Miracle-dz/UniFAR-FTUs huggingface dataset:
Spatial Heterogeneity in Production Functions Models

Controlling for unobserved heterogeneity is a fundamental challenge in empirical research, as failing to do so can introduce omitted variables biases and preclude causal inference. In this paper we develop an innovative method – the Iterative Geographically Weighted Regression (IGWR) method – to identify clusters of farms that follow a similar local production econometric model, taking explicitly unobserved spatial heterogeneity into account. The proposed method is the perfect combination of the GWR approach and the adaptive weights smoothing (AWS) procedure. This method is applied to regional samples of olive growing farms in Italy. The main finding is that the conditional global IGWR model fits the data best, proving that explicitly accounting for unobserved spatial heterogeneity is of crucial importance when modeling the production function of firms particularly for those operating in land based industries
Development and Analysis of a Three-Fin Trigate Q-FinFET for a 3 nm Technology Node with a Strained-Silicon Channel System

Multi-gate field effect transistors (FETs) such as FinFETs are severely affected by short-channel effects (SCEs) below 14 nm technology nodes, with even taller fins incurring fringing capacitances. This leads to performance degradation of the devices, which inhibits further scaling of nanoFETs, deterring the progress of semiconductor industries. Therefore, research has not kept pace with the technological requirements of the International Roadmap for Devices and Systems (IRDS). Thus, the development of newer devices with superior performances in terms of higher ON currents, acceptable leakage currents and improved SCEs is needed to enable the continuance of integrated circuit (IC) technologies. The literature has advocated integration of strained-silicon technology in existing FinFETs, which is highly effective in enhancing ON currents through the strain effect. However, the ON currents can also be amplified by intensifying the number of fins in trigate (TG) FinFETs. Thus, three-fin TG quantum (Q)-FinFETs, using a novel tri-layered strained-silicon channel, are deployed here at 10 nm and 8 nm channel lengths. Threshold voltage is calculated analytically to validate the designs. The electrical parameters and quantum effects of both devices are explored, analysed and compared with respect to existing heterostructure-on-insulator (HOI) FinFETs and the proposed existing standard requirement of IRDS 2022 for a 3 nm technology node. The comparisons demonstrated a significant increase in the drive currents upon employing three fins of the same dimensions (8 nm gate length) and specifications in a device-based system. The performance is augmented in contrast to the 3 nm technology node device of IRDS 2022, with SCEs within the limits. Thus, employing a tri-layered strained-silicon channel system in each fin allowed for forming a three-fin Q-FinFET that, in our opinion, is the technique for consolidating the performance of the devices and enabling future generation device for faster switching operation in a sub-nano regime.
Concurrent Invasive Ductal Carcinoma and Pancreatic Intraepithelial Neoplasia in Duodenal Heterotopic Pancreas: A Case Report

Heterotopic pancreatic tissue is defined as the presence of pancreatic tissue outside of its usual site with no vascular or anatomic association with the normal pancreas. This is most commonly found in the stomach, duodenum and proximal jejunum. The majority of diagnosed pancreatic heterotopia cases are an incidental finding. Yet, the heterotopic pancreas can occasionally produce symptoms such as bleeding, abdominal pain and symptoms related to gastrointestinal obstruction. They can also develop malignant and pre-malignant lesions that are normally reported in the native pancreas. The following report details a rare case of a 77-year-old male with concurrent invasive ductal carcinoma and low-grade pancreatic intraepithelial neoplasia involving a heterotopic pancreatic tissue located at the duodenum.
Sample Size Calculations for Binomial Proportions Via Highest Posterior Density Intervals

Three different Bayesian approaches to sample size calculations based on highest posterior density (HPD) intervals are discussed and illustrated in the context of a binomial experiment. The preposterior marginal distribution of the data is used to find the sample size needed to attain an expected HPD coverage probability for a given fixed interval length. Alternatively, one can find the sample size required to attain an expected HPD interval length for a fixed coverage. These two criteria can lead to different sample size requirements. In addition to averaging, a worst possible outcome scenario is also considered. The results presented here provide an exact solution to a problem recently addressed in the literature.
Assessing Customers’ Perception of Electronic Banking Services: Evidence from Ghana Commercial Banks in Kumasi Metropolis

