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The number of cybersecurity vulnerabilities keeps growing every year. Each vulnerability must be reported to the MITRE Corporation and assessed by a Counting Number Authority, which generates a metrics vector that determines its severity score. This process can take up to several weeks, with higher-severity vulnerabilities taking more time. Several authors have successfully used Deep Learning to automate the score generation process and used explainable AI to build trust with the users. However, the explanations that were shown were surface label input saliency on binary classification. This is a limitation, as several metrics are multi-class and there is much more we can achieve with XAI than just visualizing saliency. In this work, we look for actionable actions CNAs can take using XAI. We achieve state-of-the-art results using an interpretable XGBoost model, generate explanations for multi-class labels using SHAP, and use the raw Shapley values to calculate cumulative word importance and generate IF rules that allow a more transparent look at how the model classified vulnerabilities. Finally, we made the code and dataset open-source for reproducibility.

In a deregulated power system, managing congestion is crucial for effective operation and control. The goal of congestion management is to alleviate transmission line congestion while adhering to system constraints at minimal cost. This research proposes a hybrid Harris Hawk Optimization–Sine Cosine Algorithm (hHHO-SCA) for an efficient generation rescheduling approach to achieve the lowest possible congestion cost. The hybridization has been performed by introducing the features of SCA in the HHO to boost the exploration and exploitation steps of HHO, providing an efficient global solution and effectively optimizing rescheduled power output. The effectiveness of this methodology is evaluated using IEEE 30 and IEEE 118-bus test systems, taking into account system parameters. The potency of the proposed method is analyzed by comparing the results of the hHHO-SCA with those from other recent optimization techniques. The findings show that the hHHO-SCA outperforms other methods by avoiding local optima and demonstrating promising convergence characteristics.

This investigation concentrates on refining the accuracy of earthquake parameters as reported by various Saudi seismic networks, addressing the significant challenges arising from data discrepancies in earthquake location, depth, and magnitude estimations. The application of sophisticated machine learning techniques, particularly the Isolation Forest algorithm, has markedly enhanced the precision in the estimation of seismicity parameters by effectively identifying and eliminating outliers and discrepancies. A newly developed and refined seismicity catalog was employed to accurately determine key seismic parameters such as the magnitude of completeness (Mc), a-value, and b-value, thereby underlining their indispensable role in regional seismic hazard assessment. The research underscores the substantial impact of data inconsistencies on the evaluation of seismic hazards, thereby advocating for the advancement of research methodologies within the field of seismotectonics. The insights derived from this study significantly contribute to a more profound understanding of the seismotectonic processes in the region. These insights are crucial for the development of comprehensive seismic hazard assessments and the formulation of targeted earthquake preparedness strategies, thereby enhancing resilience against seismic risks in the region.

Capturing patients’ medical histories significantly influences clinical decisions. Errors in this process lead to clinical errors, which increase costs and dissatisfaction among physicians and patients. Physicians in developing countries are overloaded with patients and cannot always follow the proper history-taking procedure. The challenges have been acknowledged; however, a comprehensive understanding of the status and the remedies has remained unexplored. This paper aims to investigate the workload, history-taking challenges, and the willingness of the physicians to accept digital solutions. A cross-sectional online survey was conducted on 104 physicians across Bangladesh, featuring 22 questions regarding their professional environment, workload, digitization status of health records, challenges in history taking, and attitudes toward adopting digital solutions for managing patient histories; 92.67% of the physicians face high workloads, 88.46% struggle in medical history taking, and only 4.81% use digital medical records. About 70% struggle to complete the necessary history-taking steps, emphasizing the urgent need for solutions. A novel visualization system, the Smart Health Gantt Chart (SHGC), has been introduced for their instant feedback. A total of 93.27% of physicians expressed their willingness to use such a system. The proposed SHGC has the potential to enhance healthcare efficiency in developing nations, benefit physicians, and improve patient-centered care.

