Incretin-Based Therapies: A Promising Approach for Modulating Oxidative Stress and Insulin Resistance in Sarcopenia.

Background: Recent studies have linked sarcopenia development to the hallmarks of diabetes, oxidative stress, and insulin resistance. The anti-oxidant and insulin sensitivityenhancing effects of incretin-based therapies may provide a promising option for the treatment of sarcopenia. This review aimed to unveil the role of oxidative stress and insulin resistance in the pathogenesis of sarcopenia and explore the potential benefits of incretin-based therapies in individuals with sarcopenia. Methods: PubMed, the Cochrane Library, and Google Scholar databases were searched by applying keywords relevant to the main topic, to identify articles that met our selection criteria. Results: Incretin-based therapies manifested anti-oxidant effects by increasing the anti-oxidant defense system and decreasing free radical generation or by indirectly minimizing glucotoxicity, which was mainly achieved by improving insulin signaling and glucose homeostasis. Likewise, these drugs exhibit insulin-sensitizing activities by increasing insulin secretion, transduction, and ß-cell function or by reducing inflammation and lipotoxicity. Conclusions: Incretin-based therapies, as modulators of oxidation and insulin resistance, may target the main pathophysiological factors of sarcopenia, thus providing a promising strategy for the treatment of this disease.

Understanding Barriers to Early Rehabilitation Following the Surgery for Fragility Hip Fractures: A Single-Centre Audit.

Introduction Hip fractures are amongst the most common reasons for geriatric patients' admission to the hospital; recovery of the hip fracture patient can be enhanced by an early rehabilitation program, which should start from the time of admission. This audit aims to study the hip fracture physiotherapy rehabilitation program at a local hospital to ensure compliance with the national guidelines and to identify any potential barriers to early mobilisation after a hip fracture operation. Methods The audit was approved by the trust's audit office. A retrospective study in the Queen Elizabeth The Queen Mother Hospital in Margate, Kent, UK, has been conducted including 113 patients admitted from April to August 2023 to our local hospital; the patients were screened for the inclusion and exclusion criteria, and data collection started regarding the admitting wards and their physiotherapy assessments including the time they were first assessed by a physiotherapist and the time of first mobilisation after the hip fracture operation and any identified barriers to early mobilisation. Results There were 113 patients who underwent hip fracture surgery, including 23 males and 90 females. The mean age of the participants was 82.7 years. Ninety-three percent (n=106/113) of the patients were seen by a physiotherapist on the day or the day following the surgery for their hip fracture. Amongst the admitted patients, 40% (n=46/113) experienced delayed mobilisation. The reason for the delay was post-operative delirium (n=15/46), low haemoglobin (n=12/46), pain (n=10/46), dementia (n=2/46) and no documented reason (n=7/46). Conclusion The study outcomes emphasise the importance of admitting hip fracture patients into specialised orthopaedic wards. The study identified post-operative delirium, low haemoglobin, pain and pre-existing dementia as factors that delay rehabilitation. Most of these factors are preventable or manageable, which can facilitate early rehabilitation and subsequently hospital discharge.

Antidiabetic Agents and Bone Quality: A Focus on Glycation End Products and Incretin Pathway Modulations.

Diabetes mellitus is associated with inadequate bone health and quality and heightened susceptibility to fractures, even in patients with normal or elevated bone mineral density. Elevated advanced glycation end-products (AGEs) and a suppressed incretin pathway are among the mechanisms through which diabetes affects the bone. Accordingly, the present review aimed to investigate the effects of antidiabetic medications on bone quality, primarily through AGEs and the incretin pathway. Google Scholar, Cochrane Library, and PubMed were used to examine related studies until February 2024. Antidiabetic medications influence AGEs and the incretin pathway directly or indirectly. Certain antidiabetic drugs including metformin, glucagon-like peptide-1 receptor agonists (GLP-1RA), dipeptidyl-peptidase-4 (DDP-4) inhibitors, α-glucosidase inhibitors (AGIs), sodium-glucose co-transporter-2 inhibitors, and thiazolidinediones (TZDs), directly affect AGEs through multiple mechanisms. These mechanisms include decreasing the formation of AGEs and the expression of AGEs receptor (RAGE) in tissue and increasing serum soluble RAGE levels, resulting in the reduced action of AGEs. Similarly, metformin, GLP-1RA, DDP-4 inhibitors, AGIs, and TZDs may enhance incretin hormones directly by increasing their production or suppressing their metabolism. Additionally, these medications could influence AGEs and the incretin pathway indirectly by enhancing glycemic control. In contrast, sulfonylureas have not demonstrated any obvious effects on AGEs or the incretin pathway. Considering their favorable effects on AGEs and the incretin pathway, a suitable selection of antidiabetic drugs may facilitate more protective effects on the bone in diabetic patients.

