Tool For Selection Research Articles

Future Shorelines: A Living Shoreline Site Selection and Design Decision Support Tool that Incorporates Future Conditions Induced by Sea Level Rise

Most living shoreline site selection and design decision support tools are based upon existing environmental conditions. We developed a web-based, geospatial tool called Future Shorelines that integrates high-resolution landscape elevation data and a matrix of locally derived NOAA Interagency Sea Level Rise Scenarios to characterize future conditions of submergence and shoreline translation induced by sea level rise. Once the practitioner selects a location of interest, sea level rise scenario (e.g., high), and target year (e.g., 2050), the tool will generate plan view and cross-sectional informational graphics specific to their choices. This information can then be paired with other menu options, like parcel ownership, to facilitate the planning and construction of nature-based shoreline stabilization solutions that (1) are located where opportunities for horizontal migration are optimized, (2) remain accessible for monitoring and maintenance, and (3) perform as intended over the design life of the installation. The tool’s menu options and the user interface were informed by project partner input solicited during numerous workshops convened over the duration of the 2-year project. This coproduction created a product that was familiar to the end user and therefore increased the likelihood that it would be utilized by them during the planning and design of living shoreline projects. Although developed for use in the Indian River Lagoon, located along the east-central Florida coast, it can be seamlessly replicated for application in other coastal regions of the USA where the requisite data are available.

Total Copper and Labile Bound Copper Fraction as a Selective and Sensitive Tool in the Evaluation of Wilson Disease.

A dual filtration-based method for determination of serum labile bound copper (LBC) and LBC fraction (LBC/total copper) was developed. Reduced total copper, elevated LBC, and elevated LBC fraction have been reported in Wilson disease (WD). To evaluate the diagnostic performance of these markers, samples were obtained from 21 WD treatment-naïve (WD-TN, no WD treatment or <28 days of treatment) patients, 46 WD standard-of-care-treated (WD-SOC) patients, along with 246 patients representing other potential disorders of copper status. These were then compared to 213 reference interval population patients. Receiver operating characteristic curves for the reference population vs WD-TN yielded areas under the curve for total copper, LBC, and LBC fraction, of 0.99, 0.81, and 0.98, respectively. Using Youden cutoffs, sensitivity/specificity for WD-TN was 95%/97% for total copper, 71%/85% for LBC, and 95%/94% for LBC fraction. LBC values, but not total copper and LBC fraction, differed substantially between WD-TN and WD-SOC cohorts.We propose a dual model wherein total copper and LBC fraction results must agree to be classified as a "positive" or "negative" result for WD. This correctly classified 19/21 WD-TN patients as positive, and 194/213 reference interval patients as negative. The remaining "indeterminate" patients (representing approximately 9% of the reference and the WD-TN populations) exhibited conflicting total copper and LBC fraction results. When indeterminate results are excluded, this model exhibited apparent 100% sensitivity/specificity. Agreement of total serum copper and LBC fraction classification may constitute an effective "rule-in" and "rule-out" assessment for WD-TN patients.

Association of Inflammatory Marker C-Reactive Protein and Interleukin-6 with Stages 3-5 of Chronic Kidney Disease

