Background/Objectives: Recent advancements in artificial intelligence (AI) have spurred interest in developing computer-assisted analysis for imaging examinations. However, the lack of high-quality datasets remains a significant bottleneck. Labeling instructions are critical to improving dataset quality but are often lacking. This study aimed to establish a liver MRI segmentation protocol and assess its impact on annotation quality and inter-reader agreement; Methods: This retrospective study included 20 patients with chronic liver disease. Manual liver segmentations were performed by a radiologist in training and a radiology technician on T2 weighted imaging (wi) and T1wi at the portal venous phase. Based on the inter-reader discrepancies identified after the first segmentation round, a segmentation protocol was established, guiding the second round of segmentation, resulting in a total of 160 segmentations. Dice Similarity Coefficient (DSC) assessed inter-reader agreement pre- and post-protocol with a Wilcoxon signed-rank test. Slice selection at extreme cranial or caudal liver positions was evaluated using the McNemar test; Results: The per-volume DSC significantly increased after protocol implementation for both T2wi (p

This paper first discusses a fractional-order Liu system of order as low as 2.7 and shows its chaotic characteristics by carrying out numerical simulations such as Lyapunov exponents, bifurcation diagrams and phase portraits. Then, by using the topological horseshoe theory and computer-assisted proof, the existence of chaos in the system is verified theoretically. Finally, the fractional-order system is implemented on a Field Programmable Gate Array (FPGA) and the results obtained show that the fractional-order Liu system is indeed chaotic.

The aim of this study was to evaluate the clinical and radiographic outcomes of the All-on-4 technique in fully edentulous maxilla using R2GATE software for flapless, computer-guided surgery and immediate loading. Ten adult patients with fully edentulous maxilla, aged between 35 and 60 years, were included in the study between April 2021 and April 2022. The surgical procedure was performed under local anesthesia and followed the All-on-4 approach with immediate loading of implants using a screw-retained provisional acrylic resin prosthesis on the same day. Final prosthesis was delivered four months after the surgery. Clinical and radiographic outcomes were evaluated at four- and twelve-months post-operation. The study reported a 95% implant survival rate using the All-on-4 technique. The mean marginal bone level was recorded at 0.35 mm and 0.66 mm during the 4 and 12-month follow-up periods, respectively. Fixed provisional prosthesis fracture, abutment, and prosthesis screw loosening were the most frequent mechanical complications reported. Within the limitations of this study, the use of computer-guided surgery in the All-on-4 technique seemed to be a promising treatment option with high implant survival rates that may help reduce post-surgical discomfort and mechanical complications in the rehabilitation of the edentulous maxilla.

Periacetabular osteotomy (PAO) is an effective approach for surgical treatment of developmental dysplasia of the hip (DDH). However, due to the complex anatomical structure around the hip and limited field of view (FoV) during the surgery, it is challenging for surgeons to perform a PAO surgery. To solve this challenge, we propose a robot-assisted augmented reality (AR)-guided surgical navigation system for PAO. The system mainly consists of a robot arm, an optical tracker, and a Microsoft HoloLens 2 which is a state-of-the-art (SOTA) optical see-through head-mounted display (OST-HMD). For AR guidance, we propose an optical marker-based virtual-physical registration method to estimate a transformation from the optical tracker coordinate system (COS) to the virtual space COS, such that virtual models can be superimposed on the corresponding physical counterparts. Furthermore, to guide the osteotomy, the developed system automatically aligns the bone saw with osteotomy planes planned in preoperative images, and then provides surgeons with not only robot assistance but also AR guidance. Comprehensive experiments were conducted to evaluate both virtual-physical registration accuracy and osteotomy accuracy of the developed navigation system. The proposed virtual-physical registration method achieved an average mean absolute distance error (mADE) of 1.96 ± 0.43 mm. The developed system achieved an average distance deviation of 0.82 ± 0.17 mm and an average angular deviation of 3.77 ± 0.85 . Experimental results demonstrated high virtual-physical registration accuracy. The sub-millimeter distance deviation demonstrated the osteotomy accuracy of the proposed system.

We attempted to establish an objective method to accurately evaluate the motility of bull sperm and examined the effects of media for sperm suspensions and frame rates on data of computer-assisted sperm motility analysis (CASA). Sperm incubated in Brackett and Oliphant medium (BO) more clearly showed hyperactivation-like motility than those in synthetic oviductal fluid. Sperm images captured at 150 frames per second (fps) showed a trajectory that was closer to the real pathway than those at other frame rates (30, 50, and 75 fps). We then examined the characteristics of sex-sorted and non-sorted semen using a cluster analysis followed by a discriminant analysis of sperm motility in BO at 150 fps. The results indicated that sex-sorted semen contained sperm with hyperactivation-like motility as the main subpopulation immediately after thawing and this subpopulation decreased after 2-h incubation. The main subpopulation in non-sorted semen had progressive motility that was maintained during incubation. In conclusion, usage of BO for sperm suspensions and capturing sperm motility at 150 fps by CASA were appropriate for evaluating bovine sperm motility. A discriminant analysis using data from a cluster analysis of motile sperm has the ability to accurately describe differences in the structures of sperm motility subpopulations.

Background Bone fracture fixation surgery is one of the most commonly performed surgical procedures in the orthopedic field. However, fracture healing complications occur frequently, and the choice of the most optimal surgical approach often remains challenging. In the last years, computational tools have been developed with the aim to assist preoperative planning procedures of bone fracture fixation surgery. Objectives The aims of this review are (1) to provide a comprehensive overview of the state-of-the-art in computer-assisted preoperative planning of bone fracture fixation surgery, (2) to assess the clinical feasibility of the existing virtual planning approaches, and (3) to assess their clinical efficacy in terms of clinical outcomes as compared to conventional planning methods. Methods A literature search was performed in the MEDLINE-PubMed, Ovid-EMBASE, Ovid-EMCARE, Web of Science, and Cochrane libraries to identify articles reporting on the clinical use of computer-assisted preoperative planning of bone fracture fixation. Results 79 articles were included to provide an overview of the state-of-the art in virtual planning. While patient-specific geometrical model construction, virtual bone fracture reduction, and virtual fixation planning are routinely applied in virtual planning, biomechanical analysis is rarely included in the planning framework. 21 of the included studies were used to assess the feasibility and efficacy of computer-assisted planning methods. The reported total mean planning duration ranged from 22 to 258 minutes in different studies. Computer-assisted planning resulted in reduced operation time (Standardized Mean Difference (SMD): -2.19; 95% Confidence Interval (CI): -2.87, -1.50), less blood loss (SMD: -1.99; 95% CI: -2.75, -1.24), decreased frequency of fluoroscopy (SMD: -2.18; 95% CI: -2.74, -1.61), shortened fracture healing times (SMD:-0.51; 95% CI: -0.97, -0.05) and less postoperative complications (Risk Ratio (RR): 0.64, 95% CI: 0.46, 0.90). No significant differences were found in hospitalization duration. Some studies reported improvements in reduction quality and functional outcomes but these results were not pooled for meta-analysis, since the reported outcome measures were too heterogeneous. Conclusions Current computer-assisted planning approaches are feasible to be used in clinical practice and have been shown to improve clinical outcomes. Including biomechanical analysis into the framework has the potential to further improve clinical outcome.

The chemical graph theory is a subfield of mathematical chemistry which applies classic graph theory to chemical entities and phenomena. Chemical graphs are main data structures to represent chemical structures in cheminformatics. Computable properties of graphs lay the foundation for (quantitative) structure activity and structure property predictions - a core discipline of cheminformatics. It has a historic relevance for natural sciences, such as chemistry, biochemistry and biology, and is in the heart of modern disciplines, such as cheminformatics and bioinformatics. This review first covers the history of chemical graph theory, then provides an overview of its various techniques and applications for CASE, and finally summarises modern tools using chemical graph theory for CASE.

Objectives: Studies evaluating machine learning (ML) algorithms on cross-lingual RadLex® mappings for developing context-sensitive radiological reporting tools are lacking. Therefore, we investigated whether ML-based approaches can be utilized to assist radiologists in providing key imaging biomarkers – such as The Alberta stroke programme early CT score (APECTS). Material and Methods: A stratified random sample (age, gender, year) of CT reports (n=206) with suspected ischemic stroke was generated out of 3997 reports signed off between 2015-2019. Three independent, blinded readers assessed these reports and manually annotated clinico-radiologically relevant key features. The primary outcome was whether ASPECTS should have been provided (yes/no: 154/52). For all reports, both the findings and impressions underwent cross-lingual (German to English) RadLex®-mappings using natural language processing. Well-established ML-algorithms including classification trees, random forests, elastic net, support vector machines (SVMs) and boosted trees were evaluated in a 5 x 5-fold nested cross-validation framework. Further, a linear classifier (fastText) was directly fitted on the German reports. Ensemble learning was used to provide robust importance rankings of these ML-algorithms. Performance was evaluated using derivates of the confusion matrix and metrics of calibration including AUC, brier score and log loss as well as visually by calibration plots. Results: On this imbalanced classification task SVMs showed the highest accuracies both on human-extracted- (87%) and fully automated RadLex® features (findings: 82.5%; impressions: 85.4%). FastText without pre-trained language model showed the highest accuracy (89.3%) and AUC (92%) on the impressions. Ensemble learner revealed that boosted trees, fastText and SVMs are the most important ML-classifiers. Boosted trees fitted on the findings showed the best overall calibration curve. Conclusions: Contextual ML-based assistance suggesting ASPECTS while reporting neuroradiological emergencies is feasible, even if ML-models are restricted to be developed on limited and highly imbalanced data sets.

Recent digital transformations have significantly impacted the current society, economy, and culture. This includes adoption of rapidly emerging technologies, such as artificial intelligence (AI), immersive technology (e.g., virtual, augmented, mixed and extended reality (VR/AR/MR/XR)), cloud services, and internet of things, in our day-to-day life. `Metaverse’ has emerged as a technology that incorporates many of these transforming technologies to deliver personalised immersive experience related to specific application areas. A potential major beneficiary of Metaverse is the educational ecosystem, where highly immersive learning experiences can be delivered to students, catering to their personal learning needs and train them on life skills such as empathy, ethical qualities, and communication skills. The present-day Metaverse allows physical engagement, enabling users to utilise limb movements to interact with the presented materials. However, ethical concerns and technical limitations impede the widespread implementation of the Metaverse in the real-world. This paper provides a comprehensive survey of the existing literature covering the architecture, type and components of Metaverse, followed by practical aspects of metaverse implementation. The paper is concluded by discussing on the open challenges and issues along with their mitigation strategies and future research directions after the detailed usecase of Metaverse in Education.

Customized wrist splints, particularly for upper extremity orthoses like wrist support braces, are commonly used across numerous clinical scenarios. However, the traditional process for producing personalized wrist splints is largely manual and highly dependent on the expertise of orthopedic specialists. This experience-based approach often leads to suboptimal outcomes, necessitating further refinement of the designs. Recent advancements in Additive Manufacturing (AM) have brought significant innovation to various industries, including orthopedics. This study aims to present a comprehensive methodology that integrates advanced design tools ,like 3D Scanning, with digital manufacturing techniques to produce tailored wrist splints. The produced hand brace aims to offer enhanced mechanical performance and comfort by precisely fitting an individual’s anatomy while minimizing material usage and weight. To achieve optimal design efficiency, the study explores the application of a Topology Optimization (TO) approach for design, while the manufacturing process utilizes Fused Deposition Modeling (FDM), an evolving technology within the Additive Manufacturing (AM) sector.

Introduction: Breast ptosis is characterized by the downward nipple-areola complex displacement below the inframammary fold. Breast ptosis is associated with ageing and its concomitant changes in estrogen levels in conjunction with other factors like macromastia, weight loss, pregnancy, and both alcohol and tobacco consumption products affecting breast tissue architecture impacting the well-being and quality of life in the affected women. This study aimed to evaluate the efficacy of a novel mastopexy technique in patients with breast ptosis. Methods: A longitudinal, non-experimental, analytical study was done in a cohort of 231 women diagnosed with breast ptosis treated with a novel low-level multifrequency laser-assisted mastopexy assessing sternal notch-to-nipple distance (SNND) and breast ptosis status according to the Regnault system at the first, second, and third postoperative month. Additionally, the incidence and nature of compli-cations within the first 30 days post-surgery were evaluated according to the Clavien-Dindo system. Results: The breast ptosis proportion significantly changed across the three-month fol-low-up period from 100% to only 10% (all ptosis grade I). 90% of the patients achieved the non-ptosis grade according to the Regnault system (p

The standard treatment for early-stage lung cancer is complete tumor excision by limited resection of the lung. Pre-operative localization is used before video-assisted thoracoscopic surgery (VATS) to improve the accuracy of pulmonary nodule excision. However, lung atelectasis and hypoxia resulting from controlling apnea during the localization procedure may affect the localization accuracy. Pre-procedural pulmonary recruitment may improve respiratory mechanics and oxygenation during localization. In this study, we investigated the potential benefits of pre-localization pulmonary recruitment prior to pulmonary ground-glass nodule localization in a hybrid operating room. We hypothesized that pre-localization pulmonary recruitment would increase localization accuracy, improve oxygenation, and prevent the need for re-inflation during the localization procedure. We retrospectively enrolled patients with multiple pulmonary nodule localizations before surgical intervention in our hybrid operating room. We compared localization accuracies between patients who had undergone pre-procedure pulmonary recruitment and patients who had not. Saturation, re-inflation rate, apnea time, procedure-related pneumothorax, and procedure time were also recorded as secondary outcomes. Patients who had undergone pre-procedure recruitment had better saturation, shorter procedure time, and higher localization accuracy. The pre-procedure pulmonary recruitment maneuver was effective in increasing regional lung ventilation, leading to improved oxygenation and localization accuracy.

