Search Results (1,426)

This paper presents a scientometric review that provides a quantitative perspective on the evolution of Automation in Construction Journal (AICJ) research, emphasizing its developmental paths and emerging trends. The study aims to analyze the journal’s growth and citation impact over time. It also seeks to identify the most influential publications and the cooperation patterns among key contributors. Furthermore, the study explores the journal’s primary research themes and their evolution. Accordingly, 4084 articles were identified using the Web of Science (WoS) database and subjected to a multistep analysis using VOsviewer version 1.6.18 and Biblioshiny as software tools. First, the growth and citation of the publications over time are inspected and evaluated, in addition to ranking the most influential documents. Second, the co-authorship analysis method is applied to visualize the cooperation patterns between countries, organizations, and authors. Finally, the publications are analyzed using keyword co-occurrence and keyword thematic evolution analyses, revealing five major research clusters: (i) foundational optimization, (ii) deep learning and computer vision, (iii) building information modeling, (iv) 3D printing and robotics, and (v) machine learning. Additionally, the analysis reveals significant growth in publications (54.5%) and citations (78.0%) from 2018 to 2023, indicating the journal’s increasing global influence. This period also highlights the accelerated adoption of digitalization (e.g., BIM, computational design), increased integration of AI and machine learning for automation and predictive analytics, and rapid growth of robotics and 3D printing, driving sustainable and innovative construction practices. The paper’s findings can help readers and researchers gain a thorough understanding of the AICJ’s published work, aid research groups in planning and optimizing their research efforts, and inform editorial boards on the most promising areas in the existing body of knowledge for further investigation and development. Full article

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

Advances in plaque imaging have transformed cardiovascular diagnostics through detailed characterization of atherosclerotic plaques beyond traditional stenosis assessment. This review outlines the clinical applications of varying modalities, including dual-layer spectral CT, photon-counting CT, dual-energy CT, and CT-derived fractional flow reserve (CT-FFR). These technologies offer improved spatial resolution, tissue differentiation, and functional assessment of coronary lesions. Additionally, artificial intelligence has emerged as a powerful tool to automate plaque detection, quantify burden, and refine risk prediction. Collectively, these innovations provide a more comprehensive approach to coronary artery disease evaluation and support personalized management strategies. Full article

Acaricide usage has led to the growing problem of resistance in ticks. A heavy tick burden and the presence of ticks on animals throughout the year, despite the monthly application of acaricides, in farms in the United Arab Emirates formed the motivation for this study. The objectives of this research were as follows: (a) to assess the acaricide resistance status of the most prevalent tick Hyalomma spp. to widely used acaricides Cypermethrin and Deltamethrin; (b) to identify the association of farm management practices and farm-level risk factors with the failure of tick treatment (acaracide resistance). A total of 1600 ticks were collected from 20 farms located in three different regions of Abu Dhabi Emirate including Al Ain (n = 10), Al Dhafra (n = 5), and Abu Dhabi (n = 5). The ticks were subjected to an in vitro bioassay adult immersion test (AIT) modified with a discriminating dose (AIT-DD) against commercial preparations of Cypermethrin and Deltamethrin. A questionnaire was designed to collect metadata and information on farm management and the farm-level risk factors associated with routine farm practices relating to the treatment and control of tick and blood parasite infections in camels and small ruminant populations. Hyalomma anatolicum and Hyalomma dromedarii were identified among the collected ticks, with H. anatolicum being the most prevalent tick species (70%) in the present study. The test results of the in vitro bioassay revealed varied emerging resistance to both of the acaricides in the majority of the three regions; fully susceptible tick isolates with zero resistance to Deltamethrin were recorded in one farm at Al Ain and two farms in the Abu Dhabi region. A questionnaire analysis showed that the failure of tick treatment in farms varied with the presence or absence of vegetation areas, types of animal breeds, and management practices. This study reports the emergence of resistance in ticks to Cypermethrin and Deltamethrin across the Abu Dhabi Emirate, indicating a strict warning for the cautious use of acaricides. There is also a need to improve awareness about sound tick management and control practices among farm owners through a multidisciplinary approach adopting integrated pest management strategies that engage farmers, veterinarians, and policy makers. Full article

