Miracle-dz/UniFAR-FTUs huggingface dataset:
Face recognition for occluded face with mask region convolutional neural network and fully convolutional network: a literature review

Face recognition technology has been used in many ways, such as in the authentication and identification process. The object raised is a piece of face image that does not have complete facial information (occluded face), it can be due to acquisition from a different point of view or shooting a face from a different angle. This object was raised because the object can affect the detection and identification performance of the face image as a whole. Deep leaning method can be used to solve face recognition problems. In previous research, more focused on face detection and recognition based on resolution, and detection of face. Mask region convolutional neural network (mask R-CNN) method still has deficiency in the segmentation section which results in a decrease in the accuracy of face identification with incomplete face information objects. The segmentation used in mask R-CNN is fully convolutional network (FCN). In this research, exploration and modification of many FCN parameters will be carried out using the CNN backbone pooling layer, and modification of mask R-CNN for face identification, besides that, modifications will be made to the bounding box regressor. it is expected that the modification results can provide the best recommendations based on accuracy.
ENHANCEMENT OF STABILITY ON AUTONOMOUS WAYPOINT MISSION OF QUADROTOR USING LQR INTEGRATOR CONTROL

The ability of the quadrotor in the waypoint trajectory tracking becomes an essential requirement in the completion of various missions nowadays. However, the magnitude of steady-state errors and multiple overshoots due to environmental disturbances leads to motion instability. These conditions make the quadrotor experience a shift and even change direction from the reference path. As a result, to minimize steady-state error and multiple overshoots, this study employs a Linear Quadratic Regulator control method with the addition of an Integrator. Comparisons between LQR without Integrator and LQR with Integrator were performed. They were implemented on a quadrotor controller to track square and zig-zag waypoint patterns. From experimental results, LQR without Integrator produce of 2 meters steady-state error and -1.04 meters undershoot average with an accuracy of 64.84 % for square pattern, along 3.19 meters steady-state error, and -1.12 meters undershoot average with an accuracy of 46.73 % for a zig-zag way. The LQR method with integrator produce of 1.06 meters steady-state error with accuracy 94.96 % without multiple-overshoot for square pattern, the 1.06 meters steady-state error, and -0.18 meters undershoot average with an accuracy of 86.49 % for the zig-zag way. The results show that the LQR control method with Integrator can minimize and improve steady-state error and multiple overshoots in quadrotor flight. The condition makes the quadrotor able to flying path waypoints with the correct system specification. ABSTRAK: Kemampuan quadrotor dalam pengesanan lintasan waypoint menjadi syarat penting dalam menyelesaikan pelbagai misi pada masa kini. Walau bagaimanapun, besarnya ralat keadaan mantap dan banyak kelebihan kerana gangguan persekitaran menyebabkan ketidakstabilan pergerakan. Keadaan ini menjadikan quadrotor mengalami pergeseran dan bahkan mengubah arah dari jalur rujukan. Oleh itu, kajian ini menggunakan kaedah kawalan Linear Quadratic Regulator dengan penambahan integrator dalam meminimumkan ralat keadaan mantap dan banyak kelebihan. Perbandingan antara LQR tanpa Integrator dan LQR dengan Integrator dilakukan. Mereka dilaksanakan pada pengawal quadrotor untuk mengesan corak titik jalan persegi dan zig-zag. Dari hasil eksperimen, LQR tanpa Integrator menghasilkan ralat keadaan mantap 2 meter dan -1.04 meter rata-rata undur tembak dengan ketepatan 64.84% untuk corak persegi, sepanjang ralat keadaan tetap 3.19 meter, dan -1.12 meter rata-rata undur
Does the benthic invertebrate community reflect disturbances in the central Indian River Lagoon?

