Applications in high-performance computing (HPC) may not use all available computational resources, leaving some of them underutilized. By co-scheduling, i.e. running more than one application on the same computational node, it is possible to improve resource utilization and overall throughput. Some applications may have conflicting requirements on resources and co-scheduling may cause performance degradation, so it is important to take it into account in scheduling decisions. In this paper, we formalized co-scheduling problem and proposed multiple scheduling strategies to solve it: an optimal strategy, an online strategy and heuristic strategies. These strategies vary in terms of the optimality of the solution they produce and a priori information about the system they require. We showed theoretically that the online strategy provides schedules with a competitive ratio that has a constant upper limit. This allowed us to solve the co-scheduling problem using heuristic strategies that approximate this online strategy. Numerical simulations showed how heuristic strategies compare to the optimal strategy for different input systems. We proposed a method for measuring input parameters of the model in practice and evaluated this method on HPC benchmark applications. We showed high accuracy of measurement method, which allows to apply proposed scheduling strategies in scheduler implementation.

The requirement for integration of power plants due to the cyclical rise in electrical energy consumption is due to fluctuating load demand with the current grid systems. This integration necessitates effective allocating loads to the power plants for a minimum grid-tied transmission line cost while meeting network constraints. In this paper, we formulate an optimisation problem of minimising the total operational cost of all committed plants transmitted to the grid while meeting network constraints and ensuring economic power dispatch (EPD) and energy management system co-optimization. The developed Particle Swarm Optimization (PSO) method resolve the optimisation problem using piecewise quadratic function to describe the operational cost of the generation units, and the B coefficient approach is employed to estimate the transmission losses. Intelligent adjustments are made to the acceleration coefficients, and a brand-new algorithm is suggested for distributing the initial power values to the generation units. The developed economic power dispatch strategy successfully demonstrated an imperative cost reduction with connected load of 850MW, 1263MW and 2630MW power demand are contrasted with previous PSO application cost values, maximum yearly cost savings of (0.55%, 91.87), (46.55%, 3.78), (73.86%, 89.10) respectively, and significant environmental-benefit. The proposed co-optimisation approach can enhance a significant self-consumption ratio compared to the baseline method.

The framework proposed by Alison Druin about 20 years ago, which defined the roles children play in technology design, has been widely adopted by the Child-Computer Interaction (CCI) community. While some studies have adopted relevant roles as presented in the framework, others have argued for extending the framework to include newly identified roles. Still, other studies argue that Druin's framework failed to account for roles children may take up as they interact with peers or with tools. Consequently, this systematic literature review examines children’s emerging and changing roles in the codesign of new technologies. Specifically, we answered the research question: What new roles emerge in research as children co-design new technologies? Our effort aims at providing evidence of new roles children have adopted in literature, which may help researchers in the CCI community make more informed decisions about participatory design approaches with children.

Ocean big data is the scientific practice of using big data technology in the marine field. Data from satellites, manned spacecraft, space stations, airship, unmanned aerial vehicles, shore-based radar and observation stations, exploration platforms, buoys, underwater gliders, submersibles, and submarine observation networks are seamlessly combined into the ocean’s big data. Increasing numbers of scholars have tried to fully analyze the ocean’s big data. To explore the key research technology knowledge graphs related to ocean big data, articles between 1990 and 2020 were collected from the “Web of Science”. By comparing bibliometric software and using the visualization software Cite-Space, the pivotal literature related to ocean big data, as well as countries, institutions, categories, and keywords, were visualized and recognized. Journal co-citation analysis networks can help determine the national distribution of core journals. Co-citation analysis networks for documents show authors who are influential at key technical levels. Key co-occurrence analysis network keywords can determine research hot spots and research frontiers. The three supporting elements of marine big data research are shown in the co-citation network. These elements are author, institution, and country. By examining the co-occurrence of keywords, the key technology research directions for future marine big data were determined.

Integrating sustainable processes and innovative waste utilization not only addresses environ-mental challenges but also aligns with evaluating energy sustainability and creating the circular waste management. This study presents a comparative analysis of liquid fuel production from waste tires and water hyacinth using two thermochemical methods: co-pyrolysis (direct method) and co-gasification integrated with the Fischer-Tropsch (FT) process (indirect method). The As-pen Plus simulation software version 9 is employed to develop models, determining optimal operating conditions for maximum liquid fuel, and assessing economic viability. Co-pyrolysis simulations reveal an increase in oil products and a decrease in gas products with higher blending weight ratios of waste tires/water hyacinth (W/B) and lower pyrolyser temperatures. Operating at 400°C with a blending weight ratio of 75:25 provided the maximum liquid fuel rate of 6649.22 gal/day. For co-gasification, optimal conditions (W/B = 50:50, T = 800°C, steam to feed ratio (S/F) = 1) result in the highest syngas yield of 115.92 kmol/h. Fischer-Tropsch simulations demonstrate an increase in gasoline and a decrease in diesel with rising reactor temperatures. At 230°C, the maximum liquid fuel rate reaches 8817.15 gal/day. The economic and production yield analysis indicates that the indirect method better than the direct method, providing higher liquid fuel yields and investment worthiness.

The severe respiratory distress syndrome (SRDS) has been a significant clinical issue in modern respiratory medicine. Its role in COVID-19 pandemic is not well defined. Outside-the-hospital SRDS is usually community-acquired septic pneumonia; however, in-hospitals incidences are complicated by co-infections. While SRDS is genetically selected in European and African origins, these are not clear in Middle East, particularly in COVID-19 backgrounds. There is a severe paucity in high quality data on correlations between COVID-19, ARDS, co-infectome, and patient demographics. We have conducted a comprehensive investigation on 298 patients for associations of SRDS, coinfections, and patient demographics on COVID-19 patients’ outcomes. Of these, 9.4% (n=28) had SRDS, and the rest (90.6%) had not. 54% of those with SRDS died while 84% survived; SRDS fatality was highly significant (Chi-square test P-value = 0.00000246). Irrespective of gender, the age of patients was significantly associated with SRDS (72.9 +/-8.9) compared to those without it (56.2 +/-15.1). However, there was no significant difference neither in the age of admitted patients before COVID-19 (58.5 +/-15.3) and during COVID-19 (57.2 +/-15.5) nor in the gender and COVID-19 fatality (Fisher Exact test 2-sided 1.000, 1-sided .546) ruling out that SRDS age-specificity were selected by virus susceptibility. A 100% of SRDS patients without bacterial co-infections survived while only 25% of those with co- infectome did not; this association was highly significant (P value= 0.00041). Co-infections alone without underlying SRDS was also associated with high fatality among patients (P value= 0.00000000076). Almost all COVID-19 patients without co-infectome (99.2%) survived while 28% of those with bacterial co-infection died. The major bacterial pathogens that potentially predisposed to SRDS, were Acinetobacter baumannii, and Escherichia coli either alone or in a mixed infection with Klebsiella pneumoniae were predominant species identified during SRDS attack. Thus, Gram-negative co-infectome potentially induced fatal SRDS aggravating COVID-19 outcome. These findings have significant clinical implications in specific differential diagnosis of SRDS syndromes for subsequent empiric therapy and patient management strategies. Future vertical investigation for similar mechanisms of cytokine-induced SRDS by Gram negative pathogens is imperative since hypervirulent strains are rapidly circulating in the region. The study is limited by a single center study confined to Ha’il hospitals; large scale investigation in major national hospitals would gain more insights.

Leishmania parasites present astonishing adaptative abilities that represent a matter of life or death within disparate environments during the heteroxenous parasite life cycle. From an evolutionary perspective, organisms develop methods of overcoming such challenges. Strategies that extend beyond the genetic diversity have been discussed and include variability between parasite cells during the infections of their hosts. The occurrence of Leishmania subpopulation fluctuations with variable structural genomic contents demonstrates that a single strain might shelter the variability required to overcome inconsistent environments. Such intrastrain variability provides parasites with an extraordinary ability to adapt and thus survive and propagate. However, different perspectives on this evolution have been proposed. Strains or species living in the same environment can cooperate but also compete. These interactions might increase the replication rate of some parasites but cause the loss of more aggressive competitors for others. Adaptive responses to intra- and interspecific competition can evolve as a fixed strategy (replication is adapted to the average genetic complexity of infections) or an optional strategy (replication varies according to the genetic complexity of the current infection). This review highlights the complexity of interspecies and intrastrain interactions among Leishmania parasites as well as the different factors that influence this interplay.

New setting is introduced to study types of coloring numbers, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Different types of procedures including neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are proposed in this way, some results are obtained. General classes of neutrosophic hypergraphs are used to obtain chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic hypergraphs. Using colors to assign to the vertices of neutrosophic hypergraphs and characterizing representatives of the colors are applied in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. Some questions and problems are posed concerning ways to do further studies on this topic. Using different ways of study on neutrosophic hypergraphs to get new results about number, degree and co-degree in the way that some number, degree and co-degree get understandable perspective. Neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs are studied to investigate about the notions, coloring, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges in neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs. In this way, sets of representatives of colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges have key points to get new results but in some cases, there are usages of sets and numbers instead of optimal ones. Simultaneously, notions chromatic number, the representatives of the colors, degree of vertices, degree of hyperedges, co-degree of vertices, co-degree of hyperedges, neutrosophic degree of vertices, neutrosophic degree of hyperedges, neutrosophic co-degree of vertices, neutrosophic co-degree of hyperedges, neutrosophic number of vertices, neutrosophic number of hyperedges are applied into neutrosophic hypergraphs, especially, neutrosophic (r, n)−regular hypergraphs and neutrosophic complete r−partite hypergraphs to get sensible results about their structures. Basic familiarities with neutrosophic hypergraphs theory and hypergraph theory are proposed for this article.

At Mae-Moh power plant, Thailand, superheater tubes, which are exposed in fly ash environ-ment, often degrade due to solid particle erosion. To extend the service lifetime of the superheater tubes, the high velocity oxy-fuel (HVOF) thermal spray technique is used to deposit a protective coating on the material, SA213-T22 steel. In this work, the solid particle erosion of the Co-28Cr and WC-12Co coatings was investigated using erodent particle impingement at the angles of 30o and 90o with an average particle size of 60 μm. The erosion behavior of SA213-T22 with and without Co-28Cr and WC-12Co coatings was explained using ductile and brittle erosion modes. The erosion test resulted in the brittle mode for both Co-28Cr and WC-12Co coatings, while SA213-T22 without coating indicated the ductile mode. On the investigation of surface morphology, the Co-28Cr coating showed mostly microcracks in contrast with the WC-12Co coating, fracture, de-bonding and deep cavities were also observed. Erosion resistance of the Co-28Cr coating was significantly higher than the WC-12Co coating, due to high hardness, low porosity and high density of the coating.

The main objective of this paper is to identify the major research areas of ChatGPT through term and keyword co-occurrence network mapping techniques. For conducting the present study, total of 577 publications were retrieved from the Lens database for the network visualization. The findings of the study showed that “chatgpt” occurrence in maximum number of times followed by its related terms such as artificial intelligence, large language model, gpt, study etc. This study will be helpful to library and information science as well as computer or information technology professionals.

Advancements in Parametric Design, Generative Design, and automation in Building Information Modelling (BIM) have opened new opportunities for architects and engineers working on complex buildings, such as offices. These developments allow designers to enhance their designs, increase project efficiency, improve performance, and reduce project time and costs. To address conflicting objectives that arise during the design process, genetic algorithms with multi-objective optimization (MOO) have been employed in architectural design. The GENIUS project aims to optimize building shape, apertures, and shading systems in the concept and design stages, with a focus on energy and daylight performance. The project integrates BIM and visual programming tools with Artificial Intelligence techniques like Genetic Algorithms and RBFOpt model-based optimization. Optimization algorithms are used to identify the best solutions that meet all design objectives, helping architects optimize their designs and achieve desired outcomes. The workflow was tested on a case study of a large office building, with MOO focused on maximizing daylight performance using Spatial Daylight Autonomy metric and minimizing energy consumption using Energy Use Intensity metric. The project provides architects with a method to improve their designs using Algorithm Aided Design tools, which help identify the best solutions for complex design problems.

Frailty, an age-related decline in homeostatic reserves, markedly proceeded during the coronavirus disease 2019 pandemic. To continuously assess frailty status, a remote system is urgently required. We aimed to co-design/co-develop an online frailty check (FC) application alongside FC supporters who were facilitators in a pre-existing onsite FC program. The online FC included a screening measurement for sarcopenia and an 11-item questionnaire covering dietary, physical, and social behaviors. Using prototype applications, 55 opinions obtained from 32 FC supporters (median 74.0 years) were categorized and reflected refinement. Regarding the self-efficacy of FC supporters, a significant increase in social positioning was seen after the mock test (P=.031). For FC supporters and participants, the average system usability scale (SUS) score was 70.2±10.3 points, which was “marginally high” for acceptability and “good” for the adjective range. Multiple regression analysis showed that the SUS score was significantly correlated with online-onsite reliability but not online communication, even after adjusting by age, sex, education level, and ICT proficiency (b=0.400, 95% CI: 0.243-1.951, P=.013). Additionally, a significant association between onsite and online FC scores was observed (R=0.670, P=.001). Our online FC application was evaluated to be a valuable tool to practically assess frailty status remotely.

