Explore the vast range of titles available.

Prediction in ungauged basins (PUB) is a concerning hydrological challenge, prompting the development of various regionalization methods to improve prediction accuracy. The long short‐term memory (LSTM) model has gained popularity in rainfall‐runoff prediction in recent years and has proven applicable in PUB. Prior research indicates that incorporating static attributes in the training of regional LSTM models could improve performance in PUB. However, the underlying reasons for this enhancement have received limited exploration. This study aims to explore the role of static attributes in the training of the regional LSTM model. It is assumed that the regional LSTM model can induce streamflow generation mechanisms with the incorporation of static attributes and apply certain streamflow generation mechanisms to ungauged catchments based on their attributes. To this end, a grouping‐based training strategy is proposed, that is, training and validating regional LSTM models on catchments with similar streamflow generation mechanisms within predefined groups. The training strategies of regional LSTM models, either incorporated with static catchment attributes or based on classification, are conducted in 363 catchments. Results demonstrate a high level of consistency in the enhancement achieved by the two training strategies. Specifically, 192 and 216 catchments exhibit enhancement compared to traditionally trained models without inclusion of attributes, with 132 catchments showing improvement under both training strategies. Furthermore, the findings indicate consistent spatial patterns and attribute distributions of enhanced catchments, as well as the notable improvement in reproducing low flow‐related hydrological signatures. Key Points A classification‐based training strategy is introduced for the regional long short‐term memory (LSTM) model The influence of static attributes on the performance of the regional LSTM model in ungauged basins is investigated There is a high level of consistency in the enhancement achieved by the two training strategies, either incorporated with static catchment attributes or based on classification

Modern vehicles are proficient in establishing a spontaneous connection over a wireless radio channel, synchronizing actions and information. Security infrastructure is most important in such a sensitive scope of vehicle communication for coordinating actions and avoiding accidents on the road. One of the first security issues that need to be established is authentication via IEEE 1609.2 security infrastructure. According to our preliminary work, vehicle owners are bound to preprocess a certificate from the certificate authority. The certificate carries vehicle static attributes (e.g., licence number, brand and color) certified together with the vehicle public key in a monolithic manner. Nevertheless, a malicious vehicle might clone the static attributes to impersonate a specific vehicle. Therefore, in this paper we consider a resource expensive attack scenario involving multiple malicious vehicles with identical visual static attributes. Apparently, dynamic attributes (e.g., location and direction) can uniquely define a vehicle and can be utilized to resolve the true identity of the vehicle. However, unlike static attributes, dynamic attributes cannot be signed by a trusted authority beforehand. We propose an approach to verify the coupling between non-certified dynamic attributes and certified static attributes on an auxiliary communication channel, for example, a modulated laser beam. Furthermore, we illustrate that the proposed approach can be used to facilitate the usage of existing authentication protocols such as NAXOS, in the new scope of ad-hoc vehicle networks. We use BAN logic to verify the security claims of the protocol against the passive and active interception.

Zhang and Zhang argue that predictors are useless unless they have high precison&recall. We have a different view, for two reasons. First, for SE data sets with large neg/pos ratios, it is often required to lower precision to achieve higher recall. Second, there are many domains where low precision detectors are useful.

In this correspondence, we point out a discrepancy in a recent paper, \"data mining static code attributes to learn defect predictors,\" that was published in this journal. Because of the small percentage of defective modules, using probability of detection (pd) and probability of false alarm (pf) as accuracy measures may lead to impractical prediction models.

As the application layer in embedded systems dominates over the hardware, ensuring software quality becomes a real challenge. Software testing is the most time-consuming and costly project phase, specifically in the embedded software domain. Misclassifying a safe code as defective increases the cost of projects, and hence leads to low margins. In this research, we present a defect prediction model based on an ensemble of classifiers. We have collaborated with an industrial partner from the embedded systems domain. We use our generic defect prediction models with data coming from embedded projects. The embedded systems domain is similar to mission critical software so that the goal is to catch as many defects as possible. Therefore, the expectation from a predictor is to get very high probability of detection (pd) . On the other hand, most embedded systems in practice are commercial products, and companies would like to lower their costs to remain competitive in their market by keeping their false alarm (pf) rates as low as possible and improving their precision rates. In our experiments, we used data collected from our industry partners as well as publicly available data. Our results reveal that ensemble of classifiers significantly decreases pf down to 15% while increasing precision by 43% and hence, keeping balance rates at 74%. The cost-benefit analysis of the proposed model shows that it is enough to inspect 23% of the code on local datasets to detect around 70% of defects.

Rotation around a specific bond after photoexcitation is central to vision and emerging opportunities in optogenetics, super-resolution microscopy, and photoactive molecular devices. Competing roles for steric and electrostatic effects that govern bond-specific photoisomerization have been widely discussed, the latter originating from chromophore charge transfer upon excitation. We systematically altered the electrostatic properties of the green fluorescent protein chromophore in a photoswitchable variant, Dronpa2, using amber suppression to introduce electron-donating and electron-withdrawing groups to the phenolate ring. Through analysis of the absorption (color), fluorescence quantum yield, and energy barriers to ground- and excited-state isomerization, we evaluate the contributions of sterics and electrostatics quantitatively and demonstrate how electrostatic effects bias the pathway of chromophore photoisomerization, leading to a generalized framework to guide protein design.

