Access Time Research Articles

An Assessment of Accessibility from a Socially Sustainable Urban Mobility Approach in Mass Transit Projects: Contributions from the Northern Central American Triangle

This article aims to address the lack of research on the social dimension of sustainability, also known as social sustainability, in urban mobility projects, primarily in cities of the Global South. It proposes a strategy to partially assess social sustainability, focusing on accessibility, which is one of the key dimensions for conducting such an evaluation. To this end, a comparative analysis of three study cases is conducted in the capital cities of the Northern Central American Triangle (NCAT) before and after the construction of bus rapid transit (BRT) projects between 2000 and 2020. Accessibility is evaluated through equity and spatial efficiency indicators obtained through geographical information system (GIS) modeling, including layers representing transportation networks, populated areas, and locations of basic urban facilities. The result is an unprecedented assessment of accessibility in the NCAT capitals, which shows how the Guatemala City BRT project has improved the city’s social sustainability by reducing access times to basic urban facilities, mainly public health clinics and educational facilities, and narrowing the inequality gap as compared to projects in San Salvador and Tegucigalpa, the other capital cities in the NCAT. Additionally, it is emphasized that this methodology can be replicated in the Global South while considering the scarcity of information and the use of open-source software in the process.

Comparing short-axis versus long-axis ultrasound-guided techniques for internal jugular vein cannulation: A meta-analysis of clinical outcomes and safety.

Central venous access plays a crucial role in various clinical settings, and ultrasound guidance has become increasingly popular for improving its safety and success rates. The aim of this meta-analysis was to compare the short-axis (SAX) and long-axis (LAX) ultrasound-guided techniques for internal jugular vein (IJV) cannulation in terms of first needle pass success rate, number of cannulation attempts, access time, guidewire insertion time, posterior IJV wall puncture, arterial puncture, haematoma and catheter-related bloodstream infection. A comprehensive literature search was conducted, and randomised controlled trials (RCTs) comparing SAX and LAX techniques for IJV cannulation on adults were included. A total of 11 RCTs involving 1183 patients were included in the meta-analysis. The SAX technique demonstrated a significantly greater first needle pass success rate and faster IJV access time compared to the LAX technique. However, more posterior IJV wall puncture was significantly associated with the SAX technique. There was no significant difference between the two techniques in terms of number of cannulation attempts, guidewire insertion time, arterial puncture, haematoma and catheter-related bloodstream infection. This meta-analysis suggests that the SAX technique may have advantages over the LAX technique in terms of first needle pass success rate and potentially reducing cannulation attempts and access time. However, the occurrence of posterior IJV wall puncture raises concerns. The decision on the choice of technique should be based on individual patient factors and operator proficiency.

Students' Barriers and Emotional Presence in Online Learning: A Canonical Correlation Analysis

Addressing the barriers students face in online learning is critical to creating an inclusive, supportive, and practical educational experience that benefits many students. The aim of this study is to examine students' online learning barriers and emotional presence, which affect many variables in online learning environments, and the relationship between these variables. For this purpose, data was collected from 402 university students enrolled in the same course at a university. According to the data collected from the students, it can be said that the student's barriers to online learning are at a medium level for eight sub-factors ((a) administrative issues, (b) social interaction, (c) academic skills, (d) technical skills, (e) learner motivation, (f) time and support for studies, (g) cost and access to the Internet, and (h) technical problems). Similarly, it was found that students' emotional presence in online learning is at an average level for Receiving Emotions (x̄=3,438) and Giving Emotions (x̄=2,994) factors. Canonical correlation analysis was used to show the relationship between student barriers and emotional presence. As a result of this analysis, it was concluded that the canonical correlation was low. It was observed that the factor that contributed the most to the significant model among student barriers was the factor of administrative/instructor issues, and the factor of receiving emotions factor was one of the emotional presence factors.