The Indian River Lagoon (IRL) is home to numerous species of plants and animals, including sessile benthic invertebrates. In addition to being an important component of the ecosystem, sessile benthic organisms may be of use to detect disturbances within the lagoon. This paper presents data from a long-term monitoring program of benthic invertebrate communities at a test site in the central lagoon since 2008. Over this time, these organisms endured the declining health of the IRL along with natural disturbances (i.e., cold snaps, algal blooms, tropical cyclones). Seasonal community composition was similar to what has previously been recorded, indicating that the health of the lagoon has not caused any long-term damage to benthic invertebrate community. However significant impacts were observed with barnacle and encrusting bryozoan cover as a result of various disturbances. Of the three types of disturbances investigated, the cold snap had no effect, the hurricane only had short-term impacts (weeks) and algal blooms had shortand long-term (months) impacts. Barnacle cover was severely reduced by algal blooms and at the same time the encrusting bryozoan cover increased. As algal blooms and anthropogenic disturbances become more frequent in the IRL, barnacles and encrusting bryozoans may be used as an indicator taxa for decreases in habitat quality.
One-Class Model for Fabric Defect Detection

An automated and accurate fabric defect inspection system is in high demand as a replacement for slow, inconsistent, error-prone, and expensive human operators in the textile industry. Previous efforts focused on certain types of fabrics or defects, which is not an ideal solution. In this paper, we propose a novel one-class model that is capable of detecting various defects on different fabric types. Our model takes advantage of a well designed Gabor filter bank to analyze fabric texture. We then leverage an advanced deep learning algorithm, autoencoder, to learn general feature representations from the outputs of the Gabor filter bank. Lastly, we develop a nearest neighbor density estimator to locate potential defects and draw them on the fabric images. We demonstrate the effectiveness and robustness of the proposed model by testing it on various types of fabrics such as plain, patterned, and rotated fabrics. Our model also achieves a true positive rate (a.k.a recall) value of 0.895 with no false alarms on our dataset based upon the Standard Fabric Defect Glossary.
Ensemble Knowledge Distillation for Classification of 14 Thorax Diseases using Chest X-ray Images

Timely and accurate diagnosis of lung diseases using Chest X-ray images has been gained much attention from the computer vision and medical imaging communities. Although previous studies have presented the capability of deep convolutional neural networks by achieving competitive binary classification results, their models were seemingly unreliable to effectively distinguish multiple disease groups using a large number of x-ray images. In this paper
An Approach from Software Engineering to an IoT and Machine Learning Technological Solution that Allows Monitoring and Controlling Environmental Variables in a Coffee Crop

Context: Software engineering allows us to approach software design and development from the practical application of scientiﬁc knowledge. In the case of this IoT solution and the machine learning approach to the monitoring and control of environmental variables in a coffee crop, it allows us to visualize certain artifacts of the system in their interaction with users and their behavior with other artifacts or devices that constitute a technological solution. Method: For this work, the application of software engineering from a conceptual approach and the behavior of the system is proposed. To meet these objectives, we decided to use the Uniﬁed Modeling Language (UML) in such a way that the most important components of the technological solution could be represented from a static perspective through the use case diagrams, as well as from a dynamic viewpoint through the sequence diagrams. Results: Through the application of the UML, it was possible to develop the conceptual and behavioral modeling of certain artifacts and components. This knowledge allowed identifying the interaction bet-ween physical components and devices (machine to machine) and human-machine interaction, that is, the relationship between users and the processes that make up the technological solution. Conclusions: Through software engineering, and more speciﬁcally the UML, we were able to establish the importance of knowing the different software artifacts that make up a system or application from a different technical and functional approach, while being able to collect valuable information about the behavior of certain system artifacts, as well as the interaction between users and processes.
Processing of gyro drift for opto-electronic stabilization platform

In order to analyze the influence of gyro random drift on opto-electronic stabilization platform accuracy,the Allan variance and power spectrum judgment standards were put forward,in which the time series analysis method was used to establish the model.The FLP filter and wavelet filter were compared based on the applications of stabilization platform,digital low-pass filter,Kalman filter.The experiment proves that the FLP filter is superior to the low-pass filter in filtering the noise at medium and high frequency bands,and is superior to the Kalman filter in keeping useful signal at low frequency bands and finding out the noise characteristics.The results show that FLP filter is suitable for real-time on-line applications of high bandwidth opto-electronic stabilization platform.
The insights into calcium ion selectivity provided by ancestral prokaryotic ion channels

