Search Results (75)

In arid and semi-arid climates, where water scarcity is a persistent challenge, accurately estimating reference evapotranspiration (ET) becomes essential for sustainable water management and agricultural planning. The objectives of this study are to compare hourly ET among P–M ASCE, P–M FAO, and P–M KSA mathematical models. In addition to the accuracy assessment of daily ET derived from hourly timestep calculations for the P–M ASCE, P–M FAO, and P–M KSA. To achieve these goals, a total of 525,600-min data points from the Riyadh region, KSA, were used to compute the reference ET at multiple temporal resolutions: hourly, daily, hourly averaged over 24 h, and daily as the sum of 24 h values, across all selected Penman–Monteith (P–M) models. For hourly investigation, the comparison between reference ET computed as average hourly values and as daily/24 h values revealed statistically and practically significant differences. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001) with R² of 94.75% for ASCE, 94.87% for KSA at h_plt = 50 cm, 92.41% for FAO, and 92.44% for KSA at h_plt = 12 cm. For daily investigation, comparing the sum of 24 h ET computations to daily ET measurements revealed an underestimation of daily ET values. The Wilcoxon test confirmed a statistically significant difference (p < 0.0001), with R² exceeding 90% for all studied reference ET models. This comprehensive approach enabled a rigorous evaluation of reference ET dynamics under hyper-arid climatic conditions, which are characteristic of central Saudi Arabia. The findings contribute to the growing body of literature emphasizing the importance of high-frequency meteorological data for improving ET estimation accuracy in arid and semi-arid regions. Full article

Drought, a natural phenomenon intricately intertwined with the broader canvas of climate change, exacts a heavy toll by ushering in acute terrestrial water scarcity. Its ramifications reverberate most acutely within the agricultural heartlands, particularly those nestled in arid regions. To address this pressing issue, this study harnesses the temperature vegetation dryness index (TVDI) as a robust drought indicator, enabling a granular estimation of land water content trends. This endeavor unfolds through the sophisticated integration of geographic information systems (GISs) and remote sensing technologies (RSTs). The methodology bedrock lies in the judicious utilization of 72 high-resolution satellite images captured by the Landsat 7 and 8 platforms. These images serve as the foundational building blocks for computing TVDI values, a key metric that encapsulates the dynamic interplay between the normalized difference vegetation index (NDVI) and the land surface temperature (LST). The findings resonate with significance, unveiling a conspicuous and statistically significant uptick in the TVDI time series. This shift, observed at a confidence level of 0.05 (Z_S = 1.648), raises a crucial alarm. Remarkably, this notable surge in the TVDI exists in tandem with relatively insignificant upticks in short-term precipitation rates and LST, at statistically comparable significance levels. The implications are both pivotal and starkly clear: this profound upswing in the TVDI within agricultural domains harbors tangible environmental threats, particularly to groundwater resources, which form the lifeblood of these regions. The call to action resounds strongly, imploring judicious water management practices and a conscientious reduction in water withdrawal from reservoirs. These measures, embraced in unison, represent the imperative steps needed to defuse the looming crisis. Full article

The pavement is a complex construction subject to a range of environmental and loading conditions. Transportation organizations use pavement management systems (PMSs) to maintain satisfactory pavement performance. The pavement condition index (PCI) is a commonly used performance indicator, yet PCI evaluation is costly and time-consuming. Machine and deep learning algorithms have recently been more instrumental for forecasting pavement conditions. This research uses AI tools to develop a correlation between PCI and collected distress in urban road networks. The distresses for 15,000 pavement segments in Egypt were investigated through a desk study and field data collection. To this end, several machine learning (ML) and deep learning approaches were developed. The ML techniques include random forest (RF), support vector machine (SVM), decision tree (DT), and the deep learning approach entails artificial neural networks (ANN). The proposed techniques provide precise PCI estimates and can be seamlessly integrated with PMCs using ubiquitous spreadsheet programs. The results have shown excellent predictions of the ANN model, as demonstrated in the high coefficient of determination ($R^{2 }$ = 0.939) and the low root mean squared error (RMSE = 7.20) and the mean absolute error (MAE = 2.94). This study sets out to provide a reliable and affordable alternative to specialized tools like MicroPAVER. The ANN model exhibited greater prediction accuracy than the other developed models and can also reliably forecast PCI values by using only measured distress data. Full article

