Potential Applicability Research Articles

Identification of the Electrogenerated Hidden Nitrenium Ions by In Situ Mass Spectrometry.

The identification of the electrogenerated reactive intermediates is essential for an in-depth understanding of the electroredox processes. Although various short-lived intermediates are well characterized by coupling electrochemistry with mass spectrometry (EC/MS), many electrogenerated transient species (τ < 1 μs) are still rarely captured by the currently available EC/MS approaches. Here, we present a low-delay coupling device, which was constructed by decorating a microelectrode into the front tip of a microsized ion emitter. For the first time, the in situ detection of a previously hidden intermediate, i.e., the transient nitrenium ion of carbazole (τ = 333 ns), was achieved. The electrochemical generation of indole nitrenium ion, whose half-life is estimated to be shorter compared to the carbazole nitrenium ion due to less resonance stabilization, was also confirmed by direct observation. This clog-free microelectrode/ion emitter is cheap and easy to fabricate and offers a general and powerful approach to monitoring the fast reactions of electrogenerated reactive intermediates. We believe that our integrated EC/MS approach holds substantial potential for broad applicability, particularly in probing the intricate and ultrafast electroredox processes occurring at the electrode-solution interface.

Automated Midline Shift Detection and Quantification in Traumatic Brain Injury: A Comprehensive Review

AbstractTraumatic brain injury (TBI) often results in midline shift (MLS) that is a critical indicator of the severity and prognosis of head injuries. Automated analysis of MLS from head computed tomography (CT) scans using artificial intelligence (AI) techniques has gained much attention in the past decade and has shown promise in improving diagnostic efficiency and accuracy. This review aims to summarize the current state of research on AI-based approaches for MLS analysis in TBI cases, identify the methodologies employed, evaluate the performance of the algorithms, and draw conclusions regarding their potential clinical applicability. A comprehensive literature search was conducted, identifying 15 distinctive publications. The identified articles were analyzed for their focus on MLS detection and quantification using AI techniques, including their choice of AI algorithms, dataset characteristics, and methodology. The reviewed articles covered various aspects related to MLS detection and quantification, employing deep neural networks trained on two-dimensional or three-dimensional CT imaging datasets. The dataset sizes ranged from 11 patients' CT scans to 25,000 CT images. The performance of the AI algorithms exhibited variations in accuracy, sensitivity, and specificity, with sensitivity ranging from 70 to 100%, and specificity ranging from 73 to 97.4%. AI-based approaches utilizing deep neural networks have demonstrated potential in the automated detection and quantification of MLS in TBI cases. However, different researchers have used different techniques; hence, critical comparison is difficult. Further research and standardization of evaluation protocols are needed to establish the reliability and generalizability of these AI algorithms for MLS detection and quantification in clinical practice. The findings highlight the importance of AI techniques in improving MLS diagnosis and guiding clinical decision-making in TBI management.

Substrate-Assisted Laser-Induced Breakdown Spectroscopy Combined with Variable Selection and Extreme Learning Machine for Quantitative Determination of Fenthion in Soybean Oil

The quality and safety of edible vegetable oils are closely related to human life and health, meaning it is of great significance to explore the rapid detection methods of pesticide residues in edible vegetable oils. This study explored the applicability potential of substrate-assisted laser-induced breakdown spectroscopy (LIBS) for quantitatively determining fenthion in soybean oils. First, we explored the impact of laser energy, delay time, and average oil film thickness on the spectral signals to identify the best experimental parameters. Afterward, we quantitatively analyzed soybean oil samples using these optimized conditions and developed a full-spectrum extreme learning machine (ELM) model. The model achieved a prediction correlation coefficient (RP2) of 0.8417, a root mean square error of prediction (RMSEP) of 167.2986, and a mean absolute percentage error of prediction (MAPEP) of 26.46%. In order to enhance the prediction performance of the model, a modeling method using the Boruta algorithm combined with the ELM was proposed. The Boruta algorithm was employed to identify the feature variables that exhibit a strong correlation with the fenthion content. These selected variables were utilized as inputs for the ELM model, with the RP2, RMSEP, and MAPEP of Boruta-ELM being 0.9631, 71.4423, and 10.06%, respectively. Then, the genetic algorithm (GA) was used to optimize the parameters of the Boruta-ELM model, with the RP2, RMSEP, and MAPEP of GA-Boruta-ELM being 0.9962, 11.005, and 1.66%, respectively. The findings demonstrate that the GA-Boruta-ELM model exhibits excellent prediction capability and effectively predicts the fenthion contents in soybean oil samples. It will be valuable for the LIBS quantitative detection and analysis of pesticide residues in edible vegetable oils.

