Current Research Research Articles

Formulation and Optimization of Solid Lipid Nanoparticle-based Gel for Dermal Delivery of Linezolid Using Taguchi Design.

Linezolid (LNZ) is a synthetic oxazolidinone antibiotic approved for the treatment of uncomplicated and complicated skin and soft tissue infections caused by gram-positive bacteria. Typically, LNZ is administered orally or intravenously in most cases. However, prolonged therapy is associated with various side effects and life threatening complications. Cutaneous application of LNZ will assist in reducing the dose, hence minimizing the unwanted side/adverse effects associated with oral administration. Dermal delivery provides an alternative route of administration, facilitating a local and sustained concentration of the antimicrobial at the site of infection. The current research work aimed to formulate solid lipid nanoparticles (SLNs) based gel for dermal delivery of LNZ in the management of uncomplicated skin and soft tissue infections to maximise its benefits and minimise the side effects. SLNs were prepared by high-shear homogenisation and ultrasound method using Dynasan 114 as solid lipid and Pluronic F-68 as surfactant. The effect of surfactant concentration, drug-to-lipid ratio, and sonication time was investigated on particle size, zeta potential, and entrapment efficiency using the Taguchi design. The main effect plot of means and signal-to-noise ratio were generated to determine the optimized formulation. The optimized batch was formulated into a gel, and ex-vivo permeation study, in-vitro and in-vivo antibacterial activity were conducted. The optimised process parameters to achieve results were 2% surfactant concentration, a drug-to-lipid ratio of 1:2, and 360 s of sonication time. The optimized batch was 206.3± 0.17nm in size with a surface charge of -24.4± 4.67mV and entrapment efficiency of 80.90 ± 0.45%. SLN-based gel demonstrated anomalous transport with an 85.43% in vitro drug release. The gel showed a 5 cm zone of inhibition while evaluated for in vitro antibacterial activity against Staphylococcus aureus. Ex-vivo skin permeation studies demonstrated 20.308% drug permeation and 54.96% cutaneous deposition. In-vivo results showed a significant reduction in colony-forming units in the group treated with LNZ SLN-based gel. Ex-vivo studies ascertain the presence of the drug at the desired site and improve therapy. In-vivo results demonstrated the ability of SLN-based gel to significantly reduce the number of bacteria in the stripped infection model. The utilization of SLN as an LNZ carrier holds significant promise in dermal delivery.

Edge Caching Placement Strategy based on Evolutionary Game for Conversational Information Seeking in Edge Cloud Computing

In Internet applications, network conversation is the primary communication between the user and server. The server needs to efficiently and quickly return the corresponding service according to the conversation sent by the user to improve the users’ Quality of Service. Thus, Conversation Information Seeking (CIS) research has become a hot topic today. In Cloud Computing (CC), a central service mode, the conversation is transmitted between the user and the remote cloud over a long distance. With the explosive growth of Internet applications, network congestion, long-distance communication, and single point of failure have brought new challenges to the centralized service mode. People put forward Edge Cloud Computing (ECC) to meet the new challenges of the centralized service mode of CC. As a distributed service mode, ECC is an extension of CC. By migrating services from the remote cloud to the network edge closer to users, ECC can solve the above challenges in CC well. In ECC, people solve the problem of CIS through edge caching. The current research focuses on designing the edge cache strategy to achieve more predictable caching. In this article, we propose an edge cache placement method Evolutionary Game based Caching Placement Strategy (EG-CPS). This method consists of three modules: the user preference prediction module, the content popularity calculation module, and the cache placement decision module. To maximize the predictability of the cache strategy, we are committed to optimizing the cache hit rate and service latency. The simulation experiment compares the proposed strategy with several other cache strategies. The experimental results illustrate that EG-CPS can reduce up to 2.4% of the original average content request latency, increase the average direct cache hit rate by 1.7%, and increase the average edge cache hit rate by 3.3%.

