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Intrusion detection datasets are highly likely to contain numerous redundant, irrelevant, and noisy features that slow the performance of the machine learning techniques and classifiers that may be applied to them. The feature selection approach is used for reducing the number of features in intrusion detection datasets and eliminating those that are not important. One of the most powerful structured population approaches is the Cellular Automata approach, which is used to enhance the diversity and convergence of population-based optimization algorithms. In this work, the Cellular Automata approach, Mixed opposition-based learning, and the K-Nearest Neighbor classifier are incorporated with the Prairie dog optimization algorithm (PDO) in a new intrusion detection framework called Binary Opposition Cellular Prairie dog optimization algorithm (BOC-PDO). The proposed framework contains four key features. First, the Cellular Automata model is utilized to enhance the population of feasible solutions in the PDO. Second, four S-shaped and four V-shaped Binary Transfer Functions are used to convert the continuous solutions in BOC-PDO to binary ones. Third, the Mixed opposition-based learning approach is used at the end of the optimization loop of BOC-PDO to improve capacity for exploration. Fourth, the K-Nearest Neighbor classifier is used as the main learning model in BOC-PDO. Eleven famous intrusion detection datasets were employed in the evaluation of the effectiveness of BOC-PDO compared to eight popular binary optimization algorithms and four machine learning approaches. According to the overall simulation results, BOC-PDO scored the highest accuracy, best objective value, and fewest selected features for each of the eleven intrusion detection datasets. Besides, the reliability and consistency of the simulation results of BOC-PDO compared to the other tested algorithms were established using Friedman and Wilcoxon statistical tests. [ABSTRACT FROM AUTHOR]

Snake optimizer (SO) is an optimization algorithm drawn from the reproductive habits of serpents. It exhibits outstanding effectiveness in solving continuous optimization problems. However, SO may face some performance challenges related to its population diversity and early convergence behavior. In this paper, we address the challenges of SO by introducing the Hybrid snake optimizer algorithm (HSOA). HSOA is a novel approach to optimization that incorporates two new optimization techniques into the SO algorithm. First, it incorporates a new opposition-based learning technique called Oppositional-mutual learning into the initialization stage of the SO algorithm. Second, it integrates dynamic polynomial mutation, which is an intelligent mutation method, into the initialization and optimization stages of the SO algorithm. These integrated approaches aim to increase the population's diversity of SO, while improving its searchability during its optimization stage. In power systems, the economic load dispatch (ELD) is an intricate optimization problem that becomes more challenging when the restrictions of the valve point effect (VPE) are incorporated into it. ELD with VPE is non-convex, lacking smoothness, and exhibiting nonlinearity that considers operational limitations expressed as both equality and inequality constraints to generate electricity. The suggested HSOA algorithm underwent evaluation and was compared with 47 renowned optimization algorithms across five real-world ELD problems with different specifications: generators with different unit capacities, transmission losses, prohibited operation zones, and Ramp Rate restrictions. The experimental results demonstrate that HSOA produces competitive solutions for the five real-world ELD problems. In detail, HSOA achieves the top rank in three cases of ELD problems with a 3-unit generator, and it secures the second and third positions in high-dimensional ELD problems with 40-unit and 80-unit generators, respectively. The statistical tests confirm the reliability and efficiency of HSOA. In addition, the effectiveness of HSOA was evaluated using the single-objective IEEE-CEC 2014 functions and compared to the results of eight popular metaheuristic algorithms. The results demonstrate that HSOA is a competitive optimization algorithm capable of solving the functions of IEEE-CEC 2014. [ABSTRACT FROM AUTHOR]

