Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of a Reduced-Order Kalman Filter to Initialize a Coupled Atmosphere-Ocean Model: Impact on the Prediction of El Nino

Abstract A reduced-order Kalman filter is used to assimilate observed fields of the surface wind stress, sea surface temperature, and sea level into the coupled ocean–atmosphere model of Zebiak and Cane. The method projects the Kalman filter equations onto a subspace defined by the eigenvalue decomposition of the error forecast matrix, allowing its application to high-dimensional systems. The Zebiak and Cane model couples a linear, reduced-gravity ocean model with a single, vertical-mode atmospheric model. The compatibility between the simplified physics of the model and each observed variable is studied separately and together. The results show the ability of the empirical orthogonal functions (EOFs) of the model to represent the simultaneous value of the wind stress, SST, and sea level, when the fields are limited to the latitude band 10°S–10°N, and when the number of EOFs is greater than the number of statistically significant modes. In this first application of the Kalman filter to a coupled ocean–atm.
Edinburgh Research Explorer On the study of replay and voice conversion attacks to text-dependent speaker verification

Automatic speaker veriﬁcation (ASV) is to automatically accept or reject a claimed identity based on a speech sample. Recently, individual studies have conﬁrmed the vulnerability of state-of-the-art text-independent ASV systems under replay, speech synthesis and voice conversion attacks on various databases. However, the behaviours of text-dependent ASV systems have not been systematically assessed in the face of various spooﬁng attacks. In this work, we ﬁrst conduct a systematic analysis of text-dependent ASV systems to replay and voice conversion attacks using the same protocol and database, in particular the RSR2015 database which represents mobile device quality speech. We then analyse the interplay of voice conversion and speaker veriﬁcation by linking the voice conversion objective evaluation measures with the speaker veriﬁcation error rates to take a look at the vulnerabilities from the perspective of voice conversion.
A Preliminary Study of the Global Application of HAZUS and ShakeMap for Loss Estimation from a Scenario Earthquake in the Korean Peninsular

Efficiency and limitations of HAZUS-MH, a GIS based systematic and informative system developed by FEMA and NIBS for natural hazard loss estimations, are discussed by means of a pilot study in the Korean Peninsula. Gyeongsang-do has been selected for the test after careful reviews of previous studies including historical and modern seismicity in the peninsula. A ShakeMap for the selected scenario earthquake with magnitude 6.7 in Gyeongju area is prepared. Then, any losses due to the scenario event have been estimated using HAZUS. Results of the pilot test show that the study area may experience significant physical, economic, and social damages. Detailed study in the future will provide efficient and crucial information to the decision makers and emergency agents to mitigate any disaster posed by natural hazards.
PREDICTION LASER BEAM EFFECT UNDER HAZY WEATHER FOR FREE SPACE OPTICAL PROPAGATION

This paper focus impact of haze in free space optic transmission focusing on Malaysia-Indonesia region. Haze is one of the factors that contributed to atmosphere attenuation that can affecting this system’s performance. It will cause the beam propagation will attenuate and wander from line of sight. Ultimately drag the transmission into burst error. The analysis is carried out using simulation optical software with original data. In the simulation, analyses are carried out looking in bit error rate, eye diagram pattern and received power at receiver. From there, the prediction of laser beam effect can be estimated. The performance for free space optic transmission will investigate under different parameters values such as wavelength, visibility, receiver sensitivity and beam divergence are analyzed. From the result, as increases the haze effect it will deteriorate the signal beam. The system maximum system can support to operate is attenuation at 45 dBm/km.
THE DYNAMIC TRANSMISSION MAP FOR DEHAZING METHOD IN SINGLE IMAGE WITH TROPICAL ATMOSPHERIC CONDITION

