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Aim: Insulin has been reported to have positive effects on intestinal adaptation after short bowel syndrome when applicated oral or subcutaneously. The purpose of this study is to compare the intestinal adaptation effects of subcutaneous and oral routes of insulin in rats with short bowel syndrome. Materials and Methods: The short bowel syndrome (SBS) was performed through 70-75% of small intestinal resection and an end-to-end anastomosis. The control group rats underwent SBS only. In the second group, oral insulin (1 U/ml) was administrated twice-daily. In the last group, the insulin was administrated subcutaneously (1 U/kg) as in the control group. All rats were killed on day 15. Outcome parameters were weight of small intestine, the crypt length, villous depth, the blood levels of vascular endothelial growth factor (VEGF), and granolocyt-monocyst colony-stimulating factor (GMCSF). Results: Intestinal weight was significantly more in oral insulin group and subcutaneous insulin group than in the control group (72.6 ± 4.3, 78.6 ± 4.8 and 59.7 ± 4.8) (P < 0.05). There was no difference between the groups according to villus length, crypt depth, and villous/crypt ratio both in proximal and distal parts of the resected bowel (P > 0.05). VEGF values were not statistically significant between the groups (200.3 ± 41.6, 178.9 ± 30.7 and 184.3 ± 52.2) (P > 0.05). GMCSF was statistically higher in the control group than in other groups (3.34 ± 1.34, 1.56 ± 0.44 and 1.56 ± 0.44) (P < 0.05). Conclusion: Insulin has positive effects on intestinal adaptation in short bowel syndrome. Subcutaneous administration is slightly more effective than the oral route.

Abstract Predicting evaporation is an essential topic in water resources management. It is critical to plan irrigation schedules, optimize hydropower production, and accurately calculate the overall water balance. Thus, researchers have developed many prediction models for predicting evaporation. Despite the development of these models, there are still unresolved challenges. These challenges include selecting the most important input parameters, handling nonstationary data, extracting critical information from data, and quantifying the uncertainty of predicted values. Thus, the main aim of this study is to address these challenges by developing a new prediction model. The new prediction model, named Gated Recurrent Unit–Multi-Kernel Extreme Learning Machine (MKELM)–Gaussian Process Regression (GPR), was used to predict one-month ahead evaporation in the Kashafrood basin, Iran. This model was executed in multiple stages. First, a feature selection algorithm was used to determine the most critical input parameters. A data processing technique was then employed to decompose nonstationary data into stationary intrinsic mode functions (IMFs). The GRU model then processed these components to extract their essential information. In the following step, the extracted information was inserted into the MKELM model to predict evaporation. Finally, the GPR model quantified the uncertainty of predicted values. Our research also introduces a new optimizer called the Salp Swarm Optimization Algorithm–Sine Cosine Optimization Algorithm. This algorithm was used to tune the model parameters. This algorithm's performance and the prediction models’ accuracy were evaluated using several error indices. According to the study results, the GRU–MKELM–GPR model performed better than other models in predicting monthly evaporation. It improved the training and testing mean absolute error values of the other models by 21%-43% and 8.2–33%, respectively. Moreover, the new model improved the R2 (R-squared or coefficient of determination) values of other models by 5–12%. Generally, the main findings of this paper included the superior performance of the new model in predicting evaporation data and the superior performance of a new optimizer in adjusting model parameters. These findings highlighted the effectiveness of the suggested model in addressing the challenges associated with evaporation prediction.

Abstract Background The impact of routine preoperative anesthesia evaluations on potential perioperative complications remains unclear. This study aimed to investigate the effect of preoperative evaluation on early perioperative hemodynamic and respiratory complications. Methods This prospective observational study analyzed data from patients aged 18 to 80 who underwent elective surgery between October 15, 2023, and February 15, 2024. The study evaluated the effect of preoperative anesthesia evaluation on hemodynamic and respiratory complications occurring during surgery and within the first 24 h postoperatively, as well as its impact on the length of hospital stay. Results The analysis included 1117 patients for whom complete data was available. Hemodynamic and respiratory complications were observed in 545 patients (48.7%), occurring within the first 24 h intraoperatively and postoperatively. Because no additional examinations beyond routine blood tests, radiological imaging, and electrocardiograms were performed in the preoperative period, the impact of these tests on the development of hemodynamic and respiratory complications could not be determined. There was no statistically significant association between the presence or absence of preoperative consultation and the occurrence of early perioperative hemodynamic and respiratory complications [OR (95% CI): 0.879 (0.646–1.195); P = 0.411], nor did it affect the length of hospital stay [median (IQR); 2 (3) vs. 2 (3); P = 0.245]. Conclusion While the impact of routinely requested laboratory and imaging methods before surgery could not be assessed in this study, consultations that were requested did not affect hemodynamic and respiratory complications in the early perioperative period or on the duration of hospital stay. Trial registration number Samsun University Samsun Training and Research Hospital, following ethics committee approval (Samsun University clinical research ethics committee (KAEK) 2.12.2023) and Clinical Trials (NCT06203171 / 04.18.2024) registration.

