Miracle-dz/UniFAR-FTUs huggingface dataset:
Printed Gujarati Text Detection and Recognition (PGTDR)

India is the land of many historic manuscripts and parchments. More than one-third Indian manuscripts are written in Gujarati, according to statistics from the National Mission for Manuscripts. Since handwritten data on paper is difficult to maintain, digitizing these manuscripts and storing them in a database proves helpful in their preservation. The research will use machine learning models trained on a custom dataset to recognize Gujarati text and convert it into a machine-readable format. The research presents an effective, robust and high-precision system to conveniently digitize these manuscripts, using YOLOv8 for character detection with 98% precision and DenseNet model for text recognition with 99.2% accuracy. The system is also capable of detecting text blocks in newspaper articles and this proof of concept will be extremely efficient for detection of texts in Gujarati manuscripts.
New Particle Formation in the Mid-Latitude Upper Troposphere

Primary aerosol production due to new particle formation (NPF) in the upper troposphere and the impact that this might have on cloud condensation nuclei (CCN) concentration can be of sufficient magnitude to contribute to the uncertainty in radiative forcing. This uncertainty affects our ability to estimate how sensitive the climate is to greenhouse gas emissions. Therefore, new particle formation must be accurately defined, parametrized and accounted for in models. This research involved the deployment of instruments, data analysis and interpretation of particle formation events during the Mid-latitude Airborne Cirrus Properties Experiment (MACPEX) campaign. The approach combined field measurements and observations with extensive data analysis and modeling to study the process of new particle formation and growth to CCN active sizes. Simultaneous measurements of O3, CO, ultrafine aerosol particles and surface area from a high-altitude research aircraft were used to study tropospheric-stratospheric mixing as well as the frequency and location of NPF. It was found that the upper troposphere was an active region in the production of new particles by gas-to-particle conversion, that nucleation was triggered by convective clouds and mixing processes, and that NPF occurred in regions with high relative humidity and low surface area. In certain cases, mesoscale and
Association of Pre-PCI Blood Pressure and No-Reflow in Patients with Acute ST-Elevation Coronary Infarction

Background: Previous studies have established blood pressure (BP) as a pivotal factor influencing no-reflow following primary percutaneous coronary intervention (PCI) in patients with ST-elevation acute coronary infarction (STEMI). However, no relevant study has been conducted to investigate the optimal range of BP associated with the lowest risk of no-reflow among STEMI patients so far. Therefore, our objective was to evaluate the association between pre-PCI BP and the occurrence of no-reflow in patients with STEMI. Method: We included 1025 STEMI patients undergoing primary PCI. The BP pre-PCI was categorized into 20-mmHg increments. Logistic models were employed to assess the association of no-reflow with systolic blood pressure (SBP) or diastolic blood pressure (DBP). Three sensitivity analyses were conducted to further confirm the robustness of the association between blood pressure and no-reflow. Results: SBP or DBP exhibited a U-shaped curve association with no-reflow. No-reflow was higher in patients with lower SBP (<100 mmHg) (adjusted hazard ratio (OR) 3.64, 95% confidence interval (CI) 1.84,7.21; p < 0.001) and lower DBP (<60 mmHg) (OR 3.28, 95% CI 1.63,6.49; p < 0.001) [reference: 120 ≤SBP <140; 80 ≤DBP <100 mmHg], respectively. Furthermore, no-reflow was higher in patients with higher SBP (≥160 mmHg) (OR 2.07, 95% CI 1.27,3.36; p = 0.003) and DBP (≥100 mmHg) (OR 3.36, 95% CI 2.07,5.46; p < 0.001), respectively. The results of sensitivity analyses were consistent with the above findings. Conclusion: Maintaining a pre-PCI SBP within the range of 120 to 140 mmHg and a DBP within the range of 80 to 100 mmHg may be confer benefits to patients with STEMI in no-reflow.
EXPLORING THE THERAPEUTIC POTENTIAL OF AM114: A BORONIC CHALCONE DERIVATIVE INDUCE APOPTOSIS AND SUPPRESS PROINFLAMMATORY CYTOKINES AND CHEMOKINES IN INTERLEUKIN-1β STIMULATED HUMAN THP-1 DERIVED MACROPHAGES

