The impact of geopolitical risk, institutional governance and green finance on attaining net-zero carbon emission.

This research investigates the impact of geopolitical risk, institutional governance and green finance on environmental outcomes, specifically focusing on carbon emissions and ecological footprint. Utilizing the dynamic CS-ARDL method and aggregated mean group analysis on a panel dataset covering 21 nations from 2000 to 2021, our findings reveal that heightened geopolitical risk leads to both short and long run increases in carbon emissions and the ecological footprint. Our study finds both a direct as well as indirect connection between governance, green finance and environmental outcomes in both the short and long run, highlighting the nuanced impact of governance on the formulation of environmental policies and regulatory frameworks. The results emphasize the need for targeted strategies, including focused investments and incentives for sustainable finance, particularly in conflict-affected regions. Furthermore, our research underscores the enduring impact of historical events, such as wars, on contemporary environmental indicators, emphasizing the importance of proactive conflict prevention measures. Our research suggests that policymakers should adopt comprehensive strategies that prioritize emission reduction during short-run spikes in geopolitical risk while maintaining a steadfast commitment to long-run sustainability.

An automated approach for real-time informative frames classification in laryngeal endoscopy using deep learning.

PURPOSE: Informative image selection in laryngoscopy has the potential for improving automatic data extraction alone, for selective data storage and a faster review process, or in combination with other artificial intelligence (AI) detection or diagnosis models. This paper aims to demonstrate the feasibility of AI in providing automatic informative laryngoscopy frame selection also capable of working in real-time providing visual feedback to guide the otolaryngologist during the examination. METHODS: Several deep learning models were trained and tested on an internal dataset (n = 5147 images) and then tested on an external test set (n = 646 images) composed of both white light and narrow band images. Four videos were used to assess the real-time performance of the best-performing model. RESULTS: ResNet-50, pre-trained with the pretext strategy, reached a precision = 95% vs. 97%, recall = 97% vs, 89%, and the F1-score = 96% vs. 93% on the internal and external test set respectively (p = 0.062). The four testing videos are provided in the supplemental materials. CONCLUSION: The deep learning model demonstrated excellent performance in identifying diagnostically relevant frames within laryngoscopic videos. With its solid accuracy and real-time capabilities, the system is promising for its development in a clinical setting, either autonomously for objective quality control or in conjunction with other algorithms within a comprehensive AI toolset aimed at enhancing tumor detection and diagnosis.

A novel fusion of genetic grey wolf optimization and kernel extreme learning machines for precise diabetic eye disease classification.

In response to the growing number of diabetes cases worldwide, Our study addresses the escalating issue of diabetic eye disease (DED), a significant contributor to vision loss globally, through a pioneering approach. We propose a novel integration of a Genetic Grey Wolf Optimization (G-GWO) algorithm with a Fully Convolutional Encoder-Decoder Network (FCEDN), further enhanced by a Kernel Extreme Learning Machine (KELM) for refined image segmentation and disease classification. This innovative combination leverages the genetic algorithm and grey wolf optimization to boost the FCEDN's efficiency, enabling precise detection of DED stages and differentiation among disease types. Tested across diverse datasets, including IDRiD, DR-HAGIS, and ODIR, our model showcased superior performance, achieving classification accuracies between 98.5% to 98.8%, surpassing existing methods. This advancement sets a new standard in DED detection and offers significant potential for automating fundus image analysis, reducing reliance on manual examination, and improving patient care efficiency. Our findings are crucial to enhancing diagnostic accuracy and patient outcomes in DED management.

Xanthan gum-based edible coating effectively preserve postharvest quality of 'Gola' guava fruits by regulating physiological and biochemical processes.

