First Group Of Experiments Research Articles

An efficient decoder for size optimization of trusses with automatic member grouping

This paper presents an efficient decoding method (namely SORTED decoding) for the de facto encoding in size optimization of trusses with automatic member grouping. The efficiency of the proposed method is evaluated through numerical experiments driven by two metaheuristic algorithms of different search mechanisms: Colliding Bodies Optimization and Jaya Algorithm. The first group of experiments show that the SORTED decoding method significantly outperforms the de facto and the two other decoding methods introduced in this study (ACCUMULATED and SORTED-ACCUMULATED) in terms of solution quality; moreover, the authors proved that the superiority of the SORTED decoding is not algorithm-dependent. Considerably better member-grouping configurations that provide up to 15% material economy are discovered for some of the well-known pre-grouped benchmark problems in the second group of experiments; surprisingly, the worst member-grouping configuration discovered by the SORTED decoding for one of the problems is better than that of the pre-grouped version.

Aspects Regarding of Passive Filters Sustainability for Non-Linear Single-Phase Consumers

The efficient use of electrical energy (an important component of sustainability) has become increasingly important for electrical consumers (industrial and non-industrial) as we face the challenges of climate change and the need to protect the environment. This theme is essential for guaranteeing a secure and sustainable future for both present and future generations. The power quality and the efficiency of electrical energy are connected to each other. Some power quality problems are caused by natural and unpredictable events, but many disturbances affecting power quality are caused by suppliers and consumers. One of the most important parameters in power engineering is the power factor, which indicates the degree of efficient use of electrical energy. Harmonics is the most important dynamic component of power quality, which affects the operation of electrical equipment and, at the same time, reduces the power factor. Harmonic sources in power systems are generally associated with nonlinear loads. To analyze the operating of passive filters (series L, shunt LC, T type LCL), two groups of experiments (relevant consumers were chosen for the industry as well as from the household sector) were carried out with single-phase nonlinear consumers: in the first group of experiments, a variable-frequency drive is used to supply a three-phase induction motor with variable load; in the second group of experiments, compact fluorescent lamps and LED lamps were used. Following the experiments, it was found that the difficulty of calibrating coils (to size a filter), especially the coils with a core, and the change in electrical properties over time for capacitors. For a certain type of consumer, the improvement of the current waveform depends on the type of filter used, the possibility of improving the power factor (to use electrical energy efficiently), and the role of the source impedance, which is particularly important to improve the efficiency of passive filters. Through the appropriate choice of the passive filter, a decrease in the deforming regime is obtained, with a slight decrease in the active power, and by increasing the power factor, a decrease in the losses of electrical energy from the electrical networks is obtained, with direct implications for the emission of greenhouse gases.

Probability-Tuned Market-Based Allocations for UAV Swarms Under Unreliable Observations.

Unmanned aerial vehicle (UAV) swarms are becoming increasingly attractive as highly integrated miniature sensors and processors deliver extraordinary performance. The employment of UAV swarms on complex real-life tasks has motivated exploration on allocation problems involving multiple UAVs, complex constraints, and multiple tasks with coupling relationships. Such problems have been summarized domain independently as multirobot task allocation problems with temporal and ordering constraints (MRTA/TOC). The majority of MRTA/TOC works have hitherto focused on deterministic settings, while their stochastic counterparts are sparsely explored. In this article, allocation problems incorporating classification uncertainty of targets and soft ordering constraints of tasks are considered. To address such problems, a novel market-based allocation algorithm, the probability-tuned market-based allocation (PTMA), is proposed. PTMA consists of iterations between two phases: 1) the first phase updates local perception of global situational awareness and 2) the second phase is a market-based allocation scheme with embedded artificial randomness to locally generate allocation results. Under reasonable assumptions on classification uncertainty, the proposed PTMA algorithm is proven to guarantee a superior performance compared with conventional auctions, verifying the feasibility of using randomness to counter randomness theoretically. Three groups of numerical experiments have been conducted to assess the performance of the proposed PTMA. The first group of experiments compares PTMA with the consensus-based auction algorithm (CBAA), the chance-constrained CBAA, and the PTMA-I (specialization of PTMA with the Identity matrix), on simulated task scenarios with varying degrees of classification uncertainty. The second group evaluates the stability of PTMA. The third group tests the scalability of PTMA on expanded task scenarios. Simulation results demonstrate a satisfactory performance in effectiveness, robustness, stability, and scalability of the proposed PTMA.