The pursuit of more efficient transport has led engineers to develop a wide variety of 1 aircraft configurations with the aim of reducing fuel consumption and emissions. However, these 2 innovative designs introduce significant aeroelastic couplings that can potentially lead to structural 3 failure. Consequently, aeroelastic analysis and optimization has become an integral part of modern 4 aircraft design. In addition, aeroelastic testing of scale models is a critical phase in aircraft devel- 5 opment, requiring accurate prediction of aeroelastic behaviour during scale model construction to 6 reduce costs and mitigate risks associated with full-scale flight testing. Achieving a high degree of 7 similarity between the stiffness, mass distribution and flow field characteristics of scaled models 8 and their full-scale counterparts is of paramount importance. However, achieving similarity is 9 not always straightforward due to the variety of configurations of modern lightweight aircraft, as 10 identical geometry cannot always be directly scaled down. This configuration diversity has a direct 11 impact on the aeroelastic response, necessitating the use of computational aeroelasticity tools and 12 optimization algorithms. This paper presents the development of an aeroelastic scaling framework 13 using multidisciplinary optimization. Specifically, a parametric Finite Element Model (FEM) of the 14 wing is created, incorporating parameterisation of both thickness and geometry, primarily using 15 shell elements. Aerodynamic loads are calculated using the Doublet Lattice Method (DLM) em- 16 ploying twist and camber correction factors, and aeroelastic coupling is established using infinite 17 plate splines. The aeroelastic model is then integrated within an Ant Colony optimization (ACO) 18 algorithm to achieve static and dynamic similarity between the scaled model and the reference wing. 19 A notable contribution of this work is the incorporation of internal geometry parameterisation into 20 the framework, increasing its versatility and effectiveness.

As the COVID-19 pandemic has caused enormous damage to society, economy and public health security, it is crucial to combat the epidemic and allocate social and health resources effectively. Global optimization algorithms are extensively used in many domains to find optimal solutions to complex problems. Thus, a survey of three typical global optimizations applied on the COVID-19 pandemic is timely. This article is structured into three typical global optimization algorithms - Genetic Algorithm, Particle Swarm Optimization, and Cat Swarm Optimization - and summarizes the framework models and practical applications, as well as other optimization methods that may be applied to COVID-19. Finally, current unresolved challenges and future research opportunities to be explored are summarized.

Calculating a minimum-time trajectory requires the solving of a boundary value problem resulting from invocation of necessary conditions of optimality to set up a set of ordinary differential equations to solve the trajectory between the initial position and desired ending position with set constraints on the path between the two. This manuscript expresses the minimum time optimum trajectory between a satellite in a geosynchronous orbit and a target set on earth’s surface utilizing a non-rotating earth centric coordinate system. Expressing motion in coordinates of rotating reference frames necessitates transformation between reference frames, and one such transformation is embodied in the Direction Cosine Matrices (DCM) formed by a sequence of three successive frame rotations. Rotation about the local wing of an aerospace vehicle is almost never the pitch angle, yet modern application of kinematics often assumes such (with accompanying angular error). The same assertion is usually true about the nature of roll and yaw angles. This manuscript evaluates which sequence is the most advantageous for an object starting in space and then travels through the atmosphere to a target on the Earth’s surface. Simulation precision (validated by the quaternion normalization condition) reveals the so-called 132 rotation is the most accurate with an average error of 0.14° and a computational time of 0.013 seconds: resulting in a 97.95% increase in accuracy over the ubiquitous 321 “aerospace” rotation sequence and a 99.84% increase over the so-called 313 “orbital” rotation sequence. Utilizing the proposed optimal trajectory candidate yields a total flight time of 2 hours, 34 minutes and 46 seconds, or an average velocity of 3.85 kilometers per second, while impacting the target with a velocity of 11.54 kilometers per second.

Multi-objective optimization (MOO) is an optimization involving minimization or maximization of several objective functions more than the conventional one objective optimization, which is useful in many fields. Many of the current methodologies addresses challenges and solutions that attempt to solve simultaneously several Objectives with multiple constraints subjoined to each. Often MOO are generally subjected to linear inequality, equality and or bounded constraint that prevent all objectives from being optimized at once. This paper reviews some recent articles in area of MOO and presents deep analysis of Random and Uniform Entry-Exit time of objectives. It further break down process into sub-process and then provide some new concepts for solving problems in MOO, which comes due to periodical objectives that do not stay for the entire duration of process lifetime, unlike permanent objectives which are optimized once for the entire process duration. A methodology based on partial optimization that optimizes each objective iteratively and weight convergence method that optimizes sub-group of objectives are given. Furthermore, another method is introduced which involve objective classification, ranking, estimation and prediction where objectives are classified based on their properties, and ranked using a given criteria and in addition estimated for an optimal weight point (pareto optimal point) if it certifies a coveted optimal weight point. Then finally predicted to find how far it deviates from the estimated optimal weight point. A Sample Mathematical Tri-Objectives and Real world Optimization was analyzed using partial method, ranking and classification method, the result showed that an objective can be added or removed without affecting previous or existing optimal solutions. Therefore suitable for handling time governed MOO. Although this paper presents concepts work only, it’s practical application are beyond the scope of this paper, however base on analysis and examples presented, the concept is worthy of igniting further research and application.

A Robust Optimization framework with original concepts and fundamentals also admitting a fusion of ideals from relative regret models and static robust optimization, containing conservatism concepts is disclosed. The algorithm uses a fine-tune strategy to tune the model so the robustness and a target ideality can be mutually achieved with a specified risk. The framework comprises original concepts, a mathematical approach and an algorithm. The statistical treatment of the data with the original concepts from the framework make it able to make short, middle or long-term decision-making setting. The framework has high tractability since the algorithm forces the creation of a setting that makes a robust optimization with the specified risk. The framework can be applied in linear and nonlinear mathematical models since that the objective function is monotonic in the domain of the active convex region. Several examples are solved to best understand the framework and all results demonstrated high tractability and performance. There is a wide range of applications. Along all the text, there is a profound discussion about its philosophy, objective, original concepts, fields of application, statistical and probabilistic fundamentals.

This study aimed to develop a laboratory-scale direct air capture unit for evaluating and comparing amine-based adsorbents under temperature vacuum swing adsorption conditions. The experimental campaign conducted with the direct air capture unit allowed for the determination of equilibrium loading, CO2 uptake capacity, and other main performance parameters of the investigated adsorbent Lewatit VP OC 1065®. The investigations also helped to understand the co-adsorption of both CO2 and H2O on the tested material, which is crucial for improving temperature vacuum swing adsorption processes. This was achieved by obtaining pure component isotherms for CO2 and H2O as well as three different co-adsorption isotherm models from literature. It was found that the weighted average dual-site Toth model emerged as the most accurate and reliable model for simulating this co-adsorption behavior. Its predictions closely align with experimental data, particularly in capturing the adsorption equilibrium at various temperatures. It was also observed that this lab-scale unit offers advantages over thermogravimetric analysis when conducting adsorption experiments on the chosen amine. The final aim of this study is to provide a pathway to develop devices for testing and developing efficient and cost-effective adsorbents for direct air capture.

Rare diseases are characterized by a wide diversity of signs and symptoms and vary not only from disease to disease, but also from person to person, and living with a disease leads patients to peculiar experiences and treatments, without limits of time and space, as they extend to various environments and relationships of their lives. The objective of this study is the theoretical interaction between Value Co-creation (VC) and the Stakeholder Theory (ST) with the Shared Decision Making (SDM) health care theory. It is configured as a multiparadigmatic proposal by enabling the analysis of multiple perspectives of different stakeholders in health care. Thus, Co-created Decision Making (CDM) emerges in a logic dominated by service, with emphasis on intangible aspects and the interactivity of the relationships. It goes beyond the clinical office and the doctor-patient relationships, as studied in SDM, extending to all environments and interactions that add value to the patient's treatment. It was concluded that the essence of this new theory proposed here is neither in patient-centered care nor in patient self-care, but in co-created relationships with and between stakeholders in both directions, including non-health care environments that are important to the patient, such as relationships with friends, family, other patients with the same disease, social media, public policies, and the practice of pleasurable activities.

Recently, many authors have been interested to introduce fuzzy implications over t-norms and t-conorms. In this paper, we introduce (S,N) and residuum fuzzy implication for Dubois t-norm and Hamacher's t-norm. Also, new concepts so-called (T,N) and residual fuzzy co-implication in dual Heyting Algebra are investigated. Some examples as well as application are discussed as well.

In this work, we propose a variation of the cellular particle swarm optimization algorithm with differential evolution hybridization (CPSO-DE), to include constrained optimization in it, named Ts-CPD. It is implemented as a kernel of electronic design automation (EDA) tool capable of sizing circuit components considering a single-objective design with restrictions and constraints. The aim is to improve the optimization solutions in the sizing of analog circuits. To evaluate our proposal’s performance, we present the design of three analog circuits: a deferential amplifier, a two-stage operational amplifier, and a folded cascode operational transconductance amplifier. Numerical simulation results indicate that Ts-CPD can find better solutions, in terms of the design objective and the accomplishment of constraints, than those reported in previous works.

When using meta-heuristic optimization approaches for optimization, a large number of samples are required. In particular, when generating subgeneration, the utilization of existing samples is low and the number of individuals is high. Therefore, surrogate-based optimization has been developed, which greatly reduces the number of individuals in the subgeneration and the cost of optimization. In complex air supply scenarios, single-objective optimization results may not be comprehensive; therefore, this paper developed a double-objective air supply optimization method based on the Kriging surrogate model and Non-dominated Sorting Genetic Algorithms-II. And proposed the infill criteria based on clustering to advance the Pareto Frontier. The method was validated by an inverse prediction case, and in particular, the problems when based on 3D steady-state simulations were analyzed. The results showed that the method can quickly achieve an approximate prediction of the boundary conditions (when prediction were made based on experimental data, the number of simulations was 82 and the average error was 6.8%). Finally, the method was used to optimize the air supply parameters of a dual-aisle, single-row cabin. The Pareto set suggested that an airflow organization with dual circulation may be optimal.

As one of the important components of underwater propulsion systems, water lubricated bearings are often failure due to mechanical wearing and vibrating, especially under high loads and prolonged friction. In this paper, a dual-network co-crosslinking strategy based on HNBR is proposed, in which the epoxy network connects with the rubber network through Epoxidized Eommia ulmoides gum. The damping, tribological and mechanical properties of the prepared composite are systematically investigated. The results show that this material has excellent friction and vibration damping properties, with a water-lubricated coefficient of friction as low as 0.022 and a wear resistance as high as 3.87 × 10-6mm3/Nm. Preparing HNBR-based composite by dual network co-crosslinked is proved being a feasible solution to improve the reliability and service life of water-lubricated bearings.

The 2022-2023 influenza season in Romania was characterized by high pediatric hospitalization rates, predominated by influenza A subtypes H1N1 and H3N2. Lowered population immunity to influenza after the SARS-CoV-2 pandemic and subsequent stoppage of influenza circulation, particularly in children who had limited pre-pandemic exposures, influenced hospitalization among children immunosuppressed, and patients with concurrent medical conditions who are at increased risk for developing severe forms of influenza. This study focused on the characteristics of influenza issues among paediatric patients, as well as the relationship between different influenza virus types and viral and bacterial coinfections and illness severity in the 2022-2023 season after the SARS-CoV-2 pandemic. We conducted a retrospective clinical analysis on 301 cases of influenza in pediatric inpatients (age ≤ 18 years), hospitalized at the National Institute of Infectious Diseases "Prof. Dr. Matei Balș" IX Pediatric Infectious Diseases Clinical Section between October 2022 and February 2023. The most significant age group was 57.8% representing children between one to four years old and female. The average clinical forms were found in 61.7%, whereas severe versions represented 18.2% of cases. Most of the complications were respiratory (acute interstitial pneumonia, 76.1%), hematological (72.1%), represented by intra-infectious and deficiency anemia, leukopenia, thrombocytopenia), 33.6% were digestive, such as diarrheal disease, liver cytolysis syndrome, and the acute dehydration syndrome associated with electrolyte imbalance (71.4%). Severe complications were associated with a risk of unfavorable evolution: acute respiratory failure and neurological complications (convulsions, encephalitis). No deaths were reported. We noticed that the flu season 2022-2023 was characterized by the association of co-infections (viral, bacterial, fungal and parasitic) more frequent than in previous years (26.2% vs. 16%), which evolved more severely, with prolonged hospitalization and more complications (p<0.05), and time of use of oxygen therapy was statistically significant (p > 0.05); influenza vaccination in this group was zero. In conclusion, coinfections with respiratory viruses increase the severity of the pediatric population's immunity to influenza, especially among young children who are more vulnerable to developing a serious illness. All people above the age of six months should get vaccinated against influenza to prevent the illness and its severe complications.

