Study of strange quark density fluctuations in Au+Au Collisions at sNN\sqrt{s_{NN}} = 7.7-200 GeV from AMPT Model

The strangeness production is an important observable to study the QCD phase diagram. The yield ratios of strange quark can be helpful to search for the QCD critical point and/or first order phase transition. In this work, we studied the production of $K^{\pm}$, $\Xi^-(\bar{\Xi}^{+})$, $\phi$ and $\Lambda (\bar \Lambda)$ in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7, 11.5, 14.5, 19.6, 27, 39, 54.4, 62.4 and 200 GeV from A Multi-Phase Transport model with string melting version (AMPT-SM). We calculated the invariant yield of these strange hadrons using a different set of parameters reported in earlier studies and also by varying the hadronic cascade time ($t_{max}$) in the AMPT-SM model. We also calculated the yield ratios, $\mathcal{O}_{K^{\pm}-\Xi^{-}(\bar \Xi^{+})-\phi-\Lambda (\bar \Lambda)}$ which are sensitive to the strange quark density fluctuations and found that the AMPT-SM model fails to describe the non-monotonic trend observed by the STAR experiment. The negative particle ratio are found to be higher than the ratio of positive particles which is consistent with the experimental data. A significant effect is also seen on these ratios by varying the $t_{max}$. This study based on the transport model can be helpful to provide possible constraints as well as reference for the search of CEP in future heavy-ion experiments. Our findings suggest that the ongoing Beam Energy Scan program at RHIC and the future heavy-ion experiments will be able to find/locate the possible CEP in the QCD phase diagram which results large quark density fluctuations.Comment: 7 pages, 2 figure

COMPARATIVE EFFICACY OF DETOMIDINE AND DETOMIDINE - KETAMINE COCKTAIL IN QUAILS

Twenty adult healthy quails (Coturnix coturnix) were divided into two equal groups. One group was administered detomidine (2.4 mg/kg, I/M) and other group was administered detomidine-ketamine cocktail (1.2 mg/kg + 30 mg/kg, I/M). Detomidine slowly and smoothly induced a light sedation accompanied by superficial analgesia, hypoventilation, hypothermia and bradycardia in all birds. Detomidine-ketamine cocktail rapidly and smoothly induced a deep anaesthesia accompanied by deep analgesia, hypoventilation, hypothermia and bradycardia and complete loss of all reflexes in all birds. In both groups, recovery from sedation and anaesthesia was smooth and of short duration. From this study it was concluded that for minor and least painful procedures in quails detomidine can be used alone, while for major and painful surgical procedures detomidine-ketamine combination should be preferred

Pair production of heavy charged gauge bosons in pppp collisions at LHC

Two opposite charged new heavy gauge boson pair production at the Large Hadron Collider (LHC) is presented in this paper. These bosons are known as $W^{'}$ boson due to the reason that it is the heavy version of Standard Model's weak force carrier, the $W$ boson. The production cross section and decay width in proton-proton ($pp$) collision at \sqrts~= 8 TeV are calculated for different masses and coupling strengths of $W^{'}$. Efficiencies for different signal regions and branching ratios for different decay channels are computed. In this study, the pair production ($W^{'^{+}}W^{'^{-}}$) is considered in emerging new physics as a result of $pp$ collision at \sqrts~= 8 TeV at the LHC with final state containing two tau ($\tau$) leptons and two neutrinos (each $W^{'}$ decay to $\tau$ and its neutrino). The event selection efficiency similar to the CMS experiment is used for the mass of $W^{'}$ to set lower limits for different coupling strengths of $W^{'}$ and results are presented in this work. For heavy gauge bosons, when coupling strength is similar to that of Standard Model's $W$ boson, the mass of $W^{'}$ below 305 GeV are excluded at confidence level of $95\%$.Comment: 21 pages, 16 figure

Unusual rainfall shift during monsoon period of 2010 in Pakistan: Flash flooding in Northern Pakistan and riverine flooding in Southern Pakistan

Floods due to “blocking event” in the jet stream during 2010 caused intense rainfall and flash floods in northern Pakistan which resulted to riverine flooding in southern Pakistan. In the beginning of July 2010, changes in summer monsoon rainfall patterns caused the most severe flooding in Pakistan history. Process control charts suggest that monsoon pattern was not normal which made one-fifth of the country to be inundated. In this study, our main concern was to check the upward shifts (floods) in the rainfall pattern of all provinces of Pakistan. Results indicate that there was significant and sudden shift in the rainfall pattern of monsoon in 2010 which might be due to prolong “blocking event” in the jet stream. In late July, rainwater from the highlands entered major rivers which affected nearby areas of the Indus River. More than 250 mm of rain fell over a 36-h period in late July. Abeyant policies by the Pakistan Irrigation Department (PID) caused destruction in Jacobabad which was not a normal Indus waterway. The first week of August marked the worst week of extreme flooding in southern Pakistan. Flood simulation overylay technique showed the affected areas of the country in comparison with normal waterways by using vector and raster data images.Key words: Indus River, monsoon, flooding in 2010, rainfall pattern, Climate Change, Floods

