Miracle-dz/UniFAR-FTUs huggingface dataset:
Static Balance and Chair-Rise Performance in Neurogeriatric Patients: Promising Short Physical Performance Battery-Derived Predictors of Fear of Falling

Background: Fear of falling (FOF) negatively affects health-related quality of life and is common in neurogeriatric patients, however, related parameters are not well understood. This study investigated the relationship between FOF, physical performance (as assessed with the Short Physical Performance Battery and its subscores) and other aspects of sarcopenia in a sample of hospitalized neurogeriatric patients. Methods In 124 neurogeriatric patients, FOF was assessed with the Falls Efficacy Scale International (FES-I). Physical performance was measured using the Short Physical Performance Battery (SPPB) including walking duration, balance and five times sit-to-stand task (5xSST) subscores. Appendicular skeletal muscle mass (ASMM) was estimated with the cross-validated Sergi equation using Bioelectrical impedance analysis measures. The Depression im Alter-Skala (DIA-S) was used to assess depressive symptoms. Multiple regression models with FES-I score as outcome variable were computed using backward selection with AICc as selection criterion, including: (i) SPPB total score, ASMM/height2, grip strength, age, gender, positive fall history, number of medications, use of a walking aid, DIA-S score and Montreal Cognitive Assessment (MoCA) score; and (ii) SPPB subscores, ASMM/height2, grip strength, age, gender, positive fall history, number of medications, DIA-S score and MoCA score, once with and once without including use of a walking aid as independent variable. Results Lower SPPB total score, as well as lower SPPB balance and 5xSST subscores were associated with higher FES-I scores, but SPPB walking duration subscore was not. Moreover, DIA-S, number of medications and use of a walking aid were significantly associated with FOF. Conclusion Our preliminary results suggest that -if confirmed by subsequent studies- it may be worthwhile to screen patients with low SPPB balance and 5xSST subscores for FOF, and to treat especially these mobility deficits in neurogeriatric patients with FOF. Moreover, training neurogeriatric patients to use their walking aids correctly, critical evaluation of medication and treating depressive symptoms may further help reduce FOF in this highly vulnerable cohort.
In vivo Experience With NRT to Increase Adherence and Smoking Abstinence Among Individuals in the Criminal Legal System: Study Protocol for a Randomized Clinical Trial

Background While tobacco use among individuals involved in the criminal legal system remains 3–4 times higher than the general population, few interventions have been targeted for this population to aid in smoking cessation. Nicotine replacement therapy (NRT) is a relatively effective and accessible smoking cessation aid; however, individuals frequently stop use of NRT early due to side effects and misperceptions about the products. The present study aims to address low medication adherence by examining the efficacy of an “in vivo” NRT sampling experience in individuals under community criminal legal supervision. Methods Following recruitment through community legal outlets, participants (N = 517) are randomized to either an “in vivo NRT sampling” group or a standard smoking cessation behavioral counseling group. The in vivo group uses NRT in session and discusses perceptions and experiences of using NRT in real time while the standard smoking cessation counseling group receives four sessions of standard behavioral smoking cessation counseling. Both groups receive four intervention sessions and 12 weeks of NRT following the intervention. The 6-month post-intervention primary outcome measures are smoking point-prevalence abstinence and medication adherence. Conclusion This is a novel smoking cessation intervention specifically aimed at increasing NRT adherence and smoking cessation among those involved in the criminal legal system, a group of individuals with high smoking rates and low rates of pharmacotherapy use. If proven effective, the present treatment could be a novel intervention to implement in criminal legal settings given the minimal requirement of resources and training. This trial is registered with www.clinicaltrials. gov-NCT02938403
A passionate partnership: understanding the origins and significance of John Sinclair’s connection with K’gari (Fraser Island)