Optimizing multimodal feature selection using binary reinforced cuckoo search algorithm for improved classification performance.

Feature selection is a vital process in data mining and machine learning approaches by determining which characteristics, out of the available features, are most appropriate for categorization or knowledge representation. However, the challenging task is finding a chosen subset of elements from a given set of features to represent or extract knowledge from raw data. The number of features selected should be appropriately limited and substantial to prevent results from deviating from accuracy. When it comes to the computational time cost, feature selection is crucial. A feature selection model is put out in this study to address the feature selection issue concerning multimodal. In this work, a novel optimization algorithm inspired by cuckoo birds' behavior is the Binary Reinforced Cuckoo Search Algorithm (BRCSA). In addition, we applied the proposed BRCSA-based classification approach for multimodal feature selection. The proposed method aims to select the most relevant features from multiple modalities to improve the model's classification performance. The BRCSA algorithm is used to optimize the feature selection process, and a binary encoding scheme is employed to represent the selected features. The experiments are conducted on several benchmark datasets, and the results are compared with other state-of-the-art feature selection methods to evaluate the effectiveness of the proposed method. The experimental results demonstrate that the proposed BRCSA-based approach outperforms other methods in terms of classification accuracy, indicating its potential applicability in real-world applications. In specific on accuracy of classification (average), the proposed algorithm outperforms the existing methods such as DGUFS with 32%, MBOICO with 24%, MBOLF with 29%, WOASAT 22%, BGSA with 28%, HGSA 39%, FS-BGSK 37%, FS-pBGSK 42%, and BSSA 40%.

Detection of Nucleic Acids of the Fish Pathogen Yersinia ruckeri from Planktonic and Biofilm Samples with a CRISPR/Cas13a-Based Assay.

Yersinia ruckeri is the cause of hemorrhagic septicemia, known as enteric redmouth disease, in salmonid fish species. This bacterial pathogen can form biofilms on abiotic surfaces of aquaculture settings or even on the surfaces of the fish themselves, contributing to their persistence in the aquatic environment. Detection methods for this and other fish pathogens can be time-consuming and lack specificity and sensitivity, limiting timely monitoring, the treatment of microbial infections, and effective control of their transmission in aquaculture settings. Rapid and sensitive detection methods for nucleic acids can be crucial for an appropriate surveillance of bacterial pathogens, and the CRISPR/Cas-based assays have emerged as a good alternative since it has been proven to be a useful tool for the rapid, specific, and sensitive detection of viruses and some bacteria. In this study, we explored the capability of the CRISPR/Cas13a system (SHERLOCK) to specifically detect both DNA and RNA (gene transcripts) from planktonic and biofilm samples of the bacterial fish pathogen Y. ruckeri. The assay was designed to detect the gyrA gene and the small noncoding RNAs (sRNAs) MicA and RprA from planktonic cultures and biofilm samples prepared in marine broth. The specific crRNA designed for these gene targets included a 28 nt specific gene sequence, and a scaffold sequence necessary for Cas13-binding. For all the assays, the nucleic acids obtained from samples were previously subjected to isothermal amplification with the recombinase polymerase amplification (RPA) method and the subsequent T7 transcription of the RPA amplicons. Finally, the detection of nucleic acids of Y. ruckeri was by means of a reporter signal released by the Cas13a collateral RNA cleavage triggered upon target recognition, measured by fluorescence- or lateral-flow-based readouts. This CRISPR/Cas13a-based assay was able to specifically detect both DNA and sRNAs from the Y. ruckeri samples, and the sensitivity was comparable to that obtained with qPCR analysis, highlighting the potential applicability of this CRISPR/Cas13a-based assay for fish pathogen surveillance.

