Mixed Set Research Articles

Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.

Early identification of sepsis has been shown to significantly improve patient prognosis. Therefore, the aim of this meta-analysis is to systematically evaluate the diagnostic efficacy of machine-learning algorithms for sepsis prediction. Systematic searches were conducted in PubMed, Embase and Cochrane databases, covering literature up to December 2023. The keywords included machine learning, sepsis and prediction. After screening, data were extracted and analysed from studies meeting the inclusion criteria. Key evaluation metrics included sensitivity, specificity and the area under the curve (AUC) for diagnostic accuracy. The meta-analysis included a total of 21 studies with a data sample size of 4,158,941. Overall, the pooled sensitivity was 0.82 (95% confidence interval [CI] = 0.70-0.90; P< 0.001; I2=99.7%), the specificity was 0.91 (95% CI = 0.86-0.94; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.91-0.96). The subgroup analysis revealed that in the emergency department setting (6 studies), the pooled sensitivity was 0.79 (95% CI = 0.68-0.87; P< 0.001; I2= 99.6%), the specificity was 0.94 (95% CI 0.90-0.97; P< 0.001; I2= 99.9%), and the AUC was 0.94 (95% CI = 0.92-0.96). In the Intensive Care Unit setting (11 studies), the sensitivity was 0.91 (95% CI = 0.75-0.97; P< 0.001; I2= 98.3%), the specificity was 0.85 (95% CI = 0.75-0.92; P< 0.001; I2= 99.9%), and the AUC was 0.93 (95% CI = 0.91-0.95). Due to the limited number of studies in the in-hospital and mixed settings (n< 3), no pooled analysis was performed. Machine-learning algorithms have demonstrated excellent diagnostic accuracy in predicting the occurrence of sepsis, showing potential for clinical application.

Machine Learning-Assisted Optimization of Mixed Carbon Source Compositions for High-Performance Denitrification.

Appropriate mixed carbon sources have great potential to enhance denitrification efficiency and reduce operational costs in municipal wastewater treatment plants (WWTPs). However, traditional methods struggle to efficiently select the optimal mixture due to the variety of compositions. Herein, we developed a machine learning-assisted high-throughput method enabling WWTPs to rapidly identify and optimize mixed carbon sources. Taking a local WWTP as an example, a mixed carbon source denitrification data set was established via a high-throughput method and employed to train a machine learning model. The composition of carbon sources and the types of inoculated sludge served as input variables. The XGBoost algorithm was employed to predict the total nitrogen removal rate and microbial growth, thereby aiding in the assessment of the denitrification potential. The predicted carbon sources exhibited an enhanced denitrification potential over single carbon sources in both kinetic experiments and long-term reactor operations. Model feature analysis shows that the cumulative effect and interaction among individual carbon sources in a mixture significantly enhance the overall denitrification potential. Metagenomic analysis reveals that the mixed carbon sources increased the diversity and complexity of denitrifying bacterial ecological networks in WWTPs. This work offers an efficient method for WWTPs to optimize mixed carbon source compositions and provides new insights into the mechanism behind enhanced denitrification under a supply of multiple carbon sources.

Rotation from methadone to buprenorphine using a micro-dosing regime in patients with opioid use disorder and serious mental illness: A case series.

Inducting buprenorphine from methadone has traditionally involved initial opioid withdrawal, with risk of mental state deterioration in patients with serious mental illness (SMI). Micro-dosing of buprenorphine, with small incremental doses, is a novel off-label approach to transitioning from methadone and does not require a period of methadone abstinence. Given the limited literature about buprenorphine microdosing, we aimed to evaluate the feasibility and safety of inducting buprenorphine in a series of patients on methadone with SMI. For this retrospective case series, we reviewed the records of 16 patients with SMI at a Melbourne addiction treatment centre, from January 2021 to July 2022, who transitioned via micro-dosing, from high-dose methadone (>30 mg) to buprenorphine and depot-buprenorphine. Psychiatric diagnoses, mental state, other substance withdrawal, transfer success, transition time, opioid withdrawal symptoms and overall patient experience were collected via objective and subjective reporting. Methadone to buprenorphine transfer was completed by 88% of patients. Mental health measures remained stable with the exception of mildly increased anxiety. Median transfer time was 6.5 days for inpatients, 9 days for mixed setting and 10 days for outpatients. Most patients (93%) rated their experience 'manageable' reporting mild withdrawal symptoms. One patient met study criteria for precipitated withdrawal. This retrospective case series provides evidence that the use of a micro-dosing buprenorphine induction for methadone to buprenorphine transitions, including to depot-buprenorphine, has negligible risk, is tolerated by patients with SMI and is unlikely to precipitate an exacerbation of their mental illness.

