Miracle-dz/UniFAR-FTUs huggingface dataset:
Disentangling the complexity of coupled vibrations by two-dimensional electronic-vibrational spectroscopy

We employ two-dimensional electronic-vibrational (2DEV) spectroscopy to study the coherent dynamics of coupled vibrational modes in an excitonically-coupled dimer model. The advantage of separating excitation and detection in different frequency regimes allows us to directly probe the electronic and vibrational coherences in the time-evolved 2DEV spectra. The complexity of vibrational coherence of two coupled modes is directly revealed by cross peaks in the 2DEV spectra. With the help of the ensuing time traces, we can follow how the vibrational coherence changes over time in the monomer model and, subsequently, in the dimer model. We show that the complexity of two coupled vibrational modes and the interaction between electronic and vibrational coherences in molecular systems can be effectively disentangled using the 2DEV spectroscopy technique.
Two New Closed Form Approximate Maximum Likelihood Location Methods Based on Time Difference of Arrival Measurements

In this paper, we propose two new closed form approximate maximum likelihood location methods via time difference of arrival (TDOA) measurements to determine the location of a target. For both two methods, an initial estimation is acquired by least square method in the first step. Then, supposing that the statistical structure of measurement noise are already known, the maximum likelihood function is derived. At last, for method one, we use first order Taylor expand at the initial point to approximate the residual error for nonlinear measurement equation and substitute it into the maximum likelihood function. While for method two, we use second order Taylor expand at the initial point to approximate the maximum likelihood function. At last, we derive the closed-form solution to both of the methods respectively. The computational complexity of our methods and AML method is derived and we analyze the performance of our two methods. The simulation results show that our methods are more accurate because our methods based on maximum likelihood function and have low computational complexity because we iterate only once. Keywords—TDOA location; maximum likelihood; closed form solution; performance analysis
Understanding and quantifying uncertainty due to multiple biases in meta-analyses of observational studies

There has been considerable interest recently in quantifying uncertainty beyond that due to random error in meta-analyses. This is particularly relevant to meta-analyses of observational studies, since error in estimates from these studies cannot be attributed to a randomization mechanism. Typically, observational studies are also subject to error due to measurement error, non-participation, and incomplete adjustment for confounding. Errors due to these sources are often referred to as bias. To quantify uncertainty due to bias, researchers have proposed using “bias models” and giving subjectively elicited probability distributions to parameters that are not identifiable in the models. In a typical meta-analysis, probability distributions involving tens of parameters will have to be elicited. At the same time, the resulting estimate and uncertainty interval of the overall (metaanalytic) effect measure will generally be very sensitive to this multi-dimensional subjectively-elicited distribution. To overcome some of the problems associated with the use of such a distribution, I propose an alternative method for eliciting and quantifying uncertainty due to bias. In the method of this thesis, the lower and upper bounds of bias parameters are elicited instead of probability distributions. The most extreme Bayesian posterior inference for the target parameter of interest within the specified bounds is sought through an algorithm. The resulting lower and upper bounds for the target parameter of interest have interpretation of a Robust Bayes analysis. In this thesis, the method is applied to a meta-analysis of childhood leukaemia and exposure to electromagnetic fields. The method of this thesis was found to produce uncertainty intervals that are generally more conservative in comparison with the standard approach. It is also proposed that the method be used as a tool for sensitivity analysis, and some interesting insight is gained from the childhood leukaemia data.
Clinical Results of Reconstruction of Bilateral Pathological Quadriceps Tendon Rupture in Chronic Renal Patients

Background: Bilateral pathological quadriceps tendon rupture is a rare, challenging problem in chronic debilitating patients. A paucity of research are present in the literature and most of them are case report. Objective: This case series study aimed to evaluate the results of quadriceps tendon repair reinforced by autologous tendon augmentation in chronic renal patients. Methods: Six chronic renal patients with bilateral quadriceps tendon rupture (5 males and a female) underwent quadriceps tendon repair augmented with an autologous tendon graft with a new special configuration that allowed early rehabilitation. Results: The mean Lysholm score at the final follow-up was 93 ± 6.09 ranging from 85 to 100. We had no extension lag in this study and the mean knee flexion range was 134.16 ± 7.63 0 ranging from 125 to 145 0. Conclusion: Quadriceps tendon repair augmented with autologous tendon graft gives well to excellent results with no re-rupture complication in short-term results. Level of evidence: Therapeutic level IV.
On long term portfolio optimality via large deviations

