Pest Infestation of Dipterocarpus retusus (Blume) Fruit at Different Heights in KPHP BATULANTEH

Keruing gunung (Dipterocarpus retusus) is a non-timber forest product (NTFP) as a fruit producer that can be used as raw material for vegetable fats. One of the problems faced in planting programs for both production and conservation forests is the presence of fruit pests. The purpose of this study was to determine the fruit pests infestation fruits of D. retusus and the effect of altitude on fruit size and weight in Batulanteh Sumbawa. Fruit samples were collected at locations with different heights, namely below 1000 masl (T 22ºC, RH 83%) and above 1000 masl (T 20ºC, RH 88%). The samples of invading pests were observed and measured morphometry and morphology. The results showed that the insect infestation on the fruit was Alcidodes crassus. The percentage of fruit severity level reached more than 50% at each location. Elevation has a significant effect on fruit diameter and fruit weight, both infested by pests and whole fruit.

Evaluation of Influence Factors on the Visual Inspection Performance of Aircraft Engine Blades

Background—There are various influence factors that affect visual inspection of aircraft engine blades including type of inspection, defect type, severity level, blade perspective and background colour. The effect of those factors on the inspection performance was assessed. Method—The inspection accuracy of fifty industry practitioners was measured for 137 blade images, leading to N = 6850 observations. The data were statistically analysed to identify the significant factors. Subsequent evaluation of the eye tracking data provided additional insights into the inspection process. Results—Inspection accuracies in borescope inspections were significantly lower compared to piece-part inspection at 63.8% and 82.6%, respectively. Airfoil dents (19.0%), cracks (11.0%), and blockage (8.0%) were the most difficult defects to detect, while nicks (100.0%), tears (95.5%), and tip curls (89.0%) had the highest detection rates. The classification accuracy was lowest for airfoil dents (5.3%), burns (38.4%), and tears (44.9%), while coating loss (98.1%), nicks (90.0%), and blockage (87.5%) were most accurately classified. Defects of severity level S1 (72.0%) were more difficult to detect than increased severity levels S2 (92.8%) and S3 (99.0%). Moreover, visual perspectives perpendicular to the airfoil led to better inspection rates (up to 87.5%) than edge perspectives (51.0% to 66.5%). Background colour was not a significant factor. The eye tracking results of novices showed an unstructured search path, characterised by numerous fixations, leading to longer inspection times. Experts in contrast applied a systematic search strategy with focus on the edges, and showed a better defect discrimination ability. This observation was consistent across all stimuli, thus independent of the influence factors. Conclusions—Eye tracking identified the challenges of the inspection process and errors made. A revised inspection framework was proposed based on insights gained, and support the idea of an underlying mental model.

Rationale of the need for adaptive clothes for disabled children

The study conducted among medical personnel identified that children with infantile cerebral paralysisof the V severity level need clothes with improved properties that ensure the patient’s quality of life and the correct conduct of medical and hygienic procedures by medical personnel.

Severity assessment of aircraft engine fan blades under airborne collision of unmanned aerial vehicles comparable to bird strike certification standards

Airborne drone collision on commercial manned aircraft has received extensive awareness due to the increasing drone operations in the restricted airspace. In addition, the bird strike certification for aircraft engines is likely to be inadequate for a drone collision with identical kinetic energy due to the difference in damage levels. Thus, it is important to understand and compare the risk between drones and bird strikes. This study aims to understand the damage severity from bird and drone strikes on the manned commercial aircraft engine. The finite element method (FEM) simulation is adopted to obtain the damage of engine fan blades under the drone collision and bird strikes at different collision positions. The Lagrangian and smoothed-particle hydrodynamics approaches are employed for the drone and bird simulations, respectively. In addition, three different drone and bird weight categories were considered in this study, namely, small, medium, and large, to investigate the effect of kinetic energy on the damage of fan blades. Results from the FEM simulation demonstrated that the damage of the engine fan blades due to drone collisions were more severe when comparing bird strikes of the same weight category. The damage severity level was proposed based on the damage of engine fan blades. In the event of a drone ingestion, the damage severity level assists in the identification of potential damage to engine fan blades and its performance.

