Correct Rate Research Articles

A Pilot Study of the Computerized Brief Smell Identification Test

Objectives: A self-administered Computerized Brief Smell Identification Test (cB-SIT) was developed recently to perform the olfactory identification test under computer control. The aim of this study was to evaluate the clinical applicability of the cB-SIT as compared with the traditional Brief Smell Identification Test (B-SIT). Methods: Sixty healthy volunteers with self-reported normal olfactory function, 30 hyposmic patients, and 30 anosmic patients were enrolled from June 2023 to May 2024. All enrolled participants received both B-SIT and cB-SIT in a random order to measure their odor identification ability. Thirty healthy volunteers took the second B-SIT and cB-SIT at least one week later. Results: The score was significantly different in both B-SIT and cB-SIT among healthy volunteers, hyposmic, and anosmic patients. The correct answer rate was significantly different in 10 items of the B-SIT and in 7 items of the cB-SIT among the three groups, but the post hoc test showed significant differences in correct answer rates between healthy volunteers and hyposmic patients in 7 items of both the B-SIT and cB-SIT. Test–retest results showed the score of the second B-SIT test was significantly higher than that of the first test, but the scores of the two tests of the cB-SIT were not significantly different. In the B-SIT, the lemon odorant had a higher correct answer rate in the second test than in the first test, but in the cB-SIT, the correct answer rate was not significantly different between the first and second tests in all 12 items. Conclusions: Our findings demonstrate that the cB-SIT was similar to the B-SIT and can be administered in the diagnosis of patients with olfactory dysfunction.

Effects of long-term exposure to high-altitude on episodic memory: The moderating role of daytime dysfunction

Chronic exposure to high-altitude hypoxic environments may influence short-term memory and working memory. However, its impact on long-term memory, specifically episodic memory, remains understudied. In this study, we systematically investigated the effects of long-term exposure to high altitude (3650 m) on episodic memory, including item memory and source memory. Moreover, we examined the moderating role of daytime dysfunction on the relationship between altitude and episodic memory. In total, 97 participants were enrolled in the study: 49 were from the high-altitude (HA) group, comprising those born and raised in low altitude (LA) areas (< 500 m) and had migrated to HA for 2∼3 years after turning 18; and 48 were from the LA group, who had never lived at high altitudes. Episodic memory was evaluated using a what-when-where task, whereas daytime dysfunction was measured by the Pittsburgh Sleep Quality Index. For item memory, hit rate and correct rejection rate were significantly lower in the HA group relative to the LA group. For source binding memory, the performance in what-where binding and what-when-where binding were decreased in the HA group, but the what-when binding did not differ between the two groups. Furthermore, the effects of altitude on hit rate, what-where binding, and what-when-where binding were significantly pronounced in individuals with higher levels of daytime dysfunction. These findings suggest that long-term exposure to high-altitude hypoxic environments influences episodic memory performance, including item recognition and source-binding memory. Specifically, spatial source memory is affected by high-altitude exposure, whereas temporal source memory remains unaffected. Moreover, these results highlight the importance of considering sleep quality, especially good daytime function, in maintaining optimal episodic memory function following chronic exposure to high altitudes.

Geographical origin identification of cinnamon using HPLC-DAD fingerprints and chemometrics

The commercial value of cinnamon depends largely on its geographical origin. This study proposed high-performance liquid chromatography-diode array detection (HPLC-DAD) fingerprints combined with chemometrics to identify geographical origins of cinnamons. Alternating trilinear decomposition (ATLD) algorithm was applied to extract meaningful chemical information from the HPLC fingerprint data. Then, non-targeted HPLC fingerprints and chemical component information were used as chemical descriptors to distinguish geographical origins using principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA)/N-way partial least squares-discriminant analysis (NPLS-DA). Satisfactory classification results were acquired for each discriminant model and the correct classification rates (CCRs) of training and test sets were 100%. Moreover, four characteristic variables were screened by variable importance in projection (VIP) method, three of which were reasonably identified as cinnamic acid, cinnamaldehyde and 2-methoxycinnamaldehyde, which can be regarded as potential markers to distinguish cinnamon from different geographical origins. The results indicated that HPLC-DAD fingerprints combined with chemometrics can be a promising means to identify the geographical origins and screen potential markers of cinnamon samples, especially in the absence of high-cost and sophisticated analytical instrumentals (e.g., high-resolution mass spectrometry and nuclear magnetic resonance spectroscopy). This work can help to prevent cinnamon fraud practices and maintain the normal order of the cinnamon market.

