Miracle-dz/UniFAR-FTUs huggingface dataset:
Numerical and binomial optimal samplings of arthropods of wet paddy ecosystem in Malaysia

The optimum sample size (OSS) is the smallest sample size with userpredetermined acceptable reliability of estimation. As such, to ensure efficiency of assignments, OSS information is necessary for both numerical and binomial sampling. Data from visual inspection of 204 samples, with 40 hills of paddy plant per sample, were used to determine the OSS numerical and binomial sampling curves for each of the 22 categories of arthropods. To determine the OSS, the data on counts per hill were analyzed to obtain variance (s2) and mean density (x–) at each combination of arthropod category, sampling date and sampling time for each of the 22 arthropod categories (8 pests and 14 predators). Taylor’s a and b coefficients were then obtained, and incorporated into Wilson and Room’s (1983) algorithms to generate proportions of infestation and OSS information for enumerative and binomial samplings for each arthropod category. The pattern of OSS curves for binomial sampling indicates two groups of arthropods, i.e. those whose OSS increases slowly as x– increases and those showing a rapid increase in OSS. The first group consists of species having clumped distribution while the second group comprises species showing random distribution. The former group shows approximately three individuals a hill where the minimum sample size occurs, whereas the latter shows approximately two individuals a hill. Generally, at a certain population density, binomial sampling necessitates a larger sample size than numerical sampling for optimal characterization. In binomial sampling, the sample size required decreases with increasing population density up to a point, and then increases. In numerical sampling, the sample size required decreases with increasing population density. For each arthropod category, on the binomial OSS curve, there is no P(I) (proportion of infested hills) value below the minimum OSS point. Their spatial distributions were determined, and found to be clumped (most of the pests) and random (most of the predators). Thus, in binomial sampling at similar population densities, a larger number of samples are required to optimally estimate randomly distributed populations than those showing clumped distribution at higher densities.
Building interpolating and approximating implicit surfaces using moving least squares

This dissertation addresses the problems of building interpolating or approximating implicit surfaces from a heterogeneous collection of geometric primitives like points, polygons, and spline/subdivision surface patches. The user can choose to generate a surface that exactly interpolates the geometric elements, or a surface that approximates the input by smoothing away features smaller than some user-specified size. The implicit functions are represented using a scattered data interpolation formulation known as moving least-squares with constraints at input points or integrated over the parametric domain of each polygon or surface patch. This dissertation also proposes an improved technique for enforcing normal constraints that overcomes undesirable oscillatory behavior produced by previous methods. Multiple points, polygons and surface patches can be blended together by a single implicit function whose isosurface is a manifold envelope that either interpolates or approximates the original input, even when self-intersections, holes, or other defects are present. With an iterative procedure for ensuring that the implicit surface tightly encloses the input elements, the resulting clean, manifold surface can then be used for generating volume meshes for finite elements, manufacturing rapid prototyping models, and other applications that require manifold surfaces.
Factors impacting strategic management accounting adoption: Empirical evidence from an emerging market

Strategic management accounting (SMA) is an advanced management tool that provides valuable information to aid managers in making strategic decisions for both long-term and short-term choices. Many factors motivated managers to adopt strategic management accounting (SMA) practices in their enterprises. Furthermore, strategic management accounting (SMA) techniques also consider external stakeholders such as customers, suppliers and regulators. Therefore, there may be various aspects of the external working environment that impact SMA implementations. On the other hand, strategic management accounting (SMA) also helps managers manage costs and resources effectively. Other internal factors such as managers’ perceptions about SMA, the knowledge of accountants and technologies also significantly impact the adoption of SMA techniques. The study’s main objective is to investigate the factors that affect SMA implementation in Vietnamese enterprises. Purposive techniques are used in the study’s explorative quantitative survey approach and data collection. Data from 305 Vietnamese enterprises are included in the sample. The findings demonstrate that the adoption of SMA practices is positively associated with four factors including top management teams, corporate culture, perceived environmental uncertainty and management networking.
Recent massive expansion of wildfire and its impact on active layer over pan-Arctic permafrost

