Quantum encoding of dynamic directed graphs

In application domains such as routing, network analysis, scheduling, and planning, directed graphs are widely used as both formal models and core data structures for the development of efficient algorithmic solutions. In these areas, graphs are often evolving in time: for example, connection links may fail due to temporary technical issues, meaning that edges of the graph cannot be traversed for some time interval and alternative paths have to be followed. In classical computation graphs have been implemented both explicitly through adjacency matrices/lists and symbolically as ordered binary decision diagrams. Moreover, ad-hoc visit procedures have been developed to deal with dynamically evolving graphs. Quantum computation, exploiting interference and entanglement, has provided an exponential speed-up for specific problems, e.g., database search and integer factorization. In the quantum framework everything must be represented and manipulated using reversible operators. This poses a challenge when one has to deal with traversals of dynamically evolving directed graphs. Graph traversals are not intrinsically reversible because of converging paths. In the case of dynamically evolving graphs also the creation/destruction of paths comes into play against reversibility. In this paper we propose a novel high level graph representation in quantum computation supporting dynamic connectivity typical of real-world network applications. Our procedure allows to encode any multigraph into a unitary matrix. We devise algorithms for computing the encoding that are optimal in terms of time and space and we show the effectiveness of the proposal with some examples. We describe how to react to edge/node failures in constant time. Furthermore, we present two methods to perform quantum random walks taking advantage of this encoding: with and without projectors. We implement and test our encoding obtaining that the theoretical bounds for the running time are confirmed by the empirical results and providing more details on the behavior of the algorithms over graphs of different densities

Postpartum women’s use of medicines and breastfeeding practices: a systematic review

The objectives of this article are to systematically review i) the extent of medicine use in postpartum women, and ii) the impact of maternal medicine use (excluding contraceptives and galactogogues) on breastfeeding outcomes (initiation and/or duration). PubMed, Medline (Ovid), Scopus (Elsevier), Cinahl (EBSCO), PsycINFO (Ovid), Embase (Ovid) and Web of Science (ISI) databases were searched to find original studies on medicine use in women after the birth. Additional studies were identified by searching Google Scholar, Wiley Online Library, Springer Link, selected journals and from the reference list of retrieved articles. Observational studies with information about postpartum women’s use of any type of medicine either for chronic or acute illnesses with or without breastfeeding information were included. The majority of relevant studies suggest that more than 50 % of postpartum women (breastfeeding or not) required at least one medicine. Due to the lack of uniform medication use reporting system and differences in study designs, settings and samples, the proportion of medicine use by postpartum women varies widely, from 34 to 100 %. Regarding the impact of postpartum women’s medicine use on breastfeeding, a few studies suggest that women’s use of certain medicines (e.g. antiepileptics, propylthiouracil, antibiotics) during lactation can reduce initiation and/ or duration of breastfeeding. These studies are limited by small sample size, and with one exception, all were conducted in Canada more than a decade ago. Large scale studies are required to establish the relationship between maternal medicine use and breastfeeding, considering type of illness, period of use and total duration of medicine use

The impact of surgical delay on resectability of colorectal cancer: An international prospective cohort study

AIM: The SARS-CoV-2 pandemic has provided a unique opportunity to explore the impact of surgical delays on cancer resectability. This study aimed to compare resectability for colorectal cancer patients undergoing delayed versus non-delayed surgery. METHODS: This was an international prospective cohort study of consecutive colorectal cancer patients with a decision for curative surgery (January-April 2020). Surgical delay was defined as an operation taking place more than 4 weeks after treatment decision, in a patient who did not receive neoadjuvant therapy. A subgroup analysis explored the effects of delay in elective patients only. The impact of longer delays was explored in a sensitivity analysis. The primary outcome was complete resection, defined as curative resection with an R0 margin. RESULTS: Overall, 5453 patients from 304 hospitals in 47 countries were included, of whom 6.6% (358/5453) did not receive their planned operation. Of the 4304 operated patients without neoadjuvant therapy, 40.5% (1744/4304) were delayed beyond 4 weeks. Delayed patients were more likely to be older, men, more comorbid, have higher body mass index and have rectal cancer and early stage disease. Delayed patients had higher unadjusted rates of complete resection (93.7% vs. 91.9%, P = 0.032) and lower rates of emergency surgery (4.5% vs. 22.5%, P < 0.001). After adjustment, delay was not associated with a lower rate of complete resection (OR 1.18, 95% CI 0.90-1.55, P = 0.224), which was consistent in elective patients only (OR 0.94, 95% CI 0.69-1.27, P = 0.672). Longer delays were not associated with poorer outcomes. CONCLUSION: One in 15 colorectal cancer patients did not receive their planned operation during the first wave of COVID-19. Surgical delay did not appear to compromise resectability, raising the hypothesis that any reduction in long-term survival attributable to delays is likely to be due to micro-metastatic disease

