Low-level Operations Research Articles

Paving the Way for SQIsign: Toward Efficient Deployment on 32-bit Embedded Devices

The threat of quantum computing has spurred research into post-quantum cryptography. SQIsign, a candidate submitted to the standardization process of the National Institute of Standards and Technology, is emerging as a promising isogeny-based signature scheme. This work aimed to enhance SQIsign’s practical deployment by optimizing its low-level arithmetic operations. Through hierarchical decomposition and performance profiling, we identified the ideal-to-isogeny translation, primarily involving elliptic curve operations, as the main bottleneck. We developed efficient 32-bit finite field arithmetic for elliptic curves, such as basic operations, like addition with carry, subtraction with borrow, and conditional move. We then implemented arithmetic operations in the Montgomery domain, and extended these to quadratic field extensions. Our implementation offers improved compatibility with 32-bit architectures and enables more fine-grained SIMD acceleration. Performance evaluations demonstrated the practicality in low-level operations. Our work has potential in easing the development of SQIsign in practice, making SQIsign more efficient and practical for real-world post-quantum cryptographic applications.

A generic deep learning architecture optimization method for edge device based on start-up latency reduction

In the promising Artificial Intelligence of Things technology, deep learning algorithms are implemented on edge devices to process data locally. However, high-performance deep learning algorithms are accompanied by increased computation and parameter storage costs, leading to difficulties in implementing huge deep learning algorithms on memory and power constrained edge devices, such as smartphones and drones. Thus various compression methods are proposed, such as channel pruning. According to the analysis of low-level operations on edge devices, existing channel pruning methods have limited effect on latency optimization. Due to data processing operations, the pruned residual blocks still result in significant latency, which hinders real-time processing of CNNs on edge devices. Hence, we propose a generic deep learning architecture optimization method to achieve further acceleration on edge devices. The network is optimized in two stages, Global Constraint and Start-up Latency Reduction, and pruning of both channels and residual blocks is achieved. Optimized networks are evaluated on desktop CPU, FPGA, ARM CPU, and PULP platforms. The experimental results show that the latency is reduced by up to 70.40%, which is 13.63% higher than only applying channel pruning and achieving real-time processing in the edge device.

Design and analysis of solar hybrid combined cooling, heating and power system: A bi-level optimization model

The planning and operation optimization of hybrid combined cooling, heating and power (CCHP) systems is the prerequisite and foundation for its advantages such as economy, energy saving, and high efficiency. This study constructed a bi-level optimization model of a hybrid CCHP system. Firstly, an upper-level capacity planning model of the hybrid CCHP system is constructed considering system economy, energy, and environmental performance. Secondly, a lower-level operation optimization model is proposed, considering the operation and maintenance cost of the system. Thirdly, a multi-objective honey badger algorithm (MOHBA) is developed based on the characteristics of the studied problem and the features of the bi-level optimization model. Finally, the optimization results indicate that the hybrid CCHP can save 32.79 % of the total cost, 49.74 % of primary energy, and 60.18 % of carbon emissions compared to the separation production (SP) system. In addition, compared with improving the strength Pareto evolutionary algorithm (SPEA2), non-dominated sorting genetic algorithm-II (NSGA-II), multi-objective evolutionary algorithm based on decomposition (MOEAD), and multi-objective particle swarm optimization (MOPSO), the proposed MOHBA can provide superior optimization results. The proposed bi-level planning model achieves improved economic, energy, and environmental performance of the hybrid CCHP system compared to the single-level planning model for the given operation modes.

Spatial–temporal distribution characteristics and traceability analysis of organic matter in Shahe Reservoir (Beijing, China)

Shahe Reservoir is a key node in the upstream of the North Canal, and the water quality has gradually improved after the implementation of low water operation in 2018. The organic matter(OM) in the sediment decreased from 16.66 to 14.22%.In this study, the FI index and parallel factor method were used to investigate the traceability of OM and dissolved organic matter (DOM) in the Shahe Reservoir before and during low water level operation(LWLO), and the results showed that the terrestrial source fraction of OM in sediments was mainly related to organic-rich terrestrial plant residues carried by tributaries and overflows/outfalls during the rainy season, and the FI index indicated that the organic matter (OM) in the Shahe Reservoir before and during LWLO in each DOM in the area is derived from authigenic sources of autotrophic microorganisms, algae, etc. The parallel factor method shows that more than most of the pollutants in the DOM are input from endogenous sources and a small proportion of pollutants are input from exogenous sources. Nutrients in both sediment and interstitial water increased during the LWLO, with TN and TP levels increasing by 262.38 and 204.45 mg·kg−1 in sediment, NH4+–N, PO43−−P, TN and TP in interstitial water increasing by 0.98, 1.36, 2.07 and 4.33 mg·L−1, respectively. Pearson correlation analysis and principal component analysis showed that OM was significantly correlated with nutrients: OM and TN (p < 0.01) and OM and TP (p < 0.05) in the pre-LWLO; OM and TN and TP (p < 0.01) in the LWLO.The results suggested that organic matter pollution control should be mainly carried out from the perspective of endogenous input, focusing on controlling the release of nutrients in sediments.

