Laboratory-scale Tests Research Articles

Factors influencing the survival time of patients with advanced cancer at the end of life: a retrospective study

BackgroundPredicting the survival time of patients at the end of life can provide more accurate treatment and care programs for patients. The purpose of this study was to investigate the factors impacting 14-day survival at the end of life.MethodThis was a retrospective study. Patients with advanced cancer admitted to the Department of Palliative Medicine in a tertiary hospital in China in 2021 were included and classified into group A (survival time ≤ 14 days) or group B (survival time > 14 days). Patient demographic characteristics, palliative performance scale (PPS) scores, Barthel index scores, Fracture Risk Assessment Scale (FRAIL) scale scores, clinical features and laboratory test results were extracted from medical records. Univariable and multivariable logistic regression analyses were used to identify predictors of death within 14 days. Survival time was compared between frail and nonfrail patients.ResultsA total of 261 patients were included (122 in group A and 139 in group B), with a median survival time of 17 (13.04, 20.96) days. There were significant differences in age, FRAIL score, PPS, Barthel index, dyspnea, edema, C-reactive protein and white blood cell count between the two groups. According to the multivariable logistic regression analysis, the PPS could predict the risk of death within 14 days (OR = 6.818, 95% CI = 3.944–11.785, p < 0.001). The median survival time was 48 (33.71, 62.29) days in the nonfrail group (n = 34) and 15 (12.46, 17.54) days in the frail group (n = 227) (p < 0.001).ConclusionsA lower PPS increases the risk of 14-day mortality in patients at the end of life. Frailty may shorten the survival time of patients at the end of life.

Advanced self-sensing road stud: Integrating deep learning-based speed detection and energy harvesting

Abstract This study introduces a self-powered and self-sensing vehicle speed detection sensor, representing a significant advancement in transportation. The system employs mechanical components like a slider crank, bevel gears, and one-way bearings for unidirectional rotation, converting translational motion into electrical energy upon the impact of vehicle tyres on road studs. The electrical power generation module, including a DC generator, rectifier, and battery circuit, captures and stores this energy. In addition to energy harvesting, the system integrates a deep learning model using Long Short-Term Memory (LSTM) networks to precisely calculate vehicle speed from the displacement signals of the road studs. Displacement data from an SR-04 sensor is processed and fed into the LSTM network, achieving a classification accuracy of 99.45% for vehicle speeds of 5, 10, 15, and 20 km/h. A mathematical model and MATLAB Simscape simulations were developed, followed by experimental validation using a Mechanical Testing and Sensing System (MTS) under laboratory conditions. Lab-scale testing, a maximum output power of 3.72W and an efficiency of 62.7% were recorded at 8 Hz. Field tests were performed at various vehicle speeds. A peak voltage output of 10V was recorded for a single phase of a three-phase DC generator at 15 km/h. The displacement sensor beneath the road stud was used to record the relative time signal between adjacent peaks to calculate vehicle speed. The sensor is sustainable in energy and easily installable without infrastructure changes, enhances transportation efficiency, and is useful for traffic management, road safety, and smart transportation networks.&amp;#xD;

Polyvinyl alcohol/cellulose nanocrystals incorporated with apple waste-derived carbon dots as an excellent UV food packaging material

Ultraviolet (UV) radiation has significantly reduced food quality. With the increase in UV radiation to Earth due to ozone layer depletion, the need for eco-friendly UV-blocking food packaging materials is becoming very important. This work synthesized carbon dots (CD) from apple waste and then incorporated them into polyvinyl alcohol (PVA)/cellulose nanocrystals (CNC) to develop UV-blocking packaging materials. The CD was even distributed and highly compatible with the PVA/CNC matrix (5 wt% of CNC) to form a compact and continuous film without defects. In particular, the UV-blocking ability of PVA/CNC/CD was substantially improved. PVA/CNC/CD 1 % (1 wt% of CD) exhibits excellent UV-blocking ability, which can block about 78.2 % of UVA and 91 % of UVB. The UV protection factor of this composite was 5.9 times higher than that of PVA/CNC. In addition, the prepared composite films keep high transparency with over 70 % transmittance of visible light at 660 nm. The potential use of developed composites as UV-blocking packaging materials was demonstrated by lab-scale packaging tests with cherry tomatoes.

