Hasil untuk "Balance of trade"

F Niknam, L Rodríguez-Suné, K Agarwal et al.

Traditional optical microscopes face an inherent trade-off between speed, spatial resolution, and implementation complexity. A promising strategy to balance them relies on techniques that exploit temporal fluctuations in fluorescence images. Readily implemented in widefield systems without the need for specialized dyes or hardware, these methods enable enhanced resolution imaging, down to the nanoscale. However, they typically require the acquisition of a large number of frames, which imposes a fundamental limitation on imaging speed. Here, we address this issue by combining the high-speed fluctuation microscopy multiple signal classification algorithm (MUSICAL) with acousto-optic-enabled patterned illumination. The microsecond-scale pattern switching enabled by acousto-optic systems and MUSICAL’s ability to operate without precise knowledge of the fluctuation source are synergistically combined to deliver high-resolution imaging at timescales as short as 32 ms (using only 6 frames acquired at 185 fps). As our results demonstrate, our approach delivers lateral resolution enhancement with low implementation effort, paving the way for extending the usage of high spatiotemporal resolution microscopies.

Jun XU, Xiaohu ZHAO, Jie WANG

The combination of object detection and mixed reality (MR) technology has shown broad application prospects in the field of mining equipment maintenance. In order to meet the requirements of MR equipment for lightweight and efficient detection models, and to address the problem of target recognition in current mixed reality assisted maintenance that cannot balance accuracy and real-time performance, we propose a lightweight recognition model YOLOv8-CLRS based on YOLOv8. First, we introduce a heterogeneous kernel-based convolution (HetConv) module to replace the native C2f structure. This redesign significantly reduces computational complexity and floating-point operations, resulting in faster inference speeds while effectively maintaining rich feature representations. Second, we integrate linearly deformable convolution modules into the backbone network, replacing standard convolutions. This enhancement improves the model’s ability to adapt to objects with diverse geometries and dynamic spatial layouts, thereby increasing robustness in cluttered and variable industrial settings. Third, we reconstruct the neck of the network using a reparameterized generalized feature pyramid network, which promotes more efficient multi-scale feature fusion and strengthens semantic interaction across different feature levels. In addition, we refine the bounding box regression process by incorporating shape-IoU, a novel loss function that emphasizes geometric shape alignment between predictions and ground truths. This results in superior localization performance, particularly for non-rectangular or intricately shaped mechanical components. The proposed model was rigorously evaluated on a custom-built dataset containing images of key components of a tunneling machine, captured under diverse conditions including varying illumination, occlusion, and viewpoints. The experimental results show a 0.4% improvement in precision, a 30% reduction in parameter count, and a 13.9% increase in inference speed compared to the baseline YOLOv8 model. These performance enhancements confirm the model’s strong suitability for real-time MR applications that require both reliability and efficiency. This study not only presents a viable and efficient solution for MR-assisted industrial maintenance systems but also offers valuable insights into lightweight model design that could benefit a wide range of edge-computing applications in industry. The proposed architecture effectively balances the trade-off between computational load and detection accuracy, addressing a major barrier to the widespread adoption of AI-enhanced MR systems in real-world maintenance environments.

Mosleh M. Abualhaj, Hani Al-Mimi, Mahran Al-Zyoud et al.

Abstract The exponential growth of malware attacks, particularly those exploiting malicious URLs, poses a significant threat to cybersecurity in real-time digital environments. To address the challenges of high-dimensional feature spaces and the need for fast, accurate detection, this study proposes a hybrid bio-inspired optimization framework that combines Harris Hawks Optimization (HHO) and the Bat Algorithm (BA) for effective feature selection. The framework evaluates two strategies—union (HHO∪BA) and intersection (HHO∩BA)—to balance detection performance and computational efficiency. After feature selection, classifiers including XGBoost and Extra Trees are fine-tuned using Grid Search to ensure optimal performance. Experiments are conducted on the ISCX-URL2016 dataset, which includes a comprehensive set of benign and malware-labeled URLs. Results show that the HHO∪BA approach achieves the highest detection accuracy (up to 99.52%) and robust classification metrics, making it ideal for high-security applications where accuracy is critical. In contrast, the HHO∩BA method offers significantly faster training and inference times, making it more suitable for real-time or resource-constrained environments. These findings highlight the trade-off between accuracy and speed and provide a flexible framework that can be adapted to various cybersecurity deployment scenarios.

