High Level Of Precision Research Articles

Validation of the Microreader™ 36A ID System for forensic genetic casework and database construction

ABSTRACT Autosomal short tandem repeats (STRs) are crucial in identifying humans. Herein, a developmental validation was performed for a newly generated autosomal STR kit (Microreader™ 36A ID System). The sensitivity of this kit was fine when the input DNA dropped down to 0.02 ng/uL. Mixture analysis showed that when male:female = 200 pg: 5400 pg, full profiles of both individuals could be obtained. When inhibited substances existed, all alleles were acquired up to 2000 μM haematin, 1000 ng/μL humic acid or 250 ng/μL tannic acid. The kit exhibited a high precision level with no more than 0.29 nt standard deviation of allele size, with the accuracy (the difference of allele size between the true allele and ladder allele) no more than 0.4 nt. The total discrimination power of this system in the Chinese Han and Hui population was larger than 1–0.1 × 10−30. For the full-sibling test, the identical-by-state (IBS) score varied from 37 to 46, while it was from 21 to 28 when using the AGCU Express marker 22 STR Kit. This kit can increase the autosomal STR information of a single PCR reaction and is promising in the casework of forensic genetic as well as database construction.

The Plutonium Temperature Effect Program

Various theoretical studies have shown that highly diluted plutonium solutions could have a positive temperature effect, but up to now, no experimental program has confirmed this effect. The French Plutonium Temperature Effect Experimental Program (or PU+ in short) aims to effectively show that such a positive temperature effect exists for diluted plutonium solutions. The PU+ experiments were conducted in the “Apparatus B” facility at the CEA VALDUC research center in France. It involved several sub-critical approach-type experiments using plutonium nitrate solutions with concentrations of 14.3, 15, and 20 g/L at temperatures ranging from 20 to 40 °C. Fourteen (five at 20 g/L, four at 15 g/L, and five at 14.3 g/L) phase I experiments (consisting of independent sub-critical approaches) were performed between 2006 and 2007. The impact of the uncertainties on solution acidity and plutonium concentration made it difficult to demonstrate the positive temperature effect, requiring an additional phase II experiment (with a unique plutonium solution) from 22 to 28 °C that was performed in July 2007. This phase II experiment has shown the existence of a positive temperature effect of ~+5.17 pcm/°C (from 22 to 28 °C for a plutonium concentration of 14.3 g/L). It has recently been possible to confirm the results of this program with MORET 5 calculations by generating thermal scattering data S(α,β) at the correct experimental temperatures. This paper finally presents a fully documented experimental program highlighting the Plutonium Temperature Effect theoretically described in the literature. Its high level of precision and its “one-step” approach to criticality allowed it to show a significant positive temperature effect for a rather small variation of temperature (+6 °C). The order of magnitude of the effect was confirmed with Monte Carlo calculations using thermal scattering data for hydrogen in the solution produced by IRSN for the purpose of the comparison.

Best-Fit Circle for the Replication of Humeral Head Anatomy in Total Shoulder Arthroplasty.