Daylighting Through Electrochromic Glazing Under Overcast Skies: Impacts on Visual Task Performance and Perception

ABSTRACT Electrochromic (EC) glazing allows modulating the intensity of visual and solar transmission by dynamically switching between bleached (clear) and tinted (colored) states, leading to changes in spectral power distribution (SPD), correlated color temperature (CCT), and illuminance of the incoming daylight. In this experimental study, we investigated the impact of spectral composition of daylight filtered through a 6-pane EC façade on visual task performance and visual perception. In a semi-controlled test room, 19 subjects were exposed to five setting scenarios of the EC façade (fully bleached, mixed setting after fully bleached, fully tinted, mixed setting after fully tinted, and user-controlled). Test subjects performed a series of visual tasks, and their assessments of the indoor environment and of the outside view were collected along with luminous and spectral measurements under each condition. The analysis provided statistical evidence that the changes in spectral composition of daylight do not have a practically relevant effect on visual task performance in terms of visual acuity and color naming accuracy. However, the daylight filtered through the fully tinted glazing had a substantive impact on visual perception, evoking negative responses to the color rendering of the indoor environment and of the outside view. A mixed settings of the EC façade could improve a natural assessment of the incoming light compared to the fully tinted state, achieving better ratings in terms of perception of the indoor and outdoor environments. When given control of the EC glazing, subjects expressed higher acceptance and satisfaction compared to the tinted and mixed scenarios.

Histological tissue classification with a novel statistical filter-based convolutional neural network.

Deep networks have been of considerable interest in literature and have enabled the solution of recent real-world applications. Due to filters that offer feature extraction, Convolutional Neural Network (CNN) is recognized as an accurate, efficient and trustworthy deep learning technique for the solution of image-based challenges. The high-performing CNNs are computationally demanding even if they produce good results in a variety of applications. This is because a large number of parameters limit their ability to be reused on central processing units with low performance. To address these limitations, we suggest a novel statistical filter-based CNN (HistStatCNN) for image classification. The convolution kernels of the designed CNN model were initialized by continuous statistical methods. The performance of the proposed filter initialization approach was evaluated on a novel histological dataset and various histopathological benchmark datasets. To prove the efficiency of statistical filters, three unique parameter sets and a mixed parameter set of statistical filters were applied to the designed CNN model for the classification task. According to the results, the accuracy of GoogleNet, ResNet18, ResNet50 and ResNet101 models were 85.56%, 85.24%, 83.59% and 83.79%, respectively. The accuracy was improved by 87.13% by HistStatCNN for the histological data classification task. Moreover, the performance of the proposed filter generation approach was proved by testing on various histopathological benchmark datasets, increasing average accuracy rates. Experimental results validate that the proposed statistical filters enhance the performance of the network with more simple CNN models.

EDMD: An Entropy based Dissimilarity measure to cluster Mixed-categorical Data

The effectiveness of clustering techniques is significantly influenced by proximity measures irrespective of type of data and categorical data is no exception. Most of the existing proximity measures for categorical data assume that all attributes contribute equally to the distance measurement which is not true. Usually, frequency or probability-based approaches are better equipped in principle to counter this issue by appropriately weighting the attributes based on the intra-attribute statistical information. However, owing to the qualitative nature of categorical features, the intra-attribute disorder is not captured effectively by the popularly used continuum form of entropy known as Shannon or information entropy. If the categorical data contains ordinal features, then the problem multiplies because the existing measures treat all attributes as nominal. To address these issues, we propose a new Entropy-based Dissimilarity measure for Mixed categorical Data (EDMD) composed of both nominal and ordinal attributes. EDMD treats both nominal and ordinal attributes separately to capture the intrinsic information from the values of two different attribute types. We apply Boltzmann’s definition of entropy, which is based on the principle of counting microstates, to exploit the intra-attribute statistical information of nominal attributes while preserving the order relationships among ordinal values in distance formulation. Additionally, the statistical significance of different attributes of the data towards dissimilarity computation is taken care of through attribute weighting. The proposed measure is free from any user-defined or domain-specific parameters and there is no prior assumption about the distribution of the data sets. Experimental results demonstrate the efficacy of EDMD in terms of cluster quality, accuracy, cluster discrimination ability, and execution time to handle mixed categorical data sets of different characteristics.

