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This paper presents the analysis and implementation of the least-squares method based on the Gauss-Seidel scheme for solving nuclear mass formulas. The least-squares method leads to the solution of the system by iterations. The main advantages of the discussed method are simplicity and high accuracy. Moreover, the method enables us to process large data quickly in practice. To demonstrate the effectiveness of the method, implementation using the FORTRAN language is carried out. The steps of the algorithm are detailed. Using 2331 nuclear masses with Z ≥ 8 and N ≥ 8, it was shown that the performance of the liquid drop mass formula with six parameters improved in terms of root mean square (r.m.s. deviation equals 1.28 MeV), compared to the formula of liquid drop mass with six parameters without microscopic energy, deformation energy and congruence energy (r.m.s. deviation equals 2.65 MeV). The nuclear liquid drop model is revisited to make explicit the role of the microscopic corrections (shell and pairing). Deformation energy and the congruence energy estimate have been used to obtain the best fit. It is shown that the performance of the new approach is improved by a model of eight parameters, compared to the previous model of six parameters. The obtained r.m.s. result for the new liquid drop model in terms of masses is equal to 1.05 MeV.

New nest supports used by the white stork Ciconia ciconia ciconia (Linnaeus, 1775) are mobile phone network relay antennas, which are not without danger because they emit electromagnetic waves that can have a major, although insidious, impact on the species. The aim of this study is to assess the effects of electromagnetic waves from mobile phone network base stations on white stork reproduction. We monitored the breeding phenology of the storks as a function of the distance of their nests from the base stations over 2 consecutive seasons, 2020 and 2021. The work took place in the North-East of Algeria, in Annaba and El-Tarf provinces. The nests were counted and divided into 3 distinct groups. The first is located on the relay antennae, the second less than 200 m and the third more than 300 m from the antennae. We calculated nest occupancy, number of young in the nest and number of nests without young. The results show that nests located directly on base stations have a reduced clutch size, not exceeding two storks and a low reproductive success since the majority of nests (51.9%) remained without young. The number of young storks tends to increase in nests far from base stations. Broods with 3 and 4 young are generally those located more than 300 m from relay antennas.

Recently, the emergence of multi-tenant services-based systems (SBS) in cloud has gained significant attention. These systems, commonly adopted by Cloud software vendors, offer the advantage of building software as a service (SaaS) in a multi-tenant environment with distinct variability plans. However, the task of managing the SBS is challenging due to the interdependence among its diverse components. The difficulty primarily stems from the specific performance requirements of different variability plans and the interdependence between these plans. The main concerns involve effectively representing the components and levels of this system, and establishing synchronization rules to ensure its seamless operation. Refining these rules is crucial for improving coordination among the various components and levels of the SBS. However, it becomes particularly challenging when there are correlation issue among services that fall under different plan characteristics. In this context, it is vital to guarantee performance differentiation for each tenant by adhering to predefined quality of service (QoS) specified in Service Level Agreements (SLAs).To address these challenges, we propose a modular modeling approach based on multi-level Petri-Nets (n-NLS). By adopting these approach, the ambiguity of SBS system can be significantly reduced. An example of a SBS application illustrates our modular modeling approach using n-NLS.

In this study, we are interested in the evaluation of the department of the Earth Sciences and Universe (Science de la Terre & l'Univers-STU), Faculty of Science Ben MSik, Casablanca, Morocco, through teachers of the department of Geology. The accomplishment of this work passes through an evaluation that was carried out firstly through a satisfaction survey and secondly, a questionnaire containing questions related to the profile of the teachers, program and educational strategies of discipline offering training to the level of Bachelor. We used Sphinx software to analyze the data through the application, namely, flat table, the crossing of the questions and the specifications table. The goal is to evaluate training within the department of STU in quality and quantity. This research allowed us to identify many problems of the students who enrolled in this department. In other words these results are response elements that would help improve the teaching-learning of the STU field.

