Albania is now implementing a range of steps as part of its journey towards European Union integration, based on agreements that have been achieved. Key to these initiatives is the extensive adoption of circular economy concepts through comprehensive waste management systems. This collaboration is based on systematically implementing measures that align with the fundamental principles of the waste management hierarchy. Albania wants to lead in waste-to-energy conversion exploration by focusing on trash minimization, reuse, recycling, and energy generation from residual waste. Although there has been notable advancement, especially in aligning laws with EU requirements, there are practical obstacles, especially in the execution of waste-to-energy projects. The challenges involve the need for effective waste segregation, higher recycling rates, and the use of advanced waste-to-energy technologies. The essay utilizes meticulously selected data on Albania’s waste generation from reputable organizations and the legal framework regulating waste management to assess the current situation and predict future possibilities, which may be advantageous for government ministries and agency platforms. Full article

This work marks a significant advancement in the field of cognitive science and gaming technology. It offers an in-depth analysis of the effects of various video game genres on brainwave patterns and concentration levels in virtual reality (VR) settings. The study is groundbreaking in its approach, employing electroencephalograms (EEGs) to explore the neural correlates of gaming, thus bridging the gap between technology, psychology, and neuroscience. This review enriches the dialogue on the potential of video games as a therapeutic tool in mental health. The study’s findings illuminate the capacity of different game genres to elicit varied brainwave responses, paving the way for tailored video game therapies. This review contributes meaningfully to the state of the art by offering empirical insights into the interaction between gaming environments and brain activity, highlighting the potential applications in therapeutic settings, cognitive training, and educational tools. The findings are especially relevant for developing VR gaming content and therapeutic games, enhancing the understanding of cognitive processes, and aiding in mental healthcare strategies. Full article

In recent years, the widespread application of Mg alloy casting and Mg alloy products has generated a large amount of Mg alloy waste. This experiment used a single factor experimental analysis method to study the optimal process for removing Fe from Mg alloy AM50A waste, and developed an efficient Fe removal and regeneration process for Mg alloy AM50A. It was found that the optimal refining temperature for removing Fe ions was 670 °C, the optimal refining (RJ-2) agent mass ratio was 1.5%, and the optimal refining time was 40 min. Regenerated J40-1.5-AM50A Mg alloy was prepared using the best refining process, and its composition and mechanical properties were tested and analyzed. The experimental results show that the composition of the regenerated J40-1.5-AM50A Mg alloy prepared by this method is consistent with AM50A, with an Fe removal rate of 96.2%. The mechanical properties were improved compared to the original AM50A sample, with a maximum tensile strength increase of 1.611 KN and a tensile strength increase of 26.333 MPa. The elongation after fracture is 2.25 times that of the original sample. Research has shown that the RJ-2 refining agent can provide mechanical properties of magnesium alloys during the refining process. By analyzing the composition, XRD, SEM, and EDS of AM50A (Fe) and J40-1.5-AM50A, it was found that the refining process accelerates the removal of Fe in the form of Fe deposition. Full article

Background: Previous neuroimaging studies have identified brain regions related to respiratory motor control and perception. However, little is known about the resting-state functional connectivity (FC) associated with respiratory impairment. We aimed to determine the FC involved in mild respiratory impairment without altering transcutaneous oxygen saturation. Methods: We obtained resting-state functional magnetic resonance imaging data from 36 healthy volunteers during normal respiration and mild respiratory impairment induced by resistive load (effort breathing). ROI-to-ROI and seed-to-voxel analyses were performed using Statistical Parametric Mapping 12 and the CONN toolbox. Results: Compared to normal respiration, effort breathing activated FCs within and between the sensory perceptual area (postcentral gyrus, anterior insular cortex (AInsula), and anterior cingulate cortex) and visual cortex (the visual occipital, occipital pole (OP), and occipital fusiform gyrus). Graph theoretical analysis showed strong centrality in the visual cortex. A significant positive correlation was observed between the dyspnoea score (modified Borg scale) and FC between the left AInsula and right OP. Conclusions: These results suggested that the FCs within the respiratory sensory area via the network hub may be neural mechanisms underlying effort breathing and modified Borg scale scores. These findings may provide new insights into the visual networks that contribute to mild respiratory impairments. Full article