Background and Objectives: Flexible ureteroscopic surgery is a common minimally invasive procedure utilized for the management of various urological conditions. While effective, postoperative complications such as fever can occur, necessitating the identification of reliable biomarkers for early detection and management. In this study, we specifically evaluated the predictive performance of three preoperative hematologic indices: the neutrophil-to-lymphocyte ratio (NLR), platelet-to-lymphocyte ratio (PLR), and systemic immune–inflammation index (SII). Materials and Methods: By systematically comparing these biomarkers through receiver operating characteristic (ROC) curve analysis and logistic regression modeling, we aimed to identify the most accurate predictor of postoperative fever development. Our cohort included patients who developed postoperative fever, many of whom exhibited normal WBC counts, allowing us to evaluate the discriminatory power of alternative inflammatory biomarkers. Results: Among the 150 patients, 32 developed postoperative fever. Conventional WBC counts did not predict fever, with 91% of feverish individuals having normal WBC values. In the ROC curve analysis, NLR outperformed SII (AUC 0.847, cutoff 796) and PLR (AUC 0.743, cutoff 106), with an AUC of 0.996 at 2.96. A combined logistic model achieved 100% sensitivity and 91% specificity (AUC = 0.996). Conclusions: This study addresses a critical gap in perioperative monitoring by validating readily available complete blood count-derived ratios as clinically meaningful predictors of postoperative inflammatory responses. Full article

This study aims to investigate the release of potentially toxic elements from disposable paper and plastic cups when exposed to hot water, simulating the scenario of their use in hot beverage consumption, and to assess the associated health risks. By using ICP-MS, twelve potentially toxic elements, namely As, Ba, Cd, Co, Cr, Cu, Mn, Mo, Pb, Sb, V, and Zn, were determined in leachates, revealing significant variability in mass fractions between paper and plastic cups, with plastic cups demonstrating greater leaching potential. Health risk assessments, including hazard quotient (HQ) and excess lifetime cancer risk (ELCR), indicated minimal non-carcinogenic and carcinogenic risks for most elements, except Pb, which posed elevated non-carcinogenic risk, especially in plastic cups. Children showed higher relative exposure levels compared to adults due to their lower body weights (the HQ in children is two times greater than in adults). Overall, the findings of the current study underscore the need for stricter monitoring and regulation of materials used in disposable cups, especially plastic ones, to mitigate potential health risks. Future investigations should assess the leaching behavior of potentially toxic elements under conditions that accurately mimic real-world usage. Such investigations ought to incorporate a systematic evaluation of diverse temperature regimes, varying exposure durations, and different beverage types. Full article

The insulin-like growth factor binding protein, acid-labile subunit (IGFALS), plays a crucial role in glucose metabolism and immune regulation, key processes in recovery from surgery. Here, we studied the perioperative serum IGFALS dynamics and explored potential clinical implications. A total of 79 patients undergoing elective cardiac surgery with implementation of cardiopulmonary bypass had their serum isolated at baseline, 24 h, seven days, and three months postoperatively to assess serum concentrations of IGFALS and insulin growth factor 1 (IGF-1). Markers of perioperative injury included troponin I (TnI), high-mobility group box 1 (HMGB-1), and heat shock protein 60 (Hsp-60). Inflammatory status was assessed via interleukin-6 (IL-6) and interleukin-8 (IL-8). Additionally, we measured in vitro cytokine production to viral stimulation of whole blood and monocytes. Surrogates of neuronal distress included neurofilament light chain (NF-L), total tau (τ), phosphorylated tau at threonine 181 (τp181), and amyloid β40 and β42. Renal impairment was defined by RIFLE criteria. Cardiac dysfunction was denoted by serum N-terminal pro-brain natriuretic peptide (NT-proBNP) levels. Serum IGFALS levels declined significantly after surgery and remained depressed even at 3 months. Administration of acetaminophen and acetylsalicylic acid differentiated IGFALS levels at the 24 h postoperatively. Serum IGFALS 24 h post-operatively correlated with production of cytokines by leukocytes after in vitro viral stimulation. Serum amyloid-β1-42 was significantly associated with IGFALS at baseline and 24 h post-surgery Patients discharged home had higher IGFALS levels at 28 days and 3 months than those discharged to healthcare facilities or who died. These findings suggest that IGFALS may serve as a prognostic biomarker for recovery trajectory and postoperative outcomes in cardiac surgery patients. Full article

