Average Deviation Research Articles

Pressure-Compensated Fiber-Optic Photoacoustic Sensors for Trace SO2 Analysis in Gas Insulation Equipment.

A high-sensitivity fiber-optic photoacoustic sensor with pressure compensation is proposed to analyze the decomposition component SO2 in high-pressure gas insulation equipment. The multiple influence mechanism of pressure on photoacoustic excitation and cantilever detection has been theoretically analyzed and verified. In the high-pressure environment, the excited photoacoustic signal is enhanced, which compensates for the loss of sensitivity of the cantilever. A fiber-optic F-P cantilever is utilized to simultaneously measure static pressure and dynamic photoacoustic wave, and a spectral demodulation method based on white light interference is applied to calculate the optical path difference of the F-P interferometer (FPI). The real-time pressure is judged through the linear relationship between the average optical path difference of FPI and the pressure, which gives the proposed fiber-optic photoacoustic sensor the inherent advantages of being uncharged and resistant to electromagnetic interference. The average optical path difference of FPI is positively related to pressure, with a responsivity of 0.6 μm/atm, which is based on changes in the refractive index of gas. In the range of 1-4 atm, the SO2 sensor has a higher detection sensitivity at high-pressure, which benefits from the pressure compensation effect. With the pressure environment of gas insulation equipment at 4 atm as the application background, the SO2 gas is tested. The detection limit is 20 ppb with an averaging time of 400 s.

Real-Life Experience on the Effect of SGLT2 Inhibitors vs. Finerenone vs. Combination on Albuminuria in Chronic Kidney Disease.

There have been several recent advances in the care of patients with chronic kidney disease (CKD), including the use of sodium glucose cotransporter 2 (SGLT2) inhibitors and selective mineralocorticoid receptor antagonists (MRAs). There are very few data reporting the outcomes of these treatments in real-world experience. The aim of this retrospective study is to report the effects of SGLT2 inhibitors, finerenone, and their combination in CKD patients in our community-based setting. Ninety-eight patients with CKD with an estimated glomerular filtration rate (eGFR) between 25 and 90 mL/min per 1.73 m2 and a urine albumin-to-creatinine ratio (UACR) ≥ 30 mg/g were included. Patients were divided into three groups: two monotherapy groups of SGLT2 inhibitors or finerenone and a third combination group of therapy with SGLT2 inhibitors for the first 4 months and SGLT2 inhibitors and finerenone subsequently. The primary outcomes were the timing and percentage of patients achieving a >50% reduction in UACR from baseline. Group 1 comprised 52 patients on SGLT2i, group 2 had 22 patients on finerenone, and group 3 had 24 patients on combination therapy. The baseline median UACR and mean eGFR were 513 mg/g and 47.9 mL/min per 1.73 m2 in group 1, 548.0 mg/g and 50.5 mL/min per 1.73 m2 in group 2, and 800 mg/g and 60 mL/min per 1.73 m2 in group 3. At baseline, 71 (72.4%) patients were on the angiotensin-converting enzyme inhibitor (ACEi) or the angiotensin receptor blocker (ARB), and 78 (79.5%) patients had type 2 diabetes. After 8 months of follow-up, a >50% decrease in albuminuria was achieved in 96% of patients in group 3, compared to 50% in group 1 and 59% in group 2 (p-values were <0.01 and <0.01, respectively). There was a statistically but not clinically significant change in mean potassium levels in group 2 (+0.4 mmol/L) compared to either group 1 (0.0 mmol/L with p-value: <0.01) or group 3 (-0.01 mmol/L with p-value: <0.01). However, there was no difference in potassium levels when comparing groups 1 and 3. At the end of the follow-up, the average difference in eGFR was -3.4 (8.8), -5.3(10.1), and -7.8 (11.2) mL/min per 1.73 m2 in groups 1, 2, and 3, respectively, without a statistically significant difference between groups. In this real-world experience in our community setting, the combination of SGLT2 inhibitors and finerenone in our adult patients with CKD was associated with a very significant and clinically relevant reduction in UACR, without an increased risk of hyperkalemia. Combination therapy of SGLT2 inhibitor and finerenone regarding background use of ACEi/ARB is feasible and should be encouraged for further albuminuria reductions in CKD patients.