Effect of hadronic cascade time on freeze-out properties of Identified Hadrons in Au+Au Collisions at sNN\sqrt{s_{NN}} = 7.7-39 GeV from AMPT Model

We report the transverse momentum $p_T$ spectra of identified hadrons ($\pi^\pm$, $K^\pm$ and $p(\bar p)$) in Au+Au collisions at $\sqrt{s_{NN}}$ = 7.7 - 39 GeV from A Multi Phase Transport Model with string melting effect (AMPT-SM). During this study, a new set of parameters are explored to study the effect of hadronic cascade by varying hadronic cascade time $t_{max}$ = 30 $f$m/$c$ and 0.4 $f$m/$c$. No significant effect of this change is observed in the $p_T$ spectra of light hadrons and the AMPT-SM model reasonably reproduces the experimental data. To investigate the kinetic freeze-out properties the blast wave fit is performed to the $p_T$ spectra and it is found that the blast wave model describes the AMPT-SM simulations well. We additionally observe that the kinetic freeze-out temperature ($T_{kin}$) increases from central to peripheral collisions, which is consistent with the argument of short-lived fireball in peripheral collisions. Whereas the transverse flow velocity, shows a decreasing trend from central to peripheral collisions indicating a more rapid expansion in the central collisions. Both, $T_{kin}$ and show a weak dependence on the collision energy at most energies. We also observe a strong anti-correlation between $T_{kin}$ and $$. The extracted freeze-out parameters from the AMPT-SM simulations agree with the experimental data as opposed to earlier studies that reported some discrepancies. Whereas, no significant effect is found on the freeze-out parameters by varying the $t_{max}$. We also report the $p_T$ spectra of light hadrons and their freeze-out parameters by AMPT-SM simulations at $\sqrt{s_{NN}}$ = 14.5 GeV, where no experimental data is available for comparison. Overall, the set of parameters used in this study well describes the experimental data at BES energies.Comment: 12 pages, 7 figures, 2 table

Sheared bioconvection in a horizontal tube

The recent interest in using microorganisms for biofuels is motivation enough to study bioconvection and cell dispersion in tubes subject to imposed flow. To optimize light and nutrient uptake, many microorganisms swim in directions biased by environmental cues (e.g. phototaxis in algae and chemotaxis in bacteria). Such taxes inevitably lead to accumulations of cells, which, as many microorganisms have a density different to the fluid, can induce hydrodynamic instabilites. The large-scale fluid flow and spectacular patterns that arise are termed bioconvection. However, the extent to which bioconvection is affected or suppressed by an imposed fluid flow, and how bioconvection influences the mean flow profile and cell transport are open questions. This experimental study is the first to address these issues by quantifying the patterns due to suspensions of the gravitactic and gyrotactic green biflagellate alga Chlamydomonas in horizontal tubes subject to an imposed flow. With no flow, the dependence of the dominant pattern wavelength at pattern onset on cell concentration is established for three different tube diameters. For small imposed flows, the vertical plumes of cells are observed merely to bow in the direction of flow. For sufficiently high flow rates, the plumes progressively fragment into piecewise linear diagonal plumes, unexpectedly inclined at constant angles and translating at fixed speeds. The pattern wavelength generally grows with flow rate, with transitions at critical rates that depend on concentration. Even at high imposed flow rates, bioconvection is not wholly suppressed and perturbs the flow field.Comment: 19 pages, 9 figures, published version available at http://iopscience.iop.org/1478-3975/7/4/04600

Study of Baryon number transport using model simulations in pppp collisions at LHC Energies

We report on the excitation function of anti-baryon to baryon ratios ($\overline{p}/p$, {\alam /\lam} and {\axi / \xim}) in $pp$ collisions at {\sqrts} = 0.9, 2.76, 7 TeV from DPMJET-III, Pythia~8, EPOS~1.99, and EPOS-LHC model simulations. To study the predictions of these models at {\sqrts} = 13.6 TeV. The anti-baryon to baryon ratios are extremely important for the study of baryon number transport mechanisms. These ratios help determine the carriers of the baryon number and in the extraction of baryon structure information. Even though all models show a good agreement between model simulations and data, the ratios extracted from DPMJET-III model closely describes data at all energies. It is observed that these ratios converge to unity for various model predictions. This convergence also indicates that the anti-baryon to baryon ratios follow the mass hierarchy, such that the hyperon specie containing more strange quarks ({\alam /\lam} and {\axi / \xim}) approaches unity faster than specie containing fewer strange quarks ($\overline{p}/p$). It is also observed that the $\overline{B}/B$ ratio approaches unity more rapidly with the increase in {\sqrts} energy. At lower energies we observe an excess production of baryons over anti-baryons. However, this effect vanishes at higher energies due to the baryon-anti-baryon pair production and the baryon-anti-baryon yield becomes equal. Using model simulations, we additionally compute the asymmetry, (A\equiv\frac{N_{p}-N_{\bar{p}}}N_{p}+N_{\bar{p}}}) for protons. The asymmetry shows a decreasing trend with increase in energy from 0.9 to 7 TeV for all energies. This asymmetry trend is confirmed by model predictions at {\sqrts} = 13.6 TeV which will help to put possible constraints on model calculations at {\sqrts} = 13.6 TeV once the Run-III data for LHC becomes available.Comment: 14 pages, 8 figures, 2 table

