Local Maximum Research Articles

An MPPT method using phasor particle swarm optimization for PV‐based generation system under varying irradiance conditions

AbstractThe dependency of photovoltaic (PV) systems‐based generation systems on constantly varying temperatures and incident sunlight affect the non‐linear behaviour of PV panels. Therefore, tracking the maximum power output of the PV panels for efficient utilization becomes a challenge, resulting in power losses and the creation of intense heating spots in the shaded areas of the PV modules when the PV modules receive varying degrees of insolation, that is, under partial shading conditions (PSCs). The inclusion of bypass diodes in parallel to each PV module mitigates this problem to an extent but leads to the formation of several peaks in the P‐V characteristics. As a result, maximum power point tracking (MPPT) to deliver maximum power at the load becomes a limitation for conventional optimization algorithms as they are normally based on hill climbing algorithm and get stuck at local maxima of the P‐V curve. Therefore, metaheuristic algorithms are used thereby eliminating the possibility of getting trapped at the local optima. However, particle swarm optimization (PSO) suffers from delayed convergence, more iterations to reach the optimal point, and random parameter selection. Hence, this study employs an improved version of PSO called Phasor‐PSO (P‐PSO) in an MPPT controller. The proposed algorithm is parameter‐less which results in reduced computational complexity and thus provides quick decision in achieving the maximum power point (MPP). The hardware‐in‐loop real‐time analysis demonstrates the supremacy of P‐PSO over PSO in faster convergence, higher efficiency, and reduced power losses during tracking under various PSCs. The P‐PSO based MPPT method will find application in grid connected and stand‐alone solar PV system with better efficiency of power transfer.

Mean-Field-Type Transformers

In this article, we present the mathematical foundations of generative machine intelligence and link them with mean-field-type game theory. The key interaction mechanism is self-attention, which exhibits aggregative properties similar to those found in mean-field-type game theory. It is not necessary to have an infinite number of neural units to handle mean-field-type terms. For instance, the variance reduction in error within generative machine intelligence is a mean-field-type problem and does not involve an infinite number of decision-makers. Based on this insight, we construct mean-field-type transformers that operate on data that are not necessarily identically distributed and evolve over several layers using mean-field-type transition kernels. We demonstrate that the outcomes of these mean-field-type transformers correspond exactly to the mean-field-type equilibria of a hierarchical mean-field-type game. Due to the non-convexity of the operators’ composition, gradient-based methods alone are insufficient. To distinguish a global minimum from other extrema—such as local minima, local maxima, global maxima, and saddle points—alternative methods that exploit hidden convexities of anti-derivatives of activation functions are required. We also discuss the integration of blockchain technologies into machine intelligence, facilitating an incentive design loop for all contributors and enabling blockchain token economics for each system participant. This feature is especially relevant to ensuring the integrity of factual data, legislative information, medical records, and scientifically published references that should remain immutable after the application of generative machine intelligence.

Peak-Based Machine Learning for Plastic Type Classification in Time-of-Flight Secondary Ion Mass Spectrometry.

Time-of-flight secondary ion mass spectrometry (ToF-SIMS) measurement data and machine learning were used in this work to classify six different types of plastics. In order to take into account the characteristics of the measurement data, the local maxima of the measurement data were first examined in a preprocessing step. Several machine learning methods were then implemented to create a model that could successfully classify the plastics. To visualize the data distribution, we applied a dimensionality reduction method, namely, principal component analysis. Finally, to distinguish between the six types of plastics, we conducted an ensemble analysis using four tree-based algorithms: decision tree, random forest, gradient boosting, and LIGHTGBM. This approach can identify the feature importance of plastic samples and allow the inference of the chemical properties of each plastic type. In this way, ToF-SIMS data could be utilized to successfully classify plastics and enhance explainability.

