Search

Background: Metastatic renal cell carcinoma (RCC) poses a huge challenge once it has become resistant to targeted therapy. Vasculogenic mimicry (VM) is a novel blood supply system formed by tumor cells that can circumvent molecular targeted therapies. As one of the herbal remedies, curcumin has been demonstrated to play antineoplastic effects in many different types of human cancers; however, its function and mechanism of targeting VM in RCC remains unknown., Objective: Here, in the work, we explored the role of curcumin and its molecular mechanism in the regulation of VM formation in RCC., Methods: RNA-sequencing analysis, immunoblotting, and immunohistochemistry were used to detect E Twenty Six-1(ETS-1), vascular endothelial Cadherin (VE-Cadherin), and matrix metallopeptidase 9 (MMP9) expressions in RCC cells and tissues. RNA sequencing was used to screen the differential expressed genes. Plasmid transfections were used to transiently knock down or overexpress ETS-1. VM formation was determined by tube formation assay and animal experiments. CD31-PAS double staining was used to label the VM channels in patients and xenograft samples., Results: Our results demonstrated that VM was positively correlated with RCC grades and stages using clinical patient samples. Curcumin inhibited VM formation in dose and time-dependent manner in vitro . Using RNA-sequencing analysis, we discovered ETS-1 as a potential transcriptional factor regulating VM formation. Knocking down or overexpression of ETS-1 decreased or increased the VM formation, respectively and regulated the expression of VE-Cadherin and MMP9. Curcumin could inhibit VM formation by suppressing ETS-1, VE-Cadherin, and MMP9 expression both in vitro and in vivo ., Conclusion: Our finding might indicate that curcumin could inhibit VM by regulating ETS-1, VE-Cadherin, and MMP9 expression in RCC cell lines. Curcumin could be considered as a potential anti-cancer compound by inhibiting VM in RCC progression., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Immune checkpoint inhibitors (ICIs) offer significant advantages for the treatment of urologic tumors, enhancing the immune function of anti-tumor T cells by inhibiting PD-1 and PDL1 binding. They have been shown to be well tolerated and remarkably effective in clinical practice, offering hope to many patients who are not well treated with conventional drugs. Clinical trials in recent years have shown that anti-PD-1 and PD-L1 antibodies have good efficacy and safety in the treatment of urologic tumors. These antibodies can be applied to a variety of urologic tumors, such as bladder cancer, renal cell carcinoma, and prostate cancer. They have been approved for the first-line treatment or as an option for follow-up therapy. By blocking the PD-1/PD-L1 signaling pathway, ICIs can release immune functions that are suppressed by tumor cells and enhance T-cell killing, thereby inhibiting tumor growth and metastasis. This therapeutic approach has achieved encouraging efficacy and improved survival for many patients. Although ICIs have shown remarkable results in the treatment of urologic tumors, some problems remain, such as drug resistance and adverse effects in some patients. Therefore, further studies remain important to optimize treatment strategies and improve clinical response in patients. In conclusion, PD-1/PD-L1 signaling pathway blockers have important research advances for the treatment of urologic tumors. Their emergence brings new hope for patients who have poor outcomes with traditional drug therapy and provides new options for immunotherapy of urologic tumors. The purpose of this article is to review the research progress of PD-1 and PD-L1 signaling pathway blockers in urologic tumors in recent years., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

Cu(I)-catalyzed azide-alkyne 1,3-dipolar cycloaddition (CuAAC) - commonly known as the "click reaction" - serves as the most effective and highly reliable tool for facile construction of simple to complex designs at the molecular level. It relates to the formation of carbon heteroatomic systems by joining or clicking small molecular pieces together with the help of various organic reactions such as cycloaddition, conjugate addition, ring-opening, etc. Such dynamic strategy results in the generation of triazole and its derivatives from azides and alkynes with three nitrogen atoms in the five-membered aromatic azole ring that often forms gel-assembled structures having gelating properties. These scaffolds have led to prominent applications in designing advanced soft materials, 3D printing, ion sensing, drug delivery, photonics, separation, and purification. In this review, we mainly emphasize the different mechanistic aspects of triazole formation, which includes the synthesis of sugar-based and non-sugar-based triazoles, and their gel applications reported in the literature for the past ten years, as well as the upcoming scope in different branches of applied sciences., (Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.net.)

This research addresses the imperative need for efficient underwater exploration in the domain of deep-sea resource development, highlighting the importance of autonomous operations to mitigate the challenges posed by high-stress underwater environments. The proposed approach introduces a hybrid model for Underwater Object Detection (UOD), combining Bi-directional Long Short-Term Memory (Bi-LSTM) with a Restricted Boltzmann Machine (RBM). Bi-LSTM excels at capturing long-term dependencies and processing sequences bidirectionally to enhance comprehension of both past and future contexts. The model benefits from effective feature learning, aided by RBMs that enable the extraction of hierarchical and abstract representations. Additionally, this architecture handles variable-length sequences, mitigates the vanishing gradient problem, and achieves enhanced significance by capturing complex patterns in the data. Comprehensive evaluations on brackish, and URPC 2020 datasets demonstrate superior performance, with the BiLSTM-RBM model showcasing notable accuracies, such as big fish 98.5 for the big fish object in the brackish dataset and 98 for the star fish object in the URPC dataset. Overall, these findings underscore the BiLSTM-RBM model's suitability for UOD, positioning it as a robust solution for effective underwater object detection in challenging underwater environments. [ABSTRACT FROM AUTHOR]

Purpose: Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed adaptive threshold gradient feature (ATGF) model. A received signal strength indicator (RSSI) model with node estimated features is implicated with localization problem and enhanced with hybrid cumulative approach (HCA) algorithm for node optimizations with distance predicting. Design/methodology/approach: Using a theoretical or empirical signal propagation model, the RSSI (known transmitting power) is converted to distance, the received power (measured at the receiving node) is converted to distance and the distance is converted to RSSI (known receiving power). As a result, the approximate distance between the transceiver node and the receiver may be determined by measuring the intensity of the received signal. After acquiring information on the distance between the anchor node and the unknown node, the location of the unknown node may be determined using either the trilateral technique or the maximum probability estimate approach, depending on the circumstances using federated learning. Findings: Improvisation of localization for wireless sensor network has become one of the prime design features for estimating the different conditional changes externally and internally. One such feature of improvement is observed in this paper, via HCA where each feature of localization is depicted with machine learning algorithms imparting the energy reduction problem for each newer localized nodes in Section 5. All affected parametric features on energy levels and localization problem for newer and extinct nodes are implicated with hybrid cumulative approach as in Section 4. The proposed algorithm (HCA with AGTF) has implicated with significant change in energy levels of nodes which are generated newly and which are non-active for a stipulated time which are mentioned and tabulated in figures and tables in Section 6. Originality/value: Localization of the nodes is crucial for gaining access of different nodes which would provision in extreme areas where networks are unreachable. The feature of localization of nodes has become a significant study where multiple features on distance model are implicated on predictive and heuristic model for each set of localization parameters that govern the design on energy minimization with proposed ATGF model. An RSSI model with node estimated features is implicated with localization problem and enhanced with HCA algorithm for node optimizations with distance predicting. [ABSTRACT FROM AUTHOR]

