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This study proposes a promising candidate for organic solar cells through the creation of a novel nano-hybrid system composed of caffeine molecules encapsulated within a semiconducting single-walled carbon nanotube known as NT14 (14,0). The stability and optoelectronic properties of this hybrid system, designated as CA@NT14, were thoroughly investigated. We determined the optimal diameter for the NT14 nanotube using molecular mechanics, Density Functional Theory, spectral moment's method, and bond polarizability model, finding it to be approximately 1.18 nm. The encapsulation's impact on the Raman-active modes, specifically the radial breathing mode and tangential mode, was analyzed through Raman spectra calculations, revealing structural stability and charge transfer from CA to NT14. Notably, the NT14's Fermi level position increased upon encapsulation, indicating charge transfer and a type-II band alignment. The study further examined the bandgap characteristics of the hybrid system, finding that the encapsulation of CA within semiconducting NT14 nanotubes minimally impacts the nanotube's bandgap, thus retaining its excellent visible light absorption properties. Conversely, encapsulating CA within metallic nanotubes significantly reduces their bandgap, limiting their light absorption range. The nonresonant Raman responses of NT14 pre- and post-encapsulation corroborated the charge transfer between CA and NT14. Future research will focus on computing the electronic transport characteristics, transmission spectra, I-V characteristics, and yield of CA@NT14 using DFT and Non-Equilibrium Green's Function (formalism. An experimental study will be conducted to validate these theoretical findings. These results indicate the potential of CA@NT14 hybrids as effective active layers in organic solar cells, highlighting their significance in advancing optoelectronic applications. [ABSTRACT FROM AUTHOR]

Plant species diversity is one of the most widely used indicators in ecosystem management. The relation of species diversity with the size of the sample plot has not been fully determined for Oriental beech forests (Fagus orientalis Lipsky), a widespread species in the Hyrcanian region. Assessing the impacts of plot size on species diversity is fundamental for an ecosystem-based approach to forest management. This study determined the relation of species diversity and plot size by investigating species richness and abundance of both canopy and forest floor. Two hundred and fifty-six sample plots of 625 m2 each were layout in a grid pattern across 16 ha. Base plots (25 m × 25 m) were integrated in different scales to investigate the effect of plot size on species diversity. The total included nine plots of 0.063, 0.125, 0.188, 0.250, 0.375, 0.500, 0.563, 0.750 and 1 ha. Ten biodiversity indices were calculated. The results show that species richness in the different plot sizes was less than the actual value. The estimated value of the Simpson species diversity index was not significantly different from actual values for both canopy and forest floor diversity. The coefficient of variation of this index for the 1-ha sample plot showed the lowest amount across different plot sizes. Inverse Hill species diversity was insignificant difference across different plot sizes with an area greater than 0.500 ha. The modified Hill evenness index for the 1-ha sample size was a correct estimation of the 16-ha for both canopy and forest floor; however, the precision estimation was higher for the canopy layer. All plots greater than 0.250-ha provided an accurate estimation of the Camargo evenness index for forest floor species, but was inaccurate across different plot sizes for the canopy layer. The results indicate that the same plot size did not have the same effect across species diversity measurements. Our results show that correct estimation of species diversity measurements is related to the selection of appropriate indicators and plot size to increase the accuracy of the estimate so that the cost and time of biodiversity management may be reduced. [ABSTRACT FROM AUTHOR]

In IEC-61850-based digital substations, the protection IED's performance is dependent on merging unit's vendor implementation, communication networks, and measurement circuit's health conditions. As the process bus Sampled Value(SV) enables the availability of all sensor data on a communication network, this paper proposes a Backup Subscription scheme (BSS) for a transformer differential protection (87T, PDIF) function. BSS utilizes sensor data in digital substations to achieve a flexible protection scheme using a dynamic subscription feature. Thus, in case of failure of one sensor, differential protection would be maintained. The paper presents the implementation and verification of a prototyped scheme using a Hardware-in-the-loop simulation test bed. The main result is that BSS integration into differential protection ensures its dependability and security. Moreover, delay compensation and seamless switching feature increases the availability of differential protection. [ABSTRACT FROM AUTHOR]

Nowadays, composite materials are widely used in various industries, including aerospace, automotive, and military sectors. In many of these applications, there is a need to understand the dynamic and vibrational responses due to changes in different parameters for a more precise structural analysis. This study examines the impact of varying fiber types under different boundary conditions on the natural frequency of a three-dimensional woven composite rectangular plate. For this purpose, the Ritz theory has been used to calculate the system's natural frequency, and to validate the results, the current analysis method has been compared with previous research findings and results from finite element software simulations. The results obtained from the analytical solution and finite element simulation correlate well. [ABSTRACT FROM AUTHOR]

Significant concerns with corrosion might occur in terms of assurance, the natural world, and financial damage. One key piece of innovation toward preventing metallic corrosion includes the application of corrosion inhibitors. The polymer inhibitions of corrosion constitute the most promising areas for advancing of new corrosion inhibitors because they have more excellent adherence locations, higher temperature rebellion, versatility, viscosity, and superior filming capacity than smaller molecule corrosion inhibitors. The present review examines the development of natural and synthetic polymers for anti-corrosion agents. Such polymeric anti-corrosion agents work very well at inhibiting corrosion and do not need a high molecular weight. At this point, we examine the published literature on polymeric corrosion inhibitors and discuss how to improve them for use in the industry. [ABSTRACT FROM AUTHOR]

This work investigates the use of N-(2-(2-pentadecyl-2,5-dihydro-1H-imidazol-1-yl)ethyl)palmitamide palmitate (PIP) in 1 M H2SO4 solution as an anti-corrosive agent for Q235 steel. By using a range of methods including electrochemical tests, weight loss, scanning electron microscopy, atomic force microscopy, contact angler, and X-ray photoelectron spectroscopy were used for the analysis of PIP inhibition potential. The extract molecules formed an adherent and homogenous protective film on the MS surface. When the PIP concentration increased, a stronger inhibition effect against corrosive attack was obtained. The highest obtained inhibition efficiency is 97.65% at 100 mg/L PIP alone and 98.04% at 25 mg/L (PIP) + 0.5 mM potassium iodide. Polarization measurements showed the mixed type inhibitive nature of the PIP with cathodic dominancy. The various kinetic and thermodynamic parameters of metal dissolution and PIP adsorption processes were evaluated from the weight loss methods in order to elaborate the adsorption mechanism. Adsorption of inhibitor obeyed Langmuir adsorption isotherm. Furthermore, an active relationship involving PIP and the Q235 steel surface was highlighted by computational studies, such as density functional theory and molecular dynamics simulation that are demonstrating outstanding mitigation properties. [ABSTRACT FROM AUTHOR]

The effect of direct fire on flexural behavior of small-scale fiber reinforced concrete beams under sustained loading has been studies through an experimental investigation. A mix of synthetic polypropylene structural macro and micro fibers has been used to obtain the effect of mixed fiber dosage on bearing capacity of simply supported beam under direct flame at its bottom face. The load-deflection, temperature gradient, spalling and durability time until collapse has been recorded and compared in different conditions. In order to know the effect of load level, the tests have been performed for 40% and 70% of the ultimate member capacity at normal temperature. Based on obtained test results, the use of macro synthetic fibers can increase the flexural ductility more than 50% and the durability time until failure up to 27%. The post fire behavior curves of all samples at the cooling phase have also been checked until the beam failure has been occurred including the weight losses due to spalling for both heating and cooling phases. A comparison with the results of conventional post fire loading tests on cooled samples showed that the results can be underestimated about 1.5 times compared to the direct fire test. It has also been shown that the level of sustained load directly affects the capacity, durability and ductility while the increase in the number of fibers do not necessarily improve the behavior in the same manner. [ABSTRACT FROM AUTHOR]

