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The evolution of mega-ships and maritime alliances, in tandem with global changes, has intensified hub port competitiveness, increasing the need for innovation and adaptation, with a focus on sustainability. This study analyzed the competitiveness of the Mediterranean maritime hub port with Northern European ports. It provided a comprehensive analysis of the competitiveness of ten leading international hub ports in the Mediterranean and Northern Europe. Using the Hybrid F-AHP TOPSIS method, this research first identified and assigned weights to 10 criteria across five categories of port competitiveness from a shipping liner perspective in the Mediterranean and Northern Europe. It then ranked those ten leading ports based on the weighted criteria. The results showed that handling cost, operational efficiency, and infrastructure were the main factors for defining a competitive hub port, with growing emphasis on sustainability considerations. The results also highlight that Tangier Med, Algeciras, and Port Said were the leading ports in the region, excelling in technical efficiency and maritime costs compared to Northern European ports. The analysis further provided a holistic overview of competitiveness in the region, identifying the strengths and opportunities of each port based on 5 aspects with a special focus on the hub ports of the Southern Mediterranean. This research offers valuable insights for maritime shippers and port authorities in understanding hub port performance and competitiveness in a sustainable context.

Customer segmentation is seen as one of the pillars of a successful advertising campaign. Marketers give great importance to this flagship phase in the process of marketing new products. Successful segmentation will involve successful “Customer Targeting” and therefore a profitable customer marketing campaign. Many works have dealt with customer segmentation using unsupervised Machine Learning algorithms such as K-Means by applying the famous Recency, Frequency and Monetary model. That model suffers from insufficiency by ignoring other important parameters according to the field of application. In this paper, we have modified the model by adding diversity “D” as a fourth parameter, referring to the diversification of products purchased by a given customer. The segmentation based on RFM-D is applied in a retail market in order to detect behavior patterns for a customer. The proposed model increases the quality of prediction of customer behavior; Companies could predict, customers who will respond positively.

Although the EVRP (Electric Vehicle routing problem) has promising results on the environmental scale, its implementation has proved challenging. The difficulty of the EVRP resides in the limited driving range of the electric vehicles, combined with the significant charging time. While the charging cost is less than the cost of fuel, this charge time adds to the overall travel time and may overlap with customers’ time windows. All these factors increased the computational time exponentially and resulted in the need to overlook some constraints such as traffic congestion, road conditions, weather impact on energy consumption, and driving style, to name a few, in order to speed up execution time. While this method proved effective in accelerating the process of the EVRP, it did, however, render the approach unrealistic, as it steered far from real-life settings and made the approach unpredictable when facing dynamic and changing parameters. In this paper, we try to remedy this issue by proposing an approach in which we try to replicate real-life parameters such as heterogenous fleets, energy consumption, and infrastructure data. The objective of our approach was to minimize the total travel time, travel distance, energy consumed, and the number of vehicles deployed. To solve this problem, we propose a three-stages approach, in which the first stage consists of a newly developed dispatching approach where customers are assigned to vehicles. The second stage uses the genetic algorithm to find a set of optimal paths, and, finally, in the third stage, charging stations are inserted in the selected paths. Upon testing our approach on Solomon’s instances, our approach proved effective in finding optimal solutions in a reasonable time for five- to fifteen-customer datasets. However, when trying to solve larger datasets, the approach was slowed down by the extreme number of constraints it had to satisfy.

The residential area represents a sector that consumes more electricity especially with the rapid urban growth and the transition towards the automation and electrification of several daily activities of human beings, namely: urban mobility and the residents’ indoor comfort. As a result, meeting this increased demand requires a proportional rise in fuel use to generate energy. This type of conventional power production has significant impacts on the environment. The eco-friendly alternative is the use of renewable energies in its distributed form on buildings. However, this solution gives rise to some issues related to energy management especially with the penetration of a new domestic device, namely: the plug-in electric vehicle. For this purpose, we propose a management system for a future household equipped with controllable electric loads and an electric vehicle equipped with a PV–Wind–Battery hybrid renewable system connected to the national grid. The proposed management system is based on a linear programming model with non-linear constraints solved with MATLAB toolboxes. The simulation is based on a database of meteorological conditions resulting from TRNSYS and processed to achieve a frequency of one hour. The system decisions provide switch control states of the connection architecture as well as the variation according to the V2H (vehicle to home), H2V (home to vehicle) and involved G2V (grid to vehicle) scenarios when grid comes into play during H2V mode.

