Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness Of Mix Police (Fiscal And Monetary Police) In Reducing Unemployment In 5 Southeast Asia Countries (Indonesia, Malaysia, Thailand, Singapore And Philipnes)

This study aims to analyze the contribution of the effectiveness of the mix police (fiscal and monetary police) in reducing unemployment in 5 Southeast Asian countries (Indonesia, Malaysia, Thailand, Singapore and the Philippines). This study uses secondary data or time series, namely from the first quarter of 2001 to the fourth quarter of 2018. The data analysis model in this study is the Vector Autoregression (VAR) model and the ARDL Panel then sharpened by analysis of Impulse Response Function (IRF) and Forecast Error Variance Decomposition (FEVD). The results of the Vector Autoregression analysis show that the past variable (t-1) contributes to the present variable both on itself and on other variables. From the estimation results, it turns out that there is a reciprocal relationship between one variable and the other variables that contribute to each other.
Exogenous Selenium Improves Physio-Biochemical and Performance of Drought-Stressed Phaseolus vulgaris Seeded in Saline Soil

Water and salt stresses are among the most important global problems that limit the growth and production of several crops. The current study aims at the possibility of mitigating the effect of deficit irrigation of common bean plants growing in saline lands by foliar spraying with selenium via the assessment of growth, productivity, physiological, and biochemical measurements. In our study, two field-based trials were conducted in 2017 and 2018 to examine the influence of three selenium (Se) concentrations (0 (Se0), 25 (Se25), and 50 mg L−1 (Se50)) on common bean plants grown under full irrigation (I100 = 100% of the crop evapotranspiration; ETc) and deficit irrigation (I80 = 80% of ETc, and I60 = 60% of ETc). Bean plants exposed to water stress led to a notable reduction in growth, yield, water productivity (WP), water status, SPAD value, and chlorophyll a fluorescence features (Fv/Fm and PI). However, foliar spraying of selenium at 25 or 50 mg L−1 on stressed bean plants attenuated the harmful effects of water stress. The findings suggest that foliage application of 25 or 50 mg L−1 selenium to common bean plants grown under I80 resulted in a higher membrane stability index, relative water content, SPAD chlorophyll index, and better efficiency of photosystem II (Fv/Fm, and PI). Water deficit at 20% increased the WP by 17%; however, supplementation of 25 or 50 mg L−1 selenium mediated further increases in WP up to 26%. Exogenous application of selenium (25 mg L−1 or 50 mg L−1) to water-stressed bean plants elevated the plant defense system component, given that it increased the free proline, ascorbic acid, and glutathione levels, as well as antioxidant enzymes (SOD, APX, GPX, and CAT). It was concluded that the application of higher levels (25 or/and 50 mg L−1) of Se improves plant water status as well as the growth and yield of common beans cultivated in saline soil.
A workplace wellness program protects against COVID-19 effects on mental wellbeing

Abstract The outbreak of the COVID-19 pandemic had deleterious effects on workers’ mental wellbeing. The current study investigated the impact of an online workplace wellness program on teacher mental wellbeing and mindfulness during the first four months of nation-wide quarantine in New Zealand. Longitudinal data at three time points were examined: baseline, end of program, and 3 months post-program in a sample of 32 teachers. Tests of paired data of baseline versus the later time points found statistically significant improvements in mental wellbeing (Warwick-Edinburg Mental Wellbeing Scale) and mindfulness (The Five Facets Mindfulness Questionnaire) with large effect sizes. Mindfulness scores were higher at 3 months follow up compared to end-of-program suggesting teachers’ mindfulness ratings continued to improve after program completion. Implications for research and workplace training are discussed.
Integrated tools to evaluate environmental conditions in estuarine streams of Northeastern Brazil

