Specific Categories Research Articles

Health Service Utilization Patterns Among Medicaid Enrollees With Intellectual and Developmental Disabilities Before and During the COVID-19 Pandemic: Implications for Pandemic Response and Recovery Efforts.

To assess the impact of COVID-19 on health service utilization of adults with intellectual and developmental disabilities (IDDs) through an analysis of Medicaid claims data. Retrospective cohort study of Medicaid claims. Medicaid members aged 25 to 64years from January 1, 2018, to March 31, 2021, from the states of Louisiana, Pennsylvania, and Wyoming. We analyzed data from two 12-month time periods (pre-COVID-19 and during COVID-19) and assessed the potential impact of the COVID-19 pandemic on health service utilization and service intensity for 3 cohorts: (1) IDD with preexisting mental health diagnoses, (2) IDD without mental health diagnoses, and (3) all other Medicaid members. Health service utilization determined by specific claims data classifications. The analysis showed reduced utilization for nonmental health service types with differing utilization patterns for IDD with preexisting mental health diagnoses, IDD without mental health diagnoses, and all other Medicaid members. Change in utilization varied, however, for mental health service types. Measures of service intensity showed decreased numbers of members utilizing services across most service types and increased Medicaid claims per person across most mental health service categories but decreased Medicaid claims per person for most nonmental health services. Results suggest a need for mental health services among all Medicaid members during the COVID-19 pandemic. By anticipating these needs, communities may be able to expand outreach to Medicaid members through enhanced case management, medication checks, and telemedicine options.

Investigations of particle-process-part quality relationships in electron beam melting

Electron beam melting is a powder bed fusion process capable of manufacturing parts from a variety of high-temperature alloys. Given that the process relies on feedstock recycling for process economics, understanding process-part quality relationships is critical. This work investigates process-part quality relationships in terms of the internal and external defects and component microstructure relative to a feedstock subjected to 33 build cycles without replacement. To accomplish this, a volume of fluid mesoscale model consisting of three different powder distributions were considered: (1) Monomodal; (2) As-measured; and (3) Irregular. Particle morphology was characterized using shape factors examined via optical microscopy. To approximate the particle shapes in three-dimensions, a method is presented that utilizes a binarized domain to define low frequency, macroscale particle “base” shapes implicitly and is thus not restricted to starlike particles. The discrete element method was also used to investigate velocity distributions and packing densities of the as-measured and irregular particles with respect to deviations in the nominal layer thickness of 50 μm. In general, beam power and scan speed were found to have an appreciable effect on microstructure formation and surface roughness. Finally, correlations were found between specific classifications of irregular particles and lack of fusion defect formation.Graphical abstractOverview of the discrete element method simulation domain for the electron beam melting powder bed fusion process

Nurturing Innovation, Transforming Education: A Longitudinal Study

In an era of rapid technological advancement and evolving societal needs, fostering innovation in education has become paramount. This study examines the six-year trajectory of the Guru Inovatif Program (GIP), a unique initiative designed to cultivate a culture of innovation among educators in Malaysia. Through a mixed-methods longitudinal approach, we analyzed the program's evolution from 2019 to 2024, focusing on participation trends, structural changes, and qualitative shifts in innovation quality. The results reveal a significant growth in participation, with numbers more than doubling from 34 to 71 over the study period. Statistical analysis showed a strong positive trend (R² = 0.893), indicating consistent program appeal. The GIP's structure evolved from a generalized approach to specialized categories, reflecting a nuanced understanding of diverse educational innovation needs. Qualitative analysis revealed a marked improvement in the sophistication of innovations, transitioning from basic idea generation to evidence-based, impactful projects. The implementation of a design thinking framework proved crucial in enhancing the quality and applicability of innovations. The program's success was underpinned by its adaptive structure, collaborative partnerships, and emphasis on real-world impact. This study provides valuable insights into sustaining long-term educational innovation initiatives, offering a model that balances structure with flexibility, and theory with practical application. The findings have significant implications for educational policymakers and administrators seeking to foster a culture of continuous innovation in education.