Spike Optimization to Improve Properties of Ferroelectric Tunnel Junction Synaptic Devices for Neuromorphic Computing System Applications.

The continuous advancement of Artificial Intelligence (AI) technology depends on the efficient processing of unstructured data, encompassing text, speech, and video. Traditional serial computing systems based on the von Neumann architecture, employed in information and communication technology development for decades, are not suitable for the concurrent processing of massive unstructured data tasks with relatively low-level operations. As a result, there arises a pressing need to develop novel parallel computing systems. Recently, there has been a burgeoning interest among developers in emulating the intricate operations of the human brain, which efficiently processes vast datasets with remarkable energy efficiency. This has led to the proposal of neuromorphic computing systems. Of these, Spiking Neural Networks (SNNs), designed to closely resemble the information processing mechanisms of biological neural networks, are subjects of intense research activity. Nevertheless, a comprehensive investigation into the relationship between spike shapes and Spike-Timing-Dependent Plasticity (STDP) to ensure efficient synaptic behavior remains insufficiently explored. In this study, we systematically explore various input spike types to optimize the resistive memory characteristics of Hafnium-based Ferroelectric Tunnel Junction (FTJ) devices. Among the various spike shapes investigated, the square-triangle (RT) spike exhibited good linearity and symmetry, and a wide range of weight values could be realized depending on the offset of the RT spike. These results indicate that the spike shape serves as a crucial indicator in the alteration of synaptic connections, representing the strength of the signals.

An Estimation of Distribution Algorithm-Based Hyper-Heuristic for the Distributed Assembly Mixed No-Idle Permutation Flowshop Scheduling Problem

The distributed assembly mixed no-idle permutation flowshop scheduling problem (DAMNIPFSP), a common occurrence in modern industries like integrated circuit production, ceramic frit production, fiberglass processing, and steel-making, is a new model that considers mixed machines with no-idle restrictions as well as conventional machines. This article introduces an estimation of distribution algorithm-based hyper-heuristic (EDA-HH) to solve the DAMNIPFSP. Ten simple heuristic rules as low-level operations are utilized to search the solution space. The estimation of distribution algorithm is integrated into the framework of hyper-heuristic as the high-level strategy to control the low-level heuristics sequence in the solution space. The destruction and construction procedures are conducted on products and jobs in order to enhance the exploitation competence of EDA-HH. The computational simulation is carried out and the experimental results show that the proposed EDA-HH is significantly superior to the competitors in the statistical sense. The results of the 810 large-scale problem instances show the effectiveness of the EDA-HH in solving the DAMNIPFSP. Moreover, the CPLEX solver is utilized to verify the correctness of the model with some small instances.

Naturalistic Spoken Language Comprehension Is Supported by Alpha and Beta Oscillations.

Brain oscillations are prevalent in all species and are involved in numerous perceptual operations. α oscillations are thought to facilitate processing through the inhibition of task-irrelevant networks, while β oscillations are linked to the putative reactivation of content representations. Can the proposed functional role of α and β oscillations be generalized from low-level operations to higher-level cognitive processes? Here we address this question focusing on naturalistic spoken language comprehension. Twenty-two (18 female) Dutch native speakers listened to stories in Dutch and French while MEG was recorded. We used dependency parsing to identify three dependency states at each word: the number of (1) newly opened dependencies, (2) dependencies that remained open, and (3) resolved dependencies. We then constructed forward models to predict α and β power from the dependency features. Results showed that dependency features predict α and β power in language-related regions beyond low-level linguistic features. Left temporal, fundamental language regions are involved in language comprehension in α, while frontal and parietal, higher-order language regions, and motor regions are involved in β. Critically, α- and β-band dynamics seem to subserve language comprehension tapping into syntactic structure building and semantic composition by providing low-level mechanistic operations for inhibition and reactivation processes. Because of the temporal similarity of the α-β responses, their potential functional dissociation remains to be elucidated. Overall, this study sheds light on the role of α and β oscillations during naturalistic spoken language comprehension, providing evidence for the generalizability of these dynamics from perceptual to complex linguistic processes.SIGNIFICANCE STATEMENT It remains unclear whether the proposed functional role of α and β oscillations in perceptual and motor function is generalizable to higher-level cognitive processes, such as spoken language comprehension. We found that syntactic features predict α and β power in language-related regions beyond low-level linguistic features when listening to naturalistic speech in a known language. We offer experimental findings that integrate a neuroscientific framework on the role of brain oscillations as "building blocks" with spoken language comprehension. This supports the view of a domain-general role of oscillations across the hierarchy of cognitive functions, from low-level sensory operations to abstract linguistic processes.