Bias learning improves data driven models for streamflow prediction

Study regionAndun river basin of Southern China and 273 watersheds across the continental United States. Study focusOwing to the data incompleteness and the changing environment, there is bias existing in the data driven models for streamflow prediction, greatly limiting their application in actual practice. In this paper, we incorporate the bias learning components into the data driven models to establish the mapping-bias-learning models, and design two groups of experiments, one in the Andun River basin in China and the other in 273 watersheds in the continental United States (CONUS). In the first group of experiments, we respectively apply three machine learning algorithms and one traditional statistical method to generate sixteen mapping-bias-learning models ae well as four mapping-learning-alone models. We also explore the effectiveness of different bias learning strategies, including multiple bias learning, stacking bias learning and incremental updating bias learning. In the second group of experiments, we apply the mapping-bias-learning models to the streamflow prediction in 273 watersheds of CONUS to verify the universality and robustness of the bias learning method. New hydrological insights for the regionThe mapping-bias-learning models significantly outperform the mapping-learning-alone models, and the machine learning methods are superior to the traditional statistical method in term of the ability of bias learning. In addition, adopting the appropriate bias learning strategies can further improve the streamflow forecast performance of data-driven models. The bias learning method is applicable to various watersheds with different hydrological conditions and play efficient roles at different streamflow levels. The median of MRE drops from 0.156 for mapping-learning-alone models to 0.131 for mapping-bias-learning models, while median of NSE increases from 0.918 for mapping-learning-alone models to 0.932 for mapping-bias-learning models. More inspiringly, the bias learning method can still play roles in data driven models under the environmental conditions that never occurred before, suggesting its remarkable superiority. This study highlights the power of the bias learning method for data driven models in streamflow prediction, and the promising prospect of the combination between bias learning and machine learning methods in hydrological modelling.

Spectral Camouflage Characteristics and Recognition Ability of Targets Based on Visible/Near-Infrared Hyperspectral Images

Hyperspectral imaging can simultaneously obtain the spatial morphological information of the ground objects and the fine spectral information of each pixel. Through the quantitative analysis of the spectral characteristics of objects, it can complete the task of classification and recognition of ground objects. The appearance of imaging spectrum technology provides great advantages for military target detection and promotes the continuous improvement of military reconnaissance levels. At the same time, spectral camouflage materials and methods that are relatively resistant to hyperspectral reconnaissance technology are also developing rapidly. In order to study the reconnaissance effect of visible/near-infrared hyperspectral images on camouflage targets, this paper analyzes the spectral characteristics of different camouflage targets using the hyperspectral images obtained in the visible and near-infrared bands under natural conditions. Two groups of experiments were carried out. The first group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage clothing with grassland as the background; the second group of experiments verified the spectral camouflage characteristics and camouflage effects of different types of camouflage paint sprayed on boards and steel plates. The experiment shows that the hyperspectral image based on the near-infrared band has a good reconnaissance effect for different camouflage targets, and the near-infrared band is an effective “window” band for detecting and distinguishing true and false targets. However, the stability of the visible/near-infrared band detection for the target identification under camouflage paint is poor, and it is difficult to effectively distinguish the object materials under the same camouflage paint. This research confirms the application ability of detection based on the visible/near-infrared band, and points out the direction for the development of imaging detectors and camouflage materials in the future.