Abstract: Aim This work proposes the application and development of environmental quality indexes for the evaluation of tropical estuarine streams in different spatial scales. The main goal was to understand the biological responses of the ichthyofauna in relation to different spatial indices in each group of streams, according to their predominant land use. Our hypothesis is that the impact on the stream riparian zones and in the land use in adjacent areas to the stream interfere in the structure of the fish assembly. Methods The Physical Habitat Integrity Index (PHI) on the local scale (80.0 m) and the Microbasin Integrity Index (MII) on catchment scale (1.6 km radius) was applied in all streams. In parallel, fish collections with electric fishing equipment were carried out in the 80.0 m reach. With the data from the PHI, MII and ecological estimators (species richness and percentage of Poecilia reticulata), the Kruskal-Wallis non-parametric test and Dunn’s post hoc test were carried out to verify the differences between the groups of land use, followed by a linear and polynomial regression analysis with trend line to show a relationship among indexes used and the biological responses. Results We observed that all streams’ groups presented a high positive correlation among PHI and MII. However, ecological estimators did not respond to changes in land use linearly, but in the form of a parable in a polynomial regression. Conclusions Our main conclusion is that the use of indexes and estimators as tools for environmental assessment is an efficient way to assess the health of the streams. The results also show that the integration of local and spatial indexes reduces the distortions observed in the indexes in isolation.
Identifying Alzheimer's Disease Through Speech Using Emotion Recognition

Alzheimer's disease is the most common neurodegenerative dementia in elderly people in the world and its diagnosis requires a wide medical evaluation, supported by cognitive tests, clinical and imaging exams. Identifying the disease through speech can reduce the cost and time of medical diagnosis. Emotional states are important performance indicators of cognitive processes. Intelligent and non-invasive computational techniques can become relevant support tools for an early medical diagnosis. Therefore, this article addresses the use of emotion recognition through voice as a biomarker to identify the presence of Alzheimer's disease. The proposed method is based on the extraction of emotional features from speech and pattern recognition using neural networks. The results of the experiments reached an accuracy of 72.61%, a precision of 72.90% and a recall of 72.50% through cross-validation of the data.
Unlocking the enigma: unraveling multiple cognitive dysfunction linked to glymphatic impairment in early Alzheimer’s disease

Background Alzheimer’s disease (AD) is one of the world’s well-known neurodegenerative diseases, which is related to the balance mechanism of production and clearance of two proteins (amyloid-β and tau) regulated by the glymphatic system. Latest studies have found that AD patients exhibit impairments to their glymphatic system. However, the alterations in the AD disease continuum, especially in the early stages, remain unclear. Moreover, the relationship between the glymphatic system and cognitive dysfunction is still worth exploring. Methods A novel diffusion tensor image analysis method was applied to evaluate the activity of the glymphatic system by an index for diffusivity along the perivascular space (ALPS-index). Based on this method, the activity of the glymphatic system was noninvasively evaluated in 300 subjects, including 111 normal controls (NC), 120 subjects with mild cognitive impairment (MCI), and 69 subjects with AD. Partial correlation analysis was applied to explore the association between glymphatic system and cognitive impairment based on three domain-general scales and several domain-specific cognitive scales. Receiver operating characteristic curve analysis was used to evaluate the classification performance of ALPS-index along the AD continuum. Results ALPS-index was significantly different among NC, MCI and AD groups, and ALPS-index decreased with cognitive decline. In addition, ALPS-index was significantly correlated with the scores of the clinical scales (p<0.05, FDR corrected), especially in left hemisphere. Furthermore, combination of ALPS and fractional anisotropy (FA) values achieved better classification results (NC vs. MCI: AUC = 0.6610, NC vs. AD: AUC = 0.8214). Conclusion Here, we show that the glymphatic system is closely associated with multiple cognitive dysfunctions, and ALPS-index can be used as a biomarker for alterations along the AD continuum. This may provide new targets and strategies for the treatment of AD, and has the potential to assist clinical diagnosis.
Narrowband Wavelength Selective Thermal Emitters by Confined Tamm Plasmon Polaritons