Prokaryotic channels play an important role in the structural biology of ion channels. At the end of the 20th century, the first structure of a prokaryotic ion channel was revealed. Subsequently, the reporting of structures of various prokaryotic ion channels have provided fundamental insights into the structure of ion channels of higher organisms. Voltage-dependent Ca2+ channels (Cavs) are indispensable for coupling action potentials with Ca2+ signaling. Similar to other proteins, Cavs were predicted to have a prokaryotic counterpart; however, it has taken more than 20 years for one to be identified. The homotetrameric channel obtained from Meiothermus ruber generates the calcium ion specific current, so it is named as CavMr. Its selectivity filter contains a smaller number of negatively charged residues than mutant Cavs generated from other prokaryotic channels. CavMr belonged to a different cluster of phylogenetic trees than canonical prokaryotic cation channels. The glycine residue of the CavMr selectivity filter is a determinant for calcium selectivity. This glycine residue is conserved among eukaryotic Cavs, suggesting that there is a universal mechanism for calcium selectivity. A family of homotetrameric channels has also been identified from eukaryotic unicellular algae, and the investigation of these channels can help to understand the mechanism for ion selection that is conserved from prokaryotes to eukaryotes.
In search of autophagy biomarkers in breast cancer: Receptor status and drug agnostic transcriptional changes during autophagy flux in cell lines

Autophagy drives drug resistance and drug-induced cancer cell cytotoxicity. Targeting the autophagy process could greatly improve chemotherapy outcomes. The discovery of specific inhibitors or activators has been hindered by challenges with reliably measuring autophagy levels in a clinical setting. We investigated drug-induced autophagy in breast cancer cell lines with differing ER/PR/Her2 receptor status by exposing them to known but divergent autophagy inducers each with a unique molecular target, tamoxifen, trastuzumab, bortezomib or rapamycin. Differential gene expression analysis from total RNA extracted during the earliest sign of autophagy flux showed both cell- and drug-specific changes. We analyzed the list of differentially expressed genes to find a common, cell- and drug-agnostic autophagy signature. Twelve mRNAs were significantly modulated by all the drugs and 11 were orthogonally verified with Q-RT-PCR (Klhl24, Hbp1, Crebrf, Ypel2, Fbxo32, Gdf15, Cdc25a, Ddit4, Psat1, Cd22, Ypel3). The drug agnostic mRNA signature was similarly induced by a mitochondrially targeted agent, MitoQ. In-silico analysis on the KM-plotter cancer database showed that the levels of these mRNAs are detectable in human samples and associated with breast cancer prognosis outcomes of Relapse-Free Survival in all patients (RSF), Overall Survival in all patients (OS), and Relapse-Free Survival in ER+ Patients (RSF ER+). High levels of Klhl24, Hbp1, Crebrf, Ypel2, CD22 and Ypel3 were correlated with better outcomes, whereas lower levels of Gdf15, Cdc25a, Ddit4 and Psat1 were associated with better prognosis in breast cancer patients. This gene signature uncovers candidate autophagy biomarkers that could be tested during preclinical and clinical studies to monitor the autophagy process.
Cardiopulmonary parameters and organ blood flows for workers expressed in terms of VO2 for use in physiologically based toxicokinetic modeling

