MOEA/D with Adaptive Weight Adjustment

Recently, MOEA/D (multi-objective evolutionary algorithm based on decomposition) has achieved great success in the field of evolutionary multi-objective optimization and has attracted a lot of attention. It decomposes a multi-objective optimization problem (MOP) into a set of scalar subproblems using uniformly distributed aggregation weight vectors and provides an excellent general algorithmic framework of evolutionary multi-objective optimization. Generally, the uniformity of weight vectors in MOEA/D can ensure the diversity of the Pareto optimal solutions, however, it cannot work as well when the target MOP has a complex Pareto front (PF; i.e., discontinuous PF or PF with sharp peak or low tail). To remedy this, we propose an improved MOEA/D with adaptive weight vector adjustment (MOEA/D-AWA). According to the analysis of the geometric relationship between the weight vectors and the optimal solutions under the Chebyshev decomposition scheme, a new weight vector initialization method and an adaptive weight vector adjustment strategy are introduced in MOEA/D-AWA. The weights are adjusted periodically so that the weights of subproblems can be redistributed adaptively to obtain better uniformity of solutions. Meanwhile, computing efforts devoted to subproblems with duplicate optimal solution can be saved. Moreover, an external elite population is introduced to help adding new subproblems into real sparse regions rather than pseudo sparse regions of the complex PF, that is, discontinuous regions of the PF. MOEA/D-AWA has been compared with four state of the art MOEAs, namely the original MOEA/D, Adaptive-MOEA/D, [Formula: see text]-MOEA/D, and NSGA-II on 10 widely used test problems, two newly constructed complex problems, and two many-objective problems. Experimental results indicate that MOEA/D-AWA outperforms the benchmark algorithms in terms of the IGD metric, particularly when the PF of the MOP is complex.</jats:p

Assessing the effects of agricultural management practices on crop ecosystems with the LPJ-GUESS model

