Limited Availability Research Articles

Traditional Chinese Medicine as the Preventive and Therapeutic Remedy for COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic still has tremendous impacts on the global socio-economy and quality of living. The traditional Chinese Medicines (TCM) approach showed encouraging results during previous outbreaks of Severe Acute Respiratory Syndrome-related Coronavirus (SARS-CoV) and the Middle East Respiratory Syndrome Coronavirus (MERS-CoV). With limited treatment availability, TCM herbs and formulations could be useful to reduce COVID-19 symptoms and potential sources for discovering novel therapeutic targets. We reviewed 12 TCM herbs and formulations recommended for COVID-19 management by the National Health Commission and as National Administration of Traditional Chinese Medicine of the People's Republic of China. This article explored the Chinese national authorities' guidelines from 2003 to 2020, the scientific data in public databases for the recommended TCM remedies, and their potential mechanistic actions in COVID-19 management. Several TCM herbs and formulations could potentially benefit COVID-19 management. The recommended TCM oral preparations list includes Huoxiang zhengqi, Jinhua Qinggan, Lianhua Qingwen, and Shufeng jiedu; the recommended injection preparations comprise Xiyanping Xuebijing, Re-Du-Ning, Tanreqing, Xingnaojing, Shenfu, Shengmai, and Shenmai. TCM remedies are viable options for symptom alleviation and management of COVID-19. The current SARS-CoV-2 pandemic presents an opportunity to find novel therapeutic targets from TCM-active ingredients. Despite the recommendations in Chinese National guidelines, these remedies warrant further assessments in well-designed clinical trials to ascertain their efficacy in the treatment of COVID-19.

Outage Performance of Underlay CR-NOMA-based D2D Communications under Imperfect CSI and SIC

Background: Non-orthogonal multiple access (NOMA) is a promising technique for improving wireless communication performance in the future. In addition to NOMA, cognitive radio (CR) is another technology that can address the issue of limited spectrum availability and meet the increasing demands for wireless connectivity. Methods: This paper focuses on investigating underlay CR-NOMA-based D2D communications. The study assumes a decode-and-forward (DF) mode used in the system, where nearby users (D1, D2) act as helper users to assist distant users (D3, D4). The paper derives the closed-form expressions for outage probability (OP) and throughput at the far users in three scenarios: (1) Perfect successive interference cancellation (SIC) and perfect channel state information (CSI), (2) Imperfect CSI, and (3) Imperfect SIC. Results: In CR-NOMA mode, the results indicate that the performance of user D4 was better than user D3. Additionally, the OP performance of distant users employing CR-NOMA mode surpasses that of users using CR-OMA mode. The optimal power allocation (PA) values are investigated. Conclusion: The presence of imperfect CSI and SIC has an unfavorable influence on the outage performance. Monte Carlo simulations are used to validate the derived analytical expressions.

An extreme gradient boost based classification and regression tree for network intrusion detection in IoT

Nowadays, modern technology includes various devices, networks, and apps from the internet of things (IoT), which consist of both positive and negative impacts on social, economic, and industrial effects. To address these issues, IoT applications and networks require lightweight, quick, and adaptable security solutions. In this sense, solutions based on artificial intelligence and big data analytics can yield positive outcomes in the realm of cyber security. This study presents a method called extreme gradient boost (XGBoost) based classification and regression tree to identify network intrusions in the IoT. This model is ideally suited for application in IoT networks with restricted resource availability because of its distributed structure and builtin higher generalization capabilities. This approach is thoroughly tested using botnet internet of things (BoT-IoT) new-generation IoT security datasets. All trials are conducted in a range of different settings, and a number of performance indicators are used to evaluate the effectiveness of the proposed method. The suggested study's findings provide recommendations and insights for situations involving binary classes and numerous classes. The suggested XGBoost model achieved 99.53% of accuracy in attack detection and 99.51% in precision for binary class and multiclass classifications, respectively.

Prostate Cancer Detection from MRI Using Efficient Feature Extraction with Transfer Learning.

