Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hydrometeorological Assessment of the Historic 2019 Flood of Nebraska, Iowa, and South Dakota

During early 2019, a series of events set the stage for devastating floods in eastern Nebraska, western Iowa, and southeastern South Dakota. When the floodwaters hit, dams and levees failed, cutting off towns while destroying roads, bridges, and rail lines, further exacerbating the crisis. Lives were lost and thousands of cattle were stranded. Estimates indicate that the cost of the flooding has topped $3 billion as of August 2019, with this number expected to rise. After a warm and wet start to winter, eastern Nebraska, western Iowa, and southeastern South Dakota endured anomalously low temperatures and record-breaking snowfall. By March 2019, rivers were frozen, frost depths were 60–90 cm, and the water equivalent of the snowpack was 30–100 mm. With these conditions in place, a record-breaking surface cyclone rapidly developed in Colorado and moved eastward, producing heavy rain toward the east and blizzard conditions toward the west. In areas of eastern Nebraska, western Iowa, and southeastern South Dakota, rapid melting of the snowpack due to this rain-on-snow event quickly led to excessive runoff that overwhelmed rivers and streams. These conditions brought the region to a standstill. In this paper, we provide an analysis of the antecedent conditions in eastern Nebraska, western Iowa, and southeastern South Dakota and the development of the surface cyclone that triggered the historic flooding, along with a look into the forecast and communication of flood impacts prior to the flood. The study used multiple datasets, including in situ observations and reanalysis data. Understanding the events that led to the flooding could aid in future forecasting efforts.
Low-Diversity Tropical Rain Forests: Some Possible Mechanisms for Their Existence

Tropical rain forests do not always have a high diversity in tree species. In some cases, most of the trees in the canopy layer are a single species. This results in a low diversity among the other canopy tree species (fig. 1). Increasing dominance by one canopy species has little effect on the diversity of subcanopy trees (fig. 2). Such single-dominant forests are of two types: the dominant either persists at the site beyond one generation or it does not. In forests with a persistent dominant species (type I), one species may achieve dominance either by colonizing most of a large open patch and persisting thereafter or by gradually replacing the existing residents. The latter could take place either because the dominant is the species most resistant to deleterious physical or biotic conditions or because it is superior in competition to all others. One possible mechanism enabling one species to replace and exclude many others is the possession of an ectomycorrhizal (EM) association. Most tropical tree species sampled in the Neotropics and Africa have a different type, the vesicular-arbuscular mycorrhizal (VAM) association. Various characteristics of the EM association, such as its greater host specificity, the greater protection it gives its host from natural enemies and deleterious physical factors, and its ability to secure nutrients in both organic and inorganic form before they are available to a VAM association, may confer an advantage on its host tree species that allows it to replace itself and to displace or exclude VAM host trees. This "mycorrhiza hypothesis" needs to be tested; various types of surveys and experiments are suggested as appropriate tests. In forests where the VAM type is more common, most tree species are associated with the same set of fungal species and thus may be nearly equivalent in competitive ability for securing resources. Such equivalence promotes diversity, as suggested by earlier work. Examples of nonpersistent dominants (type II) are those that first colonize large open patches (>1 ha). Dominance probably results when few species are available for colonization during the short period open to invasion after a disturbance and few are adapted to the conditions in recently disturbed large patches. We suggest that after a single EM tree species achieves dominance in a type-I forest, other species in the same family are more likely to invade than are those of a different family. Thus, a many-species VAM rain forest might gradually shift to one with a single EM species dominant, leading to a forest of higher diversity dominated by several species from a few families that associate with EM fungi (e.g., as found in dipterocarp forests in southeastern Asia). The latter forests should tend to maintain their diversity because, like VAM forests, EM species in the same family may be nearly equivalent in competitive ability.
3-D Partitioning for Large-Scale Graph Processing

