Miracle-dz/UniFAR-FTUs huggingface dataset:
Current concepts in vital primary pulp therapy.

REVIEW Recent progress in understanding the molecular and cellular changes during tooth development and how they are mimicked during tissue repair, offers the opportunity to assess the biologic validity of the various vital pulp treatments. Under this light, indirect pulp treatment can be an acceptable procedure for primary teeth with reversible pulp inflammation, provided that this diagnosis is based on a good history, a proper clinical and radiographic examination, and the tooth had been sealed with a leakage-free restoration. Several articles report the success of this technique of direct pulp capping (DPC) and calcium hydroxide has been widely used with high success rates in young permanent teeth, but the results in primary teeth are less satisfactory. Recent studies have reported successful results with direct adhesive capping of exposed pulps, while others showed pulp inflammation and unacceptable results using this technique. Thus, the traditional rationale for the use of calcium hydroxide should be maintained, and this treatment modality reserved for iatrogenic exposures in asymptomatic teeth that are expected to exfoliate within a short period of time. In younger children, iatrogenic or carious exposures should be treated by pulpotomy. Formocresol has been the most popular pulp dressing material for pulpotomized primary molars for many years but, due to its deleterious effect, the use of formocresol is decreasing considerably worldwide. Ferric sulphate has been proposed as a substitute to formocresol, and the success rates were comparable to those of formocresol. More recently, considerably better results have been obtained with MTA (Mineral Trioxide Aggregate), and statistically significant differences were reported when compared with formocresol. Internal root resorption, a finding seen both in ferric sulphate and formocresol, was not observed in the MTA treated teeth. MTA is commercially available, but its cost is very high, and cannot be kept once opened. Thus, ferric sulphate can still be a valid and inexpensive solution for pulpotomies in primary teeth.
SENSEVAL : An Exercise in Evaluating Word SenseDisambiguation

There are now many computer programs for automatically determining which sense a word is being used in. One would like to be able to say which were better, which worse, and also which words, or varieties of language, presented particular problems to which programs. In this paper I describe a pilot evaluation exercise (`SEN-SEVAL') taking place under the auspices of ACL SIGLEX (the Lexicons Special Interest Group of the Association for Computational Linguistics) and EURALEX (Eu-ropean Association for Lexicography) in 1998. 1 Word Sense Disambiguation As dictionaries tell us, most common words have more than one meaning. When a word is used in a book or in conversation, generally speaking, just one of those meanings will apply. This is not a problem for people. We are very rarely slowed down in our comprehension by the need to work out which meaning of a word applies. But it is for computers. The clearest case is in Machine Translation. If English drug translates into French as either drogue or m edicament, then an English-French MT system needs to disambiguate drug if it is to make the correct translation. For forty years now, people have been writing computer programs to do Word Sense Disambigua-tion (WSD). Early programs (Kelly and Stone, 1975; Small, 1980) required human experts to write sets of disambiguation rules for each multi-sense word. This was a problem. It involved a huge amount of labour to write rule-sets or \Word Experts" for a substantial amount of the vocabulary. The WSD problem can be divided into two parts. The rst is, how do you express what meaning number 1 and meaning number 2 of a word are, to the computer. The second is, how do you work out which of those meanings matches an occurrence of a word to be disambiguated. (Lesk, 1986) took a novel tack, using the text of dictionary deenitions as an oo-the-shelf answer to the rst problem. He then measured the overlap, in terms of words-in-common, between each of the deenition texts and the context of the word to be disambiguated. Much recent work uses sophisticated variants of this idea. Dictionary-based approaches remain tied to a particular dictionary, with concomitant errors, imperfections and copyright constraints. With the advent of huge computer corpora, and computers powerful enough to computer complex functions over them, the 1990s has seen new strategies which nd the contexts indicative of each sense in a …
Eco-socio Impact of Mineral Resource Mining in the Karamoja Region of Uganda

Mining is one of the prolific activities geared towards poverty eradication, but an elusive goal for most government leaders in developing countries. Oftentimes, the economic aspect of mining overshadows ecological and social needs in context of host communities. For that reason, Karamoja region has experienced violent ethnic conflicts, which has made it difficult to trust mining activities to transform the area and contribute to healing of the deep-seated animosity within ethnic groups. This study used interviews and focus group discussions among artisanal miners and the data obtained was content analysed. The outcomes indicated that mineral resource mining activities are still in their infancy, yet environmental degradation is visible and human rights violations and escalating disagreements between communities are also evident. Therefore, peaceful co-existence is a prerequisite between mining companies and the Karamajong to achieve sustainable eco-socio development in Karamoja region. Moreover, the local people‘s participation in mining activities is instrumental in achieving sustainable development. Key words · Eco-socio · Mineral resource · Mining · Karamajong
Predictive Analytics and AI for Personalized Treatment Plans in Genetic Heart Diseases

