On ``A Note on the Economic Lot Size of the Integrated Vendor-Buyer Inventory System Derived without Derivatives'' by Wee and Chung

[[abstract]]Wee and Chung [3] incorporated the integrated single-vendor single-buyer inventory model with backorder, JIT delivery and inspection cost. They used a simple algebraic approach and proved that the model has an optimal solution for the condition of ˜ C = Hb + Hv“2d p − 1” − “ H2 b b + Hb ” > 0. However, they did not provide the optimal solution to the problem when the restriction is not satisfied. In this note, the authors provide some patch works to enhance the volubility of Wee and Chung’s paper.[[notice]]補正完畢[[journaltype]]國內[[incitationindex]]EI[[incitationindex]]TSSC

Electrodynamics Modified by Some Dimension-five Lorentz Violating Interactions: Radiative Corrections

We study radiative corrections to massless quantum electrodynamics modified by two dimension-five LV interactions $\bar{\Psi} \gamma^{\mu} b'^{\nu} F_{\mu\nu}\Psi$ and $\bar{\Psi}\gamma^{\mu}b^{\nu} \tilde{F}_{\mu\nu} \Psi$ in the framework of effective field theories. All divergent one-particle-irreducible Feynman diagrams are calculated at one-loop order and several related issues are discussed. It is found that massless quantum electrodynamics modified by the interaction $\bar{\Psi} \gamma^{\mu} b'^{\nu} F_{\mu\nu}\Psi$ alone is one-loop renormalizable and the result can be understood on the grounds of symmetry. In this context the one-loop Lorentz-violating beta function is derived and the corresponding running coefficients are obtained.Comment: 13 pages, 1 figure. Version to appear in EPJ

Prediction of Near-Room-Temperature Quantum Anomalous Hall Effect on Honeycomb Materials

Recently, this long-sought quantum anomalous Hall effect was realized in the magnetic topological insulator. However, the requirement of an extremely low temperature (approximately 30 mK) hinders realistic applications. Based on \textit{ab-initio} band structure calculations, we propose a quantum anomalous Hall platform with a large energy gap of 0.34 and 0.06 eV on honeycomb lattices comprised of Sn and Ge, respectively. The ferromagnetic order forms in one sublattice of the honeycomb structure by controlling the surface functionalization rather than dilute magnetic doping, which is expected to be visualized by spin polarized STM in experiment. Strong coupling between the inherent QSH state and ferromagnetism results in considerable exchange splitting and consequently an FM insulator with a large energy gap. The estimated mean-field Curie temperature is 243 and 509 K for Sn and Ge lattices, respectively. The large energy gap and high Curie temperature indicate the feasibility of the QAH effect in the near-room-temperature and even room-temperature regions.Comment: 6 pages, 4 figures and 1 tabl

W±H∓W^{\pm}H^{\mp} associated production at LHC in the general 2HDM with Spontaneous CP Violation

Spontaneous CP violation motivates the introduction of two Higgs doublets in the electroweak theory. Such a simple extension of the standard model has three neutral Higgs bosons and a pair charged Higgs, especially it leads to rich CP-violating sources including the induced Kobayashi-Maskawa CP-violating phase, the mixing of the neutral Higgs bosons due to the CP-odd Higgs and the effective complex Yukawa couplings of the charged and neutral Higgs bosons. Within this model, we present the production of a charged Higgs boson in association with a W boson at the LHC, and calculate in detail the cross section and the transverse momentum distribution of the associated W boson.Comment: 16 pages, 6 figures, omitted 3 figures, motivations for Type III 2HDM with SCPV is emphasized, to be published in PR

SU(3)FSU(3)_{F} Gauge Family Model and New Symmetry Breaking Scale From FCNC Processes

