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The objective of this study was to develop a flexible and free image processing and analysis solution, based on the Public Domain ImageJ platform, for the segmentation and analysis of complex biological plant root systems in soil from x-ray tomography 3D images. Contrasting root architectures from wheat, barley and chickpea root systems were grown in soil and scanned using a high resolution micro-tomography system. A macro (Root1) was developed that reliably identified with good to high accuracy complex root systems (10% overestimation for chickpea, 1% underestimation for wheat, 8% underestimation for barley) and provided analysis of root length and angle. In-built flexibility allowed the user interaction to (a) amend any aspect of the macro to account for specific user preferences, and (b) take account of computational limitations of the platform. The platform is free, flexible and accurate in analysing root system metrics.

Experimental observations are presented of a single surface-piercing column subject to a wide range of surface gravity waves. With the column diameter, D, chosen such that the flow lies within the drag-inertia regime, two types of high-frequency wave scattering are identified. The first is driven by the run-up and wash-down on the surface of the column in the vicinity of the upstream and downstream stagnation points. The second concerns the circulation of fluid around the column, leading to the scattering of a pair of non-concentric wavefronts. The phasing of the wave cycle at which this second mode evolves is dependent upon the time taken for fluid to move around the column. This introduces an additional time-scale, explaining why existing diffraction solutions, based upon a harmonic analysis of the incident waves, cannot describe this scattered component. The interaction between the scattered waves and the next (steep) incident wave can produce a large amplification of the scattered waves, particularly the second type. Evidence is provided to show that these interactions can produce highly localized free-surface effects, including vertical jetting, with important implications for the setting of deck elevations, the occurrence of wave slamming and the development of large run-up velocities.

Brain resection is curative for a subset of patients with drug resistant epilepsy but up to half will fail to achieve sustained seizure freedom in the long term. There is a critical need for accurate prediction tools to identify patients likely to have recurrent postoperative seizures. Results from preclinical models and intracranial EEG in humans suggest that the window of time immediately before and after a seizure (“peri-ictal”) represents a unique brain state with implications for clinical outcome prediction. Using a dataset of 294 patients who underwent temporal lobe resection for seizures, we show that machine learning classifiers can make accurate predictions of postoperative seizure outcome using 5 min of peri-ictal scalp EEG data that is part of universal presurgical evaluation (AUC 0.98, out-of-group testing accuracy > 90%). This is the first approach to seizure outcome prediction that employs a routine non-invasive preoperative study (scalp EEG) with accuracy range likely to translate into a clinical tool. Decision curve analysis (DCA) shows that compared to the prevalent clinical-variable based nomogram, use of the EEG-augmented approach could decrease the rate of unsuccessful brain resections by 20%.

The general area of understanding is inclusions in steel both metallic and nonmetallic in nature. This work has also used the concepts of inclusions in steel in general other than Ti however mainly the research works done on precipitation, solute segregation, grain developments and equilibrium aspects of important inclusions like Ti in steel have been probed. Interaction of inclusions with slag oxides has also been incorporated. Interdependence of elements common in-between many inclusions has been marked. TiN, TixOy and MnS inclusions have been very outstanding in the confines of present research. Ratios and effective concentration have been highlighted in certain cases around the topic. Type of steels, compositions of the constituent elements and temperature correlation has been spotted in certain environments. A suggestive relation with the steel properties has also been inferred. Hardness, corrosion behaviour and strength stand out to be the parameters of vital importance when considering Ti inclusions in the form of either TiN or TixOy. Certain inclusions like MnS seem to nucleate on TiN inclusions and there is a correlation evident certainly in case of complex alloys.

A problem is defined to investigate the effect of titanium traces on the corrosion behaviour of low carbon steel. In theory titanium effects surface properties like abrasion resistance in medium carbon steels and corrosion resistance in low as well as medium carbon steels. The present research as indicated by the topic is aimed to experimentally mark the effect of titanium traces on corrosion resistance in the available low carbon steel specimens.The effect of microalloying with titanium (i.e.0.02wt.%) on the corrosion behavior of low carbon steel in a 3.5 wt.% NaCl solution was studied by electrochemical, SEM, and Raman spectroscopy techniques. The electrochemical results showed that the corrosion of the Ti-bearing steel improved by around 30% compared with the Ti-free steel. The titanium microalloying led to the formation of a more compact corrosion product layer on the metal surface. The SEM analysis showed that the Ti-bearing sample had a smoother surface compared with the Ti-free steel.

Phase change materials (PCMs) have a large number of applications for thermal energy storage (TES) and temperature reduction in buildings due to their thermal characteristics and latent heat storage capabilities. The thermal mass of typical brick walls can be substantially increased using a suitable PCM primarily based on phase change temperature and heat of fusion for different weather conditions in summer and winter. This study proposed a novel dual-layer PCM configuration for brick walls to maintain human comfort for hot and cold climatic conditions in Islamabad, Pakistan. Numerical simulations were performed using Ansys Fluent for dual PCMs layered within a brick wall for June and January with melting temperatures of 29 °C and 13 °C. This study examined and discussed the charging and discharging cycles of PCMs over an extended period (one month) to establish whether the efficacy of PCMs is hindered due to difficulties in discharging. The results show that the combined use of both PCMs stated above provides better human comfort with reduced energy requirements in Islamabad throughout the year than using a single PCM (29 °C) for summer or winter (13 °C) alone.

