Growth efficiency,intestinal biology,and nutrient utilization and requirements of black soldier fly(Hermetia illucens)larvae compared to monogastric livestock species:a review

In recent years,interest in the larvae of black soldier fly(BSF)(Hermetia illucens)as a sustainable protein resource for livestock feed has increased considerably.However,knowledge on the nutritional and physiological aspects of this insect,especially compared to other conventional farmed animals is scarce.This review presents a critical comparison of data on the growth potential and efficiency of the BSF larvae(BSFL)compared to conventional monogastric livestock species.Advantages of BSFL over other monogastric livestock species includes their high growth rate and their ability to convert low-grade organic waste into high-quality protein and fat-rich biomass suitable for use in animal feed.Calculations using literature data suggest that BSFL are more efficient than broilers,pigs and fish in terms of conversion of substrate protein into body mass,but less efficient than broilers and fish in utilization of substrate gross energy to gain body mass.BSFL growth efficiency varies greatly depending on the nutrient quality of their dietary substrates.This might be associated with the function of their gastrointestinal tract,including the activity of digestive enzymes,the substrate particle characteristics,and their intestinal microbial community.The conceived advantage of BSFL having an environmental footprint better than conventional livestock is only true if BSFL is produced on low-grade organic waste and its protein would directly be used for human consumption.Therefore,their potential role as a new species to better close nutrient cycles in agro-ecological systems needs to be reconsidered,and we conclude that BSFL is a complementary livestock species efficiently utilizing organic waste that cannot be utilized by other livestock.In addition,we provide comparative insight into morpho-functional aspects of the gut,characterization of digestive enzymes,gut microbiota and fiber digestion.Finally,current knowledge on the nutritional utilization and requirements of BSFL in terms of macro-and micronutrients is reviewed and found to be rather limited.In addition,the research methods to determine nutritional requirements of conventional livestock are not applicable for BSFL.Thus,there is a great need for research on the nutrient requirements of BSFL.

Transparent open-box learning network and artificial neural network predictions of bubble-point pressure compared

The transparent open box(TOB)learning network algorithm offers an alternative approach to the lack of transparency provided by most machine-learning algorithms.It provides the exact calculations and relationships among the underlying input variables of the datasets to which it is applied.It also has the capability to achieve credible and auditable levels of prediction accuracy to complex,non-linear datasets,typical of those encountered in the oil and gas sector,highlighting the potential for underfitting and overfitting.The algorithm is applied here to predict bubble-point pressure from a published PVT dataset of 166 data records involving four easy-tomeasure variables(reservoir temperature,gas-oil ratio,oil gravity,gas density relative to air)with uneven,and in parts,sparse data coverage.The TOB network demonstrates high-prediction accuracy for this complex system,although it predictions applied to the full dataset are outperformed by an artificial neural network(ANN).However,the performance of the TOB algorithm reveals the risk of overfitting in the sparse areas of the dataset and achieves a prediction performance that matches the ANN algorithm where the underlying data population is adequate.The high levels of transparency and its inhibitions to overfitting enable the TOB learning network to provide complementary information about the underlying dataset to that provided by traditional machine learning algorithms.This makes them suitable for application in parallel with neural-network algorithms,to overcome their black-box tendencies,and for benchmarking the prediction performance of other machine learning algorithms.