Pemulsaan Organik terhadap Intensitas Serangan Bercak Ungu Serta Produksi Bawang Putih Varietas Lumbu Putih dan Lumbu Hijau

Penggunaan mulsa umumnya dilakukan di daerah-daerah yang sering mengalami kekeringan dan rentan terhadap pertumbuhan gulma. Gutomo et. al, (1998) menyatakan bahwa mulsa daun cengkeh selain memiliki peran dalam menekan kehilangan air tanah yang mempertahankan kelembaban, menjaga suhu tanah, erosi menekan pukulan melalui reduksi langsung dari air hujan ke tanah juga dapat menekan intensitas jamur yang merupakan salah satu kendala dalam mencapai hasil yang optimal bawang putih dataran tinggi. Penelitian ini dilakukan dikampus III Universitas Widyagama Malang dengan menggunakan desain, percobaan faktorial dilakukan dalam rancangan acak yang terdiri dari dua faktor dan diulang 3 kali, antara faktor M0: tidak ada mulsa; M1: cengkeh mulsa daun: M2: kubis limbah daun mulsa sedangkan faktor kedua V1: kultivar Lumbu putih dan V2: Lumbu kultivar hijau. Variabel pengamatan adalah jumlah siung, berat kering umbi dan tingkat serangan dengan menggunakan skor 0-6. Hasil analisis varians menunjukkan jumlah umbi M1V1 12,5 cengkeh / tanaman. Sementara itu, berat kering 9,82 g / tanaman. Kemudian pengamatan bercak ungu dengan tingkat serangan tertinggi pada perlakuan M2V1 (daun limbah mulsa kubis menggunakan Lumbu kultivar putih) yaitu dengan tingkat serangan 2.67%. Key word : bawang putih, daun cengkeh dan kubi

Microstructure evolution, texture development, and mechanical properties of hot-rolled 5052 aluminum alloy followed by annealing

Aluminum alloys, especially the 5000 series, have drawn the attention of the transportation industry due to their lightweight and consequently reduced fuel consumption. In this regard, one of the major problems of this alloy is its low strength and ductility that can be solved using rolling and post-annealing. Accordingly, the present study concentrates on this issue. Microstructural images showed that the rolling process develops a lot of tangled and trapped dislocations in the sample, which gradually lead to the formation of dislocation bundles and networks. Subsequent annealing can produce a more homogeneous structure with clear grain boundaries and low dislocation density in the inner region of the grains. However, grain refinement efficiency through rolling is retained even after annealing. Initial and rolled Al5052 with the maximum intensity of 2.87 and 6.33 possess the lowest and highest overall texture. Also, post-annealing decreases the texture intensity to 6.33 and 4.87 at 150 and 200 °C, respectively. In this context, deformation texture components strengthen considerably after the rolling process due to the formation of shear bands, and they slightly weaken during heat treatment. Although the initial annealing of the as-received material does not cause discontinuous recrystallization during rolling, it may facilitate the material recovery before rolling. Post-annealing was found to decrease the improved effect of strength by rolling and increase the negative influence of ductility due to the inhibition of dislocation strengthening. The results showed that both dislocation density and the precipitation of Mg atoms are influential for electrical resistivity

Determinación de algunos metales inorgánicos en aceites vegetales comestibles mediante espectroscopia de emisión atómicacon fuente de plasma acoplado inductivamente (ICP-AES)

Seventeen edible vegetable oils were analyzed spectrometrically for their metal (Cu, Fe, Mn, Co, Cr, Pb, Cd, Ni, and Zn) contents. Toxic metals in edible vegetable oils were determined by Inductively Coupled Plasma Atomic Emission Spectrometry (ICP-AES). The highest metal concentrations were measured as 0.0850, 0.0352, 0.0220, 0.0040, 0.0010, 0.0074, 0.0045, 0.0254 and 0.2870 mg/kg for copper in almond oil, for iron in corn oil-(c), for manganese in soybean oil, for cobalt in sunflower oil-(b) and almond oil, for chromium in almond oil, for lead in virgin olive oil, for cadmium in sunflower oil-(e), for nickel almond oil and for zinc in almond oil respectively. The method for determining toxic metals in edible vegetable oils by using ICP-AES is discussed. The metals were extracted from low quantities of oil (2-3 g) with a 10% nitric acid solution. The extracted metal in acid solution can be injected into the ICPAES. The proposed method is simple and allows the metals to be determined in edible vegetable oils with a precision estimated below 10% relative standard deviation (RSD) for Cu, 5% for Fe, 15% for Mn, 8% for Co, 10% for Cr, 20% for Pb, 5% for Cd, 16% for Ni and 11% for Zn.En este estudio se analizó espectrométricamente el contenido en metales (Cu, Fe, Mn, Co, Cr, Pb, Cd, Ni, and Zn) de 17 aceites vegetales comestibles mediante ICP-AES. Las concentaciones más elevadas se encontraron para el cobre en el aceite de almendra (0.0850 mg/kg), para el hierro en el aceite de maiz(c),(0.0352 mg/kg), para el manganeso en el aceite de soja (0.0220 mg/kg), para el cobalto en el aceite de girasol (b) (0.0040 mg/kg), para el cromo en el aceite de almendra (0.0010 mg/kg), para el plomo en el aceite de oliva virgen (0.0074 mg/kg), para el cadmio en el aceite de girasol (e) (0.0045 mg/kg), para el niquel en el aceite de almendra (0.0254 mg/kg) y para el zincen el aceite de almendra (0.2870 mg/kg). Los metales se extrajeron a partir de bajas cantidades de aceite (2-3 g), con una solución de ácido nítrico al 10%. Se discute el método y se conclluye que el método propuesto es simple y permite la determinación en aceites vegetales comestibles con una precisión estimada inferior al 10% para Cu, 5% para Fe, 15% para Mn. 8% para Co, 20% para Pb, 5% para Cd, 16% para Ni y 11% para Zn