Water, often described as the elixir of life, is a critical resource that sustains life on Earth. The acute water scarcity in the major basins of the Arabian Peninsula has been further aggravated by rapid population growth, urbanization, and the impacts of climate change. This situation underscores the urgent need for a comprehensive analysis of the region’s morphometric characteristics. Such an analysis is essential for informed decision-making in water resource management, infrastructure development, and conservation efforts. This study provides a foundational basis for implementing sustainable water management strategies and preserving ecological systems by deepening the understanding of the unique hydrological processes within the Arabian Peninsula. Additionally, this research offers valuable insights to policymakers for developing effective flood mitigation strategies by identifying vulnerable areas. The study focuses on an extensive investigation and assessment of morphometric parameters in the primary basins of the Arabian Peninsula, emphasizing their critical role in addressing water scarcity and promoting sustainable water management practices. The findings reveal that the Arabian Peninsula comprises 12 major basins, collectively forming a seventh-order drainage system and covering a total land area of 3.24 million km². Statistical analysis demonstrates a strong correlation between stream order and cumulative stream length, as well as a negative correlation between stream order and stream number (R² = 99%). Further analysis indicates that many of these basins exhibit a high bifurcation ratio, suggesting the presence of impermeable rocks and steep slopes. The hypsometric integral (HI) of the Peninsula is calculated to be 60%, with an erosion integral (EI) of 40%, indicating that the basin is in a mature stage of geomorphological development. Importantly, the region is characterized by a predominantly coarse drainage texture, limited infiltration, significant surface runoff, and steep slopes, all of which have critical implications for water resource management. Full article

The conflicts among the landscape water demand and other urban water requirements are motivating improvements in water sustainability in arid urban areas. The accurate estimation of urban landscape plants’ evapotranspiration (ET_PLT) is crucial for effective irrigation management practices. This study examined two factor-based approaches—the Water Use Classification of Landscape Species (WUCOLS) and the Landscape Irrigation Management Program (LIMP)—in conjunction with the formula developed by Penman–Monteith to calculate the landscape irrigation water demand in Saudi Arabia. The reference evapotranspiration (ET_r) was calculated utilizing 40 years of recorded meteorological data from various locations in Saudi Arabia. Notable variations in ET_r were observed both geographically between different regions and seasonally within regions. The highest, lowest, and moderate ET_r values were recorded in Riyadh, Mecca, and Asir, measuring 9.5, 6.7, and 5.3 mm, respectively. Regarding the decoupling approaches, the moderate species factor (K_s) of WUCOLS was compared to the three levels of managed stress (K_sm) in LIMP, categorized as “low, moderate, and high”. The statistical analysis revealed a significant advantage of K_s moderate over K_sm low, with a 37.5% reduction in the average ET_PLT. Although no significant differences were observed between moderate K_s and K_sm, the ET_PLT derived from WUCOLS was 16.7% lower than that from LIMP. Conversely, the advantage shifted towards K_sm high, which demonstrated a 20% decrease in the ET_PLT estimates. These results support the Saudi Green Initiative by furnishing essential data for sustainable water management in arid regions, promoting a science-driven approach to enhance water use efficiency and alleviate water scarcity. Full article

The present investigation utilized artificial neural networks (ANN) and gene expression programming (GEP) in comparison with the two-point method (TPM) to develop a generalized solution for predicting infiltrated water volume (∀_Z) across various soil types under furrow conditions. This work assesses infiltration behavior with respect to experimental data from several temporal contexts. Data distribution and model performance are evaluated via descriptive statistics and correlation tests. Artificial intelligence (AI) models (ANN and GEP) trained and evaluated utilizing input variables—inflow rate ($Q_{in}$); furrow length ($L$); waterfront advance time at the end of the furrow ($T_L$); infiltration opportunity time ($T_o$); and cross-sectional area of the inflow ($A_o$) are compared with TPM performance. More precisely and consistently than the water advance power function, AI-based algorithms hope to be invading water volume. Statistical analysis shows that ANN and GEP have lower error metrics, increased generalizability, and better representation of complex infiltration dynamics. The determination coefficient (R²) of ANN data produced 98.1% for testing and 97.8% for validation, while TPM showed accuracy reductions of 2.5% and 4.6%, respectively. On the other side, the R² of GEP produced 95.7% for testing and 96.1% for validation, while TPM showed accuracy reductions of 0.7% and 3%, respectively. During ANN model computation, TPMs root mean square error (RMSE) of 0.0135 m³/m exceeded all mean values. Errors within 10% relative deviation were displayed using the ANN model ${\forall }_Z$. Particularly, ANN and GEP, the study revealed that AI techniques predict furrow irrigation penetration of water volume better than the water advance power function. These models advance soil and furrow adaptation, extrapolation, and accuracy. Results show that AI-driven modeling may maximize hydrological assessments and irrigation control. Full article

