Optimal Point Research Articles

Optimization of a syngas-fueled SOFC-based multigeneration system: Enhanced performance with biomass and gasification agent selection

In light of abundant sources of biomass feedstocks and regarding the higher performance in syngas-fueled-based SOFC systems, this work proposes a novel multigeneration system based on the combination of syngas-fuel SOFC, Kalina cycle, humidification-dehumidification desalination unit, and proton exchange membrane electrolyzer. This system is designed for simultaneous production of power, heating, freshwater, and hydrogen. Comprehensive energy, exergy, exergoeconomic, environmental, and economic analyses have been conducted to evaluate its performance. The optimum input biomasses, gasification agents, and operating conditions were determined using a coupled approach of an artificial neural network, multi-objective Particle Swarm Optimization algorithm, and the Linear Programming Technique for Multidimensional Analysis of Preference decision-making method. Under base input conditions, the energy and exergy efficiencies of the proposed system were found to be 53.66 % and 38.2 %, respectively. The system also demonstrated the capability to produce 0.1633 kg/s of freshwater and generate a net power output of 377.6 kW. The results indicate that using oxygen-enriched air in the gasification process notably enhances efficiency while reducing emissions. Straw and CO₂ were identified as the optimal feedstock and gasification agent, yielding an exergy efficiency of 42.71 % and a total product cost of 6.29 $/GJ at the optimum point. Moreover, with a selling price of $0.20/kWh for electricity and a fuel cost of $6/GJ, the system can achieve total revenue of approximately $1.65 million with a payback period of 4.01 years. These findings underscore the proposed plant's profitability under specific pricing conditions, making it an attractive investment opportunity.

Self-Switching Wireless Power Transfer System Design with Constant Current/Constant Voltage Output Features Based on LCC-LCL/S Topology

To meet the demand for constant current and constant voltage charging of batteries and to increase the system output power, the LCC-LCL/S-type self-switching wireless power transfer system is proposed. The system does not require communication between the primary and secondary circuits, and its output mode is controlled by simply changing the status of two switches on the secondary side. Notably, the system satisfies the zero phase angle characteristic before and after mode switching. The parameter design method is proposed based on the circuit topology and the maximum safe current constraint. To make the output voltage fluctuation as small as possible, the optimum load switching point for the system has been designed, and its optimality has been validated by simulation. Furthermore, the switching control strategy is proposed, considering the effects of the system no-load and load short-circuit in real situations. Finally, an experimental platform was built to achieve the high efficiency output of the system with the maximum output voltage of 46.21 V, the maximum output power of 180 W, and the maximum efficiency of 93.4%, which verified the applicability of the proposed method.

Sensitivity analysis of natural convection in a porous cavity filled with nanofluid and equipped with horizontal fins using various optimization methods and MRT-LB

In the present study, natural convection heat transfer is investigated in a porous cavity filled with Cu/water nanofluid and equipped with horizontal fins. Optimization and sensitivity analysis of the fin’s geometry, porous medium and nanofluid properties to maximize heat transfer rate is the aim of this work. To achieve this purpose, a design space is created by input parameters which include length, number of fins, distance between fins, porosity, Darcy number and volumetric fraction of the nanoparticles. Several tools have been used to implement optimization methods including the Taguchi method (TM) for design points generation, sensitivity analysis of design variables by using signal-to-noise ratio (SNR) and analysis of variance (ANOVA), response surface method (RSM) for interpolation and regression by using nonparametric regression, and genetic algorithm (GA) for finding optimum design point. The double multi-relaxation time lattice Boltzmann method (MRT-LBM) is used to analyze and simulate the flow field and heat transfer in each design point. The results show that the optimal configuration leads to an average Nusselt number of 5.56. This optimal configuration is at the length of fins L/2, the number of fins 2, the distance between fins L/12, porosity 0.8, Darcy number 0.1, and the volumetric fraction of the nanoparticles 0.02. By using the SNR results, the Darcy number and the number of fins have the most and the least effect in maximizing the average Nusselt number, respectively. The ANOVA results and global sensitivity analysis (GSA) findings further validated this conclusion.