In den letzten Jahrzehnten ist es weltweit zu erheblichen Verlusten an organischem Kohlenstoff (SOC) im Boden gekommen, die auf die Intensivierung der Landwirtschaft und die Umwandlung natürlicher Böden in landwirtschaftliche Nutzflächen zur Ernährung der wachsenden Bevölkerung zurückzuführen sind. Die Erhöhung der SOC-Bestände in Ackerflächen durch verbesserte Bewirtschaftungspraktiken - wie die Verringerung der Bodenbearbeitung, die Ausbringung von Ernterückständen und der Anbau von Zwischenfrüchten - wurde als vielversprechende Option für die Eindämmung des Klimawandels identifiziert, mit gleichzeitigen Vorteilen für die Bodenfruchtbarkeit und die Ernteerträge. Die großflächige Quantifizierung dieser Bewirtschaftungspraktiken auf landwirtschaftliche Ökosysteme, einschließlich der Auswirkungen des Anbaus von Leguminosen, ist jedoch nach wie vor unsicher. Um die globale landwirtschaftliche Produktion besser abzubilden, integriere ich in dieser Arbeit zunächst zwei Körnerleguminosen (Sojabohne und Ackerbohne) und eine krautige Leguminose (Weißklee) mit biologischer Stickstofffixierung (BNF) in das dynamische Vegetationsmodell LPJ-GUESS. Die räumlichen und zeitlichen Muster der BNF-Raten in Sojabohnen und Ackerbohnen werden über den historischen Zeitraum 1981-2016 quantifiziert. Anschließend wird der Großflächige Einfluss alternativer Bewirtschaftungsstrategien auf die Ernteerträge und die Kohlenstoff- (C) und Stickstoff- (N) Bilanzen der Anbauflächen unter gegenwärtigen und zukünftigen Klimabedingungen untersucht, indem die Ergebnisse der aktualisierten Modellversion angewendet und analysiert werden. Die Modellsimulationen zeigen, dass die globale N-Fixierung in Sojabohnen und allen Hülsenfrüchten (die im Modell die Ackerbohne repräsentieren) im Zeitraum 1981-2016 bei 11,6±2,2 Tg N yr-1 bzw. 5,6±1,0 Tg N yr-1 beträgt. Räumlich gesehen sind die höchsten BNF-Raten in tropischen und gemäßigten Regionen mit warmem und feuchtem Klima zu finden. Die Bodenwasserverfügbarkeit und die Temperatur sind neben der N-Düngung die wichtigsten Einflussfaktoren für die N-Fixierung. Insgesamt macht die modellierte Gesamt-N-Fixierung durch Körnerleguminosen 12 % des jährlich in globalen terrestrischen Ökosystemen fixierten N aus (ca. 140 Tg N yr-1), was auf die Bedeutung des BNF-Eintrags in Ackerflächen für den globalen terrestrischen N-Kreislauf schließen lässt, obwohl ein großer Teil des fixierten N jedes Jahr durch die Ernte aus den Ökosystemen entfernt wird. Der Anbau von Leguminosen als Deckfrucht in der Zwischenseason unterscheidet sich deutlich vom reinen Anbau von Körnerleguminosen, da der in Deckfrüchten fixierte Stickstoff in der Regel in den Boden zurückgeführt wird. Unter der Annahme, dass weltweit alle Anbauflächen konservierende Landwirtschaftstechniken verwenden, ergibt sich basierend auf den Modelldaten, dass die Kombination von N-fixierenden Deckfrüchten und minimaler Bodenbearbeitung den Kohlenstoffgehalt des Bodens um 7 % (+0,32 Pg C yr-1 in den globalen Anbauflächen) erhöhen und gleichzeitig die N-Auswaschungsverluste um 41 % (-7,3 Tg N yr-1) nach 36 Jahren der Umsetzung reduzieren kann (die maximale Dauer, die in Feldversuchen mit Deckfrüchten in dieser Dissertation ermittelt wurde). Diese integrierte Praxis geht mit einem Anstieg der gesamten pflanzlichen Produktion um 2 % (+37 Millionen Tonnen pro Jahr, einschließlich Weizen, Mais, Reis und Soja) im letzten Jahrzehnt der Simulation einher. Im Vergleich zu Nicht-Leguminosen-Deckungskulturen trägt der Einsatz von N-fixierendem Deckungsanbau in den Modellexperimenten stärker zur Ertragssteigerung in den feuchten Tropen bei, während die Produktionsverluste in den nördlichen gemäßigten Klimazonen gemildert werden. Diese räumliche Variation hängt mit den Hauptkulturen und dem Stickstoffdüngereinsatz zusammen, wobei bei Sojabohnensystemen und stark gedüngten landwirtschaftlichen Böden nur geringe Ertragsveränderungen simuliert werden. Am Beispiel von Ostafrika werden Leguminosen zusammen mit sechs alternativen Bewirtschaftungsstrategien untersucht, um ihre Auswirkungen auf die Ökosysteme von Nutzpflanzen zu quantifizieren. Die regionalen Simulationen zeigen, dass die verbesserten Bewirtschaftungsmethoden, die in den tropischen Ökosystemen umgesetzt werden, den Klimawandel abmildern und gleichzeitig die Ernteerträge steigern können, insbesondere bei einer integrierten konservierenden Landwirtschaft, die alle bodenschonenden Techniken kombiniert. In den untersuchten Regionen führt diese kombinierte Strategie, die keine Bodenbearbeitung, die Ausbringung von Rückständen und Dung sowie den Anbau von Deckfrüchten umfasst, langfristig zu einer Erhöhung der simulierten SOC-Vorräte um 11 %, begleitet von einer Steigerung der gesamten Pflanzenproduktion um 25 %. Der Anbau von N-fixierenden Deckfrüchten ist ebenfalls vielversprechend, um den C-Gehalt im Ackerboden (+4 %) und die landwirtschaftliche Produktion (+16 %) zu erhöhen, wobei die Umweltkosten in Bezug auf die gesamten N-Verluste (+28 %; einschließlich gasförmiger Emissionen und N-Auswaschung) zu berücksichtigen sind. Diese Bewirtschaftungseinflüsse würden bei drei Klimaszenarien möglicherweise auch in Zukunft bestehen bleiben. Zusammenfassend zeigen die Ergebnisse dieser Arbeit, wie wichtig die Berücksichtigung von N-Fixierern bei der Bewertung großräumiger C-N-Zyklen in Systemen der konservierenden Landwirtschaft ist. Sie zeigen auch die Möglichkeit einer verbesserten landwirtschaftlichen Bewirtschaftung auf, um ökologische Nachhaltigkeit zu erreichen und die Ernährungssicherheit in globalen Anbauflächen zu gewährleisten

