Input Combinations Research Articles

Application of a hybrid fuzzy-based algorithm to investigate the environmental impact of sewer overflow

PurposeThis study underscores the critical importance of well-functioning sewer systems in achieving smart and sustainable urban drainage within cities. It specifically targets the pressing issue of sewer overflows (SO), widely recognized for their detrimental impact on the environment and public health. The primary purpose of this research is to bridge significant research gaps by investigating the root causes of SO incidents and comprehending their broader ecological consequences.Design/methodology/approachTo fill research gaps, the study introduces the Multi-Phase Causal Inference Fuzzy-Based Framework (MCIF). MCIF integrates the fuzzy Delphi technique, fuzzy DEMATEL method, fuzzy TOPSIS technique and expert interviews. Drawing on expertise from developed countries, MCIF systematically identifies and prioritizes SO causes, explores causal interrelationships, prioritizes environmental impacts and compiles mitigation strategies.FindingsThe study's findings are multifaceted and substantially contribute to addressing SO challenges. Utilizing the MCIF, the research effectively identifies and prioritizes causal factors behind SO incidents, highlighting their relative significance. Additionally, it unravels intricate causal relationships among key factors such as blockages, flow velocity, infiltration and inflow, under-designed pipe diameter and pipe deformation, holes or collapse, providing a profound insight into the intricate web of influences leading to SO.Originality/valueThis study introduces originality by presenting the innovative MCIF tailored for SO mitigation. The combination of fuzzy techniques, expert input and holistic analysis enriches the existing knowledge. These findings pave the way for informed decision-making and proactive measures to achieve sustainable urban drainage systems.

Methods for projecting health workforce planning in Ireland: a collaborative approach

Abstract Background Health workforce planning (HWFP) is the process of analysing, forecasting, and planning workforce supply and demand, assessing gaps, and determining target workforce to ensure that health systems are prepared for future demand. HWFP elicits recommendations that are ideally utilised by the health system to support medical workforce over a defined time. Methods The objective of this project is to inform intake for medical and surgical specialties in Ireland to reach the necessary consultant workforce by 2038. A literature search was conducted to help inform the methodology for this project. The most common approach was a needs-based model, and additional reviews discussed the integration of medical engagement into the modelling approach. Results This project will employ a collaborative approach, combining mathematical modelling and a combination of quantitative and qualitative inputs, including the following phases: 1. The research phase will estimate baseline supply and demand data across medical and surgical specialties in Ireland using statistical models; 2. The presentation phase will include input from clinical leads to allow for more accurate assumptions in the baseline of each model; 3. Review of projections will include feedback from the clinical leads to help clarify and improve the initial model; 4. Presentation of the revised projection will include further collaboration with clinical leads to finalise the model. Conclusions The robust methodology for this HWFP project will ideally inform medical and surgical specialities in Ireland through 2038. Specific and extensive predictions of health workforce supply and demand will enhance strategic preparation, which is a key priority for the Health Service Executive in Ireland. Key messages • A collaborative approach to HWFP is essential for health service preparation and improvement. • The project will be finalised by the end of 2024, and the methodology for conducting similar HWFP projects can be adapted by other EU states.

Impact of non-contrast-enhanced imaging input sequences on the generation of virtual contrast-enhanced breast MRI scans using neural network.

