Miracle-dz/UniFAR-FTUs huggingface dataset:
Imatinib resistance in CML and its association with age & gender.

Objective: To evaluate the association of gender and age with the resistance shown to Imatinib in CML with evaluation of WBCC in different age groups. Study Design: Cross Sectional Analytical study. Setting: Islamic International Medical College, Riphah International University, Islamabad, Pakistan. Patients were enrolled from the CML clinic in Holy Family Hospital, Rawalpindi. Period: January 2019 to December 2021. Material & Methods: Sampling technique was non probability consecutive sampling. We included 75 newly diagnosed CML patients, who were taking Imatinib 400 mg. Blood samples of all these patients were analysed at the start of treatment and also after 3 months to determine the complete haematological response (CHR) and patients were labelled as resistant who failed to achieve complete haematological response at this stage according to the Leukemia Net guidelines. Results: Our study demonstrates that 54.7% of the patients were Imatinib responders while 45.3 % patients were Imatinib resistant. Patients who were labelled as responders have shown complete haematological remission at 3 months of treatment. (Platelet Count < 450 x 10 9/L - WBCC < 10 x 10 9/L - Differential without Immature Granulocytes (MC, PMC, MB) and With Less Than 5% Basophils - Non-Palpable Spleen) A chi square test of independence was performed to examine the relation between gender and response and between age groups and response. The relationship between age groups and response is significant with p value of 0.003. While the relationship when determined between gender and response, it was found to be non-significant with p value of 0.08. It was also found out that WBC count in responders is at lowest in second age group and also highest in second age group in case of resistant patients that is age between 31-40 years. Conclusion: Imatinib resistance is common while treating patients with chronic myeloid leukemia. There is significant association with the different age groups and non-significant association with the gender in terms of response to the treatment of Imatinib. It was also found that age also effects the WBC count with Imatinib though it was not significant.
Driver Drowsiness Alert Using Machine Learning

Human error-related traffic accidents result in an increasing number of fatalities and injuries worldwide each year. It has been determined that driving when fatigued is a major contributor to auto accidents. Growing weariness has been shown to impair driving performance, and the collisions that result from this impairment account for more than 20% of all motor accidents. The goal of the driver drowsiness detection system is to increase road safety by reducing the frequency of accidents caused by sleepy drivers. Accidents caused by tiredness have increased recently. Many facial expressions, such as fatigue in the eyes, can be used to detect drowsiness. The suggested device watches the driver's eyes and uses a webcam pointed directly at the driver's face to identify signs of fatigue. A warning signal is issued to the driver to alert them when drowsiness is detected.
An Improved YOLOv5 Detection Method for Strip Surface Defect

Traditional steel surface defect detection methods have issues such as low detection accuracy and efficiency. It is suggested a technique based on enhanced YOLOv5 steel surface fault identification. In order to improve the network's capacity to handle feature information, this study suggests adding SE attention mechanisms to the YOLOv5s network's Backbone, Neck, and SPPF and replacing the C3 structure at the end of the Backbone with C3TR. To achieve network model lightweight, it is proposed to replace the Neck part in the YOLOv5s model and the convolution in SPPF with Ghost convolution. Then, to improve network feature fusion, extend the improved SPPF to the Neck part. Introduce the SPD convolution in the Backbone section to reduce the information loss caused by the neural network's convolution. The tested mAP is 75.3%, which is 5.3% higher than the original model, 24.6% higher than the SSD method, and 4.6% higher than the YOLOv6 algorithm after the upgraded model has been trained on the NEU-DET dataset. The observed FPS is 81.3, which is 6.3 more than the relative literature algorithm and 18.0 more than the SSD algorithm.
Improvement in Psychological Well-Being and Changes in Brain and Autonomic Nervous System Activity in Novices After Taoist Meditation Training

There is a growing body of scientific evidence that supports positive effects of mindfulness practices on physical and mental health. To further extend the knowledge about the effects of meditation that occur during and after meditation practice, this study examined changes in physiological and brain activity parameters following 8 weeks of meditation training (2 sessions per week). The current study also sought to investigate whether an individual's physiological approach could be detected in the early stages of meditation training, as we are considering the development of a device that could assist individuals in the early stages of their meditative practice.
Mapping the Scope and Impacts of the EPA Environmental Justice Small Grants Program