The primary worldwide variables limiting plant development and agricultural output are the ever-present threat that environmental stressors such as salt (may trigger osmotic stress plus ions toxicity, which impact on growth and yield of the plants), drought (provokes water stress, resulting in lowering photosynthesis process and growth rate), heavy metals (induced toxicity, hindering physiological processes also lowering crop quantity and quality), and pathogens (induce diseases that may significantly affect plant health beside productivity). This review explores the integrated effects of these stressors on plant productivity and growth rate, emphasizing how each stressor exceptionally plays a role in physiological responses. Owing to developments in technology that outclass traditional breeding methods and genetic engineering techniques, powerful alleviation strategies are vital. New findings have demonstrated the remarkable role of nanoparticles in regulating responses to these environmental stressors. In this review, we summarize the roles and various applications of nanomaterials in regulating abiotic and biotic stress responses. This review discusses and explores the relationship between various types of nanoparticles (metal, carbon-based, and biogenic) and their impact on plant physiology. Furthermore, we assess how nanoparticle technology may play a role in practices of sustainable agriculture by reducing the amount of compounds used, providing them with a larger surface area, highly efficient mass transfer abilities, and controlled, targeted delivery of lower nutrient or pesticide amounts. A review of data from several published studies leads to the conclusion that nanoparticles may act as a synergistic effect, which can effectively increase plant stress tolerance and their nutritional role. Full article

Sepsis-associated acute kidney injury (S-AKI) represents a significant health problem associated with adverse outcomes. Our study aimed to assess the value of serum cystatin-C (sCysC) and TNF-α (rs361525) in combination for diagnosing S-AKI patients and predicting their adverse outcomes. The study included 100 critically ill neonates and 100 controls. Patients were categorized into an S-AKI group and a non-AKI group. TNF-α (−238, rs361525) genotyping was performed using RT-PCR, and sCysC was assessed using ELISA. Our study showed a fundamental difference in the genotype frequencies of TNF-α (−238, rs361525) and SNP between S-AKI and non-AKI patients. Furthermore, there was a significant relationship between cystatin C and TNF-α (−238, rs361525), where cystatin C was higher in patients with AA alleles than in patients with GA and GG alleles. Combining GA + AA genotypes with elevated serum cystatin-C levels can serve as a potential diagnostic and prognostic biomarker for AKI development in this population. The GA/AA genotypes independently predicted S-AKI risk (OR = 6.64, p < 0.001). At the same time, elevated sCysC (>9.4 mg/L) emerged as a sensitive biomarker (AUC = 0.848) and independent predictor of adverse outcomes. Collectively, these findings contribute to the growing field of personalized medicine and represent a strategic advantage, enabling prevention-focused care rather than the treatment of established disease. Full article

Sulfur-containing polymers are unique sustainable materials with promise for the development of various adsorbents for environmental remediation. However, they have not been explored for CO₂ capture despite reports on its ability to decontaminate various aqueous pollutants. This study reports on the single-step synthesis of a diamine-functionalized sulfur-containing copolymer by the thermally induced radical copolymerization of N2,N2-Diallylmelamine (NDAM), a difunctional monomer, with sulfur and explores its use for CO₂ capture. The influence of reaction parameters such as the weight ratios of sulfur to NDAM, reaction temperature, time, and the addition of a porogen on the properties of aminated copolymer was investigated. The resulting copolymers were characterized using FTIR, TGA, DSC, SEM, XRD, and BET surface area analyses. The incorporation of NDAM directly imparted amine functionality while stabilizing the polysulfide chains by crosslinking, leading to a thermoset copolymer with an amorphous structure. The addition of a NaCl particle porogen to the S/NDAM mixture generated a mesoporous structure, enabling the resulting copolymer to be tested for CO₂ adsorption under varying pressures, leading to an adsorption capacity as high as 517 mg/g at 25 bar. This work not only promotes sustainable hybrid materials that advance green chemistry while aiding CO₂ mitigation efforts but also adds value to the abundant amount of sulfur by-products from petroleum refineries. Full article