The Impact of ZnO and Fe2O3 Nanoparticles on Sunflower Seed Germination, Phenolic Content and Antiglycation Potential.

The enhancement of seed germination by using nanoparticles (NPs) holds the potential to elicit the synthesis of more desired compounds with important biomedical applications, such as preventing protein glycation, which occurs in diabetes. Here, we used 7 nm and 100 nm ZnO and 4.5 nm and 16.7 nm Fe2O3 NPs to treat sunflower seeds. We evaluated the effects on germination, total phenolic content, and the anti-glycation potential of extracted polyphenols. Sunflower seeds were allowed to germinate in vitro after soaking in NP solutions of different concentrations. Polyphenols were extracted, dosed, and used in serum albumin glycation experiments. The germination speed of seeds was significantly increased by the 100 nm ZnO NPs and significantly decreased by the 4.5 nm Fe2O3 NPs. The total phenolic content (TPC) of seeds was influenced by the type of NP, as ZnO NPs enhanced TPC, and the size of the NPs, as smaller NPs led to improved parameters. The polyphenols extracted from seeds inhibited protein glycation, especially those extracted from seeds treated with 7 nm ZnO. The usage of NPs impacted the germination speed and total polyphenol content of sunflower seeds, highlighting the importance of NP type and size in the germination process.

Solubility of lumiracoxib in supercritical carbon dioxide.

This study aims to use a static-based solubility method for measuring the solubility of lumiracoxib at a temperature of 308-338 K and pressure of 120-400 bar for the first time. The obtained solubility data for lumiracoxib is between 4.74 × 10-5 and 3.46 × 10-4 (mole fraction) for the studied ranges of pressure and temperature. The solubility values reveal that the lumiracoxib experiences a crossover pressure of about 160 bar. Moreover, the measured solubility data of these two drugs are correlated with density-based semi-empirical correlations namely Bartle et al., Mendez-Santiago-Teja, Kumar and Johnstone, Chrastil and modified Chrastil models with an average absolute relative deviation of 10.7%, 9.5%, 9.8%, 7.8%, and 8.7% respectively for lumiracoxib. According to these findings, it is obvious that all of the examined models are rather accurate and there is no superiority between these models for both examined drugs although the Chrastil model is slightly better in the overall view.

Exploring the Interplay between Vitamin D, Insulin Resistance, Obesity and Skeletal Health.

Vitamin D (ViD), plays an important role in calcium absorption and bone mineralization, is associated with bone mineral density. Severe deficiency in ViD has long been linked to conditions such as rickets in children and osteomalacia in adults, revealing its substantial role in skeletal health. Additionally, investigations show an existing interconnection between ViD and insulin resistance (Ins-R), especially in patients with type 2 diabetes mellitus (T2DM). Obesity, in conjunction with Ins-R, may augment the risk of osteoporosis and deterioration of skeletal health. This review aims to examine recent studies on the interplay between ViD, Ins-R, obesity, and their impact on skeletal health, to offer insights into potential therapeutic strategies. Cochrane Library, Google Scholar, and Pubmed were searched to investigate relevant studies until December 2023. Current research demonstrates ViD's impact on pancreatic ß-cell function, systemic inflammation, and insulin action regulation. Our findings highlight an intricate association between ViD, Ins-R, obesity, and skeletal health, providing a perspective for the prevention and/or treatment of skeletal disorders in patients with obesity, Ins-R, and T2DM.