The FreiBurger: a new optotype for P300-based acuity estimation.

Accurate objective assessment of visual acuity is crucial, particularly in cases of suspected malingering, or when the patient's inability to cooperate makes standard psychophysical acuity tests unreliable. The P300 component of the event-related potentials offers a potential solution and even allows for the use of standard optotypes like the Landolt C. However, low-vision patients with large eccentric visual field defects often struggle to locate the Landolt C gap quickly enough for a P300 to be reliably produced. Addressing this challenge, we introduce a novel optotype (the "FreiBurger") with a critical detail that extends through the optotype's center. Two experiments, with 16 and 12 participants, respectively, were conducted. In the first, psychophysical acuity estimates were obtained with both the FreiBurger and the Landolt C. In the second, we tested the performance of the FreiBurger, relative to the Landolt C, in eliciting a P300 with undegraded vision, simulated low vision, and in a simulated combination of low vision and visual field constriction. Comparable psychophysical acuity values (average difference 0.03 logMAR) were obtained for both optotypes. In the P300 recordings, both optotypes produced similar P300 responses under conditions of undegraded vision and low vision. However, with the combination of low vision and constricted visual field, the P300 could only be reliably obtained with the FreiBurger, while the amplitude was drastically reduced with the Landolt C (9.1µV vs. 2.2µV; p < 0.0005). The new optotype extends the applicability of P300-based acuity estimation to the frequently encountered combination of low vision and constricted visual field, where Landolt C optotypes fail. Although impairments were simulated in the present study, we assume that the advantages of the new optotype will also manifest in patients with such impairments. We furthermore expect the advantages to apply to time-sensitive psychophysical examinations as well.

A Prospective Randomized Controlled Trial Using Virtual Reality in Pediatric Pre-intervention Echocardiograms to Decrease Child Anxiety and Fear.

Virtual reality (VR) as a distraction tool decreases anxiety and fear in children undergoing procedures, but its use has not been studied during transthoracic echocardiograms (TTEs). We hypothesized that VR in children undergoing pre-intervention TTEs decreases anxiety and fear and increases TTE study comprehensiveness and diagnostic accuracy when compared with standard distractors (television, mobile devices). Patients (6-18years old) scheduled for pre-intervention TTEs at Lucile Packard Children's Hospital in 2021 and 2022 were prospectively enrolled and randomized to VR and non-VR groups. Patients completed pre- and post-TTE surveys using the Children's Anxiety Meter-State (CAM-S) and Children's Fear Scale (CFS). Patients, parents, and sonographers completed post-TTE experience surveys. TTEs were reviewed by pediatric cardiologists for study comprehensiveness and compared with electronic medical records for diagnostic accuracy. Among 67 enrolled patients, 6 declined VR, 31 randomized to the VR group, and 30 to the non-VR group. Anxiety (average CAM-S difference 0.78 ± 1.80, p = 0.0012) and fear (average CFS difference 0.36 ± 0.74, p = 0.0005) decreased in both groups. There was no difference between groups in the change in anxiety and fear pre- and post-TTE (p = 0.96-1.00). TTE study comprehensiveness and diagnostic accuracy were high in both groups. Procedure time (time in the echocardiography room) was less for the VR group (48.4 ± 18.1min) than the non-VR group (58.8 ± 24.4min), but without a statistically significant difference (p = 0.075). VR is similar to standard distractors and may decrease procedure time. Patients, parents, and sonographers rated the VR experience highly and encouraged its use with future procedures.