Prostate cancer is a common cancer with significant implications for global health. Prompt and precise identification is crucial for efficient treatment strategizing and enhanced patient results. This research study investigates the utilization of machine learning techniques to diagnose prostate cancer. It emphasizes utilizing deep learning models, namely VGG16, VGG19, ResNet50, and ResNet50V2, to extract relevant features. The random forest approach then uses these features for classification. The study begins by doing a thorough comparison examination of the deep learning architectures outlined above to evaluate their effectiveness in extracting significant characteristics from prostate cancer imaging data. Key metrics such as sensitivity, specificity, and accuracy are used to assess the models' efficacy. With an accuracy of 99.64%, ResNet50 outperformed other tested models when it came to identifying important features in images of prostate cancer. Furthermore, the analysis of understanding factors aims to offer valuable insights into the decision-making process, thereby addressing a critical problem for clinical practice acceptance. The random forest classifier, a powerful ensemble learning method renowned for its adaptability and ability to handle intricate datasets, then uses the collected characteristics as input. The random forest model seeks to identify patterns in the feature space and produce precise predictions on the presence or absence of prostate cancer. In addition, the study tackles the restricted availability of datasets by utilizing transfer learning methods to refine the deep learning models using a small amount of annotated prostate cancer data. The objective of this method is to improve the ability of the models to generalize across different patient populations and clinical situations. This study's results are useful because they show how well VGG16, VGG19, ResNet50, and ResNet50V2 work for extracting features in the field of diagnosing prostate cancer, when used with random forest's classification abilities. The results of this work provide a basis for creating reliable and easily understandable machine learning-based diagnostic tools for detecting prostate cancer. This will enhance the possibility of an early and precise diagnosis in clinical settings such as index terms deep learning, machine learning, prostate cancer, cancer identification, and cancer classification.

The Impact of Psychosocial Support Services On Students’ Development at a South African TVET College

South African Technical and Vocational Education and Training (TVET) lecturers and student development practitioners are not sufficiently equipped to address psycho-social challenges encountered by students, and that negatively affects learning and teaching. The main aim of the study was to examine the impact of psycho-social support services on students’ development at a South African TVET college. The study used a qualitative phenomenological research technique. The participants of the study comprised a total number of 9 participants. The participants included 2 lecturers (one from NCV and the other from Report 191 programmes), 3 students from National Certificate Vocational (NCV) programme (L2-L4), 3 students from Report 191 programme (N4-N6), and 1 Student Development Practitioner of a South African TVET college who were selected through a simple random technique. Face-to-face interviews were used for data collection. Thematic analysis was adopted as a tool to analyse the data and findings revealed inadequate provision of counselling and the unavailability of peer tutoring as psycho-social support services that had a negative bearing on their development. The participants' low socio-economic level and the restricted availability of specialised psycho-social assistance for student development from a South African TVET institution were additional factors that negatively impacted their relatedness, competence, and autonomy. Future research is needed to examine the impact of psycho-social support services on students’ development at South African TVET colleges by using a larger sample of participants. The paper's findings indicate that the provision of counselling to students, understanding the impact of socio-economic status, and the availability of peer tutoring could develop students psychosocially at the South African TVET college in question. Furthermore, empowering lecturers and SDPs with psychosocial support skills helps them respond to the psychosocial challenges to student development.

Seed Production in Jalandhar

A key factor in agricultural resilience, crop diversity, and productivity is seed production—particularly in areas like Jalandhar that must meet changing market demands and climate change. This project explores the complexities of seed production in Jalandhar and how it affects agricultural development with the goal of educating researchers, policymakers, and agricultural stakeholders on how to support sustainable seed production systems. Located in the rich plains of Punjab, Jalandhar has become a major centre for the production of seeds, especially for pulses, wheat, and rice. The region's favorable soil and climate, together with cutting-edge farming techniques, are what propel the expansion of the seed business. With the growing use of hybrid and genetically modified seeds, government programs encouraging agricultural output, and growing farmer awareness of the advantages of high-quality seeds, Jalandhar has a huge market potential for seed production. Nevertheless, in order to fully realize the potential of this industry, stakeholders must work together and implement strategic interventions in response to issues including volatile market pricing, restricted financial availability for small-scale farmers, and environmental sustainability concerns. The sector's growth trajectory is further aided by the existence of well-established agricultural research institutions and government policies that are supportive of the industry, providing growing opportunities in both domestic and foreign markets High-quality seeds improve crop yields while also contributing to overall sustainability and profitability in farming operations. Agro-ecologically-suited certified seeds are essential for reducing the danger of pests, illnesses, and unfavorable weather. Encouraging and supporting sustainable seed production systems in Jalandhar benefits all related departments and financial institutions by promoting agricultural development, economic success, and food security. Keywords : Seed production ,Agricultural development ,Market potential ,Sustainable seed systems ,Government initiatives ,Economic prosperity.