Abstract The paper explores the transformation of environmental activist John Sinclair, OA, from a conservative member of the Country Party, through a position of cautious conservationism, to preeminence as a leading environmentalist with some very significant achievements. This paper aims to show some correlations between his work and ideas and major strands of environmental education research. His allegiance to K’gari (Fraser Island) and the way in which he was able to learn from the traditional custodians, the Butchulla people, and other leading environmentalists is described in this paper through his own memoir writing and the viewpoints of informants interviewed by the author. John Sinclair’s profound connection with K’gari, how it was formed and sustained, and its historical and environmental consequences make a remarkable story of a modern citizen turned activist. The development of John Sinclair’s ideas and practice show interesting parallels with the development of both environmental activism and environmental education in Australia. The story has importance in a range of areas connected with environmental stewardship and environmental education.
MSAAM: A Multiscale Adaptive Attention Module for IoT Malware Detection and Family Classification

Nowadays, the attack and defense of malware have presented asymmetric characteristic threats, which has disrupted the pace of IoT research. Traditional detection and family classification methods based on feature extraction, as well as the classical machine learning algorithms, have been afflicted with the problems of high time consuming and unbalanced numbers of malware samples. This paper designs a universal and effective Multiscale Attention Adaptive Module called MSAAM that can combine local and global feature information. It can automatically adjust the arrangement and proportion of channel and spatial submodules by auxiliary classifiers according to actual tasks. The traditional CliqueNet uses a circular feedback structure to improve the DenseNet, optimizes the information flow in a deep network, enhances the utilization of its parameters, and uses a multiscale strategy to prevent a sharp increase of its parameters. As a result, it shows a good effect in the study of image classification. By replacing the attention module in the traditional CliqueNet with the designed MSAAM, we present a new method to process the produced gray-scale images converted from the malware and thus get better results in malware processing. The improved CliqueNet runs on the benchmark datasets of MalImg and Microsoft’s BIG 2015 to verify our presented method. After validation on the experimental benchmark datasets, the detection accuracy reaches 99.8%, while the family classification accuracy reaches 99.2% and 98.2% on the above two datasets, respectively. The presented method can solve the problem of unbalanced samples in malware family classification and is also effective against obfuscation attacks.
Cyclostationary Feature Detection using Various Modulation Schemes

In cognitive radio various spectrum sensing schemes have always been researched and discussed. An ideal detection scheme should be fast, precise and effective. Cyclostationary feature detection is a detection scheme that fulfils all these criteria (fast, precise and effective). Cyclostationary feature detection also holds the capability to distinguish between the primary user signal and unwanted signal called noise. One major advantage of cyclostationary feature detection method is that in addition to identifying the primary user signal, it also identifies the modulation scheme used by the primary user. This paper explores the cyclostationary feature detection method under different modulation schemes that are BPSK, QPSK, and 8-PSK. In BPSK One primary peak (centre peak) and one secondary peak obtained, in the case of QPSK One primary peak (centre peak) and two secondary peaks obtained and in the case of 8-PSK One primary peak (centre peak) and four secondary peaks are obtained. The output is plotted on graph and various modulation schemes are studied in Cyclostationary Feature Detection method. The Spectral Correlation Function (SCF) is used in this research paper which shows a peak in the centre of graph if primary user is present. Keywords: Primary User (PU), Energy Detection (ED), Signal to Noise Ratio (SNR), Cognitive Radio (CR), Cyclostationary Feature Detection (CFD)
P-20 Affective Priming as an Indirect Measure of Sense of Warmth of Furniture Design

“Warmth” is one of the key concepts of interior design involved in choosing living room furniture. Using an affective priming paradigm, the present study investigated whether participants truly feel the warmth of furniture when they view it. In the experiment, prime pictures of a dining table with warm, cold, or neutral impressions were briefly presented, followed by target words with happy or sad meanings. Participants reported whether the target word was happy or sad. Results showed that participants judged happy target words faster when the primes were tables with warm impressions than when they were neutral ones. This finding suggests that the mere existence of a warm table modulated the subsequent processing of valenced words, which implies that affective priming is applicable as an indirect measure of impressions of furniture design.
The impact of self-efficacy beliefs on first-year accounting students’ performance: a South African perspective

