Radar Chart Research Articles

Optimal Light Intensity for Lettuce Growth, Quality, and Photosynthesis in Plant Factories.

In agriculture, one of the most crucial elements for sustained plant production is light. Artificial lighting can meet the specific light requirements of various plants. However, it is a challenge to find optimal lighting schemes that can facilitate a balance of plant growth and nutritional qualities. In this study, we experimented with the light intensity required for plant growth and nutrient elements. We designed three light intensity treatments, 180 μmol m-2 s-1 (L1), 210 μmol m-2 s-1 (L2), and 240 μmol m-2 s-1 (L3), to investigate the effect of light intensity on lettuce growth and quality. It can be clearly seen from the radar charts that L2 significantly affected the plant height, fresh weight, dry weight, and leaf area. L3 mainly affected the canopy diameter and root shoot ratio. The effect of L1 on lettuce phenotype was not significant compared with that of the others. The total soluble sugar, vitamin C, nitrate, and free amino acid in lettuce showed more significant increases under the L2 treatment than under the other treatments. In addition, the transpiration rate and stomatal conductance were opposite to each other. The comprehensive evaluation of the membership function value method and heatmap analysis showed that lettuce had the highest membership function value in L2 light intensity conditions, indicating that the lettuce grown under this light intensity could obtain higher yield and better quality. This study provides a new insight into finding the best environmental factors to balance plant nutrition and growth.

Non-traumatic brachial plexopathy identification from routine MRIs: Retrospective studies with deep learning networks

PurposeThis study aims to seek an optimized deep learning model for differentiating non-traumatic brachial plexopathy from routine MRI scans. Materials and methodsThis retrospective study collected patients through the electronic medical records (EMR) or pathological reports at Mayo Clinic and underwent BP MRI from January 2002 to December 2022. Using sagittal T1, fluid-sensitive and post-gadolinium images, a radiology panel selected BP’s region of interest (ROI) to form 3 dimensional volumes for this study. We designed six deep learning schemes to conduct BP abnormality differentiation across three MRI sequences. Utilizing five prestigious deep learning networks as the backbone, we trained and validated these models by nested five-fold cross-validation schemes. Furthermore, we defined a ’method score’ derived from the radar charts as a quantitative indicator as the guidance of the preference of the best model. ResultsThis study selected 196 patients from initial 267 candidates. A total of 256 BP MRI series were compiled from them, comprising 123 normal and 133 abnormal series. The abnormal series included 4 sub-categories, et al. breast cancer (22.5 %), lymphoma (27.1 %), inflammatory conditions (33.1 %) and others (17.2 %). The best-performing model was produced by feature merging mode with triple MRI joint strategy (AUC, 92.2 %; accuracy, 89.5 %) exceeding the multiple channel merging mode (AUC, 89.6 %; accuracy, 89.0 %), solo channel volume mode (AUC, 89.2 %; accuracy, 86.7 %) and the remaining. Evaluated by method score (maximum 2.37), the feature merging mode with backbone of VGG16 yielded the highest score of 1.75 under the triple MRI joint strategy. ConclusionDeployment of deep learning models across sagittal T1, fluid-sensitive and post-gadolinium MRI sequences demonstrated great potential for brachial plexopathy diagnosis. Our findings indicate that utilizing feature merging mode and multiple MRI joint strategy may offer satisfied deep learning model for BP abnormalities than solo-sequence analysis.

Kendall correlations and radar charts to include goals for and goals against in soccer rankings

AbstractThis paper deals with the challenging themes of the way sporting teams and athletes are ranked in sports competitions. Starting from the paradigmatic case of soccer, we advance a new method for ranking teams in the official national championships through computational statistics methods based on Kendall correlations and radar charts. In detail, we consider the goals for and against the teams in the individual matches as a further source of score assignment beyond the usual win-tie-lose trichotomy. Our approach overcomes some biases in the scoring rules that are currently employed. The methodological proposal is tested over the relevant case of the Italian “Serie A” championships played during 1930–2023.

