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The purpose of this study was to understand how middle school teachers cope with trauma. Case studies were employed with four middle school teachers. The findings from this study illuminated the unique experiences of rural middle school teachers. These findings highlight significant gaps in how teachers are supported and respond to stress and trauma. However, it also highlights the resiliency of educators in response to significant stresses and trauma. The findings suggest that if appropriate steps are taken to address teacher trauma there can be significant improvement in the overall function of the classroom and school.

Some state legislatures have introduced a rash of bills designed to control how K-12 teachers discuss so-called 'divisive issues,' such as racism, sexism, and privilege. This legislation has prompted substantial news coverage on the impact of these laws. Sidelined in this discourse are the perspectives of teachers. This mixed methods study seeks to understand the impact this legislation may have on teachers. We identified three themes salient to how teachers as 'gatekeepers' (Thornton, 1991) anticipate these legislative efforts influencing their practice: curricular autonomy, context, and institutional guidance. The paper concludes with the significance of these findings, including potential policy implications at the national, state, and local levels.

Forty-four states have recently taken formal policy steps to limit how teachers can teach about so-called "divisive issues." This paper examines teachers' perceptions of what they need to be prepared to teach in states where "divisive issues" legislation exists. Teacher interview data where restrictive legislation exists were used to explore what inservice teachers need to teach within the constraints of states' divisive issues legislation, and what teacher preparation programs should focus on when preparing new teachers to do the same. Analysis revealed, among other things, that all feel they need clear guidance from their district, and better training.

Abstract Transparent wood with high transmittance and versatility has attracted great attention as an energy-saving building material. Many studies have focused on luminescent transparent wood, while the research on organic afterglow transparent wood is an interesting combination. Here, we use luminescent difluoroboron β-diketonate (BF2bdk) compounds, methyl methacrylate (MMA), delignified wood, and initiators to prepare room-temperature phosphorescent transparent wood by thermal initiation polymerization. The resultant PMMA has been found to interact with BF2bdk via dipole-dipole interactions and consequently enhance the intersystem crossing of BF2bdk excited states. The transparent wood matrix can provide a rigid environment for BF2bdk triplets and serve as oxygen barrier to suppress non-radiative decay and oxygen quenching. The prepared afterglow material has the characteristics of diverse composition, long afterglow emission lifetimes, and high photoluminescence quantum yield. This afterglow transparent wood also demonstrates potential application value in areas such as high mechanical strength, good hydrophobicity, and high cost-effectiveness.

Abstract Geothermal energy is a sustainable resource for power generation, particularly in Yemen. Efficient utilization necessitates accurate forecasting of subsurface temperatures, which is challenging with conventional methods. This research leverages machine learning (ML) to optimize geothermal temperature forecasting in Yemen’s western region. The data set, collected from 108 geothermal wells, was divided into two sets: set 1 with 1402 data points and set 2 with 995 data points. Feature engineering prepared the data for model training. We evaluated a suite of machine learning regression models, from simple linear regression (SLR) to multi-layer perceptron (MLP). Hyperparameter tuning using Bayesian optimization (BO) was selected as the optimization process to boost model accuracy and performance. The MLP model outperformed others, achieving high $$\text {R}^{2}$$ R 2 values and low error values across all metrics after BO. Specifically, MLP achieved $$\text {R}^{2}$$ R 2 of 0.999, with MAE of 0.218, RMSE of 0.285, RAE of 4.071%, and RRSE of 4.011%. BO significantly upgraded the Gaussian process model, achieving an $$\text {R}^{2}$$ R 2 of 0.996, a minimum MAE of 0.283, RMSE of 0.575, RAE of 5.453%, and RRSE of 8.717%. The models demonstrated robust generalization capabilities with high $$\text {R}^{2}$$ R 2 values and low error metrics (MAE and RMSE) across all sets. This study highlights the potential of enhanced ML techniques and the novel BO in optimizing geothermal energy resource exploitation, contributing significantly to renewable energy research and development.

