Miracle-dz/UniFAR-FTUs huggingface dataset:
Point: User Involvement Key to Success

The evidence is voluminous, consistent, and incontrovertible. It applies to corporate, government agency, and military software development. Quite simply, the software we build does not meet our customers' needs: those of us who build large software programs fail miserably-90 percent of the time-to deliver what customers want, when they want it, at the agreed upon price; we fail to adequately manage the software development process, user-developer communication breaks down, the requirements control process breaks down, we have runaway requirements, budgets, schedules, and "death march" projects. The Best Practices Framework of the Software Program Manager's Network outlines some solutions. First, we must identify what can go wrong. Precedents give ample hints regarding risks. We need to manage the development process with more attention, particularly to what might go wrong. Second, we must manage the most fundamental part of our task: defining our goal. We fail to use requirements management to surface (early) errors or problems, to baseline and track changes, and to improve user-developer communication.
Drug Level Modelling with Difference and Differential Equations

In the current study, drug level in the human body is studied using mathematical modelling, where the difference equations have been used as a means of analyzing the dosage over a prescribed time, reduction in dose half-life, and the total amount of residual drug in the blood-stream for a given time-period. Further, the differential equations approach is investigated for data fitting using Mathematica to extend the application adhering to the continuous nature of the inherent function. The perspective helps to preserve critical resources and render comparative results. In this approach, zero-order, first-order, and fractional order kinetics are explored and compared.
Combination Spectrum Sensing Algorithm for Wireless Sensor Network Based on Random Forest

In this paper, a multi-conditional spectrum sensing combination algorithm based on random forest is proposed to address the current shortage of spectrum resources in the sensor network. The algorithm combines sensor's velocity, signal energy, the traces, and the average eigenvalue of the covariance matrix as random forest characteristic parameters, which are achieved through the strong multi-classification ability of random forest. To improve the successful rate of spectrum sensing and the utilization rate of the spectrum, we focus on analyzing the selection of parameter in theory as well as the low signal-to-noise ratio caused by channel fading and shadow effect. Meanwhile, the Doppler effective caused by car moving is also our consideration. Under low signal-to-noise ratio, the simulation results show that the proposed algorithm has better detection performance than existing spectrum sensing algorithms.
An Immune Signature for Risk Stratification and Therapeutic Prediction in Helicobacter pylori-Infected Gastric Cancer

Simple Summary Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC), and over half of the world’s population is colonized with HP. Up to now, many gene signatures are designed for predicting the prognosis of GC patients, while there are no signatures designed specifically for HP+ GC patients. Considering the tight association between HP infection and tumor immune microenvironment, we constructed an immune-related signature named IRSHG in this study, aiming to provide new insight into the treatment for HP+ GC patients. IRSHG was the first prognostic signature for HP+ GC patients, demonstrating high reliability and feasibility in predicting the prognosis. In addition, IRSHG could help investigate potential therapies and guide anti-PD-1 immunotherapy for HP+ GC patients, providing new insight for the personalized treatment of GC. Abstract Helicobacter pylori (HP) infection is the greatest risk factor for gastric cancer (GC). Increasing evidence has clarified that tumor immune microenvironment (TIME) is closely related to the prognosis and therapeutic efficacy of HP-positive (HP+) GC patients. In this study, we aimed to construct a novel immune-related signature for predicting the prognosis and immunotherapy efficacy of HP+ GC patients. A total of 153 HP+ GC from three different cohorts were included in this study. An Immune-Related prognostic Signature for HP+ GC patients (IRSHG) was established using Univariate Cox regression, the LASSO algorithm, and Multivariate Cox regression. Univariate and Multivariate analyses proved IRSHG was an independent prognostic predictor for HP+ GC patients, and an IRSHG-integrated nomogram was established to quantitatively assessthe prognostic risk. The low-IRSHG group exhibited higher copy number load and distinct mutation profiles compared with the high-IRSHG group. In addition, the difference of hallmark pathways and immune cells infiltration between the two groups was investigated. Notably, tumor immune dysfunction and exclusion (TIDE) analysis indicated that the low-IRSHG group had a higher sensitivity to anti-PD-1 immunotherapy, which was validated by an external pabolizumab treatment cohort. Moreover, 98 chemotherapeutic drugs and corresponding potential biomarkers were identified for two groups, and several drugs with potential ability to reverse IRSHG score were identified using CMap analysis. Collectively, IRSHG may serve as a promising biomarker for survival outcome as well as immunotherapy efficacy. Furthermore, it can also help to prioritize potential therapeutics for HP+ GC patients, providing new insight for the personalized treatment of HP-infected GC.
Sensor Fault/Failure Correction and Missing Sensor Replacement for Enhanced Real-time Gas Turbine Diagnostics