Effect of Blending Essential Oil on Abdominal Discomfort Using Ayurveda Tridosha

To find out the effect of abdominal discomfort using Blending Essential oil and abdominal discomfort Using Ayurvbeda Tridosha (Vata, Pitta, Kapha), of 45 female students in their 20s treated for abdominal discomfort, the control group applied Carrier Sweet Almond Oil and the first group of experiments applied Lavender, Juniper, Fennel, Cinnamon, Grapefruit, Marjoram to Carrier Sweet Almond Oil. the second group of experiments applied Lavender, Clary Sage, Rosemary, Lemon Gras, Grapefruit, Peppermint to Carrier Sweet Almond Oil and made them apply on the abdomen three times a day and for three days from the date of abdominal discomfort. As a result, 2.22 points (t=6.494, p<.001) decreased the most in the second group of experiments, and Kappa decreased by 4.00 points (t=4.536, p<.05), showing a significant difference in abdominal discomfort according to Ayurveda's Tridosha constitution. As a result, the reduction of abdominal discomfort was verified, and the application of Blending Essential oil according to Ayurveda's Tridosha constitution is presented as clinical basic data that can screen abdominal discomfort.

Near-Infrared Photobiomodulation of Living Cells, Tubulin, and Microtubules In Vitro

We report the results of experimental investigations involving photobiomodulation (PBM) of living cells, tubulin, and microtubules in buffer solutions exposed to near-infrared (NIR) light emitted from an 810 nm LED with a power density of 25 mW/cm2 pulsed at a frequency of 10 Hz. In the first group of experiments, we measured changes in the alternating current (AC) ionic conductivity in the 50–100 kHz range of HeLa and U251 cancer cell lines as living cells exposed to PBM for 60 min, and an increased resistance compared to the control cells was observed. In the second group of experiments, we investigated the stability and polymerization of microtubules under exposure to PBM. The protein buffer solution used was a mixture of Britton-Robinson buffer (BRB aka PEM) and microtubule cushion buffer. Exposure of Taxol-stabilized microtubules (~2 μM tubulin) to the LED for 120 min resulted in gradual disassembly of microtubules observed in fluorescence microscopy images. These results were compared to controls where microtubules remained stable. In the third group of experiments, we performed turbidity measurements throughout the tubulin polymerization process to quantify the rate and amount of polymerization for PBM-exposed tubulin vs. unexposed tubulin samples, using tubulin resuspended to final concentrations of ~ 22.7 μM and ~ 45.5 μM in the same buffer solution as before. Compared to the unexposed control samples, absorbance measurement results demonstrated a slower rate and reduced overall amount of polymerization in the less concentrated tubulin samples exposed to PBM for 30 min with the parameters mentioned above. Paradoxically, the opposite effect was observed in the 45.5 μM tubulin samples, demonstrating a remarkable increase in the polymerization rates and total polymer mass achieved after exposure to PBM. These results on the effects of PBM on living cells, tubulin, and microtubules are novel, further validating the modulating effects of PBM and contributing to designing more effective PBM parameters. Finally, potential consequences for the use of PBM in the context of neurodegenerative diseases are discussed.

Technical and Environmental Assessment of Chemical Pretreatment of Rice Straw

Pretreatment process selection and optimization is one of the main challenges for obtaining the highest benefits from rice straw. This work aims at studying the effect of alkali type and loading on rice straw composition, delignification percentage and black liquor characteristics. The first group of experiments has been conducted using only alkaline pretreatment (NaOH, Ca(OH)2 ,and a mixture of them) with loadings (2-10 g/l) at 90 °C for 2 h. The second group of experiments, conducted at ambient temperature, has included alkaline, acidic, and acidic followed by alkaline pretreatment. Results have showed that at 90 °C both the type and the loading of alkali have a significant effect on rice straw delignification percentage, which has ranged from 59.3-87.17% at reduced reaction time. Furthermore, at ambient temperature conditions, the delignification percentage has ranged between (47.2-66.48%) with a significant decrease in ash content. Pre-treatment at ambient temperature could significantly decrease both lignin and ash in a longer period. Finally, environmental assessment related to chemical pretreatment showed that black liquor with high COD, BOD, ash contents, and low biodegradability represents a major constraint for environmentally accepted rice straw pretreatment. Endeavors to mitigate this effect are mandatory through the recovery of a great portion of the solid content of the black liquor.