ABSTRACT This study measures the levels of self-efficacy beliefs to determine how this correlates with academic success in introductory tertiary accounting within a South African context. Also, self-efficacy beliefs are compared to determine if significant differences exist based on gender, academic language, type of study funding and different professional programmes studied. The study applies social cognitive theory and gathers quantitative data using questionnaires with statistical analysis to determine self-efficacy belief levels in a first-year accounting module. The means of several variables were compared via t-tests or ANOVAs. Hereafter, regression analysis was applied to determine if self-efficacy beliefs significantly impact academic performance. Findings indicate that most first-year accounting students felt confident in their self-efficacy beliefs. Significant differences were observed in students’ self-efficacy beliefs based on gender, academic language and type of programme studied, but not based on type of study funding. Several self-efficacy beliefs correlated significantly with academic performance, though differences were observed for different types of programmes. As limited consideration of the impact of self-efficacy beliefs within the discipline of accounting prevails in the literature, and even less so in South Africa, the study serves to enhance accounting educators’ understanding on how self-efficacy beliefs impact academic performance at the first-year level.
RESIST-HCV Criteria to Monitor Progression of Low-Risk Esophageal Varices in Patients With Compensated Cirrhosis After HCV Eradication: The SIMPLE Study

INTRODUCTION: Noninvasive criteria to predict the progression of low-risk esophageal varices (EV) in patients with compensated hepatitis C virus (HCV) cirrhosis after sustained virological response (SVR) by direct-acting antivirals (DAAs) are lacking. Our aim was to assess the diagnostic performance of Rete Sicilia Selezione Terapia–HCV (RESIST-HCV) criteria for EV progression compared with elastography-based criteria (Baveno VI, Expanded Baveno VI, and Baveno VII-HCV criteria). METHODS: All consecutive patients observed at 3 referral centers with compensated HCV cirrhosis with or without F1 EV who achieved sustained virological response by DAAs were classified at last esophagogastroduodenoscopy (EGDS) as RESIST-HCV low risk (i.e., low probability of high-risk varices [HRV]) if platelets were >120 × 109/L and serum albumin >3.6 g/dL or RESIST-HCV high risk (i.e., high probability of HRV) if platelets were <120 × 109/L or serum albumin <3.6 g/dL. The primary outcome was the progression to HRV. The area under the receiver operating characteristic curve and decision curve analysis of noninvasive criteria were calculated. RESULTS: The cohort consisted of 353 patients in Child-Pugh class A (mean age 67.2 years, 53.8% males). During a mean follow-up of 44.2 months, 34 patients (9.6%, 95% CI 6.7%–13.5%) developed HRV. At the last EGDS, 178 patients (50.4%) were RESIST-low risk, and 175 (49.6%) were RESIST-high risk. RESIST-HCV criteria showed the highest area under the receiver operating characteristic curve (0.70, 95% confidence interval 0.65–0.75), correctly sparing the highest number of EGDS (54.3%), with the lowest false-positive rate (45.7%), compared with elastography-based criteria. Decision curve analysis showed that RESIST-HCV had higher clinical utility than elastography-based criteria. DISCUSSION: Biochemical-based RESIST-HCV criteria are useful to easily predict HRV development after HCV eradication by DAAs in patients with compensated cirrhosis and low-risk EV.
ADAPTIVE MODEL PREDICTIVE CONTROLLER FOR TRAJECTORY TRACKING AND OBSTACLE AVOIDANCE ON AUTONOMOUS VEHICLE

Advancement in active steering technology is vital as the autonomous vehicle technology is preparing to enter the commercialization phase. Accurate trajectory tracking and collision free motion have become an active topic being discussed in research field recently. During an emergency obstacle avoidance manoeuvre conditions, tyre force saturation can easily happened when availability of lateral tyre forces is limited by the law of tyre friction circle. This greatly affects the trajectory tracking performance of the vehicle. Existing controllers such as generic model predictive controller (MPC) and geometric controller (Stanley) need a proper gain tuning to cope with this condition. This is due to the control gains were determined by trial and error basis via linearization process at a certain targeted speed. Therefore, the control performance is limited considering the presence of speed variation as well as extreme manoeuvre trajectory. This paper proposes an Adaptive Model Predictive Controller (MPC) controller to solve aforementioned issues. First, optimized weighting gains for the steering control were obtained using PSO algorithm. The optimised weighting gains were then scheduled into the proposed Model predictive Controller via a look-up table strategy. In this work, the proposed adaptive MPC controller was designed by using the linearization of the 7 degree-of-freedom (DOF) non-linear vehicle model. Here, the linearized model for controller design was update based on the instantaneous longitudinal speed of the vehicle system plant.
Data Management: Knowledge and skills required in research, scientific and technical organisations

