Electrical Flows, Laplacian Systems, and Faster Approximation of Maximum Flow in Undirected Graphs

We introduce a new approach to computing an approximately maximum s-t flow in a capacitated, undirected graph. This flow is computed by solving a sequence of electrical flow problems. Each electrical flow is given by the solution of a system of linear equations in a Laplacian matrix, and thus may be approximately computed in nearly-linear time. Using this approach, we develop the fastest known algorithm for computing approximately maximum s-t flows. For a graph having n vertices and m edges, our algorithm computes a (1-\epsilon)-approximately maximum s-t flow in time \tilde{O}(mn^{1/3} \epsilon^{-11/3}). A dual version of our approach computes a (1+\epsilon)-approximately minimum s-t cut in time \tilde{O}(m+n^{4/3}\eps^{-8/3}), which is the fastest known algorithm for this problem as well. Previously, the best dependence on m and n was achieved by the algorithm of Goldberg and Rao (J. ACM 1998), which can be used to compute approximately maximum s-t flows in time \tilde{O}(m\sqrt{n}\epsilon^{-1}), and approximately minimum s-t cuts in time \tilde{O}(m+n^{3/2}\epsilon^{-3})

Pengaruh Pengurangan Diameter Valvestem Dan Penambahan Radius Valveneck Terhadap Performa Motor Bakar Honda Supra Fit 100 CC

Salah satu untuk meningkatkan performa dari sepeda motor adalah dengan melakukan penggantian katup standard ke katup racing. Katup racing ini bertujuan untuk memperlancar aliran udara yang masuk ke ruang bakar. Katup racing, biasanya, cara memodifikasinya adalah dengan melakukan pengurangan diameter pada batang katup. Pemilihan diameter katup pada penelitian ini adalah pengurangan sebesar 0,7 mm, 0,9 mm, dan 1,1 mm, dan membandingkan ketiga diameter dengan katup ukuran standard. Metode pengujian yang dilakukan pada penelitian ini adalah dengan melakukan simulasi aliran dengan menggunakan program komputer ANSYS. Serta dengan melakukan uji dynotest untuk mengetahui peningkatan daya dan torsi, sebelum dan sesudah dilakukan penggantian pada masing-masing variasi pada katup. Hasil yang didapatkan setelah melakukan simulasi adalah katup dengan diameter batang yang diperkecil menghasilkan pressure drop yang lebih kecil dan velocity yang besar. Hasil tersebut dicapai oleh katup dengan pengurangan sebesar 0,9 mm. Untuk hasil dynotest, daya tertinggi dicapai oleh katup dengan pengurangan sebesar 1,1 mm. yaitu 6,8 HP, mengalami pengingkatan sebesar 17,24%. Sedangkan torsi maksimum dicapai oleh katup dengan pengurangan 0,9 mm, yaitu 7,39 N.m, meningkat sebesar 14,75% dari katup standard

Influence of Conditional Cash Transfer on Household Economic Outcomes: Perspective of Livelihood Empowerment Against Poverty (LEAP) Beneficiaries in Builsa North District of Ghana

Abject poverty remains widespread in many parts of the world today despite the remarkable progress achieved since the Second World War. Though poverty levels in Ghana have declined in terms of hard-core poor, the decline in poverty has, however, not been geographically spread equally. Achieving poverty reduction goals in Ghana, the Livelihood Empowerment Against Poverty (LEAP) Social Grants scheme was introduced as an effective long-term response to extreme poverty among vulnerable groups. This study assessed the influence of LEAP Cash Transfer on the economic outcomes of beneficiaries in the Builsa North District of Upper East Region, Ghana. The study employed a case study design. Data was collected from 156 randomly selected respondents using qualitative questionnaires, and focused group discussions. The study found that LEAP has played a pivotal role at strengthening the economic and social fortunes of majority of vulnerable households. It has enhanced households’ ability to participate in social functions, access education/literacy as human capital asset, and access vast agricultural lands to undertake crop farming activities. The study recommends to the implementing ministry, the Ministry of Gender and its subsidiary agencies at the District level to educated beneficiaries of the CCT to believe more in their individual efforts with little support such as the cash transfer. Keywords: Conditional Cash Transfer, Household Economic Outcome, LEAP, Builsa North, Livelihood framework DOI: 10.7176/RHSS/10-14-04 Publication date:July 31st 202