Improved fusion model for generating hourly fine scale land surface temperature data under all-weather condition

Existing Land Surface Temperature (LST) fusion models encounter some challenges due to missing data, complex weather areas, and rapid land cover changes. To overcome these limitations, we proposed the Integrated SpatioTemporal Fusion Algorithm (ISFAT). ISFAT is developed based on contemporary fusion models but in addition incorporates data from partially contaminated LSTs using the masked weight function. This helps to predict fine-scale LST on prediction date while considering error resulting from landcover changes between the base and prediction date. This algorithm also factors in the calculation of model residuals, which are distributed back to the predicted fine-scale LST using the thin-plate spline function. The fine-scale LST on prediction can thereafter employed for predicting hourly fine-scale LST images by integrating a coarse resolution LST with hourly temporal resolution. Compared to contemporary LST fusion models, ISFAT demonstrates superior performance, with mean average differences of 0.1 K and 0.27 K over SADFAT and STITFM, respectively. Additionally, diurnal LST predictions from ISFAT compare well with air temperatures from automatic weather stations. Notably, on February 18, 2020, ISFAT effectively optimized fine-scale LST for Hong Kong, achieving an RMSE of 3.33 K, despite the limitation of cloud cover in the base date. The newly developed ISFAT could facilitate better LST retrieval over a large spatial coverage under different degrees of cloud contamination.

Temporal variability can promote migration between habitats

Understanding the conditions that promote the evolution of migration is important in ecology and evolution. When environments are fixed and there is one most favorable site, migration to other sites lowers overall growth rate and is not favored. Here we ask, can environmental variability favor migration when there is one best site on average? Previous work suggests that the answer is yes, but a general and precise answer remained elusive. Here we establish new, rigorous inequalities to show (and use simulations to illustrate) how stochastic growth rate can increase with migration when fitness (dis)advantages fluctuate over time across sites. The effect of migration between sites on the overall stochastic growth rate depends on the difference in expected growth rates and the variance of the fluctuating difference in growth rates. When fluctuations (variance) are large, a population can benefit from bursts of higher growth in sites that are worse on average. Such bursts become more probable as the between-site variance increases. Our results apply to many (≥ 2) sites, and reveal an interplay between the length of paths between sites, the average differences in site-specific growth rates, and the size of fluctuations. Our findings have implications for evolutionary biology as they provide conditions for departure from the reduction principle, and for ecological dynamics: even when there are superior sites in a sea of poor habitats, variability and habitat quality across space determine the importance of migration.

Problem Based Learning Berbantuan Wordwall untuk Meningkatkan Kemampuan Pemecahan Masalah Matematis pada Siswa Kelas III SD

This study aims to determine the improvement of students' mathematical problem-solving skills by applying wordwall-assisted problem-based learning to mathematics learning of diagrams, figures, and bars in grade III elementary school students. The approach taken in this study is quantitative with a quasi-experimental method with a nonequivalent control group design. The data collection technique uses a question test. Quantitative data analysis techniques with hypothesis tests. The results of this study are that there is an increase in students' mathematical problem-solving skills between pretest and posttest in the experimental class. There was a difference in the average result of the pretest of 21.41 and the average result of the posttest of 53.94. Then, an average difference test was carried out using the wilcoxon test with the acquisition of Asymp.Sig values. (2-tailed) of 0.000 &lt; 0.05 hypotheses were accepted. There was a difference in the improvement of students' mathematical problem-solving skills between the results of pretet and posttest in the experimental class. The calculation of the N-gain test in the experimental class was 0.43 with medium interpretation and the control class was 0.08 with low interpretation. Based on the results of this study, it can be concluded that the use of problem-based learning assisted by wordwalls can improve students' mathematical problem-solving skills

Exploring the relationship between digital gaming, language attitudes, and academic success in EFL university students

The potential of digital games in education has been widely discussed, yet there is a significant gap in the literature regarding their influence on foreign language learning, particularly in Central Europe, and using mixed-method research designs. This study aims to fill this gap by providing empirical data from a new cultural context. Using a mixed-method research design, it investigates the impact of gaming on language learning outcomes of English as a Foreign Language (EFL) university students. A total of 99 Slovak university students of English as a foreign language were selected by convenience sampling, of which 10 were chosen for qualitative interviews to triangulate the data. The results reveal a significant improvement in vocabulary knowledge and a reported increase in positive language attitudes among gamers compared to non-gamers, with no significant difference in grade point averages between the two groups. These findings highlight the potential of digital gaming to enhance vocabulary learning and foster positive attitudes towards language learning among EFL students. Recommendations for future research are provided.

