Capacity Data Research Articles

ENHANCING OPERATIONAL EFFECIENCY IN CRUSHERS THROUGH THE USE OF AN INDUSTRY 4.0 BASED CRUSHER CONTROL SYSTEM

In the aggregate and mining industry, an excessive flow rate of raw material from the feeder, caused by irregularities in the raw material being processed by crushers, can lead to blockages or excessive strain on the crusher. Conversely, a low flow rate of raw material can result in high energy consumption by the crusher, despite operating at a low capacity. The issues encountered in the first group result in excessive energy usage in the secondary and tertiary groups. The study focuses on a system that utilizes artificial intelligence and is based on industry 4.0 principles. The system aims to maintain production in the crusher within a specific range by controlling the flow rates of the feeders using an algorithm. This control is done automatically without the need for user intervention. The system optimizes energy consumption while maximizing production capacity and ensures uninterrupted operation. The system was developed during the installation phase at an aggregate pilot plant in the Kahramanmaraş Evri region. It assesses the material capacity data using a belt scale on the crusher, feeder, and output conveyor. This data is then compared to the limit values stored in the database, and the system generates an information signal to initiate the required control actions. Based on this matching result, it sends information to the inverter, coordinates the production cycle, manages and documents the process stages using a structured learning system and artificial intelligence logic.The installation procedure was conducted using two distinct density gradation inputs. As a consequence of the reporting, the records in the report were compared during both the active and inactive states of the system. The project achieved an efficiency of 22% in terms of energy consumption per unit capacity. Based on the whole yearly energy usage, a total of 368609.7 kg of carbon emissions were averted. The facility's aggregate crushing capacity was increased by 40%.

Disruptions and adaptations of an urban nutrition intervention delivering essential services for women and children during a major health system crisis in Dhaka, Bangladesh.

Systematic crises may disrupt well-designed nutrition interventions. Continuing services requires understanding the intervention paths that have been disrupted and adapting as crises permit. Alive & Thrive developed an intervention to integrate nutrition services into urban antenatal care services in Dhaka, which started at the onset of COVID-19 and encountered extraordinary disruption of services. We investigated the disruptions and adaptations that occurred to continue the delivery of services for women and children and elucidated how the intervention team made those adaptations. We examined the intervention components planned and those implemented annotating the disruptions and adaptations. Subsequently, we detailed the intervention paths (capacity building, supportive supervision, demand generation, counselling services, and reporting, data management and performance review). We sorted out processes at the system, organizational, service delivery and individual levels on how the intervention team made the adaptations. Disruptions included decreased client load and demand for services, attrition of providers and intervention staff, key intervention activities becoming unfeasible and clients and providers facing challenges affecting utilization and provision of services. Adaptations included incorporating new guidance for the continuity of services, managing workforce turnover and incorporating remote modalities for all intervention components. The intervention adapted to continue by incorporating hybrid modalities including both original activities that were feasible and adapted activities. Amidst health system crises, the adapted intervention was successfully delivered. This knowledge of how to identify disruptions and adapt interventions during major crises is critical as Bangladesh and other countries face new threats (conflict, climate, economic downturns, inequities and epidemics).

A Spontaneous Magnetic Moment in an Organic Radical: Synthesis and Characterization of Benzodioxepinyl-1,3,2-dithiazolyl.

The synthesis of the 1,3,2-dithiazolyl radical (1) derived from 3,4-dihydro-2H-1,5-benzodioxepine is described. Crystals of 1 were grown by vacuum sublimation and adopt the orthorhombic space group Pbca. DC SQUID magnetometry reveals Curie-Weiss behavior for T > 20 K (C = 0.376 emu K mol-1 and θ = +5.7 K) consistent with local ferromagnetic interactions. The presence of weaker antiferromagnetic interactions led to magnetic ordering as a canted antiferromagnet (TN = 3.8 K) with a small spontaneous moment (2.5 × 10-4 Nβ) and a small coercivity (ca. 8 Oe) at 2 K. Magnetic ordering was reflected in a field dependence of the magnetic susceptibility below 3.8 K and a peak in the low temperature heat capacity data. A canting angle of 0.08° was estimated from M vs H data.

