Miracle-dz/UniFAR-FTUs huggingface dataset:
Early Identification of Locally Emerging Trends in Psychoactive Substance Use – Experience and Best Practice in Four European Localities

Abstract Background: Timely information about trends in psychoactive substance use could yield tailored interventions and reduce potential harms. However, conventional epidemiological tools might have limited capacity to detect trends emerging on a local level. Objectives: The aim of this study was to explore best practice in the identification of new drug trends at the local level. Methods: A total of 33 key informants from seven European municipalities/regions were interviewed to describe trends in substance use in their locality and to provide expert insights on how these were identified. Semi-structured interviews were analyzed with open coding method. Results: Four case studies that described local trends and responses were compiled: onset of problematic GHB use in the Dutch municipality of Breda (1); emerging retail shops selling new psychoactive substances (NPS) across the regions of Czech Republic (2) and in the Portuguese Agueda and Coimbra (3); and use of drugs with unknown content in the Italian region of Emilia Romagna, and its city Bologna (4). “Early identifiers” in the four case studies were organizations that work directly with people who use drugs (PWUD), emergency departments, and local police. Efficient methods of horizontal and vertical information sharing, sometimes facilitated by communication platforms, were in place, such that included early warning systems on local, national, and supra-local level. Local-level identification systems appeared as best suited to provide locally relevant information. Conclusions: Best practice in identifying emerging trends should involve all relevant “early identifiers”, should consist of supra-local exchange platforms, integrate the qualities of local-level identification, and be facilitated by local-level coordinators.
Ultra-high sensitive light-induced thermoelastic spectroscopy sensor with a high Q-factor quartz tuning fork and a multipass cell.

An ultra-high sensitive light-induced thermoelastic spectroscopy (LITES) sensor based on a resonant high Q-factor quartz turning fork (QTF) and a Herriot multipass cell was demonstrated for the first time, to the best of our knowledge. The performance of LITES and widely used tunable diode laser absorption spectroscopy (TDLAS) were experimentally investigated and compared at the same conditions. Carbon monoxide (CO) was chosen as the analyte to verify the reported sensors' performance. With a minimum detection limit (MDL) of 470 ppb for 60 ms integration time and a noise equivalent absorption (NEA) coefficient of 2.0×10-7 cm-1 Hz-1/2, and a MDL of 17 ppb with an optimum integration time of 800 s, the reported LITES sensor showed a superior sensing capability compared with a TDLAS sensor and a conventional quartz-enhanced photoacoustic spectroscopy (QEPAS) sensor.
Kesulitan Kemampuan Pemecahan Masalah Matematis Siswa SMP di Desa Mulyasari pada Materi Statistika

Every day we are faced with various problems that require problem-solving skills. The purpose of this study was to analyze the difficulties experienced by junior high school students in solving statistical problems. This research is a qualitative descriptive study with data collection techniques using observation, problem-solving ability tests, and interviews. Data analysis went through several stages of data collection, data reduction, data presentation, and drawing conclusions. This research was conducted on students in grades VIII and IX in Mulyasari Village, Bayongbong District, Garut Regency. The results of this study indicate that the difficulties experienced by students are: 1) presenting statistical reports orally, in writing, tables, diagrams, and graphs; 2) make mathematical modeling; 3) apply strategies to solve problems; 4) draw conclusions; 5) recheck the answers. Factors that affect the difficulty of students' mathematical problem-solving abilities, among others: 1) lack of skills in planning solutions; 2) students do not re-examine; 3) loss of motivation to learn; 4) not confidence; 5) the application of an inappropriate learning model.
Laboratory Predictors of Prognosis in Cardiogenic Shock Complicating Acute Myocardial Infarction

