Miracle-dz/UniFAR-FTUs huggingface dataset:
Breast Lesions Detection and Classification via YOLO-Based Fusion Models

: With recent breakthroughs in artificial intelligence, the use of deep learning models achieved remarkable advances in computer vision, ecom-merce, cybersecurity, and healthcare. Particularly, numerous applications provided efficient solutions to assist radiologists for medical imaging analysis. For instance, automatic lesion detection and classification in mammograms is still considered a crucial task that requires more accurate diagnosis and precise analysis of abnormal lesions. In this paper, we propose an end-to-end system, which is based on You-Only-Look-Once (YOLO) model, to simultaneously localize and classify suspicious breast lesions from entire mammograms. The proposed system first preprocesses the raw images, then recognizes abnormal regions as breast lesions and determines their pathology classification as either mass or calcification. We evaluated the model on two publicly available datasets, with 2907 mammograms from the Curated Breast Imaging Sub-set of Digital Database for Screening Mammography (CBIS-DDSM) and 235 mammograms from INbreast database. We also used a privately collected dataset with 487 mammograms. Furthermore, we suggested a fusion models approach to report more precise detection and accurate classification. Our best results reached a detection accuracy rate of 95.7%, 98.1% and 98% for mass lesions and 74.4%, 71.8% and 73.2% for calcification lesions, respectively on CBIS-DDSM, INbreast and the private dataset.
A Bayesian regression framework for concrete creep prediction improvement: application to Eurocode 2 model

Concrete is the most widely used material in the construction industry due to its strength, workability and durability. But under a sustained load, concrete is prone to creep causing excessive long-term deflection of structural members, cracks in tensile members, redistribution of stresses over time in composite structures, and loss of prestressing force in prestressed concrete elements. Therefore, structural engineers must accurately predict the concrete creep over the long-term. The concrete creep coefficient is an important entry in many calculations and analyses of reinforced concrete structures. Currently, many models such as the Eurocode 2 model have predicted the concrete creep coefficient. Based on a large database for creep tests, this study aims to improve the prediction of the Eurocode 2 creep coefficient model at long-term by implementing correction coefficients into the model. Since Bayesian-type inferences are suitable tools for revising and updating design codes, the correction coefficients are calculated based on Bayesian linear regression. Statistical indicators demonstrate the accuracy and effectiveness of the proposed improvement and modification.
Differential Privacy for Tensor-Valued Queries

Private individual information are increasingly exposed through high-dimensional and high-order data, with the wide deployment of learning techniques. These data are typically expressed in form of tensors, but there is no principled way to guarantee privacy for tensor-valued queries. Conventional differential privacy is typically applied to scalar values without a precise definition on the shape of the queried data. Realizing that the conventional mechanisms do not take the data structural information into account, we propose Tensor Variate Gaussian (TVG), a new $(\epsilon,\delta) $ -differential privacy mechanism for tensor-valued queries. We further introduce two mechanisms based on TVG with an improved utility by imposing the unimodal differentially-private noise. With the utility space available, the proposed mechanisms can be instantiated with an optimized utility, and the optimization problem has a closed-form solution scalable to large-scale problems. Finally, we experimentally test our mechanisms on a variety of datasets and models, demonstrating that TVG is superior than other state-of-the-art mechanisms on tensor-valued queries.
Specific Determination of Inorganic Monochloramine in Chlorinated Wastewaters

The two conventional methods for wastewater chloramination control, residual maintenance and oxidation–reduction potential control, are not specific for the desired disinfectant, monochloramine. Organochloramines, nitrite, manganese, and chromate positively interfere with classical residual control measurements. Oxidation–reduction potential control may not be able to distinguish between monochloramine and weaker disinfecting organochloramines typically found in chlorinated domestic wastewaters. A new method of control is presented that is shown to be specific for the inorganic monochloramine species. The new method is based on a modification of the classical Berthelot analysis method for ammonia. Improvements to the method have greatly increased its selectivity toward monochloramine in chlorinated wastewater matrices. Breakpoint chlorination profiles of various chlorinated wastewaters demonstrate that the conventional control methods could potentially overestimate germicidal efficiency, especially near the breakpoint area. The new method has demonstrated good specificity for monochloramine in the samples studied and has been adapted for automated chlorine control in wastewater.
Research on Recognition of Electricity Fittings in High Voltage Transmission Lines

On the basis of analyzing the structure of common power fittings in high-voltage transmission lines and their image features, combined with the DNN deep neural network in machine learning, we proposed a model suitable for high-voltage transmission line inspection robots to identify the types of electric power fittings on the transmission lines. And design a fast ROI generation method suitable for recognizing fittings on power transmission lines. Then we verify the feasibility and rationality of the fitting identification model.
RELATIONSHIP BETWEEN HUSBAND SUPPORT AND MIDWIFE ROLE WITH POST PARTUM CONTRACEPTION IN KONAWE KEPULAUAN DISTRICT

