Artificial neural networks offer a viable route in assessing and understanding the presence and concentration of heavy metals that can cause dangerous complications in the wider context of water quality prediction for the sustainability of the ecosystem. In order to estimate the heavy metal concentrations in Iznik Lake, which is an important water source for the surrounding communities, characterization data were taken from five different water sources flowing into the lake between 2015 and 2021. These characterization results were evaluated with IBM SPSS Statistics 23 software, with the addition of the lake water quality system. For this purpose, seven distinct physicochemical parameters were measured and monitored in Karasu, Kırandere, Olukdere and Sölöz water sources flowing into the lake, to serve as input data. Concentration levels of 15 distinct heavy metals in Karsak Stream originating from the lake were as the output. Specifically, Sn for Karasu (0.999), Sb for Kırandere (1.000), Cr for Olukdere (1.000) and Pb and Se for Sölöz (0.995) indicate parameter estimation R2 coefficients close to 1.000. Sn stands out as the common heavy metal parameter with best estimation prospects. Given the importance of the independent variable in estimating heavy metal pollution, conductivity, COD, COD and temperature stood out as the most effective parameters for Karasu, Olukdere, Kırandere and Sölöz, respectively. The ANN model emerges as a good prediction tool that can be used effectively in determining the heavy metal pollution in the lake as part of the efforts to protect the water budget of Lake Iznik and to eliminate the existing pollution.

Lateral flow assays have been widely used for detecting coronavirus disease 2019 (COVID-19). A lateral flow assay consists of a Nitrocellulose (NC) membrane, which must have a specific lateral flow rate for the proteins to react. The wicking rate is conventionally used as a method to assess the lateral flow in membranes. We used multiple regression and artificial neural networks (ANN) to predict the wicking rate of NC membranes based on membrane recipe data. The developed ANN predicted the wicking rate with a mean square error of 0.059, whereas the multiple regression had a square error of 0.503. This research also highlighted the significant impact of the water content on the wicking rate through images obtained from scanning electron microscopy. The findings of this research can cut down the research and development costs of novel NC membranes with a specific wicking rate significantly, as the algorithm can predict the wicking rate based on the membrane recipe.

Assembly line efficiency is one of the most important parameters that determine the overall efficiency of a manufacturing company. The production of a product under optimum conditions is ensured by a balanced assembly. With a balanced assembly line, machinery, material and labour costs are reduced. Within the scope of this research, real data about the daily production capacity and assembly line efficiency of a company producing Emergency Luminaire were taken, the same assembly line was balanced with 4 different Heuristic ALB methods and the results were compared. According to the results obtained, a high line efficiency of 93.955% was achieved using the Hoffman, Comsoal and Moodie&Young (M&Y) methods, and 84.414% was achieved with the Ranked Positional Weight (RPW) method. As a result of this, it was observed that the daily production capacity increased from 250 units to 375 units. As a result of the study, it was revealed that the efficiency of the existing assembly line and accordingly the daily production capacity increased. In addition, the study results of this assembly line were taught to an artificial neural network model for training purposes, and the work station results of the operations of a different assembly line were obtained with 99.940 accuracy. In this context, it has been revealed that the artificial neural networks method can be used in addition to the use of the heuristic method in the solution of ALB problems.

How might artificial neural networks (ANNs) inform cognitive science? Often cognitive scientists use ANNs but do not examine their internal structures. In this paper, we use ANNs to explore how cognition might represent musical properties. We train ANNs to classify musical chords, and we interpret network structure to determine what representations ANNs discover and use. We find connection weights between input units and hidden units can be described using Fourier phase spaces, a representation studied in musical set theory. We find the total signal coming through these weighted connection weights is a measure of the similarity between two Fourier structures: the structure of the hidden unit\'s weights and the structure of the stimulus. This is surprising because neither of these Fourier structures is computed by the hidden unit. We then show how output units use such similarity measures to classify chords. However, we also find different types of units-units that use different activation functions-use this similarity measure very differently. This result, combined with other findings, indicates that while our networks are related to the Fourier analysis of musical sets, they do not perform Fourier analyses of the kind usually described in musical set theory. Our results show Fourier representations of music are not limited to musical set theory. Our results also suggest how cognitive psychologists might explore Fourier representations in musical cognition. Critically, such theoretical and empirical implications require researchers to understand how network structure converts stimuli into responses.