Bi-directional DC-AC converters are widely used in the field of electric vehicle-to-grid. However, the inductance of the grid-side interface filter is affected by the length of the grid connection and the power level, which presents nonlinear characteristics. This poses challenges for high-performance grid waveform control. In this paper, a modeling method for bi-directional DC-AC grid-connected converters based on type-II T-S fuzzy models is proposed, and the corresponding type-II T-S fuzzy control strategy is designed to address the parameter uncertainty and non-linearity issues. Simulation results show that type-II T-S fuzzy control offers superior control performance and better current waveform quality compared to type-I T-S fuzzy control under uncertainty parameter conditions. The effectiveness of the proposed strategy is further validated through a 1 kW prototype of a bi-directional DC-AC converter. Full article

The investigation of magnesium (Mg) isotopes in dolomite has mainly focused on marine dolomite environments, leaving a significant gap in the understanding of their dynamics within lacustrine settings, especially in saline lake basins. In this study, a total of 16 sediment core samples from Well BX-7 in the Qianjiang Depression were sequentially selected for scanning electron microscope observation, whole-rock analysis for major and minor elements, and isotopic measurements including δ¹⁸O_carb, δ¹³C_carb, δ²⁶Mg_dol, and δ²⁶Mg_Si. In addition, two intact cores were subjected to detailed analysis on the centimeter scale. Sedimentation models were established to elucidate dolomite formation under contrasting climatic conditions, specifically humid climates with a significant riverine Mg input versus relatively dry conditions with a lower Mg input. Furthermore, a quantitative model was developed to assess the magnesium flux and isotopic mass balance within lacustrine systems, simulating the magnesium isotope variations in lake water under different climatic scenarios. The dolomite sample data at a smaller scale (sampling interval ≈ 3~5 mm) demonstrate a consistent trend with the established model, providing additional confirmation of its reliability. Dolomite precipitated under humid climatic conditions exhibits a lower and relatively stable δ²⁶Mg_dol, lower δ¹⁸O, and higher CIA, indicating higher river inputs and relatively stable Mg isotope values of lake water controlled by river input. Nevertheless, dolomite formed under relatively dry climatic conditions shows a relatively high δ²⁶Mg_dol, higher δ¹⁸O, and lower CIA, suggesting reduced river inputs and weathering intensity, as well as relatively high magnesium isotope values of the lake water controlled by dolomite precipitation. This study contributes to the understanding of magnesium isotopes in lacustrine dolomite systems. Full article

Aim: This in vitro study aimed to compare the light-transmission properties of two chairside CAD/CAM lithium disilicate (LD) ceramics (a novel fully crystallized and a traditional pre-crystallized) across varying thicknesses. Materials and Methods: One hundred flat specimens were obtained from precrystallized (e.max CAD, Ivoclar Vivadent, Schaan, Liechtenstein) and fully crystallized (LiSi GC Block; GC, Tokyo, Japan) LD at five different thicknesses (0.5, 0.75, 1.0, 1.50 and 2.0 mm). All specimens were polished with a polishing system for lithium disilicate restorations following recommendations from the manufacturer. Light transmission was evaluated with a radiometer. The statistical analysis between e.max CAD and LiSi GC Block was performed using a Mann–Whitney test for each thickness at a significance level of 0.05 (p < 0.05), followed by a Kruskal–Wallis test to compare the light transmission between the thicknesses of e.max CAD and LiSi GC Block. Results: Light transmittance was significantly affected by ceramic thickness. The 0.5 mm thick specimens exhibited the highest transmittance values compared to all other groups, while a light transmittance of 0.00 was observed in the 2.0 mm thick specimens for both e.max CAD and LiSi GC Block. In the comparison between e.max CAD and LiSi GC Block according to thickness, there was a statistically significant difference exclusively between groups with a thickness of 1.50 mm (p = 0.002). Conclusions: Light transmission for pre- and fully crystallized CAD/CAM lithium disilicate ceramics only showed a statistical difference at the thickness of 1.50 mm (p = 0.002). E.max CAD demonstrated acceptable light transmission up to a thickness of 1.5 mm. Clinical Significance: A thickness of 2 mm for chairside CAD/CAM lithium disilicate ceramics, whether pre-crystallized or fully crystallized, necessitates the use of dual-cure resin luting cement due to reduced light transmission. Full article