An effective ensemble learning approach for classification of glioma grades based on novel MRI features.

The preoperative diagnosis of brain tumors is important for therapeutic planning as it contributes to the tumors' prognosis. In the last few years, the development in the field of artificial intelligence and machine learning has contributed greatly to the medical area, especially the diagnosis of the grades of brain tumors through radiological images and magnetic resonance images. Due to the complexity of tumor descriptors in medical images, assessing the accurate grade of glioma is a major challenge for physicians. We have proposed a new classification system for glioma grading by integrating novel MRI features with an ensemble learning method, called Ensemble Learning based on Adaptive Power Mean Combiner (EL-APMC). We evaluate and compare the performance of the EL-APMC algorithm with twenty-one classifier models that represent state-of-the-art machine learning algorithms. Results show that the EL-APMC algorithm achieved the best performance in terms of classification accuracy (88.73%) and F1-score (93.12%) over the MRI Brain Tumor dataset called BRATS2015. In addition, we showed that the differences in classification results among twenty-two classifier models have statistical significance. We believe that the EL-APMC algorithm is an effective method for the classification in case of small-size datasets, which are common cases in medical fields. The proposed method provides an effective system for the classification of glioma with high reliability and accurate clinical findings.

Impact of calcium channel blockers and angiotensin receptor blockers on hematological parameters in type 2 diabetic patients.

Antihypertensive medications have been associated with a reduction in hemoglobin (Hb) levels, leading to clinically significant anemia. We aimed to provide valuable insights into the impact of angiotensin receptor blockers (ARBs) and calcium channel blockers (CCBs) on hematological parameters by measuring the levels of erythropoietin (EPO), ferritin, and complete blood count (CBC) in individuals with type 2 diabetes mellitus (T2DM), particularly considering the duration of the antihypertensives use. In addition to comparing their effects on blood pressure, glycemic status, and renal function, a retrospective cohort study was conducted at the consultation unit of Alsalam Teaching Hospital, Mosul, Nineveh Province, between October 2022 and February 2023. A total of 160 participants were enrolled after being fully examined by the consultants to detect their eligibility for inclusion in the study and to rule out any abnormality. They consisted of 40 healthy controls, 30 T2DM patients (T2DM group), 30 T2DM patients with newly diagnosed hypertension (HT) (T2DM+HT group), 30 type 2 diabetic-hypertensives on ARBs (T2DM+HT+ARBs group), and 30 type 2 diabetic-hypertensives on CCBs (T2DM+HT+CCBs group). Five milliliters of blood was drawn from a vein and divided into two parts. Two milliliters was transferred into an anticoagulant tube for the measurement of HbA1c and complete blood picture. Serum was obtained from the remaining blood and used for assessment of ferritin, EPO, FSG, creatinine, urea, and uric acid. Significantly reduced FSG and HbA1c levels were observed in T2DM+HT+CCBs and T2DM+HT+ARBs groups vs T2DM+HT group (p < 0.05). The T2DM+HT+CCBs group had statistically higher urea levels than the T2DM group (p < 0.05). Both CCBs and ARBs use resulted in reduced creatinine clearance (CrCl). T2DM+HT+CCBs group exhibited slightly higher uric acid levels compared to controls (p < 0.05). Prolonged use of CCBs and ARBs led to disturbances in hematological parameters, with CCBs users showing the lowest levels of hemoglobin (Hb), RBCs, and hematocrit (Hct) among the groups. ARBs users displayed the lowest values of EPO and ferritin compared to other patient groups, along with reduced levels of Hb, RBCs, and Hct, albeit slightly higher than CCBs users. Our study highlights the importance of a balanced approach in prescribing ARBs and CCBs to patients with T2DM, given their potential to induce blood abnormalities, particularly with prolonged usage.

Intelligent Grapevine Disease Detection Using IoT Sensor Network.

