New alternatives to the Lennard-Jones potential.

We present a new method for approximating two-body interatomic potentials from existing ab initio data based on representing the unknown function as an analytic continued fraction. In this study, our method was first inspired by a representation of the unknown potential as a Dirichlet polynomial, i.e., the partial sum of some terms of a Dirichlet series. Our method allows for a close and computationally efficient approximation of the ab initio data for the noble gases Xenon (Xe), Krypton (Kr), Argon (Ar), and Neon (Ne), which are proportional to r - 6 and to a very simple d e p t h = 1 truncated continued fraction with integer coefficients and depending on n - r only, where n is a natural number (with n = 13 for Xe, n = 16 for Kr, n = 17 for Ar, and n = 27 for Neon). For Helium (He), the data is well approximated with a function having only one variable n - r with n = 31 and a truncated continued fraction with d e p t h = 2 (i.e., the third convergent of the expansion). Also, for He, we have found an interesting d e p t h = 0 result, a Dirichlet polynomial of the form k 1 6 - r + k 2 48 - r + k 3 72 - r (with k 1 , k 2 , k 3 all integers), which provides a surprisingly good fit, not only in the attractive but also in the repulsive region. We also discuss lessons learned while facing the surprisingly challenging non-linear optimisation tasks in fitting these approximations and opportunities for parallelisation.

Continued fractions and the Thomson problem.

We introduce new analytical approximations of the minimum electrostatic energy configuration of n electrons, E(n), when they are constrained to be on the surface of a unit sphere. Using 453 putative optimal configurations, we searched for approximations of the form [Formula: see text] where g(n) was obtained via a memetic algorithm that searched for truncated analytic continued fractions finally obtaining one with Mean Squared Error equal to [Formula: see text] for the model of the normalized energy ([Formula: see text]). Using the Online Encyclopedia of Integer Sequences, we searched over 350,000 sequences and, for small values of n, we identified a strong correlation of the highest residual of our best approximations with the sequence of integers n defined by the condition that [Formula: see text] is a prime. We also observed an interesting correlation with the behavior of the smallest angle [Formula: see text], measured in radians, subtended by the vectors associated with the nearest pair of electrons in the optimal configuration. When using both [Formula: see text] and [Formula: see text] as variables a very simple approximation formula for [Formula: see text] was obtained with MSE= [Formula: see text] and MSE= 73.2349 for E(n). When expanded as a power series in infinity, we observe that an unknown constant of an expansion as a function of [Formula: see text] of E(n) first proposed by Glasser and Every in 1992 as [Formula: see text], and later refined by Morris, Deaven and Ho as [Formula: see text] in 1996, may actually be very close to -1.10462553440167 when the assumed optima for [Formula: see text] are used.

International manufacturing and trade in colistin, its implications in colistin resistance and One Health global policies: a microbiological, economic, and anthropological study.

