Optimal Selection Research Articles

A GIS-based catastrophe approach for optimal site selection for installation of solar power plants, East Azerbaijan province case study, Iran.

Due to depletion of fossil fuels and environmental issues, renewable energy consumption is increasingly growing. Solar energy as the most abundant renewable energy source available is becoming more popular around the world. In the current study, the optimal sites for solar photovoltaic power plants in East Azerbaijan province, Northwest Iran, were investigated. A total of 17 variables were categorized into four groups: climatic, geomorphological, environmental, and access-economic. In order to integrate the variables, a model based on catastrophe theory in the context of GIS was applied. The relative importance and weight of the criteria are computed based on the internal mechanism of the catastrophic system, thus greatly reducing subjectivism and uncertainties of the decision-making process. Five optimal sites located in the western part of the province within the counties of Malekan, Bonab, Ajabshir, Shabestar, and Tabriz were identified as suitable sites for the construction of solar photovoltaic power plants, where there are ideal conditions in terms of many environmental-human variables such as high potential of solar energy, high sunshine hours, low relative humidity, suitable slope, poor vegetation, distance to protected areas, proximity to the population centers, excellent access to the roads and to the main power lines.

Enhancing speech emotion recognition through deep learning and handcrafted feature fusion

In this paper, we introduce an innovative investigation in speech emotion recognition (SER). The proposed model combines deep learning-based and handcrafted audio features to achieve optimal accuracy. The proposed model employs an iterative feature selection and majority voting pipeline to obtain better results by fusing deep learning-based and handcrafted features. The wav2vec2 model and the openSmile audio processing library are used in order to extract audio features from audio data. Then the feature selection and majority voting techniques are used to identify the optimal feature selection methods for diverse feature sets and to combine their strengths. The experiments are performed using a diverse and extensive corpus to ensure the robustness of the proposed method. In the construction of this multi-corpus dataset, we used four well-known benchmark datasets, namely Ravdess, Savee, Crema-D, and Tess. All of these datasets are publicly available. These datasets are combined on six common emotions: sadness, happiness, fear, anger, surprise, and disgust. The resultant dataset comprises 11,511 samples across these categories. The proposed method has been shown to achieve results comparable to those reported in the existing literature. The experimental results indicate that the proposed pipeline leads to a 3 % improvement in classification accuracy. The highest achieved accuracy on the multi-corpus dataset is 92.55 %.

Predictors of Hepatic Decompensation after Yttrium90 Transarterial Radioembolization—Optimizing Patient Selection

Abstract Objective Yttrium 90 (Y90) transarterial radioembolization (TARE) is effective for unresectable hepatocellular carcinoma (HCC) or to bridge/downstage before transplant; however, optimal patient selection is not well-described. This study aims to identify factors that increase risk of liver decompensation resulting in hospital admissions after TARE. Methods Patients who received Y90 as their first treatment during 2012 to 2022 were identified from a prospectively collected database of 1675 HCC patients. Clinically significant hepatic decompensation was defined as total bilirubin more than or equal to 3 mg/dL or any increase in Model for End-stage Liver Disease (MELD) score resulting in readmission within 60 days or death. Results Of 137 patients, 7 (5.1%) developed hepatic decompensation requiring admission within 30 days and an additional 8 (10.9%) within 60 days. Two of these patients (1.4%) died and two (1.4%) required urgent transplant within 2 months. Preprocedure albumin less than 3.5 gm/dL (p = 0.0207), international normalized ratio more than 1.2 (p = 0.017), ascites (p = 0.036), elevated MELD (p = 0.012), and Child-Pugh (p = 0.007) scores were significant predictors of decompensation, while creatinine and sodium were not. Patients with Child-Pugh B score were three to four times more likely to decompensate (28 vs. 8%) compared to Child-Pugh A. For every unit increase in Child-Pugh score more than 6, odds of decompensation increased by a factor of 2.15. Conclusion Y90 TARE is safe and effective; however, 10.9% patients require readmission for worsened liver function. Because ascites is a significant factor in predicting decompensation and all patients require adequate renal function to receive Y90 TARE, Child-Pugh score may be more useful than MELD for patient selection. Further risk stratification may be required for those with a Child-Pugh score more than or equal to 7.