Determination of surface density of nonporous membranes with ultrasonic immersion measurements

ABSTRACT The surface density of nonporous membranes is estimated with a conventional ultrasonic immersion measurement system without need for a priori information about the membrane such as wave speed, density, thickness or impedance under most testing conditions. The derivation, theoretical limitations and practical restrictions of the technique are discussed. The broadband nature of the measurement allows the surface density values to be estimated at multiple discrete frequencies within the useful bandwidth of the transducer. In experimentation, the technique was used to measure surface densities in a range of 0.044–0.328 kg/m2. All calculated surface densities are within 5% or less of their nominal values with an average percent difference of 1.7%.

Sub-surface geospatial intelligence in carbon capture, utilization and storage: A machine learning approach for offshore storage site selection

This study introduces an innovative data-driven and machine-learning framework designed to accurately predict site scores in the site screening study for specific offshore CO2 storage sites. The framework seamlessly integrates diverse sub-surface geospatial data sources with human aided expert-weighted criteria, thereby providing a high-resolution screening tool. Tailored to accommodate varying data accessibility and the significance of criteria, this approach considers both technical and non-technical factors. Its purpose is to facilitate the identification of priority locations for projects associated with Carbon Capture, Utilization, and Storage (CCUS). Through aggregating and analyzing geospatial datasets, the study employs machine learning algorithms and an expert-weighted model to identify suitable geologic CCUS regions. This process adheres to stringent safety, risk control, and environmental guidelines, addressing situations where human analysis may fail to recognize patterns and provide detailed insights in suitable site screening techniques. The primary emphasis of this research is to bridge the gap between scientific inquiry and practical application, facilitating informed decision-making in the implementation of CCUS projects. Rigorous assessments encompassing geological, oceanographic, and eco-sensitivity metrics contribute valuable insights for policymakers and industry leaders. To ensure the accuracy, efficiency, and scalability of the established offshore CO2 storage facilities, the proposed machine learning approach undergoes benchmarking. This comprehensive evaluation includes the utilization of machine learning algorithms such as Extreme Gradient Boosting (XGBoost), Random Forest (RF), Multilayer Extreme Learning Machine (MLELM), and Deep Neural Network (DNN) for predicting more suitable site scores. Among these algorithms, the DNN algorithm emerges as the most effective in site score prediction. The strengths of the DNN algorithm encompass nonlinear modeling, feature learning, scale invariance, handling high-dimensional data, end-to-end learning, transfer learning, representation learning, and parallel processing. The evaluation results of the DNN algorithm demonstrate high accuracy in the testing subset, with values of AAPD (Average Absolute Percentage Difference) = 1.486 %, WAAPD (Weighted Average Absolute Percentage Difference) = 0.0149 %, VAF (Variance Accounted For) = 0.9937, RMSE (Root Mean Square Error) = 0.9279, RSR (Root Sum of Squares Residuals) = 0.0068, and R2 (Coefficient of Determination) = 0.9937.

Retrieval-Based Diagnostic Decision Support: Mixed Methods Study.

Diagnostic errors pose significant health risks and contribute to patient mortality. With the growing accessibility of electronic health records, machine learning models offer a promising avenue for enhancing diagnosis quality. Current research has primarily focused on a limited set of diseases with ample training data, neglecting diagnostic scenarios with limited data availability. This study aims to develop an information retrieval (IR)-based framework that accommodates data sparsity to facilitate broader diagnostic decision support. We introduced an IR-based diagnostic decision support framework called CliniqIR. It uses clinical text records, the Unified Medical Language System Metathesaurus, and 33 million PubMed abstracts to classify a broad spectrum of diagnoses independent of training data availability. CliniqIR is designed to be compatible with any IR framework. Therefore, we implemented it using both dense and sparse retrieval approaches. We compared CliniqIR's performance to that of pretrained clinical transformer models such as Clinical Bidirectional Encoder Representations from Transformers (ClinicalBERT) in supervised and zero-shot settings. Subsequently, we combined the strength of supervised fine-tuned ClinicalBERT and CliniqIR to build an ensemble framework that delivers state-of-the-art diagnostic predictions. On a complex diagnosis data set (DC3) without any training data, CliniqIR models returned the correct diagnosis within their top 3 predictions. On the Medical Information Mart for Intensive Care III data set, CliniqIR models surpassed ClinicalBERT in predicting diagnoses with <5 training samples by an average difference in mean reciprocal rank of 0.10. In a zero-shot setting where models received no disease-specific training, CliniqIR still outperformed the pretrained transformer models with a greater mean reciprocal rank of at least 0.10. Furthermore, in most conditions, our ensemble framework surpassed the performance of its individual components, demonstrating its enhanced ability to make precise diagnostic predictions. Our experiments highlight the importance of IR in leveraging unstructured knowledge resources to identify infrequently encountered diagnoses. In addition, our ensemble framework benefits from combining the complementary strengths of the supervised and retrieval-based models to diagnose a broad spectrum of diseases.