Sunana Alam

This study examines how construction of the Karakoram Highway (KKH) has influenced the livelihoods of residents in Pakistan’s Hunza Valley (HV) through tourism growth, using the Sustainable Livelihood Framework for Tourism (SLFT). Although transport infrastructure such as the KKH can generate economic opportunities, these effects remain underexplored in the literature from a localized and multidimensional livelihood perspective, particularly in tourism contexts. Using a qualitative design, the study draws on in-depth interviews and field observations to assess changes in human, social, natural, physical, financial, institutional, and informational assets associated with the KKH and subsequent tourism expansion. Fifteen semi-structured interviews were conducted between June and September 2025 and were complemented by systematic field observations in central, lower, and upper Hunza. The findings indicate that while the KKH has expanded economic opportunities, it has also created trade-offs across livelihood capitals; for example, financial gains have often been accompanied by pressures on natural and cultural assets. Notably, the study identifies information capital as a critical emerging resource that enables residents to increase tourism-related income, attract visitors, and adapt to rapidly changing market dynamics. Consistent with prior SLFT-based research, community responses vary depending on social relations, resource availability, and gaps in policy implementation. Sustainable livelihood outcomes therefore require tourism planning that meaningfully involves local communities and policies that balance economic development with cultural sustainability and environmental protection. The study contributes to tourism scholarship on mountainous regions and offers recommendations for infrastructure development that is sensitive to local livelihood patterns in developing economies.

Sang Hyun Ahn, Junhewk Kim

BackgroundHealth care leaders face a strategic dilemma: traditional expert-led content development ensures safety but is too slow for digital innovation, whereas artificial intelligence (AI) automation offers speed but introduces risks from hallucinations. Resolving this tension requires governance frameworks that balance operational efficiency with rigorous accountability for patient safety. ObjectiveThis study describes the development process and conducts a formative evaluation of the Semiautomatic Clinical Algorithm Development (S-ACAD) framework as an industry-driven implementation strategy. We aimed to assess the feasibility of this “human-in-the-loop” governance model in balancing the need for operational efficiency with the rigorous safety standards required for pediatric emergency guidance. MethodsWe conducted a prospective, single-day proof-of-concept case study focusing on pediatric febrile seizures. A single physician expert executed a 4-phase workflow: (1) parallel data collection using multiple AI agents, (2) AI-assisted synthesis, (3) iterative refinement via “AI sparring,” and (4) final clinical validation. The resulting algorithm was reviewed by 2 independent external pediatric specialists. We benchmarked this process against a fully automated system (Fully Autonomous Clinical Algorithm Development [F-ACAD]) to illustrate comparative efficiency and safety trade-offs. ResultsIn this single execution, the S-ACAD framework produced a parent-actionable febrile seizure algorithm in approximately 245 minutes. Two independent pediatric specialists (N=2) reviewed the output and did not identify medically inaccurate sections or critical safety errors requiring mandatory correction, and both rated overall clinical validity highly (9.0 and 9.5 out of 10). During the workflow, 19 human expert interventions were recorded, with clinical judgment (n=8, 42.1%) and safety review (n=5, 26.3%) as the most frequent categories in an exploratory post hoc analysis. By comparison, the fully automated approach (F-ACAD) completed the task in approximately 68 minutes, but its own AI critics identified 17 issues (9 high-priority), including concerns related to emergency response clarity and standard-of-care alignment. ConclusionsThese preliminary findings suggest that the S-ACAD framework may offer a potential pathway for “active governance” in AI-assisted clinical content development. In this proof-of-concept case, the framework combined rapid AI-assisted drafting with continuous expert oversight and independent clinical review, suggesting the potential to reduce turnaround time while maintaining safety safeguards. However, these results are based on a single expert applying the workflow to a single clinical topic, and validation across multiple experts, topics, and institutional contexts is needed before generalizability can be established.

