Mobility Increases the Data Offloading Ratio in D2D Caching Networks

Caching at mobile devices, accompanied by device-to-device (D2D) communications, is one promising technique to accommodate the exponentially increasing mobile data traffic. While most previous works ignored user mobility, there are some recent works taking it into account. However, the duration of user contact times has been ignored, making it difficult to explicitly characterize the effect of mobility. In this paper, we adopt the alternating renewal process to model the duration of both the contact and inter-contact times, and investigate how the caching performance is affected by mobility. The data offloading ratio, i.e., the proportion of requested data that can be delivered via D2D links, is taken as the performance metric. We first approximate the distribution of the communication time for a given user by beta distribution through moment matching. With this approximation, an accurate expression of the data offloading ratio is derived. For the homogeneous case where the average contact and inter-contact times of different user pairs are identical, we prove that the data offloading ratio increases with the user moving speed, assuming that the transmission rate remains the same. Simulation results are provided to show the accuracy of the approximate result, and also validate the effect of user mobility.Comment: 6 pages, 5 figures, accepted to IEEE Int. Conf. Commun. (ICC), Paris, France, May 201

Patient safety in developing countries: retrospective estimation of scale and nature of harm to patients in hospital

OBJECTIVE: To assess the frequency and nature of adverse events to patients in selected hospitals in developing or transitional economies. DESIGN: Retrospective medical record review of hospital admissions during 2005 in eight countries. SETTING: Ministries of Health of Egypt, Jordan, Kenya, Morocco, Tunisia, Sudan, South Africa and Yemen; the World Health Organisation (WHO) Eastern Mediterranean and African Regions (EMRO and AFRO), and WHO Patient Safety. PARTICIPANTS: Convenience sample of 26 hospitals from which 15,548 patient records were randomly sampled. MAIN OUTCOME MEASURES: Two stage screening. Initial screening based on 18 explicit criteria. Records that screened positive were then reviewed by a senior physician for determination of adverse event, its preventability, and the resulting disability. RESULTS: Of the 15,548 records reviewed, 8.2% showed at least one adverse event, with a range of 2.5% to 18.4% per country. Of these events, 83% were judged to be preventable, while about 30% were associated with death of the patient. About 34% adverse events were from therapeutic errors in relatively non-complex clinical situations. Inadequate training and supervision of clinical staff or the failure to follow policies or protocols contributed to most events. CONCLUSIONS: Unsafe patient care represents a serious and considerable danger to patients in the hospitals that were studied, and hence should be a high priority public health problem. Many other developing and transitional economies will probably share similar rates of harm and similar contributory factors. The convenience sampling of hospitals might limit the interpretation of results, but the identified adverse event rates show an estimate that should stimulate and facilitate the urgent institution of appropriate remedial action and also to trigger more research. Prevention of these adverse events will be complex and involves improving basic clinical processes and does not simply depend on the provision of more resources

2nd IEEE International Mediterranean Conference on Communications and Networking (IEEE MeditCom 2022) 5-8 September 2022, Athens, Greece

International audienc

Precision medicine in the era of artificial intelligence: implications in chronic disease management.

Aberrant metabolism is the root cause of several serious health issues, creating a huge burden to health and leading to diminished life expectancy. A dysregulated metabolism induces the secretion of several molecules which in turn trigger the inflammatory pathway. Inflammation is the natural reaction of the immune system to a variety of stimuli, such as pathogens, damaged cells, and harmful substances. Metabolically triggered inflammation, also called metaflammation or low-grade chronic inflammation, is the consequence of a synergic interaction between the host and the exposome-a combination of environmental drivers, including diet, lifestyle, pollutants and other factors throughout the life span of an individual. Various levels of chronic inflammation are associated with several lifestyle-related diseases such as diabetes, obesity, metabolic associated fatty liver disease (MAFLD), cancers, cardiovascular disorders (CVDs), autoimmune diseases, and chronic lung diseases. Chronic diseases are a growing concern worldwide, placing a heavy burden on individuals, families, governments, and health-care systems. New strategies are needed to empower communities worldwide to prevent and treat these diseases. Precision medicine provides a model for the next generation of lifestyle modification. This will capitalize on the dynamic interaction between an individual's biology, lifestyle, behavior, and environment. The aim of precision medicine is to design and improve diagnosis, therapeutics and prognostication through the use of large complex datasets that incorporate individual gene, function, and environmental variations. The implementation of high-performance computing (HPC) and artificial intelligence (AI) can predict risks with greater accuracy based on available multidimensional clinical and biological datasets. AI-powered precision medicine provides clinicians with an opportunity to specifically tailor early interventions to each individual. In this article, we discuss the strengths and limitations of existing and evolving recent, data-driven technologies, such as AI, in preventing, treating and reversing lifestyle-related diseases

Identification and characterization of copy number variations in cattle

Copy number variations (CNVs) are an important source of genetic changes. They are defined as a gain or loss of genomic region ranging from 50 bp to several megabases. CNVs have been shown to be associated with many diseases and some phenotypic traits in several species, including cattle. We used Pindel, Delly, BreakDancer, and CNVnator to identify CNVs using whole-genome sequencing data of 200 animals from eight French dairy and beef cattle breeds. We selected only deletions and duplications predicted by at least two tools and present in at least two animals. We identified a total of 29,132 autosomal deletions and duplications which cover between 31 to 34% (784 to 865 Mb) of the autosomal genome, with an average of 6,000 events per animal. Among these deletions and duplications, 27,690 were present in at least two animals. Out of theses, 26,417 events were deletions, 674 were duplications and 599 regions were both (deletion and duplication within the same region). We defined a CNV as deletion and duplication in the same region, and we termed this region as CNV-Region (CNVR). The size of CNVRs ranged from 100 bp to 9.3 Mb with a median of 1.3 kb and a mean of 45 kb. From the identified deletions and duplications, 8,283 overlapped with 9,733 annotated genes including 290 CNVRs overlapping with 974 annotated genes, including some genes known to be implicated in some traits of economic importance. Our study provides an extensive view of the CNVRs in French dairy and beef breeds. CNVRs with an effect on some commercially interesting phenotypes could be used to improve genetic selection of these eight French breeds