The first objective of this study is to estimate the potential of solar irradiance in Algeria using an artificial intelligence approach, namely, convolutional neural networks (CNNs). The second objective is to visualize the different solar components DNI, DHI, and GHI as spatial maps. Data from 267 locations for 2005–2022, obtained from the NREL database, subdivided into training and testing data are used to build the different forecasting models. Testing data are not used when training CNNs to provide an indication of the performance at unknown locations. CNN models with 9 input variables average temperature, relative humidity, sunshine duration, wind speed, precipitation, longitude, latitude, altitude, and month were used to estimate the monthly values of GHI, DNI, and DHI. Statistical error analysis is conducted using mean absolute error (MAE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE) and coefficient of determination R2. This study shows that CNNs can be a better solution to estimate solar irradiance data. Seasonal solar mapping was developed in a GIS environment, representing locations and values of solar irradiance. These maps offer valuable insights into solar energy resources, aiding in the implementation of solar energy systems.
Xanthene derivatives of the 14-aryl-14H dibenzo[a.j] xanthene type were exploited in our study because of their interesting biological profile as well as their innumerable applications described in previous studies. They were synthesized by one-pot condensation of β�-naphthol with arylaldehydes and catalyzed by zinc trifluoromethanesulfate Zn(OTf)2 under conventional heating without solvent. Their characterization was carried out by Ultraviolet–Visible spectrophotometry (UV–Vis), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD) and scanning electron microscopy (SEM). First, spectral analysis in the UV–Vis range showed that all xanthene derivatives had similar absorption spectra at an absorption maximum (λmax)(�max) in the region of 310–340nm. Similarly, the infrared spectra suggested the presence of characteristic bands for each product in the range of 500–3500cm−1−1. In addition, the analysis of their crystalline structures was carried out by the XRD technique and reported in our study. Finally, their sizes and morphologies were highlighted by SEM. The obtained results revealed the presence of microcrystals in the form of micro-cubes having an average length of 10μ�m for the compound C2727H1818O and micro-shuttles of average size of 20μ�m for the compound C2727H1717BrO. It was therefore concluded that the xanthene derivatives synthesized have characteristic functional groups and structures which will provide them with various therapeutic properties and can therefore be exploited as bioactive molecules in several fields.
The use of X-ray micro-CT imaging (X-ray microtomography), which is an effective imaging technique that provides multiple advantages across different fields (non-destructive, high resolution, density contrast, versatility, in-situ analysis, and 3D modeling), for experimentally determining shrinkage during fruit drying has not been extensively researched. Therefore, the main goal of this paper is to examine the shrinkage phenomenon during convective drying of tomato slices, using three different temperatures (40, 50, and 60°C) at constant air velocity of 1 m.s-1 and very low humidity. Indeed, the shrinkage curves showing the evolution of the volume ratio (V*) versus the water content ratio (X*) are determined by X-ray microtomography. According to the findings, the tomato exhibits anisotropic shrinkage that is not affected by temperature, and a single shrinkage curve is proposed. On average, the volume decreases of 70% of its initial value. The water content affects both thickness and diameter shrinkage in a linear manner until X* is 0.2. However, when the values are below 0.2, the diameter and especially thickness decrease quickly, with a polynomial and logarithmic trend, respectively.
This paper presents a critical review of Swarm Intelligence (SI) algorithms–Particle Swarm Optimization (PSO), Bee Colony Optimization (BCO), and Ant Colony Optimization (ACO)–and their applications in traffic signal control (TSC). SI techniques, inspired by natural processes, have emerged as effective tools for optimizing traffic signal timings to improve urban traffic flow, reduce delays, and enhance overall efficiency. This review highlights the distinct strengths and limitations of each algorithm, with PSO excelling in simpler scenarios, BCO offering dynamic adaptability, and ACO providing robust path-finding capabilities. The paper also explores comparative analyses and hybrid models that combine these algorithms to leverage their complementary strengths. Despite significant advancements, challenges such as scalability, data dependency, and practical implementation remain. Future research directions include bridging the gap between theoretical and real-world applications, developing hybrid and adaptive models, and incorporating advanced data-driven techniques. The insights provided aim to guide further exploration and application of SI in creating more efficient and responsive traffic management systems.
