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Big-medical-data classification and image detection are crucial tasks in the field of healthcare, as they can assist with diagnosis, treatment planning, and disease monitoring. Logistic regression and YOLOv4 are popular algorithms that can be used for these tasks. However, these techniques have limitations and performance issue with big medical data. In this study, we presented a robust approach for big-medical-data classification and image detection using logistic regression and YOLOv4, respectively. To improve the performance of these algorithms, we proposed the use of advanced parallel k-means pre-processing, a clustering technique that identified patterns and structures in the data. Additionally, we leveraged the acceleration capabilities of a neural engine processor to further enhance the speed and efficiency of our approach. We evaluated our approach on several large medical datasets and showed that it could accurately classify large amounts of medical data and detect medical images. Our results demonstrated that the combination of advanced parallel k-means pre-processing, and the neural engine processor resulted in a significant improvement in the performance of logistic regression and YOLOv4, making them more reliable for use in medical applications. This new approach offers a promising solution for medical data classification and image detection and may have significant implications for the field of healthcare. [ABSTRACT FROM AUTHOR]

Vertebral tumors have a percentage of back pain that causes other vertebral region-born symptoms. Cancers that affect the vertebral column are visceral organ cancer metastases that are mostly seen in older patients. Vertebral dysfunction and neurological failure vertebral column cancers are the most important occurred cancers for patients. In the past, only few methods have been used to combat main and metastatic vertebral tumors. These methods are accessible for short-term monitoring and possess standardized classification consistency for vertebral diagnosis. In this paper, geometric rough propagation neural network has been used for the identification of genetic factors in the examination of a clinical sample with vertebral columns. The proposed neural network has C-statistics of 79.1%, a parameter pitch of 96.1%, and configuration for measurement in the study range with the Brier's score of 95.6%. The algorithm shows great net gain on the decision curve study, with promising performance results of 98.5% on internal testing for preoperative non-routine estimation of discharges with 0.5% error rate and 96% accuracy range. Also, these models have been externally validated by the online healthcare careers cloud-based open access web application on Internet of Medical Things Platform with 97.9% specificity ratio. [ABSTRACT FROM AUTHOR]

Recently, the leading cause of preventable blindness is diabetic retinopathy (DR). Although there are several undiagnosed and non-treated cases of DR, accurate and adequate retinal screening could facilitate the early detection and treatment of DR. The goal of this research is to develop a reliable DR screening and detection model to reduce the risk of DR-related blindness. DR-infected eyes describe ophthalmologist for further examination and diagnosis might reduce the risk of vision loss and provide timely and accurate diagnostic information. Hence, this paper proposes a hybrid inductive machine learning algorithm (HIMLA) as an automated DR detection diagnostic tool. HIMLA processes and classifies colored fundus images as healthy (no retinopathy) or unhealthy (presence of DR) by identifying the appropriate medical DR cases. The proposed algorithm comprises four stages: pre-processing, segmentation, feature extraction, and classification. At the pre-processing stage, colored fundus images are normalized to a specific brightness level to enhance the quality of the images. In the segmentation stage, the processed image is encoded and decoded to segment the images for improving image quality. Furthermore, feature extraction and classification are performed using multiple instance learning (MIL). The proposed method was evaluated on CHASE datasets for the detection of DR. The accuracy, sensitivity, and specificity of the proposed approach are 96.62%, 95.31%, and 96.88%, respectively. These results indicates that HIMLA outperforms other DR models, such as ML-based neovascularization detection in the optic disc (MLB-NVD), genetic algorithm–based diabetic retinopathy (GAB-DR), DL algorithm diabetic retinopathy (DLA-DR), and diagnostic assessment–based DL for diabetic retinopathy (DAD-DR), which reduces the risk of vision loss. [ABSTRACT FROM AUTHOR]

Washingtonia is a palm tree that enriched with lignocellulosic biomass, which provided it with the potentiality for various applications. In this study, different biomass from Washingtonia plant, i.e. leaf stalk (W-LS), trunk core (W-TC), and trunk spine (W-TS) fibers, were collectively investigated by characterization. Under scanning electron microscopy (SEM) examination, the spine-shape structure of W-TS fiber along with its small particle size might facilitate it for fabrication reactivity. From chemical and elemental composition analysis, both W-TC and W-TS fibers were found with high cellulose content, while with the closely similar residual elements, suggesting them from the same source of trunk segment. However, the low cellulose content of W-LS fiber with its high ash content had somehow reduced its crystallinity and thermal stability properties. From X-ray diffraction (XRD) analysis, the crystallinity was estimated the highest for W-TS (68.2%), and while with the lowest for W-LS fiber (57.3%), which in line with the detected functional groups by Fourier Transform Infrared-ray (FTIR) spectrophotometry. In thermogravimetry (TGA) analysis, all fibers possessed great thermal resistance with Tonset in the range of 307.4–328.1°C and Tpeak in the range of 352.4–347.9°C. Washingtonia fiber had a high potential to be applied as a bio-material for versatile applications in the future. [ABSTRACT FROM AUTHOR]