Background/Objectives: Tooth discoloration is a common concern associated with the use of intra-canal medicaments, particularly those containing antibiotics. This study aims to evaluate the tooth discoloration potential of an experimental antibiotic paste modified with Nano Chitosan (APNC) and compare it with two antibiotic pastes and two calcium hydroxide-based pastes over different time intervals. Methods: Fifty bovine incisors were standardized and prepared up to size 60. The teeth were randomly assigned into five groups based on the medicament applied: Metapaste, Metapex, modified Triple Antibiotic Paste (mTAP), Double Antibiotic Paste (DAP), and APNC. A digital spectrophotometer was used to measure the color parameters (L*, a*, and b*) at two zones, above and below the cemento-enamel junction (CEJ), across four-time points: before application (T0- baseline), immediately after application (T1), after two weeks (T2), and after one month (T3). The color changes (ΔE) were calculated and statistically analyzed using repeated-measure ANOVA. Results: Statistically significant differences in discoloration were observed between the tested medicaments after one month (p < 0.05). mTAP caused the highest ΔE values both above and below the CEJ at all time points, particularly after one month (p < 0.05). Conversely, APNC, Metapaste, and DAP demonstrated the least discoloration, with no significant differences among them. The degree of discoloration was time-dependent and more pronounced below the CEJ in all groups. Conclusions: After one month, the experimental APNC paste induced tooth discoloration comparable to that of Metapaste, indicating minimal esthetic compromise. APNC may be a promising alternative to traditional antibiotic pastes with minimal discoloration effects. Full article

Growth traits are the most important economic traits in red tilapia (Oreochromis spp.) production, and are the main targets for its genetic improvement. Increasing salinity levels in the environment are affecting the growth, development, and molecular processes of aquatic animals. Red tilapia tolerates saline water to some degree. However, few credible genetic markers or potential genes are available for choosing fast-growth traits in salt-tolerant red tilapia. This work used genome-wide association study (GWAS) and RNA-sequencing (RNA-seq) to discover genes related to four growth traits in red tilapia cultured in saline water. Through genotyping, it was determined that 22 chromosomes have 12,776,921 high-quality single-nucleotide polymorphisms (SNPs). One significant SNP and eight suggestive SNPs were obtained, explaining 0.0019% to 0.3873% of phenotypic variance. A significant SNP peak associated with red tilapia growth traits was located on chr7 (chr7-47464467), and plxnb2 was identified as the candidate gene in this region. A total of 501 differentially expressed genes (DEGs) were found in the muscle of fast-growing individuals compared to those of slow-growing ones, according to a transcriptome analysis. Combining the findings of the GWAS and RNA-seq analysis, 11 candidate genes were identified, namely galnt9, esrrg, map7, mtfr2, kcnj8, fhit, dnm1, cald1, plxnb2, nuak1, and bpgm. These genes were involved in ‘other types of O-glycan biosynthesis’, ‘glycine, serine and threonine metabolism’, ‘glycolysis/gluconeogenesis’, ‘mucin-type O-glycan biosynthesis’ and ‘purine metabolism signaling’ pathways. We have developed molecular markers to genetically breed red tilapia that grow quickly in salty water. Our study lays the foundation for the future marker-assisted selection of growth traits in salt-tolerant red tilapia. Full article

