Miracle-dz/UniFAR-FTUs huggingface dataset:
A method to calibrate roadway lighting warrants and levels with crash history

Every year hundreds of night time road collisions result in fatalities and injuries in Canada. Roadway lighting is considered as the main countermeasure to prevent nighttime crashes. Provision of lighting follows two industry standards in North America: one for the warrants and the other for minimum recommended levels. The warrant system assigns scores to highway segments based on geometrical, operational and functional characteristics as well as the collision history through a night-to-day crash-ratio. Transportation agencies in North America had manifested their interest in simplifying the warrant system; eliminating elements and/or modifying lighting values, however, there is a need for a method to support lighting decisions over its effectiveness as a countermeasure. This thesis presents a novel method to calibrate lighting warrants and to identify effective levels of lighting in order to reduce the severity and frequency of night-time collisions. This new method uses an evidence-based mechanism to connect lighting warrants with statistical analysis of collisions in order to adjust the scores of the warrant. It also connects the estimation of lighting levels with evidence-based statistical analysis of crash-history in order to identify recommended levels of luminance, illuminance and uniformity variations. The method expands the industry standards by providing the decision maker with two alternate non-exclusive approaches supported over collisions’ frequency and severity criteria. A large-scale case study for highways in Quebec was used to calibrate the warrants and identified recommended levels of luminance of at least 1.5cd/m2, maximum uniformities of illuminance of 1.5 and of luminance of 8 times to prevent severe and frequent collisions. A case study of Arthabaska region in Quebec found that levels of luminance should be increased for every functional classification of roads that values of uniformity must be reduced, and that levels of illuminance increased to reduce severe collisions. The novel methods developed in this research will provide province/state transportation agencies and municipalities with the capability of not only allocating lighting when is needed and justified, but also of selecting the optimal levels that result in effective reductions in nighttime road collision frequency and severity, signifying safer roads for the society at large.
Performance Comparison Between Centralized and Hierarchical Predictive Functional Control for Adaptive Cruise Control Application

This paper presents a performance comparison between two different control structures of Predictive Functional Control (PFC), namely centralized and hierarchical architectures for an Adaptive Cruise Control (ACC) system. Centralized PFC utilizes a linearized model of the vehicle longitudinal dynamics to compute the control law, while hierarchical PFC uses a simple kinematic model with inverse vehicle longitudinal dynamics. Based on the simulation results, both control structures produced a satisfactory performance. However, in the presence of disturbances such as road slopes and wind gusts, the centralized PFC becomes more sensitive and conservative than the hierarchical PFC. The main reason is that the linearization model used in centralized PFC is only effective around the selected nominal operating points. Since the decision-making process depends on the internal model performance, centralized PFC cannot compensate for this effect. Besides, it also takes a longer computation time than hierarchical PFC since more mathematical operations must be solved than the kinematic model, especially when physical and operational constraints are considered. The standard performance parameters such as Root Mean Squared Error (RMSE), settling time, and rise time are also used for the analysis. These findings can become a solid justification for using the hierarchical PFC structure in designing an ACC system for future work.
Diverse Filters from a Single Topology Employing Current Mode Computing Blocks

This paper is related to an important area of electronics and communication engineering research that deals with the processing of analog signals. The paper proposes new filters of diverse characteristics. The building blocks used for designing the circuits are based on current mode approach. Simple current mode computing blocks are employed for the purpose. The proposed topology can realize filters with different frequency responses. Thus low-pass, high-pass, all-pass, shelving and band-reject filters are realized. The realized filters can also provide differing phase responses by simple specialization of passive elements. The actual SPICE based results using CMOS technology with ±2.5 V supply voltage are included in support of the new proposed topology. The circuits are expected to be useful for wide ranging applications from low, medium to high frequency domains.
Enhancing Network Intrusion Detection Using Effective Stacking of Ensemble Classifiers With Multi-Pronged Feature Selection Technique