Abstract The current study aimed to evaluate the accuracy of Willems, Cameriere’s and Greulich and Pyle method in age estimation among a sample of Egyptian children aged 8–16 years based on analysis of 140 panoramic dental X-ray and hand-wrist radiographs (70 girls and 70 boys). Using Willems method, the mean dental age underestimated chronological age by (0.20 ± 0.91 years) for boys and by (0.24 ± 1.33 year) for girls; these differences were not statistically significant. Also, Cameriere’s method underestimated chronological age in both sexes by mean difference of (1.10 ± 1.22 year) in boys and (1.13 ± 1.31 year) in girls; these differences were statistically significant. Regarding Greulich and Pyle atlas, the mean of skeletal age was overestimated when compared to the real age in boys with a mean difference (0.04 ± 0.86 year). In girls; the age was underestimated by a mean difference of (0.15 ± 1.32 years) when compared to chronological age; these differences were not statistically significant. Comparing the two dental methods, the mean absolute error using Willems method was less than one year (0.94 and 0.96 years) in boys and girls respectively, while in Cameriere’s method, it was more than one year in both groups. Moreover; the mean absolute error using Greulich and Pyle atlas was less than two years (1.02 and 1.38 years) in boys and girls respectively. Since Willems and the atlas methods were more accurate, the combination of both of them could be used for age estimation in the Egyptian population.

Spinal cord injury (SCI) is a debilitating condition of the nervous system that imposes considerable challenges for subjects, such as bladder and bowel incontinence and infections. The standard therapeutic strategy is methylprednisolone utilization accompanied by surgical decompression. However, achieving an effective therapy with the minimum side effects for SCI is still a puzzle. Nowadays, mesenchymal stem cell (MSC) therapy has received much consideration in scientific communities in light of its pharmacological and therapeutic properties, for instance, anti-inflammatory, regenerative, analgesic, and immunomodulatory influences. Despite the mentioned advantages for MSCs, their tumorigenic potential is a limiting agent for its wide therapeutic application. Recent documents show that the use of conditioned medium (CM) derived from MSCs can largely solve these problems. CM encompasses neuroprotective growth factors and cytokines, such as stem cell factor (SCF), vascular endothelial growth factor (VEGF), and glial cell line-derived neurotrophic factor (GDNF). The persuasive evidence from experimental studies revealed that CM originating from MSCs can have a considerable role in the amelioration of SCI. Hence, in the current papers, we will review and summarize evidence indicating the anti-SCI mechanisms of MSC-derived CM by relying the current experimental data.

Abstract This study aimed to evaluate the performance of a Parabolic Shaped Solar Dryer (PSSD) developed at Nigerian Stored Products Research Institute (NSPRI) Kano for drying Tomatoes compared to traditional Open Sun Drying methods. During the experiment, 139.3 kg of Tomato slices of Eka variety with 5 mm thickness were dried in the Parabolic Shaped Solar Dryer. The drying rate, physiochemical properties, functional properties, and bacteria count were determined. The result showed that Parabolic Shaped Solar Dryer recorded a maximum temperature of 68.2 °C, and a drying rate of 5.2 kg/day leading to a moisture content of 14.5% within the 6 days drying period. In contrast, Open Sun Drying resulted in a slightly higher moisture content of 17.8%, a lower drying rate of 1.3 kg/day, and a maximum temperature of 47.5 °C. The result also indicates an increase in Titratable acidity while Vitamin C retention significantly declines in both drying systems. A decrease in pH and Total Soluble solids was also observed. Oil Absorption capacity was observed to be 10% higher in a Parabolic Shaped Solar Dryer than in Open Sun Drying while Water Absorption capacity was also highest in samples dried in Parabolic Shaped Solar Dryer. Compared to Open Sun drying; Parabolic Shaped Solar dried tomatoes showed a much better nutrient retention for all quality parameters.

Cette étude examine l'influence du soutien social et de l'estime de soi sur la résilience des jeunes filles séropositives au VIH/SIDA, enceintes ou mères. Elle a impliqué dix (10) adolescentes âgées de 15 à 19 ans, suivies à la maternité de l’Hôpital Sœurs de la Providence de Kouvé. Les enquêtées ont été évaluées avec l’échelle d’estime de soi de Rosenberg, le questionnaire de soutien social de Sarason, et la boîte à mémoire. Les données ont été analysées via SPSS 21, Excel et analyse de contenu. Des scores élevés en soutien social et estime de soi sont associés à une meilleure résilience.

