Battery ensures power solutions for many necessary portable devices such as electric vehicles, mobiles, and laptops. Owing to the rapid growth of Li-ion battery users, unwanted incidents involving ...Li-ion batteries have also increased to some extent. In particular, the sudden breakdown of industrial and lightweight machinery due to battery failure causes a substantial economic loss for the industry. Consequently, battery state estimation, management system, and estimation of the remaining useful life (RUL) have become a topic of interest for researchers. Considering this, appropriate battery data acquisition and proper information on available battery data sets may require. This review paper is mainly focused on three parts. The first one is battery data acquisitions with commercially and freely available Li-ion battery data set information. The second is the estimation of the states of battery with the battery management system. And third is battery RUL estimation. Various RUL prognostic methods applied for Li-ion batteries are classified, discussed, and reviewed based on their essential performance parameters. Information on commercially and publicly available data sets of many battery models under various conditions is also reviewed. Various battery states are reviewed considering advanced battery management systems. To that end, a comparative study of Li-ion battery RUL prediction is provided together with the investigation of various RUL prediction algorithms and mathematical modelling.
Automatic classification of colon and lung cancer images is crucial for early detection and accurate diagnostics. However, there is room for improvement to enhance accuracy, ensuring better ...diagnostic precision. This study introduces two novel dense architectures (D1 and D2) and emphasizes their effectiveness in classifying colon and lung cancer from diverse images. It also highlights their resilience, efficiency, and superior performance across multiple datasets. These architectures were tested on various types of datasets, including NCT-CRC-HE-100K (set of 100,000 non-overlapping image patches from hematoxylin and eosin (H&E) stained histological images of human colorectal cancer (CRC) and normal tissue), CRC-VAL-HE-7K (set of 7180 image patches from N = 50 patients with colorectal adenocarcinoma, no overlap with patients in NCT-CRC-HE-100K), LC25000 (Lung and Colon Cancer Histopathological Image), and IQ-OTHNCCD (Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases), showcasing their effectiveness in classifying colon and lung cancers from histopathological and Computed Tomography (CT) scan images. This underscores the multi-modal image classification capability of the proposed models. Moreover, the study addresses imbalanced datasets, particularly in CRC-VAL-HE-7K and IQ-OTHNCCD, with a specific focus on model resilience and robustness. To assess overall performance, the study conducted experiments in different scenarios. The D1 model achieved an impressive 99.80 % accuracy on the NCT-CRC-HE-100K dataset, with a Jaccard Index (J) of 0.8371, a Matthew's Correlation Coefficient (MCC) of 0.9073, a Cohen's Kappa (Kp) of 0.9057, and a Critical Success Index (CSI) of 0.8213. When subjected to 10-fold cross-validation on LC25000, the D1 model averaged (avg) 99.96 % accuracy (avg J, MCC, Kp, and CSI of 0.9993, 0.9987, 0.9853, and 0.9990), surpassing recent reported performances. Furthermore, the ensemble of D1 and D2 reached 93 % accuracy (J, MCC, Kp, and CSI of 0.7556, 0.8839, 0.8796, and 0.7140) on the IQ-OTHNCCD dataset, exceeding recent benchmarks and aligning with other reported results. Efficiency evaluations were conducted in various scenarios. For instance, training on only 10 % of LC25000 resulted in high accuracy rates of 99.19 % (J, MCC, Kp, and CSI of 0.9840, 0.9898, 0.9898, and 0.9837) (D1) and 99.30 % (J, MCC, Kp, and CSI of 0.9863, 0.9913, 0.9913, and 0.9861) (D2). In NCT-CRC-HE-100K, D2 achieved an impressive 99.53 % accuracy (J, MCC, Kp, and CSI of 0.9906, 0.9946, 0.9946, and 0.9906) with training on only 30 % of the dataset and testing on the remaining 70 %. When tested on CRC-VAL-HE-7K, D1 and D2 achieved 95 % accuracy (J, MCC, Kp, and CSI of 0.8845, 0.9455, 0.9452, and 0.8745) and 96 % accuracy (J, MCC, Kp, and CSI of 0.8926, 0.9504, 0.9503, and 0.8798), respectively, outperforming previously reported results and aligning closely with others. Lastly, training D2 on just 10 % of NCT-CRC-HE-100K and testing on CRC-VAL-HE-7K resulted in significant outperformance of InceptionV3, Xception, and DenseNet201 benchmarks, achieving an accuracy rate of 82.98 % (J, MCC, Kp, and CSI of 0.7227, 0.8095, 0.8081, and 0.6671). Finally, using explainable AI algorithms such as Grad-CAM, Grad-CAM++, Score-CAM, and Faster Score-CAM, along with their emphasized versions, we visualized the features from the last layer of DenseNet201 for histopathological as well as CT-scan image samples. The proposed dense models, with their multi-modality, robustness, and efficiency in cancer image classification, hold the promise of significant advancements in medical diagnostics. They have the potential to revolutionize early cancer detection and improve healthcare accessibility worldwide.
