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Containers have emerged as the most promising lightweight virtualization technology in providing cloud services due to its flexible deployment, portability, and scalability especially in micro-services, smart vehicles, IoTs, and fog/edge computing. An important and vital role in cloud container services is played by the scheduler’s component to optimize performance and reduce cost due to the diverse nature of the workload and cloud resources. Despite the immense traction of containers in cloud computing, there is no comprehensive survey that covers container scheduling techniques. In this timely survey, we investigate the landscape of the state-of-the-art container scheduling techniques aiming to inspire more research work in this active area of research. The survey is structured around classifying the scheduling techniques into four categories based on the type of optimization algorithm employed to generate the schedule namely mathematical modeling, heuristics, meta-heuristics and machine learning. Then for each class of scheduling algorithms, we analyze and identify key benefits and pitfalls, together with key challenges of the available techniques based on the performance metrics. Finally, this paper highlights fertile future research opportunities to realize the full potential of the emergent container technology.

Anatase TiO2 nanoparticles (NPs) were successfully prepared through a hydrothermal approach, and Au NPs at various Au (0.1–2 wt%) contents were photodeposited onto the TiO2 NPs surface. The photocatalytic efficiency for the Au/TiO2 NPs for resorcinol photodegradation throughout UVA illumination was assessed. The TEM images and XPS findings indicated that the Au NPs are highly distributed onto TiO2 surface in the metallic state. The 0.1%Au/TiO2 NPs exhibited the highest photocatalytic efficiency of about 95.34%; however, 72.36% is given by pure TiO2 NPs. It was found that the photodegradation rate of 0.1% Au/TiO2 NPs exhibited 1.5 times of magnitude higher than pure TiO2 NPs. 0.1%Au/TiO2 NPs was considered to be the outstanding photoactive due to the ultimate efficient charge-carriers separation through charge transfer between Au and TiO2 NPs. The Au NPs sizes, its dispersity on TiO2 surface and surface plasmon resonance (SPR) were believed the critical factors for the higher photocatalytic performance of 0.1% Au/TiO2 NPs. The prepared photocatalysts are found to be the promising materials for toxic organic compounds remediation and solar conversion.

A comprehensive concept for coal conversion is proposed in this work, from which a series of products generated including light volatiles, liquid oils, and pyrolysis chars. The result indicates the optimal temperature for the pyrolysis from organic solvent extraction residue is between 700 °C to 900 °C, producing largest amount of liquid oils and giving rise to a chemically stable pyrolysis char sample with considerable BET specific surface area. The pyrolysis chars are then utilized as an alternative material for the fabrication of heat-insulating building material (char clay plaster-CCP, char brick-CB and foam glass-FA). Due to the inherent large specific surface area and ample pore channels, the water could be absorbed and stored in these pores, resulting in the effects of regulating indoor humidity and also low thermal conductivity. The char-based building material makes the exterior walls good breathing properties, able to maintain a proper humidity level. The building energy saving will be benefited from the excellent insulation property of these building products.

The advent of porous polymers capable of both CO2 capture and conversion is an emerging research area to tackle ever-increasing CO2 emissions into the atmosphere and also to use CO2 as a sustainable C1 source. Porous cationic polymers, which are analogous to zeolites, with exchangeable counteranions offer ideal platform for the simultaneous capture and conversion of CO2. Here, we report on the porous cationic polymers (Ru-PCPs) with increased number of charges synthesized through condensation reaction between tris(1,10-phenanthroline-5,6-dione)Ru(II) dichloride and ortho-aromatic amines in AlCl3 at 400 °C. Ru-PCPs exhibited surface areas up to 526 m2 g−1 and CO2 uptake capacities as high as 3.76 mmol g−1, which is among the highest reported to date for porous cationic polymers. The high CO2-philicity of Ru-PCPs originating from the presence of multiple charges and nitrogen sites coupled with the nucleophilic Cl− anions rendered Ru-PCPs as highly efficient heterogeneous catalysts for the conversion of CO2 and propylene oxide to propylene carbonate through atom economy reaction. Notably, unlike previously reported porous cationic organocatalysts which require CO2 pressures of 10–30 bar, Ru-PCPs were shown to operate efficiently even under atmospheric pressure of CO2 mainly arising from the increased number of charges and counteranions.

The phase transformations of hydrothermally prepared H2Ti3O7 nanowires (NWs) upon calcination at different temperatures were addressed to interpret the impact of H2Ti3O7 NWs crystallization on their photocatalytic performance for resorcinol photodegradation under UV illumination. Bicrystalline brookite/anatase NWs were obtained by calcination at 800 °C, which indicated a superior photocatalyst compared with rutile/brookite NWs or TiO2(B)/anatase NWs as well as commercial P-25 photocatalysts. The brookite/anatase NWs photocatalyst exhibited high photocatalytic efficiency up to 99% owing to its long charge transfer channels and high resorcinol adsorption capacity, while the commercial P-25 photocatalyst is 72% within 3 h. The rate constant k (min−1) value of brookite/anatase heterostructure NWs is greater than that of TiO2(B)/anatase NWs and commercial P-25 for 5.6 and 2 times, respectively.

Ultraviolet (UV) radiation (UVR) has well-known adverse effects on the skin and eyes. Little attention is given to physical means of photoprotection, namely glass, window films, sunglasses, and clothing. In general, all types of glass block UV-B. For UV-A, the degree of transmission depends on the type, thickness, and color of the glass. Adding window films to glass can greatly decrease the transmission of UV-A. Factors that can affect the transmission of UVR through cloth include tightness of weave, thickness, weight, type of fabrics, laundering, hydration, stretch, fabric processing, UV absorbers, color, and fabric-to-skin distance.