Targeting the Main Sources of Reactive Oxygen Species Production: Possible Therapeutic Implications in Chronic Pain.

Humans have long been combating chronic pain. In clinical practice, opioids are firstchoice analgesics, but long-term use of these drugs can lead to serious adverse reactions. Finding new, safe and effective pain relievers that are useful treatments for chronic pain is an urgent medical need. Based on accumulating evidence from numerous studies, excess reactive oxygen species (ROS) contribute to the development and maintenance of chronic pain. Some antioxidants are potentially beneficial analgesics in the clinic, but ROS-dependent pathways are completely inhibited only by scavenging ROS directly targeting cellular or subcellular sites. Unfortunately, current antioxidant treatments do not achieve this effect. Furthermore, some antioxidants interfere with physiological redox signaling pathways and fail to reverse oxidative damage. Therefore, the key upstream processes and mechanisms of ROS production that lead to chronic pain in vivo must be identified to discover potential therapeutic targets related to the pathways that control ROS production in vivo. In this review, we summarize the sites and pathways involved in analgesia based on the three main mechanisms by which ROS are generated in vivo, discuss the preclinical evidence for the therapeutic potential of targeting these pathways in chronic pain, note the shortcomings of current research and highlight possible future research directions to provide new targets and evidence for the development of clinical analgesics.

Mapping the Landscape of Obesity Effects on Male Reproductive Function: A Bibliometric Study.

Due to changes in lifestyle and dietary habits, the global population with obesity is increasing gradually, resulting in a significant rise in the number of individuals having obesity. Obesity is caused by an imbalance between energy intake and consumption, leading to excessive fat accumulation, which interferes with normal human metabolism. It is also associated with cardiovascular disease, metabolic syndrome, male reproductive endocrine regulation disorders, systemic and local inflammatory reactions, excessive oxidative stress, and apoptosis. All these factors can damage the internal environment for sperm generation and maturation, resulting in male sexual dysfunction, a decline in sperm quality, and lower fertility. This study analyzes the trends and priorities of the effects of obesity on male reproductive disorders from a bibliometric perspective. This study uses the Web of Science as the statistical source, covering all time spans. Tools like Web of Science, VOSviewer, and CiteSpace are used to analyze countries, institutions, authors, journals, and keywords in the field. Total publications, total citations, and average number of citations are selected for statistics. The results show that the research on the impact of obesity on male reproductive function can be roughly divided into three stages: the initial stage, the slow development stage, and the rapid development stage. Our statistical scope includes 463 highly relevant articles that we have screened. We found that the journal with the most publications in this field is Andrologia, and the institution with the highest total citations is the University of Utah. The most influential countries, institutions, and authors in this field are the United States, the University of Utah, and Carrell, Douglas. Currently, research related to the impact of obesity on male reproduction focuses mainly on three aspects: biochemistry, molecular biology, and reproductive biology. The keyword explosion results indicate that sperm, obesity, and male reproduction are at the forefront and trends of future research in this field. There has been a shift from basic biochemical and molecular research to research on molecular mechanisms relying on omics technologies. However, we have observed that the number of papers published in 2022 is lower than in 2021, indicating a growth interruption during this period. Considering that this deviation may be due to the impact of the COVID-19 pandemic, it may hinder the progress of certain experiments in 2022. In recent years, China has rapidly developed research in this field. However, the average citation rate is relatively low, indicating the need for Chinese scholars to improve the quality of their articles further. Based on our research and in the context of global obesity, men are at risk of increased infertility. Addressing this issue relies on our continued research into the mechanisms of obesity-related male reproductive disorders. Over the past forty-three years, with the contributions of scientists worldwide, research in this field has flourished. The impact of obesity on male reproductive disorders has been extensively studied. Currently, research in this field primarily focuses on male sperm function, sperm quality, and the effects or mechanisms of cells on male reproduction. Future trends in this field should concentrate on the relationship between male fertility and energy metabolism, as well as the endocrine function of adipose tissue. This study comprehensively analyzes the current research status and global trends in obesity and male reproductive disorders. We also discuss the future developments in this field, making it easier for researchers to understand its developmental history, current status, and trends, providing valuable reference for effective exploration in this area.