The study sought to investigate customers’ satisfaction about the quality, accessibility and challenges associated with electronic banking in Ghana with the Kumasi Metropolis as Focus. Quantitative research method with descriptive research design was used. The primary data obtained through purposive questionnaire were analyzed using Statistical Package for Social Sciences (SPSS), Principal Component Factor Analysis, and Relative Importance Index. The quality of electronic banking (e-banking), additional support services, benefits to banks, risks in internet banking, prompt responses and content format were regarded satisfactory; sophisticated software, diverse features, ease of use, banking cards, support services and environmental conditions were regarded quite satisfactory; internet connection, user’s ability to access, and availability of Automated Teller Machines (ATM) machines were however, unsatisfactory. The study concludes that customers have a mixed of positive and negative perceptions of e-banking services offered by commercial banks and suggests better means of raising funds to provide ATMs at vantage points and intensify their marketing to create more awareness.
Different Dietary Ratios of Camelina Oil to Sandeel Oil Influence the Capacity to Synthesise and Deposit EPA and DHA in Zucker Fa/Fa Rats

Plant-based food provides more ALA (α-linolenic acid) and less EPA (eicosapentaenoic acid) and DHA (docosahexanoic acid) than marine food. Earlier studies indicate that cetoleic acid (22:1n-11) stimulates the n-3 pathway from ALA to EPA and DHA. The present study aimed to investigate the dietary effects of camelina oil (CA) high in ALA and sandeel oil (SA) high in cetoleic acid on the conversion of ALA to EPA and DHA. Male Zucker fa/fa rats were fed a diet of soybean oil (Ctrl) or diets of CA, SA, or a combination of CA and SA. Significantly higher levels of DPA (docosapentaenoic acid) and DHA in blood cells from the CA group compared to the Ctrl indicate an active conversion of ALA to DPA and DHA. Increasing the uptake and deposition of EPA and DHA meant that a trend towards a decrease in the liver gene expression of Elovl5, Fads1, and Fads2 along with an increase in the dietary content of SA was observed. However, 25% of the SA could be exchanged with CA without having a significant effect on EPA, DPA, or DHA in blood cells, indicating that bioactive components in SA, such as cetoleic acid, might counteract the inhibiting effect of the high dietary content of DHA on the n-3 biosynthetic pathway.
Modeling Ring Current Proton Fluxes Using Artificial Neural Network and Van Allen Probe Measurements

Terrestrial ring current dynamics are a critical part of the near‐space environment, in that they directly drive geomagnetic field variations that control particle drifts, and define geomagnetic storms. The present study aims to specify a global and time‐varying distribution of ring current proton using geomagnetic indices and solar wind parameters with their history as input. We train an artificial neural network (ANN) model to reproduce proton fluxes measured by the Radiation Belt Storm Probes Ion Composition Experiment instrument onboard Van Allen Probes. By choosing optimal feature parameters and their history length, the model results show a high correlation and a small error between model specifications and satellite measurements. The modeled results well capture energy‐dependent proton dynamics in association with geomagnetic storms, including inward radial diffusion, acceleration and decay. Our ANN model produces proton fluxes with their corresponding 3D spatiotemporal variations, capturing the latitudinal distribution and local time asymmetry that are consistent with observations and that can further inform theory.
Application of big data in ophthalmology

The advents of information technologies have led to the creation of ever-larger datasets. Also known as big data, these large datasets are characterized by its volume, variety, velocity, veracity, and value. More importantly, big data has the potential to expand traditional research capabilities, inform clinical practice based on real-world data, and improve the health system and service delivery. This review first identified the different sources of big data in ophthalmology, including electronic medical records, data registries, research consortia, administrative databases, and biobanks. Then, we provided an in-depth look at how big data analytics have been applied in ophthalmology for disease surveillance, and evaluation on disease associations, detection, management, and prognostication. Finally, we discussed the challenges involved in big data analytics, such as data suitability and quality, data security, and analytical methodologies.
Detection of Ciguatoxins and Tetrodotoxins in Seafood with Biosensors and Other Smart Bioanalytical Systems