Comparing PFAS analysis in batch leaching and column leaching tests.

Laboratory leaching tests are tools to assess the mobility of environmental contaminants released from granular materials. Comparative leaching tests were performed using four PFAS-contaminated soils whose concentration patterns of 10 selected perfluoroalkyl and polyfluoroalkyl substances (PFAS) differed due to the two types of contamination sources. This study aimed to evaluate the equivalence of two usual laboratory-scale leaching test procedures, batch and column percolation tests, at liquid-to-solid ratios (L/S) of 2l/kg, which is the current practice within the German assessment framework, and 10l/kg (relevant for some EU regulations such as the landfill directive). The differences between the replicates of leaching tests investigating PFAS were smaller for column percolation tests than for batch tests, probably mainly due to the greater sample size and the better representativeness of the sample portion analyzed. It was observed that batch tests overestimate the release of shorter-chain PFAS, whereby the effect was greater with carboxylic than with sulfonic acids. Currently, the limits of detection of analyses given by the DIN standard with regard to PFCA and PFSA in soils are partly not sufficient to detect very low contents, whereas the detection of selected PFCA and PFSA in eluates is more sensitive, in accordance with the available standards. This results in limitations when calculating mass balances.

Exploring Putative Kokumi Oligopeptides in Classic Sparkling Wines with a UHPLC-ESI-MS/MS Targeted Protocol.

Some oligopeptides can impart kokumi flavor to foods and beverages, a topic still not addressed in wine. A targeted ultra-high performance liquid-chromatography-mass spectrometry (UHPLC-MS/MS) metabolomics method capable of quantifying both amino acids and oligopeptides in wines was therefore developed and validated, confirming the presence of 50 oligopeptides in wine, most of which had been previously unexplored. In silico screening of the affinity of these oligopeptides to interact with CaSR, the protein necessary to activate kokumi sensations, highlighted 8 dipeptides and 3 tripeptides as putative kokumi compounds. These compounds were ubiquitous in a representative set of Trentodoc classic method sparkling wines, with an average concentration of kokumi oligopeptides of 19.8 mg/L, ranging between 9.1 and 33.3 mg/L. Half of the sparkling wine samples also contained glutamic acid at concentrations equal to or greater than the threshold for the umami taste in wine, namely, 48 mg/L. Sensory tests on the dipeptide Gly-Val confirmed the ability of this novel kokumi compound to significantly modify the perception of complex real wine matrices but not of the simple model one. Preliminary laboratory-scale fermentation tests showed that the oligopeptide profile in wines is linked to the starting grape matrix and that the patterns change by fermenting barley or apple juice with the same yeast.

Vertical z-axis discontinuous carbon fibers for improved lightning strike performance of continuous fiber-reinforced polymer composites

Effective lightning strike protection for critical aerospace and wind applications requires high electrical conductivity to dissipate current efficiently. However, polymer matrix composites face a challenge due to their inherently insulating nature. While conventional carbon fiber-reinforced composites (CFRP) exhibit electrical conductivity in the planar direction, achieving through-thickness conductivity remains an ongoing challenge. In this work, we have undertaken the fabrication of CFRP interleaved with vertically oriented carbon fibers (Z-fiber) to impart higher electrical conductivity along the thickness direction. Two Z-fiber composite variations are prepared: Z-1 with a single layer of Z-fiber and Z-5 with five interleaved layers and compared with no Z-fiber layer (Z-0) composite. The composite panels were subjected to lab-scale lightning strike tests with a current magnitude of 100 kA. To emulate real-world service conditions, an aerospace-grade paint coating was applied to the composite laminates. Comparative analysis shows Z-1 reduces damage diameter to ∼22 mm compared to Z-0 (∼26 mm), while Z-5 exhibits the least damage (∼16.7 mm), confirmed by optical microscopy. Z-5 demonstrates nine times higher through-thickness electrical conductivity than Z-0, reducing electrical anisotropy substantially. Thermal-electric finite element damage modeling predicts surface damage within 6% of experimental values for both Z-0 and Z-5 composites. Flexural tests post-lightning reveal Z-5 retains 66% flexural strength and 86% modulus, significantly better than Z-0, which retains less than 40% for both properties. This study highlights the efficacy of Z-fiber composites in lightning strike protection, offering improved through-thickness conductivity and mechanical property retention.