ABSTRACT Minute ventilation rates (VE), alveolar ventilation rates (VA), cardiac outputs (Q), liver blood flow (LBF) and kidneys blood flows (KBF) for physiologically based toxicokinetic modeling and occupational health risk assessment in active workers have apparently not been determined. Minute energy expenditure rates (E) and oxygen consumption rates (VO2) in workers during exertions and their aggregate daytime activities are obtained by using open-circuit wearable devices for indirect calorimetry measurements and the doubly labeled water method respectively. Hundreds of E (in kcal/min) and VO2 (in L of O2/min) were previously reported for workers. The oxygen uptake factors of 0.2059 ± 0.0019 and 0.2057 ± 0.0018 L of O2/kcal during postprandial and fasting phases respectively enabled conversion of E into VO2. Equations determined in this study based upon more than 25 000 published measurements enable the calculation of 15 parameters in the same worker only by using the VO2 reflecting workload. These parameters, notably VE, VA, VE/VO2 VA/Q, Q, LBF and KBF were found to be interrelated. Altering one of these changes the order of magnitude of the others. Q, LBF and KBF decrease when supine adults at rest switch to an upright position. This effect of gravity diminished when VO2 increased. The fall in LBF and KBF during exertion might enhance muscle blood flow as reported previously. Taken together these equations and data may improve the accuracy of physiologically based toxicokinetic modeling as well as occupational health assessment studies in active workers exposed to xenobiotics. List of main abbreviations: AVOD: arterioveinous oxygen content difference.BMI: body mass index (in kg/m2). BSA: body surface area (in m2). BTPS: body temperature and saturated with water vapor.Bw: body weight (in kg). E: minute energy expenditure rate (in kcal/min). FGE: organ blood flow factor for the gravitational effect on blood circulation.H: oxygen uptake factor, volume of oxygen (at STPD) consumed to produce 1 kcal of energy expended.KBF: kidneys blood flow (in ml/min). LBF: liver blood flow (in ml/min). PBF: liver or kidneys blood flows expressed in terms of percentages (in %) of Qsup C values: namely PBF = (LBF or KBF/Qsup C) x 100.Q: cardiac output (in L/min or ml/min). Qsup C: cardiac output for the cohort of males or females in supination (in ml/min). STPD: standard temperature and pressure, dry air.sup: values measured when adults are in the supine position.up: values measured when adults are in the upright position.VDphys: physiological dead space at BTPS (in L). VT: tidal volume at BTPS (in L). VA: alveolar ventilation rate at BTPS (in L/min). VA/Q: ventilation-perfusion ratio (unitless). VE: minute ventilation rate at BTPS (in L/min). VO2: oxygen consumption rate (i.e. the oxygen uptake) at STPD (in L/min). VQ: ventilatory equivalent for VO2 (VE at BTPS /VO2 at STPD).
Influence of Fasting Plasma Glucose Level on Admission of COVID-19 Patients: A Retrospective Study

Background The coronavirus disease 2019 (COVID-19) is a serious global health threat and has spread dramatically worldwide. Prolonged viral shedding is associated with a more severe disease course and inflammatory reaction. Blood glucose levels were significantly associated with an increased hazard ratio (HR) for poor outcomes in COVID-19 patients. Objective Previous studies focused primarily on the relationship between blood glucose and mortality or severe outcomes, but there were few research studies on the relationship between fasting plasma glucose (FPG) and duration of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) RNA positive status. To explore the relationship between FPG levels and prolonged duration of SARS-CoV-2 viral positivity, the clinical data of COVID-19 patients were analyzed. Method In this retrospective study, 99 cases of COVID-19 patients in Beijing Ditan Hospital were recruited, and their clinical and laboratory findings at admission were collected and analyzed. Furthermore, the risk factors for prolonged duration of SARS-CoV-2 RNA shedding were identified, and the relationship between FPG levels and the prolonged presence of SARS-CoV-2 RNA was evaluated. Result We found that elevated FPG levels were correlated with longer duration of SARS-CoV-2 RNA positivity, classification of COVID-19, imaging changes of chest CT, inflammation-related biomarkers, and CD8+ T cell number in COVID-19 patients. In a logistic regression model, after adjusting for gender and age, COVID-19 patients with elevated FPG were more likely to had longer duration of SARS-CoV-2 RNA positivity than those with normal FPG levels (OR 3.053 [95% CI 1.343, 6.936]). Conclusion Higher FPG levels (≥6.1 mmol/l) at admission was an independent predictor for prolonged SARS-CoV-2 shedding, regardless of a known history of diabetes. It suggests that intensive monitoring and control of blood glucose are important for all COVID-19 patients.
Robust Deep 3D Blood Vessel Segmentation Using Structural Priors

Deep learning has enabled significant improvements in the accuracy of 3D blood vessel segmentation. Open challenges remain in scenarios where labeled 3D segmentation maps for training are severely limited, as is often the case in practice, and in ensuring robustness to noise. Inspired by the observation that 3D vessel structures project onto 2D image slices with informative and unique edge profiles, we propose a novel deep 3D vessel segmentation network guided by edge profiles. Our network architecture comprises a shared encoder and two decoders that learn segmentation maps and edge profiles jointly. 3D context is mined in both the segmentation and edge prediction branches by employing bidirectional convolutional long-short term memory (BCLSTM) modules. 3D features from the two branches are concatenated to facilitate learning of the segmentation map. As a key contribution, we introduce new regularization terms that: a) capture the local homogeneity of 3D blood vessel volumes in the presence of biomarkers; and b) ensure performance robustness to domain-specific noise by suppressing false positive responses. Experiments on benchmark datasets with ground truth labels reveal that the proposed approach outperforms state-of-the-art techniques on standard measures such as DICE overlap and mean Intersection-over-Union. The performance gains of our method are even more pronounced when training is limited. Furthermore, the computational cost of our network inference is among the lowest compared with state-of-the-art.
Outcomes and Predictors of 28-Day Mortality in Patients With Hematologic Malignancies and Septic Shock Defined by Sepsis-3 Criteria.