Abstract This study investigates the efficacy of hybrid artificial neural network (ANN) methods, incorporating metaheuristic algorithms such as particle swarm optimization (PSO), genetic algorithm (GA), gray wolf optimizer (GWO), Aquila optimizer (AO), Runge–Kutta (RUN), and the novel ANN-based Runge–Kutta with Aquila optimizer (LSTM-RUNAO). The key novelty of this research lies in the developing and applying the LSTM-RUNAO model, which combines Runge–Kutta and Aquila optimizer to enhance streamflow prediction accuracy. The models’ performance is compared against the conventional ANN method, analyzing monthly streamflow prediction across three data split scenarios (50–50%, 60–40%, and 75–25%). Results show that the LSTM-RUNAO model outperformed conventional ANN methods, achieving a 28.7% reduction in root mean square error (RMSE) and a 20.3% reduction in mean absolute error (MAE) compared to standard ANN models. In addition, the model yielded a Nash–Sutcliffe Efficiency (NSE) improvement of 12.4% and an R-squared value increase of 7.8%. The study advocates for the 75–25% train-test data splitting scenario for optimal performance in data-driven methodologies. Furthermore, it elucidates the nuanced influence of input variables on prediction accuracy, emphasizing the importance of thoughtful consideration during model development. In summary, this research contributes valuable insights and introduces an innovative hybrid model to enhance the reliability of streamflow prediction models for practical applications.

Abstract The world has arguably identified both output and population growth over time. Thus, the demand for agricultural commodities such as biofuels and the prominent challenges associated with sustainable land use, water, and food development all play a significant role in the development process, as all countries strive to achieve the use of different approaches, such as convergence in output per capita, energy use, water availability, and food production, more safely and ecologically. Therefore, this study is led by Access and Neo-Malthusian theories to investigate the effects of food, energy, and water security sustainability for achieving sustainable development goals using annual data covering 1980 to 2020 to estimate the asymmetric autoregressive distributive lag approach. Accordingly, the results revealed that the effects of positive changes in food, energy, and water security on sustainable development differ significantly from those of negative changes. A 1% change in food production, water supply development, or industrial value addition has a significant positive impact on sustainable development; however, a 1% change in energy use and carbon emission, population growth, or depletion of natural resources has substantial adverse effects on sustainable economic development in both the short run and the long run in Nigeria. The findings also revealed that restoring equilibrium convergence to achieve 2030 SDGs requires 16.2% each year. The study recommends adopting innovative systems that control and help manage available human and natural resources in productive sectors and increase value-added and renewable energy production in Nigeria.

Abstract Background The purpose of this study was to examine the reliability and validity of the Turkish version of the Nursing Activities Score. Methods To validate the Turkish version of the Nursing Activities Score, a sample comprising 30 adult intensive care patients and 30 nurses was employed. After a pilot study, the internal consistency, Cronbach’s alpha coefficient, was found to be p = 0.718. Following the significant internal consistency obtained in the pilot study, the correlation between Nursing Activities Score internal consistency and Critical Nursing Situation Index scores was examined in 150 adult intensive care patients and 150 nurses. Results The assessment of inter-rater reliability showed a high level of agreement (99%) and an average Kappa index of 0.598 (p