A library of N-benzylpyridinium-based compounds, 7a-j and 8a-j, was designed and synthesised as potential acetylcholinesterase) AChE (inhibitors. An in vitro assay for the synthesised compounds showed that most compounds had significant AChE inhibitory activities at the nanomolar and submicromolar levels. The benzyl (8a) and fluoro (8b) derivatives were the most active, with IC50 values ≤56 nM. Compound 7f, which had a benzyl moiety, showed the highest potency among all the target compounds, with an IC50 value of 7.5 ± 0.19 nM against AChE, which was higher than that of the activities of tacrine (IC50 = 30 ± 0.2 nM) and donepezil (IC50 = 14 ± 0.12 nM). Compounds with vanillin moieties exhibited antioxidant activity. Among the tested compounds, four derivatives (7f, 7 g, 8f, and 8 g) exhibited superior AChE inhibitory activity, with Ki values of 6–16 nM, which were potent in the same range as the approved drug, donepezil. These compounds showed moderate antioxidant activities, as indicated by the results of the ABTS assay. [ABSTRACT FROM AUTHOR]

Although several interventions are utilized for pain management, opioids remain the most effective intervention for moderate to severe pain. Despite opioids being the most potent analgesics used in different pain settings, several factors impede the optimal prescribing of opioids. The study seeks to identify and address the barriers physicians face to prescribing opioids in managing pain. This study was conducted in a tertiary care center in Riyadh, Saudi Arabia. It involved distributing questionnaires to the participants. The questionnaires sought to identify prescribers' knowledge and current practices as well as obstacles that they face when prescribing opioids. A total of 109 questionnaires were completed by participants. More than half [59.6%] of the respondents thought that opioid use was an optimal way to treat moderate to severe pain. About 33% chose "never" when asked if they fear legal sanctions when prescribing opioids. Fear of side effects limited almost 90% of the respondents from prescribing opioids. The study confirmed the perception that opioids are the most potent pharmacological intervention in treating pain. Several barriers were identified and discussed in this study. Further studies from different settings to understand these barriers are highly recommended. [ABSTRACT FROM AUTHOR]

Rapid urbanization, aging infrastructure, and climate change impacts have put a strain on existing stormwater drainage systems. One commonly acknowledged solution to relieve such stress is Green Stormwater Infrastructure (GSI). Interest in GSI technology has been growing. However, the level of implementation in many areas around the world lags behind the interest level. This study aims to critically review the body of literature from the last decade to determine the main barriers to wide adoption and the offered solutions to overcome them. Based on a review of 92 peer‐reviewed journal articles published between 2012 and 2022, we classify barriers and solutions into six categories: socio‐cultural, financial, institutional and governance, legislative and regulatory, technical, and biophysical. Based on observations and conclusions from the reviewed articles, we recommend the following pillars and considerations for more GSI adoption: increasing awareness and outreach programs; enhancing knowledge and data co‐production and dissemination; acknowledging interdependency and context‐specificity of many of the challenges and solutions; prioritizing integrated and participatory watershed planning; overcoming institutional path‐dependencies; prioritizing innovative solutions; giving specific consideration to maintenance protocols; considering the role of public entities; and actively engaging with communities. This article is categorized under:Engineering Water > Planning WaterWater and Life > Conservation, Management, and Awareness [ABSTRACT FROM AUTHOR]

To generate high-yielding cultivars with favorable fiber quality traits, cotton breeders can use information about combining ability and gene activity within a population to locate elite parents and potential F1 crosses. To this end, in the current study, twelve cotton parents (eight genotypes as female parents and four testers) and their F1 crosses obtained utilizing the linex tester mating design were evaluated for their general and specialized combining abilities (GCA and SCA, respectively) of yield traits. The findings showed that for all the investigated variables, variances owing to genotypes, parents, crosses, and parent vs cross showed extremely significant (P ≤ 0.01) differences. Additionally, throughout the course of two growing seasons, the mean squares for genotypes (parents and crosses) showed strong significance for all the variables under study. The greatest and most desired means for all the examined qualities were in the parent G.94, Pima S6, and tester G.86. The best crossings for the qualities examined were G.86 (G.89 × G.86), G.93 × Suvin, and G.86 × Suvin. The parents' Suvin, G89x G86 and TNB were shown to have the most desired general combining ability effects for seed cotton yield/plant, lint yield/plant, boll weight, number of bolls/plants, and lint index, while Suvin, G.96 and pima S6 were preferred for favored lint percentage. For seed cotton yield, lint percentage, boll weight, and number of bolls per plant per year, the cross-G.86 x (G.89 × G.86) displayed highly significant specific combining ability impacts. The crosses G.86 × Suvin, Kar x TNB, G.93 × Suvin, and G.93 × TNB for all the studied traits for each year and their combined were found to have highly significant positive heterotic effects relative to better parent, and they could be used in future cotton breeding programs for improving the studied traits. [ABSTRACT FROM AUTHOR]