The purpose of this paper is to investigate the knowledge and skill requirements for people working in the data management space in universities and scientific research organisations. In order to do so 25 data professionals in universities and scientific research organisations were interviewed. The most common set of skills required were interpersonal skills and related personal characteristics, closely followed by data specific knowledge and skills, including broad understanding of data types, metadata, and legal and regulatory frameworks. Contextual knowledge, knowledge of appropriate information technologies and training and advocacy skills were also highly sought after. Few current data professionals had a data specific qualification, most had learned “on the job” and through targeted professional development such as workshops, webinars and in house training. This paper presents information specific to data professionals in context and will enable educators understand the professional context and therefore to more effectively plan appropriate forms of delivery.
Power Allocation and Energy Cost Minimization in Cloud Data Centers Microgrids: A Two-Stage Optimization Approach

Data centers are becoming an important asset in today’s connected and intelligent world. Indeed, with the emergence of $5G$ networks, the growth of Internet of Things (IoT) and cloud computing, the data centers are increasingly becoming an essential component in delivering our daily services. On the other hand, cloud data centers, are outfitted with power-hungry servers and electrical installations that require enormous quantities of energy. In addition, operating cloud data centers in microgrids can be more reliable and sustainable than having them managed directly by the smart grid operator. However, microgrids have to deal with the high energy cost that their data centers may incur. In this context, managing the power allocated to the data centers and minimizing microgrids energy cost is a challenging problem. In this paper, we model a two-stage optimization approach in a microgrid-cloud architecture. In the first stage, we consider that the cloud data centers are run by multiple providers and try to get as much power as possible from the microgrid, to store this energy and to be able to execute the maximum of users applications. Such a power behavior requires an optimal power assignment from the microgrid to mitigate the risk of blackouts and power outages. Thus, the data center’s huge power demand was modeled as a non-cooperative game. The microgrid controller calculates the optimal power that will be assigned to each data center according to its Power Usage Effectiveness (PUE), number of real-time applications and its network bandwidth usage. In the second stage, the microgrids try to minimize their energy cost by optimally purchasing power from the main grid and the other migrogrids, and by selling back their stored energy. We compare our approach with three existing power minimization approaches: (i) our previously presented scheme called “Basic Game Scheme” (BGS), (ii) traditional and (iii) price-based approaches. Simulations results show that our two stage optimization approach is up to 25% more effective in terms of energy cost. In addition, our scheme promotes MGs green energy usage which significantly reduces power load rate and CO2 emission with a percentage up to 52% comparing to “BGS” and price-based scheme.
LaST: Label-Free Self-Distillation Contrastive Learning With Transformer Architecture for Remote Sensing Image Scene Classification