Objective: Chalcones and their derivatives display a wide range of pharmacological activities. This study examined the effects of AM114, a boronic-chalcone derivative, on human THP-1-derived macrophages with and without interleukin-1β (IL-1β) stimulation. Methods: AM114 and Aspirin-treated THP-1-derived macrophages underwent activation with or without interleukin-1β. The IC50 concentrations of AM114 and Aspirin were determined through an MTT test. Apoptosis was measured using various techniques, including staining with acridine orange/Ethidium bromide, Hoechst 33342, and rhodamine 123 assays. Caspase-3 activity was measured using the spectrofluorimetric technique, while DNA fragmentation was assessed via agarose gel electrophoresis. Pro-inflammatory cytokines such as interleukin-6 (IL-6) and chemokines like interleukin-8 (IL-8) were measured using enzyme-linked immunosorbent assays. Results: AM114 and Aspirin showed dose-dependent cytotoxic effects on THP-1 macrophages. Induction of apoptosis was detected in AM114-treated THP-1 macrophages activated with IL-1β compared to macrophages without IL-1β. The gradation of dye uptake, membrane blebbing, increased caspase-3 activity, and DNA fragmentation ensures the induction of apoptosis, which indicates the cell's morphological changes, biochemical processes, and mitochondrial activity. Treating AM114 in IL-1β-activated THP-1 macrophages significantly reduced pro-inflammatory cytokines (IL-6) and chemokines (IL-8), suggesting its anti-cytokine potential in inflammatory diseases. Conclusion: The study results emphasize that AM114 could act as an anti-inflammatory agent by triggering apoptosis and reducing the release of cytokines and chemokines in inflammatory conditions. As a result, it may be used as a therapeutic option for inflammatory diseases.
Potential Application of Nanotechnology on Cement Based Materials

The tensile strength of cement paste is one of the most important of the mechanical properties that influence shrinkage cracks in cementious materials. Cement pastes which exhibit low tensile strength, tend to exhibit greater shrinkage crack potential and reduced durability. If the tensile strength in cement paste can be increased, then the shrinkage cracking potential can be minimized. It is believed that the strength and cohesion of cement paste is controlled by the formation of Calcium Silicate Hydrate (C-S-H) gel. In order to be able to enhance macroscopic mechanical properties (tensile strength) it is necessary to understand the structure and behavior of C-S-H gel at the atomic level. Molecular statics was used to determine minimal potential energy and the mechanical properties of crystalline C-S-H structures. It was found that when bonds in the silicate chains of crystalline C-S-H structures were broken at certain places, the computed elastic modulus was comparable with macro level experimental values. From this study a plausible atomic structure of C-S-H gel is proposed. Further research effort builds upon the previous work by using molecular dynamics to derive tensile and compressive strength of C-S-H structures from uniaxial stress-strain data. The results from the molecular dynamics simulations showed that the maximum strength (i.e., compressive and tensile) for the proposed C-S-H gel is magnitudes higher than the strength at the macro level. However, the tensile strength of the proposed C-S-H gel is 23% of the compressive strength. This research also concludes that electrostatic forces and bond forces in the silicate chains are the main contributors to cement strength at the atomic level and that breakage in the silicate chains leads to low tensile strength in C-S-H gel.
The use of machine learning on administrative and survey data to predict suicidal thoughts and behaviors: a systematic review