The emergence of marine toxins such as ciguatoxins (CTXs) and tetrodotoxins (TTXs) in non-endemic regions may pose a serious food safety threat and public health concern if proper control measures are not applied. This article provides an overview of the main biorecognition molecules used for the detection of CTXs and TTXs and the different assay configurations and transduction strategies explored in the development of biosensors and other biotechnological tools for these marine toxins. The advantages and limitations of the systems based on cells, receptors, antibodies, and aptamers are described, and new challenges in marine toxin detection are identified. The validation of these smart bioanalytical systems through analysis of samples and comparison with other techniques is also rationally discussed. These tools have already been demonstrated to be useful in the detection and quantification of CTXs and TTXs, and are, therefore, highly promising for their implementation in research activities and monitoring programs.
Novel Transformer-Based Fusion Models for Aero-Engine Remaining Useful Life Estimation

Remaining Useful Life (RUL) estimation is a crucial technology in prognostic and health management (PHM) for modern aero-engines, as it ensures the reliability and safety of aircraft. With advances in sensor technology, data-driven approaches for RUL estimation have gained significant interest in recent years, especially deep learning-based methods. To further contribute to the field and improve the accuracy of RUL estimation, this paper, proposes novel Transformer-based fusion models for aero-engine RUL estimation. The vanilla Transformer is adapted for RUL estimation by modifying its structure based on the characteristics of aero-engine sensor data. The modified Transformer is then fused with Long Short-Term Memory (LSTM) and Convolutional Neural Network (CNN) to extract degradation features from multiple aspects. Specifically, the LSTM and CNN layers are incorporated into the decoder and encoder of the Transformer. The effectiveness and superiority of the proposed models are demonstrated through experiments on the C-MAPSS benchmark dataset. The experimental results show that the proposed LSTM-Transformer fusion model outperforms the existing state-of-the-art approaches, with up to 66.53% and 84.86% improvement in RMSE and score metrics, respectively.
An Expert System for Screening and Prognosis of Diseases: An Instance of Healthcare Management

The background of this study is that a Medical Expert System made in Visual Prolog is proposed. This expert system makes a differential diagnosis among heart, lung, kidney, skin, and brain diseases. This system is designed to give help to a medical expert in making the appropriate diagnosis of a patient. Based on a patient's symptoms and medical background, SWI Prolog offers the diagnosis through the declarative knowledge representation methodologies. On the basis of the diagnosis and current medical regulations, it might also offer other treatment options. Medical data analysis using SWI Prolog is used to spot trends or patterns in patient outcomes or disease development. Making better-educated choices concerning patient care and treatment could be made easier by healthcare providers as a result. An SWI Prolog-based medical expert system's output will be influenced by the quality of the data and code used to generate it, as well as by the medical specialists that worked on its design and implementation. The creation of more sophisticated expert systems can be particularly beneficial for early disease detection, helping to reduce the burden of diseases by detecting them more accurately and efficiently.
A design methodology for the realisation of dedicated function VLSI digital filters

Outlines a design methodology for realising finite impulse response digital filters with fixed coefficients. The technique is based on the use of a primitive operator graph, which represents the filter multipliers using a minimum or near-minimum number of addition/subtraction operations. Vertex rearrangement, retiming and edge elimination techniques are then applied to form a logical graph with an efficient allocation of pipeline registers. An example of this process is given for a bit-serial realisation employing a bit-level pipeline. >
The Research Progress of Areal Interpolation