Abstract Improving the forecasting accuracy of agricultural commodity prices is critical for many stakeholders namely, farmers, traders, exporters, governments, and all other partners in the price channel, to evade risks and enable appropriate policy interventions. However, the traditional mono-scale smoothing techniques often fail to capture the non-stationary and non-linear features due to their multifarious structure. This study has proposed a CEEMDAN (Complete Ensemble Empirical Mode Decomposition with Adaptive Noise)-TDNN (Time Delay Neural Network) model for forecasting non-linear, non-stationary agricultural price series. This study has evaluated its suitability in comparison with the other three major EMD (Empirical Mode Decomposition) variants (EMD, Ensemble EMD and Complementary Ensemble EMD) and the benchmark (Autoregressive Integrated Moving Average, Non-linear Support Vector Regression, Gradient Boosting Machine, Random Forest and TDNN) models using monthly wholesale prices of major oilseed crops in India. Outcomes from this investigation reflect that the CEEMDAN-TDNN hybrid models have outperformed all other forecasting models on the basis of evaluation metrics under consideration. For the proposed model, an average improvement of RMSE (Root Mean Square Error), Relative RMSE and MAPE (Mean Absolute Percentage Error) values has been observed to be 20.04%, 19.94% and 27.80%, respectively over the other EMD variant-based counterparts and 57.66%, 48.37% and 62.37%, respectively over the other benchmark stochastic and machine learning models. The CEEMD-TDNN and CEEMDAN-TDNN models have demonstrated superior performance in predicting the directional changes of monthly price series compared to other models. Additionally, the accuracy of forecasts generated by all models has been assessed using the Diebold-Mariano test, the Friedman test, and the Taylor diagram. The results confirm that the proposed hybrid model has outperformed the alternative models, providing a distinct advantage.

In the present study, some microbial, chemical, and physicochemical characteristics occurring during ripening were observed in unpackaged and vacuum-packed Kashar cheese samples. Some microbial and chemical properties of Kashar cheese samples were studied Also, the free fatty acid ratio was determined with the SDS-Page Electrophoresis to determine proteolysis during the ripening period and with the High-Performance Thin Layer Chromatography (HPTLC) for lipolysis. Physical, chemical, and microbiological analyzes were made at 0, 15, 30, 45, 60, and 75 days of ripening during which the changes in the number of microbiologically total aerobic mesophilic bacteria (TAMB), Lactobacillus spp., Lactococcus spp., Pseudomonas spp., yeast-mold, lipolytic, and proteolytic bacteria were determined. In the present study, % lactic acid, pH, dry matter percentage, color parameters (L, a and b values) and water activity (aw) were analyzed during ripening and the changes during storage were defined. Lactobacillus spp., Lactococcus spp., TAMB, and proteolytic bacteria counts and % lactic acid ratios were higher in vacuum-packed Kashar cheeses. It was found that lipolysis and proteolysis were higher in cheese samples stored open during ripening.

Abstract A multi-season research trial entitled ‘crop residue management effects on yield and water productivity of summer mung bean (Vigna radiata L.) under different irrigation regimes in Indian Punjab’ was conducted at Punjab Agricultural University (PAU), Regional Research Station (RRS), Bathinda, during rabi 2020 and 2021. The field experiment was conducted in a split-plot layout with nine treatment combinations and replicated thrice. The treatments consisted of T1 (no wheat residue along with tillage), T2 (leftover wheat residue with zero tillage), and T3 (incorporated wheat residue along with tillage) in main plots and irrigation regimes viz., I1 (vegetative growth and flowering stage), I2 (vegetative growth, flowering, and pod filling stage) and I3 (vegetative growth, flowering, pod formation and pod filling stage) in sub-plots, respectively. The growth and yield attributing characters were significantly higher under T3 than T1 but statistically at par with T2 during both years. An increase of 24.1% and 19.0% in grain yield was found in residue incorporation (T3) and residue retention (T2) over residue removal (T1), respectively. Maximum crop and irrigation water productivity was observed under T3 due to reduced water use and increased yield. Among the irrigation regimes, the I3 recorded significantly higher grain yield (0.70 and 0.79 t ha− 1) than I1. It was at par with I2 during both years due to higher irrigation frequency at the pod formation and pod filling stage. Crop water productivity (CWP) was higher under I3, whereas irrigation water productivity (IWP) was higher under I1 during both years. Additional irrigation at the pod-filling stage increased the grain yield by 36.5%, and two additional irrigations at the pod-formation and pod-filling stage further increased yield by 46.2% compared to only two irrigations at the vegetative and flowering stages. The treatment combinations of T2I2 and T3I2 outperformed T1I3 in terms of growth and yield attributing characters viz. plant height, dry matter accumulation (DMA), leaf area index (LAI), pods plant− 1, seeds pod− 1, and 1000-seed weight, which resulted in higher grain yield in these treatment combinations over T1I3. Applying crop residue can help minimize water use and increase crop water productivity. So, retaining crop residue in summer mung bean resulted in saving irrigation water due to lesser evapotranspiration from the soil surface.

