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Self-supervised learning has been shown to be effective in various fields, proving its usefulness in contrastive learning. Recently, graph contrastive learning has shown state-of-the-art performance in the recommendation task. They created two views and learned node embeddings so that target nodes in the two views attract each other based on the target node, and non-target nodes in the two views repel each other. However, they overlooked the fact that false negatives can occur when negative pairs are repelled. It has been shown through various studies that false negatives in contrastive learning in various fields can have a negative impact on model training, but research on the impact of false negatives in link prediction tasks, such as recommendation tasks, where classes cannot be clearly defined, is still hardly explored. In this paper, we propose an approach to define false negatives in link prediction tasks and fully utilize them in learning. Learning by defining false negatives and removing them from negative pairs showed consistent improvements over existing graph contrastive learning on five benchmark datasets. In addition, we found through comprehensive experimental studies that learning by removing false negatives is of great advantage, especially for low-density datasets. On top of these advantages, our false negative detection and false negative elimination can be naturally integrated into any graph contrastive learning architecture.

Abstract We present two methods for enhancing the efficiency of mitochondrial DNA (mtDNA) editing in mice with DddA-derived cytosine base editors (DdCBEs). First, we fused DdCBEs to a nuclear export signal (DdCBE-NES) to avoid off-target C-to-T conversions in the nuclear genome and improve editing efficiency in mtDNA. Second, mtDNA-targeted TALENs (mitoTALENs) are co-injected into mouse embryos to cleave unedited mtDNA. We generated a mouse model with the m.G12918A mutation in the MT-ND5 gene, associated with mitochondrial genetic disorders in humans. The mutant mice show hunched appearances, damaged mitochondria in kidney and brown adipose tissues, and hippocampal atrophy, resulting in premature death.

Abstract Background Noma is a rare disease that occurs mainly in malnourished patients in developing countries. Noma starts as facial swelling and gingival necrosis that eventually necrotizes underlying tissues including the jaw bone, leaving severe disfigurement. It is reported extremely rarely in patients with severe immunosuppression or blood dyscrasia. Case presentation The gingivitis that occurred in a 12-year-old Asian female patient with acute myeloid leukemia was getting increasingly worse. Although the proper treatment was done, the patient’s condition did not improve, and eventually, a large full-thickness defect was left in the maxillofacial part. Conclusions Early diagnosis and management is the only way to prevent the progression, which leads to facial disfigurement. We present a case of noma in a pediatric acute myeloid leukemia patient, in which oral function was restored through surgical intervention.

Summary Biofilms are communities of bacteria, fungi or yeasts that form on diverse biotic or abiotic surfaces, and play important roles in pathogenesis and drug resistance. A generic saw palmetto oil inhibited biofilm formation by Staphylococcus aureus, Escherichia coli O157:H7 and fungal Candida albicans without affecting their planktonic cell growth. Two main components of the oil, lauric acid and myristic acid, are responsible for this antibiofilm activity. Their antibiofilm activities were observed in dual‐species biofilms as well as three‐species biofilms of S. aureus, E. coli O157:H7 and C. albicans. Transcriptomic analysis showed that lauric acid and myristic acid repressed the expressions of haemolysin genes (hla and hld) in S. aureus, several biofilm‐related genes (csgAB, fimH and flhD) in E. coli and hypha cell wall gene HWP1 in C. albicans, which supported biofilm inhibition. Also, saw palmetto oil, lauric acid and myristic acid reduced virulence of three microbes in a nematode infection model and exhibited minimal cytotoxicity. Furthermore, combinatorial treatment of fatty acids and antibiotics showed synergistic antibacterial efficacy against S. aureus and E. coli O157:H7. These results demonstrate that saw palmetto oil and its main fatty acids might be useful for controlling bacterial infections as well as multispecies biofilms.

Mitochondria are subcontractors dedicated to energy production within cells. In human mitochondria, almost all mitochondrial proteins originate from the nucleus, except for 13 subunit proteins that make up the crucial system required to perform ‘oxidative phosphorylation (OX PHOS)’, which are expressed by the mitochondria’s self-contained DNA. Mitochondrial DNA (mtDNA) also encodes 2 rRNA and 22 tRNA species. Mitochondrial DNA replicates almost autonomously, independent of the nucleus, and its heredity follows a non-Mendelian pattern, exclusively passing from mother to children. Numerous studies have identified mtDNA mutation-related genetic diseases. The consequences of various types of mtDNA mutations, including insertions, deletions, and single base-pair mutations, are studied to reveal their relationship to mitochondrial diseases. Most mitochondrial diseases exhibit fatal symptoms, leading to ongoing therapeutic research with diverse approaches such as stimulating the defective OXPHOS system, mitochondrial replacement, and allotropic expression of defective enzymes. This review provides detailed information on two topics: (1) mitochondrial diseases caused by mtDNA mutations, and (2) the mechanisms of current treatments for mitochondrial diseases and clinical trials.