The Internet of Things (IoT) has gained significance in agriculture, using remote sensing and machine learning to help farmers make high-precision management decisions. This technology can be applied in viticulture, making it possible to monitor disease occurrence and prevent them automatically. The study aims to achieve an intelligent grapevine disease detection method, using an IoT sensor network that collects environmental and plant-related data. The focus of this study is the identification of the main parameters which provide early information regarding the grapevine's health. An overview of the sensor network, architecture, and components is provided in this paper. The IoT sensors system is deployed in the experimental plots located within the plantations of the Research Station for Viticulture and Enology (SDV) in Murfatlar, Romania. Classical methods for disease identification are applied in the field as well, in order to compare them with the sensor data, thus improving the algorithm for grapevine disease identification. The data from the sensors are analyzed using Machine Learning (ML) algorithms and correlated with the results obtained using classical methods in order to identify and predict grapevine diseases. The results of the disease occurrence are presented along with the corresponding environmental parameters. The error of the classification system, which uses a feedforward neural network, is 0.05. This study will be continued with the results obtained from the IoT sensors tested in vineyards located in other regions.

Reduction in Radiologist Interpretation Time of Serial CT and MR Imaging Findings with Deep Learning Identification of Relevant Priors, Series and Finding Locations.

RATIONALE AND OBJECTIVES: Finding comparison to relevant prior studies is a requisite component of the radiology workflow. The purpose of this study was to evaluate the impact of a deep learning tool simplifying this time-consuming task by automatically identifying and displaying the finding in relevant prior studies. MATERIALS AND METHODS: The algorithm pipeline used in this retrospective study, TimeLens (TL), is based on natural language processing and descriptor-based image-matching algorithms. The dataset used for testing comprised 3872 series of 246 radiology examinations from 75 patients (189 CTs, 95 MRIs). To ensure a comprehensive testing, five finding types frequently encountered in radiology practice were included: aortic aneurysm, intracranial aneurysm, kidney lesion, meningioma, and pulmonary nodule. After a standardized training session, nine radiologists from three university hospitals performed two reading sessions on a cloud-based evaluation platform resembling a standard RIS/PACS. The task was to measure the diameter of the finding-of-interest on two or more exams (a most recent and at least one prior exam): first without use of TL, and a second session at an interval of at least 21 days with the use of TL. All user actions were logged for each round, including time needed to measure the finding at all timepoints, number of mouse clicks, and mouse distance traveled. The effect of TL was evaluated in total, per finding type, per reader, per experience (resident vs. board-certified radiologist), and per modality. Mouse movement patterns were analyzed with heatmaps. To assess the effect of habituation to the cases, a third round of readings was performed without TL. RESULTS: Across scenarios, TL reduced the average time needed to assess a finding at all timepoints by 40.1% (107 vs. 65 seconds; p < 0.001). Largest accelerations were demonstrated for assessment of pulmonary nodules (-47.0%; p < 0.001). Less mouse clicks (-17.2%) were needed for finding evaluation with TL, and mouse distance traveled was reduced by 38.0%. Time needed to assess the findings increased from round 2 to round 3 (+27.6%; p < 0.001). Readers were able to measure a given finding in 94.4% of cases on the series initially proposed by TL as most relevant series for comparison. The heatmaps showed consistently simplified mouse movement patterns with TL. CONCLUSION: A deep learning tool significantly reduced both the amount of user interactions with the radiology image viewer and the time needed to assess findings of interest on cross-sectional imaging with relevant prior exams.

Three-dimensional printing of patient-specific computed tomography lung phantoms: a reader study.

In modern clinical decision-support algorithms, heterogeneity in image characteristics due to variations in imaging systems and protocols hinders the development of reproducible quantitative measures including for feature extraction pipelines. With the help of a reader study, we investigate the ability to provide consistent ground-truth targets by using patient-specific 3D-printed lung phantoms. PixelPrint was developed for 3D-printing lifelike computed tomography (CT) lung phantoms by directly translating clinical images into printer instructions that control density on a voxel-by-voxel basis. Data sets of three COVID-19 patients served as input for 3D-printing lung phantoms. Five radiologists rated patient and phantom images for imaging characteristics and diagnostic confidence in a blinded reader study. Effect sizes of evaluating phantom as opposed to patient images were assessed using linear mixed models. Finally, PixelPrint's production reproducibility was evaluated. Images of patients and phantoms had little variation in the estimated mean (0.03-0.29, using a 1-5 scale). When comparing phantom images to patient images, effect size analysis revealed that the difference was within one-third of the inter- and intrareader variabilities. High correspondence between the four phantoms created using the same patient images was demonstrated by PixelPrint's production repeatability tests, with greater similarity scores between high-dose acquisitions of the phantoms than between clinical-dose acquisitions of a single phantom. We demonstrated PixelPrint's ability to produce lifelike CT lung phantoms reliably. These phantoms have the potential to provide ground-truth targets for validating the generalizability of inference-based decision-support algorithms between different health centers and imaging protocols and for optimizing examination protocols with realistic patient-based phantoms. Classification: CT lung phantoms, reader study.