Magnesium and its alloys have tremendous potential as biodegradable implant materials but finding the right compromise between corrosion resistance and mechanical properties is an ongoing investigation. This study utilized equal channel angular pressing (ECAP) to fabricate Magnesium-ZX 30 alloy with a refined uniform microstructure. ECAP processing via routes Bc and A for up to four passes resulted in the evolution of dynamically recrystallized textured α-Mg ultrafine grains with a high fraction of low-angle boundaries. 4Bc and 4A processing via ECAP yielded significant grain refinements of 91.6% and 86.5%, respectively compared to the as-annealed condition. X-ray diffraction verified that the α-Mg phase dominated the microstructure as a result of ECAP processing promoting the dissolution of second phases due to the high density of dislocations and vacancies it generated. Electrochemical analysis revealed that ECAP reduced galvanic corrosion and pitting corrosion. Compared to the as-annealed sample, all ECAP-processed conditions displayed a significant decrease in corrosion rates, most notably among them is the 4A condition (97%). Moreover, the surge in grain boundary density and in grain boundary misorientations accelerated the formation of the passivation film, which enhanced the corrosion resistance compared to the as-annealed conditions by 254%, 418% for the 4Bc and 4A processed conditions, respectively. This was corroborated by an immersion test conducted in ringer lactate solution for 360 h. This current work signifies that ECAP processing is capable of producing ZX30 alloy medical implants with high mechanical properties and excellent corrosion resistance that make it suitable for future in-vitro research. [ABSTRACT FROM AUTHOR]

The present research applies different statistical analysis and machine learning (ML) approaches to predict and optimize the processing parameters on the wear behavior of ZK30 alloy processed through equal channel angular pressing (ECAP) technique. Firstly, The ECAPed ZK30 billets have been examined at as-annealed (AA), 1-pass, and 4-passes of route Bc (4Bc). Then, the wear output responses in terms of volume loss (VL) and coefficient of friction (COF) have been experimentally investigated by varying load pressure (P) and speed (V) using design of experiments (DOE). In the second step, statistical analysis of variance (ANOVA), 3D response surface plots, and ML have been employed to predict the output responses. Subsequently, genetic algorithm (GA), hybrid DOE–GA, and multi-objective genetic algorithm techniques have been used to optimize the input variables. The experimental results of ECAP process reveal a significant reduction in the average grain size by 92.7% as it processed through 4Bc compared to AA counterpart. Furthermore, 4Bc exhibited a significant improvement in the VL by 99.8% compared to AA counterpart. Both regression and ML prediction models establish a significant correlation between the projected and the actual data, indicating that the experimental and predicted values agreed exceptionally well. The minimal VL at different ECAP passes was obtained at the highest condition of the wear test. Also, the minimal COF for all ECAP passes was obtained at maximum wear load. However, the optimal speed in the wear process decreased with the number of billets passes for minimum COF. The validation of predicted ML models and VL regression under different wear conditions have an accuracy range of 70–99.7%, respectively. [ABSTRACT FROM AUTHOR]

The patient admission scheduling problem (PASP) has been studied for many years as one of the most important scheduling problems in the health sector. The primary goal of PASP is to assign patients to appropriate hospital beds while considering some hard and soft constraints. PASP is an NP-hard problem, which implies that the optimization approach is one of the best approaches that can be used to solve it. The arithmetic optimization algorithm (AOA) is a new optimization algorithm that can effectively solve both continuous and discrete optimization problems. However, it may suffer from poor exploration and premature convergence to sub-optimal solutions due to some problems with its exploration operators. In this paper, we propose a new method for the PASP using an improved AOA algorithm called the island-based AOA (iAOA). The new algorithm is based on a structured population model called the island model. This model distributes the population of candidate solutions among islands that periodically exchange some of the candidate solutions among each other based on a migration protocol. In iAOA, some modifications were applied to AOA's parameters to make it capable of handling discrete space. We evaluated iAOA using a public benchmark for the PASP and compared our results with those of other baseline algorithms (DFPA, SA, HS, GD-NLGD, HTS, TS, I-BBO, and MBBO). The simulation results revealed that iAOA achieved the minimum average execution time on 5 out of 6 instances of the public benchmark. It also showed that iAOA achieved the second minimum average violation of the objective function over 10 independent runs. Finally, the statistical examination of the experimental results using Friedman and Wilcoxon tests confirms the significance of the results of iAOA compared with the other baseline algorithms. [ABSTRACT FROM AUTHOR]