AG-MSTLN-EL: A Multi-source Transfer Learning Approach to Brain Tumor Detection.

The analysis of medical images (MI) is an important part of advanced medicine as it helps detect and diagnose various diseases early. Classifying brain tumors through magnetic resonance imaging (MRI) poses a challenge demanding accurate models for effective diagnosis and treatment planning. This paper introduces AG-MSTLN-EL, an attention-aided multi-source transfer learning ensemble learning model leveraging multi-source transfer learning (Visual Geometry Group ResNet and GoogLeNet), attention mechanisms, and ensemble learning to achieve robust and accurate brain tumor classification. Multi-source transfer learning allows knowledge extraction from diverse domains, enhancing generalization. The attention mechanism focuses on specific MRI regions, increasing interpretability and classification performance. Ensemble learning combines k-nearest neighbor, Softmax, and support vector machine classifiers, improving both accuracy and reliability. Evaluating the model's performance on a dataset with 3064 brain tumor MRI images, AG-MSTLN-EL outperforms state-of-the-art models in terms of all classification measures. The model's innovative combination of transfer learning, attention mechanism, and ensemble learning provides a reliable solution for brain tumor classification. Its superior performance and high interpretability make AG-MSTLN-EL a valuable tool for clinicians and researchers in medical image analysis.

The role of immunotherapy in patients with lung cancer and brain metastases: a narrative review of the literature.

Worldwide, approximately half of the patients diagnosed with lung cancer (LC) will develop, simultaneously or asynchronously, brain metastases (BMs). The existence of BMs negatively affects the quality of life and constitutes a poor prognostic factor, linked with high mortality. Locoregional therapy with surgery or radiation is, until now, the treatment of choice, especially for symptomatic patients; however, both options are linked to a high complication rate. The question arising here is whether, in asymptomatic patients, the benefit outweighs the risk and whether an alternative method can be used to treat this special category of patients. Over the last decade, immune checkpoint inhibitors (ICIs) have represented a major breakthrough in the field of oncology, and several molecules have been approved as a treatment option for LC. This review tried to analyze the tumor microenvironment of both the primary lung tumor and the BMs in order to evaluate the intracranial activity of ICIs, outline the main challenges of including these agents in the treatment of LC with BMs, highlight the available information from the main clinical trials, and mark the potential positive effect of choosing a combination therapy. In conclusion, it appears that immunotherapy has a positive effect, inhibiting the progression of BMs, but more data should be published specifically for this category of patients.

Development and Analysis Validation Instrument Critical Thinking Heat Concept (CTHT) Using Rash Model

This research aims to design and determine the validity and reliability of critical thinking skills instruments related to heat concepts using the Rasch model. This research uses 4D research and development methods. The research subjects or validation trials were 193 Depok City High School students majoring in Mathematics and Natural Sciences who had studied heat concepts and content validation was carried out with six expert lecturers. Content validity data analysis used Aiken V Microsoft Excel calculations with a validity limit value of < 0.78 and construct validity and reliability used the Rasch model. The results of data analysis show that the content validity of the instrument is valid and suitable for use with several revisions and expert input, while the results of content validity show a validity level of 43.21% with all valid items in the fulfilled category, while the Pearson and item reliability values show a value of 0.62 is in the sufficient category and 0.92 is in the very special category. The results of the statistical analysis trial showed that the instrument was significant and became a benchmark for further research in the application of students' critical thinking skills in learning.

Discriminant function for grouping subjects into legal age of 12 and 14 years using maturity indices of seven mandibular teeth on panoramic radiograph – A cross sectional study