Abstract Climate change is currently one of humanity’s greatest threats. To help scholars understand the psychology of climate change, we conducted an online quasi-experimental survey on 59,508 participants from 63 countries (collected between July 2022 and July 2023). In a between-subjects design, we tested 11 interventions designed to promote climate change mitigation across four outcomes: climate change belief, support for climate policies, willingness to share information on social media, and performance on an effortful pro-environmental behavioural task. Participants also reported their demographic information (e.g., age, gender) and several other independent variables (e.g., political orientation, perceptions about the scientific consensus). In the no-intervention control group, we also measured important additional variables, such as environmentalist identity and trust in climate science. We report the collaboration procedure, study design, raw and cleaned data, all survey materials, relevant analysis scripts, and data visualisations. This dataset can be used to further the understanding of psychological, demographic, and national-level factors related to individual-level climate action and how these differ across countries.

This study was aimed to determine the possibility of reusing sewage sludge in the cultivation of Taurus snowdrop (Galanthus elwesii Hook. f.). Taurus snowdrop bulbs with a circumference of 4 cm were used as plant material in the experiment. Bulbs were planted in five different growing media, 100% soil, 25% sewage sludge+75% soil, 50% sewage sludge+50% soil, 75% sewage sludge+25% soil, and 100% sewage sludge. The bulb diameter ranged between 16.8 and 18.5 mm, and bulb weight varied between 2.74 g and 6.33 g. The greatest bulblet weight (2.17 g) and leaf thickness (0.90 mm) obtained was in 75% sewage sludge+25% soil mixture. The number of bulblets ranged from 1.0 to 1.33 piece/bulb. Leaf length was between 7.30 and 9.25 cm, and leaf width was 9.7 to 11.6 mm. The effects of growing media on pedicle length, pedicle diameter, and fruit diameter were not found to be significant. Pedicle lengths were between 4.39 and 6.38 cm, pedicle diameters ranged from 1.59 to 1.80 mm, and fruit diameters were between 4.40 and 7.40 mm. It was concluded based on the present findings that 75% sewage sludge+25% soil mixture could reliably be used as a growing media for Taurus snowdrop culture.

Objective: Transitional care is essential to maintain the continuity of care in younger patients with rheumatic diseases. In this study, we aimed to assess the transition readiness of rheumatology patients who had already transferred from pediatric to adult care using a questionnaire. Materials and Methods: We included young adult rheumatology patients who had already transferred to adult rheumatology care. The Transition Readiness Assessment Questionnaire (TRAQ) was used in the adult rheumatology clinic to assess the patients’ readiness; a retrospective chart review was conducted to include diagnosis, age at diagnosis, age at transfer, and current age. Results: Three hundred and ten patients (184 female and 126 male) participated in this study. The mean age at diagnosis, the mean age at transfer, and the mean age at the time of the study were 10.7 ± 4.29, 21.1 ± 1.69, and 24.0 ± 2.26 years, respectively. Most of the patients had familial Mediterranean fever, followed by arthritis, connective tissue disorders, and other diseases. Tracking health issues was the lowest-scored domain. Females scored significantly higher than males in the tracking health issue domain (P = .006) and managing health issue domain (P = .028) but not in the overall TRAQ score (P = .053). Patients in different diagnosis and transfer age groups scored similarly across the domains. Conclusion: In this study, females performed better than males in 2 domains of the TRAQ questionnaire. Diagnoses or transfer age groups were not associated with TRAQ outcomes.