Astrogeodetic deflections of the vertical (DoVs) are close indicators of the slope of the geoid. Thus, DoVs observed along horizontal profiles may be integrated to create geoid undulation profiles. In this study, we collected DoV data in the Eastern Swiss Alps using a Swiss Digital Zenith Camera, the COmpact DIgital Astrometric Camera (CODIAC), and two total station-based QDaedalus systems. In the mountainous terrain of the Eastern Swiss Alps, the geoid profile was established at 15 benchmarks over a two-week period in June 2021. The elevation along the profile ranges from 1185 to 1800 m, with benchmark spacing ranging from 0.55 km to 2.10 km. The DoV, gravity, GNSS, and levelling measurements were conducted on these 15 benchmarks. The collected gravity data were primarily used for corrections of the DoV-based geoid profiles, accounting for variations in station height and the geoid-quasigeoid separation. The GNSS/levelling and DoV data were both used to compute geoid heights. These geoid heights are compared with the Swiss Geoid Model 2004 (CHGeo2004) and two global gravity field models (EGM2008 and XGM2019e). Our study demonstrates that absolute geoid heights derived from GNSS/leveling data achieve centimeter-level accuracy, underscoring the precision of this method. Comparisons with CHGeo2004 predictions reveal a strong correlation, closely aligning with both GNSS/leveling and DoV-derived results. Additionally, the differential geoid height analysis highlights localized variations in the geoid surface, further validating the robustness of CHGeo2004 in capturing fine-scale geoid heights. These findings confirm the reliability of both absolute and differential geoid height calculations for precise geoid modeling in complex mountainous terrains. [ABSTRACT FROM AUTHOR]

The monitoring and diagnostic system has been suggested as a non-destructive diagnostic method. The structure and operation of the suggested system can be described by the concept of digital shadow (DS). One of the main DS subsystems is a set of sensors properly placed on the monitored object and coupled with a discrete data processing model created in Matlab/Simulink. The discrete model, as another important DS subsystem of the monitored facility, transfers information about its technical condition to the operator based on data recorded by the sensor system. The digital monitoring model processes the recorded data in the form of the object's response to actions caused by its operating conditions. This work formalized a mathematical model determining the coupling of the digital model with the sensors placed on the monitored object. The formulated method using DS, due to its sensitivity, enables the detection of the damage in the object at an early stage. The tests allowed for detecting the regularities enabling the determination of the area of damage to the shaft and its size. [ABSTRACT FROM AUTHOR]

The elementary cellular automata (ECAs) under the chaotic rule possess long periodicity and are widely used in pseudo-random number generators. However, their period is limited, related to the rule and the number of cells. Meanwhile, the Boolean functions of some ECAs are linear and vulnerable to linear analysis. Thus, the ECA cannot be directly implemented in the stream cipher. In this paper, a hybrid ECA (HECA) with dynamic mask (HECA-M) is designed. The HECA-M consists of two parts: the driving and mask parts. The driving part based on a HECA is used in generating the keystream, and the mask part based on a chaotic ECA is utilized to determine the iterative rule of the driving part. Subsequently, a stream cipher based on the HECA-M and SHA-512 is proposed. The statistic and secure analyses indicate that the proposed stream cipher possesses good randomness and can resist stream cipher analyses, such as exhaustive search, Berlekamp–Massey synthesis, guess and determine attack, time–memory–data tradeoff attack, etc. Hence, the proposed scheme can meet security requirements. Moreover, the time and space consumption of the proposed stream cipher is qualified. [ABSTRACT FROM AUTHOR]

This paper comprehensively reviews friction stir welding (FSW) as applied to copper and its alloys. FSW is a solid-state joining process that offers significant advantages over traditional fusion welding methods, particularly for materials like copper that are difficult to weld conventionally due to their high thermal conductivity and oxidation issues. Over time, the FSW process has been developed for different industries. Copper structures joined through FSW are utilized for nuclear waste storage, electrical connectors, chemical and petrochemical storage, refrigeration systems, heat exchangers, and the aerospace industry. This covers recent advancements in FSW technology, the geometry of the tools used, the process parameters, and the microstructural characteristics and mechanical properties of the joints. It examines the shapes, sizes, and materials of the tools used for welding copper and its alloys, along with process parameters such as rotational speed and traverse speed, and their influence on the quality of the joints. Additionally, the paper presents syntheses of previously published results, highlighting the values of parameters that indicate the quality of the welds, including grain size, microhardness, mechanical strength, and elongation. The challenges and potential solutions in applying FSW to copper are also discussed, providing a starting point for future research and industrial applications. [ABSTRACT FROM AUTHOR]

In recent years, the efficiency of high-efficiency Cu(In,Ga)Se2 (CIGS) solar cells has been significantly improved, particularly for narrow-gap types. One of the key reasons for the enhancement of narrow-gap device performance is the formation of the "Spike" structure at the CdS/CIGS heterojunction interface. Wide-gap CIGS solar cells excel in modular production but lag behind in efficiency compared to narrow-gap cells. Some studies suggest that the "Cliff" structure at the heterojunction of wide-gap CIGS solar cells may be one of the factors contributing to this decreased efficiency. This paper utilizes the SCAPS software, grounded in the theories of semiconductor physics and photovoltaic effects, to conduct an in-depth analysis of the impact of "Cliff" and "Spike" heterojunction structures on the performance of wide band gap CIGS solar cells through numerical simulation methods. The aim is to verify whether the "Spike" structure is also advantageous for enhancing wide-gap CIGS device performance. The simulation results show that the "Spike" structure is beneficial for reducing interfacial recombination, thereby enhancing the VOC of wide-gap cells. However, an electronic transport barrier may form at the heterojunction interface, resulting in a decrease in JSC and FF, which subsequently reduces device efficiency. The optimal heterojunction structure should exhibit a reduced "Cliff" degree, which can facilitate the reduction of interfacial recombination while simultaneously preventing the formation of an electronic barrier, ultimately enhancing both VOC and device performance. [ABSTRACT FROM AUTHOR]