Practical design considerations on adaptive controllers for PWM DC/DC converters

The paper analyzes the sensitivity to discrete and continuous parameters of an adaptive filter employed to control pulse width modulation DC/DC converters. The optimal transfer function to compensate the power cell is chosen measuring and digitalizing the inputs variables of the systems. Its synthesis is done by a digitally programmable switched capacitor circuit. The effects which can lead to a wrong estimation of the operating point and to miss the target dynamic behavior of the external feedback are discussed

Embedding a multichannel environmental noise cancellation algorithm into an electronic stethoscope

The paper describes a multichannel adaptive algorithm for the enhancement of cardiac sounds with respect to environmental noise. It combines sounds acquired from a couple of microphones to reconstruct the transfer function of a stethoscope head and its interaction with the patient\u2019s body. This identification process allows to perform a distortion-less noise reduction. The filter is embedded into an electronic stethoscope, composed of a traditional acoustic head and an electronic section. This instrument allows to show on a display the heart sound and to store the acquisition into a removable media transferring data to a PC. A software tool able to reproduce, visualize, store and analyze cardiac sounds, for performing assisted diagnoses of cardiac diseases, completes the system. A demonstrator of the tool has been realized. Experimental results show significant improvements in noise reduction, when the filtering algorithm is applied

Massive Generation of Customer Load Profiles for Large Scale State Estimation Deployment: An Approach to Exploit AMI Limited Data

The management of the distribution network is becoming increasingly important as the penetration of distributed energy resources is increasing. Reliable knowledge of the real-time status of the network is essential if algorithms are to be used to help distribution system operators define network configurations. State Estimation (SE) algorithms are capable of producing such an accurate snapshot of the network state but, in turn, require a wide range of information, e.g., network topology, real-time measurement and power profiles from customers/productions. Those profiles which may, in principle, be provided by smart meters are not always available due to technical limitations of existing Advanced Metering Infrastructure (AMI) in terms of communication, storage and computing power. That means that power profiles are only available for a subset of customers. The paper proposes an approach that can overcome these limitations: the remaining profiles, required by SE algorithms, are generated on the basis of customer-related information, identifying clusters of customers with similar features, such as the same contract and pattern of energy consumption. For each cluster, a power profile estimator is generated using long-term power profiles of a limited sub-set of customers, randomly selected from the cluster itself. The synthesized full power profile, representing each customer of the distribution network, is then obtained by scaling the power profile estimator of the cluster to which the customer belongs, by the monthly energy exchanged by that customer, data that are easily available. The feasibility of the proposed approach was validated considering the distribution grid of Unareti SpA, an Italian Distribution System Operator (DSO), operating in northern Italy and serving approximately one million customers. The application of the proposed approach to the actual infrastructure shows some limitations in terms of the accuracy of the estimation of the power profile of the customer. In particular, the proposed methodology is not fully able to properly represent clusters composed of customers with a large variability in terms of power exchange with the distribution network. In any case, the root mean square error of the synthesized full power profile with the respect to validation power profiles belonging to the same cluster is, in the worst case, on the order of 6.3%, while in the rest of cases is well below 5%. Thus, the proposed approach represents a good compromise between accuracy in representing the behavior of customers on the network and resources (in terms of computational power, data storage and communication resources) to achieve that results