Delay-sensitive task offloading in a device-to-device assisted mobile edge computing (D2D-MEC) system with energy harvesting devices is a critical challenge due to the dynamic load level at edge nodes and the variability in harvested energy. In this paper, we propose a joint dynamic task offloading and CPU frequency control scheme for delay-sensitive tasks in a D2D-MEC system, taking into account the intricacies of multi-slot tasks, characterized by diverse processing speeds and data transmission rates. Our methodology involves meticulous modeling of task arrival and service processes using queuing systems, coupled with the strategic utilization of D2D communication to alleviate edge server load and prevent network congestion effectively. Central to our solution is the formulation of average task delay optimization as a challenging nonlinear integer programming problem, requiring intelligent decision making regarding task offloading for each generated task at active mobile devices and CPU frequency adjustments at discrete time slots. To navigate the intricate landscape of the extensive discrete action space, we design an efficient multi-agent DRL learning algorithm named MAOC, which is based on MAPPO, to minimize the average task delay by dynamically determining task-offloading decisions and CPU frequencies. MAOC operates within a centralized training with decentralized execution (CTDE) framework, empowering individual mobile devices to make decisions autonomously based on their unique system states. Experimental results demonstrate its swift convergence and operational efficiency, and it outperforms other baseline algorithms. Full article

We describe 20 datasets derived through signal filtering and feature extraction steps applied to the raw time series EEG data of 20 epileptic patients, as well as the methods we used to derive them. Background: Epilepsy is a complex neurological disorder which has seizures as its hallmark. Electroencephalography plays a crucial role in epilepsy assessment, offering insights into the brain’s electrical activity and advancing our understanding of seizures. The availability of tagged training sets covering all seizure phases—inter-ictal, pre-ictal, ictal, and post-ictal—is crucial for data-driven epilepsy analyses. Methods: Using the sliding window technique with a two-second window length and a one-second time slip, we extract multiple features from the preprocessed EEG time series of 20 patients from the Freiburg Seizure Prediction Database. In addition, we assign a class label to each instance to specify its corresponding seizure phase. All these operations are made through a software application we developed, which is named Training Builder. Results: The 20 tagged training datasets each contain 1080 univariate and bivariate features, and are openly and publicly available. Conclusions: The datasets support the training of data-driven models for seizure detection, prediction, and clustering, based on features engineering. Full article

Leukemias are among the most prevalent types of cancer worldwide. Bone marrow mesenchymal stem cells (MSCs) participate in the development of a suitable niche for hematopoietic stem cells, and are involved in the development of diseases such as leukemias, to a yet unknown extent. Here we described the effect of secretome of bone marrow MSCs obtained from healthy donors and from patients with acute myeloid leukemia (AML) on leukemic cell lineages, sensitive (K562) or resistant (K562-Lucena) to chemotherapy drugs. Cell proliferation, viability and death were evaluated, together with cell cycle, cytokine production and gene expression of ABC transporters and cyclins. The secretome of healthy MSCs decreased proliferation and viability of both K562 and K562‑Lucena cells; moreover, an increase in apoptosis and necrosis rates was observed, together with the activation of caspase 3/7, cell cycle arrest in G0/G1 phase and changes in expression of several ABC proteins and cyclins D1 and D2. These effects were not observed using the secretome of MSCs derived from AML patients. In conclusion, the secretome of healthy MSCs have the capacity to inhibit the development of leukemia cells, at least in the studied conditions. However, MSCs from AML patients seem to have lost this capacity, and could therefore contribute to the development of leukemia. Full article

