Predictive Software Engineering: Transform Custom Software Development into Effective Business Solutions

The paper examines the principles of the Predictive Software Engineering (PSE) framework. The authors examine how PSE enables custom software development companies to offer transparent services and products while staying within the intended budget and a guaranteed budget. The paper will cover all 7 principles of PSE: (1) Meaningful Customer Care, (2) Transparent End-to-End Control, (3) Proven Productivity, (4) Efficient Distributed Teams, (5) Disciplined Agile Delivery Process, (6) Measurable Quality Management and Technical Debt Reduction, and (7) Sound Human Development.

NITRATE REMOVAL IN WOODCHIP DENITRIFICATION BIOREACTOR – AN APPROACH COMBINING MATHEMATICAL MODELLING AND PI CONTROL

A mathematical model of nitrate removal in woodchip denitrification bioreactor based on field experiment measurements was developed in this study. The approach of solving inverse problem for nonlinear system of differential convection-reaction equations was applied to optimize the efficiency of nitrate removal depending on bioreactor’s length and flow rate. The approach was realized through the developed algorithm containing a nonlocal condition with an incorporated PI controller. This allowed to adjust flow rate for varying inflow nitrate concentrations by using PI controller. The proposed model can serve as a useful tool for bioreactor design. The main outcome of the model is a mathematical relationship intended for bioreactor length selection when nitrate concentration at the inlet and the flow rate are known. Custom software was developed to solve the system of differential equations aiming to ensure the required nitrate removal efficiency.

Prediction of abnormal left ventricular geometry on the ECG through machine learning with a focus on obesity

Background Cardiac remodeling, an important aspect of cardiovascular disease (CVD) progression, is emerging as a significant therapeutic target. However, the ECG is not a sensitive method of detecting left ventricular hypertrophy (LVH), and as far as we know, it cannot detect changes in left ventricular geometry (LVG) at early stages, especially before LVH is present. Its sensitivity is particularly low for obese patients. Purpose To use a machine learning (ML) classifier to detect abnormal LVG from EKG parameters/markers, even before it becomes LVH, and to propose some indicative markers useful for practitioners. We also looked at the results of our model for obese patients to test the markers in this population. Methods We enrolled 594 consecutive subjects, aged 30 years or older (mean age: 61.6±12 years old) with and without essential hypertension and no indications of CVD. We tried to build a “clean” dataset through which we can target the clinical, anthropometric, and electrocardiogram measurements indicative of abnormal LVG. All patients underwent a full echocardiographic evaluation and were classified into 2 groups; those with normal geometry (NG) vs. those with concentric remodeling (CR) or LVH. Abnormal LVG was identified as increased relative wall thickness (RWT) and/or left ventricular mass index (LVMi). We analyzed the EKG waveforms deduced to single beat averages for each lead using custom software and extracted 70 markers. We then trained a Random Forest machine learning model to classify subjects with abnormal LVG and calculated SHAP values to perform feature importance and interaction. Results The percentage of women was 56.5%, while 71.3% of all patients were hypertensive. Hypertension, age, body mass index divided by the Sokolow-Lyon voltage (BMI/S-L), QRS-T angle, and QTc duration were among the most important parameters (Figure, left panel) identified by the model as being predictive of abnormal LVG (AUC/ROC = 0.84, sensitivity = 0.94, specificity 0.61). Specifically for obese patients, whose prevalence in our population was 60.3%, our model performed well (sensitivity = 0.71, specificity = 0.92. When we tried our model without the the BMI/S-L parameter, the specificity dropped to 0.88. We also found that a cut-off point of 18 for the BMI/S-L marker predicted the patients who were more probable to have developed abnormal LVG (Figure 1). Conclusions This study is the first to demonstrate the promising potential of ML modeling for the efficient and cost-effective diagnostic screening of abnormal LVG through ECG. We found specific clinical and ECG parameters that can predict early pathological changes of LVG in patients without established CVD and detect the population who will benefit from a detailed echocardiographic evaluation. Our model contributes to the development of human-centered and autonomous technologies and can optimize patient-management and treatment. FUNDunding Acknowledgement Type of funding sources: None. Figure 1

