Assessment Methods Research Articles

Studies on the Assessment of Hair Cortisol Concentration in Dogs by Radioimmunoassay (RIA)

Background: Hair cortisol Concentration (HCC) is often used as a measure of chronic stress responses in humans. The use of Radioimmunoassay (RIA) and HCC to assess stress in hospitalized dogs has been poorly reported in the literature in India and Central East Asia. This paper presents a modified protocol for measuring HCC in dog hair samples for stress assessment using RIA. Methods: Estimation of HCC concentrations of healthy (n=20) and hospitalized (n=20) dogs was done using RIA. Hair samples were collected from a dog’s nape region and weighed to measure HCC concentration. The study used RIA-enabled dissevered spiking and detectable concentration-based modifications to measure HCC. Five known cortisol concentration standards (viz 19.0 nmol/l, 47.9 nmol/l, 190 nmol/l, 238.66 nmol/l and 713.33 nmol/l) were used, with the first three readily available with a cortisol RIA kit. The samples were spiked with 50 µL of the standard and analysed in a gamma counter to take counts per minute (CPM). The final concentration of HCC was estimated in nmol/l and derived in pg/mg as per standard methods. Result: Healthy dogs had a mean HCC of 6.24±0.84 pg/mg, while hospitalized dogs had 31.65±5.87 pg/mg. A significant deviation was observed in both groups. A box and whisker graph showed an HCC range of 0.36 to 13.78 pg/mg for healthy dogs and 8.70 to 65.25 pg/mg for hospitalized dogs. The interquartile range (25th to 75th percentile) was 3.55 to 9.24 pg/mg for healthy dogs and 14.43 to 46.22 for hospitalized dogs. The observed median for healthy dogs was 6.16 pg/mg and 22.62 pg/mg for hospitalized dogs. The study shows higher HCC in hospitalized sick dogs compared to clinically healthy ones, suggesting hair sample collection as a simple stress assessment method and RIA as a promising HCC detection technique.

Impact of obstruction size on ureter dynamics: A computational investigation

Kidney stones, known medically as nephrolithiasis, present a significant health concern globally, particularly in regions such as India and the Middle East. Despite extensive research, the complexity of ureteral obstruction due to kidney stones remains a challenge in urology. This study addresses this gap by analysing the effects of obstruction in a three-dimensional model of the ureter with a constant diameter, simulating various percentages of blockage. Through computational analysis, the pressure and velocity effects on the ureter wall are explored, providing insights into the dynamics of obstructed ureters. Building upon previous research, this study contributes to a deeper understanding of the factors influencing flow patterns and pressure variations within the urinary tract. Additionally, findings from this study may lead to advancements in non-invasive assessment methods for diagnosing and treating ureteral obstruction caused by kidney stones. Furthermore, insights gained from this research could potentially inform clinical decision-making, leading to improved management strategies and better outcomes for patients affected by this debilitating condition. The novelty of this work lies in its comprehensive analysis of the effects of obstruction on the ureteral wall, offering valuable implications for urological practice and patient care.

Prognostic Value of Objective Social Isolation and Loneliness in Older Patients With Heart Failure: Subanalysis of FRAGILE-HF and Kitasato Cohort.

Social factors encompass a broad spectrum of nonmedical factors, including objective (social isolation [SI]) and perceived (loneliness) conditions. Although social factors have attracted considerable research attention, information regarding their impact on patients with heart failure is scarce. We aimed to investigate the prognostic impact of objective SI and loneliness in older patients with heart failure. This study was conducted using the FRAGILE-HF (Prevalence and Prognostic Value of Physical and Social Frailty in Geriatric Patients Hospitalized for Heart Failure; derivation cohort) and Kitasato cohorts (validation cohort), which included hospitalized patients with heart failure aged ≥65 years. Objective SI and loneliness were defined using the Japanese version of Lubben Social Network Scale-6 and diagnosed when the total score for objective and perceived questions on the Lubben Social Network Scale-6 was below the median in the FRAGILE-HF. The primary outcome was 1-year death. Overall, 1232 and 405 patients in the FRAGILE-HF and Kitasato cohorts, respectively, were analyzed. Objective SI and loneliness were observed in 57.8% and 51.4% of patients in the FRAGILE-HF and 55.4% and 46.2% of those in the Kitasato cohort, respectively. During the 1-year follow-up, 149 and 31 patients died in the FRAGILE-HF and Kitasato cohorts, respectively. Cox proportional hazard analysis revealed that objective SI, but not loneliness, was significantly associated with 1-year death after adjustment for conventional risk factors in the FRAGILE-HF. These findings were consistent with the validation cohort. Objective SI assessed using the Lubben Social Network Scale-6 may be a prognostic indicator in older patients with heart failure. Given the lack of established SI assessment methods in this population, further research is required to refine such methods.