In forensic science, accurate age estimation becomes crucial, especially in cases where information about the deceased is unavailable. The legal categorization of children into age groups, notably at 12 and 14 years, holds significant importance in various legal and social contexts. Despite the importance of such categorizations, the existing literature lacks ample studies addressing the specific classification of children into these legal age groups. This study aims to identify the key variables that distinguish between the categories of 12 and 14 years of age. Additionally, it seeks to calculate and compare the sensitivity, specificity, and overall accuracy of the discriminant function in accurately determining the required age. The oral panoramic radiographs (OPGs) of 3058 children aged between 5 and 16 years from six South Indian states were evaluated. The dataset was divided into train and test data sets. Dental maturity for each individual was estimated by considering the number of fully developed permanent mandibular teeth on the left side and the normalized measurement of the open apices of all seven teeth. Linear discriminant functions for the age groups of 12 and 14 years were computed using the training data. The discriminant functions for the 12-year age group demonstrated an accuracy of, 78.0 %, while those for the 14-year age group achieved an accuracy of .71.2 %. The sensitivity and specificity of the discriminant functions for the 12-year age group were 74.5 % and, 84.6 %, respectively. In the case of the 14-year age group, the sensitivity and specificity were 72.5 % and 70.0 %, respectively. This study concludes that the discriminant functions utilized herein exhibited commendable performance in accurately classifying individuals within the specified age groups of 12 and 14 years.

Road car image target detection and recognition based on YOLOv8 deep learning algorithm

In this paper, target detection of car images in roads is performed based on the YOLOv8 model of YOLO family of models, which improves the accuracy and generalisation of the target detection task by combining multi-scale prediction, CSPNet structure and optimisation techniques such as BoF and BoS. The input images contain five types of vehicles such as Ambulance, Bus, Car, Motorcycle and Truck, which are analysed and learnt to have a classification accuracy of 75.4% on Ambulance, 53.5% on Bus, 55.1% on Car, 51.1% on Motorcycle and 42.5% on Truck. Despite the gap in specific classification accuracy, the YOLOv8 model can detect 100% of vehicles on the road, demonstrating good target detection capability. This research is of great significance for improving road traffic safety, intelligent traffic management, and the development of future autonomous driving technology. By optimising the deep learning model to achieve more accurate and efficient vehicle target detection, it can help to improve road safety and traffic efficiency, and promote the progress of intelligent transportation systems.

Interlinking environmental and food composition databases: An approach, potential and limitations

Connecting entries from environmental and nutritional databases of food products is needed to identify sustainable food options. To overcome the hurdle for a successful data standardization, this paper aimed to explore the general structure of food composition and life cycle inventory (LCI) databases and to provide a semi-automatized approach on how to successfully interlink data of two example databases.The structure, the data availability and accessibility of food entries (FI) from the LCI database Agribalyse and selected food composition EuroFIR databases were analyzed. Harmonized food names from LanguaL™ codes from food classification systems were gathered and validated manually in order to use as descriptors to tag database entries in an automated way.Both databases, EuroFIR and Agribalyse, provided sufficient amount of meta data to interlink FI with the standardization approach proposed. Information on food name, food specification, food processing and the type of production was used for data interlinkage purposes. Manual validation of data interlinkage showed that two out of a sample of 54 entries were found to have incorrectly assigned descriptors.Agreeing on common principles (e.g., use of a specific classification system, common database formats) and improving meta data availability would facilitate database interlinkage and improve both, accuracy and efficiency. Developing solutions to increase meta data availability and accessibility of FI in food databases should become a key area of research in order to transition into more reliable database connection systems.

Preliminary findings and research perspectives on the German modal sollen in L2 written assessments

Modal verbs represent a special verb category and constitute an essential component of L2 German semantics. While Abrams and Rott (2017) suggest that German learner texts depict a 90% accuracy in the correct use of modal verbs in integrative writing tasks, the correct semantic use of the modal sollen (‘supposed to’; ‘should’) seems to pose a particular challenge (Jentges 2016), especially in an assessment context. In this corpus analysis, 1,032 L2 German assessment texts across all CEFR levels were analyzed with respect to the L2 semantic use of sollen against the backdrop of the assessment prompts. 68% of instances of all uses of sollen in A1 texts and 53% of all uses of sollen in A2 texts showed an inappropriate semantic use of sollen. The discrepancy between these results and Abrams and Rott (2017) can be attributed to the prompts used in the assessments analyzed in this paper: They do not take into consideration the semantic mismatch between the use of the modal in the prompt and its inappropriate use in the texts produced.

