Search Results (257)

Corn steep liquor is a waste product from the process of treating corn grain for starch extraction. It is used as a substrate in anaerobic digestion with simultaneous hydrogen and methane production in a cascade of two anaerobic bioreactors. For process research and optimisation, adequate mathematical models are required. So, the authors aim to present a high-quality model of the corn steep liquor process for the sequential production of H₂ and CH₄. This paper proposes a technique for identifying the best mathematical model of the process using the metaheuristics crow search algorithm (CSA). The CSA was applied for the first time to mathematical modelling of the considered two-stage anaerobic digestion process, using real experimental data. Based on the analysis of the numerical data from the model parameter identification procedures, the influence of the main CSA parameters—the flight length, fl, and the awareness probability, AP—was investigated. Applying classical statistical tests and an innovative approach, InterCriteria Analysis, recommendations about the optimal CSA parameter tuning were proposed. The best CSA algorithm performance was achieved for the AP = 0.05, fl = 3.0, followed by AP = 0.10, fl = 2.5, and AP = 0.15, fl = 3.0. The optimal tuning of the CSA parameters resulted in a 29% improvement in solution accuracy. As a result, a mathematical model of the considered two-stage anaerobic digestion process with a high degree of accuracy was developed. Full article

Node localization is critical for accessing diverse nodes that provide services in remote places. Single-anchor localization techniques suffer co-linearity, performing poorly. The reliable multiple anchor node selection method is computationally intensive and requires a lot of processing power and time to identify suitable anchor nodes. Node localization in wireless sensor networks (WSNs) is challenging due to the number and placement of anchors, as well as their communication capabilities. These senor nodes possess limited energy resources, which is a big concern in localization. In addition to convention optimization in WSNs, researchers have employed nature-inspired algorithms to localize unknown nodes in WSN. However, these methods take longer, require lots of processing power, and have higher localization error, with a greater number of beacon nodes and sensitivity to parameter selection affecting localization. This research employed a nature-inspired crow search algorithm (an improvement over other nature-inspired algorithms) for selecting the suitable number of anchor nodes from the population, reducing errors in localizing unknown nodes. Additionally, the weighted centroid method was proposed for identifying the exact location of an unknown node. This made the crow search weighted centroid localization (CS-WCL) algorithm a more trustworthy and efficient method for node localization in WSNs, with reduced average localization error (ALE) and energy consumption. CS-WCL outperformed WCL and distance vector (DV)-Hop, with a reduced ALE of 15% (from 32%) and varying communication radii from 20 m to 45 m. Also, the ALE against scalability was validated for CS-WCL against WCL and DV-Hop for a varying number of beacon nodes (from 3 to 2), reducing ALE to 2.59% (from 28.75%). Lastly, CS-WCL resulted in reduced energy consumption (from 120 mJ to 45 mJ) for varying network nodes from 30 to 300 against WCL and DV-Hop. Thus, CS-WCL outperformed other nature-inspired algorithms in node localization. These have been validated using MATLAB 2022b. Full article

The days of long lines at banks and piles of paperwork are long gone. The lending landscape has been completely transformed by digital loans, which provide a quick, easy, and frequently paperless transactions. A payee can apply for a digital loan anytime, anywhere. The entire lending procedure is accessible and efficient, but the availability of data on the Internet provides many risks and threats, where there are certain difficulties in the world of digital lending and data security, and privacy are major concerns. It is important to address the possibility of predatory lending practices that target weaker payees, especially with flash loans, which are considered a critical type of digital loan, as they add additional pressure to banks in terms of security because they return to the same block of the blockchain, and the possibility of tampering with them is considered great. Therefore, we have developed a security protocol based on the principle of digital cryptocurrency wallets: these digital wallets are protected by our use of the Xsalsa20 algorithm. It has high specifications and is enhanced with the usage of the Crow search algorithm, which guarantees fast and efficient search results. After the analysis of the proposed system in the Tamarin Prover tool, we obtained proof of a lot of security properties like data authenticity, perfect forward secrecy, and many others, which constitute the matter that gives our system the power of security. Also, we obtained 0.6667 ms. as the rate of processing speed, 2990 ms. as the time required by the proposed system to repay loans after the conditions are met, and, finally, our proposed system has the power to present a flexible property by creating 29,700 loans every 3 s; by this, we obtained a system that is secure, authentic, fast, and flexible. Full article

