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Abstract Load balancing is crucial in distributed systems like fog computing, where efficiency is paramount. Offloading with different approaches is the key to balancing the load in distributed environments. Static offloading (SoA) falls short in heterogeneous networks, necessitating dynamic offloading to reduce latency in time-sensitive tasks. However, prevalent dynamic offloading (PoA) solutions often come with hidden costs that impact sensitive applications, including decision time, networks congested and distance offloading. This paper introduces the Hybrid Offloading (HybOff) algorithm, which substantially enhances load balancing and resource utilization in fog networks, addressing issues in both static and dynamic approaches while leveraging clustering theory. Its goal is to create an uncomplicated low-cost offloading approach that enhances IoT application performance by eliminating the consequences of hidden costs regardless of network size. Experimental results using the iFogSim simulation tool show that HybOff significantly reduces offloading messages, distance, and decision-offloading consequences. It improves load balancing by 97%, surpassing SoA (64%) and PoA (88%). Additionally, it increases system utilization by an average of 50% and enhances system performance 1.6 times and 1.4 times more than SoA and PoA, respectively. In summary, this paper tries to introduce a new offloading approach in load balancing research in fog environments.

In the realm of intelligent transportation, pedestrian detection has witnessed significant advancements. However, it continues to grapple with challenging issues, notably the detection of pedestrians in complex lighting scenarios. Conventional visible light mode imaging is profoundly affected by varying lighting conditions. Under optimal daytime lighting, visibility is enhanced, leading to superior pedestrian detection outcomes. Conversely, under low-light conditions, visible light mode imaging falters due to the inadequate provision of pedestrian target information, resulting in a marked decline in detection efficacy. In this context, infrared light mode imaging emerges as a valuable supplement, bolstering pedestrian information provision. This paper delves into pedestrian detection and tracking algorithms within a multi-modal image framework grounded in deep learning methodologies. Leveraging the YOLOv4 algorithm as a foundation, augmented by a channel stack fusion module, a novel multi-modal pedestrian detection algorithm tailored for intelligent transportation is proposed. This algorithm capitalizes on the fusion of visible and infrared light mode image features to enhance pedestrian detection performance amidst complex road environments. Experimental findings demonstrate that compared to the Visible-YOLOv4 algorithm, renowned for its high performance, the proposed Double-YOLOv4-CSE algorithm exhibits a notable improvement, boasting a 5.0% accuracy rate enhancement and a 6.9% reduction in logarithmic average missing rate. This research’s goal is to ensure that the algorithm can run smoothly even on a low configuration 1080 Ti GPU and to improve the algorithm’s coverage at the application layer, making it affordable and practical for both urban and rural areas. This addresses the broader research problem within the scope of smart cities and remote ends with limited computational power.

1,1,4,4-Tetramethyl-2-tetrazene (TMTZ), a potential candidate for replacing 1,1-dimethylhydrazine (UDMH), has excellent properties including a high specific impulse, non-toxicity and non-volatility. However, most synthetic methods depicted in the literature use stoichiometric oxidative coupling, and the oxidizing agents are usually heavy metals or halogens, leading to low synthetic efficiency and high environmental pollution. In order to overcome these problems, it is necessary to explore more environmentally friendly and catalytic synthetic methods. In this paper, a method of photocatalytic oxidative coupling for the preparation of TMTZ was reported for the first time. By using an organic photocatalyst Eosin Y, TMTZ was synthesized rapidly and efficiently. The thermal decomposition properties of TMTZ were studied, and different performance characteristics between TMTZ and UDMH were determined and analyzed, which provided a basis for the application of TMTZ based new liquid propellant.

Abstract Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Customer acquisition cost can be five to six times that of customer retention, hence investing in customers with churn risk is wise. Causal analysis of the churn model can predict whether a customer will churn in the foreseeable future and identify effects and possible causes for churn. In general, this study presents a conceptual framework to discover the confounding features that correlate with independent variables and are causally related to those dependent variables that impact churn. We combine different algorithms including the SMOTE, ensemble ANN, and Bayesian networks to address churn prediction problems on a massive and high-dimensional finance data that is usually generated in financial institutions due to employing interval-based features used in Customer Relationship Management systems. The effects of the curse and blessing of dimensionality assessed by utilising the Recursive Feature Elimination method to overcome the high dimension feature space problem. Moreover, a causal discovery performed to find possible interpretation methods to describe cause probabilities that lead to customer churn. Evaluation metrics on validation data confirm the random forest and our ensemble ANN model, with %86 accuracy, outperformed other approaches. Causal analysis results confirm that some independent causal variables representing the level of super guarantee contribution, account growth, and account balance amount were identified as confounding variables that cause customer churn with a high degree of belief. This article provides a real-world customer churn analysis from current status inference to future directions in local superannuation funds.