Efficient Recovery of Silver and Aluminum from End-of-Life Photovoltaics: A Two-Step Leaching Approach

Recycling solar panels is crucial to mitigating the environmental impact of the growing volume of end-of-life photovoltaic waste and to conserve valuable resources, while achieving high purity in recovered materials ensures their effective reuse in the manufacturing of new solar panels, contributing to a sustainable and circular economy. This study proposes a two-step leaching process to recover Ag and Al from the silicon fraction of EoL photovoltaics (PVs). In the first-stage laboratory scale tests, 99% Al was extracted using 5% HCl at room temperature (19 °C) for 3 h The Al was precipitated as oxide from the solution. The silicon residue was then leached with 0.5 M HNO3 at 85 °C for 2 h, extracting 99% Ag and producing Si with 99.83% purity. The silver was precipitated to produce metallic Ag with &gt;99.9% purity. Then, 95.63% of Pb in the nitric acid effluent was removed using ion exchange resins. Further, methods to mitigate the effluent solutions were recommended and the entire process was presented in a flowsheet.

Effect of Fiber Cross-Sectional and Surface Properties on the Degradation of Biobased Polymers.

Biobased polymers such as polylactic acid (PLA) and polybutylene succinate (PBS) break down naturally under certain environmental conditions. The efficiency of degradation can be linked directly to fiber surface properties, which influence polymer accessibility. Here, the degradation of PLA and PBS fibers with six different cross-sections was investigated. The fibers were aged by hydrolysis and UV exposure in an accelerated weathering test, followed by an ISO 20200 laboratory-scale disintegration test with non-aged fibers as controls. The polymers were analyzed by differential scanning calorimetry, Fourier transform infrared spectroscopy, and gel permeation chromatography, comparing the polymer granulate, virgin fibers, and UV-exposed fibers. It was found that the molecular mass and crystallinity of PBS changed more than PLA during spinning. Several PLA samples were completely degraded, whereas all the PBS samples remained intact. Furthermore, surface openings appeared on the PLA fibers during weathering, suggesting greater sensitivity to UV exposure and hydrolysis than PBS. A clear correlation between the fiber surface area and the degradation rate was observed for all samples, but the correlation was positive for PLA and negative for PBS. The slower degradation of PBS fibers with a larger surface area may reflect the ability of PBS to preserve itself by further crystallization during degradation processes at temperatures higher than the glass transition point. The data clearly show that the analysis of single degradation mechanisms is insufficient to predict the behavior of material under real-world conditions, where different degradation mechanisms may work in parallel or consecutively, and may show interdependencies.

Influence of initial state and residual stresses on the modulus-density relationship of geomaterials: insights from multiple experimental setups

ABSTRACT Quality control in pavement construction traditionally relies on density measurements of geomaterials. With the introduction of modulus measurement methods, a shift has occurred due to the operational advantages of modulus-estimating devices over conventional density measurement techniques. Modulus-based methods also provide valuable information for the mechanistic-empirical (ME) design of pavement layers. However, a challenge emerges as a single modulus measurement does not directly correspond to a specific density measurement, leading to concerns about replacing density measurements in quality control processes. While it is generally accepted that a geomaterial's modulus primarily depends on its moisture content and density or void ratio, this study explores the complex relationship between modulus and density, particularly in field scenarios with consistent moisture content. Using three different laboratory-scale test set-ups, characterised by unique loading and boundary conditions, the research highlights the significant role of a geomaterial's initial state in obtaining accurate modulus estimates. The results reveal that identical densities can exhibit variations in modulus due to differences in initial densities and the development of residual lateral stresses. These variations in field conditions often stem from different paving or spreading techniques, emphasising the need for a comprehensive approach when establishing density-modulus correlations..

