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Smart cities increasingly rely on the Internet of Things (IoT) to enhance infrastructure and public services. However, many existing IoT frameworks face challenges related to security, privacy, scalability, efficiency, and low latency. This paper introduces the Blockchain and Federated Learning for IoT (BFLIoT) framework as a solution to these issues. In the proposed method, the framework first collects real-time data, such as traffic flow and environmental conditions, then normalizes, encrypts, and securely stores it on a blockchain to ensure tamper-proof data management. In the second phase, the Data Authorization Center (DAC) uses advanced cryptographic techniques to manage secure data access and control through key generation. Additionally, edge computing devices process data locally, reducing the load on central servers, while federated learning enables distributed model training, ensuring data privacy. This approach provides a scalable, secure, efficient, and low-latency solution for IoT applications in smart cities. A comprehensive security proof demonstrates BFLIoT’s resilience against advanced cyber threats, while performance simulations validate its effectiveness, showing significant improvements in throughput, reliability, energy efficiency, and reduced delay for smart city applications.

Information-centric networking (ICN) changes the way data are accessed by focusing on the content rather than the location of devices. In this model, each piece of data has a unique name, making it accessible directly by name. This approach suits the Internet of Things (IoT), where data generation and real-time processing are fundamental. Traditional host-based communication methods are less efficient for the IoT, making ICN a better fit. A key advantage of ICN is in-network caching, which temporarily stores data across various points in the network. This caching improves data access speed, minimizes retrieval time, and reduces overall network traffic by making frequently accessed data readily available. However, IoT systems involve constantly updating data, which requires managing data freshness while also ensuring their validity and processing accuracy. The interactions with cached data, such as updates, validations, and replacements, are crucial in optimizing system performance. This research introduces an ICN-IoT method to manage and process data freshness in ICN for the IoT. It optimizes network traffic by sharing only the most current and valid data, reducing unnecessary transfers. Routers in this model calculate data freshness, assess its validity, and perform cache updates based on these metrics. Simulation results across four models show that this method enhances cache hit ratios, reduces traffic load, and improves retrieval delays, outperforming similar methods. The proposed method uses an artificial neural network to make predictions. These predictions closely match the actual values, with a low error margin of 0.0121. This precision highlights its effectiveness in maintaining data currentness and validity while reducing network overhead.

Today, facility location planning primarily pertains to the long-term strategic and operational decision-making of large public and private organizations, and the significant costs associated with facility location, construction, and operation have turned location research into long-term decision-making. Presenting a hub location model for the green supply chain can address the current status of facilities and significantly improve demand coverage at an acceptable cost. Therefore, in this study, a network of facilities for hub location in the service site domain, considering existing and potential facilities under probable scenarios, has been proposed. After presenting the mathematical model, validation was performed on a small scale, followed by sensitivity analysis of the main parameters of the model. Furthermore, a metaheuristic algorithm was employed to analyze the NP-Hardness of the model. Additionally, two metaheuristic algorithms, NSGAII and MOPSO, were developed to demonstrate the efficiency of the model. Based on the conducted analysis, it can be observed that the computational time increases exponentially with the size of sample problems, indicating the NP-Hardness of the problem. However, the NSGAII algorithm performs better in terms of computational time for medium-sized problems compared to the MOPSO algorithm. These algorithms were chosen due to their proven efficiency in handling NP-hard optimization problems and their ability to balance exploration and exploitation in search spaces.

Due to the complex relationship between various parameters affecting gas holdup in bubble column reactors, a unique correlation for gas holdup does not exist. The available correlations proposed in the literature for gas holdup prediction in aqueous alcohol solutions in bubble columns fail to predict gas holdup over a wide range of conditions. Therefore, based on around 1000 data points from the previous studies, an empirical correlation and a trained model were derived using the dimensionless numbers Reynolds, Froude, Eötvös to Morton ratio, and alcohol carbon number. The predictions were compared to experiments with different water–alcohol mediums at various concentrations to validate the correlation and trained model, and a good agreement was observed. However, the ML model was predicting more accurately, and it was indicated that the Reynolds number had the most significant impact on gas holdup, followed by the Eötvös to Morton ratio.

Bitcoin (BTC-USD) is a virtual currency that has grown in popularity after its inception in 2008. BTC-USD is an internet communication network that makes using digital money, including digital payments, easy. It offers decentralized clearing of transactions and money supply. This study attempts to accurately anticipate the BTC-USD prices (Close) using data from September 2023 to September 2024, comprising 390 observations. Four machine learning models—Multi-layer Perceptron, Extreme Learning Machine, Neural Network AutoRegression, and Extreme-Gradient Boost—as well as four time series models—Auto-Regressive Integrated Moving Average, Auto-Regressive, Non-Parametric Auto-Regressive, and Simple Exponential Smoothing models—are used to achieve this end. Various hybrid models are then proposed utilizing these models, which are based on simple averaging of these models. The data-splitting technique, commonly used in comparative analysis, splits the data into training and testing data sets. Through comparison testing with training data sets consisting of 30%, 20%, and 10%, the present work demonstrated that the suggested hybrid model outperforms the individual approaches in terms of error metrics, such as the MAE, RMSE, MAPE, SMAPE, and direction accuracy, such as correlation and the MDA of BTC. Furthermore, the DM test is utilized in this study to measure the differences in model performance, and a graphical evaluation of the models is also provided. The practical implication of this study is that financial analysts have a tool (the proposed model) that can yield insightful information about potential investments.

Since SQL injection allows attackers to interact with the database of applications, it is regarded as a significant security problem. By applying machine learning algorithms, SQL injection attacks can be identified. Problem: In the training stage of machine learning methods, effective features are used to develop an optimal classifier that is highly accurate. The specification of the features with the highest efficacy is considered to be an NP-complete combinatorial optimization challenge. Selecting the most effective features refers to the procedure of identifying the smallest and most effective features in the dataset. The rationale behind this paper is to optimize the accuracy, precision, and sensitivity parameters of the SQL injection attack detection method. Method: In this paper, a method for identifying SQL injection attacks was suggested. In the first step, a particular training dataset that included 13 features was developed. In the second step, to specify the best features of the dataset, a specific binary variety of the Olympiad optimization algorithm was developed. Various machine learning algorithms were used to create the optimal attack detector. Results: Based on the experiments carried out, the suggested SQL injection detector using an artificial neural network and the feature selector can achieve 99.35% accuracy, 100% precision, and 100% sensitivity. Owing to selecting about 30% of the effective features, the proposed method enhanced the efficacy of SQL injection detectors.