Anatomic restoration of the humeral head is critical for successful shoulder replacement. Accurate measurements of the humeral head are essential for anatomic substitution. To evaluate whether a best-fit circle of the humeral head, as determined from different projections of plain radiographs, can predict the humeral head implant size for either the left or right shoulder. Descriptive laboratory study. Bilateral shoulder radiographs of 200 patients without arthropathy or other abnormal findings of the humeral head were evaluated. The best-fit circle was obtained based on 3 points: the medial and lateral endpoints of the anatomic neck and the lateral cortex below the greater tuberosity. This circle was drawn on 5 different radiographic projections (shoulder anteroposterior [AP], glenoid AP, outlet, axillary, and 30° caudal tilt) of the left and right shoulders of each patient, and the radius of each circle was measured. Agreement in the best-fit circle radius between the left and right shoulders was statistically analyzed using the intraclass correlation coefficient (ICC). There were 2 independent blinded observers who performed each measurement twice to evaluate inter- and intraobserver reliability. Overall agreement in the radius between the right and left shoulders was excellent (all ICCs ≥0.990). The ICCs according to the radiographic view were 0.990 (95% CI, 0.986-0.993) for shoulder AP, 0.992 (95% CI, 0.989-0.995) for glenoid AP, 0.996 (95% CI, 0.994-0.997) for outlet, 0.994 (95% CI, 0.991-0.996) for axillary, and 0.993 (95% CI, 0.990-0.995) for 30° caudal tilt. Interobserver ICCs demonstrated a high level of precision: 0.987 (95% CI, 0.978-0.993) for shoulder AP, 0.986 (95% CI, 0.974-0.992) for glenoid AP, 0.974 (95% CI, 0.954-0.985) for outlet, 0.991 (95% CI, 0.984-0.995) for axillary, and 0.987 (95% CI, 0.977-0.993) for 30° caudal tilt. Intraobserver ICCs demonstrated excellent test-retest reliability: 0.983 (95% CI, 0.970-0.991) for shoulder AP, 0.989 (95% CI, 0.980-0.994) for glenoid AP, 0.987 (95% CI, 0.978-0.993) for outlet, 0.985 (95% CI, 0.973-0.991) for axillary, and 0.970 (95% CI, 0.947-0.983) for 30° caudal tilt. The best-fit circle to calculate the ideal size of a humeral head implant was able to be determined from different projections of plain radiographs. Anatomic restoration of a deformed humeral head can be achieved using the best-fit circle of the contralateral humeral head as obtained from plain radiographs. This method can facilitate preoperative planning and postoperative evaluation in the anatomic restoration of the shoulder to avoid the problems of overstuffing.

Astronomical observations in Bologna, Montpellier, and Genoa in the early 14th century: Iohannes de Luna Theutonicus revisited

MS Vienna, Österreichische Nationalbibliothek, 5311, fol. 137r, contains an unusual record of astronomical observations, among which are measurements of solar and stellar altitudes carried out with large quadrants in Bologna, Montpellier, and Genoa between 1305 and 1312. An analysis of this observational data reveals a high level of precision and accuracy, as the astronomer responsible achieved a mean error of only 0;2° during an extended run of measurements of solar noon altitudes made between December 1305 and March 1306. Other passages on the same manuscript page mention Johannes de Luna, a German astrologer-astronomer active in Bologna between c.1299 and 1312. References to his name in 14th-century manuscripts shed light on his involvement in contemporary attempts to correct the Toledan Tables and revise their model of precession on an observational basis.

Ultrasonic guided wave imaging of pipelines based on physics embedded inversion neural network

Pipeline corrosion quantification plays a vital role in guaranteeing the safety of critical industrial structures and thus significant work has been carried out to address such an issue. Although quantitative imaging is crucial for non-destructive testing, research in guided wave pipeline testing has primarily centered on qualitative approaches. Here, we propose a deep neural network built upon physical model to reconstruct pipe wall thickness from ultrasonic guided wave (UGW) signals. The workflow of reconstruction contains three layers, where each layer consists of a fixed forward network and a residual inversion network. The forward model is represented by an agent convolutional neural network which would be embedded into the entire inversion network. The residuals between data from the forward model and real signals are then mapped into velocity profile differences through sub-inversion network. Numerical experiments were conducted to verify the inversion performance of the deep neural network using thickness maps obtained from guided wave frequency domain information. Results show that inversion images are capable to reveal the positions, shapes, and depths of corrosion with high resolution and precision, yielding an average inversion of 87.37% in the test set. In addition, by utilizing the periodicity of the pipeline, the inversion accuracy of eight pairs of transducers were improved from 67.7% to 89.43% with high-order helical guided wave. Compared with traditional high-precision inversion methods such as full waveform inversion, the proposed method achieved approximately 300 times faster inversion speed at the cost of some accuracy. The research demonstrates that real-time quantitative imaging of defects on pipes can be achieved accurately by physics embedded network. Furthermore, an experimental verification of the method was carried out through UGW pipeline testing, demonstrating its feasibility. The mean squared error of wall thickness reconstruction was 0.0070, achieving a high level of precision.