Background: Postpartum contraceptive coverage in Konawe Kepulauan Regency in 2018, through data obtained at the Konawe Kepualaun District Health Office, Southeast Sulawesi Province as much as 8.4% and data in 2019 decreased to 1.26%. Methods:This quantitative research is observational with a cross sectional study design. The population in this study was 96 people with the determination of the sample using stratified cluster sampling, totaling 77 all post-partum mothers in the working area of the health centre in Konawe Kepulauan Regency. Results:The significance value (p) value of husband support and the role of midwife is 0.000 which means that p <0.05 so that Ha is accepted and Ho is rejected, which means that there is a significant correlation or relationship between husband's support and the role of midwives on the use of postpartum contraception. Both have a value of phi C which is 0.100 which meansvery weak relationship. Conclusion: There is a relationship between husband's support and the role of the midwife against the use of postpartum contraceptives in the Konawe Islands district.
A bioinspired MAV with nanocomposite wings and flexure joints: design and structural dynamic analysis

ABSTRACT A bioinspired tethered MAV model is designed, constructed and studied for structural dynamic characteristics using novel materials and methods. The model consists of two flapping wings, a compliant flapping mechanism, and a supporting chassis. Large size insects such as dragonfly and cicada are considered as inspiration for the designing purposes. The wings and mechanism designed in this study are a simplified form of the natural counterparts. These idealized bioinspired structures are first studied for structural dynamic characteristics using finite element methods. Carbon nanotubes/polypropylene (CNTs/PP) nanocomposite stiffeners and a thin LDPE membrane are used to realize the designed wing model. The use of this novel material composition resulted in a lightweight, thin, and flexible wing capable of large amplitude passive bending-twisting motion. For constructing the flapping mechanism, a novel compliant mechanism is fabricated and attached with a piezoelectric actuator of cantilever form. The compliant mechanism is designed with CNTs/PP nanocomposite flexure joints supported using rigid carbon fiber/epoxy composite linkages. The fabrication of wing stiffeners and mechanism flexures from lab developed CNTs/PP nanocomposite materials is carried out using laser micromachining based manufacturing technique. The complete MAV model is constructed by assembling the wings and mechanism with a carbon fiber/epoxy composite chassis. The final assemblage is a two-winged MAV model of a total mass, body length, and wingspan of 0.6 1g, 60.46 mm, and 90.14 mm, respectively. An external customized power supply is developed and used with a microcontroller to actuate the flapping mechanism and wings during experiments. The computational structural analysis showed that no resonance occurs at the mechanism, all the first four fundamental modes are at the wings. This is advantageous for accurate and efficient motion transmission to the wings. The model generates large-amplitude wing deflections with a very small amplitude input excitation from the actuator - useful for higher aerodynamic performance. Experimentally, the structural dynamic analysis of the MAV model is carried out using an in-house high-speed 3D digital image correlation (DIC) technique, which reveals that the wings generate bending dominated deforming shape with marginal twisting and positive camber (during downstroke). Structural dynamic results from computations are in good agreement with the experiments that validates both of the approaches for further advancements in the developed MAV model. Overall outcome of the current work is a simplified biomimetic MAV design, a potential candidate for developing mechanically efficient MAV models.
Man-in-the-Middle Attack Mitigation in Internet of Medical Things

The Internet of Medical Things are susceptible to Man-in-the-Middle (MitM) attack, which can identify healthcare emergency of monitored patients and replay normal physiological data to prevent the system from raising an alarm. In this article, we propose a framework to prevent a MitM from disrupting the operations and prohibiting the raise of alarms by the remote healthcare monitoring system. To reduce energy consumption for normal data transmission, and preserve the privacy of health data, our framework transmits a smaller size signature derived from acquired data with message authentication code, where the key is derived from received signal strength indication. Our experimental results for emergency detection show that our approach can achieve a high detection accuracy with a low false alarm rate of 3%.
Effect of geographical distributions on the phytochemical profile of methanolic extract of dried leaves of Rosmarinus officinalis L.