BACKGROUND: Guava is a fruit prone to rapid spoilage following harvest, attributed to continuous and swift physicochemical transformations, leading to substantial postharvest losses. This study explored the efficacy of xanthan gum (XG) coatings applied at various concentrations (0.25, 0.5, and 0.75%) on guava fruits (Gola cultivar) over a 15-day storage period. RESULTS: The results indicated that XG coatings, particularly at 0.75%, substantially mitigated moisture loss and decay, presenting an optimal concentration. The coated fruits exhibited a modified total soluble soluble solids, an increased total titratable acidity, and an enhanced sugar-acid ratio, collectively enhancing overall quality. Furthermore, the XG coatings demonstrated the remarkable ability to preserve bioactive compounds, such as total phenolics, flavonoids, and antioxidants, while minimizing the levels of oxidative stress markers, such as electrolyte leakage, malondialdehyde, and H2O2. The coatings also influenced cell wall components, maintaining levels of hemicellulose, cellulose, and protopectin while reducing water-soluble pectin. Quantitative analysis of ROS-scavenging enzymes, including superoxide dismutase, peroxidase, catalase, and ascorbate peroxidase, revealed significant increases in their activities in the XG-coated fruits compared to those in the control fruits. Specifically, on day 15, the 0.75% XG coating demonstrated the highest SOD and CAT activities while minimizing the reduction in APX activity. Moreover, XG coatings mitigated the activities of fruit-softening enzymes, including pectin methylesterase, polygalacturonase, and cellulase. CONCLUSIONS: This study concludes that XG coatings play a crucial role in preserving postharvest quality of guava fruits by regulating various physiological and biochemical processes. These findings offer valuable insights into the potential application of XG as a natural coating to extend the shelf life and maintain the quality of guava fruits during storage.

Viability of Free and Alginate-Carrageenan Gum Coated Lactobacillus acidophilus and Lacticaseibacillus casei in Functional Cottage Cheese.

The survivability of encapsulated and nonencapsulated probiotics consisting of Lactobacillus acidophilus and Lacticaseibacillus casei and the nutritional, physicochemical, and sensorial features of cottage cheese were investigated under refrigeration storage at 4 °C for 28 days. Microbeads of L. acidophilus and L. casei were developed using 2% sodium alginate, 1.5% sodium alginate and 0.5% carrageenan, and 1% sodium alginate and 1% carrageenan using an encapsulation technique to assess the probiotic viability in cottage cheese under different gastrointestinal conditions (SGF (simulated gastric juice), SIF (simulated intestinal fluid)), and bile salt) and storage conditions. Scanning electron microscopy (SEM) elucidated the stable structure of microbeads, Fourier transform infrared spectroscopy (FTIR) confirmed the presence probiotics in the microcapsules, and X-ray diffraction (XRD) demonstrated the amorphous state of microbeads. Furthermore, the highest encapsulation efficiency was observed for alginate 1% and carrageenan 1% microbeads (T3), i.e., 95%. Likewise, viability was recorded in T3 against SGF, SIF, and bile salt solution, i.e., 8.5, 8.8, and 8.9 log CFU/g at 80 min of exposure, compared to the control. The results of pH showed a significant (p < 0.05) decline that ultimately increased the titratable acidity. Nutritional analysis of cottage cheese revealed the highest levels of ash, protein, and total solids in T3, exhibiting mean values of 3.2, 22, and 43.2 g/100 g, respectively, after 28 days of storage. The sensory evaluation of cottage cheese demonstrated better color, flavor, and textural attributes in T3. Conclusively, synergistic addition of L. acidophilus and L. casei encapsulated with alginate-carrageenan gums was found to be more effective in improving the viability of probiotics in cottage cheese than noncapsulated cells while carrying better magnitudes of ash and protein, lower acidity, and pleasant taste.

Bilateral Acute Posterior Multifocal Placoid Pigment Epitheliopathy With Bacillary Layer Detachment Following Sinopharm COVID-19 Vaccination: A Case Report.