Investigating the influence of clustering techniques and parameters on a hybrid PSO-driven ANFIS model for electricity prediction

The availability of reliable electrical power, which is essential for a comfortable lifestyle worldwide, requires realistic power usage projections for electric utilities and policymakers, leading to the adoption of machine learning-based modelling tools due to the limitations of traditional power usage projection approaches. However, successful modeling of power usage in neuro-fuzzy models depends on the optimal selection of hyper-parameters. Consequently, this research looked at the major impact clustering methods and hyper-parameter modifications on a particle swarm optimization (PSO)-based adaptive neuro-fuzzy inference system (ANFIS) model. The study examined two distinct clustering methods and other key hyperparameters such as the number of clusters and cluster radius, resulting in a total of 10 sub-models. The performance of the developed models was assessed using four widely recognized performance indicators: root mean square error, mean absolute percentage error (MAPE), mean absolute error (MAE), and coefficient of variation of the root mean square error (CVRMSE). Additionally, the robustness of the optimal sub-model was evaluated by comparing it with other hybrid models based on three different PSO variants. The results revealed that the combination of the ANFIS approach and PSO, specifically with two clusters, yielded the most accurate forecasting scheme with the optimal values for MAPE (7.7778%), MAE (712.6094), CVRMSE (9.5464), and RMSE (909.4998).

Retraction Note: Features optimization selection in hidden layers of deep learning based on graph clustering

Retraction Note: Features optimization selection in hidden layers of deep learning based on graph clustering

A novel model for optimal selection of relay bus with maximum link reliability in VANET using hybrid fuzzy niching grey wolf optimization

A novel model for optimal selection of relay bus with maximum link reliability in VANET using hybrid fuzzy niching grey wolf optimization

Is breast conservation surgery feasible in ipsilateral cancer recurrence with previous radiotherapy?

Introduction: Breast-conserving surgery (BCS) is the preferred modalityfor early breast cancer, yet there is a significant research gap in understandingthe feasibility of BCS in ipsilateral recurrent breast cancer(IRBC) post-radiotherapy. This systematic review aims to investigate theoutcomes of repeat BCS (rBCS) in managing IRBC, providing crucialinsights for clinicians and patients in evaluating treatment options.Methods: The PRISMA framework guided data collection from PubMedand Embase. A qualitative systematic approach involved descriptive andthematic analysis, examining patterns and consistencies in outcome findingsacross identified studies. Due to the nature of the research, no statisticalanalysis was conducted.Results: Four papers, employing a 95% confidence level, were included.Studies revealed a 2.103 risk ratio for second local recurrence (SLR) afterrBCS compared to mastectomy. Pooled data indicated SLR rates of15.7% for BCS and 10.3% for mastectomy, with a 5-year overall survival(OS) rate of 86.8 and 79.8%, respectively. Repeat radiotherapy showed aprotective effect for SLR and a small OS benefit favoured rBCS. However,this came with overall evidence certainty. Other studies found a 5-year OSof 77 and 87% after rBCS, with oncological advantages for adjunctiveradiotherapy. Post-IBTR, 84% had a 5-year survival rate, while multivariateanalysis highlighted factors influencing SLR.Conclusion: This systematic review suggests that rBCS may be consideredin a subset of patients with IBCR after BCS and RT. Individual assessmentencompassing tumour-size and duration prior to relapse showsimportance approach feasibility. However, further research wouldimprove subject knowledge and aid in optimal patient selection.