Hereditary cardiac conditions, which are the focus of this abstract, pose a serious problem for medical science. The article “Predictive Analytics and AI for Personalized Treatment Plans in Genetic Heart Diseases” explores novel techniques that utilize the potential of predictive analytics and artificial intelligence (AI) to revolutionize the diagnostic and therapeutic landscape of cardiac illnesses. In this paper, we discuss how predictive analytics and AI may be used to design individualized therapies for hereditary heart problems. We take a look at the influence that genetic variants have on illness manifestation and how typical one-size-fits-all treatments fall short. Data-driven insights, risk classification, and prognostic powers are just a few of the ways in which predictive analytics is ready to transform the industry. Together with artificial intelligence (AI), which can mimic human intellect, they hold the possibility of customizing treatment strategies with unparalleled accuracy. In this article, we'll go over the value of genetic profiling and how it's being used in conjunction with machine learning algorithms to forecast things like illness susceptibility and response to therapy. The moral and legal implications of using AI in medicine are also discussed. The confidence and privacy of our patients are protected by our emphasis on ethical procedures and data protection. Predictive analytics and artificial intelligence have already started to improve the treatment of hereditary heart disorders, as evidenced by case studies and success stories. The applications are as varied as the genetic differences themselves, ranging from early illness prediction to individualized pharmaceutical regimes and surgical procedures. Predictive analytics and artificial intelligence are merging to change how we think about inherited heart problems. Readers will walk away from this piece with a solid grasp of how predictive analytics and AI might influence the future of individualized treatment strategies for those with hereditary heart disorders. All people who are afflicted with these diseases have reason to hope that this path will lead to healthier hearts, happier lives, and a better future.
Natural Infection and Transmission of a Retrovirus Closely Related to Myeloblastosis-Associated Virus Type 1 in Egg-Type Chickens

SUMMARY. Myeloblastosis-associated virus type 1 (MAV-1) is an exogenous avian retrovirus with oncogenic potential. MAV-1 was detected in young chicks hatching from eggs produced by an experimental genetic line of egg-type chickens. Transmissibility of MAV-1 had not been documented previously. This investigation was intended to partially characterize the virus involved and to study its transmissibility and oncogenicity in naturally and contact-infected chickens. Commercially produced white and brown layer pullets free of exogenous avian leukosis viruses were commingled at hatch with naturally MAV-1–infected chickens. The original MAV-1–infected chickens were discarded after approximately 8 wk, and the contact-exposed chickens were maintained in isolation for 36 wk. Young specific-pathogen-free (SPF) single comb white leghorn chickens were added to the group to study possible horizontal transmission of MAV-1 in young chickens. Upon weekly virus isolation attempts, MAV-1 was readily isolated from the contact-exposed white layers but not from the brown layers between 36 and 53 wk of age (18 wk in total). Three-week-old SPF chickens were readily infected with MAV-1 by contact as early as 1 wk postexposure. Throughout 22 hatches derived from the white and brown MAV-1–contact-exposed layers (between 36 and 53 wk of age), MAV-1 was frequently detected in the white layer progeny, whereas the virus was seldom isolated from the progeny produced by the brown layers during the same 18-wk period. MAV-1 induced a persistent infection in some of the SPF chickens that were exposed by contact at 3 wk of age. Gross tumors were not detected in any of the originally infected experimental chickens at 8 wk of age, in the contact-exposed brown or white layers at the termination of the study at 53 wks of age, or in the contact-exposed SPF chickens at the end of the study at 12 wk of age. Exogenous avian leukosis-related viruses may still be detected in egg-type chickens, emphasizing the importance of thorough screening before incorporation of experimental genetic material into commercial genetic lines of egg-type chickens.
Cloud Process Optimization: A Hybrid GA-PSO Methodology

Within the realm of computing in clouds, optimising cloud operations is a major problem, especially when it comes to striking a balance between resource utilisation and effective completion of tasks that is regulated by complex mutual dependence. In the study article "clouds Service Optimisation: A Hybrid GA-PSO The technique," a combination of approaches to efficient task assignment to services is presented. This approach is designed to reduce runtimes, reduce expenses in cloud settings, and reorganise the allocation of the load of interrelated duties across various resources. The hybrid technique that combines PSO and GA shows impressive performance in terms of shorter completion times and lower costs for execution in comparison to other current technologies. It is noteworthy because it greatly maximises the distribution of tasks across available resources, resulting in an important rise in resource utilisation. Experimental results empirically validate the effectiveness of the GAPSO algorithms and demonstrate its advantage over earlier computer approaches. Compared to traditional approaches, the algorithm shows greater convergence to optimum outcomes while retaining superior quality. This highlights the hybrid approach’s effectiveness and productivity and establishes it as a strong contender for resolving the internet of things paradigm problems with work distribution, resource optimisation, and cost containment.
An AIOT-based Health Monitoring System Integrating Contact and Non-contact Detections