Cardiogenic shock is a state of reduced cardiac output leading to hypotension, pulmonary congestion, and hypoperfusion of tissues and vital organs. Despite the advances in intensive care over the last years, the morbidity and mortality of patients remain high. The available studies of patients with cardiogenic shock suggest a connection between clinical variables, the level of biomarkers, the results of imaging investigations, strategies of management and the outcome of this group of patients. The management of patients with cardiogenic shock initially complicating acute myocardial infarction is challenging, and the number of studies in this area is growing fast. The purpose of this review is to summarize the currently available evidence on cardiogenic shock initially complicating acute myocardial infarction with particular attention to predictors of prognosis, focusing on laboratory variables (established and new), and to discuss the practical implementation. Currently available scoring systems developed during the past few decades predict the clinical outcome of this group of patients using some of the established biomarkers among other variables. With the new laboratory biomarkers that have shown their predictive value in cardiogenic shock outcomes, a new design of scoring systems would be of interest. Identifying high-risk patients offers the opportunity for early decision-making.
Parametric ANCOVA vs. Rank Transform ANCOVA when Assumptions of Conditional Normality and Homoscedasticity Are Violated.

Parametric analysis of covariance was compared to analysis of covariance with data transformed using ranks. Using a computer simulation approach the two strategies were compared in terms of the proportion of Type I errors made and statistical power when the conditional distribution of errors were: a) normal and homoscedastic, b) normal and heteroscedastic, c). non-normal and homoscedastic, and d) non-normal and heteroscedastic. The results indicated that parametric ANCOVA was robust to violations of either normality or homoscedasticity. However when both assumptions were violated the observed cA, a levels underestimated the nominal a level when sample sizes were small and a = . 05. Rank ANCOVA led to a slightly liberal test of the hypothesis when the covariate was non-normal and the errors were heteroscedastic. Practical significant power differences favoring the rank ANCOVA procedure were , observed with moderate sample sizes and skewed conditional errv distributions. 4.
Trajectory attractor approximation of the 3D Navier-Stokes system by a Leray-α model

1 In [1], a Leray-α model of turbulence was introduced. Its justification as a large eddy simulation sub-grid model of turbulence was also presented. In this paper, we study the connection between the long-time dynamics of the Leray-α model and the 3 D Navier– Stokes equations as α → 0+. In particular, we show that the trajectory attractor of the Leray-α model converges to the trajectory attractor of the 3 D Navier–Stokes system when α → 0+. 1. A 3 D LERAY-α MODEL WITH PERIODIC BOUNDARY CONDITIONS We consider the system (1) (2) where ν > 0 is the viscosity and P is the Leray projector to be defined below. System (1) and (2) was introduced in [1] (see, also, [2, 3]) and considered as a large eddy simulation subgrid model of 3 D turbulence, where the Green's function associated with the Helmholtz operator I – α 2 ∆ is considered as filtering kernel with the spatial width α. The following physical motivations justify our interest in the study of the Leray-α model. Using the Leray-α system as a closure model for the Reynolds averaged equations in channels and pipes leads to exactly the same system of equations as the 1 This article was submitted by the authors in English. ∂ t v ν∆v P u ∇ ⋅ ()v [ ] – g x (), + = x ‫ޔ‬ 3 := ‫ޒ‬mod 2πL () [ ] 3 , t 0; ≥ ∈ v u α 2 ∆u, ∇ v ⋅ – 0 , ∇ u ⋅ 0, = = = Navier–Stokes-α model (also known as the viscous Camassa–Holm system or the Lagrangian averaged Navier–Stokes-α model) under the corresponding symmetries (see, e.g., [4–6]). Hence, the analytical solutions of the Leray-α model agree well with the empirical data for a wide range of Reynolds numbers in turbulent channel and pipe flows (see [4–6]). Let us recall, however, that Leray considered system (1), (2) with a general smoothing kernel φ α , u = φ α * v , in order to approximate solutions of the 3 D Navier–Stokes system in ‫ޒ‬ 3 (see [7]). In system (1), (2), the vector fields v = v (x , t) = u 3 (x , t)) are unknown and the external force g (x) = (g 1 (x), g 2 (x), g 3 (x)) is given. We assume that the averages of these functions over …
Perbandingan Kemampuan Pemahaman Matematis Siswa melalui Mood-Understand-Recall-Digest-Expand-Review dan Discovery Learning