Gas turbine sensors are prone to bias and drift. They may also become unavailable due to maintenance activities or failure through time. It is, therefore, important to correct faulty signal or replace missing sensors with estimated values for improved diagnostic solutions. Coping with a small number of sensors is the most difficult to achieve since this often leads to underdetermined and indistinguishable diagnostic problems in multiple fault scenarios. On the other hand, installing additional sensors has been a controversial issue from cost and weight perspectives. Gas path locations with difficult conditions to install sensors is also among other sensor installation related challenges. This paper proposes a sensor fault/failure correction and missing sensor replacement method. Auto-regressive integrated moving average models are employed to correct measurements from faulty and failed sensors. To replace additional sensors needed for further diagnostic accuracy improvements, neural network models are devised. The performance of the developed approach is demonstrated by applying to a three-shaft turbofan engine. Test results verify that the method proposed can well-recover measurements from faulty/failed sensors, no matter with small or major failures. It can also compensate key missing temperature and pressure measurements on the gas path based on the data from other available sensors.
A 2.5 GS/s 7-Bit 5-Way Time-Interleaved SAR ADC With On-Chip Background Offset and Timing-Skew Calibration

This brief presents the on-chip background offset and timing-skew calibration of the 1-then-2b/cycle time-interleaved successive-approximation-register analog-to-digital converter (TI SAR ADC). For timing-skew between sub-ADC’s sampling clocks, a comparator offset-based window detector (WD) is used to adjust the clock edge misalignment. In addition, comparator offset calibration is considered both in terms of 1) global offset (between the offset-free reference comparator and the local reference comparator in each sub-ADC) and 2) local offset (between the local reference comparator and the rest of the comparators in the same sub-ADC). The proposed calibration sufficiently suppresses noise floor and spurs, and all calibrations are performed in the background without interfering with normal ADC operation. The prototype 5-way TI SAR ADC is fabricated in a 28 nm CMOS process and occupies a 0.03 mm2 area including on-chip calibration. With the proposed calibration, the prototype achieves SNDR of 40 dB at Nyquist input and consumes 7.57 mW, leading to the Walden figure of merit ( ${\text {FoM}_{W}}$ ) of 37.2 fJ/conversion-step.
Post-exercise hypotension in hypertensive individuals submitted to aerobic exercises of alternated intensities and constant intensity-exercise

