Miracle-dz/UniFAR-FTUs huggingface dataset:
Translation and adaptation of the Gross Motor Function Measure-88 to the Croatian language

The Gross Motor Function Measure-88 (GMFM-88) is a standardized observational instrument measuring gross motor function for children with cerebral palsy and Down syndrome. Clinicians in Croatia have been showing a growing interest in GMFM-88. However, only the English version of the measure was available, which possessed a barrier for its use in Croatia. The aim of this study was to translate the GMFM-88 test from English into Croatian and adapt it for use in clinical practice in Croatia. In this study, we followed the first five steps of the Sousa & Rojjanasrirat (2010) guideline for translation and adaptation. First, two authors independently did “forward” translation; a translation of the score sheet from English into Croatian. Then, the third author performed “backward” translation, which was checked and revised by the measure’s author. The pre-final version of the translated measure was tested through cognitive debriefing with seven clinicians. Participants pointed out the importance of the consistent use of terms and provided suggestions for improvement, such as changed word order or shortening of the translated items. Seventy two of 88 items were changed. With this study, we have initiated the adaptation of the GMFM-88 to Croatian. The next steps for fully validated GMFM-88 in the Croatian language is to do psychometric testing with children with cerebral palsy and Down syndrome in Croatia.
An efficient method for combining adaptive echo and noise canceller in hands‐free systems

Acoustic interferences severely degrade the quality and the intelligibility of the desired speech signal, thus posing a severe problem for many speech applications. Several acoustic echo cancellation (AEC) techniques have been proposed with a view to solving this problem. There are, however, few reports of AEC methods working under real noisy conditions. In this paper, we investigate the maximum positive synergies of the combination of acoustic echo canceller with a new adaptive beamformer. The proposed system achieves both the AEC and noise reduction of speech in an actual environment with real noise sources. Since the AEC is located behind the fixed beamformer of the new adaptive beamformer only one AEC is required for an arbitrary number of array elements, and the AEC does not feel any repercussions from the new adaptive beamformer. The proposed system was implemented in a real environment using National Instruments NI‐PXI‐1042Q controller system and data acquisition card PXI‐4472. Experimental results show that the proposed system has successfully improved the performance of hands‐free systems. Copyright © 2008 John Wiley & Sons, Ltd.
Linear and nonlinear effects of light propagation in low‐index photonic crystal slabs

We investigate theoretically and experimentally photonic crystals realised in slab waveguides made of materials with a low refractive index. Although in such systems a two‐dimensional photonic bandgap for TE polarised light can be achieved, the low index imposes several restrictions influencing the performance. We investigate the transmission and dispersion properties of photonic crystal defect waveguides, high‐quality factor microcavities and unusual phenomena of light propagation and localisation in truly periodic systems without defects. This is compared to results obtained in high‐index materials. Furthermore we investigate theoretically frequency conversion and solitons in nonlinear photonic crystals. An optical parametric oscillator in a photonic crystal microcavity and strongly localised solitons in coupled microcavities are proposed. (© 2007 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)
Electromagnetic Radiation From DielectricAnd Conducting Bodies Of Revolution In ThePresence Of Arbitrary Surfaces

A formulation is developed to treat radiation from structures consisting of dielectric and conducting bodies of revolution (BOR) in the presence of multiple arbitrarily shaped three-dimensional objects. A surface integral equation (SIE) approach is used. The integral equations are set up using both E and H-field boundary conditions on the dielectric and conducting surfaces. They are solved using the well known method of moments after an appropriate set of boundary conditions has been defined. The resulting system matrix has a partial block-diagonal nature which provides a more economical solution for structures that have some rotational symmetry.
A Time Sensitive Network-Based Data Distribution Service Implementation Method

