Search Results (18)

The growing use of machine learning (ML) and artificial intelligence across sectors has shown strong potential to improve decision-making processes. However, the adoption of ML by non-technical professionals remains limited due to the complexity of traditional development workflows, which often require software engineering and data science expertise. In recent years, low-code and no-code platforms have emerged as promising solutions to democratize ML by abstracting many of the technical tasks typically involved in software engineering pipelines. This paper investigates whether these platforms can offer a viable alternative for making ML accessible to non-expert users. Beyond predictive performance, this study also evaluates usability, setup complexity, the transparency of automated workflows, and cost management under realistic “out-of-the-box” conditions. This multidimensional perspective provides insights into the practical viability of LC/NC tools in real-world contexts. The comparative evaluation was conducted using three leading cloud-based tools: Amazon SageMaker Canvas, Google Cloud Vertex AI, and Azure Machine Learning Studio. These tools employ ensemble-based learning algorithms such as Gradient Boosted Trees, XGBoost, and Random Forests. Unlike traditional ML workflows that require extensive software engineering knowledge and manual optimization, these platforms enable domain experts to build predictive models through visual interfaces. The findings show that all platforms achieved high accuracy, with consistent identification of key features. Google Cloud Vertex AI was the most user-friendly, SageMaker Canvas offered a highly visual interface with some setup complexity, and Azure Machine Learning delivered the best model performance with a steeper learning curve. Cost transparency also varied considerably, with Google Cloud and Azure providing clearer safeguards against unexpected charges compared to Sagemaker Canvas. Full article

No-code (NC) platforms empower individuals without IT experience to create tailored applications and websites. While these platforms are accessible to a broader audience, their usability for people with disabilities remains underexplored. This study investigated whether, with targeted training, people with disabilities could effectively use NC platforms to develop customized tools for their workplace, and whether these tools would be adopted by their peers. Conducted in collaboration with a sheltered workshop in Germany, the study had three phases. Phase I involved a brainstorming session with employees, which shaped the study design and product development. In Phase II, six participants with disabilities received a one-week training to develop chatbots. Phase III implemented the chatbots in the workshop. In Phase II, each participant successfully developed four chatbots, which increased the participants’ skills and motivation. Based on the phase III results, users rated the developed chatbots highly (the System Usability Scale (SUS) questionnaire was delivered in the form of a chatbot), indicating their user-friendliness (M = 88.9, SD = 11.2). This study suggests that with appropriate training, individuals with disabilities can use NC platforms to create impactful, customized tools that are user-friendly and accessible to their peers. Full article

This work describes the synthesis, characterization, and in vitro and in silico evaluation of the biological activity of new functionalized isoxazole derivatives. The structures of all new compounds were analyzed by IR and NMR spectroscopy. The structures of 4c and 4f were further confirmed by single crystal X-ray and their compositions unambiguously determined by mass spectrometry (MS). The antibacterial effect of the isoxazoles was assessed in vitro against Escherichia coli, Bacillus subtilis, and Staphylococcusaureus bacterial strains. Isoxazole 4a showed significant activity against E. coli and B. subtilis compared to the reference antibiotic drugs while 4d and 4f also exhibited some antibacterial effects. The molecular docking results indicate that the synthesized compounds exhibit strong interactions with the target proteins. Specifically, 4a displayed a better affinity for E. coli, S. aureus, and B. subtilis in comparison to the reference drugs. The molecular dynamics simulations performed on 4a strongly support the stability of the ligand–receptor complex when interacting with the active sites of proteins from E. coli, S. aureus, and B. subtilis. Lastly, the results of the Absorption, Distribution, Metabolism, Excretion and Toxicity Analysis (ADME-Tox) reveal that the molecules have promising pharmacokinetic properties, suggesting favorable druglike properties and potential therapeutic agents. Full article

Composites based on recycled polypropylene (PP) reinforced with cellulose nanocrystals whit lignin corncob were prepared. The effect of the ratio composites prepared via a compression molding process on the mechanical and thermal properties was analyzed. Corncobs is a little-used agroindustrial residue with a high cellulose content. The corncob was milled and then delignified via the organosolve process in order to get the cellulose unbleached. An acid hydrolysis process was then carried out to obtain lignocellulose nanocrystals (LCNCs). Subsequently, LCNC/PP composites were obtained via termocompression molding using different concentrations of LCNC (0, 0.5, 1 and 2% by weight) previously mixed via extrusion. The residual lignin present in the LCNCs improved the compatibility between the reinforcement and the PP matrix. This was evidenced by the increase in mechanical properties and the stabilization of thermal properties. The results of the mechanical tests showed that the LCNC increases the tensile and flexural modules and strength with respect to neat PP. Composites with 2% of LCNC showed an increase of 36% and 43% in modulus and tensile strength, respectively, while the flexural modulus and strength increased by 7.6%. By using reinforcements of natural and residual origin (corncob) and improving the properties of recycled polymers, their reuse will increase, and this can lead to reducing waste in the environment. Full article