Assessing the Efficacy of Synthetic Optic Disc Images for Detecting Glaucomatous Optic Neuropathy Using Deep Learning.

Deep learning architectures can automatically learn complex features and patterns associated with glaucomatous optic neuropathy (GON). However, developing robust algorithms requires a large number of data sets. We sought to train an adversarial model for generating high-quality optic disc images from a large, diverse data set and then assessed the performance of models on generated synthetic images for detecting GON. A total of 17,060 (6874 glaucomatous and 10,186 healthy) fundus images were used to train deep convolutional generative adversarial networks (DCGANs) for synthesizing disc images for both classes. We then trained two models to detect GON, one solely on these synthetic images and another on a mixed data set (synthetic and real clinical images). Both the models were externally validated on a data set not used for training. The multiple classification metrics were evaluated with 95% confidence intervals. Models' decision-making processes were assessed using gradient-weighted class activation mapping (Grad-CAM) techniques. Following receiver operating characteristic curve analysis, an optimal cup-to-disc ratio threshold for detecting GON from the training data was found to be 0.619. DCGANs generated high-quality synthetic disc images for healthy and glaucomatous eyes. When trained on a mixed data set, the model's area under the receiver operating characteristic curve attained 99.85% on internal validation and 86.45% on external validation. Grad-CAM saliency maps were primarily centered on the optic nerve head, indicating a more precise and clinically relevant attention area of the fundus image. Although our model performed well on synthetic data, training on a mixed data set demonstrated better performance and generalization. Integrating synthetic and real clinical images can optimize the performance of a deep learning model in glaucoma detection. Optimizing deep learning models for glaucoma detection through integrating DCGAN-generated synthetic and real-world clinical data can be improved and generalized in clinical practice.

Ferrock cement and oxalic acid for enhanced concrete strength and durability against sulphate attack

Concrete is widely known for being plentiful, durable, and strong, but it can be damaged by different chemicals and environments, that can shorten its lifespan. Consequently, there is a growing interest among researchers to find more durable, stronger, and environmentally friendly cement alternatives. This study focuses on evaluating the workability, strength, durability properties, and morphological aspects of ferrock cement-based concrete, which involves substituting varying percentages of ferrock cement (0%, 5%, 10%, 15%, and 20%) for conventional cement. Ferrock, comprising Iron Powder, Fly Ash, Lime Powder, and glass powder offers pozzolanic potential as a sustainable cement substitute. Additionally, oxalic acid is introduced into the mixes to explore its role in catalyzing reactions between CO2 and water, thereby enhancing the concrete’s strength and durability. Compressive and flexural strength are analyzed after replacing the cement with ferrock, with and without oxalic acid, while durability is assessed through water absorption and resistance to sodium sulphate solution after 28 days of curing. Nine sets of mixes, all with a consistent water-cement ratio of 0.49, are investigated. Results indicate that concrete containing ferrock-based cement, particularly with oxalic acid, exhibits superior strength and enhanced resistance to sulphate attack. The addition of 5% and 10% ferrock with the integration of oxalic acid has 32.28% and 38.7% of compressive strength respectively, the same proportion has 0.2% and 0.1% of mass loss regarding resistance to sulphate attack. Notably, a 10% ferrock replacement with oxalic acid demonstrates the highest performance of concrete as determined by the multi-decision methods.

Filling the gap in competency-based training for medical oncology with supervised online patient counseling: A qualitative study.