Efficient detection of aortic stenosis using morphological characteristics of cardiomechanical signals and heart rate variability parameters

Recent research has shown promising results for the detection of aortic stenosis (AS) using cardio-mechanical signals. However, they are limited by two main factors: lacking physical explanations for decision-making on the existence of AS, and the need for auxiliary signals. The main goal of this paper is to address these shortcomings through a wearable inertial measurement unit (IMU), where the physical causes of AS are determined from IMU readings. To this end, we develop a framework based on seismo-cardiogram (SCG) and gyro-cardiogram (GCG) morphologies, where highly-optimized algorithms are designed to extract features deemed potentially relevant to AS. Extracted features are then analyzed through machine learning techniques for AS diagnosis. It is demonstrated that AS could be detected with 95.49–100.00% confidence. Based on the ablation study on the feature space, the GCG time-domain feature space holds higher consistency, i.e., 95.19–100.00%, with the presence of AS than HRV parameters with a low contribution of 66.00–80.00%. Furthermore, the robustness of the proposed method is evaluated by conducting analyses on the classification of the AS severity level. These analyses are resulted in a high confidence of 92.29%, demonstrating the reliability of the proposed framework. Additionally, game theory-based approaches are employed to rank the top features, among which GCG time-domain features are found to be highly consistent with both the occurrence and severity level of AS. The proposed framework contributes to reliable, low-cost wearable cardiac monitoring due to accurate performance and usage of solitary inertial sensors.

Overcoming methamphetamine withdrawal syndrome through cognitive behavioural therapy in female correctional inmates

Cognitive-behavioural therapy (CBT) approaches have among the highest level of empirical support for drug and alcohol use disorder treatment. The unbearable impact of withdrawal syndrome such as physical related problems, psychological, social and behavioural can take a long-term impact such as affective and anxiety disorder that can lead to depression. CBT as an intervention that improves coping-skill, and strategy to change a maladaptive mindset should be convenient to reduce withdrawal symptoms. The effect of CBT intervention on the severity of the symptoms of methamphetamine withdrawal syndrome is still narrow. This research is aimed to find out the CBT effect on withdrawal symptoms in both qualitative and quantitative methods on female inmates. This research uses a case study design. Data were conducted using Amphetamine Withdrawal Questionnaire (AWQ) before and after CBT intervention. Data were analyzed using univariate analysis presented as distribution frequency on both before and after the intervention and discussed with single case analysis. The result showed that CBT affects reducing withdrawal syndrome symptoms severity after 4 weeks and 4 session intervention. CBT affects decreasing withdrawal syndrome severity level. Based on these findings, the correctional nurse needs to develop comprehensive nursing care by providing CBT on a rehabilitation program to decrease female inmates’ withdrawal syndrome severity level.

Identification of Lignite as One of Usable Alternative Energy Using Difference Normalized Burn Ratio (dNBR) Calculated from Landsat-8 Data and Gravity Method in Lam Apeng Village, Aceh Besar District, Aceh Province

One of the alternative energy that exists in Indonesia, including in Aceh is coal energy. In 2016, a fire happened caused of coal in Lam Apeng Village, proven by coal’s landscape formed after the fire. The research purposes to identify the coal existence as one alternative energy in Indonesia using difference Normalized Burn Ratio (dNBR) calculated from Landsat-8 data. The difference between NBR’s which is able to show the only burn location based on its index with high severity level representes the burnt location. Gravity survey validates the results and proves that the low-density zone indicated coal existence has the same pattern as the high severity level. Both of the results give information about lignite dissemination in Lam Apeng Village.

A LIME-Based Explainable Machine Learning Model for Predicting the Severity Level of COVID-19 Diagnosed Patients

The fast and seemingly uncontrollable spread of the novel coronavirus disease (COVID-19) poses great challenges to an already overloaded health system worldwide. It thus exemplifies an urgent need for fast and effective triage. Such triage can help in the implementation of the necessary measures to prevent patient deterioration and conserve strained hospital resources. We examine two types of machine learning models, a multilayer perceptron artificial neural networks and decision trees, to predict the severity level of illness for patients diagnosed with COVID-19, based on their medical history and laboratory test results. In addition, we combine the machine learning models with a LIME-based explainable model to provide explainability of the model prediction. Our experimental results indicate that the model can achieve up to 80% prediction accuracy for the dataset we used. Finally, we integrate the explainable machine learning models into a mobile application to enable the usage of the proposed models by medical staff worldwide.