This study experimentally demonstrates infrared wavelength selective thermal emission based on Tamm plasmon polaritons (TPPs). Unlike conventional TPP structures, which have a thin metal layer on a distributed Bragg reflector (DBR), the proposed structure has a thick metal under a DBR that is more robust for thermal radiation. The number of DBR pairs is a critical factor in maximizing the narrowband emission needed to satisfy the impedance matching condition, which varies with the choice of metal film. Optimum designs for four different metals, aluminum, gold, molybdenum, and tungsten, are presented. The temporal coupled-mode theory was introduced to explain the origin of the high Q-factor of the proposed structure, which can achieve a twice higher Q-factor for the measured emissivity compared to typical plasmonic thermal emitters. The structure is one-dimensional, consisting of only multilayers and free from nanopatterning, offering a practical design in applications such as gas sensing, narrowband IR so.
Molecular Characterization of Staphylococcus aureus Complex Isolated from Free-Ranging Long-Tailed Macaques at Kosumpee Forest Park, Maha Sarakham, Thailand

The Staphylococcus (S.) aureus complex, including methicillin-resistant S. aureus (MRSA) and methicillin-susceptible S. aureus (MSSA), and S. argenteus are bacterial pathogens that are responsible for both human and animal infection. However, insights into the molecular characteristics of MRSA, MSSA, and S. argenteus carriages in wildlife, especially in long-tailed macaques, rarely have been reported in Thailand. The objective of this study was to assess molecular characterization of MRSA, MSSA, and S. argenteus strains isolated from free-ranging long-tailed macaques (Macaca fascicularis) at Kosumpee Forest Park, Maha Sarakham, Thailand. A total of 21 secondary bacterial isolates (including 14 MRSA, 5 MSSA, and 2 S. argenteus) obtained from the buccal mucosa of 17 macaques were analysed by a Polymerase chain reaction (PCR) to identify several virulence genes, including pvl, tst, hla, hlb clfA, spa (x-region), spa (IgG biding region), and coa. The most prevalent virulence genes were clfA, coa, and the spa IgG biding region which presented in all isolates. These data indicated that MRSA, MSSA, and S. argenteus isolates from the wild macaques at Kosumpee Forest Park possess a unique molecular profile, harbouring high numbers of virulence genes. These findings suggest that wild macaques may potentially serve as carriers for distribution of virulent staphylococcal bacteria in the study area.
YOLO-PD: Abnormal Signal Detection in Gas Pipelines Based on Improved YOLOv7

Natural gas pipelines are an important mode of transportation. How to quickly and accurately detect abnormal signals that occur during pipeline operation is crucial to judging the pipeline status. In this article, an abnormal signal detection method based on improved YOLOv7 (YOLO-PD) was proposed to address the low detection accuracy of abnormal signals and the dependence on manual signal segmentation scale setting in pipeline operation. First, a new refined channel attention (RCA) module was proposed to enhance network feature extraction. Second, a MaxPooling (MP) with RCA module was designed to strengthen network feature extraction and fusion capabilities. Furthermore, the atrous spatial pyramid pooling (ASPP) with RCA module was used to expand the model’s receptive field and enhance the core information features, thereby improving the detection accuracy of the model for multiscale objects. Finally, the SIoU loss function was utilized to expedite network convergence and optimize the model training process. The experimental results demonstrate that YOLO-PD exhibits high accuracy and speed, with an mAP at 0.5 of 0.996 and a frame rate of 65. In comparison to other prevalent target detection algorithms, the method proposed in this article can effectively identify abnormal signals in pipelines and achieve higher detection accuracy.
Privacy-Preserving Traceable Encrypted Traffic Inspection in Blockchain-Based Industrial IoT

Blockchain-based Industrial Internet of Things (IIoT) integrates the blockchain technology into the traditional IIoT infrastructure to provide secure and collaborative services. In IIoT, the traffic is usually encrypted using a cipher suite (SSL/TLS) for secure communication, which makes it hard for middleboxes (MBs) to detect malicious activity in the traffic. To address this problem, secure MBs that directly perform encrypted traffic inspection have been presented. Recently, a new privacypreserving deep packet inspection (DPI) system on MB for IoT scenarios was proposed, but it suffered from the following two limitations: 1) no support for fast token detection and 2) no support for tracing abnormal sources. To address the two limitations, we propose BTDPI, a privacy-preserving traceable DPI system that efficiently performs inspection over encrypted traffic in blockchain-based IIoT. Technically, we adopt a two-layer filter architecture to improve the efficiency of detection and moreover introduce a new online–offline certificateless aggregate signature with smart contract to design an identity traceability mechanism. The experiment result shows that BTDPI runs $26.7\times $ faster for token detection with 3000 tokens and 3000 rules than the state-of-the-art work.
Stable single-mode operation of 894.6 nm VCSEL at high temperatures for Cs atomic sensing