9036 Background: The Higher School of Oncology (HSO) is a collaborative US-Russian postgraduate program that improves learning outcomes for young oncologists. Many HSO medical oncology (MO) residents/graduates are also engaged in online supervised patient counseling, known as ‘Just Ask.’ We aimed to explore the educational role of ‘Just Ask’ in shaping the competency-based learning outcomes of MO graduates, who have recently begun independent clinical practice. Methods: This qualitative study included 10 HSO alumni (2019-2023) who practice MO in Russia. All participants were current or prior ‘Just Ask’ consultants. Anonymous, online semi-structured in-depth interviews were conducted to explore interviewees reflection on competencies obtained at HSO and ‘Just Ask.’ The interview guide included 42 open-ended questions based on the American Accreditation Council for Graduate Medical Education (ACGME) list of competencies for MO physicians. All interviews were recorded and transcribed. Inductive-deductive coding with code categorization was followed by thematic analysis. Themes were mapped against the ACGME competencies. Results: Interviews, with a median duration of 69 (IQR: 54-80) minutes,involved participants with 0.5-3 years of practice. Six (60%) interviewees worked in hospitals, while 2 (20%) were in outpatient and another 2 (20%) were in mixed settings. We identified 85 codes and categorized them into 10 educational experience themes. Interviewees attributed learning an evidence-based approach, basic oncology knowledge, and patient communication skills through the HSO curriculum. Themes associated with learning from online counseling included integration into real-world practice, specific types of expertise (rare tumors), and advanced communication (explaining differences in management, written communication), which are less covered by HSO. This activity enhanced skills in patient navigation, advocacy, and the ability to detect care not supported by evidence. Safe-space learning provided by ‘Just Ask’ was a unique theme reflecting the ability of trainees to hone skills in a secure and resource-enriched environment with easy access to mentoring. The association of the selected 'Just Ask'-related themes with ACGME competencies is presented in the table. Conclusions: Supervised online counseling has emerged as a valuable educational tool aimed at supplementing competencies insufficiently covered by medical oncology postgraduate curriculum. [Table: see text]

Influence of seawater mixing on fresh and mechanical properties of oil palm shell lightweight aggregate concrete containing spent garnet

Abstract The rapid advancement in construction practices has led to a higher demand for natural aggregates in the production of concrete. Simultaneously, there has been an increase in pollution due to the improper disposal of industrial waste. Effective waste management has become imperative to prevent environmental harm. A sustainable solution involves incorporating waste materials such as oil palm shells (OPS) from the palm oil industry and spent garnet from shipbuilding activities into concrete production. This research aims to explore the impact of introducing spent garnet as a partial substitute for sand on the fresh and hardened properties of lightweight aggregate concrete (LWAC) made with OPS, mixed with seawater. Two sets of concrete mixes were prepared, varying the proportion of spent garnet as a partial replacement for fine aggregate, and both seawater and freshwater were used in the mixing process. Laboratory tests were conducted to evaluate workability, oven-dry density, and compressive strength of the concrete. The findings indicate that the workability decreases in freshwater but improves in both freshwater and seawater when spent garnet is added. All mixtures containing spent garnet qualify as LWAC. Notably, seawater-mixed OPS LWAC, with 10 % spent garnet, demonstrates the highest compressive strength. The use of seawater accelerates hydration, enhancing concrete strength compared to freshwater. In conclusion, incorporating spent garnet as a replacement for sand in concrete production results in environmentally friendly OPS LWAC. This approach contributes to waste reduction and minimizes reliance on landfills, promoting sustainable construction practices.

Is being elite the same as living an easy life? Two distinct ways of experiencing subjective socioeconomic status.

Socioeconomic status (SES) predicts a large number of thoughts, feelings, and behaviors; here, we build on these findings to try to paint a comprehensive picture of what people who occupy different SES ranks are like. Existing findings attribute a mixed set of psychological patterns to people who consider themselves near the top of the socioeconomic hierarchy; these individuals are variously portrayed as selfish yet generous, entitled yet happy, and narcissistic yet tolerant. Building on previous efforts to characterize distinct dimensions of SES, we wondered whether there might be distinct but overlapping ways of experiencing one's status in the socioeconomic hierarchy, each linked to a different psychological profile, and each potentially corresponding to a different theoretical approach to the study of SES. We employed a bottom-up, participant-driven approach (total N = 3,338) to identify the thoughts, feelings, and behaviors that factor into people's subjective SES. Exploratory and confirmatory factor analyses showed that these experiences are best represented by two distinct dimensions-a sense that one belongs to a historical cultural elite (corresponding to SES as early life cultural context) and a sense that one's life is easy (corresponding, though less conclusively, to SES as current rank). We developed scales to measure each dimension and, using these scales, found that the two dimensions help categorize the known correlates of SES into two separate but internally coherent sets of psychological patterns-one magnanimous and one self-focused. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Leveraging the potential of the German operating room benchmarking initiative for planning: A ready-to-use surgical process data set