Objective: Antimicrobial resistance is a real threat to humanity. Pentavalent antimonials are reported non-effective in leishmaniasis treatment today, in countries like India. New treatment options have been assessed worldwide lately. Antimicrobial peptides (AMP) are the leading antibiotic candidates due to their large spectrum, fast efficacy, and low resistance risks. Cathelicidins are the AMP with well-documented antimicrobial activities against bacteria, fungi, and protozoa, over their positively charged membranes. Here, we aim to design cathelicidine-like helical peptides (CLHP), and compare their anti-Leishmanial efficacies in vitro, with meglumine antimoniate (MA) on Leishmania tropica. Methods: A total of five study [TN-1-5] and two control (MA and non-drug) groups were formed. Cryopreserved L. tropica isolate was thawed and cultivated in Novy-MacNeal-Nicolle medium and then in RPMI. Five different CLHPs (TN1-5) were diluted in dimethyl sulphoxide. A total of 150 uL of CLHPs and MA were added into the first wells of the test plaques, followed by serial dilutions that revealed doses within 4 and 512 ug/mL. Then, 100 uL of cultures including 1x108/mL of L. tropica promastigotes were added into each well. Viability of promastigotes was checked with XTT, while the parasite count was assessed at 24th and 48th hours. Results: TN3 was effective at 32 ug/mL. All tested CLHPs exhibited varying degrees of anti-Leishmanial activities, except TN5, even at its highest dose. Conclusion: TN3 showed a particular efficacy against L. tropicain vitro. Further studies including in vivo testing of the candidate’s both efficacy and toxicity are essential.

Abstract The study investigates the interplay of land use dynamics and climate change on the hydrological regime of the Ravi River using a comprehensive approach integrating Geographic Information System (GIS), remote sensing, and hydrological modeling at the catchment scale. Employing the Soil and Water Assessment Tool (SWAT) model, simulations were conducted to evaluate the hydrological response of the Ravi River to both current conditions and projected future scenarios of land use and climate change. This study differs from previous ones by simulating future land use and climate scenarios, offering a solid framework for understanding their impact on river flow dynamics. Model calibration and validation were performed for distinct periods (1999–2002 and 2003–2005), yielding satisfactory performance indicators (NSE, R2, PBIAS = 0.85, 0.83, and 10.01 in calibration and 0.87, 0.89, and 7.2 in validation). Through supervised classification techniques on Landsat imagery and TerrSet modeling, current and future land use maps were generated, revealing a notable increase in built-up areas from 1990 to 2020 and projections indicating further expansion by 31.7% from 2020 to 2100. Climate change projections under different socioeconomic pathways (SSP2 and SSP5) were derived for precipitation and temperature, with statistical downscaling applied using the CMhyd model. Results suggest substantial increases in precipitation (10.9 − 14.9%) and temperature (12.2 − 15.9%) across the SSP scenarios by the end of the century. Two scenarios, considering future climate conditions with current and future land use patterns, were analyzed to understand their combined impact on hydrological responses. In both scenarios, inflows to the Ravi River are projected to rise significantly (19.4 − 28.4%) from 2016 to 2100, indicating a considerable alteration in seasonal flow patterns. Additionally, historical data indicate a concerning trend of annual groundwater depth decline (0.8 m/year) from 1996 to 2020, attributed to land use and climate changes. The findings underscore the urgency for planners and managers to incorporate climate and land cover considerations into their strategies, given the potential implications for water resource management and environmental sustainability.

Objective: The ideal tension on a cable is affected by several factors, and the tensioning process is a relatively complex procedure. Individual variations can occur during tensioning, and the strain applied by residents at different training levels has not been previously questioned. For this purpose, we investigated the variations in the peak tensions applied by orthopedic residents, using a cable fixation model created on sheep femur. Methods: The experiment was conducted on the distal diaphysis of a 1-year-old sheep femur. A cobalt chrome cable, tensioned with a strain-meter integrated tensioner, was utilized to stabilize a prosthetic stem. The minimum tension required for a stable fixation under 8 N/m torque was recorded. Strain measurements of 120 trials of 12 residents were recorded and compared to their experience level and to the measured optimal tension. Results: The mean tension required for a stable fixation was 550 ± 45 N. The overall measured tension load was 540.3 ± 262.9 N. The mean tension provided by the 12 residents were between 175.7 ± 33.7 N and 875.5 ± 222.9 N. A significant difference was found between their highest and the lowest values (P < .0001). The level of experience was not correlated with the mean tension applied (P = .89) or with the difference of the minimum and the maximum tension (P = .81). The intraclass correlation coefficient was calculated as 0.1361 (95% CI, 0.02338-0.4242). Conclusion: Our results suggest that the residents’ decisions for the cable strain are not consistent or predictable, with high variations from the optimal value, regardless of their experience level.