Multi-Dimensional Landscape Connectivity Index for Prioritizing Forest Cover Change Scenarios: A Case Study of Southeast China

Predicting forest cover change (FCC) and screening development scenarios are crucial for ecological resilience. However, quantitative evaluations of prioritizing forest change scenarios are limited. Here, we took five shared socio-economic pathways (SSPs) representing potential global changes, namely SSP1: sustainability, SSP2: middle of the road, SSP3: regional rivalry, SSP4: inequality, and SSP5: fossil-fueled development, which were constructed by integrated assessment and climate models. We modeled them with the patch-generating land use simulation (PLUS) and constructed a multi-dimensional landscape connectivity index (MLCI) employing forest landscape connectivity (FLC) indices to assess forest development in Fujian Province, Southeast China. The MLCI visualized by radar charts was based on five metrics, including forest patch size (class area (CA), number (patch density (PD), isolation (landscape division index (DIVISION), aggregation (mean nearest-neighbor index (ENN_MN), and connectance index, (CONNECT). The results indicate that FC will remain above 61.4% until 2030, with growth observed in SSP1 and SSP4. Particularly, FC in SSP4 substantially increased, converted from cropland (1140.809 km2) and grassland (645.741 km2). SSP4 has the largest MLCI values and demonstrates significant enhancements in forest landscape integrity, with CA, ENN_MN and CONNECT increasing greatly. Our study offers valuable approaches to and insights into forest protection and restoration.

Exploration of PVT1 as a biomarker in prostate cancer.

Prostate cancer is a malignant tumor originating from the prostate gland, significantly affecting patients' quality of life and survival rates. Public data was utilized to identify differentially expressed genes (DEGs). Weighted gene co-expression network analysis was constructed to classify gene modules. Functional enrichment analysis was performed through Kyoto Encyclopedia of Genes and Genomes and gene ontology annotations, with results visualized using the Metascape database. Additionally, gene set enrichment analysis evaluated gene expression profiles and related pathways, constructed a protein-protein interaction network to predict core genes, analyzed survival data, plotted heatmaps and radar charts, and predicted microRNAs for core genes through miRTarBase. Two prostate cancer datasets (GSE46602 and GSE55909) were analyzed, identifying 710 DEGs. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analyses revealed that DEGs were primarily involved in organic acid metabolism and the P53 signaling pathway. Gene set enrichment analysis and Metascape analyses further confirmed the significance of these pathways. After constructing the weighted gene co-expression network analysis network, 3 core genes (DDX21, NOP56, plasmacytoma variant translocation 1 [PVT1]) were identified. Survival analysis indicated that core genes are closely related to patient prognosis. Through comparative toxicogenomics database and miRNA prediction analysis, PVT1 was considered to play a crucial role in the development of prostate cancer. The PVT1 gene is highly expressed in prostate cancer and has the potential to become a diagnostic biomarker and therapeutic target for prostate cancer.

Utilization of Sintered Sludge Ash with Different Mechanical-Thermal Activation Parameters as a Supplementary Cementitious Material: Mechanical Properties and Life Cycle Assessment of Cement-Based Paste.

The proposal of sintered sludge cement (SSC) paste aligns with the low-carbon development goals of building materials. However, there is a lack of scientific guidance for the preparation of sintered sludge ash (SSA). Herein, this study systematically investigates the influence mechanism of mechanical-thermal activation parameters of SSA on the mechanical properties and life cycle assessment (LCA) of SSC paste, and conducts a comprehensive evaluation using a radar chart and the TOPSIS method. The results show that with the increase in calcination temperature and duration, the compressive and flexural strengths of the SSC paste are improved, especially at 600 °C and above, increasing by 57.92% and 62.52%, respectively. The longer calcination time at 1000 °C results in a decrease in its mechanical properties. The addition of SSA significantly reduces the LCA indicators of cement paste. Specifically, 30% SSA only contributes 8.1% to the global warming potential. Compared to calcination, the LCA indicators have less sensitivity to ball milling, and prolonging the time hardly increases them. Based on performance and environmental impact, the optimal SSA is obtained by calcining at 800 °C for 2 h and ball milling for 10 min. This study can provide theoretical guidance for efficient building material utilization of dredged sludge.