Ocular trauma due to dog bite injury on eyelid, a special wound category

Ocular trauma due to dog bite injury on eyelid, a special wound category

Fast entangling quantum gates with almost-resonant modulated driving

Recently, the method of using a special category of synthetic analytical pulses under off-resonant conditions has improved the experimental performance of two- and multi-qubit gates in the cold atom qubit platform. In order to explore more possibilities and wider ranges of options, we design and analyze the method of almost-resonant modulated driving (ARMD) in constructing fast-speed and high-fidelity quantum logic gates. Whilst the modulation forms the key concept of high-fidelity Rydberg blockade gates so far, the on-off resonance condition can lead to nontrivial nuances in the styles of dynamics, and the ARMD gate protocols have its different characteristic mechanisms in quantum physics compared with the off-resonant gate processes. In particular, ARMD gates usually have abrupt phase changes and at certain points during the time evolution. From a more fundamental point of view, both the resonant and off-resonant methods all together belong to the unitary operation family of fast modulated driving with respect to precisely characterized inter-qubit interactions, which usually allows the quantum logic gate to conclude within one continuous pulse. We anticipate the ARMD gates to work well with the Rydberg dipole-dipole interaction and will serve as a helpful tool in the future studies of the cold atom qubit platform.

Electron Spin Resonance Spectroscopy on Magnetic van der Waals Compounds

AbstractThe field of research on magnetic van der Waals compounds—a special subclass of quasi-two-dimensional materials—is currently rapidly expanding due to the relevance of these compounds to fundamental research where they serve as a playground for the investigation of different models of quantum magnetism and also in view of their unique magneto-electronic and magneto-optical properties pertinent to novel technological applications. The electron spin resonance (ESR) spectroscopy plays an important role in the exploration of the rich magnetic behavior of van der Waals compounds due to its high sensitivity to magnetic anisotropies and unprecedentedly high energy resolution that altogether enable one to obtain thorough insights into the details of the spin structure in the magnetically ordered state and the low-energy spin dynamics in the ordered and paramagnetic phases. This article provides an overview of the recent achievements in this field made by the ESR spectroscopic techniques encompassing representatives of antiferro- and ferromagnetic van der Waals compounds of different crystal structures and chemical composition as well as of a special category of these materials termed magnetic topological insulators.

Alternative Legal Bases for Processing Health Data for Scientific Research Purposes

The processing of health data for scientific research purposes requires a legal basisunder Article 6 and a justification under Article 9 (2) GDPR by way of an exceptionto the general prohibition in Art. 9(1) of the processing of special category data.Consent tends to be highly advocated for in this regard, in both literature and practice.However, the GDPR permits an alternative option: processing for scientific researchpurposes based on Union or Member State law which provides for suitable andspecific safeguarding measures. This paper undertakes an in-depth examination ofthe ‘research exception’ in Art. 9 (2) (j) GDPR permitting the processing of healthdata for scientific research purposes, thoroughly considering its elements and itsimplications. It refers to examples of Member State implementing legislation andthe proposed European Health Data Space Regulation for illustration purposes andargues that if implemented faithfully, Art. 9 (2) (j) strikes a better balance betweenthe interests of the various stakeholders than consent, which is overall burdensomeand may hinder research. Finally, in light of the uneven implementation of theGDPR’s research exception by the Member States which creates considerable legaluncertainties and results in barriers to the free flow of research data across the EU,this paper calls for a harmonised implementing Union law in this regard.

Legal Regulation of “Skinning” Games

The development of video games has formed a complex and diversified huge system. It is precisely because of the variety of modern video games and abundant layers, "skinning" games and other infringement problems are endless. What kind of electronic games are worth protecting, and which part of them need to be protected, is the problem of today's legislation and justice. Judicial practice is basically through the copyright law or anti-unfair competition law to regulate the infringement of electronic games, but both have different degrees of inadequacy. If the development of "skinning" games is allowed to continue, the excellent but small-scale and medium-sized game developers will be driven out of the game market, and the future of electronic games will not go too far. Therefore, it is essential to strengthen intellectual property protection and improve the level of innovation by setting up a special category of protection through the Copyright Law and leading the development of the video game industry by judicial means.