Abstract Analysis of bridge failures due to scouring has been extensively studied by different researchers in recent years and as a result various methods of controlling local scour cavity have been given. Since most of the research in this field has been done on straight paths and also the complexity of the flow pattern in bends, studying the scouring pattern around the bridge piers located in the bends has become a necessity. Therefore, one of the main objectives of this study is to investigate the scouring around the circular piers which are protected by collars at the river bends. The performance of the collar significantly depends on its level around the pier, which has been studied in this study. The results showed that the optimal performance of this structure, in positions 60°, 90° and 120° at the level of 0.2 pier width under the bed with 3 mm thickness (0.06 of pier width) by approximately 68, 63 and 70% of the maximum pier scour depth was reduced compared with collarless pier, respectively. By analyzing the results of this research, it was observed that the optimum level range of collar around the pier at various bend angles is the initial bed level up to 0.4 times the pier width at the bottom of the incipient level of the bed. Moreover, getting closer to the plate placement level toward the initial bed elevation enhances the collar’s function in decreasing scouring.

Abstract Introduction Intracranial fungal infections’ (IcFIs) varying clinical manifestations lead to difficulties in diagnosis and treatment. African populations are disproportionately affected by the high burden of the disease. There is a lack of clarity as to the diagnostic and treatment modalities employed across the continent. In this review, we aim to detail the management, and outcome of IcFIs across Africa. Methods This scoping review was conducted using the Arksey and O'Malley framework. MEDLINE, EMBASE, Cochrane Library, African Index Medicus, and African Journals Online were searched for relevant articles from database inception to August 10th, 2021. The Preferred Reporting Items for Systematic Review and Meta-Analysis extension for Scoping Reviews guidelines were used to report the findings of the review. Results Of the 5,779 records identified, 131 articles were included. The mean age was 35.6 years, and the majority (56.4%) were males. The majority (n = 8,433/8,693, 97.0%) of IcFIs presented as a meningitis, the most common communicable predisposing factor of IcFIs was HIV/AIDS (n = 7,815/8,693, 89.9%), and the most common non-communicable risk factor was diabetes mellitus (n = 32/8,693, 0.4%). Cryptococcus species was the most common (n = 8,428/8,693, 97.0%) causative organism. The most commonly used diagnostic modality was cerebrospinal (CSF) cultures (n = 4,390/6,830, 64.3%) for diffuse IcFIs, and MRI imaging (n = 12/30, 40%) for focal IcFIs. The most common treatment modality was medical management with antifungals only (n = 4,481/8,693, 51.6%). The most commonly used antifungal agent in paediatric, and adult patients was amphotericin B and fluconazole dual therapy (51.5% vs 44.9%). The overall mortality rate was high (n = 3,475/7,493, 46.3%), and similar for both adult and paediatric patients (47.8% vs 42.1%). Conclusion Most IcFIs occurred in immunosuppressed individuals, and despite the new diagnostic techniques, CSF culture was mostly used in Africa. Antifungals regimens used was similar between children and adults. The outcome of IcFIs in Africa was poor for both paediatric and adult patients.

Abstract Images collected during NASA’s Double Asteroid Redirection Test (DART) mission provide the first resolved views of the Didymos binary asteroid system. These images reveal that the primary asteroid, Didymos, is flattened and has plausible undulations along its equatorial perimeter. At high elevations, its surface is rough and contains large boulders and craters; at low elevations its surface is smooth and possesses fewer large boulders and craters. Didymos’ moon, Dimorphos, possesses an intimate mixture of boulders, several asteroid-wide lineaments, and a handful of craters. The surfaces of both asteroids include boulders that are large relative to their host body, suggesting that both asteroids are rubble piles. Based on these observations, our models indicate that Didymos has a surface cohesion ≤ 1 Pa and an interior cohesion of ∼10 Pa, while Dimorphos has a surface cohesion of