We present a freely available data set of surgical case mixes and surgery process duration distributions based on processed data from the German Operating Room Benchmarking initiative. This initiative collects surgical process data from over 320 German, Austrian, and Swiss hospitals. The data exhibits high levels of quantity, quality, standardization, and multi-dimensionality, making it especially valuable for operating room planning in Operations Research. We consider detailed steps of the perioperative process and group the data with respect to the hospital’s level of care, the surgery specialty, and the type of surgery patient. We compare case mixes for different subgroups and conclude that they differ significantly, demonstrating that it is necessary to test operating room planning methods in different settings, e.g., using data sets like ours. Further, we discuss limitations and future research directions. Finally, we encourage the extension and foundation of new operating room benchmarking initiatives and their usage for operating room planning.

The influence of educational reform on the self-perceived readiness to practice of newly registered pharmacists in Ireland

Abstract Introduction Newly registered pharmacists serve important roles in our healthcare system. Investigation of their readiness to practice confirms whether their education has furnished them with the knowledge, skills and competencies to be practice-ready. In 2015, a five-year integrated master’s programme in Pharmacy replaced a 4 + 1 model and included integrated learning with dispersed experiential learning placements. Factors known to influence self-perceived readiness to practice are prior experiential learning, education, placement environment and preceptor relationship. Aim This study examined the self-perceived readiness to practice of newly registered pharmacists in their first year of qualification and the influence of educational reform. Methods The participants were newly registered pharmacists who had completed the undergraduate Pharmacy programme within the State. Participants were identified using the Pharmaceutical Society of Ireland’s database of newly qualified registrants. An email was circulated to registrants inviting them to participate in a cross-sectional online survey consisting of 25 items. Variables were self-perceived abilities to perform practice-related competencies (on a five-point Likert scale), relationship with preceptor, placement experience, and demographic information. Three cohorts were surveyed. The first cohort (2020) were graduates from the last year of the 4 + 1 programme. The second (2021) and third (2022) cohorts were graduates from the new five-year integrated programme. All analyses were performed using SPSS 24.0 (SPSS Inc, Chicago, IL, USA) and Microsoft Excel. Results Of 458 registrants, 71 completed the survey (response rate=15%). A greater proportion of females (65%, n=46) than males (31%, n=22) responded to this survey, with 4% (n=3) not disclosing a gender. Most respondents (83%, n=59) had undertaken a community pharmacy placement, while others had either a hospital placement (11%, n=8) or a mixed setting placement (5%, n=4). Participants perceived that they were adept at performing practice-related competencies. Participants agreed that they developed fruitful relationships with preceptors and had positive experiences on placement; when asked about preceptor feedback, there was a positive response: 55% for (n=17), 83% for 2021 (n=15), and 95% for 2022 (n=21). A curriculum effect was observed, as graduates from the post-reform cohort declared readiness to practice in more areas than pre-reform graduates, e.g. in leadership: (58% for 2020 [n=18], 61% for 2021 [n=11], and 73% for 2022 [n=16]), communication (58% for 2020 [n=27], 61% for 2021 [n=16], and 73% for 2022 [n=21]), and recommending drug therapies (77% for 2020 [n=24], 72% for 2021 [n=13], and 95% for 2022 [n=21]). Conclusion This study found that most respondents self-perceived themselves as ready for autonomous practice. Whilst this study found that the pharmacists surveyed were ready to practice, they may still lack practical skills. This study suggests adopting an integrated curriculum that incorporates experiential learning is supportive of readiness to practice and has a positive impact on self-perceived readiness to practice, placement setting, and preceptor relationships. The repeated cross-sectional nature of the study is a strength, but a limitation is the low response rate and there may be response bias by participants. This research seeks to contribute to curriculum development to support students in their transition to professional practice.

