Miracle-dz/UniFAR-FTUs huggingface dataset:
Disruption of white matter connectivity in chronic obstructive pulmonary disease

Background Mild cognitive impairment is a common systemic manifestation of chronic obstructive pulmonary disease (COPD). However, its pathophysiological origins are not understood. Since, cognitive function relies on efficient communication between distributed cortical and subcortical regions, we investigated whether people with COPD have disruption in white matter connectivity. Methods Structural networks were constructed for 30 COPD patients (aged 54–84 years, 57% male, FEV1 52.5% pred.) and 23 controls (aged 51–81 years, 48% Male). Networks comprised 90 grey matter regions (nodes) interconnected by white mater fibre tracts traced using deterministic tractography (edges). Edges were weighted by the number of streamlines adjusted for a) streamline length and b) end-node volume. White matter connectivity was quantified using global and nodal graph metrics which characterised the networks connection density, connection strength, segregation, integration, nodal influence and small-worldness. Between-group differences in white matter connectivity and within-group associations with cognitive function and disease severity were tested. Results COPD patients’ brain networks had significantly lower global connection strength (p = 0.03) and connection density (p = 0.04). There was a trend towards COPD patients having a reduction in nodal connection density and connection strength across the majority of network nodes but this only reached significance for connection density in the right superior temporal gyrus (p = 0.02) and did not survive correction for end-node volume. There were no other significant global or nodal network differences or within-group associations with disease severity or cognitive function. Conclusion COPD brain networks show evidence of damage compared to controls with a reduced number and strength of connections. This loss of connectivity was not sufficient to disrupt the overall efficiency of network organisation, suggesting that it has redundant capacity that makes it resilient to damage, which may explain why cognitive dysfunction is not severe. This might also explain why no direct relationships could be found with cognitive measures. Smoking and hypertension are known to have deleterious effects on the brain. These confounding effects could not be excluded.
Effects of Leucine-Enriched Whey Protein Supplementation on Physical Function in Post-Hospitalized Older Adults Participating in 12-Weeks of Resistance Training Program: A Randomized Controlled Trial

Age-related strength and muscle mass loss is further increased after acute periods of inactivity. To avoid this, resistance training has been proposed as an effective countermeasure, but the additional effect of a protein supplement is not so clear. The aim of this study was to examine the effect of a whey protein supplement enriched with leucine after resistance training on muscle mass and strength gains in a post-hospitalized elderly population. A total of 28 participants were included and allocated to either protein supplementation or placebo supplementation following resistance training for 12 weeks (2 days/week). Physical function (lower and upper body strength, aerobic capacity and the Short Physical Performance Battery (SPPB) test), mini nutritional assessment (MNA) and body composition (Dual X-ray Absorptiometry) were assessed at baseline and after 12 weeks of resistance training. Both groups showed improvements in physical function after the intervention (p < 0.01), but there were no further effects for the protein group (p > 0.05). Muscle mass did not improve after resistance training in either group (p > 0.05). In conclusion, 12 weeks of resistance training are enough to improve physical function in a post-hospitalized elderly population with no further benefits for the protein-supplemented group.
Central arterial stiffness and retinal vessel calibers: the Atherosclerosis Risk in Communities Study-Neurocognitive Study.

BACKGROUND The retinal microvasculature provides a window to the cerebral vasculature and enables examination of changes in retinal caliber that may mimic those occurring in cerebrovascular disease. The association of central arterial stiffness and retinal vessel caliber in a population sample is not fully understood. METHODS In 1706 older adults (mean age 76.3, 58.1% women) from the population-based Atherosclerosis Risk in Communities Study, we examined the cross-sectional association of central arterial stiffness [carotid-femoral pulse wave velocity (cfPWV)] with retinal vessel calibers [central retinal arteriolar equivalent (CRAE) and central retinal vein equivalent (CRVE)]. We estimated the association of cfPWV with CRAE narrowing (<25th percentile) and CRVE widening (>75th percentile) after adjustment for age, sex, race-field center, BMI, smoking, and type 2 diabetes. We tested for effect modification by sex, hypertension, and type 2 diabetes. RESULTS Carotid-femoral PWV (m/s) was not associated with the odds of CRAE narrowing [odds ratio (OR): 0.99; 95% CI: 0.95-1.03]. The association of cfPWV with CRVE widening was stronger in those without hypertension (OR: 1.10; 95% CI: 1.01-1.20) versus those with hypertension (OR: 1.01 95% CI: 0.96-1.05) and slightly stronger in those with type 2 diabetes (OR: 1.07; 95% CI: 1.00-1.14) versus without type 2 diabetes (OR: 1.01; 95% CI: 0.96-1.06). CONCLUSIONS In older adults, cfPWV was associated with wider retinal venular caliber, particularly in individuals without hypertension. Central arterial stiffening may be associated with cerebral microvascular changes, as exhibited in its retinal vasculature component.
Does the Application of Tecar Therapy Affect Temperature and Perfusion of Skin and Muscle Microcirculation? A Pilot Feasibility Study on Healthy Subjects