Toward Future Installations: Mutual Interactions of Short Intakes With Modern High Bypass Fans

In this paper, we investigate the coupled interaction between a new short intake design with a modern fan in a high-bypass ratio civil engine, specifically under the off-design condition of high incidence. The interaction is expected to be much more significant than that on a conventional intake. The performance of both the intake-alone and rotor-alone configurations are examined under isolation. Subsequently, a comprehensive understanding on the two-way interaction between intake and fan is presented. This includes the effect of fan on intake angles of attack (AoA) tolerance (FoI) and the effect of circumferential and radial flow distortion induced by the intake on the fan performance (IoF). In the FoI scenario, the rotor effectively redistributes the mass flow at the fan-face. The AoA tolerance of the short-intake design has increased by ≈4 deg when compared with the intake-alone configuration. Dynamic nature of distortion due to shock unsteadiness has been quantified. ST plots and power spectral density (PSD) of pressure fluctuations show the existence of a spectral gap between the shock unsteadiness and blade passing, with almost an order of magnitude difference in the corresponding frequencies. In the IoF scenario, both the “large” (O(360 deg)) and “small” scale distortion (O(10–60 deg)) induced by the intake results in a non-uniform inflow to the rotor. Sector analysis reveals a substantial variation in the local operating condition of the fan as opposed to its steady characteristic. Streamline curvature, upwash, and wake thickening are identified to be the three key factors affecting the fan performance. These underlying mechanisms are discussed in detail to provide further insights into the physical understanding of the fan-intake interaction. In addition to the shock-induced separation on the intake lip, the current study shows that shorter intakes are much more prone to the upwash effect at higher AoA. Insufficient flow straightening along the engine axis is reconfirmed to be one of the limiting factors for the short-intake design

Isoprene Peroxy Radical Dynamics

Approximately 500 Tg of 2-methyl-1,3-butadiene (isoprene) is emitted by deciduous trees each year. Isoprene oxidation in the atmosphere is initiated primarily by addition of hydroxyl radicals (OH) to C_4 or C_1 in a ratio 0.57 ± 0.03 (1σ) to produce two sets of distinct allylic radicals. Oxygen (O_2) adds to these allylic radicals either δ (Z or E depending on whether the allylic radical is cis or trans) or β to the OH group forming six distinct peroxy radical isomers. Due to the enhanced stability of the allylic radical, however, these peroxy radicals lose O_2 in competition with bimolecular reactions. In addition, the Z-δ hydroxy peroxy radical isomers undergo unimolecular 1,6 H-shift isomerization. Here, we use isomer-resolved measurements of the reaction products of the peroxy radicals to diagnose this complex chemistry. We find that the ratio of δ to β hydroxy peroxy radicals depends on their bimolecular lifetime (τ_(bimolecular)). At τ_(bimolecular) ≈ 0.1 s, a transition occurs from a kinetically to a largely thermodynamically controlled distribution at 297 K. Thus, in nature, where τ_(bimolecular) > 10 s, the distribution of isoprene hydroxy peroxy radicals will be controlled primarily by the difference in the relative stability of the peroxy radical isomers. In this regime, β hydroxy peroxy radical isomers comprise ∼95% of the radical pool, a much higher fraction than in the nascent (kinetic) distribution. Intramolecular 1,6 H-shift isomerization of the Z-δ hydroxy peroxy radical isomers produced from OH addition to C_4 is estimated to be ∼4 s^(–1) at 297 K. While the Z-δ isomer is initially produced in low yield, it is continually reformed via decomposition of the β hydroxy peroxy radicals. As a result, unimolecular chemistry from this isomer contributes about half of the atmospheric fate of the entire pool of peroxy radicals formed via addition of OH at C_4 for typical atmospheric conditions (τ_(bimolecular) = 100 s and T = 25 C). In contrast, unimolecular chemistry following OH addition at C_1 is slower and less important