Deionized water is replacing fluorinated liquids as the preferred choice for two-phase immersion cooling in data centers. Yet, insufficient bubble removal capability at low saturated pressure is a key challenge hindering the widespread application. To solve this issue, this study employs non-ionic surfactant (Tween 20) and asymmetric structures (expanding microchannel) to enhance the boiling performances of deionized water under sub-atmospheric pressure. The research examines the effects of pressure (8.8~38.5 kPa), surfactant concentration (0.1~0.5 mL/L), and heat flux density (10~180 W/cm2) on the boiling heat transfer characteristics and analyzes the mechanism of unusual temperature oscillations induced by surfactants. It was found that the trade-off between the sub-atmospheric pressure, surface tension coefficient, and reduced static contact angle results in pronounced intermittent boiling on the heated surface. Even with the addition of surfactants, the improvement in heat transfer requires demanding conditions. Boiling enhancement throughout all heat flux conditions was achieved when the surfactant concentration was higher than 0.2 mL/L for the expanding microchanneled surface. The heat transfer coefficient reached 6.89 W·cm−2·K−1 under 8.8 kPa, which was 45% higher than without the surfactant. Under the same heat flux and sub-atmospheric pressure, as the concentration increased from 0.1 to 0.5 mL/L, the amplitudes of temperature fluctuation of the plane surface and expanding microchanneled surface decreased from 10 K to 2 K and 18 K to 1 K, respectively. The onset of nucleate boiling and wall superheat of the expanding microchanneled surface gradually decreased with the increase in surfactant concentration, where the onset of nucleate boiling decreased by 10.54 K. When the heat flux is 160 W/cm2, the wall superheat is reduced by 12.8 K. [ABSTRACT FROM AUTHOR]

This paper presents the process of manufacturing mechanical joint components using additive manufacturing (AM) techniques such as Material Extrusion (Fused Deposition Modelling (FDM)), Material Jetting (PolyJet), and Vat Photopolymerization (VAT)/Stereolithography (SLA). Using the PolyJet technique and a photopolymer resin, spline and threaded joint components were produced. For comparative analysis, the threaded joint was also fabricated using FDM and SLA techniques. PLA material was used for the FDM technique, while photopolymer resin was utilized for the SLA process. The components produced underwent a surface analysis to evaluate the accuracy of the dimensions in relation to the nominal dimensions. For the spline connection components, the dimensional deviations recorded by a 3D scanner ranged from −0.11 to +0.18 mm for the shaft and up to 0.24 mm for the sleeve. Measurements of screw and nut diameters showed the highest accuracy for screws produced using the PolyJet technique, while the nuts exhibited the best accuracy when fabricated with the SLA method. The profile of the screw threads using a contour gauge revealed the most accurate thread profile on the screw manufactured with the PolyJet technique. [ABSTRACT FROM AUTHOR]

We have studied the molecular properties of water in physically and chemically cross-linked gelatin hydrogels by FTIR-spectroscopy, NMR relaxation, and diffusivity and broadband dielectric spectroscopy, which are sensitive to dynamical properties of water, being a structural marker of polymer network. All experiments demonstrated definite reinforcement of the hydrogel net structure and an increase in the amount of hydrate water. FTIR experiments have shown that the chemical cross-linking of gelatin molecules initiates an increase in the collagen-like triple helices "strength", as a result of infused restriction on protein molecular mobility. The "strengthening" of protein chains hinders the mobility of protein fragments, introducing complex modifications into the structural properties of water which are remained practically unchanged up to up to 30–40 °C. [ABSTRACT FROM AUTHOR]

In recent years, π-conjugated liquid crystalline molecules with optoelectronic functionalities have garnered considerable attention, and integrating these molecules into side-chain liquid crystalline polymers (SCLCPs) holds potential for developing devices that are operational near room temperature. However, it is difficult to design SCLCPs with excellent processability because liquid crystalline mesogens are rigid rods, have low solubility in organic solvents, and have a high isotropization temperature. Recently, we developed near-room-temperature π-conjugated nematic liquid crystals based on "bridged stilbene". In this work, we synthesized a polyacrylate SCLCP incorporating a bridged stilbene that exhibited a nematic phase near room temperature and could maintain liquid crystallinity for more than three months. We conducted a thorough phase structure analysis and evaluated the optical properties. The birefringence values of the resulting polymers were higher than those of the corresponding monomers because of the enhanced order parameters due to the polymer effect. In addition, the synthesized polymers inherited mesogen-derived AIE properties, with high quantum yields (Φfl = 0.14–0.35) in the solid state. It is noteworthy that the maximum fluorescence wavelength exhibited a redshift of greater than 27 nm as a consequence of film formation. Thus, several unique characteristics of the SCLCPs are unattainable with small molecular systems. [ABSTRACT FROM AUTHOR]

We developed dye-sensitized solar cells (DSSCs) using 1,5-carboxy-2-[[3-[(2,3-dihydro-1,1-dimethyl-3-ethyl-1H-benzo[e]indol-2-ylidene)methyl]-2-hydroxy-4-oxo-2-cyclobuten-1-ylidene]methyl]-3,3-dimethyl-1-octyl-3H-indolium and 1,3,3-trimethyl indolino-6′-nitrobenzopyrylospiran. The DSSCs incorporate photochromic molecules to regulate photoelectric conversion properties. We irradiated photoelectrodes adsorbed with SQ2/SPNO2 using both UV and visible light and observed the color changes in these photoelectrodes. Following UV irradiation, the transmittance at 540 nm decreased by 20%, while it increased by 15% after visible light irradiation. This indicates that SPNO2 on the DSSCs is photoisomerized from the spiropyran form (SP) to the photomerocyanine (PMC) form under UV light. The photoelectric conversion efficiency (η) of the DSSCs increased by 0.15% following 5 min of UV irradiation and decreased by 0.07% after 5 min of visible light irradiation. However, direct electron injection from PMC seems challenging, suggesting that the mechanism for improved photoelectric conversion in these DSSCs is likely due to Förster resonance energy transfer (FRET) from PMC to the SQ2 dye. The findings suggest that the co-sensitization of DSSCs by PMC-SQ2 and SQ2 alone, facilitated by their respective photoabsorption, results in externally responsive and co-sensitized solar cells. This study provides valuable insights into the development of advanced DSSCs with externally controllable photoelectric conversion properties via the strategic use of photochromic molecules and energy transfer mechanisms, advancing future solar energy applications. [ABSTRACT FROM AUTHOR]

