Miracle-dz/UniFAR-FTUs huggingface dataset:
Effectiveness of compassion-based online therapy on suicidal thoughts and pain catastrophizing in female patients with multiple sclerosis in the relapsing–remitting phase

Introduction According to research, multiple sclerosis is related to suicidal thoughts and pain catastrophizing as psycho-pathological variables, and on the other hand, compassion-based treatment can reduce mental disorders by targeting dimensions such as self-compassion. Also, since this disease is progressive and over time can cause movement restrictions in these people, online psychotherapy can be a better option for these people. So, the purpose of this study was to investigate the effectiveness of online compassion therapy on suicidal thoughts and pain catastrophizing in female patients with multiple sclerosis. Methods The current research was applied and quasi-experimental in a pre-test-post-test manner with a control group. The research sample consisted of 30 patients with multiple sclerosis living in Shiraz in 2019, who were randomly divided into two 15-person experimental and control groups. The participants were tested on scales of suicidal thoughts and pain catastrophizing, and the treatment plan based on compassion therapy was presented to the participants of the experimental group in 8 two-hour sessions online. The control group was placed on the waiting list until the completion of the treatment sessions and the post-test implementation, and after the post-test implementation, they received the treatment. Control variables in this study included gender and disease phase. Then, their scores were measured, after completing the treatment, and a three-month follow-up period. Descriptive statistics and covariance test were used to analyze the data. Results Patients showed a clear improvement in the severity of symptoms in both variables. So that the scores of suicidal thoughts and pain catastrophizing in the experimental group after receiving the treatment, as well as after a three-month follow-up period, were significantly reduced (p < 0.001). Conclusion Confirming the effectiveness of online therapy based on compassion on improving psychological outcomes in these patients, as an effective and online treatment method, is a clear step towards continuing the implementation of psychological interventions and paying attention to the dimension of mental health in this group of people.
Accelerating Deep Reinforcement Learning via Phase-Level Parallelism for Robotics Applications

Deep Reinforcement Learning (DRL) plays a critical role in controlling future intelligent machines like robots and drones. Constantly retrained by newly arriving real-world data, DRL provides optimal autonomous control solutions for adapting to ever-changing environments. However, DRL repeats inference and training that are computationally expensive on resource-constraint mobile/embedded platforms. Even worse, DRL produces a severe hardware underutilization problem due to its unique execution pattern. To overcome the inefficiency of DRL, we propose Train Early Start, a new execution pattern for building the efficient DRL algorithm. Train Early Start parallelizes the inference and training execution, hiding the serialized performance bottleneck and improving the hardware utilization dramatically. Compared to the state-of-the-art mobile SoC, Train Early Start achieves 1.42x speedup and 1.13x energy efficiency.
Theoretical Models for Evaluating and Predicting the Diffusion of Safety Technologies in Construction

Several studies have investigated the benefits of applying emerging technologies in the construction industry, but information about how these technologies can be successfully implemented in construction, particularly for safety management is limited. Different theories and frameworks have been posited and successfully implemented in different industries. However, a detailed exploration of the utility of these frameworks within the context of construction safety management is missing. The main purpose of this research is to synthesize available information on different applicable theoretical models for evaluating and predicting the diffusion of safety technologies in construction. Using an integrated review process, the authors evaluated 12 promising theories and models that are rarely utilized in construction research. Potential applications, strengths, and limitations are discussed herein. The results of the review are used to propose multiple theoretical models that could be utilized to better understand and predict the diffusion of safety technologies in the construction industry. This study reveals the key factors, within multiple frameworks, that affect the adoption, adaptation, and infusion of technologies for safety management in construction. Practitioners and researchers could utilize information from this study to evaluate the diffusion of specific technologies within an organization and the construction industry.
Weighted Ensemble LSTM Model with Word Embedding Attention for E-Commerce Product Recommendation

