combined hospice users. The outcomes were set as patterns of care, including intense care and supportive care. To identify differences in care patterns between hospice types, a generalized linear model with zero-inflated negative binomial distribution was applied. Hospice enrollment was associated with less intense care and more supportive care near death. Notably, those who used combined hospice care had the lowest probability and frequency of receiving intense care (aOR: 0.18, 95% CI: 0.17-0.19, aRR: 0.47, 95% CI: 0.44-0.49), while home-based hospice single users had the highest probability and frequency of receiving supportive care (Prescription for narcotic analgesics, aOR: 2.95, 95% CI: 2.69-3.23, aRR: 1.45, 95% CI: 1.41-1.49; Mental health care, aOR: 3.40, 95% CI: 3.13-3.69, aRR: 1.35, 95% CI: 1.31-1.39). Our findings suggest that although intense care for life-sustaining decreases with hospice enrollment, QoL at the EoL actually improves with appropriate supportive care. This study is meaningful in that it not only offers valuable insight into hospice care for terminally ill patients, but also provides policy implications for the introduction of patient-centered community-based hospice services.

신분증 OCR 인식 확률을 높일 수 있는 인공조명환경 연구

While the number of uses for ID(Identification) OCR(Optical Character Recognition) is increasing and the scope of its use is increasing, it has limitations in increasing the probability due to the problem of not being able to controlexternal conditions. The feedback currently being provided is not scientific, so it is unable to provide appropriate guidance to users. This study created a formula to avoid veiling reflection, which is the main cause of poor OCR, and allowed the angle of lighting to be adjusted according to the distance between the ID card and the camera. In addition, the BRDF(Bidirectional Reflectance Distribution Function) of the ID card was measured and simulated, and the point where the color luminance contrast was greatest was presented at an angle of 36 degrees between the light and the ID card, and this was verified in an actual situation. Through this, points where OCR can be good in an artificial lighting environment were presented in a scientific way, and machine learning was additionally conducted to provide a way for users to conveniently find places with a high OCR probability.

Przerywanie ciąży (aborcja) w świetle orzecznictwa polskiego Trybunału Konstytucyjnego

The purpose of this study is primarily to analyse the jurisprudence of the Polish Constitutional Tribunal regarding the compliance with constitutional norms of the conditions for the admissibility of termination of pregnancy (abortion). The Constitution of the Republic of Poland provides every person with legal protection of life. Although its provisions do not explicitly specify that it is about the legal protection of life from conception to natural death, this type of interpretation seems to be adopted by the Constitutional Tribunal, which results in subsequent judgments on the assessment of compliance with this constitutional model of the conditions for the admissibility of abortion in Poland. In its jurisprudence, the Constitutional Tribunal first drew attention to the constitutional guarantees for the protection of human life at every stage of its development, and thus questioned the admissibility of termination of pregnancy due to difficult living conditions or the difficult personal situation of a pregnant woman, as well as in the case when prenatal examinations or other medical conditions indicate a high probability of severe and irreversible impairment of the fetus or an incurable disease threatening its life. In this context, however, it is worth noting that abortion is permissible when a high probability of severe and irreversible impairment of the fetus or an incurable disease threatening its life simultaneously poses a substantial risk to the life and health of the mother. The analysis of the jurisprudence of the Constitutional Tribunal therefore allows to formulate a conclusion on the conservative and restrictive approach of this body regarding the conditions for the admissibility of abortion in Poland and the recognition of the legal protection of life as a cardinal constitutional value of the Polish legal system.

Assessment of the Upper Cretaceous Abu Roash carbonate source rocks from the Beni Suef field, Western Desert, Egypt

