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Currently, the viscoelastic properties of conventional asphalt cement need to be improved to meet the increasing demands caused by larger traffic loads, increased stress, and changing environmental conditions. Thus, using modifiers is suggested. Furthermore, the Sustainable Development Goals (SDGs) promote using waste materials and new technologies in asphalt pavement technology. The present study aims to fill this gap by investigating the use of pulverized oil palm industry clinker (POPIC) as an asphalt–cement modifier to improve the fatigue life of bituminous concrete using an innovative prediction approach. Thus, this study proposes an approach that integrates statistically based machine learning approaches and investigates the effects of applied stress and temperature on the fatigue life of POPIC-modified bituminous concrete. POPIC-modified bituminous concrete (POPIC-MBC) is produced from a standard Marshall mix. The interactions between POPIC concentration, stress, and temperature were optimized using response surface methodology (RSM), resulting in 7.5% POPIC, 11.7 °C, and 0.2 MPa as the optimum parameters for fatigue life. To improve the prediction accuracy and robustness of the results, RSM and ANN models were used and analyzed using MATLAB and JMP Pro, respectively. The performance of the developed model was assessed using the coefficient of determination (R2), root mean square error (RMSE), and mean relative error (MRE). The study found that using RSM, MATLAB, and JMP Pro resulted in a comprehensive analysis. MATLAB achieved an R² value of 0.9844, RMSE of 3.094, and MRE of 312.427, and JMP Pro achieved an R² value of 0.998, RMSE of 1.245, and MRE of 126.243, demonstrating higher prediction accuracy and superior performance than RSM, which had an R² value of 0.979, RMSE of 3.757, and MRE of 357.846. Further validation with parity, Taylor, and violin plots demonstrates that both models have good prediction accuracy, with the JMP Pro ANN model outperforming in terms of accuracy and alignment. This demonstrates the machine learning approach's efficiency in analyzing the fatigue life of POPIC-MBC, revealing it to be a useful tool for future research and practical applications. Furthermore, the study reveals that the innovative approach adopted and POPIC modifier, obtained from biomass waste, meets zero-waste and circular bioeconomy goals, contributing to the UN's SDGs 9, 11, 12, and 13. [ABSTRACT FROM AUTHOR]

Sign language is an essential means of communication for individuals with hearing disabilities. However, there is a significant shortage of sign language interpreters in some languages, especially in Saudi Arabia. This shortage results in a large proportion of the hearing-impaired population being deprived of services, especially in public places. This paper aims to address this gap in accessibility by leveraging technology to develop systems capable of recognizing Arabic Sign Language (ArSL) using deep learning techniques. In this paper, we propose a hybrid model to capture the spatio-temporal aspects of sign language (i.e., letters and words). The hybrid model consists of a Convolutional Neural Network (CNN) classifier to extract spatial features from sign language data and a Long Short-Term Memory (LSTM) classifier to extract spatial and temporal characteristics to handle sequential data (i.e., hand movements). To demonstrate the feasibility of our proposed hybrid model, we created a dataset of 20 different words, resulting in 4000 images for ArSL: 10 static gesture words and 500 videos for 10 dynamic gesture words. Our proposed hybrid model demonstrates promising performance, with the CNN and LSTM classifiers achieving accuracy rates of 94.40% and 82.70%, respectively. These results indicate that our approach can significantly enhance communication accessibility for the hearing-impaired community in Saudi Arabia. Thus, this paper represents a major step toward promoting inclusivity and improving the quality of life for the hearing impaired. [ABSTRACT FROM AUTHOR]

The application of nitrogen (N) improves the winter wheat yield. Excessive N application affects winter wheat yields, leading to low net incomes and negative environmental impacts, therefore, optimizing N application is essential. In this study, the effects of N rates on crop growth yield, net income (NI), water use efficiency (WUE), and nitrogen use efficiency (NUE) in the irrigated districts of the eastern loess plateau, China, were investigated using seven N application rates (N0, N90, N180, N210, N240, N270, and N300 kg ha−1) during the 2016–2017 and 2017–2018 seasons. N application significantly increased the total water consumption at 0–200 cm during the growth period, the aboveground dry matter at maturity, the grain nitrogen accumulation, yield, NI, and WUE. However, N exceeding 240 kg ha−1 did not favor dry matter and nitrogen accumulation or translocation from the anthesis stage to the maturity stage, thus leading to reduced yield, NI, and WUE. The transpiration rate and stomatal conductance N240 was highest 21–28 day after anthesis, at 187–276 kg ha−1, which can achieve a high yield and profitability with relatively low environmental costs. [ABSTRACT FROM AUTHOR]

Nowadays, there is a constant focus on implementing the net-zero emission (NZE) concept in the manufacturing supply chain (MSC). To reduce emissions and improve organisational efficiency, adopting the net-zero concept is a prevalent trend in today's highly competitive global business environment. Governments and stakeholders are pressuring the manufacturing sector to use natural resources efficiently and reduce environmental impacts. As a result, the manufacturing industry is focusing on cleaner production using net-zero practices. This study aims to identify and analyse the interaction among the drivers of net-zero adoption in the MSC. Through a systematic literature review (SLR), a list of drivers was recognised. To validate these drivers, we conducted an empirical study with 173 respondents from the Indian manufacturing industry. Further, we employed an artificial neural network (ANN) to weigh the nonlinear effect of drivers. Fuzzy interpretive structural modelling (F-ISM) was used to identify the interaction relationships among the drivers and construct a hierarchical structure among these identified drivers. The fuzzy matrix of cross-impact multiplications applied to the classification (F-MICMAC) method was used to categorise these drivers into driving and dependent categories. The outcomes of ANN show that Environmental predictors (100%) emerged as the most significant drivers, followed by Economic drivers (60.38%) and Technological drivers (59.05%). This study is a valuable resource for academia and industry professionals, providing essential insights into how adopting net zero facilitates the manufacturing industry's ability to achieve net zero across the supply chain. [ABSTRACT FROM AUTHOR]

Following calls for more research on the circular economy and open innovation, this study sheds light on the scant mechanisms influencing the relationship between open circular innovation and business circularity. This study theorizes that achieving business circularity requires a radical shift, and those circular exploitative activities may not be an effective strategy for this transition. Therefore, circular-based dynamic capabilities and circular ambidexterity are proposed as moderated-mediation mechanisms that explain the relationship between open circular innovation and business circularity. This study employs a quantitative approach to examine the hypothesized relationships. Data were purposively collected from owners and managers of marble manufacturing units due to the significant amount of waste generated by these units. A total of 292 valid responses were analyzed using SmartPLS version 4.1.0.5. The results revealed that the combined effect of open circular innovation and circular-based dynamic capabilities stimulates circular ambidexterity, which in turn leads to business circularity. This signifies that the complementary role of circular ambidexterity and circular-based dynamic capabilities foster the implementation of business circularity. This study empirically confirmed the application of three frameworks into a single new structure called the circular resource-based view, which has been neglected in previous literature. This study concluded that an organization's circular resources, capabilities, and competencies, when combined harmoniously with circular strategies, could lead to business circularity. [ABSTRACT FROM AUTHOR]

Diagnosis of bruxism is challenging because not all contractions of the masticatory muscles can be classified as bruxism. Conventional methods for sleep bruxism detection vary in effectiveness. Some provide objective data through EMG, ECG, or EEG; others, such as dental implants, are less accessible for daily practice. These methods have targeted the masseter as the key muscle for bruxism detection. However, it is important to consider that the temporalis muscle is also active during bruxism among masticatory muscles. Moreover, studies have predominantly examined sleep bruxism in the supine position, but other anatomical positions are also associated with sleep. In this research, we have collected EMG data to detect the maximum voluntary contraction of the temporalis and masseter muscles in three primary anatomical positions associated with sleep, i.e., supine and left and right lateral recumbent positions. A total of 10 time domain features were extracted, and six machine learning classifiers were compared, with random forest outperforming others. The models achieved better accuracies in the detection of sleep bruxism with the temporalis muscle. An accuracy of 93.33% was specifically found for the left lateral recumbent position among the specified anatomical positions. These results indicate a promising direction of machine learning in clinical applications, facilitating enhanced diagnosis and management of sleep bruxism. [ABSTRACT FROM AUTHOR]