Disk I/O is the major performance bottleneck of existing out-of-core graph processing systems. We found that the total I/O amount can be reduced by loading more vertices into memory every time. Although task partitioning of a graph processing system is traditionally considered equivalent to the graph partition problem, this assumption is untrue for many Machine Learning and Data Mining (MLDM) problems: instead of a single value, a <italic>vector</italic> of data elements is defined as the property for each vertex/edge. By dividing each vertex into multiple sub-vertices, more vertices can be loaded into memory every time, leading to less amount of disk I/O. To explore this new opportunity, we propose a category of <italic>3-D partitioning</italic> algorithm that considers the hidden dimension to partition the property vector. The 3-D partitioning algorithm provides a new tradeoff to reduce communication costs, which is adaptive to both distributed and out-of-core scenarios. Based on it, we build a distributed graph processing system <italic/><sc>Cube</sc><italic/> and an out-of-core system <sc>SingleCube</sc>. Since network traffic is significantly reduced, <sc>Cube</sc> outperforms state-of-the-art graph-parallel system PowerLyra by up to <inline-formula><tex-math notation="LaTeX">$4.7\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>4</mml:mn><mml:mo>. </mml:mo><mml:mn>7</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wu-ieq1-2986736.gif"/></alternatives></inline-formula>. By largely reducing the disk I/O amount, the performance of <sc>SingleCube</sc> is significantly better than state-of-the-art out-of-core system GridGraph (up to <inline-formula><tex-math notation="LaTeX">$4.5\times$</tex-math><alternatives><mml:math><mml:mrow><mml:mn>4</mml:mn><mml:mo>.</mml:mo><mml:mn>5</mml:mn><mml:mo>×</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="wu-ieq2-2986736.gif"/></alternatives></inline-formula>).
Anytime stochastic routing with hybrid learning

Increasingly massive volumes of vehicle trajectory data hold the potential to enable higher-resolution traffic services than hitherto possible. We use trajectory data to create a high-resolution, uncertain road-network graph, where edges are associated with travel-time distributions. In this setting, we study probabilistic budget routing that aims to find the path with the highest probability of arriving at a destination within a given time budget. A key challenge is to compute accurately and efficiently the travel-time distribution of a path from the travel-time distributions of the edges in the path. Existing solutions that rely on convolution assume independence among the distributions to be convolved, but as distributions are often dependent, the result distributions exhibit poor accuracy. We propose a hybrid approach that combines convolution with estimation based on machine learning to account for dependencies among distributions in order to improve accuracy. Since the hybrid approach cannot rely on the independence assumption that enables effective pruning during routing, naive use of the hybrid approach is costly. To address the resulting efficiency challenge, we propose an anytime routing algorithm that is able to return a "good enough" path at any time and that eventually computes a high-quality path. Empirical studies involving a substantial real-world trajectory set offer insight into the design properties of the proposed solution, indicating that it is practical in real-world settings.
SL-RPL: Stability-Aware Load Balancing for RPL

The Internet of Things (IoT) is exponentially growing and requires reliable and efficient protocols to grow with it. Routing Protocol for LLN (RPL) is designed to meet the different requirements of IoT. However, RPL does not support load balancing. Without a proper load balancing mechanism, IoT devices may experience severe congestion problem, resulting in packet loss and delay. However, when using congestion-aware routing metrics to achieve load balancing, frequent parent switching may occur, which can induce instability in IoT networks. In this project, we propose a stability-aware load balancing method for RPL. We evaluate the improved performance of our solution in simulations. Our simulation results indicate that the proposed method can significantly improve the stability and performance of RPL.
Exploiting Uses of Uninitialized Stack Variables in Linux Kernels to Leak Kernel Pointers