Various ocular manifestations associated with COVID-19 and vaccines, affecting both the anterior and posterior segments of the eye have been documented in the literature. In this report, we present the case of a 25-year-old male who complained of sudden-onset blurred vision and metamorphopsia in both eyes one day after receiving the second dose of the Sinopharm COVID-19 vaccine. The visual loss was painless, with no reported flashes or floaters. The patient had no significant medical or surgical history, no history of trauma, and no drug intake. Upon ocular examination, the best-corrected visual acuity was 6/60 (Snellen chart) in both eyes. The anterior segments appeared unremarkable, while fundoscopy revealed multiple yellowish-white subretinal lesions at the posterior pole of both eyes. Spectral domain optical coherence tomography (SD-OCT) confirmed the presence of subretinal fluid (SRF) with neurosensory detachment in each eye, along with bacillary layer detachment (BALAD). There were no signs of inflammation in the vitreous cavity. A diagnosis of acute posterior multifocal plaque pigment epitheliopathy (APMPPE) was established. The patient was prescribed nepafenac 0.1% drops to be instilled three times a day in both eyes and was advised to return for a follow-up examination in two weeks. At the follow-up visit, the patient's vision had improved to 6/9 in the right eye and 6/6 in the left eye, with most of the SRF absorbed. Unilateral APMPPE with BALAD has been mentioned in the literature following various COVID-19 vaccinations, but, to the best of our knowledge, this is the first case report where bilateral APMPPE with BALAD is reported. This case emphasizes the importance of a thorough eye examination for individuals experiencing ocular symptoms after receiving the COVID-19 vaccine.

Modulating plant-soil microcosm with green synthesized ZnONPs in arsenic contaminated soil.

Biogenic nanoparticle (NP), derived from plant sources, is gaining prominence as a viable, cost-effective, sustainable, and biocompatible alternative for mitigating the extensive environmental impact of arsenic on the interplay between plant-soil system. Herein, the impact of green synthesized zinc oxide nanoparticles (ZnONPs) was assessed on Catharanthus roseus root system-associated enzymes and their possible impact on microbiome niches (rhizocompartments) and overall plant performance under arsenic (As) gradients. The application of ZnONPs at different concentrations successfully modified the arsenic uptake in various plant parts, with the root arsenic levels increasing 1.5 and 1.4-fold after 25 and 50 days, respectively, at medium concentration compared to the control. Moreover, ZnONPs gradients regulated the various soil enzyme activities. Notably, urease and catalase activities showed an increase when exposed to low concentrations of ZnONPs, whereas saccharase and acid phosphatase displayed the opposite pattern, showing increased activities under medium concentration which possibly in turn influence the plant root system associated microflora. The use of nonmetric multidimensional scaling ordination revealed a significant differentiation (with a significance level of p < 0.05) in the structure of both bacterial and fungal communities under different treatment conditions across root associated niches. Bacterial and fungal phyla level analysis showed that Proteobacteria and Basidiomycota displayed a significant increase in relative abundance under medium ZnONPs concentration, as opposed to low and high concentrations, respectively. Similarly, in depth genera level analysis revealed that Burkholderia, Halomonas, Thelephora and Sebacina exhibited a notably high relative abundance in both the rhizosphere and rhizoplane (the former refers to the soil region influenced by root exudates, while the latter is the root surface itself) under medium concentrations of ZnONPs, respectively. These adjustments to the plant root-associated microcosm likely play a role in protecting the plant from oxidative stress by regulating the plant's antioxidant system and overall biomass.

Effects of perioperative clinical hypnosis on heart rate variability in patients undergoing oncologic surgery: secondary outcomes of a randomized controlled trial.