Parallel hyper heuristic algorithm based on reinforcement learning for the corridor allocation problem and parallel row ordering problem

Hyper heuristics is a relatively new optimisation algorithm. Numerous studies have reported that hyper heuristics are well applied in combinatorial optimisation problems. As a classic combinatorial optimisation problem, the row layout problem has not been publicly reported on applying hyper heuristics to its various sub-problems. To fill this gap, this study proposes a parallel hyper-heuristic approach based on reinforcement learning for corridor allocation problems and parallel row ordering problems. For the proposed algorithm, an outer layer parallel computing framework was constructed based on the encoding of the problem. The simulated annealing, tabu search, and variable neighbourhood algorithms were used in the algorithm as low-level heuristic operations, and Q-learning in reinforcement learning was used as a high-level strategy. A state space containing sequences and fitness values was designed. The algorithm performance was then evaluated for benchmark instances of the corridor allocation problem (37 groups) and parallel row ordering problem (80 groups). The results showed that, in most cases, the proposed algorithm provided a better solution than the best-known solutions in the literature. Finally, the meta-heuristic algorithm applied to three low-level heuristic operations is taken as three independent algorithms and compared with the proposed hyper-heuristic algorithm on four groups of parallel row ordering problem instances. The effectiveness of Q-learning in selection is illustrated by analysing the comparison results of the four algorithms and the number of calls of the three low-level heuristic operations in the proposed method.

Mobile Multiple Sink Path Planning for Large-Scale Sensor Networks Based on Hyper-Heuristic Artificial Bee Colony Algorithm

Large-scale wireless sensor networks consists a terrific amount of nodes, a wide range of deployment, extended data transmission time, and large network energy consumption. To solve the above problems, a mobile multiple Sink path planning based on hyper-heuristic artificial bee colony algorithm is proposed. The artificial bee colony algorithm is used as the high-level strategy. In view of the changes in the network operation process, namely the number of nodes and energy changes, the design of three stages of the artificial bee colony algorithm is used to choose and manipulate the low-level heuristic operator. The selected lower layer operator set plans the path of each mobile sink nodes. Compared with other famous meta-heuristic algorithms, it is proved that the proposed hyper-heuristic algorithm is an effective, efficient and robust algorithm, which can effectively solve the path planning problem in view of multiple sink nodes, reduce network delay and reduce network energy consumption.

Accelerated linear algebra compiler for computationally efficient numerical models: Success and potential area of improvement.

The recent dramatic progress in machine learning is partially attributed to the availability of high-performant computers and development tools. The accelerated linear algebra (XLA) compiler is one such tool that automatically optimises array operations (mostly fusion to reduce memory operations) and compiles the optimised operations into high-performant programs specific to target computing platforms. Like machine-learning models, numerical models are often expressed in array operations, and thus their performance can be boosted by XLA. This study is the first of its kind to examine the efficiency of XLA for numerical models, and the efficiency is examined stringently by comparing its performance with that of optimal implementations. Two shared-memory computing platforms are examined-the CPU platform and the GPU platform. To obtain optimal implementations, the computing speed and its optimisation are rigorously studied by considering different workloads and the corresponding computer performance. Two simple equations are found to faithfully modell the computing speed of numerical models with very few easily-measureable parameters. Regarding operation optimisation within XLA, results show that models expressed in low-level operations (e.g., slice, concatenation, and arithmetic operations) are successfully fused while high-level operations (e.g., convolution and roll) are not. Regarding compilation within XLA, results show that for the CPU platform of certain computers and certain simple numerical models on the GPU platform, XLA achieves high efficiency (> 80%) for large problems and acceptable efficiency (10%~80%) for medium-size problems-the gap is from the overhead cost of Python. Unsatisfactory performance is found for the CPU platform of other computers (operations are compiled in a non-optimal way) and for high-dimensional complex models for the GPU platform, where each GPU thread in XLA handles 4 (single precision) or 2 (double precision) output elements-hoping to exploit the high-performant instructions that can read/write 4 or 2 floating-point numbers with one instruction. However, these instructions are rarely used in the generated code for complex models and performance is negatively affected. Therefore, flags should be added to control the compilation for these non-optimal scenarios.