Analisa Metode Backpropagation Pada Prediksi Rata-rata Harga Beras Bulanan Di Tingkat Penggilingan Menurut Kualitas

Rice is a staple food in Indonesia and plays a crucial role in the food structure as a source of nutrition. The diverse population of Indonesia, spread across various islands, makes rice availability highly important. The government continues to strive for food security, particularly by increasing domestic production. These considerations become even more significant for Indonesia due to its growing population and extensive geographical distribution. To meet the population's food needs, Indonesia needs sufficient food supply and distribution to fulfill consumption and maintain adequate reserves for extensive logistical operations. Rice shortage can be seen as a threat to economic and political stability. The significance of rice as a food commodity means that it is constantly in demand by people from all walks of life. Price fluctuations over time due to imbalances between supply and demand have a significant impact on the middle class and working class. The instability of rice prices greatly affects both the general public and farmers. Generally, prices are determined by the interaction of supply and demand. If supply is high and demand is low, prices will decrease. Conversely, if supply is low and demand is high, prices will increase. Prediction is an important tool to anticipate future events by recognizing patterns from the past. Backpropagation can be used as a method to predict rice prices. The data used in this study are the average monthly rice prices at the milling level according to the quality of large-scale traders from January 2023 to December 2023, in Indonesian Rupiah per kilogram. This research utilizes data obtained from the website of the Indonesian Central Bureau of Statistics (BPS) from 2013 to 2022. The study employed 5 different architectures for data testing, namely the 15-15-1 architecture with a testing mean square error (MSE) of 0.00644604, the 15-19-1 architecture with a testing MSE of 0.01005532, the 15-30-1 architecture with a testing MSE of 0.02119922, the 15-31-1 architecture with a testing MSE of 0.00009938. The best architecture in this study was the 15-17-1 model with 5206 iterations and a runtime of 18 seconds, achieving the smallest testing MSE of 0.00000105 and the highest accuracy of 100%. From the obtained architectures, it is evident that backpropagation can perform with a high level of precision. This research can serve as a guideline for the government to determine rice availability and establish average rice prices based on quality, thus preventing future rice shortages.

Ultrasound-Assisted Anthocyanins Extraction from Pigmented Corn: Optimization Using Response Surface Methodology.

This study aimed to determine the optimal UAE conditions for extracting anthocyanins from pigmented corn using the Box-Behnken design (BBD). Six anthocyanins were identified in the samples and were used as response variables to evaluate the effects of the following working variables: extraction solvent pH (2-7), temperature (10-70 °C), solvent composition (0-50% methanol in water), and ultrasound power (20-80%). The extraction time (5-25 min) was evaluated for complete recovery. Response surface methodology suggested optimal conditions, specifically 36% methanol in water with pH 7 at 70 °C using 73% ultrasound power for 10 min. The method was validated with a high level of accuracy (>90% of recovery) and high precision (CV < 5% for both repeatability and intermediate precision). Finally, the proposed analytical extraction method was successfully applied to determine anthocyanins that covered a wide concentration range (36.47-551.92 mg kg-1) in several pigmented corn samples revealing potential varieties providing more health benefits.

Dynamic control of aluminum morphology by wire and arc additive manufacturing based on image feedback

The stability of the forming layer shape is a critical factor that impacts the final quality of sample morphology in wire and arc additive manufacturing (WAAM). This paper investigates the forming process and control methods to identify ways to optimize the process and improve the quality of the final product. The study aims to enhance the quality and precision of samples produced by the WAAM process by establishing a morphology control method based on image feedback. The focus is on real-time image acquisition using a CCD and simultaneous extraction of forming process parameter characteristics. Using a central composite experimental design, a prediction model is developed to estimate key process parameters and feature sizes of deposits, including deposition height and width. To further analyze the dynamic characteristics of feature sizes of deposited layers, step response identification is conducted using three process parameters as the system input: forming speed, arc current, and wire feeding speed. The experiments are designed to determine the weight of each process parameter and achieve a high level of response speed and precision. The findings indicate that the feature size of the deposited layer is most sensitive to changes in forming speed, followed by wire feeding speed, while arc current has the least impact on feature size. After verification, the monitoring of the deposition height and width was found to be in good agreement with the prediction model, with an accuracy of over 90%. The results of this study can be used for size measurement and optimization of large aviation aluminum alloy components using WAAM technology and to improve the quality of products produced using these processes.