Background Machine learning is a promising tool in the area of suicide prevention due to its ability to combine the effects of multiple risk factors and complex interactions. The power of machine learning has led to an influx of studies on suicide prediction, as well as a few recent reviews. Our study distinguished between data sources and reported the most important predictors of suicide outcomes identified in the literature. Objective Our study aimed to identify studies that applied machine learning techniques to administrative and survey data, summarize performance metrics reported in those studies, and enumerate the important risk factors of suicidal thoughts and behaviors identified. Methods A systematic literature search of PubMed, Medline, Embase, PsycINFO, Web of Science, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Allied and Complementary Medicine Database (AMED) to identify all studies that have used machine learning to predict suicidal thoughts and behaviors using administrative and survey data was performed. The search was conducted for articles published between January 1, 2019 and May 11, 2022. In addition, all articles identified in three recently published systematic reviews (the last of which included studies up until January 1, 2019) were retained if they met our inclusion criteria. The predictive power of machine learning methods in predicting suicidal thoughts and behaviors was explored using box plots to summarize the distribution of the area under the receiver operating characteristic curve (AUC) values by machine learning method and suicide outcome (i.e., suicidal thoughts, suicide attempt, and death by suicide). Mean AUCs with 95% confidence intervals (CIs) were computed for each suicide outcome by study design, data source, total sample size, sample size of cases, and machine learning methods employed. The most important risk factors were listed. Results The search strategy identified 2,200 unique records, of which 104 articles met the inclusion criteria. Machine learning algorithms achieved good prediction of suicidal thoughts and behaviors (i.e., an AUC between 0.80 and 0.89); however, their predictive power appears to differ across suicide outcomes. The boosting algorithms achieved good prediction of suicidal thoughts, death by suicide, and all suicide outcomes combined, while neural network algorithms achieved good prediction of suicide attempts. The risk factors for suicidal thoughts and behaviors differed depending on the data source and the population under study. Conclusion The predictive utility of machine learning for suicidal thoughts and behaviors largely depends on the approach used. The findings of the current review should prove helpful in preparing future machine learning models using administrative and survey data. Systematic review registration https://www.crd. york.ac. uk/prospero/display_record. php? ID=CRD42022333454 identifier CRD42022333454.
Enhanced Gray Wolf Optimizer For Exploring Social Network Communities

The identification of communities within social networks holds paramount importance in scientific investigations and social network analyses. Communities refer to compact clusters of nodes that exhibit stronger interconnections with each other rather than with nodes outside their community. By accurately detecting and understanding these communities, we can achieve profound insights into the structure and functionality of social networks. In this research, we introduce an innovative and improved meta-heuristic algorithm specifically designed for detecting community in social networks. Leveraging the powerful gray wolf algorithm as its foundation, our proposed algorithm employs a combination of mutation, combination, and local search operators to significantly enhance its performance. Moreover, to further refine the outcomes, we have integrated the label propagation algorithm into our approach. To measure the efficacy of our proposed algorithm, extensive evaluations were conducted on various social network datasets. The findings consistently validate our algorithm's ability to converge towards optimal results, thanks to its exceptional accuracy and precision. By introducing this advanced meta-heuristic algorithm, we make contributions to the field of social network analysis by providing a robust and efficient solution for community detection. Our algorithm empowers researchers and analysts to acquire a more profound comprehension of social network structures and functions, thereby facilitating improved decision-making and problem-solving.
Structure-Aware Scheduling Algorithm for Deadline-Constrained Scientific Workflows in the Cloud

—Cloud computing provides pay-per-use IT services through the Internet. Although cloud computing resources can help scientific workflow applications, several algorithms face the problem of meeting the user’s deadline while minimising the cost of workflow execution. In the cloud, selecting the appropriate type and the exact number of VMs is a major challenge for scheduling algorithms, as tasks in workflow applications are distributed very differently. Depending on workflow requirements, algorithms need to decide when to provision or de-provision VMs. Therefore, this paper presents an algorithm for effectively selecting and allocating resources. Based on the workflow structure, it decides the type and number of VMs to use and when to lease and release them. For some structures, our proposed algorithm uses the initial rented VMs to schedule all tasks of the same workflow to minimise data transfer costs. We evaluate the performance of our algorithm by simulating it with synthetic workflows derived from real scientific workflows with different structures. Our algorithm is compared with Dyna and CGA approaches in terms of meeting deadlines and execution costs. The experimental results show that the proposed algorithm met all the deadline factors of each workflow, while the CGA and Dyna algorithms met 25% and 50%, respectively, of all the deadline factors of all workflows. The results also show that the proposed algorithm provides more cost-efficient schedules than CGA and Dyna.
Peculiarities of Adolescent, Qualified Female Volleyball Players’ Shoulder Girdle

