Aggregation pheromone for the pepper weevil,Anthonomus eugenii cano (Coleoptera: Curculionidae): Identification and field activity

This study describes the identification of an aggregation pheromone for the pepper weevil,Anthonomus eugenii and field trials of a synthetic pheromone blend. Volatile collections and gas chromatography revealed the presence of six male-specific compounds. These compounds were identified using chromatographic and spectral techniques as: (Z)-2-(3,3-dimethylcyclohexylidene)ethanol, (E)-2-(3,3-dimethylcyclohexylidene)ethanol, (Z)-(3,3-dimethylcyclohexylidene)acetaldehyde, (E)-(3,3-dimethylcyclohexylidene)acetaldehyde, (E)-3,7-dimethyl-2,6-octadienoic acid (geranic acid), and (E)-3,7-dimethyl-2,6-octadien-1-ol (geraniol). The emission rates of these compounds from feeding males were determined to be about: 7.2, 4.8, 0.45, 0.30, 2.0, and 0.30µg/male/day, respectively. Sticky traps baited with a synthetic blend of these compounds captured more pepper weevils (both sexes) than did unbaited control traps or pheromone-baited boll weevil traps. Commercial and laboratory formulations of the synthetic pheromone were both attractive. However, the commercial formulation did not release geranic acid properly, and geranic acid is necessary for full activity. The pheromones of the pepper weevil and the boll weevil are compared. Improvements for increasing trap efficiency and possible uses for the pepper weevil pheromone are discussed. A convenient method for purifying geranic acid is also described.The mention of firm names or trade products does not imply endorsement or recommendation by the U.S. Department of Agriculture over other firms or similar products not mentioned. All programs and services of the U.S. Department of Agriculture are offered on a nondiscriminatory basis without regard to race, color, national origin, religion, sex, age, marital status or handicap.     

Mechanical Properties of Partially Dense Alumina Produced from Powder Compacts

The elastic modulus ( E ), the critical strain energy release rate ( G _c), and the flexural strength (σ) have been determined for two partially dense alumina bodies produced from the same powder but with different initial densities. The mechanical properties were measured for specimens fabricated at four different relative densities. The measured elastic modulus, critical strain energy release rate, and a calculated critical stress intensity factor ( K _c) were observed to be linearly related to (ρ–ρ₀)/(1 –ρ₀), where ρ is the current relative density and ρ₀ is the initial relative density of the powder compact. With the observed linear relations for E, G _c (or K _c), and the assumption that the crack length responsible for failure was present in the initial powder compact and shrunk in proportion to the relative density change, a Griffith equation was constructed to estimate the strength at any relative density. This relation was in good agreement with measurements.     

Arsenic removal by iron-modified activated carbon

Iron-impregnated activated carbons have been found to be very effective in arsenic removal. Oxyanionic arsenic species such as arsenate and arsenite adsorb at the iron oxyhydroxide surface by forming complexes with the surface sites. Our goal has been to load as much iron within the carbon pores as possible while also rendering as much of the iron to be available for sorbing arsenic. Surface oxidation of carbon by HNO3/H2SO4 or by HNO3/KMnO4 increased the amount of iron that could be loaded to 7.6-8.0%; arsenic stayed below 10 ppb until 12,000 bed volumes during rapid small-scale tests (RSSCTs) using Rutland, MA groundwater (40-60 ppb arsenic, and pH of 7.6-8.0). Boehm titrations showed that surface oxidation greatly increased the concentration of carboxylic and phenolic surface groups. Iron impregnation by precipitation or iron salt evaporation was also evaluated. Iron content was increased to 9-17% with internal iron-loading, and to 33.6% with both internal and external iron loading. These iron-tailored carbons reached 25,000-34,000 bed volumes to 10 ppb arsenic breakthrough during RSSCTs. With the 33.6% iron loading, some iron peeled off.     