Transporting cattle in tropical climates substantially impacts oxidative stability and meat quality due to increased stress levels. The objective of this research is to assess the impacts of road transport with two different distances and three stocking densities on meat quality indices and malondialdehyde (MDA) levels in the infraspinatus (IF) and semitendinosus muscles of Brahman crossbred heifers in a tropical climate. Sixty Brahman crossbred heifers were exposed to two different road travel distances: 450 and 850 km. Each travel distance was divided into three different stocking densities: low density of 200 kg/m2, medium density of 400 kg/m2 and high density of 600 kg/m2. The number of animals in each stocking density was kept equal. Following transit, IF and semitendinosus muscle samples were collected and assessed for meat quality characteristics, including colour attributes, pH, muscle glycogen contents, Warner-Bratzler shear force (WBSF), cooking loss and lipid oxidation. The findings showed notable impacts of livestock densities and road transport distances on meat quality parameters and MDA levels in Brahman crossbred heifers’ muscles. Similarly, increased livestock densities resulted in higher levels of MDA and alterations in meat quality measures than lower densities following both distances of road transportation. These findings highlight the significance of optimising livestock density and reducing road transport lengths to maintain the oxidative stability of meat quality in heifers.

For many years, Western researchers have extensively studied the history, language, culture, religion, and mysticism of the Kurds for specific objectives. These works are elevated to the status of sacred texts by Kurdish researchers. Their hesitancy to form critical opinions and accurately cite quotes from these texts stems from a lack of courage, conviction, and self-esteem leading them to rely unquestioningly on the translated content. An illustration of this is “Agha, Shaikh, and State, a book by Professor Martin van Bruinessen, this piece certainly stands out as one of the most crucial publications in the realm of Kurdish studies, serving as an enduring reference for Kurdish researchers to this day. A chapter of this book is specifically focused on exploring Kurdish mysticism and Tariqa. This chapter requires a thorough revision and analysis grounded in mysticism, a branch of Islamic studies and theology. As we observe, analyze, and validate, this impact is subject to criticism from a pathological perspective, and there is a necessity to rectify their viewpoints.The author has incorporated this chapter into his sociological research as a report, leaning on the statements of unidentified individuals and unspecified sources. Furthermore, his viewpoints contain numerous scientific inaccuracies, writing prejudices, and broad generalizations. This results from his research conducted solely from a sociological standpoint, lacking comprehensive and accurate information about mysticism and relying on inadequate works previously done by Western orientalists. Consequently, this has led to a decline in the scientific and historical credibility of the work.

Abstract Organic phosphorescent materials have great prospects for application, whose performance particularly depends on the preparation method. Inspired by nature’s wisdom, we report a phosphor that can utilize monomers in its environment by polymerization to construct a rigid microenvironment under light illumination, leading to a glow-in-the-dark emulsion with a phosphorescence lifetime of 1 s in water. This phosphor can achieve active growth of the aqueous emulsion with the introduction of more monomers. In the presence of trace amounts of oxygen (which has adverse effects on both polymerization and afterglow), this phosphor can still undergo photo-induced polymerization, removing the influence of oxygen and obtaining afterglow emulsion, demonstrating its adaptability to the environment. This phosphor can also catalyze the polymerization of monomers containing yellow fluorophore, obtaining long-lifetime yellow afterglow emulsion through excited state energy transfer. We have also conducted in-depth studies on the photo-catalytic and phosphorescent properties of this phosphor in model systems. This biomimetic intelligent manufacturing provides a new approach for organic phosphorescent materials and is significant for future applications.