Sugarcane holds global promise as a biofuel feedstock, necessitating a deep understanding of factors that influence biomass yield. This study unravels the intricate dynamics of plant hormones that govern growth and development in sugarcane. Transcriptome analysis of F2 introgression hybrids, derived from the cross of Saccharum officinarum "LA Purple" and wild Saccharum robustum "MOL5829", was conducted, utilizing the recently sequenced allele-specific genome of "LA Purple" as a reference. A total of 8059 differentially expressed genes were categorized into gene models (21.5%), alleles (68%), paralogs (10%), and tandemly duplicated genes (0.14%). KEGG analysis highlighted enrichment in auxin (IAA), jasmonic acid (JA), and abscisic acid (ABA) pathways, revealing regulatory roles of hormone repressor gene families (Aux/IAA, PP2C, and JAZ). Signaling pathways indicated that downregulation of AUX/IAA and PP2C and upregulation of JAZ repressor genes in high biomass segregants act as key players in influencing downstream growth regulatory genes. Endogenous hormone levels revealed higher concentrations of IAA and ABA in high biomass, which contrasted with lower levels of JA. Weighted co-expression network analysis demonstrated strong connectivity between hormone-related key genes and cell wall structural genes in high biomass genotypes. Expression analysis confirmed the upregulation of genes involved in the synthesis of structural carbohydrates and the downregulation of inflorescence and senescence-related genes in high biomass, which suggested an extended vegetative growth phase. The study underscores the importance of cumulative gene expression, including gene models, dominant alleles, paralogs, and tandemly duplicated genes and activators and repressors of disparate hormone (IAA, JA, and ABA) signaling pathways are the points of hormone crosstalk in contrasting biomass F2 segregants and could be applied for engineering high biomass acquiring varieties. [ABSTRACT FROM AUTHOR]

Conserving water resources from scarcity and pollution is the basis of water resource management and water quality monitoring programs. However, due to industrialization and population growth in Malaysia, which have resulted in poor water quality in many areas, this program needs to be improved. A smart water quality monitoring system based on the internet of things (IoT) paradigm was designed to analyze water conditions in real time and enable effective water management. Long-range (LoRa) application of the low-power, wide-area networking concept has become a phenomenon in IoT smart monitoring applications. This study proposes the implementation of a LoRa network in a water quality monitoring system-based IoT approach. The LoRa nodes were embedded with measuring sensors pH, turbidity, temperature, total dissolved solids, and dissolved oxygen, in the designated water stations. They operate at a transmission power of 14 dB and a bandwidth of 125 kHz. The network properties were tested with two different antenna gains of 2.1 dBi and 3 dBi, with three different spread factors of 7, 9, and 12. The water stations were located on the Sungai Pantai and Sungai Anak Air Batu rivers on the Universiti Malaya campus, Malaysia. Following a dashboard display and K-means analysis of the water quality data received by the LoRa gateway, it was determined that both rivers are Class II B rivers. The results from the evaluation of LoRa performance on the received strength signal indicator, signal noise ratio, loss packet, and path loss at best were −83 dBm, 7 dB, <0%, and 64.41 dB, respectively, with a minimum received sensitivity of −129.1 dBm. LoRa has demonstrated its efficiency in an urban environment for smart river monitoring purposes. [ABSTRACT FROM AUTHOR]

Simple Summary: Diabetes and cancer are major global health problems that cause significant illness and death. Many cancer patients also have diabetes, which complicates their treatment, especially if surgery is needed. This review highlights the link between diabetes and cancer. It looks into how cancer treatments can worsen blood sugar control, examines the risks that surgery holds for patients with both conditions, and outlines the strategies that can be used to manage diabetes around the time of surgery. More research is needed to provide new strategies that can reduce complications following surgery in patients with diabetes and cancer. Background: Both diabetes and cancer are major global health issues that are among the leading causes of morbidity and mortality. There is a high prevalence of diabetes among cancer patients, many of whom require a surgical procedure. This review focuses on the operative complications in patients with diabetes and cancer, and the perioperative management of diabetes in cancer patients. Methodology: A literature search of articles in English—published between January 2010 and May 2024—was carried out using the databases PubMed, MEDLINE, Google Scholar, and the Cochrane Database of Systematic Reviews. The search primarily focused on the operative complications in patients with diabetes and cancer, and perioperative management strategies. Results: The relationship between cancer and diabetes is complex; cancer patients have a high risk of developing diabetes, while diabetes is a risk factor for certain cancers. In addition, various cancer therapies can induce or worsen diabetes in susceptible patients. Many individuals with cancer and diabetes require surgery, and due to underlying diabetes, they may have elevated risks for operative complications. Optimal perioperative management for these patients includes managing perioperative glycemia and other comorbid illnesses, adjusting diabetic and cancer treatments, optimizing nutrition, minimizing the duration of fasting, supporting early mobilization, and providing patient education to enable self-management. Conclusions: While evidence is limited, optimal perioperative management for patients with both diabetes and cancer is necessary in order to reduce surgical complications. Future studies are needed to develop evidence-informed perioperative strategies and improve outcomes for these patients. [ABSTRACT FROM AUTHOR]

Nowadays, industries across the globe are acknowledging the need for a Net Zero Supply Chain (NZSC) by 2050, particularly within the Manufacturing Supply Chain (MSC) due to its significant contribution to Greenhouse Gas (GHG) emissions. Government and regulatory bodies pressure the industry to reduce GHG emissions. Industries focus on cleaner and sustainable production by adopting net zero technique practices. Achieving the net zero goal needs technological integration, such as Caron Capture Technology (CCT) and Life Cycle Assessment (LCA). Are LCA and CCT unlocking the potential for net-zero practices in MSC? This still needs to be answered. Therefore, this study aims to identify and prioritize potential enablers of Life Cycle Assessment (LCA) to facilitate the NZSC. We identified potential enablers using a Systematic Literature Review (SLR) and expert opinion. An empirical study validated these enablers, followed by the application of the "Best-Worst Method (BWM)" to rank them and the "Fuzzy Decision-making trial and evaluation laboratory (F-DEMATEL)" to identify causal relationships. The outcomes of this study reveal that 'Energy Management and Emission Reduction' is the most critical category of enablers, followed by 'Governance and Collaboration', 'Assessment and Digitalization', and 'Sustainable Materials and Production'. Outcomes highlight the importance of integrating technological innovations and stakeholder engagement to achieve net-zero goals. The implications of this study provide valuable insights for policymakers and practitioners. By focusing on the prioritized enablers, the manufacturing industry can effectively implement LCA, develop the NZSC business model, and enhance competitiveness in the global market. This study contributes to the ongoing discourse on sustainable manufacturing practices and offers a strategic framework for achieving NZSC by 2050. [ABSTRACT FROM AUTHOR]

Lithium-ion battery recycling includes discharging and processing exhausted batteries to recover valuable metals for reuse in new battery production. The improper disposal of e-waste draws attention to the possibility of reprocessing used lithium-ion batteries to make progress in recovering valuable metals. In this study, using biodegradable mixed organic acids, valuable metals were extracted from used batteries by a hydrometallurgical process under optimal conditions such as a stirring speed of 200 rpm, mixed acid concentration of ascorbic acid/citric acid (AA/CA) of 50:50 mM, temperature of 50 °C, time of 50 min, and slurry density of 20 g/L. Kinetic studies verified that the apparent activation energies, 43.6, 70.5, 49.8, 60.6, 45, and 6 kJ/mol, and surface chemical reactions controlled the leaching process for Li, Mn, Co, Ni, and Cu from cathode powder obtained from used LIBs. XRD and FT-IR confirmed the crystalline nature of the cathode powder. UV–visible spectra showed a Co(II) complex with λmax at 380 nm by reduction of the Co(III) complex. Lithium was recovered by LiF and as MnO2 using ammonium persulfate. Our efforts aimed to recover it through an economical and environmentally friendly approach. [ABSTRACT FROM AUTHOR]