MohammadTaghi Hajiaghayi, Gary Peng, Suho Shin

We initiate the study of single-sample bilateral trade with a broker, drawing an analogy to the setting of single-sample bilateral trade without a broker considered in Babaioff et al. (2020) and Cai and Wu (2023). Our model captures the three-sided interaction in which a broker mediates trade between a buyer and seller, each described by a valuation distribution from which a single sample can be drawn. We consider two settings in particular: one where the valuation distributions of the buyer and seller are identical and one where the valuation distributions are stochastically ordered. We analyze simple mechanisms that rely only on a single sample from each agent's distribution and show that these mechanisms achieve constant-factor approximations to the first-best gains-from-trade (GFT), first-best social welfare (SW), and optimal profit under the standard monotone-hazard-rate assumption. We then complement these results with matching or nearly matching upper bounds on the GFT and SW of our mechanisms. Notably, in both settings, we observe fairly small losses in the approximation factors to the first-best GFT and first-best SW due to the existence of the broker (benchmarked against the corresponding approximation factors in the setting without a broker). Furthermore, our results stand in stark contrast to those of Hajiaghayi et al. (2025), who show inapproximability results under a strategic broker with full distributional knowledge. Our results provide insight into the design of data-efficient brokerage mechanisms for online marketplaces and decentralized trading platforms, where intermediaries must facilitate trade under severe informational constraints. They highlight how even minimal data can enable robust and incentive-compatible brokerage in uncertain markets for both the broker and the market participants.

Michael O Dioha, Jeffrey Dankwa Ampah, Lily Odarno

Understanding how net-zero emissions timelines affect sustainable development is essential for climate planning in Africa. We apply the Global Change Analysis Model to explore the continent’s energy–land–water systems under four scenarios: a business-as-usual (BAU) and three net-zero scenarios targeting 2050 (NZ50), 2070 (NZ70), and 2100 (NZ100). Our analysis quantifies how the pace of decarbonization influences Africa’s interconnected energy, land, and water systems. Without new climate policy (BAU), Africa’s net CO _2 emissions could increase by nearly six-fold; from 1.8 GtCO _2 yr ^−1 in 2020 to 10.4 GtCO _2 yr ^−1 by 2100. All net-zero scenarios constrain this growth, with NZ50, NZ70, and NZ100 achieving net-zero emissions by their respective target years through deployment of multiple carbon dioxide removal approaches (e.g. BECCS). Across all scenarios, primary energy supply expands, but its composition shifts under net-zero conditions. Over 2020–2100, renewables account for an average of 49%–53% of primary energy in the net-zero cases, displacing fossil fuels. Net-zero pathways also drive land-use shifts, reducing cropland area by 29%–31% and lowering water demand for food crops by 14%–15%, while increasing water use for BECCS (∼0.9 km ^3 yr ^−1 in NZ50). These land constraints raise staple food prices, averaging $1.16 kg ^−1 in NZ50—about 96% above BAU—with the steepest increase in Western Africa. In terms of mitigation cost, NZ50 is the most expensive pathway ($78 tCO _2 ^−1 ), compared to $68 tCO _2 ^−1 in NZ100. While earlier action enables deeper emissions cuts and faster clean energy transitions, it also imposes higher economic and resource trade-offs. Delayed net-zero deadlines reduce near-term disruption but result in higher cumulative emissions. Given Africa’s development context, we argue that net-zero timelines must balance technical feasibility with economic realities and social justice.