Objective The livestock handler attitude and their handling of animals is crucial for improving animal welfare standards, minimizing stress, improving productivity and meat quality. The present study was undertaken to assess the effect of training livestock handlers on behavioral, physiological, and hormonal responses during preslaughter handling in goats. Methods A total of 6 handlers were divided into trained (trained in basic animal handling practices, animal behavior, and animal welfare), contact trained (not trained directly but interacted and saw the working of trained handlers), and untrained groups (no formal training). The handling experiment was conducted on 18 male goats by following a cross-over design. The goats were moved from lairage to slaughter point by trained, contact-trained, and untrained handlers. Various behavioral, physiological, and hormonal parameters were recorded at the lairage before handling and at the slaughter point after handling the goats. Results The training of livestock handlers had a significant effect on behavioral, physiological, and hormonal responses in goats. The goats handled by untrained and contact-trained handlers were recorded with intense vocalization, significant (p

Accumulated evidence suggests a tangible increase in breast cancer (BC) patients in the Kurdistan region of Iraq, yet data on their histological and molecular subtypes remain limited. This study aims to assess the prevalence, histopathologic features, and molecular profiles of BC incidences in Erbil. For this purpose, 261 clinical records of histologically confirmed BC cases from Rizgary Teaching Hospital are analyzed. Results reveal that invasive ductal carcinoma (IDC) emerged as the most frequent histological subtype. In terms of hormonal receptor status, mastectomy patients who tested positive for estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) are 52.49%, 50.57%, and 18.01%, respectively, and the corresponding figures for Tru-Cut biopsy (TCB) patients are 24.90% for both ER and PR and 6.13% for HER2. Regarding tumor grading, mastectomy patients aged 51–60 have the highest grade II frequency, and TCB patients under 40 years and those between 41 and 50 years show the highest occurrence of the same grade. Moreover, tumor stages II and III made up nearly two-thirds of all cases across all age groups. For the molecular subtypes, luminal A is the most prevalent in both mastectomy and TCB patients. In conclusion, IDC is the predominant BC subtype in the region, with a higher prevalence of ER and PR positivity compared to HER2. Luminal A is the dominant molecular subtype. While stages II and III are common across all ages, tumors of grade II and stage II are frequently observed in older ages.

This study was aimed to investigate data homogeneity, detect trend and changes in historical rainfall and forecast future rainfall pattern. Rainfall historical records taken from 22 gauging and non-gauging stations distributed all over Duhok governorate. The dataset includes long time series of monthly and annual rainfall totals (mm) from 1998 to 2020. To detect data variability, four different homogeneity tests were used. To analyze trends and sequential shifts in historical data, parametric and non-parametric tests were applied. Theil-Sen's slope estimator test was applied to calculate the magnitude of change over time. Rainfall forecasting was based on the Box-Jenkins methodology. The homogeneity test results revealed that the majority of the monthly and annual rainfall series were labeled as useful. Furthermore, the annual rainfall at most of the study stations presented positive trend with sen’s slope ranged between 0.545 -43.03 mm yr-1. At five stations tended to beyond the upper limit of the CUSUM chart during 2019 and 2020. Conversely, it tended to be below the lower limit at 3 stations during the period from 2000 t0 2002. ARIMA (0, 1,1) was the best model for predicting yearly rainfall for 75% of the stations, while the model (1,1,1)(1, 0, 1)12 was the best suited model for predicting and forecasting monthly rainfall for more than 83% of the selected stations.

Indonesia, one of the most earthquake-prone countries in the world, is currently developing an Earthquake Early Warning (EEW) system. A key component of this system, the Regional EEW, relies on Ground Motion Prediction models (GMPMs) to issue end-user alerts. However, in West Java, one of the pilot regions for this project, there is a lack of region-specific GMPMs essential for accurate early warnings. Traditionally, GMPMs are developed using linear regression based on complex, predefined mathematical equations and coefficients. However, Machine learning offers the advantages of bypassing the need for predefined equations and effectively capturing the nonlinear behavior present in ground motion data. To address this gap, we evaluated three machine learning algorithms (i.e. Artificial Neural Network [ANN], Gradient Boosting [GB], and Random Forest [RF]) to develop GMPMs for three tectonic categories: shallow-crustal, interface, and intraslab. These models were used to predict Peak Ground Acceleration (PGA) in West Java, utilizing 3116 strong ground motion records from 365 earthquakes with moment magnitude ranging from 2.4 to 7 and epicentral distance between 5.5 and 867 km, recorded since 2010. Our results show that The Gradient Boosting model outperformed the others across all three tectonic categories, with the lowest Mean Squared Error values (0.94, 0.60, 0.65), and Standard Deviation of Residuals (0.97, 0.77, 0.80), as well as the highest Pearson correlation coefficient-value (0.83, 0.88, 0.90) for shallow-crustal, interface, and intraslab events, respectively, demonstrating strong accuracy in predicting PGA. The model was further validated with recent earthquake data and from 2024 showing good agreement and confirming its robustness. Epicentral Distance and Moment Magnitude were the most influential in predicting PGA among the six explanatory variables used in this study. These findings highlight the potential of machine learning models to improve the accuracy of ground-shaking predictions, contributing to the success of Indonesia's Earthquake Early Warning System (EEWS).