Enhanced tetracycline degradation through nonradical processes by modulating the exposure of active sites on the iron nanoconfined catalysts

There is an urgent need to develop effective and sustainable methods for reducing water pollution. Non-homogeneous Fenton catalysts are commonly used to eliminate pollutants from water. However, the effectiveness of these catalysts is limited by the restricted availability and accessibility of the active site (AS). In this work, we presented a strategy for modulating the exposure of active nanometallic particles to fully expose the AS at the nanoscale. We also confined the AS in carbon frameworks with micropores and mesopores to enhance the effectiveness of AS utilization in Fenton-like reactions. The experimental results indicate that the Fe-out@N-C/PMS system achieved complete degradation of tetracycline within 10 min and maintained an efficient degradation rate of over 91% after five consecutive cycles. The degradation of contaminants was due to an effective non-radical mechanism, as shown by free radical quenching experiments, EPR reactive species capture tests, in situ Raman, and PMSO probe experiments. Furthermore, density-functional theory (DFT) calculations revealed that iron nanoparticles supported on nitrogen-doped carbon acted as active sites, enhancing electron transfer capacity (N-C (0.24 e) vs. Fe-out@N-C (0.69 e)). Finally, the intermediates have a limited environmental impact because of their extremely low concentrations, despite having a high relative toxicity. The exposed metal nanoparticle catalysts provide a new approach for effectively activating PMS to efficiently degrade organic contaminants with a high mineralization rate via the non-radical pathway.

The hot sex: is there evidence for climate‐driven sex allocation in mammals?

Abstract Climate change predictions indicate that future conditions will be hotter and drier, and rainfall will be more variable. Climatic conditions can affect individual condition and shape offspring sex ratios. The implications that climate change have for mammalian sex allocation are vast but are currently poorly understood. There are several scenarios under which climate‐driven sex allocation responses might emerge. Here, I make predictions on the direction of offspring sex ratio biases under hypothetical situations of restricted resource availability and altered social/spatial structures that may result from hotter, drier conditions. I searched the literature for existing evidence of climate‐driven offspring sex ratio biases among individuals studied in wild populations. There was no strong, directional support for the production of sons or daughters under ‘good’ or ‘poor’ climatic conditions among the 30 studies had reported on climatic variables and offspring sex ratio. I also searched the literature for studies that assessed maternal condition and offspring sex ratio in natural populations (i.e. there is a link between climate, resource availability and maternal condition). Of the 27 investigations, half reported a bias in favour of sons born to mothers in ‘good’ condition (56%). I discuss how this outcome could be due to a publication bias, or that it may indicate that low resource availability has the potential to lead to mothers in poor condition and the production of fewer sons. I consider the conservation implications of climate‐driven offspring sex ratios skews being carried through to the population level. I discuss the challenge of being able to account for the multiple factors that influence offspring sex ratios in natural populations and emphasise the growing need for more research in this important area of mammalian evolutionary ecology.

3D-DGGAN: A Data-Guided Generative Adversarial Network for High Fidelity in Medical Image Generation.

Three-dimensional images are frequently used in medical imaging research for classification, segmentation, and detection. However, the limited availability of 3D images hinders research progress due to network training difficulties. Generative methods have been proposed to create medical images using AI techniques. Nevertheless, 2D approaches have difficulty dealing with 3D anatomical structures, which can result in discontinuities between slices. To mitigate these discontinuities, several 3D generative networks have been proposed. However, the scarcity of available 3D images makes training these networks with limited samples inadequate for producing high-fidelity 3D images. We propose a data-guided generative adversarial network to provide high fidelity in 3D image generation. The generator creates fake images with noise using reference code obtained by extracting features from real images. The generator also creates decoded images using reference code without noise. These decoded images are compared to the real images to evaluate fidelity in the reference code. This generation process can create high-fidelity 3D images from only a small amount of real training data. Additionally, our method employs three types of discriminator: volume (evaluates all the slices), slab (evaluates a set of consecutive slices), and slice (evaluates randomly selected slices). The proposed discriminator enhances fidelity by differentiating between real and fake images based on detailed characteristics. Results from our method are compared with existing methods by using quantitative analysis such as Fréchet inception distance and maximum mean discrepancy. The results demonstrate that our method produces more realistic 3D images than existing methods.