Critical comparison of potential machine learning methods for lightning thermal damage assessment of composite laminates

The present study assesses the potential of using machine learning (ML) methods to predict the extent of lightning thermal damage in fiber-reinforced composite laminates using three supervised machine learning (SML) algorithms: (1) linear regression (LR), (2) decision tree (DT)-based, and (3) MLP models. These models were based on the 10 most significant factors that influence the severity of lightning damage, including three current waveform parameters, four material configurations, and three orthogonal electrical conductivities of each composite. All models demonstrated good performance with coefficient of determination (R2) values between 0.84 ~ 0.96. The multilayer perceptron (MLP) regression model trained with the lightning matrix damage dataset showed the most promising results (R2 > 0.94). Additional hyperparameter optimization was performed to improve the prediction performance of the baseline MLP model. The hyperparameter optimization (Adam optimizer, tanh activation function, and three hidden layers with 234 neurons) slightly improved the performance of the baseline MLP model by ~0.02, but achieved faster convergence. This result suggests that the baseline MLP model trained with the lightning matrix damage dataset is sufficiently accurate and robust. This paper highlights that ML-informed regression models can serve as an efficient first pass-estimator of lightning matrix damage in composite laminates, potentially reducing the amount of extremely time-consuming and expensive laboratory-scale lightning tests or streamlining the development of complex lightning damage models for future design.

Predictive modeling for coiled tubing fatigue using advanced machine learning techniques

Coiled tubing CT plays Pivotal role in oil and gas well intervention operations due to its advantages such as flexibility, fast mobilization, safe, low cost and wide range of applications, including: well intervention, cleaning, stimulation, fluid displacement, cementing, and drilling. However, CT is subject of fatigue and mechanical damage caused by repeated bending cycles, internal pressure and environmental factors which can lead to a premature failure, high operational cost and production downtime. With the development of CT proprieties and application traditional fatigue life prediction methods based on analytical models integrated in tracking process showed in some cases an underestimate or overestimate of the actual fatigue life of CT particularly when complex factor like welding type, corrosive environment, and high-pressure variation are involved. This study addresses this limitation by introducing a comprehensive machine learning-based approach to improve the accuracy of CT fatigue life prediction, using a data set derived from over 350 tests both lab scale and full-scale fatigue test. The study has incorporated the impact of different parameters such as CT, grades, wall, thickness, CT diameter, internal pressure and welding types. By using advanced machine learning techniques such as artificial network (ANN), Gradient boosting regressor we got a more precise estimation of the number of cycles to failure than traditional models. The results of this study shown the importance of integration of machine learning for CT fatigue life analysis and demonstrating its capacity to enhance the prediction accuracy and reduce the uncertainty. A detailed machine Learning models is presented, emphasizing their capability to handle complex data and improve prediction under diverse operational conditions. This study contributes to more reliable, CT management and safer more cost-efficient well intervention operations.

A self-adaptive fractional-order PID controller for the particle velocity regulation in a pneumatic conveying system

This paper presents the methodical development of a state-error-driven self-adaptive fractional-order proportional–integral–derivative (AFOPID) control algorithm to efficiently regulate the velocity of pneumatically conveyed particles at the desired set point and to prevent the blockage of particles in a pipe due to an imbalanced combination of their velocity and corresponding mass flow rate. The proposed fractional control law is constituted by adaptively modulating fractional orders of the integral and differential operators in the control based on the state-error variations in the velocity of solid particles. The particle’s velocity is measured and updated via electrostatic sensors in conjunction with cross-correlation signal processing algorithms. All the other fixed hyper-parameters associated with the proportional–integral–derivative (PID) control scheme are meta-heuristically optimized by using a genetic algorithm. The proposed AFOPID is benchmarked against conventional integer-order PID and the fractional-order proportional–integral–derivative (FOPID) controllers. Experiments are performed on a laboratory-scale test rig to comparatively analyze the aforesaid control schemes, where each controller is examined for three velocity set points and three disturbance levels. The experimental results validate the superior time optimality and robustness of the proposed AFOPID controller against bounded disturbances and abrupt velocity set-point variations by manifesting relatively faster settling time, low overshoots (and undershoots), and smaller steady-state fluctuations.