This study is based on a ruderal species: Malva subovata = Lavatera maritima which should normally grow in the littoral, under maritime influence. Its presence in the matorrals is highlighted for the first time. as it has been found far from its natural habitat. The comparison of the different biological and morphological spectra shows the importance of the therophytes that confirms the phenomenon of degradation. This degradation shows a depletion of the floristic procession, especially concerning sylvatic species which have given way to ephemeral therophytes and chamæphytes. Despite the presence of a shrubby layer, it is no longer a question of a forest ecosystem but of a pre-forest ecosystem. To better understand the characteristics of this Malvaceae in terms of resistance and adaptation in matorrals, we often use the phytomass of all anthropozoic formations and more specifically the phytomass of the species that characterize this group. In our case, this is Malva subovata. The results of the morphometric study constitute a basic element for the evaluation of the adaptive potential of our Lavatère against the degradation factors.

The emergence of the Internet of things (IoT), associated with the explosion in the number of connected objects, and the growth in user needs, makes the Internet network very complex. IoT objects are diverse and heterogeneous, which requires establishing interoperability and efficient identity management on the one hand. On the other hand, centralized architectures such as cloud-based ones can have overhead and high latency, with a potential risk of failure. Facing these challenges, Blockchain technology, with its decentralized architecture based on a distributed peer-to-peer network, offers a new infrastructure that allows IoT objects to interact reliably and securely. In this paper, a new approach is proposed with a three-layer architecture: layer of sensing and collection of data made up of the IoT network, layer of processing and saving of data exchanges at the Blockchain level, and access and visualization layer via a web interface. The prototype implemented in this study allows all transactions (data exchanges) generated by IoT devices to be recorded and stored on a dedicated Blockchain, assuring the security of IoT objects' communications. This prototype also enables access to and visualization of all data and information, thus enhancing the IoT network's transparency.

In this work, a maxi dual axis solar tracker is proposed. This tracker is used to track the sun path within a day and during a year. The tracking system is composed of two parts: mechanical and electrical parts. The mechanical part is composed of panel carrier, panel carrier rotator according to horizontal/vertical axis, two actuators. The electrical part is composed of light sensor, microcontroller, relays. The light sensor receives an artificial light and sends a potential difference as an analog signal to microcontroller. The microcontroller converts the analog signal into a digital signal using ADC and sends it to relays. The relays activate the two actuators to orient the maxi solar PV into the artificial light with the purpose to produce maximum energy.

Summary: Data aggregation, in its basic form, has been widely used, and several solutions have been proposed for IoT environments. However, to calculate statistical metrics, detect anomalies, and predict future trends, we need to perform various data analysis functions on the aggregated data. Recently, multifunctional data aggregation (MFDA) has been proposed to calculate various statistical functions such as sum, mean, variance, covariance, and analyze of variance (ANOVA). The purpose of MFDA is to enable the improvement of decision making, resource allocation and system performance by providing diverse and varied statistical data. However, the existing solutions involving MFDA generate significant communication and calculation costs. Furthermore, they cannot prevent malicious aggregators from sending fake data. Recently, the Fog computing paradigm has been adopted in IoT environments to address various challenges and enhance the efficiency of data processing and storage. The blockchain technology has been integrated in various IoT applications to enhance the security, increase transparency, and facilitate decentralized data exchange and transactions. In this article, we propose BMDA, a blockchain‐based secure multifunctional data aggregation method for IoT‐Fog environments. BMDA employs an encoding function to structure the data before their transmission. Furthermore, to ensure privacy preservation, authentication, data integrity and to resist malicious aggregators, we employ Paillier homomorphic encryption, BLS signature, and blockchain technology. The security analysis demonstrates the robustness of our proposal, and the performance analysis in terms of computations and communications shows the effectiveness of BMDA compared to existing solutions. [ABSTRACT FROM AUTHOR]