Recently, the amount of waste generated has been rapidly increasing, there have been difficulties disposing of waste in Korea. As a solution to this, treating waste using a cement kiln has suggested, but the environmental and economic effects have not been specifically studied. In this study, the effects of alternative resources, and reducing the social costs(Installation and Operation) associated with waste treatment facilities were analyzed. Through a co-processing method, a reduction of approximately 53kg of CO2 can be realized during the production of one ton of cement, and cost savings of about 3,815 milion USD. Another effect is an extension of the expiration date for landfills by 7.55 years.

New setting is introduced to study quasi-degree and quasi-co-degree arising from co-neighborhood. quasi-degree and quasi-co-degree is about a vertex which are applied into the setting of neutrosophic graphs. . The structure of set is studied and general results are obtained. Also, some classes of neutrosophic graphs namely path-neutrosophic graphs, cycle-neutrosophic graphs, complete-neutrosophic graphs and star-neutrosophic graphs, complete-bipartite-neutrosophic graphs and complete-multipartite-neutrosophic graphs are investigated in the terms of a vertex which is called either quasi-degree or quasi-co-degree. Neutrosophic number is reused in this way. It’s applied to use the type of neutrosophic number in the way that, three values of a vertex are used and they’ve same share to construct this number to compare with other vertices. Summation of three values of vertex makes one number and applying it to a comparison. This approach facilitates identifying vertices which form quasi-degree and quasi-co-degree. Quasi-degree is a value of a vertex which is maximum amid all values of vertices which are neighbors to a fixed vertex. Quasi-co-degree is a value of an edge which is maximum amid all values of edges which are neighbors to a fixed vertex but corresponded vertex is representative for this notion. Using different values which are related to a vertex inspire us to focus on edge and vertices which are corresponded to a fixed vertex. The notion of neighborhood is used to collect either vertices are titled neighbors or edges are incident to fixed vertex. In both settings, some classes of well-known neutrosophic graphs are studied. Some clarifications for each result and each definitions are provided. Using fixed vertex has key role to have these notions in the form of vertex or edge. The value of an edge has eligibility to call quasi-co-degree but the value of a vertex has eligibility to call quasi-degree. Some results get more frameworks and perspective about these definitions. The way in that, two vertices have connection together, open the way to define neighborhood and co-neighborhood. The maximum values in neighborhood and co-neighborhood introduces quasi-degree and quasi-co-degree, respectively. New name is chosen from degree. Since amid all vertices with different degrees, one vertex is chosen. In other words, one vertex is fixed and its degree turns out quasi-degree where two degrees could be assigned to a vertex. Degree of edges and degree of vertices. The number of edges which are incident to the vertex and the number of vertices which are neighbors to the vertex. Degree and co-degree are the notions which are transformed to use in quasi-style. Two neutrosophic values introduce two neutrosophic vertices separately in each settings. These notions are applied into neutrosophic graphs as individuals but not family of them as drawbacks for these notions. Finding special neutrosophic graphs which are well-known, is an open way to purse this study. Some problems are proposed to pursue this study. Basic familiarities with graph theory and neutrosophic graph theory are proposed for this article.

Potential of co-digestion mixing thickened secondary sludge (TS) from extended aeration wastewater treatment plant and locally available substrates (whey, grease and septage) has been studied using three steps. The first step was a batch test to determine biological methane potential (BMP) of different mixtures of the three co-substrates with TS. The second step has been carried out with lab-scale reactors (20 L) simulating anaerobic continuous stirred tank reactors fed by three mixtures of co-substrates determined according to previous step results. Modelling using ADM1 as a mechanistic model was applied in the third step to help understanding the co-digestion process. According to BMP step, septage used as co-substrate has a negative effect on performance and addition of 10 to 30% grease or 10% whey would lead to a higher production of biogas and with an increase of the methane content. The results from the reactor showed less evi-dence of the positive effects observed with the BMP assay. Protein and lipid fractions of particu-late biodegradable COD are important variables for digester stability and methane production as predicted by modelling. Results of simulations with ADM1 model adapted to co-digestion confirmed that this model is a powerful tool to optimize the process of biogas production.

There is compelling evidence that the nuclear receptor TRβ, a member of the thyroid hormone receptor (TR) family, is a tumor suppressor in thyroid, breast and other solid tumors. Cell-based and animal studies reveal that the liganded TRβ induces apoptosis, reduces an aggressive phenotype, decreases stem cell populations, and slows tumor growth through modulation of a complex interplay of transcriptional networks. TRβ-driven tumor suppressive transcriptomic signatures include repression of known drivers of proliferation such as PI3K/Akt pathway and activation of novel signaling (JAK1/STAT1) and metabolic reprogramming in both thyroid and breast cancers. The presence of TRβ is also correlated with a positive prognosis and response to therapeutics in BRCA+ and triple-negative breast cancers respectively. Ligand activation of TRβ enhances sensitivity to chemotherapeutics. TRβ co-regulators and bromodomain-containing chromatin remodeling proteins are emergent therapeutic targets. This review considers TRβ as a potential biomolecular diagnostic and therapeutic target.

The sense of uncertainty and fragility due to the effects and magnitude of global challenges we are facing (from pandemic circumstances to climate change impacts) requires – much more than in the past – the capacity to generate a visionary and forefront design approach in the young gen-erations aiming at stimulating their reaction attitude rather than providing consolidated tools from past conditions that no longer exist or will rapidly evolve. Within this general framework, we have investigated the effectiveness and impacts of experienced-based methods of learning and innovative educational tools in architecture aimed at shaping expertise in which the environ-mental dimension and the climate-change challenge dialogues with the context's complexity in terms of socio-cultural dynamics, real potentialities and constrains, addressing their transdisci-plinary trajectories. The paper analyses 5 international pioneering teaching experiences that provide the opportunity to understand the outcomes of collaborative and experiential learning processes in which the educational activities leverage a dialogue between diverse communities (academia-citizens-policymakers-practitioners). The study outcomes show that shifting the pedagogical paradigm towards in-field-experience-based models can improve the awareness of future practitioners for climate implications of architectural design, implement their analysis and project skills while triggering processes of knowledge transfer and co-production at community level, and allow them to better address the societal and cultural issues involved within decision making.

The purpose of this study is to explore conceptual approaches in co-production studies and to examine current research trends of the study. The conceptual paper includes research articles related to co-production in public administration field. By thoroughly scrutinizing 32 research works of co-production, this study highlights major loopholes in the field of the study. The contributions of the study are: (1) identifying two common characteristics of co-production, (2) categorising three types of co-producing by end-users, and (3) finding that goals and success of co-production are more beneficial for service providers though its initial approach is citizen-centric approach. We suggest that future studies should be (1) to focus on reasons for co-production failures or success, (2) to discover further hindrances for co-production in service production, (3) to examine influencing factors on service providers as well as institutional impacts on co-production process, and (4) to include practical assessment in co-production study.

Elevated antimony concentrations in aqueous environments from anthropogenic sources is becoming of global concern, here iron oxides are known to strongly adsorb aqueous antimony species with different oxidation states, but the effect of silica on the removal characteristics is not well understood despite being a common component in the environment. In this study, ferrihydrite was synthesized at various Si/Fe molar ratios to investigate its adsorption and co-precipitation behaviors with aqueous antimony anionic species, Sb(III) and Sb(V). The XRD analyses of the precipitates showed two broad diffraction features at approximately 35° and 62° 2θ, which are characteristic of 2-line ferrihydrite, no significant shifts in peak positions in the ferrihydrite regardless of the Si/Fe ratios. The infrared spectra showed a sharp band at ~990 cm⁻¹, corresponding to asymmetric stretching vibrations of Si-O-Fe bonds which increased in intensity with increasing Si/Fe molar ratios. Further, the surface charge on the precipitates became more negative with increasing Si/Fe molar ratios. The adsorption experiments indicated that Sb(V) was preferentially adsorbed at acidic conditions and decreased dramatically with increasing pH while the adsorption rate of Sb(III) ions was independent of pH, however, the presence of silica suppressed the adsorption of both Sb(III) and Sb(V) ions. The results showed that Sb(III) and Sb(V) ions were significantly inhibited by co-precipitation with ferrihydrite even in the presence of silica by isomorphous substitution in the ferrihydrite crystal structure.

Background: Previously, our laboratory has provided evidence that pre-administration of the antioxidant, lipoic acid covalently bonded to various naturally occurring antioxidants, enhanced neuroprotective capacity compared to the administration of lipoic acid on its own. The naturally occurring compound scopoletin, a coumarin derivative, has been shown in various in vitro studies to have both antioxidant and anti-inflammatory mechanism of actions. To date, the effect of scopoletin on neuronal cell death in an in vivo model of ischemia or ischemia-reperfusion has not been investigated. Therefore, the present investigation was designed to determine if scopoletin on its own, or a co-drug consisting of lipoic acid and scopoletin covalent bond, named UPEI-400, would be capable of demonstrating a similar neuroprotective efficacy. Methods: Using a rodent model of stroke in male rats (anesthetized with Inactin®; 100 mg/kg, iv), the middle cerebral artery was permanently occluded for 6 hours (pMCAO), or in separate animals, occluded for 30 min followed by 5.5 hrs of reperfusion (ischemia/reperfusion; I/R). Results: Pre-administration of either scopoletin or UPEI-400 significantly decreased infarct volume in the I/R model (p<0.05), but not in the pMCAO model of stroke. However, UPEI-400 was ~1000 times more potent as compared to scopoletin on its own. The optimal dose of UPEI-400 was then injected during the occlusion and at several time points during reperfusion and significant neuroprotection was observed for up to 150 mins following the start of reperfusion (p<0.05). Conclusion: The data suggest that synthetic combination of scopoletin with lipoic acid (UPEI-400) is a more effective neuroprotectant that either compound on their own. Also, since UPEI-400 was only effective in a model of I/R, it is possible that it may act to enhance neuronal antioxidant capacity and/or upregulate anti-inflammatory pathways to prevent the neuronal cell death.

Poly(vinyl butyral-co-vinyl alcohol-co-vinyl acetate) named further PVBA was investigated as protective coating for copper corrosion in 0.9 % NaCl solution using electrochemical measurements such as, electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization associated with Atomic Force Microscopy (AFM). The PVBA coating on the copper surface (Cu-PVBA) was modeled in methanol containing PVBA. Its inhibitory properties against corrosion was comparatively discussed with those of the copper sample treated in methanol without polymer (Cu-Me) and of untreated sample (standard copper). A protective performance of PVBA coating of 80 % was computed from electrochemical measurements, for copper corrosion in NaCl solution. Also, AFM images designed a specific surface morphology of coated surface with PVBA, clearly highlighting a polymer film adsorbed on the copper surface, which presents certain deterioration after corrosion, but metal surface was not significantly affected compared to those of untreated samples or treated in methanol, in the absence of PVBA.

Camera malfunctions or loss of storage elements in imaging devices may lead to loss of important image information or random impulse noise interference. Low rank is one of the important prior information in image optimization processing. This paper uses different low-rank constraint models of the image matrices to recover the impulse interference satellite images. Firstly, an overview of image inpainting models based on nuclear norm, truncated nuclear norm, weighted nuclear norm, and matrix-factorization-based F-norm is presented, and corresponding optimiza-tion iterative algorithms are provided. Then, we conducted experiments under three types of pulse interference and provided visual and numerical results. Finally, it is concluded that the WSVT_ADMM method based on the weighted nuclear norm can obtain the best image inpainting results; The UV_ADMM method based on F norm of matrix factorization has the least computa-tion time and can be used for large-scale low-rank matrix computation; The WSVT_ADMM method based on weighted nuclear norm and the TSVT_ ADMM method based on truncated nuclear norms can significantly improve the repair effect compared to the nuclear norm-based methods such as SVT, SVP, and n_ADMM.

Portfolio optimization is a mathematical formulation whose objective is to maximize returns while minimizing risks. A lot of improvement in the model has been made, including adding practical constraints. With the growing of shares trading, the problem becomes dimensionally very large. In this paper, we propose the usage of modified Biogeography-Based Optimization to solve the large scale constrained portfolio optimization. Results indicate the effectiveness of the method used.