Background The use of drug nanocarriers to encapsulate drugs for oral administration may become an important strategy in addressing the challenging oral absorption of some drugs. In this study—with the premise of controlling single variables—we prepared model nanoparticles with different particle sizes, surface charges, and surface hydrophobicity/hydrophilicity. The two key stages of intestinal nanoparticles (NPs) absorption—the intestinal mucus layer penetration stage and the trans-intestinal epithelial cell stage—were decoupled and analyzed. The intestinal absorption of each group of model NPs was then investigated. Results Differences in the behavioral trends of NPs in each stage of intestinal absorption were found to result from differences in particle properties. Small size, low-magnitude negative charge, and moderate hydrophilicity helped NPs pass through the small intestinal mucus layer more easily. Once through the mucus layer, an appropriate size, positive surface charge, and hydrophobic properties helped NPs complete the process of transintestinal epithelial cell transport. Conclusions To achieve high drug bioavailability, the basic properties of the delivery system must be suitable for overcoming the physiological barrier of the gastrointestinal tract.

The relation between static and dynamic stiffness in shales is important for many engineering applications. Dynamic stiffness, calculated from wave velocities, is often related to static stiffness through simple empirical correlations. The reason for this is that dynamic properties are often easier to obtain; however, it is the static properties that define the actual subsurface response to stress or pore pressure changes. Rocks are not elastic media, and stiffness depends on the stress state, stress change amplitude, loading rate, drainage conditions, fluid saturation, and scale. All these factors require consideration when static and dynamic stiffness properties are to be related. Two mechanisms that may have a strong effect on the stiffness of shales were studied in this experimental work: (1) a reduction of undrained static stiffness with an increase in stress amplitude and (2) a frequency dependence (or dispersion) of dynamic stiffness. Laboratory tests were performed on four fully brine-saturated undrained field shales from different overburden formations. Experiments were conducted using a low-frequency apparatus—a triaxial loading cell with the ability to measure dynamic stiffness at seismic frequencies (1–150 Hz) and ultrasonic velocities (500 kHz). Shale anisotropy was characterized by testing differently oriented core plugs. The results demonstrated that all the tested shales exhibited a dispersion of dynamic stiffness from seismic to ultrasonic frequencies. Young’s modulus dispersion for the tested shales ranged from nearly 30% to above 100%. Wave velocity dispersion was on the order of 10–20% for P-waves and 20–40% for S-waves. In static tests, the undrained rock stiffness gradually decreased with increasing stress amplitude. For one shale, the static undrained Young’s modulus was reduced by 50% when amplitude of the loading–unloading measurement cycle increased from 1 to 3 MPa. This finding is explained by non-elastic deformations that increase with the stress level. A method of zero-stress extrapolation of static stiffness was used to obtain the purely elastic response. The stiffness for the limit of zero stress change amplitude agreed well with the dynamic response at seismic frequency, providing a link between static and dynamic stiffness.

The effects of growth phase on the lipid content and surface properties of oleaginous microalgae Botryococcus sp. FACGB-762, Chlorella sp. XJ-445 and Desmodesmus bijugatus XJ-231 were investigated in this study. The results showed that throughout the growth phases, the lipid content of microalgae increased. The surface properties like particle size, the degree of hydrophobicity, and the total concentration of functional groups increased while net surface zeta potential decreased. The results suggested that the growth stage had significant influence not only on the lipid content but also on the surface characteristics. Moreover, the lipid content was significantly positively related to the concentration of hydroxyl functional groups in spite of algal strains or growth phases. These results provided a basis for further studies on the refinery process using oleaginous microalgae for biofuel production.

Striated muscle contraction is regulated by the translocation of troponin-tropomyosin strands over the thin filament surface. Relaxation relies partly on highly-favorable, conformation-dependent electrostatic contacts between actin and tropomyosin, which position tropomyosin such that it impedes actomyosin associations. Impaired relaxation and hypercontractile properties are hallmarks of various muscle disorders. The α-cardiac actin M305L hypertrophic cardiomyopathy-causing mutation lies near residues that help confine tropomyosin to an inhibitory position along thin filaments. Here, we investigate M305L actin in vivo, in vitro, and in silico to resolve emergent pathological properties and disease mechanisms. Our data suggest the mutation reduces actin flexibility and distorts the actin-tropomyosin electrostatic energy landscape that, in muscle, result in aberrant contractile inhibition and excessive force. Thus, actin flexibility may be required to establish and maintain interfacial contacts with tropomyosin as well as facilitate its movement over distinct actin surface features and is, therefore, likely necessary for proper regulation of contraction. The α-cardiac actin M305L hypertrophic cardiomyopathy-causing mutation is located near residues that help confine tropomyosin to an inhibitory position along thin filaments. Here the authors assessed M305L actin in vivo, in vitro, and in silico to characterize emergent pathological properties and define the mechanistic basis of disease.