The introduction of highly oxidative explosophoric groups is an efficient strategy to enhance the detonation properties of energetic materials due to its improvement effect of oxygen balances. Chlorodinitromethyl and fluorodinitromethyl groups are widely applied as oxygen-rich building blocks for the construction of energetic materials and their combinations with FOX-7 derived 1,3,5-tetrahydrogentriazine backbone provide perfect structures for a systematic and comparative thermal study of these two similar explosophoric groups. Decomposition behaviors of the obtained insensitive energetic materials were investigated through DSC-TG approaches and chlorodinitromethyl based one exhibited sharper exothermic peak shape and higher heat release rate. Non-isothermal kinetics of the thermal decomposition was achieved based on DSC experiments under different heating rates, showing an observable melting process which could only be observed from the chlorodinitromethyl group based energetic structure under high the heating rates. Possible mechanism of the thermal decomposition was proposed by exploring the intermediates during the heating process through the combination of in-situ FTIR spectroscopy technology with calculation methods, which proved that the decompositions were triggered by the cleavage of nitro groups in chlorodinitromethyl and fluorodinitromethyl moieties. Despite their effectiveness to improve the energy density levels, this study showed the chlorodinitromethyl and fluorodinitromethyl explosophoric groups can also interact with the backbones under high temperatures, especially with the ones with active N-H moieties, and result in low decomposition temperatures which may greatly limit their applications.

In recent years, the emergence of distributed machine learning has enabled deep learning models to ensure data security and privacy while training efficiently. Anomaly detection for network traffic in distributed machine learning scenarios is of great significance for network security. Although deep neural networks have made remarkable achievements in anomaly detection for network traffic, they mainly focus on closed sets, that is, assuming that all anomalies are known. However, in a real network environment, unknown abnormalities are fatal risks faced by the system because they have no labels and occur before the known anomalies. In this study, we design and implement XFinder, a dynamic unknown traffic anomaly detection framework in distributed machine learning. XFinder adopts an online mode to detect unknown anomalies in real-time. XFinder detects unknown anomalies by the unknowns detector, transfers the unknown anomalies to the prior knowledge base by the network updater, and adopts the online mode to report new anomalies in real-time. The experimental results show that the average accuracy of the unknown anomaly detection of our model is increased by 27% and the average F1-Score is improved by 20%. Compared with the offline mode, XFinder’s detection time is reduced by an average of approximately 33% on three datasets, and can better meet the network requirement.

Design and synthesis of new compounds with both high detonation performances and good safety properties have always been a formidable task in the field of energetic materials. By introducing -ONO2 and -NHNO2 moieties into 1,2,4-oxadiazole- and 1,2,5-oxadiazole-based backbones, a new family of energetic materials, including ammonium 3-nitramino-4-(5-hydroxymethyl-1,2,4-oxadiazol-3-yl)-furazan (4), 3,3′-bis[5-nitroxymethyl-1,2,4-oxadiazol-3-yl]-4,4′-azofuroxan (6), [3-(4-nitroamino-1,2,5-oxadiazol-3-yl)-1,2,4-oxadiazol-5-yl]-methylene nitrate (8), and its energetic ionic salts (10–12), were synthesized and fully characterized. The energetic and physical properties of the materials were investigated through theoretical calculations and experimental determination. The results show that the oxadiazole-based compounds exhibit high enthalpy of formations, good detonation performances, and extraordinary insensitivities. In particular, the hydrazinium salt (11) shows the best energetic properties (11: d = 1.821 g cm−3; P = 35.1 GPa, vD = 8,822 m s−1, IS = 40 J, FS > 360N). The ESP and Hirshfeld surface analysis indicated that a large number of hydrogen bonds as well as π-π stacking interactions within molecules might be the key reason for their low sensitivities and high energy-density levels.

