Miracle-dz/UniFAR-FTUs huggingface dataset:
Acute postoperative pain prevalence and intensity in the first 72 hour in Dessie Comprehensive Specialized Hospital, Ethiopia: a prospective single center observational study

Background: Postoperative pain is poorly studied in developing countries. Severe pain after surgery remains a major problem, occurring in 50–70% of the patients. Differences exist across countries. Despite numerous published studies, the degree of pain following many types of surgery in everyday clinical practice is unknown. To improve postoperative pain-treatment and develop procedure-specific, optimized pain-treatment protocols, the prevalence and severity of postoperative pain must first be identified. Objective: This study aimed to determine the prevalence and intensity of acute postoperative pain and the practice of postoperative pain management. Methods: A total of 180 patients were enrolled in the study, with 160 calculated using a single population proportion formula and a 10% attrition rate added, with proportionate stratification according to the size of each ward to obtain the number of participants from each specialty ward. A multistage sampling with stratified proportionate at the initial stages and a simple random sampling technique at the final stage was used. The patients completed a numerical pain rating scale for pain immediately after surgery for the first 72 h after surgery. The prevalence of mild, moderate, or severe pain was calculated. The evaluation was performed at eight time points including immediately after surgery: T2, T4, T8, T12, T24, T48, and T72. Result: The prevalence of severe pain in patients who underwent elective surgery in the study period measured by numeric rating scale was 21.6, 43.9, 45.6, 28.9, 39.4, 10.5, and 13.4% at 2nd, 4th, 8th, 12th, 24th, 48th and 72 h, respectively. Conclusion: The prevalence of moderate-to-severe pain within 72 h postoperatively was slightly lower than that other studies conducted in Ethiopia reported. This was still high, indicating better pain management in the postoperative period. Involvement of the anesthetist in pain management is crucial for better patient management.
Fluorescent In Situ Hybridization for the Detection of Intracellular Bacteria in Companion Animals

Simple Summary Bacterial infections have traditionally been identified by culture; however, with advances in the understanding of the role of bacteria in disease, it is recognized that intracellular bacteria can be visualized within cells. Fluorescent in situ hybridization (FISH) is a technique used to not only identify intracellular organisms but also allows researchers to see them, in situ. In this review, we discuss the use of FISH for the identification of intracellular bacteria in companion animals. Most studies have focused on the identification of Escherichia coli, particularly within the gastrointestinal tract. Additionally, bacterial associations with inflammatory diseases affecting the liver, kidney, skin, and cardiovascular systems have been investigated with mixed results. Abstract FISH techniques have been applied for the visualization and identification of intracellular bacteria in companion animal species. Most frequently, these techniques have focused on the identification of adhesive-invasive Escherichia coli in gastrointestinal disease, although various other organisms have been identified in inflammatory or neoplastic gastrointestinal disease. Previous studies have investigated a potential role of Helicobacter spp. in inflammatory gastrointestinal and hepatic conditions. Other studies evaluating the role of infectious organisms in hepatopathies have received some attention with mixed results. FISH techniques using both eubacterial and species-specific probes have been applied in inflammatory cardiovascular, urinary, and cutaneous diseases to screen for intracellular bacteria. This review summarizes the results of these studies.
The Application of NMR-based Metabolomics in Assessing the Sub-lethal Toxicity of Organohalogenated Pesticides to Earthworms

The extensive agricultural usage of organohalogenated pesticides has raised many concerns about their potential hazards especially in the soil environment. Environmental metabolomics is an emerging field that investigates the changes in the metabolic profile of native organisms in their environment due to the presence of an environmental stressor. Research presented here explores the potential of Nuclear Magnetic Resonance (NMR)-based metabolomics to examine the sub-lethal exposure of the earthworm, Eisenia fetida to sub-lethal concentrations of organohalogenated pesticides. Various one-dimensional (1-D) and twodimensional (2-D) NMR techniques were compared in a contact filter paper test earthworm metabolomic study using endosulfan, a prevalent pesticide in the environment. The results determined that both the H Presaturation Utilizing Gradients and Echos (PURGE) and the HC Heteronuclear Single Quantum Coherence (HSQC) NMR techniques were most effective in discriminating and identifying significant metabolites in earthworms due to contaminant exposure. These two NMR techniques were further explored in another metabolomic study using various sub-lethal concentrations of endosulfan and an organofluorine pesticide, trifluralin to E. fetida. Principal component analysis (PCA) tests showed increasing separation between the
Integrated Framework for Enhancing SDN Security and Reliability