Background: Tecar therapy (TT) is an endogenous thermotherapy used to generate warming up of superficial and deep tissues. TT capability to affect the blood flow is commonly considered to be the primary mechanism to promote tissue healing processes. Despite some preliminary evidence about its clinical efficacy, knowledge on the physiologic responses induced by TT is lacking. Objective: The aim of this quantitative randomized pilot study was to determinate if TT, delivered in two modes (resistive and capacitive), affects the perfusion of the skin microcirculation (PSMC) and intramuscular blood flow (IMBF). Design: A randomized controlled pilot feasibility study. Subjects: Ten healthy volunteers (n = 4 females, n = 6 males; mean age 35.9 ± 10.7 years) from a university population were recruited and completed the study. Intervention: All subjects received three different TT applications (resistive, capacitive, and placebo) for a period of 8 min. Outcome measures: PSMC, IMBF, and the skin temperature (ST) were measured pre- and post-TT application using power Doppler sonography, laser speckle contrast imaging (LSCI), and infrared thermography. Results: Compared with placebo application, statistically significant differences in PSMC resulted after both the resistive (p = 0.0001) and the capacitive (p = 0.0001) TT applications, while only the resistive modality compared with the placebo was capable to induce a significant change of IMBF (p = 0.013) and ST (p = 0.0001). Conclusions: The use of power Doppler sonography and LSCI enabled us to evaluate differences in PSMC and IMBF induced by TT application.
Development of small molecule chimeras that recruit AhR E3 ligase to target proteins.

Targeted protein degradation using chimeric small molecules such as proteolysis-targeting chimeras (PROTACs) and specific and nongenetic inhibitors of apoptosis protein [IAP]-dependent protein erasers (SNIPERs) is an emerging modality in drug discovery. Here, we expand the repertoire of E3 ligases capable of ubiquitylating target proteins using this system. By incorporating β-naphthoflavone (β-NF) as a ligand, we developed a novel class of chimeric molecules that recruit the arylhydrocarbon receptor (AhR) E3 ligase complex. β-NF-ATRA, a chimeric degrader directed against cellular retinoic acid binding proteins (CRABPs), induced the AhR-dependent degradation of CRABP-1 and CRABP-2 via the ubiquitin-proteasome pathway. A similar compound ITE-ATRA, in which an alternative AhR ligand was used, also degraded CRABP proteins. Finally, we developed a chimeric compound β-NF-JQ1 that is directed against bromodomain-containing (BRD) proteins using β-NF as an AhR ligand. β-NF-JQ1 induced the interaction of AhR and BRD proteins and displayed effective anticancer activity that correlated with protein knockdown activity. These results demonstrate a novel class of chimeric degrader molecules based on the ability to bring a target protein and an AhR E3 ligase into close proximity.
EVA (ErwerbungsVorschlags-Assistant) assists in collection building! Using ILL data for patron-driven acquisition

Purpose – The purpose of this paper is to describe the “ErwerbungsVorschlags-Assistent” (EVA), its underlying concepts and first usage statistics and experiences at the University Library of Duisburg-Essen, and explore its methodology of patron driven acquisition in regard to printed material, specifically interlibrary loan requests. Design/methodology/approach – The authors use a case study approach, describing the effects of the new system on existing ILL workflows and collection development decision-making. Findings – EVA has been in production in several German university libraries since October 2011. ILL requests are automatically compared to specific criteria in regard to their suitability as an acquisition, enriched with external data, and presented to the appropriate subject librarian. Originality/value – This paper was originally presented at the 2012 IFLA World Library and Information Congress in Helsinki, Finland, session 139, “Treating print in an increasingly digital collection: issues, dilem.
Secure, User-level Resource-constrained Sandboxing

The popularity of mobile and networked applications has resulted in an increasing demand for execution ``sandboxes''''---environments that impose irrevocable qualitative and quantitative restrictions on resource usage. Existing approaches either verify application compliance to restrictions at start time (e.g., using certified code or language-based protection) or enforce it at run time (e.g., using kernel support, binary modification, or active interception of the application''s interactions with the operating system). However, their general applicability is constrained by the fact that they are either too heavyweight and inflexible, or are limited in the kinds of sandboxing restrictions and applications they can handle. This paper presents a secure user-level sandboxing approach for enforcing both qualitative and quantitative restrictions on resource usage of applications in distributed systems. Our approach actively monitors an application''s interactions with the underlying system, proactively controlling it as desired to enforce the desired behavior. Our approach leverages a core set of user-level mechanisms that are available in most modern operating systems: fine-grained timers, monitoring infrastructure (e.g., the /proc filesystem), debugger processes, priority-based scheduling, and page-based memory protection. We describe implementations of a sandbox that imposes quantitative restrictions on CPU, memory, and network usage on two commodity operating systems: Windows NT and Linux. Our results show that application usage of resources can be restricted to within 3% of desired limits with minimal run-time overhead.
Neural Network Implementation in Foreign Exchange Kurs Prediction