Autonomous driving systems are increasingly becoming a necessary trend towards building smart cities of the future. Numerous proposals have been presented in recent years to tackle particular aspects of the working pipeline towards creating a functional end-to-end system, such as object detection, tracking, path planning, sentiment or intent detection. Nevertheless, few efforts have been made to systematically compile all of these systems into a single proposal that effectively considers the real challenges these systems will have on the road, such as real-time computation, hardware capabilities, etc. This paper has reviewed various techniques towards proposing our own end-to-end autonomous vehicle system, considering the latest state on the art on computer vision, DSs, path planning, and parallelization.

Computer and Smart Device-assisted Learning (CSDL) has gained increasing attention from educational researchers and practitioners in recent years. However, it remains controversial whether student can benefit from CSDL and what moderator could affect the impact of CSDL. Within the specific context of Korea, where the interest in digital education is steadily increasing, the number of empirical studies exploring the causal effect of CSDL remains relatively scarce. The primary objective of this empirical study was to investigate the impact of CSDL on students' academic achievement in Korea. To achieve this objective, a two-way fixed effect model was employed, utilizing a panel dataset spanning three years derived from the 'Korean Education Longitudinal Study 2013'. The findings revealed a significant positive impact of CSDL on students’ Mathematics achievement. Notably, higher income levels, increased availability of computer resources provided by schools, and the implementation of more individualized education were identified as factors that moderate the effect of CSDL on students' achievement levels in Korean and English subjects. These findings underscore the need for an approach that optimizes the educational benefits of CSDL by considering subject-specific characteristics. Furthermore, this study highlights the importance of allocating educational resources, such as computers and smart devices, and integrating individualized educational activities within the classroom environment.

Geometrical assessments of human skull have been done based on the anatomical landmarks. Automatic detection of the landmarks, if developed, will be a great help not only medically, but also anthropologically. An automated system with multi-phased deep learning networks to predict three-dimensional coordinate values of cranio-facial landmarks, was developed. From a publicly available database, computed tomography images of craniofacial area were obtained. They were digitally reconstructed into three-dimensional models. Sixteen anatomical landmarks were plotted on each of the models and coordinate values of them were recorded. Three-phased regression deep learning networks were trained respectively with 90 training datasets. For evaluation, 30 testing datasets were employed. Three-dimensional error for the first phase, testing 30 data, was 11.60 pixels in average. (1 pixel = 500 / 512 mm) For the second phase, it was significantly improved to 4.66 pixels. For the third phase, it was significantly progressed to 2.88. This was comparable to the gaps between the landmarks, plotted by two experienced practitioners. Our proposing method of multi-phased prediction, coarse detection first and narrowing down the detection area, may be a possible solution, within the physical limitation of memory and computation.

The optimal surgical approach for thymoma resection is still an object of debate. The increasing experience in robotic-assisted thoracic surgery (RATS) has led to the progressive affirmation of this technique as a valid alternative to Video-Assisted Thoracic Surgery (VATS), sternotomy and thoracotomy in this setting. The present study aims to compare the post-operative and short-term results of RATS Thymectomy for thymoma with those of other main surgical approaches (sternotomy, thoracotomy, and VATS) from a high-volume single center.Between May 2021 - September 2023, 40 consecutive patients underwent RATS Thymectomy for I to limited-stage III thymoma in our center. Three homogenous groups of consecutive patients who received thymic surgery by main alternative approaches (sternotomy, thoracotomy, VATS) over the last 5 years, were identified in order to perform a comparative analysis. Data of operative technique, time of surgery, associated resections, conversion rate, overall morbidity, tumor size, radicality, postoperative pain, length of hospital stay, and cosmetic results were recorded and compared between each control group and the RATS group. Tumor size was higher in the sternotomy group, but not significantly. The mean operative time of RATS interventions was significantly lower than that of sternotomy and VATS. It was significantly shorter compared to thoracotomy if excluding docking-undocking time. Higher rate of associated adjacent structures resection was reported in the sternotomy group (p=0.005). Conversion rate was significantly higher in VATS group (p= 0.026) compared to RATS. Postoperative pain at 24 and 48 hours was significantly lower in RATS group compared to the others. Improved cosmetics results were reported after RATS compared to sternotomy (p= 0.0001) and thoracotomy (p= 0.001) groups, with a trend towards better results compared to VATS (p=0.05). Length of hospital stay was shorter in the RATS group with a significant difference vs. the sternotomy group (p<0.001). These results from a single center confirm the safety and effectiveness of RATS for the treatment of limited stage thymoma. Advantage in terms of operative outcomes, post-operative pain, cosmetic results, and hospital stay was observed if compared to the alternative approaches. Short-term oncologic outcome was excellent based on the high complete excision rate of the tumor.

In recent years, a range of problems under the broad umbrella of computer vision based analysis of ancient coins have been attracting an increasing amount of attention. Notwithstanding this research effort, the results achieved by the state of the art in published literature remain poor and far from sufficiently well performing for any practical purpose. In the present paper we present a series of contributions which we believe will benefit the interested community. We explain that the approach of visual matching of coins, universally adopted in existing published papers on the topic, is not of practical interest because the number of ancient coin types exceeds by far the number of those types which have been imaged, be it in digital form (e.g., online) or otherwise (traditional film, in print, etc.). Rather, we argue that the focus should be on understanding the semantic content of coins. Hence, we describe a novel approach—to first extract semantic concepts from real-world multimodal input and associate them with their corresponding coin images, and then to train a convolutional neural network to learn the appearance of these concepts. On a real-world data set, we demonstrate highly promising results, correctly identifying a range of visual elements on unseen coins with up to 84% accuracy.

In laser-assisted milling, higher temperature in shear zone softens the material potentially resulting in a shift of mean residual stress, which significantly affects the damage tolerance and fatigue performance of product. In order to guide the selection of laser and cutting parameters based on the preferred mean residual stress, inverse analysis is conducted by predicting residual stress based on guessed process parameters, which is defined as the forward problem, and applying iterative gradient search to find process parameters for next iteration, which is defined as the inverse problem. An analytical inverse analysis is therefore proposed for the mean residual stress in laser-assisted milling. The forward problem is solved by analytical prediction of mean residual stress after laser-assisted milling. The residual stress profile is predicted through the calculation of thermal stress, by treating laser beam as heat source, and plastic stress by first assuming pure elastic stress in loading process, then obtaining true stress with kinematic hardening followed by the stress relaxation. The variance-based recursive method is applied to solve inverse problem by updating process parameters to match the measured mean residual stress. Three cutting parameters including depth of cut, feed per tooth, and cutting speed, and two laser parameters including laser-tool distance and laser power, are updated with respected to the minimization of resulting residual stress and measurement in each iteration. Experimental measurements are referred on the laser-assisted milling of Ti-6Al-4V grade 5 and ELI. The percentage difference between experiments and predictions is less than 5% for both materials, and the selection is completed within 50 loops.

Animal-assisted interventions (AAI) use human-animal interactions to positive effect in various contexts including cancer care. This systematic literature review is the first part of a two-part paper series focusing on the research methods and quantitative results of AAI studies in oncology. We find methodological consistency in the use of canines as therapy animals, in the types of high-risk patients excluded from studies, and in the infection precautions taken with therapy animals throughout cancer wards. The investigated patient endpoints are not significantly affected by AAI, with the exceptions of improvements in oxygen consumption, quality of life, depression, mood, and satisfaction with therapy. The AAI field in oncology has progressed significantly since its inception and has great potential to positively impact future patient outcomes. To advance the field, AAI research in oncology should consistently improve the methodological design of studies, report data more completely, and focus on the therapy animal’s well-being.

We introduce a novel microscopic image dataset augmented with segmentation and detection labels specifically designed for microplastic analysis in sewage environments. Recognizing the increasing concern over microplastics — particles of synthetic polymers smaller than 5mm — and their detrimental effects on marine ecosystems and human health, our research focuses on enhancing detection and analytical methodologies through advanced computer vision and deep learning techniques. The dataset comprises high-resolution microscopic images of microplastics collected from sewage, meticulously labeled for both segmentation and detection tasks, aiming to facilitate accurate and efficient identification and quantification of microplastic pollution. In addition to dataset development, we present example deep learning models optimized for segmentation and detection of microplastics within complex sewage samples. The models demonstrate significant potential in automating the analysis of microplastic contamination, offering a scalable solution to environmental monitoring challenges. Furthermore, we ensure the accessibility and reproducibility of our research by making the dataset and model codes publicly available, accompanied by detailed documentation on GitHub and LabelBox. The dataset and example deep learning models are publicly available at the following GitHub link. https://anonymous.4open.science/r/Microplastics-in-Sewage-1BEF

The point cloud is a form of three-dimensional data that comprises various detailed features at multiple scales. Due to this characteristic and its irregularity, point cloud analysis based on deep learning is challenging. While previous works utilize the sampling-grouping operation of PointNet++ for feature description and then explore geometry by means of sophisticated feature extractors or deep networks, such operations fail to describe multi-scale features effectively. Additionally, these techniques have led to performance saturation. And it is difficult for standard MLPs to directly "mine" point cloud. To address above problems, we propose the Detail Activation (DA) module, which encodes data based on Fourier transform after sampling and grouping. We activate the channels at different frequency levels from low to high in the DA module to gradually recover finer point cloud details. As training progresses, the proposed Point-MDA can uncover local and global geometries of point cloud progressively. Our experiments show that Point-MDA achieves superior classification accuracy, outperforming PointNet++ by 3.3% and 7.9% in terms of overall accuracy on the ModelNet40 and ScanObjectNN dataset, respectively. Furthermore, it accomplishes this without employing complicated operations, while exploring the full potential of PointNet++.

We evaluate performance of Isogeometric analysis (IGA) for structural deformation and automobile crash. In automobile crash analysis, ensuring the accuracy of the acceleration, velocity and load in time series as well as the deformation behavior is important. To maintain the aforementioned consistency, accurately reproducing the bending and buckling of structural members is indispensable. In this study, we firstly compute the bending and buckling of structural members using IGA and validate its performance by comparing the results with conventional finite element analysis and experiments. In addition, we utilize IGA for the crash analysis of an automobile body.

Mice are one of the frequently used animal models in science research whose behavioral characteristics can provide much valuable information in biology, neuroscience, and pharmacology. Nowadays, artificial intelligence is widely used in mice behavior analysis. Integrated AI systems such as ChatGPT and VisualGPT are already available, and we discuss the feasibility of MiceGPT to help researchers identify and classify mouse behavior more easily. We review the applications of mice behavior analysis, analyze the tasks of deep learning on these applications based on an AI pyramid, and finally summarize the AI approaches to solve these tasks. Based on these summaries, we propose three MiceGPT architectures to demonstrate the theoretical feasibility of MiceGPT.

Image analysis is widely applied in plant science for phenotyping and monitoring botanic and agricultural species. Although a lot of software is available, tools integrating image analysis and statistical assessment of seedling growth in large groups of plants are limited or absent, and do not cover the needs of the researchers. In this study, we developed Morley, a free, open-source graphical user interface written in Python. Morley automates the following workflow: (1) group-wise analysis of a few thousand seedlings from multiple images; (2) recognition of seeds, shoots and roots in seedling images; (3) calculation of shoot and root lengths and surface areas, (4) evaluation of statistically significant differences between plant groups, (5) calculation of germination rates, (6) visualization and interpretation. Morley is designed for laboratory studies of biotic effects on seedling growth, when molecular mechanisms underlying morphometric changes are analyzed. Performance was tested using cultivars of T. aestivum, P. sativum on seedlings of up to 1 week old. Accuracy of the measured morphometric parameters was comparable with the ones obtained using ImageJ and manual measurements. Dose-dependent laboratory tests for germination affected by new bioactive compounds and fertilizers, assuming extraction of seedlings from a substrate and/or dissection are among the suggested applications.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) is a commonly used technique for analyzing large biomolecules. However, the utilization of organic matrices limits the small-molecule analysis because of the interferences in the low-mass region and reproducibility issues. To overcome these limitations, a surface-assisted laser desorption/ionization (SALDI), which utilizes nanostructured metallic surfaces, has been developed. Herein, a novel approach for SALDI–MS was proposed using nanoengineered gold shell with nanogaps on the silica core (SiO2@Au NGS), which is an emerging material due to its excellent heat-generating capabilities. The gold shell thickness was controlled by adjusting the concentration of gold precursor for the growth of gold nanoparticles. SALDI-MS measurements were performed on a layer formed by drop-casting a mixture of SiO2@Au NGS and analytes. At the optimized process, the gold shell thickness was observed to be 17.2 nm, which showed the highest absorbance. The ion desorption efficiency was confirmed with a survival yield upon fragmentation. Based on the enhanced SALDI capability, SiO2@Au NGS was utilized to detect various small molecules including amino acids, sugars, and flavonoids. The limits of detection, reproducibility, and salt tolerance of SiO2@Au NGS demonstrate its potential as an effective and reliable SALDI material for small-molecule analyses.