The crow search arithmetic optimization algorithm (CSAOA) method is introduced in this article as a novel hybrid optimization technique. This proposed strategy is a population-based metaheuristic method inspired by crows’ food-hiding techniques and merged with a recently created simple yet robust arithmetic optimization algorithm (AOA). The proposed method’s performance and superiority over other existing methods is evaluated using six benchmark functions that are unimodal and multimodal in nature, and real-time optimization problems related to power systems, such as the weighted dynamic economic emission dispatch (DEED) problem. A load-shifting mechanism is also implemented, which reduces the system’s generation cost even further. An extensive technical study is carried out to compare the weighted DEED to the penalty factor-based DEED and arrive at a superior compromise option. The effects of CO₂, SO₂, and NOx are studied independently to determine their impact on system emissions. In addition, the weights are modified from 0.1 to 0.9, and the effects on generating cost and emission are investigated. Nonparametric statistical analysis asserts that the proposed CSAOA is superior and robust. Full article

Accurate prognosis and diagnosis are crucial for selecting and planning lung cancer treatments. As a result of the rapid development of medical imaging technology, the use of computed tomography (CT) scans in pathology is becoming standard practice. An intricate interplay of requirements and obstacles characterizes computer-assisted diagnosis, which relies on the precise and effective analysis of pathology images. In recent years, pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection have witnessed the considerable potential of artificial intelligence, especially deep learning techniques. In this context, an artificial intelligence (AI)-based methodology for lung cancer diagnosis is proposed in this research work. As a first processing step, filtering using the Butterworth smooth filter algorithm was applied to the input images from the LUNA 16 lung cancer dataset to remove noise without significantly degrading the image quality. Next, we performed the bi-level feature selection step using the Chaotic Crow Search Algorithm and Random Forest (CCSA-RF) approach to select features such as diameter, margin, spiculation, lobulation, subtlety, and malignancy. Next, the Feature Extraction step was performed using the Multi-space Image Reconstruction (MIR) method with Grey Level Co-occurrence Matrix (GLCM). Next, the Lung Tumor Severity Classification (LTSC) was implemented by using the Sparse Convolutional Neural Network (SCNN) approach with a Probabilistic Neural Network (PNN). The developed method can detect benign, normal, and malignant lung cancer images using the PNN algorithm, which reduces complexity and efficiently provides classification results. Performance parameters, namely accuracy, precision, F-score, sensitivity, and specificity, were determined to evaluate the effectiveness of the implemented hybrid method and compare it with other solutions already present in the literature. Full article

Strategies for successful aging, including the use of food supplements, are part of the approach to support skin youthfulness. To demonstrate the efficacy of fermented bilberry extract (FBE) against skin aging and uneven complexion, a clinical trial was carried out on 66 subjects with visible “crow’s feet” wrinkles, mild-to-moderate skin slackness, and uneven skin tone. The wrinkle depth, skin smoothness (Ra) and roughness (Rz), skin firmness (R0) and elasticity (R2), skin coloration (ITA°), and skin antioxidant capacity were measured before and after 28 (D28), 56 (D56), and 84 (D84) days of product use (either FBE or a placebo). These parameters were also integrated with a clinical evaluation, carried out by a dermatologist, and a self-assessment questionnaire to align the measured efficacy with the visual or perceived efficacy. At D84, the wrinkle depth had decreased by 10.6%, Ra had improved by 7.9%, Rz had decreased by 7.3%, R0 had improved by 13.3%, R2 had improved by 12.4%, and skin antioxidant capacity had increased by 20.8%. ITA° increased by 20.8% and was accompanied by a decrease in the skin’s redness component by 16.8% and an increase in the lightness component by 2.2%. The variation of all the above-mentioned parameters was statistically significant between the FBE and PL groups. Our findings demonstrate the efficacy of FBE in improving skin aging and complexion evenness. Full article

Objectives: Investigate factors contributing to the effective management of age-related hearing loss (ARHL) rehabilitation. Methods: A systematic review was conducted following PRISMA guidelines. The protocol was registered in PROSPERO (CRD42022374811). Articles were identified through systematic searches in the Scopus, PubMed, Web of Science, and Cochrane databases in May 2024. Only articles published between January 2005 and May 2024 were included. Studies were assessed for eligibility by two independent researchers and evaluated using the Crowe Critical Appraisal Tool v1.4 (CCAT). Results: Of the 278 articles identified, 54 were included. Three factors explain effective HA use. First, hearing aid signal processing, with directional microphones and noise reduction, improves user comfort and understanding regarding noise. Second, there is hearing aid fitting, with the NAL prescription rules as the gold standard, and bilateral, high-level HA performance for spatial localization and noise comprehension. Third, there is a patient-centered approach, using patient-related outcome measures (PROMs), questionnaires, counseling, and regular follow-up to involve patients in their therapeutic rehabilitation. Conclusions: Reaching a consensus on acoustic parameters is challenging due to variability in audiological results. Involving patients in their rehabilitation, addressing their needs and expectations, and offering individualized care are crucial. Full article