The Olympic Games are a sporting event and a catalyst for urban development in their host city. In this study, we utilized remote sensing and GIS techniques to examine the impact of the Olympic infrastructure on the surface temperature of urban areas. Using Landsat Series Collection 2 Tier 1 Level 2 data and cloud computing provided by Google Earth Engine (GEE), this study examines the effects of various forms of Olympic Games facility urban planning in different historical moments and location typologies, as follows: monocentric, polycentric, peripheric and clustered Olympic ring. The GEE code applies to the Olympic Games that occurred from Paris 2024 to Montreal 1976. However, this paper focuses specifically on the representative cases of Paris 2024, Tokyo 2020, Rio 2016, Beijing 2008, Sydney 2000, Barcelona 1992, Seoul 1988, and Montreal 1976. The study is not only concerned with obtaining absolute land surface temperatures (LST), but rather the relative influence of mega-event infrastructures on mitigating or increasing the urban heat. As such, the locally normalized land surface temperature (NLST) was utilized for this purpose. In some cities (Paris, Tokyo, Beijing, and Barcelona), it has been determined that Olympic planning has resulted in the development of green spaces, creating “green spots” that contribute to lower-than-average temperatures. However, it should be noted that there is a significant variation in temperature within intensely built-up areas, such as Olympic villages and the surrounding areas of the Olympic stadium, which can become “hotspots.” Therefore, it is important to acknowledge that different planning typologies of Olympic infrastructure can have varying impacts on city heat islands, with the polycentric and clustered Olympic ring typologies displaying a mitigating effect. This research contributes to a cloud computing method that can be updated for future Olympic Games or adapted for other mega-events and utilizes a widely available remote sensing data source to study a specific urban planning context.

This study investigates the impact of incorporating TiC and TiB2 inoculants on the microstructure and corrosion performance of an arc-directed energy-deposited PH 13-8Mo martensitic stainless steel. The microstructural characterizations revealed partial dissolution of the incorporated ceramic-based nanoparticles, resulting in the formation of in situ TiC phase in the TiC-inoculated sample, while TiC and chromium-enriched M3B2 phases were formed in the TiB2-inoculated sample. Further investigations into the electrochemical response of the fabricated samples confirmed that the applied inoculation strategy slightly enhanced the corrosion resistance of the alloy, offering a valuable advantage for in-service performance for applications in harsher environments. The slight improvement in the corrosion resistance of the inoculated samples was found to be attributed to the formation of a higher fraction of low-angle grain boundaries and enhanced retained austenite content in the microstructure. However, it is essential to note that the formation of chromium-enriched M3B2 phases in the TiB2-inoculated sample led to a slight deterioration in its corrosion resistance compared to the TiC-inoculated counterpart.

Background: Intra-abdominal pressure (IAP) is a critical parameter in the care of critically ill patients, as elevated IAP can lead to reduced cardiac output and organ perfusion, potentially resulting in multiple organ dysfunction and failure. The current gold standard for measuring IAP is an indirect technique via the bladder. According to the Abdominal Compartment Society’s Guidelines, new measurement methods/devices for IAP must be validated against the gold standard. Objectives: This study introduces Ventra, an abdominal phantom designed to simulate different IAP levels, abdominal compliance, respiration-related IAP variations, and bladder dynamics. Ventra aims to facilitate the development and validation of new IAP measurement devices while reducing reliance on animal and cadaveric studies. Additionally, it offers potential applications in training and education for biomedical engineering students. This study provides a thorough explanation on the phantom’s design and fabrication, which provides a low-cost solution for advancing IAP measurement research and education. The design concept, technical aspects, and a series of validation experiments determining whether Ventra is a suitable tool for future research are presented in this study. Methods: Ventra’s performance was evaluated through a series of validation tests using a pressure gauge and two intra-gastric (Spiegelberg and CiMON) and two intra-bladder (Accuryn and TraumaGuard) pressure measurement devices. The mean and standard deviation of IAP recordings by each device were investigated. Bland–Altman analysis was used to evaluate bias, precision, limits of agreement, and percentage error for each system. Concordance analysis was performed to assess the ability of Ventra in tracking IAP changes. Results: The phantom demonstrated excellent agreement with reference pressure measurements, showing an average bias of 0.11 ± 0.49 mmHg. A concordance coefficient of 100% was observed for the phantom as well. Ventra accurately simulated different abdominal compliances, with higher IAP values resulting in lower compliance. Abdominal volume changes showed a bias of 0.08 ± 0.07 L/min, and bladder fill volume measurements showed an average difference of 0.90 ± 4.33 mL for volumes ranging from 50 to 500 mL. Conclusion: The validation results were in agreement with the research guidelines of the world abdominal society. Ventra is a reliable tool that will facilitate the development and validation of new IAP measurement devices. It is an effective educational tool for biomedical engineering students as well.

This study evaluates the reproducibility of machine learning models that integrate radiomics and deep features (features extracted from a 3D autoencoder neural network) to classify various brain hemorrhages effectively. Using a dataset of 720 patients, we extracted 215 radiomics features (RFs) and 15,680 deep features (DFs) from CT brain images. With rigorous screening based on Intraclass Correlation Coefficient thresholds (>0.75), we identified 135 RFs and 1054 DFs for analysis. Feature selection techniques such as Boruta, Recursive Feature Elimination (RFE), XGBoost, and ExtraTreesClassifier were utilized alongside 11 classifiers, including AdaBoost, CatBoost, Decision Trees, LightGBM, Logistic Regression, Naive Bayes, Neural Networks, Random Forest, Support Vector Machines (SVM), and k-Nearest Neighbors (k-NN). Evaluation metrics included Area Under the Curve (AUC), Accuracy (ACC), Sensitivity (SEN), and F1-score. The model evaluation involved hyperparameter optimization, a 70:30 train–test split, and bootstrapping, further validated with the Wilcoxon signed-rank test and q-values. Notably, DFs showed higher accuracy. In the case of RFs, the Boruta + SVM combination emerged as the optimal model for AUC, ACC, and SEN, while XGBoost + Random Forest excelled in F1-score. Specifically, RFs achieved AUC, ACC, SEN, and F1-scores of 0.89, 0.85, 0.82, and 0.80, respectively. Among DFs, the ExtraTreesClassifier + Naive Bayes combination demonstrated remarkable performance, attaining an AUC of 0.96, ACC of 0.93, SEN of 0.92, and an F1-score of 0.92. Distinguished models in the RF category included SVM with Boruta, Logistic Regression with XGBoost, SVM with ExtraTreesClassifier, CatBoost with XGBoost, and Random Forest with XGBoost, each yielding significant q-values of 42. In the DFs realm, ExtraTreesClassifier + Naive Bayes, ExtraTreesClassifier + Random Forest, and Boruta + k-NN exhibited robustness, with 43, 43, and 41 significant q-values, respectively. This investigation underscores the potential of synergizing DFs with machine learning models to serve as valuable screening tools, thereby enhancing the interpretation of head CT scans for patients with brain hemorrhages.