Steel is one of the essential materials in the world's civilization. It is essential to produce many products such as pipelines, mechanical elements in machines, vehicles, profiles, and beam sections for buildings in many industries. Until the '50s of the 20th century, steel products required a complex process known as ingot casting; for years, steelmakers focused on developing and simplifying this process. The result was the con-tinuous casting process (CCP); it is the most productive method to produce steel. The CCP allows producing significant volumes of steel sections without interruption and is more productive than the formal ingot casting process. The CCP begins by transferring the liquid steel from the steel-ladle to a tundish. This tundish or vessel distributes the liquid steel, by flowing through its volume, to one or more strands having wa-ter-cooled copper molds. The mold is the primary cooling system, PCS, solidifying a steel shell to withstand a liquid core and its friction forces with the mold wall. Further down the mold, the rolls drive the steel section in the SCS. Here the steel section is cooled, solidifying the remaining liquid core, by sprays placed in every cooling segment all around the billet and along the curved section of the machine. Finally, the steel strand goes towards a horizontal-straight free-spray zone, losing heat by radiation mechanism, where the billet cools down further to total solidification. A moving torch cutting-scissor splits the billet to the desired length at the end of this heat-radiant zone.

Background/Objectives: The introduction of robotic surgery has revolutionized surgical procedures bringing significant improvements in procedures such as radical prostatectomy. In Poland, robotic technology was first used in public healthcare in 2010 for colorectal surgery in Wrocław. Public funding for robotic urologic surgery began in 2022. The aim of this study is to evaluate the impact of integrating a stable source of funding on the development and utilization of robotic surgery. Methods: Using data from the National Health Fund, a retrospective analysis of these procedures was conducted, focusing on radical prostatectomies from the beginning of the funding period to November 2023. Results: The study showed that in the first year of public funding (2022), 2,149 radical prostatectomies were performed using robotic systems, and this number almost doubled to 4,331 by October 2023. Patients aged 60 years and older accounted for more than 80% of those undergoing robotic surgery. The rise in the popularity of robotic surgery is attributed to its advantages, including high precision, reduced invasiveness in the surgical field, minimized bleeding and pain, and reduced postoperative wound size. The use of robotic surgery also brings environmental benefits. Conclusions: The growing number of robot-assisted surgeries in Poland reflects the increasing role of robotics in medicine. These advances meet the growing demand for healthcare services and demonstrate the precision and effectiveness of robotic technology, potentially improving healthcare access and outcomes. Further implementation and refinement of robotic solutions are necessary to meet the changing needs of medical specialties.

People working in the field of Animal-Assisted Interventions (AAI) often state that they perceive animal welfare as a matter of paramount importance, nevertheless most of scientific literature focuses on the effectiveness of interventions from the user's perspective. Before focusing on animals’ management and welfare during the interaction with users, it would be important to ensure animal welfare during their “ordinary lives”. This article reports information and considerations about the management of donkeys involved in AAI in Northern Italy. Six facilities with several years of experience in Donkey-Assisted Interventions were visited for the purpose of an initial data collection regarding the management of donkeys involved in AAI. A general lack of knowledge regarding the nutritional needs of the donkey and a heterogeneity of practices in the field of its preventive medicine have been identified; the study also highlighted a need for efforts to create a more stimulating and enriched environment for animals involved in AAI. Some possible areas for improvement in the management of donkeys involved in AAI have been highlighted. Further studies are necessary to provide a more comprehensive picture of the welfare of donkeys involved in this context.

Animal-assisted interventions (AAI) are a unique class of complementary medical treatments that can improve a patient’s quality of life, both physically and psychologically. Part I of this two-paper systematic literature review series focused on the study methods and quantitative results of researchers in this field. We continue this in-depth review here in Part II by discussing the common theories associated with AAI in the context of cancer. Of all the factors at work in human-animal interactions, researchers explicitly cite compatible animal personality, physical touch, physical movement, distraction/entertainment, and increased human interaction as the mechanisms responsible for the positive clinical outcomes observed in AAI. In various combinations, these mechanisms group under broader theoretical frameworks that attempt to fully explain the AAI context as it relates to cancer care. The social support hypothesis and the conception of a human-animal bond are the most referenced overarching frameworks. The cognitive activation theory of stress, the science of unitary human beings, and the self-object hypothesis are also referenced. We briefly consider other relevant theories commonly noted in the human-animal interactions literature that have the potential to clarify aspects of cancer-related AAI. We also discuss the neurobiological transduction mechanisms needed to connect theoretical frameworks and their mechanisms directly to the observed clinical outcomes. To advance the field, researchers should consider overarching theories with testable hypotheses when designing studies, and use consistent terminology when reporting results. This review lays a foundation for progress towards a unified theoretical framework and for effective treatment of the whole cancer patient.

Gender has been a rarely addressed aspect in digital game-based learning (DGBL). Likewise, mixed results have been presented regarding the effects according to gender and the conditions that generate these effects. The present work studies the effects of a drill-and-practice mathe-matical game on primary students. The study focused on an analysis by gender, measuring learning performance, and motivation in the practice activity. Also, two instructional mechanics were considered regarding the question answering, a multiple-try (MTF) and a single-try (STF) condition, to search for possible differences. A total of 81 students from 4 courses and 2 schools participated in the experiment. The study's main findings were that: (a) the girls outperformed boys in terms of students' learning gains, (b) girls presented lower levels of competence and autonomy than boys, (c) in the MTF, girls presented lower levels of autonomy but no differences in competence contrasted to boys, (d) in the STF, girls presented lower levels of competence but no differences in autonomy against boys, and (e) no differences existed in interest, effort and value among gender overall o per instructional condition. This study enhances the knowledge of gender differences under diverse instructional settings, in particular providing insights into possible differences by gender when varying the number of attempts provided to students.

In the recent years, Cannabis and hemp-based products have become increasingly popular for various applications ranging from recreational use, edibles, beverages to health care products and medicines. The rapid detection and differentiation of phytocannabinoids is, therefore, essential to assess the potency, therapeutic and nutritional values of cannabis cultivars. Here, we implemented the SpiderMass technology for the in vivo detection of cannabidiol acid (CBDA) and tetrahydrocannabinol acid (THCA) and other endogenous organic plant compounds to access distribution gradients within the plants and differentiate cultivars. The SpiderMass system is composed of an IR- laser handheld microsampling probe connected to the mass spectrometer through a transfer tube. The analysis was performed in situ on different plant organs from freshly cultivated Cannabis plants in only a few seconds. SpiderMass analysis easily discriminated the two acid phytocannabinoid isomers by MS/MS and the built statistical models differentiated between four Cannabis cultivars. Different abundancies of acid phytocannabinoids were also found along the plant as well as between different cultivars. All together, these results introduce the direct analysis by SpiderMass as a compelling analytical alternative for forensic and hemp industrial analysis.

Currently, there is an increasing interest to develop eco-friendly and efficient pigment extraction processes for diverse applications. In this study, we report the comparison between the assisted extraction performed by ultrasonication and microwave with a conventional method to obtain cochineal pigments from Dactylopius coccus Costa. Our independent variables were solvent amount, temperature and irradiation time, and the corresponding optimization was performed by three factors and three levels with a Box-Behnken design. The pigment contents were quanti-fied through UV-vis spectroscopy, and the pigment mixture of extracts was assessed by infrared spectroscopy. It is highlighted that after optimization, the extraction assisted by ultrasound gave a yield of 47% with an efficiency of 31.3 mg/min, whereas the yield obtained from the extraction assisted by microwave was 41% with 27.3 mg/min efficiency, both higher that the efficiency ob-tained with a conventional method (3.5 mg/ml).

The Mediterranean Diet, characterized by high intake of plant foods and unsaturated fats, has been suggested to improve assisted reproductive technology (ART) outcomes. This literature review aimed to summarize the evidence from observational studies on the associations between preconception Mediterranean Diet and ART outcomes. PubMed/MEDLINE, Embase, ScienceDirect, and Google Scholar databases were searched to identify relevant studies. Seven observational studies (n=2,321 women on ART) were included. Adherence to the Mediterranean Diet was assessed by food frequency questionnaires with 6-195 items. Three studies found that higher Mediterranean Diet scores improved Clinical Pregnancy or Live Birth rates, while two studies showed a positive effect on ART eggs and embryos. However, two studies reported no significant associations with ultimate ART success, and four studies found no effects on oocyte and embryo number or quality. Evidence for the effects of greater adherence to the Mediterranean Diet on ART outcomes is limited but promising. Additional rigorous research is needed to clarify and weigh the degree of association between the Mediterranean Diet and ART success.

Medical image analysis is an important branch in the field of medicine, which mainly uses image processing and analysis techniques to interpret and diagnose medical image data. Medical image data helps doctors to effectively observe and diagnose patients' body structures, tissues and lesions. Medical image analysis has been an important research area in the medical field, and it is important for disease diagnosis, treatment planning, and condition monitoring. In recent years, the rapid development of deep learning and computer vision technologies has contributed greatly to the automation, multimodal data fusion, real-time application, and accuracy improvement of medical image analysis. In addition, the development of deep learning has given rise to some new research areas in medical image analysis, such as Generative Adversarial Networks (GANs) for synthetic medical images, self-supervised learning for unsupervised feature learning, and neural network interpretability. In this paper, we will introduce some optimisation methods for medical images which are effective in improving the accuracy, efficiency and reliability of medical image analysis.

The human eye is a vital source of collecting information worldwide via daily observation. The computer science area recognizes it in research as a human-computer interaction. Human blink is an integral part of this observation as a research topic. Several studies have been conducted on eye blinking and left-right eye movements. Previous studies conducted on the eye blink consider the availability of hardware devices high in the budget, high Precision, and low light expo-sure, unlike our study, which uses low-budget devices as simple as a webcam and more versatile usable techniques, incredibly both dim and high light to name a few. This study offers the observational approach via ResNet101v2, VGG-19, and Convolution Neural Network (CNN) architectures. Outperforming both VGG-19 and CNN in this study, ResNet101v2 achieves an impressive accuracy of 98.2%. In contemporary times, deep learning in AI is taking an advanced form. This study attempts to provide a new insight into the real-world implementation of the eye blink.

Over the last few decades, advancements in astrophysics have been closely linked to the development of powerful machine-learning models that can accurately classify celestial bodies. At the same time, however, many astronomical datasets are filled with new features collected by increasingly powerful telescopes. These features can cause overfitting, clouding predictive abilities, and dampening the ability of many models to classify images. Therefore, motivation to design more efficient models has skyrocketed—aiming to optimize for lower run times and high accuracies, even with fewer provided features. In our project, we seek to optimize a convolutional neural network model using a technique known as wavelet analysis. This technique allows us to pick the key features of an astronomical image and accentuate niche details, saving time and boosting accuracy. We applied it to the MiraBest Dataset, a dataset compiled from the FIRST sky survey using a virtual telescope. In the end, after training our neural network on the original images and the five filters (approximation, horizontal, vertical, diagonal, and combined), we found that with fewer features and less overfitting, the vertical Daubechies-family wavelet filter outperformed the original runs with the unaltered images by over 10%. Our findings suggest that wavelet analysis can help harvest the most valuable features in images of celestial bodies–leading to enhanced predictions in astronomical applications and perhaps bolstering modern astrophysical theory.

Youth mental health interventions incorporating trained therapy animals are increasingly popular, but more research is needed to understand the specific interactive behaviors between participants and therapy dogs. Understanding the role of these interactive behaviors is important for supporting both intervention efficacy and animal welfare and well-being. The goal of this study was to develop ethograms to assess interactive behaviors (including both affiliative and stress-related behaviors) of participants and therapy dogs during a social stress task, explore the relationship between human and dog behaviors, and assess how these behaviors may vary between experimental conditions with varying levels of physical contact with the therapy dog. Using video data from a previous experimental study (n = 50 human-therapy dog interactions, n = 25 control group), we successfully developed behavioral ethograms that could be used with a high degree of interrater reliability. Results indicated differences between experimental condition in dog and human behaviors based on whether participants were interacting with a live or a stuffed dog, and whether they were allowed to touch the dog. These findings suggest that physically interacting with a live dog may be an important feature of these interventions, with participants demonstrating increased positive behaviors such as laughing and smiling in these conditions. Dog behaviors also varied based on whether they were in the touching/petting condition of the study. Future research should focus on identifying specific patterns of interactive behaviors between dogs and humans that predict anxiolytic outcomes.