Inhibition of pyrite oxidation through forming biogenic K-jarosite coatings to prevent acid mine drainage production

This study proposes a novel method by forming biogenic K-jarosite coatings on pyrite surfaces driven by Acidithiobacillus ferrooxidans (A. ferrooxidans) to reduce heavy metal release and prevent acid mine drainage (AMD) production. Different thicknesses of K-jarosite coatings (0.7 to 1.1 μm) were able to form on pyrite surfaces in the presence of A. ferrooxidans, which positively correlated with the initial addition of Fe2+ and K+ concentrations. The inhibiting effect of K-jarosite coatings on pyrite oxidation was studied by electrochemical measurements, chemical oxidation tests, and bio-oxidation tests. The experimental results showed that the best passivation performance was achieved when 20 mM Fe2+ and 6.7 mM K+ were initially introduced with a bacterial concentration of 4 × 108 cells·mL−1, reducing chemical and biological oxidation by 70 % and 98 %, respectively (based on the concentration of total iron dissolved into the solution by pyrite oxidation). Similarly, bio-oxidation tests of two mine waste samples also showed sound inhibition effects, which offers a preliminary demonstration of the potential applicability of this method to actual waste rock. This study presents a new perspective on passivating the oxidation of metal sulfide tailings or waste and preventing AMD.

A comparative patient-level prediction study in OMOP CDM: applicative potential and insights from synthetic data

The emergence of collaborations, which standardize and combine multiple clinical databases across different regions, provide a wealthy source of data, which is fundamental for clinical prediction models, such as patient-level predictions. With the aid of such large data pools, researchers are able to develop clinical prediction models for improved disease classification, risk assessment, and beyond. To fully utilize this potential, Machine Learning (ML) methods are commonly required to process these large amounts of data on disease-specific patient cohorts. As a consequence, the Observational Health Data Sciences and Informatics (OHDSI) collaborative develops a framework to facilitate the application of ML models for these standardized patient datasets by using the Observational Medical Outcomes Partnership (OMOP) common data model (CDM). In this study, we compare the feasibility of current web-based OHDSI approaches, namely ATLAS and “Patient-level Prediction” (PLP), against a native solution (R based) to conduct such ML-based patient-level prediction analyses in OMOP. This will enable potential users to select the most suitable approach for their investigation. Each of the applied ML solutions was individually utilized to solve the same patient-level prediction task. Both approaches went through an exemplary benchmarking analysis to assess the weaknesses and strengths of the PLP R-Package. In this work, the performance of this package was subsequently compared versus the commonly used native R-package called Machine Learning in R 3 (mlr3), and its sub-packages. The approaches were evaluated on performance, execution time, and ease of model implementation. The results show that the PLP package has shorter execution times, which indicates great scalability, as well as intuitive code implementation, and numerous possibilities for visualization. However, limitations in comparison to native packages were depicted in the implementation of specific ML classifiers (e.g., Lasso), which may result in a decreased performance for real-world prediction problems. The findings here contribute to the overall effort of developing ML-based prediction models on a clinical scale and provide a snapshot for future studies that explicitly aim to develop patient-level prediction models in OMOP CDM.

Integrated Waterflooding Effect Evaluation Methodology for Carbonate Fractured–Vuggy Reservoirs Based on the Unascertained Measure–Mahalanobis Distance Theory

The waterflooding effect evaluation of carbonate fractured–vuggy reservoirs constitutes a comprehensive multiple-information decision-making process involving quantitative unascertained measure theory. This paper establishes a novel comprehensive methodology to evaluate the waterflooding effects of carbonate fractured–vuggy reservoirs for the first time. A new evaluation grading criteria is proposed using the Mahalanobis distance method based on the multi-index comprehensive unascertained measure theory derived from a modified five-scale analytical hierarchy process–entropy weight method. The actual field data from the carbonate fractured–vuggy reservoirs and the nine evaluation indices are specifically applied to demonstrate the calculation process for the construction of the grading system model on the waterflooding effects and to verify the accuracy of the Mahalanobis distance method by comparing the calculation results with the Minkowski and Euclidean distance methods. The proposed methodology facilitates the effective evaluation of the waterflooding strategies implemented in carbonate fractured–vuggy reservoirs with three categories; the ones with outstanding performance usually demonstrated favorable index characteristics, with substantial contributions to the enhanced oil recovery, manifesting with complete well patterns, a balance in the injection–production dynamics, excellent waterflooding utilization, and control competence. In contrast, fractured–vuggy units with fair waterflooding performance revealed limitations in the enhanced oil recovery. It can also be inferred that the mediocre waterflooding performance of the fractured–vuggy units is associated with incomplete well patterns, an imbalance in the injection–production dynamics, low waterflooding utilization, and a negligible waterflooding effect. The results in this study show that this newly proposed integrated model can effectively assess the waterflooding effects quantitatively and provide a more precise scientific basis for evaluating the waterflooding effects in carbonate fractured–vuggy reservoirs, with potential applicability in other fields.