Corn (Zea mays L.) is an important annual grain that is cultivated as a food staple around the world. The current study examined the effect of wastewater and a combination of biological and organic fertilizers on the morphological and phytochemical traits of corn, using a factorial experiment based on a randomized complete block design with three replications. The first factor was biological and organic fertilizers at seven levels, including the control (no fertilization), bacterial biological fertilizers (NPK) along with iron and zinc Barvar biofertilizers, fungal biofertilizers made from Mycorrhiza and Trichoderma, biochar, a combination of bacterial and fungal biofertilizers, and a combination of bacterial and fungal biofertilizers with biochar. The second factor was irrigation at two levels (conventional irrigation and irrigation with wastewater). The traits studied included the morphological yield, phenols, flavonoids, polyphenols, glomalin, cadmium content in plant parts, and translocation factor (TF). The results disclosed that the best treatment in regard to the morphological traits was related to conventional water + biochar + mycorrhiza + Trichoderma + NPK. The highest phenol and flavonoid content were observed when biochar + mycorrhiza + Trichoderma + NPK treatments were used in both water treatments. Also, the wastewater + biochar + mycorrhiza + Trichoderma + NPK treatment demonstrated the highest total glomalin and phenylalanine ammonia-lyase (PAL) activity. The obtained results demonstrate that combined biological and organic fertilizer use on corn plants can effectively alleviate the deleterious effects of cadmium present in wastewater.

Purpose: While tuberculosis remains a significant global health concern, prosthetic joint infections (PJIs) caused by members of the Mycobacterium tuberculosis complex are exceptionally rare. Our objective is to perform a retrospective search of new cases of this disease and analyze all cases available in the literature of tuberculous PJIs, aiming to detect factors that may influence patient outcomes. Methods: The ESGIAI and ESGMYC study groups were used to collect information on non-published cases of tuberculous prosthetic joint infections (PJIs). Additionally, a literature review of all published cases of tuberculous PJIs was conducted. All identified cases in the retrospective study and in the literature review were merged and included in the statistical analysis, involving both univariate and multivariate analyses. Results: Fifteen previously unreported cases of tuberculous prosthetic joint infections (PJIs) from four countries were detailed. Among them, ten patients were female, with a median age of 76 years. The hip was affected in 13 cases. Seven patients experienced co-infection with another microorganism. Treatment approaches varied, with 13 patients undergoing implant removal, one treated with DAIR (debridement, antibiotics, and implant retention), and one case was treated with an unknown treatment method. All patients received antibiotic therapy and achieved a cure. The literature review that was conducted detected 155 published cases. Univariate analysis revealed a statistical significance for previous tuberculosis, joint, and no importance of surgery for cure. Conclusions: Tuberculous prosthetic joint infection (PJI) is a rare condition, typically presenting as a localized chronic infection. Antibiotic treatment is essential for the management of these patients, but neither surgical treatment nor duration of treatment seems to have importance in the outcome.

The development of analytical procedures, in line with the recent regulatory requirements ICH Q2 (R2) and ICH Q14, is progressing, and it must be able to manage the entire life cycle of the methodology. This is also applicable to and especially challenging for combinations of drug substances and dosage form. A reliable and efficient, stability-indicating, MS-compatible, reverse-phase ultra-performance liquid chromatographic (UPLC®) method was developed for the determination of carvedilol and felodipine in a combination oral dosage form. The development of the method, performed using analytical quality by design (AQbD) principles, was in line with the future regulatory requirements. Furthermore, the fixed-dose combination dosage forms are a clear solution to the polypharmacy phenomenon in the elderly population. The main factors evaluated were the mobile phase buffer, organic modifier, column, flow, and column temperature. The optimum conditions were achieved with a Waters Acquity HSS T3 (100 × 2.1 mm i.d., 1.8 µm) column at 38 °C, using ammonium acetate buffer (5 mM, pH 4.5) (Solution A) and MeOH (Solution B) as mobile phases in gradient elution (t = 0 min, 10% B; t = 1.5 min, 10% B; t = 12.0 min, 90% B; t = 13.0 min, 10% B; t = 15.5 min, 10% B) at a flow rate of 0.2 mL/min and UV Detection of 240 and 362 nm for carvedilol (CAV) and felodipine (FLP), respectively. The linearity was demonstrated over concentration ranges of 30–650 µg/mL (R2 = 0.9984) (CAV) and 32–260 µg/mL (R2 = 0.9996) (FLP). Forced degradation studies were performed by subjecting the samples to hydrolytic (acid and base), oxidative, and thermal stress conditions. Standard solution stability was also performed. The proposed validated method was successfully used for the quantitative analysis of bulk, stability, and fixed-dose combination dosage form samples of the desired drug product. Using the AQbD principles, it is possible to generate methodologies with improved knowledge, leading to high-quality data, lower operation costs, and minimum regulatory risk. Furthermore, this work paves the way for providing a platform of robust analytical methods for the simultaneous quantification of innovative on-demand new dose combinations.

Ever-increasing penetration of distributed energy resources (DERs) in the power grids, alongside their numerous benefits, brings new challenges that call for enhanced solutions in the field of control and management of power grids. The majority of the available research have considered either distribution or transmission grids in their studies. In this paper, a comprehensive review of the effects of DERs on the distribution and transmission grids is performed. The focus of this paper is on hierarchical management methods in order to categorize different approaches and highlight the gaps. Moreover, a review is conducted in the field of the newly introduced distributed energy resources management system (DERMS) concept. A DERMS can facilitate the hierarchical energy management procedure due to its functionalities and broad capabilities. Hence, its implementation in energy management and its impact on the power grid will be assessed with the aid of a co-simulation platform that considers both transmission and distribution grids.