This paper proposes a TSN-based data distribution service (DDS) implementation method for solving the application flexibility problem of Time Sensitive Network (TSN). First, we propose stream monitoring method for a system-level network controlling; Second, a network management methods is proposed, which adjusts the gate control strategy in accordance with real-time flow; Finally, we have merged TSN driver to DDS for improving the publishing and subscription performance. The experiment results show that the proposed method has established a certainty DDS system with great communication efficient and flexibility.
The Efficacy of Virtual Reality on Climate Change Education Increases with Amount of Body Movement and Message Specificity

: Climate change impacts are felt globally, and the impacts are increasing in severity and intensity. Developing new interventions to encourage behaviors that address climate change is crucial. This pre-registered field study investigated how the design of a virtual reality (VR) experience about ocean acidification could impact participants’ learning, behavior, and perceptions about climate change through the manipulation of the experience message framing, the sex of voice-over and the pace of the experience, and the amount of participants’ body movement. The study was run in 17 locations such as museums, aquariums, and arcades in the U.S., Canada, U.K., and Denmark. The amount of body movement was a causal mechanism, eliciting higher feelings of self-efficacy while hindering learning. Moreover, linking the VR narrative about ocean acidification linguistically to climate change impaired learning compared to a message framing that did not make the connection. As participants learned more about the experience, they perceived the risks associated with ocean acidification as higher, and they were more likely to engage in pro-climate behavior. The results shed light on the mechanisms behind how VR can teach about ocean acidification and influence climate change behavior.
SCANN: Side Channel Analysis of Spiking Neural Networks

Spiking neural networks (SNNs) are quickly gaining traction as a viable alternative to deep neural networks (DNNs). Compared to DNNs, SNNs are computationally more powerful and energy efficient. The design metrics (synaptic weights, membrane threshold, etc.) chosen for such SNN architectures are often proprietary and constitute confidential intellectual property (IP). Our study indicates that SNN architectures implemented using conventional analog neurons are susceptible to side channel attack (SCA). Unlike the conventional SCAs that are aimed to leak private keys from cryptographic implementations, SCANN (SCA̲ of spiking n̲eural n̲etworks) can reveal the sensitive IP implemented within the SNN through the power side channel. We demonstrate eight unique SCANN attacks by taking a common analog neuron (axon hillock neuron) as the test case. We chose this particular model since it is biologically plausible and is hence a good fit for SNNs. Simulation results indicate that different synaptic weights, neurons/layer, neuron membrane thresholds, and neuron capacitor sizes (which are the building blocks of SNN) yield distinct power and spike timing signatures, making them vulnerable to SCA. We show that an adversary can use templates (using foundry-calibrated simulations or fabricating known design parameters in test chips) and analysis to identify the specifications of the implemented SNN.
Hypoxia-Inducible Factor 1-Alpha and Glucose Metabolism during Cardiac Remodeling Progression from Hypertrophy to Heart Failure

In pathological cardiac hypertrophy, the heart is more dependent on glucose than fatty acids. This shift in energy metabolism occurs due to several factors, including the oxygen deficit, which activates hypoxia-inducible factor-1α (HIF-1α), a critical molecule related to glucose metabolism. However, there are gaps regarding the behavior of key proteins in the glycolytic pathway and HIF-1α during the transition from hypertrophy to heart failure (HF). This study assesses the hypothesis that there is an early change and enhancement of HIF-1α and the glycolytic pathway, as well as an association between them during cardiac remodeling. Sham and aortic stenosis Wistar rats were analyzed at 2, 6, and 18 weeks and in HF (n = 10–18). Cardiac structure and function were investigated by echocardiogram. Myocardial glycolysis, the aerobic and anaerobic pathways and glycogen were analyzed by enzymatic assay, Western blot, and enzyme-linked immunosorbent assay (ELISA). The following were observed: increased left ventricular hypertrophy; early diastolic function change and severe systolic and diastolic dysfunction in HF; increased HIF-1α in the 2nd week and in HF; precocious alteration and intensification of glycolysis with a shift to anaerobic metabolism from the 6th week onwards; association between HIF-1α, glycolysis, and the anaerobic pathway. Our hypothesis was confirmed as there was an early change and intensification in glucose metabolism, alteration in HIF-1α, and an association between data during the progression from hypertrophy to heart failure.
Cellular Localization of Orexin 1 Receptor in Human Hypothalamus and Morphological Analysis of Neurons Expressing the Receptor