System for Remote Collaborative Embedded Development

This paper explores the challenges and devised solutions for embedded development which arose during the COVID-19 pandemic. While software development, nowadays with modern tools and services such as git, virtual machines and commu-nication suits, is relatively una˙ected by resource location. That is not the case for firmware and embedded systems, which relies on physical hard-ware for design, development, and testing. To overcome the limitations of remote work and ob-structed access to actual hardware, two ideas were implemented and tested. First, based on inte-grated circuit emulation using QEMU to emulate an ARM core and custom software to facilitate communication with the embedded system. Sec-ond, remote programming and debugging over the internet with a dedicated computer system acting as a middle man between a development environ-ment and physical hardware using OpenOCD de-bugger.

Screening Coarse Airborne Dust for Lead-Rich Phase Occurrence during Characterisation of Particle Mineralogy, Chemistry and Provenance: Application to Deposits in the Vicinity of an Integrated Steelworks

A method has been developed to screen large numbers (~103–104 per sample) of coarse airborne dust particles for the occurrence of Pb-rich phases, together with quantification of the particles’ mineralogy, chemistry, and inferred provenance. Using SEM-EDS spectral imaging (SI) at 15 kV, and processing with the custom software PARC, screening of individual SI pixels is performed for Pb at the concentration level of ~10% at a length-scale of ~1 µm. The issue of overlapping Pb-Mα and S-Kα signal is resolved by exploiting peak shape criteria. The general efficacy of the method is demonstrated on a set of NIST particulate dust standard reference materials (SRMs 1649b, 2580, 2584 and 2587) with variable total Pb concentrations, and applied to a set of 31 dust samples taken in the municipalities surrounding the integrated steelworks of Tata Steel in IJmuiden, the Netherlands. The total abundances of Pb-rich pixels in the samples range from none to 0.094 area % of the (total) particle surfaces. Overall, out of ca. 92k screened particles, Pb was found in six discrete Pb-phase dominated particles and, more commonly, as superficial sub-particles (sub-micron to 10 µm) adhering to coarser particles of diverse and Pb-unrelated provenance. No relationship is apparent between the samples’ Pb-rich pixel abundance and their overall composition in terms of particle provenance.

Resident Case Tracker

Context.— Acquiring objective, timely, and comprehensive feedback on resident diagnostic performance is notoriously difficult. Objective.— To implement a custom software application (Resident Case Tracker) to improve evaluative diagnostic analysis for residency programs. Design.— Residents and faculty use a graphical user interface with restricted access to their own cases and evaluations. For each sign-out, residents enter their diagnoses and comments for each case. Faculty are provided a sign-out queue to review the resident diagnosis and select their level of agreement alongside optional comments. After sign-out, residents can review the agreement level and comments for each case, overall sign-out statistics, and organ-specific performance, and they have the option of opening and reviewing groups of cases by agreement status. A sign-out evaluation is automatically generated and stored alongside additional reports. Administrative access allows privileged users to readily review data analytics at both an individual and residency-wide global level. Results.— A marked increase in completed evaluations and feedback was noted in the initial 36 months of implementation. During a 3-year academic period, faculty completed individual feedback on 33 685 cases and 1073 overall sign-out evaluations. Conclusions.— Resident Case Tracker is an invaluable tool for our residency program and has provided unparalleled feedback and data analytics. Throughout residency, trainees have access to each completed sign-out with the ability to learn from discrepant cases while also monitoring improvements in diagnostic acumen over time. Faculty are able to assess resident milestones much more effectively while more readily identifying residents who would benefit from targeted study.

