Joint Product Framing (Display, Ranking, Pricing) and Order Fulfillment Under the Multinomial Logit Model for E-Commerce Retailers

Problem definition: We study a joint product framing and order fulfillment problem with both inventory and cardinality constraints faced by an e-commerce retailer. There is a finite selling horizon and no replenishment opportunity. In each period, the retailer needs to decide how to “frame” (i.e., display, rank, price) each product on his or her website as well as how to fulfill a new demand. Academic/practical relevance: E-commerce retail is known to suffer from thin profit margins. Using the data from a major U.S. retailer, we show that jointly planning product framing and order fulfillment can have a significant impact on online retailers’ profitability. This is a technically challenging problem as it involves both inventory and cardinality constraints. In this paper, we make progress toward resolving this challenge. Methodology: We use techniques such as randomized algorithms and graph-based algorithms to provide a tractable solution heuristic that we analyze through asymptotic analysis. Results: Our proposed randomized heuristic policy is based on the solution of a deterministic approximation to the stochastic control problem. The key challenge is in constructing a randomization scheme that is easy to implement and that guarantees the resulting policy is asymptotically optimal. We propose a novel two-step randomization scheme based on the idea of matrix decomposition and a rescaling argument. Managerial implications: Our numerical tests show that the proposed policy is very close to optimal, can be applied to large-scale problems in practice, and highlights the value of jointly optimizing product framing and order fulfillment decisions. When inventory across the network is imbalanced, the widespread practice of planning product framing without considering its impact on fulfillment can result in high shipping costs, regardless of the fulfillment policy used. Our proposed policy significantly reduces shipping costs by using product framing to manage demand so that it occurs close to the location of the inventory.

A Scalable Bidimensional Randomization Scheme for TLC 3D NAND Flash Memories

Data randomization has been a widely adopted Flash Signal Processing technique for reducing or suppressing errors since the inception of mass storage platforms based on planar NAND Flash technology. However, the paradigm change represented by the 3D memory integration concept has complicated the randomization task due to the increased dimensions of the memory array, especially along the bitlines. In this work, we propose an easy to implement, cost effective, and fully scalable with memory dimensions, randomization scheme that guarantees optimal randomization along the wordline and the bitline dimensions. At the same time, we guarantee an upper bound on the maximum length of consecutive ones and zeros along the bitline to improve the memory reliability. Our method has been validated on commercial off-the-shelf TLC 3D NAND Flash memory with respect to the Raw Bit Error Rate metric extracted in different memory working conditions.

Comparison of four COVID-19 screening strategies to facilitate early case identification within the homeless shelter population: A structured summary of a study protocol for a randomised controlled trial

Objectives 1. To compare the effectiveness of four different surveillance strategies in detecting COVID-19 within the homeless shelter population. 2. To assess the participant adherence over time for each surveillance method. Trial Design This is a prospective cluster-randomized study to compare the effectiveness of four different surveillance regimens across eight homeless shelters in the city of Hamilton. Participants Participants will include both residents of, and the staff working within, the homeless shelters. All participants aged 18 or older who consent to the study and are able to collect a swab sample (where relevant) are eligible for the study. The study will take place across eight homeless shelters (four men-only and four women-only) in the City of Hamilton in Ontario, Canada. Intervention and Comparator Groups The comparator group will receive active daily surveillance of symptoms and testing will only be completed in symptomatic participants (i.e. those who fail screening or who seek care for potential COVID-19 related symptoms). The three intervention arms will all receive active daily surveillance of symptoms and testing of symptomatic participants (as in the comparator group) in addition to one of the following: 1. Once weekly self-collected oral swabs (OS) regardless of symptoms using written and visual instructions. 2. Once weekly self-collected oral-nares swab (O-NS) regardless of symptoms using written and visual instructions. 3. Once weekly nurse collected nasopharyngeal swab (NPS) regardless of symptoms. Participants will follow verbal and written instructions for the collection of OS and O-NS specimens. For OS collection, participants are instructed to first moisten the swab on their tongue, insert the swab between the cheek and the lower gums and rotate the swab three times. This is repeated on the other side. For O-NS collection, after oral collection, the swab is inserted comfortably (about 2-3 cm) into one nostril, parallel to the floor and turned three times, then repeated in the other nostril. NPS specimens were collected by the nurse following standard of care procedure. All swabs were placed into a viral inactivation medium and transported to the laboratory for COVID-19 testing. Briefly, total nucleic acid was extracted from specimens and then amplified by RT-PCR for the UTR and Envelope genes of SARS-CoV-2 and the human RNase P gene, which is used as a sample adequacy marker. Main Outcomes 1. Primary outcome: COVID-19 detection rate, i.e. the number of new positive cases over the study period of 8 weeks in each arm of the study. 2. Secondary outcomes: Qualitative assessment of study enrollment over 8 weeks. Percentage of participants who performed 50% or more of the weekly swabs in the intervention arms in the 8 week study period. Randomization We will use a computer-generated random assignment list to randomize the shelters to one of four interventions. Shelters were stratified by gender, and the simple randomization scheme was applied within each stratum. The randomization scheme was created using WinPEPI. Blinding This is an open-label study in which neither participants nor assessors are blinded. Numbers to be randomized (sample size) Since we are including our total sample frame, a sample size estimation at the cluster level is not required. However, if we succeed to enroll 50 participants per shelter from 8 shelters (n=400), and the detection rate is 3 times higher in the intervention groups (0.15) than in the comparator groups (0.05), we will have 90% power to detect a statistically significant and clinically important difference at a type I error rate of alpha=0.05 (one tailed), assuming an intraclass correlation of ~0.008. These computations were done using WinPEPI, and informed by conservative estimates from other studies on respiratory illness in the homeless (see Full protocol). Trial Status The protocol version number is 3.0. Recruitment began on April 17, 2020 and is ongoing. Due to low numbers of COVID cases in the community and shelter system during the initial study period, the trial was extended. The estimated date for the end of the extended recruitment period is Feb 1, 2021. Trial Registration The trial was registered with ClinicalTrials.gov on June 18, 2020 with the identifier NCT04438070. Full protocol The full protocol is attached as an additional file, accessible from the Trials website (Additional file 1). In the interest in expediting dissemination of this material, the familiar formatting has been eliminated; this Letter serves as a summary of the key elements of the full protocol.