This paper addresses the issues of fault tolerance (FT) and intrusion detection (ID) in the Software-defined networking (SDN) environment. We design an integrated model that combines the FT-Manager as an FT mechanism and an ID-Manager, as an ID technique to collaboratively detect and mitigate threats in the SDN. The ID-Manager employs a machine learning (ML) technique to identify anomalous traffic accurately and effectively. Both techniques in the integrated model leverage the controller-switches communication for real-time network statistics collection. While the full implementation of the framework is yet to be realized, experimental evaluations have been conducted to identify the most suitable ML algorithm for ID-Manager to classify network traffic using a benchmarking dataset and various performance metrics. The principal component analysis method was utilized for feature engineering optimization, and the results indicate that the Random Forest (RF) classifier outperforms other algorithms with 99.9% accuracy, precision, and recall. Based on these findings, the paper recommended RF as the ideal choice for ID design in the integrated model. We also stress the significance and potential benefits of the integrated model to sustain SDN network security and dependability.
Kirchhoff’s Voltage Law at Neural Synapse From Electric Currents Based at Neurotransmiters

Because the neural complexity at brain and the demonstrated electric activity, this paper triggers the hypothesis that the neural synapse might be a tangible outcome of kirchhoff’s laws. Thus, when a direct association between the kirchhoff’s voltage law and the Fokker-Planck equation is assumed, then the synapse would acquire an electric interpretation as a bioelectric device composed by two capacitors and a single resistor. It is actually in a fully agreement to the process of synapse. Thus, while neurotransmitters inside the synaptic vesicles are together, them can behave as an electric capacitor. Indeed, after the releasing of neurotransmitters most of them can form bunching at the zone of cleft. Thus, the stopped neurotransmitters at the cleft would collapse the neural synapse. Hypothetically, this would origin neurodegenerative diseases such as dementia, Alzheimer, etc. In this manner, under the assumption that neurotransmitters can transport a surface charge density, then the presence of electric and magnetic fields provided by an external source might to change their inherent electrical processes. Some scenarios are proposed.
Intelligent Innovation and Entrepreneurship Platform Based on PSO Algorithm + Big Data

Traditional innovation and entrepreneurship platforms face problems such as low resource utilization efficiency and inaccurate decision-making. In order to solve these problems, this paper adopts the combination of PSO (Particle Swarm Optimization) algorithm and big data to improve the efficiency, accuracy and intelligence level of the algorithm. This article describes how to apply the PSO algorithm to the innovation and entrepreneurship platform to perform task optimization and decision support in the big data environment, and also optimize the convergence speed of the PSO algorithm, and effectively control the error. By optimizing resource utilization, the overall efficiency and cost-effectiveness of the platform is improved. The improved platform resource utilization rate in this paper has reached 79% −89%, and the intelligent innovation and entrepreneurship platform based on PSO algorithm and big data has achieved significant improvements in algorithm efficiency, accuracy and resource utilization rate. This provides innovative entrepreneurs with a more efficient, accurate and intelligent platform.
A Miniaturized Crossed Dipole Antenna with a Wide Axial Ratio Bandwidth

A technique for miniaturizing a crossed-polarized dipole antenna is presented in this paper. The antenna consists of a pair of dipole antennas and a halved dipole antenna. Two arms in the dipole antenna are arranged perpendicular to each other, producing two resonant frequencies. Tuning the arms’ length changes the frequency ratio for the axial ratio below 3 dB, leading to a wide axial ratio bandwidth of over 50%.
Eigenfrequency Shift of Piezoelectric Backplate in Vibro-Acoustic Energy Harvesting