An electric vehicle, as the name suggests, runs purely on electricity provided by battery packs located under its floor. This electricity fuels the engine and converts electric power to mechanical motion. Accordingly, electric vehicles do not emit pollutants as no combustion process takes place within, making it an eco-friendly alternative to combustion vehicles. However, electric vehicles face one major issue and that is their limited range. Recharging electric vehicles’ batteries requires an important amount of time. Since time is an important factor, recent works started focusing more and more on optimizing the use of recharge within an electric vehicle to make the most of it while avoiding frequent stops at the charging stations. Another riddle to solve, is the routing of vehicles which gets more complicated when electric vehicles are thrown into the mix. Not only an electric vehicle has a limited range, adding charging stations location to the problem affects the final path, the time and the cost of the routing problem. To help researchers advance in this direction, our work discuss the recent methods used to solve the vehicle routing problem, as well as the issues facing charging of electric vehicles. This will help establish a picture of where improvements need to be made and inspire future works to accelerate the rate at which electric vehicles will be roaming our roads. Our review shows that literature focuses increasingly on adapting electric VRP to real life settings by adding more and more constraints. Moreover, recent works are considering the recharging patterns of electric vehicles in order to reduce the overall travel time, distance and cost. The results of this review prove that complexity of electric VRP stems from the difficulty of satisfying all imposed constraints, as well as the inability to predict the outcome of the solution proposed face to real-life scenarios.

LoRa technology is being integrated into industrial applications as part of Industry 4.0 owing to its longer range and low power consumption. However, noise, interference, and the fading effect all have a negative impact on LoRa performance in an industrial environment, necessitating solutions to ensure reliable communication. This paper evaluates and compares LoRa’s performance in terms of packet error rate (PER) with and without forward error correction (FEC) in an industrial environment. The impact of integrating an infinite impulse response (IIR) or finite impulse response (FIR) filter into the LoRa architecture is also evaluated. Simulations are carried out in MATLAB at 868 MHz with a bandwidth of 125 kHz and two spreading factors of 7 and 12. Many-to-one and one-to-many communication modes are considered, as are line of sight (LOS) and non-line of Sight (NLOS) conditions. Simulation results show that, compared to an environment with additive white Gaussian noise (AWGN), LoRa technology suffers a significant degradation of its PER performance in industrial environments. Nevertheless, the use of forward error correction (FEC) contributes positively to offsetting this decline. Depending on the configuration and architecture examined, the gain in signal-to-noise ratio (SNR) using a 4/8 coding ratio ranges from 7 dB to 11 dB. Integrating IIR or FIR filters also boosts performance, with additional SNR gains ranging from 2 dB to 6 dB, depending on the simulation parameters.

This study investigates the importance of using engineering ventilation in healthcare settings, especially during widespread disease outbreaks such as COVID-19. Ventilation can be used to improve indoor air quality in care homes, hospitals, and quarantine locations. In the research, two scenarios of engineering ventilation are simulated using a Fire Dynamic Simulator (FDS), with sulfur hexafluoride employed as the contaminant emitted by the patient in the hospital room. The volume of the room selected for the present study is 60 m3, and the ventilation mode is designed according to the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), with 12 air changes per hour and negative pressure. The mean concentration of the pollutants is estimated for both scenarios, giving concentrations in the range of 920 ± 397 to 1260±580 ppm. The estimated indoor air quality (IAQ) values varied from 2.06 to 2.90. According to the obtained results, ventilation plays a critical role in eliminating pollutants, indicating that suitably engineered ventilation strategies can reduce the impact of COVID-19 spread in closed buildings.