A New Workflow for Instance Segmentation of Fish with YOLO

The application of deep-learning technology for marine fishery resource investigation is still in its infancy stage. In this study, we applied YOLOv5 and YOLOv8 methods to identify and segment fish in the seabed. Our results show that both methods could achieve superior performance in the segmentation task of the DeepFish dataset. We also expanded the labeling of specific fish species classification tags on the basis of the original semantic segmentation dataset of DeepFish and completed the multi-class instance segmentation task of fish based on the newly labeled tags. Based on the above two achievements, we propose a general and flexible self-iterative fish identification and segmentation standard workflow that can effectively improve the efficiency of fish surveys.

Optical coherence tomography analysis of lesion characteristics and thrombus types in non ST-segment elevation myocardial infarction patients

The precise features of lesions in non-ST-segment elevation myocardial infarction (NSTEMI) patients with total occlusion (TO) of the infarct-related artery (IRA) are still unclear. This study employs optical coherence tomography (OCT) to investigate pathological features in NSTEMI patients with or without IRA TO and explores the relationship between thrombus types and IRA occlusive status. This was a single-center retrospective study. A total of 202 patients diagnosed with NSTEMI were divided into two groups: those with Thrombolysis In Myocardial Infarction (TIMI) flow grade 0 before percutaneous coronary intervention (PCI) (referred to as the TO group, n = 100) and those TIMI flow grade 1–3 (referred to as the Non-TO group, n = 102). Baseline characteristics, coronary angiography findings, and OCT results were collected. Multivariate logistic analysis identified factors influencing TO in NSTEMI. The category of NSTEMI was further subdivided based on the type of electrocardiogram (ECG) into two subgroups: ST segment unoffset myocardial infarction (STUMI) and ST segment depression myocardial infarction (STDMI). This division allows for a more specific classification of NSTEMI cases. The TO group had a younger age, higher male representation, more smokers, lower hypertension and cerebrovascular disease incidence, lower left ventricular ejection fraction (LVEF), and higher creatine kinase myocardial band (CKMB) and creatine kinase (CK) peak levels. In the TO group, LCX served as the main IRA (52.0%), whereas in the Non-TO group, LAD was the predominant IRA (45.1%). Compared to the Non-TO group, OCT findings demonstrated that red thrombus/mixed thrombus was more common in the TO group, along with a lower occurrence of white thrombus (p < 0.001). The TO group exhibited a higher prevalence of STUMI (p = 0.001), whereas STDMI was more commonly observed in the Non-TO group (p = 0.001). NSTEMI presents as STUMI and STDMI distinct entities. Red thrombus/mixed thrombus in IRA often indicates occlusive lesions with STUMI on ECG. White thrombus suggests non-occlusive lesions with STDMI on ECG.

A Novel Classification System of Renal Hilar Tumors for Surgical Guidance: Technique, Outcome, and Safety.

This study was designed to develop an innovative classification and guidance system for renal hilar tumors and to assess the safety and effectiveness of robot-assisted partial nephrectomy (RAPN) for managing such tumors. A total of 179 patients undergoing RAPN for renal hilar tumors were retrospectively reviewed. A novel classification system with surgical techniques was introduced and the perioperative features, tumor characteristics, and the efficacy and safety of RAPN were compared within subgroups. We classified the tumors according to our novel system as follows: 131 Type I, 35 Type II, and 13 Type III. However, Type III had higher median R.E.N.A.L., PADUA, and ROADS scores compared with the others (all p < 0.001), indicating increased operative complexity and higher estimated blood loss [180.00 (115.00-215.00) ml]. Operative outcomes revealed significant disparities between Type III and the others, with longer operative times [165.00 (145.00-200.50) min], warm ischemia times [24.00 (21.50-30.50) min], tumor resection times [13.00 (12.00-15.50) min], and incision closure times [22.00 (20.00-23.50) min] (all p < 0.005). Postoperative outcomes also showed significant differences, with longer durations of drain removal (77.08 ± 18.16h) and hospitalization for Type III [5.00 (5.00-6.00) d] (all p < 0.05). Additionally, Type I had a larger tumor diameter than the others (p = 0.009) and pT stage differed significantly between the subtypes (p = 0.020). The novel renal hilar tumor classification system is capable of differentiating the surgical difficulty of RAPN and further offers personalized surgical steps tailored to each specific classification. It provides a meaningful tool for clinical practice.