Students’ mathematical understanding is still low, so it is needed model learning that helps students to be active and creative in learning; including the Mood–Understand-Recall-Digest-Expand-Review (MURDER) learning model with the Discovery Learning learning model. The purpose of this study is to determine the ability and increase of mathematical understanding of students who get the MURDER learning model and those who get the Discovery Learning learning model. This research is a quasi-experimental study in Cisurupan State Middle 1 class VII with a sample of two classes, namely class VII F as an experimental class using MURDER and VII G classes using the model Discovery Learning, as well as the material taught about rectangular flat builds. Data analysis was performed with normality tests and non parametric statistics. Based on the results of the analysis, the ability of students' mathematical understanding between those who get the learning model MURDER is better than students who get the Discovery Learning learning model. Increasing students' mathematical understanding of the MURDER learning model with the Discovery Learning model, which is high - medium.
An Online Power-Aware Routing in SDN with Congestion-Avoidance Traffic Reallocation

Software-Defined Networks (SDN) can be seen as a promising alternative to achieve the long-awaited power efficiency in current communications systems. In these programmable networks a power-aware mechanism could be easily implemented leveraging the capabilities provided by control and data plane separation. For such purpose, this paper proposes a novel solution minimizing the number of active elements required in an SDN with multiple controllers and in-band control traffic. In order to provide a complete and fine-grained strategy, this proposal comprises two crucial modules: GrIS, a green initial setup and DyPAR, a dynamic power-aware routing. Besides being compatible with SDN environments without a dedicated control network, the proposed strategy is able to handle demanding traffic arrival without degrading the performance of higher priority traffic. Simulation results show that our heuristic approach allows to obtain close-to-optimal power savings with differences under 8%. Moreover, comparison with existing related methods using real topologies validates the improvements achieved by our solution in terms of power efficiency and performance degradation avoidance. For instance, after routing all the incoming traffic, a reduction of power consumption of up to 26.5% and an increase of allocated demands of up to 26.7% can be reached by our solution.
Mushy-layer growth and convection, with application to sea ice

Sea ice is a reactive porous medium of ice crystals and liquid brine, which is an example of a mushy layer. The phase behaviour of sea ice controls the evolving material properties and fluid transport through the porous ice, with consequences for ice growth, brine drainage from the ice to provide buoyancy fluxes for the polar oceans, and sea-ice biogeochemistry. We review work on the growth of mushy layers and convective flows driven by density gradients in the interstitial fluid. After introducing the fundamentals of mushy-layer theory, we discuss the effective thermal properties, including the impact of salt transport on mushy-layer growth. We present a simplified model for diffusively controlled growth of mushy layers with modest cooling versus the solutal freezing-point depression. For growth from a cold isothermal boundary, salt diffusion modifies mushy-layer growth by around 5–20% depending on the far-field temperature and salinity. We also review work on the onset, spatial localization and nonlinear development of convective flows in mushy layers, highlighting recent work on transient solidification and models of nonlinear convection with dissolved solid-free brine channels. Finally, future research opportunities are identified, motivated by geophysical observations of ice growth. This article is part of the theme issue ‘The physics and chemistry of ice: scaffolding across scales, from the viability of life to the formation of planets’.
The impact of preexisting and posttransplant diabetes mellitus on outcomes following liver transplantation.

BACKGROUND Diabetes mellitus (DM) is said to adversely affect transplant outcomes. The aim of this study was to investigate the impact of preexisting and new-onset DM on liver transplant (LT) recipients. METHOD A single-center retrospective analysis of prospectively collected data of LT recipients (1990-2015) was undertaken. RESULTS Of the 2,209 patients, 13% (n=298) had Pre-DM, 16% (n=362) developed PTDM, 5% (n=118) developed transient hyperglycemia (t-HG) post-LT, and 65% (n=1,431) never developed DM (no DM). Baseline clinical characteristics of patients with PTDM was similar to that of patients with pre-DM. Incidence of PTDM peaked during first-year (87%) and plateaued thereafter. On multivariate analysis (Bonferroni-corrected), nonalcoholic fatty liver disease and the use of Tacrolimus and Sirolimus use were independently associated with PTDM development. Both Pre-DM and PTDM patients had satisfactory and comparable glycaemic control throughout the follow-up period. Those who developed t-HG seems to have a unique characteristic compared to others. Overall, 9%, 5%, and 8% developed end-stage renal disease (ESRD), major cardiovascular event (mCVE), and de novo cancer, respectively. Both Pre-DM and PTDM did not adversely affect patient survival, retransplantation, or de novo cancer. The risks of ESRD and mCVE were significantly higher in patients with Pre-DM followed by P|TDM and no DM. CONCLUSIONS In this largest nonregistry study, patients with pre-DM and PTDM share similar baseline clinical characteristics. Pre-DM increases the risk of ESRD and mCVE; however, patient survival was comparable to those with PTDM and without diabetes. Understanding the impact of PTDM would need prolonged follow-up.
A Novel Deep Learning Stack for APT Detection