Te chnology neural network system has been implemented in various applications, especially in terms of forecasting (forecasting), including backpropagation that can be applied to predict foreign exchange rates. There are two steps being taken in this backpropagation method of training and testing phases. In this network backpropagation algorithm is given a pair of pattern - a pattern that consists of the input pattern and desired pattern. When a pattern is given to the network, weights - weights modified to minimize the differences in the pattern of output and the desired pattern. This exercise is performed over and over - re-issued so that all the patterns the network can meet the desired pattern. The next stage is the testing stage. This stage begins by using the best weights obtained from the training phase to process the input data to produce the appropriate output. It is used to test whether the ANN can work well is that it can predict the pattern of data that has been drilled with a small error rate. From the test results using data from the monthly period in the training process the network can recognize input patterns are provided so entirely in accordance with the target. While testing with the use of data daily and weekly period that does not comply with the given target, it is because the network requires more data to identify patterns provided. As more data are trained, the better the network will recognize the pattern - a pattern so that the results more accurate predictions, but will be impacted by slowing the process of training. Keywords : Foreign currency exchange rate prediction, Neural Network, Bacpropagation
Low-Power Wide Area Network Technologies for Internet-of-Things: A Comparative Review

The rapid growth of Internet-of-Things (IoT) in the current decade has led to the development of a multitude of new access technologies targeted at low-power, wide area networks (LP-WANs). However, this has also created another challenge pertaining to technology selection. This paper reviews the performance of LP-WAN technologies for IoT, including design choices and their implications. We consider Sigfox, LoRaWAN, WavIoT, random phase multiple access (RPMA), narrowband IoT (NB-IoT), as well as LTE-M and assess their performance in terms of signal propagation, coverage and energy conservation. The comparative analyses presented in this paper are based on available data sheets and simulation results. A sensitivity analysis is also conducted to evaluate network performance in response to variations in system design parameters. Results show that each of RPMA, NB-IoT, and LTE-M incurs at least 9 dB additional path loss relative to Sigfox and LoRaWAN. This paper further reveals that with a 10% improvement in receiver sensitivity, NB-IoT 882 MHz and LoRaWAN can increase coverage by up to 398% and 142%, respectively, without adverse effects on the energy requirements. Finally, extreme weather conditions can significantly reduce the active network life of LP-WANs. In particular, the results indicate that operating an IoT device in a temperature of −20 °C can shorten its life by about half; 53% (WavIoT, LoRaWAN, Sigfox, NB-IoT, and RPMA) and 48% in LTE-M compared with environmental temperature of 40 °C.
Source Code Review of the Sequoia Voting System 1

under contract to the California Secretary of State as part of a " Top-to-Bottom " review of electronic voting systems certified for use in the State of California. 2 Team leader. 3 All author affiliations are for identification only. at the request of the California Secretary of State, as part of a " top-to-bottom " review of the state's electronic voting systems. This document is the final report of the team that examined the Sequoia voting system source code. We found significant security weaknesses throughout the Sequoia system. The nature of these weaknesses raises serious questions as to whether the Sequoia software can be relied upon to protect the integrity of elections. Every software mechanism for transmitting election results and every software mechanism for updating software lacks reliable measures to detect or prevent tampering. We detail these weaknesses, and their implications, in Chapters 3 and 4. In certain cases, audit mechanisms may be able to detect and recover from some attacks, depending on county-specific procedures; other attacks may be more difficult to detect after-the-fact even with very rigorous audits. There were numerous programming, logic, and architectural errors present in the software we reviewed. Some of these errors may be relatively harmless and reflect the large size and heterogeneous nature of the codebase. But other errors we found clearly have serious security implications. Many of the most significant vulnerabilities we found — those likely to be especially useful to an attacker seeking to alter election results — arise from four pervasive structural weaknesses, discussed in detail in Chapter 3: • Data Integrity. The Sequoia system lacks effective safeguards against corrupted or malicious data injected onto removable media, especially for devices entrusted to poll workers and other temporary staff with limited authority. This lack of input validation has potentially serious consequences, including: – Precinct election results stored on DRE Results Cartridges and optical scan memory packs are not effectively protected against tampering. A poll worker with physical access to a Results Cartridge or MemoryPack before results are counted (e. g. when returning results to the county elections board) can change recorded votes, and, in some cases, can introduce spurious results for other precincts. Under some conditions, a corrupted Results Cartridge may be able to cause damage to the WinEDS system itself when it is loaded for vote counting. – The safeguards against introduction of corrupt firmware into the precinct voting hardware are …
Energy-Efficient Cooperative Resource Allocation in Wireless Powered Mobile Edge Computing