Information leaks are the most prevalent type of vulnerabilities among all known vulnerabilities in Linux kernel. Many of them are caused by the use of uninitialized variables or data structures. It is generally believed that the majority of information leaks in Linux kernel are low-risk and do not have severe impact due to the difﬁculty (or even the impos-sibility) of exploitation. As a result, developers and security analysts do not pay enough attention to mitigating these vulnerabilities. Consequently, these vulnerabilities are usually assigned low CVSS scores or without any CVEs assigned. Moreover, many patches that address uninitialized data use bugs in Linux kernel are not accepted, leaving billions of Linux systems vulnerable. Nonetheless, information leak vulnerabilities in Linux kernel are not as low-risk as people believe. In this paper, we present a generic approach that converts stack-based information leaks in Linux kernel into kernel-pointer leaks, which can be used to defeat modern security defenses such as KASLR. Taking an exploit that triggers an information leak in Linux kernel, our approach automatically converts it into a highly impactful exploit that leaks pointers to either kernel functions or the kernel stack. We evaluate our approach on four known CVEs and one security patch in Linux kernel and demonstrate its effectiveness. Our ﬁndings provide solid evidence for Linux kernel developers and security analysts to treat information leaks in Linux kernel more seriously.
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1974;124:413–419. 3. BAPTISTA T. Bodyweight gain induced by antipsychotic drugs: mechanisms and management. Acta Psychiatr Scand 1999;100:3–16. 4. BAPTISTA T, MOLINA MG, DE QUIJADA M et al. Effects of the antipsychotic drug sulpiride on reproductive hormones in healthy women: relationship with body weight regulation. Pharmacopsychiatry 1997;30:256–262. 5. BAPTISTA T, REYES D, HERNÁNDEZ L. Antipsychotic drugs and reproductive hormones: relationship to body weight regulation. Pharmacol Biochem Behav 1999;62:409–417. 6. BAPTISTA T, LACRUZ A, ANGELES F et al. Endocrine and metabolic profile in the obesity associated to typical antipsychotic drug-administration. Pharmacopsychiatry 2001;34:223–231. 7. CHOI SH, KANG SB, JOE SH. Changes in premenstrual symptoms in women with schizophrenia: a prospective study. Psychosom Med 2001;63:822–829. 8. HOFF AL, KREMEN WS, WIENEKE MH et al. Association of estrogen levels with neuropsychological performance in women with schizophrenia. Am J Psychiatry 2001; 158:1134–1139. 9. OADES RD, SCHEPKER R. Serum gonadal steroid hormones in young schizophrenic patients. Psychoneuroendocrinology 1994;19:373–385.
RandPiper  Reconfiguration-Friendly Random Beacons with Quadratic Communication

A random beacon provides a continuous public source of randomness and its applications range from public lotteries to zero-knowledge proofs. Existing random beacon protocols sacrifice either the fault tolerance or the communication complexity for security, or ease of reconfigurability. This work overcomes the challenges with the existing works through a novel communication efficient combination of state machine replication and (Publicly) Verifiable Secret Sharing (PVSS/VSS). For a system with n nodes in the synchronous communication model and a security parameter κ, we first design an optimally resilient Byzantine fault-tolerant state machine replication protocol with O(κ n2) bits communication per consensus decision without using threshold signatures. Next, we design GRandPiper (Good Pipelined Random beacon), a random beacon protocol with bias-resistance and unpredictability, that uses PVSS and has a communication complexity of O(K n2) always, for a static adversary. However, GRandPiper allows an adaptive adversary to predict beacon values up to t+1 epochs into the future. Therefore, we design BRandPiper (Better RandPiper), that uses VSS and has a communication complexity of O(κ fn2), where f is the actual number of faults, while offering a strong unpredictability with an advantage of only a single round even for an adaptive adversary. We also provide reconfiguration mechanisms to restore the resilience of the beacon protocols while still maintaining quadratic communication complexity per epoch. We implement BRandPiper and compare it against the state-of-the-art practically deployed beacon protocol, Drand, and show that we are always better than or equal to it in performance.
Thermodynamic Properties for Arsenic Minerals and Aqueous Species