Modern technology and analysis of emotions play a crucial role in enabling intelligent healthcare systems to provide diagnostics and self-assistance services based on observation. However, precise data predictions and computational models are critical for these systems to perform their jobs effectively. Traditionally, healthcare monitoring has been the primary emphasis. However, there were a couple of negatives, including the pattern feature generating the method’s scalability and reliability, which was tested with different data sources. This paper delves into the Discriminant Input Processing Scheme (DIPS), a crucial instrument for resolving challenges. Data-segmentation-based complex processing techniques allow DIPS to merge many emotion analysis streams. The DIPS recommendation engine uses segmented data characteristics to sift through inputs from the emotion stream for patterns. The recommendation is more accurate and flexible since DIPS uses transfer learning to identify similar data across different streams. With transfer learning, this study can be sure that the previous recommendations and data properties will be available in future data streams, making the most of them. Data utilization ratio, approximation, accuracy, and false rate are some of the metrics used to assess the effectiveness of the advised approach. Self-assisted intelligent healthcare systems that use emotion-based analysis and state-of-the-art technology are crucial when managing healthcare. This study improves healthcare management’s accuracy and efficiency using computational models like DIPS to guarantee accurate data forecasts and recommendations.

This work uses the direct current magnetron sputtering (DCMS) of equi-atomic (AlTiZrHfTa) and Si targets in dynamic sweep mode to deposit nano-layered (AlTiZrHfTa)Nx/SiNx refractory high-entropy coatings (RHECs). Transmission electron microscopy (TEM), field emission scanning electron microscopy (FESEM), thermogravimetric analysis (TGA), X-ray diffraction (XRD), and X-ray photoelectron spectroscopy (XPS) are used to investigate the effect of Si addition on the oxidation behavior of the nano-layered coatings. The Si-free nitride coating exhibits FCC structure and columnar morphology, while the Si-doped nitride coatings present a FCC (AlTiZrHfTa)N/amorphous-SiNx nano-layered architecture. The hardness decreases from 24.3 ± 1.0 GPa to 17.5 ± 1.0 GPa because of the nano-layered architecture, whilst Young’s modulus reduces from 188.0 ± 1.0 GPa to roughly 162.4 ± 1.0 GPa. By increasing the thickness of the SiNx nano-layer, kp values decrease significantly from 3.36 × 10−8 g2 cm−4 h−1 to 6.06 × 10−9 g2 cm−4 h−1. The activation energy increases from 90.8 kJ·mol−1 for (AlTiZrHfTa)Nx nitride coating to 126.52 kJ·mol−1 for the (AlTiZrHfTa)Nx/SiNx nano-layered coating. The formation of a FCC (AlTiZrHfTa)-Nx/a-SiNx nano-layered architecture results in the improvement of the resistance to oxidation at high temperature.

A comprehensive geostatistical analysis was conducted on a dataset comprising 24,321 seismic events in the Red Sea region, spanning from 1997 to 2020. This analysis involved the creation of a new seismic activity database, incorporating data from both Egyptian and Saudi Seismic Networks. This enriched database provided a robust foundation for a detailed examination of the seismic patterns and activities in the region. Utilizing geographic information systems and various spatial analytic methods, it identifies seismic patterns and tectonic influences. The findings reveal significant seismic clustering along the Central Red Sea axis, indicative of active rifting between the Nubian and Arabian plates. The study demonstrates spatial autocorrelation in seismic activities, with high-high clusters marking zones of elevated seismicity. Kernel Density Estimator analyses highlight concentrated seismic activity in the Gulfs of Aqaba and Suez. Higher magnitude events are shown to localize in areas of greater tectonic stress, aligning with known geological features. This research provides critical insights into the seismic dynamics of the Red Sea, showcasing the effectiveness of geostatistical techniques in analyzing seismic data in tectonically active regions.