This study aimed to evaluate the effect of dentin hypersensitivity treatments on immediate and long-term shear bond strength (SBS) of composite restorations. Ninety non-carious extracted human molars were cut to expose dentin, which was embedded in acrylic resin, and randomly divided into three groups (n = 30/group) according to surface treatment: 1) no treatment (C and C*; control); 2) silver diamine fluoride with potassium iodide (SDF/KI and SDF/KI*; Riva Star); and 3) nano-hydroxyapatite (nHAp and nHAp*; PrevDent). The specimens were etched through the etch-and-rinse technique, followed by universal adhesive application and resin composite cylinders (2.38 mm in diameter × 3.5 mm high). The SBS was tested immediately (24 h after the restoration) and after thermocycling (*) (5000 cycles, 5 °C to 55 °C) at a 0.5 mm/min crosshead speed using a universal testing machine. A stereomicroscope was used to evaluate the mode of failure, and representative scanning electron microscopy (SEM) images were also acquired. Data normality was verified, and two-way ANOVA and Tukey's post hoc tests were performed for multiple comparisons (α = 0.05). The control group presented the highest SBS (27.10 MPa), while SDF/KI* had the lowest values (6.87 MPa). nHAp-based desensitizer exhibited higher SBS than SDF/KI for both immediate (22.6 MPa) and thermocycled (19.03 MPa) conditions. No intragroup difference was evidenced between immediate and thermocycled samples for any group. Most specimens for the C and nHAp groups presented mixed failure, while the SDF/KI groups presented comparable adhesive and mixed failures. The SBS of adhesive restorations after the application of desensitizing agents is material dependent, where SDF/KI reduces SBS values below the acceptable minimum bond strength, while the nHAp application meets the minimally required bond strength. [ABSTRACT FROM AUTHOR]

The biofuel yield from lignocellulose biomass depends strongly on the fermentable sugar yield from the hydrolysis step. Enzymatic hydrolysis, the preferable hydrolysis method, gives low sugar yield due to the lignin existence and the cellulose crystallinity. To increase the sugar yield, pretreatment is required to breakdown the recalcitrant nature of lignocellulose biomass. This review paper presents a comprehensive critical review of the lignocellulosic biomass (LCB) pretreatment methods for enhanced fermentable sugar yield. There is a need for an effective and cost-efficient pretreatment method that curbs inhibitory products and reduces the use of chemicals and energy. This paper highlighted recent advances in agricultural-based LCB pretreatment; discussed current challenges, advantages, and disadvantages; and suggested future solutions for agricultural-based biofuel production. Examined methods include pulsed electric energy (PEE), ionic liquid, co-solvent enhanced lignocellulosic fractionation pretreatment, and deep eutectic solvent. Each method was reviewed by its conditions (indicate the use of energy and chemicals), sugar yield, and inhibitory products. The review also researched the synergistic effect of combining more than one pretreatment method as a potential approach to overcome the drawbacks of the individual methods. In addition, the paper suggested improvement for each method and identified the research gaps to be bridged. Also, a comparison, summary, and research perspectives were provided. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this paper is to describe the design and implementation of a multidisciplinary, integrated approach to supporting pregnant, postpartum, and parenting people (PPPP) and their families affected by substance use disorders (SUD). Description: Between 2015 and 2022, the Moms Do Care (MDC) Program, sponsored by the Massachusetts Department of Public Health Bureau of Substance Addiction Services, established or expanded 11 co-located medical and behavioral health teams in locations across Massachusetts. These teams provided trauma-informed primary and obstetrical health care, SUD treatment and recovery services, parenting support, and case management for approximately 1048 PPPP with SUD. Assessment: By enhancing the capacity of medical and behavioral health providers offering integrated care across the perinatal health care continuum, MDC created a network of support for PPPP with SUD. Lessons learned include the need to continually invest in staff training to foster teambuilding and improve integrated service delivery, uplift the peer recovery coach role within the care team, improve engagement with and access to services for communities of color, and conduct evaluation and sustainability planning. Conclusion: MDC prioritizes trauma-informed integrated care, peer recovery, and commits to addressing inequities and stigma; thus, this program represents a promising approach to supporting PPPP impacted by SUD. The MDC model is relevant for those working to build multidisciplinary, integrated systems of health care and perinatal SUD services for marginalized populations. Significance: The Moms Do Care (MDC) Program is an integrated, multidisciplinary model of service provision originally created in response to the rising rates of perinatal opioid use disorder in Massachusetts. By partnering with and enhancing the capacities of medical, substance use disorder, and behavioral health providers along the perinatal continuum of care, and by leveraging the expertise of perinatal recovery coaches, MDC established and expanded webs of regional support for families impacted by any SUD. The MDC model is especially noteworthy to those building sustainable, multidisciplinary models of health care and perinatal SUD services for this population. [ABSTRACT FROM AUTHOR]