Quantitative geochemical calculations are not possible without thermodynamic databases and considerable advances in the quantity and quality of these databases have been made since the early days of Lewis and Randall (1923), Latimer (1952), and Rossini et al. (1952). Oelkers et al. (2009) wrote, “ The creation of thermodynamic databases may be one of the greatest advances in the field of geochemistry of the last century . ” Thermodynamic data have been used for basic research needs and for a countless variety of applications in hazardous waste management and policy making (Zhu and Anderson 2002; Nordstrom and Archer 2003; Bethke 2008; Oelkers and Schott 2009). The challenge today is to evaluate thermodynamic data for internal consistency, to reach a better consensus of the most reliable properties, to determine the degree of certainty needed for geochemical modeling, and to agree on priorities for further measurements and evaluations. Recent attention has been directed to arsenic (As) thermodynamic data, partly because of the worldwide recognition of arsenic poisoning in more than 70 countries (Nordstrom 2002; Ravenscroft et al. 2009) and the need to interpret As mobility more quantitatively in groundwater and surface-water systems. Unfortunately, not as many useful thermodynamic measurements have been made on reactions involving As compared to other major solutes and trace elements. Grenthe et al. (1992), when reviewing As data for the Organization for Economic Cooperation and Development/Nuclear Energy Agency (OECD/NEA) thermodynamic database on U, stated “ Although needed, a complete reanalysis of the chemical thermodynamic data for arsenic species is not within the scope of the current review . ” Although a complete reanalysis is not feasible at this time, this chapter reviews As thermodynamic data with a focus on internal consistency and the quality of the original measurements, updates with new data, …
A Genetic Based Research Framework to Discover Optimal Frequent Patterns Using Association Rule Mining

The rapid advances in data generation, availability of automated tools in data collection and continued decline in data storage cost enabled with high volumes of data. In addition, the datais non scalable, high dimensional, heterogeneous and complex in its nature. This situation creates inevitably increasing challenges in extracting desired information. Thus, Data mining evolves into a fertile area and got the focus by many researchers and business analysts. Data mining is a methodology the blends traditional techniques with sophisticated algorithms. Among all, the association rule mining is efficient pattern discovery technique, which finds hidden, valid, novel, useful, understandable, interesting and ultimately correlated patterns in large databases.Such correlated rules create great business value to any organization as they make use in decision making process. However, in real time applications the correlation changes continuously as the source data updates dynamically. This motivation necessitates finding and updating the frequent item sets with different supports efficiently and optimally. In order to overcome the challenges inherited in conventional association rule mining, the authors in the present paper propose an Optimal Frequent Patterns System (OFPS). The OFPS takes radically a different approach and design as a three-fold system that discovers optimal frequent patterns efficiently, using the genetic algorithm. Initially, the first-fold of OFPS focuses on preparation of domain specific data that includes data selection, cleaning, integration and transformation under the guidance of knowledge expert. Subsequently, the second-fold of OFPS emphasizes on construction of a Frequent Pattern Tree (FP-Tree) and then discovery of frequent patterns by exploring the tree in the bottom-up fashion to facilitate rapid access of individual frequent patterns quickly. The third-fold of OFPS finally concentrates on generation of optimal frequent patterns using genetic algorithm that simulates biological evaluation procedure having the self learning capability. To validate the performance of proposed OFPS in several orders of magnitude, many experiments were conducted and results have proven this as claimed.
Experimental validation of a dynamic modelling of a Reversible Solid Oxide Cells (rSOCs)