To investigate how different combinations of T1-weighted (T1w), T2-weighted (T2w), and diffusion-weighted imaging (DWI) impact the performance of virtual contrast-enhanced (vCE) breast MRI. The IRB-approved, retrospective study included 1064 multiparametric breast MRI scans (age: 52 ± 12 years) obtained from 2017 to 2020 (single site, two 3-T MRI). Eleven independent neural networks were trained to derive vCE images from varying input combinations of T1w, T2w, and multi-b-value DWI sequences (b-value = 50-1500 s/mm2). Three readers evaluated the vCE images with regard to qualitative scores of diagnostic image quality, image sharpness, satisfaction with contrast/signal-to-noise ratio, and lesion/non-mass enhancement conspicuity. Quantitative metrics (SSIM, PSNR, NRMSE, and median symmetrical accuracy) were analyzed and statistically compared between the input combinations for the full breast volume and both enhancing and non-enhancing target findings. The independent test set consisted of 187 cases. The quantitative metrics significantly improved in target findings when multi-b-value DWI sequences were included during vCE training (p < 0.05). Non-significant effects (p > 0.05) were observed for the quantitative metrics on the full breast volume when comparing input combinations including T1w. Using T1w and DWI acquisitions during vCE training is necessary to achieve high satisfaction with contrast/SNR and good conspicuity of the enhancing findings. The input combination of T1w, T2w, and DWI sequences with three b-values showed the best qualitative performance. vCE breast MRI performance is significantly influenced by input sequences. Quantitative metrics and visual quality of vCE images significantly benefit when multi b-value DWI is added to morphologic T1w-/T2w sequences as input for model training. Question How do different MRI sequences impact the performance of virtual contrast-enhanced (vCE) breast MRI? Findings The input combination of T1-weighted, T2-weighted, and diffusion-weighted imaging sequences with three b-values showed the best qualitative performance. Clinical relevance While in the future neural networks providing virtual contrast-enhanced images might further improve accessibility to breast MRI, the significant influence of input data needs to be considered during translational research.

Estimating Ross 308 Broiler Chicken Weight Through Integration of Random Forest Model and Metaheuristic Algorithms

For accurate estimation of broiler chicken weight (CW), a novel hybrid method was developed in this study where several benchmark methods, including Particle Swarm Optimization (PSO), Genetic Algorithm (GA), Ant Colony Optimization (ACO), Differential Evolution (DE), and Gravity Search Algorithm (GSA), were employed to adjust the Random Forest (RF) hyperparameters. The performance of the RF models was compared with that of classic linear regression (LR). With this aim, data (temperature, relative humidity, feed consumption, and CW) were collected from six poultry farms in Samsun, Türkiye, covering both the summer and winter seasons between 2014 and 2021. The results demonstrated that PSO and ACO significantly enhanced the performance of the standard RF model in all periods. Specifically, the RF-PSO model achieved a significant improvement by reducing the Mean Absolute Error (MAE) by 5.081% to 60.707%, highlighting its superior prediction accuracy and efficiency. The RF-ACO model also showed remarkable MAE reductions, ranging from 3.066% to 43.399%, depending on the input combinations used. In addition, the computational time required to train the RF models with PSO and ACO was considerably low, indicating their computational efficiency. These improvements emphasize the effectiveness of the PSO and ACO algorithms in achieving more accurate predictions of CW.

Optimizing Nitrogen Input Increased Yield and Efficiency in Maize-Soybean Strip Intercropping System

Optimizing nitrogen (N) fertilizer combination is a crucial measure to maximize yield and production efficiency in a maize-soybean strip intercropping system (MSSI). In this field experiment, six maize/soybean N input combinations (0 kg ha−1, F0; 255/30 kg ha−1, F1; 255/45 kg ha−1, F2; 255/60 kg ha−1, F3; 210/45 kg ha−1, F4; 300/45 kg ha−1, F5) were set in 2022 and 2023. The results indicated that optimizing N combination (maize/soybean, 255/45 kg ha−1) could synergistically increase yield and economic benefits. Path analysis results showed that the grain numbers in maize and soybean emerged as the most critical yield-affecting factors. Compared with F0, F5 showed the highest grain yield during the 2-year experiment, and the net return increased by 86.1% (F1), 133.3% (F2), 87.4% (F3), 104.7% (F4), and 128.3% (F5), respectively. Optimizing N input under F2 and F5 notably enhanced the leaf area index (LAI) of maize at the milk stage (R3) and soybean at the full pod stage (R4). Additionally, optimization of N distribution in maize stems at the tassel stage (VT) and soybean leaves at the initial flowering stage (R1) facilitated increased dry matter and N accumulation at the maturity stage, resulting in final land equivalent ratios (LER) of 1.44 and 1.55, respectively. Our results provide a more valuable field N combination for summer maize planting areas (sandy soil areas) in Huang-Huai-Hai and southern China, thus promoting the wider application of MSSI.