Analytical Method Development and Validation for Estimation of Lapatinib in Formulation by RP-HPLC with Stability Indicating

Reverse-phase high-performance liquid chromatography (RP-HPLC) approach for Lapatinib in pharmaceutical dosage form development and validation. Reverse phase chromatography is easy, practical, and superior in terms of effectiveness, stability, and repeatability. Lapatinib was chosen to be separated using the C18 column, a 250 x 4.6 mm column with 5.0µm particle packing. As lapatinib was exhibiting good peak morphologies and a large amount of resolution, it was created with a mobile phase of water, methanol, and trifluroacetic acid (30:70:0.1) v/v. The analytes were detected at 262nm using a UV detector while the mobile phase was flowing at 1.1 ml/min. Specificity, linearity, accuracy, precision, robustness, limit of detection, and limit of quantitation were used to develop the method and validate it.The method demonstrated a dynamic linear response at 25-75 µg/ml and was shown to be linear with a correlation coefficient (r2) of above 0.999 and limits of detection and quantitation (LOD and LOQ) of 0.45 and 1.35µg, respectively. Degradation of the sample was used to establish the stability indicating RP-HPLC procedures, which were then compared to standards. The relative standard deviation as a percentage was also less than 2%, demonstrating the suggested method's high level of precision.

Sex differences in the relationship between cannabis use motives and cannabis craving in daily life in emerging adults.

Cannabis use motives and craving are associated with increased risk for cannabis-related problems and are ideal targets for prevention and early intervention. Patterns of motives and craving reactivity to cannabis cues differ by sex; however, few studies closely examine the relationship between motives and craving and how it may differ by valence (±) across men and women. The present study used Cue Reactivity Ecological Momentary Assessment to assess reward (+) and relief (-) craving four semirandom times per day for 2 weeks in a sample of 63 emerging adults (age 18-21; 54% cisgender women; 85.7% White) who frequently use cannabis (≥ 3 times per week). We assessed craving before and after exposure to brief neutral or cannabis image cues and examined within- and between-participant effects of cue type, motives, sex/gender, and their interactions, on postcue cannabis craving. Regardless of cue type, women with high coping motives (-) reported less postcue relief (-) craving, and men with high enhancement motives (+) reported more postcue reward (+) craving. High enhancement motives (+), regardless of sex/gender, were associated with elevated relief (-) craving reactivity to cannabis cues, and women with high coping motives (-) reported elevated reward (+) craving reactivity to cannabis cues. Sex/gender differences in the relationships between cannabis motives and craving reactivity indicate the value of a more targeted examination of valence (±) of craving experiences in addition to motives for use. Higher levels of precision may better inform interventions for emerging adults at risk for experiencing cannabis-related problems. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Predicting the Risk of SME Loan Repayment using AI Technology-Machine Learning Techniques: A Perspective of Malaysian Financing Institutions

This study aimed to predict the likelihood of small and medium-sized (SMEs) defaulting in loan repayment by developing a model using artificial intelligence technology, namely Machine Learning algorithms. The research employed the Louvain clustering algorithm to effectively group the loan recipients based on their cumulative repayment amounts over time, and two distinct machine learning techniques, namely logistic regression (LR) and k-nearest neighbour (k-NN) were harnessed to evaluate their efficacy in classifying recipients' risk levels, namely low-risk or high-risk. The LR model achieved mean accuracy score of 100% which indicates a high level of precision that can effectively predict the risk of SME loan repayment. This is further supported by the Area Under the Curve (AUC) value of 1.0 obtained by the LR model which suggests that the model has achieved optimal separation of the two classes, and therefore it is highly reliable for risk prediction. This technique is believed to enhance the efficiency and accuracy of credit risk assessment that could enable financial institutions (FIs) to optimize their decision-making processes and mitigate potential losses caused by the defaulting loans. Hence, this study is significant as it proves the effectiveness of machine learning technique in predicting loan repayment risk in FIs in Malaysia.