Construction of Athletes' Physical Condition Monitoring and Analysis System Using Biometrics Recognition Technology

Biometrics recognition technology utilizes unique biological characteristics such as fingerprints, iris patterns, facial features, or voiceprints to identify and authenticate individuals. Through advanced algorithms and pattern recognition techniques, biometric systems capture and analyze these physiological or behavioral traits to verify a person's identity. This technology offers high levels of security and accuracy, making it valuable for various applications including access control, time and attendance tracking, border security, and digital payments. The construction of an athletes' physical condition monitoring and analysis system utilizing biometric recognition technology represents a significant advancement in sports science. By integrating cutting-edge biometric sensors and recognition algorithms, this system provides real-time insights into athletes' physiological parameters and performance metrics. Athlete performance monitoring and analysis play pivotal roles in optimizing training strategies, preventing injuries, and enhancing overall athletic performance. In this paper, we propose a novel approach leveraging the Hybrid Multi-Instance Ensemble Classifier (HMIEC) combined with biometric recognition technology to accurately classify athlete data and assess their physical condition. Our study explores the effectiveness of HMIEC across multiple biometric modalities, including ECG-based, fingerprint-based, and facial recognition, in identifying athletes and monitoring their physical parameters. Through a series of experiments and analyses, demonstrate HMIEC's superior classification performance compared to other classifiers, with high accuracy, sensitivity, specificity, and area under the curve (AUC). The reductions in heart rate from 75 to 65 beats per minute, increase in oxygen saturation levels from 98% to 99%, decreases in blood pressure readings from 120/80 mmHg to 110/70 mmHg, and enhancements in flexibility from 30 to 35 centimeters, muscle strength from 100 lbs to 120 lbs, and endurance capacity from 20 to 25 minutes.

A mixed behavioural and data-driven method for assessing the shift potential to electric micromobility: evidence from Rome

Electric micromobility, both as a private option and as a shared service, can represent an alternative to cars, particularly for given user groups (market segments) and specific classes of travel distance. The paper explores the potential for shifting from cars to electric micromobility (specifically, e-bikes and e-scooters) for commuting trips, investigated through floating car data (FCD). The methodology combines the calibration of random utility models (RUMs) and the subsequent simulation through the adoption of FCD spanning the entire city of Rome (Italy). The data used for the calibration of RUM models have been sourced from an online revealed preferences and stated preferences survey carried out between November 2020 and January 2021. Socioeconomic factors, along with transport features (travel time, access time, monetary costs, and perceived safety levels), enter into the definition of the mode choice probability. The first results showed that in Rome, the potential demand for electric micromobility could range between 14% of the FCD sample in the best case (low cost, high accessibility, and road infrastructures with a high perceived level of safety) and about 2% in the worst case (high cost, low accessibility, and a low perceived level of safety).

From Vision to Evaluation: A Metrics Framework for the ACCESS Allocations Service

The Allocations Service for the Advanced Cyberinfrastructure Coordination Ecosystem: Services & Support (ACCESS) program is charged with accepting, reviewing, and processing researchers’ requests to use resources that are integrated into the ACCESS ecosystem. We present as a case study the metrics framework used to evaluate the Allocations Service project, a metrics framework that aligns with the project’s goals and identifies key performance indicators (KPIs). Several of our top-level KPIs reflect complex concepts and are composite measures built from suites of metrics compiled from two primary sources: a well-instrumented allocations and accounting system and an annual survey of the ACCESS researcher community. This approach allows us to describe and measure complex concepts such as “democratization” and “ecosystem access time” in a quantitative manner and to target improvements to project activities. The metrics framework is augmented by metrics to measure the performance of the project team, to describe general ecosystem and allocations activity, and to capture publications from the researcher community. We used this framework to gather and present data as part of the ACCESS Allocations Service first annual NSF panel review. The metrics were largely successful at communicating our progress, but we also encountered a few unexpected technical issues with the data and calculations themselves, which we are continuing to refine. Presented here as a case study, this approach to a metrics framework for the Allocations Service has proved valuable in complementing more subjective descriptions of the project, its accomplishments, and progress toward our goals.