Coconut palms (Cocos nucifera L.) are globally significant palms with both economic and cultural value. Despite the increasing demand for coconut products, production is decreasing globally due to palm senility, pests, and diseases. It has been estimated that over half of the world’s coconut palms need to be replaced immediately. The coconut industry has acknowledged that conventional propagation methods are unlikely to yield sufficient high-quality planting material. Therefore, coconut tissue culture is considered a potential solution to this problem. By using coconut tissue culture, a large number of plantlets can be obtained in a short period of time. In this study, the quality of explants and the development stage (visible shoot/non-visible shoot) of coconut used for micropropagation were examined. To our knowledge, little research has been undertaken on this aspect of coconut micropropagation. Our results indicated that tender coconut fruit exhibited an advantage over mature fruits. In addition, coconut plumule explants subjected to an extended storage of 15 days demonstrated enhanced development compared to those without storage. Notably, smaller embryos utilized as explants displayed superior callus formation compared to their larger counterparts. Finally, embryos possessing shoots exhibited improved callus initiation, albeit accompanied by a more pronounced browning effect. Further investigations are required to obtain more knowledge about the most suitable conditions for plumule explants that lead to optimal callus initiation. Full article

In this work, we propose a new three-dimensional constitutive equation related to a third-grade fluid. This proposal is based on experimental work for which the viscosity term and the terms related to viscoelasticity may depend on the shear rate, in accordance with a power-law type model. The numerical implementation of this fluid model is rather demanding in terms of computational calculation and, in this sense, we use the Cosserat theory related to fluid dynamics, which makes the transition from the three-dimensional fluid model to a one-dimensional fluid model for a specific geometry under study which, in this case, is a straight tube with constant circular cross-section. Based on this approximation theory, the one-dimensional fluid model is solved by assuming an ordinary differential equation involving: an unsteady mean pressure gradient; an unsteady volume flow rate; the Womersley number; and the viscosity and viscoelasticity parameters. Consequently, for specific data, and using the Runge–Kutta method, we can obtain the solution for the unsteady volume flow rate and we can present simulations to the three-dimensional velocity field. Full article

Investigating latent heat flux (LHF) variations in the western boundary current region is crucial for understanding air–sea interactions. In this study, we examine the LHF trend in the East China Sea Kuroshio Region (ECSKR) from 1959 to 2021 using atmospheric and oceanic reanalysis datasets and find that the LHF has a significant strengthening trend. This strengthening can be attributed to sea surface warming resulting from the advection of sea surface temperatures. More importantly, the LHF trend has an apparent seasonal dependence: the most substantial increasing trend in LHF is observed in spring, while the trends are weak in other seasons. Further analysis illustrates that the anomaly of air–sea humidity difference plays a pivotal role in controlling the seasonal variations in LHF trends. Specifically, as a result of the different responses of the East Asian Trough to global warming across different seasons, during spring, the East Asian Trough significantly deepens, resulting in northerly winds that facilitate the intrusion of dry and cold air into the ECSKR region. This intensifies the humidity difference between the sea and air, promoting the release of oceanic latent heat. These findings can contribute to a better understanding of the surface heat budget balance within western boundary currents. Full article