Aerodynamic Design of Cooling Guide for Electrical Machine in Electric Ducted Fan

Abstract Electric ducted fan has newly emerged as the most compact type of propulsion fan for flying cars, because of its flexible configuration arrangement, low noise level and high safety level operation. Typically, an electric ducted fan is composed of a hub-driven fan and a hub-mounted inner rotor electrical machine. The thermal management of the hub-mounted electrical machine is essential because it determines the machine's continuous output power and reliability, as well as the fan thrust force. The heat transmitted from the stator winding to the cooling guide vane (CGV) is dissipated by introducing the air flow at hub region to the CGV. Therefore, in order to better improve the thermal performance of the CGV, an aerodynamic design of CGV is proposed in this paper for better thermal management of stator end-windings. First, for a certain desired power output of electrical machine under temperature constraints of stator winding, lumped thermal network is established to derive the requirement of heat transfer coefficients of the CGV. Then, from engineering feasibility, three different CGVs including straight type, arc-shape type and airfoil-shape type are tested by computational fluid dynamics. The evaluation of the CGV designs include the flow characteristics, power requirements, and thermal characteristics for stator winding cooling purposes. In comparison with the conventionally straight type CGV, improvements are gained from the designed arc-shape type and airfoil-shape type cooling configurations, whereas the fan thrust force remains the same simultaneously. It is verified that the arc-shape type and airfoil-shape type CGVs are promising in enhancing the thermal management performance of the electrical machine stator winding without power requirement.

OC-005 ENDOSCOPIC SURGICAL SKILL QUALIFICATION SYSTEM IN JAPAN: PROVEN PERFORMANCE IN HERNIA SURGERY

Abstract Endoscopic Surgical Skill Qualification System by Japan Society for Endoscopic Surgery is implemented from 2004. Laparoscopic inguinal hernia repair (TAPP, TEP) is positioned as a low difficulty level operation under general surgery field in this system. An application qualification to the system demands the list of a specified operative cases, to participate in education seminars, and training of endoscopic surgery over 2 years after the Board Certified. A specified case quantity is the operative experience of above 5 examples of a high-difficulty level and 45 examples of a low difficulty level operation. Candidates submit 3 no edited videos of the endoscopic surgery performed for indirect hernia with the orifice above 1.5 cm in a male-sex. Which to examine among 3 videos is decided randomly by the society. Two hernia-specified referees examine one candidate. When both results don't agree, an extra referee will examine newly and fixes the result. An evaluation is estimated within 60 points of common standards and 40 points by an hernia. Equal to or more than 70 points in amount become a pass. Acceptance rate in hernia was 16% in 2021. The Nationwide Survey of Endoscopic Surgery reported recurrence rates of after laparoscopic hernia surgery as 5% in TEP in and 4% in TAPP in 2012–2013, 2.0% and 1.3% in 2018∼2019, what is improved beyond the period. It's no exaggeration to say that the Qualification System has played a big part. The Qualification System like no other in the world is to be introduced.

International Evaluation of China’s Beef Cattle Industry Development Level and Lagging Points

Quality, efficiency, safety and environmental protection are important directions for the development of animal husbandry in China. Taking China’s beef industry as an example, this study establishes a comprehensive index system from six industrial subsystems: resource endowment, production, consumption, quality, trade and environment. By comparison with the beef cattle industry in other countries, great effort is being made to position the development level of China’s beef industry and to determine its lagging points, according to the coupling coordination degree and relative development degree of each subsystem. Under the multidimensional development goals, the development level of China’s beef cattle industry shows a fluctuating upward-downward trend, and the resource endowment has a certain advantage, but the development of the production, consumption, quality and environmental subsystems is insufficient and lacks competitiveness. China’s beef cattle industry is less developed than in Brazil, the United States, Argentina, Australia and other countries in terms of production, consumption, quality, trade and environment. The industrial subsystems mainly present low-level coordination and operation, with lagging development of the production quantity and quality. According to the analysis of the industry’s weaknesses, China’s beef industry needs to promote the combination of planting and breeding, cost reduction, efficiency increase, and green breeding.