Normally, outdoor images are degraded by light scattering and absorption from the atmosphere's dust, mist, haze, and smoke. These affect the image captured and cause poor visibility, dimmed luminosity, low contrast, and colour distortion. Therefore, it is crucial to restoring images captured, especially in haze conditions called image dehazing. The crucial aim of image dehazing is to improve the details on visibility, edge, and texture and retain the structure and colours of the image without data loss. Most algorithmic methods, considering the large number of algorithms suggested for single image dehazing, introduce dehazing at a certain haze level. There is a lack of a dehazing algorithm focused on the visibility range to overcome several haze levels. This paper proposes an improvement of the dehazing algorithm based on the meteorological visibility range with a dynamic transmission map to fix this problem This algorithm focuses on removing haze at different levels based on the determination of the visibility range, which is different from most existing dehazing algorithms. The dehazing algorithm emphasizes this proposed method contribute to better image quality than the existing state-of-the-art dehazing process.
LINC01852 inhibits tumorigenesis and chemoresistance in colorectal cancer by regulating SRSF5-mediated PKM alternative splicing

Background Colorectal cancer (CRC) is a major cause of cancer-related deaths worldwide, and chemoresistance is a major obstacle in the treatment of this disease. Despite advances in therapy, the molecular mechanism underlying chemoresistance in CRC is not fully understood. Recent studies have implicated the key roles of long noncoding RNAs (lncRNAs) in the regulation of CRC chemoresistance. Methods In this study, we investigated the role of lncRNA LINC01852 in CRC chemoresistance. The expression of LINC01852 was evaluated in multiple CRC cohorts using quantitative reverse transcription PCR. We conducted in vitro and in vivo functional experiments using cell culture and mouse models. RNA pull-down, RNA immunoprecipitation, chromatin immunoprecipitation, and dual luciferase assays were used to investigate the molecular mechanism of LINC01852 in CRC. Results Our �ndings a lncRNA with tumor-inhibiting properties, LINC01852, that is downregulated in CRC and inhibits cell proliferation and chemoresistance both in vitro and in vivo. Further mechanistic investigations revealed that LINC01852 enhances TRIM72-mediated ubiquitination and degradation of SRSF5, inhibiting SRSF5-mediated PKM alternative splicing and thereby decreasing the production of PKM2. Overexpression of LINC01852 causes a metabolic switch from aerobic glycolysis to oxidative phosphorylation, which attenuates the chemoresistance of CRC by inhibiting PKM2-mediated glycolysis. Conclusions Our results demonstrate that LINC01852 plays an important role in repressing CRC malignancy and chemoresistance by regulating SRSF5-mediated PKM alternative splicing, and that targeting the LINC01852/TRIM72/SRSF5/PKM2 signaling axis may represent a potential therapeutic strategy for the
of Birmingham Metabolomic analysis of human disease and its application to the eye

Metabolomics, the analysis of the metabolite profile in body fluids or tissues, is being applied to the analysis of a number of different diseases as well as being used in following responses to therapy. While genomics involves the study of gene expression and proteomics the expression of proteins, metabolomics investigates the consequences of the activity of these genes and proteins. There is good reason to think that metabolomics will find particular utility in the investigation of inflammation, given the multi-layered responses to infection and damage that are seen. This may be particularly relevant to eye disease, which may have tissue specific and systemic components. Metabolomic analysis can inform us about ocular or other body fluids and can therefore provide new information on pathways and processes involved in these responses. In this review, we explore the metabolic consequences of disease, in particular ocular conditions, and why the data may be usefully and uniquely assessed using the multiplexed analysis inherent in the metabolomic approach.
β-arrestin-dependent and -independent endosomal G protein activation by the vasopressin type 2 receptor