Inhibition of branched-chain alpha-keto acid dehydrogenase kinase augments the sensitivity of ovarian and breast cancer cells to paclitaxel.

CONTEXT: Many cancer patients who initially respond to chemotherapy eventually develop chemoresistance, and to address this, we previously conducted a RNAi screen to identify genes contributing to resistance. One of the hits from the screen was branched-chain α-keto acid dehydrogenase kinase (BCKDK). BCKDK controls the metabolism of branched-chain amino acids (BCAAs) through phosphorylation and inactivation of the branched-chain α-keto acid dehydrogenase complex (BCKDH), thereby inhibiting catabolism of BCAAs. METHODS: We measured the impact on paclitaxel sensitivity of inhibiting BCKDK in ovarian and breast cancer cell lines. RESULTS: Inhibition of BCKDK using siRNA or two chemical inhibitors (BCKDKi) was synergistic with paclitaxel in both breast and ovarian cancer cells. BCKDKi reduced levels of BCAA and the addition of exogenous BCAA suppressed this synergy. BCKDKi inactivated the mTORC1-Aurora pathway, allowing cells to overcame M-phase arrest induced by paclitaxel. In some cases, cells almost completed cytokinesis, then reverted to a single cell, resulting in multinucleate cells. CONCLUSION: BCKDK is an attractive target to augment the sensitivity of cancer cells to paclitaxel.

Restrained Shrinkage of High-Performance Ready-Mix Concrete Reinforced with Low Volume Fraction of Hybrid Fibers.

Cracking due to restrained shrinkage is a recurring issue with concrete bridge decks, impacting durability and ultimately service life. Several scholars' research has proven that the incorporation of fibers in concrete mitigates restrained shrinkage cracking when utilizing high (0.5-3%) fiber volumes. This often presents a mixing and placement issue when used for ready-mixed concretes, which discourages their use in bridge decks. This study aims to optimize the incorporation of fibers for their benefits while producing concrete that is conducive to ready-mix, jobsite use. A series of tests were performed on a high-performance concrete (HPC) mix which incorporated blended, multiple fiber types (steel crimped, macro polypropylene, and micro polypropylene) while maintaining low total fiber (0.19-0.37%) volume. These "hybrid" fiber mixes were tested for multiple mechanical properties and durability aspects, with a focus on the AASHTO T334 ring test, to evaluate fiber efficiency under restrained conditions. Promising results indicate the use of a low-volume hybrid fiber addition, incorporating a macro and micro polypropylene fiber (0.35% by volume) blend, reduced the cracking area by 16.6% when compared to HPC incorporating a single fiber type, and 39% when compared to nonfibrous HPC control mixture.

Smart Healthcare System for Severity Prediction and Critical Tasks Management of COVID-19 Patients in IoT-Fog Computing Environments.