BACKGROUND To describe short-term outcomes and independent predictors of 28-dayx mortality in adult patients with hematologic malignancies and septic shock defined by the new Third International Consensus Definitions (Sepsis-3) criteria. METHODS We performed a retrospective cohort study of patients admitted to the medical ICU with septic shock from April 2016 to March 2019. Demographic and clinical features and short-term outcomes were collected. We used descriptive statistics to summarize patient characteristics, logistic regression to identify predictors of 28-day mortality, and Kaplan-Meier plots to assess survival. RESULTS Among the 459 hematologic patients with septic shock admitted to the ICU, 109 (23.7%) had received hematopoietic stem cell transplant. The median age was 63 years (range, 18-89 years), and 179 (39%) were women. Nonsurvivors had a higher Charlson comorbidity index (P=.007), longer length of stay before ICU admission (P=.01), and greater illness severity at diagnosis and throughout the hospital course (P<.001). The mortality rate at 28 days was 67.8% and increased with increasing sequential organ failure assessment score on admission (odds ratio [OR], 1.11; 95% CI, 1.03-1.20), respiratory failure (OR, 3.12; 95% CI, 1.49-6.51), and maximum lactate level (OR, 1.16; 95% CI, 1.10-1.22). Aminoglycosides administration (OR, 0.42; 95% CI, 0.26-0.69), serum albumin (OR, 0.51; 95% CI, 0.31-0.86), and granulocyte colony-stimulating factor (G-CSF) (OR, 0.40; 95% CI, 0.24-0.65) were associated with lower 28-day mortality. Life support limitations were present in 81.6% of patients at death. At 90 days, 19.4% of the patients were alive. CONCLUSIONS Despite efforts to enhance survival, septic shock in patients with hematologic malignancies is still associated with high mortality rates and poor 90-day survival. These results demonstrate the need for an urgent call to action with higher awareness, including the further evaluation of interventions such as earlier ICU admission, aminoglycosides administration, and G-CSF treatment.
Stroke self-efficacy questionnaire – Denmark (SSEQ-DK): test–retest of the Danish version

ABSTRACT Background In stroke rehabilitation, measurement tools measuring self-efficacy with sound psychometric properties are needed. The Stroke Self-Efficacy Questionnaire (SSEQ) has recently been translated and validated into a Danish version (SSEQ–DK). Objectives To evaluate the test–retest reliability of the SSEQ-DK. Methods Fifty people with stroke ≥ 18 years in the sub-acute and chronic phase were included from February 2019 to August 2020. The SSEQ-DK was completed twice; on day 1 and day 7–14. Test–retest reliability of the single items was assessed using weighted Cohen’s kappa and percentage agreement. The activity and self-management scales were assessed by the intraclass correlation coefficient (ICC). Measurement error was assessed by calculating the Smallest Detectable Change (SDC) based on the standard error of measurement. Results Overall, kappa values showed fair to substantial test–retest reliability of the single items. However, several kappa values were missing as the statistical prerequisites were not present. The percentage agreement ranged from 78% to 94%. Based on the reported confidence interval of the estimated intraclass correlation coefficient, the test–retest reliability of the activity and self-management scales was poor to excellent in all analysis. Ceiling effects appeared in the single items. Conversely, no floor effect was seen. Conclusion The SSEQ-DK showed good test–retest reliability of the single items based on agreement among a population with stroke in the subacute and chronic phase. Broad ICC confidence intervals bar any firm conclusions concerning the test–retest reliability of the activity and self-management scales. Trial registration ClinicalTrials.gov NCT03183960. Reg. 15 June 2017.