On the Security of Selectively Encrypted HEVC Video Bitstreams

With the growing applications of video, ensuring its security has become of utmost importance. Selective encryption (SE) has gained significant attention in the field of video content protection due to its compatibility with video codecs, favorable visual distortion, and low time complexity. However, few studies consider SE security under cryptographic attacks. To fill this gap, we analyze the security concerns of encrypted bitstreams by SE schemes and propose two known plaintext attacks (KPAs). Then the corresponding defense is presented against the KPAs. To validate the effectiveness of the KPA, it is applied to attack two existing SE schemes with superior visual degradation in HEVC videos. Firstly, the video sequences are encoded using H.265/HEVC. During encoding, the selected syntax elements are recorded. Secondly, the recorded syntax elements are imported into the HEVC decoder. By utilizing the encryption parameters and the imported data in the decoder, it becomes possible to reconstruct a significant portion of the original syntax elements before encryption. Finally, the reconstructed syntax elements are compared with the encrypted syntax elements in the decoder, allowing the design of a pseudo-key stream (PKS) through the inverse of the encryption operations. The PKS is used to decrypt the existing SE scheme, and the experimental results provide evidence that the two existing SE schemes are vulnerable to the proposed KPAs. In the case of single bitstream estimation (SBE), the average correct rate of key stream estimation exceeds 93%. Moreover, with multi-bitstream complementation (MBC), the average estimation accuracy can be further improved to 99%.

Impaired facial emotion recognition in individuals with bipolar disorder

BackgroundIndividuals with bipolar disorder (BD) often struggle with emotional regulation and social interactions, partly due to difficulties in accurately recognizing facial emotions. MethodsFrom September 2021 to February 2023, 69 BD individuals-comprising 23 with bipolar manic/hypomanic episode (BME), 23 with bipolar depressive episode (BDE), 23 with bipolar euthymic (EUT)-and 23 healthy controls (HCs) were enrolled. Diagnosis adhered to DSM-IV criteria using M.I.N.I 5.0, alongside assessments via Hamilton Depression Scale 17 and Young Manic Rating Scale. Recognition tasks involving 84 facial expression images across six categories. The Wilcoxon rank-sum test compares two groups, while the Kruskal-Wallis test compares multiple groups with subsequent adjusted pairwise comparisons. ResultsThe overall correct recognition rate of facial expressions in the BD group (79 %) was significantly lower than that of the HC group (83 %) (P=0.004). Primary differences were noted in neutral (93 % vs. 100 %, P=0.012) and fear (79 % vs. 86 %, P=0.023) expressions. Within the BD group, correct recognition rates were 71 % for BME, 80 % for BDE, and 80 % for EUT, all lower than in the HC group. Significant differences in correct recognition rates of neutral, fear, and joy expressions were observed among the four groups (P<0.05), with the BME group exhibiting the lowest rate. Misidentification of facial expressions was more frequent in the BD group compared to the HC group, particularly among negative expressions. ConclusionPatients with BD demonstrate lower correct recognition and higher misidentification rates of facial expressions, with those experiencing manic episodes showing impaired recognition of neutral, joy, and fear expressions.

Sparse representation of finger knuckle print images for personal identification

SummaryFraud keeps increasing in our society and security applications become crucial and needed in our daily life. Biometric technology attempts to stop fraud and falsification in different opportunities such as bank services, access to controlled areas or crossing frontiers, by recognizing the identity of a person using his physiological (fingerprint, iris, face) or behavioral modalities (gait, signature). In this article, we focus on an emerging biometric modality called the finger knuckle print (FKP). In fact, this modality has several advantages such as the easy distinction between different persons, stability over time and user acceptance. So, an FKP identification approach is proposed using scale invariant feature transform descriptors based sparse representation method. The classification step, between training and testing FKP samples, is made using the support vector machines method. Experiments applied on two public FKP databases: The Hong Kong Polytechnic University (PolyU) Contactless Finger Knuckle Images Database and the Indian Institute of Technology Delhi (IITD) Finger Knuckle Database, containing respectively 2500 and 790 images, demonstrate high correct identification rates by reaching 98.58% and 99.15% for these two databases.