Acute exercise may result in post-exercise hypotension (PEH), which has been observed both for normotensive and hypertensive individuals, especially after continuous exercise (low to moderate intensity). The purposes were to compare the hypotensive effects of continuous exercise performed with alternated (AI) and constant intensities (CI) and verify if AI is more effective on inducing PEH. Eleven hypertensive subjects (56.8 ± 2.6 years; BMI of 26.5 ± 0.3 kg/m²) performed, on different days, an incremental test (IT) and two submaximal exercise sessions (45 min) on treadmill (AI and CI). The AI consisted of 2 min at 55.9 ± 2.6% and 1 min at 74.5 ± 4.0% of heart rate reserve (HRR) while the CI consisted of 45 min at 60 ± 2.5% of HRR. On both sessions participants rested for 10 min before exercise for blood pressure (BP) and heart rate (HR) measurements and then performed a 5 min warm-up followed by 45 min of either AI or CI. BP and HR were monitored at each 5 min of exercise and at the 5th, 10th, 15th, 30th, 60th, 90th and 120th min of post-exercise recovery (rec). ANOVA and Student t-test evidenced PEH of systolic blood pressure (SBP) after both sessions when compared to resting (p < 0.001) at all moments of rec with no differences between AI and CI. PEH of diastolic blood pressure (DBP) was observed at the 5th, 10th, 15th and 30th min of rec after CI (p < 0.05), but not after AI. PEH of mean blood pressure (MBP) was observed after CI and AI at all moments of rec. In spite of the similar PEH for SBP, the CI resulted in PEH of DBP, with longer reduction of MBP. No differences were observed between treatments for the absolute values of BP during the rec period. The authors conclude that the exercise intensities applied during AI did not induce additional hypotensive effects in relation to CI during the rec.
Comportamiento esfuerzo-deformación del suelo: Evolución e Importancia

Los modelos que emplean elemento finito son ampliamente usados en la Ingeniería Geotécnica para investigar el comportamiento esfuerzo–deformación del suelo en condiciones tanto estáticas como dinámicas. En la práctica algunos modelos constitutivos no incluyen aspectos importantes como son: la no linealidad de la rigidez en el estado de esfuerzos, la anisotropía, la dependencia del endurecimiento plástico en las deformaciones volumétricas y tampoco el efecto de la historia de esfuerzos reciente. Si bien el uso de paquetes computacionales se ha popularizado en las últimas décadas, el conocimiento de los modelos constitutivos, la adecuada selección de los parámetros de entrada y la pericia del modelador juegan un papel muy importante. Este trabajo inicia con la definición de trayectorias de esfuerzo y de la teoría del estado crítico como fundamento para el desarrollo de los diferentes modelos constitutivos (de los cuales se hace un breve recuento). Además, se abordan otros temas como: la obtención en laboratorio de los parámetros mecánicos necesarios para el modelado, la influencia del modelo utilizado y el uso del retro análisis. Finalmente, se ejemplifica la importancia de la adecuada selección del modelo numérico y de los datos de entrada en la obtención de resultados a través de casos sencillos. Artículo recibido: 20 de agosto de 2021 Artículo aceptado: 20 de diciembre de 2021
Selective Run-Length Constrained Encoding Scheme on Extended Nucleic Acid Memory

The progress of mankind into the technological revolution and rapid digitalization has caused a sharp rise in data harvesting as a global phenomenon. Human life nowadays has moved into a data-driven society where everyday problems and their solutions are dependent upon voluminous data. This, in turn, has increased the requirement of data storage technology. At this juncture, nucleic acid memory had been proposed as an alternate source of data storage and management technology that possessed great data density and high retentivity capabilities. The proposed work exploits the capability of extended nucleotides to enhance the data density of nucleic acid memory and introduces a lossless data compression algorithm named, "Selective Run-Length Constrained Encoding", which effectively accounts for the constraints related to GC content and homopolymer run. This paper also provides an elaborate comparative analysis with numerous works previously proposed in the research literature to provide a descriptive analysis in support of the proposed work.
A Distributed Intrusion Detection System using Machine Learning for IoT based on ToN-IoT Dataset