While available treatments have addressed a variety of complications in the dentoalveolar region, associated challenges have resulted in exploration of tissue engineering techniques. Often, scaffold biomaterials with specific properties are required for such strategies to be successful, development of which is an active area of research. This study focuses on the development of a copolymer of poly (N-isopropylacrylamide) (pNIPAM) and chitosan, used for 3D printing of scaffolds for dentoalveolar regeneration. The synthesized material was characterized by Fourier transform infrared spectroscopy, and the possibility of printing was evaluated through various printability tests. The rate of degradation and swelling was analyzed through gravimetry, and surface morphology was characterized by scanning electron microscopy. Viability of dental pulp stem cells seeded on the scaffolds was evaluated by live/dead analysis and DNA quantification. The results demonstrated successful copolymerization, and three formulations among various synthesized formulations were successfully 3D printed. Up to 35% degradability was confirmed within 7 days, and a maximum swelling of approximately 1200% was achieved. Furthermore, initial assessment of cell viability demonstrated biocompatibility of the developed scaffolds. While further studies are required to achieve the tissue engineering goals, the present results tend to indicate that the proposed hydrogel might be a valid candidate for scaffold fabrication serving dentoalveolar tissue engineering through 3D printing.

The energy crisis and climate change are two of the most concerning issues for human beings nowadays. For that reason, the scientific community is focused on the search for alternative biofuels to conventional fossil fuels as well as the development of sustainable processes to develop a circular economy. Bioelectrochemical processes have been demonstrated to be useful for producing bioenergy and value-added products from several types of waste. Electro-fermentation has gained great attention in the last few years due to its potential contribution to biofuel and biochemical production, e.g., hydrogen, methane, biopolymers, etc. Conventional fermentation processes pose several limitations in terms of their practical and economic feasibility. The introduction of two electrodes in a bioreactor allows the regulation of redox instabilities that occur in conventional fermentation, boosting the overall process towards a high biomass yield and enhanced product formation. In this regard, key parameters such as the type of culture, the nature of the electrodes as well as the operating conditions are crucial in order to maximize the production of biofuels and biochemicals via electro-fermentation technology. This article comprises a critical overview of the benefits and limitations of this emerging bio-electrochemical technology and its contribution to the circular economy.

In tsunami studies, understanding the intricate dynamics in the swash area, characterised by the shoaling effect, remains a challenge. In this study, we employed the adaptive mesh refinement (AMR) method to model tsunami inundation and propagation in the Onagawa town physical flume experiment. Using the open-source flow solver Basilisk, we implemented the Saint-Venant (SV) equations, Serre–Green–Naghdi (SGN) equations, and a nonhydrostatic multilayer (ML) extension of the SGN equations. A hydraulic bore tsunami-like wave was used as the input boundary condition. The objective was to assess the efficiency of the AMR method with nonhydrostatic tsunami models in overcoming limitations in 2D and quasi-3D models in flume experiments, particularly with respect to improving accuracy in arrival time and run-up detection. The results indicate improved performance of the SGN and SV models in determining tsunami arrival times. The ML model demonstrated enhanced wave run-up simulations on complex built-in terrain. The refined roughness coefficient determined using the ML solver captured the arrival time well in the northern section of the Onagawa model, albeit with a 1 s delay. The AMR method offered a computationally stable solution with an 86.3% reduction in computational time compared to a constant grid. While effective, the nonhydrostatic models entail the use of a great deal of computational resources.

The process of software development is incomplete without software testing. Software testing expenses account for almost half of all development expenses. The automation of the testing process is seen to be a technique for reducing the cost of software testing. An NP-complete optimization challenge is to generate the test data with the highest branch coverage in the shortest time. The primary goal of this research is to provide test data that covers all branches of a software unit. Increasing the convergence speed, the success rate, and the stability of the outcomes are other goals of this study. An efficient bioinspired technique is suggested in this study to automatically generate test data utilizing the discretized Bat Optimization Algorithm (BOA). Modifying and discretizing the BOA and adapting it to the test generation problem are the main contributions of this study. In the first stage of the proposed method, the source code of the input program is statistically analyzed to identify the branches and their predicates. Then, the developed discretized BOA iteratively generates effective test data. The fitness function was developed based on the program’s branch coverage. The proposed method was implemented along with the previous one. The experiments’ results indicated that the suggested method could generate test data with about 99.95% branch coverage with a limited amount of time (16 times lower than the time of similar algorithms); its success rate was 99.85% and the average number of required iterations to cover all branches is 4.70. Higher coverage, higher speed, and higher stability make the proposed method suitable as an efficient test generation method for real-world large software.

Physical activity results in oxidative stress, as evidenced by the increased production of reactive oxygen, nitrogen species, and inflammatory mediators. The management of these components is instrumental for antioxidant adaptation to exercise and post-exercise recovery. Therefore, the present report aims to study the antioxidant response to two types of exercise (a 2000 m run and a burpee test) in healthy volunteers after a long period of inactivity (1–2 months). Antioxidant enzyme activities and oxidative stress markers (protein carbonyls and malondialdehyde content) were measured in neutrophils, peripheral blood mononuclear cells, and plasma. These parameters were determined under basal conditions and immediately post-exercise. Compared to those in basal state, neutrophil superoxide dismutase (28.3 vs. 22.9 pkat/109 cells), glutathione peroxidase (147.5 vs. 120.1 nkat/109 cells), and catalase (106.3 vs. 57.9 k/109 cells) were activated significantly (p < 0.05) after the burpee test. Peripheral blood mononuclear cells exhibited only significant (p < 0.05) catalase activation (113.6 vs. 89.4 k/109 cells) after the burpee test. Other enzymes, such as glutathione reductase and myeloperoxidase, tended to increase post-exercise, although the differences from baseline were not significant. Finally, compared to basal conditions, the protein carbonyl (24.5 vs. 14.5 mmol/L) and malondialdehyde (39.6 vs. 18.3 mmol/L) contents increased significantly (p < 0.05) in neutrophils and in plasma (115.1 vs. 97.8 and 130.2 vs. 123.4 μmol/L, respectively) after the burpee test. In conclusion, high-intensity exercise seems to induce immediate oxidative stress in inactive individuals, and the acute antioxidant response was slightly greater after the burpee test than after the 2000 m run. Glutathione-dependent antioxidant systems are activated immediately as protective mechanisms.

This study uncovered the impacts of microwave (MW) treatments compared to conventional pasteurization (TP) on the quality of functional citrus–maqui beverages, with added sucrose or stevia. The influence of these thermal treatments on the microbiological burden and phytochemical composition was determined by processing under two MW power levels (600 W and 800 W) and TP at 85 °C for 15 s for 60 days at room temperature (20 °C). The results indicated that, beyond the microbiological quality achieved in the juices treated by both MW and TP technology, there were no differences among the treatments regarding the stability of vitamin C, anthocyanin, and flavanone concentrations. However, anthocyanins were more stable in those beverages with sucrose added, rendering a better red color. Besides, all treatments ensured microbiological stability throughout the entire storage time. In conclusion, MW treatment could be considered as an alternative to TP, which ensures microbial safety, protecting functional compounds associated with health effects.