Drosophila tan Encodes a Novel Hydrolase Required in Pigmentation and Vision

Many proteins are used repeatedly in development, but usually the function of the protein is similar in the different contexts. Here we report that the classical Drosophila melanogaster locus tan encodes a novel enzyme required for two very different cellular functions: hydrolysis of N-β-alanyl dopamine (NBAD) to dopamine during cuticular melanization, and hydrolysis of carcinine to histamine in the metabolism of photoreceptor neurotransmitter. We characterized two tan-like P-element insertions that failed to complement classical tan mutations. Both are inserted in the 5′ untranslated region of the previously uncharacterized gene CG12120, a putative homolog of fungal isopenicillin-N N-acyltransferase (EC 2.3.1.164). Both P insertions showed abnormally low transcription of the CG12120 mRNA. Ectopic CG12120 expression rescued tan mutant pigmentation phenotypes and caused the production of striking black melanin patterns. Electroretinogram and head histamine assays indicated that CG12120 is required for hydrolysis of carcinine to histamine, which is required for histaminergic neurotransmission. Recombinant CG12120 protein efficiently hydrolyzed both NBAD to dopamine and carcinine to histamine. We conclude that D. melanogaster CG12120 corresponds to tan. This is, to our knowledge, the first molecular genetic characterization of NBAD hydrolase and carcinine hydrolase activity in any organism and is central to the understanding of pigmentation and photoreceptor function

Theoretical prediction of diffusive ionic current through nanopores under salt gradients

In charged nanopores, ionic diffusion current reflects the ionic selectivity and ionic permeability of nanopores which determines the performance of osmotic energy conversion, i.e. the output power and efficiency. Here, theoretical predictions of the diffusive currents through cation-selective nanopores have been developed based on the investigation of diffusive ionic transport under salt gradients with simulations. The ionic diffusion current I satisfies a reciprocal relationship with the pore length I correlates with a/L (a is a constant) in long nanopores. a is determined by the cross-sectional areas of diffusion paths for anions and cations inside nanopores which can be described with a quadratic power of the diameter, and the superposition of a quadratic power and a first power of the diameter, respectively. By using effective concentration gradients instead of nominal ones, the deviation caused by the concentration polarization can be effectively avoided in the prediction of ionic diffusion current. With developed equations of effective concentration difference and ionic diffusion current, the diffusion current across nanopores can be well predicted in cases of nanopores longer than 100 nm and without overlapping of electric double layers. Our results can provide a convenient way for the quantitative prediction of ionic diffusion currents under salt gradients

Internal Leakage Fault Detection and Tolerant Control of Single-Rod Hydraulic Actuators

The integration of internal leakage fault detection and tolerant control for single-rod hydraulic actuators is present in this paper. Fault detection is a potential technique to provide efficient condition monitoring and/or preventive maintenance, and fault tolerant control is a critical method to improve the safety and reliability of hydraulic servo systems. Based on quadratic Lyapunov functions, a performance-oriented fault detection method is proposed, which has a simple structure and is prone to implement in practice. The main feature is that, when a prescribed performance index is satisfied (even a slight fault has occurred), there is no fault alarmed; otherwise (i.e., a severe fault has occurred), the fault is detected and then a fault tolerant controller is activated. The proposed tolerant controller, which is based on the parameter adaptive methodology, is also prone to realize, and the learning mechanism is simple since only the internal leakage is considered in parameter adaptation and thus the persistent exciting (PE) condition is easily satisfied. After the activation of the fault tolerant controller, the control performance is gradually recovered. Simulation results on a hydraulic servo system with both abrupt and incipient internal leakage fault demonstrate the effectiveness of the proposed fault detection and tolerant control method

Adaptive Robust Actuator Fault Accommodation for a Class of Uncertain Nonlinear Systems with Unknown Control Gains

An adaptive robust fault tolerant control approach is proposed for a class of uncertain nonlinear systems with unknown signs of high-frequency gain and unmeasured states. In the recursive design, neural networks are employed to approximate the unknown nonlinear functions, K-filters are designed to estimate the unmeasured states, and a dynamical signal and Nussbaum gain functions are introduced to handle the unknown sign of the virtual control direction. By incorporating the switching function σ algorithm, the adaptive backstepping scheme developed in this paper does not require the real value of the actuator failure. It is mathematically proved that the proposed adaptive robust fault tolerant control approach can guarantee that all the signals of the closed-loop system are bounded, and the output converges to a small neighborhood of the origin. The effectiveness of the proposed approach is illustrated by the simulation examples

Modulation Mechanism of Ionic Transport through Short Nanopores by Charged Exterior Surfaces