The thermal physiological and psychological responses in vehicles, influenced by gender and age, play a crucial role in ensuring passengers' comfort. However, these differences have often been overlooked. This study aims to comprehensively examine passengers' thermal comfort and investigate gender and age disparities based on their physiological and psychological responses. Experiments were conducted inside a vehicle placed in a climate chamber under cooling and heating conditions, with the collected data subjected to statistical analysis. The findings reveal that males had significantly higher mean skin temperatures in cooling conditions and lower skin temperatures in heating conditions than females. However, overall thermal sensation and comfort did not significantly differ between genders. Interestingly, age-related differences were observed to a limited extent in both conditions. This study provides valuable insights into passengers' thermal responses in vehicles, considering the factors of gender and age, thereby contributing to a comprehensive understanding of thermal comfort in a vehicle environment.

CO2 emission is one of the biggest environmental problems and contributes to global warming. The climatic changes due to the damage to nature is triggering a climate crisis globally. To prevent a possible climate crisis, this research proposes an engineering design solution to reduce CO2 emissions. This research proposes an optimization-machine learning pipeline and a set of models trained for the prediction of the design variables of an ecofriendly concrete column. In this research, the harmony search algorithm was used as the optimization algorithm, and different regression models were used as predictive models. Multioutput regression is applied to predict the design variables such as section width, height, and reinforcement area. The results indicated that the random forest algorithm performed better than all other machine learning algorithms that have also achieved high accuracy.

Staphylococcus aureus is one of the major pathogens responsible for antimicrobial resistance-associated death. S. aureus can secrete various exotoxins, and staphylococcal biofilms play critical roles in antibiotic tolerance and the persistence of chronic infections. Here, we investigated the inhibitory effects of 18 hydroquinones on biofilm formation and virulence factor production by S. aureus. It was found that 2,5-bis(1,1,3,3-tetramethylbutyl) hydroquinone (TBHQ) at 1 µg/mL efficiently inhibits biofilm formation by two methicillin-sensitive and two methicillin-resistant S. aureus strains with MICs of 5 µg/mL, whereas the backbone compound hydroquinone did not (MIC > 400 µg/mL). In addition, 2,3-dimethylhydroquinone and tert-butylhydroquinone at 50 µg/mL also exhibited antibiofilm activity. TBHQ at 1 µg/mL significantly decreased the hemolytic effect and lipase production by S. aureus, and at 5–50 µg/mL was non-toxic to the nematode Caenorhabditis elegans and did not adversely affect Brassica rapa seed germination or growth. Transcriptional analyses showed that TBHQ suppressed the expression of RNAIII (effector of quorum sensing). These results suggest that hydroquinones, particularly TBHQ, are potentially useful for inhibiting S. aureus biofilm formation and virulence.

Metaheuristic optimization techniques are widely applied in the optimal design of structural members. This paper presents the application of the harmony search algorithm to the optimal dimensioning of reinforced concrete circular columns. For the objective of optimization, the total cost of steel and concrete associated with the construction process were selected. The selected variables of optimization include the diameter of the column, the total cross-sectional area of steel, the unit costs of steel and concrete used in the construction, the total length of the column, and applied axial force and the bending moment acting on the column. By using the minimum allowable dimensions as the constraints of optimization, 3125 different data samples were generated where each data sample is an optimal design configuration. Based on the generated dataset, the SHapley Additive exPlanations (SHAP) algorithm was applied in combination with ensemble learning predictive models to determine the impact of each design variable on the model predictions. The relationships between the design variables and the objective function were visualized using the design of experiments methodology. Applying state-of-the-art statistical accuracy measures such as the coefficient of determination, the predictive models were demonstrated to be highly accurate. The current study demonstrates a novel technique for generating large datasets for the development of data-driven machine learning models. This new methodology can enhance the availability of large datasets, thereby facilitating the application of high-performance machine learning predictive models for optimal structural design.