Intrusion Detection (ID) is an essential task in the cyberattacks domain built to secure Internet applications and networks from malicious actors. The main shortcoming of the current Intrusion Detection Systems (IDSs) is their attack detection performance because the existing solutions fail to detect the threats efficiently. Therefore, improving the prediction model of the IDS can improve its performance. Several studies suggest improving the Machine Learning (ML)-based IDS prediction models by selecting the most informative features from the security data before utilizing it in the ML-IDS model. Thus, meta-heuristic algorithms such as White Shark Optimizer (WSO) are adapted to deal with such feature selection problems. For the IDS prediction model, in this paper, the WSO algorithm is improved to cope with the binary domain of the feature selection task as follows: Firstly, two transfer functions are used to map the continuous domain into binary. Secondly, the modified K-means algorithm is proposed to assess building the initial population with a high level of diversity. Finally, several crossover operators are utilized to improve the evolution process of the binary WSO. The three improved versions are BIWSO1 which is WSO with transfer functions, BIWSO2, which is BIWSO1 with a modified k-means algorithm, and BIWSO3, which is BIWSO2 with crossover operators. The proposed versions of BIWSO are tested using twelve public real-world IDS and IoT datasets. Comparative evaluations against well-established meta-heuristic algorithms are conducted where the BIWSO3 proves its efficiency in terms of classification accuracy, precision, recall, and F1 measurements. For further validation, statistical evidence using Friedman's, Wilcoxon's, and Mann–Whitney U tests has been conducted where BIWSO3 proves its performance at a remarkably significant level. [ABSTRACT FROM AUTHOR]

This article introduces a dynamic theoretical analysis, which is not available in the literature, for the average hourly and monthly performances of a solar-driven multistage water gap membrane distillation system. Based on simultaneous solutions of the combined heat and mass transfer equations using EES software, the dynamic model successfully predicted the average hourly and monthly output from the system. The mid-latitude meteorological data from Dhahran city, Saudi Arabia, are employed in this analysis. This analysis is carried out to examine the variations of the system productivity and energy efficiency with the operating and design parameters of the integrated system, which include the average monthly and hourly solar radiation, the number of the MD units and the number of solar collectors that are used for water heating. Results reveal that an eight-stage MD system with three solar collectors can produce up to 70 L/day of freshwater. The integrated solar MD system is able to effectively improve energy efficiency, with a maximum GOR of 0.95. [ABSTRACT FROM AUTHOR]

The performance of a novel integrated desalination system of sweeping gas membrane distillation (SGMD) module and a bubble column dehumidifier (BCD) was experimentally investigated to enhance freshwater productivity and reduce energy consumption of the system. The SGMD module acts as a humidifier by separating water vapor into the sweeping air channel to deliver humid air to the bubble column dehumidifier. The sweeping air stream was created using a vacuum pump between the SGMD module and the BCD. To evaluate the system performance, different operating conditions were tested for closed and open sweeping air cycles and with different cooling methods. Results showed a marginal enhancement of 7 to 10% in productivity and 10 to 20% in the system's gained output ratio when the closed-air cycle was used compared to the open-air cycle. Feed temperature and flow rate and sweeping air flow rate mainly control the system's performance, while the water column height in dehumidifier and its temperature have moderate impacts. In addition, both air cooling and no cooling modes of operation showed promising results regarding energy efficiency, especially with elevated dehumidifier water column heights between 6 and 9 cm. [ABSTRACT FROM AUTHOR]