Wildfire is recognized as an increasing threat to the southern boreal forests and the permafrost beneath them, with less occurring over the cold continuous permafrost than before. However, we show that continuous permafrost was a major contribution to wildfire expansion in the pan-Arctic over the last two decades. The expansion rate of burned area over continuous permafrost was 0.9 Mha decade−1, in contrast to a decreasing trend (−0.5 Mha decade−1) over the entire permafrost areas. Burned area has been rapidly growing in the north of the Arctic Circle in particular, where the total burned area in the major fire seasons during 2011–2020 nearly doubled that during 2001–2010. Wildfire expansion is closely linked to an increased soil moisture deficit, considering wildfires there combust more than 90% of belowground fuel. Continuous permafrost experiences more severe fire-induced degradation. Active layer thickening following wildfires over continuous permafrost lasts more than three decades to reach a maximum of more than triple the pre-fire thickness. These new findings highlight the massive expansion of wildfires over continuous permafrost, which can dramatically modify ecological processes, disturb organic carbon stock, and thus accelerate the positive feedback between permafrost degradation and climate warming.
Ka-/K-Band Frequency-Reconfigurable Single-Input Differential-Output Low-Noise Amplifier for 5G Applications

This letter describes the design, analysis, and performance measurements of a <inline-formula> <tex-math notation="LaTeX">$Ka$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>-band frequency-reconfigurable single-input differential-output (SIDO) low-noise amplifier (LNA) with a compact core size of 0.157 mm2. At 28 and 39 GHz, respectively, the noise figures of the LNA were 2.8 and 4.4 dB; it had high differential gains of 20.1 and 14.9 dB. The proposed LNA was fabricated using the 65-nm CMOS process. The LNA includes a two-stage common-source (CS) buffer amplifier to achieve low noise and an active balun for converting single-ended signals into differential signals. Moreover, a substrate-shield-based inductor and tunable matching networks were used to achieve the frequency reconfiguration function of the LNA. The gain and phase imbalances were 0.3 and 0.2 dB, and 5.4° and 6° at 28 and 39 GHz, respectively. Compared with published multiband SIDO LNAs, the proposed amplifier achieved higher figures of merit (FoMs) of 3.52 and 1.77 while maintaining a compact core size. Therefore, the LNA is suitable for <inline-formula> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula>- and <inline-formula> <tex-math notation="LaTeX">$Ka$ </tex-math></inline-formula>-band 5G communication applications.
Implications of Electronic Word-of-Mouth on Hotel Profitability: An Investigation beyond Room Revenue

Interest has been growing in the financial impact of online reviews, an important form of electronic word-of-mouth (eWOM), on hotel performance. Existing research in this area is overwhelmingly focused on the relationship between online reviews and room-related revenue. For a more comprehensive understanding of online reviews’ financial impacts, this study investigates the value of these reviews relative to hotels’ non-room revenue, total revenue and gross operating profit (GOP). To the authors’ best knowledge, this study is the first to link hotels’ online reviews or any form of eWOM to GOP, a firm-level outcome. A panel dataset comprising the annual operating performance of 104 hotels in Asia was assembled and matched with 50,732 TripAdvisor reviews. Analyses indicated that review valence, variance and volume correlated to varying degrees with hotels’ GOP but demonstrated limited association with non-room revenue. Our findings also indicated that online reviews more strongly influenced room revenue and total revenue than non-room revenue and GOP. Overall, the positive effects associated with room revenue demonstrated selective generalizability to GOP. This in turn reflects the insufficiency of relying solely on room revenue indicators (i.e. average daily rate, occupancy and revenue per available room) to fully understand the impact of online reviews.
Can we diagnose mental disorders in children? A large‐scale assessment of machine learning on structural neuroimaging of 6916 children in the adolescent brain cognitive development study