Modelling Prospects of Bio-Electrochemical Immunosensing Platforms

Electrochemistry is a hotspot in today’s research arena. Many different domains have been extended for their role towards the Internet of Things, digital health, personalized nutrition, and/or wellness using electrochemistry. These advances have led to a substantial increase in the power and popularity of electroanalysis and its expansion into new phases and environments. The recent COVID-19 pandemic, which turned our lives upside down, has helped us to understand the need for miniaturized electrochemical diagnostic platforms. It also accelerated the role of mobile and wearable, implantable sensors as telehealth systems. The major principle behind these platforms is the role of electrochemical immunoassays, which help in overshadowing the classical gold standard methods (reverse transcriptase polymerase chain reaction) in terms of accuracy, time, manpower, and, most importantly, economics. Many research groups have endeavoured to use electrochemical and bio-electrochemical tools to overcome the limitations of classical assays (in terms of accuracy, accessibility, portability, and response time). This review mainly focuses on the electrochemical technologies used for immunosensing platforms, their fabrication requirements, mechanistic objectives, electrochemical techniques involved, and their subsequent output signal amplifications using a tagged and non-tagged system. The combination of various techniques (optical spectroscopy, Raman scattering, column chromatography, HPLC, and X-ray diffraction) has enabled the construction of high-performance electrodes. Later in the review, these combinations and their utilization will be explained in terms of their mechanistic platform along with chemical bonding and their role in signal output in the later part of article. Furthermore, the market study in terms of real prototypes will be elaborately discussed.

Ultra-Low Power 9T FinFET Based SRAM Cell for IoT Applications

The rapid proliferation of Internet of Things (IoT) devices has escalated the demand for energy-efficient memory solutions. The development of SRAM (Static Random-Access Memory) has gathered significant attention, particularly for low-power applications and portable devices. This research introduces a novel approach to ensure robust performance and operational integrity, all while adhering to stringent power constraints amidst process and temperature fluctuations. This quest for alternative nano-devices has been ignited by the scaling and channel control challenges intrinsic to CMOS technology. Among the array of alternatives, including FinFETs, TFETs, and CNTFETs, FinFETs have emerged as strong contenders. FinFET SRAMs contribute to the ongoing drive for electronic devices to become more compact and energy-efficient. They offer a distinct advantage as a promising CMOS substitute, attributed to their high performance, stability, with low power consumption characteristics at the nanoscale. The study proposes an ultra-low power 9T FinFET based SRAM cell meticulously designed for IoT applications. Leveraging the unique attributes of FinFET based 9T cell, the proposed SRAM cell is tailored to achieving minimal power consumption while maintaining inherent stability. The study assesses the performance of the proposed SRAM cells for power dissipation, stability, speed, and energy efficiency, and compares them with existing SRAM structures. The results are obtained through the Monte Carlo simulation technique, which evaluates circuit performance under specified conditions. The study achieves remarkable efficiency gains with 9T SRAM cells compared to other configurations. Additionally, the reduced read and write access times facilitate expedited data retrieval and storage operations.

A novel SpaSA based hyper-parameter optimized FCEDN with adaptive CNN classification for skin cancer detection

Skin cancer is the most prevalent kind of cancer in people. It is estimated that more than 1 million people get skin cancer every year in the world. The effectiveness of the disease’s therapy is significantly impacted by early identification of this illness. Preprocessing is the initial detecting stage in enhancing the quality of skin images by removing undesired background noise and objects. This study aims is to compile preprocessing techniques for skin cancer imaging that are currently accessible. Researchers looking into automated skin cancer diagnosis might use this article as an excellent place to start. The fully convolutional encoder–decoder network and Sparrow search algorithm (FCEDN-SpaSA) are proposed in this study for the segmentation of dermoscopic images. The individual wolf method and the ensemble ghosting technique are integrated to generate a neighbour-based search strategy in SpaSA for stressing the correct balance between navigation and exploitation. The classification procedure is accomplished by using an adaptive CNN technique to discriminate between normal skin and malignant skin lesions suggestive of disease. Our method provides classification accuracies comparable to commonly used incremental learning techniques while using less energy, storage space, memory access, and training time (only network updates with new training samples, no network sharing). In a simulation, the segmentation performance of the proposed technique on the ISBI 2017, ISIC 2018, and PH2 datasets reached accuracies of 95.28%, 95.89%, 92.70%, and 98.78%, respectively, on the same dataset and assessed the classification performance. It is accurate 91.67% of the time. The efficiency of the suggested strategy is demonstrated through comparisons with cutting-edge methodologies.