The orexin system is related to food behavior, energy balance, wakefulness and the reward system. It consists of the neuropeptides orexin A and B, and their receptors, orexin 1 receptor (OX1R) and orexin 2 receptor (OX2R). OX1R has selective affinity for orexin A, and is implicated in multiple functions, such as reward, emotions, and autonomic regulation. This study provides information about the OX1R distribution in human hypothalamus. The human hypothalamus, despite its small size, demonstrates a remarkable complexity in terms of cell populations and cellular morphology. Numerous studies have focused on various neurotransmitters and neuropeptides in the hypothalamus, both in animals and humans, however, there is limited experimental data on the morphological characteristics of neurons. The immunohistochemical analysis of the human hypothalamus revealed that OX1R is mainly found in the lateral hypothalamic area, the lateral preoptic nucleus, the supraoptic nucleus, the dorsomedial nucleus, the ventromedial nucleus, and the paraventricular nucleus. The rest of the hypothalamic nuclei do not express the receptor, except for a very low number of neurons in the mammillary bodies. After identifying the nuclei and neuronal groups that were immunopositive for OX1R, a morphological and morphometric analysis of those neurons was conducted using the Golgi method. The analysis revealed that the neurons in the lateral hypothalamic area were uniform in terms of their morphological characteristics, often forming small groups of three to four neurons. A high proportion of neurons in this area (over 80%) expressed the OX1R, with particularly high expression in the lateral tuberal nucleus (over 95% of neurons). These results were analyzed, and shown to represent, at the cellular level, the distribution of OX1R, and we discuss the regulatory role of orexin A in the intra-hypothalamic areas, such as its special role in the plasticity of neurons, as well as in neuronal networks of the human hypothalamus.
Study on the cause classification and prevention methods of casing damage

The casing damage wells in non oil layers are mainly distributed in the N2 and SI-SII oil shale interlayer, and the casing damage types are mainly deformation and dislocation. The main reason is that the injected water enters into the oil shale, the cementing quality is poor, and it is easy to form an invasion water area after invading along the channeling channel, which reduces the stability of the formation. Under the effect of geostress, the force generated by the formation sliding along the weak surface is directly applied to the casing, causing shear casing damage; The casing damage in the oil reservoir is mainly concentrated in SII1-5 layers, which is analyzed as a result of high pressure water injection and large difference in plane and interlayer pressure; The casing damage on the fault plane is mainly caused by slip caused by water softening on the fault plane and fault activity caused by excessive difference in pore pressure of oil layers on both sides of the fault. This paper expounds the measures taken in daily management from the aspect of casing damage prevention, which provides a basis for the prevention and control of casing damage wells in the future.
Investigation of The Impact of Extended Marketing Mix and Subjective Norms on Visitors’ Revisit Intention: A Case of Beach Tourism Destinations

Tourists' revisit intention, which refers to their anticipated likelihood of returning to the same destination, is a subset of positive post-purchase behavior and a critical component of tourist loyalty. Thus, the purpose of this study was to determine the effect of an extended marketing mix and subjective norms on visitors' propensity to revisit beach tourism sites in Bangladesh. This study used a descriptive research design. Purposive sampling was utilized in this investigation. Both primary and secondary data were analyzed. Using a standardized questionnaire, primary data were acquired using a five-point Likert scale from 221 Bangladeshi tourists visiting beach tourism sites. The data were analyzed using the Statistical Package for the Social Sciences (SPSS) 25.0 version software. It was investigated with descriptive statistics, a multicollinearity test, a reliability test, and multiple regression. The regression analysis results demonstrated that people, physical evidence, and subjective norms had a significant and positive impact on revisiting intention to beach tourism destinations in Bangladesh. The assessment results can be created consciousness among all stakeholders about visitors' revisit intention related to the extended marketing mix and subjective norms, as well as in the research area. Administrators of the Bangladesh Tourism Board (BTB) and Bangladesh Parjatan Corporation (BPC) will be focused appropriately on evolving the approach for using extended marketing policies and subjective norms of revisiting intention to beach tourism destinations in Bangladesh.
MeDERT: A Metal Surface Defect Detection Model