Noise is increasingly recognised as a serious, worldwide public health concern. Noise is a type of occupational hazard that can cause damage to hearing and other health effects in workers who are exposed to high levels of noise in their work environment. Traditionally, noise can be controlled and suppressed but recently, noise can be useful and converted into electrical energy. The process of converting noise or acoustic waves into electrical energy by using a quarter wavelength resonator tube embedded with a flexible piezoelectric backplate was used in this study. The results show the maximum output voltage of 1.41 V/Pa at 112 Hz and 0.44 V/Pa at 225 Hz in the experiment, and 1.44 V/Pa at 106 Hz and 0.41 V/Pa at 226.5 Hz with incident sound waves at 90 dB. A parametric study was then performed by adjusting the lumped pointed mass at the piezoelectric backplate to tune the resonant frequencies of the system and the optimal power output. The point mass has given significant change in the acoustic properties. The maximum output power increased from 20.4 at 112 Hz using a flexible back plate to 711 at 119.75 Hz. Various small power applications can benefit from the approach by reducing and absorbing specific low-frequency bandwidths of continuous noise in the environment.
Evaluation of recombinant baculovirus clearance during rAAV production in Sf9 cells using a newly developed fluorescent-TCID50 assay

Introduction Recombinant adeno-associated virus (rAAV) vectors provide a safe and efficient means for in vivo gene delivery, although its large-scale production remains challenging. Featuring high manufacturing speed, flexible product design, and inherent safety and scalability, the baculovirus/Sf9 cell system offers a practical solution to the production of rAAV vectors in large quantities and high purity. Nonetheless, removal and inactivation of recombinant baculoviruses during downstream purification of rAAV vectors remain critical prior to clinical application. Methods The present study utilized a newly developed fluorescent-TCID50 (F-TCID50) assay to determine the infectious titer of recombinant baculovirus (rBV) stock after baculovirus removal and inactivation, and to evaluate the impact of various reagents and solutions on rBV infectivity. Results and discussion The results showed that a combination of sodium lauryl sulfate (SLS) and Triton X-100 lysis, AAVx affinity chromatography, low pH hold (pH3.0), CsCl ultracentrifugation, and NFR filtration led to effective removal and/or inactivation of recombinant baculoviruses, and achieved a log reduction value (LRV) of more than 18.9 for the entire AAV purification process. In summary, this study establishes a standard protocol for downstream baculovirus removal and inactivation and a reliable F-TCID50 assay to detect rBV infectivity, which can be widely applied in AAV manufacturing using the baculovirus system.
A Novel Coarse-to-Fine Image Registration for Repeat-Pass InSAR Based on Gabor Filter Feature and Its Application in Terahertz Region

In this study, a novel coarse-to-fine image registration algorithm based on feature matching using the Gabor filters is provided for repeat-pass SAR interferometry (InSAR). In the coarse registration, we propose a feature-based algorithm developed using the Gabor odd filter ratio-based operator (GOFRO), and the innovative feature based on the GOFRO (GOFRO-F) consists of the improved nonlinear scale space and the descriptor. The establishment of nonlinear scale spaces informed by the GOFRO enhances the stability of feature detection. Also, an improved 64-dimensional descriptor based on the GOFRO is presented to optimize the capability of feature matching. In the fine registration, we employ the normalized correlation coefficient algorithm (NCCA) and the random consensus sample (RANSAC) for determining offsets and the sub-pixel projection model calculation. The datasets of interferograms are derived from the 0.3 THz stepped-frequency radar. Compared to other algorithms, registration experiments of both simple and complex scatters show that GOFRO-F is more suitable for high precision image registration because of its enhanced robustness to the speckle noise. Furthermore, the three-dimensional (3D) reconstruction is conducted based on our proposed algorithm. The results demonstrate that our proposed algorithm facilitates the attainment of millimeter-level spatial resolutions for InSAR in THz band (THz InSAR).
Sustainability and Displacement: Assessing the Spatial Pattern of Residential Moves Near Rail Transit in Los Angeles County