Increased number of left ventricular assist device placement in patients with end stage heart failure as well as years of survival increases the likelihood of need for non-cardiac procedures. Prostate artery embolization is a safe, minimally invasive procedure performed in the setting of lower urinary tract symptoms or refractory gross hematuria of prostatic origin. These patients require a multidisciplinary approach to weigh the benefits and risks of the procedure and provide optimal periprocedural care. We report a case of technically successful prostate artery embolization performed in a patient with HeartWare HVAD presenting with refractory hematuria of prostatic origin (RHPO).

This paper develops predictive models for optimal dimensions that minimize the construction cost associated with reinforced concrete retaining walls. Random Forest, Extreme Gradient Boosting (XGBoost), Categorical Gradient Boosting (CatBoost), and Light Gradient Boosting Machine (LightGBM) algorithms were applied to obtain the predictive models. Predictive models were trained using a comprehensive dataset, which was generated using the Harmony Search (HS) algorithm. Each data sample in this database consists of a unique combination of the soil density, friction angle, ultimate bearing pressure, surcharge, the unit cost of concrete, and six different dimensions that describe an optimal retaining wall geometry. The influence of these design features on the optimal dimensioning and their interdependence are explained and visualized using the SHapley Additive exPlanations (SHAP) algorithm. The prediction accuracy of the used ensemble learning methods is evaluated with different metrics of accuracy such as the coefficient of determination, root mean square error, and mean absolute error. Comparing predicted and actual optimal dimensions on a test set showed that an R2 score of 0.99 could be achieved. In terms of computational speed, the LightGBM algorithm was found to be the fastest, with an average execution speed of 6.17 s for the training and testing of the model. On the other hand, the highest accuracy could be achieved by the CatBoost algorithm. The availability of open-source machine learning algorithms and high-quality datasets makes it possible for designers to supplement traditional design procedures with newly developed machine learning techniques. The novel methodology proposed in this paper aims at producing larger datasets, thereby increasing the applicability and accuracy of machine learning algorithms in relation to optimal dimensioning of structures.

Bacterial biofilm formation is a major cause of drug resistance and bacterial persistence; thus, controlling pathogenic biofilms is an important component of strategies targeting infectious bacterial diseases. Cinnamaldehyde (CNMA) has broad-spectrum antimicrobial and antibiofilm activities. In this study, we investigated the antibiofilm effects of ten CNMA derivatives and trans-CNMA against Gram-negative uropathogenic Escherichia coli (UPEC) and Gram-positive Staphylococcus aureus. Among the CNMA analogs tested, 4-nitrocinnamaldehyde (4-nitroCNMA) showed antibacterial and antibiofilm activities against UPEC and S. aureus with minimum inhibitory concentrations (MICs) for cell growth of 100 µg/mL, which were much more active than those of trans-CNMA. 4-NitroCNMA inhibited UPEC swimming motility, and both trans-CNMA and 4-nitroCNMA reduced extracellular polymeric substance production by UPEC. Furthermore, 4-nitroCNMA inhibited the formation of mixed UPEC/S. aureus biofilms. Collectively, our observations indicate that trans-CNMA and 4-nitroCNMA potently inhibit biofilm formation by UPEC and S. aureus. We suggest efforts be made to determine the therapeutic scope of CNMA analogs, as our results suggest CNMA derivatives have potential therapeutic use for biofilm-associated diseases.

In this paper, an optimum design algorithm is presented for reinforced concrete folded plate structures. The design provisions are implemented by ACI 318-11 and ACI 318.2-14, which are quite complex to apply. The design variables are divided into three classes. The first class refers to the variables involving the plates, which are the number of supports, thicknesses of the plates, configurations of longitudinal and transverse reinforcement, span length of each plate, and angle of inclination of the inclined plates. The second class consists of the variables involving the auxiliary members’ (beams and diaphragms) depth and breadth and the configurations of longitudinal and shear reinforcement. The third class of variables can be the supporting columns, which involve the dimensions of the column along each axis and the configurations of longitudinal and shear reinforcement. The objective function is considered as the total cost of the folded plate structure, which consists of the cost of concrete, reinforcement, and formwork that is required to construct the building. With such formulation, the design problem becomes a discrete nonlinear programming problem. Its solution is obtained by using three different soft computing techniques, which are artificial bee colony, differential evolution, and enhanced beetle antennae search. The enhancement suggested makes use of the population of beetles instead of one, as is the case in the standard algorithm. With this novel improvement, the beetle antennae search algorithm became very efficient. Two folded plate structures are designed by the proposed optimum design algorithm. It is observed that the differential evolution algorithm performed better than the other two metaheuristics and achieved the cheapest solution.