Abstract Background Prediction of mental disorders based on neuroimaging is an emerging area of research with promising first results in adults. However, research on the unique demographic of children is underrepresented and it is doubtful whether findings obtained on adults can be transferred to children. Methods Using data from 6916 children aged 9–10 in the multicenter Adolescent Brain Cognitive Development study, we extracted 136 regional volume and thickness measures from structural magnetic resonance images to rigorously evaluate the capabilities of machine learning to predict 10 different psychiatric disorders: major depressive disorder, bipolar disorder (BD), psychotic symptoms, attention deficit hyperactivity disorder (ADHD), oppositional defiant disorder, conduct disorder, post‐traumatic stress disorder, obsessive‐compulsive disorder, generalized anxiety disorder, and social anxiety disorder. For each disorder, we performed cross‐validation and assessed whether models discovered a true pattern in the data via permutation testing. Results Two of 10 disorders can be detected with statistical significance when using advanced models that (i) allow for non‐linear relationships between neuroanatomy and disorder, (ii) model interdependencies between disorders, and (iii) avoid confounding due to sociodemographic factors: ADHD (AUROC = 0.567, p = 0.002) and BD (AUROC = 0.551, p = 0.002). In contrast, traditional models perform consistently worse and predict only ADHD with statistical significance (AUROC = 0.529, p = 0.002). Conclusion While the modest absolute classification performance does not warrant application in the clinic, our results provide empirical evidence that embracing and explicitly accounting for the complexities of mental disorders via advanced machine learning models can discover patterns that would remain hidden with traditional models.
Impaired Personality Functioning in Children and Adolescents Assessed with the LoPF-Q 6-18 PR in Parent-Report and Convergence with Maladaptive Personality Traits and Personality Structure in School and Clinic Samples

To investigate if the Personality Disorder (PD) severity concept (Criterion A) of the ICD-11 and DSM-5 AMPD is applicable to children and adolescents, following the ICD-11 lifespan perspective of mental disorders, age-specific and informant-adapted assessment tools are needed. The LoPF-Q 6-18 PR (Levels of Personality Functioning Questionnaire Parent Rating) was developed to assess Impaired Personality Functioning (IPF) in children aged 6–18 in parent-reported form. It is based on the established self-report questionnaire LoPF-Q 12-18. Psychometric properties were investigated in a German-speaking clinical and school sample containing 599 subjects. The final 36-item version of LoPF-Q 6-18 PR showed good scale reliabilities with 0.96 for the total scale IPF and 0.90-0.87 for the domain scales Identity, Self-direction, Empathy, and Intimacy/Attachment and an acceptable model fit in a hierarchical CFA with CFI = 0.936, RMSEA = 0.078, and SRMR = 0.068. The total score discriminated significantly and with large effect sizes between the school population and (a) adolescent PD patients (d = 2.7 standard deviations) and (b) the younger patients (6–11-year-olds) with internalizing and externalizing disorders (d = 2.2 standard deviations). Informant agreement between parent and self-report was good at 0.47. Good construct validity can be assumed given sound covariation with related measures of psychopathology (CBCL 4-18, STiP-5.1, OPD-CA2-SQ PR) and maladaptive traits (PID5BF+ M CA IRF) in line with theory and matching the result patterns obtained in older samples in self-report. The results suggest that parent-reported assessments of IPF and maladaptive traits are equivalent to self-reported measures for Criterion A and B. Assessing IPF as early as age six might be a valuable step to foster early detection of PD, or maladaptive personality development, respectively individuals at risk.
A Comprehensive Characterization of the TI-LGAD Technology

Pixelated low-gain avalanche diodes (LGADs) can provide both precision spatial and temporal measurements for charged particle detection; however, electrical termination between the pixels yields a no-gain region, such that the active area or fill factor is not sufficient for small pixel sizes. Trench-isolated LGADs (TI-LGADs) are a strong candidate for solving the fill-factor problem, as the p-stop termination structure is replaced by isolated trenches etched in the silicon itself. In the TI-LGAD process, the p-stop termination structure, typical of LGADs, is replaced by isolating trenches etched in the silicon itself. This modification substantially reduces the size of the no-gain region, thus enabling the implementation of small pixels with an adequate fill factor value. In this article, a systematic characterization of the TI-RD50 production, the first of its kind entirely dedicated to the TI-LGAD technology, is presented. Designs are ranked according to their measured inter-pixel distance, and the time resolution is compared against the regular LGAD technology.
Combined treatment with gibberellic acid and thidiazuron improves fruit quality of ‘Red Dream’ grape cultivar