The phenomenon of luminescence and quenching is widely applied in the forensic examination of various ink materials. Here, we introduce a hybrid spectroscopic and chromatography approach to gain insights into the underlying cause of infrared luminescence (IRL) in blue ballpoint (BP) pen inks. A total of thirty BP pen ink samples from the Egyptian market were employed in this study. Insights into the origin of luminescence and quenching in the studied samples were gained based on video spectral comparator (VSC), thin layer chromatography (TLC), ultraviolet–visible (UV–Vis) spectrophotometry, and photoluminescence (PL) spectroscopy. Results showed that some of the studied inks possessed IRL due to the presence of crystal violet and some other triaryl methane dyes. Nevertheless, some ink samples did not possess IRL, despite the presence of the dyes responsible for IRL in their matrix. Interestingly, the inclusion of phthalocyanine dye in those non-luminescent ink matrices resulted in luminescence quenching, mainly due to the overlap between the absorption of phthalocyanine dye and the luminescence of the triarylmethane dyes. The IRL behavior of the ink sample under the first illumination wave band (400–485 nm) exerted control over the IRL activity across subsequent illumination wavebands, and the most effective differentiation was achieved by utilizing the first and second preset filters in VSC. The results revealed the luminescent components present in studied inks and unraveled their distinct luminescence behavior present within the ink matrix. The combination of optical spectroscopy and chromatography techniques could provide a distinctive tool to reveal the luminescence and quenching behaviors of ink dyes for the successful forensic discrimination of several BP writing pens.

Modern electrical power systems now require the spread of microgrids (MG), where they would be operating in either islanded mode or grid-connected mode. An inherent mismatch between loads and sources is introduced by changeable high renewable share in an islanded MG system with stochastic load demands. The system frequency is directly impacted by this mismatch, which can be alleviated by incorporating cutting-edge energy storage technologies and FACTS tools. The investigated islanded MG system components are wind farm, solar PV, Electric vehicles (EVs), loads, DSTATCOM, and diesel power generator. An aggregated EVs model is connected to the MG during uncertain periods of the generation of renewable energy (PV and wind) to support the performance of MGs. The ability to support ancillary services from the EVs is checked. DSTATCOM is used to provide voltage stability for the MG during congestion situations. The MG is studied in three scenarios: the first scenario MG without EVs and DSTATCOM, the second scenario MG without DSTATCOM, and the third scenario MG with all components. These scenarios are addressed to show the role of EVs and DSTATCOM, and the results in the third scenario are the best. The system voltage and frequency profile is the best in the last scenario and is entirely satisfactory and under the range of the IEEE standard. The obtained results show that both EVs and DSTATCOM are important units for improving the stability of modern power grids. The Matlab/Simulink program is considered for checking and validating the dynamic performance of the proposed configuration.

There is a great deficiency in the collection and disposal of solid waste, with a considerable amount disposed of in dumps instead of in landfills. In this sense, the objective of this research is to propose a solid waste mitigation plan through recovery in the District of Santa Rosa, Ayacucho. For this, a solid waste characterization plan was executed in eight days, and through ANOVA it was shown that there is a significant difference in means between business pairs except between a bakery and a hotel. Through clustering, zones A and B are highly correlated, reflecting that the amount of organic waste was greater than inorganic waste. In the organic waste valorization plan, the results through ANOVA indicate a significant difference for monthly and daily averages, and the clustering shows the different behavior of each month, drawing attention to August, concluding that the valorization pilot plan is viable due to the contribution of a large amount of organic solid waste to the valorization plant.

Field operations report that at least half of the fracturing fluid used in shale reservoirs is trapped. These trapped fluids can trigger various geochemical interactions. However, the impact of these interactions on well performance is still elusive. We modeled a hydraulic fracture stage where we simulated the initial conditions by injecting the fracturing fluid and shutting the well to allow the fluids to soak into the formation. Our results suggest a positive correlation between the dissolution and precipitation rates and the carbonate content of the rock. In addition, we observed that gas and load recovery are overestimated when geochemical interactions are overlooked. We also observed promising results for sea water as a good alternative fracturing fluid. Moreover, we observed better performance for cases with lower-saline connate water. The reactions of carbonates outweigh the reactions of clays in most cases. Sensitivity analysis suggests that the concentration of SO4, K and Na ions in the fracturing fluid, and the illite and calcite mineral content, along with the reservoir temperature, are the key factors affecting well performance. In conclusion, geochemical interactions should be considered for properly modeling the fate of the fracturing fluids and their impact on well performance.

