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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.

Two new fusarochromanone derivatives, deacetylfusarochromene (1) and deacetamidofusarochrom-2′,3-diene (2), along with the previously reported metabolites fusarochromanone TDP-2 (3), fusarochromene (4), 2,2-dimethyl-5-amino-6-(2′E-ene-4′-hydroxylbutyryl)-4-chromone (5), fusarochromanone (6), (−)-chrysogine (7), and equisetin (8), were isolated from the marine fungus Fusarium equiseti UBOCC-A-117302. The structures of the compounds were determined by extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses, as well as specific rotation. Among them, 2 and 5 showed inhibition of three protein kinases with IC50 values ranging from 1.42 to 25.48 μM. Cytotoxicity and antimicrobial activity of all isolated compounds were also evaluated. Six fusarochromanone derivatives (1–6) exhibited diverse activities against three cell lines, RPE-1, HCT-116, and U2OS (IC50 values ranging from 0.058 to 84.380 μM). Equisetin (8) showed bactericidal activities against Bacillus cereus and Listeria monocytogenes (MBC values of 7.8 and 31.25 µM, respectively), and bacteriostatic activity against Enterococcus faecalis (MIC value of 31.25 µM). Compounds 2 and 4 showed bacteriostatic activities against Listeria monocytogenes (MIC of 125 µM).

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.

Prenatal alcohol exposure (PAE) refers to fetal exposure to alcohol during pregnancy through placental barrier transfer from maternal blood. The postnatal outcomes of PAE differ among exposed individuals and range from overt (serious) alcohol-related behavioral and neurophysiological impairments to covert (silenced) symptoms. The aims of the present investigation were to assess the postnatal neurobehavioral disturbances, particularly, motor coordination and sensory-motor function in mice with PAE. Female mice with positive vaginal plugs were divided into three groups: group 1: Et + Pyr: received two i.p injections of ethanol (1 g/kg) followed by pyrazole (100 mg/kg). Group 2: Pyr: received an i.p injection of pyrazole (100 mg/kg). Group 3: C: of saline controls received, in equal volume, saline solution (NaCl 0.9%). After birth, mice pups were weighed and subjected to behavioral tests for motor function screening using the motor ambulation test, cliff aversion, surface righting, and negative geotaxis, while at the adult stage, mice were subjected to the open field, rotarod, parallel bars, and static rods tests. Our data show an obvious decrement of body weight from the first post-natal day (P1) and continues over the adult stage. This was accompanied by an obvious impaired sensory-motor function which was maintained even at the adult stage with alteration of the locomotor and coordination abilities. The current data demonstrate the powerful neurotoxic effect of prenatal ethanol exposure on the sensory-motor and coordination functions, leading to suppose possible structural and/or functional neuronal disturbances, particularly the locomotor network.

Extreme precipitation events play a crucial role in shaping the vulnerability of regions like Algeria to the impacts of climate change. To delve deeper into this critical aspect, this study investigates the changing patterns of extreme precipitation across five sub-regions of Algeria using data from 33 model simulations provided by the NASA Earth Exchange Global Daily Downscaled Climate Projections (NEX-GDDP-CMIP6). Our analysis reveals a projected decline in annual precipitation for four of these regions, contrasting with an expected increase in desert areas where annual precipitation levels remain low, typically not exceeding 120 mm. Furthermore, key precipitation indices such as maximum 1-day precipitation (Rx1day) and extremely wet-day precipitation (R99p) consistently show upward trends across all zones, under both SSP245 and SSP585 scenarios. However, the number of heavy precipitation days (R20mm) demonstrates varied trends among zones, exhibiting stable fluctuations. These findings provide valuable foresight into future precipitation patterns, offering essential insights for policymakers and stakeholders. By anticipating these changes, adaptive strategies can be devised to mitigate potential climate change impacts on crucial sectors such as agriculture, flooding, water resources, and drought.

The Verongida order comprises several sponge families, such as Aplysinellidae, Aplysinidae, Ianthellidae, and Pseudoceratinidae, reported for producing bromotyrosine-derived compounds. First identified in 1913, bromotyrosine derivatives have since captivated interest notably for their antitumor and antimicrobial properties. To date, over 360 bromotyrosine derivatives have been reported. Our review focuses specifically on bromotyrosine derivatives newly reported from 2004 to 2023, by summarizing current knowledge about their chemical diversity and their biological activities.

Development of biobased aliphatic polyesters with better mechanical (tensile) properties in film has attracted considerable attention. This report presents the synthesis of soluble network biobased aliphatic polyesters by acyclic diene metathesis (ADMET) polymerization of bis(undec-10-enyl)isosorbide diester [M1, dianhydro-D-glucityl bis(undec-10-enoate)] in the presence of a tri-arm crosslinker [CL, glycerol tris(undec-10-enoate)] using a ruthenium–carbene catalyst, and subsequent olefin hydrogenation using RhCl(PPh3)3. The resultant polymers, after hydrogenation (expressed as HCP1) and prepared in the presence of 1.0 mol% CL, showed better tensile properties than the linear polymer (HP1) with similar molecular weight [tensile strength (elongation at break): 20.8 MPa (282%) in HP1 vs. 35.4 MPa (572%) in HCP1]. It turned out that the polymer films prepared by the addition of CL during the polymerization (expressed as a 2-step approach) showed better tensile properties. The resultant polymer film also shows better tensile properties than the conventional polyolefins such as linear high density polyethylene, polypropylene, and low density polyethylene.

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.