Natural fibre–reinforced epoxy composites have been increasingly utilised in construction and building applications. These materials introduce cost-effective alternatives to conventional materials and their utilisation often has related to financial advantages that are immediate and can be expected over the structure service life. In this work, the results of thermal, dynamic-mechanical, and thermal-mechanical properties of date palm fibre (DPF)/bamboo fibre (BF) hybrid composite were compared to bamboo fibre-reinforced epoxy to demonstrate the importance of hybridization. The thermal stability was improved when DPF/BF fillers were added in epoxy resin comparatively to BF reinforced epoxy. The glass transition temperature (T g) was increased by incorporating the date palm fibre/bamboo hybrid composite in epoxy compared to the BF reinforced epoxy. The thermal expansion was enhanced by modifying the hybrid composites in epoxy in contrast to the single fibre composites, without hybridisation. Hence, the hybridisation technique of date palm fibre with bamboo has improved the thermal and thermal-mechanical properties suitable for several applications including non-structural applications. [ABSTRACT FROM AUTHOR]

In this research NaOH treated date palm fibres were used as reinforcement in phenolic based composites. The composites were prepared by hot press technique and evaluated tensile flexural, structural, thermo gravimetric dynamic mechanical properties. Overall properties of modification/treatment of fibers' surface enhanced, that have been studied as compared to untreated samples. The treated samples of 50% DPF composites showed highest Tensile properties among all composites but flexural properties declined compared to the untreated composites but this decline is very less. Fibre treatment showed declined properties of three point bending of DPF composites. Scanning Electron Microscopy studied the behavior of fibre and matrix bonding before and after treatment. Treated 50% DPF showed better fibre distribution, 60% DPF showed void content however 40%DPF showed poor fibre/matrix interfacial bonding. Thermogravimetric analysis studied the behavior of Treated DPF/Phenolic composites at high temperature, and found thermal stability enhanced because good interfacial bonding. Dynamic mechanical analysis showed that stability at stress of material with temperature and also studied the energy dissipation and internal friction. Treated 50%DPF showed better properties due to the ratio of mixing of fibre/matrix and better interfacial bonding. DPF composite have potential to use for exterior applications and false wall and roof. [ABSTRACT FROM AUTHOR]

[Display omitted] • A new framework successfully identified flood hotspots in an urban drainage system. • Inlet structure depth was found to be the main parameter that predicts flooding. • GSI can significantly mitigate stormwater inlet floods. • Combining GSI and gray infrastructure provides the most effective mitigation. • Cost of overflow mitigation from GSI is lower than gray infrastructure. Mitigation of localized pluvial flooding is one of the major resiliency goals in urban environments, and Green Stormwater Infrastructure (GSI) has the potential to deliver such an outcome. However, there is a lack of systematic approaches to prioritize investment in different candidate areas. This study provides a framework to identify vulnerable stormwater drainage inlets and their contributing areas, prioritize them, identify dominant factors in their selection, assess the potential of GSI in mitigating their overflows, and compare the impact and its cost to gray infrastructure upgrade alternatives. Using SWMM 5.1.013, decision trees, and a volumetric-based assessment of GSI overflow capture, we applied the framework to the City of Dallas, Texas, for three design storms with three GSI practices— bioretention cells, raingardens, and rainwater harvesting tanks. Results showed that there was a significant increase in the number of overflowing stormwater drainage inlets, referred to as hotspots, and their contributing subwatersheds, referred to as opportunity areas, with more intense storms especially in problematic watersheds. Also, prioritization results provided a series of maps to rank the opportunity areas based on overflow severity, recurrence of the overflows, and GSI availability. Moreover, classification results showed that inlet features, especially the inlet depth, were the dominant factors in the identification of the non-problematic inlets. Finally, the GSI impact assessment showed substantial overflow mitigation even at the "very high" severity levels when GSI is comprehensively deployed across opportunity areas. Despite gray infrastructure upgrades yielding higher reduction levels, their cost per cubic meter was higher than GSI. Therefore, a combination of GSI and gray results in maximum overflow reduction at a lower cost compared to common practices. [ABSTRACT FROM AUTHOR]