— Nowadays, the proliferation of social media and e-commerce platforms is largely due to the development of internet technology. Additionally, consumers are used to the idea of using these platforms to share their thoughts and feelings with others through text or multimedia data. However, it is difficult to identify the best categorization methods for this type of data. Furthermore, users are seen to have difficulty understanding aspect-based feelings conveyed by other users, and the currently existing models’ accuracies are not up to par. Deep learning models used for sentiment analysis (SA) provide improved performance by finding out the actual emotions in the presented data. The aim of this research is to develop a weighted ensemble with Long Short-Term Memory (LSTM), and a specialised deep learning model using unique word embedding approaches to enhance its performance in sentiment analysis. The words with a strong connection to a particular class are given more weight by the Word Embedding Attention (WEA) technique. The weighted ensemble with LSTM yields superior outcomes because of its excellent generalization capabilities. By integrating the advantages of several models and mitigating the effects of each model’s shortcomings, ensemble voting raises the prediction accuracy. By lessening the influence of outliers or errors in individual model categorization, ensemble voting increases the robustness of categorization. This LSTM weighted ensemble achieves 99.82 % accuracy, 99.4% precision, 99.02% f-score, and 99.7% recall in sentiment analysis which is much higher when compared to the outcomes of conventional methods.
SEMANTIC KNOWLEDGE EMBEDDING DEEP LEARNING NETWORK FOR LAND COVER CLASSIFICATION

Abstract. Land cover classification is essential basic information and key parameters for environmental change research, geographical and national monitoring, and sustainable development planning. Deep learning can automatically and multi-level extract the features of complex features, which has been proven to be an effective method for information extraction. However, one of the major challenges of deep learning is its poor interpret-ability, which makes it difficult to understand and explain the reasoning behind its classification results. This paper proposes a deep cross-modal coupling model (CMCM) for integrating semantic features and visual features. The representation of knowledge map is indicatively introduced into remote sensing image classification. Compared to previous studies, the proposed method provides accurate descriptions of the complex semantic objects within a complex land cover environment. The results showed that the integration of semantic knowledge improved the accuracy and interpret-ability of land cover classification.
ANPR Streamlining for Enhanced Service with CRM

This research paper presents a comprehensive exploration into the integration of Automatic Number Plate Recognition System (ANPRS) with Customer Relationship Management (CRM), aiming to optimize notification delivery to registered vehicle owners. In this study, we leverage cutting-edge technologies, including Optical Character Recognition (OCR) and the Haar Cascade algorithm, to enhance the accuracy and efficiency of the ANPRS. The OCR technology enables the extraction of alphanumeric information from license plates, while the Haar Cascade algorithm ensures robust object detection, contributing to the system's overall precision. The primary focus of this research is to showcase the usability of the integrated system through a practical use case scenario. By seamlessly merging ANPRS with CRM, the system is capable of automatically identifying registered vehicles and delivering timely notifications to their owners. This functionality proves particularly valuable in scenarios such as parking management, security surveillance, and law enforcement, where instant communication with vehicle owners is crucial. Through this integration, businesses and organizations can enhance their operational efficiency and provide a more responsive and personalized service to customers.
Biometric fusion methods for adaptive face recognition in computer vision

v List of Figures . x List of Tables . xiii List of Symbols . xiv Abbreviations/Acronyms . xv Chapter 1 : Introduction . 1 1.1 Chapter Outline . 1 1.2 Overview of biometrics systems . 3 1.1.1 Biometrics Authentication . 4 1.1.2 Measurement requirements . 5 1.1.3 Modules of biometrics systems . 6 1.2 Biometric Facial Recognition . 8 1.2.1 Geometrical Facial Measurement.9 1.2.2 Approaches of Face Recognition . 9 1.3 The challenges in face recognition and this work . 11 1.4 Work Motivation . 12 1.4. 1 Geometric parameters . 12 1.4. 2 Face recognition across poses . 13 1.4. 3 Quadtree decomposition . 14 1.5 Contributions . 15 1.6 Thesis structure . 17 Chapter 2 : Camera Calibration and Face Recognition . 19 2.1 Chapter outline . 19 2.2 Introduction . 20 2.2.1 Perspective projection in camera. 26 2.2.2 Pinhole camera model . 29 2.3 Camera and projective parameters . 30 2.4 Classification of face recognition techniques . 32 2.4. 1 Face recognition across poses. 34 2.4. 2 The one sample per person problem . 36 2.5 Image quadtree decomposition in face recognition . 37 2.6 Summary . 39 Chapter 3 :Camera calibration based on perspective projection . 40
Life-Cycle Assessment of a Concrete Pedestrian Bridge: A Comparative Analysis of 3D Printing and Precast Techniques

