Recent publications
Soil salinity has a negative impact on the microbial populations and their activities in hot arid lands. This study aimed to evaluate and compare the microbial abundance and activity in non-saline (NS) and saline (SS) soils, focusing on the impact of salinity on the mineralization of soil endogenous carbon and nitrogen in the region of Ouargla (southern Algeria). The mineralization of organic C and N was estimated by respirometric test (CO2 release) and the extraction of two forms of mineral nitrogen (NH4+–N and NO3––N), respectively. The experiment was conducted on incubations of soil samples under controlled parameters (28 ± 1 °C and 80% of water holding capacity). Numeration of microbial densities was performed either on solid medium of extract agar soil, oxytetracycline agar (OGA) and Katinsky medium, respectively for bacterial microflora, fungal microflora and actinomycetes (Mycelial bacteria) or in liquid medium for certain functional groups involved in mineralization of carbon and nitrogen. After 56 days of incubation, both soils showed a low potential for mineralization of carbon and nitrogen. The cumulative amounts of CO2–C released are 62.53 and 50.03mg 100 g–1of dry soil, respectively for the non-saline and saline soils. Regarding nitrogen mineralization, the cumulative quantities of ammoniacal NH4+–N and nitric NO3––N, nitrogen released were 0.53 and 0.49 mg 100 g–1 of dry soil and 1.19 and 0.91 mg 100 g–1 of dry soil, for the non-saline and saline soils, respectively for the two forms of mineral nitrogen. The reduction rates of the two forms of mineral nitrogen are 7.54 and 23.50%, for NH4+–N and NO3––N, respectively. The microbial groups studied revealed a predominance of fungal microflora in saline soil. In contrast, a high sensitivity of nitrifying germs to salinity was reported. Findings indicate that despite the lower microbial abundance and activity recorded for both soils, they respond to salinity differentially depending on the type of microbial species present in the soil as well as the nature of the microbial activity itself. On the one hand, the microbial diversity recorded in both soils demonstrates an appreciable potential for adaptation of microorganisms to the hard ecological conditions characterizing arid regions, in particularly the high salinity of the soil.
Digital watermarking is an essential technology in multimedia and information processing, addressing the ever-mounting concerns related to data integrity and protecting intellectual property rights. In content authentication, copyright protection, and data integrity, digital watermarking plays a critical role. Nonetheless, the current application of digital watermarking faces crucial challenges, most notably adversarial attacks such as compression and noise interference, which pose substantial threats to the integrity of embedded watermarks. In this article, we introduce a semi-blind watermarking scheme that fuses the capabilities of the ACM and DWT techniques to facilitate the efficient embedding and extraction of watermarks in digital images. This approach achieves a trade-off between robustness and imperceptibility, thereby ensuring that the embedded watermark remains resilient against commonplace attacks, all while preserving the visual quality of the image. Experimental results prove the effectiveness of the proposed scheme in terms of watermark invisibility and its robustness against common attacks, including compression, noise addition, and filtering. Our watermarking technique maintains imperceptibility, achieving an average PSNR of 53.95 dB and an SSIM of 0.99996. In computation complexity and resource allocation terms, the experiments prove that our proposed watermarking demands fewer computational resources, with embedding and extraction times clocking in at an astonishingly swift 0.01218 s and 0.006875 s, respectively.
This chapter explores the impact of green chemistry on material design and synthesis, emphasizing sustainable approaches to nanoscale material fabrication. Nanotechnology, which involves manipulating matter at scales from one to 100 nanometers, presents unique challenges in synthesis that necessitate a deep understanding of fundamental principles to ensure efficient and environmentally responsible practices. The chapter discusses the design and synthesis of nanomaterials and highlights their applications across various fields, detailing essential pre- and post-synthesis treatments, such as annealing and drying, that optimize material properties. Furthermore, it reviews characterization techniques crucial for assessing synthesis success and functional performance, offering insights into green strategies for enhancing material effectiveness.