A randomized nonmonotone adaptive trust region method based on the simulated annealing strategy for unconstrained optimization

PurposeThe purpose of this paper is to employ stochastic techniques to increase efficiency of the classical algorithms for solving nonlinear optimization problems.Design/methodology/approachThe well-known simulated annealing strategy is employed to search successive neighborhoods of the classical trust region (TR) algorithm.FindingsAn adaptive formula for computing the TR radius is suggested based on an eigenvalue analysis conducted on the memoryless Broyden-Fletcher-Goldfarb-Shanno updating formula. Also, a (heuristic) randomized adaptive TR algorithm is developed for solving unconstrained optimization problems. Results of computational experiments on a set of CUTEr test problems show that the proposed randomization scheme can enhance efficiency of the TR methods.Practical implicationsThe algorithm can be effectively used for solving the optimization problems which appear in engineering, economics, management, industry and other areas.Originality/valueThe proposed randomization scheme improves computational costs of the classical TR algorithm. Especially, the suggested algorithm avoids resolving the TR subproblems for many times.

An Automated Security Approach of Video Steganography–Based LSB Using FPGA Implementation

Steganography has become one of the most significant techniques to conceal secret data in media files. This paper proposes a novel automated methodology of achieving two levels of security for videos, which comprise encryption and steganography techniques. The methodology enhances the security level of secret data without affecting the accuracy and capacity of the videos. In the first level, the secret data is encrypted based on Advanced Encryption Standard (AES) algorithm using Java language, which renders the data unreadable. In the second level, the encrypted data is concealed in the video frames (images) using FPGA hardware implementation that renders the data invisible. The steganographic technique used in this work is the least significant bit (LSB) method; a 1–1–0 LSB scheme is used to maintain significantly high frame imperceptibility. The video frames used as cover files are selected randomly by the randomization scheme developed in this work. The randomization method scatters the data throughout the video frames rendering the retrieval of the data in its original order, without a proper key, a challenging task. The experimental results of concealment of secret data in video frames are presented in this paper and compared with those of similar approaches. The performance in terms of area, power dissipation, and peak signal-to-noise ratio (PSNR) of the proposed method outperformed traditional approaches. Furthermore, it is demonstrated that the proposed method is capable of automatically embedding and extracting the secret data at two levels of security on video frames, with a 57.1[Formula: see text]dB average PSNR.

A Countermeasure against DPA on SIMON with an Area-Efficient Structure

Differential power analysis (DPA) is an effective side channel attack method, which poses a critical threat to cryptographic algorithms, especially lightweight ciphers such as SIMON. In this paper, we propose an area-efficient countermeasure against DPA on SIMON based on the power randomization. Firstly, we review and analyze the architecture of SIMON algorithm. Secondly, we prove the threat of DPA attack to SIMON by launching actual DPA attack on SIMON 32/64 circuit. Thirdly, a low-cost power randomization scheme is proposed by combining fault injection with double rate technology, and the corresponding circuit design is implemented. To the best of our knowledge, this is the first scheme that applies the combination of fault injection and double rate technology to the DPA-resistance. Finally, the t-test is used to evaluate the security mechanism of the proposed designs with leakage quantification. Our experimental results show that the proposed design implements DPA-resistance of SIMON algorithm at certain overhead the cost of 47.7% LUTs utilization and 39.6% registers consumption. As compared to threshold implementation and bool mask, the proposed scheme has greater advantages in resource consumption.