This paper explores new techniques that are based on a hidden‐layer linear transformation for fast speaker adaptation used in deep neural networks (DNNs). Conventional methods using affine transformations are ineffective because they require a relatively large number of parameters to perform. Meanwhile, methods that employ singular‐value decomposition (SVD) are utilized because they are effective at reducing adaptive parameters. However, a matrix decomposition is computationally expensive when using online services. We propose the use of an extended diagonal linear transformation method to minimize adaptation parameters without SVD to increase the performance level for tasks that require smaller degrees of adaptation. In Korean large vocabulary continuous speech recognition (LVCSR) tasks, the proposed method shows significant improvements with error‐reduction rates of 8.4% and 17.1% in five and 50 conversational sentence adaptations, respectively. Compared with the adaptation methods using SVD, there is an increased recognition performance with fewer parameters.

Fiber-reinforced polymer (FRP) rebars are increasingly being used as an alternative to steel rebars in reinforced concrete (RC) members due to their excellent corrosion resistance capability and enhanced mechanical properties. Extensive research works have been performed in the last two decades to develop predictive models, codes, and guidelines to estimate the axial load-carrying capacity of FRP-RC columns. This study utilizes the power of artificial intelligence and develops an alternative approach to predict the axial capacity of FRP-RC columns more accurately using data-driven machine learning (ML) algorithms. A database of 117 tests of axially loaded FRP-RC columns is collected from the literature. The geometric and material properties, column shape and slenderness ratio, reinforcement details, and FRP types are used as the input variables, while the load-carrying capacity is used as the output response to develop the ML models. Furthermore, the input-output relationship of the ML model is explained through feature importance analysis and the SHapely Additive exPlanations (SHAP) approach. Eight ML models, namely, Kernel Ridge Regression, Lasso Regression, Support Vector Machine, Gradient Boosting Machine, Adaptive Boosting, Random Forest, Categorical Gradient Boosting, and Extreme Gradient Boosting, are used in this study for capacity prediction, and their relative performances are compared to identify the best-performing ML model. Finally, predictive equations are proposed using the harmony search optimization and the model interpretations obtained through the SHAP algorithm.

In this study, the tuned liquid damper (TLD) device was optimized by the harmony search (HS) and adaptive harmony search algorithms (AHS). Using the harmony search algorithm, seismic excitations were directed at single and ten-story structures, and TLD parameters were optimized to minimize building movement. To improve design parameters, the optimization process was repeated by adapting the design factors of the harmony search algorithm. For this purpose, both the harmony memory consideration ratio (HMCR) and fret width (FW) were gradually reduced by providing an initial value, and optimum algorithm parameters were obtained. As a result of both optimizations, in a critical seismic analysis, the displacements of the adaptive harmony search showed smaller means and standard deviations than those of the classical harmony search.

This study focuses on tuned liquid dampers (TLDs) using liquids with different characteristics optimized with the adaptive harmony search algorithm (AHS). TLDs utilize the characteristic features of the liquid to absorb the dynamic forces entering the structure and benefit from the sloshing movement and the spring stiffness created by the liquid mass. TLDs have been optimized to investigate the effect of liquid characteristics on the control by analyzing various liquids. For optimization, the memory consideration ratio (HMCR) and fret width (FW) values were adapted from the classical harmony search (HS) algorithm parameters. The TLDs were used on three types of structure models, such as single-story, 10, and 40 stories. The contribution of the liquid characteristics to the damping performance was investigated by optimizing the minimum displacement under seismic excitation. According to the results, it was understood that the liquid density and kinematic viscosity do not affect single-story structures alone. However, two characteristic features should be evaluated together. As the structure mass increases, the viscosity and density become more prominent.

