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Objective: This study aimed to investigate the diagnostic performance of volume mismatch sign on discriminating paramedial bithalamic tumors from non-tumors. Methods: In this study, we recruited patients with tumors or non-tumors of the paramedial bithalamus. We confirmed the diagnosis by pathology, laboratory tests documented in medical records, medical imaging at the baseline, or through at least 1 year of follow-up. Cases with paramedial thalamic lesions on only one side or purely midbrain illnesses were excluded. Additionally, any case without involvement of the medial thalami (i.e., those with one or both-sided anterior, lateral, or posterior thalamic lesions) was excluded. Two neuroradiologists were trained independently to evaluate volume mismatch sign on magnetic resonance T2-weighted images or T2 fluid-attenuated inversion recovery images. A positive volume mismatch sign means that the ratio of the larger-sided lesion volume to the smaller-sided lesion volume is >150%. The volume of each lesion was calculated by multiplying the anteroposterior diameter by the left-right diameter and by the height of the lesion and then dividing by 2. The kappa value was calculated to show the consistency between the two observers. The chi-square test was used to evaluate differences in volume mismatch sign between the bilthalamicmidline tumor and non-tumor groups. The positive (PPV) and negative (NPV) predictive values, sensitivity, and specificity were calculated to evaluate the ability of volume mismatch sign to differentiate paramedial bilateral thalamus tumors from non-tumors. A two-tailed P = 0.05 was considered to be statistically significant. The analyses were performed using the statistical software SPSS version 26. Results: A total of 96 patients were enrolled in this study between March 2012 and October 2022. A high agreement between the two observers on the volume mismatch sign of bilateral paramedian thalamic diseases was found, and the Kappa value was 0.828. A statistically significant difference was observed for the volume mismatch sign between the paramedial bithalamic tumor and the non-tumorous groups (χ² = 35.465, P < 0.001). The presence of volume mismatch sign in paramedial bithalamic illnesses predicted the presence of tumors with a sensitivity and specificity of 69.2% and 90.9%, respectively, and PPV and NPV were 90.0% and 71.4%. Conclusion: Volume mismatch sign may indicate tumors in paramedian bithalamic diseases. [ABSTRACT FROM AUTHOR]

Single event upset (SEU) has always been an important factor affecting the reliability of spacecraft electronic equipment, which can cause anomalies in electronic equipment in orbit, and can result in serious spacecraft failure. In order to master signal event upset discriminating and positioning technology, and to study the mechanism between SEU physical position and particle energy, a technology method about SEU discriminating and positioning was proposed in this paper, and the principle prototype system was developed. The principle prototype system consists of silicon detector and electronics module. The silicon detector and the irradiation�sensitive device are mounted in close proximity to each other in the vertical direction, and the position region where the particles are incident to the silicon detector corresponds to the physical position of the logical upset of the target device. On the one hand, the silicon detector is divided into an array grid, electronics module includes multiple detection channels, and high�energy particles hitting a grid position of the silicon detector can be detected. On the other hand, the logical position of the target device where a single particle upset occurs can be detected by the circuit. Position sensitive detector is a silicon PIN type semiconductor particle detector dividing into a 4×4 array grid corresponding to each of the 16 measurement channels. Each grid is 4�5 mm wide square with a thickness of 300 μm. The silicon detector is packaged on a PCB board, where the detector grids are connected to the pads of the PCB by gold wire crimping. The electronics module mainly includes pre�amplifier circuit, main amplifier and signal conditioning circuit, multichannel waveform signal peak�seeking circuit, particle�triggered circuit, SEU logic detection circuit for SRAM devices, FPGA control and communication circuit, etc. Waveform digitization technology is adopted, particle waveform peak value and particle arrival time are obtained through high speed analog�to�digital conversion and digital signal acquisition and processing. The basic method of SEU detection circuit for SRAM is the data read�back comparison method, the FPGA writes a cyclically accumulating 8�bit number to the address range of the SRAM at the maximum rate of the SRAM read/write, then reads it out of the SRAM and compares it with the written data for the difference. If the result is “1”, it means that a SEU event has occurred; if “0” appears, it proves that no SEU has occurred. According to the results of laboratory test and single�particle irradiation test, the LET of detectable energetic particle is greater than 6�06×10-3 MeV·cm2/mg, the position resolution of incident particles is better than 5 mm, the maximum count rate is more than 104 s-1, and the SEU inspection cycle time resolution of SRAM devices is 13�76 ms. By mastering the SEU discriminating and positioning of large�capacity SRAM device and the radiation environment sensing capability, it can improve the performance of space electronics in orbit. By mastering the single�particle flip screening and positioning of the large�capacity SRAM devices and their radiation environment sensing capability, it can improve the on�orbit performance of space electronics.

Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor seeds. A 23 factorial design was used to optimize the LIBS experimental parameters for spectral analysis. A total of 120 samples from two distinct cultivars of Brachiaria brizantha seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-one-out cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination, regardless of the cultivar or the classification algorithm. [ABSTRACT FROM AUTHOR]

This article interrogates the issue relating to employees living with cancer taking part in employment without being discriminated against based on their medical condition. It will be clearly outlined that cancer does not take away the ability of employees living with cancer to continue with work or enter into employment, which is what most employers and fellow employees believe based on the myth and stigma attached to cancer. This needs to be discouraged through proper education and creating awareness about cancer. This article will interrogate what cancer is and how it develops in the human body as well as the extent or impact of cancer on a patient to a point of leading to disability. The debate of whether cancer amounts to a disability in the South African context will be entertained and recommendations outlined with the aim of ensuring that employees living with cancer are not excluded in taking part in employment among other things. Focus will then shift to the most important aspect of this article which is discrimination, and to explore the different forms of discriminations as well as outline why employers tend to discriminate against employees living with cancer and can this conduct of employers be justified in any way in line with the South African legal system and the article will be incomplete if reference is not made to the English legal system. This is attributed to the fact that the South African legal system is built on the English legal system to a lesser or greater extent and lessons can be drawn from the English legal system due to the advances that have been made when it comes to the protection of employees living with cancer in the workplace. Recommendations will follow with the aim of providing a way forward for employees living with cancer in the South African market.

The present article is aimed at analyzing the Council of Indonesian Ulama (MUI, Majelis Ulama Indonesia) fatwa on Shia in Indonesia. The decree of MUI fatwa should be described and analyzed due to potential powerful to influence Indonesian Muslim community. Besides, there are three dialectics of discovering the fatwa such as the author, the content, and the reader of fatwa. The effect of such decree tends to occur a violence among religious minority such as discrimination and intimidation. The process of producing fatwa does not properly consider five contingencies as the parameter of authoritative fatwa. According to MUI, Shia is one of the deviate belief systems in Indonesia because it has primary different doctrine from ten indicators. To avoid the authoritarianism of fatwa and violence against religious minority, MUI as an official institution who produces the fatwa should consider the five contingencies, namely honesty, diligence, reasonableness, comprehensiveness, and self restraint.

Laser-induced breakdown spectroscopy (LIBS) associated with machine learning algorithms (ML) was used to evaluate the Brachiaria seed physiological quality by discriminating the high and low vigor seeds. A 23 factorial design was used to optimize the LIBS experimental parameters for spectral analysis. A total of 120 samples from two distinct cultivars of Brachiaria brizantha seeds exhibiting high vigor (HV) and low vigor (LV) in standard tests were studied. The raw LIBS spectra were normalized and submitted to outlier verification, previously to the reduction data dimensionality from principal component analysis. Supervised machine learning algorithm parameters were chosen by leave-one-out cross-validation in the test samples, and it was tested by external validation using a new set of data. The overall accuracy in external validation achieved 100% for HV and LV discrimination, regardless of the cultivar or the classification algorithm.