Rail transit’s association with gentrification has been a presence in the public discourse for some time and Los Angeles is no different. There is a prevailing public perception that Los Angeles' recent boom in rail transit development causes an influx of high income residents and an outflow of low income residents near rail stations. The authors' research asks whether the presence of rail transit increases the outflow lower-income neighborhood residents. The authors use a unique dataset of tax filers in Los Angeles County to address this question. This database tracks the income and location of households across 21 years at a fine spatial scale. This analysis aggregates household data to provide station-area population out mobility rates for 35 rail station neighborhoods and 35 paired control neighborhoods along two Los Angeles Metro transit lines. Their sample consists of 15 stations along the Red/Purple subway line and 20 station along the Gold light rail line that opened between 1993 and 2013. The authors measure effects on four income brackets: below 30% of Area Median Income (AMI) ( $40,000 in 2013).
Investigating the Effectiveness of Modifying the Parent-Child Interaction Pattern Based on Filial Play Therapy on Internet Addiction, Sleep Quality, and Self-efficacy in 9-12-Year-Old Children

Background: Despite all the positive features of the internet, the risk of addiction threatens users. Sleep disorders and the negative effects on self-efficacy are important risks of this technology. Accordingly, the present study aimed to investigate the effectiveness of modifying the parent-child interaction pattern based on play therapy (filial therapy) on internet addiction, sleep quality, and self-efficacy in 9-12-year-old children. Methods: This quasi-experimental study was conducted using a pre-test-post-test design with a control group. The statistical population of the study consisted of all 3617 boys and girls aged 9-12 years studying in selected schools in Kerman, Iran (12 elementary schools) in the academic year 2020-2021. A total of 72 students were randomly selected from among the children who met the benchmark score of internet addiction and were not under medical and psychological treatment. The participants were divided into two experimental and control groups (36 students each). A demographic information questionnaire, the Internet Addiction Scale (YIAS), the Self-Efficacy Questionnaire for Children (SEQ-C), and the Pittsburgh Sleep Quality Index (PSQI) were used to collect data. Data were analyzed using SPSS software. Findings: The mean scores of internet addiction in the pre-test and post-test were 86.67 and 86.23 in the control group and 88.14 and 75.14 in the experimental group, respectively, which showed a decrease in internet addiction. The scores of sleep quality in the pre-test and post-test were 1.81 and 1.78 in the control group and 1.75 and 0.5 in the experimental group, respectively. Moreover, the overall scores of self-efficacy in the pre-test and post-test were 63.64 and 63.94 in the control group and 62.08 and 81.75 in the experimental group, respectively. The results indicated an improvement in the overall quality of sleep and selfefficacy. Conclusion: The results showed that the severity of internet addiction in children decreased after the treatment based on modifying the parent-child interaction, and this treatment was also effective on self-efficacy and sleep disorders.
The anti-nutritional factors of legumes and their treatment possibilities: a review

Today the demand for plant-based protein is growing rapidly due to increased awareness of animal protein growing costs and limited supply and has been highly related to biodiversity loss, climate change, and freshwater depletion. Legumes are in demand for their high content of protein, minerals, vitamins, and carbohydrates, also including dietary fibre. Legumes are rich not only in macronutrients and micronutrients but also contain anti-nutritional factors. One of the most important anti-nutritive properties of legumes is their high trypsin activity. The length of time required for the preparation of legumes has limited their frequency of use compared to recommended intake levels. By heat treatment, an anti-nutritional component in legumes can be mostly separated. The possibility of using extrusion cooking, microwave dryer, roasting equipment, etc., is widely studied. Roasting is one of the widespread methods for treatment of legumes that significantly enhances the texture, flavour, colour, and product appearance. The latest studies in the legume treatments report valuable results after the combined treatments, wet roasting, which includes: dehulling, soaking, and roasting. Heat treatment can be a potential way to improve legumes use in food production: reducing the time required for treatment, preparation and improving nutritional value.
Protecting Multiple Sensitive Attributes in Synthetic Micro-data