Aims: Non-alcoholic fatty liver disease is strongly linked to obesity and insulin resistance. The triglyceride-glucose index (TyG index) has been proposed as a reliable biomarker of insulin resistance. Our aims is to study the contribution of the TyG index as a marker in the association of non-alcoholic fatty liver disease, obesity and insulin resistance. Material and method:This was a cross-sectional study of obese women (age>18years,BMI≥29.9Kg/m2). Non-alcoholic hepatitis steatosis was confirmed by abdominal ultrasound. The viral origin of the liver disease was eliminated by microbiological test. A lipid profile was performed. The TyG index was calculated according to the formula Ln[fasting triglycerides(mg/dL)×fasting glucose(mg/dL)/2. The relations between TyG index body composition and lipid profile was measured. Results: 42 obese women participated in our work. Age=50.80±10.33 years. BMI=40.80±5.09 Kg/m2; weight=98.1±15.99 Kg. Fasting blood sugar was 1.18±0.34 g/L. Triglycerides were 1.37±0.47 g/L. Our study demonstrated that 83% (n= 35) have, in addition to nonalcoholic fatty liver disease, a high TyG index (4.75+/-0.25) in favour of insulin resistance. The TyG index is strongly linked to fasting glucose (rs=0.83) and fasting triglycerides (rs=0.78). A statistically significant link was found between the TyG index and age (τ = 0.21, p=0.04), the TyG index and total cholesterol (τ = 0.33, p=0.001). Conclusions: The TyG index is a topical biomarker. This clue is practical. It appears to have a place in the association, obesity, non-alcoholic fatty liver disease and insulin resistance. Keywords: TyG index, insulin resistance, obesity, non-alcoholic fatty liver disease
Nowadays, different kinds of Industrial Control Systems (ICS) are used in heavy industries like oil and gas industry to monitor and control production and operation processes. In a same industrial plant, major engineering and design efforts are made during each system implementation, to comply with process requirements and features. Moreover, other efforts are made regarding to operation and maintenance issues due to diverse hardware and software architectures of such systems. This paper aim is to make a comparative study between most used control systems by highlighting their strengths and features, each according to its preferred domain of process control. Then to discuss how to develop a unified system that will have core features to monitor and control different kinds of processes. This approach is applied for the design of an oilfield telemetry project. The purpose is to propose technically optimized and cost-effective hardware and software solution.
Wetlands are present within intricate ecological settings, exhibiting dynamic changes over time and across different areas with regard to their functional and structural diversity. Several approaches have been introduced for ecosystem analysis and management since 1990s, where Driver – Pressure – State – Impact – Response (DPSIR) framework is one of the widely used approaches for assessing and managing environmental problems. In this study, we proposed a DPSIR framework as a model in the Sebkhates of Aures wetland complex (SAWC, Northeast of Algeria), in order to create a regional wetland observatory to deliver ecosystem services within current state of changes resulted from socioeconomic drivers. DPSIR facilitates the identification of essential indicators to detect threats to the Sebkahtes of Aures wetlands complex (SAWC), influencing policymakers to enhance the protection and management of these wetlands. In line with the established DPSIR framework. This same approach and methodology could be applied to the other 15 wetlands complexes known in Algeria, culminating in the creation of a national wetland observatory. Furthermore, DPSIR can be employed as an analytical tool for policy making in the context of sustainable management of wetland ecosystems and can serve as a model in the establishment and development of various observatory programs in Algeria.