Using a 3D handheld scanner to capture trochlear groove shape: proof of concept study

Introduction Trochleoplasty is a surgical procedure used to treat patellar instability by modifying the trochlear groove. Analysis of the groove with a handheld scanner would enable accurate real-time planning and facilitate tailormade correction. We aimed to measure trochlear depth, sulcus angle, trochlear facet ratio, trochlear angle and lateral trochlear inclination angle and to establish inter- and intra-rater reliability for knee models to determine reliability and repeatability. Methods The trochlear grooves of three knee models were scanned by two investigators. Three-dimensional reference models were created and surface-matched. Custom software was used to determine the desired parameters. The intraclass correlation coefficient (ICC) was used to determine test–retest reliability and the parameter results for each model that showed best reproducibility. Results There was good interobserver reliability (trochlear depth, 1.0mm; sulcus angle, 2.7°; trochlear angle, 4.0°; lateral trochlear inclination angle, 4.0°), except in the trochlear facet ratio (32.0%) of one knee model. With outliers removed, the ICC was moderate to excellent in 73.34% of measurements, with trochlear depth showing the best reproducibility. Discussion This feasibility study showed a handheld scanner in conjunction with supporting software can measure trochlear parameters with good to excellent inter- and intra-observer reliability.

uPIC–M: efficient and scalable preparation of clonal single mutant libraries for high-throughput protein biochemistry

New high-throughput biochemistry techniques complement selection-based approaches and provide quantitative kinetic and thermodynamic data for thousands of protein variants in parallel. With these advances, library generation rather than data collection has become rate limiting. Unlike pooled selection approaches, high-throughput biochemistry requires mutant libraries in which individual sequences are rationally designed, efficiently recovered, sequence-validated, and separated from one another, but current strategies are unable to produce these libraries at the needed scale and specificity at reasonable cost. Here, we present a scalable, rapid, and inexpensive approach for creating User-designed Physically Isolated Clonal–Mutant (uPIC–M) libraries that utilizes recent advances in oligo synthesis, high-throughput sample preparation, and next-generation sequencing. To demonstrate uPIC–M, we created a scanning mutant library of SpAP, a 541 amino acid alkaline phosphatase, and recovered 94% of desired mutants in a single iteration. uPIC–M uses commonly available equipment and freely downloadable custom software and can produce a 5000 mutant library at 1/3 the cost and 1/5 the time of traditional techniques.

A Novel Algorithm using Within-leg Calibration for Enhanced Accuracy of Detection of Arthritis by Infrared Thermal Imaging in Children

Objective To standardize and improve the accuracy of detection of arthritis by thermal imaging. Methods Children with clinically active arthritis in the knee or ankle, as well as healthy controls, were enrolled to the development cohort and another group of children with knee symptoms were enrolled to the validation cohort. Ultrasound was performed for the arthritis subgroup for the development cohort. Joint exam by certified rheumatologists was used as a reference for the validation cohort. Infrared thermal data were analyzed using a custom software. Temperature after within-limb calibration (TAWiC) was defined as the temperature differences between joint and ipsilateral midtibia. TAWiC of knees and ankles was evaluated using ANOVA across subgroups. Optimal thresholds were determined by receiver operating characteristic (ROC) analysis using Youden index. Results There were significant differences in mean and 95th TAWiC of knee in anterior, medial, lateral views, and of ankles in anterior view, between inflamed and uninflamed counterparts (p<0.05). The area under the curve (AUC) was higher by 36% when using TAWiCKnee than those when using absolute temperature. Within validation cohort, the sensitivity of accurate detection of arthritis in knee using both mean and 95th TAWiC from individual views or combined all 3 views ranged from 0.60 to 0.70 and the specificity was greater than 0.90 in all views. Conclusion Children with active arthritis or tenosynovitis in knees or ankles exhibited higher TAWiC than healthy joints. Our validation cohort study showed promise of the clinical utility of infrared thermal imaging for arthritis detection.