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\"Explore Twisted, the Python-based event-driven networking engine, and review several of its most popular application projects. It is written by community leaders who have contributed to many of the projects covered, and share their hard-won insights and experience.Expert Twisted starts with an introduction to event-driven programming, explaining it in the context of what makes Twisted unique. It shows how Twisted's design emphasizes testability as a solution to common challenges of reliability, debugging, and start-to-finish causality that are inherent in event-driven programming. It also explains asynchronous programming, and the importance of functions, deferreds, and coroutines. It then uses two popular applications, treq and klein, to demonstrate calling and writing Web APIs with Twisted./The second part of the book dives into Twisted projects, in each case explaining how the project fits into the Twisted ecosystem and what it does, and offers several examples to bring readers up to speed, with pointers to additional resources for more depth. Examples include using Twisted with Docker, as a WSGI container, for file sharing, and more.\"-- Provided by publisher

Mobile networks are expected to face major problems such as low network capacity, high latency, and limited resources but are expected to provide seamless connectivity in the foreseeable future. It is crucial to deliver an adequate level of performance for network services and to ensure an acceptable quality of services for mobile users. Intelligent mobility management is a promising solution to deal with the aforementioned issues. In this context, modeling user mobility behaviour is of great importance in order to extract valuable information about user behaviours and to meet their demands. In this paper, we propose a hybrid user mobility prediction approach for handover management in mobile networks. First, we extract user mobility patterns using a mobility model based on statistical models and deep learning algorithms. We deploy a vector autoregression (VAR) model and a gated recurrent unit (GRU) to predict the future trajectory of a user. We then reduce the number of unnecessary handover signaling messages and optimize the handover procedure using the obtained prediction results. We deploy mobility data generated from real users to conduct our experiments. The simulation results show that the proposed VAR-GRU mobility model has the lowest prediction error in comparison with existing methods. Moreover, we investigate the handover processing and transmission costs for predictive and non-predictive scenarios. It is shown that the handover-related costs effectively decrease when we obtain a prediction in the network. For vertical handover, processing cost and transmission cost improve, respectively, by 57.14% and 28.01%.

Same-day HIV testing and antiretroviral therapy (ART) initiation is being widely implemented. However, the optimal timing of ART among patients with tuberculosis (TB) symptoms is unknown. We hypothesized that same-day treatment (TB treatment for those diagnosed with TB; ART for those not diagnosed with TB) would be superior to standard care in this population. We conducted an open-label trial among adults with TB symptoms at initial HIV diagnosis at GHESKIO in Haiti; participants were recruited and randomized on the same day. Participants were randomized in a 1:1 ratio to same-day treatment (same-day TB testing with same-day TB treatment if TB diagnosed; same-day ART if TB not diagnosed) versus standard care (initiating TB treatment within 7 days and delaying ART to day 7 if TB not diagnosed). In both groups, ART was initiated 2 weeks after TB treatment. The primary outcome was retention in care with 48-week HIV-1 RNA <200 copies/mL, with intention to treat (ITT) analysis. From November 6, 2017 to January 16, 2020, 500 participants were randomized (250/group); the final study visit occurred on March 1, 2021. Baseline TB was diagnosed in 40 (16.0%) in the standard and 48 (19.2%) in the same-day group; all initiated TB treatment. In the standard group, 245 (98.0%) initiated ART at median of 9 days; 6 (2.4%) died, 15 (6.0%) missed the 48-week visit, and 229 (91.6%) attended the 48-week visit. Among all who were randomized, 220 (88.0%) received 48-week HIV-1 RNA testing; 168 had <200 copies/mL (among randomized: 67.2%; among tested: 76.4%). In the same-day group, 249 (99.6%) initiated ART at median of 0 days; 9 (3.6%) died, 23 (9.2%) missed the 48-week visit, and 218 (87.2%) attended the 48-week visit. Among all who were randomized, 211 (84.4%) received 48-week HIV-1 RNA; 152 had <200 copies/mL (among randomized: 60.8%; among tested: 72.0%). There was no difference between groups in the primary outcome (60.8% versus 67.2%; risk difference: -0.06; 95% CI [-0.15, 0.02]; p = 0.14). Two new grade 3 or 4 events were reported per group; none were judged to be related to the intervention. The main limitation of this study is that it was conducted at a single urban clinic, and the generalizability to other settings is uncertain. In patients with TB symptoms at HIV diagnosis, we found that same-day treatment was not associated with superior retention and viral suppression. In this study, a short delay in ART initiation did not appear to compromise outcomes. This study is registered with ClinicalTrials.gov NCT03154320.