A Hyperheuristic Algorithm Based on Evolutionary Strategy for Complex Mission Planning of AUVs in Marine Environment

Autonomous underwater vehicles (AUVs) have been widely implemented to explore marine resources or perform marine missions; meanwhile, complex mission planning has been expected to enhance the intelligence level, improve energy efficiency, and expand AUVs' application areas. The mission planning model needs to consider energy consumption, mission quality, and mission quantity of long-term working in the marine environment. In this article, one unified and robust model was proposed based on the above requirements in the complex marine environment to improve the automaticity of AUV. However, solving this mission planning model is a nontrivial problem, especially when high robustness, high efficiency, fast response, and near-optimal results are required. Therefore, we propose a novel hyperheuristic algorithm based on evolutionary strategy (ES-HH). The proposed ES-HH combines a metaheuristic framework with a selection function to evaluate the performance of low-level heuristic operators online. This evolutionary strategy endows the hyperheuristic algorithm with the online learning feature, giving the ES-HH algorithm better computing efficiency, robustness, and near-optimal results. The experiment results show that the ES-HH algorithm can achieve better convergence and higher robustness than other algorithms, such as ant colony optimization and biogeography-based optimization algorithms. Compared with ACO and BBO algorithms, the proposed ES-HH algorithm can improve the mission completion rate by 3.42% and reduce the average energy consumption of a single task by 8.02%.

Entanglement detection with near-zero cost

Recent research on parallel functional programming has culminated in a provably efficient (in work and space) parallel memory manager, which has been incorporated into the MPL (MaPLe) compiler for Parallel ML and shown to deliver practical efficiency and scalability. The memory manager exploits a property of parallel programs called disentanglement, which restricts computations from accessing concurrently allocated objects. Disentanglement is closely related to race-freedom, but subtly differs from it. Unlike race-freedom, however, no known techniques exists for ensuring disentanglement, leaving the task entirely to the programmer. This is a challenging task, because it requires reasoning about low-level memory operations (e.g., allocations and accesses), which is especially difficult in functional languages. In this paper, we present techniques for detecting entanglement dynamically, while the program is running. We first present a dynamic semantics for a functional language with references that checks for entanglement by consulting parallel and sequential dependency relations in the program. Notably, the semantics requires checks for mutable objects only. We prove the soundness of the dynamic semantics and present several techniques for realizing it efficiently, in particular by pruning away a large number of entanglement checks. We also provide bounds on the work and space of our techniques. We show that the entanglement detection techniques are practical by implementing them in the MPL compiler for Parallel ML. Considering a variety of benchmarks, we present an evaluation and measure time and space overheads of less than 5% on average with up to 72 cores. These results show that entanglement detection has negligible cost and can therefore remain deployed with little or no impact on efficiency, scalability, and space.

Research on the optimal allocation method of source and storage capacity of integrated energy system considering integrated demand response

The optimal allocation of energy storage capacity is an important issue for integrated energy systems (IES). To reduce the impact of volatility and intermittency of renewable energy sources, the impact of volatility needs to be smoothed out by rational allocation of energy storage. To address the above issues, this paper proposes a method for the optimal allocation of source storage capacity considering integrated demand response(IDR). Firstly, the basic mechanism of IES based on energy hub(EH) is constructed, and the model and coupling relationship of equipment components in the system are introduced. The IDR model is built to optimize the load curve according to the real-time market tariff scheme, and then the confidence interval method is used to analyze the scenery uncertainty in IES and determine the scenery output curve under different confidence levels. On this basis, a bi-level optimization model is built that takes into account both source and storage capacity allocation and operational optimization. The upper-level planning model takes into account the uncertainty of wind power and photovoltaic output, and solves the allocation scheme of energy storage intending to minimize the total planning cost; the lower-level operation optimization model takes into account the output constraints of each unit and optimizes the output of the equipment to minimize the operation cost. Finally, it is verified that considering IDR can reasonably optimize the allocation of the system’s source storage capacity and reduce the system’s investment and operating costs.

Shaping Information Processing: The Role of Oscillatory Dynamics in a Working Memory Task.