Background: Despite different strategy to retard the progression of CKD, majority still progress to ESRD. Other than conventional risk factors inflammation is considered as one of the reversible factors responsible for CKD progression which can be intervened. Therefore, we have studied the level of inflammatory marker CRP and IL-6 at stages 3-5 of CKD patients and their association with CKD stage progression determined by eGFR. Methods: We have conducted a cross sectional study among 150 CKD patients and 32 healthy controls at OPD of Nephrology department of National Institute of Kidney Diseases and Urology, Dhaka from January`2016 to December`2016. Purposive sampling method was the tool for case selection and CKD was defined as eGFR &lt;60ml/min/1.73m2. AKI, history of dialysis, kidney transplant, patients having acute infection, malignancy or liver disease, received chemotherapy or immunotherapy (&lt;6 months) and patients with connective tissue disease were the exclusion criteria. Fasting blood glucose, Hb%, serum Ca++, serum PO4, serum total cholesterol and serum triglyceride level were measured for both case and controls. eGFR has been calculated using MDRD equation. For the cases CRP had been measured on two occasions at an interval of one and half month. Those cases who had raised CRP level in both occasions were selected for measurement of IL-6 from second sample. CRP had been measured by turbidimetry method using fully automated chemistry analyzer. IL-6 had been measured using enzyme linked immunosorbent assay (ELISA) based on the Biotin double antibody sandwich technology. Scatter diagram and multiple regression models were used to examine the association between CRP and IL-6 with stage 3-5 of CKD denoted by eGFR. Results: The mean of CRP were 9.5±2.7 mg/L in CKD patients vs. 2.9±0.5 mg/L in controls without CKD and the mean of IL-6 were 29.5±13.0 pg/ml in CKD patients vs. 3.3±0.3 pg/ml in controls without CKD. Both were elevated and significant statistically (p=.001). Scatter diagram showing correlation coefficient (r value) -0.592 and -0.615 for eGFR vs. CRP and eGFR vs. IL-6 respectively which indicate both are negatively correlated with eGFR. But IL-6 had a more strong negative correlation. Multiple regression analysis shows regression co efficient (B) for CRP and IL-6 is -1.251 and -2.826 respectively after taking into account other factors which can alter eGFR level (age, Hb%, serum Calcium, S. Total Cholesterol, S.PO4, S. Triglyceride) that indicates in the prediction of eGFR, IL-6 had significant negative association than CRP after adjusting the conventional factors responsible for CKD progression. Conclusion: Our data suggest that though CRP and IL-6 both are significantly raised in advanced CKD stage 3-5, IL-6 is more rapidly increasing and more strongly associated than CRP with the severity of CKD measured by eGFR.

A screw algorithm for congenital C2-3 fusion with high-riding vertebral arteries: feasibilities and clinical outcomes of five different fixation techniques.

To propose a screw algorithm and investigate the anatomical feasibilities and clinical outcomes of five distinct fixation methods for C2-3 fused vertebra with high-ridding vertebral arteries (VA) (HRVA) when the C2 pedicle screw placement is unfeasible. Thirty surgical patients with congenital C2-3 fusion, HRVA, and atlantoaxial dislocation (AAD) were included. We designed a algorithm for alternative screw implantation into C2-3 fused vertebrae, including C2 pedicle screw with in-out-in (passing VA groove) technique (in-out-in screw), subfacetal screw, translaminar screw, lateral mass screw, C3 pedicle screw. VA diameter and position, C2 and C3 pedicles, superior facets, fused lamina, and fused lateral mass dimensions were evaluated for screw implantation indication. Implant failure, reduction loss, implant placement accuracy were investigated by computed tomography. A total of 5 VAs were identified as distant VAs; a total of 2 VAs were categorized as occlusive VAs. Sufficient dimension of lateral mass and lamina provided the broadest indications for screw implantation, while the distant or occlusive VA provided the most limited indications for in-out-in screw. The indications of five alternative methods ranged from narrowest to widest as follows: in-out-in screw, C3 pedicle screw, subfacetal screw, translaminar screw, lateral mass screw. The translaminar screws and the lateral mass screws increased the probability of implant failure. All patients who received in-out-in screws, C3 pedicle screws, and subfacetal screws achieved fusion. The accuracy ranged from lowest to highest as follows: C3 pedicle screw, lateral mass screw, in-out-in screw, subfacetal screw, translaminar screw. No translaminar screws deviated. The algorithm proved to be a valuable tool for screw selection in cases of C2-3 fused vertebrae with HRVAs. The subfacetal screw, boasting broad indications, a high fusion rate, and exceptional accuracy, stood as the primary preferred alternative.