X-ray Image Recognition of Pneumonia Based on Three Different Neural Networks

With the outbreak of new coronary pneumonia, a variety of pneumonia diseases are emerging, doctors often have misdiagnoses and omissions. To assist doctors in better clinical diagnosis, a variety of high-performance neural network structures have been applied to the X-ray recognition of pneumonia, and the X-ray image recognition accuracy will be improved, will effectively improve the efficiency of the hospital detection, at the same time, can also avoid the patient to miss the best rescue time. To this end, this paper compares and analyses the recognition performance of the three currently used neural network structures in the recognition of pneumonia X-ray images, uses some publicly available datasets and a mixed set composed of different datasets to conduct experiments, and summarises the advantages of the models and the direction of possible improvement. The three neural network models are presented, compared, and analysed to suggest useful references for the recognition of pneumonia X-ray images. Finally, an analysis and outlook for future neural network models for pneumonia x-ray image recognition is presented.

DFT investigation of Pt-doped CTF-1 covalent triazine frameworks for adsorption and sensing of SF6 decomposition products

We report a computational investigation of the gas adsorption and sensing properties of pristine covalent triazine framework CTF-1 and its platinum atom (Pt)-doped counterpart towards SF6 decomposition products (i.e., H2S, SO2, SOF2, and SO2F2 gases). For this purpose, density-functional theory calculations are employed with the TPSS exchange-correlation functional (with the Grimme-D3 empirical dispersion correction) and a 6-31+G**/Lanl2DZ mixed basis set. Pt was found to interact with a nitrogen atom, distorting the planar structure. Our results reveal that the electronic bandgap of the pristine CTF-1 lowers noticeably following surface doping with a Pt atom (3.48 vs. 1.06 eV for pristine vs. Pt-doped CTF-1, respectively), enhancing the electrical conductivity of the gas sensor. In contrast to pristine CTF-1, which displays weak adsorption performance (with adsorption energies of −0.25, −0.27, −0.31, and −0.18 eV for H2S, SO2, SOF2, and SO2F2, respectively), Pt-doped CTF-1 exhibits strong interactions with the gas species (with adsorption energies of −1.93, −2.41, −6.27, and −7.98 eV for H2S, SO2, SOF2, and SO2F2, respectively) and high sensitivity in detecting the target gas species under study. Moreover, a density of states (DOS) analysis is conducted to further characterize the mechanisms involved in gas adsorption and sensing. All four degradation products decrease the bandgap of pristine CTF-1 and increase the bandgap in Pt-doped CTF-1. The latter is generally more pronounced. We conclude that Pt-doped CTF-1 could be a novel SF6 decomposition gas adsorbent and sensor and provide experimentalists with the essential physicochemical characteristics of the designed materials for future work.

Depositional setting and trace fossil suites of the early Cambrian (series 2, stage 4) Khussak Formation, east-central salt range, north-west sub-Himalayas, Pakistan