Global optimization is an imperative development of local optimization because there are many problems in artificial intelligence and machine learning requires highly acute solutions over entire domain. There are many methods to resolve the global optimization, which can be classified into three groups such as analytic methods (purely mathematical methods), probabilistic methods, and heuristic methods. Especially, heuristic methods like particle swarm optimization and ant bee colony attract researchers because their effective and practical techniques which are easy to be implemented by computer programming languages. However, these heuristic methods are lacking in theoretical mathematical fundamental. Fortunately, minima distribution establishes a strict mathematical relationship between optimized target function and its global minima. In this research, I try to study minima distribution and apply it into explaining convergence and convergence speed of optimization algorithms. Especially, weak conditions of convergence and monotonicity within minima distribution are drawn so as to be appropriate to practical optimization methods.

Particle swarm optimization (PSO) is an effective algorithm to solve the optimization problem in case that derivative of target function is inexistent or difficult to be determined. Because PSO has many parameters and variants, I propose a general framework of PSO called GPSO which aggregates important parameters and generalizes important variants so that researchers can customize PSO easily. Moreover, two main properties of PSO are exploration and exploitation. The exploration property aims to avoid premature converging so as to reach global optimal solution whereas the exploitation property aims to motivate PSO to converge as fast as possible. These two aspects are equally important. Therefore, GPSO also aims to balance the exploration and the exploitation. It is expected that GPSO supports users to tune parameters for not only solving premature problem but also fast convergence.

Increasing demand for food production with limited available water resources pose the threat to agricultural activities. The conjunctive allocation of water resources maximizes the net benefit of farmers efficiently. In this study, a novel hybrid optimization model was developed based on a genetic algorithm (GA), bacterial foraging optimization (BFO) and ant colony optimization (ACO) to maximize the net benefit of water deficit Sathanur reservoir command. The GA-based opti-mization model considered crop-related physical and economical parameters to derive optimal cropping patterns for three different conjunctive use policies and further allocation of surface and groundwater for different crops are enhanced with the BFO. The allocation of surface and groundwater for the head, middle and tail reach obtained from BFO is considered as input to ACO as a guiding mechanism to attain an optimal cropping pattern. Comparing the average produc-tivity values Policy 3 (3.665 Rs/m3) has better values relating to Policy 1 (3.662 Rs/m3) and Policy 2 (3.440 Rs/m3). Thus, the developed novel hybrid optimization model (GA-BFO-ACO) is very promising to enhance the farmer's net income as well as for the command area water conservation and can be replicated in other irrigated regions of the globe to overcome chronic land and water problems.

New setting is introduced to study co-neighborhood, neutrosophic t-neighborhood, neutrosophic quasi-vertex set, neutrosophic quasi-order, neutrosophic neighborhood, neutrosophic co-t-neighborhood, neutrosophic quasi-edge set, neutrosophic quasi-size, Neutrosophic number, neutrosophic co-neighborhood, co-neutrosophic number, quasi-number and quasi-co-number. Some classes of neutrosophic graphs are investigated.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Wind energy is becoming increasingly important as a renewable energy source due to its environmental and economic benefits. Wind turbines are key components in wind energy systems, and their performance is critical for efficient power generation. Wind turbine blades are the most critical components as they interact with the wind, and their design has a significant impact on the overall system performance. Therefore, it is essential to optimize the design of wind turbine blades to enhance their efficiency and reduce their costs. This paper presents an aero-structural optimization approach for wind turbine blade design. The optimization aims to maximize the torque generated by the blade while minimizing its mass. The optimization is implemented using DAFoam software for CFD simulation, TACS for FEM simulation, and Mphys under the OpenMDAO framework for fluid-structure interaction between the CFD and FEM. The optimization results show a 6.78% increase in torque and a 4.22% decrease in mass, which demonstrates the effectiveness of the proposed approach. Aerodynamic optimization focuses on maximizing the blade's torque by modifying the blade's shape. The optimization results show that the optimized blade generates more torque than the original blade design. Additionally, structural optimization aims to minimize the blade's mass while maintaining its structural integrity. This is achieved by adjusting the thickness of the blade's cross-section. The proposed aero-structural optimization approach presents an effective solution for the design optimization of wind turbine blades. The approach considers the interaction between the aerodynamic and structural aspects of the blade and optimizes them simultaneously. This leads to an optimized design that is efficient and cost-effective, which is crucial for the widespread adoption of wind energy systems. The results of this study highlight the importance of considering the interaction between the aerodynamic and structural aspects of wind turbine blades and the effectiveness of the proposed optimization approach in enhancing their performance.

Recent research has enabled the integration of traditional Volt-VAr Control (VVC) resources, such as capacitors banks and transformer tap changers, with Distributed Energy Resources (DERs), such as photo-voltaic sources and energy storage, in order to achieve various Volt-VAr Optimization (VVO) targets, such as Conservation Voltage Reduction (CVR), minimizing VAr flow at the transformer, minimizing grid losses, minimizing asset operations and more. When more than one target function can be optimized, the question of multi-objective optimization is raised. In this work, we propose a general formulation of the multi-objective Volt-VAr optimization problem. We consider the applicability of various multi-optimization techniques and discuss the operational interpretation of these solutions. We demonstrate the methods using simulation on a test feeder.

Type 2 Diabetes Mellitus (T2DM) is one of the most common metabolic disorders, with a major involvement of oxidative stress in its onset and progression. Pioglitazone (Pio) is an antidiabetic drug which primarily works by reducing insulin resistance while Curcumin (Cur) is powerful antioxidant with o important hypoglycemic effect, also. The both drugs are associated with several drawbacks, such as reduced bioavailability, a short half-life time (Pio) as well as instability and poor water solubility (Cur), which limit their therapeutic use. In order to overcome these disadvantages, a new co-delivery (Pio, Cur) nanosystem based chitosan (CS) (CS-Pio-Cur NPs), was developed, and characterized in reference with simple nanosystems (CS-Pio NPs, CS-Cur NPs). The developed CS-based NPs (CS-Pio-Cur NPs, CS-Pio NPs, CS-Cur NPs) were physico-chemical characterized in terms of particle size (PS), entrapment efficiency (EF%) and loading capacity (LC%). The CS-Pio-Cur NPs proved increased or similar values of EE (88.95% ± 7.79 for Cur; 94.83% ± 9.89 for Pio) and LC% (24.25 % ± 1.62 for Cur; 3.5% ± 0.98 for Pio), in reference with simple nanosystems, CS-Cur NPs (EE = 82.46% ± 1.74; LC = 20.34% ± 0.94) and CS-Pio NPs) (EE = 96.31% ± 0.68; LC = 4.11% ± 0.47), respectively. The identification of APIs (Cur, Pio) loaded into CS matrix was performed using FR-IR spectroscopy and for simultaneously quantification of APIs (Cur, Pio) released from CS-Pio-Cur NPs, a HLPC method was developed and validated. Based on release study performed in different simulated fluids (SGF, SIF, SCF) it can be appreciated that developed CS-APIs NPs have favorable pharmacokinetic profile with good release in gastrointestinal tract.

The COVID-19 pandemic and tuberculosis have epidemiological similarities, being transmitted airborne, favored by direct contact, crowded environments and vulnerable biological status. We performed a retrospective study of 45 cases of pulmonary tuberculosis associated with COVID-19 (TB+COV+) compared to 45 cases with tuberculous monoinfection (TB+COV-), hospitalized during 2021-2022. The demographic characteristics were similar in the two groups, predominating men, a median age of 51 years, living in rural areas, medium level of education and smoking. Common symptoms of the two groups were cough, weight loss, profuse sweating, loss of appetite and hemoptysis, while fever, headache, myo-arthralgias and digestive symptoms characterized the TB+COV+ forms. The scores of radiological lesions in the TB+COV+ compared to TB+COV- group were significantly higher and persistent, revealing more frequent bilateral extensive lung lesions. There were no significant differences in the biological parameters between the two groups. Mortality was 2.2%, regardless of the association of COVID-19. The frequency of infections with Clostridioides difficile was higher in TB+COV+ cases. In conclusion, the co-infection of COVID-19 had an impact on the clinical and biological expression of tuberculosis diagnosed in a pandemic context.

The amalgamation of mineral and targeted bacterial preparations represents a new generation of agricultural technology. Inoculation with combined preparations of microorganisms is more effective than inoculation with a single microorganism in stimulating plant growth by providing a more balanced diet for various crops. In this work, the effect of inoculation of 20 consortium variants on the yield indicators of three crops (wheat, buckwheat, corn) and the soil microbiome in the open field was investigated. The soil microbiome was defined by 16S rRNA sequences through NGS. Species richness of the soil microbial community (alpha diversity) was similar for all studied samples. Beta-diversity analysis revealed that the microbial diversity of three soil samples (C.bw, F.bw and Soil.bw) differed significantly from all others. At the phylum level, the number of Acidobacteriota and Firmicutes in these samples was increased. For the combination “Consortium C (Rothia endophytic GMG9 and Azotobacter chroococcum GMG39) - buckwheat”, a systemic positive improvement in all growth and yield indicators was observed. The soil of the site where buckwheat grew, inoculated by Consortium C, contained significantly more available phosphorus than all other soil samples. Such results can be explained both by the direct action of a consortium of phosphate-immobilizing and nitrogen-fixing bacteria and to acidification of the medium due to an increase in phylum Acidobacteriota bacteria in the soil.

This paper presents a novel cheminformatics approach for the design and synthesis of hydroxyapatite/collagen nanocomposites, which have potential biomedical applications in tissue engineering, drug delivery, orthopedic and dental implants. The nanocomposites are synthesized by the co-precipitation method with different ratios of hydroxyapatite and collagen, and their mechanical, biological, and degradation properties are analyzed using various experimental and computational techniques, such as attenuated total reflection Fourier-transform infrared spectroscopy, X-ray diffraction, thermogravimetric analysis, scanning electron microscopy with energy-dispersive X-ray spectroscopy, and molecular simulation. The antimicrobial activity of the nanocomposites is evaluated in vitro against several bacterial and fungal strains, demonstrating their medical potential. Additionally, in silico analyses are performed to predict the absorption, distribution, metabolism, and excretion properties and the bioavailability of the collagen samples. This work opens new avenues for the development of novel biomaterials using cheminformatics tools and methods, which can facilitate the optimization of the design and synthesis parameters, as well as the prediction of the biological outcomes. Future research directions include the investigation of the biocompatibility and bioactivity of the nanocomposites in vivo, as well as the exploration of other applications and functionalities of these nanocomposites.

Recently, there has been a lot of interest in the phenolics of pomegranate peels because of their health-promoting effects. The incorporation of encapsulated phenolic extracts in functional foods, beverages, and dietary supplements can enhance their nutritional and health benefits. This paper aims to provide an overview of the encapsulation of pomegranate peel phenolic extract by co-crystallization, focusing on the properties of the encapsulated extract. Pomegranate peel extract encapsulated in sucrose by co-crystallization at conditions determined in our previous work is characterized by evaluating its properties; moisture content, bulk density, solubility, hygroscopicity, color, degree of encapsulation by thermograms, crystallinity by X-ray scattering, microstructure by scanning electron microscope, and storage stability in terms of total phenolic content and antioxidant activity. The co-crystallized powder had a low moisture content (0.59%) and hygroscopicity (0.011%) and a high bulk density (0.803 g/cm3) and solubility (61 s). Its total phenolic content decreased by only 0.56% after storage at 60oC for 45 days, whereas its antioxidant activity was maintained at levels higher than 84%. The differential scanning calorimetry and the X-ray scattering techniques proved the successful encapsulation in the sucrose matrix and the fact that the extract remained liquid inside the porosity of the sucrose crystals.

This paper presents a bibliometric analysis of highly cited papers published in the journal Sustainability between 2009 and 2023. The aim is to identify influential countries, authors, and papers, as well as patterns in authorship and citation trends. The analysis focuses on frequently cited sources, the most cited research papers and references within the journal, and trends in author keywords. The analysis shows that Sustainability is a leading publication in sustainability research, with an annual growth rate of 53.57%. The selected papers, published between 2013 and 2022, have an average age of 2.11 years and an average of 88.12 citations each. The study highlights the most influential countries in sustainability research published in Sustainability, with China leading, followed by the United States and Germany. The most prolific authors are from China, Spain, and Italy. Interdisciplinary collaboration is essential to address complex sustainability challenges, as shown in the papers published in Sustainability. The study emphasizes the importance of staying informed about emerging trends and research directions in the field of sustainability published in this journal Sustainability. It underscores the significance of fostering international collaborations and partnerships to address complex sustainability challenges facing our world today, as evidenced by the papers published in Sustainability. Overall, this bibliometric analysis provides valuable insights into the current state of sustainability research published in Sustainability and its implications for policy and practice. It highlights key areas for future research and underscores the need for interdisciplinary collaboration to address complex sustainability challenges, as demonstrated by the papers published in Sustainability. These findings have important implications for policymakers, researchers, and practitioners interested in promoting sustainable development in the field of sustainability., as published in Sustainability.