Information security depends on Network Intrusion Detection (NID), which properly identifies network threats. This work explores simulating a NID system by stacking ensemble classifiers with various feature selection methods. We used the NSL-KDD dataset for the investigation. The binary classification performance integrates all assaults into one class and treats another target variable as usual. Random Forest (RF), Bagging Classifier (BC), and Extra Tree Ensemble (ETE) using Logistics Regression (LR) as a meta estimator. Chi2-square (Chi2) and Recursive Feature Elimination (RFE) are also used for feature selection. The experimental findings suggest that the proposed technique is ideal for developing a classification model with high accuracy and outperforms standard machine learning classification methods. The Chi 2 attribute selection strategy with modest characteristics has the greatest accuracy of 99.84 %. A careful comparison with prior research shows that our technique produces state-of-the-art performance with reduced computing costs.
Evaluation of Loquat Jam Quality at Different Cooking Times Based on Physicochemical Parameters, GC-IMS and Intelligent Senses

Highlights What are the main findings? The color, energy, and texture properties of loquat jam changed with the prolongation of cooking time. The changes in the content of free amino acids, organic acids and volatile flavor substances with the cooking time caused variation in the olfactory and gustatory senses of loquat jam. There was a significant difference in the E-nose and E-tongue results of loquat jam cooked for 100 min and 120 min. What is the implication of the main finding? Heating and the resulting intramolecular interactions caused changes in the physicochemical parameters of loquat jam. Free amino acids, organic acids, and volatile substances contributed significantly to the flavor composition of loquat jam. When the cooking time reached 120 min, the olfactory and gustatory senses of loquat jam underwent significant changes. Abstract The study compared and analyzed the quality of loquat jam with different cooking times through physicochemical parameters, headspace-gas chromatography-ion migration spectroscopy (HS-GC-IMS) and intelligent senses. The results showed that with the prolongation of the cooking time, the color of loquat jam slowly deepened, the energy significantly increased, the adhesiveness, gumminess, hardness and chewiness enhanced, the free amino acid content increased from 22.40 to 65.18 mg/g. The organic acid content increased from 1.64 to 9.82 mg/g. Forty-seven volatile flavor compounds were identified in five types of loquat jam using HS-GC-IMS, among which the relative content of aldehydes was sharply higher than that of other chemical substances, playing an important role in the flavor formation of loquat jam. LJ0, LJ1 and LJ2 had higher aldehyde content, followed by LJ3 and LJ4 had the lowest aldehyde content. The orthogonal partial least squares-discriminant analysis (OPLS-DA) screened 15 marker compounds that could distinguish five types of loquat jam. The E-nose results showed a significant difference in olfactory sense between loquat jam cooked for 100 and 120 min. The E-tongue results corroborated the results of free amino acids (FAAs) and organic acids, indicating that the gustatory sense of loquat jam changed significantly when the cooking time reached 120 min. The results provided a basis for further research on the relationship between the cooking process and quality characteristics of loquat jam.
A TCAD Augmented Machine Learning Based Drain Current Model for InGaAs FinFETs

In this paper, we describe a framework for model development of InGaAs FinFET based on machine learning. An artificial neural network is used to create the model, and TCAD simulation data was used to train it. The model accurately and efficiently captures changes in gate length, drain, and gate bias voltages. In order to enable circuit simulation utilizing the SPICE environment, the trained model is exported to the Verilog-A language, and circuit simulation is demonstrated.
Predicting Students' Academic Success using Machine Learning Algorithms

This research attempts to predict the academic success of a student using Machine Learning algorithms and a thorough strategy that takes into account a variety of variables that affect students' results. The study examines a large dataset that contains information on the socioeconomic situation, involvement, and academic success of the students. To create predictive models that is able to find out the student who is at an academic risk of quitting and offer timely interventions to enhance their academic performance and prevent dropout, machine learning methods like Classifier based on Random Forest, Classifier Based on Decision Trees, Classifier using Logistic Regression, Voting based Classifier, and others will be used. To enhance students' achievement and raise the standard of education, policy decisions and resource allocation can be made using the knowledge gathered from the analysis.
NPSAT1: assessment of risk for human casualty from atmospheric reentry