Estrogen receptors (ERs) play a critical role in breast cancer (BC) development and progression, with ERα being oncogenic and ERβ exhibiting tumor-suppressive properties. The interaction between ER signaling and other molecular pathways, such as PI3K/AKT/mTOR, influences tumor growth and endocrine resistance. Emerging research highlights the role of prebiotics in modulating gut microbiota, which may influence estrogen metabolism, immune function, and therapeutic responses in BC. This review explores the impact of prebiotics on estrogen receptor modulation, gut microbiota composition, immune regulation, and metabolic pathways in breast cancer. The potential of prebiotics as adjunctive therapies to enhance treatment efficacy and mitigate chemotherapy-related side effects is discussed. A comprehensive analysis of recent preclinical and clinical studies was conducted, examining the role of prebiotics in gut microbiota modulation, immune regulation, and metabolic reprogramming in breast cancer. The impact of short-chain fatty acids (SCFAs) derived from prebiotic fermentation on epigenetic regulation and endocrine resistance was also evaluated. Prebiotics were found to modulate the gut microbiota-estrogen axis, reduce inflammation, and influence immune responses. SCFAs demonstrated selective estrogen receptor downregulation and metabolic reprogramming, suppressing tumor growth. Synbiotic interventions mitigate chemotherapy-related side effects, improving the quality of life in breast cancer patients. Prebiotics offer a promising avenue for breast cancer prevention and therapy by modulating estrogen metabolism, immune function, and metabolic pathways. Future clinical trials are needed to validate their efficacy as adjunctive treatments in breast cancer management. Full article

Background/Objectives: Parkinson’s disease (PD) is a progressive neuro-degenerative disorder characterized by α-synuclein aggregation, which promotes neuronal death and accelerates neurodegeneration. Small interfering RNA (siRNA) can reduce α-synuclein levels, but its therapeutic potential is limited by poor stability and delivery challenges. Similarly, Selegiline (Sel), a monoamine oxidase-B (MAO-B) inhibitor, has low bioavailability, restricting its effectiveness. This study aims to develop an intranasal (IN) albumin-coated liposomal system (C-Lip_Sel-siSNCA2) for the co-delivery of Sel and α-synuclein-targeting siRNA (siSNCA2) to enhance brain targeting and therapeutic efficacy. Methods: Liposomes were prepared using the ethanol injection method and optimized via D-optimal design for size, charge, and encapsulation efficiency (EE%). The optimized formulation was coated with human serum albumin (HSA) and characterized for stability, cellular uptake, and gene silencing. In vivo pharmacokinetics and pharmacodynamics were assessed in a rotenone-induced PD rat model to evaluate the motor function, biochemical markers, and brain-targeting efficiency. Results: Optimized liposomes had a particle size of 113.5 ± 6.8 nm, zeta potential of 6.2 ± 0.8 mV, and high EE% (Sel: 92.35%; siRNA: 78.66%). Albumin coating increased size to 136.5 ± 10.3 nm and shifted zeta potential to −13.5 ± 1.4 mV, enhancing stability and targeting. IN administration achieved a 3-fold increase in brain area under the concentration-time curve (AUC) versus intravenous delivery. In PD rats, C-Lip_Sel-siSNCA2 improved motor and non-motor functions, restored dopamine levels, enhanced catalase activity, and reduced MAO-B levels, mitigating dopamine degradation and α-synuclein aggregation. Conclusions: This non-invasive, dual-action nanoplatform offers a targeted therapy for PD, combining siRNA gene silencing and MAO-B inhibition, with the potential for clinical translation in neurodegenerative diseases. Full article

