Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring the effects of a wearable biocueing app (Sense-IT) as an addition to aggression regulation therapy in forensic psychiatric outpatients

Objective Preventing and reducing violence is of high importance for both individuals and society. However, the overall efficacy of current treatment interventions aimed at reducing aggressive behavior is limited. New technological-based interventions may enhance treatment outcomes, for instance by facilitating out-of-session practice and providing just-in-time support. Therefore, the aim of this study was to assess the effects of the Sense-IT biocueing app as an addition to aggression regulation therapy (ART) on interoceptive awareness, emotion regulation, and aggressive behavior among forensic outpatients. Methods A combination of methods was used. Quantitatively, a pretest-posttest design was applied to explore group changes in aggression, emotion regulation, and anger bodily sensations associated with the combination of biocueing intervention and ART. Measures were assessed at pretest, after 4 weeks posttest, and after one-month follow-up. During the 4 weeks, a single-case experimental ABA design was applied for each participant. Biocueing was added in the intervention phase. During all phases anger, aggressive thoughts, aggressive behavior, behavioral control, and physical tension were assessed twice a day, and heart rate was measured continuously. Qualitative information regarding interoceptive awareness, coping, and aggression was collected at posttest. 25 forensic outpatients participated. Results A significant decrease in self-reported aggression was found between pre- and posttest. Furthermore, three-quarters of participants reported increased interoceptive awareness associated with the biocueing intervention. However, the repeated ambulatory measurements of the single-case experimental designs (SCEDs) did not indicate a clear effect favoring the addition of biocueing. On group level, no significant effects were found. On the individual level, effects favoring the intervention were only found for two participants. Overall, effect sizes were small. Conclusion Biocueing seems a helpful addition to increase interoceptive awareness among forensic outpatients. However, not all patients benefit from the current intervention and, more specifically, from its behavioral support component aimed at enhancing emotion regulation. Future studies should therefore focus on increasing usability, tailoring the intervention to individual needs, and on integration into therapy. Individual characteristics associated with effective support by a biocueing intervention should be further investigated, as the use of personalized and technological-based treatment interventions is expected to increase in the coming years.
Agricultural supply response and price risk of maize and sorghum in South Africa

The study used the Autoregressive Distributed Lag-Error Correction Model (ARDL-ECM) approach to estimate the responsiveness of South African maize and sorghum producers to price risk, price incentives and non-price incentives. The price risk variable was incorporated in the supply models to examine its impact on maize and sorghum production decisions. The study used annual historical time series data of 49 observations for the period 1970 to 2018 was used in the analysis. The empirical results reveal that maize and sorghum producers' response to own price is reasonably low. The study further shows that both maize and sorghum crops demonstrate a high speed of adjustment to the long-run equilibrium, which means that in the event of a shock to the system, grain output will quickly re-establish itself at a faster rate. The findings underscore the relevance of price risk in determining production output in South Africa.
Life after detention: challenges encountered by released juvenile offenders in Accra, Ghana

Abstract This article explores the challenges encountered by released juvenile offenders in Accra, employing the qualitative research method, using the phenomenological approach. Twelve young male adults released from the senior correctional center in Accra took part in interviews which analyzed the Interpretative Phenomenological Approach. The findings of the study reveal that the participants encountered educational and employment challenges. Factors that fueled these challenges include the lack of financial resources and stigma. The lack of state support for the reintegration of released juvenile offenders was also another major contributing factor, the study identified. The findings of this study reveal the need for implementing the Justice for Children policy in Ghana. Context-related reintegration processes for children in conflict with the law suitable for the African and the Ghanaian terrain must be incorporated into the juvenile justice administration. This move is to ensure that released juvenile offenders are cared for. The findings further call for more research into reintegration programs fit for the African context and curated to meet the needs of these released juvenile offenders.
Skeleton-based 3D Surface Parameterization Applied on Texture Mapping