Introduction: Clinical hypnosis has been proposed for post-surgical pain management for its potential vagal-mediated anti-inflammatory properties. Evidence is needed to understand its effectiveness for post-surgical recovery. Iin this secondary outcome study, it was hypothesized that surgical oncology patients randomized to receive perioperative clinical hypnosis (CH) would demonstrate greater heart-rate variability (HRV) during rest and relaxation at a 1-month post-surgery assessment compared to a treatment-as-usual group (TAU). Methods: After REB approval, trial registration and informed consent, 92 participants were randomized to receive CH (n = 45) or TAU (n = 47). CH participants received a CH session before surgery and during post-surgical in-hospital stay HRV was assessed during rest (5 min) and relaxation (10 min) before and 1-month after surgery. Pain intensity was obtained using a 0-10 numeric rating scale pre and post 1-week and 1-month post surgery. Results: One month after surgery, HRV was significantly higher in CH group (n = 29) during rest and relaxation (both p < 0.05, d = 0.73) than TAU group (n = 28). By contrast, rest and relaxation HRV decreased from pre- to 1-month post-surgery for the TAU (both p < 0.001, d > 0.48) but not the CH group. Pain intensity increased from pre-surgery to 1-week post-surgery (p < 0.001, d = 0.50), and decreased from 1-week to 1-month post-surgery (p = 0.005, d = 0.21) for all participants. Discussion: The results suggest that hypnosis prevents the deleterious effects of surgery on HRV by preserving pre-operative vagal activity. These findings underscore the potential of clinical hypnosis in mitigating the adverse effects of surgery on autonomic function and may have significant implications for enhancing post-surgical recovery and pain management strategies. Clinical Trial Registration: ClinicalTrials.gov, identifier (NCT03730350).

A Comprehensive Evaluation of Large Language Models in Mining Gene Interactions and Pathway Knowledge.

Background: Understanding complex biological pathways, including gene-gene interactions and gene regulatory networks, is critical for exploring disease mechanisms and drug development. Manual literature curation of biological pathways is useful but cannot keep up with the exponential growth of the literature. Large-scale language models (LLMs), notable for their vast parameter sizes and comprehensive training on extensive text corpora, have great potential in automated text mining of biological pathways. Method: This study assesses the effectiveness of 21 LLMs, including both API-based models and open-source models. The evaluation focused on two key aspects: gene regulatory relations (specifically, 'activation', 'inhibition', and 'phosphorylation') and KEGG pathway component recognition. The performance of these models was analyzed using statistical metrics such as precision, recall, F1 scores, and the Jaccard similarity index. Results: Our results indicated a significant disparity in model performance. Among the API-based models, ChatGPT-4 and Claude-Pro showed superior performance, with an F1 score of 0.4448 and 0.4386 for the gene regulatory relation prediction, and a Jaccard similarity index of 0.2778 and 0.2657 for the KEGG pathway prediction, respectively. Open-source models lagged their API-based counterparts, where Falcon-180b-chat and llama1-7b led with the highest performance in gene regulatory relations (F1 of 0.2787 and 0.1923, respectively) and KEGG pathway recognition (Jaccard similarity index of 0.2237 and 0. 2207, respectively). Conclusion: LLMs are valuable in biomedical research, especially in gene network analysis and pathway mapping. However, their effectiveness varies, necessitating careful model selection. This work also provided a case study and insight into using LLMs as knowledge graphs.

Real-Time Laryngeal Cancer Boundaries Delineation on White Light and Narrow-Band Imaging Laryngoscopy with Deep Learning.