Defects in various products are unavoidable because of measurement errors and equipment accuracy limitations in the production process. Recent advances in metal surface defect detection have focused on optimizing traditional methods, developing new detection techniques, and exploring deep learning-based algorithms, providing technological support to improve metal manufacturing quality and production efficiency. To ensure the highest yield rate and meet production requirements, all products must undergo defect inspections before leaving the factory. However, Traditional methods for detecting metal surface defects require a lot of manual involvement, are difficult to accurately detect small defects, are susceptible to environmental interference, and lack stability and reliability. To address this issue, we propose the MeDERT model for metal surface defect detection. Our approach involves a new Span-sensitive Texture Fusion (STF) module structure that focuses on multi-headed attention modules to recover lost details and boost inspection speed and on top of that use the Jump-sensitive detail recovery feature fusion module to ensure the validity of the extracted textures. Additionally, we introduce singular value decomposition and pretzel noise to model the noise and enhance model robustness through data augmentation. Our MeDERT model achieved state-of-the-art (SOTA) results on a specified dataset, demonstrating its effectiveness in enhancing inspection efficiency and accuracy.
Novel Aspects Targeting Platelets in Atherosclerotic Cardiovascular Disease—A Translational Perspective

Platelets are important cellular targets in cardiovascular disease. Based on insights from basic science, translational approaches and clinical studies, a distinguished anti-platelet drug treatment regimen for cardiovascular patients could be established. Furthermore, platelets are increasingly considered as cells mediating effects “beyond thrombosis”, including vascular inflammation, tissue remodeling and healing of vascular and tissue lesions. This review has its focus on the functions and interactions of platelets with potential translational and clinical relevance. The role of platelets for the development of atherosclerosis and therapeutic modalities for primary and secondary prevention of atherosclerotic disease are addressed. Furthermore, novel therapeutic options for inhibiting platelet function and the use of platelets in regenerative medicine are considered.
A Modification of Forney's Scheme and Bounds on the Probability of Error and Erasure over a Discrete Channel

Abstract Forney's generalization of maximum likelihood decoding scheme takes care of erasures. From a tactical point of view it may be desired to consider multiple decoding schemes. This problem for a discrete memoryless channel has been handled in this paper and bounds on probability of error and erasure have been derived after modifying Forney's scheme for multiple decoders.
A method for reducing drawing load by omitting the drawing process based on impression deterioration

With the recent development of 3D computer graphics technology, the required hardware processing speed has been increasing. As a solution to this problem, there are methods to reduce the drawing load by omitting some drawing processes. Level-of-Detail and Occlusion Culling are examples of conventional methods, but both have the problem that they cannot reduce the rendering load for near objects. In this method, the drawing of near objects that exist in the shadow and are not illuminated by light is omitted, thereby reducing the drawing load while suppressing the loss of impression.
Interval Coalition Game-Based Peer-to-Peer Energy Trading Method for Multi-microgrids

Multi-microgrids (MMGs) are derived from microgrids (MGs) penetration into the distribution networks. To promote the local consumption of renewable energy and reduce the power transmission losses of distribution networks, an interval coalition game-based peer-to-peer energy trading method for MMGs is proposed in this paper. First, an interval optimization-based many-to-one sealed bid auction mechanism is designed to determine the coalition for MGs and alleviate the impacts of uncertainties in peer-to-peer energy trading processes. Second, the interval Shapley value method is applied to fairly allocate the utility of each MG in the corresponding coalition. Finally, compared with the traditional transaction methods, simulation results verify the feasibility and effectiveness of the proposed method.
Nonparametric maximum likelihood estimators for ar and ma time series

