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The importance of sentiment analysis in the rapidly evolving financial markets is widely recognized for its ability to interpret market trends and inform investment decisions. This study delves into the target-level financial sentiment analysis (TLFSA) of news headlines related to stock. The study compares the performance in the TLFSA task of various sentiment analysis techniques, including rule-based models (VADER), fine-tuned transformer-based models (DistilFinRoBERTa and Deberta-v3-base-absa-v1.1) as well as zero-shot large language models (ChatGPT and Gemini). The dataset utilized for this analysis, a novel contribution of this research, comprises 1476 manually annotated Bloomberg headlines and is made publicly available (due to copyright restrictions, only the URLs of Bloomberg headlines with the manual annotations are provided; however, these URLs can be used with a Bloomberg terminal to reconstruct the complete dataset) to encourage future research on this subject. The results indicate that the fine-tuned Deberta-v3-base-absa-v1.1 model performs better across all evaluation metrics than other evaluated models in TLFSA. However, LLMs such as ChatGPT-4, ChatGPT-4o, and Gemini 1.5 Pro provide similar performance levels without the need for task-specific fine-tuning or additional training. The study contributes to assessing the performance of LLMs for financial sentiment analysis, providing useful insights into their possible application in the financial domain.

Predicting students’ academic performance is a critical research area, yet imbalanced educational datasets, characterized by unequal academic-level representation, present challenges for classifiers. While prior research has addressed the imbalance in binary-class datasets, this study focuses on multi-class datasets. A comparison of ten resampling methods (SMOTE, Adasyn, Distance SMOTE, BorderLineSMOTE, KmeansSMOTE, SVMSMOTE, LN SMOTE, MWSMOTE, Safe Level SMOTE, and SMOTETomek) is conducted alongside nine classification models: K-Nearest Neighbors (KNN), Linear Discriminant Analysis (LDA), Quadratic Discriminant Analysis (QDA), Support Vector Machine (SVM), Logistic Regression (LR), Extra Tree (ET), Random Forest (RT), Extreme Gradient Boosting (XGB), and Ada Boost (AdaB). Following a rigorous evaluation, including hyperparameter tuning and 10 fold cross-validations, KNN with SmoteTomek attains the highest accuracy of 83.7%, as demonstrated through an ablation study. These results emphasize SMOTETomek’s effectiveness in mitigating class imbalance in educational datasets and highlight KNN’s potential as an educational data mining classifier.

This study examines industrial process optimization approaches using case studies, literature reviews, and original research. It explores techniques such as linear programming, simulation modelling, Lean, Six Sigma, and advanced machine learning algorithms. Case studies exemplify the application of these methods in beverages and aerospace sectors, the study employs specific strategies and data collection methods, providing insights into the applicability, strengths, and limitations of various methodologies. Results highlight both quantitative and qualitative outcomes, offering valuable insights for practitioners and decision-makers.

Objective: To evaluate the clinical presentation and outcome of Falciparum malaria in 100 hospitalized patients. Methodology: This hospital based descriptive study was conducted at Department of Medicine, Khyber Teaching Hospital, Peshawar from January 2011 to November 2011. One hundred patients of either gender, aged more than 12 years, diagnosed to have falciparum malaria on the basis of peripheral blood smear findings were included. Detailed information was recorded regarding presenting symptoms and signs of patients, response of patients to drugs used for Falciparum malaria, and complications of malaria. Results: Main presenting symptoms were fever (100%), headache (85%), chills (76%), nausea/vomiting (73%), altered level of consciousness (28%), fits (7%) and oliguria (5%). The main signs of Falciparum malaria were splenomegaly (75%), hepatomegaly (67%), anemia (60%), herpes labialis (42%) and jaundice (20%). Female patients were 42%, of whom 17% were pregnant. Ninety eight (98%) patients were treated with quinine + doxycycline, out of which seven (7%) patients died. Two (2%) patients were treated with combination of artemether and lumafantrine, both recovered completely. The common complications were anemia (60%), cerebral malaria (28%), hepatitis (20%), pulmonary edema (10%), hypoglycemia (8%), thrombocytopenia (7%), fundal hemorrhages (6%), renal failure (6%) and disseminated intravascular coagulation (5%). Conclusion: Falciparum malaria can present in a diverse spectrum. Fever with chills, headache, vomiting and altered level of consciousness are the common presenting symptoms. Anemia, cerebral malaria, hepatitis and pulmonary edema are the common complications of falciparum malaria. Response to quinine with doxycycline is good and mortality rate is 7%. Key Words: Falciparum Malaria, Quinine, Artemether, Artemisin-Based Combination Therapy, Complications.