This study was conducted to investigate the effect of treatment conditions of plant growth regulators on the marketability of ‘Red Dream’, a triploid grape. In order to achieve the purpose of this study, gibberellic acid 3 (GA3) 100 ppm single use treatment group was set as a control group. In addition, the fruit characteristics expressed when cytokinins such as thidiazuron (TDZ) or forchlorfenuron (CPPU) 2.5 ppm or 5.0 ppm mixed with GA3 100 ppm were compared with the control group. Cluster weight was commonly higher in the mixed treatment of TDZ or CPPU than in the GA3 single use treatment, and it was found that the cytokinin mixed treatment could effectively induce the enlargement of ‘Red Dream’ berries. Although the harvesting period was slightly delayed compared to GA3 single use treatment in the case of mixed treatment of GA3 and TDZ or CPPU, there was no inhibition of coloration, soluble solid sugar content and functionality, which are important indicators of marketability. In particular, in the GA3 100 ppm + TDZ 5 ppm treatment group, where the enlargement of fruits was the largest, the occurrence rate of non-commercial berry was also found to be the lowest. Therefore, it is expected to contribute to strengthening the marketability of ‘Red Dream’ if such plant growth regulator treatment condition is applied during cultivation.
Generalized half-logistic Poisson distributions

In this article, we proposed a new three-parameter distribution called generalized half-logistic Poisson distribution with a failure rate function that can be increasing, decreasing or upside-down bathtub-shaped depending on its parameters. The new model extends the half-logistic Poisson distribution and has exponentiated half-logistic as its limiting distribution. A comprehensive mathematical and statistical treatment of the new distribution is provided. We provide an explicit expression for the rth moment, moment generating function, Shannon entropy and Rényi entropy. The model parameter estimation was conducted via a maximum likelihood method; in addition, the existence and uniqueness of maximum likelihood estimations are analyzed under potential conditions. Finally, an application of the new distribution to a real dataset shows the flexibility and potentiality of the proposed distribution.
Predicting the Efficacy of Neoadjuvant Chemotherapy for Pancreatic Cancer Using Deep Learning of Contrast-Enhanced Ultrasound Videos

Contrast-enhanced ultrasound (CEUS) is a promising imaging modality in predicting the efficacy of neoadjuvant chemotherapy for pancreatic cancer, a tumor with high mortality. In this study, we proposed a deep-learning-based strategy for analyzing CEUS videos to predict the prognosis of pancreatic cancer neoadjuvant chemotherapy. Pre-trained convolutional neural network (CNN) models were used for binary classification of the chemotherapy as effective or ineffective, with CEUS videos collected before chemotherapy as the model input, and with the efficacy after chemotherapy as the reference standard. We proposed two deep learning models. The first CNN model used videos of ultrasound (US) and CEUS (US+CEUS), while the second CNN model only used videos of selected regions of interest (ROIs) within CEUS (CEUS-ROI). A total of 38 patients with strict restriction of clinical factors were enrolled, with 76 original CEUS videos collected. After data augmentation, 760 and 720 videos were included for the two CNN models, respectively. Seventy-six-fold and 72-fold cross-validations were performed to validate the classification performance of the two CNN models. The areas under the curve were 0.892 and 0.908 for the two models. The accuracy, recall, precision and F1 score were 0.829, 0.759, 0.786, and 0.772 for the first model. Those were 0.864, 0.930, 0.866, and 0.897 for the second model. A total of 38.2% and 40.3% of the original videos could be clearly distinguished by the deep learning models when the naked eye made an inaccurate classification. This study is the first to demonstrate the feasibility and potential of deep learning models based on pre-chemotherapy CEUS videos in predicting the efficacy of neoadjuvant chemotherapy for pancreas cancer.
Prevalence of Malnutrition in People with Dementia in Long-Term Care: A Systematic Review and Meta-Analysis