Occupational therapy assessments and interventions for patients with ankylosing spondylitis: a scoping review protocol.

The proposed scoping review aims to explore the literature on the occupational therapy (OT) scope of practice for patients with ankylosing spondylitis (AS), including assessment methods and intervention approaches used by OT practitioners, areas of impairment addressed, and practice settings where OT practitioners provide services. AS is a type of spondyloarthritis primarily involving inflammation of the spine. Studies have begun to examine the role of OT in addressing pain, function, and disability among AS patients. Given the increased recognition of OT services for this population, a comprehensive understanding of the assessment methods and intervention approaches used by OT practitioners when working with AS would benefit clinicians, providers, and patients and support future research efforts. The review will consider studies that include participants of any age diagnosed with AS and any form of OT assessment and intervention. All relevant published and unpublished studies will be considered, without date or language limitations, including all primary studies, gray literature, text and opinion papers, and clinical guidelines. The review will follow the JBI methodology for scoping reviews. Searches will be conducted in MEDLINE (PubMed), Embase, CINAHL (EBSCOhost), Scopus (EBSCOhost), PsycINFO, ProQuest Dissertations and Theses Global, SportDiscus (EBSCOhost), OTDBase, OTSeeker, and Google Scholar. Two independent reviewers will extract data from selected papers using a standardized tool modified for the review. The results will be presented using frequency tables and will be accompanied by a narrative summary. https://doi.org/10.17605/OSF.IO/VPY56.

Probabilistic Performance-based Fire Design of Structures: A Hazard-Centred and Consequence-Oriented Perspective

AbstractRisk-based design and assessment methods are gaining popularity in performance-based structural fire engineering. These methods usually start by defining a set of hazard scenarios to use as analysis inputs. This approach, proven highly effective for other hazard types such as earthquakes, may not be optimal for fire safety design. Indeed, the strong coupling between the fire phenomenon and structural features enables an ad-hoc design variable selection (and/or optimisation) to reduce fire intensity, making fire scenarios additional design outputs. In addition, such a coupling effect implies that fire scenarios maximising consequences are structure specific. Building on these considerations, this paper discusses the limitations that arise at different analysis steps (i.e., fire-scenario and intensity treatment, identifying fire intensity measures, probabilistic fire hazard analysis, developing fire fragility models, and risk calculation) when using conventional risk-based approaches for design purposes. Furthermore, it compares such approaches with a fire safety design methodology (the Consequence-oriented Fire intensity Optimisation, CFO, approach) that addresses the identified limitations. The potential benefits of integrating the two approaches are also discussed. Finally, the fire design of a simplified steel-girder bridge is introduced as an illustrative example, comparing the consequence metrics and design updating strategies resulting from the two approaches.

Influence of life history characteristics on data-limited stock status assertions and minimum size limit evaluations using Length-Based Spawning Potential Ratio (LBSPR)

Data-limited fisheries frequently lack age-structure data and instead rely on length-based management and assessment methods. However, the performance of such length-based methods and control rules are often poorly understood when confronted with varying life-history characteristics. In this study, we used Length-Based Spawning Potential Ratio (LBSPR) to conduct a data-limited assessment for a subset of high-value recreational fisheries in California and make inferences about the cumulative performance of existing management regulations. We then simulated stock status under alternative size limit scenarios to estimate the benefit of new or alternative minimum size limits on a species-by-species basis. Here, we present a validated procedure for evaluating minimum size limit scenarios and provide valuable insight on the status of five unassessed fisheries in California, including one species that appears heavily overfished. Furthermore, we identified a range of situations with variable confidence in LBSPR results dependent on life-history characteristics. Most notably, failures in length-based inferences can occur with species that exhibit sporadic recruitment success and spawning aggregation-based fisheries, leading to a hyperstable size structure and potential stock collapse.