This is the first integrated ichnologic-sedimentologic study of the Khussak Formation at the Salt Range, Pakistan. This integrated approach led to identifying thirteen lithofacies and six recurring facies associations. Facies association 1 (FA-1) is made up of massive to parallel laminated mudstone with rare interbeds of sandstone. FA-1 is moderately bioturbated with trace fossils belonging to the Cruziana ichnofacies (Cruziana, Planolites, Rusophycus). FA-1 is interpreted to record deposition in the upper intertidal mud flat setting. Facies association 2 (FA-2) consists of thinly interbedded sandstone (wavy and current rippled) and mudstone (massive to parallel-laminated). The heterolithic wavy bedding is the dominant bedding type. FA-2 is variably bioturbated (BI = 0–5). Trace fossils of moderate diversity of the Cruziana ichnofacies are present. FA-2 represents deposition in the middle intertidal mixed flat setting. Facies association 3 (FA-3) comprises gradational-based, laterally extensive, crudely bedded to massive, medium to lower fine-grained sandstone with thin (cm-scale) mudstone beds. FA-3 is intensely bioturbated (BI = 3–5). Robust and diverse trace fossils belonging to the Skolithos ichnofacies are present (Arenicolites, Cylindrichnus, Diplocraterion, Phycodes, Planolites, Siphonichnus, Skolithos, and Treptichnus). FA-3 records deposition in the lower intertidal sand flat setting. Facies association 4 (FA-4) consists of muddy to silty lower-upper fine-grained sandstone with rare mudstone drapes. FA-4 displays inclined heterolithic stratification (IHS). Tidal couplets (mud and mica drapes) and mud clasts are common. Bioturbation is absent to sparse (BI = 0–2) with low-diversity trace fossils of Skolithos ichnofacies (Cylindrichnus, Diplocraterion, Palaeophycus, Planolites, Skolithos, and rare fugichnia). FA-4 is interpreted to represent deposition in an intertidal creek point bar. Facies association 5 (FA-5) consists of well sorted sandstone (upper medium to fine-grained). The sandstone is erosionally based, and laterally extensive. Planar parallel lamination, trough cross-stratification, reactivation surfaces, and mud drapes are present only locally. The degree of biogenic reworking is low to moderate (BI = 0–4), and traces are sporadically distributed. Trace fossils present are of moderate diversity and abundance, including Arenicolites, Cylindrichnus, Diplocraterion, Monocraterion, Palaeophycus, Planolites, Siphonichnus, and Skolithos. FA-5 is interpreted as subtidal bar deposits. Facies association 6 (FA-6) is characterized by bioturbated muddy sandstone, heterolithic sandstone, and massive mudstone. Physical bedforms are commonly obliterated by bioturbation. Planar horizontal laminae, wavy laminae, small ripples, and synaeresis cracks may occur locally. FA-6 is moderate to intensely bioturbated (BI = 3–6). The distribution of traces (within beds and across the FA) is heterogeneous (sporadically) to homogeneous (locally). Robust Bergaueria, Cylindrichnus, Planolites, Rhizocorallium, Siphonichnus, Teichichnus and Zoophycos are present. FA-6 records deposition in the subtidal bay region. The tidal flat deposits of the Khussak Formation show a distinctive ichnofacies trend. The Cruziana ichnofacies characterizes the lower energy proximal deposits (intertidal mixed and mud flat). In contrast, the high-energy distal deposits (lower intertidal sand flat and shallow subtidal) are characterized by Skolithos ichnofacies. The ichnologic variation between the mud flat, mixed flat, sand flat, and subtidal deposits are gradational, but they can be used as facies boundary indicators. The bioturbation intensity and diversity of trace fossils within the lower Cambrian (Series 2, Stage 4) tidal flat deposits of the Khussak Formation suggest elevated levels of bioturbation intensity during stable environmental conditions in the Cambrian. Furthermore, it implies that the Agronomic Revolution was already emplaced in lower Cambrian (Series 2, Stage 4) tidal flat settings.

“I’m a bit out of place here.”—Preservice teachers’ positioning in the figured world of university mathematics

Preservice mathematics teachers are sometimes trained in programs so that they have both their own courses and joint courses with mathematics majors from the beginning of their studies. While this is thought to provide them with both deep mathematical knowledge and teaching-specific content right from the start, many of them report disaffection with and disengagement from mathematics during their very first semester. Current approaches often frame this from an individual’s perspective, investigating cognitive and affective individual differences among students. In contrast, we aim to understand this issue from a sociocultural perspective, examining underlying social processes. In this study, we thus explored preservice teachers’ experiences of the mathematics component of their training, using Holland and colleagues’ theory of figured worlds as a theoretical lens. Three group interviews with 14 preservice higher secondary teachers in a common mixed setting in Germany (one course specific to preservice teachers, one general mathematics course together with major students) were analyzed. Our findings displayed that the preservice teachers experienced two dichotomous figured worlds of mathematics and mathematics teaching. Our further analysis of their positioning between these worlds provided new insights to explain their disaffection, disengagement, and consequent learning behavior. We discuss practical implications, focusing on different teaching systems and interventions in teacher education.