Optimizing with Attractor: A Tutorial

This tutorial presents a novel search system—the Attractor-Based Search System (ABSS)—that can solve the Traveling Salesman Problem very efficiently with optimality guarantee. From the perspective of dynamical systems, a heuristic local search algorithm for an NP-complete combinatorial problem is a discrete dynamical system. In a local search system, an attractor drives the search trajectories into the vicinity of a globally optimal point in the solution space, and the convergence of local search trajectories makes the search system become a global and deterministic system. The attractor contains a small set of the most promising solutions to the problem. The attractor can reduce the problem size exponentially, and thus make the exhaustive search feasible. Therefore, this new search paradigm is called optimizing with attractor. The ABSS consists of two search phases: local search phase and exhaustive search phase. The local search process is used to quickly construct the attractor in the solution space, and the exhaustive search process is used to completely search the attractor to identify the optimal solution. Therefore, the exact optimal solution can be found quickly by combining local search and exhaustive search. This tutorial introduces the concept of an attractor in a local search system, and describes the process of optimizing with the attractor, using the Traveling Salesman Problem as the study platform.

Research on Off-Design Characteristics and Control of an Innovative S-CO2 Power Cycle Driven by the Flue Gas Waste Heat

Recently, supercritical CO2 (S-CO2) has been extensively applied for the recovery of waste heat from flue gas. Although various cycle configurations have been proposed, existing studies predominantly focus on the steady analysis and optimization of different S-CO2 structures under design conditions, and there is a noticeable deficiency in off-design research, especially for the innovative S-CO2 cycles. Thus, in this work aimed at the proposed novel S-CO2 power cycle, off-design characteristics and corresponding control strategies are investigated for the waste heat recovery. Based on the design parameters of the S-CO2 cycle, structural dimensions of printed circuit heat exchangers (PCHEs) and shell-and-tube heat exchangers are determined, and design values of turbines and compressors are specified. On this basis, off-design models for these key components are formulated. By manipulating variables such as cooling water inlet temperature, cooling water mass flow rate, flue gas inlet temperature and flue gas mass flow rate, cycle performances of the system are analyzed under off-design conditions. The simulation results show that when the inlet temperature and the mass flow rate of cooling water vary separately, the thermal efficiency both can reach the maximum value of 28.43% at the design point. For the changes in heat source parameters, the optimum point is slightly deviated from the design condition. Amidst the fluctuations in flue gas inlet temperature, the thermal efficiency optimizes to a peak of 28.56% at 530 °C. In the case of variation in the flue gas mass flow rate, the highest thermal efficiency 28.75% can be obtained. Furthermore, to maintain the efficient and stable operation of the S-CO2 power cycle, the corresponding control strategy of the cooling water mass flow rate is proposed for the cooling water inlet temperature variation. Generally, when the inlet temperature of cooling water increases from 23 °C to 27 °C, the cooling water mass flow should increase from 82.3% to 132.7% of the design value to keep the system running as much as possible at design conditions.

اختبار أداء محطة تحلية مياه البحر بالزويتينة

The present study is applied on the seawater multi-stage flash (MSF) desalination plant that is currently under operation in E-Zuetina operations plant located in Libya. The plant contains 21 evaporator stages at capacity of 10025 (ton/day). The presented operating data has been collected during a visit of the plant, a mathematical model for multistage flash (MSF) desalination plants was developed. The model was based on basic principles of physics and chemistry that describe the stages occurring in the desalination process. The input plant parameters that are known to affect the operation of the MSF desalination plant and its performance were taken into account in the construction of the model. These parameters included make-up flow, brine recycle flow, seawater flow, seawater temperature, seawater concentration, steam temperature and the plant load. For each stage, the developed model was used for predicting the temperatures and pressure of the brine, distillate, cooling brine, and the flow rates of brine outlet and distillate production. The developed model was evaluated with the MSF plant vendor simulation results and its actual operating data. The evaluation indicated that model predictions matched well with the vendor simulation results and the plant operating data. The developed model is sufficiently accurate and model predictions can be relied upon. Therefore, it may be recommended for determining optimum set point of a running MSF desalination plant at different loads to maximize the water production or minimize energy consumption. It can also be used to calculate controller set points for different loads of the plant. Keywords:E-Zuetina MSF Desalination Plant, case study, stage model, brine heater.