Contrast-Enhanced Intraoperative Ultrasound Shows Excellent Performance in Improving Intraoperative Decision-Making.

The aim of this study was to evaluate the performance and the impact of contrast-enhanced intraoperative ultrasound (CE-IOUS) on intraoperative decision-making, as there is still no standardized protocol for its use. Therefore, we retrospectively analyzed multiple CE-IOUS performed in hepato-pancreatic-biliary surgery with respect to pre- and postoperative imaging and histopathological findings. Data of 50 patients who underwent hepato-pancreatic-biliary surgery between 03/2022 and 03/2024 were retrospectively collected. CE-IOUS was performed with a linear 6-9 MHz multifrequency probe connected to a high-resolution device. The ultrasound contrast agent used was a stabilized aqueous suspension of sulphur hexafluoride microbubbles. In total, all 50 lesions indicated for surgery were correctly identified. In 30 cases, CE-IOUS was used to localize the primary lesion and to define the resection margins. In the remaining 20 cases, CE-IOUS identified an additional lesion. Fifteen of these findings were identified as malignant. In eight of these cases, the additional malignant lesion was subsequently resected. In the remaining seven cases, CE-IOUS again revealed an inoperable situation. In summary, CE-IOUS diagnostics resulted in a high correct classification rate of 95.7%, with positive and negative predictive values of 95.2% and 100.0%, respectively. CE-IOUS shows excellent performance in describing intraoperative findings in hepato-pancreatic-biliary surgery, leading to a substantial impact on intraoperative decision-making.

Study on the Influence of Spatial Attributes on Passengers’ Path Selection at Fengtai High-Speed Railway Station Based on Eye Tracking

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger’s wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger’s wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers’ The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers’ route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time &gt; density of information &gt; density of key information &gt; diameter of the pupil. The study analyses the influencing factors affecting passengers’ wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations.

Abstract A008: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry

Abstract Background: Cancer disparities exist within the Hispanic/Latino/a/x (H/L) population by nativity status. Non-US born H/L colorectal cancer (CRC) patients and Prostate Cancer (PCa) patients in California have a statistically significant lower risk of cancer-specific survival when compared to Non-Hispanic White patients after controlling for confounders. However, birthplace information is largely incomplete in population-based cancer registries. For example, more than 40% of H/L patients have missing information on nativity in the California Cancer Registry (CCR) database. With the goal of better imputing missing birthplace, we built a Machine Learning (ML) model to predict nativity among CCR H/L cancer patients. Methods: H/L cases with primary invasive CRC (n = 75,613) and PCa (n = 93,042) at least 18 years old, diagnosed between 1988 and 2021 contained in the CCR research file were analyzed (46.36% had missing birthplace). A binary indicator variable assigning cases with known birthplace to US-born vs. Non-US born was constructed as the target for prediction. A stratified split by levels of the outcome was used to generate training (70%, n = 63,323) and testing (30%, n = 27,139) datasets. We used a pruned classification tree to perform feature selection, with the 10 features with the highest importance (measured by the overall reduction in impurity for any split containing each feature) used to predict nativity. Missing observations of the quantitative and qualitative features were mean, or mode imputed, respectively. The classification methods considered were classification trees, Random Forest (RF), and Boosting. Model performance was evaluated using the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and the rate of correctly classified observations. Results: The top three features with the highest importance were the NHIA (NAACCR Hispanic/Latino Identification Algorithm) label, the Hispanic/Spanish identity, and the year of social security number issuance (SSN); the latter is part of a current SSN-based algorithm used to impute nativity status in this population. Classification models showed similar prediction performance. The highest AUC was reported by the RF (AUC = 0.9822), followed by Boosting (AUC = 0.9785), and the pruned classification tree (AUC = 0.9647). A similar rate of correctly classified observations was reported for the Boosting and RF models (∼94%). The RF model ranked feature importance in a similar order as the pruned classification tree. Ultimately, the RF model was the best performing classifier with a sensitivity and specificity of 0.61 and 0.90, respectively. This classifier substantially outperforms the SSN-based algorithm, which reported a correct classification rate of 81%, a sensitivity = 0.54, and a specificity = 0.41 in this dataset. Conclusions: This proof-of-concept analysis showcases a potential for developing a ML model to overcome missing nativity status in H/L cancer patients to enable more accurate classification by nativity in future research using CCR data. Citation Format: Proof-of-Concept: A machine learning classifier to impute nativity status in Hispanic/Latino/a/x cancer patients in the California Cancer Registry. Joel Sanchez Mendez, Lihua Liu, Juan P. Lewinger, Laura Fejerman, Mariana C. Stern [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A008.