Role of Electronegativity on the Elemental Diversity in High-Entropy Alloys.

High-entropy alloys (HEAs) or multi-principal element alloys (MPEAs) have found extensive applications in high-precision devices. While the increased configurational entropy for HEAs favors more elemental diversity, it also increases the possibility of phase separation into multiple heterogeneous systems. This article reports that these two mutually competing effects are balanced for 3- and 4-component alloys. Analysis of all of the n-component ABCD···-type (∼5 × 105) available compounds in the materials' database shows that more than 70% are either 3- or 4-component ones. Their high propensity is explained on the basis of their optimal average difference of electronegativity (EN) ∼0.5-1.0 and the average sum of electronegativity (EN) ∼5.0-6.5 between the constituent atoms in the Oganov scale. Effectively, these 3- and 4-component alloys lie in the intermediate (centroid) region of the van Arkel-Ketelaar triangle, indicating their metalloid nature.

Evaluation of the predictors and frequency of silent hypoxemia in COVID-19 patients and the gap between pulse oximeter and arterial blood gas levels: A cross-sectional study.

Silent hypoxemia is when patients do not experience breathing difficulty in the presence of alarmingly low O2 saturation. It could cause rapid deterioration and higher mortality rates among patients, so prompt detection and identifying predictive factors could result in significantly better outcomes. This study aims to document the evidence of silent hypoxemia in patients with COVID-19 and its clinical features. A total of 78 hospitalized, nonintubated patients with confirmed COVID-19 infection were included in this study. Their O2 saturation was measured with a pulse oximeter (PO), and arterial blood gas (ABG) was taken. Demographic and clinical features were recorded. The Borg scale was used to evaluate dyspnea status, and patients with a score of less than two accompanied by O2 saturation of less than 94% were labeled as silent hypoxic. Univariate analysis was utilized to evaluate the correlation between variables and their odds ratio (OR) and 95% confidence interval (CI). Silent hypoxemia was observed in 20 (25.6%) of the participants. The average difference between the PO and ABG methods was 4.36 ± 3.43. Based on regression analysis, dyspnea and respiratory rate demonstrated a statistically significant correlation with the O2 saturation difference between PO and ABG (OR: 2.05; p = 0.026; 95% CI: 0.248-3.847 and OR: 0.144; p = 0.048, 95% CI: 0.001-0.286). Furthermore, the Borg scale (OR: 0.29; p = 0.009; 95% CI: 0.116-0.740) had a significant reverse correlation with silent hypoxia. Silent hypoxemia can be a possible complication that affects some COVID-19 patients. Further care should be bestowed upon the younger population and those with underlying neurological or mental illnesses. Furthermore, the respiratory rate, pulse oximeter, and arterial blood gas O2 levels should be considered alongside each other.