Migraine is one of the most common neurological disorders in the US. Currently, the diagnosis and management of migraine are based primarily on subjective self-reported measures, which compromises the reliability of clinical diagnosis and the ability to robustly discern candidacy for available therapies and track treatment response. In this study, we used a computational pipeline for the automated, rapid, high-throughput, and objective analysis of encephalography (EEG) data at Cleveland Clinic to identify signatures that correlate with migraine. We performed two independent analyses, a prospective analysis (n = 62 subjects) and a retrospective age-matched analysis on a larger cohort (n = 734) obtained from the sleep registry at Cleveland Clinic. In the prospective analysis, no significant difference between migraine and control groups was detected in the mean power spectral density (PSD) of an all-electrodes montage in the frequency range of 1–32 Hz, whereas a significant PSD increase in single occipital electrodes was found at 12 Hz in migraine patients. We then trained machine learning models on the binary classification of migraine versus control using EEG power features, resulting in high accuracies (82–83%) with occipital electrodes’ power at 12 Hz ranking highest in the contribution to the model’s performance. Further retrospective analysis also showed a consistent increase in power from occipital electrodes at 12 and 13 Hz in migraine patients. These results demonstrate distinct and localized changes in brain activity measured by EEG that can potentially serve as biomarkers in the diagnosis and personalized therapy for individuals with migraine.

This work is an experimental study of thermo-mechanical surface hardening of mild steel with trace elements like titanium in negligible concentrations. This is somewhat an advanced technique used to harden steel surface which can be hardened in many typical ways. The concept is combining the thermal as well as mechanical technique to attain better results. It is quite obvious that mechanical refers to the compressive loading during machining and thermal refers to producing heat on the surface of work piece. The ideal conditions are when the heat produced is enough to achieve austenite and then subsequent quick cooling helps in the formation of martensite, which is metallurgically the most highly strong phase of steel, in terms of hardness. The coolant used preferably is the emulsified oil which flows on the surface during machining with variable rate of flow as the optimum effect is. This process hardens the surface of steel and increases its resistance against wear and abrasion. Preference is to achieve surface hardening using the conventional equipment so that operational cost is kept low and better results are attained. This technique has been quite successful in the laboratory. It can be termed as friction hardening. Some improvements in the process scheme and working environment can be made to get better results.

With widely deployed smart meters, non-intrusive energy measurements have become feasible, which may benefit people by furnishing a better understanding of appliance-level energy consumption. This work is a step forward in using graph signal processing for non-intrusive load monitoring (NILM) by proposing two novel techniques: the spectral cluster mean (SC-M) and spectral cluster eigenvector (SC-EV) methods. These methods use spectral clustering for extracting individual appliance energy usage from the aggregate energy profile of the building. After clustering the data, different strategies are employed to identify each cluster and thus the state of each device. The SC-M method identifies the cluster by comparing its mean with the devices’ pre-defined profiles. The SC-EV method employs an eigenvector resultant to locate the event and then recognize the device using its profile. An ideal dataset and a real-world REFIT dataset are used to test the performance of these two techniques. The f-measure score and disaggregation accuracy of the proposed techniques demonstrate that these two techniques are competitive and viable, with advantages of low complexity, high accuracy, no training data requirement, and fast processing time. Therefore, the proposed techniques are suitable candidates for NILM.

Rate of FEV 1 decline in COPD is heterogeneous and the extent to which inhaled corticosteroids (ICS) influence the rate of decline is unclear. The majority of previous reviews have investigated specific ICS and non-ICS inhalers and have consisted of randomised control trials (RCTs), which have specific inclusion and exclusion criteria and short follow up times. We aimed to investigate the association between change in FEV 1 and ICS-containing medications in COPD patients over longer follow up times. MEDLINE and EMBASE were searched and literature comparing change in FEV 1 in COPD patients taking ICS-containing medications with patients taking non-ICS-containing medications were identified. Titles, abstract, and full texts were screened and information extracted using the PICO checklist. Risk of bias was assessed using the Cochrane Risk of Bias tool and a descriptive synthesis of the literature was carried out due to high heterogeneity of included studies. Seventeen studies met our inclusion criteria. We found that the difference in change in FEV 1 in people using ICS and non-ICS containing medications depended on the study follow-up time. Shorter follow-up studies (1 year or less) were more likely to report an increase in FEV 1 from baseline in both patients on ICS and in patients on non-ICS-containing medications, with the majority of these studies showing a greater increase in FEV 1 in patients on ICS-containing medications. Longer follow-up studies (greater than 1 year) were more likely to report a decline in FEV 1 from baseline in patients on ICS and in patients on non-ICS containing medications but rates of FEV 1 decline were similar. Further studies are needed to better understand changes in FEV 1 when ICS-containing medications are prescribed and to determine whether ICS-containing medications influence rate of decline in FEV 1 in the long term. Results from inclusive trials and observational patient cohorts may provide information more generalisable to a population of COPD patients.