We study the problem of optimal long term portfolio selection with a view to beat a benchmark. Two kinds of objectives are considered. One concerns the probability of outperforming the benchmark and seeks either to minimise the decay rate of the probability that the portfolio exceeds the benchmark or to maximise the decay rate that the portfolio falls short. The other criterion concerns the growth rate of the risk--sensitive utility of wealth which has to be either minimised, for the risk--averse investor, or maximised, for the risk--seeking investor. It is assumed that the mean returns and volatilities of the securities are affected by an economic factor, possibly, in a nonlinear fashion. The economic factor and the benchmark are modelled with general It\^o differential equations. The results identify optimal portfolios and produce decay, or growth, rates. The portfolios have the form of time--homogeneous functions of the economic factor. Furthermore, a uniform treatment is given to the out-- and under-- performance probability optimisation as well as to the risk--averse and risk--seeking optimisation. In particular, it is shown that there exists a portfolio that optimises the decay rates of both the outperfomance probability and the underperformance probability. Whereas earlier research on the subject has relied on the techniques of stochastic optimal control and dynamic programming, in this contribution the quantities of interest are studied directly by employing the methods of the large deviation theory. The key to the analysis is to recognise the setup in question as a case of coupled diffusions with time scale separation, with the economic factor representing "the fast process".
Last-mile delivery model for E-commerce businesses using self-storage COMP4801 - Final Year Project

Last-mile delivery is an essential part of eCommerce logistics. Most eCommerce will use third-party logistics (3PL) to handle the final delivery. Nevertheless, the solution is not adaptive to a real-time environment, leading to frequently failed delivery. Therefore, a new delivery approach, the self-storage-as-parcel model (SSAP), has been proposed to facilitate last-mile delivery. The objective is to investigate the best-fit algorithm to optimize routes for SSAP. In this study, the SSAP will be treated as a Vehicle Routing Problem with Time Windows (VRPTW), an extension of the Vehicle Routing Problem (VRP). The objection function in this model is the minimization of total travel cost and the number of vehicle routes. Constraints on time window, vehicle capacity and vehicle route are assumed to be tight. A comparative study will be carried out on the existing VRPTW algorithms. Each algorithm will be tested on VRPTW data sets. The comparison results will show the algorithms' effectiveness and superiority over other algorithms. Currently, the team has identified the mathematical formation and complexity of VRPTW. The team also finalized three heuristic algorithms for the comparative experiment. The current progress is considered acceptable, and the team will focus on the experiment demo and prepare for the first presentation in this phase. Given the limitation that the routing model omits other delivery factors, such as multi-depot, it is suggested that more constraints can be added to the model in the future.
Associations Between Physical Activity, a Healthy Lifestyle Score, and Subsequent Morbidity in Childhood Cancer Survivors: a report from the Childhood Cancer Survivor Study

List of investigators: Name e-mail address Aron Onerup Aron.onerup@stjude.org Stephanie Dixon Stephanie.dixon@stjude.org Greg Armstrong Greg.armstrong@stjude.org Rebecca Howell rhowell@mdanderson.org Matt Ehrhardt Matt.ehrhardt@stjude.org Yutaka Yasui Yutaka.yasui@stjude.org Shizue Izumi shizue-izumi@biwako.shiga-u.ac. jp Lucie Turcotte turc0023@umn.edu Kumar Srivastava kumar.srivastava@stjude.org Tara Brinkman tara.brinkman@stjude.org Kevin Oeffinger Kevin.oeffinger@duke.edu Claire Snyder csnyder@jhu.edu Paul Nathan Paul.nathan@sickkids.ca Kirsten Ness Kiri.ness@stjude.org
A Brief Survey On Recommendation System

: Today recommendation system has become very convenient as it is the best technique to make online websites. It has a large number of applications in many fields including economic, education and scientific research. Recommendation systems are the search engines which analyses the user’s browsing history and suggest the product according to his taste. Recommendation system applies various approaches to predict users interest on information, products and services among the tremendous amount of available items. In this paper we first describe the recommendation system and then explaining the related research done in this field. The vast growth of information on the internet as well as number of visitors to websites add some key challenges to recommendation system which we discussed lastly in the paper.
Exploiting Invariance Properties to Certify Always and Eventually Signal Temporal Logic Operators for Hybrid Dynamical Systems

In this letter, semantics and characterizations of signal temporal logic formulas for hybrid dynamical systems are presented. Hybrid dynamical systems are given in terms of constrained differential and difference inclusions, which, respectively, capture the continuous evolution and the instantaneous events exhibited by solutions. For such systems, the always and eventually operator of signal temporal logic are studied and characterizations in terms of dynamical properties of hybrid systems are presented – in particular, using invariance and finite-time attractivity properties. Sufficient conditions that guarantee the satisfaction of a signal temporal logic formula for a given system through the satisfaction of an untimed formula for an appropriately defined new system are introduced. Specifically, it is shown that satisfying an (untimed) temporal logic formula involving until operators suffices to certify always and eventually signal temporal logic formulas for hybrid systems.
Changes in Thermal, Textural, Color and Microstructure Properties of Oleogel Made from Beeswax with Grape Seed Oil under the Effect of Cooling Rate and Oleogelator Concentration