Abstract : The United States government, through the Department of Defense and the National Aeronautics and Space Administration, has established clear guidelines that limit the acceptability of orbital debris fragments striking the surface of the Earth. This thesis addresses both the survivability of the soon-to-be-launched NPSAT1 satellite on its eventual reentry into the earths atmosphere, and any potential risk to the human populace that may result. After reviewing the history of tracking objects in space, and the policies in place to limit the creation of and risk presented by orbital debris, this research analyzes each of NPSAT1s individual components for its uncontrolled reentry into the earths atmosphere. The analysis conducted in this paper shows that although a few pieces of debris from NPSAT1 would strike the earths surface with varying degrees of impact energy, these impacts are not expected to exceed the standards set forth by the Department of Defense.
Encapsulated Rose Bengal Enhances the Photodynamic Treatment of Triple-Negative Breast Cancer Cells

Among breast cancer subtypes, triple-negative breast cancer stands out as the most aggressive, with patients facing a 40% mortality rate within the initial five years. The limited treatment options and unfavourable prognosis for triple-negative patients necessitate the development of novel therapeutic strategies. Photodynamic therapy (PDT) is an alternative treatment that can effectively target triple-negative neoplastic cells such as MDA-MB-231. In this in vitro study, we conducted a comparative analysis of the PDT killing rate of unbound Rose Bengal (RB) in solution versus RB-encapsulated chitosan nanoparticles to determine the most effective approach for inducing cytotoxicity at low laser powers (90 mW, 50 mW, 25 mW and 10 mW) and RB concentrations (50 µg/mL, 25 µg/mL, 10 µg/mL and 5 µg/mL). Intracellular singlet oxygen production and cell uptake were also determined for both treatment modalities. Dark toxicity was also assessed for normal breast cells. Despite the low laser power and concentration of nanoparticles (10 mW and 5 µg/mL), MDA-MB-231 cells experienced a substantial reduction in viability (8 ± 1%) compared to those treated with RB solution (38 ± 10%). RB nanoparticles demonstrated higher singlet oxygen production and greater uptake by cancer cells than RB solutions. Moreover, RB nanoparticles display strong cytocompatibility with normal breast cells (MCF-10A). The low activation threshold may be a crucial advantage for specifically targeting malignant cells in deep tissues.
Changes in depression trends during and after the COVID-19 pandemic among older adults in Korea

This study aimed to complement the previous studies conducted within a short period of time by examining the long-term trends in depression levels of older adults through a longitudinal study that includes the period following the onset of COVID-19. For analysis, this study used the data from the 12th (2017) through 16th (2021) Korea Welfare Panel Study, and 2,716 data of older adults 65 years and older were included. Variables included gender, age, equalized annual income (USD $), education background, residential area, living alone and disability status. As a result, two types of changes in depression were identified: the ‘rapidly rising’ type and the ‘steadily increasing’. We found that the lower the equalized annual income, women, the lower the education, living in urban and living alone showed a higher likelihood of belonging to the ‘rapidly rising’ type. The result suggests that those at risk of depression are at much higher risk during and after the COVID-19 pandemic. It is also necessary to provide timely and effective guidelines to prevent depression in older adults when implementing policies in response to an outbreak of infectious diseases, such as COVID-19.
Analysis of the possibility of applying the machine learning methods for the prediction the surface geometry after its technological treatment

The paper presents an idea and preliminary analysis of a new approach to modeling the surface geometry by applying advanced computational and analysis of data methods. It assumes that the material's surface may be treated as a random field and can be analyzed using the time series methods. The research aims to develop a model to predict surface geometry textured by a laser beam. Worked out predictive models supported by machine learning methods would indicate proper texturing process parameters to obtain specific surface geometry proprieties. The final goal of the research is to develop a model to predict the surface geometry parameters according to texturing process parameters. As a data set for the development and testing of the model, data from the profilometric test of samples with a cermet coating after laser with different beam power will be used.
Deep Convolutional Neural Network for Robust Detection of Object-Based Forgeries in Advanced Video