The Machine-Part Cell Formation Problem with Non-Binary Values: A MILP Model and a Case of Study in the Accounting Profession

The traditional machine-part cell formation problem simultaneously clusters machines and parts in different production cells from a zero–one incidence matrix that describes the existing interactions between the elements. This manuscript explores a novel alternative for the well-known machine-part cell formation problem in which the incidence matrix is composed of non-binary values. The model is presented as multiple-ratio fractional programming with binary variables in quadratic terms. A simple reformulation is also implemented in the manuscript to express the model as a mixed-integer linear programming optimization problem. The performance of the proposed model is shown through two types of empirical experiments. In the first group of experiments, the model is tested with a set of randomized matrices, and its performance is compared to the one obtained with a standard greedy algorithm. These experiments showed that the proposed model achieves higher fitness values in all matrices considered than the greedy algorithm. In the second type of experiment, the optimization model is evaluated with a real-world problem belonging to Human Resource Management. The results obtained were in line with previous findings described in the literature about the case study.

Kinetic Analysis of Methane-Propane Hydrate Formation by the Use of Different Impellers.

In the present study, the effect of different kinds of impellers with different baffles or no baffle was investigated. Up-pumping pitched blade turbine (PBTU) and Rushton turbine (RT) were the two types of impellers tested. The reactor was equipped with different designs of baffles: full, half and surface baffles, or no baffles. Single (PBTU or RT) and dual (PBTU/PBTU or RT/RT) use of impellers with full (FB), half (HB), surface (SB), and no baffle (NB) combinations formed two sets of 16 experiments. The first group of experiments was close to the equilibrium line (P = 26.5 bars and T = 8.5 °C), and the second group was deep in the equilibrium line (P = 24.5 bars and T = 2 °C). There was estimation of rate of hydrate formation, induction time, hydrate productivity, overall power consumption, split fraction, and separation factor. In both single and dual impellers, the results showed that RT experiments are better compared to PBTU in the rate of hydrate formation. The induction time is almost the same because we are deep in the equilibrium line while, hydrate productivity values are higher in PBTU compared to RT experiments. As a general view, RT experiments consume more energy compared to PBTU experiments.

IMPACT OF WATER AND CO2 ON THE MECHANICAL PROPERTIES OF LOW PERMEABLE ROCKS

This article features experiments on triaxial compression of low-permeable dolomite samples with different confining pressures (2-20 MPa), different pore fluids (dry air, water, CO2), and different temperatures (25-150 °C). The authors have studied the effect of confining pressure, pore fluid and temperature on the strength properties of the studied samples. The results show an increase in the strength with grwoing confining pressure. When the confining pressure increases from 2 to 20 MPa, the compressive strength increases from 86 to 370 MPa. Temperature has a significant effect on rock strength under low confining pressure conditions. With the increasing confining pressure reaching 15 MPa, increasing temperature has little effect on the strength of dolomite samples. Under an effective confining pressure of 5 MPa, the temperature weakening occurs on the dolomite specimens when the temperature exceeds 90 °C. During compression, liquid diffusion occurs in the specimens. Higher water viscosity can cause a temporary decrease in effective confining pressure, which can increase the strength of the rock. More prominent fractures are observed in the samples, and more fluid is injected under CO2 injection conditions, which may be useful for increasing the permeability of the geothermal reservoir. Two groups of experiments have been performed on the samples in this study: the first group of experiments investigated the effect of confining pressure on the fracture stress of core samples, without pore fluid injection; the second group of experiments investigated the effect of water or CO2 and temperature on the mechanical properties of core samples.