Syntheses, Crystal Structure and Theoretical Properties of Schiff Bases Obtained from Isoniazid and Pyridine‐2‐, 3‐, 4‐Carboxaldehyde and Their Zinc(II) Complexes

AbstractThe series of Schiff base ligands, N’‐(pyridine‐2‐ylmethylene)isonicotinohydrazide (I), N’‐(pyridine‐3‐ylmethylene)isonicotinohydrazide (II) and N’‐(pyridine‐4‐ylmethylene)isonicotinohydrazide (III), and the Zn(II) complexes of the ligands (I and II), [N’‐(pyridine‐2‐ylmethylene)isonicotinohydrazide]‐trichlorozincoate (IV) and [N’‐(Pyridin‐3‐ylmethylene) isonicotinohydrazide] trichlorozincoate (V) have been newly synthesized and their structures have been characterized by 1H, 13C NMR and FT‐IR spectroscopies, elemental analysis and single crystal X‐ray diffraction technique. The Hirshfeld surface analyses have been confirmed the importance of H‐atom contacts in establishing the packings. The ground state equilibrium geometries of the synthesized compounds are optimized at DFT/B3LYP/6‐311++G (d, p) and HF/6‐311++G (d, p) levels of theory (for the ligands) and B3LYP/GENECP method is used as the mixed basis sets of LANL2DZ (for Zn) and 6–311G (for other atoms) in the gas‐phase. The structural parameters obtained from the theoretical studies of the compounds are compared with their experimental ones. Their linear and nonlinear optical (NLO) properties are also calculated by the same methods. In the ground state, the energy band gaps (Eg) of the Schiff bases (I, II and III) have been calculated as 4.38 eV, 4.36 eV and 4.46 eV (at B3LYP) and 9.94 eV, 9.81 eV and 10.20 eV (at HF), respectively.

Mechanical characteristics of waste-printed circuit board-reinforced concrete with silica fume and prediction modelling using ANN.

The use of electronic waste in cement concrete as a fibre additive has proven to be very promising for improving mechanical characteristics and developing sustainable construction materials to reduce the waste dumped in landfills. The following study investigated the effect of electronic waste (printed circuit boards (PCBs)) on the mechanical properties of concrete and predicted the same properties with an appropriate machine learning technique. PCB fibres 45mm in length and 1.5mm in width were manufactured and added as fibre additions to two sets of concrete mixes with and without silica fume. A 10% volume replacement of cement was substituted with silica fume (SF) to enhance the characteristics of PCB fibre-reinforced concrete and minimize cement consumption. The study included an evaluation of the fresh properties and mechanical characteristics after a 28-day curing period; thereafter, the results were compared and studied using the Levenberg-Marquardt backpropagation algorithm for predictions. The results show that the mechanical properties improved up to a 5% addition of PCB fibres, resulting in strengths of 63.55MPa and 69.92MPa for mixtures of PCB5% and SFPCB5%, respectively. A similar trend was achieved for other properties, such as the tensile and flexural strengths. The results of the ANN model predicted values with R2 values ranging from 0.94 to 0.99, indicating the efficacy of the model.

The acceptability and feasibility of a cognitive behavioural therapy managing emotions group on a male psychiatric intensive care unit: a mixed methods evaluation

Research suggests that the provision of psychological interventions within acute inpatient settings is effective and important for improving clinical outcomes. There remains limited research on the application of such interventions for individuals admitted to psychiatric intensive care units (PICU), and notably none for male-only PICU environments specifically. Two published protocols have been evaluated in female and mixed gender PICU settings, one a cognitive behavioural therapy (CBT) anger management group and one dialectical behaviour therapy (DBT) skills group. No studies have reported on a general CBT-based group in a male PICU. This study aimed to assess the acceptability and feasibility of a CBT-based managing emotions skills group, delivered in brief 30-minute sessions, within a 7-bed male PICU. All individuals admitted to the PICU during the study period were invited to attend a five-session group. A mixed methods design was used incorporating measuring attendance and retention, a pre–post quasi-experimental design and a qualitative component. Pre- and post-group self-report outcome measures of calmness, anxiety, recovery, and qualitative feedback were gathered and analysed. Pre- and post-outcome measures demonstrate significant improvements in calmness and reduction in anxiety following attendance. Attendance, retention and qualitative feedback data suggests that the group was acceptable and feasible for the participants. Initial findings suggest that adapted CBT skills group-based interventions tailored to the male PICU population are acceptable, feasible, and beneficial to participants. The clinical and research implications are discussed.