Key technologies of smart antenna in WLAN based on adaptive array

In order to improve the communication quality and transmission rate of WLAN, the key technologies of smart antenna in WLAN based on adaptive array are proposed. The adaptive array is used to suppress the WLAN interference signal, and the feedback from the output is used to adjust the weight loaded on the signal, and the optimal weight corresponding to the linear constraint minimum variance is obtained to achieve beamforming optimization. On this basis, the optimal antenna selection algorithm and the sub-optimal antenna selection algorithm are designed, and the sub-channel matrix with the maximum capacity can be obtained by repeated iterative calculation, so as to realize the smart antenna selection and further optimize the performance of WLAN. Experimental results show that the smart antenna signal has 100% coverage in WLAN compared with conventional methods. When the transmission rate is greater than 35 mb/s, the delay of the study method is not jitter compared to the contrast method. When the transmission rate is greater than 35 mb/s, the packet loss rate of the proposed method remains stable, while the packet loss rate of the two comparison methods begins to increase. When the transmission rate is greater than 12 mb/s, the throughput of the proposed method is more stable than the other methods. In conclusion, this method has the characteristics of large signal coverage, low delay, low packet loss rate and high throughput, and can be widely used in practice. The contribution of this study is that the weight of the antenna array can be dynamically adjusted through the adaptive algorithm, thus optimizing the quality of the received signal and improving the receiving sensitivity.

Empowering African vultures optimizer using Archimedes optimization algorithm for maximum efficiency for global optimization and feature selection

Empowering African vultures optimizer using Archimedes optimization algorithm for maximum efficiency for global optimization and feature selection

Analysis of the outage performance of energy-harvesting cooperative-NOMA system with relay selection methods

Recent years have witnessed the remarkable progress in wireless communication systems due to the escalating demand for higher data rates, improved reliability, and increased energy efficiency. In this regard, Non-Orthogonal Multiple Access (NOMA) has emerged as a promising technology, enhancing spectral efficiency and accommodating multiple users concurrently within the same time and frequency resources. Simultaneously, the energy harvesting has surfaced as a sustainable solution, converting ambient environmental energy into usable electrical power for operating communication nodes. This paper proposes a cooperative NOMA transmission scheme integrating energy harvesting and utilizing Least Squares (LS) channel estimation for precise Channel State Information (CSI) acquisition. The objective is to establish an optimal communication path from source to destination. Relay selection methods: Optimal Relay Selection (ORS) and Max-Min Relay Selection (MMRS), are compared, focusing on their impact on the system performance. The analysis considers the influence of the number of relays and power allocation factor on the system, with a specific emphasis on the outage probability expressions. Comparative analysis between the cooperative-NOMA and the traditional cooperative relaying without NOMA reveals the superior performance of the cooperative-NOMA. Additionally, the ORS scheme outperforms MMRS in terms of the outage performance.

Optimal selection of manufacturing enterprise financialization based on simulated annealing investment portfolio theory

The global market competition is becoming increasingly fierce, and manufacturing enterprises need to invest and expand. However, the traditional financial optimization of manufacturing enterprises has faced problems such as low efficiency and inaccurate search for the optimal solution, which has made manufacturing enterprises likely to face financial risks. The investment portfolio can enable enterprises to obtain the maximum profit on a certain risk level, or reduce their investment risk on a certain return level as far as possible. If the combinatorial optimization is realized, it can be applied to the optimal selection of manufacturing enterprises’ financialization. This article analyzed the respective characteristics of Genetic Algorithm (GA) and Simulated Annealing (SA) algorithms, and analyzed the combination of GA and SA algorithms to solve the optimal investment portfolio through GA-SA algorithm, thereby helping manufacturing enterprises to make the optimal choice for financialization. The experimental results of this article indicated that the GA-SA algorithm solved the problem of GA algorithm easily falling into local optima, SA algorithm’s initial temperature and generation mechanism, and improved the efficiency of finding the optimal solution. Meanwhile, the experimental results showed that the average optimal solutions of Genetic Algorithm, Simulated Annealing algorithm, and GA-SA algorithms for 36 stock portfolios in Enterprise 1 were 69, 69, and 107, respectively. The average optimal solutions of the three algorithms for 36 stock portfolios in Enterprise 2 were 73, 90, and 112, respectively. This proves that the number of optimal solutions searched by GA-SA algorithm is higher than that of GA and SA algorithm, and also proves that it is effective to use GA-SA algorithm to optimize investment portfolio and help manufacturing enterprises to make optimal financial choices.

A Nomogram Prediction Model for Persistent Pulmonary Hypertension of the Newborn in Neonates Hospitalized for the First Time After Birth.