Rowshan is a distinctive architectural element and a primary component of the façades of traditional houses in western Saudi Arabia. It is window type primarily made from wood and is incorporated in contemporary architecture to express the identity of the Hejazi architectural heritage without the realization of the advantages of this rich architectural element. This study examines the Rowshan design philosophy by identifying its visual, functional, and structural determinants, and applying them to a model to reflect it in a modern context. The study adopts an analytical approach in addition to interviewing elders who lived in traditional houses. The results show that the Rowshan philosophy must apply all its structural, functional, and aesthetic characteristics like seating function, privacy, visual continuity, use of light, exploitation for shade, and the use of ornamental patterns and, openings, while adopting modularity, flexibility, and adaptability as important principles in its composition. This design philosophy can be a fundamental resource for creative designs to preserve Hejazi architectural identity. This study aims to renew interest in Rowshan elements in contemporary houses in the Mecca region and highlights the advantages of this unique architectural element by applying the Rowshan design philosophy. [ABSTRACT FROM AUTHOR]

Background and Aims: Hepatitis B virus (HBV) reactivation has been reported in patients co-infected with hepatitis C virus (HCV) during direct acting antiviral (DAA) therapy, leading the United States Food and Drug Administration (U.S. FDA) to issue a black box warning on all DAA drug labels recommending monitoring for HBV reactivation. We conducted a comprehensive evaluation to assess the rate of HBV reactivation among patients with chronic hepatitis C (CHC) during DAA therapy. Methods: Patients with CHC and recovered HBV infection (hepatitis B surface antigen negative (HBsAg)/anti-hepatitis B core positive), treated with DAAs were included if stored sera were available. Samples were tested for HBV DNA, HBsAg, and ALT. HBV reactivation was considered if (1) HBV DNA was undetectable pre-DAA therapy and became detectable post-therapy, or (2) HBV DNA was detectable pre-treatment, but not quantifiable (< 20 IU/mL) and became quantifiable post-treatment. Result: 79 patients with median age of 62 years were included. 68% were male and Caucasian. Various DAA regimens were administered for 12–24 weeks. Reactivation occurred in 8/79 (10%) of patients and occurred more frequently in men compared to women: 6 during treatment and 2 after treatment. Neither an ALT flare nor HBsAg seroreversion were observed. Detectable HBV DNA was transient in 5/8 and could not be determined in 3/8 but ALT flares were not observed in follow-up of these patients. Conclusion: The risk of HBV reactivation was low in CHC patients with resolved HBV during DAA therapy. Our data support testing for HBV DNA only in selected patients with ALT flares or failure of ALT normalization during DAA treatment. [ABSTRACT FROM AUTHOR]

This paper proposes new improved binary versions of the Sine Cosine Algorithm (SCA) for the Feature Selection (FS) problem. FS is an essential machine learning and data mining task of choosing a subset of highly discriminating features from noisy, irrelevant, high-dimensional, and redundant features to best represent a dataset. SCA is a recent metaheuristic algorithm established to emulate a model based on sine and cosine trigonometric functions. It was initially proposed to tackle problems in the continuous domain. The SCA has been modified to Binary SCA (BSCA) to deal with the binary domain of the FS problem. To improve the performance of BSCA, three accumulative improved variations are proposed (i.e., IBSCA1, IBSCA2, and IBSCA3) where the last version has the best performance. IBSCA1 employs Opposition Based Learning (OBL) to help ensure a diverse population of candidate solutions. IBSCA2 improves IBSCA1 by adding Variable Neighborhood Search (VNS) and Laplace distribution to support several mutation methods. IBSCA3 improves IBSCA2 by optimizing the best candidate solution using Refraction Learning (RL), a novel OBL approach based on light refraction. For performance evaluation, 19 real-wold datasets, including a COVID-19 dataset, were selected with different numbers of features, classes, and instances. Three performance measurements have been used to test the IBSCA versions: classification accuracy, number of features, and fitness values. Furthermore, the performance of the last variation of IBSCA3 is compared against 28 existing popular algorithms. Interestingly, IBCSA3 outperformed almost all comparative methods in terms of classification accuracy and fitness values. At the same time, it was ranked 15 out of 19 in terms of number of features. The overall simulation and statistical results indicate that IBSCA3 performs better than the other algorithms. [ABSTRACT FROM AUTHOR]

The document is a correction to a theoretical investigation into the dynamic performance of a solar-powered multistage water gap membrane distillation system. It addresses missing information in the original publication related to the solar collector model and model validation. The correction provides specific details and references to rectify the errors in the original article. The study was conducted at King Fahd University of Petroleum & Minerals in Saudi Arabia. [Extracted from the article]