Background: Targeted treatment of different types of cancers through highly expressed cancer cell surface receptors by fusion proteins is an efficient method for cancer therapy. The HER2 receptor is a member of the tyrosine kinase receptors family, which plays a notable role in breast cancer tumor development. About 25–30% of breast cancers overexpress human epidermal growth factor receptor 2 (HER2). Methods: In this study, we evaluated the particulars of a designed recombinant protein formed by HER2-specific Mab Herceptin linked with Arazyme on a HER2-overexpressing breast cancer cell line (SKBR3). Arazyme, a metalloprotease produced by Serratia proteamaculans was fused to the variable area of light and heavy chains of the Herceptin. The cytotoxic assay of the Arazyme-linker-Herceptin in the SKBR3 and MDA-MB-468 cells was evaluated by the MTT and flow cytometry techniques. The Caspase‑3 activity determination and adhesion assay were performed to evaluate the antitumor activity of the Arazyme-linker-Herceptin against SKBR3 cells. Furthermore, RT-PCR was used to measure the expression levels of the Bcl-2, Bax, MMP2, MMP9, and RIP3 genes. Results: The Arazyme-linker-Herceptin showed higher cytotoxicity in SKBR3 cells compared to MDA-MB-468 cells. In addition, flow cytometry results revealed that the Arazyme-linker-Herceptin can significantly induce apoptosis in the HER2-overexpressing breast cancer cell line (SKBR3), which was confirmed by Bax upregulation and the decrease in adhesion of tumor cells and MMP2/MMP9. Conclusion: The findings of this study demonstrated that the Arazyme-linker-Herceptin induced apoptosis and decreased metastatic genes in SKBR3 cells; however, further research is required to confirm the effectiveness of the fusion protein. [ABSTRACT FROM AUTHOR]

Today, with the increasing expansion of IoT devices and the growing number of user requests, processing their demands in computational environments has become increasingly challenging.The large volume of user requests and the appropriate distribution of tasks among computational resources often result in disordered energy consumption and increased latency. The correct allocation of resources and reducing energy consumption in fog computing are still significant challenges in this field. Improving resource management methods can provide better services for users. In this article, with the aim of more efficient allocation of resources and service activation management, the metaheuristic algorithm CSO (Cat Swarm Optimization) is used. User requests are received by a request evaluator, prioritized, and efficiently executed using the container live migration technique on fog resources. The container live migration technique leads to the migration of services and their better placement on fog resources, avoiding unnecessary activation of physical resources. The proposed method uses a resource manager to identify and classify available resources, aiming to determine the initial capacity of physical fog resources. The performance of the proposed method has been tested and evaluated using six metaheuristic algorithms, namely Particle Swarm Optimization (PSO), Ant Colony Optimization, Grasshopper Optimization algorithm, Genetic algorithm, Cuckoo Optimization algorithm, and Gray Wolf Optimization, within iFogSim. The proposed method has shown superior efficiency in energy consumption, execution time, latency, and network lifetime compared to other algorithms. [ABSTRACT FROM AUTHOR]

Ships and other maritime objects are often unable to endure the harsh and dynamic sea environment. Collecting real-time data and detecting these objects using various sensors such as RADARs, Synthetic Aperture RADARs, and mounted RADARs present significant challenges due to numerous influencing factors. To address this issue, our research aims to develop an Internet of Things (IoT)-based multi-scale and multi-scene ship identification system. This system leverages a multi-scale neural network integrated with a high-response convolutional neural network (CNN)-based Kalman filter architecture. To construct this model, we selected various ship categories and initially employed a base CNN model to develop a new model with different convolutional layers. Our approach utilizes mixed methods for tracking and detecting objects, with a focus on small ships. The dataset is processed through multiple neural network layers, and we implemented the Kalman filter to estimate and predict the ships' positions. Additionally, using the YOLOv3 model, we achieved improved accuracy and reduced error rates through mathematical optimization. Our method utilizes a dataset of 5,604 samples and incorporates a hybrid approach with YOLOv3. Our model demonstrates significant improvements for both medium-sized and small ships. The proposed work provides both qualitative and quantitative advancements. Our model exceeded the best results from parallel experiments by 3.9% and 1.2% in terms of Average Precision (AP). Furthermore, YOLOv3 achieved a performance score of 97.34% across various metric parameters, while our proposed approach attained the highest scores of 97.8% and 94.87%, respectively. [ABSTRACT FROM AUTHOR]

The importance and growth of the Internet of Things (IoT) in computer networks and applications have been increasing. Additionally, many of these applications generate large volumes of data, which are critical and require protection against attacks. Various techniques have been proposed to identify and counteract these threats. In this paper, we offer a hybrid machine learning approach (using the k-nearest neighbors and random forests as supervised classifiers) to enhance the accuracy of intrusion detection systems and minimize the risk of potential attacks. Also, we employ backward elimination and linear discriminant analysis algorithms for feature reduction and to lower computational costs. Following the training phase, when discrepancies arose between the decisions of the classifiers, the ultimate determination was supported by ISO/IEC 27001 regulations. The performance of the proposed model was assessed within a Python programming framework, utilizing the CICIDS 2017, NSL-KDD, and TON-IoT datasets. The outcomes illustrated that the proposed approach attained a noteworthy accuracy of 99.96% in the multi-class classification of CICIDS 2017, 99.37% in the binary classification of the NSL-KDD dataset, and 99.96% in the multi-class classification of TON-IoT dataset. Furthermore, the attack success rate for each dataset stands at 0.05%, 0.24%, and 0% respectively, demonstrating a significant reduction compared to other methods. [ABSTRACT FROM AUTHOR]

The emergence of the Internet of Things (IoT) has enabled the proliferation of interconnected devices and sensors, generating vast amounts of often complex and unstructured data. Deep learning (DL), a subfield of machine learning (ML), has shown great promise in addressing the challenges of processing and analyzing such data. Considering the increasing importance of DL and data analysis, we decided to review the articles of the last few years in this field to pave the way for researchers. In this article, we used the systematic literature review (SLR) method, and in line with that, we selected and analyzed 56 articles published from 2019 to April 2024. We first discuss the DL models used in the IoT field and clarify their specific use cases. Secondly, we outline an analysis of research areas in DL-based IoT. In addition, our research extends to the tools and simulators used to evaluate studies in the DL-based IoT domain. We also examine the DL-based IoT research datasets. Finally, our review identifies future directions and open issues in DL-based IoT. We aim to contribute to an accurate understanding of the current state, challenges, and potential breakthroughs at the intersection of DL and the IoT. [ABSTRACT FROM AUTHOR]

Purpose: Currently, significant medical practice variation exists in thermal ablation (TA) of malignant liver tumors with associated differences in outcomes. The IMaging and Advanced Guidance for workflow optimization in Interventional Oncology (IMAGIO) consortium aims to integrate interventional oncology into the standard clinical pathway for cancer treatment in Europe by 2030, by development of a standardized low-complex-high-precision workflow for TA of malignant liver tumors. This study was conducted at the start of the IMAGIO project with the aim to explore the current state and future role of modern technology in TA of malignant liver tumors. Materials and Methods: A cross-sectional questionnaire was conducted followed by an expert focus group discussion with core members and collaborating partners of the consortium. Results: Of the 13 participants, 10 respondents filled in the questionnaire. During the focus group discussion, there was consensus on the need for international standardization in TA and several aspects of the procedure, such as planning based on cross-sectional images, the adoption of different techniques for needle placement and the importance of needle position- and post-ablative margin confirmation scans. Yet, also considerable heterogeneity was reported in the adoption of modern technology, particularly in navigational systems and computer-assisted margin assessment. Conclusion: This study mirrored the current diversity in workflow of thermal liver ablation. To obtain comparable outcomes worldwide, standardization is needed. While advancements in tools and software hold the potential to homogenize outcome measurement and minimize operator-dependent variability, the rapid increase in availability also contributes to enhanced workflow variation. [ABSTRACT FROM AUTHOR]