Virtual Tools for Testing Autonomous Driving: A Survey and Benchmark of Simulators, Datasets, and Competitions

Traditional road testing of autonomous vehicles faces significant limitations, including long testing cycles, high costs, and substantial risks. Consequently, autonomous driving simulators and dataset-based testing methods have gained attention for their efficiency, low cost, and reduced risk. Simulators can efficiently test extreme scenarios and provide quick feedback, while datasets offer valuable real-world driving data for algorithm training and optimization. However, existing research often provides brief and limited overviews of simulators and datasets. Additionally, while the role of virtual autonomous driving competitions in advancing autonomous driving technology is recognized, comprehensive surveys on these competitions are scarce. This survey paper addresses these gaps by presenting an in-depth analysis of 22 mainstream autonomous driving simulators, focusing on their accessibility, physics engines, and rendering engines. It also compiles 35 open-source datasets, detailing key features in scenes and data-collecting sensors. Furthermore, the paper surveys 10 notable virtual competitions, highlighting essential information on the involved simulators, datasets, and tested scenarios involved. Additionally, this review analyzes the challenges in developing autonomous driving simulators, datasets, and virtual competitions. The aim is to provide researchers with a comprehensive perspective, aiding in the selection of suitable tools and resources to advance autonomous driving technology and its commercial implementation.

Exploring the Benefits and Applications of Video-Span Selection and Search for Real-Time Support in Sign Language Video Comprehension among ASL Learners

People learning American Sign Language (ASL) and practicing their comprehension skills will often encounter complex ASL videos that may contain unfamiliar signs. Existing dictionary tools require users to isolate a single unknown sign before initiating a search by selecting linguistic properties or performing the sign in front of a webcam. This process presents challenges in extracting and reproducing unfamiliar signs, disrupting the video-watching experience, and requiring learners to rely on external dictionaries. We explore a technology that allows users to select and view dictionary results for one or more unfamiliar signs while watching a video. We interviewed 14 ASL learners to understand their challenges in understanding ASL videos, strategies for dealing with unfamiliar vocabulary, and expectations for an in-situ dictionary system. We then conducted an in-depth analysis with 8 learners to examine their interactions with a Wizard-of-Oz prototype during a video comprehension task. Finally, we conducted a comparative study with 6 additional ASL learners to evaluate the speed, accuracy, and workload benefits of an embedded dictionary search feature within a video player. Our tool outperformed a baseline in the form of an existing online dictionary across all three metrics. The integration of a search tool and span selection offered advantages for video comprehension. Our findings have implications for designers, computer vision researchers, and sign language educators.

Selection of media tools for community planning: A study from the perspective of communicative planning

In community planning, due to the lack of evidence regarding the selection of media tools, this study examines how a common but differentiated ideal speech situation can be created as well as how more appropriate media tools can be defined and selected in the community planning process. First, this study describes the concept and theoretical basis of media used in community planning from the perspectives of the multiple effects of media evolution on communicative planning. Second, the classification criteria and typical characteristics of media tools used to support community planning are clarified from three dimensions: acceptability, cost-effectiveness, and applicability. Third, strategies for applying media tools in the four phases of communicative planning—namely, state analysis, problem identification, contradictory solution and optimization—are described. Finally, trends in the development of media tools for community planning are explored in terms of multistakeholder engagement, supporting scientific decision-making and multiple-type media integration. The results provide a reference for developing more inclusive, effective, and appropriate media tools for enhancing decision-making capacity and modernizing governance in community planning and policy-making processes.

Newton-Raphson Meets Sparsity: Sparse Learning Via a Novel Penalty and a Fast Solver.

In machine learning and statistics, the penalized regression methods are the main tools for variable selection (or feature selection) in high-dimensional sparse data analysis. Due to the nonsmoothness of the associated thresholding operators of commonly used penalties such as the least absolute shrinkage and selection operator (LASSO), the smoothly clipped absolute deviation (SCAD), and the minimax concave penalty (MCP), the classical Newton-Raphson algorithm cannot be used. In this article, we propose a cubic Hermite interpolation penalty (CHIP) with a smoothing thresholding operator. Theoretically, we establish the nonasymptotic estimation error bounds for the global minimizer of the CHIP penalized high-dimensional linear regression. Moreover, we show that the estimated support coincides with the target support with a high probability. We derive the Karush-Kuhn-Tucker (KKT) condition for the CHIP penalized estimator and then develop a support detection-based Newton-Raphson (SDNR) algorithm to solve it. Simulation studies demonstrate that the proposed method performs well in a wide range of finite sample situations. We also illustrate the application of our method with a real data example.