Utilizing the Explicit Finite Element Method for Studying Granular Flows

Granular flows are systems of complex dry particulates whose behavior is difficult to predict during sliding contact. Existing computational tools used to simulate granular flows are particle dynamics, cellular automata (CA), and continuum modeling. In the present investigation, another numerical tool—the explicit finite element method (FEM)—is analyzed as a potential technique for simulating granular flow. For this purpose, explicit dynamic finite element models of parallel shear cells were developed. These models contained 52 particles and consisted of granules that are both round and multi-shaped (diamond, triangle, and rectangle). Each parallel shear cell consisted of a smooth stationary top wall and a rough bottom surface that was given a prescribed velocity of U = 0.7 in/sec (1.78 cm/s). The coefficient of friction (COF) between the particle–particle and particle–wall collisions was varied between 0.0 and 0.75. Utilizing the output of the simulations, results are presented for the shear behavior, particle kinetic energy, and particle stresses within the shear cell as a function of time. As a means of validating the explicit technique for granular flow, a 75 particle, zero roughness, couette shear cell model (solid fraction of 0.50) is subsequently presented for which direct comparisons are made to the results published by Lun. [Lun, C.K. et al.: Phys. Fluids 8, 2868–2883 (1996)] Overall, the results indicate that the explicit FEM is a powerful tool for simulating granular flow phenomena in sliding contacts. In fact, the explicit method demonstrated several advantages over existing numerical techniques while providing equivalent accuracy to the molecular dynamics (MD) approach. These advantages included being able to monitor the collision (sub-surface and surface) stresses and kinetic energies of individual particles over time, the ability to analyze any particle shape, and the ability to capture force chains during granular flow.     

What’s the Big Deal About Big Data Loss (Actually Theft)?

I get confused about the way security deals with so-called data loss (actually theft). Recently we had what the media called, in various forms, a crisis, a huge cyberattack against, and so forth against JPMorgan Chase (JP), one of many such instances in the last year. So I looked at the various claims surrounding this instance, and found consistently that the only known “loss” of data was names, addresses, phone numbers, and e-mail addresses. And it was not lost! It was stolen, and JP still has copies. So, to be really clear, this is more or less the same information found in the phone book, available from various mailing list providers for less than a tenth of a penny a piece, and of no significant consequence I can identify. The cost of “protection” for consumers is $90 per person per year, although I do not quite get what this cost covers.     

Axial Compression of Footings in Cohesionless Soils. II: Bearing Capacity

An extensive database of full-scale field load tests was used to examine the bearing capacity for footings in cohesionless soils. Each load test curve was evaluated consistently to determine the interpreted failure load (i.e., bearing capacity) using the L1-L2 method. This test value then was compared with the theoretical bearing capacity, computed primarily using the basic Vesi? model. The comparisons show that, for footing widths B>1?m, the field results agree very well with the Vesi? predictions. However, for B<1?m, the results indicated a relationship between B and the predicted-to-measured bearing capacity ratio. Accordingly, a simple modification was made to the bearing capacity equation, and the resulting predictions are very good.     

The goal specificity effect on strategy use and instructional efficiency during computer-based scientific discovery learning

Using a computer-based scientific discovery learning environment on buoyancy in fluids we investigated the effects of goal specificity (nonspecific goals vs. specific goals) for two goal types (problem solving goals vs. learning goals) on strategy use and instructional efficiency. Our empirical findings close an important research gap, because in earlier studies the goal specificity effect either was restricted to one goal type or goal type was confounded with goal specificity. In addition, there is hardly a study with empirical evidence for the goal specificity effect on strategy use, which counts even more for a cognitive cost-benefit ratio as a dependent variable. Instead, in earlier studies the goal specificity effect has been attributed to differences in strategy use and cognitive cost-benefit ratio in a rather theoretical way. In the present study for strategy use an interaction was found between goal specificity and goal type, indicating that the goal specificity effect occurs only in case of problem solving goals, but not in case of learning goals. Compared to students provided with specific problem solving goals, students who worked on nonspecific problem solving goals, used a control of variables-strategy more frequently. Additionally, we found a main effect of goal specificity on instructional efficiency for both of the goal types, pointing at a more favorable relationship between performance gain and cognitive load caused by nonspecific goals.     

Physiological compliance and team performance

Physiological compliance (PC) refers to the correlation between physiological measures of team members over time. The goals of this study were to examine ways of measuring PC in heart rate variability (HRV) data and the relationship between PC and team performance. Teams were tasked with entering both real and simulated rooms and “shooting” individuals with a weapon and identifying individuals without a weapon. The linear correlation and directional agreement PC methods were shown to be the most sensitive to differences in performance, with greater PC being associated with better performance. The correlation method when applied to a measure of respiratory sinus arrhythmia (RSA) revealed a significant difference between high and low performers (t[8] = −2.31, p = 0.03) and the directional agreement applied to inter-beat-intervals and RSA revealed trend-level differences (t[4.62] = −1.86, p = 0.06 and t[8] = −1.68, p = 0.07). These results suggest that PC may have merit for predicting team performance.