Assessment of blood pressure measurement technique amongst nurses working at a tertiary care cardiac center

Background Accurate measurement is indispensable for effectively managing hypertension (HTN); any error in technique or instrumentation can lead to misdiagnosis and improper management. Thus, the present study aimed to assess the knowledge and skills of blood pressure (BP) measurement among nurses at a tertiary care cardiac center in Karachi. Materials and methods Nursing staff responsible for BP assessment at various stations were identified, observed, and interviewed to evaluate their skill and knowledge levels regarding BP measurement techniques. Nurses’ skill levels were assessed using a checklist based on the American Heart Association (AHA) guidelines for BP assessment. Results Seventy-five nurses participated in the study, with 49 (65.3%) being male and a mean age of 32.1 ± 6.2 years. Only 25 (33.3%) nurses reported reading the AHA guidelines for BP measurement. None of the nurses demonstrated excellent skills; 19 (25.3%) showed good skills, while 56 (74.7%) showed poor skills in BP measurement. A poor compliance was observed on a total of 14/31 steps with compliance rate of less than 50%. Similarly, none of the nurses exhibited excellent knowledge; only 3 (4%) had good knowledge, while 72 (96%) had poor knowledge about BP measurement. A poor knowledge was observed on a total of 18/36 items with correct response rate of less than 50%. Conclusion Nurses working at various stations of a tertiary cardiac center exhibited inadequate skills and knowledge regarding BP measurement. This underscores the necessity for comprehensive training and education to enhance the accurate assessment of BP.

Non-Targeted Nuclear Magnetic Resonance Analysis for Food Authenticity: A Comparative Study on Tomato Samples.

Non-targeted NMR is widely accepted as a powerful and robust analytical tool for food control. Nevertheless, standardized procedures based on validated methods are still needed when a non-targeted approach is adopted. Interlaboratory comparisons carried out in recent years have demonstrated the statistical equivalence of spectra generated by different instruments when the sample was prepared by the same operator. The present study focused on assessing the reproducibility of NMR spectra of the same matrix when different operators performed individually both the sample preparation and the measurements using their spectrometer. For this purpose, two independent laboratories prepared 63 tomato samples according to a previously optimized procedure and recorded the corresponding 1D 1H NMR spectra. A classification model was built using the spectroscopic fingerprint data delivered by the two laboratories to assess the geographical origin of the tomato samples. The performance of the optimized statistical model was satisfactory, with a 97.62% correct sample classification rate. The results of this work support the suitability of NMR techniques in food control routines even when samples are prepared by different operators by using their equipment in independent laboratories.