North Pacific warmth synchronous with the Miocene Climatic Optimum

Peak Neogene warmth and minimal polar ice volumes occurred during the Miocene Climatic Optimum (MCO, ca. 16.95−13.95 Ma) followed by cooling and ice sheet expansion during the Middle Miocene Climate Transition (MMCT, ca. 13.95−12.8 Ma). Previous records of northern high-latitude sea surface temperatures (SSTs) during these global climatic transitions are limited to Atlantic sites, and none resolve orbital-scale variability. Here, we present an orbital-resolution alkenone SST proxy record from the subpolar North Pacific that establishes a local maximum of SSTs during the MCO as much as 16 °C warmer than modern with rapid warming initiating the MCO, cooling synchronous with Antarctic ice sheet expansion during the MMCT, and high variability on orbital time scales. Persistently cooler North Pacific SST anomalies than in the Atlantic at equivalent latitudes throughout the Miocene suggest enhanced Atlantic northward heat transport under a globally warm climate. We conclude that a global forcing mechanism, likely elevated greenhouse gas concentrations, is the most parsimonious explanation for synchronous global high-latitude warmth during the Miocene.

Magnetic resonance imaging diagnosis of ankle joint athletic injury based on machine learning algorithms

The diagnosis of ankle joint athletic injuries using traditional magnetic resonance imaging (MRI) relies on the subjective judgment and experience of doctors, and small structural changes in athletic injuries are difficult to accurately detect and diagnose. By using machine learning (ML) algorithms and image processing techniques to obtain objective and consistent diagnostic results, the accuracy of diagnosing ankle joint athletic injuries can be improved. This article collected a large number of MRI images of ankle joint athletic injuries, and preprocessed the collected images to extract morphological and texture features, and perform feature fusion. The Residual Network (ResNet) was improved, and the Leaky linear rectification function (ReLU, Corrected linear unit) activation function was introduced. The transfer learning was utilized to increase the convergence speed of the model, and the global maximum pooling layer and softmax classifier were used to construct the fully connected layer. After sufficient training on the training set, the findings on the test set indicated that the average accuracy of the improved ResNet model for ankle joint injury classification was 98.3%. The use of an improved ResNet model can effectively improve the diagnostic effectiveness of ankle joint athletic injuries, providing a new method for medical diagnosis of MRI.

Curing monitoring of adhesive layers between metal adherends by ultrasonic resonance technique

Abstract Layer resonance induced by ultrasonic wave incidence is applied to monitor the viscoelastic properties of a curing adhesive layer between metal plates. The theoretical analysis shows that when the adhesive layer is modeled as a linear viscoelastic material, the ultrasonic reflection spectrum takes local minima at the layer resonance frequencies depending on the wave velocity of the adhesive. On the other hand, the local minima of the reflection spectrum can be expressed as a function of the loss factor of the adhesive. Based on these results, a characterization technique for the wave velocity and the loss factor of a curing adhesive layer is proposed. This technique enables the evaluation of the viscoelastic properties even if the reflected waves from both faces of a bond layer cannot be separated. The proposed method is employed to investigate the curing behavior of bi-component epoxy adhesives. As a result, it is shown that the bonding condition affects the variation of the wave velocity and loss factor. The estimated wave velocity increases as the curing proceeds, while the loss factor sometimes takes a local maximum depending on the bonding condition and the frequency range. When the mixing ratio of the main and curing agents is imbalanced, the variation of the wave velocity becomes gradual. Furthermore, the increase in the curing temperature leads to fast changes in the wave velocity and loss factor. The proposed technique has the potential to provide insight into the curing behavior of the adhesive layer by incorporating it into other measurement methods and theories.

Evaluating methods for identifying and quantifying Streptococcus pneumoniae co-colonization using next-generation sequencing data.