—The internet of things (IoT) is a collection of common physical things which can communicate and synthesize data utilizing network infrastructure by connecting to the internet. IoT networks are increasingly vulnerable to security breaches as their popularity grows. Cyber security attacks are among the most popular severe dangers to IoT security. Many academics are increasingly interested in enhancing the security of IoT systems. Machine learning (ML) approaches were employed to function as intrusion detection systems (IDSs) to provide better security capabilities. This work proposed a novel distributed detection system based on machine ML approaches to detect attacks in IoT and mitigate malicious occurrences. Furthermore, NSL-KDD or KDD-CUP99 datasets are used in the great majority of current studies. These datasets are not updated with new attacks. As a consequence, the ToN-IoT dataset was used for training and testing. It was created from a large-scale, diverse IoT network. The ToN-IoT dataset reflects data from each layer of the IoT system, such as cloud, fog, and edge layer. Various ML methods were tested in each specific partition of the ToN-IoT dataset. The proposed model is the first suggested model based on the collected data from the same IoT system from all layers. The Chi2 technique was used to pick features in a network dataset. It reduced the number of features to 20. Another feature selection tool employed in the windows dataset was the correlation matrix, which was used to extract the most relevant features from the whole dataset. To balance the classes, the SMOTE method was used. This paper tests numerous ML approaches in both binary and multi-class classification problems. According to the findings, the XGBoost approach is superior to other ML algorithms for each node in the suggested model
Experimental and Numerical Analyses of Stud Shear Connectors in Steel–SFRCC Composite Beams

To investigate the shear performance and failure mechanism of stud shear connectors in steel fiber-reinforced cementitious composite (SFRCC) beams, six steel-SFRCC and six steel-normal strength concrete (NC) push-out specimens with two heights (80 mm, 120 mm) and three diameters (14 mm, 18 mm, 22 mm) of stud connectors were prepared. The experimental results revealed that the stud shearing failure was the main failure mode of all push-out specimens. In comparison to the steel-NC specimens, the development of cracks in the SFRCC beams was efficiently restrained due to the existence of high-strength steel fibers added to the normal concrete. The shear resistance and stiffness of studs in the steel-SFRCC beams were, respectively, 22.3% and 15.1% greater than those in the steel-NC specimens; however, their ductility was reduced, and the stud shear connectors failed in advance. The finite element (FE) model was developed and verified by push-out test results. FE analysis results indicated that the shear resistance of stud shear connectors was significantly improved with the increase in the concrete compressive strength, the stud diameter and tensile strength, whereas the aspect ratio of studs had a small impact on the ultimate resistance of stud shear connectors. Based on the as-obtained push-out experiment and FE analysis results, empirical formulas were presented to predict the load-slip curves and ultimate shear resistance of stud shear connectors in the steel-SFRCC specimens, and higher accuracy and a wider application range were obtained than with previous formulas.
Prediction of cardiovascular disease

Nowadays, cardiovascular diseases are one of the leading causes of death. Earlier and better detection of such diseases would lead to earlier treatment and eventually to better chances of patients being able to overcome those diseases. Machine learning algorithms have been proven useful in detecting several medical conditions based on patients’ characteristics. In this paper, we are trying to predict whether a patient has a cardiovascular disease based on their characteristics. Using decision trees (C4.5), k-NN, and Naïve Bayes, in combination with cross-validation and holdout methods, we were able to achieve relatively good results. Even better results were achieved, for some specific cases such as patients having hypertension stage 2 or 3.
Cancer patients ’ concerns regarding access to cancer care : perceived impact of waiting times along the diagnosis and treatment journeyecc

C. PAUL, ba hons, phd, senior research academic, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, M. CAREY, bsc hons, dpsych, senior research academic, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, A. ANDERSON, bpsych hons, phd candidate, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, L. MACKENZIE, bpsych hons, phd candidate, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, R. SANSON-FISHER, bpsych hons, mpsych phd dsc ao, professor, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, R. COURTNEY, bpsych hons, phd candidate, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, & T. CLINTON-MCHARG, bpsych hons, phd, research academic, The University of Newcastle, Health Behaviour Research Group and Priority Research Centre for Health Behaviour (PRCHB), Hunter Medical Research Institute (HMRI), Callaghan, NSW, Australia
La Theorie de la Chiasmatypie