OBJECTIVE: To investigate the potential of deep learning for automatically delineating (segmenting) laryngeal cancer superficial extent on endoscopic images and videos. METHODS: A retrospective study was conducted extracting and annotating white light (WL) and Narrow-Band Imaging (NBI) frames to train a segmentation model (SegMENT-Plus). Two external datasets were used for validation. The model's performances were compared with those of two otolaryngology residents. In addition, the model was tested on real intraoperative laryngoscopy videos. RESULTS: A total of 3933 images of laryngeal cancer from 557 patients were used. The model achieved the following median values (interquartile range): Dice Similarity Coefficient (DSC) = 0.83 (0.70-0.90), Intersection over Union (IoU) = 0.83 (0.73-0.90), Accuracy = 0.97 (0.95-0.99), Inference Speed = 25.6 (25.1-26.1) frames per second. The external testing cohorts comprised 156 and 200 images. SegMENT-Plus performed similarly on all three datasets for DSC (p = 0.05) and IoU (p = 0.07). No significant differences were noticed when separately analyzing WL and NBI test images on DSC (p = 0.06) and IoU (p = 0.78) and when analyzing the model versus the two residents on DSC (p = 0.06) and IoU (Senior vs. SegMENT-Plus, p = 0.13; Junior vs. SegMENT-Plus, p = 1.00). The model was then tested on real intraoperative laryngoscopy videos. CONCLUSION: SegMENT-Plus can accurately delineate laryngeal cancer boundaries in endoscopic images, with performances equal to those of two otolaryngology residents. The results on the two external datasets demonstrate excellent generalization capabilities. The computation speed of the model allowed its application on videolaryngoscopies simulating real-time use. Clinical trials are needed to evaluate the role of this technology in surgical practice and resection margin improvement. LEVEL OF EVIDENCE: III Laryngoscope, 134:2826-2834, 2024.

Dual-chamber versus single chamber pacemakers, a systemic review and meta-analysis on sick sinus syndrome and atrioventricular block patients.

Aims: The atrioventricular block (AVB) is a conduction system problem that results from the impairment in the transmission of an impulse from the atria to the ventricle, the disease has many etiologies. This article aimed to evaluate the efficacy and safety of dual and single-chamber pacemakers in patients with SSS and AVB. Methods: An electronic search of PubMed (Medline), EMBASE, and Google Scholar was performed from 2000 till August 15th, 2022. Retrieved articles were exported to Endnote Reference Library Software, where duplicate studies were removed from the list, and only articles meeting the eligibility criteria of this study were selected. RevMan 5.4 and STATA 16 software were used for the analysis. The modified Cochrane Collaboration's risk of bias and New-castle Ottawa scale were used for quality assessment of RCTs and observational studies respectively. Results: This study is composed of 8953 patients with sick-sinus syndrome and atrioventricular block. A total of thirteen outcomes are included in this meta-analysis, out of which atrial fibrillation significantly favored dual chamber [OR = 1.29; 95 % CI = 1.05-1.59; P = 0.01 I2 = 29 %] and overall complications [OR = 0.48; 95 % CI = 0.29-0.77; p = 0.03 I2 = 0 %] and pneumothorax [OR = 0.31; 95 % CI = 0.10-0.93; p = 0.04, I2 = 0 %] were satisfied by single-chamber pacing. Conclusion: This study concluded that neither single-chamber nor dual-chamber pacemakers are superior to each other, but they are unique in their own ways as the results of this study manifest remarkable reduction in atrial fibrillation rates and pneumothorax using dual-chamber and single-chamber pacemakers respectively.

Control charts using half-normal and half-exponential power distributions using repetitive sampling.

This manuscript presents the development of an attribute control chart (ACC) designed to monitor the number of defective items in manufacturing processes. The charts are specifically tailored using time-truncated life test (TTLT) for two lifetime data distributions: the half-normal distribution (HND) and the half-exponential power distribution (HEPD) under a repetitive sampling scheme (RSS). To assess the effectiveness of the proposed control charts, both in-control (IC) and out-of-control (OOC) scenarios are considered by deriving the average run length (ARL). Various factors, including sample sizes, control coefficients, and truncated constants for shifted phases, are taken into account to evaluate the performance of the charts in terms of ARL. The behavior of ARLs is analyzed in the shifted process by introducing shifts in its parameters. The superiority of the HEPD-based chart is highlighted by comparing it with both the HND-based ACC and the ACC based on the Exponential distribution (ED) under TTLT using RSS. The results showcase the superior performance of the proposed HEPD-based chart, indicated by smaller ARL values. Additionally, the benefits of another proposed ACC using HND are compared with the ED-based ACC under RSS, further confirming the effectiveness of the HND-based approach through smaller ARLs Finally, the proposed control charts are evaluated through simulation testing and real-life implementation, emphasizing their practical applicability in real-world manufacturing settings.