Persistent pulmonary hypertension of the newborn (PPHN) is one of the critical neonatal diseases associated with high morbidity and mortality. This study attempted to conduct a nomogram prediction model for performing early identification of PPHN and providing effective information for clinical practice. A total of 456 newborns who first admitted to the hospital after birth were included in the analysis, including 138 newborns with PPHN and 318 newborns without PPHN (as controls). The optimal predictive variables selection was performed based on LASSO (least absolute shrinkage and selection operator) regression and multivariate logistic regression. Using the selected variables, a nomogram prediction model was developed. To validate the model, the model was assessed using the receiver operating characteristic curve, calibration plot, and clinical impact curve. Six predictors, namely, gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pao2, were identified by LASSO and multivariate logistic regression analysis from the original 30 variables studied. The constructed model, using these predictors, exhibited favorable predictive ability for PPHN, with an area under the receiver operating characteristic of 0.897 (sensitivity = 0.876, specificity = 0.785) in the training set and 0.871 (sensitivity = 0.902, specificity = 0.695) in the validation set, and was well calibrated, as indicated by the PHosmer-Lemeshow test values of 0.233 and 0.876 for the training and validation sets, respectively. The model included gestational age, neonatal respiratory distress syndrome, the levels of hemoglobin and creatine kinase-MB, gestational thyroid dysfunction, and Pao2 had good prediction performance for predicting PPHN among newborns first admitted to the hospital after birth.

Use of THRIVE for Apneic Oxygenation in Transoral Laser Microlaryngeal Surgery: A Scoping Review.

Trans-nasal humidified rapid insufflation ventilatory exchange (THRIVE) has demonstrated utility in extending the apneic window in the perioperative setting. Its benefits in facilitating tubeless anesthesia are recognized during elective laryngotracheal surgeries. The use of THRIVE and administering higher fractional inspired oxygen concentrations in laser laryngeal surgery (LLS) remains controversial due to the theoretical risk of airway fires. A scoping review of the literature describing institutional experiences with THRIVE during LLS was conducted. A systematic scoping review of the literature was performed including PubMed, Medline, Embase, Scopus, JBI EBP Database, and Cochrane Library from inception to April 2023. From the 472 articles identified in our review, nine articles were included representing 271 cases. THRIVE was used for preoxygenation and to maintain apneic oxygenation during LLS. Different institutional practices related to THRIVE parameters and intraoperative modifications during lasing were described in the literature, including cessation of THRIVE, reduction of FiO2 to 30%, and continuous 100% FiO2 oxygenation. One study described a brief ignition of the coating of a KTP laser fiber without injury to the patient. No adverse patient outcomes have been documented in the literature with THRIVE during LLS. THRIVE is a safe and effective form of tubeless anesthesia and apneic oxygenation during LLS, with no adverse patient safety events reported in the literature. Key determinants to maintain safety include optimal patient and team selection, effective surgeon-anesthetist cooperation, and institutional protocols that govern intraoperative practice. Laryngoscope, 2024.

Stereotactic body radiotherapy takes on Lung Oligometastases: Latest breakthroughs

AbstractLung oligometastases represent an intermediate state of cancer dissemination between localized and widespread metastases. Stereotactic body radiation therapy (SBRT) has emerged as an effective treatment option, with an efficacy comparable to that of surgical resection. This review aimed to provide a comprehensive summary of the latest advancements and controversial issues regarding SBRT for lung oligometastases. It focuses on four crucial perspectives: efficacy of SBRT, optimal patient selection criteria, technological innovations, and synergistic effects of SBRT combined with systemic therapy. Relevant clinical trials investigating SBRT for lung oligometastases have been conducted, with median 1‐ and 5‐year local control rates of 90% and 79%, respectively. The origin of the primary tumor, size and number of lesions, and biomarker profiles were highlighted as pivotal considerations in patient selection. The precise dose delivery was enhanced using robotic SBRT and optimized dose fractionation schemes. Evidence suggests that dose escalation above 100 Gy biologically effective dose may improve tumor control. Combined immunotherapy and SBRT have demonstrated synergistic effects in prolonging progression‐free survival and overall survival. This review provides valuable insights into the precise treatment of oligometastatic lung diseases using SBRT. Further multicenter randomized trials are warranted to develop definitive patient selection criteria and optimize the integration with systemic therapies.