Objective: To identify the commonest potential cardiac sources of embolism in patients with ischemic stroke. Methodology: This hospital based descriptive case study was conducted at Khyber Teaching Hospital, Peshawarfrom November 2009 to December 2010 on 150 patients with first ever ischemic stroke. Patients were selected onconvenient sampling technique. All patients with the clinical diagnosis of acute stroke and confirmed by non-contrastComputerized Tomography (CT) scan of brain as ischemic stroke were recruited in the study. Trans- thoracic andTrans- esophageal echocardiography were performed in selected patients in the Cardiology Department. A questionnairewas designed comprising detailed history, general physical examination, neurological, cardiovascular examinationsand relevant investigations. Data was analyzed by SPSS windows version 17. Results: Study included 150 patients (88 male and 62 female) ranging in age from 18-85 years with mean age of 60years ± 11.20 years. Potential cardiac sources of embolism were present in 45 (30%) patients. Twenty (13.33%)patients had atrial fibrillation due to rheumatic heart disease, 13(8.67%) patients non-valvular atrial fibrillation, 04(2.66%)patients myocardial infarction at the time of stroke, 03(2%) patients had dilated cardiomyopathy, 02(1.33%) patientsinfective endocarditis and one patient each had mitral valve prolapse (0.67%), prosthetic heart valves (0.67%) and leftventricular aneurysm (0.67%). Conclusion: Potential cardiac sources of cerebral embolism were detected in about one third of patients with ischemicstroke. Atrial fibrillation was the commonest cardiac source of embolism reflecting a high prevalence ofrheumatic fever and rheumatic heart disease in our country. Key Words: Ischemic Stroke, Cardiac Embolism, Atrial Fibrillation.

Nano-biochar considers a versatile and valuable sorbent to enhance plant productivity by improving soil environment and emerged as a novel solution for environmental remediation and sustainable agriculture in modern era. In this study, roles of foliar applied nanobiochar colloidal solution (NBS) on salt stressed tomato plants were investigated. For this purpose, NBS was applied (0%, 1% 3% and 5%) on two groups of plants (control 0 mM and salt stress 60 mM). Tween-20 was used as a surfactant to prolong NBS effective stay on plant leaf surface. The results showed that 3% NBS application effectively improved the plant height, plant biomass, fruit count and fruit weight under non-stressed and stressed plants. In addition, 3% NBS application further increased the plant pigments such as chlorophyll by 72% and 53%, carotenoids by 64% and 40%, leaf relative water content by 4.1 fold and 1.07 fold under both conditions, respectively. NBS application stabilized the plasma membrane via reducing electrolyte leakage by 30% as well as reduced the lipid peroxidation rates by 46% and 29% under non-stressed and stressed plants, respectively. 3% NBS application also significantly enhanced the plants primary and secondary metabolites, as well as activities of antioxidant enzymes compared to control plants. Overall, NBS foliar application significantly improved all growth and yield indices, pigments, primary and secondary metabolites, leaf water content, antioxidant enzyme activities as well as reduced electrolyte leakage and lipid peroxidation rates in tomato to combat stress conditions. In future, studies on nano biochar interactions with soil microbiota, surface modifications, long-term environmental impacts, reduced methane gas emissions, and biocompatibility could provide insights into optimizing its use in sustainable agriculture. [ABSTRACT FROM AUTHOR]

Objectives: To determine the incidence of postoperative cerebrospinal fluid (CSF) leaks after endoscopic endonasal transsphenoidal surgery (EETS) for pituitary adenomas without sellar floor reconstruction (SFR). Methods: This retrospective observational study was conducted at Department of Neurosurgery, Punjab Institute of Neurosciences (PINS), Lahore, Pakistan from January, 2018 to December, 2022. It is a non-probability based consecutive case series. A total of 316 patients meeting the inclusion criteria were selected. Results: Among the 316 patients, 102 (32.3%) were male, while 214 (67.7%) were female. Mean age was 40.98±7.8 SD (range 23 to 65 years). Regarding size of pituitary adenomas, 19 (6%) were microadenomas and 297 (94%) were macroadenomas. Overall postoperative CSF (poCSF) leak in our patients was 2.8%. Among cases with poCSF leak, 4 (3.9%) were male and five were female (2.3%) with the P-value of 0.477. Regarding size of pituitary adenomas, only one (5.3%) microadenoma had poCSF leak whereas 8 (2.7%) macroadenomas had poCSF leak with the P-value of 0.432. Conclusion: Pituitary adenomas can be successfully treated with EETS without SFR in patients who do not experience intraoperative CSF leak. [ABSTRACT FROM AUTHOR]