In this study, the crystallization behavior of beeswax in grape seed oil in a wide range of cooling rates of 0.04, 0.08, 0.16, 0.33 and 0.66 (°C/min), from 85 to 25 °C and wax concentrations of 10, 15 and 20% were examined. Thermal behavior characteristics, texture, color and the crystal shapes of the samples were evaluated. The results showed that with increasing the percentage of wax in oleogel, the onset and melting temperatures increased in different treatments, so that the greatest increase was shown in the cooling rate of 0.16 °C/min. Temperatures of 45.90±0.46 and 46.8±0.30 for 10 wax concentration up to 62.70±0.2 and 65.80±0.17 for 20% wax concentration for onset and melting temperatures, respectively were measured. With increasing wax concentration, the factors of stiffness and adhesiveness also increased and this increase was more evident in the cooling rate of 0.66 °C/min, so that the stiffness and adhesiveness parameters increased the most at concentrations of 10% to 20%, respectively. Also, with slow cooling rate, samples with larger crystals were obtained. Color evaluation revealed that with increasing wax concentration, all color parameters except parameter a*, which did not show a significant difference, increased in all cooling rate treatments. The fat crystal engineering approach followed here offers the prospect of obtaining stronger structures at oleogelator concentrations and creating oleogel with desirable properties.
Neural Prefiltering for Correlation-Aware Levels of Detail

We introduce a practical general-purpose neural appearance filtering pipeline for physically-based rendering. We tackle the previously difficult challenge of aggregating visibility across many levels of detail from local information only, without relying on learning visibility for the entire scene. The high adaptivity of neural representations allows us to retain geometric correlations along rays and thus avoid light leaks. Common approaches to prefiltering decompose the appearance of a scene into volumetric representations with physically-motivated parameters, where the inflexibility of the fitted models limits rendering accuracy. We avoid assumptions on particular types of geometry or materials, bypassing any special-case decompositions. Instead, we directly learn a compressed representation of the intra-voxel light transport. For such high-dimensional functions, neural networks have proven to be useful representations. To satisfy the opposing constraints of prefiltered appearance and correlation-preserving point-to-point visibility, we use two small independent networks on a sparse multi-level voxel grid. Each network requires 10--20 minutes of training to learn the appearance of an asset across levels of detail. Our method achieves 70--95% compression ratios and around 25% of quality improvements over previous work. We reach interactive to real-time framerates, depending on the level of detail.
Extension of a Sparse Bayesian Learning Framework for Rational Polynomial Chaos Expansion

Engineering problems often involve the design and analysis of system using numerical models which are associated with modelling uncertainty. This uncertainty might be related to incomplete knowledge about the model structure or the model parameters. In such cases uncertainty quantification methods can be applied to obtain knowledge about the uncertainty in the system performance. For computationally demanding simulation models, one often resorts to replacing the original model with a surrogate model that can be fast evaluated. Popular choices for surrogate models are neural networks or polynomial chaos expansion (PCE). Recently, a sparse Bayesian learning approach has been proposed for rational polynomial chaos expansion (rPCE), which is a surrogate model that is specifically suitable for frequency domain models in structural dynamics [1]. The sparse Bayesian learning approach can capture the model response with relatively few numbers of model evaluations. The implementation is based on a hierarchical prior structure for the model coefficients originally proposed in [2]. However, subsequently, other authors [3] have proposed alternative prior structures that are able to identify a sparser set of coefficients in comparison to the approach in [2].
Changes in blood oxidative stress biomarker and cholinesterase activity after general vs spinal anesthesia for elective cesarean sections