Analytic network process (ANP) based decision support tool for nuclear power plant location and reactor type selection

The nuclear energy industry has seen a resurgence due to interest in sustainable energy. Regulatory agencies are evaluating innovative technology and facility designs for the first time in a long time. The nuclear energy business needs a workable instrument for decision-making in light of the shift in public and regulatory opinion. This study has two main motivations. The first is to determine the set of criteria to be used in determining suitable locations for a nuclear power plant (NPP) to be established in Turkey and to determine suitable regions with Analytic Network Process (ANP) based analyzes in the light of these criteria. The second is to select the most suitable nuclear reactor type for the specified location. Plant efficiency and lifetime vary depending on the reactor type used. For this reason, in the study, both the location of the nuclear power plant and the selection of the appropriate reactor type for the plant were evaluated together. In this context, a criterion set consisting of 3 main criteria and 20 sub-criteria to be used in NPP installation location and reactor selection was determined. In the study, site selection was made by focusing on economic, social and technical factors, and reactor selection was made by focusing on technical factors. Giresun, Trabzon, Rize and Artvin provinces located in the Eastern Black Sea region were evaluated for NPP installation. For reactor selection, we focused on reactor types according to their coolants and in this context, PWR, PHWR, GCR and BWR reactors were examined. NPP location and reactor selection in the study; It was realized as Giresun (PWR) > Rize (PWR) > Trabzon (BWR) > Artvin (BWR).

In-Depth Analysis of the Selection of PBPK Modeling Tools: Bibliometric and Social Network Analysis of the Open Systems Pharmacology Community.

Since the Open Source Initiative laid the foundation for the open source software environment in 1998, the popularity of free and open source software has been steadily increasing. Model-informed drug discovery and development (MID3), a key component of pharmaceutical research and development, heavily makes use of computational models which can be developed using various software including the Open Systems Pharmacology (OSP) software (PK-Sim/MoBi), a free and open source software tool for physiologically based pharmacokinetic (PBPK) modeling. In this study, we aimed to investigate the impact, application areas, and reach of the OSP software as well as the relationships and collaboration patterns between organizations having published OSP-related articles between 2017 and 2023. Therefore, we conducted a bibliometric analysis of OSP-related publications and a social network analysis of the organizations with which authors of OSP-related publications were affiliated. On several levels, we found evidence for a significant growth in the size of the OSP community as well as its visibility in the MID3 community since OSP's establishment in 2017. Specifically, the annual publication rate of PubMed-indexed PBPK-related articles using the OSP software outpaced that of PBPK-related articles using any software. Our bibliometric analysis and network analysis demonstrated that the expansion of the OSP community was predominantly driven by new authors and organizations without prior connections to the community involving the generation of research clusters de novo and an overall diversification of the network. These findings suggest an ongoing evolution of the OSP community toward a more segmented, diverse, and inclusive network.

Budget impact models for lung cancer interventions: A systematic literature review.

Budget impact models (BIMs) forecast the financial implications of adopting new technologies and the potential need for budget reallocation, thus playing a crucial role in reimbursement decisions. Despite the importance of accurate forecasts, studies indicate large discrepancies between estimates and reality. We are developing an artificial intelligence-based clinical decision tool to identify patients with non-small cell lung cancer who are most likely to benefit from immunotherapy. To evaluate the budgetary implications and describe a systematic literature review of published lung cancer BIMs. We searched PubMed and EMBASE for studies published between 2010 and 2023 that include BIMs that describe lung cancer interventions. Forward and backward reference searches were performed for all qualifying studies. We extracted author and publication year, country, interventions, disease stages, time horizon, analytical perspective, modeling methods used, types of costs included, sensitivity analyses conducted, and data sources used. We then evaluated adherence to the Professional Society for Health Economics and Pharmacoeconomics Research best-practice guidelines. A total of 25 BIMs were identified, spanning 14 different countries. Model structure could not be ascertained definitively for nearly half of the models. The cost calculator approach was most common among the others. Time horizons ranged from 1 to 5 years, in line with recommendations. Most models compared drugs, 4 compared nondrug interventions, and 7 compared diagnostic technologies. Assumptions about market uptake were poorly documented and poorly motivated. Inclusion of cancer-related costs was rare. Adherence to best practices was variable and did not appear to improve over time. The number of published BIMs for lung cancer exceeded expectations. There were modest trends toward publication frequency and model quality over time. Our analysis revealed variability across the models, as well as their adherence to best practices, indicating substantial room for improvement. Although none of the models were individually suitable for the purpose of evaluating an artificial intelligence-based treatment selection tool, some models provided valuable insights.