Processing of LtaS restricts LTA assembly and YSIRK preprotein trafficking into Staphylococcus aureus cross-walls.

Septal membranes of Staphylococcus aureus serve as the site of secretion for precursors endowed with the YSIRK motif. Depletion of ltaS, a gene required for lipoteichoic acid (LTA) synthesis, results in the loss of restricted trafficking of YSIRK precursors to septal membranes. Here, we seek to understand the mechanism that ties LTA assembly and trafficking of YSIRK precursors. We confirm that catalytically inactive lipoteichoic acid synthase (LtaS)T300A does not support YSIRK precursor trafficking to septa. We hypothesize that the enzyme's reactants [gentiobiosyldiacylglycerol (Glc2-DAG) and phosphatidylglycerol (PG)] or products [LTA and diacylglycerol (DAG)], not LtaS, must drive this process. Indeed, we observe that septal secretion of the staphylococcal protein A YSIRK precursor is lost in ypfP and ltaA mutants that produce glycerophosphate polymers [poly(Gro-P)] without the Glc2-DAG lipid anchor. These mutants display longer poly(Gro-P) chains, implying enhanced PG consumption and DAG production. Our experiments also reveal that in the absence of Glc2-DAG, the processing of LtaS to the extracellular catalytic domain, eLtaS, is impaired. Conversely, LTA polymerization is delayed in a strain producing LtaSS218P, a variant processed more slowly than LtaS. We conclude that Glc2-DAG binding to the enzyme couples catalysis by LtaS and the physical release of eLtaS. We propose a model for the temporal and localized assembly of LTA into cross-walls. When LtaS is not processed in a timely manner, eLtaS no longer diffuses upon daughter cell splitting, LTA assembly continues, and the unique septal-lipid pool, PG over DAG ratio, is not established. This results in profound physiological changes in S. aureus cells, including the inability to restrict the secretion of YSIRK precursors at septal membranes.IMPORTANCEIn Staphylococcus aureus, peptidoglycan is assembled at the septum. Dedicated cell division proteins coordinate septal formation and the fission of daughter cells. Lipoteichoic acid (LTA) assembly and trafficking of preproteins with a YSIRK motif also occur at the septum. This begs the question as to whether cell division components also recruit these two pathways. This study shows that the processing of lipoteichoic acid synthase (LtaS) to extracellular LtaS by signal peptidase is regulated by gentiobiosyldiacylglycerol (Glc2-DAG), the priming substrate for LTA assembly. A model is proposed whereby a key substrate controls the temporal and spatial activity of an enzyme. In turn, this mechanism enables the establishment of a unique and transient lipid pool that defines septal membranes as a targeting site for the secretion of YSIRK preproteins.

Anti-inflammatory potential of some eudesmanolide and guaianolide sesquiterpenes.

Ten sesquiterpene lactones isolated from Anvillea garcinii (Burm.f.) DC ethanolic extract were assessed for their anti-inflammatory potential by myeloperoxidase (MPO) activity assignment, and mice paw swelling model. 3α,4α-10ß-trihydroxy-8α-acetyloxyguaian-12,6α-olide (1), epi-vulgarin (3), 9a-hydroxyparthenolide (4), garcinamine C (7), garcinamine D (8), garcinamine E (9), and 4, 9-dihydroxyguaian-10(14)-en-12-olide (10) showed explicit anti-inflammatory activity in rodent paw edema and MPO assignment. The findings of this study showed that the α-methylene γ-lactone moiety does not always guarantee an anti-inflammatory effect, but the presence of proline at the C3 of the lactone ring improves the binding of sesquiterpene lactones with MPO isoenzymes, resulting in a more potent inhibition.