Abstract Assume a 2D manifold surface topologically equivalent to a sphere with handles we propose a novel 3D surface parametrization along the surface skeleton. First, we use a global mapping of the surface vertices onto a computed skeleton. Second, we use local mapping of the surrounding area of each skeleton segment into a small rectangle whose size is derived based on the surface properties around the segment. Each rectangle can be textured by assigning the local u;v texture coordinates. Furthermore, these rectangles are packed into a large squared texture called skeleton texture map (STM) by approximately solving a palette loading problem. Our technique enables the mapping of a texture onto the surface without necessity to store texture coordinates together with the model data. In other words it is enough to store the geometry data with STM and the coordinates are calculated on the fly.
Revisiting the Drivers of Firm Value; An Empirical Investigation on Manufacturing Firms

The study aims to ascertain the key determinants of firm value and to analyze the contribution of each determinant at different percentiles of the probability distribution of firm value for 1,592 firms across eight major Indian manufacturing firms. The sample consists of data from 2009 to 2020 and studies the variables such as enterprise value, effective tax rate, firm size, profitability, firms’ growth rate, financial leverage, asset tangibility, and non-debt tax shield. The study employed, linear, non-linear, and non-parametric panel regression models. Quantile regression models are further used to decompose the determinants at various quantiles of firm value. Several diagnostic tests such as Breusch–Pagan, White’s Test, and Sargan Test are used to ensure robust estimates. The results obtained from this study show the presence of a concave relationship between firm value with firm size, financial leverage, and non-debt tax shield. Effective tax rates and inflation rates substantially and positively affect the value of firms in higher percentiles. The capital structure is found to significantly and positively affect the value of firms in the lower percentile. The profitability is also found to contribute to the value of firms across all the quantiles, whereas sales growth fails to strongly drive the value.
Whole-genome analysis showed the promotion of genetic diversity and coevolution in Staphylococcus aureus lytic bacteriophages and their hosts mediated by prophages via worldwide recombination events

Prophages as a part of Staphylococcus aureus genome contribute to the genetic diversity as well as survival strategies of their host. Some S. aureus prophages also have an imminent risk of host cell lysis and become a lytic phage. Nonetheless, interactions among S. aureus prophages, lytic phages, and their hosts, as well as the genetic diversity of S. aureus prophages, remain unclear. We identified 579 intact and 1,389 incomplete prophages in the genomes of 493 S. aureus isolates obtained from the NCBI database. The structural diversity and gene content of intact and incomplete prophages were investigated and compared with 188 lytic phages. Mosaic structure comparison, ortholog group clustering, phylogenetic analysis, and recombination network analysis were performed to estimate genetic relatedness among S. aureus intact prophages, incomplete prophages, and lytic phages. The intact and incomplete prophages harbored 148 and 522 distinct mosaic structures, respectively. The major difference between lytic phages and prophages was the lack of functional modules and genes. Compared to the lytic phages, both the S. aureus intact and incomplete prophages harbored multiple antimicrobial resistance (AMR) and virulence factor (VF) genes. Several functional modules of lytic phages 3_AJ_2017 and 23MRA shared more than 99% nucleotide sequence identity with S. aureus intact (ST20130943_p1 and UTSW_ MRSA_55_ip3) and incomplete prophages (SA3_LAU_ip3 and MRSA_FKTN_ip4); other modules showed little nucleotide sequence similarity. Ortholog and phylogenetic analyses revealed a common gene pool shared between the prophages and lytic Siphoviridae phages. Moreover, most shared sequences existed within intact (43428/137294, 31.6%) and incomplete prophages (41248/137294, 30.0%). Therefore, the maintenance or loss of functional modules in intact and incomplete prophages is key to balance the costs and benefits of large prophages harboring various AMR and VF genes in the bacterial host. The shared identical functional modules between S. aureus lytic phages and prophages are likely to result in the exchange, acquisition, and loss of functional modules, and therefore contribute to their genetic diversity. Moreover, constant recombination events within prophages globally were responsible for the coevolution of lytic phages and their bacterial hosts.
The effect of combining PD-1 agonist and low-dose Interleukin-2 on treating systemic lupus erythematosus