Temperature Dependence of Aerated Turbine Lubricating Oil Degradation from a Lab-Scale Test Rig

Temperature Dependence of Aerated Turbine Lubricating Oil Degradation from a Lab-Scale Test Rig

Pure copper extrusion additive manufacturing of lattice structures for enabling enhanced thermal efficiency in hydrogen production

The high geometrical complexity allowed by Additive Manufacturing (AM) of pure copper can support the development of more efficient catalytic supports that can enable the design of efficient reactors for key processes for energy transition, i.e., hydrogen production with methane steam reforming or CO2 conversion to synthetic methane, thanks to the enhanced thermal conductivity of the catalytic support. Low relative density lattice geometries with open cell copper geometries can be adopted as catalyst supports in the new generation chemical reactors to improve the heat transfer. This work presents a manufacturability study about pure copper lattice filters produced via metal Extrusion AM i.e., via feedstock 3D printing and sintering. The results show that feasible parts design can be reached and produced and that heat-exchange performances can be increased with respect to conventional supports, as confirmed by lab-scale heat transfer tests. A specific metrological assessment based on CTscan data is implemented on the printed parts to qualify them and to ensure a good thermal coupling between the printed filters and the heat exchanger main tube elements.

Treatment of textile dyes with steel flakes via Fenton and Electro-Fenton processes: An experimental analysis

The textile production process is well recognised as one of the most water-intensive industries, with an estimated daily water consumption of several thousand cubic metres. The main objective of the study is to remove the colour and COD of three reactive dyes namely reactive blue, reactive yellow and reactive black by Fenton and Electro Fenton Process. In this research paper, new novel catalyst steel flakes have been employed as part of the Fenton and Electro-Fenton process, replacing the conventional Fenton and Electro-Fenton catalyst. Scanning Electron Microscope (SEM), Energy-Dispersive X-ray Analysis (EDAX), Inductively Coupled Plasma Mass Spectrometry (ICP-MS) and Thermo Gravimetric Analysis (TGA) was done to characterize the steel flakes. The pH, dye concentration, FeSO4 dosage and H2O2 dosage were studied. The maximum colour and Chemical Oxygen Demand (COD) removal in Fenton process was 98.47 % and 74 % respectively. In Electro Fenton Oxidation the maximum colour and COD removal was 99.65 % and 78 % respectively. The best colour and COD removal was observed by Fenton and Electro Fenton oxidation with optimum Fenton molar ratio of 50:1 and pH 3 in all three dyes. In the laboratory-scale feasibility tests on the Electro-Fenton process, the highest colour removal effectiveness was observed at catalyst dosages of 1 g/L, 132 mM H2O2, and pH 3. The novel modified heterogeneous Electro-Fenton approach presents a distinct cost-effectiveness benefit in comparison to both the conventional Electro-Fenton reaction and the Electro-Fenton reaction employing alternative iron sources.

Assessment of salt-free electrodialysis metathesis: A novel process for brine management in brackish water desalination using monovalent selective ion exchange membranes

Since reverse osmosis (RO) is often limited to a water recovery of 70–85 %, implementing a secondary process like electrodialysis metathesis (EDM) to treat concentrate can increase system water recovery. This study explores salt-free electrodialysis metathesis (SF-EDM) as an alternative to conventional EDM to investigate its performance in the zero discharge desalination (ZDD) process. SF-EDM utilizes monovalent-selective ion exchange membranes, eliminating the need for sodium chloride (NaCl) as a substitution solution. This approach generates one diluate stream and two highly soluble concentrate streams enriched in calcium and sulfate, respectively. In this article, a series of lab-scale tests based on synthetic and real RO concentrate were performed to investigate how SF-EDM performs operating at high water recovery with a feedwater with high scaling potential. We critically analyze the performance in terms of salinity reduction, selectivity, and energy consumption. Results of the study show that without the addition of NaCl, the process was able to selectively separate sulfate and calcium into two concentrate streams achieve a salinity reduction of 93 % and a total system water recovery (RO and SF-EDM) of 90 %. A technoeconomic analysis found SF-EDM to be 80 % of the cost of conventional EDM making it a competitive technology for brackish water RO concentrate management.