Global sea-level rise during the Early Turonian-Late Cenomanian Bonarelli event resulted in oceanic anoxia and deposition of organic-rich source rocks across Northern Africa, such as the Late Cretaceous Abu Roash-F carbonates. Here, using thin sections, SEM, XRF, standard core analysis, and Rock-Eval pyrolysis data, we examine the petrographic and geochemical properties of the Abu Roash-F (AR-F) carbonate source rocks. These deep marine carbonates consist of dominantly planktonic foraminifera and calcispheres and are classified as wackestone. Extensive micritization, calcite cementation, and ferroan dolomite cement replacement (filling the bioclastic tests and chambers) are identified as the principal diagenetic factors. Core measurements indicate that these carbonates have very low porosity below 3% and horizontal permeability of 0.003 mD (3x10-18 m2), which is also supported by the observed isolated nanopores in SEM. The elemental concentration from the XRF data confirms a highly reducing depositional environment that facilitated the organic richness during the Early Turonian Oceanic Anoxia Event 2 (OAE2). The studied carbonate interval consists of Type-II kerogen in the oil window with a Tmax of about 440 °C and up to 4.2 wt.% TOC indicating ‘fair’ to ‘excellent’ organic richness and a very high probability of active oil generation and expulsion. Following the wireline logs and core data, the self-sourcing unconventional reservoir potential of the AR-F carbonates has been emphasized.

Growth and Export Performance of Pineapple from India: An Economic Analysis

The present study was undertaken to know the growth and export performance of pineapple. Pineapple (Ananas Comosus) is a tropical fruit belonging to the Bromeliaceae family, and it is commercially cultivated worldwide as a high-value crop. India is the sixth largest producer in the world. The objective of the study was to elucidate the growth and export performance of the Pineapple from India.The secondary data for analysis was collected from AGMARKNET, National Horticultural Board (NHB) and Agricultural and Processed Food products Export Development (APEDA). The results of the growth rate revealed a positive and increasing trend in pineapple exports from India during the study period, with an annual growth rate of 9.78 percent in quantity and 6.39 percent in value, which is significant at a five percent level of probability. The results of the Cuddy Della Valle index indicated that pineapple exports from India exhibit moderate instability, with values of 17.17 and 19.90 percent in quantity and value terms, respectively. It reveals that the average Nominal Protection Coefficient (NPC) value for pineapple was 0.53, indicating a moderate level of export competitiveness. Throughout the entire period, it is evident that India did not demonstrate competitiveness in pineapple exports, as indicated by the Revealed Comparative Advantage Index (RCA) values consistently being below unity and the Revealed Symmetric Comparative Advantage (RSCA) values being negative. The direction of the Indian pineapple trade was conducted using the Markov chain framework. Among the major importers, the United Arab Emirates stood out as one of the most stable markets with a high probability of retention at 0.624. Overall, Indian pineapple stands out as a reliable and competitive player in the global market, with a strong performance and potential for growth. Therefore, efforts should be focused on enhancing the quantity of exports while ensuring they meet the quality standards and align with the consumer preferences of other countries.

Joint friend and item recommendation based on multidimensional feature reciprocal interaction in social e-commerce

Social e-commerce platforms need to undertake the two core tasks of recommending potential social friends and preferred consumption items for users. However, the use of traditional one-dimensional information is no longer able to accurately make personalized recommendations. Early scholars have confirmed that users’ social and consumption behaviors do not exist independently: users with the same interests are more likely to become friends, and there is a high probability of similar consumption behaviors among friends. In this paper, we propose a joint friend and item recommendation model based on multidimensional feature reciprocal interaction (MFRI). Which is based on the user’s social friends and item preference information, extracts the shallow and deep features of the user’s social and consumption behaviors, and utilizes the reciprocity between unusual behaviors to achieve mutual enhancement. The reciprocity between shallow and deep features in similar behaviors is also explored based on the attention mechanism, and the model is trained by a joint loss function. We conducted experiments on real datasets, and the results confirm the effectiveness and robustness of MFRI for potential friend and preference item recommendations.

Analysis of Japan’s Experience of Implementing Technological Sovereignty Strategy

The article discusses the purpose of technological sovereignty of each state and its role for economic sustainability. The essence of technological sovereignty in the aspect of various scientific approaches is revealed as well as the boundaries of the sovereignty type under consideration are determined, taking into account production and knowledge-intensive components. Attention is focused on the features of Japan’s transition from a state in isolation to a state interacting with the outside world through technology. The essence of Japan’s approach to ensuring its own economic stability against the background of technological production is revealed. Japan’s place in the world rankings is considered in terms of technology and the number of registered patents for inventions. The indicators of capital investments, industry priorities, labor force in technological industries, the level of exports of goods and services from GDP, the trade balance from GDP, the level of exports of high technologies for 2020–2022 against the background of strengthening Japan’s technological sovereignty are analyzed. The forecast estimates of the technological future of Japan are determined, taking into account the priority in the national economy and the specifics of entering the foreign market with a new technological development. These forward-looking estimates take into account the specifics of the import of raw materials and their intended purpose to ensure technological sovereignty in the foreseeable future. As a result, there is a high probability of having a certain economic algorithm that allows the Japanese economy to restore the desired stability indicators in a short time. Therefore, it is necessary to conduct applied research in order to identify the essence of the economic algorithm used by Japan to strengthen technological sovereignty and ensure economic stability in a short time for subsequent possible adaptation in the Russian economy.