Practical Steps towards Establishing an Underwater Acoustic Network in the Context of the Marine Internet of Things

When several hydroacoustic modems operate simultaneously in an area of mutual coverage, collisions of data packets received from several sources may occur, which lead to information loss. With an increase in the number of simultaneously operating hydroacoustic modems, physical layer algorithms do not provide stable data transmission and the likelihood of collisions increases, which makes the operation of modems ineffective. To ensure effective operation in a hydroacoustic signal propagation environment and to reduce collisions when exchanging data between two modems that do not have the ability to operate synchronously and to reduce the access time to the signal propagation environment, methods of the medium access control layer using link layer protocols are required. Typically, this problem is solved using code separation of hydroacoustic channels. If you need to transfer over a network, this option will not work, since network transfer involves working on the basis of “broadcast” messages, particularly between data source and data sink that remain too far from each other, outside of their mutual audibility. In practical use, it is convenient to place these protocols into a software environment for developing specific user applications for solving network communication problems. This software framework allows for custom modification of existing network algorithms, as well as the inclusion of new network hydroacoustic communication algorithms. To build a predictive model, the DACAP, T-Lohi, Flooding, and ICRP protocols were used in this work. The implementation is performed in Erlang. The paper presents algorithms for implementing these protocols. A comparative analysis of network operation with and without protocols is provided. Efficiency of operation, i.e., data rates and probabilities of data delivery, was assessed.

Optimization Strategies for Real-time Energy Management of Electric Vehicles Based on LSTM Network Learning

The orderly control of electric vehicle load can improve the load characteristics of regional power grid and reduce the charging cost. Since it is impossible to predict the accurate access time and charging demand of electric vehicles in the future, it is impossible to make a global optimal arrangement for accessing the grid when electric vehicles are charging. Aiming at this problem, a real-time energy management system and optimization strategy of electric vehicle based on deep long-term and short-term memory neural network are proposed. Firstly, a three-tier electric vehicle management architecture including power grid layer, regional energy management system and charging station energy management system is constructed to manage large-scale electric vehicles in layers and regions; Then, a region station interaction strategy based on deep long-term and short-term memory neural network is proposed. The historical optimal solution solved by historical load information is used to train the learning network to guide the new real-time optimization; The proposed strategy can further reduce the charging cost and improve the peak valley characteristics of regional load on the premise of ensuring the charging demand of users. Finally, a simulation example is given to verify the effectiveness and superiority of the proposed layered architecture and management strategy.

Evaluation of Patients Complaining of Staring Spells: Single Center Experience

Objective: This study aimed to analyze the demographic characteristics, spell semiology, and electroencephalographic characteristics of children with a complaint of staring spells and determine the factors that differentiate epileptic and non-epileptic etiology. Material and Methods: Fifty-six patients were included retrospectively between October 1, 2022, and December 1, 2023. The patients’ age, gender, co-morbidities, and other characteristics of the staring spells (duration, frequency, automatism, and presence of post-attack symptoms), access time to the pediatric neurologist, referring unit and access time to the final diagnosis were also recorded. Electroencephalography (EEG) was performed on all patients. Results: Fifty-six patients were divided into two according to epileptic and non-epileptic etiology. Thirty-three patients (59%) were diagnosed with non-epileptic staring spells, 15 (26.7%) were diagnosed with generalized epilepsy, and 8 (14.3%) were diagnosed with focal epilepsy. The non-epileptic group had a longer spell time and spell frequency, the presence of verbal stimulation response, and no post-attack symptoms (p<0.001). The access time to the pediatric neurologist was detected as 5.5 days, and the access time to the final diagnosis was 6.6 days. EEG was diagnostic in 100% of the epileptic group. Most of the patients were referred by pediatricians and family physicians (p<0.001). Conclusion: Identifying the cause of staring spells is crucial for further follow-up. In this study, we emphasized that history and routine EEG are important to determine the etiology. It has been observed that access time to pediatric neurologists and final diagnosis are shorter in our country compared to the literature. It can be concluded that pediatricians and family physicians have a high awareness of staring spells.