On process stability and sustainability of controlled-Electro Chemical Discharge Machining (ECDM)

ABSTRACT To mitigate the transmission losses, glass vias have the potential to replace silicon vias in wireless transmission systems. In the wake of limitations of the existing methods, ECDM is an evolving process for glass micro drilling. However, the process control of ECDM is compromised due to variation in electrolytic conditions in terms of concentration and temperature. As a result, poor stability and sustainability hampers the commercial viability of the ECDM process. Therefore, present communication attempted to enhance the stability and sustainability of the ECDM with a novel approach and termed as controlled-ECDM. Subsequently, the process stability (real time based) of the controlled-ECDM has been compared with basic-ECDM using the user’s preference rating and I-MR chart. The results indicated a 20.65% gain in overall stability by controlled-ECDM. Additionally, the assessment of electrolyte emissions and performance on the radar graph indicated an improvement in the sustainability of the process.

Mixbiotic society measures: Assessment of community well-going as living system.

Social isolation and fragmentation represent global challenges, with the former stemming from a lack of interaction and the latter from exclusive mobs-both rooted in communication issues. Addressing these challenges, the philosophical realm introduces the concept of the "mixbiotic society." In this framework, individuals with diverse freedoms and values mix together in physical proximity with diverse mingling, recognizing their respective "fundamental incapacities" and uniting in solidarity. This study aims to provide novel measures to balance freedom and solidarity, specifically the intermediate between isolation and mobbing, within a mixbiotic society. To achieve this, we introduce simplified measures to evaluate dynamic communication patterns. These measures complement traditional social network analysis of static structures and conventional entropy-based assessments of dynamic patterns. Our specific hypothesis posits that the measures corresponding to four distinct phases are established by representing communication patterns as multidimensional vectors. These measures include the mean of Euclidean distance to quantify "mobism" for fragmentation, the relative distance change for "atomism" indicating isolation, and a composite measure derived from multiplying the mean and variance of cosine similarity for "mixism," reflecting the well-going state of a mixbiotic society. Additionally, nearly negligible measures correspond to "nihilism." Through the evaluation of seven real-society datasets (high school, primary school, workplace, village, conference, online community, and email), we demonstrate the utility of the "mixism" measure in assessing freedom and solidarity in society. These measures can be employed to typify communities on a radar chart and a communication trajectory graph. The superiority of the measures lies in their ability to evaluate dynamic patterns, ease of calculation, and easily interpretable meanings compared to conventional analyses. As a future development, alongside additional validation using diverse datasets, the mixbiotic society measures will be employed to analyze social issues and applied in the fields of digital democracy and platform cooperativism.

Green analytical chemistry and quality by design: A combined approach towards simultaneous determination of Letrozole with its co-administered Zoledronic Acid for cancer patients

Nowadays, breast cancer is the most affecting and globally diagnosed malignancy among women, yet Letrozole (LTZ) was considered the first-line treatment as hormonal anticancer drug. Unfortunately, LTZ develops osteoporosis as a main side effect which was overcome by using the co-administered; Zoledronic Acid (ZDA). Thus, there was a crucial need for this simultaneous quantification innovation, especially there were no any previously reported methods regarding both drugs together. In this study, an integrated framework was conducted between the experimental analytical quality-by-design (AQbD) approach and green analytical chemistry (GAC), emerging sensitive and robust RP-HPLC method. Box-Behnken Design was the developed model for optimizing an isocratic chromatographic separation on C18 Equisil® ODS (4.6 × 250 mm, 5.0 μm) column at ambient temperature, using the mobile phase of 0.1 % aqueous trifluroacetic acid (pH 2.8): acetonitrile (54.5:45.5, v/v), at 1.0 mL/min flow rate with PDA detection at 254.0 nm and 210.0 nm for LTZ and ZDA, respectively. Model statistical and residual plots analysis was significant and normally distributed. Method was fully validated as per ICH guidelines, where good linearity was 0.20–10.00 µg/mL for both drugs in presence of Tadalafil (TDF) as an internal standard, obtaining adequate correlation coefficients (r) values. Calculated LOD results were 0.058 and 0.040 µg/mL while calculated LOQ results were 0.175 and 0.122 µg/mL for LTZ and ZDA, respectively. The proposed method was effectively applied on bulk, pharmaceutical dosage forms, and spiked human plasma. Statistical comparison of the anticipated results with the reported ones was performed. Greenness assessment was evaluated using Green Analytical Procedure Index (GAPI) and Analytical Greenness (AGREE) tools; where superiority results were achieved relative to other reported methods. Finally, an EVG method evaluation tool was assessed, and the attained results were represented through its radar chart.