Social media's potential for knowledge dissemination is under-utilized due to limited user participation. This study systematically reviews factors affecting knowledge-sharing intentions on social platforms using the ROSES protocol. We searched Scopus and Web of Science for quality, relevance, and rigor, finding that 65% of the articles shared were published in high-quality journals (Q1 or Q2), with the Journal of Knowledge Management accounting for 15%. Since 2015, 62.5% of research has been published, highlighting increased activity. Quantitative methods dominated (95%), with Zhihu being the most studied platform. We identified four key themes—psychological, technological, environmental, and social—covering 47 determinants centered on trust and attitude, primarily based on individual and social behavior theories. This is the first systematic exploration of elements influencing knowledge-sharing intentions on social media, providing insights to enhance user interaction and guide social media strategies in knowledge-centric organizations. [ABSTRACT FROM AUTHOR]

Salinity stress can significantly impact productivity in agricultural area with limited water re-sources. Our study focused on how plants under salt stress respond to phosphorus availability in terms of growth and biochemical reactions in cotton genotypes. Two cotton genotypes with different P efficiencies (SK39 and JM21) were used in a hydroponic experiment with 300 mM NaCl and three P treatments (10, 20, and 30 mM). Salinity stress decreases root growth, shoot growth, biomass production, and chlorophyll content, according to the experimental findings. In treated plants, it also increased the levels of oxidative stress. However, this effect was alleviated by phosphorus therapy, which controlled the production of proline, total soluble sugars, and hydrogen peroxide (H2O2). Interestingly, salt-sensitive JM21 responded to phosphorus supplementation more favorably than salt-tolerant SK39. Our research emphasizes the critical role that phosphorus especially P20 plays increasing the salinity stress sensitivity of cotton plants and offers insightful in-formation on the mechanisms underlying the role of phosphorus in reducing salinity stress effects. This study also revealed interspecific variability in cotton genotypes and characteristics, primarily represented by attributes related to cotton growth and morphological indicators such as dry matter biomass. [ABSTRACT FROM AUTHOR]

Olfactory perception is an important physiological function for human well-being and health. Loss of olfaction, or anosmia, caused by viral infections such as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has received considerable attention, especially in persistent cases that take a long time to recover. This review discusses the integration of different components of the olfactory epithelium to serve as a structural and functional unit and explores how they are affected during viral infections, leading to the development of olfactory dysfunction. The review mainly focused on the role of receptors mediating the disruption of olfactory signal transduction pathways such as angiotensin converting enzyme 2 (ACE2), transmembrane protease serine type 2 (TMPRSS2), neuropilin 1 (NRP1), basigin (CD147), olfactory, transient receptor potential vanilloid 1 (TRPV1), purinergic, and interferon gamma receptors. Furthermore, the compromised function of the epithelial sodium channel (ENaC) induced by SARS-CoV-2 infection and its contribution to olfactory dysfunction are also discussed. Collectively, this review provides fundamental information about the many types of receptors that may modulate olfaction and participate in olfactory dysfunction. It will help to understand the underlying pathophysiology of virus-induced anosmia, which may help in finding and designing effective therapies targeting molecules involved in viral invasion and olfaction. To the best of our knowledge, this is the only review that covered all the receptors potentially involved in, or mediating, the disruption of olfactory signal transduction pathways during COVID-19 infection. This wide and complex spectrum of receptors that mediates the pathophysiology of olfactory dysfunction reflects the many ways in which anosmia can be therapeutically managed. [ABSTRACT FROM AUTHOR]

The fabrication of welded joints in steel sheets has become a focal point, especially in meeting the demands for interconnections within battery packs for electric vehicles (EVs). This study delves into the impact arising from the initiation arc during the micro-tungsten inert gas (TIG) welding of nickel-plated steel sheets. The investigation involved the manipulation of various current modulations and arc lengths. Notably, optimal results were achieved with a 5 mm arc length paired with a 25 A current modulation. Microstructural analysis, conducted through scanning electron microscopy (SEM), unveiled a higher penetration depth, contributing to a more extensive and shallower fusion zone at the interface between the filler metal and the base material. Tensile testing revealed impressive mechanical properties, with the ultimate tensile strength peaking at 90 N/mm2, a yield strength of 85 N/mm2, and the highest elastic modulus. This underscores the weld's robustness in withstanding applied loads and resisting fracture. Furthermore, the calculation of the lowest K factor at 1.0375 indicated a reduction in resistance across the specimen, resulting in enhanced conductivity. Micro-TIG welding emerges as an efficient method for nickel-plated steel in connecting individual battery cells to form a high-capacity battery pack. These interconnections ensure efficient current flow and maintain the overall integrity and performance of the battery pack. The reliability and quality of these interconnects directly affect the battery's efficiency, safety, and lifespan in EVs application. [ABSTRACT FROM AUTHOR]

Stroke is a major cause of mortality worldwide, disrupts cerebral blood flow, leading to severe brain damage. Hemiplegia, a common consequence, results in motor task loss on one side of the body. Many stroke survivors face long-term motor impairments and require great rehabilitation. Electroencephalograms (EEGs) provide a non-invasive method to monitor brain activity and have been used in brain–computer interfaces (BCIs) to help in rehabilitation. Motor imagery (MI) tasks, detected through EEG, are pivotal for developing BCIs that assist patients in regaining motor purpose. However, interpreting EEG signals for MI tasks remains challenging due to their complexity and low signal-to-noise ratio. The main aim of this study is to focus on optimizing channel selection in EEG-based BCIs specifically for stroke rehabilitation. Determining the most informative EEG channels is crucial for capturing the neural signals related to motor impairments in stroke patients. In this paper, a binary bat algorithm (BA)-based optimization method is proposed to select the most relevant channels tailored to the unique neurophysiological changes in stroke patients. This approach is able to enhance the BCI performance by improving classification accuracy and reducing data dimensionality. We use time–entropy–frequency (TEF) attributes, processed through automated independent component analysis with wavelet transform (AICA-WT) denoising, to enhance signal clarity. The selected channels and features are proved through a k-nearest neighbor (KNN) classifier using public BCI datasets, demonstrating improved classification of MI tasks and the potential for better rehabilitation outcomes. [ABSTRACT FROM AUTHOR]

Bacillus cereus sensu stricto is a foodborne pathogen that causes food poisoning. Their spore and biofilm-forming abilities persist in various environments and foods. This study investigated the prevalence, virulence, antibiotic resistance, and genetic diversity of B. cereus s. s. strains isolated from various food samples. Of 179 samples, 22.34% were positive for B. cereus s. s., with significantly high detection rates in milk products and raw chicken meat. Forty strains were isolated from positive samples. Matrix-assisted laser desorption ionization/time of flight mass spectrometry analysis revealed nine distinct clusters and multi-locus sequence typing revealed 34 sequence types including 23 novel sequences, demonstrating high genetic diversity among the isolates. PCR analysis revealed that all the strains contained at least one toxin gene, but none contained the cytK gene. Antibiotic resistance tests revealed that all isolates were classified as multidrug-resistant, with high resistance levels, particularly to β-lactam antibiotics and vancomycin, but were susceptible to gentamicin. All isolates showed variations in biofilm formation. This study highlights the significant public health risk due to B. cereus s. s. and underscores the need for stringent monitoring and control measures in food production to manage antimicrobial resistance and ensure food safety. [ABSTRACT FROM AUTHOR]