Fake product identification for small and medium firms (FPISMF) using blockchain technology

Counterfeit products have become a significant problem for small and medium-sized businesses (SMBs), with the estimated value of counterfeit goods worldwide reaching trillions of dollars. However, SMBs often lack the resources and technical expertise to implement sophisticated anti-counterfeiting measures. Towards this end, the work proposes a blockchain-based solution named as Fake Product Identification for Small and Medium Firms (FPISMF) using Hyperledger and AES encryption to enable SMBs to identify fake products and protect their brand reputation. The details of the products and the details of customers are encrypted using AES encryption and recorded on the blockchain. The application communicates with the blockchain network to validate the product and retrieve the details of the product. Chaincode is executed in a containerized environment, which provides isolation and security for the code and data being processed. An algorithm is also proposed to substitute the missing QR-code bits and data that helps reduce customer wait time. Experiments are conducted on synthesized data sets and results showing the effectiveness of the proposed FPISMF framework and reconciliation technique. It is observed from the results that though the time taken to replace a blurry bit is greatly reduced as compared to manual replacement of the product, there is an increase in this time when associated with encryption while extraction of the corresponding code from cloud database thereby achieving a time complexity of O(n), where ‘n’ is the number of scanned products. In addition, the AES SMB time complexity is approximately recoded as O(n/2) and the Cloud access and retrieval time is O(n) as compared to O(2n) in the existing work. This shows a significant improvement in the ability to replace missing bits and perform a secure analysis respectively.

Effects of water access time and unlimited access to alfalfa straw on litter quality, performance, and behavior of breeder pullets

Between 3 and 20 wk of age (WOA), the effects of water access time and access to alfalfa during the rearing phase on the litter conditions, performance, and behavior of broiler breeder pullets was studied. A total of 480 female one-day-old chicks (Ross 308) were randomly assigned to 24 floor pens (20 pullets/pen) within a 3 × 2 factorial completely randomized block design. Between 3 and 20 WOA, pullets received water 1) between 07:30 am and 10:30 pm h (3HR), 2) in 2 periods between 07:30 am and 11:00 pm h and between 14:00 pm and 15:30 pm h (5HR), or 3) during the entire light period (8HR). Half of the pens had unlimited access to alfalfa straw (ALF+) or not (ALF-). Higher water use and water-to-feed ratios were observed in the 5HR and 8HR pullets compared to the 3HR pullets (P < 0.001), with no effect observed from unlimited alfalfa. Clear differences in water use throughout the day were observed for the different water strategies. The dry matter (DM) content in the litter was lower, and the litter friability and moisture scores were higher in the 5HR and 8HR than the 3HR pens (P < 0.001), with no differences in fresh feces DM. Alfalfa straw had no effect on litter DM content, fresh feces DM content, litter friability score, or litter moisture score. Feather cover score and feather and footpad contamination score were higher in 5HR and 8HR pullets than in 3HR pullets (P < 0.05), with no differences between the ALF+ and ALF- pullets. The 5HR and 8HR pullets showed increased pecking at alfalfa straw and drinking nipples, along with decreased foraging and perching than the 3HR pullets (P < 0.05). Additionally, ALF+ pullets showed a tendency for less object pecking behavior (P = 0.066) than ALF- pullets. In conclusion, the study demonstrated that extended access to water in breeder pullets increased water use, resulting in inferior litter quality, decreased feather cover, and decreased feather cover and footpad contamination. Moreover, unlimited access to alfalfa straw decreased object pecking behavior.

Application of three-dimensional speckle tracking technique in measuring left ventricular myocardial function in patients with diabetes

BACKGROUND Diabetic cardiomyopathy is considered as a chronic complication of diabetes mellitus (DM). Therefore, early detection of left ventricular systolic function (LVSF) damage in DM is essential. AIM To explore the use of the three-dimensional speckle tracking technique (3D-STI) for measuring LVSF in DM patients via meta-analysis. METHODS The electronic databases were retrieved from the initial accessible time to 29 April 2023. The current study involved 9 studies, including 970 subjects. We carried out this meta-analysis to estimate myocardial function in DM compared with controls according to myocardial strain attained by 3D-STI. RESULTS Night articles including 970 subjects were included. No significant difference was detected in the left ventricular ejection fraction between the control and the diabetic group (P &gt; 0.05), while differences in global longitudinal strain, global circumferential strain, global radial strain, and global area strain were markedly different between the controls and DM patients (all P &lt; 0.05). CONCLUSION The 3D-STI could be applied to accurately measure early LVSF damage in patients with DM.