Microalgae, as some of the oldest life forms on Earth, are of significant interest to industry and in terms of environmental policies, due to their ability to perform photosynthesis and consume atmospheric carbon dioxide. Moreover, they contain a wide variety of value-added compounds such as amino acids and proteins, carbohydrates, and fatty acids, which can be exploited in multiple fields like medicine, cosmetics, nutritional supplements, and for the production of biodiesel. In this article, Nannochloropsis gaditana, a type of microalgae that inhabits both fresh and salt water, is studied for fatty acid recovery using deep eutectic solvents (DES). This microalgae species is a natural source of eicosapentaenoic acid (EPA), an omega-3 compound that is commonly used in the nutritional industry. There are numerous extraction techniques and pretreatments to obtain these compounds. In this work, DES are studied as extractive agents due to their advantages as neoteric solvents. Specifically, this work focuses on an assessment of the effect of the composition of DES on the extraction yield of fatty acids from microalgae. Several DES compositions based on choline chloride, ethylene glycol, and fructose are studied to analyze the influence of water content in these phases. The results show that water content significantly influences recovery yields. The DES with higher extractive capacity were those based on choline chloride, ethylene glycol, and water at a molar ratio of 1:2:2. This composition offered 48.7% of the yield obtained with a conventional solvent like methanol for the recovery of EPA (11.2 mg/g microalgae). Furthermore, the choline chloride-fructose-based DES shows the capability of selective extractions of fatty acids with low carbon content—choline chloride:fructose:water (molar ratio 2:1:2) can extract 0.14 mg of decanoic acid/g of microalgae, indicating that this DES composition can recover 35.7% more decanoic acid in comparison to methanol.

Although human monkeypox infections had not been recorded in the Kurdistan region of Iraq as of August 2023, the rapid growth of cases worldwide and the detection of monkeypox in neighboring Middle Eastern nations call for careful planning and timely response measures. Educating and empowering frontline healthcare workers (HCWs) so that they can act to curb the spread of monkeypox infections are core elements of primary prevention and protecting public health. Therefore, this study aimed to assess HCWs’ knowledge and attitudes about monkeypox and their willingness to vaccinate against monkeypox. By employing a convenience sampling method, an online survey was disseminated via Google Forms between 1 November 2022 and 15 January 2023. The researchers utilized regression analyses to ascertain the factors associated with the three parameters: knowledge, attitude, and the willingness to vaccinate. A total of 637 HCWs were included in the analysis (ages ranged between 21 and 51 years). The mean overall scores were 8.18 of a max score of 16 (SD 3.37), 3.4 of 5 (SD 1.37), and 2.41 of 5 (SD 1.25) for knowledge, attitude, and willingness to vaccinate, respectively. A multivariate logistic regression analysis demonstrated that HCWs who had heard about monkeypox before 2022 rather than later had a higher level of knowledge (AOR: 4.85; 95% CI: 2.81–8.36; p < 0.001). In addition, those who had newly joined the workforce or had less than 1 year experience in practice had more positive attitudes about curbing monkeypox (AOR: 0.35; 95% CI: 0.20–0.59; p < 0.01) than those who practiced for longer. No significant predictors of willingness to vaccinate against monkeypox were identified. The research revealed that HCWs exhibited a relatively low level of monkeypox knowledge. They also had poor attitudes towards monkeypox vaccination and were therefore reluctant to receive the vaccines. Imparting knowledge about the infectious disease can cultivate better awareness and attitudes among HCWs as to their roles in mitigating the spread of an epidemic in the foreseeable future.

Clays generally have a low strength and capacity, and additives are usually used to stabilize them. In recent years, using fly ash to stabilize soil has decreased environmental pollution while also having an economic benefit. The objective of this study is to perform a comparative investigation on the effect of class C and class F fly ashes on geotechnical properties of high-plasticity clay using the Atterberg’s limit, compaction, California Bearing Ratio (CBR), and unconfined compressive strength tests. The results showed that with an increase in the amount of fly ash, there was a decrease in the maximum dry density and an increase in the optimum moisture content. Moreover, an addition of fly ashes of up to 25% caused a reduction of the liquid limit and plasticity index, and an increase in the maximum unconfined compressive strength and CBR. Lengthening the curing time had a positive impact on the unconfined compressive strength of the soil. The soil samples with class C fly ash were seen to possess more efficient geotechnical properties as compared to class F fly ash.

Introduction: Intra-abdominal pressure (IAP) monitoring is crucial for the detection and prevention of intra-abdominal hypertension (IAH) and abdominal compartment syndrome (ACS). In the 1970s, air-filled catheters (AFCs) for urodynamic studies were introduced as a solution to overcome the limitations of water-perfused catheters. Recent studies have shown that for correct IAP measurement with traditional AFC, the bladder needs to be primed with 25 mL of saline solution to allow pressure wave transmission to the transducer outside of the body, which limits continuous IAP monitoring. Methods: In this study, a novel triple balloon, air-filled TraumaGuard (TG) catheter system from Sentinel Medical Technologies (Jacksonville, FL, USA) with a unique balloon-in-balloon design was evaluated in a porcine and cadaver model of IAH via laparoscopy (IAPgold). Results: In total, 27 and 86 paired IAP measurements were performed in two pigs and one human cadaver, respectively. The mean IAPTG was 20.7 ± 10.7 mmHg compared to IAPgold of 20.3 ± 10.3 mmHg in the porcine study. In the cadaver investigation, the mean IAPTG was 15.6 ± 10.8 mmHg compared to IAPgold of 14.4 ± 10.4 mmHg. The correlation, concordance, bias, precision, limits of agreement, and percentage error were all in accordance with the WSACS (Abdominal Compartment Society) recommendations and guidelines for research. Conclusions: These findings support the use of the TG catheter for continuous IAP monitoring, providing early detection of elevated IAP, thus enabling the potential for prevention of IAH and ACS. Confirmation studies with the TraumaGuard system in critically ill patients are warranted to further validate these findings.