ATC-SD Net: Radiotelephone Communications Speaker Diarization Network

This study addresses the challenges that high-noise environments and complex multi-speaker scenarios present in civil aviation radio communications. A novel radiotelephone communications speaker diffraction network is developed specifically for these circumstances. To improve the precision of the speaker diarization network, three core modules are designed: voice activity detection (VAD), end-to-end speaker separation for air–ground communication (EESS), and probabilistic knowledge-based text clustering (PKTC). First, the VAD module uses attention mechanisms to separate silence from irrelevant noise, resulting in pure dialogue commands. Subsequently, the EESS module distinguishes between controllers and pilots by levying voice print differences, resulting in effective speaker segmentation. Finally, the PKTC module addresses the issue of pilot voice print ambiguity using text clustering, introducing a novel flight prior knowledge-based text-related clustering model. To achieve robust speaker diarization in multi-pilot scenarios, this model uses prior knowledge-based graph construction, radar data-based graph correction, and probabilistic optimization. This study also includes the development of the specialized ATCSPEECH dataset, which demonstrates significant performance improvements over both the AMI and ATCO2 PROJECT datasets.

Mechanical characteristics of blended Renova®/polyester microfiber double cloth woven fabrics for active sportswear applications

Purpose This study aims to accommodate the mechanical characteristics that comply with the spectra of active sportswear by taking the innovative features of double-layered fabric (double cloth) and polyester yarn. The research focused on blending two types of polyester: Renova® and polyester microfiber with different weave structures in both face and back layers for fabricated fabrics. Force resistance was investigated through several properties, the mass per unit area and thickness properties were also considered. Design/methodology/approach The research was divided into two phases. The first phase included three samples with the same double cloth structure and different weft materials to detect the most preferable performance. The second phase included three samples with the same materials and different double cloth structures. Several mechanical properties were tested in accordance with standard test methods, and the results were collected, tabulated and statistically analyzed using a chart line, an ANOVA test with a p-value = 0.05, a calculated least significant difference (value) and a radar chart area. Findings The results assigned that blending Renova®/polyester microfiber develops mechanical properties than using them individually, considering the increase in polyester microfiber ratio. The double cloth with different plain structures has a significantly different effect on the examined properties, except for elongation and thickness. Originality/value According to the radar chart area, the double cloth with a plain rib structure achieved better performance, and in the same vein, the results showed that the plain with a warp rib structure enhanced fabric behavior more than the weft rib.

Preparation and Evaluation of Clarithromycin Taste-Masking Dry Suspension Using Hot Melt Extrusion Based on Solid Dispersion Technology.

Clarithromycin (CLA) is the preferred drug for treating respiratory infections in pediatric patients, but it has the drawbacks of extreme bitterness and poor water solubility. The purpose of this study was to improve solubility and mask the extreme bitterness of CLA. We use Hot Melt Extrusion (HME) to convert CLA and Eudragit® E100 into Solid Dispersion (SD). Differential scanning calorimetry (DSC) and Powder X-ray diffraction (PXRD) were used to identify the crystalline form of the prepared SDs, which showed that the crystalline CLA was converted to an amorphous form. At the same time, an increase in dissolution rate was observed, which is one of the properties of SD. The results showed that the prepared SD significantly increased the dissolution rate of crystalline CLA. Subsequently, the SD of CLA was prepared into a dry suspension with excellent suspending properties and a taste-masking effect. The bitterness bubble chart and taste radar chart showed that the SD achieved the bitter taste masking of CLA. Principal components analysis (PCA) of the data generated by the electronic tongue showed that the bitter taste of CLA was significantly suppressed using the polymer Eudragit® E100. Subsequently, a dry suspension was prepared from the SD of CLA. In conclusion, this work illustrated the importance of HME for preparing amorphous SD of CLA, which can solve the problems of bitterness-masking and poor solubility. It is also significant for the development of compliant pediatric formulations.