Introduction: Increasing cannabis legalization raises concerns that tobacco use, frequently used with cannabis, will also increase. This study investigated the association between legal status of cannabis in place of residence and prevalence of cannabis and tobacco co-use, simultaneous use, and mixing by comparing the prevalence among adults in Canada (prior to cannabis legalization) vs. adults in US states that had legalized recreational cannabis vs. US states that had not as of September 2018. Methods: Data were drawn from the 2018 International Cannabis Policy Study, conducted with respondents aged 16-65 in Canada and the US recruited from non-probability consumer panels. Differences in the prevalence of co-use, simultaneous use, and mixing between tobacco and different cannabis products were examined using logistic regression models by legal status of place of residence among past 12-month cannabis consumers (N=6744). Results: Co-use and simultaneous use in the past 12 months were most common among respondents in US legal states. Among cannabis consumers, co-use and simultaneous use were less common in US legal states, while mixing was less frequent in US states with both legal and illegal cannabis compared to Canada. Use of edibles was associated with lower odds of all three outcomes, while smoking dried herb or hash was associated with higher odds. Conclusions: The proportion of cannabis consumers who used tobacco was lower in legal jurisdictions despite higher prevalence of cannabis use. Edible use was inversely associated with co-use suggesting that edible use does not appear to be associated with increased tobacco use.

As a typical form of value co-creation, crowdsourcing has been increasingly applied by firms to generate business value. By engaging a crowd, a platform, and other stakeholders, a crowdsourcer can foster the co-creation of a portfolio of value for diverse stakeholders. In analyzing the value co-creation in crowdsourcing, we propose a framework by combining the theories and frameworks in value co-creation and crowdsourcing. The framework examines the key stakeholders, joint purpose, engaged value co-creation processes, contributions, bidirectional relationships of the engagement, and perceived value, exhibiting a holistic view of the value co-creation in a crowdsourcing project. Results of the analysis reveal the business performance of the crowdsourcing project and identify areas of improvement regarding business sustainability. This is a major theoretical contribution of this study. The research design applied a case study approach to empirically investigate a crowdsourcing project. Both the theoretical and practical implications are discussed.

This pot-based study investigated the influence of co-composted wood-derived biochar on lettuce growth performance under salinity and drought stress conditions. Biochar of two particle sizes; > 2 mm and < 1 mm were co-composted with the mixture (1:1 ratio of dry weight) of cow and poultry manures. Co-composted biochars were applied at 5% and 7% rates in soil. Control treatments included the amendment of mixture of biochar with manure in soil. Pots were subjected to slight drought (48-55% water filled pore space (WFPS) of soil) and non-drought conditions (60% WFPS) and under 0 and 1.3 dS m-1 salinity. Results revealed that plants growth performance was significantly better under treatments of co-composted biochar and no salt stress conditions, than when mixture of biochar and manure was applied to soil as non-composted fertilizer. Under no stress condition, small particle-sized co-composted biochar increased root biomass by 786.2% than the large particle-sized co-composted biochar at same application rate. As compared to large-sized co-composted biochar, small sized co-composted biochar at high application rates increased root biomass by 167 – 245% but not leaf biomass under both stress conditions. Small particle-sized co-composted biochar amendment also increased the phosphorus use efficiency (PUE) of lettuce leaves than large particle-sized co-composted biochar under no stress condition. The amendment of small-sized co-composted biochar also increased significantly the concentration of Olsen phosphorus in soil than the amendment of large-particle-sized co-composted biochar. In conclusion, amendment of small particle-sized co-composted biochar has the potential of attenuating salinity and drought stress in lettuce and promoting P cycling in soil.

The social distancing as a response to COVID 19 pandemic has led to the exceptional reductions of daily routine people activities and vehicle uses mainly in city. This same situation was also experienced by several busy, large, and populous cities in Southeast Asia (SA) countries. Correspondingly, this study aimed to test the hypothesis that the social distancing implementation period has increased the air quality in the term of carbon monoxide (CO) emission reduction as drawn from Jakarta city as an example of the one of populated cities in SA region. The CO was measured in parts per billions (ppb) and monitored on the daily basis employing remote sensor platform. The monitor periods were started from January, February, March, and April 2020 with 10 measurement days for each month. The social distancing was implemented from mid of March to the recent April. The CO measurement data were statistically tested to justify the significant effects of social distancing on the CO levels. Based on the CO data analysis, the order of CO mean by months is February > January > March > April. The CO levels for January, February, March, and April were 87.46 ppb (95%CI: 83.54-91.37), 88.20 ppb (95%CI: 81.65-94.74), 86.38 (95%CI: 81.06-91.69), and 78.68 (95%CI: 74.03-83.32) respectively. This study also find significant difference (p<0.05) of CO levels especially in April when social distancing has been implemented. Hence, these findings illustrate the potential air pollutant reduction gained from implementing social distancing as can be seen in April.

Degussa P25 is a benchmark form of TiO₂ used worldwide in photocatalysis studies. Currently no such benchmark exists for co-catalysts, which are essential for many photocatalytic reactions. Here, we present the preparation of Pt nanocluster co-catalysts on TiO₂ using an unmodified commercial source and equipment that is commonly available. Transmission electron microscopy reveals that the procedure produces TiO₂ decorated with Pt atom and nanoclusters (1-5 atoms). Optical reflectance and X-ray diffraction measurements show that the procedure does not affect the TiO₂ polymorph or UV-Vis absorbance. Gas phase photocatalytic splitting of heavy water (D₂O) shows that the Pt nanocluster decorated TiO₂ outperforms Pt nanoparticle (produced by photodeposition) decorated TiO₂ in D₂ production. Pt nanoclusters, produced directly from a commercial source, with high co-catalyst activity are prime candidates to be used in benchmark photocatalytic reactions.

In order to understand the effect of Pb-CuI co-doping on the thermoelectric performance of Bi₂Te₃, n-type Bi₂Te₃ co-doped with x at% CuI and 1/2x at% Pb (x = 0, 0.01, 0.03, 0.05, 0.07, and 0.10) were prepared via high temperature solid state reaction and consolidated using spark plasma sintering. Electron and thermal transport properties, i.e., electrical conductivity, carrier concentration, Hall mobility, Seebeck coefficient, and thermal conductivity, of CuI-Pb co-doped Bi₂Te₃ were measured in the temperature range from 300 K to 523 K and compared to corresponding x% of CuI-doped Bi₂Te₃ and undoped Bi₂Te₃. The addition of a small amount of Pb significantly decreased the carrier concentration, which could be attributed to the holes from Pb atoms, thus the CuI-Pb co-doped samples show a lower electrical conductivity and a higher Seebeck coefficient compared to CuI-doped samples with similar x values. The incorporation of Pb into CuI-doped Bi₂Te₃ rarely changed the power factor because of the trade-off relationship between the electrical conductivity and the Seebeck coefficient. The total thermal conductivity(κ_tot) of co-doped samples (κ_tot ~1.4 W/m∙K at 300 K) is slightly lower than that of 1% CuI-doped Bi₂Te₃ (κ_tot~1.5 W/m∙K at 300 K) and undoped Bi₂Te₃ (κ_tot ~1.6 W/m∙K at 300 K) due to the alloy scattering. The 1% CuI-Pb co-doped Bi₂Te₃ sample shows the highest ZT value of 0.96 at 370 K. All data on electrical and thermal transport properties suggest that the thermoelectric properties of Bi₂Te₃ and its operating temperature can be controlled by co-doping.

Modern power system integrates more and more new energy and use a large number of power electronic equipment. This makes it face more challenges in online optimization and real-time control. Deep reinforcement learning(DRL) has the ability of processing big data and high-dimensional features, as well as the ability of independently learning and optimizing decision-making in complex environments. In this paper, we explore DRL based online combination optimization method of grid section for large complex power system. In our method, to improve the convergence speed of the model, we propose to discretize the output action of the unit and simplify the action space. We also design a reinforcement learning loss function with strong constraints to further improve the convergence speed of the model and facilitate the algorithm to obtain the stable solution. Moreover, to avoid the local optimal solution problem caused by the discretization of the output action, we propose to use the annealing optimization algorithm to make the granularity of the unit output finer. We verify our method on IEEE 118-bus system. The experimental results show that our model has fast convergence speed and better performance, and can obtain stable solutions.

We consider the problem of scheduling tasks on a single machine subject to an uncertain breakdown to minimize the flowtime. Assume the machine is unavailable during the breakdown; the starting time of the breakdown is a random variable s with distribution function D(s) and the terminating time of the breakdown has no any other information; jobs are non-resumable. Under the assumptions, starting from the perspective of statistical optimization, we first define the expected competitive ratio of an algorithm to find the optimized solution with the considered problem. Further, we propose and prove a certain result on the expected competitive ratio of the SPT rule. In particular, we show that under a quite loose condition the expected competitive ratio of the SPT rule is no more than 5/4. Meanwhile, we also make some discussions about our studies. What we have done will rich and improve the studying results on the area of scheduling to minimize flowtime and advance the development of online optimization and statistical optimization.

Malicious apps specifically aimed at the Android platform have increased in tandem with the proliferation of mobile devices. Malware is now so carefully written that it is difficult to detect. Due to the exponential growth in malware, manual methods of malware are increasingly ineffec-tive. Although prior writers have proposed numerous high-quality approaches, static and dy-namic assessments inherently necessitate intricate procedures. The obfuscation methods used by modern malware are incredibly complex and clever. As a result, it cannot be detected using only static malware analysis. As a result, this work presents a hybrid analysis approach, partially tai-lored for multiple-feature data, for identifying Android malware and classifying malware families to improve Android malware detection and classification. This paper offers a hybrid method that combines static and dynamic malware analysis to give a full view of the threat. Three distinct phases make up the framework proposed in this research. Normalization and feature extraction procedures are used in the first phase of pre-processing. Both static and dynamic features undergo feature selection in the second phase. Two feature selection strategies are proposed to choose the best subset of features to use for both static and dynamic features. The third phase involves ap-plying a newly proposed detection model to classify android apps; this model uses a neural net-work optimized with an improved version of HHO. Application of binary and multi-class classi-fication is used, with binary classification for benign and malware apps and multi-class classifica-tion for detecting malware categories and families. By utilizing the features gleaned from static and dynamic malware analysis, several machine-learning methods are used for malware classifi-cation. According to the results of the experiments, the hybrid approach improves the accuracy of detection and classification of Android malware compared to the scenario when considering static and dynamic information separately.

Ti-aluminosilicate gels were used as supports for immobilization of Fe, Co and Ni oxides (5%) by impregnation, and synthesis of efficient photocatalysts in degradation of β-lactam antibiotic from water. Titanium oxide (1 and 2%) was incorporated into zeolite network by modifying the gel during the zeolitation process. The formation of the zeolite Y structure and its microporous structure were evidenced by X-ray diffraction and N2 physisorption. The structure, composition, reduction and optical properties were studied by X-ray difraction, H2-TPR, XPS, Raman, photoluminiscence and UV-Vis spectroscopy. The obtained results indicated a zeolite Y structure for all photocatalysts with tetra-coordinate Ti4+ sites. The immobilized metals from group VIII are both in the form of metal oxides such as Fe2O3, Fe3O4, CoO, Co3O4 , NiO and also in the me-tallic state (Fe0, Co0, and Ni0). A red shift of the absorption edge was observed in UV-Vis spectra of photocatalysts upon addition of Fe, Co or Ni species. The photocatalytic performances were evaluated in degradation of cefuroxime from water under visible light irradiation. The best results were obtained for iron immobilized photocatalysts. Scavenger experiments explained the photocatalytic results and their mechanisms. A different contribution of the active species to the photocatalytic reactions was evidenced.

Photovoltaic systems and wind energy systems are generally used in distributed system. However, zero-sequence and harmonic currents occur due to photovoltaic systems, and positive-sequence harmonic currents occur due to wind turbines. Thus, the sheath current in high voltage cables increases due to these currents and poor power quality in distributed generation plants. Metal parts of the cable are grounded to prevent the negative effects of sheath currents. When long high voltage cable lines are grounded with the method in the literature, high sheath current and voltage occur on the cable sheath. Therefore, the SSBLR method is designed as a new method for the distributed generation system. The SSBLR method has been optimized with prediction methods and multi-objective optimization methods to prevent negative effects of the sheath current. Pareto optimal method is used as multi-objective optimization method, and artificial neural network, hybrid artificial neural network and regression methods are used as prediction method. The most dominant result is searched using different estimation and optimization methods in pareto optimal method. When the artificial neural network-genetic algorithm hybrid method is used as a prediction method, and genetic algorithm used as optimization method, significant decreases are observed in sheath current and voltage.