An assessment of bilateral debt swap financing indispensable for economic growth and environment sustainability: a policy implication for heavily indebted countries.

Bilateral debt swap is an innovative global financing mechanism designed to support heavily indebted countries (HICs). It is a debt-restructuring process involving donor countries forgiving debt owed by HICs in exchange for commitments to undertake projects on environment and socio-economic development. It is a unique approach designed to help heavily indebted countries get back on their feet. Effective debt swap financing can lead to both economic growth and environment sustainability, but they are challenging to implement. This study examines the impact of bilateral debt swap financing on economic growth and environment sustainability. For the purpose, we have used debt swap index developed with Kaiser-Meyer-Olkin (KMO) methodology. KMO widely used approach of Principle Component Analysis (PCA) to solve the problem of "over-identification" and make strong correlation among endogenous variables of interest. In order to validate the nexus empirically between bilateral debt swap financing with economic growth and environment sustainability, we have employed the Two-Step System Generalized Method of Moments (SYS-GMM) approach in 25 countries for the period of 2002 to 2021. This modern econometric method addresses endogeneity issues and controls for unobserved heterogeneity in panel data. At the same time, the technique addresses the simultaneity problem, reverse causality, and remove selection bias. Findings of the study shows that effective bilateral debt swap financing can boost economic growth and environment sustainability by investing domestic resources for targeted activities along with reduced debt burden. Empirical results reveal that 1% change in debt swap financing can lead to a maximum of 0.23% growth in the economy and 0.28% improvement in environment sustainability. However, it is important to note that in most empirical specifications, results are inconclusive. One possible reason for this is often ineffective debt swap practices coupled with inadequate monitoring and evaluation in HICs. Policymakers should focus on enhancing debt swap policies to promote economic growth and environment sustainability.

Efficient quality assurance for isocentric stability in stereotactic body radiation therapy using machine learning.

This study aims to predict isocentric stability for stereotactic body radiation therapy (SBRT) treatments using machine learning (ML), covers the challenges of manual assessment and computational time for quality assurance (QA), and supports medical physicists to enhance accuracy. The isocentric parameters for collimator (C), gantry (G), and table (T) tests were conducted with the RUBY phantom during QA using TrueBeam linac for SBRT. This analysis combined statistical features from the IsoCheck EPID software. Five ML models, including logistic regression (LR), decision tree (DT), random forest (RF), naive Bayes (NB), and support vector machines (SVM), were used to predict the outcome of the QA procedure. 247 Winston-Lutz (WL) tests were collected from 2020 to 2022. In our study, both DT and RF achieved the highest score on test accuracy (Acc. test) ranging from 93.5% to 99.4%, and area under curve (AUC) values from 90 to 100% on three modes (C, G, and T). The precision, recall, and F1 scores indicate the DT model consistently outperforms other ML models in predicting isocenter stability deviation in QA. The QA assessment using ML models can assist error prediction early to avoid potential harm during SBRT and ensure safe and effective patient treatments.

Swarm intelligence-based packet scheduling for future intelligent networks.

Network operations involve several decision-making tasks. Some of these tasks are related to operators, such as extending the footprint or upgrading the network capacity. Other decision tasks are related to network functions, such as traffic classifications, scheduling, capacity, coverage trade-offs, and policy enforcement. These decisions are often decentralized, and each network node makes its own decisions based on the preconfigured rules or policies. To ensure effectiveness, it is essential that planning and functional decisions are in harmony. However, human intervention-based decisions are subject to high costs, delays, and mistakes. On the other hand, machine learning has been used in different fields of life to automate decision processes intelligently. Similarly, future intelligent networks are also expected to see an intense use of machine learning and artificial intelligence techniques for functional and operational automation. This article investigates the current state-of-the-art methods for packet scheduling and related decision processes. Furthermore, it proposes a machine learning-based approach for packet scheduling for agile and cost-effective networks to address various issues and challenges. The analysis of the experimental results shows that the proposed deep learning-based approach can successfully address the challenges without compromising the network performance. For example, it has been seen that with mean absolute error from 6.38 to 8.41 using the proposed deep learning model, the packet scheduling can maintain 99.95% throughput, 99.97% delay, and 99.94% jitter, which are much better as compared to the statically configured traffic profiles.