Dementia is a common syndrome in older people. Dementia alters eating behaviors, hunger and thirst cues, swallow function, ability to self-feed, and recognition and interest in food. There is significant variation in the reported prevalence of malnutrition among older people who live in long-term care. The aim was to conduct a systematic literature review and meta-analysis of the prevalence of malnutrition in those with dementia living in long-term care using a validated nutrition assessment tool. Scopus, Web of Science, CINAHL, and Medline were searched. A random effects model was used to determine the prevalence and risk of malnutrition. Data were retrieved from 24 studies. Most of the studies were from Europe or South Asia. The prevalence of malnutrition ranged from 6.8 to 75.6%, and the risk of malnutrition was 36.5–90.4%. The pooled prevalence of malnutrition in those with dementia in long-term care was 26.98% (95% CI 22.0–32.26, p < 0.0001, I2 = 94.12%). The pooled prevalence of the risk of malnutrition in those with dementia was 57.43% (95% CI 49.39–65.28, p < 0.0001, I2 = 97.38%). Malnutrition is widespread in those with dementia living in long-term care. Further research exploring malnutrition in other industrialized countries using validated assessment tools is required.
Firm Financial Conditions and the Transmission of Monetary Policy

We study how the transmission of monetary policy to firms' investment and credit spreads depends on their financial conditions, finding a major role for their excess bond premia (EBPs), the component of credit spreads in excess of default risk. While monetary policy easing shocks compress credit spreads more for firms with higher ex-ante EBPs, it is lower-EBP firms that invest more. We rationalize these findings using a model with financial frictions in which lower-EBP firms have flatter marginal product of capital curves. We also show empirically that the cross-sectional distribution of firm EBPs determines the aggregate effectiveness of monetary policy.
Improved laser glass cutting by spatio-temporal control of energy deposition using bursts of femtosecond pulses

We demonstrate the advantage of combining non-diffractive beam shapes and femtosecond bursts for volume laser processing of transparent materials. By re-distribution of the single laser pulse energy into several sub-pulses with 25 ns time delay, the energy deposition in the material can be enhanced significantly. Our combined experimental and theoretical analysis shows that in burst-mode detrimental defocusing by the laser generated plasma is reduced, and the non-diffractive beam shape prevails. At the same time, heat accumulation during the interaction with the burst leads to temperatures high enough to induce material melting and even in-volume cracks. In an exemplary case study, we demonstrate that the formation of these cracks can be controlled to allow high-speed and high-quality glass cutting.
Global-Local Attention-Based Butterfly Vision Transformer for Visualization-Based Malware Classification

In recent studies, convolutional neural networks (CNNs) are mostly used as dynamic techniques for visualization-based malware classification and detection. Though vision transformer (ViT) proved its efficiency in image classification, a few of the earlier studies developed a ViT-based malware classifier. This paper proposes a butterfly construction-based vision transformer (B_ViT) model for visualization-based malware classification and detection. B_ViT has four phases: (1) image partitioning and patches embeddings; (2) local attention; (3) global attention; and (4) training and malware classification. B_ViT is an enhanced ViT architecture that supports the parallel processing of image patches and captures local and global spatial representations of malware images. B_ViT is a transfer learning-based model that uses a pre-trained ViT model on the ImageNet dataset to initialize the training parameters of transformers. Four B_ViT variants are experimented and evaluated on grayscale malware images collected from MalImg, Microsoft BIG datasets or converted from portable executable imports. The experiments show that B_ViT variants outperform the Input Enhanced vision transformer (IEViT) and ViT variants, achieving an accuracy equal to 99.49% and 99.99% for malware classification and detection respectively. The experiments also show that B_ViT is time effective for malware classification and detection where the average speed-up of B_ViT variants over IEViT and ViT variants are equal to 2.42 and 1.81 respectively. The analysis proves the efficiency of texture-based malware detection as well as the resilience of B_ViT to polymorphic obfuscation. Finally, the proposed B_ViT-based malware classifier outperforms the CNN-based malware classification methods in well.