Video forgery detection is a critical aspect of digital forensics, addressing the challenges posed by the manipulation of video content. This paper presents a novel approach for video forgery detection using Deep Convolutional Neural Networks (DCNN). Leveraging the power of deep learning, our method aims to improve the accuracy and efficiency of object-based forgery detection in advanced video sequences. In the proposed approach, we build upon the foundation of an existing method, which utilizes Convolutional Neural Networks, and introduce innovative modifications to the DCNN architecture. These modifications include data preprocessing, network architecture, and training strategies that enhance the model’s ability to detect tampered objects in video frames. We conduct experiments on the SYSU-OBJFORG dataset, the largest object-based forged video dataset to date, with advanced video encoding standards. Our DCNN-based approach is compared with the existing method, demonstrating superior performance. The results show increased accuracy and robustness in detecting object-based video forgery. This paper not only contributes to the field of video forgery detection but also underscores the potential of deep learning, particularly DCNN, in addressing the evolving challenges of digital video manipulation. The findings open avenues for future research in the localization of forged regions and the application of DCNN in lower bitrate or lower resolution video sequences.
Effect of professional oral hygiene measures on the periodontal health of patients undergoing fixed orthodontic treatment

Background: Plaque accumulate on orthodontic appliances when proper oral hygiene is not maintained, leading to destruction of periodontal tissues. Good oral hygiene during fixed orthodontic therapy will prevent periodontal disease, which eventually will reflect final outcome of orthodontic treatment. Objectives: To find out whether professional prophylaxis midway during fixed orthodontic treatment will reduce adverse changes on periodontal health due to poor oral hygiene maintenance when compared to homecare measures only. Methods: This analytical cross-sectional study was conducted from 2022-05-06 to 2023-02-05 after institutional ethical approval among 24 patients (eight males, 16 females) undergoing fixed orthodontic treatment in Orthodontics department, Nobel Medical College. The effect of regular homecare measures only, as compared to professional prophylaxis during orthodontic treatment in addition to regular homecare on the periodontal health was evaluated on two separate visits over a nine-month period. SPSS 11.5 was used for statistical analysis. Results: This study showed improved periodontal health in “experimental group” when oral prophylaxis was done in fifth month of fixed orthodontic therapy and worsened periodontal health in “control group” during a nine-month study period. Conclusion: Significant improvement of gingival and periodontal health with an improvement in plaque control with professional prophylaxis was observed which was in accordance with most previous studies. Plaque control with professional prophylaxis after initial alignment is also of utmost importance in the maintenance of periodontal health as well as the prevention of periodontal disease during fixed orthodontic treatment. It can be considered key to good periodontal health of patients undergoing fixed orthodontic treatment.
Image Smoothing via Multiscale Global Perception

Image smoothing provides a fundamental operation for image processing, with a broad spectrum of applications. It is a challenging task which requires global analysis on image patterns with scale awareness. Existing deep models for image smoothing are insufficiently efficient in global perception and multi-scale processing. This letter proposes a deep model with an efficient multi-scale fusion architecture and a series of global processing blocks. The architecture enhances multi-scale feature flow by incorporating features of different scales into both the encoder and decoder blocks of a U-shape network, with multi-scale feature fusion modules inserted between the encoder and the decoder. The global processing blocks leverage the multi-axis processing mechanism to achieve joint local and global perception. Benefiting from these two key designs, our proposed model enjoys superiority in both smoothing performance and computational complexity, as demonstrated in the experiments on two benchmark datasets.
Baseline risk factors of in-hospital mortality after surgery for acute type A aortic dissection: an ERTAAD study