The Antidiabetic Drug Metformin Attenuated Depressive and Anxietylike Behaviors and Oxidative Stress in the Brain in a Rodent Model of Inflammation Induced by Lipopolysaccharide in Male Rats.

Inflammation is considered to be a link between diabetes and central nervous system (CNS) disorders, including depression and anxiety. Metformin is suggested to have antioxidant, anti-inflammatory, and mood-improving effects. The aim of the current research was to investigate the effects of the antidiabetic drug metformin on depressive- and anxiety- like behaviors and oxidative stress in the brain in a rodent model of inflammation induced by lipopolysaccharide (LPS) in male rats. The rats were treated as follows: (1) Vehicle instead of metformin and lipopolysaccharide, (2) Lipopolysaccharide (1 mg/ kg) + vehicle instead of metformin, (3-5) Lipopolysaccharide + 50, 100, or 150 mg/ kg of metformin. After the behavioral tests, including open field (OF), elevated pulse maze (EPM), and force swimming (FS) tests, the brains were removed, and malondialdehyde (MDA), nitric oxide (NO) metabolites, total thiol, catalase (CAT) activity, interleukin-6 (IL-6) and superoxide dismutase (SOD) activity were determined. In the EPM, metformin increased the open arm time and entry and decreased closed arm time and entry. In the FS test, metformin lowered the immobility and increased active time compared to lipopolysaccharide. In the OF test, metformin increased total crossing and total distance, time spent, traveled distance, and crossing number in the central zone. As a result of metformin administration, IL-6, MDA, and NO metabolites were decreased while thiol content, SOD, and CAT activity were increased. The results indicated that the well-known antidiabetic drug metformin attenuated depressive- and anxiety-like behaviors induced by inflammation in rats. These beneficial effects are suggested to be due to their attenuating effects on neuroinflammation, oxidative stress, and NO in the brain.

A Mechanistic Review on Protective Effects of Mangosteen and its Xanthones Against Hazardous Materials and Toxins.

Due to its pharmacological properties, α-Mangostin, mainly found in Garcinia mangostana (G. mangostana) L. (Mangosteen, queen of fruits), treats wounds, skin infections, and many other disorders. In fact, α-Mangostin and other xanthonoid, including β-Mangostin and γ-Mangostin, are found in G. mangostana, which have various advantages, namely neuroprotective, anti-proliferative, antinociceptive, antioxidant, pro-apoptotic, anti-obesity, anti-inflammatory, and hypoglycemic through multiple signaling mechanisms, for instance, extracellular signal-regulated kinase1/2 (ERK 1/2), mitogenactivated Protein kinase (MAPK), nuclear factor-kappa B (NF-kB), transforming growth factor beta1 (TGF-β1) and AMP-activated protein kinase (AMPK). This review presents comprehensive information on Mangosteen's pharmacological and antitoxic aspects and its xanthones against various natural and chemical toxins. Because of the insufficient clinical study, we hope the current research can benefit from performing clinical and preclinical studies against different toxic agents.

A Rationalized Approach to Design and Discover Novel Non-steroidal Derivatives through Computational Aid for the Treatment of Prostate Cancer.