Investigating the effect of steel micro‐ and macro‐fibers on the bond behavior of steel rebar embedded in ultra‐high performance concrete

AbstractIn recent years, the use of ultra‐high performance concrete (UHPC) has been widely considered in special structures and retrofitting of existing structures. Since most of the relations proposed by the regulations are based on the behavior of concrete with normal strength concrete, it is necessary to conduct more experimental studies to predict the behavior of UHPC. One of these notable behaviors is the bond and cohesion between rebar and UHPC, which plays a critical role in the design and seismic behavior of structures. In the present study, a series of experimental tests of uniaxial compressive strength, splitting (Brazilian) tensile strength, elastic modulus, and pull‐out of ribbed steel bars from ultra‐high performance fiber‐reinforced concrete (UHPFRC) were performed. The tests were done considering the four‐volume fractions of hooked‐end steel macro‐fibers, straight steel micro‐fibers, a hybrid combination of these two fibers, three different diameters of ribbed steel bars, and several embedded lengths. Moreover, direct tensile and compressive stress–strain curves, Young's modulus, bonding properties of rebar with UHPFRC, and fracture mechanisms were considered. The results indicated that applying micro‐fibers may enhance the bond stress between rebar and UHPC better than macro‐fibers. Also applying hybrid fibers resulted in the concurrent enhancement of bond stress and the optimum point slip of UHPC in the stress–slip diagram. This finding maybe attributed to the satisfactory performance of micro‐fibers in reducing the growth speed of micro‐level cracks and the suitable effect of macro‐fibers in decreasing the progression speed of macro‐level cracks. Furthermore, it was revealed that the increase in rebar diameter and embedding length leads to the reduction of maximum bond stress in UHPFRC.

Vitamin C variation in citrus in response to genotypes, storage temperatures, and storage times: A systematic review and meta-analysis

Numerous published studies have highlighted discrepancies in the duration and storage temperature used for preserving vitamin C content on various citrus genotypes worldwide. The present study aimed to analyze the variation in vitamin C content as influenced by citrus genotype, duration, and storage temperature using meta-analysis approaches. Data searching, selection, and tabulation resulted in a comprehensive database constructed from 1412 data points gathered from 54 individual studies, following PRISMA-P guidelines. The vitamin C content varied widely, ranging from 0 to 76.2 mg/100 mL in whole data of citrus fruit. Meta-analysis findings revealed that the duration of storage significantly impacted the vitamin C content in citrus fruits. Specifically, for grapefruit, mandarin, and orange, the length of storage significantly influenced their vitamin C levels (P < 0.01), with a consistent decrease observed over time. The correlation coefficients (R2) were 0.63 for grapefruit, 0.9 for mandarin, and 0.69 for orange. In contrast, no significant difference was found in terms of vitamin C levels between hybrid and lime citrus concerning the impact of storage time. However, other results indicated a significant influence of storage temperature on the variation in vitamin C levels for both citrus and hybrid varieties (P < 0.001). Depending on the genotype, tangerine had significantly lower vitamin C content compared to other varieties, at 16.9 mg/100 mL, with vitamin C ranging from 13.2 to 20.9 mg/100 mL (P < 0.001). The highest vitamin C content was found in lemon and hybrid varieties, around 65.5 (range 59.3–76.2) and 48.3 (range 29.6–75.5), respectively, all in mg/100 mL (P < 0.001). Furthermore, there was a tendency for decreasing vitamin C concentration due to temperature (P = 0.078), while citrus variety experienced a decrease, although not significant. The ideal temperature (15 °C) and duration of storage (56 days) to minimize vitamin C loss in citrus fruits are at their optimum point. In conclusion, the deterioration of vitamin C in citrus fruits is influenced by both temperature and storage duration, and its content is also impacted by the variety of citrus.