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers, which include the fluid-structure interaction. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids, and then introduced as a variable of the stiffness matrix during the structural analysis. The materials used were non-linearly modeled using an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor can be expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC) where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

The main goal of this paper is to show that Bayesian optimization could be regarded as a general framework for the data driven modelling and solution of problems arising in water distribution systems. Hydraulic simulation, both scenario based, and Monte Carlo is a key tool in modelling in water distribution systems. The related optimization problems fall in a simulation/optimization framework in which objectives and constraints are often black-box. Bayesian Optimization (BO) is characterized by a surrogate model, usually a Gaussian process, but also a random forest and increasingly neural networks and an acquisition function which drives the search for new evaluation points. These modelling options make BO nonparametric, robust, flexible and sample efficient particularly suitable for simulation/optimization problems. A defining characteristic of BO is its versatility and flexibility, given for instance by different probabilistic models, in particular different kernels, different acquisition functions. These characteristics of the Bayesian optimization approach are exemplified by the two problems: cost/energy optimization in pump scheduling and optimal sensor placement for early detection on contaminant intrusion. Different surrogate models have been used both in explicit and implicit control schemes. Showing that BO can drive the process of learning control rules directly from operational data. BO can also be extended to multi-objective optimization. Two algorithms have been proposed for multi-objective detection problem using two different acquisition functions.

Many combinatorial optimization problems are hard to solve within the polynomial computational time or NP-hard problems. Therefore, developing new optimization techniques or improving existing ones still grab attention. This paper presents an improved variant of the Ant Colony Optimization meta-heuristic called Enhanced Hyper Cube Framework ACO (EHCFACO). This variant has an enhanced exploitation feature that works through two added local search movements of insertion and bit flip. In order to examine the performance of the improved meta-heuristic, a well-known structural optimization problem of laminate Stacking Sequence Design (SSD) for maximizing critical buckling load has been used. Furthermore, five different ACO variants were concisely presented and implemented to solve the same optimization problem. The performance assessment results reveal that EHCFACO outperforms the other ACO variants and produces a cost-effective solution with considerable quality.

The research paper presents a new approach towards constructing motion equations for structures with attached MTMDs (multiple tuned mass dampers). A primary system, with MDOF (multiple dynamic degrees of freedom) was reduced to an equivalent system with a SDOF (single degree of freedom) through the modal approach, and equations from additional MTMDs were added to a thus-created system. Optimization based on H2 and H∞ for the transfer function associated with the generalized displacement of an SDOF system. The research work utilized GA (genetic algorithms) and SA (simulated annealing method) optimization algorithms to determine the stiffness and damping parameters for individual TMDs. The effect of damping and stiffness (MTMD tuning) distribution depending on the number of TMDs was also analyzed. The paper also reviews the impact of primary system mass change on the efficiency of optimized MTMDs, as well as confirms the results of other authors involving greater MTMD effectiveness relative to a single TMD.

We developed a novel hybrid approach for solving global optimization, computer science, bio-medical and engineering real life applications that is based on the coupling of the Whale Optimizer and Sine Cosine Algorithms via a surrogate model. We relate the whale optimizer algorithm to balance between the exploitation and the exploration process in the proposed method. There exist confirmed techniques for searching approximate best optimal solutions, but our algorithm will further guarantee that such numerical and statistical solutions satisfy physical bounds of the standard and real life functions. Our experiments with the benchmark, bio-medical, computer science and engineering real life problems have illustrated the advantages of using a newly hybrid approach based on mixing Whale Optimizer and Sine Cosine algorithms. It holds considerable potential for reducing execution time for solving standard and real life problems and at the same time improving the quality of the solution.

With the every passing day, the demand for data traffic is increasing and this demand forces the research community not only to look for alternating spectrum for communication but also urges the radio frequency planners to use the existing spectrum smartly. Cell size is shrinking with the every upcoming communication generation which makes the base station placement planning complex and cumbersome. In order to make the next-generation cost-effective, it is important to design the network in such a way which utilizes minimum number of base stations while ensure coverage and quality of service. This paper aims at develop a new approach using hybrid metaheuristic and metamodel applied in multi-transmitter placement planning (MTPP) problem. We apply radial basis function (RBF) metamodel to assist particle swarm optimizer (PSO) in a constrained simulation-optimization (SO) of MTPP to mitigate the associated computational burden of optimization procedure. We evaluate the effectiveness and applicability of proposed algorithm in a case study by simulating MTPP model with two, three, four and five transmitters.

Baggage overweight is a significant phenomenon in the air travel in- dustry which is both regulated by the governments and highly profitable for the airlines. The recent years increase in airlines' profits is attributed to so-called ancillary payments – all consumer costs excluding the actual transportation fare. Baggage overweight fees, being a part of such pay- ments, have high profit-generation potential. Is the pricing for baggage overweight positioned correctly? The rationality of policies currently used by airlines is questionable, as they lack a customer-oriented marketing ap- proach. The general purpose of this research is to explore the possibilities of rational baggage overweight pricing for airlines by developing a multi- level price optimization model aimed to maximize company revenue. The modeling is based on determining consumer Willingness-To- Pay values for baggage overweight, and providing an optimal ticket price for them to maximize company expected revenue. The total profit value is forecasted too, based on the company market share and revenue level.

One control challenge in prosthetic legs is seamless transition from one gait mode to another. User intent recognition (UIR) is a high-level controller that tells a low-level controller to switch to the identified activity mode, depending on the user’s intent and environment. We propose a new framework to design an optimal UIR system with simultaneous maximum performance and parsimony for gait mode recognition. We use multi-objective optimization (MOO) to find an optimal feature subset that creates a trade-off between these two conflicting objectives. The main contribution of this paper is two-fold: (1) a new gradient-based multi-objective feature selection (GMOFS) method for optimal UIR design; and (2) the application of advanced evolutionary MOO methods for UIR. GMOFS is an embedded method that simultaneously performs feature selection and classification by incorporating an elastic net in multilayer perceptron neural network training. Experimental data are collected from six subjects, including three able-bodied subjects and three transfemoral amputees. We implement GMOFS and four variants of multi-objective biogeography-based optimization (MOBBO) for optimal feature subset selection, and we compare their performances using normalized hypervolume and relative coverage. GMOFS demonstrates competitive performance compared to the four MOBBO methods. We achieve a mean classification accuracy of 97.14% ± 1.51% and 98.45% ± 1.22% with the optimal selected subset for able-bodied and amputee subjects, respectively, while using only 23% of the available features. Results thus indicate the potential of advanced optimization methods to simultaneously achieve accurate, reliable, and compact UIR for locomotion mode detection of lower-limb amputees with prostheses.

Acemetacin (ACM) is a new non-steroidal anti-inflammatory drug with anti-inflammatory, analgesic, and antipyretic effects. However, the poor water solubility and gastrointestinal side effects limit its use. Recently, the co-amorphous (CAM) strategy has attracted great interest to improve solubility for poorly water-soluble drugs, and basic amino acids have the potential to protect the gastrointestinal tract. In order to develop highly efficient and low toxic ACM formulation, we propose to prepare ACM CAM, with basic amino acids (lysine, arginine and histidine) as co-formers, using a cryo-milling method. The solid-state behaviors of the ACM CAMs were characterized by polarizing light microscopy, differential scanning calorimetry and powder X-ray diffraction. Fourier transform infrared spectroscopy and molecular docking were carried out to understand the formation mechanism. Moreover, the gastro-protective effects of ACM CAMs were evaluated in a rat gastric ulcer model. The results demonstrated CAMs improved the dissolution rates of ACM compared with the neat amorphous counterpart. Furthermore, ACM CAMs are significantly effective in mitigating the ACM-induced gastric ulcer in rats, and the ulcer inhibition rates were almost 90%. More importantly, this study provided a useful method for mitigating drug-induced gastrointestinal damage and broadened the applications of drug-amino acid CAMs.

The SARS-CoV-2 have caused a devastating pandemic of all times in the recent human history. However, there is a serious paucity in high quality data on aggravating factors and mechanisms of co-infection. This study aimed to identify the trending patterns of bacterial co-infections and types and associated outcomes in three phases of the pandemic. Using quality hospital data, we have investigated the SARS-CoV-2 fatality rates, profiles, and types of bacterial co-infections before, during, and after COVID-19 vaccinations. Out of 389 isolates used in different aspects, 298 was examined before and during the pandemic (n=149 before, n=149 during), death rates were 32% during compared to only 7.4% before pandemic with significant association (P value = 0.000000075). Death rate was 34% in co-infected (n = 170) compared to non-co-infected patients (n = 128) indicating a highly significant value (P value = 0.00000000000088). However, analysis of patients without other respiratory problems (n=28) indicated that among the remaining 270 patients, death was 30% in co-infected patients (n=150) and only 0.8% in non-coinfected (n=120) with high significant P value= 0.00000000076. The trending patterns of co-infections before, during, and after vaccinations showed a significant decline in Staphylococcus aureus with concomitant peaks in Gram-negatives in totals of (n= 149 before/n= 149 during): Klebsiella pneumonia (n = 11/49 before/during; E. coli n=10/24, A. baumannii n=8/25, and Ps. Aeruginosa n= 5/16, and S. aureus 13/1. Nevertheless, in post vaccination phase, (n= 91) gender-specific co-infections were examined for potential differences in susceptibility. Methicillin resistant S. aureus (MRSA) dominated both genders followed by E. coli in males and females with the latter gender showing higher rates of isolations in both species. Klebsiella pneumoniae declined to third place mostly in male patients. The drastic decline in K. pneumoniae and Gram-negatives post-vaccination strongly imply a potential co-protection in vaccines. Future analysis would gain more insights into molecular mimicry.

In this article, we review about the nature of Kerr Blackhole, Kerr metric and express kerr metric in Boyer-Lindquist co-ordinates.

The increase in carbon dioxide emissions has a significant impact on human society and the global environment. As the most abundant and cheap C1 resource, the conversion and utilization of carbon dioxide has received extensive attention from researchers. In many methods of carbon dioxide conversion and utilization, the reverse water gas conversion reaction is considered to be one of the most effective ways. In this paper, the research progress of reverse water gas conversion reaction in recent years is introduced.

Abstract Congenital rubella syndrome is a constellation of birth defects that can have devastating consequences, impacting approximately 100,000 births worldwide each year. The incidence is much lower in countries that routinely vaccinate their population. In the US, postnatal immunization of susceptible women is an important epidemiological strategy for the prevention of rubella. However, concerns that co-administration of Rubella vaccine with other immunoglobins (i.e., Rhogam) could comprise vaccine efficacy has produced warnings that can delay the administration of Rubella vaccination postpartum, leaving women susceptible in a subsequent pregnancy. We aimed to address whether co-administration of Rubella vaccine and Rhogam decreased antibody responses as compared to those receiving only Rubella vaccination. This retrospective cohort study utilized clinical data from 80 subjects who received Rubella vaccine and Rhogam after delivery and 43 subjects received Rubella vaccine alone. Maternal demographics, pregnancy complications and rubella status at the start of a subsequent pregnancy were recorded for analysis. Overall, the two cohorts had similar baseline characteristics; however, lower parity was noted in the participants having both Rubella vaccination and Rhogam. Based on maternal antibody IgG index for rubella during the next pregnancy, we observed that 88% of the Rhogam + Rubella vaccine group had positive titers, which was not significantly different than the 81% in the Rubella vaccine only cohort (p=0.36). In conclusion, no differences in the rubella immunity status in a subsequent pregnancy were observed in those mothers administered both Rubella vaccine and Rhogam concurrently. Given these findings, warnings against co-administration of Rubella vaccine in combination with other immunoglobulins should be discontinued.

Vessel co-option correlates with resistance against anti-angiogenic agents and chemotherapy in colorectal cancer liver metastasis (CRCLM). We previously identified higher intensity of neutrophils in the tumour microenvironment of vessel co-opting CRCLM lesions compared to their angiogenic counterparts. Herein, we demonstrated that over 50% of the neutrophils in vessel co-opting lesions are expressing pro-apoptotic markers including cleaved caspase-3 and poly (ADP-ribose) polymerase-1 (PARP-1). Our previous publications suggested upregulation of transforming growth factor-beta (TGFβ1) in the microenvironment of vessel co-option CRCLM. Therefore, we examined the effect of TGFβ1 on the expression of cleaved caspase-3 and PARP-1 in neutrophils in vitro. Significantly, we noticed the upregulation of pro-apoptotic markers upon exposure to TGFβ1. This finding might pave the way to determine the role of neutrophils in developing vessel co-option in CRCLM in the future.