Multiscale evaluation of aging susceptibility of asphalt binders modified with recycled rubber and plastic pyrolytic oil composites

Changes in rheological performance, migration of chemical components, and microstructure transformation are some of the processes that occur as asphalt binders age across various stages throughout their lifecycle. Aging-related changes influence pavement performance, typically increasing its susceptibility to issues like fatigue and thermal cracking. Thus, comprehending the role of aging in the performance of asphalt binders is vital for devising effective pavement design and maintenance strategies. The use of plastic pyrolysis oil and recycled non-tire automobile rubber to create oil-rubber modified asphalt binders is becoming quite popular as a sustainable and environmentally friendly method for asphalt pavements. This study aimed to assess the aging behavior of asphalt binders modified with plastic pyrolysis oil and recycled non-tire automotive rubber, both separately and in composite modification. Binders underwent testing under four aging conditions: unaged, short-term aged, long-term aged, and extended long-term aged. The study focused on a multiscale assessment of aging characteristics, encompassing macroscale, mesoscale, microscale, and nanoscale analyses. At the macroscale, the binders were tested for rheological parameters using multiple stress creep recovery (MSCR), linear amplitude sweep (LAS), and Glover-Rowe (G-R) parameter tests, while the mesoscale analysis was conducted using Fourier-transform infrared (FTIR) spectroscopy. Additionally, microstructural analysis was performed using atomic force microscopy (AFM), and nanoscale evaluation was carried out using nuclear magnetic resonance (NMR). Finally, a comprehensive assessment index that included all the parameters under study was generated using radar graph analysis. It was observed that modifying an asphalt binder with a heat-preheated composite of plastic pyrolysis oil and recycled non-tire automotive rubber showed the lowest CAI values, thus helping to mitigate the effects of aging and achieving enhanced binder performance.

A numerical inquiry of the MHD radiative thin film nanofluid flow and heat transfer augmentation for Ni and Ta nanoparticles of different shapes dispersed in C 12 H 26–C 15 H 32 over an unsteady, curved stretching surface

Thin films are a versatile technology used in various industries, including light-emitting diodes, magnetic recording media, electronic device manufacturing, and optical coating. They are crucial for studying quantum phenomena, superlattices, and multiferroic materials. Their small size allows for better thermal transport studies due to the combination of thin film flow and nanoparticles. Therefore, this study aims to investigate thin film flow and heat transfer with the effects of thermal radiations, magnetic field, and velocity slip in association with tantalum ( Ta ) and nickel ( Ni ) nanoparticles that emerged in kerosene oil ( C 12 H 26 − C 15 H 32 ) toward a curved stretching surface. A review of the pertinent literature indicates that no research has previously been done on the present problem of an unsteady thin film flow across a curved stretching surface. The problem under consideration is modeled by choosing the curvilinear coordinate system. The controlling nonlinear system of partial differential equations is transformed into the system of ordinary differential equations by using an appropriate similarity transformation and then solved numerically using the BVP 4 C technique in MATLAB . The impact of significant physical parameters on velocity, temperature, skin friction, and Nusselt numbers are depicted through graphs and radar charts. The investigation highlights the significant influence of magnetohydrodynamics ( MHD ) , thermal radiation, volumetric fraction, and velocity slip on the enhancement of nanofluid temperature. It is also noted that cylindrical-shaped Ta nanoparticles exhibited the maximum velocity, while blade-shaped Ni nanoparticles showed the lowest. Moreover, the maximum and minimum temperatures were observed for blade-shaped Ni nanoparticles and cylindrical Ta nanoparticles, respectively. Additionally, the maximum and minimum values of skin friction are found for cylindrical Ni nanoparticles and blade-shaped Ta nanoparticles, respectively. Similarly, the maximum and minimum Nusselt numbers are recorded for cylindrical Ta nanoparticles and blade-shaped Ni nanoparticles, respectively.