Genetic Determined Iron Starvation Signature in Friedreich's Ataxia.

Early studies in cellular models suggested an iron accumulation in Friedreich's ataxia (FA), yet findings from patients are lacking. The objective is to characterize systemic iron metabolism, body iron storages, and intracellular iron regulation in FA patients. In FA patients and matched healthy controls, we assessed serum iron parameters, regulatory hormones as well as the expression of regulatory proteins and iron distribution in peripheral blood mononuclear cells (PBMCs). We applied magnetic resonance imaging with R2*-relaxometry to quantify iron storages in the liver, spleen, and pancreas. Across all evaluations, we assessed the influence of the genetic severity as expressed by the length of the shorter GAA-expansion (GAA1). We recruited 40 FA patients (19 women). Compared to controls, FA patients displayed lower serum iron and transferrin saturation. Serum ferritin, hepcidin, mean corpuscular hemoglobin and mean corpuscular volume in FA inversely correlated with the GAA1-repeat length, indicating iron deficiency and restricted availability for erythropoiesis with increasing genetic severity. R2*-relaxometry revealed a reduction of splenic and hepatic iron stores in FA. Liver and spleen R2* values inversely correlated with the GAA1-repeat length. FA PBMCs displayed downregulation of ferritin and upregulation of transferrin receptor and divalent metal transporter-1 mRNA, particularly in patients with >500 GAA1-repeats. In FA PBMCs, intracellular iron was not increased, but shifted toward mitochondria. We provide evidence for a previously unrecognized iron starvation signature at systemic and cellular levels in FA patients, which is related to the underlying genetic severity. These findings challenge the use of systemic iron lowering therapies in FA. © 2024 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

A Bayesian platform trial design with hybrid control based on multisource exchangeability modelling.

Enrolling patients to the standard of care (SOC) arm in randomized clinical trials, especially for rare diseases, can be very challenging due to the lack of resources, restricted patient population availability, and ethical considerations. As the therapeutic effect for the SOC is often well documented in historical trials, we propose a Bayesian platform trial design with hybrid control based on the multisource exchangeability modelling (MEM) framework to harness historical control data. The MEM approach provides a computationally efficient method to formally evaluate the exchangeability of study outcomes between different data sources and allows us to make better informed data borrowing decisions based on the exchangeability between historical and concurrent data. We conduct extensive simulation studies to evaluate the proposed hybrid design. We demonstrate the proposed design leads to significant sample size reduction for the internal control arm and borrows more information compared to competing Bayesian approaches when historical and internal data are compatible.

Block Scheduling for LARC in a Family Medicine Residency Program.

Only 20% of family physicians report providing long-acting reversible contraception (LARC). Clinician-related barriers include confidence and comfort with LARC counseling and insertion/removal, and limited availability and uptake. Training during residency may address barriers and increase access/availability of LARC to support reproductive autonomy. We sought to determine the impact of block scheduling LARC clinics on resident comfort and confidence with LARC counseling and insertion/removal. LARC block schedules were established in a Midwest family medicine residency's primary clinic (FMC) and in a federally qualified health center rotation clinic. Baseline and end-of-study surveys, compared by Mann-Whitney U and Wilcoxon signed-rank tests, were used to assess comfort and confidence with counseling and inserting LARC. The number of LARC devices placed at the FMC were collected for the intervention year and the year prior. Twenty of 30 residents completed the baseline survey; 13 completed the end-of-study survey. At the group and individual levels, comfort increased for counseling on Levonorgestrel (LNG) intrauterine devices (IUDs) and for inserting implants and LNG IUDs. Individual comfort increased for copper IUDs. Resident willingness to recommend LARC increased, and more devices were placed during the intervention year than the year prior in the FMC (all: P<.05). Block scheduling of LARC clinics was associated with increased residents' comfort and confidence with counseling and placement of implants (LNG IUDs) and with an increase in LARCs placed at one clinic. Changes to scheduling may be an effective educational strategy that may increase access/availability to LARC.