Sentinel 1a-2a Incorporating an Object-Based Image Analysis Method for Flood Mapping and Extent Assessment

Abstract. This study presents flood extent extraction and mapping from Sentinel images. Here we suggest an algorithm for extracting flooded areas from object-based image analysis (OBIA) using Sentinel-1A and Sentinel-2A images to map and assess the flood extent from the beginning to one week after the event. This study used multi-scale parameters in OBIA for image segmentation. First, we identified the flooded regions by applying our proposed algorithm on the Sentinel-1A. Then, to evaluate the effects of the flood on each land-use/land cover (LULC) class, Sentinel-2A images is classified using the OBIA after the event. Besides, we also used the threshold method to compare the proposed algorithm applying OBIA to determine the efficiency in computing parameters for change detection and flood extent mapping. The findings revealed the best performance for the segmentation process with an Object Fitness Index (OFI) is 0.92 when the scale parameter of 60 is applied. The results also show that 2099.4 km2 of the study area is flooded at the beginning of the flood. Furthermore, we found that the most flooded LULC classes are agricultural land and orchards with 695.28km2 (32.4%) and 708.63 km2 (33.7%), respectively. In comparison, about 33.9% of the remaining flooded area has occurred in other classes (i.e., fish farm, built-up, bare land and water bodies). The resulting object of each scale parameter was evaluated by Object Pureness Index (OPI), Object Matching Index (OMI), and OFI. Finally, our Overall Accuracy (OA) method incorporated field data using the Global Positioning System (GPS) shows 93%, 90%, and 89% for LULC, flood map (i.e., using our proposed algorithm), and threshold method, respectively.

Chest CT‐based automated vertebral fracture assessment using artificial intelligence and morphologic features

AbstractBackgroundSpinal degeneration and vertebral compression fractures are common among the elderly that adversely affect their mobility, quality of life, lung function, and mortality. Assessment of vertebral fractures in chronic obstructive pulmonary disease (COPD) is important due to the high prevalence of osteoporosis and associated vertebral fractures in COPD.PurposeWe present new automated methods for (1) segmentation and labelling of individual vertebrae in chest computed tomography (CT) images using deep learning (DL), multi‐parametric freeze‐and‐grow (FG) algorithm, and separation of apparently fused vertebrae using intensity autocorrelation and (2) vertebral deformity fracture detection using computed vertebral height features and parametric computational modelling of an established protocol outlined for trained human experts.MethodsA chest CT‐based automated method was developed for quantitative deformity fracture assessment following the protocol by Genant et al. The computational method was accomplished in the following steps: (1) computation of a voxel‐level vertebral body likelihood map from chest CT using a trained DL network; (2) delineation and labelling of individual vertebrae on the likelihood map using an iterative multi‐parametric FG algorithm; (3) separation of apparently fused vertebrae in CT using intensity autocorrelation; (4) computation of vertebral heights using contour analysis on the central anterior‐posterior (AP) plane of a vertebral body; (5) assessment of vertebral fracture status using ratio functions of vertebral heights and optimized thresholds. The method was applied to inspiratory or total lung capacity (TLC) chest scans from the multi‐site Genetic Epidemiology of COPD (COPDGene) (ClinicalTrials.gov: NCT00608764) study, and the performance was examined (n = 3231). One hundred and twenty scans randomly selected from this dataset were partitioned into training (n = 80) and validation (n = 40) datasets for the DL‐based vertebral body classifier. Also, generalizability of the method to low dose CT imaging (n = 236) was evaluated.ResultsThe vertebral segmentation module achieved a Dice score of .984 as compared to manual outlining results as reference (n = 100); the segmentation performance was consistent across images with the minimum and maximum of Dice scores among images being .980 and .989, respectively. The vertebral labelling module achieved 100% accuracy (n = 100). For low dose CT, the segmentation module produced image‐level minimum and maximum Dice scores of .995 and .999, respectively, as compared to standard dose CT as the reference; vertebral labelling at low dose CT was fully consistent with standard dose CT (n = 236). The fracture assessment method achieved overall accuracy, sensitivity, and specificity of 98.3%, 94.8%, and 98.5%, respectively, for 40,050 vertebrae from 3231 COPDGene participants. For generalizability experiments, fracture assessment from low dose CT was consistent with the reference standard dose CT results across all participants.ConclusionsOur CT‐based automated method for vertebral fracture assessment is accurate, and it offers a feasible alternative to manual expert reading, especially for large population‐based studies, where automation is important for high efficiency. Generalizability of the method to low dose CT imaging further extends the scope of application of the method, particularly since the usage of low dose CT imaging in large population‐based studies has increased to reduce cumulative radiation exposure.