We present a novel Deep Learning (DL) stack for detecting Advanced Persistent threat (APT) attacks. This model is based on a theoretical approach where an APT is observed as a multi-vector multi-stage attack with a continuous strategic campaign. To capture these attacks, the entire network flow and particularly raw data must be used as an input for the detection process. By combining different types of tailored DL-methods, it is possible to capture certain types of anomalies and behaviour. Our method essentially breaks down a bigger problem into smaller tasks, tries to solve these sequentially and finally returns a conclusive result. This concept paper outlines, for example, the problems and possible solutions for the tasks. Additionally, we describe how we will be developing, implementing and testing the method in the near future.
Design of an Intelligent Active Obstacle Avoidance Car Based on Rotating Ultrasonic Sensors

In view of obstacle avoidance based on static ultrasonic wave sensor when the low sensitivity, slow response and low obstacle avoidance rate defect exists in the intelligent cars. An omnidirectional intelligent obstacle avoidance system was designed to control the ultrasonic sensor rotating for measuring distance. PWM drive servo actuators were used in this system to control the rotation measurement of ultrasonic sensor. Through sorting and filtering the sampled data, and then determine the optimal path, which can achieve a full range of autonomous obstacle avoidance. The experimental results show that the designed system can effectively improve the speed, sensitivity and obstacle avoidance success rate of autonomous obstacle avoidance.
Cognitive Decline in Older People with Multiple Sclerosis—A Narrative Review of the Literature

Several important questions regarding cognitive aging and dementia in older people with multiple sclerosis (PwMS) are the focus of this narrative review: Do older PwMS have worse cognitive decline compared to older people without MS? Can older PwMS develop dementia or other neurodegenerative diseases such as Alzheimer’s disease (AD) that may be accelerated due to MS? Are there any potential biomarkers that can help to determine the etiology of cognitive decline in older PwMS? What are the neural and cellular bases of cognitive aging and neurodegeneration in MS? Current evidence suggests that cognitive impairment in MS is distinguishable from that due to other neurodegenerative diseases, although older PwMS may present with accelerated cognitive decline. While dementia is prevalent in PwMS, there is currently no consensus on defining it. Cerebrospinal fluid and imaging biomarkers have the potential to identify disease processes linked to MS and other comorbidities—such as AD and vascular disease—in older PwMS, although more research is required. In conclusion, one should be aware that multiple underlying pathologies can coexist in older PwMS and cause cognitive decline. Future basic and clinical research will need to consider these complex factors to better understand the underlying pathophysiology, and to improve diagnostic accuracy.
Sublethal Dose Impact of λ Cyhalothrin on Life Table Parameters of Ladybird Beetle, Coccinella septempunctata (Coleoptera: Curculionidae) Reveals Tolerance in Field Population

1Rice Research Institute, Kala Shah Kaku, Punjab, Pakistan 2Department of Entomology, University of Sargodha, Sargodha 40100, Pakistan 3Department of Entomology, Faculty of Agricultural and Environmental Sciences, Islamia University of Bahawalpur, Bahawalpur, Pakistan Article Information Received 25 September, 2020 Revised 26 October 2020 Accepted 20 November, 2020 Available online 02 June 2022 (early access)
Behavioral preference for viewing drug v. pleasant images predicts current and future opioid misuse among chronic pain patients