The aim of the present study was the validation of the already reported Bos taurus SNPs in the Sahiwal breed. A total of nine SNPs of the casein gene were studied. Out of nine, seven Bos taurus SNPs of casein protein genes were found to be significantly associated with milk productivity traits. The genomic DNA was extracted from the mammary alveolar endothelial cells of a flock of 80 purebred Sahiwal lactating dams available at Khizrabad Farm near Sargodha. New allele-specific primers were designed from the NCBI annotated sequence database of Bos taurus to obtain 100 nt-long PCR products. Each dam was tested separately for all the SNPs investigated. Animals with genotype GG for the SNPs rs43703010, rs10500451, and 110323127, respectively, exhibited high milk yield. Similarly, animals with genotype AA for the SNPs rs11079521, rs43703016, and rs43703017 showed high milk yield consistently. For the SNP rs43703015, animals with genotype CC showed high milk productivity. These above-mentioned SNPs have previously been reported to significantly up-regulate casein protein contents in Bos taurus. Our results indicated SNPs that significantly affect the milk protein contents may also significantly increase per capita milk yield. These finding suggest that the above-mentioned reported SNPs can also be used as genetic markers of milk productivity in Sahiwal cattle. Full article

Tryptophan metabolites, such as 5-hydroxytryptophan (5-HTP), serotonin, and melatonin, hold significant promise as supplements for managing various mood-related disorders, including depression and insomnia. However, their chemical production via chemical synthesis and phytochemical extraction presents drawbacks, such as the generation of toxic byproducts and low yields. In this study, we explore an alternative approach utilizing S. cerevisiae STG S101 for biosynthesis. Through a series of eleven experiments employing different combinations of tryptophan supplementation, Tween 20, and HEPES buffer, we investigated the production of these indolamines. The tryptophan metabolites were analyzed using liquid chromatography with tandem mass spectrometry (LC-MS/MS). Notably, setups replacing peptone in the YPD media with tryptophan (Run 3) and incorporating tryptophan along with 25 mM HEPES buffer (Run 4) demonstrated successful biosynthesis of 5-HTP and serotonin. The highest 5-HTP and serotonin concentrations were 58.9 ± 16.0 mg L⁻¹ and 0.0650 ± 0.00211 mg L⁻¹, respectively. Melatonin concentrations were undetected in all the setups. These findings underscore the potential of using probiotic yeast strains as a safer and conceivably more cost-effective alternative for indolamine synthesis. The utilization of probiotic strains presents a promising avenue, potentially offering scalability, sustainability, reduced environmental impact, and feasibility for large-scale production. Full article

Novel sensor solutions for sleep monitoring at home could alleviate bottlenecks in sleep medical care as well as enable selective or continuous observation over long periods of time and contribute to new insights in sleep medicine and beyond. Since especially in the latter case the sensor data differ strongly in signal, number and extent of sensors from the classical polysomnography (PSG) sensor technology, an automatic evaluation is essential for the application. However, the training of an automatic algorithm is complicated by the fact that the development phase of the new sensor technology, extensive comparative measurements with standardized reference systems, is often not possible and therefore only small datasets are available. In order to circumvent high system-specific training data requirements, we employ pre-training on large datasets with finetuning on small datasets of new sensor technology to enable automatic sleep phase detection for small test series. By pre-training on publicly available PSG datasets and finetuning on 12 nights recorded with new sensor technology based on a pre-gelled electrode grid to capture electroencephalography (EEG), electrooculography (EOG) and electromyography (EMG), an $F_1$ score across all sleep phases of 0.81 is achieved (wake 0.84, N1 0.62, N2 0.81, N3 0.87, REM 0.88), using only EEG and EOG. The analysis additionally considers the spatial distribution of the channels and an approach to approximate classical electrode positions based on specific linear combinations of the new sensor grid channels. Full article