Assessing Machine Tool Selection Process in Sustainable Production to Address Climate Change Based on Hybrid MCDM Methods

Assessing Machine Tool Selection Process in Sustainable Production to Address Climate Change Based on Hybrid MCDM Methods

Study on the influence of geometric parameters of typical titanium alloy power milling tools

Abstract Based on ABAQUS, orthogonal finite element tests were carried out when the number of keyway milling cutter blades was three and four, and the spiral rise angle was 60° and 65°, respectively. The results showed that with the maximum number of blades and spiral rise angle, the milling quality of typical titanium alloy TC4 was greatly improved. The surface roughness decreases, and the effect of spiral lift angle on surface roughness is more significant, which provides a theoretical basis for improving the quality and energy of typical titanium alloy milling tool selection.

Combining a field experiment and literature to model the regrowth probability of perennial storage organs fragmented by tillage: Case study of Cirsium arvense (L.) Scop

Management of perennial weeds has become increasingly difficult with the reduction of herbicide use. Creeping perennials accumulate reserves in specialized belowground organs from which they regenerate new plants after a disturbance. Through tool selection, tillage operations could be optimized to reduce perennial-weed reserves and limit regeneration. In the present study, the effect of five tools on the fragmentation of the creeping roots of Cirsium arvense (L.) Scop. (Canada thistle), a major perennial weed in arable crops, were analysed. A field trial was set up to measure the lengths of the root fragments left after tillage. Five tools were tested: mouldboard ploughing, rotary harrow, disc harrow, rigid-tine cultivator and goose-foot cultivator. Fragment-length distribution varied according to the tool: rotary harrow left the smallest (3.7 cm on average) and least variable fragment lengths, mouldboard ploughing the longest (12.7 cm) and most variable ones. The other tools produced intermediate-sized fragments (8–10 cm). Based on these results and literature, a model was proposed to predict perennial-weed regeneration probability from storage-organ fragments after one tillage run. The effects of six factors, which were agronomic (tillage tool), environmental (soil conditions and temperature) and biological (storage-organ fragment diameter, maximal belowground-shoot length and pre-tillage storage-organ distribution), were tested through a sensitivity analysis. According to the model, the probability of fragment regeneration success is lower for the rotary harrow than for the mouldboard plough. The most important drivers of fragment regeneration success were the biological traits: fragment diameter and maximal belowground-shoot length per unit fragment biomass. The present model should be complemented to predict the effect of tillage on perennial-weed regrowth and help improving non-chemical weed-management strategies. To achieve this, further research is needed on plant regrowth potential from storage organs and their architecture in the soil.

Evaluating English Teachers’ Artificial Intelligence Readiness and Training Needs with a TPACK-Based Model