Objective: This study aimed to investigate the diagnostic performance of volume mismatch sign on discriminating paramedial bithalamic tumors from non-tumors., Methods: In this study, we recruited patients with tumors or non-tumors of the paramedial bithalamus. We confirmed the diagnosis by pathology, laboratory tests documented in medical records, medical imaging at the baseline, or through at least 1 year of follow-up. Cases with paramedial thalamic lesions on only one side or purely midbrain illnesses were excluded. Additionally, any case without involvement of the medial thalami (i.e., those with one or both-sided anterior, lateral, or posterior thalamic lesions) was excluded. Two neuroradiologists were trained independently to evaluate volume mismatch sign on magnetic resonance T2-weighted images or T2 fluid-attenuated inversion recovery images. A positive volume mismatch sign means that the ratio of the larger-sided lesion volume to the smaller-sided lesion volume is >150%. The volume of each lesion was calculated by multiplying the anteroposterior diameter by the left-right diameter and by the height of the lesion and then dividing by 2. The kappa value was calculated to show the consistency between the two observers. The chi-square test was used to evaluate differences in volume mismatch sign between the bilthalamic midline tumor and non-tumor groups. The positive (PPV) and negative (NPV) predictive values, sensitivity, and specificity were calculated to evaluate the ability of volume mismatch sign to differentiate paramedial bilateral thalamus tumors from non-tumors. A two-tailed P ≤ 0.05 was considered to be statistically significant. The analyses were performed using the statistical software SPSS version 26., Results: A total of 96 patients were enrolled in this study between March 2012 and October 2022. A high agreement between the two observers on the volume mismatch sign of bilateral paramedian thalamic diseases was found, and the Kappa value was 0.828. A statistically significant difference was observed for the volume mismatch sign between the paramedial bithalamic tumor and the non-tumorous groups (χ 2 = 35.465, P < 0.001). The presence of volume mismatch sign in paramedial bithalamic illnesses predicted the presence of tumors with a sensitivity and specificity of 69.2% and 90.9%, respectively, and PPV and NPV were 90.0% and 71.4%., Conclusion: Volume mismatch sign may indicate tumors in paramedian bithalamic diseases., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Su and Gao.)

In this paper, a mass classification method in mammograms is proposed based on two-concentric masks and discriminating texton. First, the two-concentric masks are employed, dividing each mass region into the center region and the periphery region. Then integrating linear discriminant analysis (LDA) with traditional texton, the discriminating texton is proposed. The shortage of not considering the class information in traditional texton is improved. Finally, features are extracted with discriminating texton for both the center region and the periphery region. Thus, the problem of disregarding the spatial layout information is alleviated. The proposed method is tested on 130 mass regions from Digital Database for Screening Mammography (DDSM) database. The classification accuracy rate reaches 86.92% and the area under the receiver operating characteristics (ROC) curve is 0.91, which is higher than traditional texton and some other texture-based methods. [ABSTRACT FROM AUTHOR]

This contribution discusses the return of the ‘social question’ to the basically still wealthy and secure societies of the Global North. Referring to the case of German welfare capitalism, a historically new form of discriminating precariousness is being identified. This type of precariousness results from processes of a market driven, capitalist Landnahme. The paper argues that this specific form of precariousness should be the subject matter of a renewed, scientific social critique.

The establishment of biorelevant and discriminating dissolution procedure for drug products with limited water solubility is a useful technique for qualitative forecasting of the in vivo behavior of formulations. It also characterizes the drug product performance in pharmaceutical development. Lornoxicam, a BCS class-II drug is a nonsteroidal antiinflammatory drug of the oxicam class, has no official dissolution media available in the literature. The objective of present work was to develop and validate a discriminating and biorelevant dissolution test for lornoxicam tablet dosage forms. To quantify the lornoxicam in dissolution samples, UV spectrophotometric method was developed using 0.01M sodium hydroxide solution as solvent at λmax376 nm. After evaluation of saturation solubility, dissolution, sink conditions and stability of lornoxicam bulk drug in different pH solutions and biorelevant media, the dissolution method was optimized using USP paddle type apparatus at 50 rpm rotation speed and 500 ml simulated intestinal fluid as discriminating and biorelevant dissolution medium. The similarity factor (f2) were investigated for formulations with changes in composition and manufacturing variations, values revealed that dissolution method having discriminating power and method was validated as per standard guidelines. The proposed dissolution method can be effectively applied for routine quality control in vitro dissolution studies of lornoxicam in tablets and helpful to pharmacopoeias. [ABSTRACT FROM AUTHOR]

In this paper a new classification method called locality-sensitive kernel sparse representation classification (LS-KSRC) is proposed for face recognition. LS-KSRC integrates both sparsity and data locality in the kernel feature space rather than in the original feature space. LS-KSRC can learn more discriminating sparse representation coefficients for face recognition. The closed form solution of the l 1 -norm minimization problem for LS-KSRC is also presented. LS-KSRC is compared with kernel sparse representation classification (KSRC), sparse representation classification (SRC), locality-constrained linear coding (LLC), support vector machines (SVM), the nearest neighbor (NN), and the nearest subspace (NS). Experimental results on three benchmarking face databases, i.e., the ORL database, the Extended Yale B database, and the CMU PIE database, demonstrate the promising performance of the proposed method for face recognition, outperforming the other used methods. [ABSTRACT FROM AUTHOR]