Aiming at the deficiency of the contact health detection devices, this paper proposes a new health detection system based on millimeter wave radar, which combines noncontact and contact mechanism. In addition to collecting vital signs such as human breathing and heartbeat, the system also collects key information such as human movement, blood oxygen and body temperature through deploying various IoT (Internet of Things) node devices. Specific implementation is based on 4G/WIFI/Bluetooth communication modules by using MQTT protocol to remotely transmit the collected data to the IoT platform and client APPs. Furthermore, the cloud platform stores and analyzes the data, ensures the stability of data storage, and draws real-time curves to make the physical condition of the test subject more intuitive. Experimental results have proven that the presented system has good stability, wide application range and high application prospect, especially for the life monitoring of the elderly living alone.
Analysis and Experimental Kinematics of a Skid-Steering Wheeled Robot Based on a Laser Scanner Sensor

Skid-steering mobile robots are widely used because of their simple mechanism and robustness. However, due to the complex wheel-ground interactions and the kinematic constraints, it is a challenge to understand the kinematics and dynamics of such a robotic platform. In this paper, we develop an analysis and experimental kinematic scheme for a skid-steering wheeled vehicle based-on a laser scanner sensor. The kinematics model is established based on the boundedness of the instantaneous centers of rotation (ICR) of treads on the 2D motion plane. The kinematic parameters (the ICR coefficient χ, the path curvature variable λ and robot speed v), including the effect of vehicle dynamics, are introduced to describe the kinematics model. Then, an exact but costly dynamic model is used and the simulation of this model’s stationary response for the vehicle shows a qualitative relationship for the specified parameters χ and λ. Moreover, the parameters of the kinematic model are determined based-on a laser scanner localization experimental analysis method with a skid-steering robotic platform, Pioneer P3-AT. The relationship between the ICR coefficient χ and two physical factors is studied, i.e., the radius of the path curvature λ and the robot speed v. An empirical function-based relationship between the ICR coefficient of the robot and the path parameters is derived. To validate the obtained results, it is empirically demonstrated that the proposed kinematics model significantly improves the dead-reckoning performance of this skid–steering robot.
Utilizing Prefetch Buffers for Iterative Graph Applications

Graph applications are bound to high memory latencies due to the vast graph data they process and irregular memory access patterns. One of the several approaches to overcome this memory bottleneck is graph prefetching. Graph prefetchers can accurately anticipate the irregular memory access patterns observed in graph applications and bring the appropriate data to caches beforehand. Graph prefetching achieves significant performance improvements for well-known graph benchmarks and applications. However, those that aggressively prefetch graph data into the L1 cache suffer from cache pollution and early evictions of valuable data. We propose storing the prefetched graph data in separate prefetch buffers rather than the L1 cache to solve this issue. We suggest a prefetch buffer design for holding the graph data and dispatching it when needed by the CPU. Our technique applies to L1 cache graph prefetchers decoupled with in-order cores, achieving average speedups up to 20%. The prefetch buffers used in our design are minimal compared to the L1 cache, thus bringing negliaible storage overheads.
Optimal Integration of Metaheuristic Techniques with Correction-Based Approaches for Profit Maximization in Combinatorial Production Planning: A Comparative Analysis

To enhance production planning efficiency, it’s crucial to have well-structured models that align with metaheuristic and other optimization techniques. This research conducts a comparative analysis of three unique metaheuristic methods: sanitized-teaching-learning-based optimization (sTLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations. The primary focus lies on profit-maximizing production planning within a single-level framework, encompassing intricate domain constraints, nonlinear costs, budget limitations and raw material constraints. The paper introduces three correction-based solution approaches. Upon evaluating 300 distinct instances of the problem, the study reveals that s-TLBO integrated with correction-based approach 1 and 3 consistently outperforms the other methods when it comes to devising optimal production plans for a Saudi Arabian petrochemical industry. This industry scenario involves 24 products, 54 processes, and three raw materials.
Dashboard-camera-based Safe Driving Support System