Dental implants are crucial in contemporary oral rehabilitation, necessitating optimal integration with the surrounding soft tissues for durable success. The attachment between the implant surface and peri-implant mucosa should establish a secure seal to prevent bacterial infiltration and subsequent tissue inflammation. This concise review examines the histological and biological perspectives of peri-implant soft tissue reactions to zirconium and titanium abutments, shedding light on their respective advantages and limitations. While titanium has been the gold standard, zirconia has gained attention due to its biocompatibility and aesthetic appeal. Histological studies show comparable soft tissue attachment and inflammatory responses between the two materials. Further research is needed to explore surface treatments and optimize outcomes in dental implant rehabilitation. Full article

Steel pipeline systems carry about three-quarters of the world’s oil and gas. Such pipelines need to be coated to prevent corrosion and erosion. An alternative to the current epoxy-based coating, a multi-layered composite coating is developed in this research. The composite coatings were made from carbon fiber-reinforced thermoplastic polymer (CFRTP) material. Uniaxial carbon fiber CF/PPS prepreg tape was utilized, where the PPS (polyphenylene sulfide) is employed as a thermoplastic (TP) matrix. Compression molding was used to manufacture three flat panels, each consisting of seven plies: UD (Unidirectional), Biaxial, and Off-axis. Samples of carbon steel were coated with multi-layered composites. The physical, mechanical, and corrosion-resistant properties of steel-composite coated samples were evaluated. A better and more promising lap-shear strength of about 58 MPa was demonstrated. When compared to the Biaxial and Off-axis samples, the UD assembly had the maximum flexural strength (420 MPa); however, the Biaxial coating has the highest corrosion resistance (445 kΩ·cm²) when compared to the Off-axis and UD coatings. Full article

Background/objectives: The blood–brain barrier (BBB) significantly limits the treatment of central nervous system disorders, such as schizophrenia, by restricting drug delivery to the brain. This study explores the potential of intranasal clozapine-loaded lipid nanocapsules (IN LNCs_Clo) as a targeted and effective delivery system to the brain. Methods: LNCs_Clo were prepared using the phase inversion technique and characterized in terms of size, zeta potential, entrapment efficiency (EE%), and in vitro drug release. The pharmacokinetic, safety, and pharmacodynamic effects of LNCs_Clo were then evaluated in a rat model through intranasal (IN) administration and compared with those of oral and intravenous (IV) Clo solutions. Results: LNCs_Clo were prepared using a phase inversion technique, resulting in a nanocarrier with a particle size of 28.6 ± 3.6 nm, homogenous dispersion, and high EE% (84.66 ± 5.66%). Pharmacokinetic analysis demonstrated that IN LNCs_Clo provided enhanced Clo brain bioavailability, rapid CNS targeting, and prolonged drug retention compared to oral and intravenous routes. Notably, the area under the curve (AUC) for brain concentration showed more than two-fold and eight-fold increases with LNCs_Clo, compared to IV and oral solutions, respectively, indicating improved brain-targeting efficiency. Safety assessments indicated that LNCs_Clo administration mitigated Clo-associated metabolic side effects, such as hyperglycemia, insulin imbalance, and liver enzyme alterations. Additionally, pharmacodynamic studies showed that LNCs_Clo significantly improved antipsychotic efficacy and reduced schizophrenia-induced hyperactivity, while preserving motor function. Conclusions: These results highlight the potential of IN LNCs_Clo as a novel drug delivery system, offering improved therapeutic efficacy, reduced systemic side effects, and better patient compliance in the treatment of schizophrenia and potentially other CNS disorders. Full article

In this research, ethe performance of bifacial photovoltaic (PV) modules under varying background conditions is explored, specifically green grass, brown clay, and white gravel, on a sunny day. By leveraging both exergy and energy analysis, this research aims to provide a comprehensive evaluation of bifacial module efficiency compared to traditional monofacial modules. The experimental setup simulates diverse installation environments, including rooftops and ground-mounted systems, by varying background reflectance. Key performance metrics such as energy yield, exergy yield, and overall efficiency were measured. The findings reveal that bifacial modules installed over white gravel backgrounds achieve the highest exergy profile and efficiency during peak solar radiation periods, attributed to the enhanced reflectivity of white gravel. These insights can inform strategic decisions regarding the selection and placement of bifacial modules to optimize energy and exergy outputs in real-world scenarios. This study contributes valuable knowledge to the advancement of renewable energy technologies, offering guidance for researchers, developers, and policymakers focused on sustainable energy solutions. Full article