Emad Elabd

Abstract Deep learning is a field within artificial intelligence that uses large datasets to train models capable of recognizing patterns and making predictions. One of the serious challenges facing model creators during data training is preserving the privacy of the data. Adversaries use membership inference attack to expose the privacy of the data used in training the model. They identify whether a specific data point was used in the training process or not. To protect these models against this type of attack, differential privacy approach can be used. Differential privacy involves adding noise to the training weights during the data training phase according to a specific probability distribution. At every step during the data training process, a fixed amount of noise is consistently added to the training weights. This paper utilizes a modified version of the differential privacy technique to defend against membership inference attacks. The new algorithm does not add regular noise to the training weights in each step but it adds it randomly during training process. Adding noise randomly increases the randomness in training phase and decrease the chances of prediction in the case of an attack. The model performance is evaluated using a set of metrics, including accuracy, precision, recall, F1 score, and the privacy budget ( $$\epsilon$$ ). The results demonstrate that the Gaussian Randomized Noise Differentially Private Stochastic Gradient Descent (Gaussian RanN-DP-SGD) approach consistently outperforms other standard Differential Privacy (DP) methods across accuracy, precision, recall, and F1 score. Regarding privacy preservation, the Gaussian RanN-DP-SGD method achieves the most favorable privacy-utility trade-off, maintaining a satisfactory balance between model utility and user privacy. Notably, it delivers acceptable performance within a privacy budget range of $$\epsilon = 1-2$$ , which is suitable for most practical applications.

Zhiyu Bi, Ming Chen, Guopeng Ding et al.

The high-precision attitude estimation technique for non-cooperative targets in space, based on monocular cameras, has important application value in missions such as space debris removal, autonomous rendezvous and docking, and on-orbit services. However, due to the inherent missing information problem of monocular vision systems and the high complexity of target geometry, existing monocular pose estimation methods find it difficult to realize an effective balance between accuracy and computational efficiency. Current solutions commonly adopt deep neural network architectures to improve estimation accuracy; but, this method is often accompanied by the problems of a dramatic expansion of the number of model parameters and a significant increase in computational complexity, which limits its deployment and real-time inference capabilities in real spatial tasks. To address the above problems, this paper proposes a spacecraft pose estimation network, called Balance-URSONet, which weighs the trade-off between accuracy and the number of parameters, and makes the pose estimation model have a stronger feature extraction capability by innovatively using RepVGG as the feature extraction network. In order to effectively improve the performance and inference speed of the model, this paper proposes the feature excitation unit (FEU), which is able to flexibly adjust the feature representation of the network and thus optimize the utilization efficiency of spatial and channel information. The experimental results show that the Balance-URSONet proposed in this paper has excellent performance in the spacecraft pose estimation task, with an ESA score of 0.13 and a parameter count 13 times lower than that of URSONet.

Yidi Zhao, Hao Dong, Keming Cheng et al.

Long-endurance hypersonic vehicles face the dual challenge of withstanding extreme aerodynamic heating while meeting onboard power requirements. Integrating thermoelectric generators within thermal protection systems offers a solution by converting thermal loads into electrical power. However, accurate prediction requires resolving coupled multiphysics, where three-dimensional simulations are computationally prohibitive and existing one-dimensional models lack accuracy. This study develops a quasi-two-dimensional distributed thermal network incorporating shape-factor corrections for rapid, high-fidelity prediction. Multi-objective optimization is performed to balance specific power, thermal expansion mismatch, and thermal margin. Analysis reveals fundamental trade-offs: a maximum-power design achieves 28.1 W/kg but only a 0.8% thermal margin, whereas a balanced design delivers 24.5 W/kg with a 5.1% thermal margin and significantly reduced thermal stress. Despite geometric variations, peak conversion efficiency converges to approximately 13%. This indicates that efficiency is primarily governed by material properties, while geometric optimization effectively tunes temperature and thermal strain distributions, providing guidelines for reliable system development.

Mustapha Mukhtar, Idris Abdullahi Abdulqadir

This study employs the panel quantile regression (QR) technique to evaluate whether globalization threshold conditions are essential for achieving effective economic growth, utilizing data from 47 Sub-Saharan African (SSA) countries for the period from 2000 to 2021. The bootstrap simultaneous conditional QR analysis was conducted using the fixed-effects panel QR approach. The study findings revealed that the globalization thresholds at which the total effect of globalization as a percentage of global integration changes from negative to positive are 3.82% and 4.36%, respectively. Furthermore, the critical mass of FDI and trade thresholds at which the total effects of FDI and trade, as a percentage of knowledge spillovers, change from negative to positive is 4.66% and 2.19%, respectively. Conversely, these results revealed an asymmetric relationship between globalization and growth among SSA countries. Therefore, these triggers and globalization thresholds serve as essential conditions and catalysts that will foster economic development in SSA economies. The results also indicate significant effects of globalization thresholds on economic growth among the SSA countries. Regarding policy relevance, these findings are also crucial for policymakers when they are developing strategies that will promote equal opportunity and balance development in the region through knowledge spillovers and improvements in global integration.