Waste cooking oil is a common byproduct in the culinary industry, often posing disposal challenges. This study explores its conversion into the valuable bioplastic material, medium-chain-length polyhydroxyalkanoate (mcl-PHA), through microbial biosynthesis in controlled bioreactor conditions. Twenty-four bacterial isolates were obtained from oil-contaminated soil and waste materials in Mahd Ad-Dahab, Saudi Arabia. The best PHA-producing isolates were identified via 16S rDNA analysis as Neobacillus niacini and Metabacillus niabensis, with the sequences deposited in GenBank (accession numbers: PP346270 and PP346271). This study evaluated the effects of various carbon and nitrogen sources, as well as environmental factors, such as pH, temperature, and shaking speed, on the PHA production titer. Neobacillus niacini favored waste cooking oil and yeast extract, achieving a PHA production titer of 1.13 g/L, while Metabacillus niabensis preferred waste olive oil and urea, with a PHA production titer of 0.85 g/L. Both strains exhibited optimal growth at a neutral pH of 7, under optimal shaking -flask conditions. The bioreactor performance showed improved PHA production under controlled pH conditions, with a final titer of 9.75 g/L for Neobacillus niacini and 4.78 g/L for Metabacillus niabensis. Fourier transform infrared (FT-IR) spectroscopy and gas chromatography–mass spectrometry (GC-MS) confirmed the biosynthesized polymer as mcl-PHA. This research not only offers a sustainable method for transforming waste into valuable materials, but also provides insights into the optimal conditions for microbial PHA production, advancing environmental science and materials engineering. [ABSTRACT FROM AUTHOR]

The electrification of road freight transport on highways using an overhead contact line system is being tested in Germany. In this study, we investigated availability of an overhead contact line system based upon unique real-world data gathered in the ELISA field test. Based on our investigation, we anticipate a high system availability in future operations. We concluded that the newness of the system can justify the longest downtimes. To confirm our findings, we first categorized causes of the downtimes. Building upon these data, we analyzed these downtimes, specifically focusing on the longest occurrences as they posed significant barriers to future operation and market implementation. Subsequently, we illustrated how the system's availability changed when individual causes of downtimes were reduced over time. Our research is paving the way to chart potential future operational scenarios. Our contribution aids decision-makers and all individuals who need to determine whether overhead contact line technology should be employed on highways in the future. [ABSTRACT FROM AUTHOR]

This study investigated the impact of meteorological factors on electricity consumption in arid regions, characterized by extreme temperatures and high humidity. Statistical approaches such as multiple linear regression (MLR) and multiplicative time series (MTS), alongside the advanced machine learning method Extreme Gradient Boosting (XGBoost) were utilized to analyze historical consumption data. The models developed were rigorously evaluated using established measures such as the Coefficient of Determination ( R 2 ), Root Mean Squared Error (RMSE), and Mean Absolute Percentage Error (MAPE). The performance of the models was highly accurate, with regression-type models consistently achieving an R 2 greater than 0.9. Additionally, other metrics such as RMSE and MAPE demonstrated exceptionally low values relative to the overall data scale, reinforcing the models' precision and reliability. The analysis not only highlights the significant meteorological drivers of electricity consumption but also assesses the models' effectiveness in managing seasonal and irregular variations. These findings offer crucial insights for improving energy management and promoting sustainability in similar climatic regions. [ABSTRACT FROM AUTHOR]

Salinity is a serious abiotic stress that limits crop production and food security. Micronutrient application has shown promising results in mitigating the toxic impacts of salinity. This study assessed the impacts of zinc seed priming (ZSP) on the germination, growth, physiological and biochemical functioning of sorghum cultivars. The study comprised sorghum cultivars (JS-2002 and JS-263), salinity stress (control (0 mM) and 120 mM)), and control and ZSP (4 mM). Salinity stress reduced germination and seedling growth by increasing electrolyte leakage (EL: 60.65%), hydrogen peroxide (H2O2: 109.50%), malondialdehyde (MDA; 115.30%), sodium (Na), and chloride (Cl) accumulation and decreasing chlorophyll synthesis, relative water contents (RWC), total soluble proteins (TSPs), and potassium (K) uptake and accumulation. Nonetheless, ZSP mitigated the deleterious impacts of salinity and led to faster germination and better seedling growth. Zinc seed priming improved the chlorophyll synthesis, leaf water contents, antioxidant activities (ascorbate peroxide: APX, catalase: CAT, peroxidase: POD, superoxide dismutase: SOD), TSPs, proline, K uptake and accumulation, and reduced EL, MDA, and H2O2 production, as well as the accumulation of toxic ions (Na and Cl), thereby promoting better germination and growth. Thus, these findings suggested that ZSP can mitigate the toxicity of salinity by favoring nutrient homeostasis, antioxidant activities, chlorophyll synthesis, osmolyte accumulation, and maintaining leaf water status. [ABSTRACT FROM AUTHOR]

Designing drilling mud rheology is a complex task, particularly when it comes to preventing filter cakes from obstructing formation pores and making sure they can be easily decomposed using breakers. Incorporating both multiphysics and data-driven numerical simulations into the design of mud rheology experiments creates an additional challenge due to their symmetrical integration. In this computational intelligence study, we introduced numerical validation techniques using 498 available datasets from mud rheology and images from filter cakes. The goal was to symmetrically predict flow, maximize filtration volume, monitor void spaces, and evaluate formation damage occurrences. A neural-objective and image optimization approach to drilling mud rheology automation was employed using an artificial neural network feedforward (ANN-FF) function, a non-ANN-FF function, an image processing tool, and an objective optimization tool. These methods utilized the Google TensorFlow Sequential API-DNN architecture, MATLAB-nftool, the MATLAB-image processing tool, and a single-objective optimization algorithm. However, the analysis emanating from the ANN-FF and non-ANN-FF (with neurons of 10, 12, and 18) indicated that, unlike non-ANN-FF, ANN-FF obtained the highest correlation coefficient of 0.96–0.99. Also, the analysis of SBM and OBM image processing revealed a total void area of 1790 M µm2 and 1739 M µm2, respectively. Both SBM and OBM exhibited notable porosity and permeability that contributed to the enhancement of the flow index. Nonetheless, this study did reveal that the experimental-informed single objective analysis impeded the filtration volume; hence, it demonstrated potential formation damage. It is, therefore, consistent to note that automating flow predictions from mud rheology and filter cakes present an alternative intelligence method for non-programmers to optimize drilling productive time. [ABSTRACT FROM AUTHOR]

In this study, titanium dioxide (TiO2) was deposited onto a fluorine-doped tin oxide (FTO) substrate using the sol–gel spin coating method. Through the implementation of calcination treatment on the thin film, enhancements were observed in terms of structural, optical, and morphological properties. Various calcination temperatures were explored, with TiO2 annealed at 600 °C identified as the optimal sample. Analysis of the X-ray diffraction spectroscopy (XRD) pattern revealed the prominent orientation plane of (101), indicating the presence of anatase TiO2 with a tetragonal pattern at this temperature. Despite fluctuations in the optical spectrum, the highest transmittance of 80% was observed in the visible region within the wavelength range of 400 nm. The estimated band-gap value of 3.45 eV reaffirmed the characteristic of TiO2. Surface analysis indicated the homogeneous growth of TiO2, uniformly covering the FTO substrate. Cross-sectional examination revealed a thickness of 263 nm with dense and compact nature of TiO2 thin film. No presence of defects or pores reflects a well-organized structure and high-quality formation. Significant electrical rectification properties were observed, indicating the successful formation of a p–n junction. In summary, calcination treatment was found to be crucial for enhancing the properties of the thin film, highlighting its significance in the development of solar cell applications. [ABSTRACT FROM AUTHOR]

Simple Summary: Scale drop disease virus (SDDV) has been identified as a significant pathogen causing scale drop syndrome (SDS), leading to significant economic losses in Asian seabass production in Southeast Asia. This study utilized a metagenomic approach to investigate the bacterial and viral communities associated with SDS, with a particular focus on SDDV, and evaluated the potential of metagenomics for retrieving complete SDDV genomes. By characterizing the complete genomes of SDDV strains, we aimed to gain a deeper understanding of the virus. The insights gained from this study are expected to inform the development of comprehensive disease prevention and control strategies for SDDV, mitigating its impact on the aquaculture industry. Scale drop disease virus (SDDV), a double-stranded DNA virus in the family Iridoviridae, has been reported widely in southeast Asian countries as a causative agent of scale drop syndrome (SDS) in Asian seabass. SDS has resulted in high mortality and significant economic losses to the aquaculture industry. This study demonstrated the use of metagenomic methods to investigate bacterial and viral communities present in infected fish tissues and recover a complete genome of the causative agent named SDDV TH7_2019. Characterization of the TH7_2019 genome revealed a genome size of 131 kb with 134 putative ORFs encoding viral proteins potentially associated with host apoptosis manipulation. A comparative genome analysis showed a high degree of amino acid identity across SDDV strains, with variations in number of repeat sequences and mutations within core genes. Phylogenetic analyses indicate a close relationship among SDDV genomes. This research enhances our understanding of the genetic diversity and evolutionary relationship of SDDV, contributing valuable insights for further development of effective control strategies of SDDV. [ABSTRACT FROM AUTHOR]