Simulation of coronary fractional flow reserve and whole-cycle flow based on optical coherence tomography in individual patients with coronary artery disease

Computer simulations of coronary fractional flow reserve (FFR) based on coronary imaging have emerged as an attractive alternative to invasive measurements. However, most methods are proprietary and employ non-physiological assumptions. Our aims were to develop and validate a physiologically realistic open-source simulation model for coronary flow, and to use this model to predict FFR based on intracoronary optical coherence tomography (OCT) data in individual patients. We included patients undergoing elective coronary angiography with angiographic borderline coronary stenosis. Invasive measurements of coronary hyperemic pressure and absolute flow and OCT imaging were performed. A computer model of coronary flow incorporating pulsatile flow and the effect of left ventricular contraction was developed and calibrated, and patient-specific flow simulation was performed. Forty-eight coronary arteries from 41 patients were included in the analysis. Average FFR was 0.79 ± 0.14, and 50% had FFR ≤ 0.80. Correlation between simulated and measured FFR was high (r = 0.83, p < 0.001). Average difference between simulated FFR and observed FFR in individual patients was − 0.009 ± 0.076. Overall diagnostic accuracy for simulated FFR ≤ 0.80 in predicting observed FFR ≤ 0.80 was 0.88 (0.75–0.95) with sensitivity 0.79 (0.58–0.93) and specificity 0.96 (0.79–1.00). The positive predictive value was 0.95 (0.75–1.00) and the negative predictive value was 0.82 (0.63–0.94). In conclusion, realistic simulations of whole-cycle coronary flow can be produced based on intracoronary OCT data with a new, computationally simple simulation model. Simulated FFR had moderate numerical agreement with observed FFR and a good diagnostic accuracy for predicting hemodynamic significance of coronary stenoses.

Aerobic Exercise Improves Heart Rate Variability After an Implantable Cardioverter Defibrillator (ICD).

Clinicaltrials.gov: NCT00522340.

Decision-making Support Platform and Security Design for Rural Leisure Tour Industry based on SEA Method and SVR Model

An accurate short-term passenger flow forecast of rural tourism can avoid accidents as much as possible. However, the short-term passenger flow of rural leisure tourism shows nonlinear, seasonal, random, and other complex characteristics. Meanwhile, the traditional forecasting methods are often difficult to achieve accurate forecasting. Therefore, this study now used the nonlinear mapping function in the support vector regression to convert the passenger traffic training sample into a high-dimensional feature space and established a linear decision function. Then the influence of periodicity on the prediction effect through seasonal index adjustment was reduced. Finally, event triggering combined with core embedding technology for tamper-proof detection was adopted to improve the security of the platform. The results showed that the minimum absolute error of prediction with improved SVM model was 0.27% compared with traditional model and autoregressive integrated moving average model. After the introduction of the Internet search factor, the traffic prediction result was more accurate, which was 0.0425 smaller than that without the introduction of the Internet search factor. When the concurrency was less than 100 times/s, the average response time difference before and after adding the core embedded program was small, indicating that the security of page tampering technology was high. This research method can effectively predict the passenger flow of rural leisure tourism industry and ensure the safety of the platform.

Evaluation of Concept Maps Strategy in Learning al-Qowa’id al-Nahwiyyah l Taqwim Istiratijiyyah Kharaith al-Mafahim fi Ta’lim al-Qawa’id al-Nahwiyyah

Learning al-Qowa’id al-Nahwiyyah required an effective approach. The study evaluated use of concept maps strategy in teaching al-Qowa’id al-Nahwiyyah at Al-Hidayah Islamic boarding school in Karang Ploso and compared its effectiveness with lecture method. The approach employed both qualitative and quantitative methods. Data were collected through observation, interviews, questionnaires, and documentation. Qualitative data were analyzed using Sugiyono's model, while quantitative data were analyzed using SPSS 25. The research findings indicated that the utilization of concept maps strategy at Al-Hidayah Islamic boarding school was carried out through several steps, including Introduction of Material, mind map creation, Group Discussion, and Practice. The students perceived that the concept mapping strategy could help organize information effectively, as evidenced by the average satisfaction questionnaire score of 83.4%. Although the use of concept maps was favored by the students, this strategy did not show a significant difference compared to the lecture method, with only a slight average score difference of 1.07. The average score for the concept maps strategy was 85.25, while for the lecture method it was 84.18. As the analysis results using SPSS indicated a significance value (2-tailed) of 0.612 &gt; 0.05 and t value (0.510) &lt; t table (2.042), it was understood that the concept maps strategy did not have a significant difference compared to the lecture method in teaching al-Qowa’id al-Nahwiyyah. Therefore, teaching al-Qowa’id al-Nahwiyyah using the concept maps strategy was not more effective compared to the lecture method as both could equally enhance students' abilities optimally.