Analysis of Soil Health Parameters to Identify Important Soil Nutrients and Weights Using Feature Engineering for Multiple Agri-Advices

Introduction:: Identification of important soil nutrients is a very important task for precision farming and developing efficient machine learning models. Method:: The existing work shows that the patent is filed and published on a method and device for assessment of soil health parameters and recommendation of fertilizers. The existing work is done for one advice at a time not for several advices. Multiple advices that are taken into account for the task are appropriate crops, organic fertilizer, and combination 1 and combination 2 of fertilizers. objective: Apply feature selection techniques based on Chi-Square, ANOVA and Mutual Information Gain scoring functions such as Select K Best and Select Percentile for multiple agri-advice dataset of Pune District regions to identify important soil health features to reduce the complexity of classification models and in turn reduce space and the computational time of different classification models. Result:: This paper presented results of feature selection techniques based on Chi-Square, ANOVA and Mutual Information Gain scoring functions such as Select K Best and Select Percentile for multiple agri-advice datasets of Pune District regions to identify important soil health features. Conclusion:: As per Chi-Square, ANOVA and Mutual Information scoring functions with Select K Best and Select Percentile techniques ‘Mn’ was the most important parameter and Cu’ and ‘B’ were the least important parameters among all 11 parameters common in 4 agriculture advices. Whereas Ph, K, Fe, 'OC', 'N', 'S', 'Mn', and 'P' will be used for future research work on the development of an efficient classification algorithm for multi-advice generators.

App Based Digital Audiometer

Hearing impairment is a widespread health concern on a global scale that affects individuals across all age groups. The timely identification and regular monitoring of one's hearing ability are crucial factors for prompt intervention and an improved overall quality of life. However, the traditional method of audiometry comes with limitations such as the need for specialized equipment and professional supervision making it inaccessible and expensive for many individuals seeking hearing assessments. To address this issue a cutting-edge approach has been developed in the form of a Digital Audiometer App offering a convenient cost-efficient and user-friendly means of assessing one's auditory function. This innovative tool utilizes the widespread use of smartphones and wearable devices allowing for a portable and accessible method of hearing assessment. The system integrates advanced signal processing techniques intuitive interfaces and reliable calibration processes to guarantee precise and consistent results. Additionally, the audiometer is compatible with both iOS and Android platforms ensuring its availability to users worldwide

Determining Riverine Surface Roughness at Fluvial Mesohabitat Level and Its Influence on UAV-Based Thermal Imaging Accuracy

Water surface roughness (SR) is a highly relevant parameter governing data reliability in remote sensing applications, yet lacking appropriate methodology in riverine habitats. In order to assess thermal accuracy linked to SR of thermal imaging derived from an unmanned aerial vehicle (UAV), we developed the SR Measurement Device (SRMD). The SRMD uses the concept of in situ quantification of wave frequency and wave amplitude. Data of nine installed SRMDs in four different fluvial mesohabitat classes presented a range of 0 to 47 waves per 30 s and an amplitude range of 0 to 6 cm. Even subtle differences between mesohabitat classes run, riffle, and no-/low-flow still and pool areas could be detected with the SRMD. However, SR revealed no significant influence on the accuracy of thermal infrared (TIR) imagery data in our study case. Overall, the presented device expands existing methods of riverine habitat assessments and has the potential to produce highly relevant data of SR for various ecological and technical applications, ranging from remote sensing of surface water and habitat quality characterizations to bank stability and erosion risk assessments.