Thermal dynamic analysis and multi-objective optimization of a 10 kW dual preheating solid oxide fuel cell system

The increasingly serious environmental problems caused by the excessive development of fossil fuels necessitate the promoting of novel and effective energy technologies. As an environmentally friendly and effective promising solution in terms of its low emission, and fuel adaptability, the solid oxide fuel cell (SOFC) system has attracted widespread attention. To better promote the progress of SOFC, the SOFC testing system (SOFC TS) is very important. In this paper, we have established a dual preheating SOFC TS configuration where the fuel is first preheated by exhaust gas, and then preheated by the electric heater to the setting temperature. A comprehensive thermodynamic analysis is conducted to investigate the characteristic of this SOFC TS. Building upon this designed testing system, a multi-objective optimization approach is adopted to enhance the performance of the thermal management subsystem. The results showed that the SOFC stack is the main exergy component in the SOFC TS, the selected point of multi-objective optimization can reduce the thermal management costs by 60$ and improve system efficiency by 4.5%.

Development and validation of a risk assessment tool for uncontrolled type 2 diabetes among patients in South Karnataka, India

IntroductionDiabetes is a chronic medical condition with severe complications mainly caused due to unhealthy lifestyles in genetically susceptible individuals. This study attempts to develop a non-invasive risk assessment tool to...

Thermodynamic analysis and examining the effects of parameters in BSR-HDH system using response surface methodology

1) BackgroundIn this study, biogas steam reforming (BSR) coupled with a humidification dehumidification unit (HDH) was proposed and the novel thermodynamic analysis interaction effects on energy efficiency and exergy efficiency via the design of experiments was used. 2) MethodsComprehensive thermodynamic modelling has been performed using EES software. Thus, by utilizing the thermodynamic analysis of the combined system by EES software and transferring the experiments based on the central composite design for the input parameters (the inlet temperature of the dehumidifier, humidifier and dehumidifier circulated mass flow rate and the desalination heater inlet temperature) extracted by the design expert software, the results obtained from the EES provides responses that shows the impact of the interaction of the input parameters. In RSM model, the central composite design (CCD) is employed in the experimental design. 3) Significant FindingsFrom the thermodynamic outlet results, the energy efficiency, exergy efficiency, hydrogen mass flow rate and freshwater mass flow rate of the system are obtained 82.39% and 72.65%, 0.1071 kg/s and 0.211 kg/s, respectively. R2 values in energy and exergy efficiency responses were calculated 99.99% and 99.97%, respectively that shows the model has a good accuracy. The optimum points for parameters of the inlet temperature of dehumidifier (T14), humidifier and dehumidifier circulated mass flow rate (m19) and the desalination heater inlet temperature (T10) and also responses of energy efficiency and exergy efficiency are obtained 310 K, 8 kg/s, 450 K, 0.9051% and 0.7313%, respectively. Also, the variables of the inlet temperature of dehumidifier and humidifier and dehumidifier circulated mass flow rate (m19) have more interaction with each other based on their slope changes in the upper and lower ranges.