Detection of multiple pneumococcal serotype carriage can enhance monitoring of pneumococcal vaccine impact, particularly among high-burden childhood populations. We assessed methods for identifying co-carriage of pneumococcal serotypes from whole-genome sequences. Twenty-four nasopharyngeal samples were collected during community carriage surveillance from healthy children in Blantyre, Malawi, which were then serotyped by microarray. Pneumococcal DNA from culture plate sweeps were sequenced using Illumina MiSeq, and genomic serotyping was carried out using SeroCall and PneumoKITy. Their sensitivity was calculated in reference to the microarray data. Local maxima in the single-nucleotide polymorphism (SNP) density distributions were assessed for their correspondence to the relative abundance of serotypes. Across the 24 individuals, the microarray detected 77 non-unique serotypes, of which 42 occurred at high relative abundance (>10%) (per individual, median, 3; range, 1-6 serotypes). The average sequencing depth was 57X (range: 21X-88X). The sensitivity of SeroCall for identifying high-abundance serotypes was 98% (95% CI, 0.87-1.00), 20% (0.08-0.36) for low abundance (<10%), and 62% (0.50-0.72) overall. PneumoKITy's sensitivity was 86% (0.72-0.95), 20% (0.06-0.32), and 56% (0.42-0.65), respectively. Local maxima in the SNP frequency distribution were highly correlated with the relative abundance of high-abundance serotypes. Six samples were resequenced, and the pooled runs had an average fourfold increase in sequencing depth. This allowed genomic serotyping of two of the previously undetectable seven low-abundance serotypes. Genomic serotyping is highly sensitive for the detection of high-abundance serotypes in samples with co-carriage. Serotype-associated reads may be identified through SNP frequency, and increased read depth can increase sensitivity for low-abundance serotype detection.IMPORTANCEPneumococcal carriage is a prerequisite for invasive pneumococcal disease, which is a leading cause of childhood pneumonia. Multiple carriage of unique pneumococcal serotypes at a single time point is prevalent among high-burden childhood populations. This study assessed the sensitivity of different genomic serotyping methods for identifying pneumococcal serotypes during co-carriage. These methods were evaluated against the current gold standard for co-carriage detection. The results showed that genomic serotyping methods have high sensitivity for detecting high-abundance serotypes in samples with co-carriage, and increasing sequencing depth can increase sensitivity for low-abundance serotypes. These results are important for monitoring vaccine impact, which aims to reduce the prevalence of specific pneumococcal serotypes. By accurately detecting and identifying multiple pneumococcal serotypes in carrier populations, we can better evaluate the effectiveness of vaccination programs.

Near-Infrared Spectroscopy for Growth Estimation of Spirulina platensis Cultures

The present study proposes the use of Near-Infrared (NIR) spectroscopy as a rapid method for estimating the growth of Spirulina platensis cultures, avoiding any sample manipulation or pretreatment. NIR spectroscopy in diffuse reflectance mode was used on culture volumes as received, with Principal Component Analysis (PCA) and Partial Least Squares (PLS) linear regression, for developing the calibration model in the wavelength range of 1000–2500 nm, in order to choose the appropriate wavelength to estimate the growth of the microalga. The local reflectance maximum at 1062.6 nm, connected with reduced water absorption and scattering effects by the microalga, was identified from PCA as the positive peak in the first loading plot, correlating diffuse reflectance with dilution levels. The calibration curve of diffuse reflectance at 1062.6 nm in response to dilution presented strong linearity, supported by a coefficient of determination (R2) of 0.995. Cross-validation of NIR spectra with a S. platensis culture confirmed the method’s reliability, showing that the growth follows an exponential pattern. The study shows that diffuse reflectance NIR spectroscopy can be used for the rapid monitoring of Spirulina platensis growth.

Algorithms for optimizing systems with multiple extremum functionals

The problem of minimizing (maximizing) multiple extremum functionals (infinite-dimensional optimization) is considered. This problem cannot be solved by conventional gradient methods. New gradient methods with adaptive relaxation of steps in the vicinity of local extrema are proposed. The efficiency of the proposed methods is demonstrated by the example of optimizing the shape of a hydraulic gun nozzle with respect to the objective functional, which is the average force of the hydraulic pulse jet momentum acting on an obstacle. Two local maxima are found, the second of which is global; in the second maximum, the average force of the jet momentum is three times higher than in the first maximum. The corresponding nozzle shape is optimal. Conventional gradient methods have not found any maximum; i.e., they were unable to solve the problem.