The rapid adoption of Android devices comes with the growing prevalence of mobile malware, which leads to serious threats to mobile phone security and attacks private information on mobile devices. In this paper, we designed and implemented a model for malware detection on Android devices to protect private and financial information, for the mobile applications of the ATISCOM project. This model is based on client/server architecture, to reduce the heavy computations on a mobile device by sending data from the mobile device to the server for remote processing (i.e., offloading) of the predictions. We then gradually optimized our proposed model for better classification of the newly installed applications on Android devices. We at first adopted Naive Bayes to build the model with 92.4486% accuracy, then the classification method that gave the best accuracy of 93.85% for stochastic gradient descent (SGD) with binary class (i.e., malware and benign), and finally the regression method with numerical values ranging from −100 to 100 to manage the uncertainty predictions. Therefore, our proposed model with random forest regression gives a good accuracy in terms of performance, with a good correlation coefficient, minimum computation time and the smallest number of errors for malware detection.

Android has become the leading operating system for mobile devices, and the most targeted one by malware. Therefore, many analysis methods have been proposed for detecting Android malware. However, few of them use proper datasets for evaluation. In this paper, we propose BrainShield, a hybrid malware detection model trained on the Omnidroid dataset to reduce attacks on Android devices. The latter is the most diversified dataset in terms of the number of different features, and contains the largest number of samples, 22,000 samples, for model evaluation in the Android malware detection field. BrainShield’s implementation is based on a client/server architecture and consists of three fully connected neural networks: (1) the first is used for static analysis and reaches an accuracy of 92.9% trained on 840 static features; (2) the second is a dynamic neural network that reaches an accuracy of 81.1% trained on 3722 dynamic features; and (3) the third neural network proposed is hybrid, reaching an accuracy of 91.1% trained on 7081 static and dynamic features. Simulation results show that BrainShield is able to improve the accuracy and the precision of well-known malware detection methods.

Heterogeneous Vehicular Network (HetVNET) is a highly dynamic type of network that changes very quickly. Regarding this feature of HetVNETs and the emerging notion of network slicing in 5G technology, we propose a hybrid intelligent Software-Defined Network (SDN) and Network Functions Virtualization (NFV) based architecture. In this paper, we apply Conditional Generative Adversarial Network (CGAN) to augment the information of successful network scenarios that are related to network congestion and dynamicity. The results show that the proposed CGAN can be trained in order to generate valuable data. The generated data are similar to the real data and they can be used in blueprints of HetVNET slices.

Background HIV programs are often assessed by the proportion of patients who are alive and retained in care; however some patients are categorized as lost to follow-up (LTF) and have unknown vital status. LTF is not an outcome but a mixed category of patients who have undocumented death, transfer and disengagement from care. Estimating vital status (dead versus alive) among this category is critical for survival analyses and program evaluation. Methods We used three methods to estimate survival in the cohort and to ascertain factors associated with death among the first cohort of HIV positive patients to receive antiretroviral therapy in Haiti: complete case (CC) (drops missing), Inverse Probability Weights (IPW) (uses tracking data) and Multiple Imputation with Chained Equations (MICE) (imputes missing data). Logistic regression was used to calculate odds ratios and 95% confidence intervals for adjusted models for death at 10 years. The logistic regression models controlled for sex, age, severe poverty (living on <$1 USD per day), Port-au-Prince residence and baseline clinical characteristics of weight, CD4, WHO stage and tuberculosis diagnosis. Results Age, severe poverty, baseline weight and WHO stage were statistically significant predictors of AIDS related mortality across all models. Gender was only statistically significant in the MICE model but had at least a 10% difference in odds ratios across all models. Conclusion Each of these methods had different assumptions and differed in the number of observations included due to how missing values were addressed. We found MICE to be most robust in predicting survival status as it allowed us to impute missing data so that we had the maximum number of observations to perform regression analyses. MICE also provides a complementary alternative for estimating survival among patients with unassigned vital status. Additionally, the results were easier to interpret, less likely to be biased and provided an alternative to a problem that is often commented upon in the extant literature.

