Vehicle Routing Problem with Time Windows and Simultaneous Delivery and Pick-Up Service Based on MCPSO

This paper considers two additional factors of the widely researched vehicle routing problem with time windows (VRPTW). The two factors, which are very common characteristics in realworld, are uncertain number of vehicles and simultaneous delivery and pick-up service. Using minimization of the total transport costs as the objective of the extension VRPTW, a mathematic model is constructed. To solve the problem, an efficient multiswarm cooperative particle swarm optimization (MCPSO) algorithm is applied. And a new encoding method is proposed for the extension VRPTW. Finally, comparing with genetic algorithm (GA) and particle swarm optimization (PSO) algorithm, the MCPSO algorithm performs best for solving this problem

Measuring the regional availability of forest biomass for biofuels and the potential of GHG reduction

Forest biomass is an important resource for producing bioenergy and reducing greenhouse gas (GHG) emissions. The State of Michigan in the United States (U.S.) is one region recognized for its high potential of supplying forest biomass; however, the long-term availability of timber harvests and the associated harvest residues from this area has not been fully explored. In this study time trend analyses was employed for long term timber assessment and developed mathematical models for harvest residue estimation, as well as the implications of use for ethanol. The GHG savings potential of ethanol over gasoline was also modeled. The methods were applied in Michigan under scenarios of different harvest solutions, harvest types, transportation distances, conversion technologies, and higher heating values over a 50-year period. Our results indicate that the study region has the potential to supply 0.75–1.4 Megatonnes (Mt) dry timber annually and less than 0.05 Mt of dry residue produced from these harvests. This amount of forest biomass could generate 0.15–1.01 Mt of ethanol, which contains 0.68–17.32 GJ of energy. The substitution of ethanol for gasoline as transportation fuel has potential to reduce emissions by 0.043–1.09 Mt CO2eq annually. The developed method is generalizable in other similar regions of different countries for bioenergy related analyses

Towards Efficient Task-Driven Model Reprogramming with Foundation Models

Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. Moreover, the data used for pretraining foundation models are usually invisible and very different from the target data of downstream tasks. This brings a critical challenge for the real-world application of foundation models: one has to transfer the knowledge of a foundation model to the downstream task that has a quite different architecture with only downstream target data. Existing transfer learning or knowledge distillation methods depend on either the same model structure or finetuning of the foundation model. Thus, naively introducing these methods can be either infeasible or very inefficient. To address this, we propose a Task-Driven Model Reprogramming (TDMR) framework. Specifically, we reprogram the foundation model to project the knowledge into a proxy space, which alleviates the adverse effect of task mismatch and domain inconsistency. Then, we reprogram the target model via progressive distillation from the proxy space to efficiently learn the knowledge from the reprogrammed foundation model. TDMR is compatible with different pre-trained model types (CNN, transformer or their mix) and limited target data, and promotes the wide applications of vision foundation models to downstream tasks in a cost-effective manner. Extensive experiments on different downstream classification tasks and target model structures demonstrate the effectiveness of our methods with both CNNs and transformer foundation models

The TTYH3/MK5 Positive Feedback Loop regulates Tumor Progression via GSK3-β/β-catenin signaling in HCC

Hepatocellular carcinoma (HCC) is one of the leading causes of cancer-related death worldwide, and identification of novel targets is necessary for its diagnosis and treatment. This study aimed to investigate the biological function and clinical significance of tweety homolog 3 (TTYH3) in HCC. TTYH3 overexpression promoted cell proliferation, migration, and invasion and inhibited HCCM3 and Hep3B cell apoptosis. TTYH3 promoted tumor formation and metastasis in vivo. TTYH3 upregulated calcium influx and intracellular chloride concentration, thereby promoting cellular migration and regulating epithelial-mesenchymal transition-related protein expression. The interaction between TTYH3 and MK5 was identified through co-immunoprecipitation assays and protein docking. TTYH3 promoted the expression of MK5, which then activated the GSK3β/β-catenin signaling pathway. MK5 knockdown attenuated the activation of GSK3β/β-catenin signaling by TTYH3. TTYH3 expression was regulated in a positive feedback manner. In clinical HCC samples, TTYH3 was upregulated in the HCC tissues compared to nontumor tissues. Furthermore, high TTYH3 expression was significantly correlated with poor patient survival. The CpG islands were hypomethylated in the promoter region of TTYH3 in HCC tissues. In conclusion, we identified TTYH3 regulates tumor development and progression via MK5/GSK3-β/β-catenin signaling in HCC and promotes itself expression in a positive feedback loop

The Geozoic Supereon

Geological time units are the lingua franca of earth sciences: they are a terminological convenience, a vernacular of any geological conversation, and a prerequisite of geo-scientific writing found throughout in earth science dictionaries and textbooks. Time units include terms formalized by stratigraphic committees as well as informal constructs erected ad hoc to communicate more efficiently. With these time terms we partition Earth’s history into utilitarian and intuitively understandable time segments that vary in length over seven orders of magnitude: from the 225-year-long Anthropocene (Crutzen and Stoermer, 2000) to the ,4-billion-year-long Precambrian (e.g., Hicks, 1885; Ball, 1906; formalized by De Villiers, 1969)

An Intrusion Detection Algorithm based on D-S theory and Rough Set

Intrusion detection system is a kind of network security system which can alarm suspicious transmission or take active response measures when it real-time monitors network transmission and discovers suspicious transmission. But intrusion detection system has many problems such as wrong detection of intrusions, missed intrusions, poor real-time performance. In order to improve the performance of intrusion detection system, this paper proposes an intrusion detection algorithm based on D-S theory and Rough Set. The algorithm uses the attribute reduction algorithm in rough set to eliminate redundant attributes, form the simplest attributes set, overcome the traditional D-S theory relying on expert knowledge to provide evidence and makes each evidence body mutual independence. So it improves the evidence synthesis efficiency, shortens the evidence synthesis time and reduces the conflict phenomenon of evidence synthesis. On this basis, the paper builds an intrusion detection model based on D-S theory and rough set, and the experimental results demonstrate that the model has higher detection rate and lower false detection rate

An Intrusion Detection Algorithm based on D-S theory and Rough Set

Intrusion detection system is a kind of network security system which can alarm suspicious transmission or take active response measures when it real-time monitors network transmission and discovers suspicious transmission. But intrusion detection system has many problems such as wrong detection of intrusions, missed intrusions, poor real-time performance. In order to improve the performance of intrusion detection system, this paper proposes an intrusion detection algorithm based on D-S theory and Rough Set. The algorithm uses the attribute reduction algorithm in rough set to eliminate redundant attributes, form the simplest attributes set, overcome the traditional D-S theory relying on expert knowledge to provide evidence and makes each evidence body mutual independence. So it improves the evidence synthesis efficiency, shortens the evidence synthesis time and reduces the conflict phenomenon of evidence synthesis. On this basis, the paper builds an intrusion detection model based on D-S theory and rough set, and the experimental results demonstrate that the model has higher detection rate and lower false detection rate

Recent Developments in Modulation Spectroscopy for Methane Detection Based on Tunable Diode Laser

In this review, methane absorption characteristics mainly in the near-infrared region and typical types of currently available semiconductor lasers are described. Wavelength modulation spectroscopy (WMS), frequency modulation spectroscopy (FMS), and two-tone frequency modulation spectroscopy (TTFMS), as major techniques in modulation spectroscopy, are presented in combination with the application of methane detection