Thread Displacement and Intensity Oscillations in a Quiescent Prominence

In this paper, we investigate the thread displacement and intensity oscillations in a quiescent prominence observed by New Vacuum Solar Telescope at the Hα line center on 2019 October 31. Each individual thread is traced by the local maximum intensity among its width at various times. In total, 35 threads are detected at six heights parallel to the solar surface. We find 29/35 threads exhibiting the displacement oscillation. A sinusoidal function is used to fit them, and a mean period of 26 minutes is identified. By slicing the same thread at different positions, we find that the oscillation of the thread is very likely a standing wave, but it could also be a long-wavelength propagating wave. After integrating the intensity along the thread width, we also find 8/35 threads presenting their intensity oscillation with a mean period of 7.7 minutes. In total, 7/35 threads exhibit both the displacement and the intensity oscillations.

Deep joint subdomain alignment for unsupervised domain adaptation

Domain adaptation (DA) aims to transfer knowledge of the labeled source domain to the unlabeled target domain. Current DA methods learn domain-invariant features by aligning source and target feature spaces by distribution discrepancy minimization or adversarial training. However, existing DA methods fail to consider the semantic-level alignment, which can lead to the distortion of semantic feature structures for the target tasks. In this paper, we propose a novel domain adaptation method, called Deep Joint Subdomain Alignment (DJSA), which jointly conducts intra-subdomain and inter-subdomain alignment for fine-grained classification of target tasks. DJSA obtains the pseudo labels of unlabeled target samples by measuring the similarity between the target samples and source category centers, and then DJSA calculates an intra-subdomain loss to reduce the difference within the same class and increase the difference between different classes in both the source domain and the target domain, and DJSA utilizes a Local Maximum Mean Discrepancy (LMMD) metric to measure the inter-subdomain discrepancy between the source and target domain, and minimizes the discrepancy to align different domains. Furthermore, extensive experimental results demonstrate the effectiveness and superiority of our proposed method in unsupervised domain adaptation.

Large eddy simulation of a marine propeller with leading edge tubercles

The results of large eddy simulations on a cylindrical grid consisting of 5.8 × 109 points are reported, dealing with marine propellers with leading edge tubercles (LETs). They are compared with the performance and flow fields of the baseline geometry without tubercles. In general, the efficiency of propulsion is not improved, but a substantial effect is produced on the development of the flow across the propeller blades. The minima of pressure on the suction side of the blades are confined in the troughs of the leading edge, with the potential of reducing the overall extent of the area of cavitation (cavitation funneling effect). In addition, local maxima of turbulence are produced on the suction side of the blades by the onset of streamwise vortices at the troughs of the LETs. Although the wake development is slightly modified across blade geometries, no obvious influence of the LETs on the major wake structures is observed. Due to their early breakup, the vortices developing across the span of the propeller blades, including those originating at the LETs, are able to affect indeed a very short extent of the propeller wake. Its dynamics is still dominated by the tip and hub vortices, as for the conventional design of the propeller. Meanwhile, the intensity of the root vortices shed by the conventional propeller is substantially reduced in the wake of the tubercled propellers, thanks to the modified geometry of the blades at their root, resulting also in a slightly slower instability of the hub vortex.