Systemic lupus erythematosus (SLE) is a chronic autoimmune disease involving multiple organs. It is often called “immortal cancer” due to the difficulties in disease treatment. As the cornerstone of immune regulation, the programmed cell death protein 1 (PD-1) has been extensively studied in the context of chronic inflammation due to its ability of regulating immune response and immunosuppression. Recently, more and more studies on rheumatic immune related complications have also focused on PD-1 and proposed that the use of PD-1 agonist could inhibit the activation of lymphocytes and alleviate SLE disease activity. In this review, we summarized the role of PD-1 in SLE, implicating its potential application as a biomarker to predict SLE disease activity; we also proposed that the combination of PD-1 agonist and low-dose IL-2 may have better therapeutic efficacy, shining light on a new direction for developing specific treatment approaches.
Essential system response information for transient-based leak detection methods

Many transient-based techniques have been developed for leak detection in piping fluid systems, and they are thought to use leak-induced damping and reflection effects to detect and locate the problem. This work investigates the relative importance of the damping and reflective effects from the leak to the leak detection methods with the importance of both effects tested using four representative methods, which vary significantly in their modes of operation and provide the coverage of the major developments in the transient leak detection field. It was found that all transient-based methods are unable to detect and locate the leak in the absence of the leak-reflected signal, whereas the leak is detected and located even in the absence of leak-induced damping. The results indicate that the information previously thought to be contained in the damping of slow transients was found to be low frequency reflections from the leak.
A novel method to produce massive seedlings via symbiotic seed germination in orchids

Orchids produce large numbers of dust-like seeds that rely heavily on orchid mycorrhizal fungi (OMFs) for germination. Using OMFs to facilitate orchid proliferation is considered an effective method for orchid conservation but still presents challenges in practice. In this study, orchid seed-fungus complexes, in which orchid seeds and fungal mycelia were embedded together to form granules, were developed as platforms to facilitate seed germination and seedling production. Overall, seedlings were produced by seed-fungus complexes for five orchid species with large variations in the percentages of seedlings produced among species/treatments. For the different fungal treatments in Dendrobium officinale, Sebacinales LQ performed much better than the other fungal strains. At 90 days after sowing, 75.8±2.6% seedlings were produced in the LQ treatment, which was significantly higher than in the Tulasnella sp. JM (22.0±3.0%) and Tulasnella sp. TPYD-2 (5.3±1.0%) treatments, as well as in the LQ and TPYD-2 cocultured treatment (40.4±3.2%), while no seedlings were formed in the Tulasnella sp. SSCDO-5 or control treatments. For the other four orchid species, only one compatible fungus for each species was used, and the percentages of seedlings in epiphytic Dendrobium devonianum (67.2±2.9%) and D. nobile (38.9±2.8%) were much higher than those in terrestrial Paphiopedilum spicerianum (2.9±1.1%) and Arundina graminifolia (6.7±2.1%) at 90 days after sowing. Adding 1% polymer water-absorbent resin to the seed-fungus complexes of D. officinale seeds with fungal strain Sebacinales LQ significantly increased seedling formation, while other additional substances showed negative effects on seedling formation. For the storage of seed-fungus complexes, it is recommended to store the seed-fungus complexes in valve bags at room temperature for a short time and at a low temperature of 4°C for no more than 30 days. As a platform for symbiotic seed germination, the seed-fungus complex can facilitate seed germination, produce seedlings and support subsequent seedling growth, and its seedling productivity depends on seed germination characteristics, seed viability, and the efficiency of fungi. Seed-fungus complexes have great potential to be used as propagules in orchid conservation.
Vortex rings in classical and quantum systems