Today’s environmental issues are among the primary themes that researchers explore in their search for practical solutions to achieve the Sustainable Development Goals (SDGs), such as reducing carbon emissions and promoting sustainable practices. Renewable energy is crucial to overcoming future challenges, causing many countries to accelerate their adoption at various levels. In this context, the impact of renewable energy adoption on achieving the Sustainable Development Goals in the Ha’il region has been evaluated. Specifically, two techniques are employed. The first technique is an empirical model based on the Vector Error Correction Model (VECM), which identifies the SDGs related to renewable energy in achieving SDGH. Only three SDGs (SDG7, SDG12, and SDG13) were found to influence SDGH significantly. The second technique uses deep learning, specifically LSTM networks, to forecast SDGH behavior over a ten-year period about the three selected SDGs. The results indicate that these three SDGs play a crucial role in sustainable development in the Ha’il region. Therefore, this research produces strategic recommendations to optimize the adoption of renewable energy in the Ha’il region. These findings provide policymakers with a data-driven framework to enhance strategies, utilize resources more efficiently, and promote broader sustainability initiatives. Full article

Electricity theft remains a pressing challenge in modern smart grid systems, leading to significant economic losses and compromised grid stability. This paper presents a sensor-driven framework for electricity theft detection that leverages data collected from smart meter sensors, key components in smart grid monitoring infrastructure. The proposed approach combines prototype learning and meta-level ensemble learning to develop a scalable and accurate detection model, capable of identifying zero-day attacks that are not present in the training data. Smart meter data is compressed using Principal Component Analysis (PCA) and K-means clustering to extract representative consumption patterns, i.e., prototypes, achieving a 92% reduction in dataset size while preserving critical anomaly-relevant features. These prototypes are then used to train base-level one-class classifiers, specifically the One-Class Support Vector Machine (OCSVM) and the Gaussian Mixture Model (GMM). The outputs of these classifiers are normalized and fused in a meta-OCSVM layer, which learns decision boundaries in the transformed score space. Experimental results using the Irish CER Smart Metering Project (SMP) dataset show that the proposed sensor-based detection framework achieves superior performance, with an accuracy of 88.45% and a false alarm rate of just 13.85%, while reducing training time by over 75%. By efficiently processing high-frequency smart meter sensor data, this model contributes to developing real-time and energy-efficient anomaly detection systems in smart grid environments. Full article

Background/Objectives: Bariatric surgery improves weight and metabolic health in individuals with severe obesity; however, challenges like gut dysbiosis and nutrient deficiencies persist postoperatively. Probiotic supplementation may enhance recovery by modulating gut microbiota. This updated meta-analysis aimed to assess the effects of probiotics/synbiotics on metabolic, anthropometric, and nutritional outcomes after bariatric surgery. Methods: A systematic review and meta-analysis of randomized controlled trials (RCTs) was conducted using PubMed, SCOPUS, Web of Science, and CENTRAL through December 2024. Studies comparing probiotics/synbiotics (which contain both probiotics and prebiotics) versus a placebo in adults post-bariatric surgery were included. Meta-analyses were conducted, with subgroup analyses by surgery type, the timing of the intervention, and probiotic formulation (PROSPERO ID: CRD420251019199). Results: Thirteen RCTs involving 809 patients were included in the analysis. Probiotic use significantly reduced BMI (MD = 0.67, 95% CI: 0.33 to 1.00), HbA1c (MD = −0.19%, 95% CI: −0.36 to −0.01), triglycerides (MD = −16.56 mg/dL), and AST levels (MD = −3.68 U/L), while increasing ALP (MD = 8.12 U/L) and vitamin D (MD = 13.68 pg/mL). Ferritin levels were significantly lower (MD = −18.89 µg/L) in the probiotic group. A subgroup analysis showed enhanced benefits in patients undergoing mini-gastric bypass, with perioperative or synbiotic interventions specifically improving triglycerides, total cholesterol, and HbA1c. Conclusions: Probiotics may offer modest but significant improvements in BMI, glycemic control, lipid profile, liver enzymes, and vitamin D levels after bariatric surgery. These findings support the potential role of probiotics/synbiotics as an adjunct therapy, though further large-scale trials are warranted to confirm long-term benefits. Full article