Estimation of Cochlear Frequency Selectivity Using a Convolution Model of Forward-Masked Compound Action Potentials.

Frequency selectivity is a fundamental property of the peripheral auditory system; however, the invasiveness of auditory nerve (AN) experiments limits its study in the human ear. Compound action potentials (CAPs) associated with forward masking have been suggested as an alternative to assess cochlear frequency selectivity. Previous methods relied on an empirical comparison of AN and CAP tuning curves in animal models, arguably not taking full advantage of the information contained in forward-masked CAP waveforms. To improve the estimation of cochlear frequency selectivity based on the CAP, we introduce a convolution model to fit forward-masked CAP waveforms. The model generates masking patterns that, when convolved with a unitary response, can predict the masking of the CAP waveform induced by Gaussian noise maskers. Model parameters, including those characterizing frequency selectivity, are fine-tuned by minimizing waveform prediction errors across numerous masking conditions, yielding robust estimates. The method was applied to click-evoked CAPs at the round window of anesthetized chinchillas using notched-noise maskers with various notch widths and attenuations. The estimated quality factor Q10 as a function of center frequency is shown to closely match the average quality factor obtained from AN fiber tuning curves, without the need for an empirical correction factor. This study establishes a moderately invasive method for estimating cochlear frequency selectivity with potential applicability to other animal species or humans. Beyond the estimation of frequency selectivity, the proposed model proved to be remarkably accurate in fitting forward-masked CAP responses and could be extended to study more complex aspects of cochlear signal processing (e.g., compressive nonlinearities).

Advances in the Sustainable Development of Biobased Materials Using Plant and Animal Waste as Raw Materials: A Review

There is substantial concern about critical environmental problems related to waste in production sectors such as textile, construction, and packaging. The materials ascribed to the sector’s unsustainability are primarily fabrics, plastic, and hazardous solvents, making developing new biobased materials imperative. As such, various strategies have been investigated to convert and recycle waste and give them commercial value via the manufacture of biobased materials. This review discusses the various types of raw materials as sources to develop new biobased materials that could promote the transition toward sustainability. According to the literature, the functional qualities of biobased materials are comparable to those of synthetic materials. Raw material sources such as biomass, derived from plant and animal-based waste, are attractive due to their low cost, abundance, and biodegradability. The manufacture of biomaterials, as well as their characterization and performance, are also discussed. Further, this review will offer a comprehensive view of the potential applicability and current commercial applications of the developed biobased materials in relevant areas such as packaging, construction, textile, and wastewater remediation. This could be a potential field of research to address the environmental challenges posed by the continuous growth of the global population.

A protein-miRNA biomic analysis approach to explore neuroprotective potential of nobiletin in human neural progenitor cells (hNPCs).