In spite of extensive applications of flow modulated comprehensive two-dimensional gas chromatography (FM-GG × GC) in different research areas, its application in the field of chiral separation is very limited. From a practical point of view, the establishment of experimental parameters for enantiomer separations is possibly more demanding in this case. Since the carrier gas flows in both dimensions, it affects not only the separation parameters, but also the fill/flush volumes of the modulator and its working efficiency. In this context, a multivariate design of experiment was applied to find the optimum experimental parameters of a reversed fill/flush (RFF) modulator for enantiomer separation of organic compounds present in botrytized wine samples. The results were described both with response surface methodology and artificial neural networks (ANN). The enantiomeric composition of chiral compounds present in the botrytized wines was used to identify their geographical origin, by principal component analysis (PCA). In addition, the developed one-class partial least squares (OC-PLS) model enabled recognition of the wine samples from the Tokaj wine region with 93% effectiveness in the presence of other samples.

This study aims to adapt the Soil and Watershed Assessment Tool (SWAT), a ubiquitously used watershed model, for ground-water dominated surface waterbodies by accounting for recharge from the aquifers. Using measured flow to a headwater slope wetland in Alabama\'s coastal plain region as a case study, we present challenges and relatively simple approaches in using the SWAT model to predict flows from the draining watershed and relatively simple approaches to model groundwater upwelling. SWAT-simulated flow at the study watershed was limited by precipitation, and consequently, simulated flows were several times smaller in magnitude than observed flows. Thus, our first approach involved a separate stormflow and baseflow calibration which included the use of a regression relationship between observed and simulated baseflow (E NASH = 0.67). Our next approach involved adapting SWAT to simulate upwelling groundwater discharge instead of deep aquifer losses by constraining the range of deep losses, β deep parameter, to negative values (E NASH = 0.75). Finally, we also investigated the use of artificial neural networks (ANN) in conjunction with SWAT to further improve calibration performance. This approach used SWAT-calibrated flow, evapotranspiration, and precipitation as inputs to ANN (E NASH = 0.88). The methods investigated in this study can be used to navigate similar flow calibration challenges in other groundwater dominant watersheds which can be very useful tool for managers and modelers alike.

The complex and non-linear nature of material properties evolution during 3D printing continues to make experimental optimization of Fused Deposition Modeling (FDM) costly, thus entailing the development of mathematical predictive models. This paper proposes a two-stage methodology based on coupling limited data experiments with black-box AI modeling and then performing heuristic optimization, to enhance the viscoelastic properties of FDM processed acrylonitrile butadiene styrene (ABS). The effect of selected process parameters (including nozzle temperature, layer height, raster orientation and deposition speed) as well as their combinative effects are also studied. Specifically, in the first step, a Taguchi orthogonal array was employed to design the Dynamic Mechanical Analysis (DMA) experiments with a minimal number of runs, while considering different working conditions (temperatures) of the final prints. The significance of process parameters was measured using Lenth\'s statistical method. Combinative effects of FDM parameters were noted to be highly nonlinear and complex. Next, artificial neural networks were trained to predict both the storage and loss moduli of the 3D printed samples, and consequently, the process parameters were optimized via Particle Swarm Optimization (PSO). The optimized process of the prints showed overall a closer behavior to that of the parent (unprocessed) ABS, when compared to the unoptimized set-up.