Abstract Background The USA is currently enduring an opioid crisis. Identifying cost-effective, easy-to-implement behavioral measures that predict treatment outcomes in opioid misusers is a crucial scientific, therapeutic, and epidemiological goal. Methods The current study used a mixed cross-sectional and longitudinal design to test whether a behavioral choice task, previously validated in stimulant users, was associated with increased opioid misuse severity at baseline, and whether it predicted change in opioid misuse severity at follow-up. At baseline, data from 100 prescription opioid-treated chronic pain patients were analyzed; at follow-up, data were analyzed in 34 of these participants who were non-misusers at baseline. During the choice task, participants chose under probabilistic contingencies whether to view opioid-related images in comparison with affectively pleasant, unpleasant, and neutral images. Following previous procedures, we also assessed insight into choice behavior, operationalized as whether (yes/no) participants correctly self-reported the image category they chose most often. Results At baseline, the higher choice for viewing opioid images in direct comparison with pleasant images was associated with opioid misuse and impaired insight into choice behavior; the combination of these produced especially elevated opioid-related choice behavior. In longitudinal analyses of individuals who were initially non-misusers, higher baseline opioid v. pleasant choice behavior predicted more opioid misuse behaviors at follow-up. Conclusions These results indicate that greater relative allocation of behavior toward opioid stimuli and away from stimuli depicting natural reinforcement is associated with concurrent opioid misuse and portends vulnerability toward future misuse. The choice task may provide important medical information to guide opioid-prescribing practices.
Bullying Victimization Among Left-Behind Children in Rural China: Prevalence and Associated Risk Factors

Bullying victimization among Chinese school-aged children has recently been recognized as a serious problem. However, studies on the prevalence and correlates of bullying victimization among left-behind children—whose parents live and work in urban areas while they remain at home in a rural area—have fallen far behind. We conducted a study based on one representative sample of 742 left-behind children in Grades 6 to 10 from rural China in 2010. The results indicate that a total of 31.6% of the left-behind children report recurrent bullying victimization, which is higher than that of their rural counterparts who live with their parents. Furthermore, left-behind children who experience bullying victimization are more likely to report being drunk frequently, poorer peer and family relationships, and academic problems. Future bullying prevention and intervention programs should be designed to meet the special needs of left-behind children.
Displacement-Based Formation Control with Phase Synchronization in a Time-Invariant Flow Field

This paper discusses phase synchronization and circular formation control for multiple unicycle-type agents with fixed constant speeds in a time-invariant flow field. We describe the Lyapunov-based design of displacement formation control approach that stabilizes collective motion in a known flow field including uniform flow and non-uniform flow. The communication topology in displacement-based control utilizes all-to-all interactions. Moreover, we provide a combination of formation shape and steering control to stabilize formation and orientation simultaneously. The validity of the proposed algorithms above is confirmed by theoretical derivation and numerical simulation.
Energy-Efficient Hadamard-Based SATD Hardware Architectures Through Calculation Reuse

The Hadamard-based Sum of Absolute Transformed Differences (SATD) is a distortion metric that correlates better with other video encoding steps than the commonly used Sum of Absolute Differences (SAD) and thus potentially improves coding efficiency. However, due to the Hadamard Transform (HT), it demands a large share of total encoding time. Meanwhile, the widespread use of embedded encoders made computational complexity and energy efficiency two relevant issues, besides the usual trade-off between the rate and distortion. Hence, the adoption of SATD during video coding requires the design of energy-efficient hardware accelerators. In this paper, we show that the SATD improves coding efficiency by up to −2.99% in BD-Rate when used in motion estimation within the HEVC Model (HM). Subsequently, as the main contribution, we combined two properties featured by the SATD to devise a novel hardware architecture based on calculation reuse. These properties allow for rearranging the architecture components to reduce the hardware size, save operations, and thus, reduce energy. The synthesis and simulation results indicate that the proposed architecture can lead to area and energy savings of about 28% and 32%, respectively, compared to the state-of-the-art architecture based on reuse solely.