Prostate cancer is one of the most prevalent cancers in men, leading to the second most common cause of death in men. Despite the availability of multiple treatments, the prevalence of prostate cancer remains high. Steroidal antagonists are associated with poor bioavailability and side effects, while non-steroidal antagonists show serious side effects, such as gynecomastia. Therefore, there is a need for a potential candidate for the treatment of prostate cancer with better bioavailability, good therapeutic effects, and minimal side effects. This current research work focused on identifying a novel non-steroidal androgen receptor antagonist through computational tools, such as docking and in silico ADMET analysis. Molecules were designed based on a literature survey, followed by molecular docking of all designed compounds and ADMET analysis of the hit compounds. A library of 600 non-steroidal derivatives (cis and trans) was designed, and molecular docking was performed in the active site of the androgen receptor (PDBID: 1Z95) using Auto- Dock Vina 1.5.6. Docking studies resulted in 15 potent hits, which were then subjected to ADME analysis using SwissADME. ADME analysis predicted three compounds (SK-79, SK-109, and SK-169) with the best ADME profile and better bioavailability. Toxicity studies using Protox-II were performed on the three best compounds (SK-79, SK-109, and SK-169), which predicted ideal toxicity for these lead compounds. This research work will provide ample opportunities to explore medicinal and computational research areas. It will facilitate the development of novel androgen receptor antagonists in future experimental studies.

The influences of gender and income inequality on cross-national variations in lethal violence

The current research examines the cross-national relationship between income and gender inequality as well as their interconnected influences on both female and male homicide victimization. Using a sample of 127 heterogeneous countries, this research supports previous studies that economically stratified societies tend to have high levels of lethal violence. The study also finds that economically stratified societies tend to be male-dominated, which is also associated with increased violence against women as well as increased male-onmale violence.

Treatment Advances in Lung Cancer with Leptomeningeal Metastasis.

Leptomeningeal metastasis (LM) is a serious and often fatal complication in patients with advanced lung cancer, resulting in significant neurological deficits, decreased quality of life, and a poor prognosis. This article summarizes current research advances in treating lung cancer with meningeal metastases, discusses clinical challenges, and explores treatment strategies. Through an extensive review of relevant clinical trial reports and screening of recent conference abstracts, we collected clinical data on treating patients with lung cancer with meningeal metastases to provide an overview of the current research progress. Exciting progress has been made by focusing on specific mutations within lung cancer, including the use of EGFR tyrosine kinase inhibitors or inhibitors for anaplastic lymphoma kinase gene rearrangement, such as osimertinib, alectinib, and lorlatinib. These targeted therapies have shown impressive results in penetrating the central nervous system (CNS). Regarding whole-brain radiotherapy, there is currently some controversy among investigators regarding its effect on survival. Additionally, immune checkpoint inhibitors (ICIs) have demonstrated reliable clinical benefits due to their ability to retain anticancer activity in CNS metastases. Moreover, combination therapy shows promise in providing further treatment possibilities. Considerable progress has been made in the clinical research of lung cancer with LM. However, the sample size of prospective clinical trials investigating LM for lung cancer is still limited, with most reports being retrospective. Developing more effective management protocols for metastatic LM in lung cancer remains an ongoing challenge for the future.

Single Nucleotide Polymorphism (SNP) rs2229569 with L Selectin Gene Expression in Iraqi Female during in vitro Fertilization Program.

يُعرف العقم بأنه أحد مشاكل الصحة الإنجابية الرائدة في مناطق مختلفة من العالم. يعتبر الإخصاب في المختبرأحد أكثر علاجات العقم فعالية. في الدراسة الحالية ، تم تحديد دور جين SELL ، وخاصة تعدد الأشكال rs2229569 ، من حيث الانغراس ومستوى التعبير. اشتملت الدراسة على 67 انثى خضعن لدورة إخصاب في المختبر ، مقسمة إلى مجموعتين رئيسيتين: مجموعة الإناث التي فشلت عملية الزرع ومجموعة الإناث الناجحة في عملية الزرع. تم جمع عينات الدم من مجموعتي الاناث. بعد استخلاص الدنا من الدم وتضخيم الحمض النووي عن طريق تفاعل البلمرة المتسلسل ، تم إرسال العينات لتحليل التسلسل ، وتم تحديد SNP rs2229569 التي اظهرت ثلاثة أنماط وراثية. كانت مجموعة الفشل منحرفه عن H.W.E. ، وكانت مجموعة النجاح في اتفاق مع H.W.E. وفقًا لقيم مربع كاي ((X2 = 11.60 1.58، تشير الفروق المعنوية في مجموعة الفشل إلى تأثير العقم في مجتمع الدراسة. تُظهر نسبة الأرجحية لـ rs2229569 أن أليل T كان (2.09) ، لذلك قد يمثل T allele أليل المخاطرة لعمليةفشل الزرع .أخيرًا ، أظهر جين SELL مستوى تعبيرًا منخفضًا لدى الاناث المصابات بفشل الزرع مقارنة بالاناث في مجموعات الزرع الناجحة.