The study of vortex rings has been pursued for decades and is a particularly difficult subject. However, the discovery of quantized vortex rings in superfluid helium has greatly increased interest in vortex rings with very thin cores. While rapid progress has been made in the simulation of quantized vortex rings, there has not been comparable progress in laboratory studies of vortex rings in a viscous fluid such as water. This article overviews the history and current frontiers of classical and quantum vortex rings. After introducing the classical results, this review discusses thin-cored vortex rings in superfluid helium in section 2, and recent progress in understanding vortex rings of very thin cores propagating in water in section 3.
NIDS-CNNLSTM: Network Intrusion Detection Classification Model Based on Deep Learning

Intrusion detection is the core topic of network security, and the intrusion detection algorithm based on deep learning has become a research hotspot in network security. In this paper, a network intrusion detection classification model (NIDS-CNNLSTM) based on deep learning is constructed for the wireless sensing scenario of the Industrial Internet of Things (IIoT) to effectively distinguish and identify network traffic data and ensure the security of the equipment and operation of the IIoT. NIDS-CNNLSTM combines the powerful learning ability of long short-term memory neural networks in time series data, learns and classifies the features selected by the convolutional neural network, and verifies the applicability based on binary classification and multi-classification scenarios. The model is trained using KDD CUP99, NSL_KDD, and UNSW_NB15 classic datasets. The verification accuracy and training loss on the three datasets all show good convergence and level, and the accuracy rate is high when classifying various types of traffic. The overall performance of NIDS-CNNLSTM has been significantly improved compared with the models proposed in previous studies. The effectiveness shows a high detection rate and classification accuracy and a low false alarm rate through experimental results. It is more suitable for large-scale and multi-scenario network data in the IIoT.
A Survey on Optimal Channel Estimation Methods for RIS-Aided Communication Systems

Next-generation wireless communications aim to utilize mmWave/subTHz bands. In this regime, signal propagation is vulnerable to interferences and path losses. To overcome this issue, a novel technology has been introduced, which is called reconfigurable intelligent surface (RIS). RISs control digitally the reflecting signals using many passive reflector arrays and implement a smart and modifiable radio environment for wireless communications. Nonetheless, channel estimation is the main problem of RIS-assisted systems because of their direct dependence on the system architecture design, the transmission channel configuration and methods used to compute channel state information (CSI) on a base station (BS) and RIS. In this paper, a concise survey on the up-to-date RIS-assisted wireless communications is provided and includes the massive multiple input-multiple output (mMIMO), multiple input-single output (MISO) and cell-free systems with an emphasis on effective algorithms computing CSI. In addition, we will present the effectiveness of the algorithms computing CSI for different communication systems and their techniques, and we will represent the most important ones.
Statistical inference on graphs

The problem of graph inference, or graph reconstruction, is to predict the presence or absence of edges between a set of given points known to form the vertices of a graph. Motivated by various applications including communication networks and systems biology, we propose a general model for studying the problem of graph inference in a supervised learning framework. In our setting, both the graph vertices and edges are assumed to be random, with a probability distribution that possibly depends on the size of the graph. We show that the problem can be transformed into one where we can use statistical learning methods based on empirical minimization of natural estimates of the reconstruction risk.Convex risk minimizationmethods are also studied to provide a theoretical framework for reconstruction algorithms based on boosting and support vector machines. Our approach is illustrated on simulated graphs.
Low energy /spl gamma/-rays scintillation detection with large area avalanche photodiodes

The energy resolution of NaI:Tl, CsI:Tl, LSO and YAP crystals coupled to 16 mm diameter large area avalanche photodiodes (LAAPD) was studied for low energy /spl gamma/-rays. Energy resolutions of 11.3% and 8.4% were obtained for 59.6 keV /spl gamma/-rays from /sup 241/Am and 122 keV /spl gamma/-rays from /sup 57/Co sources, respectively, as measured with a 10 mm diameter by 10 mm high NaI:Tl crystal. The optimization of the LAAPD performance is presented, being essential for improvement of energy resolution at low energy /spl gamma/-rays. Measured numbers of primary electron-hole pairs produced in LAAPDs and evaluation of the noise contribution of these devices allowed for quantitative discussion of the results. Particularly, the intrinsic energy resolution of the crystals vs. the energy of /spl gamma/-rays has been evaluated. Obtained data for a NaI:Tl crystal showed a good agreement (within 30%) with the calculated component due to nonproportional light yield for energies above 50 keV.
A Novel CT Perfusion-Based Fractional Flow Reserve Algorithm for Detecting Coronary Artery Disease