This study is focused on the mechanical properties of WC-Co composites obtained via Selective Laser Sintering using PA12 as a binder. The as-printed samples were thermally debonded, and sintered, first in vacuum, and then sinter-HIPed at 1400oC, using 50 bar Ar, which has led to relative densities up to 66 %. Optical metallographic images show a microstructure consisting of WC, with an average grain size in the range of 1.4 – 2.0 µm, with isolated large grains, in a well-distributed Co matrix. The shrinkage of the samples was 43 %, with no significant shape distortion. The printing direction of the samples has a great impact on the transversal rupture strength (TRS). Nevertheless, the bending strength was low, with a measured maximum of 612 MPa. SEM images of the fracture surface of TRS samples show the presence of defects that constitute the cause of the low measured values. The hardness values position the obtained composites in the range of medium coarse classical cemented carbides. The results were also related to the amount of free Co after sintering, close to the initial one, as assessed by magnetic measurements, indicating a low degree of interaction with PA12 decomposition products.

Age-related alterations in the gut microbiome composition and its impacts on the host’s health have been well described; however, detailed analyses of the gut microbial structure defining ecological microbe-microbe interactions is limited. One of the ways to determine these interactions is by understanding microbial co-occurrence patterns. We previously showed promising abilities of Lactobacillus acidophilus DDS-1 on the aging gut microbiome and immune system. However, the potential of the DDS-1 strain to modulate microbial co-occurrence patterns is unknown. Hence, we aimed to investigate the ability of L. acidophilus DDS-1 to modulate the fecal, mucosal and cecal-related microbial co-occurrence networks in young and aging C57BL/6J mice. Our Kendall’s tau correlation measures of co-occurrence revealed age-related changes in the gut microbiome, which were characterized by reduced number of nodes and associations across sample types when compared to younger mice. After four-week supplementation, L. acidophilus DDS-1 differentially modulated the overall microbial community structure in fecal and mucosal samples as compared to cecal samples. Beneficial bacteria such as Lactobacillus and Akkermansia acted as connectors in aging networks in response to L. acidophilus DDS-1 supplementation. Our findings provided the first evidence of the DDS-1-induced gut microbial ecological interactions revealing the complex structure of microbial ecosystems with age.

Plastid gene expression involves many post-transcriptional maturation steps resulting in a complex transcriptome composed of multiple isoforms. Although short read RNA-seq has considerably improved our understanding of the molecular mechanisms controlling these processes, it is unable to sequence full-length transcripts. This information is however crucial when it comes to understand the interplay between the various steps of plastid gene expression. Here, the study of the Arabidopsis leaf plastid transcriptome using Nanopore sequencing showed that many splicing and editing events were not independent but co-occurring. For a given transcript, maturation events also appeared to be chronologically ordered with splicing happening after most sites are edited.

Hormones must be balanced and dynamically controlled for the Female Reproductive Tract (FRT) to function correctly during the menstrual cycle, pregnancy, and delivery. Gamete selection and successful transfer to the uterus, where it implants and pregnancy occurs, is supported by the mucosal epithelial lining of the FRT ovaries, uterus, cervix, fallopian tubes, and vagina. Successful implantation and placentation in humans and other animals rely on complex interactions between the embryo and a receptive female reproductive system. The FRT's recent breakthroughs in three-dimensional (3D) organoid systems now provide critical experimental models that match the organ's physiological, functional, and anatomical characteristics in vitro. This article summarizes the current state of the art on organoids generated from various parts of the FRT. The current analysis examines recent developments in the creation of organoid models of reproductive organs, as well as their future directions.

Antibiotic inefficacy in treating bacterial infections is largely studied in the context of developing resistance mechanisms. However, little attention has been paid to combined diseases mechanisms, interspecies pathogenesis and the resulting impact on antimicrobial treatment. This review will consider the co-infections of Salmonella and Schistosoma mansoni. It summarises the protective mechanisms that the pathophysiology of the two infections confer, which leads to an antibiotic protection phenomenon. This review will elucidate the functional characteristics of the gut microbiota in the context of these co-infections, the pathogenicity of these infections in infected mice, and the efficacy of the antibiotics used in treatment of these co-infections over time. Salmonella-Schistosoma interactions and the mechanism for antibiotic protection are not well established. However, antimicrobial drug inefficacy is an existing phenomenon in these co-infections. The treatment of schistosomiasis to ensure the efficacy of antibiotic therapy for bacterial infections should be considered in co-infected patients.

Some emergent SARS-CoV-2 variants raise concerns due to their altered biological properties. For both B.1.1.7 and B.1351 variants, named as variants of concern (VOC), increased transmissibility was reported, whereas B.1.351 was more resistant to multiple monoclonal antibodies (mAbs), as well as convalescent and vaccination sera. To test this hypothesis, we examined the proportion of VOC over time across different geographic areas where the two VOC, B.1.1.7 and B.1.351, co-circulate. Our comparative analysis was based on the number of SARS-CoV-2 sequences on GISAID database. We report that B.1.1.7 dominates over B.1.351 in geographic areas where both variants co-circulate and the B.1.1.7 was the first variant introduced in the population. The only areas where B.1.351 was detected at higher proportion were South Africa and Mayotte in Africa, where this strain was associated with increased community transmission before the detection of B.1.1.7. The dominance of B.1.1.7 over B.1.351 could be important since B.1.351 was more resistant to certain mAbs, as well as heterologous convalescent and vaccination sera, thus suggesting that it may be transmitted more effectively in people with pre-existing immunity to other VOC. This scenario would lessen the effectiveness of vaccine and urge the need to update them with new strains.

Long non-coding RNA (lncRNA) can regulate several aspects of gene expression, being associated with complex phenotypes in humans and livestock species. In taurine beef cattle, recent evidence points to the involvement of lncRNA in feed efficiency (FE), a proxy for increased productivity and sustainability. Here, we hypothesized specific regulatory roles of lncRNA in FE of indicine cattle. Using RNA-Seq data from liver, muscle, hypothalamus, pituitary and adrenal gland from Nellore bulls with divergent FE, we submitted new transcripts to a series of filters to confidently predict lncRNA. Then, we identified lncRNA that were differentially expressed (DE) and/or key regulators of FE. Finally, we explored lncRNA genomic location and interactions with miRNA and mRNA to infer potential function. We were able to identify 126 relevant lncRNA for FE in Bos indicus, some with high homology to previously identified lncRNA in Bos taurus and some possible specific regulators of FE in indicine cattle. Moreover, lncRNA identified here were linked to previously described mechanisms related to FE in hypothalamus-pituitary-adrenal axis and are expected to help elucidate this complex phenotype. This study contributes to expanding the catalogue of lncRNA, particularly in indicine cattle, and identifies candidates for further studies in animal selection and management.

Background: On late December 2019, a viral pneumonia known as coronavirus disease 2019 (COVID-19), was originated from China and spread very rapidly in the world. Therefore, COVID-19 became a global concern and health problem. Methods: We presented four patients in this study. They were selected from patients who presented with pneumonia symptoms and were suspicious for COVID-19 and referred to the intended centers for COVID-19 diagnosis and management of Shiraz University of Medical Sciences in the south of Iran. Two nasopharyngeal and oropharyngeal throat swab samples were collected from each patient and tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) detection by real-time reverse-transcriptase–polymerase-chain-reaction (RT-PCR), and also samples were sent for influenza viruses and all the respiratory panel. Results: In the present report, four patients were diagnosed in the starting days of COVID-19 disease in our center in south of Iran with co-infection of SARS-CoV-2 and influenza virus. Conclusions: This co-infection of COVID-19 and influenza highlights the importance of considering SARS-CoV-2 PCR assay regardless of other positive findings for other pathogens in the primary test during the epidemic.

Contact-based co-culture of fibroblasts and keratinocytes is important to study the structure and functions of the wound bed. Co-culture of these two cell types in direct contact with each other has been challenging, requiring high serum concentrations (up to 10%), feeder systems and a range of supplemental factors. These approaches are not only technically demanding, but also present scientific, cost and ethical limitations associated with high-serum concentrations. We have developed two reduced-serum approaches (1-2%) to support contact-based co-culture of human dermal fibroblasts (HDFa) and human epidermal keratinocytes (HaCaT). The two approaches include (1) Specialized cell culture media for each cell type mixed in a 1:1 ratio (KGM+FGM), and (2) Minimal media supplemented with cell-specific growth factors (MEM+GF). Co-culture could be successfully achieved by co-seeding (two cell types were introduced simultaneously), or in a layered fashion (keratinocytes seeded on top of confluent fibroblasts). With wound scratch assays, the co-cultured platforms could demonstrate cell proliferation, migration and wound closure. The reduced-serum conditions developed are simple, easy to formulate and adopt, and based on commonly-available media components. These contact-based co-culture approaches can be leveraged for wound and skin studies, and tissue bioengineering applications, potentially reducing concerns with high-serum formulations.

Background: The febrile patient from tropical areas, in which emerging arboviruses are endemic, represent a diagnostic challenge and potential co-infections with other pathogens (i.e bacteria or parasites) are usually overlooked. Objectives: We present a case of an elderly woman diagnosed with dengue, chikungunya and Leptospira interrogans co-infection. Study Design: Case report. Results: An 87-year old woman from Colombia complained of upper abdominal pain, arthralgia, myalgia, hyporexia, malaise and intermittent fever accompanied with progressive jaundice. She had a medical history of chronic heart failure (Stage C, NYHA III), without documented cardiac murmurs, right bundle branch block, non-valvular atrial fibrillation, hypertension, and chronic venous disease. Her cardiac and pulmonary status quickly deteriorated after 24 hours of her admission without electrocardiographic changes and she required ventilatory and vasopressor support. In the next hours the patient evolved to pulseless electrical activity and then she died. Dengue IgM, NS1 ELISA, MAT for Leptospira interrogans and RT-PCR for chikungunya, were positive. Discussion: This case illustrates a multiple co-infection in a febrile patient from a tropical area of Latin America that evolved to death.

In this work, we report synthesis of Cu, Pt and Pd doped SnO₂ powders and their comparative CO gas sensing studies. Dopants were incorporated into SnO₂ nanostructures using chemical and impregnation methods by using urea and ammonia as precipitation agents. The synthesized samples were characterized using X-ray diffraction (XRD), Raman spectroscopy, Scanning electron microscopy (SEM) and High resolution transmission electron microscopy (HR-TEM). The presence of dopants within the SnO₂ nanostructures was evidenced from HR-TEM. Doped powders utilizing chemical methods with urea as precipitation agent presented higher sensitivities compared to the remaining, which is due to the formation of uniform and homogeneous particles resulted from the temperature assisted synthesis. The particle sizes of doped SnO₂ nanostructures were in the range of 40-100 nm. An enhanced sensitivity around 1783 was achieved with Cu doped SnO₂ when compared with two other dopants i.e., Pt (1200) and Pd: SnO₂(502). The high sensitivity of Cu: SnO₂ is due to formation of CuO and its excellent association and dissociation in the presence of CO with adsorbed atmospheric oxygen at sensor operation temperatures resulted in high conductance. Cu: SnO₂ may be an alternative and cost effective sensor for industrial applications.

Good governance in natural resource management (NRM) is one of the most challenging issues in developing countries that often inappropriately embedded in national policies and political agendas. It is, in fact, even more important for countries like Bangladesh with exceptionally high pressure and dependence on its natural resources for sustaining rural livelihoods. Globally, nowadays, good governance is considered as one of the key factor for achieving the goal of sustainable development and biodiversity conservation. Bangladesh, of late has responded to that global zeal by involving local communities in the management of country’s declining forest and other natural resources. The colonial legacy of the forestry sector of Bangladesh was planned and, managed as interim projects through donors’ prescriptions. Thus, institutions, management processes and conservation outcomes were problematic. The conventional approach adopted by colonial and post-colonial regimes for forest management also proved to be inefficient due to its top-down management system. The absolute dependency on donor support, and their prescription sometimes worsened the situation both ecologically and socially. Global, regional and local trends supported the need for a different dimension in the governance paradigms. The introduction of a pluralistic approach, known as co-management in protected areas (PAs) is an example of an attempt whereby shared governance mechanism are implemented to attain the desired goals of conservation that will also address the livelihoods and aspirations of communities living in and around PAs of the country. However, in designing future forest and PA regimes the concern of the external aid support and attached conditions remain a reality that needs to be addressed. Adequate attention should be given to our vanishing biodiversity, culture and community livelihoods through devising an appropriate governance mechanism recognizing and supporting local rights, access and participation in the environmental management. It is now time to mainstream the adhoc nature of governance according to our national conservation strategy and policy frameworks in order to achieve the goals and objectives of the Bangladesh NRM sector addressing the human and community right of people in the specific context of forest protected areas management.