In a broad sense, spatial interpolation includes point interpolation and areal interpolation. In this article these two types of interpolation methods were reviewed and the areal interpolation which was not very popular to major people was especially introduced according to whether the interpolation methods use ancillary data or not. In the areal interpolation methods without using ancillary data, the algorithm and its characteristic of the following methods were introduced: point-based areal interpolation, areal-weighted interpolation, areal-weighted interpolation using control zones and pycnophylactic method. In the areal interpolation methods using ancillary data, the EM algorithm and the method using remote sensing data as ancillary data were introduced. And the special case of areal interpolation - statistics variables spatialization-was also introduced. In the end, these two types of interpolation methods were compared and the main research trend of areal interpolation was foreseen.
D FILTER VS NONLINEAR FILTERING TECHNIQUES IN NONLINEAR TARGET TRACKING APPLICATIONS

Nonlinear filtering and control techniques ( -D filter and controller) have recently gained a lot of attentions in the estimation and control community, especially in the context of robust and adaptation capabilities. This paper examines the -D filter performance for a nonlinear target tracking problem and evaluates its performance and implementation simplicity versus other nonlinear filtering techniques such as unscented Kalman Filtering (UKF) or Extended Kalman filtering (EKF) techniques. The -D filter is derived by constructing the dual of a new nonlinear regulator control technique, D   approximation which involves approximate solutions to the Hamilton Jacobi Bellman (HJB) equation. The structure of this filter is similar to the State Dependent Riccati Equation Filter (SDREF). However, this method does not require an online computationally intensive solving of the algebraic Riccati equation at each sample time as compared with the SDREF. A complex highly nonlinear tracking problem is employed to evaluate this new class of nonlinear filter vs other nonlinear filtering contenders such as UKF and/or EKF. Current evaluation of the D   filter compared with EKF and UKF via Monte Carlos simulation for this particular target tracking problem has illustrated a very attractive and encouraging result toward nonlinear estimation problems.
Clinical Characteristics of Headache in Multiple Sclerosis Patients: A Cross-Sectional Study

Primary headaches are known to be associated with multiple sclerosis (MS), but previous studies concerning this relationship are not conclusive. Nowadays, there are no studies assessing the prevalence of headaches in Polish MS patients. The aim of the study was to assess the prevalence and characterise headaches in MS patients treated with disease-modifying therapies (DMTs). In a cross-sectional study of 419 consecutive RRMS patients, primary headaches were diagnosed according to the International Classification of Headache Disorders (ICHD-3) criteria. Primary headaches were observed in 236 (56%) of RRMS patients, with a higher prevalence in women (ratio of 2:1). The most common was migraine 174 (41%) (migraine with aura 80 (45%), migraine without aura 53 (30%), and probable migraine without aura 41 (23%); less frequent was tension-type headache 62 (14%). Female sex was a risk factor for migraines but not for tension-type headaches (p = 0.002). Migraines mostly started before MS onset (p = 0.023). Migraine with aura was associated with older age, longer disease duration (p = 0.028), and lower SDMT (p = 0.002). Longer DMT time was associated with migraine (p = 0.047), particularly migraine with aura (p = 0.035). Typical for migraine with aura were headaches during clinical isolated syndrome (CIS) (p = 0.001) and relapses (p = 0.025). Age and type of CIS, oligoclonal band presence, family MS history, EDSS, 9HTP, T25FW, and type of DMT did not correlate with headache. Headaches are present in more than half of MS patients treated with DMTs; migraines occur almost three times more frequently than tension-type headaches. Migraines with aura headaches during CIS and relapses are typical. Migraine in MS patients had high severity and typical migraine characteristics. DMTs had no correlation with the presence or type of headache.
GA approach for finding Rough Set decision rules based on bireducts