Silicon nanowires are part of nanostructures, characterized by a high surface to volume ratio or large aspect ratio (AR), between 10 and 104. The new physicochemical properties of SiNWs compared to those of planar silicon are inevitable parameters for involving these nanostructures in the fields of nanotechnology, environment, medicine, pharmacy and others. Nevertheless, the passivation of nanowires by metal nanoparticles or a suitable semiconductor enhances their photocatalytic activities. Likewise, the addition of appropriate organic compounds improves the sensor of these nanostructures. In this paper, we first summarize an overview bottom-up and top-down production of silicon nanowires, and then the advantages and drawbacks of each method are described. Some potential implications of SiNWs in optoelectronics, photocatalysts and biosensors have been detailed. In each application, the main elements of enhancement of the composite-based on silicon nanowires covered with metal nanoparticles or functionalized with an organic compound are discussed. Research Highlights 1- MACE technique for SiNWs elaboration 2- Catalytic and photocatalytic efficiency of silicon nanowires 3- The effect of metal nanoparticles covered nanowires of silicon on catalytic and photocatalytic efficiency 4- The effect of sunlight on photocatalytic efficiency 5- The functionalization of nanowires by specific organic compounds to the cure of certain human diseases 6- The physicochemical properties of SiNWs and the origin of their involvement to produce probes immobilizing DNA on the surface 7- The implication of SiNWs in Schottky diodes (SD) and organic Schottky diodes (OSD) 8- The effect of organic molecules in the SiNWs layer [ABSTRACT FROM AUTHOR]

Purpose: The aim of this study was to compare the effect of a pressure-controlled strategy allowing non-synchronised unassisted spontaneous ventilation (PC-SV) to a conventional volume assist-control strategy (ACV) on the outcome of patients with acute respiratory distress syndrome (ARDS). Methods: Open-label randomised clinical trial in 22 intensive care units (ICU) in France. Seven hundred adults with moderate or severe ARDS (PaO2/FiO2 < 200 mmHg) were enrolled from February 2013 to October 2018. Patients were randomly assigned to PC-SV (n = 348) or ACV (n = 352) with similar objectives of tidal volume (6 mL/kg predicted body weight) and positive end-expiratory pressure (PEEP). Paralysis was stopped after 24 h and sedation adapted to favour patients' spontaneous ventilation. The primary endpoint was in-hospital death from any cause at day 60. Results: Hospital mortality [34.6% vs 33.5%, p = 0.77, risk ratio (RR) = 1.03 (95% confidence interval [CI] 0.84–1.27)], 28-day mortality, as well as the number of ventilator-free days and organ failure-free days at day 28 did not differ between PC-SV and ACV groups. Patients in the PC-SV group received significantly less sedation and neuro-muscular blocking agents than in the ACV group. A lower proportion of patients required adjunctive therapy of hypoxemia (including prone positioning) in the PC-SV group than in the ACV group [33.1% vs 41.3%, p = 0.03, RR = 0.80 (95% CI 0.66–0.98)]. The incidences of pneumothorax and refractory hypoxemia did not differ between the groups. Conclusions: A strategy based on PC-SV mode that favours spontaneous ventilation reduced the need for sedation and adjunctive therapies of hypoxemia but did not significantly reduce mortality compared to ACV with similar tidal volume and PEEP levels. [ABSTRACT FROM AUTHOR]

Background: Laryngeal cancer is a common advanced head and neck cancer. Surgery, radiation, chemotherapy, or a combination of these treatments are used to treat locally advanced laryngeal cancer. Total laryngectomy is generally preferred for T3-4 laryngeal cancers, despite being an invasive procedure. Post-surgical hypocalcemia is a known complication of neck compartment surgeries, and patients who have undergone laryngectomy are at a higher risk of developing hypoparathyroidism, leading to hypocalcemia. This study aims to investigate the prevalence and risk factors of hypocalcemia in individuals following laryngectomy. Method: This retrospective study examined 50 consecutive laryngectomy patients over eight years. Data on demographic characteristics, medical history, surgical details, pre-and post-operative calcium levels, neck dissection laterality, history of radiotherapy were collected. Hypocalcemia was defined as calcium levels < 8.5 mg/dl or corrected calcium < 8.5 mg/dl. Statistical analyses included paired t-tests, Wilcoxon signed-rank tests, and binary logistic regression using R studio. Results: The mean age of participants was 59 years, predominating males (94%) and habitual smokers (94%). Squamous cell carcinoma was the most prevalent (98%) pathological diagnosis, and various surgical techniques were employed. While initial comparisons showed no significant changes in calcium levels pre-and post-operatively, adjusting for albumin levels revealed a significant association. Logistic regression identified neck dissection, low pre-operative calcium, radiotherapy, and total thyroidectomy as significant predictors of post-operative hypocalcemia (p < 0.05). Conclusion: Surgical factors such as neck dissection and total thyroidectomy, alongside albumin levels, significantly impact postoperative hypocalcemia. These findings underscore the need for meticulous monitoring and potential preventive measures to manage calcium imbalance post-laryngectomy. [ABSTRACT FROM AUTHOR]

Identifying and selecting superior genotypes in different countries and regions is a valuable breeding method in walnuts. The present study evaluated 31 quantitative and qualitative traits of seven superior walnut genotypes comparable to 'Jamal', 'Lara', and 'Serr' cultivars. The comparisons followed IPGRI descriptions. We evaluated pomological traits in the superior genotypes for four consecutive years (2019, 2020, 2021, and 2022), observing high variations among the genotypes. The thickness of husk, fruit weight, nut weight, and nut length ranged between 2.47–4.08 mm, 42.26–58.38 g, 10.43–14.60 g, and 29.59–37.61 mm, respectively. Also, kernel weight, percentage, color, plumpness, and fillness ranged between 5.22–6.97 g, 40.18–54.75%, 1.33–2.67 (approximately light), 2.33 (medium)—3.00 (plump), and 4.78 (medium)—7.00 (good), respectively. Biochemical features in the kernels varied significantly among the genotypes, particularly in the case of oil content (62.26–70.08%), SFA (10.10–13.03%), MUFA (13.38–36.40%), and PUFA (52.05–74.40%). Furthermore, kernel protein content, total polyphenols, and antioxidant activity ranged between 16.79–21.61%, 58.78–95.06 mg GAE/g, and 0.15–0.22 EC50, respectively. The kernel percentage correlated significantly and negatively with the flowering type (r = −0.414) and nut weight (r = −0.467) but positively with DFO (r = 0.545), fruitfulness percentage (r = 0.471), kernel weight (r = 0.412), saturated fatty acid (r = 0.375), oil percentage (r = 0.472), and protein percentage (r = 0.422). These genotypes were superior due to closed-shell seal, light kernel color, ease of kernel removal from nuts, lateral bearing, high kernel percentage, and high nutritional value. The selected genotypes are the bearers of desirable genes for future walnut breeding programs. [ABSTRACT FROM AUTHOR]

Multiple sclerosis (MS) is a chronic immune-mediated demyelinating disease of the central nervous system. Selenium is a trace element with significant antioxidant activity. This study aimed to seek evidence concerning selenium supplementation in MS. A systematic search was performed on PubMed, Web of Science, Scopus, and Embase databases to identify the studies assessing the consumption rate, efficacy, and safety of selenium and selenium-containing supplementations in MS patients. The meta-analysis was performed using the Comprehensive Meta-Analysis and the risk of bias was evaluated using the Joanna Briggs Institute's critical appraisal tools. A total of 9 studies were included, which consisted of six studies regarding the rate of selenium supplement consumption in MS patients, with a total sample size of 2381 patients. Based on the quantitative synthesis, 14.3% (95% CI, 12.8–16.0%; I2, 3.58%) of MS patients had current selenium supplements usage, and 11.3% (95% CI, 7.6–16.6%; I2, 81.40%) of patients had used selenium supplements previously. Although there is no evidence regarding supplementation with selenium alone, three RCT studies reported the safety of selenium-containing supplementation use in MS with improved inflammation and oxidative stress conditions. The findings of this study show that over 10% of patients with MS used selenium supplements, with no clinical significance supporting the benefits. There is a lack of evidence regarding the safety and efficacy of selenium supplements in MS patients. Due to the limited number of included studies and the lack of comprehensive and specific studies regarding selenium supplements in MS, the results must be interpreted with caution, and future clinical trials are required. [ABSTRACT FROM AUTHOR]