Structural and Stratigraphic Interpretations for Mishrif Formation in Kumait Oil Field, Using Seismic Reflection Data, Southern-Eastern Iraq

The Kumait oil field is located in the Maysan Governorate southeastern Iraq, which was studied and interpreted by using 2D and 3D seismic data provided by the Oil Exploration Company using Petrel software. The study concerned the Mishrif Formation belongs to the Cretaceous Age. The reflector was detected based on synthetic seismograms and well logs for Kt-1 well. Structural maps (two-way time, velocity, and depth) were derived from seismic reflection interpretations to show several structural features, such as four isolated irregular enclosures representing the Kumait structure plunging NW-SE and demonstrating that the Kumait oil field is influenced by the Najd fault system trending NW-SE. The stratigraphic interpretation used the variance attribute to delineate the edge of the fault, which extended from the Mishrif Formation with a depth of 3090m and used the Direct Hydrocarbon Indicators (DHI) to identify the stratigraphic features in the research region and the extraction of seismic attributes. The applied seismic attributes, such as the instantaneous phase, show dim spots, flat spots, and mounds and is followed by down-lap; the instantaneous frequency, which offers low-frequency areas indicating a high probability of hydrocarbon accumulation; and the RMS amplitude, which shows high amplitude in the seismic data that conforms to the structure and confirms the presence of hydrocarbon.

An efficient active learning Kriging approach for expected fatigue damage assessment applied to wind turbine structures

Estimating fatigue damage in wind turbine structures over their lifetime is a time-demanding task, necessitating simulations encompassing all the wind and wave conditions. Typically, this involves expending several thousand hours in simulation efforts. Given formidable computational demand, this study proposes an efficient active learning Kriging approach named ALK-EFDA for estimating expected fatigue damage in wind turbines. The ALK-EFDA framework leverages a Kriging surrogate model to predict fatigue damage levels across various wind-wave scenarios, and a learning function is designed to reduce Kriging prediction error in the high probability of occurrences of wind-wave cases. An active learning approach is proposed to estimate the expected fatigue damage efficiently. To validate the effectiveness of this approach, we applied it to a 15 MW Semi-submersible floating wind turbine model. The proposed approach estimates the expected short-term fatigue damage of the wind turbine tower and mooring lines. Our findings demonstrate that the ALK-EFDA method efficiently and accurately estimates fatigue damage in wind turbine structures. Compared to simulation methods, the proposed ALK-EFDA approach significantly reduces computational expenses by over 30 times. Furthermore, the absolute error, when compared to simulated results, remains at approximately 1%. This method will be a useful tool for offshore wind turbine designers.

Forecasting the probability of successful deployment of internet of things and cloud computing in the building sector through consistent fuzzy preference relations method

Purpose This study aims to identify significant factors, analyse them using the consistent fuzzy preference relations (CFPR) method and forecast the probability of successful deployment of the internet of things (IoT) and cloud computing (CC) in Gujarat, India’s building sector. Design/methodology/approach From the previous studies, 25 significant factors were identified, and a questionnaire survey with personal interviews obtained 120 responses from building experts in Gujarat, India. The questionnaire survey data’s validity, reliability and descriptive statistics were also assessed. Building experts’ opinions are inputted into the CFPR method, and priority weights and ratings for probable outcomes are obtained to forecast success and failure. Findings The findings demonstrate that the most important factors are affordable system and ease of use and battery life and size of sensors, whereas less important ones include poor collaboration between IoT and cloud developer community and building sector and suitable location. The forecasting values demonstrate that the factor suitable location has a high probability of success; however, factors such as loss of jobs and data governance have a high probability of failure. Based on the forecasted values, the probability of success (0.6420) is almost twice that of failure (0.3580). It shows that deploying IoT and CC in the building sector of Gujarat, India, is very much feasible. Originality/value Previous studies analysed IoT and CC factors using different multi-criteria decision-making (MCDM) methods to merely prioritise ranking in the building sector, but forecasting success/failure makes this study unique. This research is generally applicable, and its findings may be utilised for decision-making and deployment of IoT and CC in the building sector anywhere globally.