Improved metric-based meta learning with attention mechanism for few-shot cross-domain train bearing fault diagnosis

Traction motor bearings, as a crucial component of subway trains, play a pivotal role in ensuring the safety of train operations. Therefore, intelligent diagnosis of train bearings holds significant importance. However, due to the complex and dynamic nature of bearing conditions coupled with limited fault data availability, traditional diagnostic methods fail to yield satisfactory results. To address this issue, we propose an improved metrics-based meta-learning approach for accurate few-shot cross-domain fault diagnosis of train bearings. Firstly, we introduce a 1D-signal channel attention mechanism that effectively extracts latent features and enhances recognition accuracy. Secondly, by incorporating the Adabound algorithm into our model framework, we further enhance its classification performance. Finally, through several case studies, we demonstrate the effectiveness of our proposed method in comparison to other approaches within similar settings.

A Convenient Synthesis of Bis-(dihydro)caffeoylspermidines

Abstract: Caffeoyl spermidines are valuable alkaloids naturally existing in many plant species. These alkaloids are characterized as pharmacologically important ingredients of many plant extracts used in traditional medicines. Bis-caffeoylspermidine and bis-dihydrocaffeoylspermidine exhibit antioxidant, anti-inflammatory, and enzyme inhibition properties that make them valuable natural sources of safer therapeutic agents. However, the limited availability of these compounds in nature derives the attention of synthetic chemists. Therefore, we synthesized bis-caffeoylspermidine and bisdihydrocaffeoylspermidine using cheaper and commercially available starting materials in 97% and 86% overall yields, respectively. The synthetic scheme can be upgraded to the commercial-scale synthesis of these compounds

Effect of Plant Density on Mechanical Harvest Efficiency of New Mexico Pod-type Green Chile Pepper

Summary. New Mexico green pod-type chile (Capsicum annuum) has significant importance as a vegetable crop. The cultivation and trade of New Mexico pod-type green chile are culturally significant within New Mexico (USA) and contribute to the state’s economy by providing income and employment to farmers and through supporting industries. However, because of the high cost and limited availability of labor, New Mexico pod-type green chile acreage has declined. Traditionally, New Mexico pod-type green chile is hand-harvested when the fruit are full-size but physiologically immature. To preserve and expand the production of New Mexico pod-type green chile, the adoption of mechanical harvest technologies is essential. In 2015 and 2016, experiments were conducted at New Mexico State University’s Los Lunas Agricultural Science Center (Los Lunas, NM, USA) to examine the effects of increased planting density on New Mexico pod-type green chile fruit size, plant architecture, and mechanical harvest efficiency. Two commercial New Mexico pod-type green chile cultivars, NuMex Joe E. Parker and AZ-1904, were direct-seeded on 17 Apr 2015 and 14 Apr 2016. On 11 Jun 2015 and 14 Jun 2016, three plant density treatments were implemented at 39,000 (high), 23,000 (medium), and 15,000 (standard) plants/acre. Before harvest, plant measurements, including height, width, height to first bifurcation, stem diameter, and number of lateral basal branches, were obtained. Plots were mechanically harvested using an inclined double helix harvester, and harvested material was sorted into marketable green fruit, machine-broken fruit, and nonpod plant material. Fruit measurements, including fruit weight, width, length, pericarp thickness, and number of locules, were obtained. Both cultivars exhibited a 9% increase in height to bifurcation accompanied by fewer basal branches grown at high density. Plant density did not significantly affect the fruit length, width, number of locules, and pericarp thickness. Plants grown at high density had an increased percentage of marketable fruit, with ‘NuMex Joe E. Parker’ having a higher percentage of marketable green fruit compared to ‘AZ-1904’. The results demonstrated that an increase in planting density in production fields to 39,000 plants/acre coupled with cultivar selection enhanced efficiency in a mechanical harvest system.

Low crude protein formulation with supplemental amino acids for its impacts on intestinal health and growth performance of growing-finishing pigs