Model-Based Prediction for Small Domains Using Covariates: A Comparison of Four Methods

Abstract We consider methods for model-based small area estimation when the number of areas with sampled data is a small fraction of the total areas for which estimates are required. Abundant auxiliary information is available from the survey for all the sampled areas. Further, through an external source, there is information for all areas. The goal is to use auxiliary variables to predict the outcome of interest for all areas. We compare areal-level random forests and LASSO approaches to a frequentist forward variable selection approach and a Bayesian shrinkage method using a horseshoe prior. Further, to measure the uncertainty of estimates obtained from random forests and the LASSO, we propose a modification of the split conformal procedure that relaxes the assumption of exchangeable data. We show that the proposed method yields intervals with the correct coverage rate and this is confirmed through a simulation study. This work is motivated by Ghanaian data available from the sixth Ghana Living Standards Survey (GLSS) and the 2010 Population and Housing Census, in the Greater Accra Metropolitan Area (GAMA) region, which comprises eight districts that are further divided into enumeration areas (EAs). We estimate the areal mean household log consumption using both datasets. The outcome variable is measured only in the GLSS for 3 percent of all the EAs (136 out of 5019) and 174 potential covariates are available in both datasets. In the application, among the four modeling methods considered, the Bayesian shrinkage performed the best in terms of bias, mean squared error (MSE), and prediction interval coverages and scores, as assessed through a cross-validation study. We find substantial between-area variation with the estimated log consumption showing a 1.3-fold variation across the GAMA region. The western areas are the poorest while the Accra Metropolitan Area district has the richest areas.

Prediction of mechanical properties of rolled steel based on dual-attention multiscale convolutional neural network

The mechanical properties of carbon steel sheet directly depend on the chemical composition and process parameters of the steel. There is a complex nonlinear relationship between chemical composition, process parameters, and mechanical properties, and the establishment of a model with the ability to automatically distinguish the importance of different parameters, and to have good prediction accuracy and generalisation in different data sets is one of the important issues in the field of carbon steel mechanical properties prediction. In this paper, we proposed a multi-scale convolutional network model based on dual-attention mechanism (DAM-MSDSC), where the dual-attention module mines from features and channels at the same time, making the model focus on the important features. The multiscale feature fusion module effectively improves the model prediction accuracy by mining features from the output of the middle layer of the network to obtain features containing multiscale fusion features. In hot-rolled steel, for the two parameters of carbon content and temperature, it is confirmed that the change of carbon content has a greater impact on TS, while FTT and CT have a greater impact on YS. Through comparative experiments on different datasets, the proposed model had the best prediction performance, in which the correct rates of YS, TS, and EL are 98 %, 98.7 %, and 99.4 %, respectively, in cold rolled steel, and 97.8 %, 98.6 %, and 99.2 %, respectively, in hot rolled steel.

A calibrated model for N-body dynamical friction acting on supermassive black holes

Abstract Black holes are believed to be crucial in regulating star formation in massive galaxies, which makes it essential to faithfully represent the physics of these objects in cosmological hydrodynamics simulations. Limited spatial and mass resolution and the associated discreteness noise make following the dynamics of black holes especially challenging. In particular, dynamical friction, which is responsible for driving massive black holes towards the centres of galaxies, cannot be accurately modelled with softened N-body interactions. A number of subgrid models have been proposed to mimic dynamical friction or directly include its full effects in simulations. Each of these methods has its individual benefits and shortcomings, while all suffer from a common issue of being unable to represent black holes with masses below a few times the simulated dark matter particle mass. In this paper, we propose a correction for unresolved dynamical friction, which has been calibrated on simulations run with the code KETJU, in which gravitational interactions of black holes are not softened. We demonstrate that our correction is able to sink black holes with masses greater than the dark matter particle mass at the correct rate. We show that the impact of stochasticity is significant for low-mass black holes (MBH ≤ 5MDM) and propose a correction for stochastic heating. Combined, this approach is applicable to next generation cosmological hydrodynamics simulations that jointly track galaxy and black hole growth with realistic black hole orbits.

Analysis of Responses of GPT-4V to the Japanese National Clinical Engineer Licensing Examination.

Chat Generative Pretrained Transformer (ChatGPT; OpenAI) is a state-of-the-art large language model that can simulate human-like conversations based on user input. We evaluated the performance of GPT-4V in the Japanese National Clinical Engineer Licensing Examination using 2,155 questions from 2012 to 2023. The average correct answer rate for all questions was 86.0%. In particular, clinical medicine, basic medicine, medical materials, biological properties, and mechanical engineering achieved a correct response rate of ≥ 90%. Conversely, medical device safety management, electrical and electronic engineering, and extracorporeal circulation obtained low correct answer rates ranging from 64.8% to 76.5%. The correct answer rates for questions that included figures/tables, required numerical calculation, figure/table ∩ calculation, and knowledge of Japanese Industrial Standards were 55.2%, 85.8%, 64.2% and 31.0%, respectively. The reason for the low correct answer rates is that ChatGPT lacked recognition of the images and knowledge of standards and laws. This study concludes that careful attention is required when using ChatGPT because several of its explanations lack the correct description.