The vasopressin type 2 receptor (V2R) is an essential GPCR in renal regulation of water homeostasis. Upon stimulation, the V2R activates Gαs and Gαq/11, which is followed by robust recruitment of β-arrestins and receptor internalization into endosomes. Unlike canonical GPCR signaling, the β-arrestin association with the V2R does not terminate Gαs activation, and thus, Gαs-mediated signaling is sustained while the receptor is internalized. Here, we demonstrate that this V2R ability to co-interact with G protein/β-arrestin and promote endosomal G protein signaling is not restricted to Gαs, but also involves Gαq/11. Furthermore, our data implies that β-arrestins potentiate Gαs/Gαq/11 activation at endosomes rather than terminating their signaling. Surprisingly, we found that the V2R internalizes and promote endosomal G protein activation independent of β-arrestins to a minor degree. These new observations challenge the current model of endosomal GPCR signaling and suggest that this event can occur in both β-arrestin-dependent and -independent manners. IMPACT STATEMENT The vasopressin type 2 receptor promotes dual Gαs and Gαq/11 signaling at early endosomes in β-arrestin-dependent and -independent manners.
Wagner’s Hypothesis In India: An Empirical Investigation Using Bivariate Framework

Over the course of many decades, economists have focused their attention on the link between public expenditure and economic growth. Public expenditure plays an important role in an economy because, without government support and backing, a poor country will be unable to make large investments that will result in a positive shift in the country's economic base. That is why the government makes such large investments. One of the prominent and earliest economists named Adolf Wagner, who had given a concept regarding public expenditure and economic growth, later became a law, which is known as "Wagner’s Law" or "Wagner’s hypothesis. " According to Wagner’s hypothesis, as the economy develops, it will lead to an increase in government expenditure. To verify Wagner’s hypothesis in India by using annual time series data from 1980–2019. Therefore, the long data set validates the consistency of our statistical and economic conclusions. We apply the unit root test, the Autoregressive Distributed Lag Model (ARDL), cointegration, and the Granger causality test. Our results indicate the presence of a long-run relationship between GDP and public expenditure, while the causality is uni-directional from GDP to public expenditure. Thus, we find support for Wagner’s hypothesis. The attempt of this paper is to re-examine Wagner’s hypothesis in India because it is rarely examined in the case of developing countries.
Performance Evaluation of Matrix-Matrix Multiplication using Parallel Programming Models on CPU Platforms

Today’s hardware platforms have parallel processing capabilities and many parallel programming models have been developed. It is necessary to research an e�cient implementation of compute-intensive applications using available platforms. Matrix-matrix multiplication is an important kernel used in many scienti�c and multimedia applications. This kernel is a compute-intensive program too. This paper aims to e�ciently implement this kernel using different parallel programming models such as SIMD, OpenMP, and OpenCL. Our experiment results for different matrix sizes show that speedups up to 6.5x, 3.2x, 16.7x, and 32x are achieved for SIMD, OpenMP, hybrid OpenMP-SIMD, and OpenCL implementations over-optimized serial implementation, respectively.
A Correlational Investigation Of Resilience And Job Stress Among Public And Private School Teachers At Secondary Level

Job stress in the current world is enormous because of multiple factors, which include competition, extra expectations, changes in curriculum, changes in educational audits, along with domestic reasons, and all these directly contribute into the stress of teachers. Furthermore, these stresses demand for teachers’ resilience abilities that enables them to cope with the changing demands or adjust with. This study investigated teachers’ job stress and their resilience capacity in public and private secondary school of district Mardan. The objectives concentrated on the investigation of jobs stress level and resilience abilities of public and private secondary school teachers along with the relationship between these two study variables. Following a quantitative correlational explanatory research design a sample of 291 respondents were selected (Public = 179, Private = 112) through stratified sampling techniques. The data were collected on two questionnaires (Brief Resilience Scale and Job Stress) and were analyzed through descriptive and inferential statistic through SPSS. The results showed that private school teachers experience more job stress in term of job demands, job control, and they were as similar the public-school teachers in support and job satisfaction. Furthermore, the resilience skills of public and private school teachers were the same average level. The need of training for the professional development of teachers to meet the job demands were recommended for the authorities.
The function of medication beliefs as mediators between personality traits and adherence behavior in people with asthma