Surbhi Rajput, Ashish Chiplunkar, Rohit Vaish

In the single stock trading prophet problem formulated by Correa et al.\ (2023), an online algorithm observes a sequence of prices of a stock. At each step, the algorithm can either buy the stock by paying the current price if it doesn't already hold the stock, or it can sell the currently held stock and collect the current price as a reward. The goal of the algorithm is to maximize its overall profit. In this work, we generalize the model and the results of Correa et al.\ by allowing the algorithm to trade multiple stocks. First, we formulate the $(k,\ell,\ell')$-Trading Prophet Problem, wherein there are $k$ stocks in the market, and the online algorithm can hold up to $\ell$ stocks at any time, where $\ell\leq k$. The online algorithm competes against an offline algorithm that can hold at most $\ell'\leq\ell$ stocks at any time. Under the assumption that prices of different stocks are independent, we show that, for any $\ell$, $\ell'$, and $k$, the optimal competitive ratio of $(k,\ell,\ell')$-Trading Prophet Problem is $\min(1/2,\ell/k)$. We further introduce the more general $\cal{M}$-Trading Prophet Problem over a matroid $\cal{M}$ on the set of $k$ stocks, wherein the stock prices at any given time are possibly correlated (but are independent across time). The algorithm is allowed to hold only a feasible subset of stocks at any time. We prove a tight bound of $1/(1+d)$ on the competitive ratio of the $\cal{M}$-Trading Prophet Problem, where $d$ is the density of the matroid. We then consider the non-i.i.d.\ random order setting over a matroid, wherein stock prices drawn independently from $n$ potentially different distributions are presented in a uniformly random order. In this setting, we achieve a competitive ratio of at least $1/(1+d)-\cal{O}(1/n)$, where $d$ is the density of the matroid, matching the hardness result for i.i.d.\ instances as $n$ approaches $\infty$.

Jacopo Timini

This paper reexamines the effects of the Latin Monetary Union (LMU) - a 19th century agreement among several European countries to standardize their currencies through a bimetallic system based on fixed gold and silver content - on trade. Unlike previous studies, this paper adopts the latest advances in gravity modeling and a more rigorous approach to defining the control group by accounting for the diversity of currency regimes during the early years of the LMU. My findings suggest that the LMU had a positive effect on trade between its members until the early 1870s, when bimetallism was still considered a viable monetary system. These effects then faded, converging to zero. Results are robust to the inclusion of additional potential confounders, the use of various samples spanning different countries and trade data sources, and alternative methodological choices.

Dimitry Borissov

Enterprises operate as complex systems embedded in dynamic environments characterized by global interdependencies, technol1ogical advancements, and systemic challenges. This paper examines the critical components, challenges, and resilience strategies necessary for modern organizations to navigate complexity and uncertainty. The COVID-19 pandemic and the global semiconductor shortage revealed vulnerabilities in interconnected supply chains, financial markets, and digital ecosystems, underscoring the need for systemic adaptability. Technological advancements, including artificial intelligence, the Internet of Things, and big data, add layers of operational complexity, demanding robust data management, cybersecurity measures, and seamless integration with legacy systems. The paper highlights the growing demand for sustainable and ethical practices, driven by regulatory pressures, consumer expectations, and advocacy group influence. Businesses are compelled to balance short-term efficiency with long-term adaptability to thrive in a volatile environment shaped by rapid technological, market, and societal changes. Case studies of organizations such as Toyota, Amazon, and Unilever illustrate how viewing enterprises as interconnected systems allows them to address root causes of challenges, implement resilience strategies, and leverage adaptability as a competitive advantage. Theoretical frameworks, including systems thinking, complex adaptive systems (CAS), and the Viable System Model, provide tools for understanding enterprise complexity. These frameworks emphasize interdependencies, nonlinearities, feedback loops, and emergent behaviors that define organizational systems. The paper explores the concept of resilience, emphasizing adaptability, recovery, and thriving amidst disruptions as critical elements of long-term sustainability. Challenges such as resistance to change, coordination across subsystems, and the trade-off between efficiency and resilience are analyzed within the context of enterprise architecture, Normal Accident Theory, and the Swiss Cheese Model. The study advocates for adopting resilience engineering and collective mindfulness to anticipate, detect, and manage errors effectively, ensuring organizational stability and growth. By framing enterprises as dynamic, adaptive systems, this paper contributes actionable insights for building resilience, fostering sustainability, and managing complexity in an era of unprecedented disruption.