The optimum cost of the structure design is one of the major goals of structural engineers. The availability of large datasets with preoptimized structural configurations can facilitate the process of optimum design significantly. The current study uses a dataset of 7744 optimum design configurations for a cylindrical water tank. Each of them was obtained by using the harmony search algorithm. The database used contains unique combinations of height, radius, total cost, material unit cost, and corresponding wall thickness that minimize the total cost. It was used to create ensemble learning models such as Random Forest, Extreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), and Categorical Gradient Boosting (CatBoost). Generated machine learning models were able to predict the optimum wall thickness corresponding to new data with high accuracy. Using SHapely Additive exPlanations (SHAP), the height of a cylindrical wall was found to have the greatest impact on the optimum wall thickness followed by radius and the ratio of concrete unit cost to steel unit cost.

Performance of convergence to the optimum value is not completely a known process due to characteristics of the considered design problem and floating values of optimization algorithm control parameters. However, increasing robustness and effectiveness of an optimization algorithm may be possible statistically by estimating proper algorithm parameters values. Not only the algorithm which utilizes these estimated-proper algorithm parameter values may enable to find the best fitness in a shorter time, but also it may supply the optimum searching process with a pragmatical manner. This study focuses on the statistical investigation of the optimum values for the control parameters of the harmony search algorithm and their effects on the best solution. For this purpose, the Taguchi method integrated hybrid harmony search algorithm has been presented as an alternative method for optimization analyses instead of sensitivity analyses which are generally used for the investigation of the proper algorithm parameters. The harmony memory size, the harmony memory considering rate, the pitch adjustment rate, the maximum iteration number, and the independent run number of entire iterations have been debated as the algorithm control parameters of the harmony search algorithm. To observe the effects of design problem characteristics on control parameters, the new hybrid method has been applied to different engineering optimization problems including several engineering-optimization examples and a real-size engineering optimization design. End of extensive optimization and statistical analyses to achieve optimum values of control parameters providing rapid convergence to optimum fitness value and handling constraints have been estimated with reasonable relative errors. Employing the Taguchi method integrated hybrid harmony search algorithm in parameter optimization has been demonstrated as it is a reliable and efficient manner to obtain the optimum results with fewer numbers of run and iteration.

As plastic has become an integral component of daily life, microplastic has become a ubiquitous, unavoidable constituent of nearly all ecosystems. Besides monitoring the amount and distribution of microplastic in the environment, it is necessary to understand the possible direct effects, especially toxicity and how it is affected by environmental factors where it is discarded. The present study investigated how microplastic derived from high-density polyethylene bottle caps collected in two climatically different cities, i.e., Singapore (tropical rainforest climate) and Lahti, Finland (continental climate), affected the essential agricultural grain crop, Triticum aestivum (L.). Wheat seedlings were exposed to microplastic derived from these collected bottle caps, as well as new and artificially aged caps, for seven days. Morphological parameters, such as root and shoot length and oxidative stress development, were measured. Exposure to microplastic derived from the caps resulted in reduced seedling root and shoot lengths compared to the controls, as well as enhanced lipid peroxidation and catalase activity. With all parameters tested, microplastic derived from Lahti bottle caps exhibited more severe effects than Singapore, which was similar to that elicited by new microplastic. The Singapore microplastic had possibly leached its toxic substances before collection due to accelerated degradation promoted by the prevailing warmer climate conditions.

Total Potential Optimization using Metaheuristic Algorithms (TPO/MA) is an alternative tool for the analysis of structures. It is shown that this emerging method is advantageous in solving nonlinear problems like trusses, tensegrity structures, cable networks, and plane stress systems. In the present study, TPO/MA, which does not need any specific implementation for nonlinearity, is demonstrated to be successfully applied to the analysis of plane strain structures. A numerical investigation is performed using nine different metaheuristic algorithms and an adaptive harmony search in linear analysis of a structural mechanics problem having 8 free nodes defined as design variables in the minimization problem of total potential energy. For nonlinear stress-strain relation cases, two structural mechanics problems, one being a thick-walled pipe and the other being a cantilever retaining wall, are analyzed by employing adaptive harmony search, which was found to be the best one in linear analyses. The nonlinear stress-strain relations considered in these analyses are hypothetical ones due to the lack of any such relationship in the literature. The results have shown that TPO/MA can solve nonlinear plane strain problems that can be encountered as engineering problems in structural mechanics.