Chemical-induced neurotoxicity is increasingly recognized to accelerate the development of neurodegenerative disorders (NDs), which pose an increasing health burden to society. Attempts are being made to develop drugs that can cross the blood-brain barrier and have minimal or no side effects. Nobiletin (NOB), a polymethoxylated flavonoid with anti-oxidative and anti-inflammatory effects, has been demonstrated to be a promising compound to treat a variety of NDs. Here, we investigated the potential role of NOB in sodium arsenate (NA)-induced deregulated miRNAs and target proteins in human neural progenitor cells (hNPCs). The proteomics and microRNA (miRNA) profiling was done for different groups, namely, unexposed control, NA-exposed, NA + NOB, and NOB groups. Following the correlation analysis between deregulated miRNAs and target proteins, RT-PCR analysis was used to validate the selected genes. The proteomic analysis showed that significantly deregulated proteins were associated with neurodegeneration pathways, response to oxidative stress, RNA processing, DNA repair, and apoptotic process following exposure to NA. The OpenArray analysis confirmed that NA exposure significantly altered miRNAs that regulate P53 signaling, Wnt signaling, cell death, and cell cycle pathways. The RT-PCR validation studies concur with proteomic data as marker genes associated with autophagy and apoptosis (HO-1, SQSTM1, LC-3, Cas3, Apaf1, HSP70, and SNCA1) were altered following NA exposure. It was observed that the treatment of NOB significantly restored the deregulated miRNAs and proteins to their basal levels. Hence, it may be considered one of its neuroprotective mechanisms. Together, the findings are promising to demonstrate the potential applicability of NOB as a neuroprotectant against chemical-induced neurotoxicity.

My Best Alaskan Life: Addressing Adolescent Mental and Reproductive Health in Alaska.

Since 2020, a multisector research team has coordinated a youth-driven, community-based participatory research project to adapt a reproductive life plan for application in a statewide initiative called My Best Alaskan Life (MBAL). The RLP is adapted for Alaskan youth and is intended to support teens in decision-making processes reflecting cultural priorities, personal goals, and sexual and reproductive health. Background. With 46% of youth in Alaska reporting not having used a condom during their last sexual intercourse and 15% not having used contraception, unintended pregnancy and transmission of STIs will continue. Furthermore, Alaskan youth also cite high rates of hopelessness and suicidality, and research shows that poor mental health among adolescents is correlated with developing and maintaining high-risk sexual behaviors. An intervention focusing on supporting mental wellness and developing personal goals in the context of sexual health decision-making may encourage adolescents to adopt safer sexual health behaviors. The MBAL research team completed a statewide pilot assessing the design and implementation of the tool, gathering feedback from over 700 survey responses (youth, ages 14-20); conducted 10 in-depth interviews (adult partners at community organizations and clinics); and hosted two youth-led design review sessions. Questionnaire respondents were overwhelmingly positive about the tool (91% "liked or loved" the tool) and its potential applicability in their community (86% cited "very applicable"). Project next steps include incorporating design recommendations, a statewide randomized control trial and ultimately, open source access for all interested parties.

A technical, economic, and environmental assessment on dimethyl ether (DME) as a renewable solvent from carbon dioxide utilization (CCU) for heavy oil recovery: A real field in Surmont, Canada as case study

The Paris Agreement represents a shared global goal to combat climate change; however, national carbon emissions vary significantly, mainly due to heavy oil production. The viscous nature of heavy oil requires heat to make it flow, which significantly contributes to emissions. Addressing this issue and finding ways to produce heavy oil in a low-carbon manner while ensuring a win–win situation for economic and energy demands are challenges. If these challenges are successfully addressed, carbon emissions could be significantly reduced in countries that have abundant heavy oil resources. In this study, dimethyl ether is proposed as a renewable solvent from syngas, and we evaluate its technical, environmental, and economic performance through laboratory experiments and numerical simulations by taking the Surmont field in Canada as a case study. Our findings are as follows: a) through in-house experiments, DME solvent expanding steam-assisted gravity drainage outperforms conventional steam-assisted gravity drainage in terms of both a recovery factor and a maximum oil rate, with increases of 24% and over two times, respectively; b) from field-scale numerical simulations, DME co-injection with steam offers over two times the maximum recovery rate increase, an 80% reduction in a steam-oil ratio, an extra 50% increase in cumulative recovery, and 86% energy savings; c) the DME recovery rate can be increased by 13% compared to conventional solvents; d) a 0.06 reduction in DME's hold-up ratio and a 13% increase in the recovery ratio suggest that it has higher solvent efficiency and lower solvent losses than conventional light solvents (propane and butane); e) DME can, in theory, be produced with zero emissions; and f) DME demonstrates high economic robustness when considering fluctuating oil prices and carbon policies. In summary, this study demonstrates the potential benefits and applicability of DME-based processes in terms of technical, environmental, and economic aspects.