Introduction Differentiated service delivery (DSD) models have been widely implemented for patients in stable HIV care. However, DSD has rarely been offered to newly diagnosed patients. We assessed the effectiveness of early fast‐track care during a period of severe civil unrest in Port‐au‐Prince, Haiti. Methods We conducted a pilot randomized trial among adults presenting with early HIV disease to determine whether early fast‐track care (8−12 weeks after same‐day HIV testing and antiretroviral therapy [ART] initiation) was associated with superior outcomes compared with standard (deferred eligibility for fast‐track care). All participants received tenofovir/lamivudine/dolutegravir (TLD), and HIV‐1 RNA <200 copies/ml was required prior to initiating fast‐track care. The primary outcome was 48‐week HIV‐1 RNA <200 copies/ml, with intention‐to‐treat analysis. Results From December 2020 to August 2022, 245 participants were randomized to standard (n = 116) and early fast‐track (n = 129) groups. All initiated TLD on the day of HIV diagnosis. In the early fast‐track group, one (0.8%) died, 12 (9.3%) were internally displaced/emigrated, five (3.9%) were lost‐to‐follow‐up (LTFU), two (1.6%) had a gap in care/later return, one (0.8%) was transferred and 108 (83.7%) were retained; 88 (68.2%) received 48‐week viral load testing and 80 (90.9% of tested; 62.0% of randomized) had HIV‐1 RNA <200 copies/ml. In the standard group, two (1.7%) died, six (5.2%) were internally displaced/emigrated, three (2.6%) were LTFU, one (0.9%) had a gap in care/later return, one (0.9%) was transferred and 103 (88.8%) were retained; 78 (67.2%) received 48‐week viral load testing and 66 (84.6% of tested; 56.9% of randomized) had HIV‐1 RNA <200 copies/ml. By design, the sample size of this pilot study was too small to provide definitive evidence of treatment effect, but the primary outcome was numerically higher in the early fast‐track group (62.0% vs. 56.9%; RD: 0.051: 95% CI: −0.072, 0.174). Conclusions Early fast‐track care was associated with high levels of viral suppression among adults initiating same‐day TLD, despite severe civil unrest in Haiti. Completion of 48‐week viral load testing was suboptimal, due to the need for participants to leave Port‐au‐Prince during peak periods of gang‐related violence, and the lack of availability of viral load testing for those receiving non‐facility‐based ART.

Long-term management of AIDS in resource-constrained settings is a tremendous challenge. This report from Haiti offers evidence that antiretroviral therapy can be successfully delivered with significant clinical benefit for more than a decade. To the Editor: We report the 10-year survival rates in the first cohort of patients with AIDS who consecutively initiated antiretroviral therapy (ART) in Port-au-Prince, Haiti. A total of 910 patients 13 years of age or older who initiated ART from 2003 through 2004 were followed for 10 years; the initiation of ART and follow-up care were performed in accordance with World Health Organization (WHO) guidelines. 1 Details regarding this cohort have been reported previously, and ethics approval for the retrospective study was received from the relevant institutional review boards. 2 , 3 Death was ascertained from medical records. Data from patients who . . .

Seawater-basalt interaction taking place at mid-ocean ridges was studied using numerical modeling to determine the compositional evolution of hydrothermal fluids and associated alteration mineralogy forming within newly emplaced crustal material. Geochemical modeling was carried out in a closed seawater-basalt system at discrete temperature intervals between 2 and 400°C at 500 bars, varying fluid/rock ratios, and secondary mineral assemblages representative of basalt alteration in natural systems. In addition to temperature, the fluid/rock ratio has a fundamental control on the resulting system chemistry. At rock-buffered conditions (low fluid/rock ratios), the mineral-solution equilibrium was characterized by high cation to proton activity ratios for aCa2+/(aH+)2 and aNa+/aH+ and very low dissolved Mg concentrations due to the precipitation of smectites and chlorite. A complex secondary mineral alteration assemblage dominated by Ca- and Na-bearing minerals including zeolites, calcite, epidote, prehnite, clinozoisite, and albite was predicted to form. The resulting fluid composition was alkaline and reduced relative to ambient seawater, with Eh values ranging between −0.2 and −0.6 V. In contrast, seawater-buffered conditions (high fluid/rock ratios) resulted in lower cation to proton activity ratios for aCa2+/(aH+)2 and aNa+/aH+ and higher dissolved Mg concentrations comparable to the value of this element in ambient seawater. A more simple mineral assemblage was predicted to form at these conditions with the predominance of Al-Si- and Mg-bearing minerals including kaolinite, quartz, and talc in addition to large amounts of anhydrite. The resulting fluid composition was mildly acidic and oxidized relative to seawater with Eh values ranging between −0.2 and 0 V. These modeling results were compared to a compilation of submarine hydrothermal vent fluid compositions from mid-ocean ridge settings and analogous basalt-dominated environments. The agreement obtained between the simulations and the compiled fluid data indicates that mid-ocean ridge hydrothermal processes can be closely reproduced by mineral-solution equilibria for a broad range of temperatures and fluid/rock ratios.