BACKGROUND: The emergence of colistin-resistant Enterobacterales is a global public health concern, yet colistin is still widely used in animals that are used for food as treatment, metaphylaxis, prophylaxis, and growth promotion. Herein, we investigate the effect of colistin-resistant Enterobacterales in Pakistan, global trade of colistin, colistin use at the farm level, and relevant socioeconomic factors. METHODS: We conducted a microbiological, economic, and anthropological study of colistin-resistant Escherichia coli in humans, animals, and the environment and international trade and knowledge of colistin in Pakistan, Bangladesh, Nigeria, China, India, and Viet Nam. We collected backyard poultry cloacal swabs, commercial broiler cloacal swabs, cattle and buffalo rectal swabs, human rectal swabs, wild bird droppings, cattle and buffalo meat, sewage water, poultry flies, chicken meat, and canal water from 131 sites across Faisalabad, Pakistan, to be tested for mcr-1-positive and mcr-3-positive Escherichia coli. We recruited new patients admitted to Allied Hospital, Faisalabad, Pakistan, with abdominal pain and diarrhoea for rectal swabs. Patients with dysentery and those who were already on antibiotic treatment were excluded. Data for colistin trade between 2017 and 2020, including importation, manufacturing, and usage, were accessed from online databases and government sources in Pakistan, Bangladesh, and Nigeria. We recruited participants from poultry farms and veterinary drug stores in Pakistan and Nigeria to be interviewed using a structured questionnaire. International manufacturing, import, and export data; value analysis; and trade routes of colistin pharmaceutical raw material (PRM), feed additive, and finished pharmaceutical products (FPPs) were accessed from 2017-21 export data sets. FINDINGS: We collected 1131 samples between May 12, 2018, and July 1, 2019: backyard poultry cloacal swabs (n=100), commercial broiler cloacal swabs (n=102), cattle and buffalo rectal swabs (n=188), human rectal swabs (n=200), wild bird droppings (n=100), cattle and buffalo meat (n=100), sewage water (n=90), poultry flies (n=100), chicken meat (n=100), and canal water (n=51). We recruited 200 inpatients at Allied Hospital, Faisalabad, Pakistan, between Nov 15, 2018, and Dec 14, 2018, for rectal swabs. We recruited 21 participants between Jan 1, 2020, and Dec 31, 2020, from poultry farms and drug stores in Pakistan and Nigeria to be interviewed. 75 (7%) of 1131 samples contained mcr-1-positive E coli, including wild bird droppings (25 [25%] of 100), commercial broiler cloacal swabs (17 [17%] of 100), backyard poultry cloacal swabs (one [1%] of 100), chicken meat (13 [13%] of 100), cattle and buffalo meat (two [2%] of 100), poultry flies (eight [8%] of 100), sewage water (six [7%] of 90), and human rectal swabs (three [2%] of 200). During 2017-20, Pakistan imported 275·5 tonnes (68·9 tonnes per year, 95% CI 41·2-96·6) of colistin as PRM, all sourced from China, 701·9 tonnes (175·5 tonnes per year, 140·9-210·1) of colistin as feed additives from China and Viet Nam, and 63·0 tonnes (15·8 tonnes per year, 10·4-21·1) of colistin as FPPs from various countries in Asia and Europe. For Bangladesh and Nigeria, colistin PRM and FPPs were imported from China and Europe. Colistin knowledge and usage practices in Pakistan and Nigeria were unsatisfactory in terms of understanding of the effects on human medicine and usage other than for treatment purposes. China is the major manufacturer of PRM and feed additive colistin and exported a total of 2664·8 tonnes (666·2 tonnes per year, 95% CI 262·1 to 1070·2) of PRM and 2570·2 tonnes (642·6 tonnes per year, -89·4 to 1374·5) of feed additive in 1330 shipments during 2018-21 to 21 countries. INTERPRETATION: Regardless of 193 countries signing the UN agreement to tackle antimicrobial resistance, trading of colistin as PRM, FPPs, and feed additive or growth promoter in low-income and middle-income countries continues unabated. Robust national and international laws are urgently required to mitigate the international trade of this antimicrobial listed on WHO Critically Important Antimicrobials for Human Medicine. FUNDING: Pakistan Agricultural Research Council and INEOS Oxford Institute for Antimicrobial Research TRANSLATION: For the Urdu translation of the abstract see Supplementary Materials section.

Predicting Characteristics Associated with Breast Cancer Survival Using Multiple Machine Learning Approaches.

Breast cancer is one of the most commonly diagnosed female disorders globally. Numerous studies have been conducted to predict survival markers, although the majority of these analyses were conducted using simple statistical techniques. In lieu of that, this research employed machine learning approaches to develop models for identifying and visualizing relevant prognostic indications of breast cancer survival rates. A comprehensive hospital-based breast cancer dataset was collected from the National Cancer Institute's SEER Program's November 2017 update, which offers population-based cancer statistics. The dataset included female patients diagnosed between 2006 and 2010 with infiltrating duct and lobular carcinoma breast cancer (SEER primary cites recode NOS histology codes 8522/3). The dataset included nine predictor factors and one predictor variable that were linked to the patients' survival status (alive or dead). To identify important prognostic markers associated with breast cancer survival rates, prediction models were constructed using K-nearest neighbor (K-NN), decision tree (DT), gradient boosting (GB), random forest (RF), AdaBoost, logistic regression (LR), voting classifier, and support vector machine (SVM). All methods yielded close results in terms of model accuracy and calibration measures, with the lowest achieved from logistic regression (accuracy = 80.57 percent) and the greatest acquired from the random forest (accuracy = 94.64 percent). Notably, the multiple machine learning algorithms utilized in this research achieved high accuracy, suggesting that these approaches might be used as alternative prognostic tools in breast cancer survival studies, especially in the Asian area.