Enhancing the Prediction of Influent Total Nitrogen in Wastewater Treatment Plant Using Adaptive Neuro-Fuzzy Inference System–Gradient-Based Optimization Algorithm

For the accurate estimation of daily influent total nitrogen of sewage plants, a novel hybrid approach is proposed in this study, where a gradient-based optimization (GBO) algorithm is employed to adjust the hyper-parameters of an adaptive neuro-fuzzy system (ANFIS). Several benchmark methods for optimizing ANFIS parameters are compared, which include particle swarm optimization (PSO), gray wolf optimization (GWO), and gradient-based optimization (GBO). The prediction accuracy of the ANFIS-GBO model is evaluated against other models using four statistical measures: root-mean-squared error (RMSE), mean absolute error (MAE), and Nash–Sutcliffe efficiency (NSE), and coefficient of determination (R2). Test results show that the suggested ANFIS-GBO outperforms the standalone ANFIS, hybrid ANFIS-PSO and ANFIS-GWO methods in daily influent total nitrogen prediction from the sewage treatment plant. The ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO models are evaluated using seven distinct input combinations to predict daily TNinf. The results from both the testing and training periods demonstrate that these models, namely ANFIS, ANFIS-PSO, ANFIS-GWO, and ANFIS-GBO, exhibit the highest level of accuracy for the seventh input combination (Qw, pH, SS, TP, NH3-N, COD, and BOD5). ANFS-GBO-7 reduced the RMSE in the prediction of ANFIS-7, ANFIS-PSO-7, and ANFIS-GWO-7 by 21.77, 10.73, and 6.81%, respectively, in the test stage. Results from testing and training further demonstrate that increasing the number of parameters (NH3-N, COD, and BOD) as input improves the models’ ability to make predictions. The outcomes show that the ANFIS-GBO model can potentially be suggested for the daily prediction of influent total nitrogen (TNinf) in full-scale wastewater treatment plants.

Students’ Interactions and Communication Skills Towards Their Maximum Learning

This paper explores the critical role of interaction and communication skills in educational Settings, especially in the context of higher education in China, and analyzes the impact of these skills on maximizing student learning outcomes. The paper emphasizes the importance of effective interaction and communication as core elements in the process of language learning, because language acquisition and ability development depend on the effective combination of input and output. The paper proposes that a comprehensive educational reform is necessary to address the identified communication barriers of students, aiming at promoting the improvement of students' interaction and communication skills. Such reforms are essential to achieve the goal of high-quality undergraduate education and to develop innovative and communicative skills.

Model-driven development for functional correctness of avionics systems: a verification framework for SysML specifications

AbstractCurrently, the most widespread software quality assurance methods in the avionics domain are semi-automated reviews and testing. However, their effort grows disproportionately to the size of the system under development. Also, these methods cannot achieve exhaustive coverage due to the complexity of today’s avionics systems and their potentially infinite set of combinations of possible inputs and system states. Furthermore, the later software issues are detected in the development process, the more expensive it is to fix them. To overcome these issues, a model-driven verification approach for modeling and analyzing avionics systems in early phases of the development is presented. To this end, semantics is given to SysML v2 models by a mapping to a theorem prover encoding. The development of a dedicated SysML v2 profile supporting event-driven data flow specifications, the encoding of corresponding structures in the theorem prover Isabelle, and a generator creating theorems from SysML v2 models are presented. The approach is evaluated by formally proving a representative liveness property of a hierarchical system model from the avionics domain. Since liveness properties can be negated only by infinite data sequences and thus cannot be covered exhaustively by testing, this case study demonstrates the added value for meeting typical safety requirements in the avionics domain. The results can be transferred from avionics to other domains, as well.

Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75–90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.

Supercomputer framework for reverse engineering firing patterns of neuron populations to identify their synaptic inputs.