This paper presents a system for medical image diagnosis that uses transfer learning (TL) and feature selection techniques. The main aim of TL on pre-trained models such as MobileNetV3 is to extract features from raw images. Here, a novel feature selection optimization algorithm called the Artificial Hummingbird Algorithm based on Aquila Optimization (AHA-AO) is proposed. The AHA-AO is used to select only the most relevant features and ensure the improvement of the overall model classification. Our methodology was evaluated using four datasets, namely, ISIC-2016, PH2, Chest-XRay, and Blood-Cell. We compared the proposed feature selection algorithm with five of the most popular feature selection optimization algorithms. We obtained an accuracy of 87.30% for the ISIC-2016 dataset, 97.50% for the PH2 dataset, 86.90% for the Chest-XRay dataset, and 88.60% for the Blood-cell dataset. The AHA-AO outperformed the other optimization techniques. Moreover, the developed AHA-AO was faster than the other feature selection models during the process of determining the relevant features. The proposed feature selection algorithm successfully improved the performance and the speed of the overall deep learning models.

Urban ecosystems interact with surroundings via land cover changes and their subsequent impact on surface temperature. In emerging countries, large urban agglomerations often form around cities, and only a few studies have evaluated their impact. This study carries out the first ever large-scale assessment of urban heat island (UHI) and reflects on its mitigation in Morocco. The analysis reveals a well-defined UHI in urban areas built within vegetated lands and an urban heat sink (UHS) in urban areas built within arid regions. Both UHI and UHS amplitudes are higher during day than nighttime, emphasizing vegetation physiological activity. This study shows a monotonic increase in UHI amplitude with urban area size. However, unlike previous studies, this analysis shows that, as urban areas built in desert-like environments grow in size, the UHS gradually decreases to ultimately turn into an UHI. On average, cities built within vegetation are warmer than the rural fringe by 1.51 °C during daytime. This suggests that daytime urban heating may exacerbate the potential climate warming. These results also suggest that adapted trees constitute a natural cooling mechanism and should be part of urban heating mitigation in Morocco.

The present study aimed to assess the radiological hazards associated with applying the investigated granite in the building materials and the infrastructures applications. The investigated granites are classified into four categories: El-Urf, barren, colourful and opaque. El Urf monzogranite intrudes metagabbro diorite complex with sharp contacts. Based on the activity concentrations, the environmental parameters such as absorbed dose rate (Dair), annual effective dose (AED), radium equivalent activity (Raeq), external (Hex) and internal (Hin) hazard indices were measured. The mineralized pegmatite is located in the southwestern foothill of the Gabal El Urf younger granite. It displays well-defined zonation of three zones: outer, middle and inner zones represented by potash feldspar, quartz and mica, respectively. The isorad map showed that El Urf monzogranite is barren (Up to 100 cps) surrounding an excavation of the studied pegmatite that exhibits moderate colorful mineralization (phase-I = 500–1500 cps) and anomalous opaque mineralization (phase-II = 1500–3500 cps) pegmatites. The obtained results of radionuclides activity concentrations illustrated that the Opaque granites have the highest values of 238U (561 ± 127 Bq kg−1), 232Th (4289 ± 891 Bq kg−1) and 40K (3002 ± 446 Bq kg−1) in the granites, which are higher than the recommended worldwide average. Many of the radiological hazard parameters were lesser than the international limits in the younger granites and barren pegmatites. All of these parameters were higher in the colorful and opaque mineralized pegmatites. The high activity and the elevated radiological hazard parameters in the mineralized pegmatites are revised to the presence of radioactive and radioelements bearing minerals, such as thorite, meta-autunite, kasolite, phurcalite, columbite, fergusonite, Xenotime and fluorapatite. Other instances of mineralization were also recorded as cassiterite, atacamite, galena, pyrite and iron oxide minerals. Thus, the granites with high radioactivity concentration cannot be applied in the different applications of building materials and ornamental stones.