Two-Dimensional Tandem Mass Spectrometry for Biopolymer Structural Analysis.

Biopolymer analysis, including proteomics and glycomics, relies heavily on the use of mass spectrometry for structural elucidation, including sequence determination. Novel methods to improve sample workup, instrument performance, and data analysis continue to be developed to address shortcomings associated with sample preparation, analysis time, data quality, and data interpretation. Here, we present a new method that couples in-source collision-induced dissociation (IS-CID) with two-dimensional tandem mass spectrometry (2D MS/MS) as a way to simplify proteomics and glycomics workflows while also providing additional insight into analyte structures over traditional MS/MS experiments. Specifically, IS-CID is employed as a gas-phase digestion method, i.e., to break down intact full-length polysaccharide or peptide ions prior to mass analysis. The resulting mixtures of oligomeric ions are analyzed by 2D-MS/MS, a technique that allows association of product ions with their precursor ions without isolation of the latter. A novel data analysis strategy is introduced to leverage the second dimension of 2D MS/MS spectra, in which stairstep patterns, representing outputs of a molecule's MSn scans, are extracted for structural interconnectivity information on the oligomer. The results demonstrate the potential applicability of 2D MS/MS strategies to the modern omics workflow and structural analysis of various classes of biopolymers.

Two‐Dimensional Tandem Mass Spectrometry for Biopolymer Structural Analysis

AbstractBiopolymer analysis, including proteomics and glycomics, relies heavily on the use of mass spectrometry for structural elucidation, including sequence determination. Novel methods to improve sample workup, instrument performance, and data analysis continue to be developed to address shortcomings associated with sample preparation, analysis time, data quality, and data interpretation. Here, we present a new method that couples in‐source collision‐induced dissociation (IS‐CID) with two‐dimensional tandem mass spectrometry (2D MS/MS) as a way to simplify proteomics and glycomics workflows while also providing additional insight into analyte structures over traditional MS/MS experiments. Specifically, IS‐CID is employed as a gas‐phase digestion method, i.e., to break down intact full‐length polysaccharide or peptide ions prior to mass analysis. The resulting mixtures of oligomeric ions are analyzed by 2D‐MS/MS, a technique that allows association of product ions with their precursor ions without isolation of the latter. A novel data analysis strategy is introduced to leverage the second dimension of 2D MS/MS spectra, in which stairstep patterns, representing outputs of a molecule's MSn scans, are extracted for structural interconnectivity information on the oligomer. The results demonstrate the potential applicability of 2D MS/MS strategies to the modern omics workflow and structural analysis of various classes of biopolymers.

Ligand‐Induced Chirality in ClMBA2SnI4 2D Perovskite**

AbstractChiral perovskites possess a huge applicative potential in several areas of optoelectronics and spintronics. The development of novel lead‐free perovskites with tunable properties is a key topic of current research. Herein, we report a novel lead‐free chiral perovskite, namely (R/S−)ClMBA2SnI4 (ClMBA=1‐(4‐chlorophenyl)ethanamine) and the corresponding racemic system. ClMBA2SnI4 samples exhibit a low band gap (2.12 eV) together with broad emission extending in the red region of the spectrum (∼1.7 eV). Chirality transfer from the organic ligand induces chiroptical activity in the 465–530 nm range. Density functional theory calculations show a Rashba type band splitting for the chiral samples and no band splitting for the racemic isomer. Self‐trapped exciton formation is at the origin of the large Stokes shift in the emission. Careful correlation with analogous lead and lead‐free 2D chiral perovskites confirms the role of the symmetry‐breaking distortions in the inorganic layers associated with the ligands as the source of the observed chiroptical properties providing also preliminary structure‐property correlation in 2D chiral perovskites.

Ligand-Induced Chirality in ClMBA2 SnI4 2D Perovskite.