Abstract This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development, extending the analysis up to 2024. It focuses on artificial intelligence's transformative role in the geothermal industry, analyzing recent literature from Scopus and Google Scholar to identify emerging trends, challenges, and future opportunities. The results reveal a marked increase in artificial intelligence (AI) applications, particularly in reservoir engineering, with significant advancements observed post‐2019. This study highlights AI's potential in enhancing drilling and exploration, emphasizing the integration of detailed case studies and practical applications. It also underscores the importance of ongoing research and tailored AI applications, in light of the rapid technological advancements and future trends in the field.

Globally, the textile industry plays a significant role in production but faces many challenges that affect its operation and sustainability. Product quality remains an essential concern despite technological advancements and quality control measures. Raw materials, production methods, and skills differ, resulting in customer dissatisfaction and damage to reputation. A quality management strategy is necessary to overcome these problems and promote quality improvement. This includes the implementation of the Lean Six Sigma approach. This article focuses on improving textile quality by developing the Six Sigma DMAIC method. An analysis of three months' data from a textile company shows that an increase in the Outgoing Quality Level (OQL) from 3% to 6% resulted in significant economic losses, amounting to PKR-240,000 due to rework, residual costs, customer returns, and lost sales time. By utilizing Six Sigma tools, the company reduced OQL from 6% to 4%, thus lowering product losses and increasing operational efficiency. As a result of these findings, the textile industry can improve quality and profitability through a Lean Six Sigma approach.

The present study evaluated the effect of training to livestock handlers on electroencephalogram (EEG) and plasma enzymes levels during preslaughter handling in goats. Six handlers were divided into three groups viz., trained (T-trained in basic animal handling practices, animal behavior, and animal welfare principles), contact trained (CT-not trained directly but interacted and saw the working of trained handlers), and untrained (UT-no formal training). Eighteen Boer cross bucks were used in the study by following a cross-over design and electroencephalogram spectrum, creatine kinase (CK), and lactate dehydrogenase (LDH) concentrations were recorded before handling (in lairage) and after handling (at slaughter point). The training of livestock handlers had a significant (p0.05) change was recorded in the alpha, beta, delta, theta, and gamma bands activity. The goats handled by trained handlers exhibited significantly (p

This study aimed to evaluate the effects of rumen-protected fat (RPF) on in vitro fermentation profiles and biohydrogenation of fatty acids. The treatment diets were basal diet (70:30 concentrate to rice straw) with no RPF (CON), basal diet plus prilled fat (PF), basal diet plus prilled fat with lecithin (PFL) and basal diet plus calcium soap of palm fatty acids (CaS). In vitro gas production, fermentation kinetics, in vitro dry matter digestibility (IVDMD), in vitro organic matter digestibility (IVOMD), metabolizable energy (ME), rumen fermentation and fatty acid profile were determined. The results show that RPF did not affect cumulative gas production and gas production kinetics. PFL significantly (p < 0.05) improved IVDMD and IVOMD, although the addition of RPF did not affect ME. The volatile fatty acid (VFA), pH, ammonia nitrogen, methane, and molar proportion of VFA were not significantly influenced by the RPF; methane was numerically reduced because of PFL treatment. The concentration of monounsaturated and polyunsaturated fatty acids increased (p < 0.05) whereas that of saturated fatty acids decreased in the control diet. The biohydrogenation of C18:2n-6 and C18 unsaturated fatty acids was enhanced (p < 0.05) by PFL. These findings suggest that PFL enhances gas production, decreases methane and increases the biohydrogenation of C18:2n6 without disrupting rumen fermentation.