In this paper, we propose a new method for code-switching (CS) automatic speech recognition (ASR) in Korean. First, the phonetic variations in English pronunciation spoken by Korean speakers should be considered. Thus, we tried to find a unified pronunciation model based on phonetic knowledge and deep learning. Second, we extracted the CS sentences semantically similar to the target domain and then applied the language model (LM) adaptation to solve the biased modeling toward Korean due to the imbalanced training data. In this experiment, training data were AI Hub (1033 h) in Korean and Librispeech (960 h) in English. As a result, when compared to the baseline, the proposed method improved the error reduction rate (ERR) by up to 11.6% with phonetic variant modeling and by 17.3% when semantically similar sentences were applied to the LM adaptation. If we considered only English words, the word correction rate improved up to 24.2% compared to that of the baseline. The proposed method seems to be very effective in CS speech recognition.

In this paper, the Harmony Search (HS) algorithm is utilized to perform single and multivariate parametric studies to acquire the optimization of both size and cost of reinforced concrete (RC) retaining walls embedded in pure frictional soils. The geotechnical properties of the backfill and foundation soil such as shear strength angle, unit weight, and the ultimate bearing pressure of the soil have been used to create different cases for evaluating the effects of site properties on the size and cost of the wall. The change of depth of excavation and surcharge loading condition is fictionalized for generating different environmental conditions for all envisaged soil profiles to predict possible rates of influences. The unit cost of the concrete has also been evaluated as a variant to show the economic constraints on the selection of structural materials. The results of the analyses represent the integrated influences of different significant parameters on the achievement of minimum cost-dimension optimization. Besides, a well-known commercial geotechnical engineering software is used to compare the appropriateness of the suggested designs in terms of both the attainment of geotechnical stability and the structural requirements. Consequently, this study can guide both researchers and designers to select the proper and optimal sections of RC-retaining wall systems with simultaneous analyses of parameters that are influenced by the design process. Furthermore, the optimization results indicate that a significant cost reduction may be achieved when compared with the traditional pre-design method.

In this study, multi-story structures with different combinations (on each floor and only the first floor) of active tendon control systems driven by a proportional–integral–derivative (PID) controller were actively controlled. The PID parameters, Kp (proportional gain), Td (derivative gain), and Ti (integral gain) for each structure, were optimally tuned by using both the harmony search algorithm (HS) and flower pollination algorithm (FPA), which are metaheuristic algorithms. In two different active-controlled structures, which are formed according to the position of the PID, the structural responses under near-fault records defined in FEMA P-695 are examined to determine the appropriate feedback which was applied for displacement, velocity, acceleration, and total acceleration. The performance of the different feedback strategies on these two active-controlled structures is evaluated. As a result, the acceleration feedback is suitable for all combinations of the active control system with a PID controller. The HS algorithm outperforms the optimum results found according to the FPA.

In this study, results are presented for depth-specific hydraulic and water quality surveys, as well as meteorological observations, at key monitoring sites in the Paldang Reservoir, South Korea. These results were used to determine the water circulation characteristics that represent the main contributors to water quality changes. In the section before the confluence point of the two rivers, a different type of two-layer flow appeared for each. In the North Han River, backwater flowing backward in the upstream direction occurred in the surface layer, and was accompanied by two-layer flow, during the zero-discharge period of the Cheongpyeong Dam. On the other hand, in the South Han River, two-layer flow was observed in the upper layer in the downstream flow, and the thickness of upper and lower layers varied depending on the discharge rate of the Paldang Dam. Strong flow fluctuations were observed near the Paldang Dam (3 km upstream), and these were dependent on its instantaneous discharge rate. In particular, if the instantaneous discharge from the Paldang Dam remained below 400 m3/s for several days, the flow velocity in the lower layer decreased below 2 cm/s or no flow occurred. The comparison between flows during different periods associated with depth-specific water temperature variations showed that the average flow prevailed across all layers in the downstream direction at all monitoring sites during the vertical mixing period. In contrast, spatiotemporal variations in flow, such as two-layer flow and backwater, were observed during the stratification period in the South Han River and the North Han River. This led to increased residence times and vertical stability, thus creating favorable conditions for algal blooms.

The shear buckling of web plates and lateral–torsional buckling are among the major failure modes of plate girders. The importance of the lateral–torsional buckling capacity of plate girders was further evidenced when several plate girders of a bridge in Edmonton, Alberta, Canada failed in 2015, because insufficient bracing led to the lateral buckling of the plate girders. In this study, we focus on the optimisation of the cross-sections of plate girders using a well-known and extremely efficient meta-heuristic optimisation algorithm called the harmony search algorithm. The objective of this optimisation is to design the cross-sections of the plate girders with the minimum area that satisfies requirements, such as the lateral–torsional buckling load and ultimate shear stress. The base geometry, material properties, applied load and boundary conditions were taken from an experimental study and optimised. It was revealed that the same amount of load-carrying capacity demonstrated by this model can be achieved with a cross-sectional area 16% smaller than that of the original specimen. Furthermore, the slenderness of the web plate was found to have a decisive effect on the cost-efficiency of the plate girder design.