This paper introduces the Efficient Metaheuristic BitTorrent (EM-BT) algorithm, aimed at optimizing the placement and sizing of photovoltaic renewable energy sources (PVRES) and capacitor banks (CBs) in electric distribution networks. The main goal is to minimize energy losses and enhance voltage stability over 24 h, taking into account varying load profiles, solar irradiance, and temperature effects. The algorithm is rigorously tested on standard distribution networks, including the IEEE 33, IEEE 69, and ZB-ALG-Hassi Sida 157-bus systems. The results reveal that EM-BT outperforms established methods like Particle Swarm Optimization (PSO), Grey Wolf Optimizer (GWO), and Whale Optimization Algorithm (WOA), demonstrating its effectiveness in reducing energy losses and maintaining stable voltage profiles. By effectively combining PVRES and CBs, this research highlights a robust approach to enhancing both technical performance and operational reliability in distribution systems. Additionally, the consideration of temperature effects on PVRES efficiency adds depth to the study, making it a valuable contribution to the field of power system optimization.
This study presents the synthesis and characterization of a novel series of ferrocenylmethylnucleobase compounds, namely, FcMeAd, FcMeCy, FcMeTh, and (FcMe)₂Ad with promising antioxidant and antidiabetic properties. Spectroscopic techniques confirmed their sandwich‐like geometry, with the nucleobase moiety coordinated to the ferrocene unit. Density functional theory (DFT) optimization revealed alignment with existing crystallographic data and indicated low frontier molecular orbital (FMO) energy gaps, suggesting facile intramolecular charge transfer and potential biological activity. The antidiabetic activity was evaluated in vitro through inhibition assays targeting α‐glucosidase and α‐amylase enzymes, which was supported by in silico molecular docking studies. Among the compounds, FcMeTh exhibited the highest antidiabetic and antioxidant properties due to the presence of carbonyl and amide functionalities, along with an electron‐donating methyl group. Molecular dynamics (MD) simulations confirmed high binding affinity and structural stability of the docked compounds, with strong interactions with the target enzymes, further validating the potential of these compounds as effective inhibitors. Pharmacokinetic and ADMET evaluations indicated their nontoxic, noncarcinogenic nature and suitability for oral administration. The combined in vitro and in silico findings, including the critical insights from MD simulations, suggest that these ferrocenylmethylnucleobase compounds, especially FcMeTh, possess enhanced antioxidant and antidiabetic properties. This highlights their potential as promising therapeutic agents for managing oxidative stress and Type 2 diabetes.
The study explored the efficacy of Salvia chudaei ethanolic extract in managing hyperlipidemia, hyperglycemia, and oxidative stress induced by Triton X‐100 in Wistar rats. Twenty‐four rats were divided into four groups: Control, Salvia chudaei‐treated, Triton‐induced hyperlipidemic, and a combination of Triton + Salvia chudaei treatment. Triton X‐100 raised serum levels of total cholesterol, triacylglycerol, and LDL while lowering HDL cholesterol, leading to oxidative stress marked by increased MDA and reduced antioxidant activity in liver and kidney tissues. Administration of Salvia chudaei extract effectively reversed these adverse effects, significantly lowering cholesterol, triacylglycerol, and LDL levels, and improving HDL cholesterol. It also enhanced antioxidant defenses and reduced oxidative stress markers, demonstrating its protective role against metabolic dysfunction. HPLC analysis confirmed the presence of bioactive compounds in the extract that contribute to these benefits. The findings suggest that Salvia chudaei ethanolic extract possesses strong antihyperlipidemic, antihyperglycemic, and antioxidant properties, making it a promising natural agent for preventing and treating hyperlipidemia and oxidative stress. This positions Salvia chudaei as a potential new therapeutic approach in the management of metabolic disorders.
The current study was conducted to explore the phytochemical composition and in vitro antioxidant activities of Moringa oleifera (MO) leaf aqueous extract, as well as its in vivo modulatory effects on abamectin (ABM) induced oxidative stress in rat erythrocytes and brain. Animals were randomly divided into four groups. The first group served as a control and received distilled water by gavage. The second group (ABM) received abamectin (1 mg/kg b.w. in drinking water). The third group (MO) received M. oleifera leaf aqueous extract (200 mg/kg b.w. by gavage). The fourth group (ABM‐MO) received a combination of ABM and MO. ABM inhibited acetylcholinesterase activity, decreased the activities of antioxidant enzymes. Supplementation with MO in ABM‐treated rats significantly ameliorated the biochemical parameters cited above. The computational modeling revealed that M. oleifera identified compounds bound human peroxiredoxin 5, catalase and glutathione peroxidase with acceptable affinities, which together with the established molecular interactions and tight embedding satisfactory support the in vivo results. Thus, it may be concluded that ABM impairs brain and erythrocyte function through oxidative damage, and these effects could be prevented by Moringa oleifera leaf aqueous extract, likely due to its antioxidant activity.