Rational bidding using reinforcement learning: an application in automated resource allocation

The application of autonomous agents by the provisioning and usage of computational resources is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic resource provisioning and usage of computational resources, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a framework for supporting consumers and providers in technical and economic preference elicitation and the generation of bids. Secondly, we introduce a consumer-side reinforcement learning bidding strategy which enables rational behavior by the generation and selection of bids. Thirdly, we evaluate and compare this bidding strategy against a truth-telling bidding strategy for two kinds of market mechanisms – one centralized and one decentralized

Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Adaptive-Aggressive Traders Don't Dominate

For more than a decade Vytelingum's Adaptive-Aggressive (AA) algorithm has been recognized as the best-performing automated auction-market trading-agent strategy currently known in the AI/Agents literature; in this paper, we demonstrate that it is in fact routinely outperformed by another algorithm when exhaustively tested across a sufficiently wide range of market scenarios. The novel step taken here is to use large-scale compute facilities to brute-force exhaustively evaluate AA in a variety of market environments based on those used for testing it in the original publications. Our results show that even in these simple environments AA is consistently out-performed by IBM's GDX algorithm, first published in 2002. We summarize here results from more than one million market simulation experiments, orders of magnitude more testing than was reported in the original publications that first introduced AA. A 2019 ICAART paper by Cliff claimed that AA's failings were revealed by testing it in more realistic experiments, with conditions closer to those found in real financial markets, but here we demonstrate that even in the simple experiment conditions that were used in the original AA papers, exhaustive testing shows AA to be outperformed by GDX. We close this paper with a discussion of the methodological implications of our work: any results from previous papers where any one trading algorithm is claimed to be superior to others on the basis of only a few thousand trials are probably best treated with some suspicion now. The rise of cloud computing means that the compute-power necessary to subject trading algorithms to millions of trials over a wide range of conditions is readily available at reasonable cost: we should make use of this; exhaustive testing such as is shown here should be the norm in future evaluations and comparisons of new trading algorithms.Comment: To be published as a chapter in "Agents and Artificial Intelligence" edited by Jaap van den Herik, Ana Paula Rocha, and Luc Steels; forthcoming 2019/2020. 24 Pages, 1 Figure, 7 Table

Increasing negotiation performance at the edge of the network

Automated negotiation has been used in a variety of distributed settings, such as privacy in the Internet of Things (IoT) devices and power distribution in Smart Grids. The most common protocol under which these agents negotiate is the Alternating Offers Protocol (AOP). Under this protocol, agents cannot express any additional information to each other besides a counter offer. This can lead to unnecessarily long negotiations when, for example, negotiations are impossible, risking to waste bandwidth that is a precious resource at the edge of the network. While alternative protocols exist which alleviate this problem, these solutions are too complex for low power devices, such as IoT sensors operating at the edge of the network. To improve this bottleneck, we introduce an extension to AOP called Alternating Constrained Offers Protocol (ACOP), in which agents can also express constraints to each other. This allows agents to both search the possibility space more efficiently and recognise impossible situations sooner. We empirically show that agents using ACOP can significantly reduce the number of messages a negotiation takes, independently of the strategy agents choose. In particular, we show our method significantly reduces the number of messages when an agreement is not possible. Furthermore, when an agreement is possible it reaches this agreement sooner with no negative effect on the utility.Comment: Accepted for presentation at The 7th International Conference on Agreement Technologies (AT 2020

Learning to trade in an unbalanced market

We study the evolution of trading strategies in double auctions as the size of the market gets larger. When the number of buyers and sellers is balanced, Fano et al.~(2011) show that the choice of the order-clearing rule (simultaneous or asynchronous) steers the emergence of fundamentally different strategic behavior. We extend their work to unbalanced markets, confirming their main result as well as that allocative inefficiency tends to zero. On the other hand, we discover that convergence to the competitive outcome takes place only when the market is large and that the long side of the market is more effective at improving its disadvantaged terms of trade under asynchronous order-clearing