Background Surgery for type A aortic dissection (TAAD) is associated with high risk of mortality. Current risk scoring methods have a limited predictive accuracy. Methods Subjects were patients who underwent surgery for acute TAAD at 18 European centers of cardiac surgery from the European Registry of Type A Aortic Dissection (ERTAAD). Results Out of 3,902 patients included in the ERTAAD, 2,477 fulfilled the inclusion criteria. In the validation dataset (2,229 patients), the rate of in-hospital mortality was 18.4%. The rate of composite outcome (in-hospital death, stroke/global ischemia, dialysis, and/or acute heart failure) was 41.2%, and 10-year mortality rate was 47.0%. Logistic regression identified the following patient-related variables associated with an increased risk of in-hospital mortality [area under the curve (AUC), 0.755, 95% confidence interval (CI), 0.729–0.780; Brier score 0.128]: age; estimated glomerular filtration rate; arterial lactate; iatrogenic dissection; left ventricular ejection fraction ≤50%; invasive mechanical ventilation; cardiopulmonary resuscitation immediately before surgery; and cerebral, mesenteric, and peripheral malperfusion. The estimated risk score was associated with an increased risk of composite outcome (AUC, 0.689, 95% CI, 0.667–0.711) and of late mortality [hazard ratio (HR), 1.035, 95% CI, 1.031–1.038; Harrell's C 0.702; Somer's D 0.403]. In the validation dataset (248 patients), the in-hospital mortality rate was 16.1%, the composite outcome rate was 41.5%, and the 10-year mortality rate was 49.1%. The estimated risk score was predictive of in-hospital mortality (AUC, 0.703, 95% CI, 0.613–0.793; Brier score 0.121; slope 0.905) and of composite outcome (AUC, 0.682, 95% CI, 0.614–0.749). The estimated risk score was predictive of late mortality (HR, 1.035, 95% CI, 1.031–1.038; Harrell's C 0.702; Somer's D 0.403), also when hospital deaths were excluded from the analysis (HR, 1.024, 95% CI, 1.018–1.031; Harrell's C 0.630; Somer's D 0.261). Conclusions The present analysis identified several baseline clinical risk factors, along with preoperative estimated glomerular filtration rate and arterial lactate, which are predictive of in-hospital mortality and major postoperative adverse events after surgical repair of acute TAAD. These risk factors may be valuable components for risk adjustment in the evaluation of surgical and anesthesiological strategies aiming to improve the results of surgery for TAAD. Clinical Trial Registration https://clinicaltrials.gov, identifier NCT04831073.
Study of Steam Generation and Distribution in a Hospital to Improve Energy Efficiency Using Thermography, Ultrasound, and Gas Analyzer

This investigation analyzed the energy efficiency of a steam system in a hospital, considering the procedure in ASME EA-3-2009 standard. The obtained boiler energy efficiency was 80.29 %, by applying an energy balance, reaching 15.33 kW for heat losses in the distribution pipes. Two consistent improvement alternatives were proposed, starting by unifying the pipe diameter of the kitchen area of 5 m, generating a reduction of heat loss from 828 to 600 W, which represented a total annual energy saving of around 2.4 GJ/year. The investment cost is USD 47.40, considering the achievement of the break-even point after 9 months, where the insulation of the pipes with glass wool was considered second and it generated a reduction in losses of 5.98 kW, representing a total annual energy saving of about 62.91 GJ/year, which corresponds to approximately USD 1,672. The investment for pipe insulation with a length of 49 m was USD 462, considering the NPV calculation and the possible savings, and the break-even point was reached after approximately 4 months, indicating the economic benefit of applying the two improvement options proposed.
Two New Species of Dendrobatid Frogs, Genus Colostethus, from the Cordillera del Condor, Ecuador.

Two new species of Colostethus are described from the physiographically iso- lated Cordillera del C6ndor in southern Ecuador. Colostethus mystax is a small species charac- terized by the absence of webbing and pale longitudinal stripes on the body; C. shuar is a large species that also lacks webbing but has an oblique lateral stripe that extends to the orbit. Both species are endemic to the Cordillera del C6ndor (Dendrobatidae, Colostethus mystax, C. shuar, Ecuador, Cordillera del C6ndor )