Deep Learning-based Thigh Muscle Investigation Using MRI For Prosthetic Development for Patients Undergoing Total Knee Replacement (TKR).

A prosthetic device is designed based on the quantitative analysis of muscle MRI which will improve the muscle control achieved with functional electrical stimulation/ guided robotic exoskeletons. Electromyography (EMG) provides muscle functionality information while MRI provides the physiological and functionality of muscles. The sensor feedbacks were used for the bionic prosthesis, but the length of muscle using image processing was not correlated. To investigate and perform qualitative and quantitative assessment of thigh muscle using MRI. The objective of the work is to improve the existing VAG signal classification method to diagnose abnormality using MRI for patients undergoing Total knee replacement (TKR). A deep learning method for qualitative and quantitative assessment of thigh muscle is done using MRI. In existing prosthetic devices, electrical measurements of a person's muscles are obtained using surface or implantable electrodes. Several methods were used for the classification and diagnostic processes. The existing methods have drawbacks in feature extraction and require experts to design the system. This work combines medical image processing and orthopaedic prosthetics to develop a therapeutic method. This design provides much more precise control of prosthetic limbs using the image processing technique. The hybrid CNN swarm-based method measures the muscle structure and functions. Along with the sensor readings, these details are combined for prosthetic control. The implementation was carried out in MATLAB, Sketchuppro, and Arduino IDE. A combined swarm intelligence and deep learning method were proposed for qualitative and quantitative assessment of thigh muscle. The prosthetic device choice was done from the scanned MRI image like Humerus-T prosthetics, segmental prosthesis and arthrodesis prosthesis. The investigation was done for the Total knee replacement (TKR) approach.

Risk assessment of high-speed railway CTC system based on improved game theory and cloud model

PurposeIn order to solve the problem of inaccurate calculation of index weights, subjectivity and uncertainty of index assessment in the risk assessment process, this study aims to propose a scientific and reasonable centralized traffic control (CTC) system risk assessment method.Design/methodology/approachFirst, system-theoretic process analysis (STPA) is used to conduct risk analysis on the CTC system and constructs risk assessment indexes based on this analysis. Then, to enhance the accuracy of weight calculation, the fuzzy analytical hierarchy process (FAHP), fuzzy decision-making trial and evaluation laboratory (FDEMATEL) and entropy weight method are employed to calculate the subjective weight, relative weight and objective weight of each index. These three types of weights are combined using game theory to obtain the combined weight for each index. To reduce subjectivity and uncertainty in the assessment process, the backward cloud generator method is utilized to obtain the numerical character (NC) of the cloud model for each index. The NCs of the indexes are then weighted to derive the comprehensive cloud for risk assessment of the CTC system. This cloud model is used to obtain the CTC system's comprehensive risk assessment. The model's similarity measurement method gauges the likeness between the comprehensive risk assessment cloud and the risk standard cloud. Finally, this process yields the risk assessment results for the CTC system.FindingsThe cloud model can handle the subjectivity and fuzziness in the risk assessment process well. The cloud model-based risk assessment method was applied to the CTC system risk assessment of a railway group and achieved good results.Originality/valueThis study provides a cloud model-based method for risk assessment of CTC systems, which accurately calculates the weight of risk indexes and uses cloud models to reduce uncertainty and subjectivity in the assessment, achieving effective risk assessment of CTC systems. It can provide a reference and theoretical basis for risk management of the CTC system.