Revisiting the Extension of SGTE Heat Capacity Data to Zero Kelvin: Combining Classical Fit Polynomials with Debye–Einstein Functions

AbstractIt is demonstrated in this work that a four parameter Debye–Einstein integral is an excellent fitting function for heat capacity values of pure elements from zero Kelvin to room temperature provided that there are no phase transformations in this temperature range. The standard errors of the four parameters of the Debye–Einstein approach are provided. As examples the temperature dependent molar heat capacities of Fe, Al, Ag and Au are calculated in the temperature range from 0 to 300 K. Standard molar entropies, enthalpies and values of a molar Gibbs energy related function are derived from the molar heat capacities and the values are compared to literature data. The next goal focuses on a seamless transition of these low temperature heat capacities to SGTE (Scientific Group Thermodata Europe) unary data. This can be achieved by penalyzing deviations in the heat capacity values and in their temperature derivatives at the transition point. Whereas the constrained heat capacities of Fe and Al mimic the experimental data, the calculated values deviate considerably in case of Ag and Au. As an alternative a smooth transition in the heat capacities and the temperature derivative is achieved by a switch function employed close to the transition region.

Optimal allocation of production quotas using an inverse DEA integrated with lean production capacity and negative data

In the past decade, the petroleum industry has faced two significant oil price crashes. The first occurred in 2014, driven by increased U.S. production, geopolitical shifts, and changes in OPEC policies. The more recent crash in 2020 was a consequence of the COVID-19 pandemic. Despite these secondary factors, industry experts widely attribute both crashes to excessive crude oil production. Considering this and aiming to prevent recurrences of unexpected price crashes in the future, this paper proposes a novel application of inverse DEA for optimizing OPEC production quotas. In this study, two versions of the inverse DEA are proposed − unbounded and bounded. Essentially, the unbounded model computes theoretical estimates of production quotas, while the bounded model computes practical estimates. To illustrate the applicability of the proposed models, a numerical real-world example is presented in OPEC member nations. Based on a preliminary analysis, only Algeria, Libya, Nigeria, and Venezuela were eligible for production allocations. The application of the unbounded model revealed all four nations were capable of maxing out their production quotas. For practical reasons, and in order to ensure market stability, the bounded model found out that only Libya could potentially receive a maximum allocation of 7.71%. This translates to an increase of 93 thousand barrels of oil per day for Libya. The bounded model not only averted excessive crude oil production but also curtailed hazardous waste associated with increased oil production, resulting in significant positive effects in both Libya and Venezuela. According to our findings, the unbounded model is appropriate for theoretical analysis, while the bounded model is suitable for regulatory bodies such as OPEC.

Transient heat-flux method for measuring heat capacity: examples from Cu and VO2

Many quantum materials undergo phase transitions above room temperature. However, thermodynamic evidence of these phase transitions is relatively scarce. For instance, detailed specific heat anomalies have rarely been reported for the transitions. In addition to considering intrinsic factors that obscure the thermodynamic manifestation of relevant degrees of freedom, it is also important to revisit measurement techniques based on firmly established physical principles. In this study, we introduce a transient heat-flux method for measuring heat capacity of solids, and report a specific heat anomaly in VO2, along with the reproduction of the standard specific heat capacity data of Cu. At present, our method is capable of measuring heat capacities ranging from 1 J/mol⋅K to 400 J/mol⋅K with an uncertainty of 5 % across a temperature range from room temperature to 100 °C.

Simultaneous generation of dual 16-QAM-modulated millimeter-wave signals enabled by precoding-based optical I/Q modulation and single photodetector detection

Integration of quadrature-amplitude-modulation (QAM) modulation and millimeter-wave (mm-wave) technology ensures a concise enhancement of transmission capacity and peak data rates in radio-over-fiber (RoF) systems. Nevertheless, with the continuous development of new-generation mobile applications, higher demands on access flexibility have also been formulated. We propose a novel, to the best of our knowledge, dual 16-QAM-modulated mm-wave signal generation scheme for the simultaneous generation and transmission of two independent 16-QAM signals with different carrier frequencies, enhancing the channel capacity, spectrum efficiency, and access flexibility of the RoF systems. In our proposed scheme, the generation of the optical dual 16-QAM-modulated mm-wave signals is implemented by a precoding-based optical in-phase/quadrature (I/Q) modulator operating at optical carrier suppression (OCS) mode, and their detection is implemented by a single photodetector (PD). At the transmitter side, two independent driving QAM vector radio-frequency (RF) signals with precoding, generated via digital signal process (DSP), are fed into two parallel Mach–Zehnder modulators (MZMs) of an I/Q modulator to implement OCS modulation. The I/Q modulator generates optical carriers carrying both information from different QAM signals simultaneously. After square-law detection in a single PD, we generate the desired dual 16-QAM-modulated mm-wave signals with photonic frequency-doubling and recover the original 16-QAM signals without information distortion. Based on our proposed scheme, we experimentally demonstrate the simultaneous generation and transmission of 2-Gbaud dual 16-QAM-modulated mm-wave signals with carrier frequencies of 26 GHz and 40 GHz, respectively. After transmission over 10-km single-mode fiber (SMF) link and 1.2-m wireless link, the dual 16-QAM signals can be successfully separated and demodulated by a common DSP. Bit-error ratios (BERs) of both recovered 16-QAM signals can reach the hard-decision forward-error-correction (HD-FEC) threshold of 3.8×10−3. Compared with the existing schemes, our scheme only requires one-half of the driving frequency to generate dual 16-QAM-modulated mm-wave signals with the same carrier frequency. The result indicates that our proposed scheme can lower bandwidth requirements for optoelectronics devices, along with implementing better spectral efficiency and receiver sensitivity, which is more applicable to the demands for increased system capacity and multi-frequency access flexibility in future systems.