This study aims to compare the environmental impact of precast and 3D concrete printing (3DCP) techniques with a pedestrian bridge case study. A detailed cradle-to-site life cycle assessment has been performed from the standpoint of material, construction, and installation stages. The results showed that the concrete used in 3DCP has a higher negative environmental impact compared to the precast method due to the higher percentage of cement used in printable concrete. However, since C3DP used less material than the precast technique, there is no significant difference in the environmental impact of the total concrete used between the 3DCP and precast bridges. In addition, due to the use of reinforcement and formwork in the precast technique, the environmental impact of the total materials used in the precast bridge was more adverse than the 3DCP bridge. Notably, due to using electricity for printing, the negative environmental impact of the construction process in 3DCP was significantly higher than in the precast technique. Finally, the total carbon dioxide equivalent emitted during the construction of the 3DCP bridge was 80% of the precast bridge.
Integrating Surrounding Vehicle Information for Vehicle Trajectory Representation and Abnormal Lane-Change Behavior Detection

The detection of abnormal lane-changing behavior in road vehicles has applications in traffic management and law enforcement. The primary approach to achieving this detection involves utilizing sensor data to characterize vehicle trajectories, extract distinctive parameters, and establish a detection model. Abnormal lane-changing behaviors can lead to unsafe interactions with surrounding vehicles, thereby increasing traffic risks. Therefore, solely focusing on individual vehicle perspectives and neglecting the influence of surrounding vehicles in abnormal lane-changing behavior detection has limitations. To address this, this study proposes a framework for abnormal lane-changing behavior detection. Initially, the study introduces a novel approach for representing vehicle trajectories that integrates information from surrounding vehicles. This facilitates the extraction of feature parameters considering the interactions between vehicles and distinguishing between different phases of lane-changing. The Light Gradient Boosting Machine (LGBM) algorithm is then employed to construct an abnormal lane-changing behavior detection model. The results indicate that this framework exhibits high detection accuracy, with the integration of surrounding vehicle information making a significant contribution to the detection outcomes.
The Effect of Laser Irradiation to Surfaces of Computer-Aided Design/Computer-Aided Fabrication Resin Blocks Coated with a Silane Coupling Agent on Bond Strength between the Resin Blocks and Composite Resin

The aim of this study was to investigate the effect of laser irradiation to computer-aided design/computer-aided fabrication (CAD/CAM) resin blocks coated with a silane coupling agent on the bond strength between resin blocks and composite resin. The CAD/CAM resin blocks used in this study were Cerasmart 300 (GC) and Vita Enamic (Vita); they were cut into plates and then subjected to a series of treatments. After processing with a silane coupling agent, treatment with a semiconductor laser was performed at 3.0, 5.0, and 7.0 W, followed by bonding procedures. The control group included those exposed to silane and bonded without laser application. After bonding, a mold with a simulated cavity was formed on the specimen and filled with flowable composite resin, and they were stored for 24 h or stressed by thermal cycling for subsequent testing that assessed the shear bond strength (n = 10). The results revealed that the bond strength was significantly enhanced by laser irradiation after applying a silane coupling agent (p < 0.03), whereas significant increase was not detected between the materials (p > 0.05). Particularly, 7 W laser irradiation had a significant increase on the bond strength between the composite resin and Cerasmart block after thermal cycling (p = 0.009). The SBS of the composite resin to CAD/CAM resin blocks was significantly enhanced by laser irradiation after silane coupling agent application.
Plants under Siege: Investigating the Relevance of ‘Ca. P. solani’ Cixiid Vectors through a Multi-Test Study