Bisphenol A (BPA), an endocrine disruptor, is commonly used to produce epoxy resins and polycarbonate plastics. Continuous exposure to BPA may contribute to the development of diseases in humans and seriously affect their health. Previous research suggests a significant relationship between the increased incidence of neurological diseases and the level of BPA in the living environment. Syringic acid (SA), a natural derivative of gallic acid, has recently considered much attention due to neuromodulator activity and its anti-oxidant, anti-apoptotic, and anti-inflammatory effects. Therefore, in this study, we aimed to investigate the effects of SA on oxidative stress, apoptosis, memory and locomotor disorders, and mitochondrial function, and to identify the mechanisms related to Alzheimer's disease (AD) in the brain of rats receiving high doses of BPA. For this purpose, male Wistar rats received BPA (50, 100, and 200 mg/kg) and SA (50 mg/kg) for 21 days. The results showed that BPA exposure significantly altered the rats' neurobehavioral responses. Additionally, BPA, by increasing the level of ROS, and MDA level, increased the level of oxidative stress while reducing the level of antioxidant enzymes, such as SOD, CAT, GPx, and mitochondrial GSH. The administration of BPA at 200 mg/kg significantly decreased the expression of ERRα, TFAM, irisin, PGC-1α, Bcl-2, and FNDC5, while it increased the expression of TrkB, cytochrome C, caspase 3, and Bax. Moreover, the Western blotting results showed that BPA increased the levels of P-AMPK, GSK3b, p-tau, and Aβ, while it decreased the levels of PKA, P-PKA, Akt, BDNF, CREB, P-CREB, and PI3K. Meanwhile, SA at 50 mg/kg reversed the behavioral, biochemical, and molecular changes induced by high doses of BPA. Overall, BPA could lead to the development of AD by affecting the mitochondria-dependent apoptosis pathway, as well as AMPK/PGC-1α/FNDC5 and CREB/BDNF/TrkB signaling pathways, and finally, by increasing the expression of tau and Aβ proteins. In conclusion, SA, as an antioxidant, significantly reduced the toxicity of BPA. [ABSTRACT FROM AUTHOR]

This study investigated the effects of microencapsulating Myrtus communis fruit extracts within intact and plasmolysed Saccharomyces cerevisiae yeast cells on the phenolic compounds and antioxidant capacity of the extracts. Spectroscopic techniques were employed to quantify polyphenols, flavonoids, and tannins, while UPLC-ESI-MS-MS analysis was utilized to identify and quantify phenolic compounds before and after microencapsulation. Molecular docking was conducted to assess the interaction between yeast cell components and the major identified compounds. Microencapsulation was found to result in a diminished content of polyphenols, flavonoids, and tannins. Phenolic compounds showed higher polyphenol content when encapsulated in plasmolysed cells compared to non-plasmolysed yeast cell. Three major compounds were detected in the microcapsules: myricetin, chlorogenic acid, and coumaric acid, with myricetin exhibiting the highest level of interaction with target proteins. FTIR analysis revealed the interaction between phenolic compounds and yeast cell components, while SEM showed aggregation of microcapsules within plasmolysed microcapsules. Encapsulation of myrtle extract in yeast cells preserved cell wall integrity and microcapsule morphology, with superior thermal stability of encapsulated extracts compared to their non-encapsulated counterparts as demonstrated by TGA–DSC analysis. An enhancement in antioxidant activity after encapsulation was also observed, with the highest activity in plasmolysed yeast cell microcapsules. [ABSTRACT FROM AUTHOR]

Over the past decade, the world has experienced a remarkable shift in the automotive landscape, as electric vehicles (EVs) have appeared as a viable and increasingly popular alternative to the long-standing dominance of internal combustion engine (ICE) vehicles and their ability to absorb the surplus of electricity generated from renewable sources. This paper presents a detailed examination of the different categories of EVs, charging methods and explores energy generation systems tailored for EVs. As vehicle complexity and road congestion increase with the growth of EVs, the need for intelligent transport systems to improve road safety and efficiency becomes imperative. Machine learning (ML), recognized as a powerful approach for adaptive and predictive system development, has gained importance in the vehicle domain. By employing a variety of algorithms, ML effectively addresses pressing issues related to electric vehicles, including battery management, range optimization, and energy consumption. This paper conducts a brief review of ML methods, including both traditional and applied approaches, to address energy consumption issues in EVs, such as range estimation and prediction, as well as range optimization. [ABSTRACT FROM AUTHOR]

The Climate Suitability Index (CSI) can increase agricultural efficiency by identifying the high-potential areas for cultivation from the climate perspective. The present study develops a probabilistic framework to calculate CSI for rainfed cultivation of 12 medicinal plants from the climate perspective of precipitation and temperature. Unlike the ongoing frameworks based on expert judgments, this formulation decreases the inherent subjectivity by using two components: frequency analysis and Particle Swarm Optimization (PSO). In the first component, the precipitation and temperature layers were prepared by calculating the occurrence probability for each plant, and the obtained probabilities were spatially interpolated using geographical information system processes. In the second component, PSO quantifies CSI by classifying a study area into clusters using an unsupervised clustering technique. The formulation was implemented in the Lake Urmia basin, which was distressed by unsustainable water resources management. By identifying clusters with higher CSI values for each plant, the results provide deeper insights to optimize cultivation patterns in the basin. These insights can help managers and farmers increase yields, reduce costs, and improve profitability. [ABSTRACT FROM AUTHOR]

The changing landscape of cancer surgery requires ongoing consideration of ethical issues to ensure patient-centered care and fair access to treatments. With technological advancements and the global expansion of surgical interventions, healthcare professionals must navigate complex ethical dilemmas related to patient autonomy, informed consent, and the impact of new technologies on the physician–patient relationship. Additionally, ethical principles and decision-making in oncology, especially in the context of genetic predisposition to breast cancer, highlight the importance of integrating patient knowledge, preferences, and alignment between goals and treatments. As global surgery continues to grow, addressing ethical considerations becomes crucial to reduce disparities in access to surgical interventions and uphold ethical duties in patient care. Furthermore, the rise of digital applications in healthcare, such as digital surgery, requires heightened awareness of the unique ethical issues in this domain. The ethical implications of using artificial intelligence (AI) in robotic surgical training have drawn attention to the challenges of protecting patient and surgeon data, as well as the ethical boundaries that innovation may encounter. These discussions collectively emphasize the complex ethical issues associated with surgical innovation and underscore the importance of upholding ethical standards in the pursuit of progress in the field. In this study, we thoroughly analyzed previous scholarly works on ethical considerations and equipoise in the field of oncological surgery. Our main focus was on the use of AI in this specific context. [ABSTRACT FROM AUTHOR]