A lot of oil is leaked into aquatic environments, significantly impacting fish health and, consequently, human populations. This study aimed to introduce an L-phenylalanine-based low-molecular-weight gelator (expressed as Z-Phe-C18) as a smart remediation tool for oil spills. Several groups of Nile tilapia were allocated in aquaria exposed to different doses of crude engine oil with/without the organogelator for 4 weeks. The results revealed a significant increase in biochemical oxygen demand, chemical oxygen demand, electrical conductivity, and total dissolved solids in water samples of fish aquaria exposed to oil pollution. The antioxidant activity levels, micronucleus formation, and expression patterns of stress-related genes were significantly higher in the livers of fish exposed to crude oil than in those of control fish. On the contrary, fish groups exposed to oil pollution and treated with the organogelator indicated that antioxidant enzymes, micronucleus incidence, and gene expression alteration of stress-related genes declined compared with those exposed to oil pollution only. The results suggest that oil pollution can induce oxidative stress via the enhancement of oxygen free radical formation. On the contrary, oil removal by the organogelator decreases oxidative stress and consequently strengthens fish immunity. So, we can conclude that organogelator treatment is promoting oxidative resistance development by increasing the activities of antioxidant enzymes, which are important in protection against oil pollution and preventing peroxidation of fish tissues. Promisingly, the organogelator could be used as a tool for the remediation of oil pollution in aquatic environments.

Infections with Mycoplasma gallisepticum (MG) in poultry are associated with a wide range of disease conditions, including those affecting the respiratory and reproductive systems. The purpose of this study was to endorse the more sensitive diagnostic scheme for MG infection and identify the best molecular marker for MG phylogenetic analysis using six housekeeping genes: mgc2, mraW, atpG, ugpA, DUF31196, and lgT. For these purposes, 55 poultry flocks of different species were screened using either qRT-PCR or PCR techniques analogous to conventional culturing from non-cultured and cultured swabs on PPLO broth. The rate of MG positivity was the highest when using qRT-PCR from cultured broth (89.0%) and the lowest when using conventional culturing (34.5%). Compared to qRT-PCR from broth, statistical analysis using the Roc curve in MedCalc statistical software showed that the PCR schemes (qRT-PCR from swabs and PCR from swabs and broth) performed better than conventional culturing in terms of sensitivity, accuracy, and area under the curve (AUC), suggesting that they may be more reliable schemes. Further support was added by Cohen’s kappa test, showing moderate agreement between the molecular approaches. Among the six screened genes, mgc2 and mraW had the highest detection rates (69% and 65.4%, respectively). The comparative phylogenetic analysis revealed that mgc2 or atpG gene sequences distinguished MG isolates into different clades with high discriminatory power.

Understanding and long-term assessment of the efficacy of soil and water conservation (SWC) techniques is essential for sustainable watershed management. There have been few studies measuring the long-term impact of SWC on soil erosion at the catchment scale due to a lack of historical data. This research aims to measure and analyze the impact of SWC interventions on sediment yield using the Soil and Water Assessment Tool (SWAT) in the Wadi Rmel watershed between 2000 and 2020. The study approach has simultaneously three main aspects: (i) long-term and temporally dynamic, (ii) large-scale landscape distribution, and (iii) empirical evidence of impacts induced by terracing measures. Historical data on SWC in 2000, 2005, 2010, 2015, and 2020 were used for running the model. The monthly calibration (2001–2014) using the SUFI2 algorithm was implemented with 22 input parameters and displayed a good model performance, with a Nash–Sutcliffe efficiency (NSE) of 0.61 for daily runoff and a coefficient of determination (R2) of 0.66. A satisfactory result was obtained for the monthly validation process (2015–2020) with NSE value equal to 0.83 and R2 value of 0.85. The result showed the increase in terracing areas led to a cumulative decrease in watershed sediment yield in long-term, i.e., the reduced erosion per one hectare of the expanded terracing area after 10–20 years were about 9–33 times higher than those in the first 5 years. This finding suggests that maximal benefits of SWC should be expected in the long-term, i.e., beyond a decade. Additionally, the study revealed variations in sediment yield contribution among sub-basins, with the southwestern region being the degradation hotspot areas (having erosion exceeding tolerable thresholds) needing prioritization for erosion controls. These findings enable stakeholders to plan effective management in semi-arid wheat-based agricultural areas with scarce data.

Photovoltaic (PV) systems are crucial to the production of electricity for a newly established community in Egypt, especially in grid-tied systems. Power quality (PQ) issues appear as a result of PV connection with the power grid (PG). PQ problems cause the PG to experience faults and harmonics, which affect consumers. A series compensator dynamic voltage restorer (DVR) is the most affordable option for resolving the abovementioned PQ problems. To address PQ difficulties, this paper describes a grid-tied PV combined with a DVR that uses a rotating dq reference frame (dqRF) controller. The main goal of this study is to apply and construct an effective PI controller for a DVR to mitigate PQ problems. The artificial rabbits optimization (ARO) is used to obtain the best tune of the PI controller. The obtained results are compared with five optimization techniques (L-SHADE, CMAES, WOA, PSO, and GWO) to show its impact and effectiveness. Additionally, Lyapunov’s function is used to analyze and evaluate the proposed controller stability. Also, a mathematical analysis of the investigated PV, boost converter, and rotating dqRF control is performed. Two fault test scenarios are examined to confirm the efficacy of the suggested control approach. The parameters’ (voltage, current, and power) waveforms for the suggested system are improved, and the system is kept running continuously under fault periods, which improves the performance of the system. Moreover, the findings demonstrate that the presented design successfully keeps the voltage at the required level with low THD% values at the load side according to the IEEE standards and displays a clear enhancement in voltage waveforms. The MATLAB/SIMULINK software is used to confirm the proposed system’s performance.

