Fishmeal Dietary Replacement up to 50%: A Comparative Study of Two Insect Meals for Rainbow Trout (Oncorhynchus mykiss)

The demand of optimal protein for human consumption is growing. The Food and Agriculture Organization (FAO) has highlighted aquaculture as one of the most promising alternatives for this protein supply gap due to the high efficiency of fish growth. However, aquaculture has been facing its own sustainability problem, because its high demand for protein has been traditionally satisfied with the use of fishmeal (FM) as the main source. Some of the most promising and sustainable protein substitutes for FM come from insects. The present manuscript provides insight into an experiment carried out on rainbow trout (Oncorhynchus mykiss) with a 50% replacement of FM with different larvae insect meals: Hermetia illucens (HI), and Tenebrio molitor (TM). TM showed better results for growth, protein utilization and more active digestive function, supported by intestinal histological changes. Liver histology and intermediary metabolism did not show relevant changes between insect meals, while other parameters such as antioxidant enzyme activities and tissue damage indicators showed the potential of insect meals as functional ingredients.

Residual Force Method for damage identification in a laminated composite plate with different boundary conditions

The strongest point about damage identification based on the dynamic measurements, is the ability perform structural health evaluation globally. Researchers in the last few years payed more attention to damage indicators based on modal analysis using either frequencies, mode shapes, or Frequency Response Functions (FRFs). This paper presents a new application of damage identification in a cross-ply (0°/90°/0°) laminated composite plate based on Force Residual Method (FRM) damage indicator. Considering single and multiple damages with different damage levels. As well as investigating the SSSS and CCCC boundary conditions effect on the estimation accuracy. Moreover, a white Gaussian noise is introduced to test the challenge the technique. The results show that the suggested FRM indicator provides accurate results under different boundary conditions. Favouring the SSSS boundary condition than the CCCC for 3% noise.

Relationship between fibrinogen/albumin ratio and early renal damage markers in patients with hypertension

Aim:To investigate the relationship between fibrinogen/albumin ratio (FAR) and early renal damage in hypertensive patients.Patients & methods:A retrospective study included 626 patients with hypertension, grouped according to the FAR tertiles and the presence or absence of early renal impairment. Early renal damage indicators[Serum cystatin c and β2 microglobulin] were detected in each group, and the differences between groups were compared, and the factors affecting early renal damage indicators were analyzed.Results:Serum cystatin c(CysC) and β2 microglobulin(β2-MG) levels in patients increased with FAR. In the renal impairment group, the fibrinogen and FAR were significantly increased, and serum albumin was decreased. FAR was positively correlated with β2-MG and CysC. Regression analysis showed that FAR level was a factor affecting blood β2-MG(β=6.632, p＜0.001)and CysC(β=1.991, p＜0.001).Conclusion:Elevated FAR is an independent risk factor for early renal damage in hypertensive patients

Damage identification in steel plate using FRF and inverse analysis

Metaheuristic algorithms have known vast development in recent years. And their applicability in engineering projects is constantly growing; however, their deferent exploration and exploitation techniques cause the engineering problems to favor some algorithms over others. This paper studies damage identification in steel plates using Frequency Response Function (FRF) damage indicator to detect and localize the healthy and damaged structure. The study is formulated as an inverse analysis, investigating the performance of three new metaheuristic algorithms of Wild Horse Optimizer (WHO), Harris Hawks Optimization (HHO), and Arithmetic Optimization Algorithm (AOA). The objective function is based on measured and calculated FRF damage indicators. The results showed that the case of four damages with different damage severity levels presented a good challenge where the HWO algorithm was shown to have the best performance. Both in convergence speed and CPU time.

Assessment of Damage to Structures and Equipment Resulting from Explosion, Fire, and Heat Events

This article addresses the effects of damage to equipment and structures due to explosions (blast), fire, and heat as well as the methodologies that are used by investigating teams to assess the damage and remaining life of the equipment. It discusses the steps involved in preliminary data collection and preparation. Before discussing the identification, evaluation, and use of explosion damage indicators, the article describes some of the more common events that are considered in incident investigations. The range of scenarios that can occur during explosions and the characteristics of each are also covered. In addition, the article primarily discusses level 1 and level 2 of fire and heat damage assessment and provides information on level 3 assessment.

INTERPRETATION OF HISTORIC STRUCTURE FOR NON-INVASIVE ASSESSMENT USING EYE TRACKING

With the aims of ensuring safety and decreasing maintenance costs, previous studies in bridge inspection research have worked to elucidate damage indicators and understand their correspondence to structural deficiency. During this process, understanding how an inspector looks at a structure comprehensively as well as how they localize on damage is vital to examining diagnostic bias and how it can play a role in the preservation and maintenance process. To understand human perception and assess the humaninfrastructure interaction during the feature extraction process, eye tracking can be useful. Eye tracking data can accurately map where a human is looking and what they are focusing on based on metrics such as fixation, saccade, pupil dilation, and scan path. The present research highlights the use of eye tracking metrics for recognizing and inferring human implicit attention and intention while performing a structural inspection. These metrics will be used to learn the behavior of human eyes and how detection tasks can change a person’s overall behavior. A preliminary study has been carried out for damage detection to analyze key features that are important for understanding human-infrastructure interaction during damage assessment. These eye tracking features will lay the foundation for human intent prediction and how an inspector performs inspection on historic structures for existing types of damage. In future, the results of this work will be used to train a machine learning agent for autonomous and reactive decision making.

MACHINE LEARNING AND ANOMALY DETECTION ALGORITHMS FOR DAMAGE CHARACTERIZATION FROM COMPLIANCE DATA IN 3-POINT BENDING FATIGUE

Three-point bending fatigue compliance datasets of multi-layer fiberglass-weave/epoxy test specimens, including five and ten mil interlayers, were analyzed using Artificial Intelligence (AI) methods along with statistical analysis, revealing the existence of three different compliance-based damage modes. Anomaly detection algorithms helped discover damage indicators observable in short intervals (of 50 cycles) in the compliance data, whose patterns vary with the material and the number of load cycles to which the material is subjected. Machine learning algorithms were applied using the compliance features to assess the likelihood that material failure may occur within a certain number of future loading cycles. High accuracy, precision, and recall rates were achieved in the classification task, for which we evaluated several algorithms, including various variations of neural networks and support vector machines. Thus our work demonstrates the utility of AI algorithms for discovering a diversity of damage mechanisms and failures.