The study of the dynamics or the progress of science has been widely explored with descriptive and statistical analyses. Also this study has attracted several computational approaches that are labelled together as the Computational History of Science, especially with the rise of data science and the development of increasingly powerful computers. Among these approaches, some works have studied dynamism in scientific literature by employing text analysis techniques that rely on topic models to study the dynamics of research topics. Unlike topic models that do not delve deeper into the content of scientific publications, for the first time, this paper uses temporal word embeddings to automatically track the dynamics of scientific keywords over time. To this end, we propose Vec2Dynamics, a neural-based computational history approach that reports stability of k-nearest neighbors of scientific keywords over time; the stability indicates whether the keywords are taking new neighborhood due to evolution of scientific literature. To evaluate how Vec2Dynamics models such relationships in the domain of Machine Learning (ML), we constructed scientific corpora from the papers published in the Neural Information Processing Systems (NIPS; actually abbreviated NeurIPS) conference between 1987 and 2016. The descriptive analysis that we performed in this paper verify the efficacy of our proposed approach. In fact, we found a generally strong consistency between the obtained results and the Machine Learning timeline.

Water Distribution System (WDS) threats have significantly grown following the Maroochy shire incident, as evidenced by proofed attacks on water premises. As a result, in addition to traditional solutions (e.g., data encryption and authentication), attack detection is being proposed in WDS to reduce disruption cases. The attack detection system must meet two critical requirements: high accuracy and near real-time detection. This drives us to propose a two-stage detection system that uses self-supervised and unsupervised algorithms to detect Cyber-Physical (CP) attacks. Stage 1 uses heuristic adaptive self-supervised algorithms to achieve near real-time decision-making and detection sensitivity of 66% utilizing Boss. Stage 2 attempts to validate the detection of attacks using an unsupervised algorithm to maintain a detection accuracy of 94% utilizing Isolation Forest. Both stages are examined against time granularity and are empirically analyzed against a variety of performance evaluation indicators. Our findings demonstrate that the algorithms in stage 1 are less favored than those in the literature, but their existence enables near real-time decision-making and detection reliability. In stage 2, the isolation Forest algorithm, in contrast, gives excellent accuracy. As a result, both stages can collaborate to maximize accuracy in a near real-time attack detection system.

Cottonseed is one of the important by-products of the cotton crop. Researchers claim that cottonseed with less than 0.45% of gossypol is quite good for human consumption and animal feeding because it is a rich source of protein, edible oil, and energy. Total and free gossypols are the influencing parameters that reduce the edible nature of the cottonseed. In the present work, multiple quadratic regression models have been prepared to predict the reduction in the free and total gossypol percent. This response surface method (RSM)-based approach was applied to investigate the combined effect between input parameters such as acetone level, time of extraction, liquid-to-solid ratio (LSR), and the number of extraction cycles, whereas output responses are free and total gossypol reduction percentage. Analysis of Variance (ANOVA) has been performed to determine the highly significant parameter. The optimum combination of input parameters was determined using the RSM-based desirability approach, and confirmatory experiments were performed to validate the combination. Results revealed that the number of extraction cycles and liquid-to-solid ratio significantly affects the reduction of free and total gossypol levels. The values of r-square were found above 0.9, which indicates that the developed models are suitable and reliable for predicting free and total gossypol reduction percentage.

Four different phosphate glass formulations (F0, F1, F2, and F3) were developed according o wheat nutrient requirements to be used as controlled-release fertilizers. These glasses contain macro-elements (P2O5-K2O-CaO-MgO), with the addition of microelements (Fe-Mn-Zn-B-Cu-Mo) in each formulation. The effects of these elements’ addition on thermal properties, glass structure, and dissolution behaviors were investigated. Results showed that these glasses are composed essentially of metaphosphate chains and that the addition of micronutrients could change the chemical durability of phosphate glasses. A greenhouse experiment was performed using wheat (Triticum durum L.) to evaluate the efficiency of the four glasses, with or without application of chemical nitrogen (N) (N + VF and VF, respectively). The different formulas were tested using two rates of 0.3 and 1 g per plant. In addition to the vitreous fertilizer formulations, two other treatments were applied: control treatment with no amendment and Nitrogen-Phosphorus-Potassium treatment with the application of the conventional fertilizers on the base of optimal rates. After four months of cultivation, vitreous fertilizers application significantly improved growth (7% to 88%), photosynthetic (8% to 49%) parameters, and yield (29% to 33%) compared to NPK treatment and to the control. It has been found that formulas F1, F2, and F3 may constitute a potential alternative to conventional fertilization due to their positive impact on wheat production and can be used in practice as an environmentally controlled-release fertilizer.