As state and national environmental justice (EJ) programs have expanded in recent years to address new risks and challenges, there are important lessons to be learned from one of the earliest federal agency EJ programs, the EPA Environmental Justice Small Grants (EJSG) program. Since 1994, over 1,000 small grants have been awarded to support communities in developing solutions to local environmental and public health problems; however, the collective impact of these investments has never been evaluated. Using GIS to map the locations of EJSG funds relative to data from the Toxics Release Inventory (TRI), this paper addresses two main questions. First, are grants being awarded to the types of communities (low-income, minority areas facing major environmental hazards) intended to be served by the program? Second, have there been any significant environmental changes in EJSG areas since the start of the program? Results of county-level spatial analysis reveal that EJ grants are only in part being awarded to minority or low-income counties facing higher than the national average TRI releases and that average toxic releases have increased significantly in EJSG counties in some EPA regions relative to nonEJSG counties. These results highlight the need for systematic EJ program evaluation and coordination.
TOWARDS SUCCESSFUL ORGANIZATIONAL CHANGE MANAGEMENT - NEW PROCESS MODEL IDENTIFICATION

The ever-evolving corporate landscape, catalyzed by the COVID-19 pandemic and rapid advancements in information technologies, has given rise to a new organizational philosophy. As novel sectors emerge and new forms of work and business processes unfold, change becomes a necessity integrated into companies' survival strategies. The dynamic, diverse, and discontinuous nature of the contemporary business environment demands a deeper understanding of organizational changes. Despite the prevalence of change, a significant number of initiated change programs fail, emphasizing the need for effective change management. While numerous change management theories exist, empirical validation and practical testing are often lacking, creating a gap between theory and application. This paper presents a critical exploration of prominent process models of organizational change, aiming to identify a unified diagnostic process model that addresses existing shortcomings and leverages the strengths of these models. The research identifies ten common phases in various process models, leading to the formulation of a 10F process model of organizational change. This model encompasses crucial stages such as problem diagnosis, creating a sense of urgency, establishing leadership roles, vision creation, planning, communication, implementation, short-term results, stabilization, and monitoring. The 10F model offers a comprehensive framework for understanding and managing diverse organizational changes. This research contributes to bridging the gap between theory and practice, providing valuable insights for managers, researchers, and practitioners engaged in organizational change initiatives.
Capturing the Spread of Information in Heterogeneous V2X Through Scalable Computation

—Emerging V2X technology enables vehicles to exchange messages with each other (V2V) and with signaling infrastructure (I2V) on the roadways. Information propagation in transportation networks is highly influenced by both vehicle mobility and wireless communication. As for vehicle mobility, realistic traffic flow changes with time, exhibiting sharp time-triggered transitions, due to external factors such as traffic lights. Thus, mobility process is temporally heterogeneous and not smooth, which fundamentally alters the dynamics of V2X (V2V and I2V together) message propagation in a complex manner. As for wireless communication, communication heterogeneity is an integral component of V2X systems - different types of vehicles may have different communication capabilities, and V2V and I2V communications coexist. We propose a mathematical framework, based on a continuous-time Markov chain (CTMC), for characterizing the spatio-temporal spread of V2X information (1) when the traffic flow exhibits sharp time-triggered transitions and (2) when there exists communication heterogeneity comprising of different V2V commutation capabilities, different wireless communication conditions, and both V2V and I2V. We prove that the state evolutions under the CTMC model converge to a set of differential equations in the asymptotic limit of a large number of vehicles, enabling computations that gracefully scale with increase in network size and the number of vehicles. Our framework can accommodate arbitrary traffic synchronization patterns corresponding for example to incorporate the presence of an arbitrary number of traffic signals. Furthermore, numerical computations using this mathematical framework answer several questions that influence the practice of V2X network design and security.
Forecasting of the Stock Market Price Using LSTM-CNN Model with Various Representations of Collected Dataset

One of the most challenging tasks in computation is stock market forecasting. Many factors, such as physiological versus physical factors, rational versus irrational behavior, investor attitude, market rumours, etc., impact the projection. We examine the potential impact of data analysis on this industry. The productive market hypothesis expresses that when all market members and financial backers have prompt admittance to data about an organization and stock market developments, the impacts of those advancements have proactively been figured into the stock price. Thus, the claim is guaranteed to be the main spot value that can be utilized to predict a market's future behavior. This study applies Machine Learning (ML) calculations on authentic stock value information to appraise the future pattern, considering the previous stock cost as the completion of every contributing variable. ML methods can be used to make highly accurate forecasts and uncover patterns and insights we were unaware of. We give a structure that utilizes the LSTM-CNN model and the net development computation technique to investigate and project an organization's future development.
Attention-Enabled Lightweight Neural Network Architecture for Detection of Action Unit Activation