Multi-objective optimization-driven machine learning for charging and V2G pattern for plug-in hybrid vehicles: Balancing battery aging and power management

This research study aims to optimize the economic energy management of a plug-in hybrid vehicle by maximizing revenue through vehicle-to-grid interactions during peak hours, while also prioritizing the reduction of battery aging as an additional objective. The study focuses on the Mitsubishi Outlander vehicle in New York City, analyzing charging and V2G times, rates, and ambient temperatures to understand their impact on lithium-ion battery aging. Using a machine learning approach, a deep neural network is trained with time, rate, and temperature inputs to improve accuracy in predicting battery aging effects. A multi-objective optimization algorithm, NSGA-II, is employed to balance the trade-off between maximizing revenue and minimizing battery aging based on the neural network's predictions. The results indicate that the best time for charging is before starting the travel, with a 3.95 % battery aging. Raising the charging amperage and implementing V2G elevate battery aging to 7.83 %. Furthermore, higher ambient temperatures can accelerate battery aging significantly. A temperature rise from 10 °C to 30 °C could potentially result in a 12.23 % increase in battery aging. Through optimum selection of charging and V2G time, rate, and temperature, the study suggests that an income of $1043 and a battery aging rate of 7.2 % can be achieved within one year.

Multi-parameter terahertz metamaterial sensors based on single-layer quarter ring patterns

A terahertz metamaterial sensor based on a single-layer quarter ring pattern is proposed to realize the simultaneous measurement of temperature, refractive index, and other parameters through a metasurface consisting of a patterned quarter ring gold layer and a silicon substrate. Field finite element numerical simulations were performed to analyse the transmission and reflection spectra. A two-parameter sensor combined with electric field distribution was realized using multiple peaks, thus revealing the physical mechanisms of the interaction in metamaterial structures. In addition, the effect of each geometrical parameter on the sensor performance is also simultaneously analysed to verify the optimal selection of structural parameters. On this basis, the sensors are theoretically analysed at different incidence and polarization angles, and the results show that the transmittance is not sensitive to the polarization angle. The simulation results show that the transmittance at the resonant frequencies of 0.48 THz, 0.64 THz, 0.79 THz and 1.04 THz are 93.7 %, 79.1 %, 38.4 % and 50.8 %, respectively, and the refractive index sensitivities at the resonant frequencies are 6.84 GHz/RIU, 7.42 GHz/RIU, 8.22 GHz/RIU and 32.11 GHz/RIU, and quality factor Q of 6.76, 9.63, 15.51 and 26.28, respectively. The devices proposed in this study are simple in structure, easy to fabricate, easy to miniaturize and can be integrated for sensitivity, multi-parameter sensors. They can be functionally designed for chemical and biological sensing and other important applications.

Fast Aerodynamic Prediction of Airfoil with Trailing Edge Flap Based on Multi-Task Deep Learning

Conventional methods for solving Navier–Stokes (NS) equations to analyze flow fields and aerodynamic forces of airfoils with trailing edge flaps (TEFs) are known for their significant time cost. This study presents a Multi-Task Swin Transformer (MT-Swin-T) deep learning framework tailored for swift prediction of velocity fields and aerodynamic coefficients of TEF-equipped airfoils. The proposed model combines a Swin Transformer (Swin-T) for flow field prediction with a multi-layer perceptron (MLP) dedicated to lift coefficient prediction. Both networks undergo gradient updates through the shared encoder component of the Swin Transformer. Such a trained network model for computational fluid dynamics simulations is both effective and robust, significantly improving the efficiency of complex aerodynamic shape design optimization and flow control. The study further investigates the impact of integrating multi-task learning loss functions, skip connections, and the network’s structural design on prediction accuracy. Additionally, the effectiveness of deep learning in improving the aerodynamic simulation efficiency of airfoils with TEF is examined. Results demonstrate that the multi-task deep learning approach provides accurate predictions for TEF airfoil flow fields and lift coefficients. The strategic combination of these tasks during network training, along with the optimal selection of loss functions, significantly enhances prediction accuracy compared with the single-task network. In a specific case study, the MT-Swin-T model demonstrated a prediction time that was 1/7214 of the time necessitated by CFD simulation.