This study aims to remove some of the ambiguities of visual plagiarism in interior design (those related to the visual composition of space represented by line, form, shape, texture, time, colour, light, etc.) by examining the main detection methods, the extent of the issue, and the experiences and roles of academic interior designers. Two main methods were used: an analysis of the primary forms of visual plagiarism and an applied approach to test the effectiveness of plagiarism. Additionally, 30 academics in the Kingdom of Saudi Arabia, who majored in interior design, were surveyed using an online questionnaire. The results demonstrated that changing the light and colour while maintaining the rest of the properties and changing the line, form, and shape are considered visual plagiarism. However, the results also indicated that visual plagiarism is not always clear for academics. It is hoped that the results of this study will increase the awareness of visual plagiarism, enhance ethics in the university environment, and help academic interior designers apply scientific methods to detect plagiarism in design projects. This study can also assist regulatory authorities to manage complaints and disputes in plagiarism cases. [ABSTRACT FROM AUTHOR]

The Permutation Flow Shop Scheduling Problem (PFSSP) is an interesting scheduling problem that has many real-world applications. It has been widely used as a benchmark to prove the efficiency of many discrete optimization algorithms. The DJaya algorithm is a discrete variation of the Jaya algorithm that has been recently proposed for solving discrete real-world problems. However, DJaya may get stuck in a local optima because of some limitations in its optimization operators. In this paper, we propose a new discrete optimization algorithm called Discrete Jaya with Refraction Learning and Three Mutation Methods (DJRL3M) for solving the PFSSP. DJRL3M incorporates five modifications into DJaya. First, it utilizes Refraction Learning (RL), which is a special type of opposition learning, to generate a diverse initial population of solutions. Second, it uses three mutation methods to explore the search space of a problem: DJaya mutation, highly disruptive polynomial mutation and Pitch Adjustment mutation. Third, it employs RL at each iteration to generate the opposite solutions of the best and worst solutions in an attempt to jump out local optima. Fourth, it uses the abandon method at the end of each iteration to discard a predefined percentage of the worst solutions and generate new random solutions. Finally, it uses the smallest position value to determine the correct values of the decision variables in a given candidate solution. The performance of DJRL3M was evaluated and compared with six well-recognized optimization algorithms [(New Cuckoo Search (NCS) (Wang et al. in SC 21:4297–4307, 2017), DJaya (Gao et al. in ITC 49:1944–1955, 2018), Hybrid Harmony Search (HHS) (Zhao et al. in EAAI 65:178-199, 2017), Modified Genetic algorithm (MGA) (Mumtaz et al. in: Advances in Manufacturing Technology XXXII: Proceedings of the 16th International Conference on Manufacturing Research, incorporating the 33rd National Conference on Manufacturing Research, 2018), Generalised Accelerations for Insertion-based Heuristics (GAIbH) (Fernandez-Viagas et al. in EJOR 282:858–872, 2020), Memetic algorithm with novel semi-constructive crossover and mutation operators (MASC) (Kurdi in ASC 94:106548, 2020)] using a set of Taillard's benchmark instances. The experimental and statistical results show that DJRL3M obtains better performance than the performance of NCS, DJaya, HHS and MGA and exhibits competitive performance compared to the performance of MASC and GAIbH. [ABSTRACT FROM AUTHOR]

Water desalination using the membrane distillation (MD) process is an emerging technique that has the potential for future commercialization. In this study, the performance of a new design multistage water-gap membrane distillation module is experimentally investigated for possible scaling up and commercialization. A new compact design is proposed for enhanced productivity, low energy consumption, and therefore low freshwater production cost. The variations of permeate flux, specific thermal energy consumption, gained output ratio, and production cost with the different operating parameters are studied. Besides, the impacts of having parallel and series flow arrangements among the module stages are investigated. Results showed that operating the multistage system at higher feed temperature increases productivity, reduces the specific energy consumption, and minimizes the water production cost. The parallel flow arrangement is slightly better than the series arrangement in terms of productivity. However, the series flow arrangement is found better in terms of energy efficiency. The best-achieved performance indicators are gained output ratio (GOR) of 0.49, specific thermal energy consumption of 1320 kWh/m3, and water production cost of 6 $/m3, which are very promising findings of the tested compact multistage water-gap MD system. [ABSTRACT FROM AUTHOR]