A multilevel approach towards unbiased sampling of random elliptic partial differential equations

Partial differential equations are powerful tools for used to characterizing various physical systems. In practice, measurement errors are often present and probability models are employed to account for such uncertainties. In this paper we present a Monte Carlo scheme that yields unbiased estimators for expectations of random elliptic partial differential equations. This algorithm combines a multilevel Monte Carlo method (Giles (2008)) and a randomization scheme proposed by Rhee and Glynn (2012), (2013). Furthermore, to obtain an estimator with both finite variance and finite expected computational cost, we employ higher-order approximations.

LO80: Ondansetron administration to non-dehydrated children with acute gastroenteritis-associated vomiting, in emergency departments in Pakistan: a randomized, blinded, phase 3, superiority trial

Introduction: In high-income countries, vomiting often impedes oral rehydration therapy, leading to intravenous rehydration fluid administration to children with acute gastroenteritis. Ondansetron administration reduces vomiting and intravenous fluid administration in this population. We evaluated whether ondansetron is similarly effective when employed in Pakistan. Methods: In this 2-hospital, double-blind, placebo-controlled, emergency department-based, randomized trial, we recruited children aged 0·5 to 5·0 years, without dehydration, who had diarrhea and 1 episode of vomiting within 4 hours of arrival. Patients were randomly assigned (1:1), via an internet-based randomization service, using a stratified, variable block randomization scheme, to receive a single dose of oral ondansetron or placebo. The primary endpoint was intravenous rehydration (administration of 20 ml/kg over 4 hours of an isotonic fluid) within 72 hours of randomization. All randomized children were analysed. Results: From July 3, 2014, to January 12, 2017, 626 children were randomized. Intravenous rehydration was provided to 10.8% (34/314) and 10.3% (27/312) of children administered placebo and ondansetron, respectively (OR: 0.946; 95% CI: 0.564, 1.587; P=0.834). A regression model fitted with treatment group and adjusted for antiemetic administration and vomiting frequency in the preceding 24 hours, yielded similar results; OR=0.952; 95% CI: 0.570, 1.589; P=0.850. There was no evidence of interaction between treatment group and age (P=0.974), 3 diarrheal stools in the preceding 24 hours (P=0.983) or 3 vomits in the preceding 24 hours (P=0.554). During the 4-hour study observation period, 24.0% (75/314) and 19.6% (61/312) of children in the placebo and ondansetron groups vomited, respectively; OR: 0.774; 95%CI: 0.528, 1.133; P=0.187. Conclusion: Ondansetron administration did not significantly reduce intravenous rehydration use, suggesting that in children without dehydration, ondansetron administration does not significantly alter the disease course and should not be administered to this group of children.

RRApp, a robust randomization app, for clinical and translational research

While junior clinical researchers at academic medical institutions across the US often desire to be actively engaged in randomized-clinical trials, they often lack adequate resources and research capacity to design and implement them. This insufficiency hinders their ability to generate a rigorous randomization scheme to minimize selection bias and yield comparable groups. Moreover, there are limited online user-friendly randomization tools. Thus, we developed a free robust randomization app (RRApp). RRApp incorporates 6 major randomization techniques: simple randomization, stratified randomization, block randomization, permuted block randomization, stratified block randomization, and stratified permuted block randomization. The design phase has been completed, including robust server scripts and a straightforward user-interface using the “shiny” package in R. Randomization schemes generated in RRApp can be input directly into the Research Electronic Data Capture (REDCap) system. RRApp has been evaluated by biostatisticians and junior clinical faculty at the Icahn School of Medicine at Mount Sinai. Constructive feedback regarding the quality and functionality of RRApp was also provided by attendees of the 2016 Association for Clinical and Translational Statisticians Annual Meeting. RRApp aims to educate early stage clinical trialists about the importance of randomization, while simultaneously assisting them, in a user-friendly fashion, to generate reproducible randomization schemes.

Guidelines for conducting split-mouth clinical studies in restorative dentistry

<p>The split-mouth design used in some clinical trials make a randomization scheme on site level where two treatments are randomly assigned to sites of one of the two halves of the mouth. The aim of this review was to summarize guidelines for conducting split-mouth clinical studies in Restorative Dentistry. This is a review performed through scientific articles published between 2004 and 2014 indexed in MEDLINE, PubMed and Scielo databases. The study evaluated USPHS and FDI criteria. The current review showed the main characteristics used in split-mouth studies presented the Restorative Dentistry literature, as ethical aspects, sample calculation, methods of selection and evaluation patients, in order to provide a guideline for clinical conduction. It showed a standard of methodologies to enable comparison among studies.<strong> </strong></p><p><strong>Keywords: </strong>Clinical studies, split-mouth design, dentistry.</p>