Adsorption cooling system (ACS) is one of the promising alternatives to the conventional vapor compression refrigeration system (VCRS) due to its advantage of driven by low grade thermal energy instead of electric power. However due to its lower efficiency, a significant research works is in progress worldwide. In view of this, the presented paper proposes a methodology to predict the required optimum heat source temperature of two different ACSs based on novel environment friendly pairs of activated carbon-methanol and silica gel-water for the ice-making and water chiller applications, respectively and their performance analysis. Performance parameters, cooling capacity, thermal efficiency, and coefficient of performance (COP) have been used to derive the limits of source temperature and applied to two different ACS. Further feasibility study has been carried out integrating economic and environmental perceptions for the El Oued city, Algeria. The performance analysis of CarboTech A35/1/CH 3 OH showed the maximum ice production of 16.17 kg/day for the generator temperatures of 358-378 K with a COP of 0.65. The analysis of S40/H 2 O application showed the maximum chilled water of 7.88 kg/day for the generator temperatures of 348-37 K having COP of 0.74. The economic analysis suggests that hot water generation with solar energy is a better option as compared to geothermal resource.
This chapter presents an analysis of the impact of neonicotinoids on the bee population. Neonicotinoids are widely used pesticides that are toxic to bees and thus pose a threat to pollinators and bee-dependent ecosystems. The importance and use of neonicotinoids were discussed and the importance of studying their impact on bees was explained. Then, the characteristics of pesticides of this group and their various applications in agriculture and other sectors of the economy are presented. The results of scientific research confirming the toxicity of neonicotinoids to bees were presented. The methods by which these chemicals affect bee organisms and influence their behaviour, health and functioning of bees working in colonies are discussed. The adverse effects of neonicotinoids have been identified at the scale of individual beekeepers and their honeybee colonies. The consequences of neonicotinoid use on bee population dynamics and the role of these pesticides in bee decline and colony breakdown were analysed. The effect of neonicotinoids on biological diversity and ecosystems is an additional factor to consider. The effects of the application of this group of pesticides on other pollinating insects and harmless organisms, as well as the impact on bee-dependent ecosystems, are discussed. The chapter also contains an analysis of the existing legal regulations regarding the use of neonicotinoids and the related controversies. Actions taken to protect bees and alternative approaches to the protection of plants and pollinators were indicated. A summary and conclusions close the chapter, presenting the key results and findings. They point to the need for further research and action to protect bees from the effects of neonicotinoids, which are important for maintaining healthy ecosystems and ensuring the necessary role of pollinators in maintaining biodiversity.
The existence of fins on a basin's flat absorber undoubtedly enhances the thermal efficiency and distillate output by increasing the exposed surface area of water to sunlight. However, the issue of shading caused by the fins hampers the productivity of distillers. Researchers are currently seeking a resolution to this problem. The current study conducted a performance comparison of hemispherical solar distillers using copper conical fins with a diameter of 4 cm and a height of 2 cm. The distillers were also equipped with copper conical fins‐filled red bricks, which were painted black. These fins were placed on the bottom of the basin at various spacing intervals (0, 1, and 2 cm) and a water depth of 2 cm. The experimental data showed that the productivity values of the hemispherical solar distiller using copper conical fins (HSD‐CCF) were 6.00, 5.40, and 5.00 L/m², while the productivity values of the hemispherical solar distiller using copper conical fins‐filled with red bricks (HSD‐CCF & RB) were 7.00, 6.30, and 5.80 L/m² at spacing distances of 0, 1, and 2 cm, respectively. The conventional hemispherical solar distiller without fins (CHSD) achieved a peak efficiency of 4.50 L/m². The HSD‐CCF achieves cumulative yields of 33.33%, 20.00%, and 11.11% when utilizing copper conical fins at spacing distances of 0, 1, and 2 cm, respectively, compared to the THSD. The efficiency of the hemispherical sun distiller using copper conical fins‐filled with red bricks (HSD‐CCF & RB) is enhanced by 55.55%, 40.00%, and 28.89% when compared to the conventional hemispherical solar distiller (THSD), at spacing distances of 0, 1, and 2 cm, respectively. The study discovered that including copper conical fins packed with red bricks improves the efficiency of solar distillers. Furthermore, the study revealed that increasing the spacing between the fins further reduces shading, hence enhancing performance.