This research addresses the lack of publicly available datasets for Bangladeshi medicinal plants by presenting a comprehensive dataset comprising 5000 images of ten species collected under controlled ...conditions. To improve performance, several preprocessing techniques were employed, such as image selection, background removal, unsharp masking, contrast-limited adaptive histogram equalization, and morphological gradient. Then, we applied five state-of-the-art deep learning models to achieve benchmark performance on the dataset: VGG16, ResNet50, DenseNet201, InceptionV3, and Xception. Among these models, DenseNet201 demonstrated the highest accuracy of 85.28%. In addition to benchmarking the deep learning models, three novel neural network architectures were developed: dense-residual–dense (DRD), dense-residual–ConvLSTM-dense (DRCD), and inception-residual–ConvLSTM-dense (IRCD). The DRCD model achieved the highest accuracy of 97%, surpassing the benchmark performances of individual models. This highlights the effectiveness of the proposed architectures in capturing complex patterns and dependencies within the data. To further enhance classification accuracy, an ensemble approach was adopted, employing both hard ensemble and soft ensemble techniques. The hard ensemble achieved an accuracy of 98%, while the soft ensemble achieved the highest accuracy of 99%. These results demonstrate the effectiveness of ensembling techniques in boosting overall classification performance. The outcomes of this study have significant implications for the accurate identification and classification of Bangladeshi medicinal plants. This research provides valuable resources for traditional medicine, drug discovery, and biodiversity conservation efforts. The developed models and ensemble techniques can aid researchers, botanists, and practitioners in accurately identifying medicinal plant species, thereby facilitating the utilization of their therapeutic potential and contributing to the preservation of biodiversity.
The present study has examined the antidiabetic effects of 21 days co-administration of xenin-8-Gln with the dual-acting fusion peptide, exendin-4/gastrin, as well as persistence of beneficial ...metabolic benefits, in high fat fed (HFF) mice. Xenin-8-Gln, exendin-4 and gastrin represent compounds that activate receptors of the gut-derived hormones, xenin, glucagon-like peptide-1 (GLP-1) and gastrin, respectively. Twice-daily administration of exendin-4/gastrin, xenin-8-Gln or a combination of both peptides significantly reduced circulating glucose, HbA1c and cumulative energy intake. Combination therapy with xenin-8-Gln and exendin-4/gastrin increased circulating insulin. All HFF mice treated with exendin-4/gastrin presented with body weight similar to lean control mice on day 21. Each treatment improved glucose tolerance and the glucose-lowering actions of glucose dependent insulinotropic polypeptide (GIP), as well as augmenting glucose- and GIP-induced insulin secretion, with benefits being most prominent in the combination group. Administration of exendin-4/gastrin alone, and in combination with xenin-8-Gln, increased pancreatic insulin content and improved the insulin sensitivity index. Pancreatic beta-cell area was significantly increased, and alpha cell area decreased, by all treatments, with the combination group also displaying enhanced overall islet area. Notably, metabolic benefits were generally retained in all groups of HFF mice, and especially in the combination group, following discontinuation of the treatment regimens for 21 days. This was associated with maintenance of increased islet and beta-cell areas. Together, these data confirm the antidiabetic effects of co-activation of GLP-1, gastrin and xenin cell signalling pathways, and highlight the sustainable benefits this type of treatment paradigm can offer in T2DM.
System frequency may change from defined values while transmitting power from one area to another in an interconnected power system due to various reasons such as load changes and faults. This ...frequency change causes a frequency error in the system. However, the system frequency should always be maintained close to the nominal value even in the presence of model uncertainties and physical constraints. This paper proposes an Active Disturbance Rejection Controller (ADRC)-based load frequency control (LFC) of an interconnected power system. The controller incorporates effects of generator inertia and generator electrical proximity to the point of disturbances. The proposed controller reduces the magnitude error of the area control error (ACE) of an interconnected power system compared to the standard controller. The simulation results verify the effectiveness of proposed ADRC in the application of LFC of an interconnected power system.