Background: Internships are a mandatory graduation requirement to help medical students transition to the work environment. Some individuals are prone to anxiety in an unfamiliar environment, which is a public concern among young adults. Here, we investigated the mechanism between internet gaming disorder and anxiety and insomnia among internship students. Methods: A convenient sample of 267 internship students was collected in a cross-sectional study module between 17 July and 27 December 2022. The survey contained a 7-item Generalized Anxiety Disorder (GAD-7), Athens Insomnia Scale (AIS), and Internet Gaming Disorder Scale—Short-Form (IGDS9-SF). The association was estimated using Pearson's correlations, and network analysis was performed to characterize these associations. Results: Our results indicate that about 60% of participants exhibited mild to severe anxiety and insomnia, while 2.28% showed symptoms of internet gaming disorder. Also, we found a moderate association between anxiety and insomnia. An item-level analysis indicated that GAD_1 "feeling anxious" and GAD_5 "unable to sit still" are essential for gaming, and that GAD_2 "uncontrollable worrying" is crucial for insomnia. This indicated an interplay between these items, supported by our centrality analysis, where we found that GAD_1 and GAD_2 depicted high centrality. Conclusions: We found high rates of anxiety and insomnia in internship students and the association between selected symptoms of anxiety and insomnia. At the same time, low rates of internet gaming disorder could be attributed to a lack of time for entertainment and an increased awareness of its risks. Given these findings, an awareness of anxiety and insomnia risk should be emphasized. [ABSTRACT FROM AUTHOR]

Background/Objectives: Nutritional status significantly influences cardiac surgery outcomes, with malnutrition contributing to poorer results and increased complications. This study addresses the critical gap in understanding by exploring the relationship between pre-operative nutritional status and post-operative cognitive dysfunction (POCD) in adult cardiac patients. Methods: A comprehensive search across key databases investigates the prevalence of malnutrition in pre-operative cardiac surgery patients, its effects, and its association with POCD. Factors exacerbating malnutrition, such as chronic illnesses and reduced functionality, are considered. The study also examines the incidence of POCD, its primary association with CABG procedures, and the impact of malnutrition on complications like inflammation, pulmonary and cardiac failure, and renal injury. Discussions: Findings reveal that 46.4% of pre-operative cardiac surgery patients experience malnutrition, linked to chronic illnesses and reduced functionality. Malnutrition significantly contributes to inflammation and complications, including POCD, with an incidence ranging from 15 to 50%. CABG procedures are particularly associated with POCD, and malnutrition prolongs intensive care stays while increasing vulnerability to surgical stress. Conclusions: The review underscores the crucial role of nutrition in recovery and advocates for a universally recognized nutrition assessment tool tailored to diverse cardiac surgery patients. Emphasizing pre-operative enhanced nutrition as a potential strategy to mitigate inflammation and improve cognitive function, the review highlights the need for integrating nutrition screening into clinical practice to optimize outcomes for high-risk cardiac surgery patients. However, to date, most data came from observational studies; hence, there is a need for future interventional studies to test the hypothesis that pre-operative enhanced nutrition can mitigate inflammation and improve cognitive function in this patient population. [ABSTRACT FROM AUTHOR]

Extraction techniques are continuously developed by the scientific community. Meanwhile, avocado peel is a by-product of avocado processing and a source of bioactive compounds. The purpose of this review was to summarize the use of advanced techniques for extracting bioactive compounds from avocado peel to help understand which techniques have and have not been applied to avocado peel. Three primary databases were used to collect the information, including Google Scholar, Scopus, and Web of Science, by using the keywords "avocado", "peel", and "extraction". Additional keywords related to the extraction technique were also used, including "Microwave-Assisted Extraction", "Ultrasound-Assisted Extraction", "Enzyme-Assisted Extraction", "Pressurized Liquid Extraction", "Supercritical Fluid Extraction", "Natural Deep Eutectic Solvents", "Three-phase partitioning (TPP)", "Pulsed-Electric Field", "High Voltage Electric Discharge Plasma", "Centrifugal Partition Extraction", and "Surfactant-Mediated Extraction". The results show that microwave-assisted extraction, ultrasound-assisted extraction, enzyme-assisted extraction, pressurized liquid extraction, supercritical fluid extraction, TPP, and natural deep eutectic solvent extraction have been used to retrieve bioactive compounds from avocado peel. Other techniques have not yet been applied for the extraction of bioactive compounds from avocado peel. This article is the first review discussing the advanced extraction technique for retrieving bioactive compounds from avocado peel. This article creates a paradigm for future studies. [ABSTRACT FROM AUTHOR]

Net metering (NM) is among the potent regulatory tools used globally for supporting distributed generation and renewable energy sources. This paper examines the trajectory of NM in a developing country such as Pakistan, analyzing the impact of regulatory changes, confidence-building strategies, hindering factors, and technical/financial issues. The three-stage methodology involves three components, namely techno-economic analysis, stakeholder engagement surveys, and impact analysis of financing mechanisms. This study emphasizes the importance of clear regulatory and financial frameworks, grid upgrades, and public–private partnerships for technology distribution in the context of a developing country with weak grid utilities and an import–export energy ratio. It also explores the role of financial incentives, such as tax breaks and subsidies, to encourage investment in NM systems from the perspective of lucrative rates, impact on paybacks, and return on investments, and proposes concrete solutions to enhance financial inclusion for ambitious renewable energy goals. Until April 2023, over 56,000 NM/distributed generation facilities were commissioned, with an installed capacity of 950 MW. By May 2024, the number of NM consumers reached ~100,000, with a 1950 MW capacity, nearly doubling. However, the import and export ratio of IESCO changed most, with 61% exports and 39% imports, directly impacting the revenue stream. A total of 60% of banks have adopted actions linked with green banking criteria, aiming to limit their environmental impact. The change in tariff will result in reduced ROI for NM consumers to 20%, and increase the payback period from less than 4 years to 13 years. Government subsidies, tax breaks, and green financing frameworks are proposed to encourage investment, but have been abruptly halted, and were previously at a 6% interest rate. This research aims to provide insights into effective market evaluation methodologies for NM programs and offer policy recommendations to strengthen legislative and institutional frameworks governing NM. [ABSTRACT FROM AUTHOR]

This study employs electrochemical impedance spectroscopy (EIS) to probe the behavior of Tau-441 protein, a key component implicated in Alzheimer's disease. Through meticulous experimentation and analysis, the impedance of Tau-441 protein suspension revealed a conductivity peak value of 1.02 S/m. The study demonstrates a high level of specificity and selectivity, particularly within the challenging nanomolar concentration range. Additionally, the EIS method enabled the prediction of Tau-441 protein's dielectrophoresis (DEP) response and the determination of the associated frequency range of 1 kHz to 1 MHz. These findings contribute to advancing our understanding of the molecular intricacies surrounding Tau-441 and hold promise for unraveling implications related to Alzheimer's disease. This study establishes a robust foundation for future research on neurodegenerative disease and biosciences, offering valuable insights into the electrochemical dynamics of Tau-441 protein. [ABSTRACT FROM AUTHOR]

