Evaluating the impacts of farmers’ behaviors on a hypothetical agricultural water market based on double auction

Agricultural water markets are considered effective instruments to mitigate the impacts of water scarcity and to increase crop production. However, previous studies have limited understanding of how farmers’ behaviors affect the performance of water markets. This study develops an agent-based model to explicitly incorporate farmers’ behaviors, namely irrigation behavior (represented by farmers’ sensitivity to soil water deficit k) and bidding behavior (represented by farmers’ rent seeking l and learning rate b), in a hypothetical water market based on a double auction. The model is applied to the Guadalupe River Basin in Texas to simulate a hypothetical agricultural water market under various hydrological conditions. It is found that the joint impacts of the behavioral parameters on the water market are strong and complex. In particular, among the three behavioral parameters, k affects the water market potential and its impacts on the performance of the water market are significant under most scenarios. The impacts of l or b on the performance of the water market depend on the other two parameters. The water market could significantly increase crop production only when the following conditions are satisfied: (1) k is small and (2) l is small and/or b is large. The first condition requires efficient irrigation scheduling, and the second requires well-developed water market institutions that provide incentives to bid true valuation of water permits

Pursuing an evader through cooperative relaying in multi-agent surveillance networks

We provide a distributed control strategy for each mobile agent in a surveillance network in the plane to cooperatively pursue an evader. The pursuit task is relayed from one agent to another when the evader crosses the boundary of the Voronoi regions divided according to the agents’ positions. The dynamics of the resulted cooperative relay-pursuit network are described by a novel model of impulsive systems. As a result, to guarantee the stability of the closed-loop network system, the controllers’ gains are chosen effectively using the solution of an algebraic Riccati equation. The proof of the stability is based on the construction of a switched Lyapunov function. We also show that the proposed controller is able to deal with delays if some sufficient conditions in the form of a set of linear inequalities are satisfied. A numerical example is provided to validate the performance of the proposed controller

Experimental Implementation of Remote State Preparation by Nuclear Magnetic Resonance

We have experimentally implemented remote state preparation (RSP) of a qubit from a hydrogen to a carbon nucleus in molecules of carbon-13 labeled chloroform $^{13}$CHCl$_{3}$ over interatomic distances using liquid-state nuclear magnetic resonance (NMR) technique. Full RSP of a special ensemble of qubits, i.e., a qubit chosen from equatorial and polar great circles on a Bloch sphere with Pati's scheme, was achieved with one cbit communication. Such a RSP scheme can be generalized to prepare a large number of qubit states and may be used in other quantum information processing and quantum computing.Comment: 10 pages,5 PS figure

Exploring the Role of Social Media and Individual Behaviors in Flood Evacuation Processes: An Agent-Based Modeling Approach

Flood warnings from various information sources are important for individuals to make evacuation decisions during a flood event. In this study, we develop a general opinion dynamics model to simulate how individuals update their flood hazard awareness when exposed to multiple information sources, including global broadcast, social media, and observations of neighbors' actions. The opinion dynamics model is coupled with a traffic model to simulate the evacuation processes of a residential community with a given transportation network. Through various scenarios, we investigate how social media affect the opinion dynamics and evacuation processes. We find that stronger social media can make evacuation processes more sensitive to the change of global broadcast and neighbor observations, and thus, impose larger uncertainty on evacuation rates (i.e., a large range of evacuation rates corresponding to sources of information). For instance, evacuation rates are lower when social media become more influential and individuals have less trust in global broadcast. Stubborn individuals can significantly affect the opinion dynamics and reduce evacuation rates. In addition, evacuation rates respond to the percentage of stubborn agents in a nonlinear manner, i.e., above a threshold, the impact of stubborn agents will be intensified by stronger social media. These results highlight the role of social media in flood evacuation processes and the need to monitor social media so that misinformation can be corrected in a timely manner. The joint impacts of social media, quality of flood warnings, and transportation capacity on evacuation rates are also discussed.Additional support was provided by Shenzhen Municipal Science and Technology Innovation Committee (#ZDSY20150831141712549)

Experimental realization of a highly structured search algorithm

The highly structured search algorithm proposed by Hogg[Phys.Rev.Lett. 80,2473(1998)] is implemented experimentally for the 1-SAT problem in a single search step by using nuclear magnetic resonance technique with two-qubit sample. It is the first demonstration of the Hogg's algorithm, and can be readily extended to solving 1-SAT problem for more qubits in one step if the appropriate samples possessing more qubits are experimentally feasible.Comment: RevTex, 11 pages + 3 pages of figure

Fld1p, a functional homologue of human seipin, regulates the size of lipid droplets in yeast