Modified RNN for Solving Comprehensive Sylvester Equation With TDOA Application.

The augmented Sylvester equation, as a comprehensive equation, is of great significance and its special cases (e.g., Lyapunov equation, Sylvester equation, Stein equation) are frequently encountered in various fields. It is worth pointing out that the current research on simultaneously eliminating the lagging error and handling noises in the nonstationary complex-valued field is rather rare. Therefore, this article focuses on solving a nonstationary complex-valued augmented Sylvester equation (NCASE) in real time and proposes two modified recurrent neural network (RNN) models. The first proposed modified RNN model possesses gradient search and velocity compensation, termed as RNN-GV model. The superiority of the proposed RNN-GV model to traditional algorithms including the complex-valued gradient-based RNN (GRNN) model lies in completely eliminating the lagging error when employed in the nonstationary problem. The second model named complex-valued integration enhanced RNN-GV with the nonlinear acceleration (IERNN-GVN) model is proposed to adapt to a noisy environment and accelerate the convergence process. Besides, the convergence and robustness of these two proposed models are proved via theoretical analysis. Simulative results on an illustrative example and an application to the moving source localization coincide with the theoretical analysis and illustrate the excellent performance of the proposed models.

Influence of stress-specific interventions on biomarker levels and cognitive function in cancer patients: Systematic review and meta-analysis.

Cancer patients' psycho-physiological health is seriously affected by long-term exposure to stress. Many studies have explored the impact of stress-specific interventions on cancer patients' biomarker levels and cognitive functions. However, the current research findings are inconsistent, and their statistical power is limited by the small samples. Therefore, we conducted this meta-analysis to verify the effect of stress-specific interventions on cancer patients. The literature involved nine databases from the inception until January 13, 2024, extracted 19 randomized controlled trials (RCTs). Review Manager (RevMan) 5.4 software was used to perform a meta-analysis, and the revised Cochrane risk of bias tool (RoB2) was utilized for quality evaluation. Nine RCTs were assessed as having a low risk of bias, and others had a moderate risk. The results showed that stress-specific interventions had beneficial effects on patients' subjective cognition but uncertain impacts on their executive function, tumour necrosis factor-α level, morning cortisol level, and no effect on cortisol at other times, interleukin (IL)-10, IL-8, IL-6, IL-1, and C-reactive protein. More rigorous studies are required to elucidate the influence of stress-specific interventions on biomarker levels. The potential mechanism by which stress-specific interventions affect the cancer patient's cognitive function remains unclear.

The Role of Resveratrol in Alzheimer's Disease: A Comprehensive Review of Current Research

: Alzheimer's disease (AD) is a neurodegenerative disorder characterized by progres-sive cognitive decline, memory loss, and impaired daily functioning. The etiology of AD is complex and multifactorial, involving various pathological mechanisms such as the accumula-tion of amyloid-beta plaques, neurofibrillary tangles, neuroinflammation, and oxidative stress. As the global prevalence of AD continues to rise, there is a growing interest in identifying po-tential therapeutic interventions to prevent or slow down the progression of the disease. Resvera-trol, a natural polyphenolic compound found in various plant sources such as grapes, berries, and peanuts, has gained considerable attention due to its potential neuroprotective effects. Numerous preclinical studies utilizing in vitro and animal models have investigated the impact of resvera-trol on AD pathology and associated cognitive impairments. This review aims to provide a com-prehensive summary of the current research on the role of resveratrol in AD. In conclusion, resveratrol holds promise as a potential therapeutic agent for AD due to its ability to target mul-tiple pathological processes involved in the disease. Further research, including well-designed clinical trials with larger sample sizes, is needed to fully elucidate the efficacy, optimal dosage, and long-term effects of resveratrol in AD patients. Nevertheless, resveratrol remains an intri-guing compound with neuroprotective properties and may contribute to the development of nov-el therapeutic approaches for AD in the future.