Crop losses caused by the plant pathogenic bacterium ‘Candidatus Phytoplasma solani’ (CaPsol) underscore the need to better understand its perplexing epidemiological pathways. Hyalesthes obsoletus (Hemiptera, Cixiidae) is a prominent CaPsol vector with three plant associations in Serbia (ex Urtica dioica/HobsUd; ex Convolvulus arvensis/HobsCa; ex Crepis foetida/HobsCf). Another cixiid planthopper, Reptalus quinquecostatus (Dufour), has been recently confirmed as a noteworthy CaPsol vector. A multi-test study assessed the relevance of H. obsoletus associations and R. quinquecostatus populations from Crataegus monogyna and Prunus spinosa in CaPsol occurrence in sugar beet, maize, and tobacco. Molecular typing of the CaPsol strains transmitted to test plants in experimental trials provided the first evidence of HobsUd transmitting CaPsol tuf-a type to sugar beet, HobsCa infecting maize and tobacco with tuf-b type, and HobsCf transmitting CaPsol tuf-b to maize. Affiliation of R. quinquecostatus with the specific CaPsol genotype, dSTOLg, was reaffirmed in this study. The possible involvement of R. quinquecostatus in maize redness disease and tobacco stolbur was suggested, given that this cixiid was identified as a vector of CaPsol to these crops. The obtained results indicate that the tested vectors pose a threat to cultivated plants in Serbia, underscoring the need to recognize their relevance in CaPsol disease occurrences.
Energy efficiency optimization of ethylene production process with respect to a novel FLPEM‐based material‐product nexus

Energy efficiency optimization and energy management can bring multiple economic benefits for the complicated petrochemical industries. However, the influence of the production scale and materials, and the uncertain data have a direct impact on the production prediction and energy efficiency optimization of ethylene production process, which is ignored by the conventional optimization strategies. To achieve energy management and reduce energy consumption considering the production technology, total energy, and material flows, a multiobjective energy efficiency optimization scheme for the ethylene production process with respect to material‐product nexus is proposed. Specifically, a total‐factor material‐product nexus model of the ethylene production is established by a novel functional link prediction error method–integrated algorithm, providing system‐level material equilibrium constraints for the energy efficiency optimization scheme. Then an optimization model with two mutually complementary energy efficiency indicators is established and implemented by the multiobjective particle swarm optimization integrating functional link prediction error method. The proposed energy efficiency optimization scheme is applied into a practical ethylene plant, and the optimization results demonstrate that energy efficiency of this plant is increased by 10.30%, synthetical energy consumption is decreased by 13.05%, and energy cost is decreased by 10.42%.
Spatial patterns of torrential rain in the Haihe River Basin and the corresponding large‐scale circulation

The classification of torrential rain is crucial to the flood mitigation and water resources, but the relevant research in the Haihe River Basin (HRB) is lacking. In this study, 156 regional torrential rain events from 1972 to 2022 were classified into two types using cluster analysis: eastern type and southern type. The eastern type is the typical torrential rain in the HRB, accounting for 61.5% of the total days, whereas the southern type mainly occurs in the piedmont plain with larger intensity and scope, which would pose more severe hazards. Corresponding large‐scale circulation patterns of the two types reveal a clear relationship between the HRB torrential rain and ascending motion, associated with the anomalies of low‐level moisture convergence and upper‐level wind divergence. The anomalous upper‐level divergence is accompanied by the northward expansion of South Asian High and intensified westerly jets. In the eastern type, strong low‐pressure anomalies north of Lake Baikal at mid‐level produce anomalous northwesterlies. The cold air from mid–high latitudes encounters enhanced southwesterly moist air accompanied by the northward expansion of Western Pacific Subtropical High (WPSH), resulting in the low‐level moisture convergence. In the southern type, the low‐level anomalous convergence is caused by a relatively weak cyclonic anomaly and significantly strengthened southeasterly moisture from the Pacific. The enhancement of southwesterlies results from the cooperative effect of the remarkably northwesterly WPSH and low‐pressure anomalies in South China Sea. As a whole, the eastern type is the result of convergence of cold air and southwesterly moisture driven by a dipole pattern at mid–high latitudes, whereas the southern type is mainly attributed to the accumulation of abundant southeasterly water vapour. This classification can identify high‐risk areas for torrential rain in the HRB and contribute to the forecast skills of extreme precipitation.
DEVELOPMENT OF A PYTHON-BASED POSITION CALCULATION SYSTEM FOR THE MOON’S VISIBLE POSITION IN EQUATORIAL COORDINATES