The cuckoo search (CS) algorithm is an effective optimization algorithm, but it is prone to stagnation in suboptimality because of some limitations in its exploration mechanisms. This paper introduces a variation of CS called exploratory CS (ECS), which incorporates three modifications to the original CS algorithm to enhance its exploration capabilities. First, ECS uses a special type of opposition-based learning called refraction learning to improve the ability of CS to jump out of suboptimality. Second, ECS uses the Gaussian perturbation to optimize the worst candidate solutions in the population before the discard step in CS. Third, in addition to the Lévy flight mutation method used in CS, ECS employs two mutation methods, namely highly disruptive polynomial mutation and Jaya mutation, to generate new improved candidate solutions. A set of 14 widely used benchmark functions was used to evaluate and compare ECS to three variations of CS:CS with Lévy flight (CS), CS with highly disruptive polynomial mutation (CS10) and CS with pitch adjustment mutation (CS11). The overall experimental and statistical results indicate that ECS exhibits better performance than all of the tested CS variations. Besides, the single-objective IEEE CEC 2014 functions were used to evaluate and compare the performance of ECS to six well-known swarm optimization algorithms: CS with Lévy flight, Grey wolf optimizer (GWO), distributed Grey wolf optimizer (DGWO), distributed adaptive differential evolution with linear population size reduction evolution (L-SHADE), memory-based hybrid Dragonfly algorithm and Fireworks algorithm with differential mutation. Interestingly, the results indicate that ECS provides competitive performance compared to the tested six well-known swarm optimization algorithms. [ABSTRACT FROM AUTHOR]

Evaluating the knowledge of patients attending radiology departments regarding ionizing radiation used in medical imaging and its associated hazards can provide knowledge of the patient's awareness level of the associated risk of the radiation used in medical imaging. The aims of this study were to evaluate the awareness of patients regarding medical radiation types used in medical diagnostic imaging and its influence on their decision to proceed with that procedure. Over an 8-months period, a total of 418 patients, 48% Men and 52% Women, presenting for diagnostic imaging in the department of radiology, completed a 15-point questionnaire. The questionnaire included demographic and radiation awareness sections. Less than 32% of the participants had a potential risk of radiation explained by the doctor before the procedure. 59% of the participants expressed that the potential risk of radiation makes them anxious; less than about 25% of the participants showed that the potential risk of radiation affects their decision to have the procedure. Overall, the data collected from this survey indicate that there is a lack of information about radiation risk provided to the patients prior to the diagnostic procedure. Efforts should be made to ensure that patients receiving multiple medical imaging tests are aware of the radiation they are receiving. [ABSTRACT FROM AUTHOR]

The optimization-based scheduling algorithms used for scheduling workflows in cloud computing environments may easily get trapped in local optima, especially in the beginning of their simulation processes because of some limitations in their exploration methods. Moreover, the performance of some optimization-based scheduling algorithms may severely degrade when dealing with medium- or large-size scheduling problems. The Island-based Cuckoo Search with highly disruptive polynomial mutation (iCSPM) algorithm is a parallel version of the Cuckoo Search (CS) algorithm. The iCSPM algorithm incorporates the island model into CS and uses an exploration function based on the highly disruptive polynomial mutation. It has been empirically proven that iCSPM performs better than popular optimization algorithms (e.g., CS and island-based Genetic algorithm). This paper presents a variation of iCSPM called Discrete iCSPM with opposition-based learning strategy (DiCSPM) for scheduling workflows in cloud computing environments based on two objectives: computation and data transmission costs. DiCSPM includes two new features compared to iCSPM. First, it uses the opposition-based learning approach (OBL) in the initialization step at the level of islands, where each island in the island model contains the opposite population of another island. Second, the smallest position value method is used in the DiCSPM algorithm to determine the correct values of the decision variables in the candidate solutions. The proposed algorithm was experimentally evaluated and compared to well-known scheduling algorithms [Best Resource Selection, Particle Swarm Optimization (PSO) and Grey Wolf Optimizer] using two types of workflows: balanced and imbalanced workflows. The overall experimental and statistical results indicate that DiCSPM provides solutions for the scheduling problem of workflows in cloud computing environment faster than the other compared algorithms. Moreover, DiCSPM was evaluated and compared to state-of-the-art algorithms, namely PSO, binary PSO and discrete binary cat swarm optimization using scientific workflows of different sizes using WorkflowSim. The obtained results suggest that DiCSPM provides the best makespan compared to the other algorithms. [ABSTRACT FROM AUTHOR]