Zhikang Fan, Weiran Shen, Shaojie Tang et al.

We study the problem of designing revenue-maximizing mechanisms for a selfish mediator who facilitates trade between a buyer and a seller. We consider a setting where the mediator does not have information advantage and the buyer's valuation is interdependent with the seller's private information. The mechanism may involve multi-round negotiations and flexible fee structures. We show that the mediator can restrict attention to a class of joint menu-selection mechanisms, where each mechanism can be represented as a two-dimensional menu. Each party privately selects an option from their own dimension and the two options together determine the menu entry. The mediator then recommends both parties whether to trade based on the jointly selected menu entry. We then establish an impossibility trilemma: no mechanism can simultaneously satisfy incentive compatibility, obedience, and informativeness. Motivated by this result, we characterize the optimal mechanisms under two relaxation conditions. First, when the seller's cost is constant, the optimal mechanism exhibits a threshold structure: trade occurs whenever the quality of the item exceeds a threshold that is decreasing in the buyer's type. Consequently, low-typed buyers receive more information, which in turn gives the mediator more power to charge from them. Second, when the mediator has veto power, the optimal mechanism also takes a threshold form, but in the opposite direction: trade occurs only if the quality falls below a threshold that is increasing in the buyer's type. As a result, items with lower qualities are more likely to be traded and the corresponding sellers benefit more, which discourages sellers of high qualities from participating and gives rise to a ``lemons market'' effect.

Patrick Chiu

In ancient China, Daoist philosophers developed the concepts of qi (energy), Wu Xing (five elements), and yin (feminine, dark, negative) and yang (masculine, bright, positive) opposite forces between 200 and 600 BCE. Based on these philosophies, Zhen Jiu (acupuncture), Ben Cao (materia medica), and the practice of Qi Gong (energy optimization movements) evolved as the three interrelated therapeutic regimens of Chinese medicine (Note 1). Since the time of Zhang Qian, who discovered China’s western regions in the 1st century BCE, Hai Yao (the exotic elements of materia medica from the maritime Silk Road countries), had been transmitted from the ancient land and maritime routes of the Silk Road to China in the past two millennia (Note 2). Since the late 17th century, the English East India Company, later called the British East India Company, introduced Yang Yao (opium) to the Manchu Qing Empire to balance a growing trade deficit for tea export from China to the British Empire. After the First Opium War ended in 1842, enterprising expatriate chemists and druggists in the treaty ports imported Xi Yao (modern medicines from the Western world) for sale to the merchant navy and the local market. From the second half of the 19th century onwards, both Hai Yao and Xi Yao have become a fully integrated part of modern China’s armamentarium for the Chinese medicine and Western hospitals and retail pharmacy sectors. This paper articulates the journey of adoption of exotic elements of materia medica from the ancient land and sea routes of the Silk Road, including the western regions and the rest of the world in the past two millennia. Opium traders, ship surgeons, medical and pharmaceutical missionaries, enterprising traders, and policymakers together transformed Ben Cao into Xi Yao during the late Manchu Qing dynasty and the early Nationalist Era.