With the rapid development and widespread adoption of artificial intelligence (AI) tools, the implementation of instructional pedagogy has transformed significantly. English teachers need to understand how AI tools can improve their teaching and must acquire the necessary technical and pedagogical knowledge to effectively utilize AI technology. Although the integration of AI into language teaching shows potential benefits, there remains a dearth of comprehensive research on English teachers’ perceptions, readiness, and professional development requirements in relation to AI.To address these knowledge research gaps, our study aims to evaluate English teachers’ current understanding of AI tools and their training needs for integrating AI into the English language classroom. Our proposed model uses the technological pedagogical content knowledge (TPACK) framework, which incorporates English language teaching and information literacy contexts. This framework allows for a holistic assessment of teachers’ readiness for integrating AI within English language teaching practices.A study was conducted with a class of preservice English teachers in Hong Kong. An online survey was designed to assess the readiness of English teachers for applying AI tools in the classroom as well as their understanding and level of information literacy. This study helped identify and address potential issues with the survey before launching it to a wider audience. Our findings confirmed the validity and reliability of the instrument and indicated that preservice English teacher participants are generally prepared to integrate AI tools into the English classroom. Corelation analysis was also conducted to assess the relationships among the constructs and showed that technological pedagogical knowledge (TPK) and instructional literacy (IL) were significant predictors of the overall TPACK construct. The study suggested professional training in the selection, implementation and progress monitoring of specific AI tools for English Language teaching; pedagogy design; and the ability to search for appropriate resources for the English classroom. The framework can be enhanced by using a mixed-method approach and incorporating a qualitative study to triangulate the findings. An explanatory sequential design will be recommended to collect quantitative data first, then qualitative data will be collected for further analysis.

Predictors of residual tricuspid regurgitation after interventional therapy: an automated deep-learning CT analysis

Computed tomography (CT) is used as a valuable tool for device selection for interventional therapy in tricuspid regurgitation (TR). We aimed to evaluate predictors of TR reduction using CT and automated deep learning algorithms. Patients with severe to torrential TR and CTs prior to either percutaneous annuloplasty (PA) or tricuspid transcatheter edge-to-edge repair (T-TEER) were enrolled. CTs were analyzed using automated deep learning algorithms to assess tricuspid valve anatomy, right heart morphology, and function. Outcome parameters comprised post-interventional TR ≤ 1 and all-cause mortality. 84 patients with T-TEER (n = 32) or PA treatment (n = 52) were enrolled. Patients with a post-interventional TR ≤ 1 presented lower tenting heights and smaller tenting angles compared to patients with a TR > 1. Tenting height showed the best accuracy for post-interventional TR > 1 with an AUC of 0.756 (95% CI 0.560–0.951) in the T-TEER and 0.658 (95% CI 0.501–0.815) in the PA group, consistent with a suggested threshold of 6.8 mm and 9.2 mm, respectively. Patients with a post-interventional TR ≤ 1 exhibited a mortality of 4% and those with a TR > 1 of 12% during a follow-up of 331 ± 300 and 370 ± 265 days, respectively (p = 0.124). To conclude, tenting is associated with procedural outcomes and should be considered during screening for interventional TR therapy.

From building blocks to digital networks: analyzing critical drivers for social media adoption in the construction sector

Despite the growing recognition of social media’s potential benefits in the construction industry, there is a lack of in-depth research examining the specific drivers that influence its adoption. Therefore, this study fills this gap by empirically investigating the drivers influencing the adoption of social media in the construction sector of a developing country such as Nigeria. This objective was achieved through a quantitative research approach, involving the utilization of a well-structured questionnaire to gather responses from construction professionals, including architects, builders, engineers, quantity surveyors and estate managers. These professionals were sampled using a combination of purposive and snowball sampling techniques. The obtained data underwent analysis using various statistical methods, including descriptive statistics and inferential statistics such as exploratory factor analysis (EFA) and Horn Parallel Analysis. The EFA revealed four clusters of drivers: communication and collaboration, operational efficiency and risk management, marketing and branding and knowledge sharing and learning. The findings of this study offer valuable guidance for construction firms and professionals seeking to harness the power of social media. Understanding the specific drivers within each cluster can inform strategic decisions regarding the selection and implementation of social media tools.