The gross composition of milk may affect the textural, compositional and microbiological properties of yoghurt. Sahiwal, Holstein Friesian milk procured from Dairy Animal Training and Research Center, B-block University of Veterinary and Animal Sciences, Pattoki and Nili Ravi buffalo milk procured from Nili Ravi buffalo Research Institute (BRI), Pattoki. All other ingredients purchased from Sigma Aldrich Germany through local suppliers. Milk was standardized to 15% total solids with 3.5% fat. Skim Milk Powder (SMP) was used for the standardization of the yogurt. Milk was pasteurized at 82oC for 5 minutes and cool down to 43℃ and at this temperature starter culture was added. Yogurt batch incubated at 43℃ for 3 to 3.5 h. During this period pH was monitored regularly and as the pH drops to 4.6, the batch was shifted to blast chilling at 1-2℃ and then stored at 4℃. Sensory evaluation of all yogurt samples was carried out on a 9-point hedonic scale. All the tests were carried out on day 0, 7, 14 and 21. The collected data was investigated through ANOVA technique under complete randomized design (CRD) using SAS 9.1 software. The results showed that yoghurt made from Nili Ravi buffalo milk (To) showed a significant (p= 0.5) overall acceptability as compared to other treatments. Yoghurt made from Nili Ravi buffalo milk (To) were followed by T3, T4, T1 and T5 respectively. The Nili Ravi buffalo milk showed higher acceptability to the consumers. [ABSTRACT FROM AUTHOR]

Crabs and lobsters are valuable crustaceans that contribute enormously to the seafood needs of the growing human population. This paper presents a comprehensive analysis of single- and multi-stage object detectors for the detection of crabs and lobsters using images captured onboard fishing boats. We investigate the speed and accuracy of multiple object detection techniques using a novel dataset, multiple backbone networks, various input sizes, and fine-tuned parameters. We extend our work to train lightweight models to accommodate the fishing boats equipped with low-power hardware systems. Firstly, we train Faster R-CNN, SSD, and YOLO with different backbones and tuning parameters. The models trained with higher input sizes resulted in lower frames per second (FPS) and vice versa. The base models were highly accurate but were compromised in computational and run-time costs. The lightweight models were adaptable to low-power hardware compared to the base models. Secondly, we improved the performance of YOLO (v3, v4, and tiny versions) using custom anchors generated by the k-means clustering approach using our novel dataset. The YOLO (v4 and it's tiny version) achieved mean average precision (mAP) of 99.2% and 95.2%, respectively. The YOLOv4-tiny trained on the custom anchor-based dataset is capable of precisely detecting crabs and lobsters onboard fishing boats at 64 frames per second (FPS) on an NVidia GeForce RTX 3070 GPU. The Results obtained identified the strengths and weaknesses of each method towards a trade-off between speed and accuracy for detecting objects in input images. [ABSTRACT FROM AUTHOR]

Wheat is a major food for many people globally. It's essential and widely grown worldwide. The effects of salinity were evaluated of 40 bread wheat genotypes at the seedling stage using heritability and genotypic association analysis. In this experiment, the pots were used to grow the seeds and were subjected to four different concentrations of salt (one control and three salt environments). Hence, the experiment was conducted using a complete randomized design CRD with four replications to determine the salinity-tolerant genotypes. The studied seedling traits namely were, germination percentage (GP), root length (RL), shoot length (SL), shoot fresh weight (SFW), root fresh weight (RFW), shoot dry weight (SDW), root dry weight (RDW), seedling length (SDL), vigor index (VI), relative water content (RWC), chlorophyll content (CC), turgid weight (TW), seedling fresh weight (SdFW), seedling dry weight (SdDW), and stomatal conductance (SC). Analysis of variance results showed that significance variability presence among genotypes and treatments (differnet salinity stressed). The genotypes G5, G27, and G37 performed well against the salinity stress and were considered salinity tolerant while the genotypes G12, G22, and G32 performed worst against stress and were considered salinity susceptible cultivars. The relative water content had a highly significant association in all salinity stressed conditions with all studied attributes while stomatal conductance had a non-significant association. The increase in the salt concentration delayed or stopped the seeds from germinating and in the case of other traits they were significantly affected by the saline environment. Our study suggests that, in future breeding programs, we may derive significant benefits from genotypes that have consistently performed well under salt stress conditions. These genotypes can be used to develop high-yielding, salt-tolerant wheat cultivars, thereby contributing to sustainable food production and global food security. [ABSTRACT FROM AUTHOR]