Ancient numismatics, that is, the study of ancient currencies (predominantly coins), is an interesting domain for the application of computer vision and machine learning, and has been receiving an increasing amount of attention in recent years. Notwithstanding the number of articles published on the topic, the variety of different methodological approaches described, and the mounting realisation that the relevant problems in the field are most challenging indeed, all research to date has entirely ignored one specific, readily accessible modality: colour. Invariably, colour is discarded and images of coins treated as being greyscale. The present article is the first one to question this decision (and indeed, it is a decision). We discuss the reasons behind the said choice, present a case why it ought to be reexamined, and in turn investigate the issue for the first time in the published literature. Specifically, we propose two new colour-based representations specifically designed with the aim of being applied to ancient coin analysis, and argue why it is sensible to employ them in the first stages of the classification process as a means of drastically reducing the initially enormous number of classes involved in type matching ancient coins (tens of thousands, just for Ancient Roman Imperial coins). Furthermore, we introduce a new data set collected with the specific aim of denomination-based categorisation of ancient coins, where we hypothesised colour could be of potential use, and evaluate the proposed representations. Lastly, we report surprisingly successful performances which goes further than confirming our hypothesis—rather, they convincingly demonstrate a much higher relevant information content carried by colour than even we expected. Thus we trust that our findings will be noted by others in the field and that more attention and further research will be devoted to the use of colour in automatic ancient coin analysis.

This article explores the required amount of time series points from a high-speed traffic network to accurately estimate the Hurst exponent. The methodology consists in designing an experiment using estimators that are applied to time series, followed by addressing the minimum amount of points required to obtain accurate estimates of the Hurst exponent in real-time. The methodology addresses the exhaustive analysis of the Hurst exponent considering bias behavior, standard deviation, mean square error, and convergence using fractional Gaussian noise signals with stationary increases. Our results show that the Whittle estimator successfully estimates the Hurst exponent in series with few points. Based on the results obtained, a minimum length for the time series is empirically proposed. Finally, to validate the results, the methodology is applied to real traffic captures in a high-speed network based on the IEEE 802.3ab standard.

Studying and understanding the behavior of people and vehicles on public roads can be of utmost importance for supporting the activities of many institutional stakeholders. It may allow automated supervision of the ongoing situation in a given place, with warnings or alarms raised in case of anomalies. It may be used to plan their interventions on road and town organization. It may provide them with advanced support to decision-making. The number of involved entities and places to manage makes it infeasible to manually handle all the traffic-related tasks. Moreover, the complexity of the tasks to be carried out requires the adoption of advanced approaches. Many AI solutions are nowadays mature to support these requirements. In some cases, the motivations and objectives of traffic management require the AI outcomes to be understandable, interpretable and explainable. In this paper, we propose TrAnSIT (TRaffic ANalysis Supervision and Interpretation Tool), an AI-based framework that combines several modules, each aimed at tackling a specific traffic-related task, so as to cover a wide landscape of traffic-related issues, from overall urban or suburban traffic management to surveying specific road segments that fall under the scope of one camera. Most of these modules are based on AI techniques that support a human-level understanding of the outcomes.

"Enhancing the Efficiency of Natural Gas Liquid (NGL) Extraction and Fractionation Trains: An Integrated Approach of Simulation Analysis, Advanced Modifications, and Technological Advancements. Through comprehensive research, innovative solutions such as the heat pump aided distillation (HPAD) system and self-heat recuperation technology (SHRT) have been developed to significantly reduce the energy consumption associated with conventional distillation systems. To identify a practical system for NGL fractionation trains, this study extensively examined and compared various HPAD and SHRT options to retrofit a single column. The objective was to find the most suitable and efficient solution for the fractionation process of natural gas liquids (NGLs). The retrofit options analyzed in this study encompassed a range of techniques, including vapor compression (VC), mechanical vapor re-compression (MVR), thermal vapor re-compression (TVR), bottom flashing (BF), side heat exchanger (SHE), intermediate heating and cooling (IHC), self-heat recuperative (SHR), and modified self-heat recuperative (MSHR) distillation. These methods were carefully examined to determine their suitability and effectiveness in improving the performance of NGL fractionation trains In this study, a depropanizer column, typically employed in conventional NGL plants, was selected as a case study. The simulation software Aspen HYSYS V7.3 was employed to model and analyze eight retrofit designs based on predefined criteria. The simulation data was carefully evaluated to identify the most efficient design for minimizing energy consumption. Among the retrofit options, the mechanical vapor re-compression (MVR) technique demonstrated the most significant energy cost reduction, with a remarkable 68.11 percent improvement compared to the base case conventional column. These findings highlight the potential of MVR as an effective solution for lowering energy costs in NGL fractionation trainsFollowing the MVR retrofit option, the study found that vapor compression (VC) achieved a considerable energy cost reduction of 66.65 percent, closely followed by modified self-heat recuperative (MSHR) at 64.02 percent, bottom flashing (BF) at 62.88 percent, self-heat recuperative (SHR) at 55.85 percent, side heat exchanger (SHE) at 54.23 percent, intermediate heating and cooling (IHC) at 39.54 percent, and thermal vapor re-compression (TVR) also at 39.54 percent. These findings highlight the significant potential for energy savings offered by these retrofit options, with VC being the most popular choice, closely followed by MSHR, BF, SHR, SHE, IHC, and TVR.

Infertility affects millions worldwide, posing a significant global health challenge. The proteomic analysis of follicular fluid provides a comprehensive view of the complex molecular landscape within ovarian follicles, offering valuable information on the factors influencing oocyte development and on the overall reproductive health. The follicular fluid is derived from the plasma and contains various proteins that can have different roles in oocyte health and infertility and this fluid is a critical microenvironment for the developing oocytes as well. Using HPLC/MS method we investigated the protein composition of the follicular fluid and after classification we did relative quantification of the identified proteins in the pregnant (P) and non-pregnant (NP) groups. Based on the protein-protein interaction analysis ALB and ApoA1 was found to be hub proteins and the quantitative comparison of the P and NP groups resulted significantly lower concentration of ApoA1 and HDL-cholesterol in the P group. As both molecules are involved in the cholesterol transport, we also investigated their role in the development of oocytes and in the prediction of fertility.

Badminton tactics refer to the techniques and strategies employed by players to win a match. Analyzing these tactics can help players improve their performance and outsmart their opponents. To study the tactics of top players, we use a gaming tree to analyze matches between two of the most powerful badminton players in history: Lin and Lee. By employing the Nash Equilibrium, we can discover the most beneficial strategies for both players, which reflect their most powerful techniques. Additionally, with the help of this gaming tree, we can precisely predict how players will implement their tactics. Empirical experimental results demonstrate that our proposed method not only evaluates and identifies each player's weaknesses and strengths but also has powerful capabilities to predict their tactics.

We previously reported our initial experience of subxiphoid single-port robotic-assisted thoracic surgery (SRATS) thymectomy using the single-port robotic surgical system (SPS). However, the potential benefits of this technique remain unknown. Thus, this study examined the multi-institutional experience of SRATS thymectomy and compared perioperative outcomes of this technique with those of subxiphoid single-port video-assisted thoracic surgery (SVATS) thymectomy. Data of patients who underwent subxiphoid SRATS and SVATS thymectomy performed by three thoracic surgeons at three institutions between September 2018 and May 2024 were retrospectively collected. In total, 110 patients were included, with 85 and 25 undergoing SRATS and SVATS thymectomy, respectively. No significant differences were noted in the total operative time (154.46±74.06 vs. 146.76±67.07 min, P=0.674) and conversion rate to median sternotomy (0% vs. 4 %, P=0.227) between the two groups. The SRATS group was associated with a lower conversion rate to multi-port surgery (2% vs. 20%, P=0.006), shorter chest tube drainage duration (1.40±0.94 vs. 2.00±1.29 days, P=0.001), and shorter postoperative hospital stay (2.87±1.26 vs. 5.08±5.20 days, P=0.007). Subxiphoid SRATS thymectomy using the SPS is feasible and can be a good alternative to conventional minimally invasive thymectomy. Further large-scale randomized controlled studies are needed to confirm its benefits.

This study deals with the computational analysis of the kinetic processes of a microbial electrolysis cell assisted-anaerobic digestion (MEC-AD) treating raw-waste activated sludge (WAS), comparatively with a conventional AD, as well as the effect of the Organic Loading Rate (OLR) on the system’s performance. The aim was to derive a single kinetic and mathematical model for the study of the MEC-AD, using the ADM1 framework, which can be utilized to extract the effect of an applied potential on the kinetics of AD. The experimental data were obtained from the long-term, continuous operation of two identical reactors (an AD and a MEC-AD), which were operated at different OLRs (1.1, 1.7 and 2.9 gCOD/(L*d)). The results showed that the MEC-AD yielded improved biomass yields, substrate consumption kinetics and 1st order disintegration kinetics, with a predominant contribution to disintegration of complex particulates, comparatively with the AD. Moreover, it enabled operation at higher OLRs (achieving the highest divergence from the AD at the OLR of 4.14 gCOD/(L*d)), therefore accelerating sludge treatment, as well as an improved performance at an increased solids retention time (SRT). These findings can serve as a quantitative guide for the effects of the operating parameters on the raw-WAS-fed MEC-AD performance.

Fractal behavior and long-range dependence are widely observed in measurements and characterization of traffic flow in high-speed computer networks of different technologies and coverage levels. This paper presents the results obtained when applying fractal analysis techniques on a time series obtained from traffic captures coming from an application server connected to the Internet through a high-speed link. The results obtained show that traffic flow in the dedicated high-speed network link have fractal behavior when the Hurst exponent is in the range of 0.5, 1, the fractal dimension between 1, 1.5, and the correlation coefficient between –0.5, 0. Based on these results, it is ideal to characterize both the singularities of the traffic and its impulsiveness during a fractal analysis of temporal scales. Finally, based on the results of the time series analyses, the fact that the traffic flows of current computer networks exhibit fractal behavior with a long-range dependency is reaffirmed.

General–purpose automatic speech recognition (ASR) systems have improved their quality and are being used for pronunciation assessment. However, the assessment of isolated short utterances, as words in minimal pairs for segmental approaches, remains an important challenge, even more for non-native speakers. In this work, we compare the performance of our own tailored ASR system (kASR) with the one of Google ASR (gASR) for the assessment of Spanish minimal pair words produced by 33 native Japanese speakers in a computer-assisted pronunciation training (CAPT) scenario. Participants of a pre/post-test training experiment spanning four weeks were split into three groups: experimental, in-classroom, and placebo. Experimental group used the CAPT tool described in the paper, which we specially designed for autonomous pronunciation training. Statistically significant improvement for experimental and in-classroom groups is revealed, and moderate correlation values between gASR and kASR results were obtained, beside strong correlations between the post-test scores of both ASR systems with the CAPT application scores found at the final stages of application use. These results suggest that both ASR alternatives are valid for assessing minimal pairs in CAPT tools, in the current configuration. Discussion on possible ways to improve our system and possibilities for future research are included.

Dog Assisted Therapy is hypothesized to lower stress in children with Autism Spectrum Disorder (ASD) and children with Down Syndrome (DS), which may be visible on a physiological level. In this study, we measured Heart Rate Variability (HRV) and salivary cortisol of 20 children with DS or ASD at the beginning and end of 6 weekly sessions of Dog-Assisted Therapy. We found a decrease of cortisol levels during single sessions, but no overall effect after 6 sessions (6 weeks). The effect of Dog Assisted Therapy on the increase of HRV could not be confirmed. This study is one of the first to use physiological measurements to test the effects of DAT.

Not a lot is known about either death or the dying process. Politicians and many in the medical profession in the UK tend to shy away from interfering with it by not allowing euthanasia as an end of life option for the patient. This is the first paper in a series of two, comparing the situation in human medicine and veterinary medicine, in which euthanasia is well practiced for relieving suffering at the end of an animal’s life. This first part takes the form of a literature review including best practice around end of life care, its deficiencies and the need for assisted dying. Veterinary surgeons are well trained in the ethics of euthanasia and put it to good use in the best interest of their animal patients. In countries which have legalized physician assisted suicide for the terminally ill reporting indicates that it works well, without increases in involuntary euthanasia and most importantly without intimidation of the vulnerable. However, there is still an ever increasing tendency to overuse sedation and opioids at the end of life, which merits further investigation. With advances in medical science able to significantly prolong the dying process, patient autonomy demands a review of the law in the UK.

Artificial Intelligence (AI) is revolutionizing decision-making procedures in healthcare information systems (HIS), increasing the efficacy, accuracy, and personalization of healthcare. Notwithstanding remarkable progresses, yet there remain gaps in the holistic comprehension and implication of AI-driven technologies across various HIS scenarios. These gaps, consisting limited integration of AI across a variety of HIS parts and inadequate emphasize on interpretability and privacy, motivate our study to conduct a structured evaluation of recent AI applications in this domain. In this study, we present a novel taxonomy for the AI implication in medical decision-making. We analyzed 30 articles and categorized them into six groups including: Clinical Decision Support Systems (CDSS), Predictive Analytics, Natural Language Processing (NLP), Computer-Aided Diagnostics (CAD), Robotic-Assisted Surgery, and Virtual Health Assistants (VHAs/chatbots). Our study aims to propose a holistic perspective of the fast evolving perspective of AI in HIS. The majority of the investigated studies were published from 2023 and 2022, with Springer as the leading publisher (33%). Python and MATLAB were the most well-known simulation languages, applied in 48% and 20% of the papers, respectively, reflecting the technical trends in the area. By emphasizing on pivotal criteria like interpretability, accuracy, and privacy, this research aims to elucidate the crucial issues and development driving AI-driven decision-making in HIS.