Simple Summary: Cancer cells often become resistant to treatment, making it harder to fight the disease effectively. This resistance can happen due to genetic changes, proteins that pump out drugs, or the environment around the tumor. Crizotinib is a drug that helps overcome this resistance in certain lung cancers and lymphomas by targeting specific proteins. It can also break down a harmful protein found in some cancers. Researchers have found that combining crizotinib with other drugs can improve its effectiveness, especially for cancers with specific genetic mutations. Chemoresistance is a major obstacle in cancer treatment, often leading to disease progression and poor outcomes. It arises through various mechanisms such as genetic mutations, drug efflux pumps, enhanced DNA repair, and changes in the tumor microenvironment. These processes allow cancer cells to survive despite chemotherapy, underscoring the need for new strategies to overcome resistance and improve treatment efficacy. Crizotinib, a first-generation multi-target kinase inhibitor, is approved by the FDA for the treatment of ALK-positive or ROS1-positive non-small cell lung cancer (NSCLC), refractory inflammatory (ALK)-positive myofibroblastic tumors (IMTs) and relapsed/refractory ALK-positive anaplastic large cell lymphoma (ALCL). Crizotinib exists in two enantiomeric forms: (R)-crizotinib and its mirror image, (S)-crizotinib. It is assumed that the R-isomer is responsible for the carrying out various processes reviewed here The S-isomer, on the other hand, shows a strong inhibition of MTH1, an enzyme important for DNA repair mechanisms. Studies have shown that crizotinib is an effective multi-kinase inhibitor targeting various kinases such as c-Met, native/T315I Bcr/Abl, and JAK2. Its mechanism of action involves the competitive inhibition of ATP binding and allosteric inhibition, particularly at Bcr/Abl. Crizotinib showed synergistic effects when combined with the poly ADP ribose polymerase inhibitor (PARP), especially in ovarian cancer harboring BRCA gene mutations. In addition, crizotinib targets a critical vulnerability in many p53-mutated cancers. Unlike its wild-type counterpart, the p53 mutant promotes cancer cell survival. Crizotinib can cause the degradation of the p53 mutant, sensitizing these cancer cells to DNA-damaging substances and triggering apoptosis. Interestingly, other reports demonstrated that crizotinib exhibits anti-bacterial activity, targeting Gram-positive bacteria. Also, it is active against drug-resistant strains. In summary, crizotinib exerts anti-tumor effects through several mechanisms, including the inhibition of kinases and the restoration of drug sensitivity. The potential of crizotinib in combination therapies is emphasized, particularly in cancers with a high prevalence of the p53 mutant, such as triple-negative breast cancer (TNBC) and high-grade serous ovarian cancer (HGSOC). [ABSTRACT FROM AUTHOR]

Ferroelectric materials, renowned for their capacity to demonstrate spontaneous electric polarization reversible through an external electric field, are essential in numerous technological applications owing to their distinctive characteristics. For this, a series of spinel Sr-Cd co-doped nickel ferrite nanomaterials Cd0.5−xSrxNi0.5Fe2O4 (x = 0.0, 0.1, 0.2 and 0.3) were prepared through the standard sol-gel auto combustion method The XRD patterns showed that the prepared samples have a cubic spinel structure. The crystallite sizes of the samples vary from 29 to 40 nm. The morphology of prepared samples showed uniformly distributed spheres. Magnetic properties showed the soft magnetic nature of the prepared ferrites. The ferroelectric study revealed that Sr-Cd substituted ferrites exhibited the elliptical nature of ferroelectric loops at normal room temperature. The maximum polarization has been achieved at x = 0.3. The understanding of current and voltage (I–V) showed a slowly decreasing tendency of leakage current on both sides symmetrically against the increasing Sr content. The conductivity of the prepared spinel increases as a function of higher Sr doping. The real part of dielectric constant increases with increasing frequency. The materials show large elliptical loops indicating high asymmetric ferroelectric energy storage capability. [ABSTRACT FROM AUTHOR]

This study investigates the relationship between corporate cash holdings and investment efficiency, with a focus on how COVID-19 and the presence of women directors may influence this relationship. Using data from Indonesian public companies during the COVID-19 period, comprising 2350 firm-year observations, we employ fixed-effect regression models with industry and year controls to test our hypotheses. Robustness and endogeneity tests are conducted to ensure the reliability of our findings. Our research reveals that companies with larger cash reserves tend to experience decreased investment efficiency during the COVID-19 crisis. Moreover, the negative impact of substantial cash reserves on investment efficiency is exacerbated by the presence of female directors on the board. However, our findings also suggest that female directors can mitigate the adverse effects of excessive cash reserves on a company's investment efficiency, particularly during unforeseen economic challenges such as the pandemic. [ABSTRACT FROM AUTHOR]

Muscle-enriched A-type lamin-interacting protein (MLIP) is an emerging protein involved in cellular homeostasis and stress adaptation. Eukaryotic cells regulate various cellular processes, including metabolism, DNA repair, and cell cycle progression, to maintain cellular homeostasis. Disruptions in this homeostasis can lead to diseases such as cancer, characterized by uncontrolled cell growth and division. This review aims to explore for the first time the unique role MLIP may play in cancer development and progression, given its interactions with the PI3K/Akt/mTOR pathway, p53, MAPK9, and FOXO transcription factors, all critical regulators of cellular homeostasis and tumor suppression. We discuss the current understanding of MLIP's involvement in pro-survival pathways and its potential implications in cancer cells' metabolic remodeling and dysregulated homeostasis. Additionally, we examine the potential of MLIP as a novel therapeutic target for cancer treatment. This review aims to shed light on MLIP's potential impact on cancer biology and contribute to developing innovative therapeutic strategies. [ABSTRACT FROM AUTHOR]

Naphthenic acid corrosion is a well-recognized factor contributing to corrosion in the construction of offshore industry pipelines. To mitigate the corrosive effects, minor quantities of alloying elements are introduced into the steel. This research specifically explores the corrosion effects arising from immersing low-carbon steel, specifically A333 Grade 6, in a naphthenic acid solution. Various weight percentages of niobium were incorporated, and the resulting properties were observed. It was noted that the addition of 2% niobium in low-carbon steel exhibited the least mass loss and a lower corrosion rate after a 12 h immersion in naphthenic acid. Microstructural analysis using scanning electron microscopy (SEM) revealed small white particles, indicating the presence of oil sediment residue, along with corrosion pits. Following the addition of 2% niobium, the occurrence of corrosion pits markedly decreased, and only minor voids were observed. Additionally, the chemical composition analysis using energy-dispersive X-Ray analysis (EDX) showed that the black spot exhibited the highest percentage of carbon, resembling high corrosion attack. Meanwhile, the whitish regions with low carbon content indicated the lowest corrosion attack. The results demonstrated that the addition of 2% niobium yielded optimal properties for justifying corrosion effects. Therefore, low-carbon steel with a 2% niobium addition can be regarded as a superior corrosion-resistant material for offshore platform pipeline applications. [ABSTRACT FROM AUTHOR]

This study aims to understand the influence of nitrogen accumulation, fungal endophyte, yield, nitrogen use efficiency, and grain nutritional quality parameters on the yield of quinoa in some areas of China. The endophytic microbial community in plants plays a crucial role in plant growth, development, and health, especially in quinoa plants under different nitrogen fertilizer levels. The results from the present study indicated that appropriate nitrogen application significantly enhanced the nitrogen accumulation and yield of quinoa grains during maturity, increasing by 34.54–42.18% and 14.59–30.71%, respectively. Concurrently, protein content, amylose, total starch, ash, and fat content also increased, with respective growth rates of 1.15–18.18%, 30.74–42.53%, 6.40–12.40%, 1.94–21.94%, and 5.32–22.22%. Our constructed interaction network of bacterial and fungal communities revealed that bacteria outnumbered fungi significantly, and most of them exhibited synergistic interactions. The moderate increase in N150 was beneficial for increasing quinoa yield, achieving nitrogen use efficiency (NUE) of over 20%. The N210 was increased, and both the yield and NUE significantly decreased. This study provides novel insights into the impact of nitrogen fertilizer on quinoa growth and microbial communities, which are crucial for achieving agricultural sustainable development. [ABSTRACT FROM AUTHOR]