Integrative Analysis of Site-Specific Parameters with Nuisance Parameters on the Common Support

AbstractHigh-throughput technologies in bioscience have pushed us into an era with high dimensionality. Swamped by thousands of predictors, detecting the valuable signal from the noise in clinical studies becomes challenging. As a common strategy, integrative analysis utilizing similarities across multiple studies might help lift the curse of dimensionality and enhance statistical power. However, due to the growing concern about individual data privacy, data-sharing constraints are often imposed in integrative analysis. These might lead to results inequivalent to ones without sharing constraints and reduce statistical power in integrative analyses. In this paper, built on Abess, we propose an integrative analysis method to estimate the site-specific parameters in the presence of high dimensional nuisance parameters in multi-site studies. Implemented with a carefully designed $$L_{2,0}$$ L 2 , 0 penalization on nuisance parameters, the proposed method satisfies both the DataSHIELD constraint, which only allows the transmission of summary statistics from sites, and the equivalence property that the solution is exactly the same as the solution merging all datasets into one on a single location. Assuming the nuisance parameters share a common support, the proposed method has support recovery and selection consistency with high probability and exhibits improved estimation accuracy on the site-specific parameters and low computational cost in numerical experiments. We demonstrate the merit of the proposed method by investigating the relationship between the CD8 T cell count and the treatment effect of zidovudine-incorporated therapy in the AIDS Clinical Trials Group Study 175.

Connected in bad times and in good times: Empathy induces stable social closeness.

Humans need social closeness to prosper. There is evidence that empathy can induce social closeness. However, it remains unclear how empathy-related social closeness is formed and how stable it is as time passes. We applied an acquisition-extinction paradigm combined with computational modelling and fMRI, to investigate the formation and stability of empathy-related social closeness. Female participants observed painful stimulation of another person with high probability (acquisition) and low probability (extinction), and rated their closeness to that person. The results of two independent studies showed increased social closeness in the acquisition block that resisted extinction in the extinction block. Providing insights into underlying mechanisms, reinforcement learning modelling revealed that the formation of social closeness is based on a learning signal (prediction error) generated from observing another's pain, whereas maintaining social closeness is based on a learning signal generated from observing another's pain relief. The results of a reciprocity control study indicate that this feedback recalibration is specific to learning of empathy-related social closeness. On the neural level, the recalibration of the feedback signal was associated with neural responses in anterior insula and adjacent inferior frontal gyrus and the bilateral superior temporal sulcus/temporo-parietal junction. Together, these findings show that empathy-related social closeness generated in bad times, i.e., empathy with the misfortune of another person, transfers to good times, and thus may form one important basis for stable social relationships.Significance Statement Humans feel close to others if they empathize with them. Here we test whether this feeling of social closeness remains if empathy is no longer elicited. Combining mathematical learning models and functional magnetic-resonance imaging, we find that empathy with others' pain establishes stable social closeness that is maintained even if the other person is feeling well again. Explaining the mechanism, we show that the stability of empathy-induced social closeness is based on the recalibration of an empathy-related learning signal in the anterior insula/inferior frontal gyrus and the temporo-parietal junction. These findings reveal how empathy maintains social closeness and thus contributes to the formation of stable social relationships.