Research on evaporation duct height prediction based on back propagation neural network

Evaporation duct is an abnormal refraction atmospheric stratification frequently appearing at sea. Accurately obtaining evaporation duct height (EDH) is significant for the effective application of electromagnetic system equipment. To overcome the limitations of theoretical prediction models, this study proposes a pure data-driven back propagation neural network (BPNN) EDH prediction model (referred to as BPNN model) for the first time, which uses BPNN suitable for the data characteristics of evaporation duct and then conducts two groups of experiments to fully test the proposed BPNN model. The Paulus–Jeske (PJ) model and support vector regression (SVR) model are introduced as the baseline methods in these experiments. The results of the first group of experiments prove that on the EDH prediction in all the experimental areas, the BPNN model has significantly better comprehensive performances than the PJ model and the SVR model. Also, the second group of experiments’ results reflects the BPNN model has great generalisation capacity, which makes it suitable for predicting the EDH, and the distributions of EDH have a strong spatial correlation.

Experimental study of metamorphic reactions and dehydration processes at the blueschist–eclogite transition during warm subduction

AbstractThe transition between blueschist and eclogite plays an important role in subduction zones via dehydration and densification processes in descending oceanic slabs. There are a number of previous petrological studies describing potential mineral reactions taking place at the transition. An experimental determination of such reactions could help constrain the pressure–temperature conditions of the transition as well as the processes of dehydration. However, previous experimental contributions have focused on the stability of spontaneously formed hydrous minerals in basaltic compositions rather than on reactions among already formed blueschist facies minerals. Therefore, this study conducted three groups of experiments to explore the metamorphic reactions among blueschist facies minerals at conditions corresponding to warm subduction, where faster reaction rates are possible on the time scale of laboratory experiments. The first group of experiments was to establish experimental reversals of the reaction glaucophane+paragonite to jadeite+pyrope+quartz+H2O over the range of 2.2–3.5 GPa and 650–820°C. This reaction has long been treated as key to the blueschist–eclogite transition. However, only the growth of glaucophane+paragonite was observed at the intersectional stability field of both paragonite and jadeite+quartz, confirming thermodynamic calculations that the reaction is not stable in the system Na2O–MgO–Al2O3–SiO2–H2O. The second set of experiments involved unreversed experiments using glaucophane+zoisite ±quartz in low‐Fe and Ca‐rich systems and were run at 1.8–2.4 GPa and 600–780°C. These produced omphacite+paragonite/kyanite+H2O accompanied by compositional shifts in the sodium amphibole, glaucophane, towards sodium–calcium amphiboles such as winchite (☐(CaNa)(Mg4Al)Si8O22(OH)2) and barroisite (☐(CaNa)(Mg3Al2)(AlSi7)O22(OH)2). This suggests that a two‐step dehydration occurs, first involving the breakdown of glaucophane+zoisite towards a paragonite‐bearing assemblage, then the breakdown of paragonite to release H2O. It also indicates that sodium–calcium amphibole can coexist with eclogite phases, thereby extending the thermal stability of amphibole to greater subduction zone depths. The third set of experiments was an experimental investigation at 2.0–2.4 GPa and 630–850°C involving a high‐Fe (Fe#=Fetotal/(Fetotal+Mg)≈0.36) natural glaucophane, synthetic paragonite and their eclogite‐forming reaction products. The results indicated that garnet and omphacite grew over most of these pressure–temperature conditions, which demonstrates the importance of Fe‐rich glaucophane in forming the key eclogite assemblage of garnet+omphacite, even under warm subduction zone conditions. Based on the experiments of this study, reaction between glaucophane+zoisite is instrumental in controlling dehydration processes at the blueschist–eclogite transition during warm subduction.