Gravity corrections for the updated italian levelling network

The Italian official height system is defined through a high precision levelling network established and maintained by the Istituto Geografico Militare - IGM. During the last 20 years, IGM has performed levelling campaigns on almost the whole peninsular area of Italy with the aim of both densifying the existing network and updating the reference heights. This paper reports about the procedure applied to correct the levelling observations for the gravity effects and the assessment on the results. The needed gravity values were predicted from the Italian gravity dataset (IGD), and both from EGM2008 and XGM2019e high resolution global gravity models. A new formulation of the normal correction as well as the standard orthometric correction were applied. The IGD derived corrections proved to be effective by reducing the misclosure error of critical loops below the tolerance level. Gravity data derived from EGM2008 and XGM2019e proved to be too poor for the correction purposes, as it was also confirmed by a comparison against available observed data, with RMS of the differences, in Alpine ares, ranging between 50 and 100 mGal.

Comparison between Ultrasound- and Microwave-Assisted Extraction Methods to Determine Phenolic Compounds in Barley (Hordeum vulgare L.).

Barley (Hordeum vulgare L.) is one of the major cereal crops worldwide. It is grown not only to be used as fodder but also for human consumption. Barley grains are a great source of phenolic compounds, which are particularly interesting for their health-promoting antioxidant properties, among other benefits. Two extraction methods, namely ultrasound-assisted extraction (UAE) and microwave-assisted extraction (MAE), have been optimized and compared by using Box-Behnken design (BBD) to determine both the antioxidant power and the phenolic compound levels of the extracts. Three variables have been assessed based on these designs: solvent composition (% MeOH in water), temperature (°C), and sample-to-solvent ratio (mg sample mL-1 solvent). The solvent composition used and the interaction between the solvent and the temperature were the most significant variables in terms of recovery of phenolic compounds and antioxidant capacity of the extracts. Short extraction times, a high precision level, and good recoveries have been confirmed for both methods. Moreover, they were successfully applied to several samples. Significant differences regarding the level of phenolic compounds and antioxidant power were revealed when analyzing three different barley varieties. Specifically, the amounts of phenolic compounds ranged from 1.08 to 1.81 mg gallic acid equivalent g-1 barley, while their antioxidant capacity ranged from 1.35 to 2.06 mg Trolox equivalent g-1 barley, depending on the barley variety. Finally, MAE was found to be slightly more efficient than UAE, presenting higher levels of phenolic compounds in the extracts.

On the applications of collocation method for numerically analyzing the nonlinear Degasperis–Procesi and Benjamin–Bona–Mahony equations

This research uses the Sinc Collocation method to numerically examine the Degasperis–Procesi and Benjamin–Bona–Mahony equations, achieving a high level of precision and accuracy on computational grounds with a variety of mesh points. The proposed technique involves global collocation using Sinc bases function (SBF) as an activation function. Initially, the time derivative discretization has been performed through finite difference scheme and the remaining spatial derivatives are approximated by [Formula: see text]-weighted scheme to minimize the complexity in numerical computations. The main contribution of research is in modifying the proposed scheme to overcome the complexity involved in the whole process of finding solutions with a cardinal expansion of Sinc functions in nonlinear and singular systems. The solutions of Degasperis–Procesi and Benjamin–Bona–Mahony stiff nonlinear systems are illustrated numerically and graphically and the validation and verification of the obtained results are examined through stability analysis by calculating the spectral radius by bounding the value of [Formula: see text]. A comparative study is presented with some of the well-known numerical techniques, which assured the accuracy and reliability of our technique.