Using Spatial Literacy for Disaster Management in Coastal Communities of Small Island Developing States (SIDS): A Case Study from Lavongai, Papua New Guinea

This study investigates the use of participatory geographic information systems (PGIS) for hazard assessment in small island developing states (SIDS), with a focus on spatial literacy and community-based disaster management. By partnering with the Lavongai community on Papua New Guinea, this research aimed to empower community members through skill development in geodata processing. The program leveraged local knowledge and the global positioning system to create participatory maps, enhancing both community capacity and researcher data quality. Workshops and focus group discussions (FGDs) were conducted to assess the community’s understanding of spatial concepts related to disaster risks. The core objective was a preliminary assessment of the community’s social and economic vulnerability to coastal disasters, using household data and GIS analysis. The results showed varied vulnerability levels within the community, highlighting the need for targeted disaster mitigation training and nature-based solutions. High-resolution satellite imagery and a simple bathtub model simulated sea level rise, identifying land-uses at risk. The program concluded with a community presentation of thematic maps, fostering collaboration and transparency. Future projects will address environmental challenges identified by local leaders and prioritize skill development, social data collection, and water resource mapping.

A pyroxene-based quantum magnet with multiple magnetization plateaus.

Pyroxenes (AMX2O6) consisting of infinite one-dimensional edge-sharing MO6 chains and bridging XO4 tetrahedra are fertile ground for finding quantum materials. Thus, here, we have studied calcium cobalt germanate (CaCoGe2O6) and calcium cobalt silicate (CaCoSi2O6) crystals in depth. Heat capacity data show that the spins in both compounds are dominantly Ising-like, even after being manipulated by high magnetic fields. On cooling below the Néel temperatures, a sharp field-induced transition in magnetization is observed for CaCoGe2O6, while multiple magnetization plateaus beneath the full saturation moment are spotted for CaCoSi2O6. Our analysis shows that these contrasting behaviors potentially arise from the different electron configurations of germanium and silicon, in which the 3d orbitals are filled in the former but empty in the latter, enabling electron hopping. Thus, silicate tetrahedra can aid the interchain superexchange pathway between cobalt(II) ion centers, while germanate ones tend to block it during magnetization.

Thermodynamic modelling of systems involved in natural gas dehydration with triethylene glycol using a group contribution association model

Natural gas (NG) dehydration through absorption into Triethylene Glycol (TEG) is one of the most important applications in the NG industry. The optimal design of the TEG dehydration process requires a deep understanding of the thermodynamic behavior of mixtures containing TEG, water, hydrocarbons, and other compounds present in natural gas. In this work, the recently developed Universal Mixing Rule – Cubic Plus Association (UMR-CPA) group contribution equation of state (EoS) is extended to these systems. UMR-CPA combines the PR-CPA EoS with the UNIFAC group contribution activity coefficient model through the Universal Mixing Rules. Parameters for pure water, TEG and NG components were determined by accurately fitting vapor pressure, density and heat capacity data. For non-associating compounds, the model leads to overall deviations of 1.2 % in vapor pressures and 6.1 % in isobaric heat capacities. Water properties are also quite accurately described, with overall deviations of approximately 0.4 %, 1.2 % and 5.7 % in vapor pressures, liquid densities and isobaric heat capacities, respectively. The model was then applied to mixtures of water and TEG with gases and hydrocarbons by correlating the proper group interaction parameters. Very satisfactory results were obtained for both vapor-liquid and liquid-liquid phase equilibria in these systems, where also an adequate reproduction of the minimum of hydrocarbon solubility in water was noted. Finally, the UMR-CPA EoS was further validated through the prediction of the phase behavior of ternary systems including TEG and/or water and NG compounds. Very good predictions were achieved for the low TEG and water content in the vapor phase of the TEG-H2O-CH4 ternary system, with absolute deviations of around 0.05 and 23.26 ppm, respectively. Overall, the model yields accurate predictions, suggesting its suitability for designing the TEG dehydration process.