The pollution from waste plastic express packages (WPEPs), especially microplastic (MP) fragments, caused by the blowout development of the express delivery industry has attracted widespread attention. On account of the variety of additives, strong complexity, and high diversity of plastic express packages (PEPs), the multi-class classification of WPEPs is a typical large-class-number classification (LCNC). The traceability and identification of microplastic fragments from WPEPs is very challenging. An effective chemometric method for large-class-number classification would be very beneficial for the comprehensive treatment of WPEP pollution through the recycling and reuse of waste plastic express packages, including microplastic fragments and plastic debris. Rather than using the traditional one-against-one (OAO) and one-against-all (OAA) dichotomies, an exhaustive and parallel half-against-half (EPHAH) decomposition, which overcomes the defects of the OAO’s classifier learning limitations and the OAA’s data proportion imbalance, is proposed for feature selection. EPHAH analysis, combined with partial least squares discriminant analysis (PLS-DA) for large-class-number classification, was performed on 750 microplastic fragments of polyethylene WPEPs from 10 major courier companies using near-infrared (NIR) spectroscopy. After the removal of abnormal samples through robust principal component analysis (RPCA), the root mean square error of cross-validation (RMSECV) value for the model was reduced to 0.01, which was 21.5% lower than that including the abnormal samples. The best models of PLS-DA were obtained using SNV combined with SG-17 smoothing and 2D (SNV+SG-17+2D); the latent variables (LVs), the error rates of Monte Carlo cross-validation (ERMCCVs), and the final classification accuracies were 6.35, 0.155, and 88.67% for OAO-PLSDA; 5.37, 0.103, and 87.33% for OAA-PLSDA; and 3.12, 0.054, and 96.00% for EPHAH-PLSDA. The results showed that the EPHAH strategy can completely learn the complex LCNC decision boundaries for 10 classes, effectively break the tie problem, and greatly improve the voting resolution, thereby demonstrating significant superiority to both the OAO and OAA strategies in terms of classification accuracy. Meanwhile, PLS-DA further maximized the covariance and data interpretation abilities between the potential variables and categories of microplastic debris, thereby establishing an ideal performance identification model with a recognition rate of 96.00%. Full article

The widely used high-density polyethylene (HDPE) polymer has inadequate mechanical and thermal properties for structural applications. To overcome this challenge, nano zinc oxide (ZnO) and nano boron oxide (B₂O₃) doped lignin-containing cellulose nanocrystals (L-CNC) were blended in the polymer matrix. The working hypothesis is that lignin will prevent CNC aggregation, and metal oxides will reduce the flammability of polymers by modifying their degradation pathways. This research prepared and incorporated safe, effective, and eco-friendly hybrid systems of nano ZnO/L-CNC and nano B₂O₃/L-CNC into the HDPE matrix to improve their physio-mechanical and fire-retardant properties. The composites were characterized using Fourier transform infrared spectroscopy, scanning electron microscopy, energy dispersive X-ray analysis, thermo-gravimetric analysis, differential scanning calorimetry, dynamic mechanical analysis, horizontal burning test, and microcalorimetry test. The results demonstrated a substantial increase in mechanical properties and a reduction in flammability. The scanning electron microscope (SEM) images showed some agglomeration and irregular distribution of the inorganic oxides. Full article