Background and purpose. Many regularly performed spikes and serves lead to a shoulder girdle adaptation that could be sport-specific and result in traumatic events. The aim was to evaluate shoulder joints’ range of movements (ROM), peak isometric strength of arm muscles, submaximal force repetition error, and upper body dynamic stability in adolescent female volleyball players. Materials and methods. 15 qualified, 17.7 ± 2.1-year-old, female volleyball players participated. The shoulder active ROM of internal rotation (IR), external rotation (ER), flexion, extension, abduction, adduction, horizontal abduction (HABD), and horizontal adduction (HADD) were measured. The peak force and the ability to repeat the same submaximal force of IR, ER, and extension were determined. Dynamic stability was detected by the Y-upper-body dynamic balance test (Y-UBDST). Results. The ER ROM was increased but the IR and HADD ROMs were decreased on both shoulders . The extension and adduction ROMs were smaller in the dominant (D) shoulder (p < 0.05). The D arm’s IR, ER, and extension muscles developed greater peak forces (p < 0.05). ER/IR muscles’ peak forces ratio was 0.94 ± 0.18 in both shoulders. The ER and extension muscles submaximal force repetition error was smaller in the D arm (p < 0.05). The mean score of the normalized reaching distances in the Y-UBDST was symmetrical: 95 ± 6% in the D and 94 ± 7% in the non-dominant (ND) arm. The correlation between the rotators’ peak force and the Y-UBDBT was not significant. Conclusions. The shoulder girdle characteristics were optimal and adaptable for volleyball spike and serve movements in those adolescent qualified female volleyball players without pathological changes in pre-season.
Differential response to climate change among populations for woody plant species : an ecological and physiological approach

XV CHAPTER 1 GENERAL INTRODUCTION . 1 1.1 BACKGROUND . 1 1.2 REVIEW OF LITERATURE . 4 1.3 OVERVIEW OF MY THESIS . 17 CHAPTER 2 ELEVATED TEMPERATURE IS MORE EFFECTIVE THAN ELEVATED [CO2] IN EXPOSING GENOTYPIC VARIATION IN TELOPEA SPECIOSISSIMA GROWTH PLASTICITY . 23 2.1 INTRODUCTION . 23 2.2 MATERIALS AND METHODS . 29 2.3 RESULTS . 35 2.4 DISCUSSION . 55
Phytoremediation of Nitrogen Impacted Soil and Groundwater at a Fertilizer Facility in Central Alberta

In-situ remediation techniques such as phytoremediation have shown promise as economical alternatives for reducing the risk of environmental contaminants at impacted sites. Research trials were initiated to determine the efficacy of phytoremediation for soil and groundwater contaminated with high levels of nitrogen fertilizer at a fertilizer plant in Alberta, Canada. Experimental trials were conducted in environmental growth chambers, and carried out for a growing degree day period equivalent to an average growing season. Initially, plant growth trials were conducted with soils artificially contaminated with varying levels of ammonium nitrate to determine the approximate upper limit of plant nitrogen tolerance. Historically contaminated soil and groundwater containing high levels of ammonium, nitrate, phosphate and sulfate fertilizers was then investigated using electromagnetic surveying, sampling and chemical analysis. Using this data, samples were collected and growth chamber experiments designed to determine if plants could assist in the remediation of naturally occurring soils and groundwater contaminated with excess fertilizer. Results indicate that plants can take up excess soil nitrogen caused by fertilizer contamination. Phytoremediation is potentially effective under conditions where soils are contaminated by high concentrations of a variety of plant nutrients so long as conditions are not phytotoxic, as well as being economical, sustainable and aesthetically pleasing. The results of this research may be used to develop phytoremediation programs at western Canadian fertilizer facilities.
Advances in Malware Analysis and Detection in Cloud Computing Environments: A Review