Welding is an extensively used technique in manufacturing, and as for every other process, there is the potential for defects in the weld joint that could be catastrophic to the manufactured products. Different welding processes use different parameter settings, which greatly impact the quality of the final welded products. The focus of research in weld defect detection is to develop a non-destructive testing method for weld quality assessment based on observing the weld with an RGB camera. Deep learning techniques have been widely used in the domain of weld defect detection in recent times, but the majority of them use, for example, X-ray images. An RGB image-based solution is attractive, as RGB cameras are comparatively inexpensive compared to X-ray image solutions. However, the number of publicly available RGB image datasets for weld defect detection is comparatively lower than that of X-ray image datasets. This work achieves a complete weld quality assessment involving lap shear strength prediction and visual weld defect detection from an extremely limited dataset. First, a multimodal dataset is generated by the fusion of image data features extracted using a convolutional autoencoder (CAE) designed in this experiment and input parameter settings data. The fusion of the dataset reduced lap shear strength (LSS) prediction errors by 34% compared to prediction errors using only input parameter settings data. This is a promising result, considering the extremely small dataset size. This work also achieves visual weld defect detection on the same limited dataset with the help of an ultrasonic weld defect dataset generated using offline and online data augmentation. The weld defect detection achieves an accuracy of 74%, again a promising result that meets standard requirements. The combination of lap shear strength prediction and visual defect detection leads to a complete inspection to avoid premature failure of the ultrasonic weld joints. The weld defect detection was compared against the publicly available image dataset for surface defect detection. [ABSTRACT FROM AUTHOR]

Background: The biomedical and health informatics (BMHI) fields have been advancing rapidly, a trend particularly emphasised during the recent COVID-19 pandemic, introducing innovations in BMHI. Over nearly 50 years since its establishment as a scientific discipline, BMHI has encountered several challenges, such as mishaps, delays, failures, and moments of enthusiastic expectations and notable successes. This paper focuses on reviewing the progress made in the BMHI discipline, evaluating key milestones, and discussing future challenges. Methods: To, Structured, step-by-step qualitative methodology was developed and applied, centred on gathering expert opinions and analysing trends from the literature to provide a comprehensive assessment. Experts and pioneers in the BMHI field were assigned thematic tasks based on the research question, providing critical inputs for the thematic analysis. This led to the identification of five key dimensions used to present the findings in the paper: informatics in biomedicine and healthcare, health data in Informatics, nurses in informatics, education and accreditation in health informatics, and ethical, legal, social, and security issues. Results: Each dimension is examined through recently emerging innovations, linking them directly to the future of healthcare, like the role of artificial intelligence, innovative digital health tools, the expansion of telemedicine, and the use of mobile health apps and wearable devices. The new approach of BMHI covers newly introduced clinical needs and approaches like patient-centric, remote monitoring, and precision medicine clinical approaches. Conclusions: These insights offer clear recommendations for improving education and developing experts to advance future innovations. Notably, this narrative review presents a body of knowledge essential for a deep understanding of the BMHI field from a human-centric perspective and, as such, could serve as a reference point for prospective analysis and innovation development. [ABSTRACT FROM AUTHOR]

The purpose of this study is to understand Green Human Resource Management (GHRM) practices, considerations that facilitate Green Organizational Culture (GOC) and Organizational Agility (OA), and the connection between these factors and the social performance (SP) and environmental performance (EP) of businesses. The target population of the current study comprises a Bangladeshi organization (manufacturing, university and service). In this quantitative study, data were collected using structured questionnaires. The final sample consisted of 445 respondents, and the data were analyzed using the Structural Equation Modeling (SEM) technique. Both Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were conducted to ensure the validity and reliability of the measurement model. The results show that social GHRM has a beneficial effect on social and environmental performance factors. This research also discovered employee's ideas about GRRM, GOC OA, and improving an organization's social and environmental performance for long-term growth. Our results show that hiring, teaching, evaluating, and rewarding employees in ways that avoid harming the natural world are all examples of HR management practices that help create a green workplace culture. This work adds to the resource-based view (RBV) and transitive leadership (TL) theory by showing the things that contribute to an organization's green culture and agility help the link between social and environmental performance and green HRM practices. Organizations in Bangladesh can benefit greatly from the GHRM model that this study suggests, as OC and OA have a good effect on both society and the environment. [ABSTRACT FROM AUTHOR]

Renal cell carcinoma (RCC) has diverse pathological subtypes, most of which have a poor prognosis. Patients with advanced RCC require systemic therapies for disease control. Although targeted therapies and immune checkpoint inhibitors have shown therapeutic efficacy, patients eventually succumb to disease progression. Therefore, additional therapies targeting different pathways are needed to provide more therapeutic options for sequential treatment. Our study explored the biological mechanisms and therapeutic outcomes for NPS-1034, a dual MET/AXL inhibitor, in RCC, both in vivo and in vitro. Our results showed that NPS-1034 can significantly inhibit tumor proliferation and induce cancer cell apoptosis. Besides MET and AXL, known targets of NPS-1034, we identified TNFRSF1A as another target gene inhibited by NPS-1034 via antibody arrays. This was further supported by next-generation sequencing, showing that the TNF signaling pathway is one of the most significant NPS-1034-regulated pathways. Furthermore, one of the identified target genes, GADD45A, responsible for NPS-1034 anticancer properties, was significantly associated with patient survival in RCC. GADD45A expression was significantly upregulated via NPS-1034 and downregulated via TNFRSF1A overexpression. Finally, its therapeutic efficacy was demonstrated in vivo, showing that NPS-1034 significantly alleviated the tumor burden and inhibited cell proliferation in a lung metastatic animal model. In conclusion, we explored the therapeutic mechanism of NPS-1034 and found that it targets not only MET and AXL but also TNFRSF1A. In a lung metastatic animal model, we confirmed that NPS-1034 is a potential candidate for systemic therapy in RCC. [ABSTRACT FROM AUTHOR]