The design of cheap, noble-metal-free, and efficient electrocatalysts for an enhanced hydrogen evolution reaction (HER) to produce hydrogen gas as an energy source from water splitting is an ideal approach. Herein, we report the synthesis of Fe2O3 nanorods-NiFe2O4 nanoparticles on cotton fiber cloth (Fe2O3-NiFe2O4/CF) at a low temperature as an efficient electrocatalyst for HERs. Among the as-prepared samples, the optimal Fe2O3-NiFe2O4/CF-3 electrocatalyst exhibits good HER performance with an overpotential of 127 mV at a current density of 10 mA cm-2, small Tafel slope of 44.9 mV dec-1, and good stability in 1 M KOH alkaline solution. The synergistic effect between Fe2O3 nanorods and NiFe2O4 nanoparticles of the heterojunction composite at the heterointerface is mainly responsible for improved HER performance. The CF is an effective substrate for the growth of the Fe2O3-NiFe2O4 nanocomposite and provides conductive channels for the active materials' HER process. [ABSTRACT FROM AUTHOR]

Agriculture is a major source of greenhouse gas (GHG) emissions. Biochar has been recommended as a potential strategy to mitigate GHG emissions and improve soil fertility and crop productivity. However, few studies have investigated the potential of biochar co-compost (BCC) in relation to soil properties, rice productivity, and GHG emissions. Therefore, we examined the potential of BC, compost (CP), and BCC in terms of environmental and agronomic benefits. The study comprised four different treatments: control, biochar, compost, and biochar co-compost. The application of all of the treatments increased the soil pH; however, BC and BCC remained the top performers. The addition of BC and BBC also limited the ammonium nitrogen (NH4+-N) availability and increased soil organic carbon (SOC), which limited the GHG emissions. Biochar co-compost resulted in fewer carbon dioxide (CO2) emissions, while BC resulted in fewer methane (CH4) emissions, which was comparable with BCC. Moreover, BC caused a marked reduction in nitrous oxide (N2O) emissions that was comparable to BCC. This reduction was attributed to increased soil pH, nosZ, and nirK abundance and a reduction in ammonia-oxidizing archaea (AOA) and ammonia-oxidizing bacteria (AOB) abundance. The application of different amendments, particularly BCC, favored rice growth and productivity by increasing nutrient availability, soil carbon, and enzymatic activities. Lastly, BCC and BC also increased the abundance and diversity of soil bacteria, which favored plant growth and caused a reduction in GHG emissions. Our results suggest that BCC could be an important practice to recycle organic sources while optimizing climate change and crop productivity. [ABSTRACT FROM AUTHOR]

Mercury (Hg) is a highly toxic contaminant posing serious ecological and human health risks. This study investigates the Hg transfer characteristics and prediction models in a soil–lettuce system, employing bioconcentration factors (BCF), path analysis (PA), and Freundlich-type functions. A pot experiment was conducted in a greenhouse, where lettuce was grown in a range of Chinese agricultural soils (n = 21) and deliberately spiked with Hg using Hg(NO3)2 solution. The results indicated that lettuce grown in Hg-spiked acidic soils (pH < 6.5) accumulated total Hg (THg) levels up to 14.01 µg kg−1, surpassing the safe consumption limit of 10 µg kg−1. The BCF for lettuce THg was less than 1.0, suggesting a low transfer of Hg from soil to lettuce. Notably, BCF values were significantly higher in acidic soils (0.02) compared to alkaline soils (0.005). Path analysis accounted for 82% of the variation in lettuce THg content, identifying soil THg, pH, and amorphous (Amo) Al and Fe oxides as primary direct factors. Additionally, soil-available Hg (AvHg), exchangeable Hg (ExHg), clay, and organic matter (OM) were significant indirect factors affecting lettuce THg content. To validate the findings of the path analysis, an extended Freundlich-type equation was developed using stepwise multiple linear regression (SMLR). This model exhibited high predictive accuracy (R2 = 0.82, p ≤ 0.001), with soil pH, THg, and amorphous Al and Fe oxides being the key variables for predicting Hg transfer in the soil–lettuce system. The insights from this study can guide the management of safe lettuce production in Hg-contaminated soils, ensuring the mitigation of Hg exposure through agricultural produce. [ABSTRACT FROM AUTHOR]

Backgrounds: Sleep disturbances are prevalent among elderly individuals. While polysomnography (PSG) serves as the gold standard for sleep monitoring, its extensive setup and data analysis procedures impose significant costs and time constraints, thereby restricting the long-term application within the general public. Our laboratory introduced an innovative biomarker, utilizing artificial intelligence algorithms applied to PSG data to estimate brain age (BA), a metric validated in cohorts with cognitive impairments. Nevertheless, the potential of exercise, which has been a recognized means of enhancing sleep quality in middle-aged and older adults to reduce BA, remains undetermined. Methods: We conducted an exploratory study to evaluate whether 12 weeks of moderate-intensity exercise can improve cognitive function, sleep quality, and the brain age index (BAI), a biomarker computed from overnight sleep electroencephalogram (EEG), in physically inactive middle-aged and older adults. Home wearable devices were used to monitor heart rate and overnight sleep EEG over this period. The NIH Toolbox Cognition Battery, in-lab overnight polysomnography, cardiopulmonary exercise testing, and a multiplex cytokines assay were employed to compare pre- and post-exercise brain health, exercise capacity, and plasma proteins. Results: In total, 26 participants completed the initial assessment and exercise program, and 24 completed all procedures. Data are presented as mean [lower 95% CI of mean, upper 95% CI of mean]. Participants significantly increased maximal oxygen consumption (Pre: 21.11 [18.98, 23.23], Post 22.39 [20.09, 24.68], mL/kg/min; effect size: −0.33) and decreased resting heart rate (Pre: 66.66 [63.62, 67.38], Post: 65.13 [64.25, 66.93], bpm; effect size: −0.02) and sleeping heart rate (Pre: 64.55 [61.87, 667.23], Post: 62.93 [60.78, 65.09], bpm; effect size: −0.15). Total cognitive performance (Pre: 111.1 [107.6, 114.6], Post: 115.2 [111.9, 118.5]; effect size: 0.49) was significantly improved. No significant differences were seen in BAI or measures of sleep macro- and micro-architecture. Plasma IL-4 (Pre: 0.24 [0.18, 0.3], Post: 0.33 [0.24, 0.42], pg/mL; effect size: 0.49) was elevated, while IL-8 (Pre: 5.5 [4.45, 6.55], Post: 4.3 [3.66, 5], pg/mL; effect size: −0.57) was reduced. Conclusions: Cognitive function was improved by a 12-week moderate-intensity exercise program in physically inactive middle-aged and older adults, as were aerobic fitness (VO2max) and plasma cytokine profiles. However, we found no measurable effects on sleep architecture or BAI. It remains to be seen whether a study with a larger sample size and more intensive or more prolonged exercise exposure can demonstrate a beneficial effect on sleep quality and brain age. [ABSTRACT FROM AUTHOR]

Ambulance vehicles face a challenging issue in minimizing the response time for an emergency call due to the high volume of traffic and traffic signal delays. Several research works have proposed ambulance vehicle detection approaches and techniques to prioritize ambulance vehicles by turning the traffic light to green for saving patients' lives. However, the detection of ambulance vehicles is a challenging issue due to the similarities between ambulance vehicles and other commercial trucks. In this paper, we chose a machine learning (ML) technique, namely, YOLOv8 (You Only Look Once), for ambulance vehicle detection by synchronizing it with the traffic camera and sending an open signal to the traffic system for clearing the way on the road. This will reduce the amount of time it takes the ambulance to arrive at the traffic light. In particular, we managed to gather our own dataset from 10 different countries. Each country has 300 images of its own ambulance vehicles (i.e., 3000 images in total). Then, we trained our YOLOv8 model on these datasets with various techniques, including pre-trained vs. non-pre-trained, and compared them. Moreover, we introduced a layered system consisting of a data acquisition layer, an ambulance detection layer, a monitoring layer, and a cloud layer to support our cloud-based ambulance detection system. Last but not least, we conducted several experiments to validate our proposed system. Furthermore, we compared the performance of our YOLOv8 model with other models presented in the literature including YOLOv5 and YOLOv7. The results of the experiments are quite promising where the universal model of YOLOv8 scored an average of 0.982, 0.976, 0.958, and 0.967 for the accuracy, precision, recall, and F1-score, respectively. [ABSTRACT FROM AUTHOR]