Introduction: Initiation of breastfeeding right after birth and exclusive breastfeeding for the first 6 months of life is highly recommended. In this survey, we assessed the knowledge and practice of ever-married women towards early initiation of breastfeeding and exclusive breastfeeding in the Nuristan province of Afghanistan. Methods: In a cross-sectional household survey, we enrolled 640 ever-married 15–49 years women having a child aged 0–23 months in the Parun district of Nuristan province from September to October 2021. Using a standardized data collection form and face-to-face interview, we measured self-reported demographic characteristics and the study outcomes (awareness and practice towards the initiation of breastfeeding within an hour of birth and exclusive breastfeeding for the first 6 months of life). We analyzed the data using survey methods and used bivariate and multiple Poisson regression to assess the covariates of the study outcomes. Results: About 46.9% of ever-married women were 25–34 years old, 67.5% never attended school, 30.4% were unemployed, and 86.4% had < $60 monthly income. Overall, 56.8% (95% CI 52.9–60.6) of the women had correct knowledge of the early initiation of breastfeeding, and 51.4% (95% CI 47.5–55.3) initiated breastfeeding early for their last baby. Also, 85.9% (95% CI 82.9–88.4) of the women had correct knowledge of exclusive breastfeeding, but only 32.6% (95% CI 29.1–36.4) exclusively breastfed their last baby for 6 months after birth. After adjustment for covariates, women who were 45–49 years old (Adjusted Prevalence Ratio [APR] = 1.26), Divorced/Separated (APR 1.68), ever attended school (APR 1.39), monthly income > $115 (APR 1.50) were more likely to start early breastfeeding. Moreover, women who were Divorced/Separated (APR 3.53) were more likely to exclusively breastfeed their babies for 6 months after birth. Discussion: Only over half of the women were aware of and initiated early breastfeeding. Although most women knew about exclusive breastfeeding, less than one-third exclusively breast their babies for 6 months after birth. We found several contributing factors, such as age, marital status, education, and income that should be considered for targeted interventions. Significance: Understanding the knowledge and practices of mothers towards knowledge and practices of exclusive breastfeeding will help social and behavior change, health and nutrition experts and public health practitioners design evidence based projects and interventions to reduce prevalence of stunting, underweight and wasting in the community. The findings will also help researchers to further explore this topic and improve health and nutrition outcomes in the community. [ABSTRACT FROM AUTHOR]

In this study, a computational analysis based on density functional theory is conducted to study the elastic, mechanical, vibrational, and thermodynamic properties of novel chalcogens, Ag2BeSnX4 (X = S, Se, and Te). We used the generalized gradient approximation (GGA) within the framework of density functional theory (DFT). The mesh parameter values (a and c) were calculated using the X-ray diffraction method. The calculated elastic constants indicate that the bond strength along the [1 0 0] directions is stronger than that along the direction [0 0 1]; according to the Born-Huang stability criterion, we can see that they are mechanically stable. A high value of the ratio (B/G) is associated with ductility for Ag2BeSnX4 (X = S, Se, and Te) materials. Additionally, the Raman shifts of all samples are calculated. Between 10 and 1000 K in temperature, the vibrational mode shifts were calculated for three chalcoginides. The thermal behavior of these movements shows that these structures can undergo deformation with increasing temperature. These results suggest a first contribution to the understanding of the effect of temperature on the vibrational modes of three kesterite structures Ag2BeSnX4 (X = S, Se, and Te) and consequently on their structures. The heat capacity (C V) , free energy (F) , entropy (S) , and enthalpy (H) are also computed. The kesterite phase of the Ag2BeSnX4 (X = S, Se, and Te) structures aligns with theoretical findings in elastic properties, exhibiting superior elastic properties. These attributes are valuable for the design of optoelectronic devices. [ABSTRACT FROM AUTHOR]

The random forest (RF) algorithm is considered as a powerful statistical classifier that is more popular in other fields but is relatively unknown in HEA(s)’s prediction phase. In this research, Random Forest (RF) technique is used to investigate phase selection principles effectively utilizing a large experimental case study on 401 distinct HEAs, comprising 174 SS , 54 IM , and 173 S S + I M phases. The accuracy of the proposed method is almost 10% higher than SVM and KNN for classifying HEA(s). Moreover, the precision of the proposed method is similar to ANN. Experimental results indicate the validity and reliability of the RF-based diagnosis method. [ABSTRACT FROM AUTHOR]

Cloud architecture and its operations interest both general consumers and researchers. Google, as a technology giant, offers cloud services globally. This paper analyzes the Google cluster usage trace, focusing on three key aspects: task execution times, rescheduling frequency, and the relationship between task priority and rescheduling. Firstly, we examine how memory and processor performance impact task execution times across different machines. Next, we investigate how the number of task constraints influences rescheduling frequency and overall environmental efficiency. Furthermore, we analyze how task priority affects rescheduling and explore its correlation with task constraints. The results reveal that doubling the memory size can accelerate tasks by a factor of nine and that 90% of rescheduling is associated with tasks having less than seven constraints. We aim to enhance data center performance by identifying bottlenecks in the Google Cluster Dataset and providing recommendations for all cloud service providers. Our key findings indicate that memory plays a more significant role than the processor, and tasks with higher constraints have a less pronounced impact on rescheduling than anticipated. [ABSTRACT FROM AUTHOR]

Pollutants are removed from the environment in a short time when water or biological fluids contain certain substances enter the hydrogel. In this research, we synthesized four kinds of acrylic hydrogels (performance about 50%) and applied for the kinetic sorption study of methylene blue (MB) from aqueous solutions. The adsorption of MB on the developed adsorbents (containing Fe3O4, ZnSe, and starch) was examined in a batch system. Characterization of the prepared acrylic adsorbents was performed by UV, FT-IR, SEM and XRD analyzes. The results showed that the four hydrogels could efficiently remove MB from solutions. The residual concentration of MB was analyzed by UV–Vis spectrophotometry at 662 nm (λmax). First the calibration curve of methylene blue dyes was obtained. Then the UV spectrum was taken from the mixture of gel and methylene blue at different concentrations (78,125, 13,950 and 19,531 ppm) and different time intervals (0–10 min) and the graph of the adsorption changes was obtained for each one. These graphs showed the kinetics of adsorption and showed this fact that the adsorption process has followed the Langmuir isotherm (the adsorption values qmax of 42 mg.g−1), and in some cases the Freundlich model. Among the kinetic models applied, the pseudo-second-order (k about 0.3 M−1.min−1) kinetic model was identified with the highest regression coefficients (the first in the high concentrations and the second in the low concentrations). A proposed adsorption mechanism to acrylic hydrogel was illustrated based on the chemical structure of MB and related hydrogel. The stability and reusability of hydrogels and the recovery of absorbed pollutants are among the advantages of this method which shows its economic efficiency. [ABSTRACT FROM AUTHOR]

The majority of optoelectronic devices are composed mostly of quantum well heterostructures. The primary attraction of this kind of structure is the energy levels discretization that charge carriers can access due to confinement of the charge carriers in the quantum well in accordance with growth direction. The various transition energies will be determined by the bursting between these levels. The width of the well and that of the barrier, the doping, the gap energy, and the shift of the energy ΔEC and ΔEg between the various bands of the two materials are some of the parameters that define the structure and affect both the confined levels and the transition energies. When holes and electrons are contained in the same area of space, it can produce considerable quantum and also optical efficiency. The resulting devices (including LEDs, LDs, QCLs, QWIPs, and others) are applied in a variety of fields (medicine, telecom, security, military, astronomy). By employing the parabolic and non-parabolic band approximation and adopting the model of the envelope function, we theoretically investigated the quantum confinement in the unstressed GaN/AlInN heterostructure. The energies of confined levels of the heterostructure and their associated envelope functions were calculated using a finite difference approach. The goal is to identify, on the one hand, the geometries that the intersubband transition energies could enable the development of optoelectronic devices for use in the terahertz field (0.4–41.33 meV) and in the optical fiber telecommunications sector (800–933 meV) on the other. Our findings demonstrate that in order to achieve wavelengths for telecommunications applications, one must take into account geometries that generate four confined levels. Indeed, for geometries with barrier width fixed at Lb=60 Å and for well widths Lw from 20 to 45 Å, the transition E14 enables us to get wavelengths λ = 1.44–1.54 µm. Additionally, we are able to transition at a frequency of ν 34 = 9.86 THz for the crucial geometry of 60/25/60 Å thanks to the transition E34. Thus, the two needed domains would be satisfied by this geometry. [ABSTRACT FROM AUTHOR]