Nanoparticle coatings present a highly effective method for significantly enhancing the performance of solar distillers by improving heat transfer, evaporation, and condensation processes. This review provides a comprehensive examination of the current research on nanoparticle-coated solar stills, highlighting how nanoparticles improve thermal conductivity, absorb solar energy more efficiently, and promote dropwise condensation to increase freshwater productivity. Various nanoparticles, including metal oxides and carbon-based materials, have been studied for their ability to optimize solar still performance. However, challenges such as nanoparticle stability, cost, and environmental impact remain. This paper consolidates research efforts, analyses key findings, and outlines future directions for advancing the application of nanoparticle coatings in sustainable water purification technologies.
Stimuli-responsive biomaterials have received great attention in pharmaceutical applications because of its unique properties and diverse functions. These materials, often derived from biological sources or designed to interact with biological systems, emerge in reaction to outside stimuli, such as pH, temperature, light, or specific biomolecules. This responsiveness allows control of drug delivery, diagnostics, and therapeutic interventions with enhanced precision and effectiveness. In this chapter, we explore different types of stimuli-responsive biomaterials and their applications in the pharmaceutical sector. The materials discussed span a wide range, including liposomes, micelles, hydrogels, nanoparticles, and nanocomposites. Each of these substances has distinct stimulus-responsive properties that can be tailored to fit specific pharmaceutical needs. Furthermore, we highlight recent developments and innovations in this field and discuss emerging technologies, new manufacturing methods, and potential challenges. The incorporation of stimuli-responsive biomaterials into personalized medicine and their role in addressing drug delivery challenges, reducing side effects, and improving patient outcomes are also being explored.
This study investigates the synthesis of CuO/Ni/Fe3O4 nanocomposite (NC) using gallic acid, as well as its catalytic performance in CO2 methanation and photocatalytic hydrogen generation. UV-visible spectroscopy analysis revealed a prominent absorption peak at 370 nm and a band gap energy of 1.26 eV, indicating favorable optical properties for photocatalysis. FTIR analysis identified key functional groups, including a significant O-H peak at 3366 cm⁻¹, C-H stretching at 2926 cm⁻¹, and metal-oxygen bonding vibrations at 580 and 461 cm⁻¹, confirming the presence of Cu-O, Fe-O, and Ni-O bonds, indicative of successful nanoparticle formation. XRD analysis showed distinct peaks at 2θ values corresponding to cubic and monoclinic crystal structures, with calculated crystallite sizes of approximately 30 nm and a surface area of 29 m²/g. The nanocomposite exhibited 37% crystallinity and a density of 6.88 g/cm³. Thermal stability tests revealed only a 5.7% weight loss between 589 and 785 °C. Catalytic tests showed a maximum CO2 conversion rate of 94.8% at 420 °C, with CH4 selectivity exceeding 90% across all temperatures. In photocatalytic hydrogen production, the NC achieved an initial rate of 165 µmol/g.h, reaching a total yield of 741 µmol/g after 5 h. The catalyst maintained efficiency over four cycles, highlighting its stability and reusability. These findings emphasize the potential of CuO/Ni/Fe3O4 NC as a promising catalyst for sustainable energy production and carbon utilization, combining a green synthesis method with high catalytic efficiency.
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This study reports an environmentally friendly and cost-effective methodology for synthesizing hematite (α-Fe2O3) nanoparticles (NPs) using a plant extract from Helianthemum Lippii (L.) pers with various aqueous ferric chloride (FeCl3) concentrations. The composition, morphology, crystallinity, and particle size of α-Fe2O3 were influenced by varying the precursor solution concentration (0.1, 0.07, and 0.04 M). Characterization of the synthesized α-Fe2O3 NPs was performed using FT-IR, UV-Visible spectroscopy, XRD, SEM, and EDS, confirming their homogeneity and physical characteristics. SEM analysis revealed that the α-Fe2O3 nanoparticles exhibited a variety of shapes, including spheres and irregular forms, with sizes ranging from 24 to 30 nm. Additionally, the study found that as the precursor concentration increased from 0.04 M to 0.1 M, the direct bandgap values decreased from 3.16 eV to 2.53 eV, while the indirect bandgap values also decreased from 2.23 eV to 1.94 eV, indicating potential for photocatalytic applications. The α-Fe2O3 NPs demonstrated high efficiency, making them suitable for use as antibacterial and antioxidant agents, as well as in photocatalytic degradation to remove dyes from wastewater generated by industrial dyeing processes.