The study is an attempt to empirically examine factors that differentiate EFL readers with different EFL reading proficiency levels. Four selected factors believed to play a role in reading comprehension are considered, namely: linguistic knowledge, reading strategies, text structure knowledge, content and world background knowledge. Discriminant analysis was employed to scrutinize the data collected on these variables. The analysis wraps up, revealing that advanced EFL readers, while they are sufficiently equipped with formal schemata that necessarily embrace both linguistic knowledge and text structure knowledge, can be speculated to consist of three groups: those who fail to activate their relevant schemata, those who fail to use their reading strategies, and those who can function both content and world background knowledge and their reading strategies with ease.

Abstract: Discriminating between potato tubers and clods is the first step in developing an automatic separation system on potato harvesters. In this study, an acoustic-based intelligent system was developed for high speed discriminating between potato tubers and soil clods. About 500kg mixture of potato tubers and clods were loaded on a belt conveyer and were impacted against a steel plate at four different velocities. The resulting acoustic signals were recorded, processed and potential features were extracted from the analysis of sound signals in both time and frequency domains. A multilayer perceptron neural network with a back propagation algorithm was used for pattern recognition. Altogether, 17 potential discriminating features were selected and fed as input vectors to the artificial neural network models. Optimal network was selected based on mean square error, correct detection rate and correlation coefficient. At the belt velocity of 1ms−1, detection accuracy of the presented system was about 97.3% and 97.6% for potatoes and clods, respectively. Increasing the belt velocity resulted in the reduction of detection accuracy and increase in the number of miss classified samples. By using this system, it is expected that a potato harvester may operate at a capacity of 20tonhr−1 with the accuracy of about 97%. [Copyright &y& Elsevier]

Abstract: ZEPLIN II is a 30-kg two-phase xenon detector designed for direct detection of cold dark matter in the form of WIMPs. Currently in the commissioning phase, it will begin operation in the Boulby Mine, UK later this year. ZEPLIN II is capable of discriminating between nuclear recoils and background events and has a design reach up to two orders of magnitude beyond current limits. It will also serve as a step in the development program for a next-generation ton-scale detector. [Copyright &y& Elsevier]

Discriminating living and dead cells is of great significance for the study of apoptosis. In this work, we have developed a unique fluorescent probe (RPIC) for discriminating live and dead cells with duel-channel fluorescence imaging under double excitation and double emission mode. Dead cells treated with RPIC shows weak fluorescence signals in red channel, however, strong fluorescence signals are appeared in red channel in live cells. Weak and strong green fluorescence signals present at live cells and dead cells, respectively. Moreover, RPIC can detect successfully apoptosis of cancer cells. For in-vivo imaging, RPIC can discriminate successfully live and dead zebrafish with the same method. More interestingly, it is found that RPIC possesses the ability of discriminating normal mice and tumor mice. Herein, the development of RPIC is described as the first paradigm of probes that could discriminate live and dead cells under double excitation and double emission mode. Unlabelled Image • RPIC could discriminate live and dead cells by under double excitation and double emission mode. • RPIC can detect successfully apoptosis of cancer cells. • RPIC can discriminate successfully live and dead zebrafish. • It is found that RPIC possess the ability of discriminating normal mice and tumor mice. [ABSTRACT FROM AUTHOR]

The eastern hemlock (Tsuga Canadensis) is declining in health and vigor in eastern North America due to infestation by an introduced insect, the hemlock woolly adelgid (Adelges isugue). Adelgid feeding activity results in the defoliation of hemlock forest canopy over several years. We investigated the application of Landsat satellite imagery and change-detection techniques to monitor the health of hemlock forest stands in northern New Jersey. We described methods used to correct effects due to atmospheric conditions and monitor the health status of hemlock stands over time. As hemlocks defoliate, changes occur in the spectral reflectance of the canopy in near infrared and red wavelengths-changes captured in the Normalized Difference Vegetation Index. By relating the differences in this index over time to hemlock defoliation on the ground, four classes of hemlock forest health were predicted across spatially heterogeneous landscapes with 82% accuracy. Using a time series of images, we are investigating temporal and spatial patterns in hemlock defoliation across the study area over the past decade. Based on the success of this methodology, we are no expanding out study to monitor hemlock health across the entire Mid-Atlantic region.