Heterogeneous Ensemble Combination Search Using Genetic Algorithm for Class Imbalanced Data Classification.

Classification of datasets with imbalanced sample distributions has always been a challenge. In general, a popular approach for enhancing classification performance is the construction of an ensemble of classifiers. However, the performance of an ensemble is dependent on the choice of constituent base classifiers. Therefore, we propose a genetic algorithm-based search method for finding the optimum combination from a pool of base classifiers to form a heterogeneous ensemble. The algorithm, called GA-EoC, utilises 10 fold-cross validation on training data for evaluating the quality of each candidate ensembles. In order to combine the base classifiers decision into ensemble's output, we used the simple and widely used majority voting approach. The proposed algorithm, along with the random sub-sampling approach to balance the class distribution, has been used for classifying class-imbalanced datasets. Additionally, if a feature set was not available, we used the (α, ß) - k Feature Set method to select a better subset of features for classification. We have tested GA-EoC with three benchmarking datasets from the UCI-Machine Learning repository, one Alzheimer's disease dataset and a subset of the PubFig database of Columbia University. In general, the performance of the proposed method on the chosen datasets is robust and better than that of the constituent base classifiers and many other well-known ensembles. Based on our empirical study we claim that a genetic algorithm is a superior and reliable approach to heterogeneous ensemble construction and we expect that the proposed GA-EoC would perform consistently in other cases.

A study on strategic provisioning of cloud computing services.

Cloud computing is currently emerging as an ever-changing, growing paradigm that models "everything-as-a-service." Virtualised physical resources, infrastructure, and applications are supplied by service provisioning in the cloud. The evolution in the adoption of cloud computing is driven by clear and distinct promising features for both cloud users and cloud providers. However, the increasing number of cloud providers and the variety of service offerings have made it difficult for the customers to choose the best services. By employing successful service provisioning, the essential services required by customers, such as agility and availability, pricing, security and trust, and user metrics can be guaranteed by service provisioning. Hence, continuous service provisioning that satisfies the user requirements is a mandatory feature for the cloud user and vitally important in cloud computing service offerings. Therefore, we aim to review the state-of-the-art service provisioning objectives, essential services, topologies, user requirements, necessary metrics, and pricing mechanisms. We synthesize and summarize different provision techniques, approaches, and models through a comprehensive literature review. A thematic taxonomy of cloud service provisioning is presented after the systematic review. Finally, future research directions and open research issues are identified.

Cloud service selection using multicriteria decision analysis.

Cloud computing (CC) has recently been receiving tremendous attention from the IT industry and academic researchers. CC leverages its unique services to cloud customers in a pay-as-you-go, anytime, anywhere manner. Cloud services provide dynamically scalable services through the Internet on demand. Therefore, service provisioning plays a key role in CC. The cloud customer must be able to select appropriate services according to his or her needs. Several approaches have been proposed to solve the service selection problem, including multicriteria decision analysis (MCDA). MCDA enables the user to choose from among a number of available choices. In this paper, we analyze the application of MCDA to service selection in CC. We identify and synthesize several MCDA techniques and provide a comprehensive analysis of this technology for general readers. In addition, we present a taxonomy derived from a survey of the current literature. Finally, we highlight several state-of-the-art practical aspects of MCDA implementation in cloud computing service selection. The contributions of this study are four-fold: (a) focusing on the state-of-the-art MCDA techniques, (b) highlighting the comparative analysis and suitability of several MCDA methods, (c) presenting a taxonomy through extensive literature review, and (d) analyzing and summarizing the cloud computing service selections in different scenarios.