Generating comprehensive lithium battery charging data with generative AI

In optimizing the performance and extending the lifespan of lithium batteries, accurate state prediction is crucial. Traditional regression and classification methods have achieved some success in predicting battery states. However, the effectiveness of these data-driven approaches largely depends on the availability and quality of public datasets. Additionally, generating electrochemical data primarily through battery experiments is a lengthy and costly process, making it extremely difficult to obtain high-quality data. This difficulty, combined with data incompleteness, significantly affects prediction accuracy. To address these challenges, this study introduces End of Life (EOL) and Equivalent Cycle Life (ECL) as supervised conditions for generative AI models. By integrating an embedding layer into the CVAE model, we developed the Refined Conditional Variational Autoencoder (RCVAE). Through preprocessing data into a quasi-video format, coupled with customized training and inference algorithms, the RCVAE generates the required charging data in real time based on the battery’s ECL and EOL. This avoids storing irrelevant information, saving space and resources. RCVAE uses a lightweight architecture, enabling fast generation of necessary voltage, current, temperature, and charging capacity data. This approach provides users with a comprehensive electrochemical dataset, pioneering a new research domain for the artificial synthesis of lithium battery data. Furthermore, based on the detailed synthetic data, various battery state indicators can be calculated, offering new perspectives and possibilities for lithium battery performance prediction.

PDOC-02 SUB-SAHARAN AFRICAN EXPERIENCE OF NEUROSURGICAL-ONCOLOGIC CARE: CHALLENGES AND BARRIERS ENCOUNTERED AT 7 CANCER TREATMENT CENTERS

Abstract BACKGROUND Wide disparities in neurosurgical-oncologic care and treatment outcomes exists globally despite recent improvements in diagnostics and cancer therapy. To better understand the challenges to neurosurgical-oncologic care in low resource settings, we collected data on national neurosurgery capacity and hospital diagnostic and treatment capacity across 7 national referral hospitals in 7 countries Sub-Saharan Africa (SSA). METHODS A 42-item self-administered questionnaire was distributed to partner neurosurgeons at the 7 sites via REDCap in April 2023 to provide country and hospital level capacity data on neurosurgical-oncologic care. RESULTS Neurosurgical and neurosurgical-oncologic care was reported to be available in a limited number of provinces/states/regions in 6 out of the 7 countries. The neurosurgery and pediatric neurosurgery workforce density across the 7 countries ranged between 0.03 – 0.67 per 100,000 and 0 – 0.05 per 100,000 respectively. Three hospitals had no pediatric ICU with the remaining four having between 2-8 bed-capacity pediatric ICU. One hospital did not have both CT and MRI scanner available and relied solely on private diagnostic facilities for neuroimaging. Histopathology services were largely limited to basic hematoxylin and eosin (H&E) and/or advanced histopathology staining. Molecular subtyping was available at only one hospital. None of the 7 hospitals had neurocritical care expertise, neuroradiologist, or neuropathologist. Four hospitals had a pediatric anesthesiologist. Only one hospital had a neuro-oncologist, but none had a pediatric neuro-oncologist. Both adjuvant chemotherapy and radiotherapy was unavailable at 3 hospitals. Rehabilitation was largely limited to basic physical and occupational therapy at all 7 hospitals. Although all 7 countries had a multiple health payer system, the payment structure differed across the 7 hospitals for different neurosurgical-oncologic services with patients making out-of-pocket payments for all services in some cases. Financial constraint was reported as a major barrier to care. CONCLUSION System-level interventions are needed to strengthen neurosurgical-oncologic care capacity in SSA.