Corneal injury is one of the substantial and challenging global concerns regarding vision loss. Corneal transplantation is the gold standard for restoring vision to a blurred cornea; however, suture-related complications, corneal haziness, and graft failure have led to ongoing research to improve graft efficiency. In this study, a highly suture-able hybrid corneal scaffold composed of decellularized corneas injected with GelMa/PEGDA in situ was fabricated to preserve the structural integrity and transparency of the injured corneas. First, GelMA/PEGDA hydrogels with different ratios (3/7, 1/1, and 7/3) were evaluated in terms of microstructure, degradation rate, cytocompatibility, and equilibrium water content. GelMa/PEGDA 7/3 was chosen based on the cytocompatibility assay, which confirmed the attachment, maintenance, and proliferation of limbal stem cells with an elongated morphology. Next, the explanted rabbit cornea was decellularized successfully to preserve its extracellular components and transparency with less than 1% DNA content. Thereafter, a hybrid cornea was developed by incorporating GelMA/PEGDA hydrogel (7/3) into the decellularized rabbit cornea under ultraviolet light. This hybrid cornea showed improved sutureability and possessed appropriate transparency. In addition, ex vivo examinations of the enucleated eyeballs of rabbits showed that this hybrid cornea effectively adhered to the corneal stromal and surrounding tissues by applying GelMA/PEGDA hydrogel, which is favorable for corneal transplantations. Based on these findings, the introduced hybrid cornea could be a potential candidate for the in situ repair of deep anterior corneal defects. [ABSTRACT FROM AUTHOR]

The increased availability of technology in higher education has led to the growth of online learning platforms. However, a significant concern exists regarding dropout rates in online higher education (OHE). In this ever-evolving landscape, student attrition poses a complex challenge that demands careful investigation. This systematic literature review presents a comprehensive analysis of the literature to uncover the reasons behind dropout rates in virtual learning environments. Following the PRISMA guidelines, this study systematically identifies and elucidates the risk factors associated with dropout in online higher education. The selection process encompassed articles published between 2013 and June 2023, resulting in the inclusion of 110 relevant articles that significantly contribute to the discourse in this field. We examine demographic, course-related, technology-related, motivational, and support-related aspects that shape students' decisions in online learning programs. The review highlights key contributors to dropout like the quality of the course, academic preparation, student satisfaction, learner motivation, system attributes, and support services. Conversely, health concerns, financial limitations, technological issues, screen fatigue, isolation, and academic workload, emerge as significant limitations reported by online learners. These insights offer a holistic understanding of dropout dynamics, guiding the development of targeted interventions and strategies to enhance the quality and effectiveness of online education. [ABSTRACT FROM AUTHOR]

This paper presents a novel Anonymization Strategy Selection Framework that combines relaxed functional dependencies (RFDs) and particle swarm optimization (PSO) to balance data privacy and utility. Our approach extracts RFDs from datasets, generates diverse anonymization strategies using domain generalization hierarchies, and employs PSO for strategy optimization. We introduce a fitness function that balances k-anonymity and information loss. The framework's innovation lies in using RFDs to capture fine-grained data dependencies, enabling more nuanced anonymization. Evaluation on widely used UCI machine learning repository datasets show our framework outperforms existing techniques, achieving higher k-anonymity levels with lower information loss. Our adaptive approach generates hybrid strategies combining elements from multiple RFDs, resulting in superior privacy-utility trade-offs. This research advances privacy-preserving data publishing by providing a flexible, effective tool for generating anonymized datasets that maintain high utility for downstream analysis. [ABSTRACT FROM AUTHOR]

Nowadays, with the advent of movies and TV shows and the competition between different movie streamer companies and movie databases to attract more users, movie recommenders have become a major prerequisite for customer satisfaction. Most of the previously introduced methods used collaborative, content-based, and hybrid filtering techniques, where neural network-based approaches and matrix completion are the major approaches of most recent movie recommender systems. The major drawbacks of previous systems are not considering side information, such as plot synopsis and cold start problem, in the context of movie recommendations. In this paper, we propose a novel inductive approach called MoRGH which first constructs a graph of similar movies by considering the information available in movies' plot synopsis and genres. Second, we construct a heterogeneous graph that includes two types of nodes: movies and users. This graph is built using the MovieLens dataset and the similarity graph generated in the first stage, where each edge between a user and a movie represents the user's rating for that movie, and each edge between two movies represents the similarity between them. Third, MoRGH mitigates the drawbacks of previous methods by employing a GNN and GAE-based model that combines collaborative and content-based approaches. This hybrid approach allows MoRGH to provide accurate and more personalized recommendations for each user, outperforming previous state-of-the-art models in terms of RMSE scores. The achieved improvement in RMSE scores demonstrates MoRGH's superior performance and its ability to deliver enhanced recommendations compared to existing models. [ABSTRACT FROM AUTHOR]

Preterm birth (PTB) remains a global health concern, impacting neonatal mortality and lifelong health consequences. Traditional methods for estimating PTB rely on electronic health records or biomedical signals, limited to short-term assessments in clinical settings. Recent studies have leveraged wearable technologies for in-home maternal health monitoring, offering continuous assessment of maternal autonomic nervous system (ANS) activity and facilitating the exploration of PTB risk. In this paper, we conduct a longitudinal study to assess the risk of PTB by examining maternal ANS activity through heart rate (HR) and heart rate variability (HRV). To achieve this, we collect long-term raw photoplethysmogram (PPG) signals from 58 pregnant women (including seven preterm cases) from gestational weeks 12–15 to three months post-delivery using smartwatches in daily life settings. We employ a PPG processing pipeline to accurately extract HR and HRV, and an autoencoder machine learning model with SHAP analysis to generate explainable abnormality scores indicative of PTB risk. Our results reveal distinctive patterns in PTB abnormality scores during the second pregnancy trimester, indicating the potential for early PTB risk estimation. Moreover, we find that HR, average of interbeat intervals (AVNN), SD1SD2 ratio, and standard deviation of interbeat intervals (SDNN) emerge as significant PTB indicators. [ABSTRACT FROM AUTHOR]

Spatial accurate mapping of land susceptibility to wind erosion is necessary to mitigate its destructive consequences. In this research, for the first time, we developed a novel methodology based on deep learning (DL) and active learning (AL) models, their combination (e.g., recurrent neural network (RNN), RNN-AL, gated recurrent units (GRU), and GRU-AL) and three interpretation techniques (e.g., synergy matrix, SHapley Additive exPlanations (SHAP) decision plot, and accumulated local effects (ALE) plot) to map global land susceptibility to wind erosion. In this respect, 13 variables were explored as controlling factors to wind erosion, and eight of them (e.g., wind speed, topsoil carbon content, topsoil clay content, elevation, topsoil gravel fragment, precipitation, topsoil sand content and soil moisture) were selected as important factors via the Harris Hawk Optimization (HHO) feature selection algorithm. The four models were applied to map land susceptibility to wind erosion, and their performance was assessed by three measures consisting of area under of receiver operating characteristic (AUROC) curve, cumulative gain and Kolmogorov Smirnov (KS) statistic plots. The results revealed that GRU-AL model was considered as the most accurate, revealing that 38.5%, 12.6%, 10.3%, 12.5% and 26.1% of the global lands are grouped at very low, low, moderate, high and very high susceptibility classes to wind erosion hazard, respectively. Interpretation techniques were applied to interpret the contribution and impact of the eight input variables on the model's output. Synergy plot revealed that the soil carbon content exhibited high synergy with DEM and soil moisture on the model's predictions. ALE plot showed that soil carbon content and precipitation had negative feedback on the prediction of land susceptibility to wind erosion. Based on SHAP decision plot, soil moisture and DEM presented the highest contribution on the model's output. Results highlighted new regions at high latitudes (southern Greenland coast, hotspots in Alaska and Siberia), which exhibited high and very high land susceptibility to wind erosion. [ABSTRACT FROM AUTHOR]