Short nanopores have various applications in biosensing, desalination, and energy conversion. Here, the modulation of charged exterior surfaces on ionic transport is investigated through simulations with sub-200 nm long nanopores under applied voltages. Detailed analysis of ionic current, electric field strength, and fluid flow inside and outside nanopores reveals that charged exterior surfaces can increase ionic conductance by increasing both the concentration and migration speed of charge carriers. The electric double layers near charged exterior surfaces provide an ion pool and an additional passageway for counterions, which lead to enhanced exterior surface conductance and ionic concentrations at pore entrances and inside the nanopore. We also report that charges on the membrane surfaces increase electric field strengths inside nanopores. The effective width of a ring with surface charges placed at pore entrances (Lcs) is considered as well by studying the dependence of the current on Lcs. We find a linear relationship between the effective Lcs and the surface charge density and voltage, and an inverse relationship between the geometrical pore length and salt concentration. Our results elucidate the modulation mechanism of charged exterior surfaces on ionic transport through short nanopores, which is important for the design and fabrication of porous membranes.Comment: 27 pages, 6 figure

Ion Transport through Short Nanopores Modulated by Charged Exterior Surfaces

Short nanopores find extensive applications capitalizing on their high throughput and detection resolution. Ionic behaviors through long nanopores are mainly determined by charged inner-pore walls. When pore lengths decrease to sub-200 nm, charged exterior surfaces provide considerable modulation to ion current. We find that the charge status of inner-pore walls affects the modulation of ion current from charged exterior surfaces. For 50-nm-long nanopores with neutral inner-pore walls, charged exterior surfaces on the voltage (surfaceV) and ground (surfaceG) sides enhance and inhibit ion transport by forming ion enrichment and depletion zones inside nanopores, respectively. For nanopores with both charged inner-pore and exterior surfaces, continuous electric double layers enhance ion transport through nanopores significantly. The charged surfaceV results in higher ion current by simultaneously weakening ion depletion at pore entrances and enhancing the intra-pore ion enrichment. The charged surfaceG expedites the exit of ions from nanopores, resulting in a decrease in ion enrichment at pore exits. Through adjustment in the width of charged-ring regions near pore boundaries, the effective charged width of the charged exterior is explored at ~20nm. Our results may provide a theoretical guide for further optimizing the performance of nanopore-based applications, like seawater desalination, biosensing, and osmotic energy conversion.Comment: 18 pages, 5 figure

Estimating the Global Influence of Cover Crops on Ecosystem Service Indicators in Croplands With the LPJ‐GUESS Model

Cover crops (CCs) can improve soil nutrient retention and crop production while providing climate change mitigation co-benefits. However, quantifying these ecosystem services across global agricultural lands remains inadequate. Here, we assess how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects agricultural soil carbon stocks, N leaching, and crop yields, using the dynamic global vegetation model LPJ-GUESS. The model performance is evaluated with observations from worldwide field trials and modeled output further compared against previously published large-scale estimates. LPJ-GUESS broadly captures the enhanced soil carbon, reduced N leaching, and yield changes that are observed in the field. Globally, we found that combining N-fixing CCs with no-tillage technique could potentially increase soil carbon levels by 7% (+0.32 Pg C yr$^{−1}$ in global croplands) while reducing N leaching loss by 41% (−7.3 Tg N yr$^{−1}$) compared with fallow controls after 36 years of simulation since 2015. This integrated practice is accompanied by a 2% of increase in total crop production (+37 million tonnes yr$^{−1}$ including wheat, maize, rice, and soybean) in the last decade of the simulation. The identified effects of CCs on crop productivity vary widely among main crop types and N fertilizer applications, with small yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long-term environmental sustainability without compromising crop production in global croplands

Retentive Network: A Successor to Transformer for Large Language Models

In this work, we propose Retentive Network (RetNet) as a foundation architecture for large language models, simultaneously achieving training parallelism, low-cost inference, and good performance. We theoretically derive the connection between recurrence and attention. Then we propose the retention mechanism for sequence modeling, which supports three computation paradigms, i.e., parallel, recurrent, and chunkwise recurrent. Specifically, the parallel representation allows for training parallelism. The recurrent representation enables low-cost $O(1)$ inference, which improves decoding throughput, latency, and GPU memory without sacrificing performance. The chunkwise recurrent representation facilitates efficient long-sequence modeling with linear complexity, where each chunk is encoded parallelly while recurrently summarizing the chunks. Experimental results on language modeling show that RetNet achieves favorable scaling results, parallel training, low-cost deployment, and efficient inference. The intriguing properties make RetNet a strong successor to Transformer for large language models. Code will be available at https://aka.ms/retnet