First published in 1909 in La Cellule, F. A. Janssens’s “The chiasmatype theory. A new interpretation of the maturation divisions” proved controversial and was for several decades resisted by many geneticists and cytologists. In this month’s Perspectives, Koszul et al. revisit Janssens's findings and the surrounding controversies. Here, translated for the first time in English, GENETICS republishes the original Janssens’s article. The article is presented as closely as possible to the original publication’s format and style.2 GENETICS wishes to thank Romain Koszul and Denise Zickler for their labors in presenting the scientific community with this new translation.
Analysis of Vibrations and Currents for Broken Rotor Bar Detection in Three-phase Induction Motors

Selecting the physical property capable of representing the health state of a machine is an important step in designing fault detection systems. In addition, variation of the loading condition is a challenge in deploying an industrial predictive maintenance solution. The robustness of the physical properties to variations in loading conditions is, therefore, an important consideration. In this paper, we focus specifically on squirrel cage induction motors and analyze the capabilities of three-phase current and five vibration signals acquired from different locations of the motor for the detection of Broken Rotor Bar generated in different loads. In particular, we examine the mentioned signals in relation to the performance of classifiers trained with them. Regarding the classifiers, we employ deep conventional classifiers and also propose a hybrid classifier that utilizes contrastive loss in order to mitigate the effect of different variations. The analysis shows that vibration signals are more robust under varying load conditions. Furthermore, the proposed hybrid classifier outperforms conventional classifiers and is able to achieve an accuracy of 90.96% when using current signals and 97.69% when using vibration signals.
Morphological and molecular evidence of the hybrid origin of Crepidiastrum ×muratagenii in Korea

The plant “Hong-do-go-deul-ppae-gi” has been considered as Crepidiastrum × muratagenii, a hybrid between C. denticulatum and C. lanceolatum, based on its morphological traits and geographical distribution. To reveal the hybrid origin of Hong-do-go-deul-ppae-gi, we examined additional morphological traits of this plant and its putative parents (C. denticulatum, C. lanceolatum, C. platyphyllum) and analyzed one nuclear ribosomal internal transcribed spacer (ITS) region and four chloroplast regions (trnT-L, trnL-F, rpl16 intron, and rps16 intron). As a result of examining the morphological traits, putative hybrid individuals were classified into three types based on the habit, cauline leaf, outer phyllary, and achene beak traits. A molecular analysis found that the ITS sequences of Type 1 and Type 2 individuals showed additive species-specific sites of C. denticulatum and C. lanceolatum. Plastid sequences of Type 1 and Type 2 individuals showed C. denticulatum and C. lanceolatum sequences, respectively. However, Type 3 individuals had ITS and plastid sequences corresponding to C. denticulatum. Accordingly, Type 1 and Type 2 individuals not only share morphological traits with C. denticulatum and C. lanceolatum but also show additive species-specific sites for C. denticulatum and C. lanceolatum, and not C. platyphyllum, supporting its origin as a hybrid between C. denticulatum and C. lanceolatum. Type 3 had morphological traits similar to other hybrid types but was distinguished with respect to outer phyllaries and demonstrated some resemblance to C. denticulatum. In a molecular analysis, Type 3 was found to be identical with regard to the sequence of C. denticulatum and was judged to be an ecological variation of C. denticulatum.
Exploring Automated Classification Approaches to Advance the Assessment of Collaborative Problem Solving Skills

Competency in skills associated with collaborative problem solving (CPS) is critical for many contexts, including school, the workplace, and the military. Innovative approaches for assessing individuals’ CPS competency are necessary, as traditional assessment types such as multiple -choice items are not well suited for such a process-oriented competency. In a move to computer-based environments to support CPS assessment, innovative computational approaches are also needed to understand individuals’ CPS behaviors. In the current study, we describe the use of a simulation-based task on electronics concepts as an environment for higher education students to display evidence of their CPS competency. We further describe computational linguistic methods for automatically characterizing students’ display of various CPS skills in the task. Comparisons between such an automated approach and an approach based on human annotation to characterize student CPS behaviors revealed above average agreement. These results give credence to the potential for automated approaches to help advance the assessment of CPS and to circumvent the time-intensive human annotation approaches that are typically used in these contexts.