Remaining Useful Life Estimation of Lithium-Ion Batteries Based on Small Sample Models

Accurate prediction of the Remaining Useful Life (RUL) of lithium-ion batteries is essential for enhancing energy management and extending the lifespan of batteries across various industries. However, the raw capacity data of these batteries is often noisy and exhibits complex nonlinear degradation patterns, especially due to capacity regeneration phenomena during operation, making precise RUL prediction a significant challenge. Although various deep learning-based methods have been proposed, their performance relies heavily on the availability of large datasets, and satisfactory prediction accuracy is often achievable only with extensive training samples. To overcome this limitation, we propose a novel method that integrates sequence decomposition algorithms with an optimized neural network. Specifically, the Complementary Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) algorithm is employed to decompose the raw capacity data, effectively mitigating the noise from capacity regeneration. Subsequently, Particle Swarm Optimization (PSO) is used to fine-tune the hyperparameters of the Bidirectional Gated Recurrent Unit (BiGRU) model. The final BiGRU-based prediction model was extensively tested on eight lithium-ion battery datasets from NASA and CALCE, demonstrating robust generalization capability, even with limited data. The experimental results indicate that the CEEMDAN-PSO-BiGRU model can reliably and accurately predict the RUL and capacity of lithium-ion batteries, providing a promising and reliable method for RUL prediction in practical applications.

Spatial lung imaging in clinical and translational settings.

For many severe lung diseases, non-invasive biomarkers from imaging could improve early detection of lung injury or disease onset, establish a diagnosis, or help follow-up disease progression and treatment strategies. Imaging of the thorax and lung is challenging due to its size, respiration movement, transferred cardiac pulsation, vast density range and gravitation sensitivity. However, there is extensive ongoing research in this fast-evolving field. Recent improvements in spatial imaging have allowed us to study the three-dimensional structure of the lung, providing both spatial architecture and transcriptomic information at single-cell resolution. This fast progression, however, comes with several challenges, including significant image file storage and network capacity issues, increased costs, data processing and analysis, the role of artificial intelligence and machine learning, and mechanisms to combine several modalities. In this review, we provide an overview of advances and current issues in the field of spatial lung imaging.

Heat capacities and thermodynamic properties of urea phosphate and its aqueous solutions at various temperatures

Urea phosphate was produced through a complexation reaction of phosphoric acid and urea and subsequent cooling crystallization. The elemental composition analysis, XRD, and FTIR all confirmed that the prepared urea phosphate had high purity and good crystallinity, and showed a clear onset melting temperature of 118.12℃ and corresponding enthalpy change of 222.10 J∙g−1 in TG/DSC measurement. Meanwhile, the heat capacities of urea phosphate and its aqueous solution were carefully measured using microcalorimetry with an expanded relative uncertainty better than 2 %. The experimental results revealed that the heat capacity of urea phosphate rises rapidly with temperature, with an increase of 41 % from −20 to 100℃. The experimental heat capacity data were correlated with semi-empirical models of Maier-Kelley equation, Khodakovsky equation and Debye-Einstein equation which all give excellent reproduction of the measured data with very close average deviation around 0.5 %. The enthalpy, entropy, and Gibbs free energy of solid urea phosphate were estimated at various temperatures by integrating heat capacity against temperature with Maier-Kelley equation and the results confirmed urea phosphate is thermally quite stable below 100℃. The heat capacity of urea phosphate aqueous solution increases with concentration and temperature, and there is a strong linear relationship between the solution’s heat capacity and temperature when the composition remains constant. The apparent molar heat capacity of urea phosphate in solution is positive and increases with concentration and temperature. The extrapolated partial molal heat capacity of urea phosphate is temperature independent and shows an average value of −709.4 J·K−1·mol−1. Due to the partial dissociation and partial ionization of urea phosphate in solution, the excess molar heat capacities show positive deviation from that of ideal solution, which also increases with the increase of concentration and temperature.

Microwave-Assisted Synthesis of Corn Husk-Based Hydrogels Grafted with Acrylamide

Corn husk waste contains cellulose, which has the potential as a raw material for hydrogel preparation. Hydrogels can be applied as water purification, diapers, and superabsorbents. This study aimed to synthesize hydrogel from corn husk cellulose grafted with acrylamide monomer using a microwave-assisted grafting method. Potassium peroxodisulfate (PPD) was used as an initiator, and the effects of acrylamide and PPD on hydrogel swelling capacity were investigated. The process involved drying and crushing corn husks into powder, then mixing the powder with acrylamide and PPD for microwave grafting to form a polymer, which was then ground into powder. The grafted polymer was combined with carrageenan to create bead gels soaked in distilled water and urea to measure swelling capacity. Results showed that swelling capacity increased with more acrylamide and decreased with more PPD. The highest swelling capacity reached 1016.16% in water and 961.6% in urea. FTIR analysis confirmed the successful grafting of acrylamide onto corn husk cellulose by detecting changes in the infrared spectrum. Based on FTIR and swelling capacity data, it was concluded that the grafting process was completed using the microwave method with PPD as the initiator.