In this study, the focus is on the magnetic path formation and its effects on the performance of a counter-rotating dual-rotor permanent-magnet flux-switching machine (CR-DRPMFSM) for direct-drive counter-rotating wind power generation, based on different stator slot and rotor pole combinations. To fully exploit rotor-shaft bore and improve fault-tolerant design, as well as increase torque density, dual-rotor topologies with the capability for dual electrical and dual mechanical ports are investigated. Moreover, the direct-drive counter-rotating wind power generation technique offers a brushless topology, thus reducing maintenance cost and improving energy conversion efficiency compared to single-blade wind turbine systems. Using finite element analysis (FEA), the inherent magnetic coupling of the series and parallel paths shows varied impacts on the electromagnetic performance of four different CR-DRPMFSMs based on the slot/pole combinations (MI to MIV) considered in this study. The key electromagnetic performance indices, such as torque, cogging torque, torque ripple, power factor, and efficiency, show proportionate variation to the coupling level. A comparative analysis shows that MI exhibits higher average torque, lower torque ripples, and high efficiency, reaching 90% with a power factor of 0.6. As an optimal design, an MI test prototype is developed. The experimental test prototype validates the FEA results under no-load and on-load conditions.

Marine natural products are well-recognized as potential resources to fill the pipeline of drug leads to enter the pharmaceutical industry. In this circumstance, marine-derived fungi are one of the unique sources of bioactive secondary metabolites due to their capacity to produce diverse polyketides and peptides with unique structures and diverse biological activities. The present review covers the peptides from marine-derived fungi reported from the literature published from January 1991 to June 2023, and various scientific databases, including Elsevier, ACS publications, Taylor and Francis, Wiley Online Library, MDPI, Springer, Thieme, Bentham, ProQuest, and the Marine Pharmacology website, are used for a literature search. This review focuses on chemical characteristics, sources, and biological and pharmacological activities of 366 marine fungal peptides belonging to various classes, such as linear, cyclic, and depsipeptides. Among 30 marine-derived fungal genera, isolated from marine macro-organisms such as marine algae, sponges, coral, and mangrove plants, as well as deep sea sediments, species of Aspergillus were found to produce the highest number of peptides (174 peptides), followed by Penicillium (23 peptides), Acremonium (22 peptides), Eurotium (18 peptides), Trichoderma (18 peptides), Simplicillium (17 peptides), and Beauveria (12 peptides). The cytotoxic activity against a broad spectrum of human cancer cell lines was the predominant biological activity of the reported marine peptides (32%), whereas antibacterial, antifungal, antiviral, anti-inflammatory, and various enzyme inhibition activities ranged from 7% to 20%. In the first part of this review, the chemistry of marine peptides is discussed and followed by their biological activity.

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Parkinson’s disease (PD) is one of the most common neurodegenerative diseases. There is a wide range of sleep disturbances in patients with PD, such as insomnia and rapid eye movement (REM) sleep behavior disorder (or REM behavior disorder (RBD)). RBD is a sleep disorder in which a patient acts out his/her dreams and includes abnormal behaviors during the REM phase of sleep. On the other hand, melatonin is the principal hormone that is secreted by the pineal gland and significantly modulates the circadian clock and mood state. Furthermore, melatonin has a wide range of regulatory effects and is a safe treatment for sleep disturbances such as RBD in PD. However, the molecular mechanisms of melatonin involved in the treatment or control of RBD are unknown. In this study, we reviewed the pathophysiology of PD and sleep disturbances, including RBD. We also discussed the potential molecular mechanisms of melatonin involved in its therapeutic effect. It was concluded that disruption of crucial neurotransmitter systems that mediate sleep, including norepinephrine, serotonin, dopamine, and GABA, and important neurotransmitter systems that mediate the REM phase, including acetylcholine, serotonin, and norepinephrine, are significantly involved in the induction of sleep disturbances, including RBD in PD. It was also concluded that accumulation of α-synuclein in sleep-related brain regions can disrupt sleep processes and the circadian rhythm. We suggested that new treatment strategies for sleep disturbances in PD may focus on the modulation of α-synuclein aggregation or expression.

Five undescribed pentaketide derivatives, (R)-6,8-dihydroxy-4,5-dimethyl-3-methylidene-3,4-dihydro-1H-2-benzopyran-1-one (1), [(3S,4R)-3,8-dihydroxy-6-methoxy-4,5-dimethyl-1-oxo-3,4-dihydro-1H-isochromen-3-yl]methyl acetate (2), (R)-5, 7-dimethoxy-3-((S)-(1-hydroxyethyl)-3,4-dimethylisobenzofuran-1(3H)-one (4b), (S)-7-hydroxy-3-((S)-1-hydroxyethyl)-5-methoxy-3,4-dimethylisobenzofuran 1(3H)-one (5), and a p-hydroxyphenyl-2-pyridone derivative, avellaneanone (6), were isolated together with the previously reported (R)-3-acetyl-7-hydroxy-5-methoxy-3,4-dimethylisobenzofuran-1(3H)-one (3), (R)-7-hydroxy-3-((S)-1-hydroxyethyl)-5-methoxy-3,4-dimethylisobenzofuran-1(3H)-one (4a) and isosclerone (7), from the ethyl acetate extract of a culture of a marine sponge-derived fungus, Hamigera avellanea KUFA0732. The structures of the undescribed compounds were elucidated using 1D and 2D NMR, as well as high-resolution mass spectral analyses. The absolute configurations of the stereogenic carbons in 1, 4b, 5, and 6 were established by X-ray crystallographic analysis. The absolute configurations of C-3 and C-4 in 2 were determined by ROESY correlations and on the basis of their common biosynthetic origin with 1. The crude fungal extract and the isolated compounds 1, 3, 4b, 5, 6, and 7 were assayed for their growth inhibitory activity against various plant pathogenic fungi viz. Alternaria brassicicola, Bipolaris oryzae, Colletotrichum capsici, C. gloeosporiodes, Curvularia oryzae, Fusarium semitectum, Lasiodiplodia theobromae, Phytophthora palmivora, Pyricularia oryzae, Rhizoctonia oryzae and Sclerotium rolfsii.

Imatinib mesylate, an anticancer drug, is prescribed to treat gastrointestinal stromal tumors and chronic myelogenous leukemia. A hybrid nanocomposite of N,S-doped carbon dots/carbon nanotube-poly(amidoamine) dendrimer (N,S-CDs/CNTD) was successfully synthesized and used as a significant modifier to design a new and highly selective electrochemical sensor for the determination of imatinib mesylate. A rigorous study with electrochemical techniques, such as cyclic voltammetry and differential pulse voltammetry, was performed to elucidate the electrocatalytic properties of the as-prepared nanocomposite and the preparation procedure of the modified glassy carbon electrode (GCE). A higher oxidation peak current was generated for the imatinib mesylate on a N,S-CDs/CNTD/GCE surface compared to the GCE and CNTD/GCE. The N,S-CDs/CNTD/GCE showed a linear relationship between the concentration and oxidation peak current of the imatinib mesylate in 0.01–100 μM, with a detection limit of 3 nM. Finally, the imatinib mesylate’s quantification in blood-serum samples was successfully performed. The N,S-CDs/CNTD/GCE’s reproducibility and stability were indeed excellent.