Fault diagnosis of wind power pitch bearings based on spatiotemporal clustering and a deep attention subdomain adaptive residual network

The pitch bearing adjusts the blade pitch angle to maintain optimal windward surface exposure. However, the challenging operating conditions often result in premature localized spalling of the bearings. This study introduces an approach utilizing spatiotemporal clustering (STC) alongside a deep subdomain adaptive residual network employing channel attention to assess bearing damage. The spatiotemporal clustering feature map is created by analyzing adjacent impact times of acoustic emission signals to determine fault occurrence and severity, with the Mel-AMDSAN network employed for intelligent fault diagnosis. By integrating the “Squeeze and Excitation” (SE) module into ResNet50, domain-invariant features associated with faults are extracted, while local maximum mean discrepancy (LMMD) is utilized to align fault-type distributions across different domains. Experimental data from wind power pitch bearing test benches is utilized. The results show that the method has strong advantages and application value in terms of real-time, accuracy and cross-domain diagnosis.

PV Array Reconfiguration for Global Maximum Power Optimizing under Partial Shading Conditions based on PV Switched System

Background: Due to the partial shading effects on different photovoltaic (PV) modules in a PV array, PV module operating conditions are inconsistent, and PV array output power is significantly reduced. Although the maximum power point (MPP) of a non-uniform irradiance PV array can be observed through global maximum power point tracking (GMPPT), no evaluation of the array's energy potential has been done. Objective: One of the most effective solutions to overcome the negative effects of partial shading in PV systems is the PV array reconfiguration process. To optimize the electrical structure of the PV array as the PV modules are partially shaded in a non-uniform manner, this study proposes a promising technique for dynamic reconfiguring a PV array in order to improve the extracted maximum power from a PV array under partial shading conditions (PSC). Methods: PV modules are rearranged by iteratively sorting them to allow the PV array with nonuniform irradiance to produce as much power as possible. This is conducted by applying a switching matrix to implement a PV-switched system approach. The proposed system with different PV array dimensions (e.g., 3×4, 4×6, and 5×8) is assessed in order to validate the proposed algorithm. A MATLAB/Simulink PV array developed model is used to find the global maximum power point for the different PV array dimensions in both pre-reconfiguration and post-reconfiguration states. Results: A detailed numerical comparison of the extracted power from the proposed system has been provided. Conclusion: The results show that the proposed system has the potential to extract the exact global maximum power for a PV-switched system under PSC, irrespective of array dimensions, according to simulation results.

Performance evaluation of different photovoltaic array configurations under partial shading

In Partial Shading Conditions (PSCs), Photo Voltaic (PV) systems often experience notable output power and efficiency reductions due to weather variations. This study is dedicated to determining the most effective PV array configuration under partial shading. Various configurations, including Series Parallel (SP), Total Cross Tied (TCT), Bridge Linked (BL), Honey Comb (HC), Double Tied (DT), and hybrid connections, are simulated and evaluated under PSCs. Nine shading patterns, such as vertical, horizontal, centre-wise, upper triangular, cross-wise, expansive, arbitrary, L-shaped, and diagonal, are examined using a 6 × 6 array of PV Configuration. Performance analysis is based on parameters such as open circuit voltage (Voc), short circuit current (Isc), Global Maximum Power Point (GMPP), maximum voltage (Vm), maximum current (Im), Fill Factor (FF), Mismatch Loss (ML)/Power Loss (PL), and efficiency (η). Additionally, hybrid configurations like Alternate- Total Cross Tied -Bridge Linked (ALT-TCT-BL), Alternate -Total Cross Tied -Double Tied (ALT-TCT-DT), and Alternate -Total Cross Tied -Triple Tied (ALT-TCT-TT) are investigated and compared with existing configurations. Hybrid configurations with fewer cross-ties are recommended to simplify circuit complexity. MATLAB/Simulink software is employed for simulation, using a Sunpower SPR-E18-295-COM panel. Comparative analysis confirms that hybrid configurations exhibit higher efficiency for various shading patterns than existing configurations, equal or at par with the TCT connection. For eight connections and nine shading scenarios along with zero (no) shading conditions, eighty distinct combinations have been analysed, and on comparative analysis with contemporary work, the optimal output is obtained from the connections considered under PSCs. For all the shading patterns considered, the proposed work fetches maximum efficiency compared to the contemporary work, and it ranges between 13.61 % and 17.84 % for different shading patterns. The maximum value of efficiencies for horizontal, vertical, diagonal, centre-wise, expansive, arbitrary, upper triangular and L-shaped shading patterns is 13.77 %, 17.24 %, 17.84 %, 15.66 %, 13.61 %, 17.21 %,17.35 % and 15.11 % respectively. The hybrid configuration achieves higher efficiency than current methods for all shading patterns and configurations, with improvements in efficiency ranging from 2.1 % to 42.22 % across all cases.