The geophysical investigation of dambo groundwater reserves using electrical resistivity methods was conducted in Linthipe 4B sub basin in Central region of Malawi. With the increasing over-utilization of shallow wells in dambos for smallholder irrigation, this study was carried out to investigate whether dambo groundwater reserves could serve as sustainable irrigation water sources and to examine the aquifer characteristics in Linthipe sub basin. Vertical Electrical Sounding (VES) points were established in the basin using the Schlumberger configuration array. Data was analysed using IPI2win and Surfer software applications. Contrary to other commercial software applications which are costly for government departments when analysing geophysical data, partial curve matching and one dimensional (1-D) computer iteration techniques were used to interpret the VES curves and the pseudo-cross section resistivity profiles due to their simplicity and cost-effectiveness. The study has revealed that the aquifer properties in the basin are exceptionally variable in terms of state of weathering, depth, thickness, lithology, aquifer recharge configuration, geologic material and hydraulic gradients. These variations showed that the shallow wells in the basin have significant fractures suggesting high water potential for climate smart irrigation usage in the study area. The potential groundwater zones for climate smart irrigated farming were detected at VES 1, 2, 3, 4, 5 and 6 noted by low aquifer resistivity with high values of aquifer thickness. However, VES points, 7, 8 and 9 were likely to be zones of low water bearing potential because they had high values of aquifer resistivity with low aquifer thickness. The findings also validated the effectiveness, timeliness, and efficiency of using vertical electrical resistivity technique in exploring groundwater for irrigation. The results from this study have highlighted that feasible shallow well depth for a sustainable irrigation system is a function of geologic resistance and aquifer thickness when the geologic material has low resistance and broader thickness. This study noted that knowledge of aquifer recharge rates in dambos is required in order to effectively control abstraction rates for sustainable irrigation in basins. There is a need to promote the usage of geophysical studies, reforestation, river basin management trainings and aquifer recharge technologies in exploring sites for shallow well development for irrigation. The study further recommends the usage of geographical information systems (GIS) and Artificial Intelligence (AI) in improving the results of groundwater monitoring studies in Malawi.

Existing methods for spatial quantification of grassland utilization intensity cannot meet the demand for accurate detection of the spatial distribution of grassland utilization intensity in the Qinghai-Tibetan Plateau with high spatial resolution. In this paper, a method based on remote-sensing observations and simulations of grassland growth dynamics is proposed. The grassland enhanced vegetation index (EVI) time-series curve during the growing season characterizes the growth of grassland in the corresponding pixel; The deviation between the observed and potential EVI curves indicates the disturbance on grassland growth imposed by human activities, and it can characterize the grassland utilization intensity during the growing season. Based on the main idea described above, absolute and relative disturbances are calculated and used as quantitative indicators of grassland utilization intensity defined from different perspectives. Livestock amount at the pixel scale is obtained by pixel-by-pixel calculations based on the function relationship at the township scale between absolute disturbance and livestock density, which is specific quantitative indicator that considers the mode of grassland utilization. In simulating the potential EVI of grassland, the lag and accumulation effects of meteorological factors are investigated at the daily scale using a multi-objective genetic algorithm. Further, the nonlinear functions between multiple environmental factors (e.g., grassland type, topography, soil, meteorology) and the grassland EVI are established using an error back-propagation feedforward artificial neural network (ANN-BP) with parameter optimization. Finally, the potential EVIs of all grassland pixels are simulated on the basis of this model. The method is applied to the Selinco basin on the Qinghai-Tibetan Plateau and validated by examining the spatial consistency of the results with township-scale livestock density and grazing pressure. The final results indicate that the proposed method can accurately detect the spatial distribution of grassland utilization intensity which is appliable in the similar regions.

This study aims to produce accurate predictions of the NO2 concentrations at a specific station of a monitoring network located in the Bay of Algeciras (Spain). Artificial neural networks (ANNs) and sequence-to-sequence long short-term memory networks (LSTMs) were used to create the forecasting models. Additionally, a new prediction method was proposed combining LSTMs using a rolling window scheme with a cross-validation procedure for time series (LSTM-CVT). Two different strategies were followed regarding the input variables: using NO2 from the station or employing NO2 and other pollutants data from any station of the network plus meteorological variables. The ANN and LSTM-CVT exogenous models used lagged datasets of different window sizes. Several feature ranking methods were used to select the top lagged variables and include them in the final exogenous datasets. Prediction horizons of t + 1, t + 4 and t + 8 were employed. The exogenous variables inclusion enhanced the model\'s performance, especially for t + 4 (ρ ≈ 0.68 to ρ ≈ 0.74) and t + 8 (ρ ≈ 0.59 to ρ ≈ 0.66). The proposed LSTM-CVT method delivered promising results as the best performing models per prediction horizon employed this new methodology. Additionally, per each parameter combination, it obtained lower error values than ANNs in 85% of the cases.