The animal gastrointestinal tract contains a complex microbiome whose composition ultimately reflects the co-evolution of microorganisms with their animal host and their host’s environment. This study aimed to gain insights into the adaptation of the microbiota of local Egyptian cattle to three different ecosystems (Upper Egypt, Middle Egypt, and Lower Egypt) distributed across 11 governorates (with an average of 12 animals per governorate) using amplicon sequencing. We analyzed the microbiota from 136 fecal samples of local Egyptian cattle through a 16S rRNA gene sequencing approach to better understand the fecal microbial diversity of this breed which developed under different ecosystems. An alpha diversity analysis showed that the fecal microbiota of the Egyptian cattle was not significantly diverse across areas, seasons, sexes, or farm types. Meanwhile, microbiota data revealed significant differences in richness among age groups (p = 0.0018). The microbial community differed significantly in the distribution of its relative abundance rather than in richness across different ecosystems. The taxonomic analysis of the reads identified Firmicutes and Actinobacteriota as the dominant phyla, accounting for over 93% of the total bacterial community in Egyptian cattle. Middle Egypt exhibited a different microbial community composition compared to Upper and Lower Egypt, with a significantly higher abundance of Firmicutes and Euryarchaeota and a lower abundance of Actinobacteriota in this region than the other two ecosystems. Additionally, Middle Egypt had a significantly higher relative abundance of the Methanobacteriaceae family and the Methanobrevibacter genera than Lower and Upper Egypt. These results suggest a difference in the adaptation of the fecal microbial communities of Egyptian cattle raised in Middle Egypt. At the genus level, eleven genera were significantly different among the three ecosystems including Bacillus, DNF00809, Kandleria, Lachnospiraceae_NK3A20_group, Methanobrevibacter, Mogibacterium, Olsenella, Paeniclostridium, Romboutsia, Turicibacter, and UCG-005. These significant differences in microbiota composition may impact the animal’s adaptation to varied environments. Full article

A series of novel thiazole-based chalcones were evaluated for their anticancer activity as potential tubulin polymerization inhibitors. In vitro anticancer screening for the thiazole derivatives 2a–2p exhibited broad-spectrum antitumor activity against various cancer cell lines particularly Ovar-3 and MDA-MB-468 cells with a GI₅₀ range from 1.55 to 2.95 μΜ, respectively. Compound 2e demonstrated significant inhibition of tubulin polymerization, with an IC₅₀ value of 7.78 μM compared to Combretastatin-A4 (CA-4), with an IC₅₀ value of 4.93 μM. Molecular docking studies of compounds 2e, 2g, and 2h into tubulin further supported these findings, revealing that they bind effectively to the colchicine binding site, mirroring key interactions exhibited by CA-4. Computational predictions suggested favorable oral bioavailability and drug-likeness for these compounds, highlighting their potential for further development as chemotherapeutic agents. Full article

Bifacial solar modules, capable of harvesting sunlight from both sides, present a promising pathway for sustainable energy generation. This study examines the performance of bifacial modules on a cloudy day through comparative exergy and energy analyses. The analysis considers both the quality and quantity of energy produced by bifacial and monofacial modules. Conducted at Qassim University in Buraydah City, Saudi Arabia, the study recorded measurements during two intervals on a cloudy day using a real-time photovoltaic measurement system. Module performance was evaluated concerning energy yield, exergy yield, and solar irradiance, factoring in ambient and module temperatures. The results demonstrate that bifacial modules outperformed monofacial modules in electrical energy and output exergy. In the first period, the bifacial modules showed a 9.5% higher exergy efficiency and a 7% greater energy efficiency compared to the monofacial modules. During the second period, the bifacial modules achieved a 4.5% higher exergy efficiency and a 3.5% increased energy efficiency over the monofacial modules. These findings contribute to global sustainability efforts by reducing fossil fuel dependence and optimizing bifacial PV module design and operation for enhanced energy and exergy efficiency, even in cloudy conditions. The study’s implications for sustainable development and energy policies underscore the essential role of advanced PV technologies in achieving sustainable energy goals. Full article