Mobile edge computing (MEC) as an emerging and prospective computing paradigm, offloads the computation-intensive tasks from resources-constrained smart mobile devices (SMDs) to edge cloud or server so as to enhance computation capability of SMDs. Meanwhile, it is expected that wireless power transfer (WPT) is applied to MEC (WPT-MEC) in order to prolong operation time of the battery. However, how to achieve the energy-effective computation offloading in WPT-MEC system under the hard constraint remains a challenge issue. To address this challenge, this paper considers energy-effective resource allocation policy in a two-user WPT-MEC system. We first formulate the maximization minimum energy efficiency (EE) problem to ensure the fairness of users. Then, due to the “doubly near–far” problem, we propose a user cooperative scheme in which the near user can forward the far user’s tasks to the edge cloud utilizing its more harvested energy. Considering the green network, we attach the scheme to maximize the users’ EE, defined as a ratio of the user throughput to its harvested or consumptive energy, subject to the constraints of the computational tasks in two schemes. We convert two problems into their equivalent parameterized subtractive form and provide the corresponding optimal solutions via two efficient optimization algorithms. Numerical results show that the optimal WPT-MEC system with cooperation has significant performance enhancement over the systems without cooperation.
Energy efficient accelerators for autonomous navigation in miniaturized robots

Autonomy is becoming an increasingly desirable feature for very small nano/pico robots to navigate cluttered and confined indoors environments such as collapsed buildings, caves, etc. Robot perception (i.e., semantic and geometric understanding) is considered the computation bottleneck in autonomous navigation systems because of the high dimensionality of the problem. For example, multi-scale object detection is desired for robustness, which requires significant data expansion. Additionally, a 3D map size grows overtime while the robot explores the environment, which requires computation power and large memory size. In this thesis, we introduce ASIC solutions that enable real-time and low power perception. First, the thesis demonstrates energy-efficient and high-throughput object detection accelerators for semantic understanding, which can process full HD (1920×1080, 60 fps) videos with energy consumption between 0.36 to 1.74 nJ/pixel. On-the-fly processing, parallel architectures, and image pre-processing are used to reduce the overhead of multi-scale detection using rigid-body models. Detection accuracy can be doubled with deformable parts models, but requires 35× more computation. To overcome this overhead, we exploit data compression, computation pruning, and basis projection for an overall 5× power reduction and 3.6× smaller memory size. Second, this thesis presents an algorithm and hardware co-design approach to enable real-time and energy-efficient localization and mapping for geometric understanding, using visual-inertial odometry. The chip (Navion) processes 752×480 stereo frames at up to 171 fps, with an energy consumption between 1.6 to 3.5 nJ/pixel. Parallelism, rescheduling, resource sharing, exploiting sparsity, and image compression are applied to overcome the high dimensionality of the problem, resulting in 4.1×memory size reduction, and enabling full integration. Navion can adapt to different environments to maximize accuracy, throughput and energy-efficiency trade-offs. To the best of our knowledge, this thesis presents the first fully integrated VIO system in an ASIC. Thesis Supervisor: Vivienne Sze Title: Associate Professor of Electrical Engineering and Computer Science
Reducing the risk of VWAP orders execution - A new approach to modeling intra-day volume

This paper proposes a new dynamic approach to modelling intra-day trading volume based on factor models. It assumes that intra-day volume can be decomposed into two parts each predicted using separate time-series models. By enabling more accurate prediction of intra-day volume, this methodology allows for a significant reduction in the cost of executing Volume weighted Average Price orders.
Performance analysis of WPM‐based transmission with equalization‐aware bit loading

Wavelet packet modulation (WPM) is a multicarrier modulation (MCM) technique that has emerged as a potential alternative to the widely used orthogonal frequency‐division multiplexing (OFDM) method. Because WPM has overlapped symbols, equalization cannot rely on the use of the cyclic prefix (CP), which is used in OFDM. This study applies linear minimum mean‐square error (MMSE) equalization in the time domain instead of in the frequency domain to achieve low computational complexity. With a modest equalizer filter length, the imperfection of MMSE equalization results in subcarrier attenuation and noise amplification, which are considered in the development of a bit‐loading algorithm. Analytical expressions for the bit error rate (BER) performance are derived and validated using simulation results. A performance evaluation is carried out in different test scenarios as per Recommendation ITU‐R M.1225. Numerical results show that WPM with equalization‐aware bit loading outperforms OFDM with bit loading. Because previous comparisons between WPM and OFDM did not include bit loading, the results obtained provide additional evidence of the benefits of WPM over OFDM.