This research presents Gradient Boosted Tree High Importance Path Snippets (gbt-HIPS), a novel, heuristic method for explaining gradient boosted tree (GBT) classification models by extracting a single classification rule (CR) from the ensemble of decision trees that make up the GBT model. This CR contains the most statistically important boundary values of the input space as antecedent terms. The CR represents a hyper-rectangle of the input space inside which the GBT model is, very reliably, classifying all instances with the same class label as the explanandum instance. In a benchmark test using nine data sets and five competing state-of-the-art methods, gbt-HIPS offered the best trade-off between coverage (0.16–0.75) and precision (0.85–0.98). Unlike competing methods, gbt-HIPS is also demonstrably guarded against under- and over-fitting. A further distinguishing feature of our method is that, unlike much prior work, our explanations also provide counterfactual detail in accordance with widely accepted recommendations for what makes a good explanation.

With only less than 10% recovery, the primary production of hydrocarbon from shale reservoirs has redefined the energy equation in the world. Similar to conventional reservoirs, Enhanced Oil Recovery (EOR) techniques could be devised to enhance the current recovery factors. However, shale reservoirs possess unique characteristics that significantly affect the fluid properties. Therefore, we are adopting a molecular simulation approach that is well-suited to account for these effects to evaluate the performance of three different gases, methane, carbon dioxide and nitrogen, to recover the hydrocarbons from rough pore surfaces. Our hydrocarbon systems consists of either a single component (decane) or more than one component (decane and pentane). We simulated cases where concurrent and countercurrent displacement is studied. For concurrent displacement (injected fluids displace hydrocarbons towards the production region), we found that nitrogen and methane yielded similar recovery; however nitrogen exhibited a faster breakthrough. On the other hand, carbon dioxide was more effective in extracting the hydrocarbons when sufficient pressure was maintained. For countercurrent displacement (gases are injected and hydrocarbons are produced from the same direction), methane was found to be more effective, followed by carbon dioxide and nitrogen. In all cases, confinement reduced the recovery factor of all gases. This work provides insights to devise strategies to improve the current recovery factors observed in shale reservoirs.

Breast cancer is one of the most prevalent cancer types with a high mortality rate in women worldwide. This devastating cancer still represents a worldwide public health concern in terms of high morbidity and mortality rates. The diagnosis of breast abnormalities is challenging due to different types of tissues and textural variations in intensity. Hence, developing an accurate computer-aided system (CAD) is very important to distinguish normal from abnormal tissues and define the abnormal tissues as benign or malignant. The present study aims to enhance the accuracy of CAD systems and to reduce its computational complexity. This paper proposes a method for extracting a set of statistical features based on curvelet and wavelet sub-bands. Then the binary grey wolf optimizer (BGWO) is used as a feature selection technique aiming to choose the best set of features giving high performance. Using public dataset, Digital Database for Screening Mammography (DDSM), different experiments have been performed with and without using the BGWO algorithm. The random forest classifier with 10-fold cross-validation is used to achieve the classification task to evaluate the selected set of features’ capability. The obtained results showed that when the BGWO algorithm is used as a feature selection technique, only 30.7% of the total features can be used to detect whether a mammogram image is normal or abnormal with ROC area reaching 1.0 when the fusion of both curvelet and wavelet features were used. In addition, in case of diagnosing the mammogram images as benign or malignant, the results showed that using BGWO algorithm as a feature selection technique, only 38.5% of the total features can be used to do so with high ROC area result at 0.871.