In this paper, stable single-mode operation at high temperatures is produced by the surface-relief-integrated vertical cavity surface emitting laser (VCSEL). The gain-cavity mode detuning technique is employed to realize high operating temperatures for the VCSEL. The surface relief is etched in the centre of the top side as a mode discriminator for the fundamental mode output, and the threshold current minimum is 1.94 mA at high temperatures by the gain-cavity mode detuning technique. Maximum single-fundamental-mode output power of 0.45 mW at 80 °C is obtained, and the side mode suppression ratios (SMSRs) are more than 30 dB with increasing temperature and current, respectively.
Neural Associative Memories as Accelerators for Binary Vector Search

Associative memories aim at matching an input noisy vector with a stored one. The matched vector satisfies a minimum distance criterion with respect to the inner metric of the device. This problem of finding nearest neighbors in terms of Euclidean or Hamming distances is a very common operation in machine learning and pattern recognition. However, the inner metrics of associative memories are often misfitted to handle practical scenarios. In this paper, we adapt Willshaw networks in order to use them for accelerating nearest neighbor search with limited impact on accuracy. We provide a theoretical analysis of our method for binary sparse vectors. We also test our method using the MNIST handwritten digits database. Both our analysis for synthetic data and experiments with real-data evidence a significant gain in complexity with negligible loss in performance compared to exhaustive search.
Evaluating the effect of using mechanically deboned chicken meat as a partial or total replacer for emulsion-type chicken sausages

Abstract The study aimed to determine the effects of using mechanically deboned chicken meat (MDCM) on the quality of bologna-type chicken sausage and chicken frankfurters. Sausages were formulated with control (0%), 20, 40, 60, 80 and 100% MDCM replacing the chicken breast meat as raw material. The use of MDCM caused an increase in pH value of both sausage types. The use of MDCM up to 20% had no significant effect on TBARS value, while 40% MDCM and higher levels caused an increase in TBARS value. L* value of sausages was affected by MDCM. MDCM had no significant effect on a* value in bologna-type sausage, however, it increased a* value in frankfurters. A dispersion between the MDCM level and the textural properties in sausage types was determined by PCA, and the 20% MDCM group showed the closest relationship to control group in PCA. The results of heatmap of textural parameters showed that the use of more than 20% MDCM was not possible in bologna type sausage, although frankfurters were the same main cluster except 100% MDCM group.
Melkersson–Rosenthal Syndrome and Migraine: A New Phenotype Associated with SCN1A Variants?

Peripheral facial palsy rarely occurs as part of Melkersson–Rosenthal syndrome (MRS), which is characterized by the classical triad of tongue cheilitis, recurrent episodes of orofacial swelling, and palsy. MRS is a disorder with variable expressivity and clinical as well as genetic heterogeneity; however, the causative gene remains to be identified. Migraine is a common neurological disorder, presenting with or without aura, which may be associated with neurological symptoms. The classical example of monogenic migraine is familial hemiplegic migraine (FHM), which has phenotypic variability in carriers of variants in the same gene or even carriers of the same variant. We present a family in which two sisters displayed recurrent migraines, one of which presented recurrent facial palsy and had clinical diagnosis of MRS. We performed WES and Sanger sequencing for segregation analysis in the available family members. We identified a c.3521C>G missense heterozygous variant in SCN1A carried only by the affected sister. Variants in the SCN1A gene can cause a spectrum of early-onset epileptic encephalopathies, in addition to FHM; therefore, our finding reasonably explains the proband phenotype, in which the main symptom was recurrent facial palsy. This report also adds knowledge to the clinical spectrum of SCN1A alterations and suggests a potential overlap between MRS and FHM.
LU Factorization Algorithm with Minimum Degree Ordering in Power Distribution Network Problems