Background Low crude protein (CP) formulations with supplemental amino acids (AA) are used to enhance intestinal health, reduce costs, minimize environmental impact, and maintain growth performance of pigs. However, extensive reduction of dietary CP can compromise growth performance due to limited synthesis of non-essential AA and limited availability of bioactive compounds from protein supplements even when AA requirements are met. Moreover, implementing a low CP formulation can increase the net energy (NE) content in feeds causing excessive fat deposition. Additional supplementation of functional AA, coupled with low CP formulation could further enhance intestinal health and glucose metabolism, improving nitrogen utilization, and growth performance. Three experiments were conducted to evaluate the effects of low CP formulations with supplemental AA on the intestinal health and growth performance of growing-finishing pigs.Methods In Exp. 1, 90 pigs (19.7 ± 1.1 kg, 45 barrows and 45 gilts) were assigned to 3 treatments: CON (18.0% CP, supplementing Lys, Met, and Thr), LCP (16.0% CP, supplementing Lys, Met, Thr, Trp, and Val), and LCPT (16.1% CP, LCP + 0.05% SID Trp). In Exp. 2, 72 pigs (34.2 ± 4.2 kg BW) were assigned to 3 treatments: CON (17.7% CP, meeting the requirements of Lys, Met, Thr, and Trp); LCP (15.0% CP, meeting Lys, Thr, Trp, Met, Val, Ile, and Phe); and VLCP (12.8% CP, meeting Lys, Thr, Trp, Met, Val, Ile, Phe, His, and Leu). In Exp. 3, 72 pigs (54.1 ± 5.9 kg BW) were assigned to 3 treatments and fed experimental diets for 3 phases (grower 2, finishing 1, and finishing 2). Treatments were CON (18.0%, 13.8%, 12.7% CP for 3 phases; meeting Lys, Met, Thr, and Trp); LCP (13.5%, 11.4%, 10.4% CP for 3 phases; meeting Lys, Thr, Trp, Met, Val, Ile, and Phe); and LCPG (14.1%, 12.8%, 11.1% CP for 3 phases; LCP + Glu to match SID Glu with CON). All diets had 2.6 Mcal/kg NE.ResultsIn Exp. 1, overall, the growth performance did not differ among treatments. The LCPT increased (P < 0.05) Claudin-1 expression in the duodenum and jejunum. The LCP and LCPT increased (P < 0.05) CAT-1, 4F2hc, and B0AT expressions in the jejunum. In Exp. 2, overall, the VLCP reduced (P < 0.05) G:F and BUN. The LCP and VLCP increased (P < 0.05) the backfat thickness (BFT). In Exp. 3, overall, growth performance and BFT did not differ among treatments. The LCPG reduced (P < 0.05) BUN, whereas increased the insulin in plasma. The LCP and LCPG reduced (P < 0.05) the abundance of Streptococcaceae, whereas the LCP reduced (P < 0.05) Erysipelotrichaceae, and the alpha diversity.ConclusionsWhen implementing low CP formulation, CP can be reduced by supplementation of Lys, Thr, Met, Trp, Val, and Ile without affecting the growth performance of growing-finishing pigs when NE is adjusted to avoid increased fat deposition. Supplementation of Trp above the requirement or supplementation of Glu in low CP formulation seems to benefit intestinal health as well as improved nitrogen utilization and glucose metabolism.

Adaptive Stacking Ensemble Techniques for Early Severity Classification of COVID-19 Patients

During outbreaks of infectious diseases, such as COVID-19, it is critical to rapidly determine treatment priorities and identify patients requiring hospitalization based on clinical severity. Although various machine learning models have been developed to predict COVID-19 severity, most have limitations, such as small dataset sizes, the limited availability of clinical variables, or a constrained classification of severity levels by a single classifier. In this paper, we propose an adaptive stacking ensemble technique that identifies various COVID-19 patient severity levels and separates them into three formats: Type 1 (low or high severity), Type 2 (mild, severe, critical), and Type 3 (asymptomatic, mild, moderate, severe, fatal). To enhance the model’s generalizability, we utilized a nationwide dataset from the South Korean government, comprising data from 5644 patients across over 100 hospitals. To address the limited availability of clinical variables, our technique employs data-driven strategies and a proposed feature selection method. This ensures the availability of clinical variables across diverse hospital environments. To construct optimal stacking ensemble models, our technique adaptively selects candidate base classifiers by analyzing the correlation between their predicted outcomes and performance. It then automatically determines the optimal multi-layer combination of base and meta-classifiers using a greedy search algorithm. To further improve the performance, we applied various techniques, including imputation of missing values and oversampling. The experimental results demonstrate that our stacking ensemble models significantly outperform existing single classifiers and AutoML approaches, with improvements of 6.42% and 8.86% in F1 and AUC scores for Type 1, 9.59% and 6.68% for Type 2, and 11.94% and 9.24% for Type 3, respectively. Consequently, our approach improves the prediction of COVID-19 severity levels and potentially assists frontline healthcare providers in making informed decisions.