Improved Bathymetry in the South China Sea from Multisource Gravity Field Elements Using Fully Connected Neural Network

Traditional bathymetry inversion methods that rely on an altimetry-derived gravity anomaly (GA) and/or a vertical gravity gradient anomaly (VGG) have been widely used for bathymetry prediction in the South China Sea. However, few studies attempt new methods to combine multisource gravity data to improve the accuracy of the bathymetry. In this study, we introduce a fully connected deep neural network (FC-DNN) to merge GA, VGG, and the deflection of vertical (DOV) to predict the bathymetry in the South China Sea. Single beam sounding depths were used as sample data for neural network training. Independent shipboard depths and GEBCO2023, topo_25.1, and ETOPO2022 models were applied as validation data. The assessment results showed that the FC-DNN model reached a high precision level with an STD of 49.20 m. More than 70% of the differences between the FC-DNN bathymetric model and other depth models were less than 100 m. Furthermore, the spectral analysis results showed that the FC-DNN bathymetry model has stronger energy in medium and short wavelengths than other models, which indicates that additional gravity field element DOVs can recover richer topographic signals in those particular bands.

O-136 Artificial intelligence to assist in surgical sperm detection and isolation

Abstract Study question Can an artificial intelligence (AI) improve the speed and accuracy of identifying sperm in complex testicular tissue samples? Summary answer Trained AI can identify sperm in real-time instantly with higher accuracy, not only reducing strain on embryologists but increasing sample coverage in a shorter time. What is known already Non-obstructive azoospermia (NOA) is a form of severe male-factor infertility, affecting nearly 5% of infertile couples seeking treatment. Isolating sperm from macerated testicular tissue for intracytoplasmic sperm injection (ICSI) has changed marginally in the last two decades, and still requires embryologists to search arduously through a background of collateral cells including red blood cells (RBC’s), white blood cells (WBC’s), leydig, sertoli and epithelial cells, causing fatigue and reducing sample coverage. Image analysis using trained AI can instantly identify shapes and forms and thus presents itself as a candidate to dramatically reduce processing times in surgical sperm cases. Study design, size, duration This proof-of-concept consists of two phases over 5 months. A training phase using 7 azoospermic patients to provide samples to train a convolutional neural network with manual annotation. Secondly, a side-by-side live test with an embryologist versus the AI model comparing time taken and accuracy of sperm identification, and precision of identifying sperm in macerated tissue samples (false positives and false negatives). Results were analysed using Mann-Whitney U-test. Differences were considered significant when p-value&amp;lt;0.05. Participants/materials, setting, methods Consenting patients at IVF Australia, Sydney attending for surgical sperm collection donated discarded macerated testicular tissue samples whereby excess tissue was used post-processing in conjunction with donor sperm, to train the machine learning software. Thereafter, testing using conventional ICSI micromanipulator microscope was performed side-by-side on test samples and time taken, accuracy and precision were recorded between a control count, computer vision and trained embryologist. Main results and the role of chance The AI model showed dramatic improvement in time taken to identify sperm per field, improved accuracy in identifying sperm as well as a high level of precision. Precision for the trained embryologist was considered to be 100%. Time taken per field of view to identify all sperm was significantly lower for the AI when compared to the trained embryologist (0.019 ± 1.4 x 10-4s vs 22.87 ± 0.98s, P &amp;lt; 0.0001). There was also a significant difference in accuracy (89.88 ± 1.56% vs 83.22 ± 2.02%, P = 0.017) for the AI model versus the trained embryologist respectively. The precision of the model is 91.27 ± 1.27% which considered false positives and the correct identification of sperm versus the control count. From a total of 688 sperm to be found, 560 were found by the embryologist and 611 were found by the AI model in less than 1000th of the time it took the embryologist. Limitations, reasons for caution This is a proof-of-concept test of a convolutional neural network AI. Following this study, side-by-side on donated clinical samples are needed, and a clinical trial comparing embryologists versus AI is required to prove efficacy and usefulness. A larger number of samples across multiple indications and surgical approaches is required. Wider implications of the findings AI-powered image analysis has the potential for seamless integration into laboratory workflows to reduce time to identify and isolate sperm from surgical sperm samples from hours to minutes, and do this with improved accuracy, thus increasing success rates from these treatments. Trial registration number N/A