The problem of estimating the parameters of moving average or autoregressive time series is studied when the error distribution is completely unknown. Four nonparametric maximum likelihood estimators (NPMLE) are presented for this purpose. These estimators are compared with the classical moment and least squares estimators in a simulation study. The behavior of these NPMLEs is much better than the classical ones, suggesting that they should be used extensively when no parametric information is known in advance about the error distribution. An application of these estimators to coal mining accidents data is also included.
Frequency-based Drone Detection using I-Hawk

Drones or Unmanned Aerial Vehicles (UAVs) have a wide range of uses in fields such as agriculture, delivery, search and rescue, and surveying. They can be incredibly beneficial but also harmful if used for illegal or malicious purposes like smuggling, illegal surveillance, terrorist attacks, industrial espionage, and privacy violation. Therefore, it’s important to regulate the use of drones and develop technology to detect and neutralize their misuse as well as strict laws and regulations to prevent them. A proposed design for a drone defense system utilizes a heat sensor and frequency jammer to detect and neutralize unauthorized drones. The system collects primary data by scanning all frequencies in use within a specific area and then compares this data to secondary data gathered by observing any illegal drone movements. By comparing primary and secondary data, the system can identify and jam any unauthorized frequencies in use. The heat sensor eliminates biological variables and safeguards the camera on the enemy drone, allowing the system to pinpoint exactly what was infiltrated. This approach not only detects the actions of the hostile drone but also preserves any information it has captured.
SNIP: Southbound Message Delivery with In-network Pruning in Clouds

In a hyper-scale cloud data center, a large number of control messages are distributed to hundreds of thousands of compute nodes from a logically centralized control plane. The delivery of these control messages, a.k. a. southbound messages, is critical to cloud infrastructure management, as it greatly affects customer experience. Existing works mainly deal with southbound message delivery by two methods: point-to-point transmission and Message Queue (MQ)-based solutions. With the point-to-point transmission method, each message is sent from the control plane to compute nodes directly, which may cause high control complexity and overhead in hyper-scale clouds. The MQ-based method can address the challenge of high complexity through message aggregation and subscribe/publish model. However, it usually brings in redundant messages, and further causes extra load on compute nodes. To solve the problem above, we design SNIP, which exploits the ability of programmable switches to perform in-network message pruning and to reduce message redundancy. Specffically, forwarding and processing information computed by the control plane is attached to the package header of every control message. Redundant messages can be identified and processed by programmable switches. In addition, we propose a rounding-based algorithm to prune messages with minimal redundancy. The simulation results show that SNIP can reduce the control overhead by 80%-85% and the total traffic of redundant messages by 35% compared with existing solutions.
Customer Churn Prediction Based on Machine Learning

In recent years, telecommunication has become the major media of communication, while the market has been saturated in many developed countries. Telecommunication companies are shifting their concerns from acquiring new customers to retaining regular customers, since the cost to win a new customer is higher than that of maintaining a regular customer. Therefore, it is essential for telecommunication operators to forecast customer churn in this competitive, accurate market. This paper aims to identify affecting customer churn and construct an efficient model, which is used to predict and analyze data with visualization results. The churn forecast consists of several phases: data prepossessing, data analysis, evaluation measure, and application of machine learning algorithms. Moreover, data pre-processing covers data cleaning, transformation, and classification. The machine learning classifiers selected are Logistic Regression, SVM, Random Forest, AdaBoost, GBDT, XGBoost, Light GBM, and CatBoost. Classifiers were evaluated using performance measures, such as accuracy, precision, recall, AUC, and F1-Score. Based on the paper, the result was shown that the Light GBM outperformed other classifiers while identifying potential churners.