COVID-19 has depleted healthcare systems around the world. Extreme conditions must be defined as soon as possible so that services and treatment can be deployed and intensified. Many biomarkers are being investigated in order to track the patient's condition. Unfortunately, this may interfere with the symptoms of other diseases, making it more difficult for a specialist to diagnose or predict the severity level of the case. This research develops a Smart Healthcare System for Severity Prediction and Critical Tasks Management (SHSSP-CTM) for COVID-19 patients. On the one hand, a machine learning (ML) model is projected to predict the severity of COVID-19 disease. On the other hand, a multi-agent system is proposed to prioritize patients according to the seriousness of the COVID-19 condition and then provide complete network management from the edge to the cloud. Clinical data, including Internet of Medical Things (IoMT) sensors and Electronic Health Record (EHR) data of 78 patients from one hospital in the Wasit Governorate, Iraq, were used in this study. Different data sources are fused to generate new feature pattern. Also, data mining techniques such as normalization and feature selection are applied. Two models, specifically logistic regression (LR) and random forest (RF), are used as baseline severity predictive models. A multi-agent algorithm (MAA), consisting of a personal agent (PA) and fog node agent (FNA), is used to control the prioritization process of COVID-19 patients. The highest prediction result is achieved based on data fusion and selected features, where all examined classifiers observe a significant increase in accuracy. Furthermore, compared with state-of-the-art methods, the RF model showed a high and balanced prediction performance with 86% accuracy, 85.7% F-score, 87.2% precision, and 86% recall. In addition, as compared to the cloud, the MAA showed very significant performance where the resource usage was 66% in the proposed model and 34% in the traditional cloud, the delay was 19% in the proposed model and 81% in the cloud, and the consumed energy was 31% in proposed model and 69% in the cloud. The findings of this study will allow for the early detection of three severity cases, lowering mortality rates.

Structural Performance of Lightweight Aggregate Concrete Reinforced by Glass or Basalt Fiber Reinforced Polymer Bars.

Lightweight aggregate concrete (LWC) and fiber reinforced polymer (FRP) reinforcement are potentially more sustainable alternatives to traditional steel-reinforced concrete structures, offering several important benefits. To further the knowledge in this area, the physical-mechanical properties of LWC produced with 0%, 50%, and 100% expanded clay aggregate were assessed. Subsequently, the flexural behavior of LWC beams reinforced with steel reinforcement and glass and basalt FRP bars was tested. The results of the experimental program allowed quantifying of the effect of expanded clay aggregate incorporation on LWC properties. The use of FRP reinforcement was also compared to steel-reinforced concrete beam behavior. The results of this study can provide additional support for the use of innovative materials such as LWA and FRP reinforcement.

Bond Behavior of FRP Bars in Lightweight SCC under Direct Pull-Out Conditions: Experimental and Numerical Investigation.

In recent decades, lightweight aggregate concrete (LWC) became a popular building material due to its desired properties. However, various attributes of LWC, such as bond behavior of used reinforcing, have not been described thoroughly. In this regard, LWC produced with 0%, 50%, and 100% expanded clay aggregate was designed, and the physical-mechanical properties were assessed for material characterization. Subsequently, the bond behaviors of LWC reinforced with steel, glass fiber reinforced polymer (GFRP), and basalt fiber reinforced polymer (BFRP) bars were evaluated by pull-out tests. The results of the experimental program allowed the effects of expanded clay aggregate incorporation on LWC properties to be quantified. The bond strength of BFRP bars was not affected by the replacement of coarse aggregate by expanded clay aggregate, whilst the GFRP bars showed lower bond strength values of LWC specimens. Contrarily, in the case of steel bars, both the bond strength and bond stiffness were higher for LWC specimens than for those of normal concrete. Finite element software ATENA 3D was used for simulation of the bond behavior of LWC, and the model validated by the experimental results referred to reasonably corresponding outputs.

EEG Channel Selection Using Multiobjective Cuckoo Search for Person Identification as Protection System in Healthcare Applications.

Recently, the electroencephalogram (EEG) signal presents an excellent potential for a new person identification technique. Several studies defined the EEG with unique features, universality, and natural robustness to be used as a new track to prevent spoofing attacks. The EEG signals are a visual recording of the brain's electrical activities, measured by placing electrodes (channels) in various scalp positions. However, traditional EEG-based systems lead to high complexity with many channels, and some channels have critical information for the identification system while others do not. Several studies have proposed a single objective to address the EEG channel for person identification. Unfortunately, these studies only focused on increasing the accuracy rate without balancing the accuracy and the total number of selected EEG channels. The novelty of this paper is to propose a multiobjective binary version of the cuckoo search algorithm (MOBCS-KNN) to find optimal EEG channel selections for person identification. The proposed method (MOBCS-KNN) used a weighted sum technique to implement a multiobjective approach. In addition, a KNN classifier for EEG-based biometric person identification is used. It is worth mentioning that this is the initial investigation of using a multiobjective technique with EEG channel selection problem. A standard EEG motor imagery dataset is used to evaluate the performance of the MOBCS-KNN. The experiments show that the MOBCS-KNN obtained accuracy of 93.86% using only 24 sensors with AR20 autoregressive coefficients. Another critical point is that the MOBCS-KNN finds channels not too close to each other to capture relevant information from all over the head. In conclusion, the MOBCS-KNN algorithm achieves the best results compared with metaheuristic algorithms. Finally, the recommended approach can draw future directions to be applied to different research areas.