Feature selection plays an important role in knowledge discovery and data mining nowadays. In traditional rough set theory, feature selection using reduct the minimal discerning set of attributes is an important area. Nevertheless, the original definition of a reduct is restrictive, so in one of the previous research it was proposed to take into account not only the horizontal reduction of information by feature selection, but also a vertical reduction considering suitable subsets of the original set of objects. Following the work mentioned above, a new approach to generate bireducts using a multi–objective genetic algorithm was proposed. Although the genetic algorithms were used to calculate reduct in some previous works, we did not find any work where genetic algorithms were adopted to calculate bireducts. Compared to the works done before in this area, the proposed method has less randomness in generating bireducts. The genetic algorithm system estimated a quality of each bireduct by values of two objective functions as evolution progresses, so consequently a set of bireducts with optimized values of these objectives was obtained. Different fitness evaluation methods and genetic operators, such as crossover and mutation, were applied and the prediction accuracies were compared. Five datasets were used to test the proposed method and two datasets were used to perform a comparison study. Statistical analysis using the one-way ANOVA test was performed to determine the significant difference between the results. The experiment showed that the proposed method was able to reduce the number of bireducts necessary in order to receive a good prediction accuracy. Also, the influence of different genetic operators and fitness evaluation strategies on the prediction accuracy was analyzed. It was shown that the prediction accuracies of the proposed method are comparable with the best results in machine learning literature, and some of them outperformed it.
Propolis and its therapeutic potential on wound healing

Propolis is a resinous mixture collected by honey bees from tree buds or other plant sources.Propolis compounds have been the focus of many studies due to their anti-microbial, anti-cancer, and antiinflammatory effects. In addition, propolis is also recognized to have regenerative properties. It has beenfound to regulate a number of cellular and molecular processes such as wound repair and regeneration,hemostasis, inflammation, cell proliferation, and tissue remodeling. Many in vivo studies on wound modelsshow that propolis has beneficial roles in wound healing. These beneficial roles have also been confirmedby clinical research studies. However, more research is needed on the dose, side effects, and clinicalefficacy of propolis on wounds. In conclusion, research with different experimental models is promising asa potential therapeutic strategy in wound healing.
A Study of Syntactic and Semantic Artifacts and its Application to Lambda Definability, Strong Normalization, and Weak Normalization in the Presence of...

Church's lambda-calculus underlies the syntax (i.e., the form) and the semantics (i.e., the meaning) of functional programs. This thesis is dedicated to studying man-made constructs (i.e., artifacts) in the lambda calculus. For example, one puts the expressive power of the lambda calculus to the test in the area of lambda definability. In this area, we present a course-of-value representation bridging Church numerals and Scott numerals. We then turn to weak and strong normalization using Danvy et al.'s syntactic and functional correspondences. We give a new account of Felleisen and Hieb's syntactic theory of state, and of abstract machines for strong normalization due to Curien, Cregut, Lescanne, and Kluge.
Improvement in the Thermal Conductivity of Silver Epoxy Adhesive by Treating with Water Vapor

With the miniaturization of electronic devices, electronic packaging has become increasingly precise and complex, which presents a significant challenge in terms of heat dissipation. Electrically conductive adhesives (ECAs), particularly silver epoxy adhesives, have emerged as a new type of electronic packaging material, thanks to their high conductivity and stable contact resistance. However, while there has been extensive research on silver epoxy adhesives, little attention has been paid to improving their thermal conductivity, which is a critical requirement in the ECA industry. In this paper, we propose a straightforward method for treating silver epoxy adhesive with water vapor, resulting in a remarkable improvement in thermal conductivity to 9.1 W/(m·K), three times higher than the sample cured using traditional methods (2.7 W/(m·K)). Through research and analysis, the study demonstrates that the introduction of H2O into the gaps and holes of the silver epoxy adhesive increases the path of electron conduction, thereby improving thermal conductivity. Furthermore, this method has the potential to significantly improve the performance of packaging materials and meet the needs of high-performance ECAs.
commentary and analysis: The Insignificance of Significance Testing

Abstract Null hypothesis significance testing (NHST) is commonplace in atmospheric research, despite the many criticisms that have been leveled against its use in other fields of research. NHST is used, in papers in almost every issue of the Society's journals, to test correlations, means, and trends. Some of the major criticisms of NHST are summarized, and possible alternatives are discussed.