Rearrangement reactions are efficient strategies in organic synthesis and contribute enormously to the development of energetic materials. Here, we report on the preparation of a fused energetic structure of 7-nitro-3,5-dihydro-4H-pyrazolo[4,3-d][1,2,3]triazin-4-one (NPTO) based on a novel Hofmann-type rearrangement. The 1,2,3-triazine unit was introduced into the fused bicyclic skeleton from a pyrazole unit for the first time. The new compound of NPTO was fully characterized using multinuclear NMR and IR spectroscopy, elemental analysis as well as X-ray diffraction studies. The thermal behaviors and detonation properties of NPTO were investigated through a differential scanning calorimetry (DSC-TG) approach and EXPLO5 program-based calculations, respectively. The calculation results showed similar detonation performances between NPTO and the energetic materials of DNPP and ANPP, indicating that NPTO has a good application perspective in insensitive explosives and propellants.

Dinitropyrazole is an important structure for the design and synthesis of energetic materials. In this work, we reported the first comparative thermal studies of two representative dinitropyrazole-based energetic materials, 4-amino-3,5-dinitropyrazole (LLM-116) and its novel trimer derivative (LLM-226). Both the experimental and theoretical results proved the active aromatic N-H moiety would cause incredible variations in the physicochemical characteristics of the obtained energetic materials. Thermal behaviors and kinetic studies of the two related dinitropyrazole-based energetic structures showed that impressive thermal stabilization could be achieved after the trimerization, but also would result in a less concentrated heat-release process. Detailed analysis of condensed-phase systems and the gaseous products during the thermal decomposition processes, and simulation studies based on ReaxFF force field, indicated that the ring opening of LLM-116 was triggered by hydrogen transfer of the active aromatic N-H moiety. In contrast, the initial decomposition of LLM-226 was caused by the rupture of carbon-nitrogen bonds at the diazo moiety.

Density, detonation property, and sensitivity may be the most valued features when evaluating an energetic material. By reasoning structure–property relationships, a nitro-free planar energetic material with high nitrogen and oxygen content, 7-hydroxy-difurazano[3,4-b:3′,4′-f]furoxano[3″,4″-d]azepine (4), was synthesized using a unique and facile approach. The structure was fully characterized by IR and NMR spectra, elemental analysis, differential scanning calorimetry (DSC), and single-crystal X-ray diffraction. The expected properties of 4, including a high density of 1.92 g cm−3, high detonation velocity of 8,875 m s−1, and low mechanical sensitivities (impact sensitivity, 21 J and friction sensitivity, >360 N), confirm our strategy. Interestingly, the single-crystal structures of 4 reveal expected face-to-face and edge-to-face π-interactions in the crystal stacking. The remarkable differences in crystal stacking of 4 provide unequivocal evidence that face-to-face π-π interactions contribute significantly to closer assembly and higher density.

The subject of this study was to explore the optimum requirements of loach (Paramisgurnus dabryanus) regarding dietary proteins and lipids and discuss the underlying mechanism. We designed nine diets to determine the effects of different levels of dietary crude protein (CP: 30%, 35%, and 40%) and ether extract (EE: 6%, 10%, and 14%) on the growth performance and metabolism of P. dabryanus. In total, 2160 healthy P. dabryanus (5.19 ± 0.01 g) were divided into nine groups with four replications at 60 fish per barrel stocking density. The trial lasted for eight weeks. Serum and liver samples were gathered for metabolomic and transcriptomic analyses. The results showed that the specific growth rate of P. dabryanus in the CP40EE10 group was the fastest and notably higher than that in other groups (P< 0.05). Analysis of the metabolome results found that the mTOR signaling pathway, glycerophospholipid metabolism, D-arginine and Dornithine metabolism were significantly enriched pathways in the CP40EE10 group compared with the other groups (P< 0.05). Moreover, the transcriptomic analysis of differentially expressed genes (DEGs) showed that the expression of ARG (arginase) involved in protein synthesis was significantly upregulated in the CP40EE10 group compared to the slowest growing group (P< 0.05). Additionally, the expression of SPLA2 (secretory phospholipase A2) involved in lipid metabolism and FBP (fructose-1,6-bisphosphatase) involved in glucose metabolism were all significantly downregulated in the CP30EE6 group compared with the CP40EE10 group (P< 0.05). Furthermore, the analysis of differentially expressed metabolites (DEMs) and DEGs co-enriched in the KEGG pathway revealed that the significantly enriched pathways were arginine and proline metabolism, glycerophospholipid metabolism, and glycolysis/gluconeogenesis in CP40EE10 compared with other groups (P< 0.05). We conclude that including 40% CP and 10% EE in the P. dabryanus diet could result in a better growth rate. We hypothesized from metabolomic and transcriptomic analyses that the CP40EE10 diet might promote the growth of P. dabryanus by promoting protein synthesis, lipid metabolism, and energy production. [ABSTRACT FROM AUTHOR]