Background: There is evidence that both personality traits and personal beliefs about medications affect adherence behavior. However, limited research exists on how personality and beliefs about asthma medication interact in influencing adherence behavior in people with asthma. To extend our knowledge in this area of adherence research, we aimed to determine the mediating effects of beliefs about asthma medication between personality traits and adherence behavior. Methods: Asthmatics (n = 516) selected from a population-based study called West Sweden Asthma Study completed the Neuroticism, Extraversion and Openness to Experience Five-Factor Inventory, the Medication Adherence Report Scale, and the Beliefs about Medicines Questionnaire. Data were analyzed using confirmatory factor analysis and structural equation modeling. Results: Three of the five investigated personality traits – agreeableness, conscientiousness, and neuroticism – were associated with both concerns about asthma medication and adherence behavior. Concerns functioned as a partial mediator for the influencing effects of agreeableness, conscientiousness, and neuroticism on adherence behavior. Conclusion: The findings suggest that personality traits could be used to identify individuals with asthma who need support with their adherence behavior. Additionally, targeting concerns about asthma medication in asthmatics with low levels of agreeableness or conscientiousness or high levels of neuroticism could have a favorable effect on their adherence behavior.
Towards a Curry-Howard Correspondence for Linear, Reversible Computation

In this paper, we present a linear and reversible language with inductive and coinductive types, together with a Curry-Howard correspondence with the positive fragment of the logic µ MALL : linear logic extended with least ﬁxed points allowing inductive statements. Linear, reversible computation makes an important sub-class of quantum computation without measure-ment. In the latter, the notion of purely quantum recursive type is not yet well understood. Moreover, models for reasoning about quantum algorithms only provide complex types for classical datatypes: there are usually no types for purely quantum objects beside tensors of quantum bits. This work is a ﬁrst step towards understanding purely quantum recursive types.
Neck dissection of Level Ib for the treatment of cN0 HPV-negative oropharyngeal squamous cell carcinoma: Should it be safely omitted?

Background: Selective neck dissection (SND) of levels II-IV is traditionally recommended for the treatment of cN0 oropharyngeal squamous cell carcinoma (OPSCC). Whether to include level Ib in the dissection of cN0 neck tumors is debated because occult lymph node metastasis is frequently located at level II, which is closely posterior to level Ib. Moreover, neck dissection focusing on HPV-negative OPSCC is insu�cient because the HPV-positive subtype accounts for most cases in many regions, especially in Western countries. The aim of this study was to clarify whether patients would potentially bene�t from including level Ib in SND for cN0 HPV-negative OPSCC. Methods: This retrospective cohort study included 198 cN0 HPV-negative OPSCC patients who underwent SND of levels II-IV (Group A) and levels Ib-IV (Group B). Neck recurrence (NR) was considered the primary endpoint. Overall survival (OS), disease-free survival (DFS), and disease-specic survival (DSS) were the secondary endpoints. Results: In Group B, the rate of occult lymph node metastasis at level Ib of the ipsilateral neck was 6.1%. For the entire cohort, the rate of NR was 7.6% in Group A and 2.3% in Group B (p = 0.074). The 5-year OS and DFS rates were also not signi�cantly different between Group A (50.0% and 43.2%) and Group B (57.6% and 51.6%), while Group B had higher 5-year DSS rate than Group A (60.7% vs. 47.9%, p = 0.018). For the tumor from the base of tongue, the rate of NR was obviously higher in Group A (12.2%) than in Group B (2.1%, p = 0.015). Patients in Group A had lower 5-year DFS (39.1% vs. 47.4%, p = 0.046) and DSS rates (46.5% vs. 56.3% p = 0.034). N2 stage was an independent predictive
SECURE AUTHENTICATION SCHEME BASED ON NUMERICAL SERIES CRYPTOGRAPHY FOR INTERNET OF THINGS