To make healthcare available and easily accessible, the Internet of Things (IoT), which paved the way to the construction of smart cities, marked the birth of many smart applications in numerous areas, including healthcare. As a result, smart healthcare applications have been and are being developed to provide, using mobile and electronic technology, higher diagnosis quality of the diseases, better treatment of the patients, and improved quality of lives. Since smart healthcare applications that are mainly concerned with the prediction of healthcare data (like diseases for example) rely on predictive healthcare data analytics, it is imperative for such predictive healthcare data analytics to be as accurate as possible. In this paper, we will exploit supervised machine learning methods in classification and regression to improve the performance of the traditional Random Forest on healthcare datasets, both in terms of accuracy and classification/regression speed, in order to produce an effective and efficient smart healthcare application, which we have termed eGAP. eGAP uses the evolutionary game theoretic approach replicator dynamics to evolve a Random Forest ensemble. Trees of high resemblance in an initial Random Forest are clustered, and then clusters grow and shrink by adding and removing trees using replicator dynamics, according to the predictive accuracy of each subforest represented by a cluster of trees. All clusters have an initial number of trees that is equal to the number of trees in the smallest cluster. Cluster growth is performed using trees that are not initially sampled. The speed and accuracy of the proposed method have been demonstrated by an experimental study on 10 classification and 10 regression medical datasets.

The compression of ammonium azide (AA) has been considered to be a promising route for producing high energy-density polynitrogen compounds. So far though, there is no experimental evidence that pure AA can be transformed into polynitrogen materials under high pressure at room temperature. We report here on high pressure (P) and temperature (T) experiments on AA embedded in N2 and on pure AA in the range 0–30 GPa, 300–700 K. The decomposition of AA into N2 and NH3 was observed in liquid N2 around 15 GPa–700 K. For pressures above 20 GPa, our results show that AA in N2 transforms into a new crystalline compound and solid ammonia when heated above 620 K. This compound is stable at room temperature and on decompression down to at least 7.0 GPa. Pure AA also transforms into a new compound at similar P–T conditions, but the product is different. The newly observed phases are studied by Raman spectroscopy and X-ray diffraction and compared to nitrogen and hydronitrogen compounds that have been predicted in the literature. While there is no exact match with any of them, similar vibrational features are found between the product that was obtained in AA + N2 with a polymeric compound of N9H formula.

Phosphate glasses have potentially interesting properties that can be used in various applications. Recently, different studies are focusing on their dissolution behaviours that can be modified to suit some environmental applications, such as controlled-release fertilisers. In this work, magnesium had been suggested to improve the glass durability of 3P2O5-2K2O-(1 − x)CaO-xMgO glasses (0 ≤ x ≤ 1). Indeed, its effect on glass structure, thermal properties and most important dissolution behaviours were studied, in order to evaluate their suitability of being used as controlled-release fertilisers. Various compositions in which calcium was partially replaced by magnesium were prepared by melting at 800 °C. The samples were characterised by differential scanning calorimetry, density measurements, X-Ray diffraction, FTIR spectroscopy and Raman spectroscopy. The dissolution behaviours were investigated using inductively coupled plasma optical emission spectrometry ICP-OES, pH measurements and SEM. Substitution of calcium by magnesium reduced the glass density, owing to the lower atomic weight of magnesium compared to calcium, and caused an increase in glass transition and crystallisation temperatures. Magnesium substitution significantly improved the chemical durability of the glasses due to more covalent Mg–O bond than the Ca–O bond. This study demonstrated that 3P2O5-2K2O-0.3CaO-0.7MgO (x = 0.7) had a dissolution profile adequate to the criteria of controlled-release fertilisers and could be used to nourish the plants with phosphorus, potassium, calcium and magnesium.