In this study, we develop new reverse engineering (RE) techniques to identify the organization of the synaptic inputs generating firing patterns of populations of neurons. We tested these techniques in silico to allow rigorous evaluation of their effectiveness, using remarkably extensive parameter searches enabled by massively-parallel computation on supercomputers. We chose spinal motoneurons as our target neural system, since motoneurons process all motor commands and have well-established input-output properties. One set of simulated motoneurons was driven by 300,000+ simulated combinations of excitatory, inhibitory, and neuromodulatory inputs. Our goal was to determine if these firing patterns had sufficient information to allow RE identification of the input combinations. Like other neural systems, the motoneuron input-output system is likely non-unique. This non-uniqueness could potentially limit this RE approach, as many input combinations can produce similar outputs. However, our simulations revealed that firing patterns contained sufficient information to sharply restrict the solution space. Thus, our RE approach successfully generated estimates of the actual simulated patterns of excitation, inhibition, and neuromodulation, with variances accounted for ranging from 75-90%. It was striking that nonlinearities induced in firing patterns by the neuromodulation inputs did not impede RE, but instead generated distinctive features in firing patterns that aided RE. These simulations demonstrate the potential of this form of RE analysis. It is likely that the ever-increasing capacity of supercomputers will allow increasingly accurate RE of neuron inputs from their firing patterns from many neural systems.

An efficient data fusion model based on Bayesian model averaging for robust water quality prediction using deep learning strategies

Accurate monitoring of dissolved oxygen (DO) levels is critical for stakeholders to effectively safeguard water resources and aquatic ecosystem health. This research presents an innovative data fusion framework based on Bayesian model averaging (BMA) by the combination of several neuroscience models (deep learning methodologies) including multilayer perceptron neural network (MLPNN), recurrent neural network (RNN), convolutional neural network (CNN), gated recurrent unit (GRU), long short-term memory (LSTM), and seasonal autoregressive integrated moving average with exogenous variables (SARIMAX). BMA has the capacity to greatly enhance the results attained by standalone approaches. In this study, two feature selection methods such as mutual information (MI) and recursive feature elimination (RFE) applied to select effective predictors and investigate the importance of each input parameters. The techniques were evaluated based on four different metrics and, finally, to demonstrate the usefulness and effectiveness of the newly implemented strategy, it was applied proficiently at the USGS stations, 01427510 and 02336152 within the USA. The findings from analyzing data based on two stations confirmed that the suggested approach (BMA) outperformed other methods such as MLPNN, RNN, CNN, LSTM, and SARIMAX when it came to predicting the levels of DO on a daily basis. In terms of RMSE, MAE and R2, BMA yielded 0.272 mg/L, 0.216 mg/L, and 0.975 using MI technique and 0.320 mg/L, 0.261 mg/L, and 0.965 using RFE method at 01427510 USGS station, respectively. Similarly, based on RMSE, MAE and R2, BMA produced DO prediction by RMSE = 0.352 mg/L, 0.264 mg/L, and 0.968 by MI approach and RMSE = 0.378 mg/L, 0.282 mg/L, and 0.963 via RFE process at 02336152 USGS station, respectively. After analyzing different combinations of input that obtained from feature selection paradigms, it was observed that the variables with the greatest impact on daily dissolved oxygen levels are the dissolved oxygen levels in the previous time period and the water temperature. On the other hand, the pH and turbidity have minimal influence on the daily DO. Finally, the results of this study confirmed that the BMA tool can be efficiently applied to predict DO concentration based on deep learning model outputs.

Research on the potential of integrated vehicle control with closed-loop phase portrait analysis

The phase portrait is an important tool for analyzing vehicle stable region widely utilized in vehicle dynamic control systems. At handling limits, traditional open-loop vehicle stable region constraints will restrict the vehicle control ability, while the closed-loop actuator interventions can help states outside the open-loop stable region converge back to stable origin, which is more suitable for constraining vehicle states under extreme working conditions. This article systematically analyzes the potential of integrated vehicle control with closed-loop phase portrait. Firstly, a vehicle dynamic model considering actuator dynamics and tire transient characteristics is established. Then, a numerical optimal control method for calculating closed-loop controllability region is introduced. The controllability region under different friction coefficients, vehicle speeds, convergence time, vehicle steering response characteristics, actuator time constants, and tire transient characteristics are discussed and analyzed. In terms of different control inputs combinations, this article displays the controllability regions under different front steering angles, rear steering angles, direct yaw moments, and their synergistic performances. Control input laws derived from numerical optimization are summarized in two specific directions. Finally, a comparative analysis of open-loop stable regions and closed-loop controllability regions confirms the superiority of the proposed method. Potential applications of the systematic analysis are also discussed.