Federated learning enables intelligent reflecting surface in fog-cloud enabled cellular network.

The intelligent reflecting surface (IRS) is a ground-breaking technology that can boost the efficiency of wireless data transmission systems. Specifically, the wireless signal transmitting environment is reconfigured by adjusting a large number of small reflecting units simultaneously. Therefore, intelligent reflecting surface (IRS) has been suggested as a possible solution for improving several aspects of future wireless communication. However, individual nodes are empowered in IRS, but decisions and learning of data are still made by the centralized node in the IRS mechanism. Whereas, in previous works, the problem of energy-efficient and delayed awareness learning IRS-assisted communications has been largely overlooked. The federated learning aware Intelligent Reconfigurable Surface Task Scheduling schemes (FL-IRSTS) algorithm is proposed in this paper to achieve high-speed communication with energy and delay efficient offloading and scheduling. The training of models is divided into different nodes. Therefore, the trained model will decide the IRSTS configuration that best meets the goals in terms of communication rate. Multiple local models trained with the local healthcare fog-cloud network for each workload using federated learning (FL) to generate a global model. Then, each trained model shared its initial configuration with the global model for the next training round. Each application's healthcare data is handled and processed locally during the training process. Simulation results show that the proposed algorithm's achievable rate output can effectively approach centralized machine learning (ML) while meeting the study's energy and delay objectives.

Influence of Polypropylene and Steel Fibers on the Performance and Crack Repair of Self-Compacting Concrete.

The research reported in this paper aims to evaluate the epoxy injection technique used to strengthen fiber-reinforced self-compacting concrete (FRSCC) with high strength. This method is carried out on ruptured concrete specimens to assess the efficiency of the epoxy resin adhesive injection retrofitting technique for strength and stiffness. Five FRSCC mixes were designed and placed using different types (steel and polypropylene) and contents (0%, 0.25%, and 0.45% by volume) of fibers. The fresh and mechanical properties in addition to the microstructure of produced mixes were evaluated to assess the impact of fibers on the behavior of FRSCC. Results showed that the workability of FRSCC is reduced by increasing steel or polypropylene fiber content; however, the rheological characteristics of placed mixes satisfied the European Guidelines for Self-Compacting Concrete recommendation for fresh concrete. Also, splitting tensile, flexural, and shear strengths were enhanced by increasing fiber content. The simultaneous application of epoxy injection in FRSCC for repairing damaged concrete beams was shown to be highly effective.

Effects of Bee Propolis on FBG, HbA1c, and Insulin Resistance in Healthy Volunteers

Objectives: Bee propolis is a natural substance that is used in traditional medicine due to its versatile pharmacological actions. This study evaluates whether short term use of bee propolis supplementation could have an impact on glycemic control in healthy individuals. Materials and Methods: A single daily dose of 1000 mg of bee propolis was administered orally to a total of 34 healthy individuals for 60 days. Body weight, body mass index (BMI), fasting blood glucose (FBG), glycosylated hemoglobin (HbA1c), and insulin resistance were measured in all participants before and after the use of bee propolis. Results: The results of this study showed that bee propolis was associated with a significant increase in body weight and BMI of healthy volunteers. Bee propolis supplementation decreased FBG and HbA1c, but did not affect insulin resistance. Conclusion: Based on these results, bee propolis supplementation has a potential effect on glycemic control in healthy individuals and this should be considered when using this supplement in medical conditions.