Vegetation net primary productivity (NPP) is a crucial indicator for assessing the carbon balance in terrestrial ecosystems. Qualitative and comparative research on the NPP influenced by human activities, climate change, and their interactions remains insufficient. The Three-North Shelter Forest Program (TNSFP), initiated in 1978, provides a valuable reference for such investigations. This study employs an improved residual trend method to analyze the spatiotemporal patterns, trends, and driving factors of vegetation NPP during the second phase of the Three-North Shelter Forest Program (2001–2020), as well as TNSFP’s contribution to vegetation NPP. The results indicate that (1) from 2001 to 2020, overall vegetation NPP exhibited a significant fluctuating upward trend at a rate of 3.69 g C/m⁻² annually; and (2) precipitation, accounting for 1.527 g C/m⁻², had a more significant impact on vegetation net productivity compared to temperature (0.002 g C/m⁻²). Climate factors (76%) significantly influenced vegetation NPP in the Three-North Shelter Forest region more than human activities (24%). In the last decade (2011–2020), the climate contribution rate decreased to 67%, while the human activity contribution rate increased by seven percentage points compared to the previous decade (2001–2010); (3) during 2001–2020, TNSFP contributed 10.9% to the total human activity contribution to vegetation net primary productivity, approximately 2.6% of the overall contribution; (4) After the second phase of TNSFP was enacted, PM2.5 levels decreased by an average of −0.57 μg/m⁻³/a⁻¹. Concurrently, soil conservation improved from 6.57 t/km² in 2001 to 14.37 t/km² in 2020. Full article

In this work, the spatial distribution, potential sources, and risk assessment of perfluoroalkyl substances (PFASs) were investigated at 22 surface water sampling sites in Hefei City. The study encompassed 11 distinct types of PFASs, which included 7 perfluoroalkyl carboxylic acids (PFCAs) and 4 perfluoroalkyl sulfonic acids (PFSAs). The findings indicated that the overall concentration of PFASs varied between 12.96 to 545.50 ng/L, with perfluorooctanoic acid (PFOA), perfluorobutanesulfonic acid (PFBS), perfluorobutyric acid (PFBA), and perfluorohexanoic acid (PFHxA) being the most prevalent, contributing to an average of 71% of the total PFASs concentration. Principal component analysis (PCA) elucidated the primary sources of PFASs, which included industrial emissions, fluoropolymer production and treatment, textile processing, and the impact of the electroplating industry. Employing the risk quotient (RQ) method facilitated the assessment of ecological risks associated with PFASs in surface water within the study area, suggesting that the current concentrations of PFASs in Hefei’s surface water pose a relatively low ecological risk. However, the long-term ecological effects of PFASs cannot be overlooked due to their potential for long-range transport and the cumulative nature of biological food chains. Full article

This study assesses the performance of the Weather Research and Forecasting (WRF) model using a high-resolution spatial grid (1 km) with various combinations of physical parameterization packages to simulate a severe event in August 2021 in the southeastern Brazilian coast. After determining the optimal set of physical parameterizations for representing wind patterns during this event, a year-long evaluation was conducted, covering forecast horizons of 24, 48, and 72 h. The simulation results were compared with observational wind data from four weather stations. The findings highlight variations in the efficacy of different physical parameterization sets, with certain sets encountering challenges in accurately depicting the peak of the severe event. The most favorable results were achieved using a combination of Tiedtke (cumulus), Thompson (microphysics), TKE (boundary layer), Monin-Obukhov (surface layer), Unified-NOAH (land surface), and RRTMG (shortwave and longwave radiation). Over the one-year forecasting period, the WRF model effectively represented the overall wind pattern, including forecasts up to three days in advance (72-h forecast horizon). Generally, the statistical metrics indicate robust model performance, even for the 72-h forecast horizon, with correlation coefficients consistently exceeding 0.60 at all analyzed points. While the model proficiently captured wind distribution, it tended to overestimate northeast wind speed and gust intensities. Notably, forecast accuracy decreased as stations approached the ocean, exemplified by the ATPM station. Full article

The walking of humanoid robots is dependent on the precise tracking of their center of gravity and foot trajectories. Trajectory tracking is achieved by mobilizing their joints to achieve the correct trajectory. Errors occur because of assumptions on tracking the center of gravity and the foot trajectories. In this study, a numerical algorithm was developed that produces an exact and single kinematic solution in which the center of gravity and foot trajectories can be tracked with the desired precision. The effectiveness of this algorithm was examined with a dynamic simulation and compared with a method given in the literature. The main highlight of this study, using the presented algorithm, is that the robot could walk even if the position of its center of gravity was lower than its hips, resulting in a tracking error that was smaller than that reported in the literature. Full article