Malakoplakia is an unusual chronic inflammatory condition that commonly occurs as a complication in immunocompromised patients. It is associated with bacterial infection, specifically Escherichia coli. Gastrointestinal malakoplakia in patients with organ transplant has rarely been described in the literature. We report the case of a 55-year-old male patient who had an orthotropic liver transplant 2 years before presentation and had been taking a combination of tacrolimus and prednisolone. He reported chronic watery diarrhea for the previous 6 months with no history of infection. His colonoscopy showed diffuse multiple mucosal nodules and ulcers appearing in the rectum and throughout the colon. A colonic biopsy revealed infiltration of the lamina propria by chronic inflammatory cells and sheets of histiocytes with the inclusion of Michaelis-Gutmann bodies. Pathological findings were consistent with malakoplakia. This is the sixth reported case of this presentation worldwide. Clinicians should be aware of this disease with its diverse pattern of presentation, as it can be a potential cause of diarrhea in liver transplant recipients. It is crucial to differentiate the pathology associated with this disease from that of other causes because it is a self-limited condition that can be managed well by prescribing antibiotics and reducing immunosuppression therapy. Keywords: Colonic malakoplakia, Immunocompromised, Liver transplant, Michaelis–Gutmann

Background: Despite the benefits associated with breast self-examination (BSE), few women perform it and many do not even know how to perform it. Objectives: The purpose of this study was to identify the proportion of women practicing BSE, factors that could affect its performance and explore women’s awareness about its practice steps. Methods: The study design can be differentiated into two components. The first was a cross-sectional survey to determine the prevalence of BSE. Recruitment efforts resulted in 510 women. BSE was practiced by 109. The second component of the study was a case-control study to identify factors associated with BSE, whereas practicing subjects (control) were compared with a randomly selected similar number of non-practicing females (cases). Results: The prevalence of BSE was 21%. Most of the socio-demographic variables have no significant effect on the practice of BSE. Practicing women had sufficient level of knowledge about BSE, clinical breast examination, and mammography. They believed significantly that bloody discharge from the nipple, presence of masses in the breasts, abnormal arm swelling, nipple retraction and discoloration of the breast were signs and symptoms of breast cancer. About 35% of practicing women in the current study performed correctly ⩽6 steps out of 12 steps. Conclusion: Only 21% of women attending PHC had ever practiced BSE. Even a high proportion of them were not aware of the correct steps of the procedure. Health education programs are essential to encourage and improve women’s practice of BSE. KEYWORDS: Breast self, Examination, Warning signs, Steps

We propose a diagnostics technique for the state space model fitting formed by deleting observations from the data and measuring the change in the estimates of the parameters. A method is proposed for distinguishing an observational outlier from an innovational one. The presented subspace system identification algorithm is robust and less sensitive to outliers. We give a numerical result to show effectiveness of the proposed method.

In this paper, a subspace system identification algorithm for the errors-in-variables (EIV) state space models subject to observation noise with outliers has been developed. By using the minimum covariance determinant (MCD) estimator, the outliers have been identified and deleted. Then the classical EIV subspace system identification algorithms have been applied to estimate the parameters of the state space models. In order to solve the MCD problem for the EIV state space models, a random search algorithm has been proposed. A Monte-Carlo simulation results demonstrate the effectiveness of the proposed algorithm.

In this paper, we propose a robust Kalman filter and smoother for the errors-invariables (EIV) state space model subject to observation noise with outliers. We introduce the EIV problem with outliers and then we present the minimum covariance determinant (MCD) estimator which is highly robust estimator to detect outliers. As a result, a new statistical test to check the existence of outliers which is based on the Kalman filter and smoother has been formulated. Since the MCD is a combinatorial optimization problem the randomized algorithm has been proposed in order to achieve the optimal estimate. However, the uniform sampling method has a high computational cost and may lead to biased estimate, therefore we apply the sub-sampling method. A Monte Carlo simulation result shows the efficiency of the proposed algorithm.

This paper advocates a new subspace system identification algorithm for the errors-in-variables (EIV) state space model via the EM algorithm. To initialize the EM algorithm an initial estimate is obtained by the errors-in-variables subspace system identification method: EIV-MOESP (Chou et al. [1997]) and EIV-N4SID (Gustafsson [2001]). The EM algorithm is an algorithm to compute the maximum value for the likelihood function that is consists of two steps; namely the E- and M-steps. The E- and M-steps in the EM algorithm are calculated by computing the conditional expectation under the assumption that the input-output data is completely observed. Numerical example shows that the EM algorithm can monotonically improve the initial estimates obtained by subspace identification methods.

The offset lithographic printing process requires the operator to make appropriate and timely on-line adjustments to compensate for color deviations from the desired print. An operator acquires proficiency by working on the same machine over a period of several years; thus he is able to apply adjustments according to its specific characteristics. It was found that this machine-specific knowledge consists of articulated and unarticulated knowledge. A connectionist representation was designed to map the observable variables to the operator's adjustments; while a forward chaining expert system was developed to represent the operator's articulated knowledge. A weight-based conflict resolution technique was constructed to dynamically update the knowledge base. This paper begins by presenting the press characterization problem. Then the development of the system is described. Finally, an analysis of results that cover all possible categories is documented.