The integration of Hydrogen technologies in different end-uses such as transport, electric microgrids, residential and industrial applications, will increase exponentially soon. Hydrogen as energy carrier allows more favourable energy conversion than other conventional systems and is crucial in worldwide decarbonize end uses. The production of green hydrogen, using RES, is a key area for the evolution of this technology. In this context, SWITCH is a Horizon 2020 European Project that aims to design, build and test an in-situ fully integrated and continuous multisource hydrogen production system, based on solid oxide cell technology. Reversible Solid Oxide Cell (rSOCs) technologies allow to convert renewable energy as hydrogen in the power-to-gas application (P2G) and in reversible mode is able to produce electricity from hydrogen stored, power-to-power application (P2P). rSOCs are really interesting to stabilize the random nature of RES because a combined electrolysis and fuel cell system should be able to switch between the two modes as quickly as possible in order to optimize the integration and the use of RES. However, rSOCs need a complex BoP from the thermal point of view, able to guarantee high efficiency even at partial load mode as well as easy start-up and shutdown procedures. In this work, a Stack Box Module dynamic model was developed in Modelica environment as a dynamic tool for the definition and optimization of BoP requirements. Stack model was validated in SOFC (Solid Oxide Fuel Cell) and SOE (Solid Oxide Electrolyser). The results of the simulation provide verification of the technical/thermodynamic behaviour and flexibility of a stack box of 70 cells. Dynamic modelling allows to evaluate the effect of the reagent inlet temperatures on the operation and hydrogen production/consumption in terms of yield as well as the transients between the different operative modes. Model has been validated by experimental measurements performed in the laboratory. In particular, the kinetics of the reactions governing steam methane reforming (SMR) was considered from data found in the literature, while the ASR (Area Specific Resistance) value was calibrated according to experimental data. The results of the dynamic model show as model can be a useful design and optimization tool for the SOCs technology.
Learning from Things: Method and Theory of Material Culture Studies ed. by W. David Kingery (review)

David Kingery, a renowned ceramic engineer, has dedicated the past decade or so of his career to archaeology. Specifically, he has focused his energy on the cross-disciplinary investigation of material culture. Through his own research, and more importantly by sponsoring interdisciplinary conferences and providing the framework for easier between-college study at the University of Arizona, he has been successful in breaking down traditional barriers. This endeavor, however, is like swimming upstream against an ever-increasing current, as the trend in science is to become more specialized and to communicate less with people outside one's narrow interest. In this climate, interdisciplinary communication is more important than ever, and Kingery is to be commended for his dogged determination to make it happen. This book, assembled from a series of papers presented at a conference held at the Smithsonian Institution, grapples with the facts that the study of material culture is inherently interdisciplinary, and that the relationship between people and things is multileveled yet understandable. Kingery's guiding principle is that "things . , are probably the truest representations we have of values and meanings within a society" (ix).
Potential and challenges for using neuroscientific tools in strategic management studies

Purpose The purpose of this study is to understand how neuroscientific tools are used and discussed in ongoing research on strategy in organizations. Design/methodology/approach The authors used a bibliometric study of bibliographic pairing to answer the research question. They collected data from the Web of Science and Scopus databases using the keywords “neuroscience*,” “neurostrategy*” and “neuroscientific*. ” Findings This study presents a framework that relates fundamental aspects discussed in current research using neuroscientific tools: Neuroscience and its research tools in organizations; emotions and information processing; interdisciplinary application of neuroscientific tools; and moral and ethical influences in the leaders' decision-making process. Research limitations/implications The inclusion of neuroscientific tools in Strategic Management research is still under development. There are criticisms and challenges related to the limitations and potential to support future research. Practical implications Despite recognizing the potential of neuroscientific tools in the mind and brain relationship, this study suggests that at this stage, because of criticisms and challenges, they should be used as support and in addition to other traditional research techniques to assess constructs and mechanisms related to strategic decisions and choices in organizations. Social implications Neuroscientific methods in organizational studies can provide insights into individual reactions to ethical issues and raise challenging normative questions about the nature of moral responsibility, autonomy, intention and free will, offering multiple perspectives in the field of business ethics. Originality/value In addition to presenting the potential and challenges of using scientific tools in strategic management studies, this study helps create methodological paths for studies in strategic management.