Ochre, waste iron sludge from the treatment of iron rich groundwater for potable use, makes up a significant waste problem. Furthermore, wastewater treatment plants with enhanced biological phosphorus removal and the digestion of sludge are in lack of iron for the prevention of hydrogen sulfide production and the release of phosphorous during anaerobic digestion. Thus, the addition of ochre to anaerobic digestion is a potential beneficial reuse of ochre. Sludge from wastewater treatment plants with enhanced biological phosphorus removal was used for the experiments. Batch and continuous pilot-scale tests were performed for the mesophilic digestion of primary and waste-activated sludge with different doses of ochre. Two different doses of ochre corresponding to molar ratios of 1 and 2 moles Fe3+/mole P released in the batch test resulted in 29% and 57% reductions of phosphates respectively in the sludge liquor compared to the control sludge without inhibiting the digestion process. In the pilot experiment, the dosing of ochre at both a high and low dose (molar ratios of 1.6 and 0.8 Fe3+/S2−, respectively) resulted in an immediate drop in the H2S concentration (from >2000 ppm down to 570 ppm), while the control reactor still produced biogas with a high hydrogen sulfide concentration. The inhibition of the digestion process was observed (accumulation of acetate) at the higher dose. In a second pilot scale experiment, lower doses of ochre were tested continuously (1.5 and 0.75 mole Fe3+/mole Preleased) to avoid any inhibition, while evaluating the phosphate precipitation. A reduction of phosphates in sludge liquor (33% and 66% for the low and high doses respectively) was obtained.

Soft robots have gained popularity, especially in intraluminal applications, because their soft bodies make them safer for surgical interventions than flexures with rigid backbones. This study investigates a pressure-regulating stiffness tendon-driven soft robot and provides a continuum mechanics model for it towards using that in adaptive stiffness applications. To this end, first, a central single-chamber pneumatic and tri-tendon-driven soft robot was designed and fabricated. Afterward, the classic Cosserat’s rod model was adopted and augmented with the hyperelastic material model. The model was then formulated as a boundary-value problem and was solved using the shooting method. To identify the pressure-stiffening effect, a parameter-identification problem was formulated to identify the relationship between the flexural rigidity of the soft robot and internal pressure. The flexural rigidity of the robot at various pressures was optimized to match theoretical deformation and experiments. The theoretical findings of arbitrary pressures were then compared with the experiment for validation. The internal chamber pressure was in the range of 0 to 40 kPa and the tendon tensions were in the range of 0 to 3 N. The theoretical and experimental findings were in fair agreement for tip displacement with a maximum error of 6.40% of the flexure’s length.

An undescribed hybrid phenalenone dimer, talaropinophilone (3), an unreported azaphilone, 7-epi-pinazaphilone B (4), an unreported phthalide dimer, talaropinophilide (6), and an undescribed 9R,15S-dihydroxy-ergosta-4,6,8 (14)-tetraen-3-one (7) were isolated together with the previously reported bacillisporins A (1) and B (2), an azaphilone derivative, Sch 1385568 (5), 1-deoxyrubralactone (8), acetylquestinol (9), piniterpenoid D (10) and 3,5-dihydroxy-4-methylphthalaldehydic acid (11) from the ethyl acetate extract of the culture of a marine sponge-derived fungus, Talaromyces pinophilus KUFA 1767. The structures of the undescribed compounds were elucidated by 1D and 2D NMR as well as high-resolution mass spectral analyses. The absolute configuration of C-9′ of 1 and 2 was revised to be 9′S using the coupling constant value between C-8′ and C-9′ and was confirmed by ROESY correlations in the case of 2. The absolute configurations of the stereogenic carbons in 7 and 8 were established by X-ray crystallographic analysis. Compounds 1,2, 4–8, 10 and 11 were tested for antibacterial activity against four reference strains, viz. two Gram-positive (Staphylococcus aureus ATCC 29213, Enterococcus faecalis ATCC 29212) and two Gram-negative (Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 27853), as well as three multidrug-resistant strains, viz. an extended-spectrum β-lactamase (ESBL)-producing E. coli, a methicillin-resistant S. aureus (MRSA) and a vancomycin-resistant E. faecalis (VRE). However, only 1 and 2 exhibited significant antibacterial activity against both S. aureus ATCC 29213 and MRSA. Moreover, 1 and 2 also significantly inhibited biofilm formation in S. aureus ATCC 29213 at both MIC and 2xMIC concentrations.

The rapid spread of monkeypox (mpox) has been declared as a public health emergency of international concern (PHEIC). The present study aimed to assess the knowledge, attitude, and worry levels of the general population in the Kurdistan region of Iraq regarding the ongoing mpox multi-country outbreak. An online cross-sectional survey was conducted between 27–30 July 2022, using a convenience sampling method. The questionnaire was adapted from previous studies addressing the same topic. The independent Student’s t-test, one-way ANOVA, and logistic regression were used to assess possible factors associated with knowledge, attitude, and worry toward mpox. A total of 510 respondents were included in the final analysis. The participants showed a moderate level of mpox knowledge, a neutral attitude towards mpox, and a relatively moderate worry level. The logistic regression analysis showed that age, gender, marital status, religion, level of education, and place of residence were associated with mpox knowledge; however, the significant variables in the multivariate regression analysis were gender, religion, level of education, and residential area. Gender and residential area were associated with attitudes toward mpox; however, the significant variables in the multivariate regression analysis were gender and residential areas. The worry toward mpox was influenced by gender, marital status, religion, and place of residence, yet the significant variables in the multivariate regression analysis were gender, religion, educational level, and residential area. In conclusion, the Kurdish population had moderate knowledge, a neutral attitude, and a moderate level of worry about mpox. Considering the continuous rapid rise in mpox cases in several countries, and its possible risk as pandemic amid the ongoing COVID-19 pandemic, proactive control measures, adequate disease prevention strategies, and preparedness plans need to be formulated and immediately implemented to tackle the appearance of fears among people, and to safeguard the mental health of the public.