Distracted driving poses a significant and widespread threat to road safety, resulting in severe accidents and loss of lives. Detecting driver distraction promptly is imperative for enhancing road safety. While various methodologies and technologies have been explored to address this issue, we present an innovative, cost-efficient, non-intrusive, and lightweight Safe Driving Support System (SDSS) that utilizes dual dashboard cameras. In addition to conventional driver gaze tracking, our system considers broader aspects, including monitoring the road environment and pedestrian safety. Our study comprises two primary modules: driver distraction detection and pedestrian safety. The driver distraction detection module evaluates distraction by analyzing the driver's gaze direction and the position of pedestrians on the road. Through extensive experimentation, we determined that one particular strategy, strategy B, outperformed others, achieving an 88.76% accuracy rate for Strictly Correct Estimation Rate (SCER) and a 96.07% accuracy rate for Loosely Correct Estimation Rate (LCER). The pedestrian safety module assesses the risk level of pedestrians based on lane lines and their relative positions. Together, these modules offer an early detection and mitigation solution for the leading causes of accidents: driver distraction and pedestrian risk. In summary, our SDSS provides a comprehensive and cost-effective approach to enhancing road safety by addressing driver distraction and pedestrian safety. The results from our experiments demonstrate the system's effectiveness in detecting and mitigating potential hazards on the road, contributing to a safer transportation environment and the prevention of accidents.
Wideband high gain metasurface-based circularly polarized patch antenna

A metasurface-based circularly polarised (CP) microstrip patch antenna has been proposed in this paper. A square microstrip patch combined with plus- and diagonal-shaped slots have been used as the host antenna designed on a FR-4 substrate. The metasurface comprising a $4 \times 4$ square array has been designed on another FR-4 substrate with identical dimensions acting as a superstrate layer and separated by a layer of Teflon from the original patch. The proposed antenna offers a 10-dB impedance bandwidth over the frequency range of 5.37 GHz and 7.44 GHz. The axial ratio (AR) bandwidth of the proposed antenna lies between 6.02 GHz and 6.35 GHz. The maximum realized gain of 6.3 dBic in the 5.37 GHz to 6.35 has been achieved using the antenna and the antenna has been found to be right handed CP (RHCP) in nature. The proposed CP antenna can be deployed for applications like satellite communication, weather radar system etc.
SwinFVC: A Swin Flow Video Colorization Example-Based Method

Color plays a vital role in our perception and interaction with the world, particularly in the realm of video where its presence or absence holds significant importance. Consequently, the process of generating accurate representations of color in videos, where original color information is absent, is a crucial area of research. Despite recent attention to this subject, there remain several unresolved issues pertaining to enhancing result quality and exploring novel approaches. To address these gaps, we propose Swin Flow Video Colorization (SwinFVC), a framework that leverages state-of-the-art computer vision techniques, such as Swin Transformers, in conjunction with classical convolutional neural networks (CNNs) to extract color information from reference images. Additionally, we introduce the Flow Color encoding block, a subnetwork responsible for generating feature representations that effectively capture color dynamics across images and video streams. Our implementation was trained using the DAVIS and LDV datasets, and the results exhibited superior effectiveness compared to current state-of-the-art methods, as evidenced by the Fréchet Inception Distance (FID) and Color Distribution Consistency (CDC) metrics.
The Effectiveness of Machine Learning to Estimate the Risk of Failure in Brazilian Public Contracts

Automatic risk estimation is paramount to prioritizing public contracts auditing efforts, and Machine Learning Risk Prediction (MLRP) models are a promising solution to the classification task of identifying high-risk contracts. Current approaches are focused on the federal level of government, at the same time, face limiting challenges such as the absence of exhaustive ground truth, the difficulty of gaining access to critical databases to build model features, as well as the absence of public literature on the relevance of proposed features. In this work, we attempt to propose MLRP models at the municipal level and overcome those issues by exploring the space of challenges and opportunities in applying MLRP to a setting of Brazilian public contracts. With grounds on the prosecutors' practical experience, we combine three data sources to produce a novel dataset that is more detailed and precise than those used in previous works, first to establish a baseline measuring the gains of applying MLRP to Brazilian Public contracts at the municipal level, second to compare the performance of MLRP and a sample of ad-hoc state-of-the-practice data. Next, we leverage semantic features from contract descriptions in order to evaluate the impact of the contract area on the model's prediction. Also, we experiment using urban and economic characteristics to improve model performance. We measure the impact of access to each datasource on model performance, quantifying the importance of non-open data for this task. Our results suggest that the ad-hoc approach at the firm level has little practical efficacy when evaluated through a more granular/actionable perspective. Contract-level MLRP may be a promising approach, especially when using economic indicators to characterize municipalities, such as GDP per capita. Also, we found no difference between the impact of each feature set on the models' predictions.