Background & Objective: There is an increased ratio of cesarean sections (CS) compared to normal deliveries the world over. Both general and regional anesthetic techniques are in practice for CS. Limited information is available on the determination of blood cholinesterase (ChE) activity in conjunction with oxidative stress biomarkers after the CS. The purpose of the present study was to determine and compare the plasma and erythrocyte ChE activity as well as malondialdehyde (MDA) level and total antioxidant status in pregnant women before anesthesia and after the use of general anesthesia (GA) or spinal anesthesia (SA) for elective CS. Methodology: A pre-post study was conducted on 102 full-term pregnant women, who underwent elective CS in four hospitals at Duhok (Iraq) from January 2022 to July 2022. GA with propofol and SA with bupivacaine 0.5% were administered to 52 and 50 women, respectively. Maternal blood samples before anesthesia and after the completion of operation were assayed for the ChE activity in the plasma and erythrocytes, and plasma oxidative stress biomarkers malondialdehyde and total antioxidant status. Results: Plasma ChE activity significantly decreased in the GA and SA groups after the cesarean-deliveries compared to the corresponding pre-anesthetic values. The plasma MDA level in both GA and SA groups, but not the total antioxidant status, was significantly reduced. Estimation of the odds ratio and the relative risk indicated that a risk factor is associated with the reduced plasma ChE activity in women after the CS when the general anesthetic propofol is used. Conclusion: Both general anesthesia with propofol and spinal anesthesia with bupivacaine inhibited plasma cholinesterase activity and reduced oxidative stress biomarker malondialdehyde after the cesarean sections. Abbreviations: CS - Cesarean section; ChE: Cholinesterase; EChE: Erythrocyte Cholinesterase; MDA: Malondialdehyde; OS: Oxidative Stress; PChE: Plasma ChE; TAS: Total Antioxidant Status. Key words: Anesthesia, Spinal; Cesarean section; General anesthesia; Malondialdehyde; Oxidative stress Citation: Karam RS, Mohammad FK. Changes in blood oxidative stress biomarker and cholinesterase activity after general vs spinal anesthesia for elective cesarean sections. Anaesth. pain intensive care 2023;27(3):396−404; DOI: 10.35975/apic.v27i3. 2244 Received: March 23, 2023; Reviewed: May 06, 2023; Accepted: May 09, 2023
The Anheuser Busch InBev Trailer Problem: An Application of Online Resource Allocation and Inventory Selection

In this paper, we introduce and study the Anheuser Busch InBev trailer problem, which considers how Anheuser Busch InBev ships its beer to wholesalers via third party delivery trucks. In this problem, Anheuser Busch InBev must select the weights and inventory levels of preloaded trailers of beer, which are then matched in an online fashion to arriving third party delivery trucks. What ultimately arises is an interesting online resource allocation problem, which also must incorporate an initial inventory decision as a precursor. We develop two distinct approaches for this problem under two different settings, which are distinguished by whether or not Anheuser Busch InBev would like to consider altering the weights of trailers that have been preloaded as the delivery trucks arrive. In both settings, we characterize optimal policies for matching trailers to arriving delivery trucks and develop efficient approaches to select the set of trailer weights to preload as well as their corresponding inventory levels. Using historical truck arrival data from Anheuser Busch InBev, we simulate our proposed approaches and find that their performances are within 1% of optimality on average.
Invited Session on: Decision Analytics and Digitization for End-to-End Supply Chain Transformation

Digitalization has altered different dimensions of life around the world in this new era marked by a dynamic and complex atmosphere and a competitive economic marketplace. It has a substantial effect on supply chain processes, and it is clear that moving from a conventional supply chain to a digitalized supply chain is a competitive advantage that provides organizations with long-term value. Furthermore, the use of business analytics applications in decision making is becoming more common in today's organisations, and the significant changes such applications have brought to both centralised and distributed organisations have prompted decision makers to rethink how they capture, process, and analyse both structured and unstructured data and make decisions. Decision-makers need a collection of tools that allow them to analyze datasets and perform descriptive, predictive, and prescriptive analytics in order to develop key performance indicators that will help them enhance organizational performance.
Misusing browsers’ login managers for data exfiltration by third parties

In this thesis a research about data exfiltration by third-party scripts has been conducted. In detail, this entails abuse of browsers’ internal login managers without the users’ consent. The user fills out a login form on a website and saves their credentials in the browsers’ login manager. When the user starts browsing on that particular website a third-party script is inserted which is autofilled by the browsers’ built-in login manager. The script then obtains the users’ email address and sends hashes of it to a third-party server. We run web crawls on 50000 websites to find evidence of such tracking. Tracker Radar Collector basic instrumentation is extended to detect whether sensitive data is being injected into the pages’ context. A new collector is added which uses the MutationObserver API to detect DOM changes. Moreover, a bait technique is used to allow sensitive data injections into websites such that third-party scripts can access and exfiltrate the data. The analysis of our results reveals leaks via URLs and POST request bodies and discovers input field sniffs. However, no cases were found where login managers were abused for the purpose of third-party web tracking. We compare these results to previous research and discuss the limitations of our own research.
EMF Exposure Analysis for a Compact Multi-Band 5G Antenna

The fifth generation (5G) wireless communication systems are projected to work at millimeter wave (mm-wave) frequency bands that would bring new challenges with the implementation of antennas and safety level of electromagnetic field exposures. In this paper, a new design of 5G mmwave antenna for multi-frequency bands has been introduced. The antenna is small enough and has a form factor that can be easily fit into the current available mobile handset devices. The proposed antenna covers all the nominated frequency bands by the FCC for 5G communications and has good radiation performances at 28GHz, 37GHz, 39GHz, and 64–71GHz. The electromagnetic field exposure to the human head model has been studied by means of numerical simulation for all above frequency bands. The feature of our proposed antenna is that all the frequency bands for the 5th generation mobile handset will be available in a single and simple antenna structure; hence, analysis of EMF exposure in a wide range of frequency can be done on a single antenna design.