Geomorphic clinoptilolite is a mechanically and thermally stable mineral substance, and such characteristics are rarely found in natural zeolites. In addition, clinoptilolite has very interesting electrical properties due to the presence of extra-framework cations in the crystal structure. Indeed, owing to the electrical transport by hopping mechanism that alkali cations may give, this ceramic material behaves like an electrical insulator at room temperature, while it changes to an electrical conductor with the increasing of temperature. Such unusual electrical property of clinoptilolite can be advantageously exploited for a number of functional applications in the industrial field like, for example, thermal sensors, electrical/thermal switches, thermistors, etc. Here, the capability of a simple natural clinoptilolite monolith to switch from electrical insulator to conductor under fast and slow thermal changes has been investigated by a.c. electrical transport measurement (i.e., time-resolved high-frequency micro-current measurements).

Lagoons ecosystems present various ecosystem services, including ecological functions and economic value contributing to human well-being. Morocco houses five lagoons from the North to the South (Nador lagoon, Moulay Bousselham Lagoon, Oualidia lagoon, Sidi Moussa lagoon, and Khenifes lagoon). These lagoons present many services and goods to the population living there, especially aquaculture (in the Oualidia & Khenifes lagoons), fishing, and agricultural activities. Moreover, the increased human activities around such ecosystems have negatively influenced their environmental quality. In this order, we evaluate the evolution of human activities in each lagoon during the last four decades. We analyze the ecosystem services and goods; these lagoons provide using Ma's conceptual framework, which incorporates ecosystem services and goods with human welfare. The current knowledge reveals these lagoons' critical role in the local population and region's economy. Besides, the study highlights that there are common services and goods that these ecosystems provide, which implies the need for developing strategies and policies based on approaches that combine all the provided services and goods for sustainable socio-economic and environmental growth.

Integrated circuits and optoelectronic devices are complex systems consisting of various materials with different characteristics that can develop mechanical stress when assembled. Since the presence of local stress influences the electronic performance of the device, it is useful to investigate the presence of stress and its distribution in different samples. Micro-Raman spectroscopy allows for determination, mapping, and quantification of local stress and is a powerful tool in understanding how various assembly methods of different materials influence the stress distribution in each layer of complex electronic systems such as LEDs.

This work focusses on the Raman investigation of both metal and semiconductor material properties in integrated circuits: silicon chips that simulate part of the structure of an optoelectronic device, and GaN LEDs. Silicon chips (120 μm thick) are soldered to copper substrates. Blue GaN-based LEDs, bonded to a silicon carrier using gold silicon, are soldered with an AuSn alloy on copper substrates, with different thicknesses. Stress is determined by 2D Raman mapping of the surface, at 514.15 nm, in a wide temperature range, from -50 to 180°C: from the determination of the Raman peak position of Silicon centered around 520 cm^-1, and GaN, centered around 568 cm^-1, the presence of tensile and compressive stresses on the samples are evaluated.

The topic of facial beauty analysis has emerged as a crucial and fascinating subject in human culture. With various applications and significant attention from researchers, recent studies have investigated the relationship between facial features and age, emotions, and other factors using multidisciplinary approaches. Facial beauty prediction is a significant visual recognition problem for the assessment of facial attractiveness, which is consistent with human perception. Overcoming the challenges associated with facial beauty prediction requires considerable effort due to the field's novelty and lack of resources.

In this vein, a deep learning method has recently demonstrated remarkable abilities in feature representation and analysis. Accordingly, this paper contains main contributions propose an ensemble based on the pre-trained convolutional neural networks models to identify scores for facial beauty prediction. These ensembles are three separate deep convolutional neural networks, each with a unique structural representation built by previously trained models from Inceptionv3, Mobilenetv2 and a new simple network based on Convolutional Neural Networks (CNNs) for facial beauty prediction problem. According to the SCUT-FBP5500 benchmark dataset the model obtains 0.9350 Pearson Coefficient Experimental results demonstrated that using this ensemble of deep network leads to better predicting of facial beauty closer to human evaluation than conventional technology that spreads the facial beauty. Finally, potential research directions are suggested for future research on facial beauty prediction.

With increasing needs of recycling composite materials in the context of circular economy and re-use of the fiber and matrix material as new resources, composite materials often pose problems as they are complex materials. The user properties of high strength and long lifetime require development of strong interfaces between the matrix and reinforcing fibers, while recycling would benefit from easy separation of both phases. Therefore, the design of an interface with reversible bonding upon thermal or chemical activation may offer a good balance. In addition, the request for bio-based composites incorporating cellulose fibers should be combined with bio-inspired interface modification avoiding traditional chemical surface modification. An impressive example of reversible bonding in nature is observed in the mussels and regulated by so-called mussel-foot proteins. The latter main component includes dopamine that presents reversible bonding upon change in pH. In present work, the cellulose fibers were modified with a catechol coating that was polymerized in contact with the cellulose surface. The chemical compatibility and covalent bonding of the coating through reaction with the cellulose hydroxyl groups was demonstrated. The adhesive properties of modified cellulose fibers were investigated by local adhesive measurements through AFM mapping, and the interface strength of fibers embedded into a PMMA matrix was evaluated through single fiber pull-out testing. The adhesion strength of the modified fibers dropped after submersion in solutions with pH above 10. The pull-out strength of the modified fibers similarly decreased after diffusion of a solution with pH above 10 through the interface. A proof of concept for recycling the cellulose/PMMA composites was demonstrated by shredding and chemical treatment in solutions at different pH, where cellulose fibers were successfully recovered at high pH.