The article is mainly concerned with radial-axial ring rolling (RARR) process of AM60 magnesium rings at elevated temperatures. First, several tubes were cast using centrifugal casting. Afterward, the cast tubes were machined and cut into primary rings for the rolling process. Duration of all the ring rolling tests was constant (20 s). The RARR operation was performed at three elevated temperatures of 200, 250 and 300 °C and with three different rotational speeds of 40, 60 and 80 rpm for the driven (main) roll. To determine the stress-strain curves of the centrifugally cast and the rolled rings, several compression tests were performed with samples in rolling direction, transverse direction and normal direction. The experimental results showed that the initial temperature of the ring and the rotational speed of the driven roll represented inverse effects on the mechanical properties, such as strength and hardness, of the rolled rings. The relation between the average grain sizes of specimens with their mechanical properties was in agreement with the Hall–Petch relation. Using the design of experiments technique, the main effects of temperature and rotational speed as well as their interaction on different mechanical and microstructural properties of the rolled rings were also investigated. Finally, several regression models were derived to predict various material properties of the rolled rings in terms of the temperature and rotational speed. [ABSTRACT FROM AUTHOR]

The physical and chemical properties of atmospheric aerosol particles are crucial in influencing global climate and ecosystem processes. Given the numerous studies highlighting adverse health effects from exposure to aerosol particulates, particularly Particulate Matter (PM), effective air quality management strategies are under consideration (Annesi-Maesano et al. Eur Respir Soc 29(3):428–431. 2007). Herein, we introduce a predictive model—PmForecast—employing a self-adaptive long short-term memory (LSTM) architecture to predict PM 2.5 values in the real atmosphere. Specifically, we explore adopting a LSTM model to better benefit from temporal dimensions. PmForecast is strategically designed with four key phases: preprocessing, temporal attention, prediction horizon, and LSTM layers. By leveraging LSTM's significant predictive ability in time-series data, the inclusion of temporal attention enhances the model's specificity. Temporal dynamics modeling entails generating insights over time, utilizing temporal attention to extract essential characteristics from historical air pollutant concentrations, with the flexibility to adjust the historical data according to the forecasting period. To assess PmForecast, we consider measurements collected from the QameleO network, a sparse network of air-quality micro-stations deployed in Dijon, France. The self-adaptive capabilities of PmForecast allow the model to be dynamically updated, evaluating its performance and continuously tuning hyper-parameters based on the latest data trends. Our empirical evaluation reports that PmForecast outperforms the state of the art, achieving notable accuracy in both short-term and long-term predictions. The PmForecast deployment at scale can serve as a valuable tool for proactive decision-making and targeted interventions to mitigate the health risks associated with air pollution. [ABSTRACT FROM AUTHOR]

The right lane and platoon assignment to a vehicle significantly impacts achieving the desired driving goals to improve occupants' safety or reduce pollution. Preparing an intelligent assistant to address this issue has exposed researchers' attention, especially in intelligent transportation systems. Implementing such a system is complex and challenging to design, mainly because of conflicting driving goals possibility and the mixed traffic of autonomous and human-driven vehicles. This paper presents a multi-tier computational architecture to utilize the cloud, fog, and edge computational resources and formulate the lane/platoon assignment as an optimization problem subject to safety combined with any other secondary goal. The problem is then solved by the meta-heuristic Genetic Algorithm. By evaluating the proposed method by real data gathering, machine learning, and numerical experiments, the results show that the proposed method satisfies the minimization of overlapping collision areas during vehicles' lane/platoon changes and harmonizes the traffic in all lanes and each platoon. [ABSTRACT FROM AUTHOR]

Algeria, a semi-arid Mediterranean country, is distinguished by a more marked interannual variability in solid inputs compared to liquid inputs. This study aimed to comprehensively analyze hydrometeorological variables, such as rainfall, suspended sediment load, and flood flows in the Wadi Mina basin (6048 km2), using both traditional and advanced statistical techniques. These techniques included the MK trend tests MMK, and TFPWMK, as well as newer methods such as ITA and IPTA. To identify breakpoints, the CUSUM test and the SQ-MK test were used. The study was based on monthly data covering 40 years of rainfall (1970–2010) and 41 years of data on flood flows and suspended sediment load (1969–2010). The results indicated stable annual rainfall across the stations, suggesting a consistent rainfall regime over time. However, notable variability in sediment loads and flood flows was observed, with stations like Ain Hamara, Sidi Aek Djilali, and Wadi El Abtal displaying marked decreases in annual sediment loads, hinting at effective erosion control or natural attenuation of erosive processes. In contrast, a significant increase in flood events was recorded at Ain Hamara, attributed to climatic fluctuations or land use changes affecting runoff. Monthly analyses further demonstrated the enhanced sensitivity of ITA and IPTA over traditional methods, effectively uncovering subtle trends not detected by MK, MMK, and TFPWMK. For example, increasing rainfall trends during winter at Takhmert station and significant sediment transport patterns at Ain Hamara during colder months suggested dynamic seasonal hydrological behaviors. Additionally, critical insights into abrupt changes were revealed through CUSUM and SQ-MK tests, such as a pivotal shift in flood regimes at Takhmert in 2003 and a critical change in rainfall patterns at Ain Hamara in 2008, necessitating adjustments in water management and agricultural practices. [ABSTRACT FROM AUTHOR]

The Internet of Vehicles (IoV) promises to revolutionize transportation in smart cities, but its interconnectedness raises critical security and privacy concerns. Limited computational power, diverse network technologies, and many sensors and vehicles challenge data integrity and trust in data exchange. Existing solutions, often dependent on specific environments and protocols, struggle to address these issues across the entire IoV ecosystem. This paper explores the potential of blockchain technology to address these challenges. We argue that blockchain's immutability and decentralization offer a unique solution for trust management in various IoV environments. We review existing blockchain-based algorithms and models proposed for IoV integration and propose a novel taxonomy to categorize these approaches. This taxonomy will help us analyze effective parameters, implementation methods, and evaluation metrics in the reviewed literature. According to our research, the most critical evaluation parameter for blockchain-based methods is time, including system-level service-related time parameters and solution implementation time, and 38% of existing papers simulated the approach using Hyperledger. Additionally, we will identify key challenges from integrating blockchain into the IoV landscape. By providing a comprehensive review and analysis of blockchain-based trust management solutions for IoV, this paper aims to contribute to the ongoing development of secure and reliable intelligent transportation systems. [ABSTRACT FROM AUTHOR]

Vehicular Ad Hoc Networks (VANETs) play a critical role in ensuring safety and welfare applications for drivers and passengers amidst the escalating vehicular population in urban environments. The efficient functioning of VANETs hinges on addressing the challenge of load balancing among Road Side Units (RSUs). This paper introduces a groundbreaking approach aimed at enhancing real-time data transmission services within VANETs. The key contribution lies in the development of a multicast routing algorithm utilizing a geo-targeting protocol, facilitating simultaneous delivery of source data packets to multiple destinations. This innovative strategy aims to alleviate RSU congestion, thereby significantly enhancing the quality of real-time data transmission services. Moreover, this study presents advancements in the Statistical Match and Queuing algorithm, refining it over time to substantially mitigate network congestion and redundancy. Additionally, a Multi-Protocol Label Switching based algorithm is implemented to elevate service quality parameters, including end-to-end latency, packet loss, and overall network efficiency within in-vehicle networks. Importantly, this approach remains adaptable across various Layer two technologies, ensuring compatibility and scalability. Simulation results validate the efficacy of the proposed methodology, showcasing its superiority over existing methods. The findings underscore the innovative algorithms' prowess in addressing load balancing challenges across diverse scenarios, affirming their potential to significantly enhance VANET service quality. [ABSTRACT FROM AUTHOR]