The pursuit of innovative combinations for the development of novel antimicrobial and antiviral medications has garnered worldwide interest among scientists in recent times. Monosaccharides and their glycosides, such as methyl α-d-mannopyranoside derivatives, play a significant role in the potential treatment of viral respiratory pathologies. This study was undertaken to investigate and assess the synthesis and spectral characterization of methyl α-d-mannopyranoside derivatives 2–6, incorporating various aliphatic and aromatic groups. The investigation encompassed comprehensive in vitro antimicrobial screening, examination of physicochemical properties, molecular docking analysis, molecular dynamics simulations, and pharmacokinetic predictions. A unimolar one-step cinnamoylation reaction was employed under controlled conditions to produce methyl 6-O-cinnamoyl-α-d-mannopyranoside 2, demonstrating selectivity at the C-6 position. This represented a pivotal step in the development of potential antimicrobial derivatives based on methyl α-d-mannopyranoside. Subsequently, four additional methyl 6-O-cinnamoyl-α-d-mannopyranoside derivatives were synthesized with reasonably high yields. The chemical structures of these novel analogs were confirmed through a thorough analysis of their physicochemical properties, elemental composition, and spectroscopic data. In vitro antimicrobial assays were conducted against six bacterial strains and two fungal strains, revealing promising antifungal properties of these methyl α-d-mannopyranoside derivatives in comparison to their antibacterial activity. Moreover, cytotoxicity testing revealed that the compounds are less toxic. Further supporting these findings, molecular docking studies were performed against the H5N1 influenza A virus, indicating significant binding affinities and nonbonding interactions with the target protein 6VMZ. Notably, compounds 4 (−7.2) and 6 (−7.0) exhibited the highest binding affinities. Additionally, a 100 ns molecular dynamics simulation was conducted to assess the stability of the complex formed between the receptor 6VMZ and methyl α-d-mannopyranoside derivatives under in silico physiological conditions. The results revealed a stable conformation and binding pattern within the stimulating environment. In silico pharmacokinetic and toxicity assessments of the synthesized molecules were performed using Osiris software (version 2.9.1). Compounds 4 and 6 demonstrated favorable computational and pharmacological activities, albeit with a low drug score, possibly attributed to their higher molecular weight and irritancy. In conclusion, this study showcases the synthesis and evaluation of methyl α-d-mannopyranoside derivatives as promising candidates for antimicrobial and antifungal agents. Molecular docking and dynamics simulations, along with pharmacological predictions, contribute to our understanding of their potential therapeutic utility, although further research may be warranted to address certain pharmacological aspects. Full article

Nucleoside analogs are frequently used in the control of viral infections and neoplastic diseases. However, relatively few studies have shown that nucleoside analogs have antibacterial and antifungal activities. In this study, a fused pyrimidine molecule, uridine, was modified with various aliphatic chains and aromatic groups to produce new derivatives as antimicrobial agents. All newly synthesized uridine derivatives were analyzed by spectral (NMR, FTIR, mass spectrometry), elemental, and physicochemical analyses. Prediction of activity spectra for substances (PASS) and in vitro biological evaluation against bacteria and fungi indicated promising antimicrobial capability of these uridine derivatives. The tested compounds were more effective against fungal phytopathogens than bacterial strains, as determined by their in vitro antimicrobial activity. Cytotoxicity testing indicated that the compounds were less toxic. In addition, antiproliferative activity against Ehrlich ascites carcinoma (EAC) cells was investigated, and compound 6 (2′,3′-di-O-cinnamoyl-5′-O-palmitoyluridine) demonstrated promising anticancer activity. Their molecular docking against Escherichia coli (1RXF) and Salmonella typhi (3000) revealed notable binding affinities and nonbonding interactions in support of this finding. Stable conformation and binding patterns/energy were found in a stimulating 400 ns molecular dynamics (MD) simulation. Structure–activity relationship (SAR) investigation indicated that acyl chains, CH₃(CH₂)₁₀CO-, (C₆H₅)₃C-, and C₂H₅C₆H₄CO-, combined with deoxyribose, were most effective against the tested bacterial and fungal pathogens. Pharmacokinetic predictions were examined to determine their ADMET characteristics, and the results in silico were intriguing. Finally, the synthesized uridine derivatives demonstrated increased medicinal activity and high potential for future antimicrobial/anticancer agent(s). Full article

Due to the uneven distribution of glycosidase enzyme expression across bacteria and fungi, glycoside derivatives of antimicrobial compounds provide prospective and promising antimicrobial materials. Therefore, herein, we report the synthesis and characterization of six novel methyl 4,6-O-benzylidene-α-d-glucopyranoside (MBG) derivatives (2–7). The structures were ascertained using spectroscopic techniques and elemental analyses. Antimicrobial tests (zone of inhibition, MIC and MBC) were carried out to determine their ability to inhibit the growth of different Gram-positive, Gram-negative bacteria and fungi. The highest antibacterial activity was recorded with compounds 4, 5, 6 and 7. The compounds with the most significant antifungal efficacy were 4, 5, 6 and 7. Based on the prediction of activity spectra for substances (PASS), compounds 4 and 7 have promising antimicrobial capacity. Molecular docking studies focused on fungal and bacterial proteins where derivatives 3 and 6 exhibited strong binding affinities. The molecular dynamics study revealed that the complexes formed by these derivatives with the proteins L,D-transpeptidase Ykud and endoglucanase from Aspergillus niger remained stable, both over time and in physiological conditions. Structure–activity relationships, including in vitro and in silico results, revealed that the acyl chains [lauroyl-(CH₃(CH₂)₁₀CO-), cinnamoyl-(C₆H₅CH=CHCO-)], in combination with sugar, were found to have the most potential against human and fungal pathogens. Synthetic, antimicrobial and pharmacokinetic studies revealed that MBG derivatives have good potential for antimicrobial activity, developing a therapeutic target for bacteria and fungi. Furthermore, the Petra/Osiris/Molinspiration (POM) study clearly indicated the presence of an important (O1^δ−----O2^δ−) antifungal pharmacophore site. This site can also be explored as a potential antiviral moiety. Full article