Study Involvement and Academic Achievement of Secondary Students in West Bengal

The term "study involvement" describes the level of mental and physical effort a student puts into their academic pursuits. Numerous causes contribute to its emergence among pupils. One may argue that interest is the essential component and motivator that keeps people interested in and paying attention to the activities they attend. This study aims to investigate the relationship between study participation and academic success. For data collection in this study, the researcher employed a mixed sample strategy in addition to a descriptive survey method. 600 samples were chosen for this investigation. The scale of study involvement was created by researchers. The study's conclusion is that (i) among secondary school pupils; there is a mean difference between study involvement and academic achievement. (ii) There is no average difference in the amount of study time spent by male and female secondary pupils. (iii) There is no average difference in the amount of study time spent by secondary pupils in rural and urban areas. (iv) Among secondary school students, there is a positive correlation between study engagement and mental health.

Colorimetric Evaluation of a Reintegration via Spectral Imaging-Case Study: Nasrid Tiling Panel from the Alhambra of Granada (Spain).

Color reintegration is a restoration treatment that involves applying paint or colored plaster to an object of cultural heritage to facilitate its perception and understanding. This study examines the impact of lighting on the visual appearance of one such restored piece: a tiled skirting panel from the Nasrid period (1238-1492), permanently on display at the Museum of the Alhambra (Spain). Spectral images in the range of 380-1080 nm were obtained using a hyperspectral image scanner. CIELAB and CIEDE2000 color coordinates at each pixel were computed assuming the CIE 1931 standard colorimetric observer and considering ten relevant illuminants proposed by the International Commission on Illumination (CIE): D65 plus nine white LEDs. Four main hues (blue, green, yellow, and black) can be distinguished in the original and reintegrated areas. For each hue, mean color difference from the mean (MCDM), CIEDE2000 average distances, volumes, and overlapping volumes were computed in the CIELAB space by comparing the original and the reintegrated zones. The study reveals noticeable average color differences between the original and reintegrated areas within tiles: 6.0 and 4.7 CIEDE2000 units for the yellow and blue tiles (with MCDM values of 3.7 and 4.5 and 5.8 and 7.2, respectively), and 16.6 and 17.8 CIEDE2000 units for the black and green tiles (with MCDM values of 13.2 and 12.2 and 10.9 and 11.3, respectively). The overlapping volume of CIELAB clouds of points corresponding to the original and reintegrated areas ranges from 35% to 50%, indicating that these areas would be perceived as different by observers with normal color vision for all four tiles. However, average color differences between the original and reintegrated areas changed with the tested illuminants by less than 2.6 CIEDE2000 units. Our current methodology provides useful quantitative results for evaluation of the color appearance of a reintegrated area under different light sources, helping curators and museum professionals to choose optimal lighting.

The effect of audit training in universities on the audit expectation gap

The audit expectation gap (AEG) is a phenomenon where there is a difference between what people expect of auditors and what they can do. This research was conducted in 2023 with 336 university students in Vietnam to assess the effect of university training on the AEG related to auditors' responsibilities. The reliability of the scale and the average difference between student groups were tested using SPSS 29 software. The results showed that AEG exists among accounting students who have studied auditing subjects. Most of these students have higher expectations than other students. This study also revealed that the university's auditing program and auditing courses effectively narrowed the AEG to two in nine auditor's responsibilities and one in nine auditor's responsibilities, respectively. However, it is important to note that these changes were only cognitive. Students still expect auditors to fulfill crucial responsibilities such as detecting fraud, predicting bankruptcy, evaluating ongoing operations and serving as the "family doctor" of the enterprise. Therefore, training for students can help narrow the AEG about certain audit responsibilities. A better approach is to strengthen the responsibility and authority of auditors. The future of the audit profession depends on the demands of stakeholders and the capabilities of the profession. The auditing profession must accept new demands for auditor responsibilities in the new context. This also has implications for audit bodies in narrowing the audit expectation gap in Vietnam.