Optimum image alignment setting selection for structure-from-motion photogrammetry using Remotely Piloted Aircraft Systems (RPAS) to support coral habitat classification

Coral reefs provide a range of ecological services and support highly diverse coastal ecosystem. However, the extent of living corals has been reduced by half globally as a result of anthropogenic natural stressors. Continuous monitoring by deploying accessible remote sensing (RS) methods to map coral habitats is necessary for effective conservation and management strategies. Recently, there has been an increased use of Remotely Piloted Aircraft Systems (RPAS) for coral reef RS. Image misalignment is considered a key problem in the Structure from Motion (SfM) workflow. Combinations of image alignment settings (IAS) and key point limit (KPL) optimizations aim to improve quality of the sparse cloud while lowering SfM reconstruction uncertainty, increasing projection accuracy, and ultimately improving coral habitat classification accuracy. In this study, the orthoimages were produced from a total of 25 IAS (lowest, low, medium, high and highest) and KPM (5k, 10k, 20k, 40k, and 60k) combinations using Agisoft™ Metashape software. Measurements of geometric distortion, efficiency, and completeness were used to evaluate these orthoimages with three visible bands. Results that satisfied the requirements for both geometric quality, spectral accuracy, processing efficiency identified the optimum alignment methods needed for routinely monitoring and mapping of coral reefs of Pulau Bidong. The SfM-image alignment techniques chosen for this study could produce the coral mapping extent, a higher number of tie points and matches, better image alignment success and coral habitat classification accuracy, less processing time and memory usage and no geometric distortions. The development of the methodology for the optimum parametrization of RPAS multispectral imagery would be beneficial to researchers studying coral reefs, marine sciences, and RS data analysts. It would provide them reliable methods for evaluating the quality of orthoimages and faster processing methods to achieve coastal RS objectives.

A back‐to‐back coordination‐based learning scheme for deceiving reactive jammers in distributed networks

AbstractReactive jammers select jamming strategies according to the users’ responses; thus, conventional anti‐jamming methods such as frequency hopping are inadequate to defeat the jamming attack. In this article, the authors propose a novel uncoupled deception scheme to trap the reactive jammer into attacking a decoy channel in distributed networks. Specifically, the authors design a multi‐functional network utility for every user to mislead the jammer with a minimum energy consumption while achieving the highest network throughput. Based on the network utility, the anti‐jamming problem is formulated as an exact potential game such that the existence of Nash equilibrium can be guaranteed theoretically. The authors further propose a back‐to‐back coordination‐based learning algorithm to reach the optimal channel selection and power adaption in a non‐cooperative way. To alleviate the lack of mutual information exchange, the back‐to‐back coordination mechanism derives all users to deceive the jammer by inferring others’ strategies based on a shared belief. Simulation results show that the proposed algorithm yields higher network throughput and efficiency‐cost ratio compared to the state‐of‐the‐art cooperative schemes.

Evaluating osteoporosis and bone quality in the aging spine: modern considerations for surgical management in the geriatric population.

Surgical management paradigms of spinal pathologies in the aging population carry inherent substantial risks, with surgical complications being more prevalent among patients with osteoporosis compared to those with normal bone mineral density. In this narrative review, we aim to highlight important clinical understanding and considerations in perioperative evaluation and management of patients elected to undergo spinal surgery. Osteoporosis is a well-defined risk factor for mechanical complications following spinal surgery, and as such, perioperative optimization of bone health in the setting of surgery for geriatric patients remains a critical research area alongside intraoperative surgical augmentation techniques. Surgical techniques to circumvent challenges with instrumentation of poor bone mineral density have included augmentation of pedicle screw fixation, including segmental bicortical screw fixation techniques, cement augmentation with fenestrated screws, or use of expandable pedicle screws to improve bone-implant interface. Judicious selection of treatment modalities and subsequent perioperative optimization is paramount to minimize surgical complications. Contemporary guidelines and evolving paradigms in perioperative evaluation, optimization, and management of the aging spine include the advent of quantitatively evaluating computed tomography (CT) via assessment of the magnitude of Hounsfield units. Prescribing pharmacotherapeutic agents and monitoring bone health requires a multidisciplinary team approach, including endocrinologists and geriatricians to coordinate high-quality care for advanced-age patients who require surgical management of their spinal disorders.