LAVOLUTION: Tunable structured light for bridge displacement measurement

Assessing bridge conditions is critical, but field measurements often face challenges like sensor installation and accuracy requirements. This paper introduces a novel approach using structured light (SL) for non-target-based measurements, eliminating the need for physical markers. A tunable SL system, featuring four laser pointers aligned with a commercial camera, was developed to estimate the relative angle between the camera and the target structure, and to accurately calculate the scale factor. The proposed method involves three main processes: (1) laser alignment, (2) angle estimation, and (3) real-time displacement measurement. By precisely aligning the SL system and minimizing discrepancies between projected SL points and an ideal numerical model, the method achieves accurate results. Validation through numerical simulations, lab-scale tests, and full-scale bridge tests demonstrated a maximum error of 2.69% in scale and 2.53% in displacement, confirming the method’s effectiveness and applicability.

Phytotoxicity Testing of Atmospheric Polycyclic Aromatic Hydrocarbons

Atmospheric polycyclic aromatic hydrocarbons (PAHs) have well-known phytotoxicity on higher plants. However, while numerous bioindication studies have been targeted on how different symptoms indicate the deleterious effects of PAHs in the field, laboratory-scale phytotoxicity tests are much rarer. While ecotoxicity tests might rely on the very same end-points as bioindication studies, they have to comply with quality assurance criteria, repeatability being the most important. As such, proper reporting involves the description of the test compound, experimental design and conditions, test organism used, and end-points measured. The recent review intends to give an overview of studies available in the literature complying with these requirements. PAHs occur in the atmosphere both in gaseous form and bound to particles. As plants are exposed to both phases, test protocols available represent different exposure pathways, fumigation chambers vs. direct foliar treatment. Reported studies, therefore, are grouped based on the exposure route they intend to simulate.

An experimental investigation of the effect of barrier trench on vibration propagation on a laboratory scale model

Vibration waves caused by construction or mining operations may cause damage to nearby structures or sensitive machinery and equipment. Some measures are implemented to eliminate or reduce this negative environmental effect of vibration. The barrier trench, one of these methods, aims to reduce the vibration by creating a suitable barrier between the structure and the vibration source on the ground. In this study, the effect of trench depth and distance from the source on vibration waves was simulated with a designed laboratory-scale test set-up to determine the effective parameters in barrier trench use. In addition, the effects of the superimposition of the source vibration wave with the reflected and refracted vibration waves from the trench, which has not been previously discussed in the literature, were also investigated. A laboratory scale gypsum-plaster block with dimensions of 200 cm × 90 cm x 70 cm was prepared and a Schmidt hammer was used as the impact energy source to generate vibration throughout the gypsum block. A trench barrier was opened at different depths on the test block and 588 vibration recordings were taken by the vibration monitor in different locations of the designed set-up. Statistical analyses were performed using vibration measurement results, trench source distance, and trench depths. As a result, vibration estimation equations depending on trench depth and the distance between the vibration source point and the trench were developed and how the presence of trench affects vibration propagation is revealed. It was found that vibrations increase due to the superposition of the source and reflected waves in front of the barrier trench. As the barrier depth increases, it is understood that the vibrations emanating from the near-surface vibration energy source are better blocked by the barrier. Thus, this study provides fundamental information on designing barrier trenches to avoid adverse effects of vibrations.

Towards maximization of parameters identifiability: Development of the CalOpt tool and its application to the anaerobic digestion model

This work addresses the need for robust approaches to estimate kinetic parameters in continuous anaerobic co-digestion plants. Specifically, it outlines a procedure for including data from batch activity tests on digestate samples, in addition to the poorly informative dataset from conventional monitoring of biogas facilities. To profitably make use of activity tests, the estimation of the biomass composition in digestate samples is required to improve parameter identifiability. To this purpose, an open-source tool (CalOpt) was developed and tested on a pilot-scale co-digester, simultaneously using data from both conventional monitoring and lab-scale activity tests. Targeting the prediction of volatile fatty acids (VFAs) concentrations in the digestate of the continuous co-digester, the kinetic parameters of different microbial groups were selected for calibration based on parameters importance ranking and collinearity index. The results demonstrate that the CalOpt tool significantly improved the fitting of acetic, propionic, and butyric acid concentrations in digestate as well as the methane flowrate from the digester. Furthermore, the study assessed the necessity of modifying the Monod kinetics to account for substrate inhibition by incorporating the Haldane term in the VFAs uptake kinetic model, which proved to be essential for better interpreting batch activity tests.