Lighting is the largest source of electricity consumption. The implementation of new technologies makes it possible to reduce the energy bill as well as the operating costs. In this work, we created a control and management system for exterior lighting using creative, intelligent, and autonomous methods. The project allows control room operators to monitor the system and interact when needed. Our project is subdivided into three major parts, ordering, data processing, and action. The first part is carried out in two modes: manually using an Optocoupler and controlled with a graphical interface by RS232 serial-type communication. The second part is processed by a microcontroller; as for the third part, it controls three poles through an MOC3023 Optotriac IC and BTA16-600B Triac for full and half lighting. For the controlled mode, we used the dimmer principle which works by essentially chopping parts out of the AC voltage. The brightness of the lamp is determined by the power transferred to it, so the more the waveform is chopped, the more it dims. A zero crossing control is needed for phase cutting. The graphical application of our project has a rather rough design. It is divided into two windows: the first consists of a connection window, and the second one is for the main control interface. On this control interface, there are basically three tabs: connection and serial connection status, control, and operating mode.

Electricity production from photovoltaic (PV) panels is maximized when the operating point is located at the maximum power point thanks to dedicated controllers. These controllers are driven to track the maximum power by using various algorithms within distributed or centralized architectures accounting for factors such as partial irradiation and temperature changes. The effect of irradiance on the optimal panel voltage is weak or even negligible, while it is strong and quasi-linear-dependent on temperature. Based on this observation, this article introduces a straightforward method for tracking the maximum power of a PV panel by using an optimizer, focusing solely on its temperature response as an input variable. The proposed approach hinges on linearizing the relationship between panel temperature and operating voltage. This relationship enables the estimation of the maximum power point through temperature measurement alone. Thus, after determination of the linear temperature coefficient of the voltage requiring only the knowledge of two optimal voltages at different temperatures, for example from the datasheet of the panel, the power tracking involves only one temperature sensor placed on the panel alongside a voltage sensor for regulation. The principle, modelling, and validation post-panel ageing of the method are detailed in this paper. Simulation, conducted using real experimental irradiation and temperature data, attests to the effectiveness of the control. Results indicate an average effectiveness of the method of >99.1% in tracking the maximum power, with the panel generating 2.33 kWh out of a possible 2.35 kWh. This performance is comparable to that of tracking devices employing more complex algorithms. The simplicity and efficiency of the method make it a promising option for maximizing the power production at low cost from PV systems in small or residential, on- or off-grid connected applications. [ABSTRACT FROM AUTHOR]

This paper presents a novel topology for photovoltaic microinverters that uses a buck-boost converter coupled with a discharge circuit. The system enables efficient conversion of electrical energy from the solar panel without requiring voltage filters or step-up transformers. For its control, an exponential action control strategy is used, as it allows effective tracking of the reference voltage. The stability of the overall control and inverter system is demonstrated and detailed using the Lyapunov method. The sizing of the components used is also detailed. The performance of this topology is evaluated under various load conditions, revealing low harmonic distortion (THD) in both current and voltage, with the best THD of 1% being obtained at a power factor of 0.8. This makes it a promising solution for photovoltaic energy conversion. [ABSTRACT FROM AUTHOR]

Accurate estimation of porosity is a critical factor in reservoir characterization. This study aims to enhance porosity prediction through the implementation and comparison of various stacking ensemble learning strategies. A dataset comprising 273 points, which consists of well logs and core measurements, was collected from two wells for model development. Four base learners, including Support Vector Regression (SVR), Multi-Layer Perceptron (MLP), Random Forest Regression (RFR), and XGBoost, were trained on this dataset. These models were then integrated using multiple stacking ensemble techniques, such as weighted averaging, Bayesian model averaging, and RFR as a metalearner. Meta-learners were trained on predictions from the base learners, generated through crossvalidation on leave-out data. Performance evaluations of both base and meta learners were conducted on a separate testing dataset using statistical and graphical error analysis. Results indicate that all learners demonstrated robust performance, with weighted averaging outperforming other strategies on testing data. The stacking ensemble approach, particularly through weighted averaging, effectively improved base learner performance on testing data by leveraging individual model strengths and mitigating weaknesses. The findings of this study are valuable for geoscientists and reservoir engineers in achieving accurate reservoir characterization and facilitating exploration activities. [ABSTRACT FROM AUTHOR]