Neural Architecture Search as Multiobjective Optimization Benchmarks: Problem Formulation and Performance Assessment

The ongoing advancements in network architecture design have led to remarkable achievements in deep learning across various challenging computer vision tasks. Meanwhile, the development of neural architecture search (NAS) has provided promising approaches to automating the design of network architectures for lower prediction error. Recently, the emerging application scenarios of deep learning (e.g., autonomous driving) have raised higher demands for network architectures considering multiple design criteria: number of parameters/weights, number of floating-point operations, inference latency, among others. From an optimization point of view, the NAS tasks involving multiple design criteria are intrinsically multiobjective optimization problems; hence, it is reasonable to adopt evolutionary multiobjective optimization (EMO) algorithms for tackling them. Nonetheless, there is still a clear gap confining the related research along this pathway: on the one hand, there is a lack of a general problem formulation of NAS tasks from an optimization point of view; on the other hand, there are challenges in conducting benchmark assessments of EMO algorithms on NAS tasks. To bridge the gap: (i) we formulate NAS tasks into general multi-objective optimization problems and analyze the complex characteristics from an optimization point of view; (ii) we present an end-to-end pipeline, dubbed EvoXBench, to generate benchmark test problems for EMO algorithms to run efficiently -without the requirement of GPUs or Pytorch/Tensorflow; (iii) we instantiate two test suites comprehensively covering two datasets, seven search spaces, and three hardware devices, involving up to eight objectives. Based on the above, we validate the proposed test suites using six representative EMO algorithms and provide some empirical analyses. The code of EvoXBench is available at https://github.com/EMI-Group/EvoXBench.

Development of a rapid tool for screening financial toxicity based on the comprehensive score for financial toxicity

ObjectiveThis study aimed to use item response theory (IRT) to develop and validate a short version of the Chinese COmprehensive Score for financial Toxicity (COST-S-C) in patients with cancer. MethodsA total of 500 patients with cancer participated in the survey. Confirmatory factor analysis (CFA) was used to assess the factor structure of the COST-S-C. A two-parameter IRT model was used to examine the item fit of the COST-S-C. Item discrimination, item characteristic curves (ICCs), item information curves (IICs), and item fit were estimated. Differential item functioning (DIF) was assessed for participants’ gender and age. Reliability, convergent and discriminant validity was examined as well as estimating the optimal cut-off points of the COST-S-C. ResultsThe CFA results supported the bi-factor structure of the COST-S-C. The IRT analysis findings revealed that several items showed problems with the ICCs, providing little information in terms of IICs as well as poor discrimination. Item 5 showed a negligible DIF problem with age. A valid 3-item COST-S-C scale and its optimal cut-off point was developed. ConclusionsThe COST-S-C is a valid and quick screening tool used to distinguish patients with cancer who report significant financial toxicity in various medical settings.

Grey-Box Bayesian Optimization for Sensor Placement in Assisted Living Environments

Optimizing the configuration and placement of sensors is crucial for reliable fall detection, indoor localization, and activity recognition in assisted living spaces. We propose a novel, sample-efficient approach to find a high-quality sensor placement in an arbitrary indoor space based on grey-box Bayesian optimization and simulation-based evaluation. Our key technical contribution lies in capturing domain-specific knowledge about the spatial distribution of activities and incorporating it into the iterative selection of query points in Bayesian optimization. Considering two simulated indoor environments and a real-world dataset containing human activities and sensor triggers, we show that our proposed method performs better compared to state-of-the-art black-box optimization techniques in identifying high-quality sensor placements, leading to an accurate activity recognition model in terms of F1-score, while also requiring a significantly lower (51.3% on average) number of expensive function queries.

Improved recovery of antioxidants from aronia juice processing residue via optimization of extraction variables based on multi-prediction models