Surgical navigation has evolved as a vital tool in maxillofacial surgery, offering precise and patient-specific data. This study explores the clinical applications and accuracy of intraoperative tool tracking in maxillofacial surgery. The research includes 37 patients with various pathologies who underwent surgery assisted by a surgical navigation system using tracked instruments. The study showcases two representative cases: one involving coronoid hyperplasia with mouth opening deficit and another addressing nose-orbital-ethmoidal-frontal ossifying fibroma resection. The results indicate that surgical navigation with tracked instruments provides high precision (<1.5 mm error), reduced surgical time, and less invasive approaches. The study highlights the potential for reproducible outcomes and increased safety, especially in complex cases. Despite some limitations, the synergy between surgical navigation and tracked instruments offers a promising approach in maxillofacial surgery, expanding its applications beyond current practices.

Abstract In This research we will describe how to deal the system with users to make the work in the company or in any work space . in application forms interact with users inside the system . application form of any system must creates a many graphical interface for user ant we should take care about who is dealing with the system , Additionally, application forms used from different type of user so that we must take care about this points during designing the system for different users . Application Forms in the system consists of many programs, or components and function we are designing it. the instructions inside program can be execute by clicking on the command button and we can create specific process in the system.

This study's main target was to analyze and design rectangular edges supported two ways solid slabs by using ES EN 1992-1-1:2015. Slab design is often carried out either manually or with the use of design and analytic software. The researcher sees that some software cannot accept some countries' standard codes. For example, currently in Ethiopia analysis and design of two-way solid slab is done using readily available Excel sheet template. But working with this might have many problems, firstly the structure that is already analyzed by SAP or SAFE or any other international software application that uses international codes but which cannot design structure using ES EN 1992; for instant euro codes and designed by excel sheet can create failure and uneconomical analysis and design result. In this paper, the slab is designed and analyzed based on the chosen concrete grade, chosen reinforcement bar diameter, chosen steel grade for design and analysis of slabs calculations like load, moment, shear, and deflection checking using the moment coefficient method for analysis and design and Microsoft Visual Basic 2010 for coding. All input values are given by the International Standard units and are also used to represent output values. Using manual calculations delays time and mostly the result is not correct. But using this Computer program can increase computation accuracy and save time. The procedure the researcher followed is, first the manual calculation has been done and then SADSE2021 has been done. The result is that both are 99.9% identical, and the disadvantage of this method is that it cannot be used to determine the detailed drawing.

Human-Computer Interaction (HCI) has evolved significantly with advances in Artificial Intelligence (AI) and Computer Vision (CV), paving the way for more intelligent and adaptive user interfaces. This paper explores the design and implementation of user interfaces that leverage AI and CV to create more intuitive, responsive, and personalized experiences. By integrating techniques such as facial recognition, gesture control, and eye tracking, interfaces can adapt in real-time to users’ actions and intentions, thus enhancing accessibility, efficiency, and engagement across various applications, from healthcare to gaming. The study reviews current advancements and challenges in using AI and CV for HCI, including data privacy, model interpretability, and computational efficiency. Experimental results demonstrate the potential of these technologies to improve interaction quality and user satisfaction. This work aims to contribute to the growing field of intelligent HCI design, offering insights into practical implementations and future research directions in AI-driven interface development.

The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

In recent years, news media has been greatly disrupted by the potential of technologically driven approaches in the creation, production, and distribution of news products and services. Artificial intelligence (AI) has emerged from the realm of science fiction and has become a very real tool that can aid society in addressing many issues, including the challenges faced by the news industry. The ubiquity of computing has become apparent and has demonstrated the different approaches that can be achieved using AI. We analyzed the news industry’s AI adoption based on the seven subfields of AI: (i) machine learning; (ii) computer vision (CV); (iii) speech recognition; (iv) natural language processing (NLP); (v) planning, scheduling, and optimization; (vi) expert systems; and (vii) robotics. Our findings suggest that three subfields are being developed more in the news media: machine learning, computer vision, as well as planning, scheduling, and optimization. Other areas have not been fully deployed in the journalistic field. Most AI news projects rely on funds from tech companies such as Google. This limits AI’s potential to a small number of players in the news industry. We make conclusions by providing examples of how these subfields are being developed in journalism and present an agenda for future research.

Computer vision is considered as an ally to solve business problems that require human intervention, intelligence and criteria. This topic of research has evolved in XXI century at faster peace, delivering various alternatives from open source until commercial platforms. With so many options and market growing, it result difficult to make a decision on which one to use, or even worse, realize it was not suited for different scenarios. In this paper we analyze five options selected arbitrarily and tested on a dataset of 755 images to detect persons in an image, using object detectors. We analyze elapsed time to process an image, error with observations by humans, number of persons detected, correlation of time and person density, object detected size and F1 Score, considering precision and recall. As we found there are score ties and similar behaviors among options available, we introduce a novel index that takes in consideration the number of persons and their pixel size, to propose the Vision Acuity Index of Computer Vision. The results demonstrate this is a good option to serve as indicator to make decisions. Also, this index proposed have a potential to be expanded for different business use cases, and to measure new proposed algorithms in the future along with the traditional metrics used previously.

This work - in two parts - tries to promote a new method in Mathematical Modeling, not ever used before, namely the use of the methods of Non-standard Analysis in Topoi. For the general mathematical theory of topoi (Topoi Theory) we refer to the book [10] . For non-standard analysis in the particular Boolean topos SET (the category of sets) we refer to the book [11]. This first part of the work is related, from an applicative point of view, mainly with Mathematical Neuroscience, more precisely a model of the human thinking, consciousness and subconsciousness. The connections between brain and mind will be also scketched, but a closer study also need some further topics to be studied in Part II: Artificial Intelligence, namely (Quantum) neural networks and (Quantum) Turing machine. However both parts (I. and II.) should be considered together, because they complement each other. The topoi model the intuitionistic logic (multi-valued) and have been used in Quantum Physics (see [9] and its references) while Non-Standard Analysis in SET (introduced by Abraham Robinson[31]) has been applied in Mathematical Economics (see [2] and its references); however, the combination was never used until now in Applied Mathematics. Even more, the Non-standard Analysis in Topoi is not too much studied from the point of view of Pure Mathematics either. One of the main objective of this work is to produce progress in this aria of Mathematics also, mainly for the SET-type topoi and exponential topoi, those topoi used in Quantum Physics and in which we need Non-stantard Analysis for our purposes. Based on the paper [13] we propose the logic of non-standard extensions in topoi as a model of the human thinking (based on infons), these theories representing top and very difficult results in Abstract Mathematics. We will analise the relation between brain and mind via the genetic-epigenetic interplay. More details will be provided in the Introduction, followed by a short presentation of the general theory, later connected with the (Quantum) Yang-Baxter equations in topoi. The connections between these equations and braid groups and knots, with intended applications in Neuroscience and Artificial Intelligence will be further analysed in Part II. Besides promoting a new idea of modeling, the present paper aims to promote the building of a Research Proposal for the European $''$Human Brain Project$''$ (https://www.humanbrainproject.eu/en/), and the NSF-Europe Program $''$Collaborative Research in Computational Neuroscience$''$ (https://www.nsf.gov/funding/pgm\_summ.jsp?pims\_id=5147), jointly with the new founded $''$National Centre for the Research of the Brain$''$ of the Romanian Academy of Science (https://acad.ro/centreAR/CNCC/CNCC.pdf, in Romanian). $''$Blue Brain Project$''$ (https://www.epfl.ch/research/domains/bluebrain/) could also be of big interest. We hope that the journals edited by MDPI (Axioms, in particular) and by the Simion Stoilow Institute of the Romanian Academy - Revue Roumaine de Mathematique Pures et Appliqu (http://imar.ro/journals/Revue\_Mathematique/home\_page.html) - among others - will host special issues related to the research subjects described in both parts of this work, starting with this one. For further details and some of the progress on this research subject, see www.ovidiufpasarescu.com.}

The goal of this research is to determine the optimal microwave-assisted sol-gel synthesis pa-rameters of TiO2 nanoparticles with the achievement of the crystalline TiO2 nanoparticles with the highest specific surface area and the lowest energy band gap. Titanium isopropoxide was used as a precursor to prepare the sol (colloidal solution) of TiO2, isopropanol as a solvent, acetylacetone for complexation, and nitric acid as a catalyst. Four samples of titanium dioxide were synthesized from the prepared colloidal solution in a microwave reactor at a temperature of 150 °C for 30 minutes and at a temperature of 200 °C for 10, 20, and 30 minutes. The phase composition of the prepared samples was determined by X-ray diffraction analysis (XRD) and Fourier-transform infrared spectroscopy (FTIR). Nitrogen adsorption/desorption isotherms were used to determine the spe-cific surface area and pore size distributions by Brunauer-Emmett-Teller (BET) method. The band gap energy values of the TiO2 samples were determined by diffuse reflectance spectroscopy (DRS). CrystallineTiO2 nanoparticles with the highest specific surface area and the lowest energy band gap were obtained for the TiO2 sample synthesized at 200 °C for 10 minutes.

: The effect that the microstructure exerts on the TRIP phenomenon and on the mechanical properties in a multiphase steel was studied. Samples of an initially cold-rolled ferrite-pearlite steel underwent different intercritical annealing treatments at 750 °C until an equal fractions of austenite/ferrite was reached; the intercritical treatment was followed by isothermal bainitic treatments before cooling the samples to room temperature. Samples in the first treatment were heated directly to the intercritical temperature, whereas other samples were heated to either 900 or 1100 °C to obtain a fully homogenized, single phase austenitic microstructure prior to the conducting the intercritical treatment. The high temperature homogenization of austenite resulted in the decrease in its stability, so a considerable austenite fraction transformed into martensite by cooling to room temperature after the bainitic heat treatment. Most of the retained austenite transformed during the tensile tests, and as a consequence, the previously homogenized steels showed the highest UTS. In turn, the steel with a ferritic-pearlitic initial microstructure, exhibited higher ductility than the other steels and texture components that favor forming processes.

Electroless etching of semiconductors was elevated to an advanced micromachining process by the addition of a structured metal catalyst. Patterning of the catalyst by lithographic techniques facilitates the patterning of crystalline and polycrystalline wafer substrates. Galvanic deposition of metals on semiconductors has a natural tendency to produce nanoparticles rather than flat uniform films. This characteristic makes possible the etching of not only wafers but also particles with arbitrary shape. While it has been widely recognized that spontaneous deposition of metal nanoparticles can be used in connection with etching to porosify wafers, it is also possible to produced nanostructured powders. MACE can be controlled to produce (1) etch track pores with shapes and sizes closely related to the shape and size of the metal nanoparticle, (2) hierarchically porosified substrates exhibiting combinations of large etch track pores and mesopores, and (3) nanowires with either solid or mesoporous cores. This review discussed the mechanisms of porosification, processing advances and the properties of the etch product with special emphasis on the etching of silicon powders.

This is the second of a series of two papers comparing the end of life issues in human and veterinary medicine. We outline the main differences between human and animal patients such as patient communication, finance and ‘conflicts of interest’ between animal, owner and veterinarian. We discuss striking similarities between human and veterinary issues such as assessing quality of life and the primary role of the attending veterinarian or doctor being the welfare and care of the patient. This paper takes the form of an ethical argument in favour of allowing euthanasia in human medicine, by providing insights into end of life issues for humans from an independent veterinary perspective. Veterinary surgeons are well trained in the ethics of euthanasia and put it to good use in the best interest of their animal patients. Doctors in the UK are limited and unwilling to put forward a case for the option of euthanasia for those patients who face a slow and agonizing death. With advances in medical science being able to significantly prolong the dying process, autonomy for the patient demands a review of the law regarding patient choice in the UK.

This study delves into the realm of heuristic evaluation within usability testing, focusing on refining and applying a tailored algorithm that simplifies the implementation of the Analytic Hierarchy Process for heuristic prioritization. Addressing the prevalent challenge of disparate evaluation methodologies and the absence of a standardized process in usability testing, our research introduces a novel method that leverages the Analytic Hierarchy Process alongside a bespoke algorithm. This algorithm utilizes transitive properties for pairwise comparisons, significantly reducing the evaluative workload. This innovative approach facilitates the estimation of heuristic relevance irrespective of the number of items per heuristic or the item scale and streamlines the overall evaluation process. In addition to rigorous simulation testing of the tailored algorithm, we applied our method in a practical scenario, engaging seven usability experts in evaluating a web interface. This empirical application underscored our method’s capacity to diminish the requisite comparison count and spotlight areas of improvement for critically weighted yet underperforming heuristics. The outcomes of this study underscore the efficacy of our approach in enhancing the efficiency and organization of heuristic evaluations, paving the way for more structured and effective usability testing methodologies in future research endeavors.