Real-time water quality forecasting in rivers using satellite data and dynamic models: an online system for operational management, control and citizen science

Increasingly scarce water resources and growing global populations have exacerbated the problems of water quality in river systems and freshwaters in general. New monitoring methodologies and tools to democratize access to water quality information are needed if we are to reach ambitious societal objectives such as the UN Sustainable Development Goals and the European Green Deal. Here we present a cloud-based system for producing publicly accessible real time water quality forecasts coupled to novel biosensor technology. Short term forecasts of water quality impairments, e.g., as cyanobacteria blooms, sediment plumes and toxic pollution incidents are increasingly relevant both to citizens and stakeholders. Here, we present a new cloud based system that utilizes satellite data to produce real time forecasts of flow and water quality using a chain of dynamic catchment-scale models at multiple locations in a river network. We demonstrate this new system using two case studies: the River Thames and the Essex Colne River (United Kingdom). These rivers are key water supply sources for London and South-East England, respectively and are of high interest to recreational water users. We show how the new system can predict and forecast water quality, estimate toxicity and connect to citizen science observations using an App (www.aquascope.com) for information synthesis and delivery.

Thrombotic Microangiopathy in Solid Organ Transplantation

Thrombotic Microangiopathy (TMA) is a syndrome characterized by microangiopathic hemolytic anemia (MAHA) and thrombocytopenia. The presence of schistocytes on peripheral smear, a negative Coombs test, elevated lactate dehydrogenase, increased reticulocyte count and low haptoglobin are often the clues for MAHA. The microvascular process often targets vasculature in kidneys, brain, gastrointestinal system, heart, and skin. A timely diagnosis and treatment are often crucial to prevent severe end organ damage and death. TMA is classified into primary and secondary forms. Primary TMA includes TTP and complement mediated or atypical hemolytic uremic syndrome (aHUS), often related to a mutation or deficiency and clinically expressed in the setting of a precipitant condition. Secondary TMA is a manifestation of underlying disorder and can occur in clinical scenarios associated with autoimmune disease, malignancy, infections, SOT (Solid Organ Transplant), pregnancy, HSCT (Hematopoietic Stem Cell Transplantation), medications, or methylmalonic acidemia. Transplant associated TMA (TA-TMA) can be complement mediated or aHUS and could be related to the ischemic reperfusion injury, induction regimen, calcineurin inhibitor (CNI) use, mammalian target of rapamycin (MTOR) inhibitor use, or could be infection related. Cost, access, and turnaround time are often the limitations for certain TTP and complement specific testing. Treatment should not be delayed while waiting for such tests. Treatment must be individualized based on the underlying cause of TMA. Terminal complement blockade utilizing monoclonal antibodies directed against C5 complement is the treatment for complement mediated TMA. C5 inhibitors have also been used successfully in treatment of secondary HUS cases where, unlike aHUS, defects in complement cannot be demonstrated. Such treatment has demonstrated improvement in renal function, MAHA and platelet counts.