Sustainable astronomy: A comparative life cycle assessment of off-grid hybrid energy systems to supply large telescopes

Abstract Purpose Supplying off-grid facilities such as astronomical observatories with renewable energy–based systems (RES) instead of diesel generators can considerably reduce their environmental impact. However, RES require oversized capacities to counter intermittency and comply with reliability requirements, hence shifting the environmental impact from operation to construction phase. We assess whether 100% RES scenarios are favorable from an environmental point of view and discuss the trade-offs in systems with backup fossil generators versus 100% renewable ones. Methods In this comparative life cycle assessment (LCA), we study various RES supply systems to power a new telescope in the Atacama Desert, Chile. We compare six setups, including 100% RES scenarios, namely, photovoltaics (PV) with batteries and hydrogen energy storage; high-renewable scenarios, with fossil fuel power generation next to RES and storage; and a system combining PV with diesel generation. We base system sizing on a techno-economical optimization for the start of operation in 2030. Foreground data stem from life cycle inventories of RES components since 2015 and 2030 electricity mix assumptions of production countries. We assess environmental impact in the categories climate change, mineral resource depletion, and water use. Results and discussion We find that 100% RES and high-renewable scenarios result in emissions of 0.077–0.115 kg CO2e/kWh supplied, compared to 0.917 kg CO2e/kWh in the reference case with solely diesel generation. One hundred percent RES scenarios have a lower CO2e impact than high-renewable scenarios. However, the latter lower the mineral resource depletion and water use by about 27% compared to 100% RES scenarios. Applying hybrid energy storage systems increases the water use impact while reducing the mineral resource depletion. Conclusions None of the six energy systems we compared was clearly the best in all environmental impacts considered. Trade-offs must be taken when choosing an energy system to supply the prospective off-grid telescope in Chile. We find high-renewable systems with some fossil generation as the better option regarding power reliability, mineral resource depletion, and water use, while inducing slightly higher greenhouse gas emissions than the 100% RES scenarios. As remote research facilities and off-grid settlements today are mainly supplied by fossil fuels, we expect to motivate more multifaceted decisions for implementing larger shares of RES for these areas. To advance the LCA community in the field of energy systems, we should strive to incorporate temporal and regional realities into our life cycle inventories. To ease the path for upcoming studies, we publish this work’s inventories as detailed activity level datasets.

A probabilistic fatigue life assessment method for wind turbine blade based on Bayesian GPR with the effects of pitch angle

AbstractThe fatigue behavior of large wind turbine blades is complex and stochastic due to their complex structure and operating environment. This paper focuses on developing a probabilistic fatigue life assessment method for wind turbine blades considering the uncertainties from wind velocity, material mechanical properties, pitch angle, and layer thickness. To improve the efficiency of stochastic fatigue behavior analysis of wind turbine blade, unidirectional fluid‐structure coupling (UFSC) and bidirectional fluid‐structure coupling (BFSC) analysis are employed to analyze the stochastic response. Then, Gaussian process regression (GPR) and Bayesian updating are combined to establish the stochastic fatigue behavior prediction model for wind turbine blade. On this basis, a modified S‐N curve formulation is proposed, and the fatigue life of wind turbine blade is analyzed by the modified S‐N curve and compared with the three‐parameter Weibull model. The results indicate that the proposed method for fatigue life assessment has better accuracy. The proposed probabilistic fatigue life assessment method with high accuracy and high efficiency, which is beneficial for the fatigue reliability design of wind turbine blades.

An nLCA approach to support consumer meal decisions: a New Zealand case study of toppings on toast