Light-emitting diodes enhanced by localized surface plasmon resonance

Light-emitting diodes [LEDs] are of particular interest recently as their performance is approaching fluorescent/incandescent tubes. Moreover, their energy-saving property is attracting many researchers because of the huge energy crisis we are facing. Among all methods intending to enhance the efficiency and intensity of a conventional LED, localized surface plasmon resonance is a promising way. The mechanism is based on the energy coupling effect between the emitted photons from the semiconductor and metallic nanoparticles fabricated by nanotechnology. In this review, we describe the mechanism of this coupling effect and summarize the common fabrication techniques. The prospect, including the potential to replace fluorescent/incandescent lighting devices as well as applications to flat panel displays and optoelectronics, and future challenges with regard to the design of metallic nanostructures and fabrication techniques are discussed

Comparative analysis of machine and deep learning models for soil properties prediction from hyperspectral visual band

Estimating various properties of soil, including moisture, carbon, and nitrogen, is crucial for studying their correlation with plant health and food production. However, conventional methods such as oven-drying and chemical analysis are laborious, expensive, and only feasible for a limited land area. With the advent of remote sensing technologies like multi/hyperspectral imaging, it is now possible to predict soil properties non-invasive and cost-effectively for a large expanse of bare land. Recent research shows the possibility of predicting those soil contents from a wide range of hyperspectral data using good prediction algorithms. However, these kinds of hyperspectral sensors are expensive and not widely available. Therefore, this paper investigates different machine and deep learning techniques to predict soil nutrient properties using only the red (R), green (G), and blue (B) bands data to propose a suitable machine/deep learning model that can be used as a rapid soil test. Another objective of this research is to observe and compare the prediction accuracy in three cases i. hyperspectral band ii. full spectrum of the visual band, and iii. three-channel of RGB band and provide a guideline to the user on which spectrum information they should use to predict those soil properties. The outcome of this research helps to develop a mobile application that is easy to use for a quick soil test. This research also explores learning-based algorithms with significant feature combinations and their performance comparisons in predicting soil properties from visual band data. For this, we also explore the impact of dimensional reduction (i.e., principal component analysis) and transformations (i.e., empirical mode decomposition) of features. The results show that the proposed model can comparably predict the soil contents from the three-channel RGB data

Soil moisture, organic carbon, and nitrogen content prediction with hyperspectral data using regression models

Soil moisture, soil organic carbon, and nitrogen content prediction are considered significant fields of study as they are directly related to plant health and food production. Direct estimation of these soil properties with traditional methods, for example, the oven-drying technique and chemical analysis, is a time and resource-consuming approach and can predict only smaller areas. With the significant development of remote sensing and hyperspectral (HS) imaging technologies, soil moisture, carbon, and nitrogen can be estimated over vast areas. This paper presents a generalized approach to predicting three different essential soil contents using a comprehensive study of various machine learning (ML) models by considering the dimensional reduction in feature spaces. In this study, we have used three popular benchmark HS datasets captured in Germany and Sweden. The efficacy of different ML algorithms is evaluated to predict soil content, and significant improvement is obtained when a specific range of bands is selected. The performance of ML models is further improved by applying principal component analysis (PCA), a dimensional reduction method that works with an unsupervised learning method. The effect of soil temperature on soil moisture prediction is evaluated in this study, and the results show that when the soil temperature is considered with the HS band, the soil moisture prediction accuracy does not improve. However, the combined effect of band selection and feature transformation using PCA significantly enhances the prediction accuracy for soil moisture, carbon, and nitrogen content. This study represents a comprehensive analysis of a wide range of established ML regression models using data preprocessing, effective band selection, and data dimension reduction and attempt to understand which feature combinations provide the best accuracy. The outcomes of several ML models are verified with validation techniques and the best- and worst-case scenarios in terms of soil content are noted. The proposed approach outperforms existing estimation techniques