With the ever-increasing amount of data collected, there is also an increased demand for data analysis and machine learning methods, which are consequently frequently deployed. However, many of the data collected are very sensitive and of a personal nature – thus, data confidentiality and privacy become important considerations. In the wake of this, the use of synthetic data as a privacy-preserving measure for micro-data is gaining more and more popularity, especially due to its ability to maintain a high level of data utility. Synthetic data is artificially generated by a model that has been trained on real data. This means that the observations in the synthetic data do not directly correspond to any individual in the original dataset. While there are many tools for creating synthetic data available, only a little research has focused on specifically treating sensitive attributes and generating synthetic data in a way that concentrates on protecting these selected attributes from inference attacks while keeping the data utility as high as possible. This can be achieved done by setting certain constraints when learning the model from the original data. Earlier work proposed a modification to extend the DataSynthesizer, an approach for synthetic data generation that uses Bayesian Networks to capture the underlying structures in the original data, to protect one sensitive attribute. In this paper, we investigate two different techniques for extending this approach to protect multiple attributes from inference and analyse the subsequent effects on the data utility.
Enhancing the Compaction Characteristics of Peat Soil through Ground Granulated Blast Furnace Slag (GGBS) Stabilisation

Peat soil is renowned for its compressibility problem that can give rise to issues such as ground settlement, particularly within the spectrum of construction endeavours. Conversely, the implementation of chemical stabilisation, involving the incorporation of suitable additives, is contemplated prior to commencing construction activities. Ground Granulated Blast Furnace Slag (GGBS), which is a byproduct of the iron and steel industry, stands out as a prominent additive that can augment the engineering attributes of peat soil. This investigation focuses on the compaction characteristics of peat soil treated with varying proportions of GGBS, as evaluated through the Modified Proctor test. It involved examining the impact of diverse GGBS additions on the liquid limit of peat soil. The findings showed that the introduction of GGBS yielded a substantial reduction in the liquid limit of peat soil, signifying an elevated susceptibility to moisture and an increase in plasticity. Moreover, the incorporation of GGBS into peat soil prompted an increase in its Maximum Dry Density (MDD) and a reduction in its Optimum Moisture Content (OMC). Notably, the peak MDD, registering at 1000 kg/m³, was achieved at a GGBS content of 50%, marking the highest value observed in this study. The OMC values further indicated the lowest moisture content, standing at 29%, with the addition of 25% GGBS to the peat soil. These outcomes convincingly advocate that the utilisation of GGBS in treating peat soil engenders positive enhancement in its compaction characteristics, specifically pertaining to MDD and OMC. Such enhancement in engineering properties holds promising implications for potential stability.
Simulations of long‐period ground motions from a large earthquake using finite rupture modeling and the ambient seismic field

Long‐period ground motions generated by large earthquakes slowly attenuate with distance and can be significantly amplified by local velocity structures even at large distances. We take advantage of the wave propagation information carried by the ambient seismic field to simulate the long‐period ground motions of the 2008 Mw 6.9 Iwate‐Miyagi Nairiku earthquake, which occurred in the Tohoku region, Japan. We extract Green's functions between pairs of stations by regarding seismometers located in the vicinity of the main shock fault plane as virtual sources and others as receivers. We calibrate the amplitude of the extracted Green's functions using records of a Mw 5.0 aftershock with a reverse‐faulting focal mechanism similar to that of the main shock. We use scaling relations between small and large earthquakes to construct several finite fault models that are used together with the extracted Green's functions to simulate the ground motions of the main shock. Among the different models, a uniform finite source model with a rupture velocity of 1.95 km/s combined with the Green's functions extracted using one virtual source station located at the center of the fault plane yields the best fit between the simulated and observed long‐period ground motions. This study demonstrates the potential of the ambient seismic field combined with finite source modeling to assess the seismic hazard related to long‐period ground motions that could be generated by forthcoming large earthquakes.