Rosmarinus officinalis L. (Rosemary) is a medicinal herb belongs to Lamiaceae family have shown a variety of pharmacological activities as cancer chemoprevention and therapy using in vitro and in vivo models. The study was conducted to the herb that was chosen from different locations (named as RO1, RO2, RO3, RO4 and RO5) with respect to qualitative and quantitative phytochemical analysis. Preliminary phytochemical screening reveals that the presence of phytoconstituents including alkaloids, phenols, cardiac glycosides, saponins, flavonoids, tannins, terpenoids, steroids, carbohydrates, proteins, coumarins and quinines, while anthraquinones was absent in all the samples collected from different locations of kodaikanal hills. RO3 sample was found to contain high level of phenolic content (62.39 mg/g) followed by RO2 (55.53 mg/g) and RO1samples (53.51 mg/g), while RO4 (44.36 mg/g) and RO5 samples (40.75 mg/g) contain low level of phenolics. The total flavonoid content was found to be maximum (35.18 mg/g) in RO3 followed by RO2 (26.17mg/g) and RO1 (23.28 mg/g) samples and minimum in RO4 (18.94 mg/g) and RO5 (17.75 mg/g) samples. The total tannin content was found to be maximum (29.70 mg/g) in RO3 followed by RO2 (22.28 mg/g) and RO1 (21.48 mg/g) and minimum in RO4 (18.16 mg/g) and RO5 (15.86 mg/g). The total carbohydrate content was maximum (31.85 mg/g) in RO3 followed by RO2 (24.88 mg/g), RO1 (23.75 mg/g), RO4 (19.92 mg/g) and RO5 (17.64 mg/g). The protein content was maximum (25.20 mg/g) in RO3 followed by RO2 (18.16 mg/g), RO1 (16.00 mg/g), RO4 (13.18 mg/g) and RO5 (11.59 mg/g).
Anterior Fontanel Size Among Term Newborns: A Systematic Review and Meta-Analysis

Background: Anterior fontanel is an integral element of an infant craniofacial system. There are six fontanels in the newborn skull, namely anterior, posterior, two mastoid, and two sphenoid fontanels. The anterior fontanel is the largest, prominent, and most important for clinical evaluation. Sex, race, genetics, gestational age, and region are the principal factors that influence anterior fontanel size. There exist inconclusive findings on the size of anterior fontanel in newborns. Therefore, this systematic review and meta-analysis aimed to determine the pooled mean size of anterior fontanel among term newborns and to identify the pooled mean difference of anterior fontanel size between males and females. Methods: PubMed/Medline, Google Scholar, Science Direct, JBI Library, embase, and Cochrane Library databases were systematically searched. All essential data were extracted using a standardized data extraction format. The heterogeneity across studies was assessed using the Cochrane Q test statistic, I2 test statistic, and p-values. A fixed-effect model and random effect model were used to estimate the pooled mean size of anterior fontanel and the pooled mean difference between male newborns and female newborns, respectively. To deal with heterogeneity, sub-group analysis, meta-regression analysis, and sensitivity analysis were considered. JBI quality appraisal checklist was used to evaluate the quality of studies. Results: In this meta-analysis, 8, 661 newborns were involved in twenty-six studies. Among studies, 13 conducted in Asia, 7 in Africa, 5 in America, and 1 in Europe. The pooled mean size of anterior fontanel was 2.58 cm (95% CI: 2.31, 2.85 cm). The pooled mean size of anterior fontanel for Asia, Africa, America, and Europe region was 2.49, 3.15, 2.35, and 2.01 cm, respectively. A statistically significant mean difference was detected between male and female newborns (D + L pooled MD = 0.15 cm, 95% CI: 0.02, 0.29 cm). Conclusion: The pooled estimate of this review does provide the mean value of the anterior fontanel size in the newborns. There was a statistically significant mean fontanel size difference between male and female newborns. Therefore, male newborns had a significantly larger mean size than female newborns.
The switching control strategy of redundant electromechanical servo system based on the compound sliding mode control

Aiming to resolve the limitations of robustness and stability in switching working modes of dual redundancy electric servo system, a control strategy based on sliding mode control is suggested. By analyzing the electromechanical servo system’s basic structure and working theory, a mathematical model is created to enable the design of new control functions. Taking into account the influence of the internal parameter perturbation of the electromechanical servo system during the switching between working modes, the sliding mode control is used to enhance system robustness, and the method of combining the latest saturation function and the exponential reaching law is used to minimize the system chattering. The simulation results and analysis show that the control strategy can effectively improve the robustness and control accuracy of the system.
Digital smile design as a tool in the planning of porcelain laminate veneers restoration

Digital smile design is an important tool for esthetic planning in dentistry as it facilitates ease of communication between professionals and patients. This clinical report has the objective of describing a clinical procedure involving digital smile design for the placement of porcelain laminate veneer restorations. The digital smile design included dental and facial analyzes. The smile curve was drawn and a dental ruler was used to delimitate the spaces between each tooth. An approximate simulation of the smile was generated using Adobe Photoshop software. For esthetic and functional evaluation of the digital plan was created a diagnostic waxing and mock-up. The laminate veneers were made with feldspathic porcelain. Digital smile design was essential for successful planning because it enabled better patient contact with the end result. This case demonstrates the importance of this tool in esthetic dentistry. Indexing terms: Dental veneers. Operative dentistry. Smiling.
Determinants of Rice Farmer Participation in the Direct Marketing Channel in Ghana