IntroductionThis study investigates the development and potential application of a nutritional Life Cycle Assessment (nLCA) method to rank meals, using a case study of a “toppings on toast” (ToTs) meal. Methodological issues are investigated in the context of application to support consumers to make more informed food choices at the meal level.MethodsFourteen selected “toppings on toast” (ToTs) commonly consumed in New Zealand (NZ) were evaluated for their climate change impacts and nutritional value using the serve size of each topping as the functional unit (FU). NZ-specific climate change values were obtained from an existing database and recent literature. Nutritional value was calculated using the NRF family of indices – specifically the NRF9.3 and NRF28.3 indices (the latter constructed for this study to include all nutrients in the selected toppings for which reference values were available) and presented in a separate midpoint nutrition impact category. The NRF and climate change scores were assigned quartile-based weights, and the weight of each index score was averaged with that of the climate change score. Based on these average values, the toppings were ranked in two ranking sets (one for each index). In a sensitivity analysis, two alternative reference units were also used (100 g and 100 kcal) to investigate how different FUs influenced the final rankings.ResultsThe results showed that use of one or other NRF index affected the magnitude of the nLCA results; however, the rankings of the ToTs based on the nLCA results did not change much between the two indices. Avocado and peanut butter performed the best (top two ranks), and bacon, butter, and cheese were the poorest performers (bottom two ranks), for both the ranking sets. The toppings which did change ranks mostly moved up or down by only one position. Thus, the results of this case study suggest that the NRF9.3 index is sufficient to determine overall the best, medium, and worst performing toppings in the ToT meal context. However, the results also showed that water-soluble vitamins and unsaturated fats included in the NRF28.3 index contributed significantly to the nutritional scores for most of the toppings and were instrumental in the rank changes for the toppings which are particularly rich in these nutrients.DiscussionThus, for a more diverse range of toppings/meals, an expanded index including these nutrients can generate more nuanced rankings. This study contributes to the nascent but fast-growing nLCA research field, particularly within the meal context. The method used in this case study could be applied in food composition databases, restaurant menus, and websites/apps that provides recipes for meals. However, the study also highlighted the potentially significant variability in climate change and nutritional values in the toppings associated with different production practices, seasonality, and different varieties of the same product. Any future development of nLCA-based meal level rankings should address this variability and communicate it to the consumer.

Investigation on the multidirectional crack vibration induced by rock fracture

Understanding crack vibrations in rock under load is crucial for comprehending and predicting rock failure. With this motive, a novel multidirectional crack vibration (MCV) monitoring experiment is carried out using miniature vibration acceleration sensors with directional sensing. The obtained results show that the crack surface produced by uniaxial compression failure of rock is rough and its direction is variable. Furthermore, the volume distribution of the sample after failure is uneven, and there is a noticeable difference in mass between the broken samples on either side of the crack. Different directions of crack vibration signals were detected during rock failure, all exhibiting a strong temporal correspondence with the stress drop, but the amplitudes and main frequencies of the vibrations differ across directions. The fracture-induced crack vibration is a typical low-frequency signal, with the highest recorded frequency at 55.63 kHz and the lowest being 0.86 kHz, with the main frequency mostly below 25 kHz, which is notably lower than acoustic emission. This difference suggests that fracture-induced crack vibration and acoustic emission are distinct physical quantities. The disparities in rock vibration amplitude and main frequency among different directions, especially those perceived by opposing sensors, indicate that the two sides of the crack cannot be considered as the same vibration system. The analysis shows that the fracture-induced crack vibration of rock under load is damped vibration with a small and variable damping. Besides, due to the difference in vibration parameters on both sides of the crack, their vibration amplitude and frequency are also different. The miniature acceleration sensors used in this work can effectively capture MCV during rock failure and can be employed to further investigate the vibration precursor of rock instability failure, so as to develop appropriate assessment and prediction methods for underground dynamic disasters.

Advanced Bayesian study on inland navigational risk of remotely controlled autonomous ship

The arise of autonomous ships has necessitated the development of new risk assessment techniques and methods. This study proposes a new framework for navigational risk assessment of remotely controlled Maritime Autonomous Surface Ships (MASS). This framework establishes a set of risk influencing factors affecting safety of navigation of a remotely-controlled MASS. Next, model parameters are defined based on the risk factors, and the model structure is developed using Bayesian Networks. To this end, an extensive literature survey is conducted, enhanced with the domain knowledge elicited from the experts and improved by the experimental data obtained during representative MASS model trials carried out in an inland river. Conditional Probability Tables are generated using a new function employing expert feedback regarding Interval Type 2 Fuzzy Sets. The developed Bayesian model yields the expected utilities results representing an accident’s probability and consequence, with the results visualized on a dedicated diagram. Finally, the developed risk assessment model is validated by conducting three axiom tests, extreme scenarios analysis, and sensitivity analysis. Navigational environment, natural environment, traffic complexity, and shore-ship collaboration performance are critical from the probability and consequence perspective for inland navigational accidents to a remotely controlled MASS. Lastly, important nodes to Shore-Ship collaboration performance include autonomy of target ships, cyber risk, and transition from other remote control centers.