The proliferation of intelligent monitoring devices has led to the widespread adoption of background extraction technology across a multitude of domains, including intelligent transportation, video surveillance, human-computer interaction, and medical diagnosis. In this work, the model employs a multi-layer convolutional neural network structure, which enables the extraction and fusion of scene features from different scales in a layer-by-layer manner. This approach facilitates the comprehensive capture of complex scene information. The convolutional network comprises multiple layers, with each layer responsible for extracting features at a specific scale. The shallower layers capture more detailed feature information, whereas the deeper layers focus on more global scene features. The strategy of multi-scale feature fusion allows the model to fully capture multi-level and multi-dimensional information in the context of large-scale scenes. Furthermore, the incorporation of an attention mechanism enables the model to adaptively allocate attention to salient regions, thereby enhancing its capacity to discern intricate scenes by assigning greater significance to pivotal features. The experimental results demonstrate that the proposed method is effective in practice, as evidenced by its performance on public datasets.

This study explores the integration of computer vision with topology optimization for additive manufacturing, with a focus on maximizing eigenfrequency in a design domain. Utilizing cus-tom-developed photogrammetry software, high-resolution images are processed to generate de-tailed 3D models, subsequently converted to STL files with precision. Adaptive meshing in COMSOL Multiphysics, controlled through a MATLAB API, ensures optimal mesh resolution. Prioritizing resource conservation in extraterrestrial environments, the original volume is reduced by 50% while preserving structural integrity. The design domain undergoes rigorous topology optimization in MATLAB, supported by COMSOL's advanced FEM simulation. The optimized design exhibits a 57% performance improvement and a 50% weight reduction, maintaining desired vibration characteristics, validating the efficacy of the modifications. Moreover, the case with an eccentric mass shows a significant 64% increase in eigenfrequency.

Background: We assess locoregional drug targeting effectiveness of intraoperative (IO) cryoprobe-assisted injection of blue dye (BD) and cytotoxic-tracer mixture (TTM), in VX2 tumor model, and its translational value to cryo-assisted breast tumor surgery with BD alone. Methods: Under computed tomography (CT) guidance, we injected two ml TTM in five aliquots in the margin of 16 frozen or normothermic VX2 tumors. We evaluated the IO and post-operative drug targeting and therapeutic efficacy in tumor-host interface (T-HI) by CT, gross examination, and histopathology. In twenty-six T1 to T4 primary breast cancer (BRCA) we performed ultrasound-guided (US) cryoprobe-assisted tumor freezing, BD guided lymphatic mapping, and surgery. We evaluated, IO and in freshly resected specimen, BD distribution pattern in T-HI, lymph node(s), breast parenchyma, and resection cavity. Results: Fluids-impervious frozen VX2 or breast tumor transported drug(s) an arc-like pattern at T-HI regardless of freeze dose, number of freeze-thaw cycles, drug dose fractionation, tumor characteristics or dimensions. During melting, TTM spread within fifty percent VX2 tumor mirrored that of T-HI; it was massive in normothermia. In VX2 twenty percent focal margin necrosis at pathology coincided with CT gap; in both studies, BD dose-staining spread in T-HI and tumor was linear. Eighty-four patients had one to twelve stained axillary lymph nodes; sixty-nine percent and all respectively, had another quadrant and resection cavity stained. Conclusion: Intraoperative freezing-assisted drug delivery and targeting techniques during cryoablation of VX2 tumor translate successfully to locoregional BD targeting, lymphatic mapping during cryo-assisted surgery of breast cancer.

In the dynamic landscape of drug discovery, Computer-rAided Drug Design (CADD) emerges as a transformative force, bridging the realms of biology and technology. This paper overviews CADD's historical evolution, categorization into structure-based and ligand-based approaches, and its crucial role in rationalizing and expediting drug discovery. As CADD advances, incorporating diverse biological data and ensuring data privacy become paramount. Challenges persist, demanding the optimization of algorithms and robust ethical frameworks. Integrating Machine Learning and Artificial Intelligence amplifies CADD's predictive capabilities, yet ethical considerations and scalability challenges linger. Collaborative efforts and global initiatives, exemplified by platforms like Open Source Malaria, underscore the democratization of drug discovery. The convergence of CADD with personalized medicine offers tailored therapeutic solutions, though ethical dilemmas and accessibility concerns must be navigated. Emerging technologies like quantum computing, immersive technologies, and green chemistry promise to redefine the future of CADD. The trajectory of CADD, marked by rapid advancements, anticipates challenges in ensuring accuracy, addressing biases in AI, and incorporating sustainability metrics. The paper concludes by highlighting the need for proactive measures in navigating the ethical, technological, and educational frontiers of CADD to shape a healthier, brighter future in drug discovery.

The field of 3D model generation has become essential across various industries, including gaming, virtual and augmented reality (VR/AR), architecture, and medical imaging. Traditionally reliant on manual efforts, 3D content creation is now being transformed by deep generative models, enabling more efficient, scalable, and dynamic generation of complex shapes and environments. This survey provides a comprehensive review of key backbone architectures used for 3D generation, including autoencoders, variational autoencoders (VAEs), generative adversarial networks (GANs), autoregressive models, diffusion models, normalizing flows, attention-based models, CLIP-guided models, and procedural generation techniques. We explore each model’s role in 3D generation, highlighting their strengths—such as the precision of VAEs, the realism of GANs, the stability of diffusion models, and the scalability of procedural methods—alongside their limitations, such as training instability, high computational costs, and the difficulty in handling multi-modal data. Additionally, we discuss the increasing relevance of attention-enhanced models and the integration of text-based CLIP supervision for improved semantic alignment in 3D outputs. The survey concludes with an analysis of open challenges, including balancing efficiency with expressiveness, managing training complexity, and addressing dataset limitations. It also identifies future research directions, such as few-shot learning, hybrid architectures, and neural-symbolic approaches, which promise to advance the field by improving the generalization and versatility of 3D generation models. This paper aims to guide researchers and practitioners in navigating the evolving landscape of 3D generative methods and inspire new innovations in the creation of realistic, high-quality 3D content.

Engineering of quantum computers requires reliable characterization of qubits, quantum operations, and even the entire hardware. Quantum tomography is an indispensable framework in quantum characterization, verification, and validation (QCVV), which has been widely accepted by researchers. According to the tomographic target, quantum tomography can be categorized into quantum state tomography (QST), quantum process tomography (QPT), gate set tomography (GST), process tensor tomography (PTT), and instrument set tomography (IST). Standard quantum tomography toolkits generally consist of basic linear inverse methods and statistical maximum likelihood estimation (MLE) based methods. Furthermore, the performance of standard methods, including effectiveness and efficiency, has been further developed by exploiting Bayesian estimation, neural networks, matrix completion techniques, etc. In this review, we introduce the fundamental quantum tomography techniques, including QST, QPT, GST, PTT, and IST. We first introduce the details of basic linear inverse methods. Then, the framework of MLE methods with constraints is summarized. Finally, we briefly introduce recent further research in developing the performance of tomography. This review provides a primary getting-start in developing quantum tomography, which promotes quantum computer development.

Background/Objectives: Forceps delivery is a crucial obstetrical technique that has been increasingly underutilized in favor of cesarean delivery, despite numerous complications related to cesarean section. The major concerns in assisted vaginal birth (AVB) are the safety and long-term consequences. Thus, we aimed to investigate the neurodevelopmental outcome of children born with forceps delivery up to the age of seven. Methods: A prospective descriptive study was conducted from January 2011 to December 2016 among 49 women from our department in the Clinic for Gynecology and Obstetrics, University Clinical Center of Serbia. The study included women with term singleton cephalic pregnancies. Descriptive statistics were calculated for the baseline demographic and clinical features, whereas the logistic regression analysis was used to explore the association between various perinatal parameters and neurological status of newborns. The Sarnat and Sarnat classification was used to evaluate neurological status of newborns, while long-term neurodevelopmental outcome was assesed with Griffiths Mental Development Scale and additional questions based on the neurologist's clinical practice and experience. Results: Pathological neurological status of the newborn was found in 16.3%, with pathological ultrasound of CNS in 3%. A statistically significant association was observed with Apgar score, odds ratio of 0.575 (95% CI: 0.407–0.813, p = 0.002) and perinatal asphyxia, odds ratio of 9.882 (95% CI: 1.111–87.902, p = 0.04). However, this was unlikely to be related to the mode of delivery. Long-term adverse neurological outcome was seen in three cases, which accounts for 6.4%. These included mild disorders, such as delayed milestone, speech delay and motor clumsiness. Conclusion: The present study highlights the safety of forceps delivery regarding children's neurological outcomes at 7 years of age. This is an important contribution to the modern management of labor, especially in light of increasing rates of cesarean deliveries worldwide.

Recent technological advances, including capsule endoscopy (CE) and balloon-assisted endoscopy (BAE), have revealed that small intestinal disease is more common than previously thought. CE has advantages including, high diagnostic yield, discom-fort-free, outpatient basis, and physiological images. BAE enabled endoscopic diagnosis and treatment in the deep small bowel. Computed tomography (CT) enterography with negative oral contrast can evaluate masses, wall thickening, and narrowing of the small intestine. In addition, Enhanced CT can detect abnormalities outside the gastrointestinal tract that endoscopy cannot evalu-ate. Each modality has its advantages and disadvantages, and a good combination of multiple modalities leads to an accurate diagnosis. As a first-line modality, three-phase enhanced CT is preferred. If CT shows a mass, stenosis, or wall thickening, a BAE should be selected. If there are no abnormal findings on CT and no obstructive symptoms, CE should be selected. If there are sig-nificant findings in the CE, determine the indication for BAE and its insertion route based on these findings. Early diagnosis of small intestinal tumors is essential for favorable outcomes. For early diagnosis, the possibility of small bowel lesions should be considered in patients with unexplained symptoms and signs after examination of the upper and lower gastro-intestinal tract.

Objectives: Chylothorax is a relatively rare condition of lymphatic fluid accumulation in the thoracic cavity due to a leakage from the thoracic duct or its tributaries. Patients present with dyspnea, malnutrition, and immunosuppression. Treatment can be conservative or surgical, depending on etiology and clinical course. The optimal management algorithm for chylothorax is still controversial. Methods: This is a retrospective study of all patients with chylothorax treated at our Department of Thoracic Surgery during the 10-year period. Results: A total of 14 patients were identified for the study. Nine patients had chylothorax after lung or esophageal cancer surgery. Four patients had chylothorax in advanced lymphoma. One patient had chylothorax after blunt chest trauma. A conservative approach was initiated in most patients (92%), including pleural drainage, nil per mouth, total parenteral nutrition, and somatostatin 0.1 mg bid subcutaneously. Surgical treatment was indicated in patients with thoracic drain production >800 mL per day beyond the fifth day of treatment and those with blunt thoracic trauma. Two patients had thoracic duct ligation via right-sided thoracotomy, and five patients had video-assisted thoracoscopic thoracic duct ligation with the immediate arrest of chylous leakage. Conclusion: Chylothorax should be treated conservatively initially. Surgical treatment should not be delayed in case of failure beyond the fifth day. In our series of patients, a video-assisted thoracoscopic approach for thoracic duct ligation proved to be minimally invasive, highly efficient, and well tolerated by patients and, therefore, should be the preferable route of surgical treatment.

Silicon-based thin-films and nanostructures are of paramount importance in a wide range of applications, including microelectronics, photovoltaics, large area sensors, as well as biomedicine. The wide accessibility of silicon and its relatively low cost have driven a continuous improvement of the technology based on this element. Plasma technology has been widely used for the synthesis of coatings and nanostructures based on silicon. Moreover, it has given a fundamental contribution for continuously improving the control of the physicochemical properties of silicon-based materials, and for allowing the synthesis of nanometric structures with well-defined shape and morphology. In this work, we have reviewed the most interesting developments of plasma-assisted processed for the synthesis of Si-based materials, both inorganic and organic, in the last five years. A special attention has been given to the new techniques, or modifications of already existing ones, that open new possibilities for the synthesis of materials with new properties, as well as nanostructures with novel characteristics.

Conventional phenotyping breeding approaches for vegetable crops like Solanaceae, Bulb, Root crops, have made a significant contribution by developing many varieties. Despite this, conventional phenotyping approaches are not sufficient due to the longer time taken to develop a variety, low genetic gain, environmental factors and some other externalities that affect the phenotype-based selection. To address the challenges of conventional phenotype, a new recent method of high throughput phenotyping (HTP) is considered a promising tool. The development of high-throughput phenotyping technology began in the preceding decade as advancements in sensor, computer vision, automation, and advanced machine learning technologies. HTP platforms are being utilized to undertake non-destructive assessments of the complete plant system in a range of crops. HTP provides the precise measurements and suggests the collection of high-quality and accurate data which is necessary for standardizing phenotyping for the collection of genetic dissection and genomic assisted breeding such as genome-wide association studies (GWAS), linkage mapping, marker-assisted selection (MAS), genomic selection (GS). The remainder of this chapter discusses how high-throughput phenotyping technologies can be used in genomic-assisted breeding for vegetable crops

Objective: The study aimed to explore the experiences of women undergoing Assisted Reproductive Technologies namely; Invitro Fertilization and Intracytoplasmic Sperm Injection at the Finney Hospital and Fertility Centre, New Bortianor, Ghana. Method: A qualitative research design was employed to analyse and describe the experiences of the women seeking Assisted Reproductive Technologies. Fifteen women were invited and interviewed using a semi-structured interview guide. The responses were transcribed verbatim and analysed using content analysis. Results: Three themes emerged from the study: the women’s experiences, challenges and the roles and contributions of significant others. The women were anxious, stressed-up, exhausted and financially burdened. Spouses and health professionals played significant roles by providing social, emotional and financial support for these women. Significant others such as spouses and close relatives were supportive and provided encouragement to the women. Conclusion: The experiences of women undergoing Assisted Reproductive Technologies are multi-dimensional. Thus psychosocial intervention as part of ART services with health insurance cover may be client-centered and more appropriate for these group of women.