The increase in self-supervised learning (SSL), especially contrastive learning, has enabled one to train deep neural network models with unlabeled data for remote sensing image (RSI) scene classification. Nevertheless, it still suffers from the following issues: 1) the performance of the contrastive learning method is significantly impacted by the hard negative sample (HNS) issue, since the RSI scenario is complex in semantics and rich in surface features; 2) the multiscale characteristic of RSI is missed in the existing contrastive learning methods; and 3) as the backbone of a deep learning model, especially in the case of limited annotation, a convolutional neural network (CNN) does not include the adequate receptive field of convolutional kernels to capture the broad contextual information of RSI. In this regard, we propose label-free self-distillation contrastive learning with a transformer architecture (LaST). We introduce the self-distillation contrastive learning mechanism to address the HNS issue. Specifically, the LaST architecture comprises two modules: scale alignment with a multicrop module and a long-range dependence capture backbone module. In the former, we present global–local crop and scale alignment to encourage local-to-global correspondence and acquire multiscale relations. Then, the distorted views are fed into a transformer as a backbone, which is good at capturing the long-range-dependent contextual information of the RSI while maintaining the spatial smoothness of the learned features. Experiments on public datasets show that in the downstream scene classification task, LaST improves the performance of the self-supervised trained model by a maximum of 2.18% compared to the HNS-impacted contrastive learning approaches, and only 1.5% of labeled data can achieve the performance of supervised training CNNs with 10% labeled data. Moreover, this letter supports the integration of a transformer architecture and self-supervised paradigms in RSI interpretation.
Parlamentų ir teisminių institucijų indėlis į pagrindinių teisių apsaugą: glaudesnio sąryšio link

Dabartiniame konstitucionalizme ginti pagrindines teises nuo daugumos despotizmo pirmiausia buvo patikėta teisėjams, vėliau – ir nepriklausomoms institucijoms. Tačiau pastaruoju metu į šią veiklą vėl įsitraukia parlamentai, čia atlikę nepamirštamą istorinį vaidmenį; jų gebėjimai prilygsta atitinkamiems teisminių institucijų gebėjimams. Tarptautiniu mastu augant politiniam sąmoningumui, atgijo parlamentų, parlamentarų ir parlamentų tarnybų domėjimasis šia tema, o pagrindinių teisių apsauga tapo bendra atsakomybe. Įstatymų priėmimas, vykdomosios valdžios kontrolė, biudžeto tvirtinimas, svarstytinų pasiūlymų, ataskaitų teikimas, tyrimas – visos šios tradicinės parlamentų funkcijos gali prisidėti prie teisių apsaugos. Tokia apsauga savotiškai nuspalvina kasdienę asamblėjų veiklą dėl piliečių politinių teisių įgyvendinimo. Esama ir iniciatyvesnio požiūrio: siekiant išskirti šią autentišką parlamento misiją, steigiami specializuoti instituciniai dariniai (pavyzdžiui, Jungtinės Karalystės Jungtinis žmogaus teisių komitetas). Ar tai iš tiesų padeda formuoti bendrą tarpinstitucinę jautrumo pagrindinėms teisėms kultūrą, empiriniu lygmeniu diskutuotina.
Crystal Engineering of Ionic Cocrystals Sustained by the Phenol–Phenolate Supramolecular Heterosynthon

Although crystal engineering strategies are generally well explored in the context of multicomponent crystals (cocrystals) formed by neutral coformers (molecular cocrystals), cocrystals comprised of one or more salts (ionic cocrystals, ICCs) are understudied. We herein address the design, preparation, and structural characterization of ICCs formed by phenolic moieties, a common group in natural products and drug molecules. Organic and inorganic bases were reacted with the following phenolic coformers: phenol, resorcinol, phloroglucinol, 4-methoxyphenol, and 4-isopropylphenol. Nine ICCs were crystallized, each of them sustained by the phenol–phenolate supramolecular heterosynthon (PhOH···PhO–). Such ICCs are of potential utility, and there are numerous examples of phenolic compounds that are biologically active, some of which suffer from low aqueous solubility. The propensity to form ICCs sustained by the PhOH···PhO– supramolecular heterosynthon was evaluated through a combination of Cambridge Structural Database (CSD) mining, structural characterization of nine novel ICCs, and calculation of interaction energies. Our analysis of these 9 ICCs and the 41 relevant entries archived in the CSD revealed that phenol groups can reliably form ICCs through charge-assisted PhOH···PhO– interactions. This conclusion is supported by hydrogen-bond strength calculations derived from CrystalExplorer that reveal the PhOH···PhO– interaction to be around 3 times stronger than the phenol–phenol hydrogen bond. The PhOH···PhO– supramolecular heterosynthon could therefore enable crystal engineering studies of a large number of phenolic pharmaceutical and nutraceutical compounds with their conjugate bases.