Meeting the challenge of varietal and geographical authentication of hazelnuts through lipid metabolite fingerprinting

Hazelnuts are high-quality products with significant economic importance in many European countries. Their market price depends on their qualitative characteristics, which are driven by cultivar and geographical origin, making hazelnuts susceptible to fraud. This study systematically compared two lipidomic fingerprinting strategies for the simultaneous authentication of hazelnut cultivar and provenance, based on the analysis of the unsaponifiable fraction (UF) and triacylglycerol (TAG) profiles by gas chromatography–mass spectrometry coupled with chemometrics. PLS-DA classification models were developed using a large sample set with high natural variability (n = 309) to discriminate hazelnuts by cultivar and origin. External validation results demonstrated the suitability of the UF fingerprint as a hazelnut authentication tool, both tested models showing a high efficiency (>94 %). The correct classification rate of the TAG fingerprinting method was lower (>80 %), but due to its faster analysis time, it is recommended as a complementary screening tool to UF fingerprinting.

A Statutory Analysis of the Role of the Courts in Protecting Financial Consumers' Rights in the Zimbabwean Banking Sector: The Penelope Case in Context

Abstract The Zimbabwean banking sector has experienced several tumultuous challenges since 2000. This is owing chiefly to the political and economic challenges the country has experienced. Poorly conceived and ill-timed policies have also wreaked havoc in the Zimbabwean banking sector. Financial consumers have sometimes lost their savings to hyper-inflation or overnight policy changes in Zimbabwe. The change in currencies is a good example of how financial consumers have seen their deposits and savings at banks and other related financial institutions eroded to nothing. In 2018, the Reserve Bank of Zimbabwe (RBZ) issued a directive for all banks to convert all deposits made prior to October 2018 from the United States (US) dollar denomination to the Zimbabwean dollar (bond notes and coins). This was done on an exchange rate of one US dollar as to one Zimbabwean dollar, yet in reality, the local currency had been devalued. The RBZ directive left individuals and corporates reeling from exchange losses. The directive was akin to a heist and many people lost trust and confidence in the Zimbabwean banking sector. While many financial consumers suffered in silence, some decided to take their banks to court, demanding a reimbursement of their deposits in the currency in which they deposited or alternatively, the equivalent of the original deposit using the correct exchange rate. This article examines the role of the courts in upholding the rights of financial consumers in the Zimbabwean banking sector in the light of the Penelope case. It also discusses how the Zimbabwean courts could assist in preserving the sanctity of the bank-client relationship among banks and financial customers. In addition, the article explores the role of the courts in challenging unjust laws that affect financial consumers' investments in banks. The strengths and weaknesses of the Zimbabwean banking regulatory framework are further discussed to assess if it is robust enough to protect financial consumers. The article recommends that the courts should objectively set aside unjust laws in the interest of justice to enable banks and other financial institutions to honour their contractual obligations. Policymakers should not adopt poor policies that infringe upon financial consumers' rights. Keywords: Banking sector; consumer protection; financial consumer; deposits; exchange rate; courts.

Evaluation of Earthquake Performance of Reinforced Concrete Buildings with Fuzzy Logic Method