Design of Self–Checking Digital Devices with Boolean Signals Correction Using Weight-Based Bose–Lin Codes

This paper proposes a method for designing self-checking digital devices with Boolean signals correction and weight-based Bose–Lin codes. Unlike previous studies, the method involves Boolean signals correction (BSC) in the concurrent error-detection (CED) circuit for those functions describing the outputs of source devices that participate in the formation of data symbols of weight-based Bose–Lin codes. In such codes, as in the absolute majority of uniform separable codes, several data vectors correspond to the same check vector; therefore, it is possible to choose a method for determining BSC functions. We describe an algorithm for determining their values for each input combination, considering the testability of the checker and transformation elements in the CED circuit. The method involves the so-called “base” structure for monitoring multi-output devices by output groups. With this method, the designer of a self-checking device has high variability in choosing the design method and can regulate important indicators (structure redundancy, controllability, energy consumption, and others). Experiments with combinational benchmarks from MCNC Benchmarks were carried out. According to the experimental data, the method has high efficiency in terms of structure redundancy compared to the duplication method widespread in practice. The method can be effective when designing real devices with fault detection used in all areas of technology, including critical application systems in industry and transport.

Medical geographical zoning in part of Uzbekistan – A regional synthesis

This study delves into the nuanced realm of medical geography, focusing on the intricate interplay between geographical factors and public health outcomes in the Samarkand and Navoi regions of Uzbekistan. Over the decades, the field of medical geography has evolved, embracing a holistic perspective that integrates ecological, socioeconomic, and accessibility elements to elucidate the complex dynamics of disease distribution and healthcare provision. Utilizing a comprehensive methodology, this research employs a combination of GIS tools, statistical analysis, and expert input to delineate medical geographical zones within the study area. By synthesizing data from diverse sources including administrative records, environmental assessments, and demographic surveys, the study constructs a detailed picture of the region's health landscape. The analysis revealed five primary medical geographical zones and a sub-zone, each characterized by distinct natural features, economic activities, and health challenges. From the bustling urban centers of Samarkand and Navoi to the remote desert expanses of Uzguduk - Tomdi North, each zone presents unique opportunities and obstacles for healthcare delivery and environmental management. Key findings highlight the disproportionate distribution of healthcare resources, with densely populated areas in the southern part of the study area benefitting from greater accessibility to medical facilities compared to remote rural regions. Moreover, environmental factors such as air pollution, water contamination, and climatic variability emerge as significant determinants of public health, exacerbating respiratory ailments, cardiovascular diseases, and other health conditions. The study underscores the importance of spatial analysis and interdisciplinary collaboration in addressing complex health issues within diverse geographical landscapes. By mapping medical geographical zones and understanding their specific health concerns, policymakers and healthcare professionals can develop targeted interventions to improve healthcare access, mitigate environmental hazards, and promote community well-being. In conclusion, this research contributes valuable insights to the field of medical geography, shedding light on the multifaceted interactions between geography, environment, and health in the Samarkand and Navoi regions of Uzbekistan. Moving forward, further research and collaborative efforts are needed to address the underlying determinants of health disparities and foster sustainable development in the region.