Aim: To develop an index for quantitative assessment of the upper limb motor function in children with cerebral palsy before and after robot-assisted therapy. Method: An upper limb motor function index was developed using kinematic, surface electromyography and three-axis inertial measurements unit data collected from 15 children with cerebral palsy (CP) and 15 typically developed children. Children with CP underwent 18 robot-assisted therapy sessions with the REAplan device. All children were evaluated, using kinematic data from the REAplan, electromyography and three-axis inertial measurements unit readings from its accelerometer. A principal component analysis was conducted to produce an evaluation index, which is able to detect the deviation from the upper limb motor function of typically developing children group. Children with CP were evaluated twice before and after the intervention with Box and Blocks test and Finger-To-Nose test. The discriminative and concurrent validity of the upper limb motor function index were investigated. Results: The upper limb motor function index was higher in children with CP post therapy (p<0.001). Finger-To-Nose test values improved after robot-assisted therapy (p<0.03). A weak but positive correlation was observed between upper limb motor function index and clinical tests (r=0.012, p=0.95 and r=0.13, p= 0.54 for Box and Blocks test and Finger-To-Nose test respectively). Interpretation: The upper limb motor function index successfully differentiated between the typically developing children and children with CP and was effective in assessing the improvement of the upper limb motor function after robot-assisted therapy. The upper limb motor function index could be extended to assess and monitor rehabilitation therapies of other populations, such as those with stroke and Parkinson’s disease.

Introduction: This review explores the literature and presents institutional cases of concurrent robotic multisite surgery—in particular, robot-assisted radical prostatectomy (RARP) and robot-assisted partial nephrectomy (RAPN) for synchronous prostate and kidney cancers. We highlight dual-console synchronous robotic procedures, emphasizing the unique docking tech-nique developed. Aim: The aims of the study were to evaluate the feasibility, safety, and clinical outcomes of concurrent RARP and RAPN for synchronous prostate cancer and small renal tumors, as well as other concurrent robotic multisite surgeries, and to compare institutional findings with the existing literature. Materials and Methods: We describe the unique docking technique used in our procedures, which facilitates atraumatic transitions between surgeries using the same port sites. Additionally, eligible studies on concurrent robotic multisite surgery that were published in English were retrieved from the MEDLINE, Embase, and Cochrane Library databases. Data on operative time, blood loss, complications, and postoperative outcomes were extracted. Results: Eight cases from our institution demonstrated the feasibility and safety of the proposed approach, which is consistent with previous reports. The unique docking technique and dual-console setup enhance surgical precision, offering significant benefits in patient outcomes. As part of the litera-ture review, nine retrospective studies involving 40 cases of concurrent RARP and RAPN were reviewed. Additionally, eleven retrospective studies involving 392 cases of concomitant RARP and robotic transabdominal preperitoneal inguinal hernia repair (RTAPPIHR) were analyzed. Conclusions: Concurrent RARP and RAPN for synchronous prostate and kidney tumors is a viable and effective approach. Our early experience suggests that this and other combined procedures can optimize patient care and reduce costs. Prospective trials are needed to validate these findings and further refine the techniques.

Recent progress in deep learning has led to accurate and efficient generic object detection networks. Training of highly reliable models depends on large datasets with highly textured and rich images. However, in real-world scenarios, the performance of the generic object detection system decreases when (i) occlusions hide the objects, (ii) objects are present in low-light images, or (iii) they are merged with background information. In this paper, we refer to all these situations as challenging environments. With the recent rapid development in generic object detection algorithms, notable progress has been observed in the field of object detection in challenging environments. However, there is no consolidated reference to cover state-of-the-art in this domain. To the best of our knowledge, this paper presents the first comprehensive overview, covering recent approaches that have tackled the problem of object detection in challenging environments. Furthermore, we present the quantitative and qualitative performance analysis of these approaches and discuss the currently available challenging datasets. Moreover, this paper investigates the performance of current state-of-the-art generic object detection algorithms by benchmarking results on the three well-known challenging datasets. Finally, we highlight several current shortcomings and outline future directions.

Carob pulp is a Mediterranean by-product highlighted as an important source of bioactive compounds potentially used in different industrial applications. This paper deals with the comparison of methods elaborated with combinations of conventional (maceration-M, organic solvents and water) and non-conventional green (ultrasound-assisted extraction -UAE, and natural deep eutectic solvents -NADES) extraction approaches and using chemometric tools for screening the polyphenol profile by HPLC-HRMS and bioactive potential measured by Folin-Ciocalteu, DPPH and ABTS assays of carob pulp from two Tunisian ubications (North and South). Regarding the factors studied (extraction technique and solvent, and geographical location), the solvent marked the higher variability found for the different families of compounds studied. The highest yield of total phenolics was achieved by the conventional method ethanol 75%-M (5023.92 µg/g) and the green approaches water-UAE (5008.32 µg/g), followed by the NADES lactic acid: sodium acetate (3:1) (3117.23 µg/g). The latter standing out for its contents in flavonols and hydrolizable tannins and antioxidant potential. The two Tunisian locations studied showed differentiation in the phenolic content depending on the extraction method. However, in general, higher contents were observed in extracts from the southern region. The chemometric evaluation confirm the suitability of the proposed green methods as excellent powerful tools for bioactive extraction from carob pulp, with similar, improved or highly selective phenolic recoveries compared to the conventional methods assessed.

In all areas of medicine, especially in radiology, computers are increasing year by year. Filmless radiology, speech recognition software, electronic application forms, and teleradiology are recent developments that have greatly improved radiologists' performance. This research explores radiology software trends, predictions, and the challenges posed by informatics and historical trend analysis. The rationale behind this research is that information technology (IT) is overgrowing almost every day. We must continuously seek new ways to apply IT to make more use of resources. Consequently, IT becomes increasingly crucial to radiology organizations' innovative thinking, workflow, and business models. This study aimed to analyze all radiology software publications in the Science Citation Index (SCI). From 1991 to July 2021, SCI was used to search for publications systematically. We have also widely used this historical method in radiology software research. The findings and discussions are base on an assessment of trends, predictions, contributions, and challenges in radiology software, and we are exploring radiology software with six evolutionary stages. The gift of this research is that radiology managers realize that the use of new information technologies is closely related to survival in a competitive environment. Radiology companies can review these new technologies to develop more innovative business models and services to improve operational deficiencies.

Recent improvements in deepfake creation have made deepfake videos more realistic. Moreover, open-source software has made deepfake creation more accessible, which reduces the barrier to entry for deepfake creation. This could pose a threat to the people’s privacy. There is a potential danger if the deepfake creation techniques are used by people with an ulterior motive to produce deepfake videos of world leaders to disrupt the order of countries and the world. Therefore, research into the automatic detection of deepfaked media is essential for public security. In this work, we propose a deepfake detection method using upper body language analysis. Specifically, a many-to-one LSTM network was designed and trained as a classification model for deepfake detection. Different models were trained by varying the hyperparameters to build a final model with benchmark accuracy. We achieved 94.39% accuracy on the deepfake test set. The experimental results showed that upper body language can effectively detect deepfakes.

This study presents a novel approach combining computer vision with topology optimization for additive manufacturing. Using specialized photogrammetry software, we capture high-resolution images of the design domain to create precise 3D models through detailed scanning. These models are converted into STL file format and remeshed using an adaptive algorithm within COMSOL 5.3 Multiphysics, managed by a custom MATLAB 2023. This integration allows optimal mesh resolution and accuracy in analyses. We applied this method to design a concrete pillar for 3D printing, aiming for a 75% volume reduction to enhance material efficiency and stability, crucial for extraterrestrial environments. The design, scanned with a 360-degree camera array, informed the MATLAB-based topology optimization process. Combining MATLAB’s optimization algorithms with COMSOL’s meshing and finite element solver solver, we explored material-efficient configurations. The results show significant volume reduction, particularly in the central design area, optimizing material use while maintaining structural stability. The optimization algorithm demonstrated rapid, stable convergence to near-optimal solutions within about 20 iterations, highlighting the method’s efficiency and robustness in complex design optimization.

In recent decades, the development of surgical systems that minimize patient impact has been a major focus for surgeons and researchers, leading to the advent of robotic systems for minimally invasive surgery. These technologies offer significant patient benefits, including enhanced outcome quality and accuracy, reduced invasiveness, lower blood loss, decreased postoperative pain, diminished infection risk, and shorter hospitalization and recovery times. Surgeons benefit from the elimination of human tremor, ergonomic advantages, improved vision systems, better access to challenging anatomical areas, and magnified 3DHD visualization of the operating field. Since 2000, Intuitive Surgical has developed multiple generations of master-slave multi-arm robots, securing over 7,000 patents, which created significant barriers for competitors. This monopoly resulted in the widespread adoption of their technology, now used in over 11 million surgeries globally. With the expiration of key patents, new robotic platforms featuring innovative designs, such as modular systems, are emerging. This review examines advancements in robotic surgery within the fields of general, urological, and gynaecological surgery. The objective is to analyse the current robotic surgical platforms, their technological progress, and their impact on surgical practices. By examining these platforms, this review provides insights into their development, potential benefits, and future directions in robotic-assisted surgery.

Primary spontaneous pneumothorax (PSP) is a pleural disease that abruptly affects healthy adolescents and young adults, representing a common thoracic disorder in this age group. This study aimed to investigate the necessity of preoperative chest tube insertion in patients with PSP and to re-evaluate current clinical treatment approaches. A retrospective review of medical records was conducted for children and young adults aged 14–30 years, diagnosed with PSP and who underwent Video-assisted thoracoscopic surgery (VATS) between January 2016 and December 2022 at Wonju Severance Christian Hospital. Patients were retrospectively divided into two groups based on their initial treatment: chest tube insertion and non-chest tube insertion. Clinical data including demographics, treatment details, and outcomes were collected and compared. Total 128 cases of PSP were included in the study. The total length of hospital stay was significantly shorter in the non-chest tube group (p=0.000), which was attributed to the omission of pre-operative chest tube insertions. No significant differences were observed between the two groups in terms of postoperative complications or recurrence rates. In cases of young patients with PSP, omitting pre-operative chest tube insertion may be a viable option if the patient is scheduled for VATS.

(1) Background: the popularity of rabbits has increased during the last decades and became the third common companion animals in EU. Their participation in animal assisted interventions (AAI) is growing. It is highly important to ensure the well-being of the animals in AAI. Whereas the needs and the advantages of people involved in AAI are becoming more and more evident, the needs of animals are not clearly determined, it is required not to affect the animals negatively. Those animals who are used for AAI purpose need toneed to be transported regulrarly; (2) Methods: the rabbits stress – caused by transportation – were measured by non-invasive faecaesl cortisol metabolite (FCM) analysis. 18 animals were involved in the study and experienced a 30 minutes transportation every second day duringfor two weeks (total 6 times) while 128 samples were collected; (3) Results: rabbits –could handle the transportation procedure by the first and second time but subsequently the stress hormone metabolites in faeces samples increased significantly regardless of the offered treatments (hay, carrot and apple) during the carriage. (4) Conclusions: those owners who use rabbits for Animal Assisted Intervention purpose need to take into account that transportation itself is a stressful experience for the animals.

Deep sky observation is a fascinating activity, but it requires favourable conditions in order to be fully appreciated. Complex equipment, light pollution around urban areas, lack of contextual information often prevents newcomers from making the most of their observations, restricting the field to a niche expert audience. In this paper, we show how combining the usage of smart telescopes with eXplainable Artificial Intelligence makes the practice of astronomy even more accessible and educative, and how it has been applied during outreach sessions In Luxembourg Greater Region.

Excitation of the acoustic field leading to the Blaha effect affects the plasticity of the material significantly in ultrasonic vibration-assisted forming. In a micro-forming field, the effects are more significant in the deformation in surface of materials [1]-[3], in which reduction of the surface roughness based on the increasing of plastic deformation of surface asperity was effective [4]. On the other hand, the effect on deformation behavior of the bulk region indicted reduction in the yield stress of materials, and not only acoustic effect [5], but also impact effect is found to generate a large amount of dislocation and produce plastic deformation [6][7]. However, the effect on the bulk is more significant as that on the surface. Differences in the effect on the surface and the bulk are not clarified. In this study, the mechanism of the deformation in the surface of the material with ultrasonic vibration assistance is investigated and compared with that in the bulk. Forging tests using a newly developed ultrasonic vibrator were carried out on pure Cu foils with various process conditions. The longitudinal vibration frequency of the ultrasonic transducer is 60∓2kHz, and the vibration amplitude is in an adjustable range of 0~10μm. Forging test was carried out at different initial stress, specimen size and amplitude. The difference in acoustic softening and impact effects on the surface and the bulk was discussed.