«Perinatal portrait», reproductive health parameters and risk factors for fat metabolism disorders in young overweight women

Overweight and obesity are one of the most serious social and medical problems today. It is appropriate to single out a special category of young women aged 18–35 years with excessive body weight, that is, the age which is capable of ensuring the reproductive potential of the future.The objective: to determine the probable factors of metabolic imbalance and the main parameters of reproductive health disorders in young overweight women.Materials and methods. Reproductive health parameters and the main predictors of its impairment were evaluated in two groups of patients – the main group (890 women with BMI &gt; 25.0 kg/m2) and a control group (40 patients with BMI of 18.5–24.9 kg/m2).The main group was divided into two subgroups taking into account the age of formation of overweight and obesity: I subgroup – 596 women with pubertal obesity (66.9%) and II subgroup – 294 women with overweight in the postpubertal period (33.1%).Results. A high rate of pregnancy complications (86.9%), gestational diabetes (25.8%), preeclampsia (34.1%), premature birth and low birth weight fetus (20.9%), as well as the birth of a girl with a large body weight (24.4%), can become modified factors in the formation of the reproductive system according to the characteristics of the “perinatal portrait” and form the basis of screening programs. The relationship between the early introduction of complementary foods and the absence of breastfeeding and the presence of obesity in the girl in the future was established (OR=7.25; 95% CI: 2.99–17.59; χ2=23.53, p&lt;0.0001).Attention should be paid to reproductive function disorders: amenorrhea was found 1.7 times more often in the case of postpubertal obesity, as well as abnormal uterine bleeding or metrorrhagia, infertility (24.4%), dyshormonal breast diseases (34.7%), polycystic ovary syndrome (59.8%), proliferative processes of the endometrium and myometrium (34.7%), as well as the formation of endometrial hyperplasia and polyposis with age (25.8%).A high percentage of stressogenic factors was determined in the examined cohort of patients, which could initiate the manifestation of metabolic and endocrine disorders in 569 cases – 63.9% (OR=4.14; 95% CI: 2.07–8.25; χ2=17.38, p&lt;0.0001).Conclusions. Probable predictors of impaired fat metabolism in young women were statistically identified: heredity from the mothers of such patients regarding metabolic and endocrine disorders, body weight at birth more than 4000 g and artificial feeding, early age of menarche, pubertal paratrophy, stress factors, infectious diseases during menarche, hypodynamia and a high level of intellectual differentiation.The reproductive health of women with overweight and obesity is characterized by a high frequency of menstrual cycle disorders, increased manifestations of changes in the vaginal microbiome and dysuric symptoms, as well as infertility, hormone-dependent diseases, and impaired reproductive function.

Secure Your Network: Building a Hybrid Intrusion Detection System in Java

Abstract: To protect the system from different types of intrusions, an intrusion detection system ID is required. It is essential to analyse the communication in order to classify the material as malicious or helpful. The usage of intrusion detection systems for cyber security shouldn't add to the processing time required for classification. These days, classification algorithms are utilized in conjunction with machine learning approaches to identify harmful data or intrusions. KDD cup 99 is the data set that was used in the experiment. Hybrid classification models can be used to adjust the performance of individual classification methods. This model blends rule-based algorithms with categorization techniques. An additional degree of security is provided by the combination of machine and human intelligence in classification. Precision, recall, F-Measure, and Mean Age Precision are used to validate an algorithm. The algorithm has a 92.35 percent accuracy rate. Even after merging our human-written criteria with traditional machine learning classification techniques, the model's accuracy is still considered satisfactory. However, there is still room for improvement and a more specific classification of the attack