Due to the recent abnormalities in global temperature and increasing carbon emissions, the world is working to reduce carbon emissions. In particular, the construction sector accounts for about 37% of all carbon emissions, so it is one of the areas where sustainable reduction efforts must be made. Therefore, in this paper, an optimal design process was performed by evaluating carbon emissions as the objective function, a choice which differed from the objective function of the existing research used in the optimal design of truss structures. The metaheuristics algorithm used for the process was the advanced crow search algorithm. The levels of carbon emissions generated when the material of a truss structure consisted of a customary material (steel) were compared to scenarios in which timber was used, and a construction scenario centered on the Republic of Korea was established for comparison. The structures used as examples were 10-, 17-, 22-, and 120-bar truss structures. As a result, it was confirmed that truss structures using timber had fewer carbon emissions than structures using steel. In addition, it was confirmed that, even in the same timber structures, domestic timber had fewer carbon emissions than imported timber. These results confirmed that in order to achieve carbon neutrality in the construction field, carbon emissions must be considered in advance, in the design stage. Full article

Accurate estimation of photovoltaic (PV) power generation can ensure the stability of regional voltage control, provide a smooth PV output voltage and reduce the impact on power systems with many PV units. The internal parameters of solar cells that affect their PV power output may change over a period of operation and must be re-estimated to produce a power output close to the actual value. To accurately estimate the power output for PV modules, a three-diode model is used to simulate the PV power generation. The three-diode model is more accurate but more complex than single-diode and two-diode models. Different from the traditional methods, the 9 parameters of the three-diode model are transformed into 16 parameters to further provide more refined estimates. To accurately estimate the 16 parameters in the model, an optimization tool that combines enhanced swarm intelligence (ESI) algorithms and the dynamic crowing distance (DCD) index is used based on actual historical PV power data and the associated weather information. When the 16 parameters for a three-diode model are accurately estimated, the I–V (current-voltage) curves for different solar irradiances are plotted, and the possible failures of PV modules can be predicted at an early stage. The proposed method is verified using a 200 kWp PV power generation system. Three different diode models that are optimized using different ESI algorithms are compared for different weather conditions. The results affirm the reliability of the proposed ESI algorithms and the value of creating more refined estimation models with more parameters. Preliminary fault diagnosis results based on the differences between the actual and estimated I–V curves are provided to operators for early maintenance reference. Full article

Plasmin is a serine protease induced by UV-irradiation in skin that contributes to inflammation. We showed that plasmin is upregulated in photo-exposed facial skin and that this correlates with increased transepidermal water loss (TEWL). Plasmin activity upregulates downstream pathways such as pro-inflammatory cytokines and matrix-metalloproteinases (MMPs). In addition, the plasminogen system modulates cutaneous melanogenesis. In this study, we investigated potential skin-aging effects of plasmin with a dual inhibitor of plasmin and its activator urokinase (uPA). We established a range of in vitro and ex vivo assays to investigate inflammation, MMP-9 activation, and collagen modulation, and the melanogenesis modulation activity of plasmin. A specific plasmin inhibitor, Amidinobenzyl Benzylsulfonyl D-Seryl Homophenylalaninamide Acetate (ABSHA), was used in these assays to downregulate these effects. We found that ABSHA was able to down-regulate UV-irradiation-induced MMP-9 expression, and subsequent collagen IV degradation, ex vivo. In addition, the increased melanin synthesis in epidermal melanocytes was reduced significantly by ABSHA. Furthermore, dermal fibroblasts treated with the plasmin inhibitor showed increased collagen I synthesis. We further investigated these effects in a two-month, monocentric, placebo-controlled human study on female Chinese volunteers. We found a significant increase in collagen density by ultrasound measurement and an increase in elasticity by cutometer assessment in the group using a formulation consisting of a 10 ppm ABSHA solution. This resulted in decreased wrinkle volumes on both the forehead and crow’s feet as shown by Primos CR. Looking at age spots, there was a decrease in overall ITA° and melanin density as well as in the total age spot area. Our results establish plasmin as a skin-aging enzyme. Using specific inhibitors against plasmin shows promise against age-induced skin conditions. Full article