Machine learning has traditionally been solely performed on servers and high-performance machines. However, advances in chip technology have given us miniature libraries that fit in our pockets and mobile processors have vastly increased in capability narrowing the vast gap between the simple processors embedded in such things and their more complex cousins in personal computers. Thus, with the current advancement in these devices, in terms of processing power, energy storage and memory capacity, the opportunity has arisen to extract great value in having on-device machine learning for Internet of Things (IoT) devices. Implementing machine learning inference on edge devices has huge potential and is still in its early stages. However, it is already more powerful than most realise. In this paper, a step forward has been taken to understand the feasibility of running machine learning algorithms, both training and inference, on a Raspberry Pi, an embedded version of the Android operating system designed for IoT device development. Three different algorithms: Random Forests, Support Vector Machine (SVM) and Multi-Layer Perceptron, respectively, have been tested using ten diverse data sets on the Raspberry Pi to profile their performance in terms of speed (training and inference), accuracy, and power consumption. As a result of the conducted tests, the SVM algorithm proved to be slightly faster in inference and more efficient in power consumption, but the Random Forest algorithm exhibited the highest accuracy. In addition to the performance results, we will discuss their usability scenarios and the idea of implementing more complex and taxing algorithms such as Deep Learning on these small devices in more details.

We are witnessing the emergence of new big data processing architectures due to the convergence of the Internet of Things (IoTs), edge computing and cloud computing. Existing big data processing architectures are underpinned by the transfer of raw data streams to the cloud computing environment for processing and analysis. This operation is expensive and fails to meet the real-time processing needs of IoT applications. In this article, we present and evaluate a novel big data processing architecture named RedEdge (i.e., data reduction on the edge) that incorporates mechanism to facilitate the processing of big data streams near the source of the data. The RedEdge model leverages mobile IoT-termed mobile edge devices as primary data processing platforms. However, in the case of the unavailability of computational and battery power resources, it offloads data streams in nearer mobile edge devices or to the cloud. We evaluate the RedEdge architecture and the related mechanism within a real-world experiment setting involving 12 mobile users. The experimental evaluation reveals that the RedEdge model has the capability to reduce big data stream by up to 92.86% without compromising energy and memory consumption on mobile edge devices.

A novel series of the title compound 4-(5-arylazo-2-hydroxystyryl)-1-methylpyridinium iodide 6 has been synthesized via condensation reactions of the arylazosalicylaldehyde derivatives 4a–i with 1-methyl-picolinium iodide 5. The structures of the new arylazo compounds were characterized by 1H NMR, IR, mass spectroscopy, as well as spectral and elemental analyses. The electronic absorption spectra of arylazomerocyanine compounds 6 were measured in different buffer solutions and solvents. The pK′s and pK*′s in both the ground and excited states, respectively, were determined for the series and their correlations with the Hammett equation were examined. The results indicated that the title arylazomerocyanine dyes 6 exist in the azo form 6A in both ground and excited states. The substituent and solvent effects (solvatochromism) of the title compound arylazomerocyanine dyes were determined using the Kamlet-Taft equation and subsequently discussed.

Cell‐ghosts representing empty cells of the archaeon Sulfolobus acidocaldarius, consisting only of their highly ordered and unusually stable outermost proteinaceous surface layer (S‐layer), were used as templates for Au nanoparticles fabrication. The properties of these archaeal Au nanoparticles differ significantly from those produced earlier by us onto bacterial S‐layer sheets. The archaeal Au nanoparticles, with a size of about 2.5 nm, consist exclusively of metallic Au(0), while those produced on the bacterial S‐layer had a size of about 4 nm and represented a mixture of Au(0) and Au(III) in the ratio of 40 to 60 %. The most impressive feature of the archaeal Au nanoparticles is that they are strongly paramagnetic, in contrast to the bacterial ones and also to bulk gold. SQUID magnetometry and XMCD measurements demonstrated that the archaeal Au nanoparticles possess a rather large magnetic moment of about 0.1 µB/atom. HR‐ TEM‐EDX analysis revealed that the archaeal Au nanoparticles are linked to the sulfur atoms of the thiol groups of the amino acid cysteine, characteristic only for archaeal S‐layers. This is the first study demonstrating the formation of such unusually strong magnetic Au nanoparticles on a non‐modified archaeal S‐layer.