Cybersecurity Challenges in Smart Grids: A Focus on Information Technology

The joining of information technology into smart grids has altered the energy area, improving proficiency and maintainability. Notwithstanding, this combination additionally delivers critical cybersecurity challenges. This paper digs into the investigation of cybersecurity challenges in smart grids, especially underlining the job of information technology. One basic viewpoint analyzed in this study is the use of organized Robust Principal Component Analysis (RPCA) with the Proximal Point identifier. Through definite estimations and examinations, the paper presents a thorough outline of the RPCA-based approach's viability. It gives experiences into the computational prerequisites for carrying out this method, featuring its true capacity in identifying oddities inside smart lattice frameworks. The exploration uses genuine data from the IEEE 30 and IEEE 118 power frameworks to assess the exhibition of the RPCA-based proximal tendency locater. Results exhibit promising results, including high detection probability and diminished recognizable proof latency. In addition, the review exhibits the calculation's ability to recognize False Data Injection Attacks (FDIA) with a great ID probability surpassing 95%. Besides, trial re-enactments led for both arbitrary and assigned assault situations on the IEEE 30 and IEEE 118 power frameworks display essentially lower detection latencies. These discoveries highlight the significance and viability of utilizing RPCA-based approaches in moderating cyber security dangers inside smart framework foundations.

Exploring the Relationship Between Key Factors and Victory and Defeat in Volleyball Matches using Bayesian Network Modeling

Identifying key factors contributing to victory and defeat in volleyball matches using Bayesian network modeling involves probabilistic graphical modeling to analyze the interrelationships among various factors and their influence on match outcomes. By integrating data on player performance, team strategies, match conditions, and opponent characteristics, Bayesian networks can uncover complex dependencies and predict the likelihood of winning or losing based on different scenarios. The research conducted on exploring the relationship between key factors and victory and defeat in volleyball matches using Bayesian network modeling. Through the utilization of Bayesian network modeling, this study aims to elucidate the intricate interplay between different variables and their impact on match outcomes in the context of volleyball. The research employs a Hidden Markov Process Bayesian Network (HMPBN) to model the temporal dependencies and uncertainties inherent in volleyball matches. The dataset comprises a comprehensive collection of volleyball match data, encompassing various observable variables such as points scored, successful serves, defensive plays, and more. The simulation setup involves training the HMPBN model on a dataset consisting of 1000 instances and evaluating its performance on a separate test dataset of 500 instances. The model parameters, including the transition matrix, emission matrix, and initial state distribution, are determined through rigorous statistical estimation techniques. The results of the simulation experiments provide valuable insights into the probabilities associated with different actions and their influence on match outcomes. The study reveals that successful serves have a high probability of contributing to match victories, with an average success rate of 80% in the dataset. Conversely, defensive plays exhibit a lower success rate, with an average probability of success of 60%. The Hidden Markov Process Bayesian Network (HMPBN) achieves an overall classification accuracy of 85% on the test dataset, demonstrating its effectiveness in predicting match outcomes.

Comprehensive analysis of bulk and single-cell transcriptomic data reveals a novel signature associated with endoplasmic reticulum stress, lipid metabolism, and liver metastasis in pancreatic cancer

BackgroundPancreatic ductal adenocarcinoma (PDAC) is a lethal malignancy with high probability of recurrence and distant metastasis. Liver metastasis is the predominant metastatic mode developed in most pancreatic cancer cases, which seriously affects the overall survival rate of patients. Abnormally activated endoplasmic reticulum stress and lipid metabolism reprogramming are closely related to tumor growth and metastasis. This study aims to construct a prognostic model based on endoplasmic reticulum stress and lipid metabolism for pancreatic cancer, and further explore its correlation with tumor immunity and the possibility of immunotherapy.MethodsTranscriptomic and clinical data are acquired from TCGA, ICGC, and GEO databases. Potential prognostic genes were screened by consistent clustering and WGCNA methods, and the whole cohort was randomly divided into training and testing groups. The prognostic model was constructed by machine learning method in the training cohort and verified in the test, TCGA and ICGC cohorts. The clinical application of this model and its relationship with tumor immunity were analyzed, and the relationship between endoplasmic reticulum stress and intercellular communication was further explored.ResultsA total of 92 characteristic genes related to endoplasmic reticulum stress, lipid metabolism and liver metastasis were identified in pancreatic cancer. We established and validated a prognostic model for pancreatic cancer with 7 signatures, including ADH1C, APOE, RAP1GAP, NPC1L1, P4HB, SOD2, and TNFSF10. This model is considered to be an independent prognosticator and is a more accurate predictor of overall survival than age, gender, and stage. TIDE score was increased in high-risk group, while the infiltration levels of CD8+ T cells and M1 macrophages were decreased. The number and intensity of intercellular communication were increased in the high ER stress group.ConclusionsWe constructed and validated a novel prognostic model for pancreatic cancer, which can also be used as an instrumental variable to predict the prognosis and immune microenvironment. In addition, this study revealed the effect of ER stress on cell–cell communication in the tumor microenvironment.