In the quest for effective environmental governance, the integration of legal and cultural pluralism within conservation strategies emerges as a critical factor, especially in regions marked by rich ethnic diversity and complex historical legacies. This paper explores the symbiotic relationship between state conservation efforts and the engagement of local communities, with a particular focus on the Indigenous Maroon communities in the Blue and John Crow Mountains (BJCMs) of Jamaica. It underscores the imperative of aligning conservation objectives with the aspirations and traditional practices of these communities to foster sustainable ecosystems and safeguard Indigenous autonomy. Central to this discourse is the development of collaborative frameworks that respect and incorporate the legal and cultural dimensions of pluralism, thereby facilitating a co-managed approach to environmental stewardship. This study emphasizes the role of collaboration and trust as pivotal elements in cultivating a mutual understanding of the interdependencies between state law and Indigenous law. This research advocates for a reciprocal exchange of knowledge between the state and community members, aiming to empower the latter with the resources necessary for effective environmental protection while respecting their legal autonomy. This approach not only enhances conservation initiatives overall, but also ensures that these efforts are informed by the rich cultural heritage and traditional ecological knowledge of the Maroon communities. By examining the conservation practices and governance challenges faced by the Maroons in the BJCMs, this paper reveals the nuanced dynamics of implementing state-led conservation laws in areas characterized by cultural and legal pluralism. The findings highlight the necessity for state regulatory frameworks to enable collaborative governance models that complement, rather than undermine, the traditional governance structures of the Maroons. This research contributes to the broader discourse on environmental governance by illustrating the potential of culturally informed conservation strategies to address environmental threats while respecting and reinforcing the social fabric of Indigenous communities. Full article

Hydrometallurgical gold recovery processes play a pivotal role in the gold mining industry, contributing to more than 90% of global gold production. Among the array of techniques available, the Merrill–Crowe process, adsorption, ion exchange, and solvent extraction are central in extracting gold from leach solutions. While the Merrill–Crowe process and gold complex adsorption onto activated carbon represent historical cornerstones, their inherent limitations have prompted the emergence of more recent innovations in ion exchange and solvent extraction, offering enhanced selectivity, control, and sustainability. The evolution of modern organic chemistry has significantly influenced the progress of ion exchange technology, mainly through the introduction of advanced polymer matrix synthetic resins. At the same time, novel solvents tailored to gold complex interactions have revitalized ion exchange and solvent extraction. Introducing ionic liquids and deep eutectic solvents has also added a new dimension to efforts to improve gold extraction metallurgy. This paper reviews these cutting-edge developments and their potential to revolutionize the hydrometallurgical gold recovery process, addressing the pressing need for improved efficiency and environmental responsibility. Full article

The extensive use of rodenticides poses a severe threat to non-target species, particularly birds of prey and scavengers. In this study, a GC-MS/MS-based method was used to unlock the cause of bird deaths in Poland. Organs (liver, heart, kidney, and lungs) collected during autopsies of two rooks (Corvus frugilegus) and one carrion crow (Corvus corone corone), as well as fecal samples, were analyzed for the presence of anticoagulant coumarin derivatives, i.e., warfarin and bromadiolone. As for warfarin, the highest concentration was found in crow samples overall, with concentrations in the feces and lungs at 5.812 ± 0.368 µg/g and 4.840 ± 0.256 µg/g, respectively. The heart showed the lowest concentration of this compound (0.128 ± 0.01 µg/g). In the case of bromadiolone, the highest concentration was recorded in the liver of a rook (16.659 ± 1.499 µg/g) and this concentration significantly exceeded the levels in the other samples. By revealing the reality of the threat, these discoveries emphasize the need to regulate and monitor the trade in rodenticides. Full article

In the process of completing large-scale and fine-grained sensing tasks for the new generation of crowd-sensing systems, the role of analysis, reasoning, and decision making based on artificial intelligence has become indispensable. Mobile crowd sensing, which is an open system reliant on the broad participation of mobile intelligent terminal devices in data sensing and computation, poses a significant risk of user privacy data leakage. To mitigate the data security threats that arise from malicious users in federated learning and the constraints of end devices in crowd-sensing applications, which are unsuitable for high computational overheads associated with traditional cryptographic security mechanisms, we propose FedCrow, which is a federated-learning-based approach for protecting crowd-sensing data that integrates federated learning with crowd sensing. FedCrow enables the training of artificial intelligence models on multiple user devices without the need to upload user data to a central server, thus mitigating the risk of crowd-sensing user data leakage. To address security vulnerabilities in the model data during the interaction process in federated learning, the system employs encryption methods suitable for crowd-sensing applications to ensure secure data transmission during the training process, thereby establishing a secure federated-learning framework for protecting crowd-sensing data. To combat potential malicious users in federated learning, a legitimate user identification method based on the user contribution level was designed using the gradient similarity principle. By filtering out malicious users, the system reduces the threat of attacks, thereby enhancing the system accuracy and security. Through various attack experiments, the system’s ability to defend against malicious user attacks was validated. The experimental results demonstrate the method’s effectiveness in countering common attacks in federated learning. Additionally, through comparative experiments, suitable encryption methods based on the size of the data in crowd-sensing applications were identified to effectively protect the data security during transmission. Full article