Algorithms have evolved from machine code to low-code-no-code (LCNC) in the past 20 years. Observing the growth of LCNC-based algorithm development, the CEO of GitHub mentioned that the future of coding is no coding at all. This paper systematically reviewed several of the recent studies using mainstream LCNC platforms to understand the area of research, the LCNC platforms used within these studies, and the features of LCNC used for solving individual research questions. We identified 23 research works using LCNC platforms, such as SetXRM, the vf-OS platform, Aure-BPM, CRISP-DM, and Microsoft Power Platform (MPP). About 61% of these existing studies resorted to MPP as their primary choice. The critical research problems solved by these research works were within the area of global news analysis, social media analysis, landslides, tornadoes, COVID-19, digitization of process, manufacturing, logistics, and software/app development. The main reasons identified for solving research problems with LCNC algorithms were as follows: (1) obtaining research data from multiple sources in complete automation; (2) generating artificial intelligence-driven insights without having to manually code them. In the course of describing this review, this paper also demonstrates a practical approach to implement a cyber-attack monitoring algorithm with the most popular LCNC platform. Full article

The chemistry and biochemistry of carbohydrate esters are essential parts of biochemical and medicinal research. A group of methyl β-d-galactopyranoside (β-MGP, 1) derivatives was acylated with 3-bromobenzoyl chloride and 4-bromobenzoyl chloride in anhydrous N,N-dimethylformamide/triethylamine to obtain 6-O-substitution products, which were subsequently converted into 2,3,4-tri-O-acyl derivatives with different aliphatic and aromatic substituents. Spectroscopic and elemental data exploration of these derivatives confirmed their chemical structures. In vitro biological experiments against five bacteria and two fungi and the prediction of activity spectra for substances (PASS) revealed ascending antifungal and antibacterial activities compared with their antiviral activities. Minimum inhibitory concentration (MIC) and minimum bactericidal concentration (MBC) experiments were performed for two derivatives, 3 and 9, based on their antibacterial activities. Most of these derivatives showed >780% inhibition of fungal mycelial growth. Density functional theory (DFT) was used to calculate the chemical descriptors and thermodynamic properties, whereas molecular docking was performed against antibacterial drug targets, including PDB: 4QDI, 5A5E, 7D27, 1ZJI, 3K8E, and 2MRW, and antifungal drug targets, such as PDB: 1EA1 and 1AI9, to identify potential drug candidates for microbial pathogens. A 100 ns molecular dynamics simulation study revealed stable conformation and binding patterns in a stimulating environment by their uniform RMSD, RMSF, SASA, H-bond, and RoG profiles. In silico pharmacokinetic and quantitative structure–activity relationship (QSAR) calculations (pIC₅₀ values 3.67~8.15) suggested that all the designed β-MGP derivatives exhibited promising results due to their improved kinetic properties with low aquatic and non-aquatic toxicities. These biological, structure–activity relationship (SAR) [lauroyl-(CH₃(CH₂)₁₀CO-) group was found to have potential], and in silico computational studies revealed that the newly synthesized MGP derivatives are potential antibacterial/antifungal candidates and can serve as therapeutic targets for human and plant pathogens. Full article

The deployment of a client–server-based distributed intelligent system involves application development in both the network domain and the device domain. In the network domain, an application server (typically in the cloud) is deployed to execute the network applications. In the device domain, several Internet of Things (IoT) devices may be configured as, for example, wireless sensor networks (WSNs), and interact with each other through the application server. Developing the network and the device applications are tedious tasks that are the major costs for building a distributed intelligent system. To resolve this issue, a low-code or no-code (LCNC) approach has been purposed to automate code generation. As traditional LCNC solutions are highly generic, they tend to generate excess code and instructions, which will lack efficiency in terms of storage and processing. Fortunately, optimization of automated code generation can be achieved for IoT by taking advantage of the IoT characteristics. An IoT-based distributed intelligent system consists of the device domain (IoT devices) and the network domain (IoT server). The software of an IoT device in the device domain consists of the Device Application (DA) and the Sensor Application (SA). Most IoT LCNC approaches provide code generation in the network domain. Very few approaches automatically generate the DA code. To our knowledge, no approach supports the SA code generation. In this paper, we propose DeviceTalk, an LCNC environment for the DA and the SA code development. DeviceTalk automatically generates the code for IoT devices to speed up the software development in the device domain for a distributed intelligent system. We propose the DeviceTalk architecture, design and implementation of the code generation mechanism for the IoT devices. Then, we show how a developer can use the DeviceTalk Graphical User Interface (GUI) to exercise LCNC development of the device software. Full article