The preparation of new materials based on starch for the development of biodegradable packaging is increasing, however, the poor properties of this biopolymer for this application causes an area of opportunities for the improvement of water vapour permeability (WVP), mechanical properties, thermal properties, hydrophilicity, water absorption, among others. Hence, starch has been combined with other polymers such as polyvinyl alcohol, which has shown an improvement in the mechanical properties of starch, also, the use of clays suggests that the properties of response to water can be improved. Therefore, in this work, the preparation and characterization of starch-PVA-nanoclay films prepared by solvent casting is reported. The results obtained suggest that the sonication of nanoclay is necessary to reach a good dispersion, which promotes a strong interaction among starch-PVA-nanoclay. In addition, the properties of WVP and mechanical properties of films improved with incorporation of nanoclay, the concentration of 0.5% w/v of nanoclay showed to be the best concentration due to concentrations of 1.0 and 1.5% w/v were poorer than 0.5% w/v. Accordingly, the successful incorporation of nanoclays into the matrix starch-PVA suggests that this material is a good candidate for use as packaging.

Cannabis is a flowering plant that has long been used for medicinal, therapeutic, and recreational purposes. Cannabis contains more than 500 different compounds, including a unique class of terpeno-phenolic compounds known as cannabinoids; Δ9-tetrahydrocannabinol (THC) and cannabidiol (CBD) are the most prevalent cannabinoids and have been associated with the therapeutic and medicinal properties of the cannabis plant. In this paper, continuous flow microwave assisted extraction (MAE) is presented and compared with other methods for commercial cannabis extraction. The practical issues of each extraction method are discussed. The main advantages of MAE are: continuous-flow method which allows for higher volumes of biomass to be processed in less time than existing extraction methods, improved extraction efficiency leading to increased final product yields, improved extract consistency and quality because the process does not require stopping and restarting material flows, and ease of scale-up to industrial scale without the use of pressurised batch vessels. Moreover, due to the flexibility of changing the operation conditions, MAE eliminates additional steps required in most extraction methods, such as biomass decarboxylation, winterisation, which typically adds at least a half day to the extraction process. Another factor that sets MAE apart is the ability to achieve high extraction efficiency even at the industrial scale. Whereas the typical recovery of active compounds using supercritical CO¬2 remains around 70-80%, via MAE up to 95% of the active compounds from cannabis biomass can be recovered at the industrial scale.

Rice (Oryza sativa L.) supplies nourishment to about half of the population of the world's inhabitants. Of them, more than 2 billion people suffer from "hidden hunger" in which they are unable to meet the recommended nutrients or micronutrients from their daily dietary intake. Bio-fortification refers to developing micronutrient-rich diet foods using traditional breeding methods and modern biotechnology, a promising approach to nutrition enrichment as part of an integrated strategy for food systems. To improve the profile of rice grain for the biofortification related traits, understanding the genetics of important biofortification traits is required. Moreover, these attributes are quantitative in nature and are influenced by several genes and environmental variables. In the course of past decades, several endeavours such, as finding the important quantitative trait loci (QTLs) for improving the nutrient profile of rice seeds were successfully undertaken. In this review, we have presented the information regarding the QTLs identified for the biofortification traits in the rice.

This research was presented the special designed microfluidic device generated for sperm separation based on assumption of different surface electrical charged of sperms X and Y. However, to avoid ethical problem, the microfluidic chip has been tested with the mimic electrical charged particles, TiO₂-coated Polystyrene beads, (TiO₂-coated Ps-beads), instead of spermatozoa. The work has been separated into three main parts. Firstly, the simply but efficient fabrication of negatively charged TiO₂-coated Ps-beads has been presented. In addition, various characterization techniques such as X-ray diffraction (XRD), Tungsten Scanning Electron Microscopy (W-SEM) with energy-dispersive X-ray spectroscopy (EDS) mode, and X-ray Absorption Spectroscopy (XAS), have been reported in this work to elucidate the reasons behind the persistence of negatively charged on the surface of TiO₂-coated Ps-beads. Results show that the fabricated TiO₂-coated Ps-beads was partly coated in the mixed forms of amorphous Ti⁴⁺ and had caused a negatively charge to appear on the surface after fabrication and had sustained its electrical charged for long. Secondly, process of simulation and fabrication of microfluidic device was presented. Finally the negatively charged TiO₂-coated Ps-beads were tested in this microfluidic devices. For design of microfluidic devices integrated with a couple of microelectrodes, the simulated structures were fabricated by photolithographic technique and tested with the Ps-beads. Percentage of validation for Ps-beads separation indicated that the 100 mm-distance-between-electrodes microfluidic device exhibits to be the highest performance prototype at 86.96%. For further confirmation, another model so called the single path prototype has been established. It is confirmed by 92.59% of validation for the utilization of the device. The successfully designed microfluidic devices can be examined with actual spermatozoa later. Furthermore, process to fabricate the negatively charged TiO₂-coated Ps-beads can be established as testified samples for development of other microfluidic devices.

The aim of this study is to develop a software solution for real-time recognition of sign language words using two arms. This will enable communication between hearing-impaired individuals and those who can hear. Several sign language recognition systems have been developed using different technologies, including cameras, armbands, and gloves. The system developed in this study utilizes surface electromyography (muscle activity) and inertial measurement unit (motion dynamics) data from both arms. Other methods often have drawbacks, such as high costs, low accuracy due to ambient light and obstacles, and complex hardware requirements, which have prevented their practical application. A software has been developed that can run on different operating systems using digital signal processing and machine learning methods specific to the study. For the test, we created a dataset of 80 words based on their frequency of use in daily life and performed a thorough feature extraction process. We tested the recognition performance using various classifiers and parameters and compared the results. The Random Forest algorithm was found to have the highest success rate with 99.875% accuracy, while the Naive Bayes algorithm had the lowest success rate with 87.625% accuracy. Feedback from a test group of 10 people indicated that the system is user-friendly, aesthetically appealing, and practically useful. The new system enables smoother communication for people with hearing disabilities and promises seamless integration into daily life without compromising user comfort or lifestyle quality.

The integration of Artificial Intelligence (AI) into architecture is reshaping the industry by enhancing design processes, project management, and sustainability efforts. This study provides a comprehensive analysis of AI software tools, focusing on their functionalities in the market, automated site analysis, building performance analysis, architectural design development, and additional features. Key findings reveal that while tools like Spacio.ai, Swapp, and Autodesk Forma offer extensive features across various design phases, their effectiveness is influenced by design capabilities, sustainability and viability assessments, overall architectural design procedures, and third-party interactions. The study underscores the need for tailored AI tools that can effectively follow the architectural process from conceptualization and design to engineering and building permits, all while ensuring full regulatory compliance and adaptability in real-time. By identifying current trends and features, this study contributes to understanding the role of AI in architecture, offering valuable insights for software developers and professionals aiming to integrate AI capabilities into a robust platform. The goal is to elevate architecture to higher levels of human engagement and construction excellence, harnessing the full potential of AI.

This study presents polyaniline (PANI) synthesis and characterization using a novel acid-assisted polymerization technique. Two PANI suspensions with different ammonium peroxydisulfate (APS) concentrations were synthesized at room temperature, i.e. the ratio of aniline to APS was 10:1 for PANI1 and 5:1 for PANI2. SEM measurements revealed distinct structures: a porous nanofibrillar structure for PANI1 and a densely packed structure for PANI2. The electrochemical performance of the fabricated PANI/glassy carbon (GC) electrodes was evaluated using a three-electrode cell configuration in the scan rates of 10 mV/s and 30 mV/s. The PANI1/GC heterostructure exhibited a specific capacitance of 160 F/g, while this value increased to 407 for the PANI2/GC. This research contributes not only to the understanding of PANI synthesis at room temperature but also to its potential applications in electrochemical energy storage devices.

This study explores the potential of using a Siamese Network as a biomarker for assessing the effectiveness of Dolphin-Assisted Therapy (DAT) in children with Cerebral Palsy (CP). The problem statement revolves around the need for objective measures to evaluate the impact of DAT on patients with CP, considering the subjective nature of traditional assessment methods. The methodology involves training a Siamese network, a type of neural network designed to compare similarities between inputs, using data collected from CP patients undergoing DAT sessions. The network is trained to learn patterns and similarities between pre- and post-therapy evaluations, in order to identify biomarkers indicative of therapy effectiveness. Notably, the Siamese Network’s architecture ensures that comparisons are made within the same feature space, allowing for more accurate assessments. The results of the study demonstrate promising findings, indicating different patterns in the output of the Siamese Network that correlate with improvements in symptoms of CP post-DAT. These findings suggest the potential utility of the Siamese Network as a biomarker to monitor therapy outcomes in children with CP who undergo DAT, offering a more objective and quantifiable approach to assessing therapeutic interventions. Significant disparities in neuronal voltage perturbations, with a mean of 7.9825 dB in specific samples compared to 6.2838 dB in the data set, indicate a marked difference from resting activity. This suggests that dolphin-assisted therapy selectively activates specific brain regions uniquely during the intervention.

Sperm capacitation involves biochemical and physiological changes that enable sperm to fertilize the oocyte. It can be induced in vitro under controlled conditions that simulate the environment of the oviduct. While extensively studied in mammals, its approach in lizards remains absent. Understanding the mechanisms that facilitate reproduction is essential for advancing the implementation of assisted reproductive technologies in this group. Fifteen males of Sceloporus torquatus were collected during the early stage of the reproductive season. The sperm were isolated from the seminal plasma and then diluted up to a volume of 150 μl using BWW medium to incubate with 5% CO2 at 30 °C for a maximum duration of 3 hours. A fraction was retrieved hourly for ongoing sperm assessment. We found that total motility is maintained up to 2 hours post-incubation, after which it decreased. However, sperm viability remained constant. From that moment on, we observed a transition to a deeper and less symmetrical flagellar bending in many spermatozoa. The chlortetracycline assay indicated a reduction in the percentage of sperm showing the F pattern and an increase in those exhibiting the capacitated (B) and reactive (RA) patterns, accompanied by an elevation in the percentage of damaged acrosomes as revealed by the lectin binding assay. In mammals, these changes are often associated with sperm capacitation. These observations support the notion that this process may also occur in saurian.

Chemical process control relies on a tightly controlled, narrow range of margins for critical variables, ensuring process stability and safeguarding equipment from potential accidents. The availability of historical process data is limited to a specific setpoint of operation. This challenge raises issues for process monitoring in predicting and adjusting to deviations outside of the range of operational parameters. Therefore, this paper proposes simulation-assisted deep transfer learning for predicting and optimizing the final purity and production capacity of the glycerin purification process. The proposed network is trained by the simulation domain to generate a base feature extractor, which is then fine-tuned using few-shot learning techniques on the target learner to extend the working domain of the model beyond the historical practice. The result shows that the proposed model improved prediction performance by 99% in predicting water content and 79.72% in glycerin prediction over the conventional deep learning model. Additionally, the implementation of the proposed model identified the production and product quality improvement for enhancing the glycerin purification process.

Alginate, a biopolymer promising in food, biomedical, pharmaceutical, and electronic materials industries, is characterized by its biodegradability, biocompatibility, low toxicity, and gel-forming properties. It is most abundantly found in brown algae. However, conventional dilute acid and alkali extraction methods face limitations in commercialization due to their long processing time, low throughput, and high solvent requirements. In this study, a microwave-assisted extraction (MAE) process for sodium alginate was designed to improve extraction efficiency. Solid/liquid ratio, extraction temperature, and extraction solvent concentration were major variables affecting sodium alginate extraction from Undaria pinnatifida(sea mustard). They were then statistically optimized using response surface methodology. Under optimal conditions (13.27 g/L, 91.86 ℃, 2.51 %(w/v), and 15 min), the yield was 38.41 %, which was 93.43 % of the theoretical content of sodium alginate in Undaria pinnatifida. Our work has confirmed the productivity and industrial feasibility of efficient extraction of sodium alginate from marine biomass, and we hope that it will serve as an encouraging case for the application of biopolymers as one of the desirable options for alternative petrochemicals to construct a sustainable society.

Catheter-based revascularization procedures developed as an alternative of systemic thrombolysis for patients at intermediate-high and high-risk pulmonary embolism. USAT IH-PE is a retrospective and prospective multicenter registry of such patients treated with ultrasound-facilitated, catheter-directed thrombolysis, whose preliminary results are presented in this study. The primary endpoint was the incidence of pulmonary hypertension (PH) at follow up. Secondary endpoints were short- and mid-term changes in echocardiographic parameters of right ventricle (RV) function, in-hospital and all-cause mortality, and procedure-related bleeding events. Between March 2018 and July 2023, 102 patients were included. The majority were at intermediate-high risk PE (86%), mostly female (57%), median age 66.5 years (IQR 56-74), and 28.4% had active cancer. Echocardiographic follow-up was available for 70 patients, and in only one the diagnosis of PH was confirmed by right heart catheterization, resulting in an incidence of 1.43% (CI 95%, 0.036-7.7). RV echocardiographic parameters improved both at 24 hours and at follow up. In-hospital mortality was 3.9% (CI 95%, 1.08-9.74) while all-cause mortality was 11% (CI 95%, 5.4-19.2). Only 12% had bleeding complications, oh whom 4.9% were BARC 3. Preliminary results from USAT IH-PE registry showed low incidence of PH, improvement in RV function and a safe profile.