Geoelectrical resistivity measurements were conducted in five locations within the eastern portion of the Dahomey basin for the purpose of subsurface evaluation and detecting saturated zones. The locations are Covenant University (L1), Bells University (L2), Oju-Ore-Ilogbo Road (L3), Obasanjo-Ijagba Road (L4), and Iyana Iyesi (L5). The study was carried out to avert the common challenges of drilling low-yield groundwater boreholes in the area. A total of 30 Vertical Electrical Soundings (VES) and five two-dimensional Electrical Resistivity Tomography (ERT) data sets have been acquired along the study areas. The geoelectrical resistivity results were integrated with the borehole logs to generate the spatial distribution of the subsurface lithologies in the area. The delineated subsurface lithologies include the topsoil (lateritic clay), clayey sand, sandy clay, fine silty sand, coarse sand, and shale/clay units. The fine silty sand and coarse sand units were identified as the two main aquifer units within the area. The depths to the upper aquifer unit in the area include 31.7–96.7 m, 38.5–94.0 m, 30.7–57.5 m, 39.1–63.4 m, and 46.9–57.5 m for locations L1, L2, L3, L4, and L5, respectively. At the same time, the depths to the lower aquifer unit in the area include 43.4–112.7 m, 52.2–108.0 m, 44.2–72.5 m, 53.7–78.5 m, and 63.5–72.9 m for locations L1, L2, L3, L4, and L5, respectively. The estimated hydraulic parameters for both aquifers show they are highly productive with mean porosity, mean hydraulic conductivity, and mean transmissivity of 20–22%, 12.4–17.0 × 10−2 m/s, 1.56–2.18 m2/s for the upper aquifer, and 48–50%, 371–478 × 10−2 m/s, 50.00–62.14 m2/s for the lower aquifer. By focusing on these aquifer systems during exploration, sustainable groundwater resources can be secured, providing relief to homeowners within the study area who might otherwise face the frustration of drilling unproductive and low-yield boreholes. However, it is crucial to consider the presence of sub-vertical faults in the study area, as these faults can significantly impact groundwater development and management. These sub-vertical structural faults may lead to changes in the permeability, hydraulic conductivity, and transmissivity of the delineated aquifers, affecting their productivity across the divide and ultimately influencing the overall water availability in the area. Careful consideration of these geological factors is essential for effective aquifer management and sustainable groundwater utilisation.

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.

Honey bee products comprise various compounds, including honey, propolis, royal jelly, bee pollen, bee wax and bee venom, which have long been recognized for their pharmacological and health-promoting benefits. Scientists have discovered that periodontal disorders stem from dental biofilm, an inflammatory response to bacterial overgrowth produced by dysbiosis in the oral microbiome. The bee products have been investigated for their role in prevention of oral diseases, which are attributed to a myriad of biologically active compounds including flavonoids (pinocembrin, catechin, caffeic acid phenethyl ester (CAPE) and galangin), phenolic acids (hydroxybenzoic acid, hydroxycinnamic acid, p-coumaric, ellagic, caffeic and ferulic acids) and terpenoids. This review aims to update the current understanding of role of selected bee products, namely, honey, propolis and royal jelly, in preventing oral diseases as well as their potential biological activities and mechanism of action in relation to oral health have been discussed. Furthermore, the safety of incorporation of bee products is also critically discussed. To summarize, bee products could potentially serve as a therapy option for people suffering from a variety of oral disorders.

Supervisory control and data acquisition (SCADA) attacks have increased due to the digital transformation of many industrial control systems (ICS). Operational technology (OT) operators should use the defense-in-depth concept to secure their operations from cyber attacks and reduce the surface that can be attacked. Layers of security, such as firewalls, endpoint solutions, honeypots, etc., should be used to secure traditional IT systems. The three main goals of IT cybersecurity are confidentiality, integrity, and availability (CIA), but these three goals have different levels of importance in the operational technology (OT) industry. Availability comes before confidentiality and integrity because of the criticality of business in OT. One of the layers of security in both IT and OT is honeypots. SCADA honeypots are used as a layer of security to mitigate attacks, known attackers’ techniques, and network and system weaknesses that attackers may use, and to mitigate these vulnerabilities. In this paper, we use SCADA honeypots for early detection of potential malicious tampering within a SCADA device network, and to determine threats against ICS/SCADA networks. An analysis of SCADA honeypots gives us the ability to know which protocols are most commonly attacked, and attackers’ behaviors, locations, and goals. We use an ICS/SCADA honeypot called Conpot, which simulates real ICS/SCADA systems with some ICS protocols and ICS/SCADA PLCs.

Planners face many obstacles during the planning phase of any new project, especially project scheduling due to the numerous details and complexity of each uniquely designed project; previous experience in similar projects and traditional scheduling methods are not sufficient. The 4D-BIM schedule is the best solution, as it can be integrated with other modern technologies such as UAS, which improves project scheduling by allowing the project team to access project plans, details, and time-related information to simulate construction sequences. Despite the benefits of using 4D-BIM, planners prefer traditional planning and scheduling methods because of the barriers to using modern technologies. This research proposes a structural equation model focusing on estimating the weights of BIM barriers in offshore construction projects, including: the investigation of barriers keeping the construction industry standing at 3D-BIM; the relationships between them; and the evaluation of the probability and impact of each, using fuzzy theory. To validate the proposed model, a case study of an offshore project was conducted. The most important latent variables were awareness, risk, demand, and management, while the most prominent observed variables were the uncertainty of the return on investment and the lack of experienced users. This research guides project managers on actions that can be taken for each key variable to enhance and develop the use of 4D-BIM in project scheduling.