Chronic kidney disease (CKD), can alter thyroid hormone production, distribution, and excretion. Thyroid dysfunctions like hypothyroidism, hyperthyroidism, and euthyroidism are identified in renal failure patients. This study aimed to determine thyroid dysfunction prevalence among CKD patients who attended Diagnostic Laboratory Services in Bulawayo from January to December 2023. The study was analytical cross-sectional and archival data collection methods were to gather information from 118 CKD patients who underwent both thyroid function tests and urea and electrolytes tests. A descriptive and analytical statistic test was used to determine the prevalence of thyroid dysfunction among CKD patients Chi square test was used to test association between thyroid’s dysfunction in CKD setting with various risk factors, P< 0.05 was considered as statistically significant. Thyroid dysfunction (TD) prevalence was 53%, increasing with CKD severity, with stage 3B having a high prevalence of 37%. Major types include subclinical hypothyroidism (11%), overt hypothyroidism (16%), and subclinical hyperthyroidism (4.2%), with 36% of females presenting with thyroid dysfunction as compared to 31% of males. Prevalence of thyroid dysfunction increased with an increase in age Majors risk factor for CKD in TD setting were hypertension, diabetes, and heart failure. In conclusion, there was a high prevalence of thyroid dysfunction (53%) among chronic kidney disease patients.

Room-temperature phosphorescence and organic afterglow materials exhibit significant applications in diverse fields. Among them, aqueous-phase organic afterglow materials display interesting biomedical and other applications, whereas afterglow material fabrication in aqueous medium remains less explored when compared to those in solid states. In view of the excellent afterglow performance in difluoroboron β-diketonate containing systems, here we report supramolecular inclusion complexation of γ-cyclodextrin (γCD) with difluoroboron β-diketonate (BF2bdk) phosphors for devising aqueous-phase room-temperature afterglow crystalline micro/nanostructures. The γCD-BF2bdk host-guest supramolecular interactions induce crystallization of the two-component system into micro/nanostructures where BF2bdk’s triplet excited states can be well protected from nonradiative decay and oxygen quenching, leading to the emergence of aqueous-phase afterglow with phosphorescence lifetimes around 1 s and high photoluminescence quantum yields (PLQY) > 50 %. The aqueous afterglow materials possess visible-light-excitable property and can serve as donor of energy transfer for constructing long-wavelength and color-tunable afterglow systems. Their potential applications for bioimaging were further demonstrated. The present study provides a simple method but a new avenue for the preparation of high-performance aqueous-phase afterglow materials.

MXenes are currently a research hotspot in the field of 2D materials, hinting to revolutionize material technology. Their layered architecture allows for molecular intercalation, defect engineering, and surface band gap functionalization, with applications as diverse as energy storage and drinking water desalination. Its structural and functional integrity has prompted the scientific community to investigate novel compositions in an effort to leverage electrochemical activity, mechanical robustness, flexibility and environmental stability. However, the current synthesis routes present a bottleneck in proposing MXenes as a sustainable material for the future. Therefore, by expanding the reach of synthetic chemistry towards efficient strategies for green production, we present the first comprehensive introspection of the use of green solvents and their impact on material properties during MXene synthesis. This review is an attempt to quantify the intriguing characteristics of MXene nanocomposites by embracing design tools like the ‘iceberg model’. To further evaluate the performance of MXenes fabricated using green strategies (such as eutectic etching) we have made an attempt to critically compare them with conventional MXenes by examining surface characteristics, electrochemical analysis, charge transfer mechanisms etc. Conclusively, we aim to instigate concern about the environmental impact of MXene synthesis and instil a multidisciplinary approach to tailor environmentally benign, scalable and efficient MXene derivatives for commercial energy applications. The review provides an immersive account linking UN sustainable development goals with the industrial outlook of green MXenes, it highlights their impact on climate change, potential to build technically advanced economies, low cost production and range of applications.