University-Based Coach Education: The E-Portfolio as a Formative Assessment Tool of Student-Coaches’ Learning

Current research highlights the need for more studies focused on how high-quality assessment strategies can contribute to coach learning in coach education. The use of e-portfolios, as formative assessment tools, has shown to contribute to student-coaches’ learning in university-based programs, but studies on this topic are still scarce. The aim of this action research project is to investigate the potentialities and challenges of the e-portfolio as a formative learning-oriented assessment tool in an undergraduate sport coach course in Brazil. By sharing this, from the perspective of student-coaches and the assistant-professors of the course, we reflect on the evidence with the intention to both inform colleagues doing similar work and contribute to an emerging body of assessment in coach education literature.

Deep Reinforcement Learning Versus Evolution Strategies: A Comparative Survey.

Deep reinforcement learning (DRL) and evolution strategies (ESs) have surpassed human-level control in many sequential decision-making problems, yet many open challenges still exist. To get insights into the strengths and weaknesses of DRL versus ESs, an analysis of their respective capabilities and limitations is provided. After presenting their fundamental concepts and algorithms, a comparison is provided on key aspects, such as scalability, exploration, adaptation to dynamic environments, and multiagent learning. Current research challenges are also discussed, including sample efficiency, exploration versus exploitation, dealing with sparse rewards, and learning to plan. Then, the benefits of hybrid algorithms that combine DRL and ESs are highlighted.

Sequential Point Clouds: A Survey.

Point clouds have garnered increasing research attention and found numerous practical applications. However, many of these applications, such as autonomous driving and robotic manipulation, rely on sequential point clouds, essentially adding a temporal dimension to the data (i.e., four dimensions) because the information of the static point cloud data could provide is still limited. Recent research efforts have been directed towards enhancing the understanding and utilization of sequential point clouds. This paper offers a comprehensive review of deep learning methods applied to sequential point cloud research, encompassing dynamic flow estimation, object detection & tracking, point cloud segmentation, and point cloud forecasting. This paper further summarizes and compares the quantitative results of the reviewed methods over the public benchmark datasets. Ultimately, the paper concludes by addressing the challenges in current sequential point cloud research and pointing towards promising avenues for future research.

Depression-reminiscent Behavior Induced by Chronic Unpredictable Mild Stress Paradigm in Mice Substantially Abrogated by Diosmin

Background: Diosmin has already been described and documented to be neuroprotective, antioxidant and anti-inflammatory. It may possess or hold depressionalleviating activity. Therefore, the purpose of the current research protocol is to investigate the depression-relieving proficiency of diosmin in stressed and unstressed mice. Methods: Male mice (Swiss albino) were imperiled to an unpredictable chronic stress paradigm every day for three sequential weeks, and depression-resembling behavioral despair was induced. Imipramine 15 mg/kg and diosmin (25, 50 and 100 mg/kg) were dispensed for 21 successive days to discrete groups of stressed and unstressed mice. Results: Both diosmin (100 mg/kg) and 15 mg/kg imipramine administration for 3 consecutive weeks substantially or significantly diminished the immobility period of mice imperiled to stress in comparison to stressed mice gauzed with the vehicle. Diosmin (25, 50 and 100 mg/kg) and imipramine considerably reinstated the diminished sucrose proclivity (sucrose preference percentage; %) in stressed mice, demonstrating their considerable or substantial depression-relieving effects. The locomotor activities of mice were not considerably altered by these drugs. Antidepressant-like activity of diosmin for immobility periods and preference for sucrose was observed to be analogous to imipramine. Diosmin (100 mg/kg) and imipramine substantially quashed CUMS- persuaded escalation of plasma corticosterone and nitrite levels, malondialdehyde levels and MAO-A activity in the brain of stressed mice. Both drugs also substantially reversed CUMS-prompted reduction in catalase activity and brain glutathione levels. Conclusion: Accordingly, diosmin revealed significant anti-depressive activity in mice imperiled to chronic mild unpredictable stress paradigm conceivably via mitigation of nitrosative and oxidative stress, reticence of brain MAO-A action, and sink drop of plasma corticosterone degrees.