Aronia melanocarpa, which has attracted attention for its outstanding antioxidant activity, is usually consumed as a juice, leaving considerable residue after processing into beverages. Recycling these by-products as feedstock for biorefineries is an important strategy for achieving a sustainable circular economy through biowaste management. In this study, the optimal extraction conditions for efficient antioxidant recovery from A. melanocarpa juice-processing residue (AJPR) were determined using statistical methods. Based on the Plackett-Burman design (PBD), four variables (shaking speed, solid loading, ethanol concentration, and total volume) were found to have significant effects on antioxidant extraction. The optimum points of the selected four variables were calculated by multi-regression models from response surface methodology (RSM), and the determined conditions were as follows:196.2 rpm, 71.3 g/L, 54.3% ethanol, and 137.8 mL of total volume. The DPPH• IC50 and ABTS•+ TEAC value of AJPR extracts were determined to be 2.02 mg/mL and 11.49 mg/mL, respectively, exhibiting promising potential as an antioxidant. The recovered polyphenol, flavonoid, and anthocyanin contents were 80.4, 42.5, and 14.1 mg/g-biomass, respectively. We show that bioengineering technologies can be used to achieve a circular economy by recycling discarded biowaste into value-added substances.

Effect of grain orientation on the energy absorption performance of Chinese fir wood under quasi-static compression

ABSTRACT The compressive response and energy absorption of Chinese fir wood with different grain orientations along the longitudinal (L) and radial (or tangential) directions were studied through uniaxial quasi-static compression tests. The results indicated that the wood compressive behavior changed from out-of-plane to in-plane, similar to honeycomb materials when the grain orientation was changed from longitudinal to transverse. The initial peak stress (σp ) and plateau stress (σpl ) decreased upon increasing the grain angle (θ), especially below 45°, and more slowly above 45°. The wood compressive failure modes and energy absorption ability were also strongly dependent on the grain orientation, and the energy absorption per unit volume and energy absorption efficiency decreased as θ increased. Energy absorption diagrams and mathematical models were established to evaluate the optimum energy absorption point to guide the selection of energy-absorbing wood.

Performance Optimization of Supercapbattery with Porous Modification on Silicon as Anode and Cathode Based on Al&lt;sub&gt;2&lt;/sub&gt;O&lt;sub&gt;3&lt;/sub&gt;/CuCrO&lt;sub&gt;2&lt;/sub&gt;

The development of the supercapbattery has become the focus of energy storage research due to their potential to increase energy and power density. This research is focused on developing a modification of silicon (Si) porous as an anode with Al2O3/CuCrO2/AC composite as a cathode of supercapacitor. These electrodes were synthesized using LA133 binder with deionized water as solvent. The supercapacitor electrode uses an aluminum foil substrate, while the Si electrode uses a cupper foil substrate. The structural and morphological characterization of the electrodes were identified through XRD, FTIR, and SEM tests, while the electrochemical performance characterization using Galvanostatic Charge-Discharge (GCD) instruments. The results of XRD data analysis of thin film electrodes of supercapacitor showed diffraction peaks which indicated the phases Al2O3/CuCrO2/AC and Si porous. The absorption functional groups of Al2O3/CuCrO2/AC and Si porous were identified through FTIR characterization. The results of SEM showed the addition of CuCrO2 and structure modification of silicon into porous caused increasing value of porosity. The electrochemical performance of the optimum point at Al2O3/CuCrO2/AC condition, showing a specific capacitance of 50.3 F/g, an energy density of 36.499 Wh/kg, and a power density of 433.6 W/kg. The combination of Al2O3/CuCrO2/AC//Si Porous 16 for supercapbattery devices shows performance with a specific capacitance of 14.4 F/g, an energy density of 6.1 Wh/kg, and a power density of 33.6 W/kg. These results indicate an increase in electrochemical performance compared with Si anodes without modification.

Optimization of minimal thermal treatment conditions for sour cherry (Prunus cerasus L.) juice using ultrasound‐ohmic combination treatment