Lignin is a natural source of UV-shielding materials, though its recalcitrant and heterogeneous structure makes the extraction and purification processes complex. However, lignin’s functionality can be directly utilised when it stays as native with cellulose and hemicellulose in plant biomass, rather than being separated. The fabrication process of this native lignin is sustainable, as it consumes less energy and chemicals compared to purified lignin; thus, it is an economic and more straightforward approach. In this study, the properties of native and purified lignin–cellulose nanocrystals (L–CNCs) sourced from hemp hurd waste were compared to explore the differences in their morphology, UV-shielding properties and chemical structure affected by their distinct fabrication process. These two kinds of L–CNCs were further added into polyvinyl alcohol (PVA) to evaluate their reinforcement characteristics. The resulting native L–CNCs/PVA film showed stronger UV-shielding ability than purified L–CNCs. Moreover, the native L–CNCs showed better compatibility with PVA, while the purified L–CNCs/PVA interfaces showed phase separation. The phase separation in purified L–CNCs/PVA films reduced the films’ tensile strength and Young’s modulus and increased the water vapour transmission. The laboratory-scale cost of native L–CNCs production (~AUD 80/kg) was only 10% of purified L–CNCs (~AUD 850/kg), resulting in a comparatively lower cost for preparing native L–CNCs/PVA composite films. Overall, this study shows that the proposed method of production and use of native L–CNCs can be an economic approach to deliver UV-shielding properties for potential applications, such as food packaging. Full article

The biodegradation of polyhydroxybutyrate-co-hydroxyvalerate (PHBV) ternary biocomposites containing nature-based plasticizer acetyl tributyl citrate (ATBC), heterogeneous nucleation agents—calcium carbonate (CaCO₃) and spray-dried lignin-coated cellulose nanocrystals (L-CNC)—in vermicomposting, freshwater biotope, and thermophilic composting have been studied. The degree of disintegration, differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), and the evaluation of surface images taken by scanning electron microscopy (SEM) were conducted for the determination influence of different environments and additives on the biodegradation of PHBV. Furthermore, the method adapted from ISO 14855-1 standard was used for thermophilic composting. It is a method based on the measurement of the amount of carbon dioxide evolved during microbial degradation. The highest biodegradation rate was observed in the thermophilic condition of composting. The biodegradation level of all PHBV-based samples was, after 90 days, higher than 90%. Different mechanisms of degradation and consequently different degradation rate were evaluated in vermicomposting and freshwater biotope. The surface enzymatic degradation, observed during the vermicomposting process, showed slightly higher biodegradation potential than the hydrolytic attack of freshwater biotope. The application of ATBC plasticizers in the PHBV matrix caused an increase in biodegradation rate in all environments. However, the highest biodegradation rate was achieved for ternary PHBV biocomposites containing 10 wt. % of ATBC and 10 wt. % of CaCO₃. A considerable increase in the degree of disintegration was evaluated, even in freshwater biotope. Furthermore, the slight inhibition effect of L-CNC on the biodegradation process of ternary PHBV/ATBC/L-CNC could be stated. Full article

The occurrence of neuroendocrine tumors among the diagnosed neoplasms is extremely rare and is associated with difficulties in undertaking effective therapy due to the histopathological differentiation of individual subtypes and the scarce clinical data and recommendations found in the literature. The choice of treatment largely depends not only on its type, but also on the location and production of excess hormones by the tumor itself. Common therapeutic approaches include surgical removal of the tumor, the use of chemotherapy, targeted drug therapy, peptide receptor radionuclide therapy, and the use of radiation therapy. This article reviews the current knowledge on the classification and application of radiotherapy in the treatment of lung NETs. Case reports were presented in which treatment with conventional radiotherapy, radical and palliative radiochemotherapy, as well as stereotactic fractionated radiotherapy in the treatment of typical (TC) and atypical (AT) lung carcinoids and large cell neuroendocrine carcinoma (LCNC) were used. We hope that the solutions presented in the literature will allow many radiation oncologists to make the best, often personalized decisions about the therapeutic qualifications of patients. Full article