Background: Sarcopenia is characterized by a loss of muscle mass and function and is often associated with frailty, a syndrome linked to physical disability and shortened survival in various patient populations, including cancer patients. Low serum alanine aminotransferase (ALT) values, serving as a biomarker for sarcopenia, were previously associated with frailty and shortened survival in several cancers. In the current study, we aimed to test the association between low ALT and shorter survival in renal cell carcinoma (RCC) patients and survivors. Methods: This was a retrospective analysis of RCC patients and survivors, both in- and outpatients. We defined patients with sarcopenia as those presenting with ALT < 17 IU/L. Results: We identified records of 3012 RCC patients. The cohort included 1830 patients (mean age 65.6 ± 13.3 years, 68% were men) of whom only 179 underwent surgical treatment. Out of the eligible cohort, 811 patients (44.3%) had ALT < 17 IU/L, with a mean ALT value of patients within the low-ALT group of 11.79 IU/L, while the mean value in the higher ALT level group was 24.44 IU/L (p < 0.001). Patients in the low-ALT group were older (67.9 vs. 63.7 years; p < 0.001) and had lower BMIs (26.6 vs. 28; p < 0.001). In addition, patients with low ALT had lower hemoglobin values (12.14 vs. 12.91 g/dL; p < 0.001), higher serum creatinine (1.49 vs. 1.14; p < 0.001) and higher platelet to lymphocyte ratios (178 vs. 156; p < 0.001). In a univariate analysis, low ALT levels were associated with a 72% increase in mortality (95% CI 1.46–2.02, p < 0.001). In a multivariate model controlled for age, gender, hemoglobin, platelets, LDH, neutrophil to lymphocyte ratios and platelet to lymphocyte ratios, low ALT levels were still associated with a 27% increase in mortality (HR = 1.27, 95% CI 1.08–1.51; p = 0.005). Conclusion. Low ALT values, associated with sarcopenia and frailty, are also associated with shortened survival in RCC patients, and survivors and could potentially be applied for optimizing individual treatment decisions. [ABSTRACT FROM AUTHOR]

Immune checkpoint inhibitors (ICI) have become the cornerstone of treatment in renal cell carcinoma (RCC), for both metastatic disease and in an adjuvant setting. However, an adaptive resistance from cancer cells may arise during ICI treatment, therefore many studies are focusing on additional immune checkpoint inhibitor pathways. Promising targets of immunotherapeutic agents under investigation include T cell immunoglobulin and ITIM domain (TIGIT), immunoglobulin-like transcript 4 (ILT4), lymphocyte activation gene-3 (LAG-3), vaccines, T cell immunoglobulin and mucin domain-containing protein 3 (TIM-3), and chimeric antigen receptor (CAR) T cells. In this review of the literature, we recollect the current knowledge of the novel treatment strategies in the field of immunotherapy that are being investigated in RCC and analyze their mechanism of action, their activity and the clinical studies that are currently underway. [ABSTRACT FROM AUTHOR]

Recent ground-breaking research in the Internet of Things (IoT) and drone technology has brought about revolutionary advancements in automation, remote sensing, and remote operations. Drones have gained massive popularity in IoT applications, like remote monitoring and task automation. Consequently, an innovative business model called Drones-as-a-Service (DaaS), which aims at leasing out drones for commercial use, is gaining momentum in the market. This spike in demand for drones is credited to their ease of deployment, flexibility of operation, and risk-free functioning. To fully utilize the advantages that drones provide and further delve into the flourishing field of drone research, it is essential to acquaint ourselves with the scope of this field and understand the capabilities of a drone. The methodology employed in this survey paper encompassed a thorough literature review to establish a foundation, followed by the design and implementation of a comprehensive survey instrument. As a result, this survey provides a means for the reader to explore diverse drone research topics, ranging from basic drone types and deployment architectures to complex drone networking and communication systems. Intelligence-based enhancements for drone communications and Blockchain-based security solutions have also been discussed. We have conducted detailed comparisons between different classes and architectures of drones, drone communication protocols (WiFi, LTE, 5G, satellites), and drone-enabled 5G networks (Ad hoc networks and Software Defined Networks). Blockchain-based and Intelligence-assisted solutions for 5G-related drone network security concerns have also been discussed. We also highlight the utility of drones in present-day Smart City Infrastructure, ranging across multitudes of applications, as well as in the Precision Agriculture domain and their future scope. Finally, we outline significant security challenges and cyber-attacks faced by drones and their countermeasures, as proposed in the state-of-the-art literature. In conclusion, we end this survey by discussing the future scope and directions of drone research. [ABSTRACT FROM AUTHOR]

When dealing with complex models in real situations, many optimization problems require the use of more than one objective function to adequately represent the relevant characteristics of the system under consideration. Multi-objective optimization algorithms that can deal with several objective functions are necessary in order to obtain reasonable results within an adequate processing time. This paper presents the multi-objective version of a recent metaheuristic algorithm that optimizes a single objective function, known as the Majority–minority Cellular Automata Algorithm (MmCAA), inspired by cellular automata operations. The algorithm presented here is known as the Multi-objective Majority–minority Cellular Automata Algorithm (MOMmCAA). The MOMmCAA adds repository management and multi-objective search space density control to complement the performance of the MmCAA and make it capable of optimizing multi-objective problems. To evaluate the performance of the MOMmCAA, results on benchmark test sets (DTLZ, quadratic, and CEC-2020) and real-world engineering design problems were compared against other multi-objective algorithms recognized for their performance (MOLAPO, GS, MOPSO, NSGA-II, and MNMA). The results obtained in this work show that the MOMmCA achieves comparable performance with the other metaheuristic methods, demonstrating its competitiveness for use in multi-objective problems. The MOMmCAA was implemented in MATLAB and its source code can be consulted in GitHub. [ABSTRACT FROM AUTHOR]

Laminin, fibronectin, and collagen IV are pivotal extracellular matrix (ECM) components. The ECM environment governs the fundamental properties of tumors, including proliferation, vascularization, and invasion. Given the critical role of cell-matrix adhesion in malignant tumor progression, we hypothesize that the concentrations of these proteins may be altered in the plasma of patients with clear cell renal cell carcinoma (ccRCC). This study aimed to evaluate the serum, urine, and tissue levels of laminin-5, collagen IV, and fibronectin among a control group and ccRCC patients, with the latter divided into stages T1–T2 and T3–T4 according to the TNM classification. We included 60 patients with histopathologically confirmed ccRCC and 26 patients diagnosed with chronic cystitis or benign prostatic hyperplasia (BPH). Collagen IV, laminin-5, and fibronectin were detected using Surface Plasmon Resonance Imaging biosensors. Significant differences were observed between the control group and ccRCC patients, as well as between the T1–T2 and T3–T4 subgroups. Levels were generally higher in plasma and tissue for fibronectin and collagen IV in ccRCC patients and lower for laminin. The ROC (Receiver operating characteristic) analysis yielded satisfactory results for differentiating between ccRCC patients and controls (AUC 0.84–0.93), with statistical significance for both fibronectin and laminin in plasma and urine. Analysis between the T1–T2 and T3–T4 groups revealed interesting findings for all examined substances in plasma (AUC 0.8–0.95). The results suggest a positive correlation between fibronectin and collagen levels and ccRCC staging, while laminin shows a negative correlation, implying a potential protective role. The relationship between plasma and urine concentrations of these biomarkers may be instrumental for tumor detection and staging, thereby streamlining therapeutic decision-making. [ABSTRACT FROM AUTHOR]