Chiral perovskites possess a huge applicative potential in several areas of optoelectronics and spintronics. The development of novel lead-free perovskites with tunable properties is a key topic of current research. Herein, we report a novel lead-free chiral perovskite, namely (R/S-)ClMBA2 SnI4 (ClMBA=1-(4-chlorophenyl)ethanamine) and the corresponding racemic system. ClMBA2 SnI4 samples exhibit a low band gap (2.12 eV) together with broad emission extending in the red region of the spectrum (∼1.7 eV). Chirality transfer from the organic ligand induces chiroptical activity in the 465-530 nm range. Density functional theory calculations show a Rashba type band splitting for the chiral samples and no band splitting for the racemic isomer. Self-trapped exciton formation is at the origin of the large Stokes shift in the emission. Careful correlation with analogous lead and lead-free 2D chiral perovskites confirms the role of the symmetry-breaking distortions in the inorganic layers associated with the ligands as the source of the observed chiroptical properties providing also preliminary structure-property correlation in 2D chiral perovskites.

Optimized protocol for high-vacuum scanning electron microscopy analysis of polyacrylamide hydrogel-attached sperm cells in a binary system.

A protocol for the analysis of a binary system comprising polyacrylamide hydrogel-attached sperm cells using high-vacuum scanning electron microscopy (SEM) is presented. This protocol focuses on optimizing the SEM procedure to obtain accurate and detailed imaging of the sperm cells and their interactions with the hydrogel scaffold. The methodology involves a stepwise sample preparation, including sample dehydration through a gradual exchange of ethanol/water ratios, followed by the application of a conductive metal coating. By employing this modified protocol, the traditional use of acetone dehydration, which may introduce chemical alterations to the materials, is avoided. The proposed approach enables a comprehensive evaluation of the morphology and interactions within the biological system in contact with the soft material scaffold. Furthermore, the potential application of this protocol extends to the study of other mammalian reproductive cells or cells of different origins adhered to hydrogel scaffolds. RESEARCH HIGHLIGHTS: Novel SEM protocol reveals precise imaging of sperm-hydrogel attachment in a binary system, enhancing our understanding of cell-material interactions. By optimizing SEM procedures, the protocol achieves precise imaging of sperm-hydrogel interactions using ethanol/water dehydration and a conductive metal coating. This modified approach enables a thorough assessment of morphology and interactions in the binary system,extending its potential applicability to other reproductive cells on hydrogelscaffolds.

Sustainability Indices and RAM Analysis for Maintenance Decision Making Considering Environmental Sustainability

Maintenance is becoming an increasingly green process, aiming to simultaneously minimize the impact of assets on the economy, society, and the surrounding environment while also mitigating any adverse consequences arising from maintenance activities. To manage maintenance from a sustainable perspective, appropriate indices able to represent environmental assessment results in an aggregated and synthetic way are necessary, along with rational decision-making tools able to simulate different maintenance alternatives considering various sustainability dimensions. In response to this challenge, a methodology rooted in sustainability indices and RAM analysis is proposed to support decision making with respect to sustainable maintenance practices. By applying the proposed methodology to a proof of concept we demonstrate that the approach enables the evaluation of both the environmental and economic impacts of different maintenance activities. Furthermore, the methodology facilitates the identification of specific environmental dimensions that need improvement, in addition to discerning the distinct contributions of individual components to the environmental and economic impacts at the system level. These insights offer valuable information to support informed decision making regarding the allocation of internal resources for impact mitigation. The obtained results underscore the potential applicability of this approach to industrial systems, signifying its contribution to the goal of achieving sustainable maintenance practices.

Inverse Data Envelopment Analysis Model to Improve Efficiency by Increasing Outputs

Inverse Data Envelopment Analysis (IDEA) has gained significant attention among researchers as an analytical tool for assessing efficiency. Estimating input values while ensuring cost efficiency through changes in output quantities is complex and sensitive. In contrast to conventional data envelopment analysis methods, IDEA enables the quantification of input/output variations resulting from output/input reductions or expansions while preserving the measurement efficiency level. This paper aims to introduce a novel approach for estimating input values by incrementally increasing the value of each output of decision-making units (DMUs) during the evaluation process, thereby maintaining or improving cost efficiency. By utilizing IDEA and manipulating the output values of the DMU under evaluation, the input values are estimated while ensuring constant or enhanced cost efficiency. A simple numerical example and a case study from a Turkish automotive company are presented to validate the proposed method. The obtained results demonstrate significant improvements and hold promising prospects, indicating the potential applicability of this approach to other similar problems and research areas.