Lipid droplets (LDs) are emerging cellular organelles that are of crucial importance in cell biology and human diseases. In this study, we present our screen of ∼4,700 Saccharomyces cerevisiae mutants for abnormalities in the number and morphology of LDs; we identify 17 fld (few LDs) and 116 mld (many LDs) mutants. One of the fld mutants (fld1) is caused by the deletion of YLR404W, a previously uncharacterized open reading frame. Cells lacking FLD1 contain strikingly enlarged (supersized) LDs, and LDs from fld1Δ cells demonstrate significantly enhanced fusion activities both in vivo and in vitro. Interestingly, the expression of human seipin, whose mutant forms are associated with Berardinelli-Seip congenital lipodystrophy and motoneuron disorders, rescues LD-associated defects in fld1Δ cells. Lipid profiling reveals alterations in acyl chain compositions of major phospholipids in fld1Δ cells. These results suggest that an evolutionally conserved function of seipin in phospholipid metabolism and LD formation may be functionally important in human adipogenesis

RORα and 25-Hydroxycholesterol Crosstalk Regulates Lipid Droplet Homeostasis in Macrophages.

Nuclear hormone receptors have important roles in the regulation of metabolic and inflammatory pathways. The retinoid-related orphan receptor alpha (Rorα)-deficient staggerer (sg/sg) mice display several phenotypes indicative of aberrant lipid metabolism, including dyslipidemia, and increased susceptibility to atherosclerosis. In this study we demonstrate that macrophages from sg/sg mice have increased ability to accumulate lipids and accordingly exhibit larger lipid droplets (LD). We have previously shown that BMMs from sg/sg mice have significantly decreased expression of cholesterol 25-hydroxylase (Ch25h) mRNA, the enzyme that produces the oxysterol, 25-hydroxycholesterol (25HC), and now confirm this at the protein level. 25HC functions as an inverse agonist for RORα. siRNA knockdown of Ch25h in macrophages up-regulates Vldlr mRNA expression and causes increased accumulation of LDs. Treatment with physiological concentrations of 25HC in sg/sg macrophages restored lipid accumulation back to normal levels. Thus, 25HC and RORα signify a new pathway involved in the regulation of lipid homeostasis in macrophages, potentially via increased uptake of lipid which is suggested by mRNA expression changes in Vldlr and other related genes

Pushing the Limits of Machine Design: Automated CPU Design with AI

Design activity -- constructing an artifact description satisfying given goals and constraints -- distinguishes humanity from other animals and traditional machines, and endowing machines with design abilities at the human level or beyond has been a long-term pursuit. Though machines have already demonstrated their abilities in designing new materials, proteins, and computer programs with advanced artificial intelligence (AI) techniques, the search space for designing such objects is relatively small, and thus, "Can machines design like humans?" remains an open question. To explore the boundary of machine design, here we present a new AI approach to automatically design a central processing unit (CPU), the brain of a computer, and one of the world's most intricate devices humanity have ever designed. This approach generates the circuit logic, which is represented by a graph structure called Binary Speculation Diagram (BSD), of the CPU design from only external input-output observations instead of formal program code. During the generation of BSD, Monte Carlo-based expansion and the distance of Boolean functions are used to guarantee accuracy and efficiency, respectively. By efficiently exploring a search space of unprecedented size 10^{10^{540}}, which is the largest one of all machine-designed objects to our best knowledge, and thus pushing the limits of machine design, our approach generates an industrial-scale RISC-V CPU within only 5 hours. The taped-out CPU successfully runs the Linux operating system and performs comparably against the human-designed Intel 80486SX CPU. In addition to learning the world's first CPU only from input-output observations, which may reform the semiconductor industry by significantly reducing the design cycle, our approach even autonomously discovers human knowledge of the von Neumann architecture.Comment: 28 page

SEIPIN Regulates Lipid Droplet Expansion and Adipocyte Development by Modulating the Activity of Glycerol-3-phosphate Acyltransferase

Berardinelli-Seip congenital lipodystrophy 2 (BSCL2) is caused by loss-of-function mutations in SEIPIN, a protein implicated in both adipogenesis and lipid droplet expansion but whose molecular function remains obscure. Here, we identify physical and functional interactions between SEIPIN and microsomal isoforms of glycerol-3-phosphate acyltransferase (GPAT) in multiple organisms. Compared to controls, GPAT activity was elevated in SEIPIN-deficient cells and tissues and GPAT kinetic values were altered. Increased GPAT activity appears to underpin the block in adipogenesis and abnormal lipid droplet morphology associated with SEIPIN loss. Overexpression of Gpat3 blocked adipogenesis, and Gpat3 knockdown in SEIPIN-deficient preadipocytes partially restored differentiation. GPAT overexpression in yeast, preadipocytes, and fly salivary glands also formed supersized lipid droplets. Finally, pharmacological inhibition of GPAT in Seipin-/- mouse preadipocytes partially restored adipogenesis. These data identify SEIPIN as an evolutionarily conserved regulator of microsomal GPAT and suggest that GPAT inhibitors might be useful for the treatment of human BSCL2 patients

A role for oxysterol-binding protein–related protein 5 in endosomal cholesterol trafficking

ORP5 works together with Niemann Pick C-1 to facilitate exit of cholesterol from endosomes and lysosomes