Soil loss (SL) and its related sedimentation in mountainous areas affect the lifetime and functionality of dams. Darbandikhan Lake is one example of a dam lake in the Zagros region that was filled in late 1961. Since then, the lake has received a considerable amount of sediments from the upstream area of the basin. Interestingly, a series of dams have been constructed (13 dams), leading to a change in the sedimentation rate arriving at the main reservoir. This motivated us to evaluate a different combination of equations to estimate the Revised Universal Soil Loss Equation (RUSLE), Sediment Delivery Ratio (SDR), and Reservoir Sedimentation (RSed). Sets of Digital Elevation Model (DEM) gathered by the Shuttle Radar Topography Mission (SRTM), Tropical Rainfall Measuring Mission (TRMM), Harmonized World Soil Database (HWSD), AQUA eMODIS NDVI V6 data, in situ surveys by echo-sounding bathymetry, and other ancillary data were employed for this purpose. In this research, to estimate the RSed, five models of the SDR and the two most sensitive factors affecting soil-loss estimation were tested (i.e., rainfall erosivity (R) and cover management factor (C)) to propose a proper RUSLE-SDR model suitable for RSed modeling in mountainous areas. Thereafter, the proper RSed using field measurement of the bathymetric survey in Darbandikhan Lake Basin (DLB) was validated. The results show that six of the ninety scenarios tested have errors 3·yr−1. Moreover, this study advises using the Modified Fournier index (MIF) equations to estimate the R factor. Avoiding the combination of the Index of Connectivity (IC) model for calculating SDR and land cover for calculating the C factor to obtain better estimates is highly recommended.

Effective nitrogen (N) management practices are critical to sustain crop production and minimize nitrate (NO3−) leaching loss from irrigated fields in the Columbia Basin (U.S.), but studies on the applied practices are limited. Therefore, from 2014 to 2016, two separate field studies were conducted in sandy loam soils in the region to evaluate the performance of various N fertilizers in spring and winter wheat. The treatments consisted of two nitrification inhibitors (NIs) (Instinct® II and Agrotain® Ultra) in combination with two N fertilizers (urea and urea ammonium nitrate [UAN]) under two application methods (single vs. split-application) and two rates (100% vs. 85% of growers’ standard). The results from these field trials demonstrated that N fertilizer treatments did not affect wheat grain yield (GY) and grain protein (GP). In the spring wheat trial, higher NH4⁺-N content but lower NO3−-N content was observed in the UAN treatments (0–30 cm). However, the application of NIs had no considerable effect on soil N content. In the winter wheat trial, the split N application generally reduced NO3−-N and total mineral nitrogen (TMN) content, especially at 30–60 cm, in comparison to a single application. The use of Instinct® II tended to reduce NO3−-N and TMN contents, while Agrotain® Ultra was not effective in inhibiting nitrification. Our findings suggest that more studies on the effectiveness of NIs and N applications would enable growers to optimize N use efficiency and crop production in the region.

In this article, we explore the production of socio-cultural values around blue-green solutions (BGS) through the perspective of care. We explore how values and preferences are formed through the complexity of everyday life engagements in a BGS environment. The data come from a questionnaire answered by 328 households in the neighborhood of Augustenborg in Malmö, Sweden. The questionnaire collects detailed information about inhabitants’ possible recreational use (through Likert scale questions) and willingness to pay (WTP) (estimated through contingent valuation). The study evaluates if and how people care to use, care to live with, and care to pay for BGS. The result shows that people in Augustenborg relate in different and sometimes contradictory ways to BGS. A well-used BGS environment does not per se make the environment successful or result in people preferring a BGS environment in the future. In addition, recreational use and building awareness about BGS flood mitigation seem to increase the willingness to pay, whereas living longer in the area seems to decrease it. The study reveals a landscape of care that is constantly being formed and transformed. This suggests that both planning and research needs to focus more on the relation between BGS and social use over time.

(1) Background: Patients with advanced chronic liver disease (ACLD) are living longer with more comorbidities because of improved medical and surgical management. However, patients with ACLD are at increased risk of perioperative morbidity and mortality; (2) Methods: We conducted a comprehensive review of the literature to support a narrative clinical guideline about the assessment of mortality risk and management of perioperative morbidity in patients with ACLD undergoing surgical procedures; (3) Results: Slight data exist to guide the perioperative management of patients with ACLD, and most recommendations are based on case series and expert opinion. The severity of liver dysfunction, portal hypertension, cardiopulmonary and renal comorbidities, and complexity of surgery and type (elective versus emergent) are predictors of perioperative morbidity and mortality. Expert multidisciplinary teams are necessary to evaluate and manage ACLD before, during, and after surgical procedures; (4) Conclusions: This clinical practice document updates the available data and recommendations to optimize the management of patients with advanced chronic liver disease who undergo surgical procedures.

Lao PDR faces seasonal power supply problems due to its heavy reliance on hydropower. Thus, the aim of this paper was to prioritize renewable energy (RE) resources for sustainable electricity generation in Lao PDR using the analytic hierarchy process (AHP) method, and to further estimate the energy available for the prioritized RE to enhance the seasonal power supply. Four RE alternatives were assessed considering technological, economic, environmental, and social criteria with twelve overall sub-criteria. The results indicated that hydropower was the most highly prioritized alternative, followed by solar. The resulting weights of the RE prioritization were in agreement with the Lao energy policy and plan. In order to address the seasonal power supply problem, setting-up floating photovoltaic (FPV) units in the existing hydropower reservoirs was proposed. The FPV potential was estimated, and the results revealed that the predicted power demand by 2030, as calculated in the latest Lao national power development strategy, could be fully covered by integrating the FPV output from 10% coverage of the water surface in four existing hydropower reservoirs with the existing power supply in 2020. The proposed FPV technology would provide a solution to enhance the seasonal power supply and reduce the power import.

The aim of this research work was to investigate the applicability of smart contracts in the context of automating the process of managing meeting minutes. To this end, smartMinutes, a proof-of-concept prototype of automating meeting minutes was designed, implemented, and validated with test cases. The smartMinutes framework improves current practices related to the meeting minutes process by providing automation in areas where possible, and doing so in a transparent, flexible, reliable, and tamper-proof manner. The last feature is of paramount importance due to the fact that meeting minutes offer legal protection, as they are considered official records of the actions taken by an organisation. Additionally, smartMinutes supports meeting agendas with non-voting items as well as voting items, offering a pool of three voting schemes, executing under three different configurations. A particular configuration, the hidden mode, provides for secrecy while at the same time guaranteeing transparency.