Neural oscillations are thought to reflect low-level operations that can be used for higher-level cognitive functions. Here, we investigated the role of brain rhythms in the 1–30 Hz range by recording MEG in human participants performing a visual delayed match-to-sample paradigm in which orientation or spatial frequency of sample and probe gratings had to be matched. A cue occurring before or after sample presentation indicated the to-be-matched feature. We demonstrate that alpha/beta power decrease tracks the presentation of the informative cue and indexes faster responses. Moreover, these faster responses coincided with an augmented phase alignment of slow oscillations, as well as phase–amplitude coupling between slow and fast oscillations. Importantly, stimulus decodability was boosted by both low alpha power and high beta power. In summary, we provide support for a comprehensive framework in which different rhythms play specific roles: slow rhythms control input sampling, while alpha (and beta) gates the information flow, beta recruits task-relevant circuits, and the timing of faster oscillations is controlled by slower ones.

Word-Embeddings Distinguish Denominal and Root-Derived Verbs in Semitic

Proponents of the Distributed Morphology framework have posited the existence of two levels of morphological word formation: a lower one, leading to loose input-output semantic relationships; and an upper one, leading to tight input-output semantic relationships. In this work, we propose to test the validity of this assumption in the context of Hebrew word embeddings. If the two-level hypothesis is borne out, we expect state-of-the-art Hebrew word embeddings to encode (1) a noun, (2) a denominal derived from it (via an upper-level operation), and (3) a verb related to the noun (via a lower-level operation on the noun's root), in such a way that the denominal (2) should be closer in the embedding space to the noun (1) than the related verb (3) is to the same noun (1). We report that this hypothesis is verified by four embedding models of Hebrew: fastText, GloVe, Word2Vec and AlephBERT. This suggests that word embedding models are able to capture complex and fine-grained semantic properties that are morphologically motivated.

How has the eutrophication evolved in the tributary bay of the Three Gorges Reservoir area

AbstractThe Three Gorges Reservoir on the Yangtze River is the largest water control project in the world. While exerting great benefits (i.e., flood control, hydropower generation, inland river navigation and scenic tourism), the eutrophication of the tributary bay of the Three Gorges Reservoir has become one of the main environmental problems. This paper is to study the causes of water environment evolution in the tributary bay and investigate the driving force of eutrophication succession after the Three Gorges Reservoir enters the regular operation. By considering the Meixi River on the left bank of the mainstream of the Three Gorges Reservoir area (a typical tributary), this paper developed a three-dimensional hydrodynamic, water quality and water eutrophication mathematical model for the Meixi tributary bay, calibrated with measured data about hydrological regime (water level, flow), hydrodynamic factors (velocity) and water quality (water temperature, Chl-a, TP, TN, etc.). The annual variation of Chl-a concentration in the tributary bay was simulated, and the response relationship between the variation of Chl-a concentration and water conditions of the mainstream and tributary bay (e.g., reservoir water level, reservoir bay velocity, exogenous nutrient input, water temperature stratification and other factors) was analyzed. Results show that the water storage operation of the Three Gorges Reservoir contributes to the low flow velocity maintenance (≤0.05 m/s) in the tributary bay, the backward flow of the mainstream and the sufficient nutrients carried by the tributary water; the water temperature stratification is more likely to occur in the slow detention area in the middle-upper part of the bay in spring and summer, which provides a potential driving force for algae blooms. With the continuous decline of pollution load input in the reservoir basin, the algae blooms in tributary bay was the result of the combined action of low water level operation, low flow velocity (≤0.02 m/s), a large number of non-point source loads input with rainfall and runoff (the proportion of wet year is &amp;gt;70%), and obvious water temperature stratification in shallow water area, and the suitable meteorological conditions are the main inducing factors. Therefore, since the ecological regulation cannot be applied to the multitudinous tributary bays, the non-point source pollution control in the tributary bay is the key to controlling factor.

When Capabilities Backfire: How Improved Hassling Capabilities Produce Worse Outcomes

I formalize interactions between an endogenously rising challenger and a rival defender who can accept the challenger’s rise, go to war before the rise comes to fruition, or degrade the challenger’s growth through low-level conflict operations that I call “hassling.” The novelty here is that the defender has private information about its hassling capabilities; this implies that the challenger does not know how much it can rise without provoking the defender to hassle or go to war. I find that when the defender’s ability to hassle improves, it can provoke a strategic response in the challenger that undermines the defender’s ability to use its private information productively and results in lower utility for the defender. This model provides insight into both Saddam Hussein’s decision-making leading up to the 2003 US invasion and the stability-instability paradox.