The Chemerpil gravimagnetic anomaly as a modern reflection of the unique Precambrian volcanic-plutonic magnetite-gold mineralisation structure of the Ukrainian Shield (Middle Bug area)

The article is devoted to the peculiarities of genesis and mineralogical potential of Chemerpol iron-magnetite structure, its difference from other similar objects of Central Bug area. As an independent geological-geophysical object it was established according to the data of gravimetric and magnetometric survey of medium scale as a complex localised maximum of subisometric shape with diameter of 1.7 km and area of 2.8 km2. In regional structural and tectonic terms, this maximum is confined to the Sinitsiv block of the Golovanovsk suture zone. As of today, the Chemerpol structure has been sufficiently investigated by mapping drilling and various geophysical methods at the level of the crystalline basement surface. Based on the obtained data, the main petrophysical complexes involved in its structure have been determined. The industrial and mineralogical potential of the Chemerpolska structure is estimated at 100 million tons of magnetite quartzites and carbonate formations, as well as 2 tons of exogenous gold in the weathering crust. No estimates have been made of the underlying gold deposits. Copper mineralisation, represented by the natural copper, chalcopyrite and chalcosine, has been identified in the weathering crust and crystalline basement. By structural and tectonic features, petrophysical and mineralogical diversity, the Chemerpolska structure can be attributed to local pipe volcanic-magmatic objects. Its closest well-studied geological analogues may be such Precambrian "stratified" iron-oxide-gold-copper deposits as Solobot and others of the Carajas ore district in Brazil. For geological verification of new conclusions about the peculiarities of the Chemerpol structure and its actual ore potential, it is necessary to continue its study based, first of all, on the data of targeted structural exploration drilling and deep seismic and electrical exploration methods. The economic efficiency of extraction of the main mineral components of the Chemerpolska structure - gold and magnetite iron - can be increased due to the accompanying use of non-metallic calcifiers for the manufacture of sintering flux, soil deoxidation, raw materials for road pavement.

Experimental validation of effective zebra optimization algorithm-based MPPT under partial shading conditions in photovoltaic systems

This study introduces a novel approach for analyzing photovoltaic (PV) systems that employ block lookup tables for speedy and efficient simulation. It introduces an innovative method for tracking the Global Maximum Power Point (GMPP) by utilizing Zebra Optimization Algorithm (ZOA). The suggested method was carefully evaluated under difficult Partial Shading Conditions (PSCs) and Dynamic Shading Conditions (DSCs) to determine its global and local search capability. ZOA’s performance was examined in four scenarios and compared to four existing MPPT algorithms: Grey Wolf Optimization (GWO), Particle Swarm Optimization (PSO), Flower Pollination Algorithm (FPA), and Whale Optimization Algorithm (WOA). ZOA surpassed its competitors with an average tracking time of 0.875 s and a tracking efficiency of 99.95% in PSCs. In comparison, ZOA increased tracking efficiency by up to 2%, increased resilience under varied circumstances, and produced a faster convergence speed—approaching the maximum Power Point 10–15% faster than the other algorithms. Furthermore, ZOA significantly decreased operating point variations. The algorithm’s overall performance was tested using an experimental setup with a DSPACE board and a PV emulator. These findings demonstrate that ZOA is a highly efficient and dependable MPPT solution for PV systems, especially in severe PSCs.