With the increase in environmental monitoring and assessing, we are gaining insight into the extent of microplastic pollution in our environment. The threat posed by microplastics to biota could come, e.g., from leached substances. As some plastic materials have been decaying in nature for extended periods already, the toxic effects of leaching compounds need to be investigated. It is furthermore essential to understand the adverse effects of new plastic and how these effects differ from the effects elicited by old plastic material. Therefore, in the present study, the effects of exposure to leachates from new and artificial aged polycarbonate as well as new and aged polycarbonate granules on various germination parameters of Lepidium sativum were studied. Germination, root, and shoot length, as well as the calculated germination rate index as a measure for germination speed, was negatively influenced in substrate-free and substrate containing exposures. From an ecological and agricultural point of view, this implies possible yield losses with less germinating seeds, slower plant germination speed, and smaller seedlings in general.

Microplastics (MPs) of varying sizes are widespread pollutants in our environment. The general opinion is that the smaller the size, the more dangerous the MPs are due to enhanced uptake possibilities. It would be of considerably ecological significance to understand the response of biota to microplastic contamination both physically and physiologically. Here, we report on an area choice experiment (avoidance test) using Enchytraeus crypticus, in which we mixed different amounts of high-density polyethylene microplastic particles into the soil. In all experimental scenarios, more Enchytraeids moved to the unspiked sections or chose a lower MP-concentration. Worms in contact with MP exhibited an enhanced oxidative stress status, measured as the induced activity of the antioxidative enzymes catalase and glutathione S-transferase. As plastic polymers per se are nontoxic, the exposure time employed was too short for chemicals to leach from the microplastic, and as the microplastic particles used in these experiments were too large (4 mm) to be consumed by the Enchytraeids, the likely cause for the avoidance and oxidative stress could be linked to altered soil properties.

Pyrroloquinone ring systems are important structural units present in many biologically active molecules including a number of marine alkaloids. For example, they are found in a series of marine metabolites, such as tsitsikammamines, zyzzyanones, wakayin, and terreusinone. Several of these alkaloids have exhibited antimicrobial, antimalarial, antifungal, antitumor, and photoprotecting activities. Synthesis of pyrroloquinone unit is the key step in the synthesis of many of these important organic molecules. Here, we present a ceric (IV) ammonium nitrate (CAN) mediated oxidative free radical cyclization reaction of 1,3-dicarbonyl compounds with aminoquinones as a facile methodology for making various substituted pyrroloquinones. 1,3-dicarbonyl compounds used in this study are ethyl acetoacetate, acetylacetone, benzoyl acetone, and N,N-dimethyl acetoacetamide. The aminoquinones used in this study are 2-(benzylamino)naphthalene-1,4-dione and 6-(benzylamino)-1-tosyl-1H-indole-4,7-dione. The yields of the synthesized pyrroloquinones ranged from 23–91%.

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures. The base isolators may lose their damping capacity over time due to environmental or dynamic effects. This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-term isolator life. In this study, an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time. With the developed model, the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity, and the decrease in the damping property was tried to be determined. For this purpose, a data set was created by collecting the behavior of structures with single degrees of freedom (SDOF), different stiffness, damping ratio and natural period isolated from the foundation under far fault earthquakes. The data is divided into 5 different damping classes varying between 10% and 50%. Machine learning model was trained in damping classes with the data on the structure's response to random seismic vibrations. As a result of the isolator behavior under randomly selected earthquakes, the recorded motion and structural acceleration of the structure against any seismic vibration were examined, and the decrease in the damping capacity was estimated on a class basis. The performance loss of the isolators, which are separated according to their damping properties, has been tried to be determined, and the reductions in the amounts to be taken into account have been determined by class. In the developed prediction model, using various supervised machine learning classification algorithms, the classification algorithm providing the highest precision for the model has been decided. When the results are examined, it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%, and it is an effective method in deciding whether there is a decrease in the damping capacity. [ABSTRACT FROM AUTHOR]