A Comparison of Additional Benefit Assessment Methods for Time-to-Event Endpoints Using Hazard Ratio Point Estimates or Confidence Interval Limits by Means of a Simulation Study.

For time-to-event endpoints, three additional benefit assessment methods have been developed aiming at an unbiased knowledge about the magnitude of clinical benefit of newly approved treatments. The American Society of Clinical Oncology (ASCO) defines a continuous score using the hazard ratio point estimate (HR-PE). The European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) developed methods with an ordinal outcome using lower and upper limits of the 95% HR confidence interval (HR-CI), respectively. We describe all three frameworks for additional benefit assessment aiming at a fair comparison across different stakeholders. Furthermore, we determine which ASCO score is consistent with which ESMO/IQWiG category. In a comprehensive simulation study with different failure time distributions and treatment effects, we compare all methods using Spearman's correlation and descriptive measures. For determination of ASCO values consistent with categories of ESMO/IQWiG, maximizing weighted Cohen's Kappa approach was used. Our research depicts a high positive relationship between ASCO/IQWiG and a low positive relationship between ASCO/ESMO. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 corresponds to ESMO categories. Using ASCO values of 21 and 38 as cutoffs represents IQWiG categories. We investigated the statistical aspects of the methods and hence implemented slightly reduced versions of all methods. IQWiG and ASCO are more conservative than ESMO, which often awards the maximal category independent of the true effect and is at risk of overcompensating with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and underpowered/overpowered studies influence all methods in some degree. Nevertheless, ESMO is the most liberal one. For the additional benefit assessment, the American Society of Clinical Oncology (ASCO) uses the hazard ratio point estimate (HR-PE) for their continuous score. In contrast, the European Society for Medical Oncology (ESMO) and the German Institute for Quality and Efficiency in Health Care (IQWiG) use the lower and upper 95% HR confidence interval (HR-CI) to specific thresholds, respectively. ESMO generously assigns maximal scores, while IQWiG is more conservative.This research provides the first comparison between IQWiG and ASCO and describes all three frameworks for additional benefit assessment aiming for a fair comparison across different stakeholders. Furthermore, thresholds for ASCO consistent with ESMO and IQWiG categories are determined, enabling a comparison of the methods in practice in a fair manner.IQWiG and ASCO are the more conservative methods, while ESMO awards high percentages of maximal categories, especially with various failure time distributions. ASCO has similar characteristics as IQWiG. Delayed treatment effects and under/-overpowered studies influence all methods. Nevertheless, ESMO is the most liberal one. An ASCO score smaller than 17, 17 to 20, 20 to 24, and greater than 24 correspond to the categories of ESMO. Using ASCO values of 21 and 38 as cutoffs represents categories of IQWiG.

Code Requirements for the Seismic Design of Irregular Elevation RC Structures

The recent seismic activity highlights the crucial need to enhance seismic design and safety assessment methods, particularly for irregular structures, in both new and existing constructions. The present study focuses on structural irregularities in elevation for buildings, as the design of structural systems involves multiple variables that often result in irregularities in many buildings. This work aims to perform a comparative assessment of the criteria adopted for the evaluation of the structural irregularities in elevation present in European and international seismic codes. This paper is structured as follows: Firtsly, it discusses structural irregularities and more specifically the most common types of structural damage due to seismic events. Then, it shows the documented experiences of structural damages in seismic events associated with structural irregularities in China, Italy, Spain, Nepal and Mexico. Additionaly, it discusses the requirements of the standards on irregularities and their limitation in that matter. At the end of this section, the different approaches of each code in irregularities in elevation are compared. All assessed seismic codes addresses the structural irregularity issue, attributing the desired characteristics of a seismic-resistant structure. However, there are considerable development differences between norms, demonstrated on ambiguity of few codes on criteria of vertical irregularies.