Mechanochemical activation, by means of high-energy ball milling, was applied to CeO₂ as a strategy to enhance its physicochemical properties. Different milling parameters such as rotational speed and milling time were screened to evaluate their effect on ceria. Fluorite-type structure of cerianite was maintained in all cases, no matter the amount of energy introduced by milling process, as observed by X-ray Diffraction (XRD). A decrease in crystallite sizes along with a consequent increase in Specific Surface Area (S_BET) were observed by XRD and N₂ sorption (BET method). Pore diameters and total pore volumes were also in line with the duration of CeO₂ milling. Moreover, redox properties and oxygen mobility studied by H₂-Temperature Programmed Reduction (H₂- TPR) showed an increase in reducibility with milling time, including signals of both bulf and surface ceria, due to the greater number of defects and/or oxygen vacancies achieved by mechanochemical activation. Obtained features could play an essential role in terms of metal-support interaction, reactants adsorption and/or oxygen supply during catalytic reactions. Thus, high-energy ball milling becomes a useful, simple and green method for materials design with catalytic applications.

In general, perovskite crystals are composed of monovalent cations, divalent metal cations, and halogen anions. Most reported high-efficiency perovskite solar cells contain toxic lead. To solve this problem, there is a need to substitute lead with low-toxic elements. In this study, we focus on copper (Cu) and investigate the effect of Cu substitution on the electronic structures and photovoltaic properties. From the band structures obtained by first-principles calculations, the energy levels of the Cu-d orbital, which formed slightly above the valence band maximum, are predicted to work as an acceptor or a defect level. Devices in which 2 % Cu compound was added to the perovskite precursor solution showed higher device performance than standard devices. On the other hand, the short-circuit current density decreased with increasing the Cu composition. The calculations and experiments indicated that the energy level of the Cu-d orbital would work as a defect level and that carrier recombination would decrease the current density. Combining first-principles calculations and experiments, the effect of Cu substitution in methylammonium-based perovskite solar cells was clarified.

This study presents a concise overview of the role that clean energy technologies can play in the decarbonisation path of Western Macedonia, which is called upon not only to adjust its production model to the new requirements, but also to proceed immediately to a comprehensive productive restructuring towards a full phase-out of coal activities. One detailed survey will summarise the main findings and estimates on the renewable energy and clean energy, technical and research potential and in addition present assessments on the potential impact this could have on job creation and regional economic development in terms of potential investments. The studies’ goal is to identify and promote actions to accelerate the pace of innovation in clean energy and, in parallel, serve as a platform for collaboration among stakeholders from business, government, civil society and selected innovation alliances who share a vision for a sustainable future, highlighting the importance of accelerating innovation in sustainable energy – across the many ways in which energy is produced, delivered and consumed.

The winery industry generates large volumes of wastewater which can be toxic if released to the environment without proper treatment. The aim of this work was to treat two winery wastewaters (from red and white wine production) by Fenton based processes. With application of the best operational conditions pH = 3.0, [ferrocene] = [FeSO₄•7H₂O] = 0.50 g/L, [H₂O₂] = 155 mM, temperature = 298 K, radiation UV (254 nm), to the treatment of a red WW, it was achieved a chemical oxygen demand (COD) removal of 98.9 and 84.5%, respectively, for homogeneous and heterogeneous photo-Fenton. The same conditions were applied in the treatment of a white WW and it was achieved a 98.9 and 84.5% COD removal. Based in the results it is concluded that Fenton based processes are effective in WW treatment.

Fatigue life prediction has been a main concern in engineering design for decades, particularly for components subjected to thermo-mechanical fatigue (TMF) loadings. Therefore, accurate fatigue life prediction is important to ensure the reliability and integrity of mechanical structures. This study aims to predict the low cycle fatigue life of 316 FR stainless steel under isothermal and thermo-mechanical loading conditions. Finite element analysis (FEA) is initially performed using ABAQUS software to study the cyclic behavior of the specimens under both loading conditions. Subsequently, the fatigue life is assessed using total strain energy density-based approach, considering both Masing and non-Masing methods. The considered strain amplitude levels range from ±0.4% to ±1.2% and temperatures vary from 500°C to 650°C for thermo-mechanical loading, while maintaining a constant temperature of 650°C for isothermal loading. A comparison is made between the predicted results and experimental data from literature in terms of hysteresis loops, total strain energy density and fatigue life. It is found that FEA accurately replicate the cyclic response of this material, and provides satisfactory results for total strain energy density. Furthermore, Masing method is found to achieve most accurate prediction when compared to non-Masing method, demonstrating higher accuracy for isothermal loading compared to TMF.