Despite of being an exceptionally biodiversity rich country, the forest coverage of Bangladesh is declining at an alarming rate. Declaration and management of protected areas in this regard is one of the efforts from government side to tackle the loss of biodiversity. The limited numbers of forest-protected areas (FPA), established to conserve the dwindling forest biodiversity of the country with high pressure on them for timber, non-timber forest products, and fuelwood - makes their management challenging. Moreover, most of the FPAs of the country declared only in the recent decades with very limited infrastructure, manpower and policy support for monitoring and governance. Some people-centred approaches for the management of FPAs and alternative livelihood and income generation subsidies although made available through a few project interventions, their number are still inadequate and performance remains less than satisfactory. This chapter provides a critical review of the FPAs of Bangladesh looking at their role in biodiversity conservation, management challenges, and key lessons from previous management interventions with recommendations for the future. It has been revealed that the FPA system of Bangladesh still poorly represents the diverse forest ecosystems with relatively small forest size and lack of corridors for the movement of wildlife. There are ample opportunities to render co-management of FPAs an effective strategy to minimize the conflicts in FPAs management in the country. It is, however, important to ensure the access of local forest-dependent people to different alternative income generating options that may adequately support their livelihoods.

In 2023, the technological landscape witnessed an unprecedented surge of transformative innovations, including Web 3.0 and machine learning, poised to revolutionize various aspects of our daily lives. At the forefront of this revolution stands artificial intelligence (AI), which has evolved from a concept once confined to science fiction into an indispensable force reshaping industries on a global scale. Notably, recent years have seen remarkable strides in AI functionalities, particularly in machine learning, computer vision, and natural language processing. These advancements have empowered AI to generate diverse interactions and media autonomously. This study ultimately provides invaluable insights into the dynamic intersection of AI and Advertising, con-tributing to a deeper comprehension of this evolving field. This study conducts a comprehensive review of Artificial Intelligence within the advertising domain, employing network analysis. By meticulously examining the evolution of AI through co-occurrences of terms, keywords, and co-authorship analysis, we aim to unveil the relationship of AI & Advertising through the lens of Generative models to identify pivotal trends, core concepts, and seminal studies that have profoundly influenced the industry. The analysis charts the trajectory leading to the emergence of Generative Artificial Intelligence (GenAI), a transformative development poised to revolutionize advertising practices and consumer engagement. In tandem with these objectives, the results highlight vital trends, emphasizing the growing prominence machine learning tools and techniques such as deep learning techniques and advanced natural language processing methods like word2vec, GANs, and more in shaping the future of advertising practices.

In this paper, the entire downhole fluid sucker rod pump system is replaced with a viscoelastic vibration model, namely a third-order differential equation with an inhomogeneous forcing term. Both Kelvin and Maxwell viscoelastic models can be implemented along with the dynamic behaviors of a mass point attached to the viscoelastic model. By employing the time-dependent polished rod force measured with a dynamometer as the input to the viscoelastic dynamic model, we have obtained the displacement responses, which match closely with the experimental measurements in actual operations, through an iterative process. The key discovery of this work is the feasibility of the so-called inverse optimization procedure which can be utilized to identify the equivalent scaling factor and viscoelastic system parameters. The proposed Newton-Raphson iterative method with some terms in the Jacobian matrix expressed with averaged rates of changes based on perturbations of up to two independent parameters provides a feasible tool for optimization issues related to complex engineering problems with mere information of input and output data from either experiments or comprehensive simulations. The same inverse optimization procedure is also implemented to model the entire fluid delivery system of a very viscous non-Newtonian polymer modeled as a first-order ordinary differential system similar to the transient entrance developing flow. The convergent parameter reproduces transient solutions which match very well with those from full-fledged computational fluid dynamics models with the required inlet volume flow rate and the outlet pressure conditions.

We present an integrated omnichannel framework designed for coordinating and improving shopping experience in the Health and Pharma (H&P) sector. The architecture of this unified purchasing system is composed by macros and subsystems designed for the scalability and flexibility of the framework in the specific context of H&P. In addition, we introduce an optimization algorithm specifically designed for the storage operations in a dark-store, with the aim of optimizing operational efficiency and reducing related costs. In the present study we propose two product allocation strategies in a vertical carousel warehouse, based on correlations between products. We show that the proposed optimized allocation strategy significantly reduces picking times compared to the random allocation strategy. Moreover, these results indicate that taking into account the correlation between products in past orders and the optimization of product arrangements within the warehouse can significantly improve the efficiency of the picking process.

In this paper the attention is focused on the analysis and the optimization of energy flows in networked systems via a fluid-dynamic model. In particular, a cost functional that represents a term proportional to the kinetic energy of an energy system is studied. First, the functional is optimized for a simple network having a unique node, with an incoming arc and two outgoing ones. The optimization deals with distribution coefficients and explicit solutions are found. Then, the global optimization is obtained using the local optimal parameters at the various nodes of the system. Considering the case study of an energy hub, interesting results are obtained for the whole network, proving the correctness of the proposed approach.

In recent years, new metaheuristic algorithms have been developed taking as reference the inspiration on biological and natural phenomena. This nature-inspired approach for algorithm development has been widely used by many researchers in solving optimization problem. These algorithms have been compared with the traditional ones algorithms and have demonstrated to be superior in complex problems. This paper attempts to describe the algorithms based on nature, that are used in fuzzy clustering. We briefly describe the optimization methods, the most cited nature-inspired algorithms published in recent years, authors, networks and relationship of the works, etc. We believe the paper can serve as a basis for analysis of the new are of nature and bio-inspired optimization of fuzzy clustering.

The growing interest in the use of energy storage systems to improve the performance of tramways has prompted the development of control techniques and optimal storage devices, displacement, and sizing to obtain the maximum profit and reduce the total installation cost. Recently, the rapid diffusion of renewable energy generation from photovoltaic panels has also created a large interest in coupling renewable energy and storage units. This study analyzed the integration of a photovoltaic power plant, supercapacitor energy storage system, and railway power system. Random optimization was used to verify the feasibility of this integration in a real tramway electric system operating in the city of Naples, and the benefits and total cost of this integration were evaluated.

The quadratic unconstrained binary optimization (QUBO) is a classic NP-hard problem with an enormous number of applications. Local search strategy (LSS) is one of the most fundamental algorithmic concepts that has been successfully applied to a wide range of hard combinatorial optimization problems. One LSS that has gained the attention of researchers is the r-flip (also known as r-Opt) strategy. Given a binary solution with n variables, the r-flip strategy ‘flips’ r binary variables to get a new solution if the changes improve the objective function. The main purpose of this paper is to develop several results for implementation of r-flip moves in QUBO, including a necessary and sufficient condition that when a 1-flip search reaches local optimality, the number of candidates for implementation of the r-flip moves can be reduced significantly. The results of the substantial computational experiments are reported to compare an r-flip strategy embedded algorithm and a multiple start tabu search algorithm on a set of benchmark instances and three very-large-scale QUBO instances. The r-flip strategy implemented within the algorithm makes the algorithm very efficient, very high-quality solutions within a short CPU time.

Due to the high computational burden and the high nonlinearity of the responses, crashworthiness optimizations are notoriously hard-to-solve challenges. Among various approaches, methods like the Successive Response Surface Method (SRSM) have stood out for their efficiency in enhancing baseline designs within a few iterations. However, these methods have limitations that restrict their application. Their minimum iterative resampling required is often computationally prohibitive. Furthermore, surrogate models are conventionally constructed using Polynomial Response Surface (PRS), a method that is poorly versatile, prone to overfitting, and incapable of quantifying uncertainty. Furthermore, the lack of continuity between successive response surfaces results in suboptimal predictions. This paper introduces the Multi-Fidelity Successive Response Surface (MF-SRS), a Gaussian process-based method, which leverages a nonlinear multi-fidelity approach for more accurate and efficient predictions compared to SRSM. After initial testing on synthetic problems, this method is applied to a real-world crashworthiness task: optimizing a bumper crossmember and crash box system. Results, benchmarked against SRSM and the Gaussian Process Successive Response Surface (GP-SRS) – a single-fidelity Gaussian process-driven extension of SRSM, show that MF-SRS offers distinct advantages. Specifically, it improves upon the specific energy absorbed optimum value achieved by SRSM by 14.1 %, revealing its potential for future applications.

Algorithms for machine learning have found extensive use in numerous fields and applications. One important aspect of effectively utilizing these algorithms is tuning the hyperparameters to match the specific task at hand. The selection and configuration of hyperparameters directly impact the performance of machine learning models. Achieving optimal hyperparameter settings often requires a deep understanding of the underlying models and the appropriate optimization techniques. While there are many automatic optimization techniques available, each with its own advantages and disadvantages, this article focuses on hyperparameter optimization for well-known machine learning models. It explores cutting-edge optimization methods and provides guidance on applying them to different machine learning algorithms. The article also presents real-world applications of hyperparameter optimization by conducting tests on spatial data collections for landslide susceptibility mapping. Based on the experiment's results, both Bayesian optimization and metaheuristic algorithms showed promising performance compared to baseline algorithms. For example, the metaheuristic algorithm improved the overall accuracy of the random forest model. Additionally, Bayesian algorithms, such as Gaussian processes, performed well for models like KNN and SVM. The paper thoroughly discusses the reasons behind the efficiency of these algorithms. By successfully identifying appropriate hyperparameter configurations, this research paper aims to assist researchers, spatial data analysts, and industrial users in developing machine learning models more effectively. The findings and insights provided in this paper can contribute to enhancing the performance and applicability of machine learning algorithms in various domains.

Optimization using Genetic Algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the Genetic Algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case of one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and damp the undesired attitudes. The proposed crossover can be achieved in two stages: the first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system shows improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.

Classical and optimal control architectures for motion mechanics with fusion of noisy sensors use different algorithms and calculations to perform and control any number of physical demands, to varying degrees of accuracy, precision, and cost. Their performances are tested for the purpose of comparison through the means of a Monte Carlo simulation that simulates how different parameters might vary under noise, representing real-world imperfect sensors. We find that improvements in one figure of merit often come at a cost in the performance in the others, especially depending on the presence of noise in the system sensors. If sensor noise is negligible, open-loop optimal control performs the best. However, in the overpowering presence of sensor noise, using a control law inversion patching filter performs as the best replacement, but has significant computational strain.

Structural system design is the process of giving form to a set of interconnected components subjected to loads and design constraints while navigating a complex design space. While safe designs are relatively easy to develop, optimal designs are not. Modern computational optimization approaches employ population based metaheuristic algorithms to overcome challenges with the system design optimization landscape. However, the choice of the initial population, or ground structure, can have an outsized impact on the resulting optimization. This paper presents a new method of generating such ground structures, using a combination of topology optimization (TO) and a novel system extraction algorithm. Since TO generates monolithic structures, rather than systems, its use for structural system design and optimization has been limited. In this paper, truss systems are extracted from topologies through morphological analysis and artificial intelligence techniques. This algorithm, and its assessment, constitutes the key contributions of this paper. The structural systems obtained are compared with ground truth solutions to evaluate the performance of the algorithms. The generated structures are also compared against benchmark designs from the literature. The results indicate that the presented truss generation algorithm produces structures comparable to those generated through metaheuristic optimization, while mitigating the need for assumptions about initial ground structures.

This paper presents a multi-swarm PSO algorithm for the Quadratic Assignment Problem (QAP) implemented on the OpenCL platform. Our work was motivated by results of time efficiency tests performed for single-swarm algorithm implementation that showed clearly that the benefits of a parallel execution platform can be fully exploited provided the processed population is large. The described algorithm can be executed in two modes: with independent swarms or with migration. We discuss the algorithm construction as well as we report results of tests performed on several problem instances from the QAPLIB library. During the experiments the algorithm was configured to process large populations. This allowed us to collect statistical data related to values of goal function reached by individual particles. We use them to demonstrate on two test cases that although single particles seem to behave chaotically during the optimization process, when the whole population is analyzed, the probability that a particle will select a near-optimal solution grows.

Powder bed fusion (PBF) process is a metal additive manufacturing process which can build parts with any complexity from a wide range of metallic materials. PBF process research has predominantly focused on the impact of only a few parameters on product properties due to the lack of a systematic approach for optimizing a large set of process parameters simultaneously. The pivotal challenges regarding this process require a quantitative approach for mapping the material properties and process parameters onto the ultimate quality; this will then enable the optimization of those parameters. In this study, we propose a two-phase framework for optimizing the process parameters and developing a predictive model for 316L stainless steel material. We also discuss the correlation between process parameters -- i.e., laser specifications -- and mechanical properties and how to achieve parts with high density (> 98%) as well as better ultimate mechanical properties. In this paper, we introduce and test an innovative approach for developing AM predictive models, with a relatively low error percentage of 10.236% that are used to optimize process parameters in accordance with user or manufacturer requirements. These models use support vector regression, random forest regression, and neural network techniques. It is shown that the intelligent selection of process parameters using these models can achieve an optimized density of up to 99.31% with uniform microstructure, which improves hardness, impact strength, and other mechanical properties.