Enhanced artificial hummingbird algorithm with chaotic traversal flight

Tackling the shortcomings of slow convergence, imprecision, and entrapment in local optima inherent in traditional meta-heuristic algorithms, this study presents the enhanced artificial hummingbird algorithm with chaotic traversal flight (CEAHA), which employs chaotic ergodicity within the foundational framework of the conventional artificial hummingbird algorithm. This approach implements chaotic motion within local regions of the solution space, ensuring a thorough exploration of potential optima and preventing algorithmic stagnation at local maxima by guaranteeing a non-repetitive traversal of all search states. This study also analyzes the intrinsic mechanisms by which eight different chaotic mappings affect optimization performance, from the perspectives of invariant measures and traversal efficiency of ergodic chaotic motion. In comparative tests with 21 meta-heuristic algorithms on the CEC2014, CEC2019, and CEC2022 benchmark suites across various dimensions, CEAHA demonstrates superior optimization performance. Furthermore, the practicability and robustness of CEAHA have been confirmed in mechanical design optimization problems through 4 engineering instances: pressure vessel, gear trains, speed reducers, and piston levers.

Enhanced MPPT Based on Zebra Optimization Algorithm for Control of a Partially Shaded Photovoltaic System

Objective: The objective of this paper is to adapt the Zebra Optimization Algorithm (ZOA) to track the Global Maximum Power Point (GMPP) of a PV system when subjected to partial shading conditions. Theoretical Framework: Photovoltaic (PV) systems are increasingly being used due to their clean nature and low maintenance cost. However, solar cells exhibit a nonlinear relationship between current and voltage, which necessitates the use of Maximum Power Point Tracking (MPPT) algorithms to ensure the system operates at its full potential. Bypass diodes are integrated into PV modules to protect solar cells from damage during partial shading, but this leads to a distorted multi-peak Power-Voltage (P-V) curve. This distortion presents a challenge for algorithms to distinguish between Global Maximum Power Point (GMPP) and Local Maximum Power Points (LMPPs). Method: The Zebra Optimization Algorithm (ZOA) is applied to track the GMPP of a PV system under partial shading conditions. Simulations were conducted using the MATLAB/Simulink platform to evaluate the algorithm’s performance. The focus was on ensuring fast and accurate tracking of the GMPPT under rapid changes in solar irradiance levels. Results and Discussion: Simulation results demonstrated that the ZOA-based MPPT algorithm effectively tracked the GMPPT of the PV system, even under rapid variations in solar irradiance. The algorithm proved its accuracy and speed in distinguishing the GMPP from LMPPs, addressing the challenges posed by the distorted P-V curve. Research Implications: The successful application of the ZOA for tracking the GMPP in partial shading conditions offers a significant improvement for PV systems. This can lead to enhanced efficiency in energy production and more reliable performance in real-world scenarios, where shading and other environmental factors frequently affect solar panels. Originality/Value: This study introduces the adaptation of the Zebra Optimization Algorithm (ZOA) in MPPT for PV systems, addressing the issue of distinguishing between GMPP and LMPPs under partial shading. The approach demonstrates both innovation and practical value in improving the efficiency of PV systems in challenging environmental conditions.

A convexity-constrained parameterization of the random effects generalized partial credit model.

An alternative closed-form expression for the marginal joint probability distribution of item scores under the random effects generalized partial credit model is presented. The closed-form expression involves a cumulant generating function and is therefore subjected to convexity constraints. As a consequence, complicated moment inequalities are taken into account in maximum likelihood estimation of the parameters of the model, so that the estimation solution is always proper. Another important favorable consequence is that the likelihood function has a single local extreme point, the global maximum. Furthermore, attention is paid to expected a posteriori person parameter estimation, generalizations of the model, and testing the goodness-of-fit of the model. Procedures proposed are demonstrated in an illustrative example.