Hospital Capacity Data and Extreme Heat Event Vulnerability

Hospital Capacity Data and Extreme Heat Event Vulnerability

Life prediction method of battery energy storage system in frequency modulation application

Abstract To tackle the challenge of lifespan reduction in lithium batteries during frequency modulation, this study introduces a novel Remaining Useful Life (RUL) prediction methodology. The proposed approach integrates Variational Mode Decomposition (VMD) with Gated Recurrent Unit (GRU) networks, thereby efficiently synthesizing data from both operational parameters and capacity metrics. Firstly, VMD is utilized to decompose the initial capacity data of lithium batteries into separate components characterized by high and low frequencies. Subsequently, for the high-frequency elements, GRU is employed for rolling iterative prediction, while for the low-frequency elements, features are extracted from the operational data and inputted into GRU for prediction. Finally, the component prediction results are restored to obtain capacity prediction values. Validation conducted using the NASA dataset shows that the root mean square error of capacity prediction is minimized to 0.0122, with a corresponding minimum average absolute error of 0.0096, and RUL prediction errors are generally within 2 cycles.

Finite Temperature Magnetism in the Triangular Lattice Antiferromagnet KErTe2

Abstract After the discovery of the ARECh2 (A = alkali or monovalent ions, RE = rare-earth, Ch = chalcogen) triangular lattice quantum spin liquid (QSL) family, a series of its oxide, sulfide, and selenide counterparts has been consistently reported and extensively investigated. While KErTe2 represents the initial synthesized telluride member, preserving its triangular spin lattice, it was anticipated that the substantial tellurium ions could impart more pronounced magnetic attributes and electronic structures to this material class. This study delves into the magnetism of KErTe2 at finite temperatures through magnetization and electron spin resonance (ESR) measurements. Based on the angular momentum J ^ after spin-orbit coupling (SOC) and symmetry analysis, we obtain the magnetic effective Hamiltonian to describe the magnetism of Er3+ in R 3 ¯ m space group. Applying the mean-field approximation to the Hamiltonian, we can simulate the magnetization and magnetic heat capacity of KErTe2 in paramagnetic state and determine the crystalline electric field (CEF) parameters and partial exchange interactions. The relatively narrow energy gaps between the CEF ground state and excited states exert a significant influence on the magnetism. For example, small CEF excitations can result in a significant broadening of the ESR linewidth at 2 K. For the fitted exchange interactions, although the values are small, given a large angular momentum J = 15/2 after SOC, they still have a noticeable effect at finite temperatures. Notably, the heat capacity data under different magnetic fields along the c axis direction also roughly match our calculated results, further validating the reliability of our analytical approach. These derived parameters serve as crucial tools for future investigations into the ground state magnetism of KErTe2. The findings presented herein lay a foundation for exploration of the intricate magnetism within the triangular-lattice delafossite family.

A new method to calculate and predict thermodynamic properties of nonpolar, polar and quantum fluids at high temperatures

As the only known equation of state (EOS) with the rigid theoretical foundation, the virial equation of state (VEOS) can be reliable enough to describe real-gas imperfection for low-to-moderate densities when truncated after the second or third virial coefficient. In our previous work, on the basis of the corresponding state principle, the generalized second and third virial coefficient models were proposed for nonpolar, polar and quantum fluids in a wide temperature range. In this work, combined with the ideal heat capacities, the high-temperature performance of the truncated VEOSs was evaluated on derived thermodynamic properties including speed of sound, isobaric heat capacity, entropy and internal energy. The criterion for being “valid” is defined as 1% relative deviation from the multiparameter EOSs, and this work presents the valid density and pressure regions of the truncated VEOSs for the derived thermodynamic properties. The truncated VEOSs have valid densities of the saturated densities below the critical temperatures, and have valid densities that tend to be constant beyond the critical temperatures. The SRK EOS is chosen as the representative of generalized EOSs. By the comparisons with the SRK EOS, the truncated VEOSs show the advantage in stability and universality. The truncated VEOSs can give a reliable extrapolation with wide applicable pressure ranges at high temperatures for nonpolar, polar and quantum fluids. The applicable density regions of the truncated VEOSs for derived thermodynamic properties are recommended to be the valid density regions of the pvT property. The applicable temperature ranges of the truncated VEOSs for derived thermodynamic properties are determined by the temperature ranges of the available ideal heat capacity data.