Wetlands are present within intricate ecological settings, exhibiting dynamic changes over time and across different areas with regard to their functional and structural diversity. Several approaches have been introduced for ecosystem analysis and management since 1990s, where Driver – Pressure – State – Impact – Response (DPSIR) framework is one of the widely used approaches for assessing and managing environmental problems. In this study, we proposed a DPSIR framework as a model in the Sebkhates of Aures wetland complex (SAWC, Northeast of Algeria), in order to create a regional wetland observatory to deliver ecosystem services within current state of changes resulted from socioeconomic drivers. DPSIR facilitates the identification of essential indicators to detect threats to the Sebkahtes of Aures wetlands complex (SAWC), influencing policymakers to enhance the protection and management of these wetlands. In line with the established DPSIR framework. This same approach and methodology could be applied to the other 15 wetlands complexes known in Algeria, culminating in the creation of a national wetland observatory. Furthermore, DPSIR can be employed as an analytical tool for policy making in the context of sustainable management of wetland ecosystems and can serve as a model in the establishment and development of various observatory programs in Algeria.
Studies on drying of the watermelon seeds or rinds are available, but not on drying of the fruit flesh. Data on the sorption of watermelon fruit (Citrullus Lanatus), which are useful and important when identifying optimal food drying and storage conditions, are also not available. Therefore, the main goal of this study is to investigate the water desorption behavior of watermelon using the dynamic method requiring the automated dynamic vapor sorption (DVS) technique based on a humidity-generating device. The moisture desorption isotherms are determined at three different temperatures (20°C, 30°C and 40°C) over a relative humidity range of 0-80%. In addition, 11 different models are applied to the experimental data in order to select the appropriate desorption curve equation. The models are compared using the correlation coefficient, the coefficient of determination, the standard error, the reduced chi-squared, the mean bias error and the root mean square error; they are predicted by a non-linear regression analysis using the Curve Expert software and MS Excel computer program. The effect of the temperature on the constants of the found equation is also examined. Besides, the desorption isosteric heat of watermelon is determined using the Clausius-Clapeyron relation. Contrary to long times taken in the static method, the results show that 4.5 days are widely sufficient to reach the equilibrium via the DVS device. In addition, the obtained curves are of the type III and the Henderson model best fits the experimental data. In the interval 0.05-0.45 kg water.kg dry matter-1 of equilibrium moisture, the values of the isosteric heat vary from 65.02 to 113.25 kJ mol-1. Finally, the equation giving the isosteric heat of desorption v.s. the equilibrium moisture content is obtained.
The issue of reducing spatial disparities is one of the most pressing concerns for policymakers and planners, which consider a crucial focus in planning and public service, especially accessibility to healthcare. Accessibility and proximity are the principal keys to providing good public service. Therefore, a healthcare system that meets the requirements of availability and affordability will be useless if spatial accessibility is not provided equally to all demands (population). Many technics and methods exist to quantify accessibility, including the two-step floating catchment area (2SFCA) method, its widely used to measure healthcare accessibility based on the travel distance threshold. This research paper aims to use the 2SFCA method to measure the spatial healthcare accessibility in Batna City because the 2SFCA method offers to measure accessibility on both spatial and functional levels. The spatial level will consider the threshold distances between the health demand (population) and the health provider location (healthcare facilities); moreover, functional accessibility is measured based on facility to population ratio that will give a better overview of Batna's healthcare provider. As a result, the optimal threshold distance that offers balanced results between the spatial accessibility score and other WHO ratios will be a distance between 1000- and 1500-meters travel distance. In addition, the central census districts have a higher access score than the rest of the city's districts; most census districts that do not have accessibility (12% of the population) to healthcare facilities are concentrated in the southwest of Batna city.
In this paper, we present a novel hybrid meta-heuristic by enhancing the Basic Bees Algorithm through the integration of a local search method namely Simulated Annealing and Variable Neighbourhood Search like algorithm. The resulted hybrid bees algorithm (BASA) is used to solve the Single Machine Scheduling Problem with Early/Tardy jobs, where the generated outcomes are compared against the Basic Bees Algorithm (BA), and against some stat-of-the-art meta-heuristics. Computational results reveal that our proposed framework outperforms the Basic Bees Algorithm, and demonstrates a competitive performance compared with some algorithms extracted from the literature.