Impacts of Assimilating ATMS Radiances on Heavy Rainfall Forecast in RMAPS-ST

The Advanced Technology Microwave Sounder (ATMS) mounted on the Suomi National Polar-Orbiting Partnership (NPP) satellite can provide both temperature and humidity information for a weather prediction model. Based on the rapid-refresh multi-scale analysis and prediction system—short-term (RMAPS-ST), we investigated the impact of ATMS radiance data assimilation on strong rainfall forecasts. Two groups of experiments were conducted to forecast heavy precipitation over North China between 18 July and 20 July 2016. The initial conditions and forecast results from the two groups of experiments have been compared and evaluated against observations. In comparison with the first group of experiments that only assimilated conventional observations, some added value can be obtained for the initial conditions of temperature, humidity, and wind fields after assimilating ATMS radiance observations in the system. For the forecast results with the assimilation of ATMS radiances, the score skills of quantitative forecast rainfall have been improved when verified against the observed rainfall. The Heidke skill score (HSS) skills of 6-h accumulated precipitation in the 24-h forecasts were overall increased, more prominently so for the heavy rainfall above 25 mm in the 0–6 h of forecasts. Assimilating ATMS radiance data reduced the false alarm ratio of quantitative precipitation forecasting in the 0–12 h of the forecast range and thus improved the threat scores for the heavy rainfall storm. Furthermore, the assimilation of ATMS radiances improved the spatial distribution of hourly rainfall forecast with observations compared with that of the first group of experiments, and the mean absolute error was reduced in the 10-h lead time of forecasts. The inclusion of ATMS radiances provided more information for the vertical structure of features in the temperature and moisture profiles, which had an indirect positive impact on the forecasts of the heavy rainfall in the RMAPS-ST system. However, the deviation in the location of the heavy rainfall center requires future work.

Research on an olfactory neural system model and its applications based on deep learning

The idea of constructing the biological neural system model as realistic as possible can not only provide a new artificial neural network (ANN), but also offer an effective object to study biological neural systems. As a very meaningful attempt about the idea, a bionic model of olfactory neural system, KIII model, is introduced in this paper. There are the unique characteristics of KIII model different from those of general ANNs. The KIII model realistically simulates the structure of the real olfactory neural system and the process of odor molecules gradually transformed by the core components of the olfactory system including olfactory receptor, olfactory bulb and olfactory cortex. The neuron model of the KIII model is constructed and optimized based on neurophysiological experimental data and accurately reflects the response of olfactory neurons to odor stimulation. In particular, the noise introduced to KIII model can further improve the performance of the model. In addition, the KIII model is analyzed based on the idea of deep learning. The qualitative analysis shows that there are obvious similarities between the KIII model and the deep learning model. Furthermore, with the epileptic electroencephalograph (EEG) recognition task, two groups of experiments are designed to comprehensively analyze the performance of the KIII model. In the first group of experiments, a typical pattern recognition experiment with feature extraction stage is shown. The features of epileptic EEG were extracted based on Empirical Mode Decomposition (EMD), and the KIII model was used as a classifier. The experimental results show that the KIII model only needs a small number of iterations to memorize different modes and has a high recognition rate, over 91%. In the second group of experiments, a direct recognition experiment without feature extraction stage is shown. The original epileptic EEG signals as KIII model inputs directly were recognized. The experimental results show that there is still an excellent performance in the KIII model, over 96%, and the recognition result is similar to the characteristics of the deep learning model. The theoretical analysis and experimental results prove that KIII model with the idea of deep learning is an excellent bionic model of olfactory neural system and gets a good balance between high bionics and good performance, which is a good reference for related research.

Influence of different non-grain commodities on the population growth of Trogoderma granarium Everts (Coleoptera: Dermestidae)