Enhancing Organizational Performance: Harnessing AI and NLP for User Feedback Analysis in Product Development

This paper explores the application of AI and NLP techniques for user feedback analysis in the context of heavy machine crane products. By leveraging AI and NLP, organizations can gain insights into customer perceptions, improve product development, enhance satisfaction and loyalty, inform decision-making, and gain a competitive advantage. The paper highlights the impact of user feedback analysis on organizational performance and emphasizes the reasons for using AI and NLP, including scalability, objectivity, improved accuracy, increased insights, and time savings. The methodology involves data collection, cleaning, text and rating analysis, interpretation, and feedback implementation. Results include sentiment analysis, word cloud visualizations, and radar charts comparing product attributes. These findings provide valuable information for understanding customer sentiment, identifying improvement areas, and making data-driven decisions to enhance the customer experience. In conclusion, promising AI and NLP techniques in user feedback analysis offer organizations a powerful tool to understand customers, improve product development, increase satisfaction, and drive business success.

Physicochemical and organoleptic characteristics of chicken sausage using red sorghum flour (Sorghum bicolor) as filler

Abstract This study aims to determine the effect of using sorghum flour as a filling material in sausages on physicochemical and organoleptic properties. This study used the complete randomized design (CRD) method with 7 treatments (H0: tapioca flour, H1: sorghum flour without treatment, H2: sorghum flour soaked overnight, H3: germinated sorghum flour for 1 day, H4: germinated sorghum flour for 2 days, H5: germinated sorghum flour for 3 days and H6: germinated sorghum flour for 4 days with 3 replications. The physicochemical properties’ data were analyzed using ANOVA and it was continued with Duncan Multiple Range Test (DMRT) analysis at α = 5%, while the sensory evaluation data were analyzed using Microsoft Excel 2021 and displayed as a spider chart. Based on the results of the research, sausage H3 has the best value in cooking loss, and chemical properties except for fat content, and sensory evaluation. The values include cooking loss of 2.20%, moisture content of 62.34%, ash content of 2.60%, and protein content of 17.59%, while the lowest fat content is in H0 with a value of 5.31%. Using sorghum flour as a filler in sausages also gave the sausages a natural color and decreased the texture value when compared to sausages using tapioca flour. Based on the organoleptic results, in general, panelists liked the research sausages, both the use of tapioca flour and sorghum flour with various treatments, with an average score of 4 (like), but there was only 1 treatment H5 / third days of germinating sorghum flour which had a score of 3.1 (somewhat like).

Evaluating the contribution of geophysics to the assessment of natural hazards

This paper discussed the role of geophysics in the evaluation of natural hazards with the view to enhancing familiarity with earth shakes, ground characteristics, landslide likelihood, and volcanic deliveries. Semantic and geophysical data were collected from seismic surveys, measurement of groundwater, electrical resistivity of the subsoil and gas emission. Seismic data analysis revealed diverse activity across sites, with magnitudes ranging from 3.234 to 6.456 Mw, emphasizing the importance of geophysical monitoring in identifying high-risk areas. Groundwater measurements indicated spatial variations in water table depth from 1.234 to 6.789 m, essential for effective resource management. Soil resistivity values ranged from 75.123 to 145.901 Ohm-m, providing insights into soil properties relevant for geotechnical and environmental studies. Landslide susceptibility assessment used the factor of slope angle, vegetation cover, and rainfall intensity; these were 10. 901° to 28. 345°, 55. 234% to 88. 456%, and 6. 789 to 20. 901 mm/hr respectively. The gas emission of SO2 ranged between 0. 123–0. 901 kg/s, that of CO2, 0. 456–1. 234 kg/s, while H2S ranged between 0. 789–1. 567 kg/s based on the emission of the several sites suitable for eruption prediction models. A statistical approach was used in which Histogram, scatter plot and radar chart were used to explain the data collected. The study conclude that the application of various geophysical methods is strikingly useful in natural hazard and risk assessment and planning and therefore beneficial in reducing disaster risks. These results provide compelling evidence about the importance of geophysics in propagating awareness in the Earth processes and improvement of hazards.