Graphical Abstract
The study investigates the performance enhancement of a conical solar distillation system by incorporating different energy storage materials, including glass balls, stainless steel balls, sandstones, and black gravel. These materials were analyzed based on their ability to improve energy and exergy efficiencies. The experimental setup involved using identical sizes (∅ 1.5 cm) of these materials to maximize water evaporation and distillate yield. The key novelty lies in the comparative analysis of these energy storage materials under similar environmental conditions, offering insights into their thermophysical properties and overall system performance. Stainless steel balls demonstrated the highest energy efficiency, reaching 54.06%, with an exergy efficiency of 3.93%, and the highest water productivity of 9.45 L/m²/day. The study’s findings emphasize that stainless steel balls are the most effective energy storage material in a conical solar still, significantly improving water yield and system efficiency. The research presents a practical solution for cost-effective and energy-efficient solar desalination, addressing the global challenge of freshwater scarcity. Future work should explore the integration of advanced materials and hybrid systems to further optimize solar distillation processes.
This study presents a sustainable approach to synthesizing carbon nanoparticles (CNPs) from orange peel waste through a process involving thermal treatment, chemical activation, and ultrasonication, producing spherical CNPs of approximately 70 nm in size. The surface of the CNPs was modified with chitosan, resulting in a chitosan@CNPs composite with enhanced functional properties. X-ray diffraction (XRD) analysis revealed that the CNPs possess a crystalline structure, while the chitosan@CNPs nanocomposite exhibited distinct crystalline peaks attributable to chitosan. SEM–EDX analysis showed a higher carbon content in chitosan@CNPs (68.09%) compared to CNPs (63.33%), indicating successful surface modification. Additionally, UV–Vis spectroscopy demonstrated that chitosan@CNPs had a slightly higher band gap energy (3.92 eV) compared to CNPs (3.86 eV), reflecting altered optical properties. The anti-hemolytic activity of chitosan@CNPs was 74.4% at 20 mg/mL, slightly higher than CNPs at 70.1%, when compared to the standard (ascorbic acid). However, the antioxidant activity of CNPs was higher (89.52%) than that of chitosan@CNPs (59.97%). Cytotoxicity assessments confirmed the biocompatibility of both materials, with minimal cytotoxic effects observed. These findings suggest that orange peel waste-derived CNPs hold potential for applications in drug delivery, biosensing, and environmental remediation.
Estimating the actual and perceived age of human faces has garnered significant interest for its wide-ranging practical applications. Various intelligent scenarios stand to gain from these computational systems capable of accurately predicting individuals’ ages. Automated age estimation systems are particularly valuable in fields such as medical diagnostics, facial product development, casting for films, assessing the impact of cosmetic procedures, and anti-aging treatments. In the realm where deep networks have demonstrated their supremacy as the fron-trunners among machine learning tools, Our approach integrates Convolutional Neural Networks (CNN) with Transformers. This novel system enhances information extraction by utilizing transformer attention mechanisms, rather than solely depending on features extracted from convolutional neural networks for estimating age. Based on the experiments conducted, the system effectively captures the sequential progression and continuous nature of the aging process. Furthermore, the proposed model surpasses the cutting-edge model by delivering exceptional results, achieving the lowest mean absolute errors of 2.31 for MORPH II, 5.35 for CACD, and 2.91 for AFAD.
Transformer health analysis using Dissolved Gas Analysis is crucial for diagnosing power transformer faults. This paper proposes an innovative approach to diagnose power transformer faults by integrating machine learning algorithms with Ensemble techniques. The method involves fusing reduced dimensional input features through Principal Component Analysis with Ensemble techniques such as Bagging, Decorate, and Boosting. Various machine learning algorithms, including Decision Tree (DT), K‐Nearest Neighbours, Radial Basis Function Network, and Support Vector Machine, are employed in conjunction with Ensemble techniques. The long short‐term memory algorithm was used to create synthetic data to solve the issue of data imbalance. A dataset of 683 samples is used in the study for training, testing, validation, and comparison with current techniques. The results highlight the effectiveness of Ensemble techniques, particularly Boosting, which demonstrates superior performance across all classification algorithms. The Boosting with DT algorithm achieves an impressive accuracy of 98.32%, surpassing alternative methods. In validation, the proposed Boosting Ensemble technique outperforms various approaches, showcasing its diagnostic accuracy and superiority over alternative methods. The research emphasises the model's effectiveness in smoothing input vectors, enhancing harmony with ensemble techniques, and overcoming limitations in prior methods.
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