GEODETIC WORKS TO DETERMINE VERTICAL MOVEMENTS AT THE ZHAMBYL GEODYNAMIC POLYGON

This article discusses the issues of determining the current movements of the Earth's surface by geodetic methods at the Zhambyl predictive geodynamic polygon. The article discusses the characteristics of predictive polygons in the Republic of Kazakhstan. The data of vertical movements of the Earth's surface on the territory of the Zhambyl geodynamic polygon are presented. The results of high-precision leveling for 2020 and 2022 are presented, which made it possible to plot vertical displacement graphs and analyze changes in the position of geodetic reference points. The analysis showed that in the study area there are slight vertical displacements from -0.01 to -0.06 mm/year, which do not exceed the allowable possible accumulation of measurement errors.

Neural Image Caption Generation with Visual Attention : Enabling Image Accessibility for the Visually Impaired

The internet is saturated with images that convey messages and emotions more effectively than words alone in today's digital age. Individuals with visual impairments, who are unable to perceive and comprehend these images, face significant obstacles in this visual-centric online environment. As there are millions of visually impaired people around the globe, it is essential to close this accessibility gap and enable them to interact with online visual content. We propose a novel model for neural image caption generation with visual attention to address this pressing issue. Our model uses a combination of CNNs and RNNs to convert the content of images into aural descriptions, making them accessible to the visually impaired. The primary objective of our project is to generate captions that accurately and effectively describe the visual elements of an image. The model proposed operates in two phases. First, a text-to-speech API is utilized to convert the image's content into a textual description. The extracted textual description is then converted to audio, allowing visually impaired individuals to perceive visual information through sound. Through exhaustive experimentation and evaluation, we intend to achieve a high level of precision and descriptivism in our system for image captioning. We will evaluate the performance of the model by undertaking comprehensive qualitative and quantitative assessments, comparing its generated captions to ground truth captions annotated by humans. By enabling visually impaired individuals to access and comprehend online images, our research promotes digital inclusion and equality. It has the potential to improve the online experience for millions of visually impaired people, enabling them to interact with visual content and enriching their lives through meaningful image-based interactions.

Rapid Method for Quantification of Iron (Fe+3) from Ferrazone (NaFe-EDTA) in Fortified Wheat Flour.

Conventional methods for quantifying the added iron in wheat flour are time-consuming and costly. A rapid method (Time/Sample: 95 min) was developed by modifying the conventional standard method (Time/Sample: 560 min) and validated. Linearity and linear regression of the rapid method presented excellent correlation coefficient (R2) values (0.9976 to 0.9991), which were close to 1, while the limits of agreement (LOA) were in the range of -0.01 to 0.06 mg/kg. The limits of detection (LOD)/specificity and limits of quantitation (LOQ)/sensitivity values were found to be 0.03 and 0.09 mg/kg, respectively. The rapid method was subjected to validation, wherein the precision of intra-assay, inter-assay, and inter-person was determined to be within the range of 1.35-7.25%. These results indicate a high level of accuracy and precision of the method. The percent relative standard deviation (RSD) for recoveries at varying spiking levels, that is, 5, 10, and 15 mg/kg, was determined at 1.33 lying far below the upper limit of acceptability (RSD < 20). Overall, the developed rapid method can be sustainably alternate for conventional methods owing to its ability to produce accurate, precise, robust, and reproducible results.

When do consumers favor overly precise information about investment returns?

Consumers are often shown investment returns with high levels of precision, which could lead them to misunderstand the inherent uncertainty. We test whether consumers are drawn to precision-that is offset the uncertainty in investment decisions by over-relying on precise numerical information. Five incentivized experiments compared decisions when expected growth is presented in precise forecasts as opposed to ranges. Consumers are more likely to prefer and invest more in precise forecasts when they are evaluated jointly with ranges and when the range features a potential loss. Under these circumstances, precise forecasts give consumers more confidence to invest. This effect holds when consumers are told investment returns are uncertain. On the other hand, experiencing discrepancies between expected and actual growth dissipates the preference for precise forecasts. We identify conditions under which consumers are more likely to favor precise forecasts and how this could be avoided if necessary. (PsycInfo Database Record (c) 2023 APA, all rights reserved).