Reinforced concrete building is a type of building whose structural system consists of reinforced concrete columns, beams, wall wall, slabs and foundations. Due to the strong earthquakes in our country, it has been observed that reinforced concrete buildings have been severely damaged or collapsed from past to present. The evaluation of buildings after earthquakes and their performances are quite important for the safety of life and property. In the literature, different methods have been developed for post-earthquake evaluation of buildings, either low-cost and fast or slow and high-cost and tool-demanding. In this study, in order to overcome the gap in the literature, the evaluation of earthquake performance of buildings with fuzzy logic method is discussed. In this context, the performance of the building was evaluated by taking into account the concrete compressive strength, number of floors, ground floor area, area of column and shear walls, ground floor area and architectural parameters. The data of 18, 28 and 146 buildings affected by the earthquakes in Afyon, Bingöl and Van provinces in 2002, 2003 and 2011, respectively, were used in the study. Out of the total 192 building data, 94 buildings were processed as data and fuzzy logic rules were applied. The remaining 98 buildings were tested with this method. The buildings considered are light, moderate and severely damaged or collapsed. Matlab program was used in the study. The method and result used in the study are in the class of fast and second stage methods in the literature, which are reliable but do not reflect the final result. With the method revealed in the study, the damage status of reinforced concrete buildings can be determined quickly and reliably. In this context, the buildings were evaluated as no damage/slight, moderate and severely damaged or collapsed. According to the results, the correct estimation rate of the damage status of 98 buildings was found to be 88%. It is seen that the fuzzy logic method can predict building performances at a quite high rate. Compared to the existing second stage assessment methods in the literature, this result is more conservative.

Accuracy of Anterior Shoulder Injections Without Image Guidance: A Prospective Controlled Study.

This study aims to assess the accuracy of glenohumeral joint injections through an anterosuperior approach using anatomical landmarks as a guide and arthroscopic visualization as the control method and to evaluate whether there is any association between accuracy, the physicians training, and the patient's pathology. A sample size of 124 patients was calculated. A prospective cohort study including 164 consecutive patients was conducted. All patients over 18 years of age who underwent shoulder arthroscopy during the study period were included. A needle was placed using an anterosuperior approach before the beginning of the surgery alternating between a shoulder surgeon and a resident. Direct visualization through a posterior arthroscopic view was used to verify correct needle placement. Each case was classified as success or failure based on the arthroscopic intra-articular visualization of the needle. Univariate and multivariate analyses were performed to evaluate the relationship between injection accuracy, operator experience, and patient pathology. A p-value less than 0.05 was considered statistically significant. Of the 164 needles placed, 131 were intra-articular, giving a total correct placement rate of 80% (95% CI, 73-86%). Experts had an accuracy of 88%, compared to a precision rate of 72% for residents (p<0.001). A logistic regression was performed to evaluate which factors are independently associated with injection accuracy failure. Patients diagnosed with adhesive capsulitis had an OR of 6.15 for injection failure. This study shows that an anterior-superior approach shoulder injection technique performed by a shoulder specialist without image guidance has a high precision rate. However, in physicians with no experience in shoulder surgery, as well as in some pathologies such as adhesive capsulitis, the accuracy of the procedure decreases significantly and thus, in these cases, the use of some type of image guidance during the procedure may be recommended to achieve greater precision.

Elemental tracer determination and modeling for geographical origin designation of sweet oranges

Sweet oranges have long been an integral part of global health and culinary practices, offering a wealth of nutrients and bioactive compounds. Ensuring the authenticity of these citrus fruits is essential for maintaining consumer confidence, promoting transparency in sourcing, and protecting producers' reputations in the marketplace. In this study, we explored the feasibility of using multi-element profiling combined with pattern recognition algorithms to trace the origin of sweet orange samples. To achieve this, we employed an optimized microwave plasma atomic emission spectroscopy (MP-AES) method to analyze the elemental composition (Al, Ba, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Mo, Na, Ni, Pb, Sr, and Zn) of 183 orange samples from four production regions in northeastern Argentina. Support vector machine (SVM), random forest (RF), and gradient boosting tree (GBT) models were then built using the collected data to identify elemental tracer's indicative of origin. Based on a comprehensive evaluation of overall accuracy, receiver operating characteristic (ROC) curves, and area under the curve (AUC), the GBT model demonstrated the best classification performance, achieving a 96.5 % correct prediction rate on test samples, as confirmed by the ROC curve (AUC = 0.973). Consequently, this approach provides compelling evidence for the potential utility of MP-AES combined with supervised modeling to determine the geographic origin of sweet oranges produced in Argentina, thereby contributing to consumer protection against fraud.