Cloud computing, integral for data storage and online services, presents significant advantages over traditional data storage and distribution methods, including enhanced convenience, on-demand storage, scalability, and cost efficiency. Its growing adoption in securing Internet of Things (IoT) and cyber-physical systems (CPS) against various cyber threats offers numerous opportunities. Despite the continuous evolution of malware and the lack of a universally effective detection method, cloud environments provide a promising approach for malware detection. Cloud computing, recognized for its efficiency, scalability, flexibility, and reliability on elastic resources, is widely utilized in the IT industry to support IT infrastructure and services. However, one of the foremost security challenges faced is malware attacks. Certain antivirus scanners struggle to detect metamorphic or encrypted malware in cloud environments due to complexity and scale, allowing such threats to evade detection. High detection rates with precision in reducing false positives are essential. Machine learning (ML) classifiers, a vital component in Artificial Intelligence (AI) systems, require training on extensive data volumes to develop credible models with high detection rates. Traditional detection methods face challenges in identifying complex malware, as modern malware employs contemporary packaging and obfuscation techniques to circumvent security measures. This paper provides a detailed discussion on detecting malware in cloud environments and the advantages of cloud computing in safeguarding IoT and CPS from cyber attacks. It presents a survey on malware analysis and detection models, aiding researchers in identifying limitations of traditional malware detection models in cloud environments and inspiring the design of innovative models with enhanced quality of service
Precipitable Water Comparisons Over Ghana using PPP Techniques and Reanalysis Data

Atmospheric Water vapor is an important greenhouse gas and contributes greatly in maintaining the Earth’s energy balance. This critical meteorological parameter is not being sensed by any of the 22 synoptic weather stations in Ghana. This study presents a highly precise tool for water vapor sensing based on the concept Global Navigation Satellite Systems (GNSS) meteorology and tests the computed results against global reanalysis data. Conventional approaches used to sense the atmospheric water vapor or Precipitable Water (PW) such as radiosondes, hygrometers, microwave radiometers or sun photometers are expensive and have coverage and temporal limitations. Whereas GNSS meteorological concept offers an easier, inexpensive and all-weather technique to retrieve PW or Integrated Water Vapor (IWV) from zenith tropospheric delays (ZTD) over a reference station. This study employed precise point positioning (PPP) techniques to quantify the extend of delays on the signal due to the troposphere and stratosphere where atmospheric water vapor resides. Stringent processing criteria were set using an elevation cut-off of 5 degrees, precise orbital and clock products were used as well as nominal tropospheric corrections and mapping functions implemented. The delays which are originally slanted are mapped unto the zenith direction and integrated with surface meteorological parameters to retrieve PW or IWV. The gLAB software, Canadian Spatial Reference System (CSRS) and Automatic Precise Positioning Service (APPS) online PPP services were the approaches used to compute ZTD. PW values obtained were compared with Japanese Metro Agency Reanalysis (JRA), European Centre for Medium-Range Weather Forecasts Reanalysis (ERA-interim) and National Center for Environmental Prediction (NCEP) global reanalysis data. Correlation analysis were run on the logged station data using the three approaches and global reanalysis data. The obtained results show stronger correlation between the retrieved PW values and those provided by the ERA-interim. Finally, the study results indicate that with a more densified network of GNSS base stations the retrieved PW or IWV will greatly improve numerical weather predictions in Ghana. Keywords : GNSS Signals, PPP, Integrated Water vapour, Precipitable Water, Reanalysis Models
Variations of skill-related fitness components in normal and overweight high school children

Adolescence means "stepping into adult life. " It is a prepubertal phase where the transmutation of childhood into adulthood occurs and the physical and psychosomatic growth of a child takes place. This study has focused on Normal & Over Weight in school going children aged between 10 and 19 years, and their status in skill related fitness. The minimum sample size for each group = 100. The subjects taken into the study were adolescent children with an age group of 10-19 years. For the study, all the students were taken from school who were studying between the 5th standard to 12th standards. After screening the children, the eligible students were randomly called to have their body composition assessed class and section-wise. The children were divided into two groups based on their Body Mass Index (BMI). The BMI was assessed with the weight in kgs divided by height in meters square of the child. The children were divided into two group and skill fitness outcome measures namely Broad Jump Test (BJT); Hand Grip Strength Test (HGS); Bent Arm Hang Test (BAH) were used. This study concluded that the physical fitness that includes skill fitness was decreased in Overweight than Normal weight adolescent boys and girls.