Power systems computations for nowadays common large distributed systems typically involve the usage of very large sparse matrices, whose analysis and verification is very time and memory consuming. When blocked, sparse matrices can be processed much more efficiently, and this made blocked sparse matrices widely used in acquiring solutions for power system problems. The established sparse matrix storage and reordering techniques however do not fully utilize the existing computer architecture, thus search for efficient sparse system solution is ongoing. This paper presents adjustments of well-known LU factorization algorithm suitable for use in power distribution network applications. LU factorization algorithm processes data in blocks and uses minimum degree ordering to accelerate the computations.
Investigation of novel imaging methods using therapeutic ion beams

Imaging techniques play an increasingly important role for treatment planning and in-situ monitoring in ion beam therapy. In this thesis, two novel methods were studied. For real-time in-vivo range verification, FLUKA Monte Carlo simulations were performed to address the detection of prompt gammas, emitted during irradiation of homogeneous and heterogeneous targets, using an ideal detector, highlighting main signal and background features relevant for clinical application. For range monitoring prior to or in-between treatment, a dedicated detector prototype based on a stack of 61 ionization chambers was assembled and its applicability to ion-based transmission imaging was investigated experimentally. Characterization of the set-up in terms of beam parameters and settings of the read-out electronics underlined the potential of heavy ion radiography and tomography as an attractive low dose imaging modality. Possible sources of image artifacts were analyzed and an original method to improve the nominal resolution of the used detector set-up was proposed. The resulting images, expressed directly in water equivalent thickness and path length, of different phantoms of increasing complexity provided a promising proof of principle of ion-based planar and volumetric imaging. Overall, the findings of this thesis strongly support the large potential of the investigated imaging techniques for future clinical use.
WORKING PAPER SERIES Default Risk and the Cross-section of UK Insurance Firms

In this paper, we use a novel data-set of UK public and non-public insurance firms between 1985-2014 to investigate the empirical relationship between insurance firm’s returns and default risk as well as between industry default risk and reinsurance activity. We investigate whether some important firm’s characteristics (particularly, size and reinsurance) can help us to understand that relationship. We employ a novel cross-sectional portfolio approach and, after splitting returns into underwriting and investment returns, find evidence that default risk is negatively related to firms’ returns, while it is closely related to size and reinsurance activities especially for small size firms. We also report empirical evidence showing that returns in the insurance industry are exposed to a common risk factor.
PEER SUPPORT PROGRAMME AMONG AMBULANCE PERSONNEL: JOB SATISFACTION, HEALTH AND SUPPORT PROGRAMA DE APOIO DE PARES COM SOCORRISTAS: SATISFAÇÃO LABORAL, SAÚDE E APOIO

: Prehospital emergency entails challenges, risks and exposure to potential traumatic events. Social support is broadly recognized as a protective factor for mental health, especially in stressful conditions. Peer support has been pointed out as a promising approach to be used in high-risk contexts, such as emergency services. However, there is a lack of research about its effectiveness. This study evaluates the impact of a peer support programme on the job satisfaction, general health and well-being
Modelling emotional valence and arousal of non-linguistic utterances for sound design support

Non-Linguistic Utterances (NLUs), produced for popular media, computers, robots, and public spaces, can quickly and wordlessly convey emotional characteristics of a message. They have been studied in terms of their ability to convey affect in robot communication. The objective of this research is to develop a model that correctly infers the emotional Valence and Arousal of an NLU. On a Likert scale, 17 subjects evaluated the relative Valence and Arousal of 560 sounds collected from popular movies, TV shows, and video games, including NLUs and other character utterances. Three audio feature sets were used to extract features including spectral energy, spectral spread, zero-crossing rate (ZCR), Mel Frequency Cepstral Coefficients (MFCCs), and audio chroma, as well as pitch, jitter, formant, shimmer, loudness, and Harmonics-to-Noise Ratio, among others. After feature reduction by Factor Analysis, the best-performing models inferred average Valence with a Mean Absolute Error (MAE) of 0.107 and Arousal with MAE of 0.097 on audio samples removed from the training stages. These results suggest the model infers Valence and Arousal of most NLUs to less than the difference between successive rating points on the 7-point Likert scale (0.14). This inference system is applicable to the development of novel NLUs to augment robot-human communication or to the design of sounds for other systems, machines, and settings.