In recent modern power systems, the number of renewable energy systems (RESs) and nonlinear loads have become more prevalent. When these systems are connected to the electricity grid, they may face new difficulties and issues such as harmonics and non-standard voltage. The proposed study suggests the application of a whale optimization algorithm (WOA) based on a fractional-order proportional-integral controller (FOPIC) for unified power quality conditioner (UPQC) and STATCOM tools. These operate best with the help of their improved control system, to increase the system’s reliability and fast dynamic response, and to decrease the total harmonic distortion (THD) for enhancing the power quality (PQ). In this article, three different configurations are studied and assessed, namely: (C1) WOA-based FOPIC for UPQC, (C2) WOA-based FOPIC for STATCOM, and (C3) system without FACTS, i.e., base case, to mitigate the mentioned drawbacks. C3 is also considered as a base case to highlight the main benefits of C1 and C2 in improving the PQ by reducing the %THD of the voltage and current system and improving the systems’ voltage waveforms. With C2, voltage fluctuation is decreased by 98%, but it nearly disappears in C1 during normal conditions. Additionally, during the fault period, voltage distortion is reduced by 95% and 100% with C2 and C1, respectively. Furthermore, when comparing C1 to C2 and C3 under regular conditions, the percentage reduction in THD is remarkable. In addition, C1 eliminates the need for voltage sag, and harmonic and current harmonic detectors, and it helps to streamline the control approach and boost control precision. The modeling and simulation of the prepared system are performed by MATLAB/Simulink. Finally, it can be concluded that the acquired results are very interesting and helpful in the recovery to the steady state of wind systems and nonlinear loads, thereby increasing their grid connection capabilities.

With the advancement of information technology and economic globalization, the problem of supplier selection is gaining in popularity. The impact of supplier selection decisions made were quick and noteworthy on the healthcare profitability and total cost of medical equipment. Thus, there is an urgent need for decision support systems that address the optimal healthcare supplier selection problem, as this problem is addressed by a limited number of studies. Those studies addressed this problem mathematically or by using meta-heuristics methods. The focus of this work is to advance the meta-heuristics methods by considering more objectives rather than the utilized objectives. In this context, the optimal supplier selection problem for healthcare equipment was formulated as a mathematical model to expose the required objectives and constraints for the sake of searching for the optimal suppliers. Subsequently, the problem is realized as a multi-objective problem, with the help of this proposed model. The model has three minimization objectives: (1) transportation cost; (2) delivery time; and (3) the number of damaged items. The proposed system includes realistic constraints such as device quality, usability, and service quality. The model also takes into account capacity limits for each supplier. Next, it is proposed to adapt the well-known non-dominated sorting genetic algorithm (NSGA)-III algorithm to choose the optimal suppliers. The results of the adapted NSGA-III have been compared with several heuristic algorithms and two meta-heuristic algorithms (i.e., particle swarm optimization and NSGA-II). The obtained results show that the adapted NSGA-III outperformed the methods of comparison.

Electronic health records (EHRs) play an important role in our life. However, most of the time, they are scattered and saved on different databases belonging to distinct institutions (hospitals, laboratories, clinics, etc.) geographically distributed across one or many countries. Due to this decentralization and the heterogeneity of the different involved systems, medical staff are facing difficulties in correctly collaborating by sharing, protecting, and tracking their patient’s electronic health-record history to provide them with the best care. Additionally, patients have no control over their private EHRs. Blockchain has many promising future uses for the healthcare domain because it provides a better solution for sharing data while preserving the integrity, the interoperability, the availability of the classical client–server architectures used to manage EHRS. This paper proposes a framework called HealthBlock for collaboratively sharing EHRs and their privacy preservation. Different technologies have been combined to achieve this goal. The InterPlanetary File System (IPFS) technology stores and shares patients’ EHRs in distributed off-chain storage and ensures the record’s immutability; Hyperledger Indy gives patients full control over their EHRs, and Hyperledger Fabric stores the patient-access control policy and delegations.

Two phenylspirodrimanes, never isolated before, stachybotrin J (1) and new stachybocin G (epi-stachybocin A) (2), along with the already reported stachybotrin I (3), stachybotrin H (4), stachybotrylactam (5), stachybotrylactam acetate (6), 2α-acetoxystachybotrylactam acetate (7), stachybotramide (8), chartarlactam B (9), and F1839-J (10) were isolated from the sponge-associated fungus Stachybotrys chartarum MUT 3308. Their structures were established based on extensive spectrometric (HRMS) and spectroscopic (1D and 2D NMR) analyses. Absolute configurations of the stereogenic centers of stachybotrin J (1), stachybocin G (2), and stachybotrin I (3), were determined by comparison of their experimental circular dichroism (CD) spectra with their time-dependent density functional theory (TD-DFT) circular dichroism (ECD) spectra. The putative structures of seventeen additional phenylspirodrimanes were proposed by analysis of their respective MS/MS spectra through a Feature-Based Molecular Networking approach. All the isolated compounds were evaluated for their cytotoxicity against five aggressive cancer cell lines (MP41, 786, 786R, CAL33, and CAL33RR), notably including two resistant human cancer cell lines (786R, CAL33RR), and compounds 5, 6, and 7 exhibited cytotoxicity with IC50 values in the range of 0.3−2.2 µM.

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.