Economic study of Binadhan-22 production in some selected areas of Bangladesh

This study was conducted in four districts of Bangladesh, namely Mymensingh, Bogura, Naogaon and Rangpur to estimate the profitability and factors affecting gross return of the variety. This study was based on primary data which were collected from 200 Binadhan-22 growing farmers. In the sampled areas data were collected through pre-designed interview schedule from January-February, 2023. Tabular, descriptive statistics and Cobb-Douglas production function model were used to fulfill the objectives. It was found that Binadhan-22 production is profitable. The average net return per hectare was Tk. 28876. The net return was highest in Rangpur (Tk. 32480/ha) followed by Naogaon (Tk. 28554/ha), Mymensingh (Tk. 27331/ha) and Bogura (Tk. 27139/ha), respectively. Benefit cost ratio was estimated at 1.39 and 2.04 on full cost and variable cost basis. Cobb-Douglas production function was chosen to determine the factor affecting gross return of Binadhan-22 production. All of the factors namely, human labour cost, power tiller cost, seed cost, fertilizer cost, irrigation cost, insecticides cost were statistically significant and positive. The regression coefficients for farming experience and agricultural training under all areas had negative but significant relationship at 5% and 10% level, respectively. The regression coefficient of age was positive and significant at 10% level. Under all areas, the regression coefficients of education and farm size were positive but not significant. Farmers faced some constraints in cultivating of the variety. The first ranked constraint was lack of availability of seeds (93%). Other constraints were lack of training (61%), lack of capital (38%) and lack of technical know-how (16%). In addition to helping farmers use a better combination of inputs that will create higher productivity and return, effective extension services may also assist farmers in cultivating rice in a way that will contribute to food security and self-sufficiency. Bangladesh J. Nuclear Agric, 38(1): 81-91, 2024

Evaluation of GPT Large Language Model Performance on RSNA 2023 Case of the Day Questions.

Background GPT-4V (GPT-4 with vision, ChatGPT; OpenAI) has shown impressive performance in several medical assessments. However, few studies have assessed its performance in interpreting radiologic images. Purpose To assess and compare the accuracy of GPT-4V in assessing radiologic cases with both images and textual context to that of radiologists and residents, to assess if GPT-4V assistance improves human accuracy, and to assess and compare the accuracy of GPT-4V with that of image-only or text-only inputs. Materials and Methods Seventy-two Case of the Day questions at the RSNA 2023 Annual Meeting were curated in this observer study. Answers from GPT-4V were obtained between November 26 and December 10, 2023, with the following inputs for each question: image only, text only, and both text and images. Five radiologists and three residents also answered the questions in an "open book" setting. For the artificial intelligence (AI)-assisted portion, the radiologists and residents were provided with the outputs of GPT-4V. The accuracy of radiologists and residents, both with and without AI assistance, was analyzed using a mixed-effects linear model. The accuracies of GPT-4V with different input combinations were compared by using the McNemar test. P < .05 was considered to indicate a significant difference. Results The accuracy of GPT-4V was 43% (31 of 72; 95% CI: 32, 55). Radiologists and residents did not significantly outperform GPT-4V in either imaging-dependent (59% and 56% vs 39%; P = .31 and .52, respectively) or imaging-independent (76% and 63% vs 70%; both P = .99) cases. With access to GPT-4V responses, there was no evidence of improvement in the average accuracy of the readers. The accuracy obtained by GPT-4V with text-only and image-only inputs was 50% (35 of 70; 95% CI: 39, 61) and 38% (26 of 69; 95% CI: 27, 49), respectively. Conclusion The radiologists and residents did not significantly outperform GPT-4V. Assistance from GPT-4V did not help human raters. GPT-4V relied on the textual context for its outputs. © RSNA, 2024 Supplemental material is available for this article. See also the editorial by Katz in this issue.

Exploring possibilities of broccoli (Brassica oleracea L. var. italica) production under organic management

Broccoli (Brassica oleracea L. var. italica) was introduced as part of the Indian government's efforts to diversify and modernize agriculture in the region. Its introduction has helped J&K farmers to increase their income through higher market prices and to gain access to new markets and trade opportunities. It being a nutrient-demanding crop, farmers use high doses of chemical fertilizers, pesticides and herbicides and if consumed as salad or half-boiled in many culinary dishes, these chemicals are likely to impact the health of consumers and affect the environment. Therefore, alternative usage to these chemicals using combinations of organic inputs was aimed at producing a healthy crop of broccoli. It was concluded that the application of FYM@ 25 t/ha as a basal dose in the presence of seedling dip treatment of biofertilizers (Azotobacter @ 0.5 kg/ha + Phosphate-Solubilizing Bacteria @l kg/ha + Potassium-Mobilizing Bacteria @ 750 ml/ha) along with the scheduled application of Neem oil +Baeuveria bassiana at 1, 3, 7 and 15 days of interval after disease symptoms was found superior in obtaining higher curd yield (109.57 q/ha) and seed yield (604.96 kg/ha).