Escherichia coli is a gram-negative commensal bacterium living in human and animal intestines. Its pathogenic strains lead to high morbidity and mortality, which can adversely affect people by causing urinary tract infections, food poisoning, septic shock, or meningitis. Humans can contract E. coli by eating contaminated food—such as raw or undercooked raw milk, meat products, and fresh produce sold in open markets—as well as by coming into contact with contaminated settings like wastewater, municipal water, soil, and faeces. Some pathogenic strains identified in Nigeria, include Enterohemorrhagic (Verotoxigenic), Enterotoxigenic, Enteropathogenic, Enteroinvasive, and Enteroaggregative E. coli. This causes acute watery or bloody diarrhoea, stomach cramps, and vomiting. Apart from the virulence profile of E. coli, antibiotic resistance mechanisms such as the presence of blaCTX-M found in humans, animals, and environmental isolates are of great importance and require surveillance and monitoring for emerging threats in resource-limited countries. This review is aimed at understanding the underlying mechanisms of evolution and antibiotic resistance in E. coli in Nigeria and highlights the use of improving One Health approaches to combat the problem of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

The firewater system is a complex system associated with the safety process of Hydrogen storage tanks. Predicting preventive maintenance strategies is essential to ensure the long-term reliability of this system. Therefore, it is necessary to evaluate the multistate reliability of the firewater system in order to predict preventive maintenance strategies and provide safety measures. A polymorphic fuzzy fault tree analysis (PFFTA) for the risk analysis of complex systems has attracted much attention because of its powerful evaluation capability and its ability to analyze relationships among basic events. However, obtaining multistate failure probability (MFP) data for basic events in PFFTA has always been a major challenge. It is also difficult to quantify the minimum cut set (MCS) in PFFTA and determine the critical components for selecting a preventive maintenance strategy. In this study, we propose the Pythagorean fuzzy cost–benefit–safety analysis by using the PFFTA, an improved consistency aggregation method (I-CAM), and fuzzy Dematel for a predictive preventive maintenance strategy. In the proposed approach, the I-CAM method was used to collect and aggregate weights of experts' opinions to evaluate the MFP of basic events in PFFTA. As a result, a triptych cost–benefit–safety analysis based on Pythagorean fuzzy sets (PFSs) and the sum-product method (SPM) was estimated to reduce expert subjectivity, support an improved cost-effectiveness index to rank critical components, and fuzzy Dematel to evaluate influence of proposed preventive maintenance actions. To clarify the effectiveness and feasibility of the proposed methodology, a case study of the firewater system related to the plant is located in SONELGAZ electricity power plant (OUMACHE Unit) was demonstrated. Both evaluations of the cost–benefit–safety analysis of the critical component were performed, and selected the influence of preventive maintenance strategy of the firewater system was predicted. [ABSTRACT FROM AUTHOR]

In deep learning (DL) frameworks, a checkpoint operation is widely used to store intermediate variable values (e.g., weights, biases, and gradients) on storage media. This operation helps to reduce the recovery time of running a machine learning (ML) model after sudden power failures or random crashes. However, the checkpoint operation can stall the overall training step of the running model and waste expensive hardware resources by leaving the GPU in idle sleep during the checkpoint operation. In addition, the completion time of the checkpoint operation is unpredictable in cloud server environments (e.g., AWS and Azure) because excessive I/O operations issued by other running applications interfere with the checkpoint operations in the storage stacks. To efficiently address the above two problems, we carefully designed Check-QZP, which reduces the amount of data required for checkpoint operations and parallelizes executions on the CPU and GPU by understanding the internal behaviors of the training step. For the evaluation, we implemented Check-QZP and compared it with the traditional approach in real-world multi-tenant scenarios. In the evaluation, Check-QZP outperformed the baseline in all cases in terms of the overall checkpoint time and the amount of data generated by the checkpoint operations, reducing them by up to 87.5% and 99.8%, respectively. In addition, Check-QZP achieved superior training speeds compared to the baseline. [ABSTRACT FROM AUTHOR]

Bifacial photovoltaic (PV) modules can capture both front and rear incident light simultaneously, thereby enhancing their power output. Achieving uniformity in rear incident light is crucial for an efficient and a stable operation. In this study, we present a simple, yet effective textured rear reflector, designed to optimize the performance and stability of bifacial PV modules. The three-dimensional textured surface was created using an ethylene vinyl acetate sheet (EVA) through a hot-press method at 150 °C. Subsequently, the textured EVA surface was coated with solution-processed silver ink, increasing the reflectance of the textured reflector through a low-temperature process. The integration of the developed textured rear reflector into bifacial crystalline silicon (c-Si) PV modules resulted in an additional 6.9% improvement in power conversion efficiency compared to bifacial PV modules without a rear reflector, particularly when the rear reflector is close to the PV module. Furthermore, the textured rear reflector may mitigate current mismatch among cells by randomizing incident light and uniformly redistributing the reflected light to the PV cells. Consequently, the proposed textured reflector contributes to the enhanced performance and stability of bifacial PV modules. [ABSTRACT FROM AUTHOR]

In recent years, transdermal drug delivery systems have gained considerable attention for their ability to enhance patient compliance and provide consistent drug release. However, the barrier function of the skin's outermost layer remains the foremost challenge in these systems. As a result, there is a growing interest in the use of ethosomes as a cutting-edge nanocarrier in drug delivery. This review presents the concept and mechanism of action of ethosomes as well as the numerous types and preparation techniques available. It also discusses the significance of characterization methods and how they impact ethosomal properties along with recent advancements and applications in the realm of transdermal drug delivery. Ethosomes exhibit tremendous potential for the pharmaceutical industry, and optimizing preparation methods, performing comprehensive property studies, and exploring their applications are critical areas for future research. [ABSTRACT FROM AUTHOR]

Background: Prostate cancer (PCa) is the second most commonly diagnosed cancer in men worldwide, with metastasis, particularly to bone, being the primary cause of mortality. Currently, prognostic markers like PSA levels and Gleason classification are limited in predicting metastasis, emphasizing the need for novel clinical biomarkers. New molecules predicting tumor progression have been identified over time. Some, such as the immune checkpoint inhibitors (ICIs) PD-1/PD-L1, have become valid markers as theranostic tools essential for prognosis and drug target therapy. However, despite the success of ICIs as an anti-cancer therapy for solid tumors, their efficacy in treating bone metastases has mainly proven ineffective, suggesting intrinsic resistance to this therapy in the bone microenvironment. This study explores the potential of immunological intratumoral biomarkers, focusing on placental growth factor (PlGF), Vascular Endothelial Growth Factor Receptor 1 (VEGFR1), and Programmed Cell Death Protein 1 (PD-1), in predicting bone metastasis formation. Methods: we analyzed PCa samples from patients with and without metastasis by immunohistochemical analysis. Results: Results revealed that PlGF expression is significantly higher in primary tumors of patients that developed metastasis within five years from the histological diagnosis. Additionally, PlGF expression correlates with increased VEGFR1 and PD-1 levels, as well as the presence of intratumoral M2 macrophages. Conclusions: These findings suggest that PlGF contributes to an immunosuppressive environment, thus favoring tumor progression and metastatic process. Results here highlight the potential of integrating these molecular markers with existing prognostic tools to enhance the accuracy of metastasis prediction in PCa. By identifying patients at risk for metastasis, clinicians can tailor treatment strategies more effectively, potentially improving survival outcomes and quality of life. This study underscores the importance of further research into the role of intratumoral biomarkers in PCa management. [ABSTRACT FROM AUTHOR]