Drought is one of the most severe natural disasters worldwide, but with a particular emphasis in sub-humid and semi-arid climates. Several indices have been created to appropriately identify drought’s characteristics and variability. The main objectives of this study consisted of analyzing the behavior of different indices applied in northeast Algeria and comparing them across a long-term data set (1961–2014). The SPI and RDI at 9-month time scales were compared to the PDSI and MedPDSI based on 123 rainfall stations and gridded PET data interpolated to all the locations. A principal component analysis (PCA) in S-mode with varimax rotation (RPC) was applied to the monthly values of all indices to analyze the spatiotemporal patterns of droughts. Two principal components were retained, which identified two sub-regions with coherent differences related to their distance from the Mediterranean Sea and the UNEP aridity index. Trends in the RPC scores were assessed using the modified Mann–Kendall (MMK) test and Sen’s slope estimator, which showed a fundamental difference between the two sub-regions. The RPC of all drought indices showed trends of decreases in the frequency and severity of droughts in the northern sub-region, and trends of increases in the frequency and severity of droughts in the southern region, where the climate is mostly semi-arid and arid. Only a few cases were statistically significant, mostly when using the PDSI and MedPDSI for the southern sub-region. The spatial patterns of moderate, severe, and extreme drought occurrences were similar for the SPI and RDI pair of indices based on the probability of rainfall anomalies, and for the Sc-PDSI and MedPDSI pair based on water balance anomalies. The interpretation of the spatial variability of droughts, mainly of the extreme ones, was supported by an analysis of semi-variograms. The novel index MedPDSI compared well with the other indices and showed advantages of performing the soil water balance following the FAO56 dual Kc method with the actual olive evapotranspiration instead of PET, and of better explaining the spatial variability of extreme droughts; in addition, the trends detected were significant for both the northern and southern sub-regions.

The synergetic control technique (SCT) has the solution for understanding the symmetry inherent in the non-linear properties of wind turbines (WTs); therefore, they achieve excellent performance and enhance the operation of the WT. Small-scale WTs are efficient and cost-effective; they are usually installed close to where the generated electricity is used. This technology is gaining popularity worldwide for off-grid electricity generation, such as in rural homes, farms, small factories, and commercial properties. To enhance the efficiency of the WT, it is vital to operate the WT at its maximum power. This work proposes an efficient and fast maximum power point tracking (MPPT) technique based on the SCT to eradicate the drawbacks of the conventional methods and enhance the operation of the WT at the MPP regardless of wind speed and load changes. The SCT has advantages, such as robustness, simplified design, fast response, no requirement for knowledge of WT characteristics, no need for wind sensors or intricate power electronics, and straightforward implementation. Furthermore, it improves speed convergence with minimal steady-state oscillations at the MPP. The investigated configuration involves a wind-driven permanent magnet synchronous generator (PMSG), uncontrolled rectifier, boost converter, and variable load. The two converters are used to integrate the PMSG with the load. Three scenarios (step changes in wind speed, stochastic changes in wind speed, and variable electrical load) are studied to assess the SCT. The results prove a high performance of the suggested MPPT control method for a fast convergence speed, boosted WT efficacy, low oscillation levels, and applicability under a variety of environmental situations. This work used the MATLAB/Simulink program and was then implemented on a dSPACE 1104 control board to assess the efficacy of the SCT. Furthermore, experimental validation on a 1 kW Darrieus-type WT driving a PMSG was performed.

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.

Of the various tumour types, colorectal cancer and brain tumours are still considered among the most serious and deadly diseases in the world. Therefore, many researchers are interested in improving the accuracy and reliability of diagnostic medical machine learning models. In computer-aided diagnosis, self-supervised learning has been proven to be an effective solution when dealing with datasets with insufficient data annotations. However, medical image datasets often suffer from data irregularities, making the recognition task even more challenging. The class decomposition approach has provided a robust solution to such a challenging problem by simplifying the learning of class boundaries of a dataset. In this paper, we propose a robust self-supervised model, called XDecompo, to improve the transferability of features from the pretext task to the downstream task. XDecompo has been designed based on an affinity propagation-based class decomposition to effectively encourage learning of the class boundaries in the downstream task. XDecompo has an explainable component to highlight important pixels that contribute to classification and explain the effect of class decomposition on improving the speciality of extracted features. We also explore the generalisability of XDecompo in handling different medical datasets, such as histopathology for colorectal cancer and brain tumour images. The quantitative results demonstrate the robustness of XDecompo with high accuracy of 96.16% and 94.30% for CRC and brain tumour images, respectively. XDecompo has demonstrated its generalization capability and achieved high classification accuracy (both quantitatively and qualitatively) in different medical image datasets, compared with other models. Moreover, a post hoc explainable method has been used to validate the feature transferability, demonstrating highly accurate feature representations.

In this paper, we will investigate the influence of the microphysics schemes on the rainfall pattern of the extreme storm that impacted Egypt on 12 March 2020. The aim is to improve rainfall forecasting using the numerical Weather Research and Forecasting (WRF) model for an effective Early Warning System (EWS). The performance of six microphysics schemes were evaluated using the Model Object-based Evaluation analysis tool (MODE) forced by three selected satellite-based datasets (CMORPH, PERSIANN, PERSIANN-CCS, etc.) and one reanalysis dataset (ERA5). Six numerical simulations were performed using the WRF model, considering the following microphysics schemes: Lin, WSM6, Goddard, Thompson, Morrison, and NSSL2C. The models were evaluated using both conventional statistical indices and MODE, which is much more suitable in such studies. The results showed that the Lin scheme outperformed the other schemes such as WSM6, Goddard, Thompson, Morrison, and NSSL2C, in rainfall forecasting. The Thompson scheme was found to be the least reliable scheme. An extension for this study is recommended in other regions where the observational rain gauges data are available.