The objective of this study was to identify, quantify, and elucidate the polyphenols, flavonoids, and anthocyanins, and their antioxidant activities (via 13 2,2-diphenyl-1-picrylhydrazyl) (DPPH) radical and 2,2ʹ-azinobis-(3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) radical cation scavenging abilities and bleaching β-carotene tested in vitro in the whole fruit, fruit skin of strawberry tree fruits of 5 genotypes and to find out the most valuable fruit for disease preventing diets. Total phenols, total flavonoids, total anthocyanins, antioxidant activity (DPPH, ABTS and β-carotene bleaching assay), pH, titrable acidity, soluble solids, and organic acids were investigated in five strawberry tree genotypes belonging to several areas in Morocco. Qualitative and quantitative analyses of individual phenolic compounds by high-performance liquid chromatography (HPLC) were also carried out. Significant differences (p˂0.05) were observed across the five genotypes in total phenols (25.37–39.06 mg GAE/g DW), total flavonoids (3.30–7.07 mg RE/g DW), total anthocyanins (0.15–0.64 mg cya-3-glu/100 g DW), pH (2.44–3.92), titrable acidity (0.65–1.01 g malic acid/100 g fw), and soluble solids (14.83–18.53%). The antioxidant activity was evaluated by three assays. The values were 3.33–21.08, 2.25–19.58, and 1.08–13 mg AAE/g DW for DPPH scavenging test, ABTS, and β-carotene bleaching, respectively. Gallocatechol and catechin were the most abundant phenolic compounds. Principal component analysis showed that the first three components formed 90.25% of the total inertia. Chlorogenic acid, ellagic acid derivative I, ellagic acid, rutin, and cyanidin-3,5-diglucoside were the most involved variables in the total variance explained.

In the present paper, new type of extension of classical beta function is introduced and its convergence is proved. Further it is used to introduce the extension of Gauss hypergeometric function and confluent hypergeometric functions. Then we study their properties, integral representation, certain fractional derivatives, and fractional integral formulas and application of these functions.

The growing demand in the field of security led to the development of interesting approaches in face classification. These works are interested since their beginning in extracting the invariant features of the face to build a single model easily identifiable by classification algorithms. Our goal in this article is to develop more efficient practical methods for face detection. We present a new fast and accurate approach based on local binary patterns (LBP) for the extraction of the features that is combined with the new classifier Neighboring Support Vector Classifier (NSVC) for classification. The experimental results on different natural images show that the proposed method can get very good results at a very short detection time. The best precision obtained by LBP-NSVC exceeds 99%.

The quest for an efficient nature-inspired optimization technique has continued over the last few decades. In this paper, a hybrid nature-inspired optimization technique has been proposed. The hybrid algorithm has been constructed using Mean Grey Wolf Optimizer (MGWO) and Whale Optimizer Algorithm (WOA). We have utilized the spiral equation of Whale Optimizer Algorithm for two procedures in the Hybrid Approach GWO (HAGWO) algorithm: (i) firstly, we used the spiral equation in Grey Wolf Optimizer algorithm for balance between the exploitation and the exploration process in the new hybrid approach; and (ii) secondly, we also applied this equation in the whole population in order to refrain from the premature convergence and trapping in local minima. The feasibility and effectiveness of the hybrid algorithm have been tested by solving some standard benchmarks, XOR, Baloon, Iris, Breast Cancer, Welded Beam Design, Pressure Vessel Design problems and comparing the results with those obtained through other metaheuristics. The solutions prove that the newly existing hybrid variant has higher stronger stability, faster convergence rate and computational accuracy than other nature-inspired metaheuristics on the maximum number of problems and can successfully resolve the function of constrained nonlinear optimization in reality.

This paper aims to comprehensively compare the risk-return profiles of Islamic and conventional equity markets, providing valuable insights into the financial performance of stock market indices in Islamic countries as compared to those in developed nations. The objective is to delve into the intricate dynamics of these markets, shedding light on how they respond to various economic and financial factors. By focusing on the performance metrics of stock market indices in both Islamic and developed countries, this study intends to analyze not only the overall returns generated but also the associated risks involved. In doing so, it seeks to unearth the nuances in investment opportunities, uncovering potential patterns of divergence or convergence between these two market segments. Furthermore, this research endeavors to discern how different economic and regulatory environments impact these markets, contributing to a more profound understanding of global investment. [ABSTRACT FROM AUTHOR]