Deep Learning Classification of Ischemic Stroke Territory on Diffusion-Weighted MRI: Added Value of Augmenting the Input with Image Transformations.

Our primary aim with this study was to build a patient-level classifier for stroke territory in DWI using AI to facilitate fast triage of stroke to a dedicated stroke center. A retrospective collection of DWI images of 271 and 122 consecutive acute ischemic stroke patients from two centers was carried out. Pretrained MobileNetV2 and EfficientNetB0 architectures were used to classify territorial subtypes as middle cerebral artery, posterior circulation, or watershed infarcts along with normal slices. Various input combinations using edge maps, thresholding, and hard attention versions were explored. The effect of augmenting the three-channel inputs of pre-trained models on classification performance was analyzed. ROC analyses and confusion matrix-derived performance metrics of the models were reported. Of the 271 patients included in this study, 151 (55.7%) were male and 120 (44.3%) were female. One hundred twenty-nine patients had MCA (47.6%), 65 patients had posterior circulation (24%), and 77 patients had watershed (28.0%) infarcts for center 1. Of the 122 patients from center 2, 78 (64%) were male and 44 (34%) were female. Fifty-two patients (43%) had MCA, 51 patients had posterior circulation (42%), and 19 (15%) patients had watershed infarcts. The Mobile-Crop model had the best performance with 0.95 accuracy and a 0.91 mean f1 score for slice-wise classification and 0.88 accuracy on external test sets, along with a 0.92 mean AUC. In conclusion, modified pre-trained models may be augmented with the transformation of images to provide a more accurate classification of affected territory by stroke in DWI.

Heat pump and thermal energy storage: Influences of photovoltaic, the control strategy, and price assumptions on the optimal design

Combining heat pump, thermal energy storage, and photovoltaic is a common option to increase renewable energy usage in building energy systems. While research finds that optimal system design depends on the control, design guidelines neglect an influence of (1) photovoltaic, (2) the supervisory control, and (3) prices assumptions on the design of heat pump and thermal energy storage. We analyze how these influences may impact the optimal design. To analyze possible impacts, a pre-screening approach is used to identify relevant model input combinations with a potentially high influence on the optimal design. Applying a simulation-based design optimization with a detailed building energy system model, we optimize three cases: no photovoltaic, no supervisory control, and a state-of-the-art supervisory control. Results indicate that cost optimal design does not change with photovoltaic or the supervisory control, but heat pump size increases with higher electricity prices. For the storage, maximizing the size achieves high self-sufficiency degrees, but cost optimal design changes only for selected price assumptions. Overall, we find that neglecting photovoltaic and surplus control in the system design is a valid assumption. However, price assumptions should be included to allow simple but optimal design rules in current guidelines.

Influence of WEDM input variables on the machinability of Ni–Cu superalloy

ABSTRACT The study investigates the influence of Wire-cut Electrical Discharge Machining (WEDM) input variables on the Ni–Cu superalloy, crucial in marine environments. Input variables include Input current (Ic), Pulse on Time (TON), Pulse off Time (TOFF), Servo Voltage (SV), and Wire Feed (WF) rate, while output variables are Machine Speed (Mc), Kerf Width (K), and Material Removal Rate (MRR). Utilizing an L16 orthogonal matrix, experiments were conducted. The Taguchi method examines single output variables, while the Grey Relational Grade Algorithm (GRA) assesses multi-objective outputs. GRA identifies optimal input combinations, crucial for maximizing the machining performance. ANOVA of GRA results highlights Pulse on Time (TON), Input current (Ic), and Servo Voltage (SV) as significant factors affecting Mc, K, and MRR. This analysis underscores their importance in optimizing the WEDM process for Ni–Cu (MONEL K-500 superalloy).