Based on the $SU(3)_{F}$ gauge family symmetry model which was proposed to explain the observed mass and mixing pattern of neutrinos, we investigate the symmetry breaking, the mixing pattern in quark and lepton sectors, and the contribution of the new gauge bosons to some flavour changing neutral currents (FCNC) processes at low energy. With the current data of the mass differences in the neutral pseudo-scalar $P^{0}-\bar{P}^{0}$ systems, we find that the $SU(3)_{F}$ symmetry breaking scale can be as low as 300TeV and the mass of the lightest gauge boson be about $100$TeV. Other FCNC processes, such as the lepton flavour number violation process $\mu^{-}\rightarrow e^{-}e^{+}e^{-}$ and the semi-leptonic rare decay $K\rightarrow \pi \bar{\nu} \nu$, contain contributions via the new gauge bosons exchanging. With the constrains got from $P^0-\bar{P}^0$ system, we estimate that the contribution of the new physics is around $10^{-16}$, far below the current experimental bounds.Comment: 3figure

Tüübituletus neljandat järku loogikavalemitele

Tänapäeval omavad nutiseadmed meie elus suurt rolli, eriti igapäevastes tegemistes. Sellepärast võib kaaluda nutitelefoni kui üht kõige huvitavamat andurit kujutamaks meie tegevusi ja meie ümbrust. Lisaks sellele on nutitelefonide arvutusjõudlus hüppeliselt kasvanud, mida kinnitavad nendes sisalduvad erinevad andurid nagu kiirendusmõõturid ja güroskoobid ning võimekus sooritada rohkem ülesandeid kui kunagi varem. Nende mugavuse ja madala hinna tõttu on nutitelefone hakatud kasutama kui kaasaskantavaid arvutusplatvorme autonoomsete sõidukite arenduses. Intelligentsete sõidukite süsteemide kriitiliseimaks probleemiks on turvalisus. Teekatte tuvastus on üks turvalise liiklemise põhikomponentidest. Enamik praeguseid lahendusi teekatte tuvastamiseks kasutavad erinevate sensorite nagu kaamerate ja LiDARite kokkusulatamist. See on küll efektiivne meetod, kuid tegemist on kallite anduritega ning mille kasutamine vajab auto enda modifitseerimist. Lõputöö pakub välja meetodi teekatte tuvastamiseks kasutades nutitelefonis oleva kiirendusmõõturi andmeid. See protsess kasutab ajaliselt jätjestatud kiirendusmõõturi andmeid, millele järgneb masiivne ajaliselt järjestatud tunnuste eraldamine ja valimine. Peale seda suunatakse eraldatud tunnused DeepSense närvivõrgu raamistikku, et teekate tuvastada. Meetod klassifitseerib kolme erinevat teekatte tüüpi: sile, munakivitee ja kruusatee. Põhjalik pakutud metoodika uurimine ja analüüs viiakse läbi kasutades üldlevinud masinõppe meetodeid nagu tugivektor-masinad, otsustusmets, täielikult ühendatud närvivõrgud ja konvulutioonteisendus närvivõrgud. Metoodikal põhinevad katsed näitavad, et pakutud lähenemine võimaldab tuvastada teekatte siledust väljapakutud kolme kategooriasse.Nowadays, Smart devices plays a big role in our lives, especially in our daily activities. Therefore, Smartphones can be considered as one of the most interesting sensor for depicting our activities and our surroundings. Furthermore, the computation power of smartphones has increased a lot recently as most of them have multiple sensors like accelerometers and gyroscopes. Besides, They are capable of processing more tasks than we ever imagined. Because of their advantages of convenience and low-cost, the portable computation platforms has been adopted in the development of autonomous vehicles. The most critical issue of the intelligent system assisted vehicles is that the safety problem. The recognition of the road surface is one of the components to ensure the safety drive. Most of the solutions use sensor fusion to recognize road surfaces such as combining cameras and LiDARs, which is costly for equipment and they usually need installations to re-equip existing cars, but these methods provide overall excellent results. This thesis proposes a method for recognizing the road surface based on using accelerometer data collected from smartphone. The process uses time series data collected from a smartphone’s accelerometer, followed by a massive time series feature extraction and selection. After that, the features are fed into trained DeepSense variant neural network framework to get the recognition of the road surfaces. The proposed method provides three classes recognition for smooth, bumpy and rough roads. Moreover, in this thesis we conducted a thorough evaluation and analysis of the proposed method by comparing it with conventional machine learning methods like SVM, random forest, fully connected neural network and convolutional neural network. The accuracy of the method in this thesis overmatch the compared examples. The road surface type will be classified into three categories which will indicate smoothness of the road surface