This article presents the results of an experimental study carried out to assess the possibility of using waste wind turbine blades as retaining wall structures for slopes and trenches. The use of Vestas and LM-type blades as retaining wall components was assumed, based on 'columns' made of Vestas-type closed profiles filled with concrete and 'slabs' of fragments extracted from LM-type blades. The results of the tests and comparisons of the displacement and strain values of the components obtained using different measurement methods are presented in this paper. The force–strain and force–displacement relationships obtained from the tests were used to validate numerical models of slope protection walls and excavations designed from used wind turbine blades. According to our research, there is a high degree of variability in the strength parameters and deformation of the composite elements made from the wind turbine blades. Therefore, in the case of this type of material, characterized by a significant variation in carrying capacity, deformability, and the nature of the failures, the use of different measurement methods makes it possible to obtain much of the data necessary for assessing the reusability of wind turbine blades in building. [ABSTRACT FROM AUTHOR]

Taking into consideration the moderating role of perceived organizational support within the framework of the natural resource-based view (RBV) theory, the purpose of this study is to investigate the connection between corporate social responsibility (CSR) and sustainable environmental performance. Specifically, this study focuses on the roles that green capability and green transformational leadership play as mediators in this relationship. Through the use of a survey questionnaire, information was collected from 420 employees working for small- and medium-sized enterprises (SMEs) in Bangladesh. The data were analyzed with the help of AMOS and SPSS. The findings indicate that the level of CSR has a significant impact on the performance of sustainable environmental practices. To a large extent, green capability and green transformational leadership serve as mediators in the relationship between CSR and sustainable environmental performance. Furthermore, perceived organizational support plays a significant role in moderating the relationship between CSR and sustainable environmental performance. The relationship between green transformational leadership and sustainable environmental performance is also significantly moderated by perceived organizational support. This is a significant contributor to the connection. This multidimensional corporate social responsibility model can be used to assess sustainable environmental performance in both industrialized and developing countries, and it can also be extended to other service sectors, according to the theoretical conclusion that can be drawn from the research. This research demonstrates that there is a direct connection between corporate social responsibility and sustainable environmental performance. As a result, practitioners are able to develop strategies that are effective in terms of corporate social responsibility. These findings should be taken into consideration by policymakers and managers who are dedicated to promoting equitable development of the country. [ABSTRACT FROM AUTHOR]

The modern healthcare system strives to provide patients with more comfortable and less invasive experiences, focusing on noninvasive and painless diagnostic and treatment methods. A key priority is the early diagnosis of life-threatening diseases, which can significantly improve patient outcomes by enabling treatment at earlier stages. While most patients must undergo diagnostic procedures before beginning treatment, many existing methods are invasive, time-consuming, and inconvenient. To address these challenges, electrochemical-based wearable and point-of-care (PoC) sensing devices have emerged, playing a crucial role in the noninvasive, continuous, periodic, and remote monitoring of key biomarkers. Due to their numerous advantages, several wearable and PoC devices have been developed. In this focused review, we explore the advancements in metal–organic frameworks (MOFs)-based wearable and PoC devices. MOFs are porous crystalline materials that are cost-effective, biocompatible, and can be synthesized sustainably on a large scale, making them promising candidates for sensor development. However, research on MOF-based wearable and PoC sensors remains limited, and no comprehensive review has yet to synthesize the existing knowledge in this area. This review aims to fill that gap by emphasizing the design of materials, fabrication methodologies, sensing mechanisms, device construction, and real-world applicability of these sensors. Additionally, we underscore the importance and potential of MOF-based wearable and PoC sensors for advancing healthcare technologies. In conclusion, this review sheds light on the current state of the art, the challenges faced, and the opportunities ahead in MOF-based wearable and PoC sensing technologies. [ABSTRACT FROM AUTHOR]

Simple Summary: The characterization of molecular alterations is continuously gaining relevance in pathology as it can contribute to explaining tumors' pathogenesis and address specific targeted therapies. As for the kidney, in recent years, much scientific research has been focused on the MiTF family genes, particularly on the TFE3 gene. In this setting, while initially just accustomed to a subtype of renal cell carcinoma, currently classified as TFE3-rearranged renal cell carcinoma, TFE3 fusions have been identified in a few mesenchymal neoplasms of the kidney viewed as PEComas. In this work, we gather the available data regarding the key clinical and pathological features of these TFE3-rearranged renal epithelial and mesenchymal neoplasms. We seek to propose a comprehensive solution regarding their pathogenesis and sort out their classification conundrum. Background: Identical translocations involving the TFE3 gene and various partners have been found in both renal and soft tissue tumors, like alveolar soft part sarcoma (ASPSCR1), ossifying fibromyxoid tumor (PHF1), epithelioid hemangioendothelioma, and the clear cell stromal tumor of the lung (YAP1). Methods: Herein, we review in detail the clinicopathologic and molecular data of TFE3-rearranged renal tumors and propose our perspective, which may shed light on this emerging conundrum. Results: Among the kidney tumors carrying TFE3 translocations, most are morphologically heterogeneous carcinomas labeling for the tubular marker PAX8. The others are mesenchymal neoplasms known as PEComas, characterized by epithelioid cells co-expressing smooth muscle actin, cathepsin-K, melanogenesis markers, and sometimes melanin pigment deposition. Over the past 30 years, numerous TFE3 fusion partners have been identified, with ASPL/ASPSCR1, PRCC, SFPQ/PSF, and NONO being the most frequent. Conclusions: It is not well understood why similar gene fusions can give rise to renal tumors with different morpho-immunophenotypes, which may contribute to the recent disagreement regarding their classification. However, as these two entities, respectively, epithelial and mesenchymal in nature, are widely recognized by the pathology community and their clinicopathologic features well established, we overall believe it is still better to retain the names TFE3-rearranged renal cell carcinoma and TFE3-rearranged PEComa. [ABSTRACT FROM AUTHOR]