We investigated the population growth of the khapra beetle, Trogoderma granarium Everts (Coleoptera: Dermestidae) on seven groups of commercially available non-grain commodities. Six powdered spices were used in the first group of experiments: black pepper, clove, nutmeg, allspice, cinnamon and turmeric. The second group of the tested commodities included seven animal products: powdered cow milk, powdered pork zelatin, sheeps’ wool, goat’s skin, ducks’ feathers, dry dog food and dry cat food. The third group of commodities was consisted of six herbs: oregano, spearmint, basil, coriander, laurel and marjoram. The fourth group of commodities contained ten pulses: chickpeas, lentil, split peas, black-eyed peas, beans, soybean flour and whole kernels, lima beans, mung bean, and broad beans. The fifth group comprised six dried fruits: Corinthian currants, sultanas, banana chips, melons, apricots and figs. The sixth group was consisted of five non-grain commodities: cottonseed cake, tobacco, black tea, Turk kahvesi and potato flour. The seventh group included seven nuts: sunflower seeds, pumpkin seeds, pistachios, roasted chickpeas (yellow), almonds, hazelnuts and walnuts. Finally, six cracked containment categories (0% cracked kernels, 5% cracked kernels and 95% intact kernels, 10% cracked kernels and 90% intact kernels, 25% cracked kernels and 75% intact kernels, 50% cracked kernels and 50% intact kernels and 100% cracked kernels) from five pulses were tested: chickpeas, black-eyed peas, mung bean, soybean and split peas. The highest progeny production (3.01 individuals per vial) was recorded on powdered cow milk. On pistachios, split peas, sunflower seeds, soybean flour, pumpkin seeds, walnuts, almonds and coriander, T. granarium built high population densities rapidly, while on roasted chickpeas, cottonseed cake, hazelnuts, chickpeas, dog food and lentils, its population growth was much less. Broad beans, melons, figs, lima beans, beans, Corinthian currants, pork zelatin and potato flour were less suitable diets for the development of this species. On black pepper, clove, nutmeg, allspice, cinnamon, turmeric, sheeps’ wool, goat’s skin, ducks’ feathers, cat food, sultanas, banana chips, apricots, tobacco, black tea, Turk kahvesi, oregano, spearmint, basil, laurel and marjoram, no progeny production was recorded. The proportion of 100% cracked black-eyed peas or mung beans was more suitable for the population growth of T. granarium. Also, the percentages of 50% cracked chickpeas or 10% cracked soybeans enhanced the development of the species, in comparison with the whole kernels of each pulse. Our study indicated that several non-grain commodities are beneficial for the population growth of T. granarium, a fact that should be seriously taken into account in international trade, as this pest may utilize them as “vehicles” of expansion.

Factors Influencing Emitter Clogging Caused by Negative Pressure in Potted Yunnan Laterite with Root-Zone Infiltration Irrigation

Abstract. In this study, an equivalent mechanical model was established for the clogging induced by soil suction under negative pressure during root-zone infiltration irrigation in a crop-free pot, and a state diagram was plotted for the clogging process induced by a single soil suction factor under negative pressure. Three groups of impact factor experiments were conducted using three different emitters: a flow-adjustable emitter with eight horizontal outlets (emitter L), a flow-adjustable emitter with two vertical outlets (emitter Q), and an anti-clogging material wrapped around the outer surface of emitter L (emitter K). The first group of experiments investigated the influence of irrigation pressure, the variation of irrigation pressure (range of 0.02 to 0.12 MPa), and the amount of sediment inside emitter L in Yunnan laterite. For the second group of experiments, two soil types (Yunnan laterite and yellow sand), two single-event irrigation volumes (170 and 250 mL), and two numbers of irrigation events (8 and 16) were used to quantitatively analyze the sediment content in emitter L. The third group of experiments used emitters L, Q, and K and involved quantitative analysis of the clogging differences in the different emitter types with two soil types (Yunnan laterite and yellow sand) and two numbers of irrigation events (24 and 64). Statistical analysis showed that irrigation pressure, soil type, irrigation pattern, and emitter type were significant (p < 0.05) for the amount of sediment due to negative suction inside the emitter. The results demonstrate that emitter clogging induced by negative pressure tends to first decrease and then increase with increasing irrigation pressure. The amount of sediment that accumulates within an emitter is influenced by both the soil suction effect under negative pressure as well as soil flushing under positive pressure. Within emitter L, clogging induced by soil suction under negative pressure was more likely to occur when using Yunnan laterite rather than yellow sand soil. When both soil types were used in the test, emitter L was observed to be more prone to clogging induced by negative pressure compared to emitter Q, whereas emitter K exhibited the best anti-clogging performance. This study provides a quantitative account of the factors associated with clogging induced by negative pressure, and it provides a theoretical and experimental basis for gaining an in-depth understanding of emitter clogging during root-zone infiltration irrigation in Yunnan laterite. Keywords: Emitter, Negative-pressure clogging, Pot environment, Root-zone infiltration irrigation, Yunnan laterite.