Background: The diagnostic accuracy of fractional flow reserve (FFR) derived from coronary computed tomography angiography (CCTA) (FFR-CT) needs to be further improved despite promising results available in the literature. While an innovative myocardial computed tomographic perfusion (CTP)-derived fractional flow reserve (CTP-FFR) model has been initially established, the feasibility of CTP-FFR to detect coronary artery ischemia in patients with suspected coronary artery disease (CAD) has not been proven. Methods: This retrospective study included 93 patients (a total of 103 vessels) who received CCTA and CTP for suspected CAD. Invasive coronary angiography (ICA) was performed within 2 weeks after CCTA and CTP. CTP-FFR, CCTA (stenosis ≥ 50% and ≥70%), ICA, FFR-CT and CTP were assessed by independent laboratory experts. The diagnostic ability of the CTP-FFR grouped by quantitative coronary angiography (QCA) in mild (30–49%), moderate (50–69%) and severe stenosis (≥70%) was calculated. The effect of calcification of lesions, grouped by FFR on CTP-FFR measurements, was also assessed. Results: On the basis of per-vessel level, the AUCs for CTP-FFR, CTP, FFR-CT and CCTA were 0.953, 0.876, 0.873 and 0.830, respectively (all p < 0.001). The sensitivity, specificity, accuracy, positive predictive value (PPV) and negative predictive value (NPV) of CTP-FFR for per-vessel level were 0.87, 0.88, 0.87, 0.85 and 0.89 respectively, compared with 0.87, 0.54, 0.69, 0.61, 0.83 and 0.75, 0.73, 0.74, 0.70, 0.77 for CCTA ≥ 50% and ≥70% stenosis, respectively. On the basis of per-vessel analysis, CTP-FFR had higher specificity, accuracy and AUC compared with CCTA and also higher AUC compared with FFR-CT or CTP (all p < 0.05). The sensitivity and accuracy of CTP-FFR + CTP + FFR-CT were also improved over FFR-CT alone (both p < 0.05). It also had improved specificity compared with FFR-CT or CTP alone (p < 0.01). A strong correlation between CTP-FFR and invasive FFR values was found on per-vessel analysis (Pearson’s correlation coefficient 0.89). The specificity of CTP-FFR was higher in the severe calcification group than in the low calcification group (p < 0.001). Conclusions: A novel CTP-FFR model has promising value to detect myocardial ischemia in CAD, particularly in mild-to-moderate stenotic lesions.
Improved method of calculating ab initio high-temperature thermodynamic properties with application to ZrC

Thermodynamic properties of ZrC are calculated up to the melting point (${T}^{\mathrm{melt}}\ensuremath{\approx}3700\phantom{\rule{0.28em}{0ex}}\text{K}$), using density functional theory (DFT) to obtain the fully anharmonic vibrational contribution, and including electronic excitations. A significant improvement is found in comparison to results calculated within the quasiharmonic approximation. The calculated thermal expansion is in better agreement with experiment and the heat capacity reproduces rather closely a CALPHAD estimate. The calculations are presented as an application of a development of the upsampled thermodynamic integration using Langevin dynamics (UP-TILD) approach. This development, referred to here as two-stage upsampled thermodynamic integration using Langevin dynamics (TU-TILD), is the inclusion of tailored interatomic potentials to characterize an intermediate reference state of anharmonic vibrations on a two-stage path of thermodynamic integration between the original DFT quasiharmonic free energy and the fully anharmonic DFT free energy. This approach greatly accelerates the convergence of the calculation, giving a factor of improvement in efficiency of $\ensuremath{\sim}50$ in the present case compared to the original UP-TILD approach, and it can be applied to a wide range of materials.