Articulating the supervisory functions of university supervisors in vocational education and training teaching internship

This article examines supervisory practices in vocational education and training teaching internships within a blended-learning system. Using a multiple-case study of five triads made up of a university supervisor, a cooperative teacher from the workplace and a student teacher, practices were analysed through the lens of supervisory functions: support, mediation between theory and practice, collaboration, evaluation and internship management. Findings show that support and evaluation practices are dominant within triads. The management function is characterized by practices surrounding the selection and use of digital tools to supervise student teachers in a blended-learning system. The collaboration function appears to be inconsistent from one triad to another, while the mediation between theory and practice function plays a limited role in the practices of all supervisors, despite being identified as one of their main responsibilities.

Exploring environmental sustainability through dimension reduction algorithms: A cost-effective UV spectroscopic method for simultaneous determination of veterinary binary mixtures of doxycycline hydrochloride and tylosin tartrate

This study presents a cost-effective and environmentally friendly approach for the simultaneous determination of a veterinary binary mixture comprising doxycycline hydrochloride (DOX) and tylosin tartrate (TYZ) utilizing UV spectroscopy alongside dimension reduction algorithms (DRAs). Seventeen DRAs were evaluated, and their performances were compared based on four metrics: mean squared error (MSE), mean absolute error (MAE), median absolute error (MedAE), and coefficient of determination (R2). Based on the performance indices, Isomap algorithm demonstrated the highest predictive capacity among all DRAs. MSE, MAE, MedAE and R2 values of (0.38, 0.28, 0.19 and 0.999) and (0.08, 0.26, 0.22 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 4.67–30 μg mL−1 for DOX. MSE, MAE, MedAE and R2 values of (0.54, 0.34, 0.19 and 0.994) and (0.07, 0.19, 0.07 and 0.998) were obtained for calibration and test datasets, respectively across a concentration range 3.51–24 μg mL−1 for TYZ. The developed method underwent validation utilizing the accuracy profile approach. An ecological impact assessment was carried out employing six greenness evaluation tools: The Green Solvent Selection Tool (GSST), National Environmental Methods Index (NEMI), the Assessment of Green Profile (AGP), carbon footprint analysis, Analytical Greenness Calculator (AGREE), and Complementary Green Analytical Procedure Index (Complex GAPI). Additionally, we applied blueness and whiteness assessments using Blue Applicability Grade Index (BAGI) and Red-Green-Blue 12 (RGB 12) algorithms, respectively. The proposed method demonstrated higher GSST scores, a more "green" profile in NEMI, a superior AGP profile, and better environmental sustainability in Complex GAPI. The calculated carbon footprint value was 0.0002 kg CO2 equivalent per sample, The AGREE score was 0.87, BAGI was assessed at 72.5, and the whiteness assessment by the RGB12 algorithm was 89.6. Statistical comparison of the proposed method with a previously reported HPLC method for dosage form analysis revealed no significant differences at a 95% confidence level. This study highlights the novelty of combining UV spectroscopy with dimension reduction algorithms, offering significant advancements over traditional UV and chemometric methods for the analysis of these drugs. This approach not only enhances the efficiency and accuracy of determining active ingredients in pharmaceutical dosage forms but also contributes to environmental sustainability.

Strategi Pengelolaan Sumber Daya Insani dalam Meningkatkan Industri Fesyen Halal

This article aims to offer management strategy of Islamic Human Resources (IHR) in the Islamic fashion industry environment. This research is library research, with data collection techniques using books, journals, and other publications. This study uses a qualitative approach that produces descriptive data in the form of words about IHR management in the Islamic fashion industry. The collected data are then analyzed, interpreted, and generalized. The results of the study indicate that IHR development not only emphasizes intellectual and emotional development, but also involves spiritual and moral development. The presence of a truly Islamic IHR can reduce or eliminate the risk of sharia compliance in the Islamic fashion industry environment. The operationalization of IHR Islamization can be carried out through several stages, namely sharia recruitment and sharia selection. Sharia selection tools that can be used include sharia curriculum vitae (CV) analysis, sharia test, sharia interview, sharia recommendation and sharia psychological test. Everyone who works in the Islamic fashion industry needs to be Islamized through the stages of sharia recruitment and selection. It is hoped that the IHR development stages will give birth to tazkiyah or inner purification which will color all activities in the Islamic fashion industry.