Antibiotic resistance (AR) is a naturally occurring phenomenon with the capacity to render useless all known antibiotics in the fight against bacterial infections. Although bacterial resistance appeared before any human life form, this process has accelerated in the past years. Important causes of AR in modern times could be the over-prescription of antibiotics, the presence of faulty infection-prevention strategies, pollution in overcrowded areas, or the use of antibiotics in agriculture and farming, together with a decreased interest from the pharmaceutical industry in researching and testing new antibiotics. The last cause is primarily due to the high costs of developing antibiotics. The aim of the present review is to highlight the techniques that are being developed for the identification of new antibiotics to assist this lengthy process, using artificial intelligence (AI). AI can shorten the preclinical phase by rapidly generating many substances based on algorithms created by machine learning (ML) through techniques such as neural networks (NN) or deep learning (DL). Recently, a text mining system that incorporates DL algorithms was used to help and speed up the data curation process. Moreover, new and old methods are being used to identify new antibiotics, such as the combination of quantitative structure-activity relationship (QSAR) methods with ML or Raman spectroscopy and MALDI-TOF MS combined with NN, offering faster and easier interpretation of results. Thus, AI techniques are important additional tools for researchers and clinicians in the race for new methods of overcoming bacterial resistance.

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.

With the advent of pandemics and infectious diseases, numerous research activities on natural products have been carried out to combat them. Researchers are investigating natural products for the treatment and/or management of various infectious diseases and/or disorders. Acacia catechu (L.f.) Willd. belongs to the family Fabaceae (subfamily Mimosoideae) known as Khair or Cutch tree, possesses diverse pharmacological actions, and has been widely used in Asia and different parts of the world. The purpose of the present study is to highlight the phytochemical profile of different parts of A. catechu, the different biological activities of A. catechu extract, and the utilization of A. catechu as food and beverage. The present work constitutes a review of A. catechu; we performed searches (books, Google, Google Scholar, and Scopus publications) to compile the work/investigations made on A. catechu to the present. From our survey, it was concluded that the main phytochemicals compounds in A. catechu are protocatechuic acid, taxifolin, epicatechin, epigallocatechin, catechin, epicatechin gallate, procyanidin, phloroglucin, aldobiuronic acid, gallic acid, D-galactose, afzelchin gum, L-arabinose, D-rhamnose, and quercetin. The whole plant of A. catechu possesses a comprehensive variety of medicinal potentials such as antimicrobial, antidiarrheal, antinociceptive, antihyperlipidemic, antiulcer, antioxidant, antidiabetic, antiproliferative, haemolytic, and anti-inflammatory properties due to the presence of bioactive compounds like flavonoids, alkaloids, and tannins. However, even though the plant’s metabolites were reported to have many different pharmacological uses, there is limited information about their toxicity or clinical trials. Further research on diverse metabolites of A. catechu should be carried out to ensure the safety or utilization of this plant in the pharma or food industries and in the development of potent plant-based drugs.

The growing need for an industrialized world, especially in Africa, cannot be feasible without adequate mineral resources. Thus, the search for more mineral deposits will continue to be necessary. An integrated approach involving geological mapping and a high-resolution geophysical (aeromagnetic) investigation was conducted to assess the manganese mineralization in parts of the Oban Massif, southeast Nigeria. The aeromagnetic data were processed using regional-residual anomaly separation techniques, first vertical derivative (1VD), analytical signals, source parameters imaging (SPI), and Euler deconvolution to better understand magnetic source distributions and their depths of occurrence. The geological investigation revealed a dominant variety of metamorphic rock types, including migmatitic (banded) gneisses hornblende granite gneisses, amphibolites, charnockites, and some quartzite ridges. Also present are some indications of pockets of dolerites. The study area also observed epithermal Mn+Fe+Qtz vein type mineralization associated with hydrothermal alteration zones whose orientation coincides with dominant structural orientation from aeromagnetic interpretation. Analysis of aeromagnetic data shows that the study area is dominated by ENE, NNE, and E-W structural directions (near-surface basement structures), with the ENE trends related to mineralization in the area. The manganese mineralization within Oban Massif is structurally controlled. The depths of the magnetic anomalies in the study area were estimated using SPI and Euler decomposition algorithms. SPI delineated the shallow, intermediate, and deep magnetic anomalies at 84–142 m, 152–200 m, and 215–656 m, respectively. Euler decomposition, however, revealed that shallow, intermediate, and deep depths occurrence of the magnetic anomalies are at 200–377 m, 393–472 m, and 499–793 m, respectively.

Stem cells are a well-known autologous pluripotent cell source, having excellent potential to develop into specialized cells, such as brain, skin, and bone marrow cells. The oral cavity is reported to be a rich source of multiple types of oral stem cells, including the dental pulp, mucosal soft tissues, periodontal ligament, and apical papilla. Oral stem cells were useful for both the regeneration of soft tissue components in the dental pulp and mineralized structure regeneration, such as bone or dentin, and can be a viable substitute for traditionally used bone marrow stem cells. In recent years, several studies have reported that plant extracts or compounds promoted the proliferation, differentiation, and survival of different oral stem cells. This review is carried out by following the PRISMA guidelines and focusing mainly on the effects of bioactive compounds on oral stem cell-mediated dental, bone, and neural regeneration. It is observed that in recent years studies were mainly focused on the utilization of oral stem cell-mediated regeneration of bone or dental mesenchymal cells, however, the utility of bioactive compounds on oral stem cell-mediated regeneration requires additional assessment beyond in vitro and in vivo studies, and requires more randomized clinical trials and case studies.

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.