Fractal triangular search: a metaheuristic for image content search

This work proposes a variable neighbourhood search (FTS) that uses a fractal-based local search primarily designed for images. Searching for specific content in images is posed as an optimisation problem, where evidence elements are expected to be present. Evidence elements improve the odds of finding the desired content and are closely associated to it in terms of spatial location. The proposed local search algorithm follows the fashion of a chain of triangles that engulf each other and grow indefinitely in a fractal fashion, while their orientation varies in each iteration. The authors carried out an extensive set of experiments, which confirmed that FTS outperforms state-of-the-art metaheuristics. On average, FTS was able to locate content faster, visiting less incorrect image locations. In the first group of experiments, FTS was faster in seven out of nine cases, being >8% faster on average, when compared to the second best search method. In the second group, FTS was faster in six out of seven cases, and it was >22% faster on average when compared to the approach ranked second best. FTS tends to outperform other metaheuristics substantially as the size of the image increases.

Multiple Representations of Space by the Cockroach, Periplaneta americana

When cockroaches are trained to a visual–olfactory cue pairing using the antennal projection response (APR), they can form different memories for the location of a visual cue. A series of experiments, each examining memory for the spatial location of a visual cue, were performed using restrained cockroaches. The first group of experiments involved training cockroaches to associate a visual cue (CS—green LED) with an odor cue (US) in the presence or absence of a second visual reference cue (white LED). These experiments revealed that cockroaches have at least two forms of spatial memory. First, it was found that during learning, the movements of the antennae in response to the odor influenced the cockroaches’ memory. If they use only one antenna, cockroaches form a memory that results in an APR being elicited to the CS irrespective of its location in space. When using both antennae, the cockroaches resulting memory leads to an APR to the CS that is spatially confined to within 15° of the trained position. This memory represents an egocentric spatial representation. Second, the cockroaches simultaneously formed a memory for the angular spatial relationships between two visual cues when trained in the presence of a second visual reference cue. This training provided the cockroaches an allocentric representation or visual snapshot of the environment. If both egocentric and the visual snapshot were available to the cockroach to localize the learned cue, the visual snapshot determined the behavioral response in this assay. Finally, the split-brain assay was used to characterize the cockroach’s ability to establish a memory for the angular relationship between two visual cues with half a brain. Split-brain cockroaches were trained to unilaterally associate a pair of visual cues (CS—green LED and reference—white LED) with an odor cue (US). Split-brain cockroaches learned the general arrangement of the visual cues (i.e., the green LED is right of the white LED), but not the precise angular relationship. These experiments provide new insight into spatial memory processes in the cockroach.

Oil Aging due to Partial Discharge Activity

Oil is the main insulation in power transformers and over long time of ageing its insulation properties can change. In this paper ageing of oil due to the exposure to electric discharges was investigated. The effect of high energy discharges (complete arc) and low energy discharges (partial discharges) on oil properties such as breakdown strength and oil conductivity was investigated.An experimental setup consisting of two spherical electrodes was designed. The adjustable distance between the two electrodes made it possible to have PD with different magnitude.The oil conductivity and breakdown strength was measured for three sets of experiments. The first group of experiments was performed on new oil in order to have a reference for comparison. In the second group of experiments the new oil samples was exposed to 1000 and 3000 lightning impulses. In the third group of experiments new oil samples was exposed to partial discharge for different duration of time. Oil conductivity and breakdown strength of these aged samples were compared with new oil. The results show that after exposure to lightning impulse oil conductivity increases and breakdown strength decreases, However PD activity for short time does not change the oil conductivity but it reduces the breakdown strength.