AbstractThis study aimed to optimize minimal thermal treatment conditions for sour cherry juice using response surface methodology. The effects of ultrasound power (UP; 54, 35, 14 W), ultrasound time (UT; 8, 5, 3 min), ohmic electrical field strength (OE; 40, 30, 20 V/cm) and ohmic time (OT; 8, 5, 3 min) on total phenolic content (TPC), antioxidant activity (AA), color values (L*, a*, b*, ), total monomeric anthocyanin content (TMAC), polymeric color (PC), color density (CD), polymeric color ratio (PC%), total mesophilic aerobic bacteria (TMAB) and yeast‐mold count (YM) were investigated. UP significantly affected TPC, L*, a*, b*, and , while OE affected TPC, TMAC, PC, PC%, and TYC. UT affected TPC, L*, AA, CD, PC and PC%, and OT level affected TPC and b* values. The optimum points were determined by considering maximum TPC as 28.7564, maximum AA as 6.7714, maximum TMAC as 4.9616, maximum L* as 21.3839, maximum a* as 5.5396, maximum b* as 3.4260, maximum color density as 27.1412, minimum polymeric color as 17.3148, minimum PC% as 4.9253, minimum TYC as 1.9251 and minimum TMBC as 2.3301. The optimum levels were found to be 54 W, 8 min, 27.06 V/cm and 8 min. Improvements in TPC, TMAC and AA were determined after ultrasound‐ohmic combination thermal treatment. Although ohmic (OH) has the effect of maintaining juice quality, its effectiveness is impacted by the electrochemical properties of food. By using ultrasound, the electrical conductivity values of juices were increased; thus, ultrasound improved the electrochemical properties of sour cherry juice. Further studies are needed to evaluate the effects of ultrasound‐ohmic combination for juice processing on a large scale.

Investigation of the geometric parameters on the performance of glass fiber reinforced polymer laminated mechanical joints under fatigue loading

AbstractThe present work optimizes the geometric parameters of glass fiber reinforced polymer (GFRP) laminated pin joints under fatigue loading conditions using response surface methodology (RSM). RSM is utilized to construct polynomial functions that optimally capture the behavior of experimental data, intending to make statistical inferences. Key geometric parameters, that is the edge and width dimensions were studied in pursuit of enhanced performance under fatigue loading of pin joints. The study aimed to validate the effectiveness of the optimized parameters, particularly for attaining load‐bearing capacity, fatigue life, and failure mode using numerical and experimental approaches. Optimal configurations were achieved, that is edge and width equal to 16 mm at which the failure mode shifts to bearing mode. Another configuration with an edge of 20 mm and a width of 22 mm was achieved for optimized fatigue life. These findings underscore the practical implications of the optimization process, with the potential for substantial gains in fatigue life and strength of GFRP laminated pin joints in engineering applications. The identified optimal configurations exhibited a non‐catastrophic bearing failure mode, emphasizing a crucial characteristic that provides prior‐warning before the occurrence of final failure. The experimental results align well with the numerical outcomes regarding fatigue life and the mode of failure.Highlights Parametric optimization of GFRP pin joint under cyclic loading. Optimization of GFRP pin joints via RSM for cyclic loading. Validation through numerical and experimental approaches. Comparison of fatigue life at optimum design points. Depiction of the failure modes using damage pattern under fatigue loading.

Conundrums of the Liquidity Determinants of Commercial Banks in Ethiopia

This study examines the conundrums of the liquidity determinants of commercial banks in Ethiopia. The study employed a causal research design by utilizing data from 2011 to 2021. The study unit of analysis is 15 commercial banks in Ethiopia. It utilized General Methods of Moment (GMM) dynamic panel regression. The finding showed that bank profit, interest rate margin, concentration ratio, and inflation have significantly adverse effects on bank liquidity. However, capital adequacy has no significant effect on the bank's liquidity. Besides, the growth of the gross domestic product and exchange rate significantly enhance the bank's liquidity. The study urges bank management and regulatory bodies to search for the optimum trade-off point between profit and interest rate spread at the required level of liquidity. Besides, policymakers and regulators should take measures that neutralize the detrimental effect of the market structure of the banking industry. Since inflation and money supply (M2) negatively affect liquidity, it is vital to transform macroeconomic variables into a robust institutional mechanism to enhance liquidity. Generally, the study recommends continued supervision and support to shield banks from a liquidity crunch.