Прогнозування РНК, siRNA з подальшим визначенням імунологічної відповіді запропонованої вакцини (Vac534) проти COVID-19

Despite the decline in Coronavirus infections, care must be taken to avoid new mutations that allow the virus to escape vaccination and treatment. Viral RNA is responsible for virus replication and assembly in the host cell. Aim. The goal of this work was to predict the RNA secondary structure of the vaccination we developed before (Vac534). In addition, the degree of RNA overlaps while translating into ribosomes, refolding, and evaluating the protein immune response are all considered. Methods. Different immunobioinformatics tools and servers were utilized for analysis. RNA folding was executed through the application of RNAstructure 6.4 and RNAfold software. The unique sequence context led to the emergence of potential RNA structures at the site where ribosome binding occurs. A new method, C-IMMSIM, which relies on immune cell epitope prediction, was employed to gain fresh insights into comprehending the immune system. Results. А high probability of ≥ 99% is shown between nucleotides with the stability of loops and motifs of the RNA 2D structure. The predicted siRNA sequences, which were located in three places, were used to calculate total energy, self-folding, and a free end with a high accessibility score. RNA translation into the ribosome is required to determine the optimum direction of translation. The short docking fragment of 24 nucleotides of RNA (Vac534) generated six robust binds with MPRO at high energy. Conclusions. The immunological evaluation of the vaccination is critical for stimulating immune cells and detecting interleukins and cytokine production. This study is viewed as a step toward developing Coronavirus treatments. The number of loops and motifs is increasing, with a probability of above 99% for most sequences. The predicted siRNA sequences, which were located in three places, were used to calculate total energy, self-folding, and a free end with a high accessibility score.

Insights into the synergistic effect of polylactam kinetic hydrate inhibitor and amino acid via molecular dynamics simulations

Technological innovation is urgently needed to solve the problem of hydrate formation in oil and gas transmission pipelines for the flow assurance guarantee. Typical kinetic hydrate inhibitors for industrial application are the combination of lactam-based polymers with solvent synergists. In this study, a novel non-solvent synergist leucine (Leu) was proposed for improving the kinetic inhibition effect of polyvinyl caprolactam (PVCap). The synergism of PVCap and Leu was investigated via molecular dynamics simulations. Results showed that the 1 wt% PVCap + 2 wt% Leu system can totally inhibit methane hydrate formation within the simulation period, and the combination systems with other concentrations have negligible effects on delaying hydrate nucleation but can significantly slow down the hydrate growth rate. The excellent synergism of PVCap and Leu may be due to the large diffusion coefficient of the molecules in the systems, which provides a high probability for the aggregation of methane molecules and induces the formation of bubbles. In addition, the bubbles can be further stabilized by the amphiphilic PVCap and Leu molecules around it. This study also provides new insights to develop powerful synergists for PVCap, which would be helpful to the design strategy of KHIs with high efficiency.

Runtime Analysis of Competitive Co-evolutionary Algorithms for Maximin Optimisation of a Bilinear Function

AbstractCo-evolutionary algorithms have a wide range of applications, such as in hardware design, evolution of strategies for board games, and patching software bugs. However, these algorithms are poorly understood and applications are often limited by pathological behaviour, such as loss of gradient, relative over-generalisation, and mediocre objective stasis. It is an open challenge to develop a theory that can predict when co-evolutionary algorithms find solutions efficiently and reliable. This paper provides a first step in developing runtime analysis for population-based competitive co-evolutionary algorithms. We provide a mathematical framework for describing and reasoning about the performance of co-evolutionary processes. To illustrate the framework, we introduce a population-based co-evolutionary algorithm called PDCoEA, and prove that it obtains a solution to a bilinear maximin optimisation problem in expected polynomial time. Finally, we describe settings where PDCoEA needs exponential time with overwhelmingly high probability to obtain a solution.