The paradox of spring: Thyroid and glucocorticoid responses to cold temperatures and food availability in free living Carneddau ponies

In seasonal environments, maintaining a constant body temperature poses challenges for endotherms. Cold winters at high latitudes, with limited food availability, create opposing demands on metabolism: upregulation preserves body temperature but depletes energy reserves. Examining endocrine profiles, such as thyroid hormone triiodothyronine (T3) and glucocorticoids (GCs), proxies for changes in metabolic rate and acute stressors, offer insights into physiological trade-offs. We evaluated how environmental conditions and gestation impact on faecal hormone metabolites (fT3Ms and fGCMs) from late winter to spring in a free-living population of Carneddau ponies. Faecal T3Ms were highest in late February and March, when temperatures were lowest. Then, fT3Ms concentrations decreased throughout April and were at the lowest in May before increasing towards the end of the study. The decline in fT3M levels in April and May was associated with warmer weather but poor food availability, diet diversity and diet composition. On the other hand, fGCM levels did not display a clear temporal pattern but were associated with reproductive status, where pregnant and lactating females had higher fGCM levels as compared to adult males and non-reproductive females. The temporal profile of fT3Ms levels highlights metabolic trade-offs in a changing environment. In contrast, the ephemeral but synchronous increase in fGCM concentrations across the population suggest a shared experience of acute stressors (i.e., weather, disturbance or social). This multi-biomarker approach can evaluate the role of acute stressors versus energy budgets in the context of interventions, reproduction, seasonality and environmental change, or across multiple scales from individuals to populations.

IRNLGD: An Edge Detection Algorithm with Comprehensive Gradient Directions for Tidal Stream Turbine

Tidal stream turbines (TSTs) harness the kinetic energy of tides to generate electricity by rotating the rotor. Biofouling will lead to an imbalance between the blades, resulting in imbalanced torque and voltage across the windings, ultimately polluting the grid. Therefore, rotor condition monitoring is of great significance for the stable operation of the system. Image-based attachment detection algorithms provide the advantage of visually displaying the location and area of faults. However, due to the limited availability of data from multiple machine types and environments, it is difficult to ensure the generalization of the network. Additionally, TST images degrade, resulting in reduced image gradients and making it challenging to extract edge and other features. In order to address the issue of limited data, a novel non-data-driven edge detection algorithm, indexed resemble-normal-line guidance detector (IRNLGD), is proposed for TST rotor attachment fault detection. Aiming to solve the problem of edge features being suppressed, IRNLGD introduces the concept of “indexed resemble-normal-line direction” and integrates multi-directional gradient information for edge determination. Real-image experiments demonstrate IRNLGD’s effectiveness in detecting TST rotor edges and faults. Evaluation on public datasets shows the superior performance of our method in detecting fine edges in low-light images.

Distributed Computation Offloading and Power Control for UAV-Enabled Internet of Medical Things

The advancement of the Internet of Medical Things (IoMT) has led to the emergence of various health and emotion care services, e.g., health monitoring. To cater to increasing computational requirements of IoMT services, Mobile Edge Computing (MEC) has emerged as an indispensable technology in smart health. Benefiting from the cost-effectiveness of deployment, unmanned aerial vehicles (UAVs) equipped with MEC servers in Non-Orthogonal Multiple Access (NOMA) have emerged as a promising solution for providing smart health services in proximity to medical devices (MDs). However, the escalating number of MDs and the limited availability of communication resources of UAVs give rise to a significant increase in transmission latency. Moreover, due to the limited communication range of UAVs, the geographically-distributed MDs lead to workload imbalance of UAVs, which deteriorates the service response delay. To this end, this paper proposes a UAV-enabled Distributed computation Offloading and Power control method with Multi-Agent, named DOPMA, for NOMA-based IoMT environment. Specifically, this paper introduces computation and transmission queue models to analyze the dynamic characteristics of task execution latency and energy consumption. Moreover, a credit assignment scheme-based reward function is designed considering both system-level rewards and rewards tailored to each MD, and an improved multi-agent deep deterministic policy gradient algorithm is developed to derive offloading and power control decisions independently. Extensive simulations demonstrate that the proposed method outperforms existing schemes, achieving \(7.1\% \) reduction in energy consumption and \(16\% \) decrease in average delay.