Management of depth to achieve timely arteriovenous fistula utilization

ObjectiveFailure to achieve timely arteriovenous fistulae (AVFs) utilization due to excessive depth (>6 mm) remains an ongoing concern for dialysis access. This study evaluates the outcomes of radiocephalic (RCF) and brachiocephalic (BCF) fistula elevation required for access utilization. MethodsA retrospective review of all patients undergoing first-time autologous access over 10 years was undertaken. RCF and BCF were analyzed, and cases of initial access failure due to depth alone were selected for study. Primary and staged brachio-basilic AVF were excluded. Outcomes of early thrombosis, line placement, maturation (successful progression to hemodialysis [HD), reintervention, and functional dialysis (continuous HD for 3 consecutive months) were examined. ResultsFrom January 2012 to December 2022, 1733 patients (67% female; mean age, 61 ± 14 years) underwent autologous AVF placement. Of these, 298 patients (17%) had depth-related AVF access issues (BCF, 71% and RCF, 29%). Nineteen percent of these AVFs underwent a primary balloon-assisted maturation (BAM), and 2% had side branch coil embolization before consideration for elevation. The average time to intervention for depth was 11 ± 4 weeks after primary creation. During elevation, side branch ligation occurred in 38% of cases, and 15% underwent intraoperative BAM, The pre-elevation depth was 8.2 ± 3.1 mm, and the mean post-elevation depth was 4.7 ± 2.9 mm (P = .002). Early thrombosis (<18 days) occurred in 4% of cases. There was no mortality, and the 30-day major adverse cardiac event rate was 2%, with a 30-day morbidity of 5%, which was driven by wound issues. Six percent of the AVFs underwent follow-up BAM within 3 months. Mean maturation of the AVFs was 74% ± 3% vs 72% ± 3% (P = .58) for the elevation vs no-elevation groups at 24 weeks, respectively. However, there was an increase in tunneled central line placement in pre-emptive fistula patients due to the delay in maturation (elevation, 17% vs no-elevation, 8%; P = .008). There was a mean successful access time of 6 ± 3 weeks after elevation (16 ± 4 weeks after access creation). There was a median of 2.4 secondary interventions per year after elevation compared with a median of 2.7 secondary interventions per year without elevation. Mean access functionality was 68% ± 8% vs 75% ± 8% at 3 years for the elevation vs no-elevation groups, respectively (P = .25). ConclusionsElevation of deep BCF and RCF occurs late after placement but can be successfully achieved with low morbidity and satisfactory long-term functionality. It results in an increase in tunneled central line placement in pre-emptive fistula patients. Elevation is a valuable adjunct to AVF maturation and enhances an autologous access policy.

Three Lessons From the 2004 “Big Bang” Enlargement

The 2004 “Big Bang” enlargement was a powerful reminder that European integration is an instrument for peace and not just prosperity. The pace of that enlargement depended more on the requirements for stability than on the transformation of the candidate countries. It was also a reminder of the importance of forward-looking analysis. Candidates might meet the criteria for membership at the time of accession, but that is no guarantee that they will develop in ways that continue to reflect those criteria once they have gained entry. Finally, it was a reminder that enlargement changes the experience of membership for all member states and not just for those countries that gain entry. A larger Union requires greater self-discipline to hold down congestion in decision-making and greater multilateral surveillance to prevent the actions of one member state from undermining the benefits of membership for the rest. These reminders are important lessons in planning the European Union’s next historic enlargement. The next enlargement will follow a pace set by security considerations more than the transformative power of the accession process. It will depend on a robust analysis of convergence together with contingency planning for any staged accession. And it will require commitment from existing member states as well as candidate countries to what will become a very different European Union. This next enlargement will be challenging for all parts of Europe. Nevertheless, it is better than the alternative of no enlargement or an accession process with no credible endgame.

Designing a Deep Neural Network engine for LLC block reuse prediction to mitigate Soft Error in Multicore

Last level cache (LLC), a major contender of chip area, exhibits the highest sensitivity to soft error. Block reuse prediction is used to exhibit selective protection of LLC blocks. However, traditional reuse predictors fail to accurately understand the cache access pattern. This work explores machine learning for block reuse prediction that realizes the features that affect reuse likelihood and therefore, better predicts the reusable blocks. To off-load the reuse calculation from the processing cores, a dedicated reuse predictor engine is designed. This engine implements a deep neural network with unsupervised learning and calculates reuse probabilities offline without affecting the execution path. Priority blocks with high reuse likelihood are protected using replications in shared LLC. Low priority blocks are invalidated to reduce their vulnerable time. A replication aware replacement policy is also developed to protect the replicated/reserved blocks from eviction. The scheme is evaluated in Multi2Sim 5.0 simulation framework with SPEC-CPU benchmarks considering bulk-CMOS, FDSOI and FinFET technologies. The results indicate 41.29% and 43.14% reductions in miss rate, 92.29% and 89.56% reductions in vulnerability, 5.73% and 6.31% increase in write-back rate, 17.55% and 16.27% reductions in average memory access time, 2.17% and 1.54% increase in Instructions-per-cycle (IPC) and 1.31% and 0.77% reductions in normalized-execution-cycle over the baseline (with SECDED protection) for integer and floating point benchmarks respectively. Marginal increase in power consumption by 1.33% and dynamic energy by 0.43% are observed at 6.32% area overhead.