Acceptance sampling plan based on difference in difference estimator with application.

An acceptance sampling plan has been designed in this study based on the Difference-in-Difference estimator. This plan is designed for the inspection of those product units whose life follows the normal distribution. The operating characteristic function is discussed for the two respective cases of the standard deviation known and unknown. The parameters of the proposed plan are determined by minimizing the sample size and followed by the satisfying optimization rule. The results are computed and tabulated for various parametric combinations of acceptable quality levels and limiting quality levels. The computations are performed by using R statistical programming software for all respective cases. The real-life application of the proposed sampling plan has been discussed and elaborated in detail.

Stress Monitoring Using Machine Learning, IoT and Wearable Sensors.

The Internet of Things (IoT) has emerged as a fundamental framework for interconnected device communication, representing a relatively new paradigm and the evolution of the Internet into its next phase. Its significance is pronounced in diverse fields, especially healthcare, where it finds applications in scenarios such as medical service tracking. By analyzing patterns in observed parameters, the anticipation of disease types becomes feasible. Stress monitoring with wearable sensors and the Internet of Things (IoT) is a potential application that can enhance wellness and preventative health management. Healthcare professionals have harnessed robust systems incorporating battery-based wearable technology and wireless communication channels to enable cost-effective healthcare monitoring for various medical conditions. Network-connected sensors, whether within living spaces or worn on the body, accumulate data crucial for evaluating patients' health. The integration of machine learning and cutting-edge technology has sparked research interest in addressing stress levels. Psychological stress significantly impacts a person's physiological parameters. Stress can have negative impacts over time, prompting sometimes costly therapies. Acute stress levels can even constitute a life-threatening risk, especially in people who have previously been diagnosed with borderline personality disorder or schizophrenia. To offer a proactive solution within the realm of smart healthcare, this article introduces a novel machine learning-based system termed "Stress-Track". The device is intended to track a person's stress levels by examining their body temperature, sweat, and motion rate during physical activity. The proposed model achieves an impressive accuracy rate of 99.5%, showcasing its potential impact on stress management and healthcare enhancement.

Unsupervised Mixture Models on the Edge for Smart Energy Consumption Segmentation with Feature Saliency.

Smart meter datasets have recently transitioned from monthly intervals to one-second granularity, yielding invaluable insights for diverse metering functions. Clustering analysis, a fundamental data mining technique, is extensively applied to discern unique energy consumption patterns. However, the advent of high-resolution smart meter data brings forth formidable challenges, including non-Gaussian data distributions, unknown cluster counts, and varying feature importance within high-dimensional spaces. This article introduces an innovative learning framework integrating the expectation-maximization algorithm with the minimum message length criterion. This unified approach enables concurrent feature and model selection, finely tuned for the proposed bounded asymmetric generalized Gaussian mixture model with feature saliency. Our experiments aim to replicate an efficient smart meter data analysis scenario by incorporating three distinct feature extraction methods. We rigorously validate the clustering efficacy of our proposed algorithm against several state-of-the-art approaches, employing diverse performance metrics across synthetic and real smart meter datasets. The clusters that we identify effectively highlight variations in residential energy consumption, furnishing utility companies with actionable insights for targeted demand reduction efforts. Moreover, we demonstrate our method's robustness and real-world applicability by harnessing Concordia's High-Performance Computing infrastructure. This facilitates efficient energy pattern characterization, particularly within smart meter environments involving edge cloud computing. Finally, we emphasize that our proposed mixture model outperforms three other models in this paper's comparative study. We achieve superior performance compared to the non-bounded variant of the proposed mixture model by an average percentage improvement of 7.828%.