Simple Summary: Stereotactic body radiotherapy (SBRT) is a treatment technique that delivers higher doses of conformal radiation precisely to specific targets. SBRT is utilized for the treatment of the more common cancers such as lung, liver and prostate malignancies, both in the early stage and the oligometastatic setting. The aim of this review is to highlight the current role of SBRT for renal cell carcinoma (RCC), a disease which was previously thought to be radioresistant, particularly to conventionally fractionated radiotherapy. There is now an increasing body of literature encompassing published phase II trials, reviews and meta-analyses demonstrating the effectiveness of SBRT in both early-stage RCC (as an alternative to patients who are not surgical candidates) and within the context of metastasis-directed therapy (MDT) for patients with oligometastatic and oligoprogressive RCC. Ongoing randomized trials are also exploring the role of SBRT as a cytoreductive treatment in metastatic RCC. Renal cell carcinoma (RCC) has been increasing in incidence by around 1.5% per year for several years. However, the mortality rate has been decreasing by 1.6% per year, and this can be attributed to stage migration and improvements in treatment. One treatment modality that has emerged in recent years is stereotactic body radiotherapy (SBRT), which is an advanced radiotherapy technique that allows the delivery of high-dose radiation to the tumor while minimizing doses to the organs at risk. SBRT has developed a role in the treatment of early-stage, oligometastatic and oligoprogressive RCC. In localized disease, phase II trials and meta-analyses have shown that SBRT provides a very high probability of long-term local control with a low risk of severe late toxicity. In oligometastatic (OMD) RCC, the same level of evidence has similarly shown good local control and minimal toxicity. SBRT could also delay the necessity to start or switch systemic treatments. Medical societies have started to incorporate SBRT in their guidelines in the treatment of localized disease and OMD. A possible future role of SBRT involves cytoreduction. It is theorized that SBRT can lower tumor burden and enhance immune-related response, but it cannot be recommended until the results of the phase II trials are published. [ABSTRACT FROM AUTHOR]

The term " Friction stir welding," abbreviated as "FSW," refers to a cutting-edge technology that has found widespread application in the metallurgical sector. In this piece of work, a complete discussion is presented on the most recent developments in the numerical analysis of FSW. These developments cover subjects like welding processes, and they are the subject of the discussion. In particular, the findings of this research lay a solid groundwork for future research into the phenomena that lie behind each of the three sub problems that are associated with FSW. Specifically, fully coupled thermo-mechanical models are used to study how different tool shapes influence the temperature distributions and material deformations that occur during (FSW). In addition to shedding light on various critical weld production methods, the results of the study also provide crucial insight into the mechanisms that are behind the construction of the FSW of steel, titanium, and other difficult materials. Nowadays, many techniques, such as computational solid mechanics (CSM) and computational fluid dynamics (CFD) methodologies, have been used to characterise FSW principles and model heat generation. Simulation of material flow has also been studied with regrard to numerical modelling approach. The results that have been summarised to understand the sophisticated numerical analytic techniques for FSW of a variety of different materials. [ABSTRACT FROM AUTHOR]

A sequence of n trials from a finite population with no replacement is described by the hypergeometric distribution as the number of successes. Calculating the likelihood that factory-produced items would be defective is one of the most popular uses of the hypergeometric distribution in industrial quality control. Very recently, several researchers have applied this distribution on certain families of analytic functions. In this study, we provide certain adequate criteria for the generalized hypergeometric distribution series to be in two families of analytic functions defined in the open unit disk. Furthermore, we consider an integral operator for the hypergeometric distribution. Some corollaries will be implied from our main results. [ABSTRACT FROM AUTHOR]

The classification of galaxies has significantly advanced using machine learning techniques, offering deeper insights into the universe. This study focuses on the typology of galaxies using data from the Galaxy Zoo project, where classifications are based on the opinions of non-expert volunteers, introducing a degree of uncertainty. The objective of this study is to integrate Fuzzy C-Means (FCM) clustering with explainability methods to achieve a precise and interpretable model for galaxy classification. We applied FCM to manage this uncertainty and group galaxies based on their morphological characteristics. Additionally, we used explainability methods, specifically SHAP (SHapley Additive exPlanations) values and LIME (Local Interpretable Model-Agnostic Explanations), to interpret and explain the key factors influencing the classification. The results show that using FCM allows for accurate classification while managing data uncertainty, with high precision values that meet the expectations of the study. Additionally, SHAP values and LIME provide a clear understanding of the most influential features in each cluster. This method enhances our classification and understanding of galaxies and is extendable to environmental studies on Earth, offering tools for environmental management and protection. The presented methodology highlights the importance of integrating FCM and XAI techniques to address complex problems with uncertain data. [ABSTRACT FROM AUTHOR]

Effectively detecting catechol (CC) and hydroquinone (HQ) simultaneously is crucial for environmental protection and human health monitoring. In the study presented herein, a novel electrochemical sensor for the sensitive simultaneous detection of CC and HQ was constructed based on an electrochemically reduced graphene oxide (ERGO)-modified multi-walled carbon nanotube paste electrode (MWCNTPE). Scanning electron microscopy, X-ray photoelectron spectroscopy, Raman spectroscopy and electrochemical techniques were utilized to characterize the sensing interface and investigate the sensing mechanism. Under the optimal detection conditions, the oxidation peak currents of CC and HQ show a good linear relationship with their concentrations in the range of 0.4–400 μM with a detection limit of 0.083 μM for CC and 0.028 μM for HQ (S/N = 3). Moreover, the sensor exhibits good performance and can be applied successfully in the simultaneous detection of CC and HQ in tap water samples and urine samples with satisfactory results, indicating its promising application prospects. [ABSTRACT FROM AUTHOR]

The research on two-dimensional materials has gained significant traction due to their potential for thermoelectric, optical, and other properties. The development of two-dimensional (2D) nanostructured-based TE generators and photodetectors has shown promising results. Over the years, researchers have played a crucial role in advancing this field, enhancing the properties of 2D materials through techniques such as doping, alloying, and various growth methods. Among these materials, black phosphorus, transition metal dichalcogenides, graphene, and IVA-VIA compounds stand out for their remarkable electronic, mechanical, and optical properties. This study presents a comprehensive review of the progress in the field, focusing on IVA-VIA compounds and their applications in TE and photodetector technologies. We summarize recent advancements in enhancing these materials' TE and optical properties and provide an overview of various synthesis techniques for their fabrication. Additionally, we highlight their potential applications as photodetectors in the infrared spectrum. This comprehensive review aims to equip researchers with a deep understanding of the TE and optical properties of 2DMs and their potential applications and to inspire further advancements in this field of research. [ABSTRACT FROM AUTHOR]