Enhancing smallholder farmers’ access to reliable, ready, and direct marketing channels is a prerequisite for sustainable food supply and poverty reduction in the developing countries including Ghana. However, it is a great challenge for smallholder farmers to access direct marketing channels in Ghana. This paper analyzes the factors that influence smallholder rice farmers’ participation, and the intensity of participation, in direct marketing channels using primary data from 199 farmers in three rice-growing districts in the Northern Region of Ghana. A double hurdle model was used in the empirical analysis. The study findings show that a lower percentage of farmers sold their rice output to processors (direct marketing). Farm size, the price of rice output per 85 kg bag, access to market information, and access to credit increased farmers’ participation in direct marketing channels, whereas payment period and ownership of a bicycle reduced their participation. The study concludes that improving farmers’ access to market information and credit availability would enhance participation in direct marketing channels.
Model-Based Time Series Clustering and Interpulse Modulation Parameter Estimation of Multifunction Radar Pulse Sequences

Multifunction radars (MFRs) are sophisticated sensors with significant intelligence, flexibility, and agility. It is important to analyze the system behavior and interpret the intentions of an MFR through the recognition of consecutive work modes in a pulse sequence. With the rapid development of MFRs, the agility capabilities in work mode modulations and the modulation parameters have increased, making the recognition task more challenging. Therefore, it is essential to develop new methods that are more adaptive and less depended on prior information. This article proposes a new method for the time series clustering of MFR work modes named as model-based radar time series clustering. The proposed method considers the variable modulation parameters of submode and employs three algorithms for different assumptions of progressive availability of the priors of a noncooperative MFR. The experiments with four typical pulse repetition interval modulations simulation samples validate the feasibility and the superior performance of the proposed method over the state-of-the-art pulse sequence clustering methods for recognition of MFR work modes.
Fuzzy arithmetic in economic research

Many tasks in economic research are based on arithmetic calculations of indicators that reflect the state of economic development. The general incompleteness of publicly available data, designed to solve such problems, has led to the emergence of numerous decision support systems based on fuzzy arithmetic. The article presents a study on the approach aimed at integrating fuzzy information about economic indicators into economic models. The definition of arithmetic operations on fuzzy values is given, and the methods of obtaining the resulting fuzzy indicators with the help of some software tools are considered. Analytical and numerical methods of obtaining the resulting indicators in the form of fuzzy numbers are described and analyzed. A direct calculation algorithm for all arithmetic operations is proposed, utilized, and used for the evaluation of resulting indicators. Also, analytical and numerical methods for obtaining fuzzy results are considered in the article, and some of them are proposed for utilization. On the example of economic indicators used in the labor rationing, the results of an evaluation of some indicators for a technological operation in the form of fuzzy numbers were obtained by different methods and compared. The practical recommendations, given in the article, on the use of fuzzy arithmetic in decision support systems outline the directions of further research.
VECMAN: A Framework for Energy-Aware Resource Management in Vehicular Edge Computing Systems

In Vehicular Edge Computing (VEC) systems, the computing resources of connected Electric Vehicles (EV) are used to fulfill the low-latency computation requirements of vehicles. However, local execution of heavy workloads may drain a considerable amount of energy in EVs. One promising way to improve the energy efficiency is to share and coordinate computing resources among connected EVs. However, the uncertainties in the future location of vehicles make it hard to decide which vehicles participate in resource sharing and how long they share their resources so that all participants benefit from resource sharing. In this paper, we propose VECMAN, a framework for energy-aware resource management in VEC systems composed of two algorithms: (i) a resource selector algorithm that determines the participating vehicles and the duration of resource sharing period; and (ii) an energy manager algorithm that manages computing resources of the participating vehicles with the aim of minimizing the computational energy consumption. We evaluate the proposed algorithms and show that they considerably reduce the vehicles’ computational energy consumption compared to the state-of-the-art baselines. Specifically, our algorithms achieve between 7 and 18 percent energy savings compared to a baseline that executes workload locally and an average of 13 percent energy savings compared to a baseline that offloads vehicles’ workloads to RSUs.
Intelligent power equipment identification model based on grid topology analysis

With the continuous development of the power grid, power equipment becomes more complex and diverse, which has increased the workload of power maintenance personnel. This paper proposes a method of intelligent identification of distribution network equipment to reduce the power maintenance personnel's workload. The model needs device photos, GPS coordinates, and device topology information of the entire power grid to infer the possible situation of the current device. The model is mainly divided into two parts: target recognition and equipment prediction. In target recognition, we propose a Self-attention target detection network (SA-TDN) that combines Faster-RCNN and Attention mechanism. In equipment prediction part, we use KD-Tree to analyse the grid topology to predict the real identification of the device. We compared this model with other convolutional neural networks (CNN) classification models. The results show that our model is ahead of current models in prediction accuracy.