Background and Aims: Endoscopic enteral stenting (ES) in malignant gastric outlet obstruction (GOO) is limited by high rates of stent obstruction. EUS-guided gastroenterostomy (EUS-GE) is a novel procedure that potentially offers sustained patency without tumor ingrowth/overgrowth. The aim of this study is to compare EUS-GE with ES in terms of (1) symptom recurrence and need for re-intervention, (2) technical success (proper stent positioning as determined via endoscopy and fluoroscopy), (3) clinical success (ability to tolerate oral intake without vomiting), and (4) procedure-related adverse events (AEs).Methods: Multicenter retrospective study of all consecutive patients who underwent either EUS-GE at four centers between 2013 and 2015 or ES at one center between 2008 and 2010.Results: A total of 82 patients (mean age 66-years ± 13.5 and 40.2% female) were identified: 30 in EUS-GE and 52 in ES. Technical and clinical success was not significantly different: 86.7% EUS-GE versus 94.2% ES (p = 0.2) and 83.3% EUS-GE versus 67.3% ES (p = 0.12), respectively. Symptom recurrence and need for re-intervention, however, was significantly lower in the EUS-GE group (4.0 vs. 28.6%, (p = 0.015). Post-procedure mean length of hospitalization was comparable at 11.3 days ± 6.6 for EUS-GE versus 9.5 days ± 8.3 for ES (p = 0.3). Rates and severity of AEs (as per the ASGE lexicon) were also similar (16.7 vs. 11.5%, p = 0.5). On multivariable analysis, ES was independently associated with need for re-intervention (OR 12.8, p = 0.027).Conclusion: EUS-GE may be ideal for malignant GOO with comparable effectiveness and safety to ES while being associated with fewer symptom recurrence and requirements for re-intervention. [ABSTRACT FROM AUTHOR]

Background and aims: Endoscopic placement of enteral self-expandable metallic stents is an alternative to surgical gastrojejunostomy (GJ) for palliation of malignant gastric outlet obstruction (GOO). Factors associated with clinical outcomes are not known. The aims of this study are to compare the overall complication rate and effectiveness (duration of oral intake) between endoscopic stenting (ES) and GJ in patients with GOO and identify predictors of clinical outcomes. Patients and methods: This was a retrospective cohort study at a single tertiary academic center. Patients who underwent ES or GJ for treatment of GOO between 1/2001 and 12/2010 were identified using an institutional claims database. The electronic medical records for each patient were reviewed. Univariate and multivariate logistic regression analyses were performed to study the association of treatment outcomes with patient factors and cancer therapy. Results: 120 patients had ES while 227 had GJ. Technical success was higher for GJ (99 vs. 96 %, p = 0.004). Complication rates were higher in the GJ group (22.10 vs. 11.66 %, p = 0.02). Reintervention was more common with ES [adjusted odds ratio (OR) 9.18, p < 0.0001]. Mean length of hospital stay (LOHS) was shorter (adjusted p = 0.005) in the ES compared with the GJ group. However, mean hospital charges, including reinterventions, were greater in the ES group (US $34,250 vs. US $27,599, p = 0.03). ES and GJ had comparable reintervention-free time in patients who had reintervention (88 vs. 106 days, respectively, p = 0.79). Chemotherapy [adjusted hazard ratio (HR) 3 > 0.57, p = 0.04] and radiation therapy (adjusted HR 0.35, p = 0.03) were associated with significantly longer duration of oral intake after ES or GJ. Conclusion: ES is associated with fewer complications, shorter LOHS, but higher reintervention rates and overall charges. [ABSTRACT FROM AUTHOR]

A correction is presented to the article "EUS-guided gastroenterostomy is comparable to enteral stenting with fewer re-interventions in malignant gastric outlet obstruction" which appeared in the previous issue.