Copolymers are valuable supports for obtaining heterogeneous catalysts that allow their recycling and therefore substantial savings, particularly in the field of asymmetric catalysis. This contribution reports the use of two comonomers: Azido-3-propylmethacrylate (AZMA) bearing a reactive azide function was associated with 2-methoxyethyl methacrylate (MEMA), used as a spacer, for the ATRP synthesis of copolymers, and then post-functionalized with a propargyl chromium salen complex. The controlled homopolymerization of MEMA by ATRP was firstly described and proved to be more controlled in molar mass than that of AZMA for conversions up to 63%. The ATRP copolymerization of both monomers made it possible to control the molar masses and the composition, with nevertheless a slight increase in the dispersity (from 1.05 to 1.3) when the incorporation ratio of AZMA increased from 10 to 50 mol%. These copolymers were post-functionalized with chromium salen units by click chemistry and their activity was evaluated in the asymmetric ring opening of cyclohexene oxide with trimethylsilyl azide. At an equal catalytic ratio, a significant increase in enantioselectivity was obtained by using the copolymer containing the largest part of salen units, probably allowing, in this case, the more favorable bimetallic activation of both the engaged nucleophile and electrophile. Moreover, the catalytic polymer was recovered by simple filtration and re-engaged in subsequent catalytic runs, up to seven times, without loss of activity or selectivity.

Even though social media contain rich information on events and public opinions, it is impractical to manually filter this information due to data’s vast generation and dynamicity. Thus, automated extraction mechanisms are invaluable to the community. We need real data with ground truth labels to build/evaluate such systems. Still, to the best of our knowledge, no available social media dataset covers continuous periods with event and sentiment labels together except for events or sentiments. Datasets without time gaps are huge due to high data generation and require extensive effort for manual labelling. Different approaches, ranging from unsupervised to supervised, have been proposed by previous research targeting such datasets. However, their generic nature mainly fails to capture event-specific sentiment expressions, making them inappropriate for labelling event sentiments. Filling this gap, we propose a novel data annotation approach in this paper involving several neural networks. Our approach outperforms the commonly used sentiment annotation models such as VADER and TextBlob. Also, it generates probability values for all sentiment categories besides providing a single category per tweet, supporting aggregated sentiment analyses. Using this approach, we annotate and release a dataset named TED-S, covering two diverse domains, sports and politics. TED-S has complete subsets of Twitter data streams with both sub-event and sentiment labels, providing the ability to support event sentiment-based research.

This paper presents a set of methods, jointly called PGraphD*, which includes two new methods (PGraphDD-QM and PGraphDD-SS) for drift detection and one new method (PGraphDL) for drift localisation in business processes. The methods are based on deep learning and graphs, with PGraphDD-QM and PGraphDD-SS employing a quality metric and a similarity score for detecting drifts, respectively. According to experimental results, PGraphDD-SS outperforms PGraphDD-QM in drift detection, achieving an accuracy score of 100% over the majority of synthetic logs and an accuracy score of 80% over a complex real-life log. Furthermore, PGraphDD-SS detects drifts with delays that are 59% shorter on average compared to the best performing state-of-the-art method.

Increasing drought incidence and infertile soils require the improvement of maize for nitrogen use efficiency (NUE) under drought conditions. The objectives were to assess tolerance and genetic effects of Algerian populations under no-nitrogen and water stress. We evaluated a diallel among six Algerian maize populations under no-nitrogen vs. 120 kg/ha N fertilization and drought vs. control. Variability was significant among populations and their crosses for NUE under drought. Additive genetic effects could be capitalized using the populations BAH and MST, with high grain nitrogen utilization efficiency (NUtE). The most promising crosses were SHH × AOR with no-nitrogen supply under both water regimes for NUtE, AOR × IGS, under water stress for partial factor productivity (PFP), and well-watered conditions with nitrogen supply for protein content; AOR × IZM for agronomic nitrogen use efficiency (AE) under water stress; and AOR × BAH for grain nutrient utilization efficiency (NUtE) under well-watered conditions with nitrogen. These parents could be promising for developing drought-tolerant or/and low nitrogen hybrids to improve these traits. Maximum heterosis could be exploited using those populations and crosses. Reciprocal recurrent selection could be used to take advantage of additive and non-additive gene effects found based on estimations of genetic parameters.

The proper and sustainable management of nitrogen fertilization is one of the most common problems of cereal cultivation in semiarid regions, which are characterized by a wide variability in climatic conditions. The current work was conducted to evaluate the effects of nitrogen fertilization on the agronomic and economic aspects of durum wheat cultivated under rainfed semiarid conditions in Algeria and to determine the most efficient nitrogen use efficiency (NUE) among four genotypes that are widespread in the country (tall and short, old and modern genotypes). The four genotypes, Bousselam, MBB, Megress, and GTAdur, were investigated under four nitrogen rates from 0 to 120 kg N ha−1 during three cropping seasons (2016 to 2018). The results indicate that the total nitrogen uptake at maturity (NM), nitrogen uptake by grain (NG), nitrogen harvest index (NHI), NUE and its components, such as nitrogen uptake efficiency (NUpE) and nitrogen utilization efficiency (NUtE), were significantly affected by year, genotype, and nitrogen level. From this study, it appears that higher nitrogen rates improved NM and NG. However, no effects on either grain yield or marginal net return (MNR) were observed; conversely, increased nitrogen levels produced a 13% reduction in the economic return. In other words, in the North African environment, the response to nitrogen is more evident in quality than in yield, which in turn is dependent on the yearly weather conditions and cultivated genotypes. Moreover, nitrogen negatively affected NUE and its components (NUpE, NUtE). On average, NUE displayed low values (14.77 kg kg−1), mostly irregular and highly dependent on weather conditions; in the best year, it did not exceed 60% (19.87 kg kg−1) of the global average value of 33 kg kg−1. Moreover, the modern genotypes Megress (tall) and GTAdur (short) showed the best capacity to tolerate different nitrogen conditions and water shortages, providing relatively superior yields, as well as more effective N use from fertilizers and the soil than the other two genotypes.