Background
To assess and compare the local control and toxicities between HDR Intracavitary Brachytherapy with 7.5 Gy and 9 Gy per fraction after EBRT in treatment of carcinoma cervix.
Methodology
A ...total of 180 patients were randomly assigned to 2 arms. Arm A received HDR intracavitary brachytherapy with a dose of 7.5 Gy per fraction, 1 fraction per week for 3 fractions and Arm B received 9 Gy per fraction, 1 fraction per week for 2 fractions. Patients were evaluated on follow up for assessment of local control and toxicities.
Results
The median follow up was 12 months (6–18 months). In arm A 89% of the patient had complete response and 11% had recurrence or metastasis. In arm B 93% of the patient had complete response and 7% had recurrence or metastasis. Grade 2/3 diarrhoea was seen in 4.4% of patients in Arm A and in 7.7% in Arm B. Grade 2/3 proctitis was seen in 3.3% of patients in 7.5 Gy arm and in 6.6% in 9 Gy arm. One patient in each arm had grade 1 haematuria. The overall duration of treatment was significant lower in Arm B compared to Arm A (59 days vs 68 days, p = 0.01).
Conclusion
The result of this clinical study shows that Intracavitary brachytherapy with a dose of 9 Gy per fraction is non inferior to other schedules in term of local control and does not result in increased toxicity.
has been frequently used to study biogenesis, functionality, and intracellular transport of various renal proteins, including ion channels, solute transporters, and aquaporins. Specific mutations in ...genes encoding most of these renal proteins affect kidney function in such a way that various disease phenotypes ultimately occur. In this context, human kidney anion exchanger 1 (kAE1) represents an important bicarbonate/chloride exchanger which maintains the acid-base homeostasis in the human body. Malfunctions in kAE1 lead to a pathological phenotype known as distal renal tubular acidosis (dRTA). Here, we evaluated the potential of baker's yeast as a model system to investigate different cellular aspects of kAE1 physiology. For the first time, we successfully expressed yeast codon-optimized full-length versions of tagged and untagged wild-type kAE1 and demonstrated their partial localization at the yeast plasma membrane (PM). Finally, pH and chloride measurements further suggest biological activity of full-length kAE1, emphasizing the potential of
as a model system for studying trafficking, activity, and/or degradation of mammalian ion channels and transporters such as kAE1 in the future.
Distal renal tubular acidosis (dRTA) is a common kidney dysfunction characterized by impaired acid secretion via urine. Previous studies revealed that α-intercalated cells of dRTA patients express mutated forms of human kidney anion exchanger 1 (kAE1) which result in inefficient plasma membrane targeting or diminished expression levels of kAE1. However, the precise dRTA-causing processes are inadequately understood, and alternative model systems are helpful tools to address kAE1-related questions in a fast and inexpensive way. In contrast to a previous study, we successfully expressed full-length kAE1 in
Using advanced microscopy techniques as well as different biochemical and functionality assays, plasma membrane localization and biological activity were confirmed for the heterologously expressed anion transporter. These findings represent first important steps to use the potential of yeast as a model organism for studying trafficking, activity, and degradation of kAE1 and its mutant variants in the future.
When the brain's thermoregulation system fails, heatstroke occurs. Not treating it within the first half hour can lead to disability and possibly death. To prevent heatstroke, a cooling mechanism for ...the brain is proposed. The prototype system has the capability to track the environment and provide on-the-spot cooling. The aim is to reduce the risk of heatstroke. The key feature of the system is the inclusion of PID controller, that automates the temperature regulation by monitoring the external environment.
The aim of this work is to provide a process for obtaining natural carob syrup of Morocco carob pods and their total and reducing sugar. Samples were collected from different regions in the ...agro-forestry system of Morocco. The total sugar and reducing sugar in pods obtained from different regions were 31.5–50.1 and 10.2–14.6g/100g “%w/w”, respectively. The yield of syrup from the different regions varies between 28.76 and 37.22g/100g “%w/w”. Populations from Essaouira and Beni-mellal have higher levels of sugar and yield of syrup. The values obtained vary according to the origin of the samples.
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