Ritwik Sinha, Zhao Song, Tianyi Zhou

Large Language Models have become popular for their remarkable capabilities in human-oriented tasks and traditional natural language processing tasks. Its efficient functioning is attributed to the attention mechanism in the Transformer architecture, enabling it to concentrate on particular aspects of the input. LLMs are increasingly being used in domains such as generating prose, poetry or art, which require the model to be creative (e.g. Adobe firefly). LLMs possess advanced language generation abilities that enable them to generate distinctive and captivating content. This utilization of LLMs in generating narratives shows their flexibility and potential for use in domains that extend beyond conventional natural language processing duties. In different contexts, we may expect the LLM to generate factually correct answers, that match reality; e.g., question-answering systems or online assistants. In such situations, being correct is critical to LLMs being trusted in practice. The Bing Chatbot provides its users with the flexibility to select one of the three output modes: creative, balanced, and precise. Each mode emphasizes creativity and factual accuracy differently. In this work, we provide a mathematical abstraction to describe creativity and reality based on certain losses. A model trained on these losses balances the trade-off between the creativity and reality of the model.

Leilei Shi, Bing Han, Yingzi Zhu et al.

It has been long that literature in financial academics focuses mainly on price and return but much less on trading volume. In the past twenty years, it has already linked both price and trading volume to economic fundamentals, and explored the behavioral implications of trading volume such as investor's attitude toward risks, overconfidence, disagreement, and attention etc. However, what is surprising is how little we really know about trading volume. Here we show that trading volume probability represents the frequency of market crowd's trading action in terms of behavior analysis, and test two adaptive hypotheses relevant to the volume uncertainty associated with price in China stock market. The empirical work reveals that market crowd trade a stock in efficient adaptation except for simple heuristics, gradually tend to achieve agreement on an outcome or an asset price widely on a trading day, and generate such a stationary equilibrium price very often in interaction and competition among themselves no matter whether it is highly overestimated or underestimated. This suggests that asset prices include not only a fundamental value but also private information, speculative, sentiment, attention, gamble, and entertainment values etc. Moreover, market crowd adapt to gain and loss by trading volume increase or decrease significantly in interaction with environment in any two consecutive trading days. Our results demonstrate how interaction between information and news, the trading action, and return outcomes in the three-term feedback loop produces excessive trading volume which includes various internal and external causes.

Liang Pei, Chunhui Wang, Liying Sun

Unconventional water is an important water resource for agricultural utilization in the drought and water shortage of Northeast China. Additionally, exploration in making full use of it is an important way to alleviate water shortage in China. This paper analyzed the effects of unconventional water through field trials on the accumulation of heavy metals in both cucumbers and the typical black clay soil (expressed as black soil) around the Anshan metallic ore. By exploring the effects of unconventional water after secondary treatment on the accumulation characteristics of heavy metals in cucumbers and the heavy metal balance in the soil–crop system under different conditions, the study shows that there are no significant differences in the heavy metal content when the quantity of unconventional water for irrigation varies. Unconventional water for short-term irrigation does not cause pollution to either the soil environment or the crops. Nor will it cause the accumulation of heavy metals, and the index for the heavy metal content is far below the critical value of the trade standard and national standard, which indicates that the crops irrigated with unconventional water during their growth turn out to be free of pollutants. Unconventional water brings less heavy metals into the black soil than crops. The input and output quantities have only small effects on the heavy metal balance in the black soil. This paper provides a reference for the safety control and evaluation of unconventional agricultural utilization.

Sangjun Son, Yong-chan Park, Minyong Cho et al.

How can we accurately and efficiently decompose a tensor stream? Tensor decomposition is a crucial task in a wide range of applications and plays a significant role in latent feature extraction and estimation of unobserved entries of data. The problem of efficiently decomposing tensor streams has been of great interest because many real-world data dynamically change over time. However, existing methods for dynamic tensor decomposition sacrifice the accuracy too much, which limits their usages in practice. Moreover, the accuracy loss becomes even more serious when the tensor stream has an inconsistent temporal pattern since the current methods cannot adapt quickly to a sudden change in data. In this paper, we propose DAO-CP, an accurate and efficient online CP decomposition method which adapts to data changes. DAO-CP tracks local error norms of the tensor streams, detecting a change point of the error norms. It then chooses the best strategy depending on the degree of changes to balance the trade-off between speed and accuracy. Specifically, DAO-CP decides whether to (1) reuse the previous factor matrices for the fast running time or (2) discard them and restart the decomposition to increase the accuracy. Experimental results show that DAO-CP achieves the state-of-the-art accuracy without noticeable loss of speed compared to existing methods.