Despite Malaysia's year-round sunny climate, vitamin D deficiency is surprisingly common among Malaysians. However, we hypothesise that vitamin D levels among coastal populations are above average. Thus, we aim to investigate vitamin D levels and correlate them with the potential contributing factors from three selected coastal villages in Johor, Melaka, and Negeri Sembilan. Convenient sampling was employed to recruit 120 Malay male and female participants, and dried blood spots (DBS) were obtained to measure 25 (OH) vitamin D3 levels via immunoassay. Participants also completed two questionnaires: the Sun Exposure and Protection Index (SEPI) and a validated food frequency questionnaire for Malaysians. The participant pool comprised 35.20% males and 64.80% females who completed all questionnaires and underwent DBS sampling. Our analysis revealed a significant difference (p < 0.05) based on skin tones, impacting various facets of the SEPI, including sunscreen usage, protective clothing utilisation, and the adoption of protective headwear. Furthermore, gender emerged as another pivotal factor, demonstrating significant distinctions in these SEPI components. Nevertheless, there is a weak correlation between SEPI scores and vitamin D levels. Subsequent regression analysis did produce statistically significant results (p = 0.018), yet the associated low R2 value indicated a weak correlation between dietary vitamin D intake that impacts vitamin D levels. In conclusion, our preliminary findings indicate that sun exposure and dietary factors are not the sole determinants of 25-OH vitamin D3 levels. However, we require more samples from various coastal locations for a definitive justification. [ABSTRACT FROM AUTHOR]

Coastal acid-sulfate soils are crucial for producing crops and thus, for food security. However, over time, these soil resources experience degradation, leading to higher agro-input, lower yields, and environmental hazards that finally threaten food security. The optimal use of this fragile resource is only attained by implementing vigorous integrated water–soil–crop management technologies amid the climate change impact. This study aimed to review the distribution, properties, use, and management of acid-sulfate soils in Kalimantan, Indonesia. Acid-sulfate soils cover about 3.5 Mha of the coastal area in Kalimantan and have high acidity, high-risk iron and aluminum toxicity, and low fertility, requiring precise water management, amelioration and fertilizer application, crop variety selection, and rice cultivation technologies. Lime, biochar, organic fertilizer, compost, ash, and fly ash are ameliorants that raise pH, reduce iron and aluminum toxicity, and improve crop yield. Rice cultivation has developed from traditional to modern but needs re-designing to fit local conditions. Depending on the soil nutrient status, rice cultivation requires 80–200 kg ha−1 of urea, 50–150 kg ha−1 of SP36, 50–150 kg ha−1 of KCl, and 125–400 kg ha−1 of NPK compound fertilizer, but is affected by CH4 and CO2 emissions. Good water management impacts the effective implementation of amelioration and fertilizer application technologies. The remaining challenges and future directions for water management, amelioration, fertilizer application, crop varieties, cultivation techniques, land use optimization, climate change adaptation and mitigation, technology adoption and implementation, and resource conservation are outlined. Acid-sulfate soils remain a resource capital that supports food security regionally and nationally in Indonesia. [ABSTRACT FROM AUTHOR]

Amyotrophic Lateral Sclerosis (ALS) is a highly fatal neurodegenerative disorder characterized by the progressive wasting and paralysis of voluntary muscle. Despite extensive research, the etiology of ALS remains elusive, and effective treatment options are limited. However, recent evidence implicates gut dysbiosis and gut–brain axis (GBA) dysfunction in ALS pathogenesis. Alterations to the composition and diversity of microbial communities within the gut flora have been consistently observed in ALS patients. These changes are often correlated with disease progression and patient outcome, suggesting that GBA modulation may have therapeutic potential. Indeed, targeting the gut microbiota has been shown to be neuroprotective in several animal models, alleviating motor symptoms and mitigating disease progression. However, the translation of these findings to human patients is challenging due to the complexity of ALS pathology and the varying diversity of gut microbiota. This review comprehensively summarizes the current literature on ALS-related gut dysbiosis, focusing on the implications of GBA dysfunction. It delineates three main mechanisms by which dysbiosis contributes to ALS pathology: compromised intestinal barrier integrity, metabolic dysfunction, and immune dysregulation. It also examines preclinical evidence on the therapeutic potential of gut-microbiota-modulating agents (categorized as prebiotics, probiotics, and postbiotics) in ALS. [ABSTRACT FROM AUTHOR]

Cadmium (Cd), as the most prevalent heavy metal contaminant poses serious risks to plants, humans, and the environment. The ubiquity of this toxic metal is continuously increasing due to the rapid discharge of industrial and mining effluents and the excessive use of chemical fertilizers. Nanoparticles (NPs) have emerged as a novel strategy to alleviate Cd toxicity. Zinc oxide nanoparticles (ZnO-NPs) have become the most important NPs used to mitigate the toxicity of abiotic stresses and improve crop productivity. The plants quickly absorb Cd, which subsequently disrupts plant physiological and biochemical processes and increases the production of reactive oxygen species (ROS), which causes the oxidation of cellular structures and significant growth losses. Besides this, Cd toxicity also disrupts leaf osmotic pressure, nutrient uptake, membrane stability, chlorophyll synthesis, and enzyme activities, leading to a serious reduction in growth and biomass productivity. Though plants possess an excellent defense mechanism to counteract Cd toxicity, this is not enough to counter higher concentrations of Cd toxicity. Applying Zn-NPs has proven to have significant potential in mitigating the toxic effects of Cd. ZnO-NPs improve chlorophyll synthesis, photosynthetic efficiency, membrane stability, nutrient uptake, and gene expression, which can help to counter toxic effects of Cd stress. Additionally, ZnO-NPs also help to reduce Cd absorption and accumulation in plants, and the complex relationship between ZnO-NPs, osmolytes, hormones, and secondary metabolites plays an important role in Cd tolerance. Thus, this review concentrates on exploring the diverse mechanisms by which ZnO nanoparticles can alleviate Cd toxicity in plants. In the end, this review has identified various research gaps that need addressing to ensure the promising future of ZnO-NPs in mitigating Cd toxicity. The findings of this review contribute to gaining a deeper understanding of the role of ZnO-NPs in combating Cd toxicity to promote safer and sustainable crop production by remediating Cd-polluted soils. This also allows for the development of eco-friendly approaches to remediate Cd-polluted soils to improve soil fertility and environmental quality. [ABSTRACT FROM AUTHOR]

Background and Objective: Patients with spinal muscular atrophy (SMA) treated with a disease-modifying therapy (DMT) are often classified as responders or non-responders based on the attainment of a specific improvement threshold on validated functional scales. This categorization may significantly impact treatment reimbursement in some countries. The aim of this research is to evaluate the perception of treatments and their benefit by patients considered as responders or non-responders. Methods: In this non-commercial multicenter study, 99 post-symptomatically treated SMA type I–III patients with a median age of 11.2 (0.39–57.4) years at treatment initiation were stratified into three groups based on their treatment outcomes, i.e., those exhibiting clinically significant improvement (N = 41), those with non-clinically significant improvement (N = 18), or those showing no improvement (N = 40). Fifteen months after treatment, the initiation patients or patients' caregivers were assessed using a patient-rated scoring system based on the Patient Global Impression of Change (PGIC) scale, comprising 22 questions targeting important aspects and tasks in the daily life of patients with SMA. Results: We found no statistical difference in the patient perception of treatment benefits in 17 out of 22 domains across patient groups. Conclusions: Our results suggest that functional motor scales do not recapitulate patients' and patients' caregivers' experience of the effect of nusinersen treatment in SMA. [ABSTRACT FROM AUTHOR]