Identification of Radar Signals Based on Time-Frequency Agility using Short-Time Fourier Transform

With modern advances in radar technologies and increased complexity in aerial battle, there is need for knowledge acquisition on the abilities and operating characteristics of intercepted hostile systems. The required knowledge obtained through advanced signal processing is necessary for either real time-warning or in order to determine Electronic Order of Battle (EOB) of these systems. An algorithm was therefore developed in this paper based on a joint Time-Frequency Distribution (TFD) in order to identify the time-frequency agility of radar signals based on its changing pulse characteristics. The joint TFD used in this paper was the square magnitude of the Short-Time Fourier Transform (STFT), where power and frequency obtained at instants of time from its Time-Frequency Representation (TFR) was used to estimate the time and frequency parameters of the radar signals respectively. Identification was thereafter done through classification of the signals using a rule-based classifier formed from the estimated time and frequency parameters. The signals considered in this paper were the simple pulsed, pulse repetition interval modulated, frequency hopping and the agile pulsed radar signals, which represent cases of various forms of agility associated with modern radar technologies. Classification accuracy was verified using the Monte Carlo simulation performed at various ranges of Signal-to-Noise Ratios (SNRs) in the presence of noise modelled by the Additive White Gaussian Noise (AWGN). Results obtained showed identification accuracy of 99% irrespective of the signal at a minimum SNR of 0dB where signal and noise power were the same. The obtained minimum SNR at this classification accuracy showed that the developed algorithm can be deployed practically in the electronic warfare field for accurate agility classification of airborne radar signals

Detecting fake news and disinformation using artificial intelligence and machine learning to avoid supply chain disruptions

Fake news and disinformation (FNaD) are increasingly being circulated through various online and social networking platforms, causing widespread disruptions and influencing decision-making perceptions. Despite the growing importance of detecting fake news in politics, relatively limited research efforts have been made to develop artificial intelligence (AI) and machine learning (ML) oriented FNaD detection models suited to minimize supply chain disruptions (SCDs). Using a combination of AI and ML, and case studies based on data collected from Indonesia, Malaysia, and Pakistan, we developed a FNaD detection model aimed at preventing SCDs. This model based on multiple data sources has shown evidence of its effectiveness in managerial decision-making. Our study further contributes to the supply chain and AI-ML literature, provides practical insights, and points to future research directions

AI-driven optimization of ethanol-powered internal combustion engines in alignment with multiple SDGs: A sustainable energy transition

With the escalating requirement for global sustainable energy solutions and the complexities linked with the complete transition to new technologies, internal combustion engines (ICEs) powered with biofuels like ethanol are gaining significance over time. However, problems linked to the performance and emissions of such ICEs necessitate accurate prediction and optimization. The study employed the integration of artificial neural networks (ANN) and multi-level historical design of response surface methodology (RSM) to address these challenges in alignment with the Sustainable Development Goals (SDGs). A single-cylinder spark ignition (SI) engine powered with ethanol-gasoline blends at different loads and speeds was used to gather data. Among six initially trained ANN models, the most efficient model with a regression coefficient (R2) of 0.9952 (training), 0.98579 (validation), 0.98847 (testing), and 0.99307 (overall) was employed to predict outputs such as brake power, brake specific fuel consumption (BSFC), brake thermal energy (BTE), concentration of carbon dioxide (CO2), carbon monoxide (CO), hydrocarbons (HC), and oxides of nitrogen NOx. Predicted outputs were optimized by incorporating RSM. On implementing optimized conditions, it was observed that BP and BTE increased by 19.9%, and 29.8%, respectively. Additionally, CO, and HC emissions experienced substantial reductions of 28.1%, and 40.6%, respectively. This research can help engine producers and researchers make refined decisions and achieve improved performance and emissions. The study directly supports SDG 7, SDG 9, SDG 12, SDG 13, and SGD 17, which call for achieving affordable, clean energy, sustainable industrialization, responsible consumption, and production, taking action on climate change, and partnership to advance the SDGs as a whole respectively