The rapid advancement of cellular networks and wireless networks has laid a solid basis for the Internet of Things. IoT has evolved into a unique standard that allows diverse physical devices to collaborate with one another. A service provider gives a variety of services that may be accessed via smart apps anywhere, at any time, and from any location over the Internet. Because of the public environment of mobile communication and the Internet, these services are highly vulnerable to a several malicious attacks, such as unauthorized disclosure by hostile attackers. As a result, the best option for overcoming these vulnerabilities is a strong authentication method. In this paper, a lightweight authentication scheme that is based on numerical series cryptography is proposed for the IoT environments. It allows mutual authentication between IoT devices. Parametric study and formal proofs are utilized to illustrate that the proposed approach is resistant to a variety of security threats.
IMAGE RETRIEVAL USING COLOR AND STRETCHED TEXTONS OF RULE BASED MOTIF

By deriving color and texture attributes, this paper develops a new framework for content-based image retrieval (CBIR). The individual histograms on the H, S, and V planes are calculated in the first step. This paper primarily divided the 3x3 window into four overlapped microgrids of size 2x2. On each micro grid, it has derived Rule-based Dynamic motif (RM) indexes. The RM extracts all probable Motifs on a 2x2 grid by overcoming all possible ambiguities. This process transforms the 3x3 window into a 2x2 grid and each grid value represents the shape, and texture features more efficiently and precisely. On this transformed grid of 2x2, this paper applied Stretched Texton (ST) patterns. The ST patterns consider the magnitude relationship among the pixels that are part and not part of texton formation. This aspect is not considered in the earlier methods. The co-occurrence features are derived from RM-ST matrix (RMSTCM). The feature vector is derived by fusing features of RMSTCM with the histogram features of color components of H, S, and V. The suggested framework is related with the other methods of significance. The outcomes specify the efficacy of the RMSTCM.
The history and purview of

Last year marked the 10th anniversary of the birth of phylogeography as a formal discipline. However, the field’s gestation began in the mid-1970s with the introduction of mitochondrial (mt) DNA analyses to population genetics, and to the profound shift toward genealogical thought at the intraspecific level (now formalized as coalescent theory) that these methods prompted. This paper traces the early history and explosive growth of phylogeography, and closes with predictions about future challenges for the field that centre on several facets of genealogical concordance.
AI-Driven Segmentation and Automated Analysis of the Whole Sagittal Spine from X-ray Images for Spinopelvic Parameter Evaluation

Spinal–pelvic parameters are utilized in orthopedics for assessing patients’ curvature and body alignment in diagnosing, treating, and planning surgeries for spinal and pelvic disorders. Segmenting and autodetecting the whole spine from lateral radiographs is challenging. Recent efforts have employed deep learning techniques to automate the segmentation and analysis of whole-spine lateral radiographs. This study aims to develop an artificial intelligence (AI)-based deep learning approach for the automated segmentation, alignment, and measurement of spinal–pelvic parameters through whole-spine lateral radiographs. We conducted the study on 932 annotated images from various spinal pathologies. Using a deep learning (DL) model, anatomical landmarks of the cervical, thoracic, lumbar vertebrae, sacrum, and femoral head were automatically distinguished. The algorithm was designed to measure 13 radiographic alignment and spinal–pelvic parameters from the whole-spine lateral radiographs. Training data comprised 748 digital radiographic (DR) X-ray images, while 90 X-ray images were used for validation. Another set of 90 X-ray images served as the test set. Inter-rater reliability between orthopedic spine specialists, orthopedic residents, and the DL model was evaluated using the intraclass correlation coefficient (ICC). The segmentation accuracy for anatomical landmarks was within an acceptable range (median error: 1.7–4.1 mm). The inter-rater reliability between the proposed DL model and individual experts was fair to good for measurements of spinal curvature characteristics (all ICC values > 0.62). The developed DL model in this study demonstrated good levels of inter-rater reliability for predicting anatomical landmark positions and measuring radiographic alignment and spinal–pelvic parameters. Automated segmentation and analysis of whole-spine lateral radiographs using deep learning offers a promising tool to enhance accuracy and efficiency in orthopedic diagnostics and treatments.