How Does Automation Shape the Process of Narrative Visualization: A Survey of Tools.

In recent years, narrative visualization has gained much attention. Researchers have proposed different design spaces for various narrative visualization genres and scenarios to facilitate the creation process. As users' needs grow and automation technologies advance, increasingly more tools have been designed and developed. In this study, we summarized six genres of narrative visualization (annotated charts, infographics, timelines & storylines, data comics, scrollytelling & slideshow, and data videos) based on previous research and four types of tools (design spaces, authoring tools, ML/AI-supported tools and ML/AI-generator tools) based on the intelligence and automation level of the tools. We surveyed 105 papers and tools to study how automation can progressively engage in visualization design and narrative processes to help users easily create narrative visualizations. This research aims to provide an overview of current research and development in the automation involvement of narrative visualization tools. We discuss key research problems in each category and suggest new opportunities to encourage further research in the related domain.

Exploration of Skillification and Its Use in Awarding Credit for Prior Learning

In applications for prior learning credit, learners with professional experience describe competencies gained during their employment history or past study, explaining how these competencies correspond to what is taught in a course sufficiently well to award them credit for prior learning. Comparing skills that are extracted algorithmically from artifacts describing a learner’s prior experience to skills similarly extracted from syllabi offers a potential solution. Current research indicates that algorithmically extracted skills can be used to identify relevance between artifacts and groups of courses but that connecting an artifact to a specific course requires guidance to learners on the artifact creation, careful use of analytical techniques, and faculty involvement. The guidance includes setting expectations for language volume and creating focused text, like an essay, rather than more general descriptive artifacts, like résumés. Effective analytical techniques accommodate observed variation in skill volume between different artifacts, optimizing the potential identification of appropriate relationships. Basing decisions on the degree of overlap in the total artifact-course skill set appears more effective than considering the number of matches to program skills. Deterministic matching is insufficient for identifying course-level correspondence, while approaches that capture information about the covariation of skills among courses offer promise.

Superimposed Semantic Communication for IoT-Based Real-Time ECG Monitoring.

Real-time electrocardiogram (ECG) monitoring and diagnosis through Internet of Things (IoT) are crucial for addressing the severity and timely treatment of cardiovascular diseases, enabling timely intervention and preventing life-threatening complications. However, current ECG monitoring research predominantly focuses on individual aspects such as signal compression, diagnostic analysis, or secure transmission, lacking joint optimization of various modules in IoT scenarios. To address this gap, this work proposes a novel framework based on superimposed semantic communication for real-time ECG monitoring in IoT. The framework comprises three hierarchical levels: the edge level for data collection and processing, the relay level for signal compression and coding, and the cloud level for data analysis and reconstruction. The proposed framework offers several unique advantages. By employing semantic encoding guided by ECG classification tasks, it selectively extracts crucial features within and between signals, improving compression ratio and adaptability to channel noise. The superimposed semantic encoding achieves content encryption without requiring any additional operations. Moreover, the framework utilizes lightweight anomaly detection neural networks, reducing edge device power consumption and conserving communication resources. Simulation and real experimental results demonstrate that the proposed method achieves real-time encoding and transmission of ECG signals with a compression ratio of 0.019 on the MIT-BIH dataset. Furthermore, it attains a heartbeat classification accuracy of 0.988 and a reconstruction error of 0.061.