Pipelines are crucial for transporting energy sources, yet corrosion especially stress corrosion cracking (SCC) poses a complex and potentially catastrophic form of material degradation. Traditional techniques like finite element analysis (FEA) have been utilized for SCC prediction, but it suffers from high computational cost and limited scalability. Deep learning (DL) with integration of FEA leverages large-scale data and learn complex nonlinear patterns for SCC prediction. Currently, literature on deep learning-enabled finite element analysis for pipelines SCC prediction is scarce, offering limited insights into this emerging approach and lack of comprehensive review. This paper reviews and investigates the current research directions and applications of DL-enabled FEA methodologies for simulation of SCC prediction. The importance of DL, technique type and network are also outlined in this review paper. This paper delves into integration of DL algorithms with FEA and their ability to grab complex interactions between mechanical stress, material properties, and environmental factors. Based on this comprehensive review, it was found that DL and FEA have proven to be strong prediction tools with high accuracy and lower training cost. DL-enabled FEA techniques are also being utilized to replace time-consuming methods and conventional codes. Furthermore, article discusses potential of this integrated approach for enhancing accuracy and efficiency of SCC prediction, leading to improved pipeline integrity management practices.Abbreviations: SCC: Stress corrosion cracking; FE: Finite element; FEA: Finite element analysis; FEM: Finite element method; ML: Machine learning; DL: Deep learning; EGIG: European gas pipeline incident data group; PHMSA: Pipeline and hazardous materials safety administration; DNNs: Deep neural networks; HpHSCC: High pH stress corrosion cracking; SMYS: Specified minimum yield strength; HE: Hydrogen embrittlement; NNpHSCC: Near-neutral pH stress corrosion cracking; ASME: American society of mechanical engineers; DNV: Det norske veritas; AI-FEM: Advanced iterative finite element method; CGR: Crack growth rate; ME: Mechanoelectrochemical; XGB: XGBoost; CAT: Catboost; CP: Cathodic Protection; AI: Artificial intelligence; ANNs: Artificial neural networks; CNNs: Convolutional neural networks; RNNs: Recurrent neural networks; ReLU: Rectified linear unit

T lymphocytes expressing CD57 and lacking costimulatory receptors CD27/CD28 have been reported to accumulate with aging, chronic infection, and cancer. These cells are described as senescent, with inability to proliferate but enhanced cytolytic and cytokine-producing capacity. However, robust functional studies on these cells taken directly from cancer patients are lacking. We isolated these T cells and their CD27/28+ counterparts from blood and tumor samples of 50 patients with previously untreated head and neck cancer. Functional studies confirmed that these cells have enhanced ability to degranulate and produce IFN-γ. They also retain the ability to proliferate, thus are not senescent. These data suggest that CD27/28-CD57+ CD8+ T cells are a subset of highly differentiated, CD45RA+ effector memory (TEMRA) cells with retained proliferative capacity. Patients with > 34% of these cells among CD8+ T cells in the blood had a higher rate of locoregional disease relapse, suggesting these cells may have prognostic significance.

IntroductionEchinococcosis is a widespread zoonotic disease caused by tapeworms of the Echinococcus genus, manifesting in mature or larval forms. Cystic echinococcosis (CE) and alveolar echinococcosis (AE) are the primary types affecting humans, linked, respectively, to Echinococcus granulosus and Echinococcus multilocularis. This study is a systematic review and meta-analysis of the risk factors associated with CE and AE in humans.MethodsRelevant English publications were found through a thorough search of eligible databases. The inclusion criteria focused on cross-sectional and case–control studies investigating risk factors for human echinococcosis. Collected data included author, country, study design, demographics, sample size, literacy, occupation, drinking water source, dog ownership, and hand hygiene.ResultsA total of 1,594 studies were found in the initial search, with only 36 papers (involving 1,207,436 cases) meeting the inclusion criteria. Most of the study population (99.35%) showed no echinococcosis infection, while 0.65% were infected. Of the infected cases, 77.92% had CE, while 22.08% had AE. Among 629,996 (52.18%) females, 4,830 (0.76%) were infected, compared to 2,968 (0.52%) infections among 565,872 (46.86%) males (p