Facial Action Unit (AU) detection is of major importance in a broad range of artificial intelligence applications such as healthcare, Facial Expression Recognition (FER), and mental state analysis. In this paper, we present an innovative, resource-efficient facial AU detection model, embedding both spatial and channel attention mechanisms into a convolutional neural network (CNN). Along with a unique data input system leveraging image data and binary-encoded AU activation labels, our model enhances AU detection capabilities while simultaneously offering interpretability for FER systems. In contrast to existing state-of-the-art models, our proposal’s streamlined architecture, combined with superior performance, establishes it as an ideal solution for resource-limited environments like mobile and embedded systems with computational constraints. The model was trained and evaluated utilizing the BP4D, CK+, DISFA, FER2013+, and RAF-DB datasets, with the latter two being particularly significant as they represent wild datasets for facial expression recognition. These datasets encompass ground truth emotions matched with corresponding AU activations according to the Facial Action Coding System. Various metrics, including F1 score, accuracy, and Euclidean distance, showcase its effectiveness in AU detection and interpretability.
Requirement Mining and Analysis of Complex Products Based on External Stakeholders

Complex products have multiple external stakeholders, and their influence is different due to different levels of power and interests. At present, enterprises mainly focus on a small number of influential external stakeholders, while ignoring others, which will affect the satisfaction of different stakeholders with complex products and is not conducive to improving the market competitiveness of enterprises. In addition, a large number of unstructured data related to complex products contain much information about the stakeholders' requirements. This paper proposes a requirement mining and analysis method, which determines the importance of top-level technical indicators by considering external stakeholders and their requirements. The proposed methods start with determining the importance of external stakeholders of complex products through the Power/Interest matrix. Afterward, based on the unstructured data requirement mining model, the requirements and importance of external stakeholders are determined from different data sources. Finally, according to the results of external stakeholder analysis and requirement mining, the house of quality is constructed to accurately map the multiple external stakeholders' requirements to the importance of the top-level technical indicators of complex products, which provides a basis for the functional design of complex products. The proposed method is verified by a case study of subway design.
Bacteriophages and Their Host Range in Multidrug-Resistant Bacterial Disease Treatment

The rapid emergence of multidrug-resistant (MDR) bacteria in recent times has prompted the search for new and more potent antibiotics. Bacteriophages (commonly known as phages) are viruses that target and infect their bacterial hosts. As such, they are also a potential alternative to antibiotics. These phages can be broadly categorized into monovalent (with a narrow host range spectrum and specific to a single bacterial genus) and polyvalent (with a broad host range and specific to more than two genera). However, there is still much ambiguity in the use of these terms, with researchers often describing their phages differently. There is considerable research on the use of both narrow- and broad-host range phages in the treatment of infections and diseases caused by MDR bacteria, including tuberculosis, cystic fibrosis, and carbapenem-resistant Enterobacterales (CRE) infectious diseases. From this, it is clear that the host range of these phages plays a vital role in determining the effectiveness of any phage therapy, and this factor is usually analyzed based on the advantages and limitations of different host ranges. There have also been efforts to expand phage host ranges via phage cocktail development, phage engineering and combination therapies, in line with current technological advancements. This literature review aims to provide a more in-depth understanding of the role of phage host ranges in the effectiveness of treating MDR-bacterial diseases, by exploring the following: phage biology, the importance of phages in MDR bacteria diseases treatment, the importance of phage host range and its advantages and limitations, current findings and recent developments, and finally, possible future directions for wide host range phages.
Artificial Intelligence on Mobile Multimedia Networks for Call Admission Control Systems

An increased demand for multimedia services necessitates the implementation of high-speed networks and call admission control (CAC) schemes. Increasingly, consumers want to be able to watch movies and TV shows on their smartphones. The primary goal of this integrated neural fuzzy-based CAC method is to enhance quality of service (QoS) while also ensuring equitable allocation of resources, with the end goal of reducing the likelihood of calls being dropped or blocked in mobile multimedia networks. This results in a more accurate and reliable control system. This hybrid technique is further developed to produce a more effective computational model for traffic management and the equitable allocation of radio resources for new calls and handoff calls. The findings of the simulation reveal that a neural fuzzy-based CAC has the potential to achieve low call-dropping probability and maximum resource utilization in high-speed networks.
Utilizing Internet of Things for Automating Food Security and Savvy Agriculture: A Review

Food security, a concept rooted in the 1970s, emphasizes ensuring the global availability and affordability of essential food items. Agriculture, land, and water stand as vital resources for human survival. However, their contamination resulting from urbanization, cultivation, and chemical usage hinders optimal production quality. In this context, the adoption of Internet of Things (IoT) paradigm has been increasingly prevalent across sectors, with agriculture benefiting from improved productivity and reduced planting costs. This comprehensive study delves into the significance of IoT in agribusiness, surveying its fundamental components and addressing prevailing challenges. Specifically, it comprehensively covers the IoT ecosystem in agriculture, encompassing the employment of drones/UAVs, pertinent protocols, and networking challenges. Through this extensive review, we aim to shed light on the manifold opportunities and complexities associated with leveraging IoT for automated food security and savvy agriculture.
The origins of the southern Scandinavian wolf Canis lupus population: potential for natural immigration in relation to dispersal distances, geography and Baltic ice