ChlorTox scale assessment, greenness, and whiteness evaluation of selective spectrophotometric analysis of dimenhydrinate and cinnarizine

Nausea and vomiting are considered common series side effects induced by chemotherapy treatment in cancer patients. This annoying side effect can impair the patient’s compliance to cancer treatment and affect their quality of life. Dimenhydrinate and cinnarizine in combined pharmaceutical dosage form is used to control chemotherapy induced nausea and vomiting in cancer patients. For safety, selective spectrophotometric methods based on novel dual resolution strategies were introduced to estimate dimenhydrinate and cinnarizine in presence of their harmful impurities namely benzophenone and 1- (diphenylmethyl)piperazine, respectively. These methods namely, dual ratio difference (DRD), dual ratio extraction (DRE) and dual absorbance extraction coupled with dual ratio extraction (DAE-DRE) were successfully performed to simultaneously analyze the drug of interests dimenhydrinate and cinnarizine in their pure form, synthetic mixtures and in market dosage form. Linearity ranges were 6.0–60.0 μg/mL and 3.0–30.0 μg/mL for dimenhydrinate and cinnarizine, respectively with good recovery% of Mean ± SD for all the proposed methods 99.82 ± 0.48, 99.79 ± 0.40, 100.14 ± 0.82, 100.03 ± 0.69, respectively. ICH guidelines were adhered in accordance with confirming validation of the proposed methods where fulfilling results were accomplished. Various unified greenness and whiteness assessment tools, such as the chlorTox scale, greenness index via spider chart, AGREE (The Analytical Greenness Metric), green certificate, and the RGB12 algorithm were employed in this research to assess the greenness and sustainability of the introduced UV-spectrophotometric methods in comparison to the reported HPLC method. As a result, these methods hold significant potential for utilization in the quality control department of pharmaceutical companies, contributing to enhanced pharmaceutical product analysis and overall sustainability practices.

Theoretical Investigations on Phytochemicals: Physical Chemistry, FMO, MEP, Lipophilicity, Water Solubility, Drug-likeness, and Bioavailability

Naturally-existing chemicals especially phytochemicals have been commonly used for medicinal purposes in terms of both traditional and contemporary respects. Allyl isothiocyanate structure exhibits antimicrobial and anticancer activity, whereas the allitridin, as one of the main components of the garlic, showed antifungal, antitumor, and antioxidant activity. Arabinose and galactose as monosaccharides also play a main role in drug-design research to facilitate drug delivery to target cells and regulate insulin resistance, respectively. Herein, the 3-isothiocyanatoprop-1-ene (Allyl isothiocyanate, AITC), 2,3,4,5-tetrahydroxypentanal (Ar, Arabinose), 1,3-diallyltrisulfane (Allitridin, DATS), 6-(hydroxymethyl)tetrahydro-2H-pyran-2,3,4,5-tetraol (Galactose, Gal), 6-methyltetrahydro-2H-pyran-2,3,4,5-tetraol (Rhamnose, Rh), and tetrahydro-2H-pyran-2,3,4,5-tetraol (cyclic-Arabinose, C-Ar) agents were investigated by using DFT. The B3LYP/6-311G** level computations were used to optimize the compounds' geometries and then to predict the reactivity indexes of the compounds. Also, lipophilicity and water-solubility features were determined to enlighten the physicochemical characteristics of the compounds. Then, the studied agents' pharmacokinetics were evaluated using the BOILED-Egg and radar graphs. Last, the bioavailability and drug-likeness behaviors were predicted. This trial work will be hoped to provide fundamental electronic and physicochemical insight into the relationship between drug-likeness and electronic structure.

Multicriteria decision tools for selection of sustainable integrated retrofits: application to the seismic and energy upgrade of a masonry building

The design of sustainable integrated retrofits is a new frontier in civil engineering that aims to minimize economic and environmental impacts of interventions on existing buildings. The enhanced methodology presented in this paper combines seismic and energy performance metrics with economic and environmental indicators associated with each integrated retrofit. The purpose is to identify the optimal retrofit through histograms, radar charts, proficiency ratios and iso-class plots, as the one corresponding to the higher seismic and energy performances and the minimum economic and environmental impacts. Such a methodology can be applied to either the installation phase of the retrofit or its entire life cycle, making it possible to compare these two scenarios in a long-term perspective. The methodology, generally valid for any building materials or structural typologies, is here applied to a two-story natural-stone masonry building, damaged by the 2016 Central Italy earthquake sequence.