Periodontal diseases are caused mainly by inflammation of the gums and bones surrounding the teeth or by dysbiosis of the oral microbiome, and the Global Burden of Disease study (2019) reported that periodontal disease affects 20–50% of the global population. In recent years, more preference has been given to natural therapies compared to synthetic drugs in the treatment of periodontal disease, and several oral care products, such as toothpaste, mouthwash, and dentifrices, have been developed comprising honeybee products, such as propolis, honey, royal jelly, and purified bee venom. In this study, we systematically reviewed the literature on the treatment of periodontitis using honeybee products. A literature search was performed using various databases, including PubMed, Web of Science, ScienceDirect, Scopus, clinicaltrials.gov, and Google Scholar. A total of 31 studies were reviewed using eligibility criteria published between January 2016 and December 2021. In vitro, in vivo, and clinical studies (randomized clinical trials) were included. Based on the results of these studies, honeybee products, such as propolis and purified bee venom, were concluded to be effective and safe for use in the treatment of periodontitis mainly due to their antimicrobial and anti-inflammatory activities. However, to obtain reliable results from randomized clinical trials assessing the effectiveness of honeybee products in periodontal treatment with long-term follow-up, a broader sample size and assessment of various clinical parameters are needed.

Thymoquinone (TQ), the key active component of Nigella sativa (NS), demonstrates very promising biomedical anti-inflammatory, antioxidant, antimicrobial and anticancer properties. Several investigations have inspected the modulative activities of TQ on different stem/progenitor cell types, but its possible role in the regulation of gingival mesenchymal stem/progenitor cells (G-MSCs) has not yet been characterized. For the first time, this study investigates the effects of TQ on G-MSCs’ stemness and Toll-like receptor expression profiles. G-MSCs (n = 5) were isolated, sorted via anti-STRO-1 antibodies and then disseminated on cell culture dishes to create colony-forming units (CFUs), and their stem/progenitor cell attributes were characterized. TQ stimulation of the G-MSCs was performed, followed by an examination of the expression of pluripotency-related factors using RT-PCR and the expression profiles of TLRs 1–10 using flowcytometry, and they were compared to a non-stimulated control group. The G-MSCs presented all the predefined stem/progenitor cells’ features. The TQ-activated G-MSCs displayed significantly higher expressions of TLR3 and NANOG with a significantly reduced expression of TLR1 (p < 0.05, Wilcoxon signed-rank test). TQ-mediated stimulation preserves G-MSCs’ pluripotency and facilitates a cellular shift into an immunocompetent-differentiating phenotype through increased TLR3 expression. This characteristic modulation might impact the potential therapeutic applications of G-MSCs.

The tomato is a member of the Solanaceae family and is a crop that is widely cultivated around the world due to its sweet, sour, salty, juicy, and nutritious berries. The processing of tomatoes generates a significant amount of waste in the form of tomato pomace, which includes seeds and skin. Tomato seeds are reservoirs of various nutrients, such as proteins, carbohydrates, lipids, minerals, and vitamins. These components make tomato seeds an important ingredient for application in food matrices. This review discusses the functional food properties of tomato seeds and their scope of utilization as major ingredients in the functional food industry. In addition, this review describes the development of tomato seeds as a potential nutritional and nutraceutical ingredient, along with recent updates on research conducted worldwide. This is the first review that demonstrates the nutritional profile of tomato seeds along with its diverse functional food properties and application as a functional food ingredient.

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.

Guava (Psidium guajava L.) fruit is also known as the apple of tropics, belongs to the family of genus Psidium, and is widely cultivated in tropical zones of the world. Recently, the importance of guava fruit has increased due to its inherent nutritional content, pleasant aroma, excellent flavor, and delicious taste. It is considered an excellent source of nutrients and phytochemicals. Guava is a climacteric fruit that continues to mature or ripen even after harvest, showing an increase in the rate of respiration and metabolic activities within a short period, leading to rapid senescence or spoilage of fruit. It has limitations in terms of commercialization due to short storage life after harvest and sensitivity to diseases and chilling injury during the storage period. Many postharvest technologies such as edible packaging, modified atmosphere packaging (MAP), composite packaging, controlled atmosphere packaging (CAP), antimicrobial/antifungal packaging, and nano packaging have been used to retard the chilling injury and enhance the keeping quality of guava fruits during the storage period to control respiration rate, reduce weight loss, minimize lipid oxidation, and maintain organoleptic properties. However, these packaging technologies have varied effects on the internal and external quality attributes of guava fruits. This review, therefore, discusses the physiology, mechanism of ripening, oxidation, and ethylene production of guava fruits. The review also discusses the packaging technologies and their effect on the postharvest characteristics of guava fruits during the storage period.

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.

Garlic (Allium sativa L.) is a bulbous flowering plant belongs to the family of Amaryllidaceae and is a predominant horticultural crop originating from central Asia. Garlic and its products are chiefly used for culinary and therapeutic purposes in many countries. Bulbs of raw garlic have been investigated for their role in oral health, which are ascribed to a myriad of biologically active compounds such as alliin, allicin, methiin, S-allylcysteine (SAC), diallyl sulfide (DAS), S-ally-mercapto cysteine (SAMC), diallyl disulphide (DADS), diallyl trisulfide (DATS) and methyl allyl disulphide. A systematic review was conducted following the PRISMA statement. Scopus, PubMed, Clinicaltrials.gov, and Science direct databases were searched between 12 April 2021 to 4 September 2021. A total of 148 studies were included and the qualitative synthesis phytochemical profile of GE, biological activities, therapeutic applications of garlic extract (GE) in oral health care system, and its mechanism of action in curing various oral pathologies have been discussed. Furthermore, the safety of incorporation of GE as food supplements is also critically discussed. To conclude, GE could conceivably make a treatment recourse for patients suffering from diverse oral diseases.