Abstract Ever since the present phase of wolf Canis lupus population growth began in southern Scandinavia in 1983 there has been controversy surrounding their origins. Genetical analyses have clarified that the wolves originate from the Finnish-Russian populations, but the debate continues about how they came to be in southern Scandinavia, with many wolf-opponents claiming they have been released in a clandestine action. By comparing the geography of Scandinavia to known wolf dispersal behaviour our analysis focuses on whether it is possible for wolves to have recolonised southern Scandinavia without human assistance. From 298 published dispersal distances for North American wolves, 10 were over 500 km, with the longest being 886 km in a straight line. When also including data on actual distance moved, several wolves have been recorded to travel more than 4,000 km, often within only a few months. However, the published data are biased towards short-distance movements. Any wolves travelling from the Finnish-Russian border to the site of the 1983 reproduction would have to have travelled > 1,000 km, with potential routes being overland, or over the ice covered Baltic Sea during winter. From their present distribution, wolves have shown a clear ability to cross areas of sea-ice of up to 70 km. Therefore, it is possible for wolves to have colonised south Scandinavia through natural dispersal, although it requires movements at the extreme edge of what has been documented. As wolves expand in both southern Scandinavia and Finland, the distance between the populations will decrease, although contact will require passing through 500 km of the conflict-full reindeer-herding areas or crossing of the Baltic Ice.
A review on functional outcomes of intra-articular distal humerus fractures treated with recon plate using Mayo Elbow Performance Score (MEPS).

Objective: To Review on Functional Outcomes of Intra-articular Distal Humerus Fractures Treated with Recon Plate Using the Mayo Elbow Performance Score (MEPS). Study Design: Retrospective study. Setting: Department of Orthopaedic & Spine Surgery, Ghurki Trust Teaching Hospital, Lahore. Period: 2015 to 2021. Material & Methods: A total of 353 patients meeting the inclusion criteria enrolled in the study. Patients were followed up at three months postoperatively to assess functional outcome using the Mayo Elbow Performance Score (MEPS). All data were recorded on the proforma. All the collected data were entered and analyzed through SPSS version 22. The chi-square test was applied post-stratification, taking p≤0.05 as significant. Results: In this study, 353 patients were included; among these majority, 220(62.4%) were males, and 133 were female (37.7%). The mean Age of patients was 42.5±14.32, ranging from 18 to 75 years. 182(51.6%) were affected on the left side compared to the right Side 170(48.2%). In most cases, the history of a mode of injury was fall 266(75.3%), and only 87(24.6%) had RTA history. The average MEPS score was 87.37±15.81, ranging from 40 to 100. According to the distribution of functional outcome, 265(75.1%) reported excellent outcome, 52(14.7%) with good, 26(7.5%) as fair, and only 10(2.8%) reported poor outcome. Stratification of functional outcome and patient demographic history showed a statistically significant association between age, gender, side effect, mode of injury, and functional outcome as p<. 05. Conclusion: It is concluded that Recon plates provided patients who had distal humerus fractures an excellent functional result. It provides reliable stabilization for fractures of the distal humerus. Due to its improved stability, the device aids early mobility even in fractures with complications. Therefore, the surgeon should use this technique to avoid complications.
Nitrogen fertilizer rate management as a nitrous oxide mitigation strategy: development of a nitrous oxide emission reduction protocol (NERP).

Nitrous oxide (N2O) is a potent agricultural greenhouse gas (GHG). More than 50% of the global anthropogenic N2O flux is attributable to emissions from soil, primarily due to large fertilizer nitrogen (N) applications to corn and other non-leguminous crops. Quantification of the trade–offs between N2O emissions, fertilizer N rate, and crop yield is an essential requirement for informing management strategies aiming to reduce the agricultural sector GHG burden, without compromising productivity and producer livelihood. There is currently great interest in developing and implementing agricultural GHG reduction offset projects for inclusion within carbon offset markets. Nitrous oxide, with a global warming potential (GWP) of 298, is a major target for these endeavours due to the high payback associated with its emission prevention. In this paper we use robust quantitative relationships between fertilizer N rate and N2O emissions, along with a recently developed approach for determining economically profitable N rates for optimized crop yield, to propose a simple, transparent, and robust N2O emission reduction protocol (NERP) for generating agricultural GHG emission reduction credits. This NERP has the advantage of providing an economic and environmental incentive for producers and other stakeholders, necessary requirements in the implementation of agricultural offset projects.