In the field of astronomy, it is important to know the position of the moon in equatorial coordinates. However, these calculations can be difficult to do manually. Therefore, a system for calculating the visible position of the moon in Python-based equatorial coordinates has been developed. This system uses a suitable algorithm to calculate the visible position of the moon in equatorial coordinates. The calculation results obtained from this system are then compared with the simulation results from the Stellarium software. System for calculating the visible position of the moon in equatorial coordinates with PythonRA values (alpha): 11h21m36.9s and declination (delta): 8° 28' 23” while the results from the Stellarium software simulation at the same time are RA (alpha): 11h18m0.33s and declination (delta): 8° 25' 18.2”. The results of the comparison show that the system for calculating the visible position of the moon in Python-based equatorial coordinates has a difference in RA of 0h3m36s and declination of 0°3'4.8” from the Stellarium simulation. The error value for the comparison of python calculations with Stellarium is RA = 2% and Declination = 0.6%. This shows that the use of python can simplify calculations with difficult algorithms and the results obtained are quite accurate. Thus, this Python-based system for calculating the visible position of the moon in equatorial coordinates can be used as a tool in the field of astronomy.
Camera Trapping and Conservation in Lambir Hills National Park, Sarawak

A rapid assessment using camera traps was carried out in Lambir Hills National Park from February 2004 to September 2004. In 1127 trap nights, six CamTrakker units and one DeerCam unit recorded a total of 225 wildlife photographs, which included seven orders, 11 families and 18 species of animals. Macaca nemestrina had the highest frequency of photo records with 63 exposures, while single exposures were recorded for Arctictis binturong, Echinosorex gymnurus, Prionodon linsang, Rheithrosciurus macrotis,Sus barbatus, and Macaca fascicularis. The rare Neofelis nebulosa was also recorded during this study. Activity patterns were calculated for five species. Low photo records of large mammals were noted and is believed to be caused by illegal hunting. The conservation of these species is discussed.
Vaping and COVID-19 pandemic: changes in e-cigarette use and conclusions for the future – narrative review

Introduction and Objective. Electronic cigarettes (e-cigarettes) have become the most common form of nicotine consumption among adolescents and young adults. Little is known about the impact of government-imposed restrictions during the COVID-19 pandemic on the vaping behaviour of both adolescents and adults. This study aimed to explore scientific research concerning the prevalence of e-cigarette usage following the onset of the COVID-19 pandemic. Review Methods. A systematic, comprehensive search of English-language literature in online databases (PubMed, Web of Science, Elsevier, Scopus, Google Scholar) was conducted from January 2023 – June 2023. A key word search was conducted for publications published from March 2020 when the COVID-19 pandemic was announced to December 2022. Brief description of the state of knowledge. Most of the analyzed articles reported changes in e-cigarette use during the COVID-19 pandemic. The reasons for the increase in the use of e-cigarettes during the pandemic included: boredom, loneliness, stress caused by lockdown, and online purchases. The decrease in e-cigarette consumption is attributed to stay-at-home orders causing barriers to e-cigarette access, limited peer group gatherings, and increased time spent at home with the family. The Covid-19 pandemic also impacted attempts to stop the use of e-cigarettes. Summary. Understanding the factors that influenced changes in e-cigarette use during the COVID-19 pandemic should be used to develop prevention programmes for adolescents and young adults. Educating young people and increasing knowledge about the harmful effects of using e-cigarettes may contribute to reducing the initiation of using e-cigarettes, and is a strong incentive to quit smoking.