Neural Radiance Fields (NeRFs), as an innovative method employing neural networks for the implicit representation of 3D scenes, have been able to synthesize images from arbitrary viewpoints and successfully apply them to the visualization of objects and room-level scenes (<50 m²). However, due to the capacity limitations of neural networks, the rendering of drone-captured scenes (>10,000 m²) often appears blurry and lacks detail. Merely increasing the model’s capacity or the number of sample points can significantly raise training costs. Existing space contraction methods, designed for forward-facing trajectory or the 360° object-centric trajectory, are not suitable for the unique trajectories of drone footage. Furthermore, anomalies and cloud fog artifacts, resulting from complex lighting conditions and sparse data acquisition, can significantly degrade the quality of rendering. To address these challenges, we propose a framework specifically designed for drone-captured scenes. Within this framework, while using a feature grid and multi-layer perceptron (MLP) to jointly represent 3D scenes, we introduce a Space Boundary Compression method and a Ground-Optimized Sampling strategy to streamline spatial structure and enhance sampling performance. Moreover, we propose an anti-aliasing neural rendering model based on Cluster Sampling and Integrated Hash Encoding to optimize distant details and incorporate an L1 norm penalty for outliers, as well as entropy regularization loss to reduce fluffy artifacts. To verify the effectiveness of the algorithm, experiments were conducted on four drone-captured scenes. The results show that, with only a single GPU and less than two hours of training time, photorealistic visualization can be achieved, significantly improving upon the performance of the existing NeRF approaches. Full article

Purpose: This study aimed to employ the incremental digital image correlation (DIC) method to obtain displacement and strain field data of the cornea from Corvis ST (CVS) sequences and access the performance of embedding these biomechanical data with machine learning models to distinguish forme fruste keratoconus (FFKC) from normal corneas. Methods: 100 subjects were categorized into normal (N = 50) and FFKC (N = 50) groups. Image sequences depicting the horizontal cross-section of the human cornea under air puff were captured using the Corvis ST tonometer. The high-speed evolution of full-field corneal displacement, strain, velocity, and strain rate was reconstructed utilizing the incremental DIC approach. Maximum (max-) and average (ave-) values of full-field displacement V, shear strain γxy, velocity VR, and shear strain rate γxyR were determined over time, generating eight evolution curves denoting max-V, max-γxy, max-VR, max-γxyR, ave-V, ave-γxy, ave-VR, and ave-γxyR, respectively. These evolution data were inputted into two machine learning (ML) models, specifically Naïve Bayes (NB) and Random Forest (RF) models, which were subsequently employed to construct a voting classifier. The performance of the models in diagnosing FFKC from normal corneas was compared to existing CVS parameters. Results: The Normal group and the FFKC group each included 50 eyes. The FFKC group did not differ from healthy controls for age (p = 0.26) and gender (p = 0.36) at baseline, but they had significantly lower bIOP (p < 0.001) and thinner central cornea thickness (CCT) (p < 0.001). The results demonstrated that the proposed voting ensemble model yielded the highest performance with an AUC of 1.00, followed by the RF model with an AUC of 0.99. Radius and A2 Time emerged as the best-performing CVS parameters with AUC values of 0.948 and 0.938, respectively. Nonetheless, no existing Corvis ST parameters outperformed the ML models. A progressive enhancement in performance of the ML models was observed with incremental time points during the corneal deformation. Conclusion: This study represents the first instance where displacement and strain data following incremental DIC analysis of Corvis ST images were integrated with machine learning models to effectively differentiate FFKC corneas from normal ones, achieving superior accuracy compared to existing CVS parameters. Considering biomechanical responses of the inner cornea and their temporal pattern changes may significantly improve the early detection of keratoconus. Full article