Background: Brown adipose tissue (BAT) is a thermogenic tissue that uncouples oxidative phosphorylation from ATP synthesis and increases energy expenditure via non-shivering thermogenesis in mammals. Cold exposure and exercise have been shown to increase BAT and browning of white adipose tissue (WAT) in mice. This study aimed to determine whether there is an additive effect of exercise during cold exposure on markers related to browning of adipose tissue. in Wistar rats. Methods: Twenty-four male Wistar rats were randomly divided into three groups: Control (C, 25˚C), Swimming in Neutral (SN, 30˚C) water, and Swimming in Cold (SC, 15˚C) water. Swimming included intervals of 2–3 min, 1 min rest, until exhausted, three days a week for six weeks, with a training load of 3–6% body weight. After the experimental protocol, interscapular BAT and inguinal subcutaneous white adipose tissue (WAT) were excised, weighed, and processed for beiging marker gene expression. Results: SN and SC resulted in lower body weight gain, associated with reduced WAT and BAT volume and increased BAT number with greater effects observed in SC. Myostatin protein expression was lower in BAT, WAT, soleus muscle, and serum NC and SC compared to the C group. Expression of the interferon regulatory factor-4 (IRF4) gene in both BAT and WAT tissues was significantly greater in the SC than in the C. Expression of the PGC-1α in BAT was significantly increased in the SC compared to C and increased in WAT in NC and SC. Expression of the UCP1 in BAT and WAT increased in the SC group compared to other groups. Conclusion: The findings demonstrate that six weeks of swimming training in cold water promotes additive effects of the expression of genes and proteins involved in the browning process of adipose tissue in Wistar rats. Myostatin inhibition may possess a regulator effect on the PGC-1α – UCP1 pathway that mediates adipose tissue browning. [ABSTRACT FROM AUTHOR]

As a promising alternative anode material for Li-ion batteries, monoclinic titanium niobate (TiNb2O7) offers the inherent safety associated with high lithiation potential combined with gravimetric capacities compared to that of graphite. Herein, evaporative sol-gel method has been employed to obtain TiNb2O7 with tailored morphology and crystallite structures. Using F127 co-polymer as structure directing soft template, the otherwise irregular morphology of the ceramic after calcination turns into an intricate assembly of interconnected particles with appropriately sized morphological voids for electrolyte contact and damping of the volumetric changes associated with lithiation process. Using cellulose as hard template will in turn increase the size scale of the interconnected particulates and associated morphological voids and enhances the crystallite characteristics known to enhance lithium diffusion and storage. The samples under study were subjected to high-resolution transmission electron microscopy, field-emission scanning electron microscopy, powder X-ray diffraction, selected area electron diffraction, Fourier transform infrared spectroscopy, diffuse reflectance spectroscopy and electrochemical analysis based on half-cells with lithium metal as counter electrode. Electrochemical characterization of the representative samples under study clearly shows a delicate balance between material characteristics which should simultaneously satisfy strict conditions for optimized performance. The optimized sample shows an initial Columbic efficiently of 99.2% at 0.1 C and cyclable capacity of 155 mAhg−1 with a 76% retention upon 100 cycles of charge-discharge. Highlights: Evaporative sol-gel approach was taken as the synthesis method for nanostructured TiNb2O7 F127 as a triblock co-polymer non-ionic surfactant was chosen as a representative soft-template. Microcrystalline cellulose was picked as a representative hard template All of the samples were characterized in terms of crystallography, morphology and performance in battery half-cells against lithium metal. The optimized sample showed an initial Columbic efficiently of 99.2% at 0.1C and cyclable capacity of 155 mAhg−1 with a 76% retention upon 100 cycles of charge-discharge. [ABSTRACT FROM AUTHOR]

In recent years, due to the rapid growth of the world's population, the demand for agricultural products and food is growing increasingly. Therefore, the agricultural supply chain optimization has been grabbed by researchers to reduce food security concerns. On the other hand, the production amount of farmers is affected by various factors, including environmental conditions. In this paper, a supply chain network is investigated by developing a Mixed-Integer Linear Programming (MILP) model to effectively improve economic objectives under uncertainty. Then, a scenario-based robust optimization approach is employed to deal with the uncertainty. One of the novelities of our paper is considering weather conditions and economic fluctuations in different scenarios. The effectiveness of the proposed mathematical model has been confirmed by a real case study of dates farms. Dates and its by-products have a significant role in GDP, job creation, export, and the creation of various packaging and processing. Moreover, three meta-heuristic algorithms including Whale Optimization Algorithm (WOA), Particle Swarm Optimization (PSO), and a hybrid algorithm based on them (WOA–PSO) are adapted to deal with the NP-hardness of the problems. Moreover, the parameters of the proposed algorithms are improved by the Taguchi method, and to achieve more exact measurements, sensitivity analysis is performed. Finally, the numerical results confirmed that the accuracy of the hybrid algorithm was between 1.9 and 2.8%. Therefore, this approach could be practical and efficient for solving large-sized problems. The obtained outcomes demonstrated that the planned model provides tactical considerations for the related managers. [ABSTRACT FROM AUTHOR]

Suicide is the second leading cause of death among youth aged 15–24 years. Identifying modifiable risk factors relevant to adolescents is crucial for suicide prevention. Sleep patterns have been linked to suicidality in adults, but lack sufficient study in youth. This ecological momentary assessment (EMA) study aimed to explore the relationship between objectively and subjectively measured sleep characteristics and next-day suicidal ideation in high-risk youth. We included 29 adolescents (12–18 years old) admitted to the inpatient psychiatric ward post-suicide attempt or due to suicidal intent within the previous month. We conducted objective (actigraphy) and subjective (sleep diary) sleep pattern assessments over ten consecutive days. Daily suicidal ideation was evaluated using a questionnaire based on the validated C-SSRS interview. A significant positive association was observed between sleep onset latency (SOL) and expressing a "death wish" the following day (OR = 1.06, 95% CI [1–1.11], p =.04), with each minute of longer SOL increased the risk for a death wish the following day by 6%. In addition, a marginally significant negative association was observed between total sleep time (TST) and expressing a "death wish" the following day (OR = 0.57, 95% CI [0.3–1.11], p = 0.1), with each one-hour decrease in objectively measured TST increasing the odds of a death wish by 43%. Our study highlights the interplay between sleep patterns and suicidal ideation, with SOL and TST playing a significant role that may function as proximal risk factors for suicidality and as a target for intervention while treating suicidal youth. [ABSTRACT FROM AUTHOR]

The use of fiber-reinforced metal laminates (FML) is increasing due to their special mechanical properties. Their properties depend on the type and thickness of the metal and reinforcing fibers. In this study, the mechanical properties of FML sheets with a 2 mm thickness stainless steel 420 intermediate layer reinforced with six layers of Woven and Mat glass fibers were investigated. The fibers were impregnated with a polyester resin and produced as three layers on the front and back of the steel sheet with different fiber angles (0°, 15°, 30°, and 45°). Finally, samples with 8 mm thickness were cut by water jet. Mechanical properties including static tensile and bending tests were performed, and after that a low-cycle dynamic tensile and bending test was also performed according to the obtained maximum static tensile and bending force for each sample. The obtained results varied depending on the fibers' angle, but in all tests, the highest strength was obtained for samples with fibers arranged at 0°. A charpy impact and high speed pressure test were performed to determine toughness and confirm the strength and adhesion between the samples' layers. SEM imaging of the fracture surface showed good adhesion and bonding between layers. [ABSTRACT FROM AUTHOR]