Abstract The etiology of tic disorders (TDs) is not precisely known, although several lines of evidence suggest involvement of the immune system in pathogenesis. Here, we aimed to determine the expression levels of pro-inflammatory and anti-inflammatory cytokines in children with TD and compare them with those of healthy controls. Furthermore, we also evaluated their association with clinical variables in the TD group. Within the study period, 88 children with tic disorders and 111 healthy control children were enrolled. Most children with tic disorders were diagnosed with Tourette’s disorder (n = 47, 53.4%) or persistent motor tic disorder (n = 39, 44.3%), while the remainder (n = 2, 2.3%) were diagnosed with persistent vocal tic disorder. We found that children with tic disorders had significantly elevated levels of IL-1β, TNF-α, IL-6 and IL-4 expression, while we detected lower expression levels of IL-17 in children with tic disorders. Our findings provide a molecular landscape of cytokine expression in children with TD, which may suggest a proinflammatory state not affected by the presence of comorbidity and symptom severity. Delineating the contribution of alterations in the immune system to the pathogenesis of tic disorders may pave the way for better therapeutic interventions.

Abstract Field observations conducted immediately following the February 6, 2023, Mw 7.8 Kahramanmaraş earthquake documented the southern surface rupture termination in the Amik Basin. The termination occurred in an en-echelon pattern, extending across the 3.5 km width of the approximately 10-km-wide stepover. This extension reached towards the northern tip of the Hacıpaşa Fault, which constitutes the main northern segment of the Dead Sea Fault Zone (DSFZ). Archaeoseismologic and paleoseismologic data show that the approximately 800-km-long DSFZ has been seismically quiet for more than 600 years in the north and 900 years in the south. A similar fault connection geometry at the western end of the 1939 Ms 7.9 Erzincan earthquake in the easternmost part of the North Anatolian Fault Zone and the subsequently triggered successive large magnitude earthquakes migrating westward within a few decades highlights an increased seismic hazard for the entire DSFZ. This heightened seismic hazard potential along the DSFZ, combined with historical population centers experiencing wars and migrations, puts millions of people at an unparalleled risk.

Hybrid epoxy composites are highly considered for low-density applications due to the excellent specific strength and specific stiffness. Enhancements made to the epoxy matrix by addition of nanofillers like carbon nanotubes (CNTs) and graphene (GNPs) have been studied in detail over the course of few decades. These enhancements not only help elevate the material properties of the matrix but also activate different failure mitigating mechanisms in the composite. Although highly beneficial, there are few shortcomings due to the challenging fabrication process of integrating such. Common problems like filler agglomeration, formation of voids, wrinkling and more can result in poor load transfer within the composite. Graphene quantum dots (GQDs), on the other hand are the smallest carbon-based filler which are known to promote more intimate contact with the matrix. Their small size enables simultaneous enhancement of stiffness, strength and toughness. In addition, functionalization of these materials enables other supramolecular interactions like hydrogen bonding which improve the interfacial interaction with the epoxy. This study provides a molecular dynamics (MD) workflow to model a single functionalized GQD embedded in an epoxy matrix and the effective mechanical response of the nanocomposite. Ten chemistries were developed with different oxygen-based functional groups which capture the effect of GQD on the mechanical properties of the nanocomposite. Uniaxial strain simulations revealed that a maximum strength gain of 56 % and stiffness gain of 18 % was computed by the oxidized GQD-epoxy nanocomposite.

An accurate solar radiation (SR) prediction with a practical training approach is vital in estimating solar energy. A hybrid machine learning (ML) model is proposed for estimating the monthly SR. The proposed model includes two ML approaches: the response surface method (RSM) and support vector regression (SVR). The RSM is used to optimize the input variables and handle the data points for the prediction of SR. The first ML approach presents two input variables to estimate data handling. In the second ML process, the SVR model provides a nonlinear regression for handling data supplied by RSM. A new model was employed to predict the SR data taken from two stations in Turkey, as the temperature and extraterrestrial radiation were used as the model inputs. The RSM, artificial neural networks (ANNs), SVR, multivariate adaptive regression spline (MARS), M5 model tree (M5Tree) and convolutional neural networks (CNN) methods as existing ML approaches were employed to compare the predictions proposed hybrid ML approaches using several criteria. Data were split into training and testing sets, and two scenarios were established to compare models’ efficiencies according to different sets. The outcomes showed that the proposed model provides better accuracy for estimating SR using limited input data than other alternatives. The accuracy of the ANNs, SVR, MARS, M5Tree, RSM and CNN models was improved using a hybrid ML model. The proposed RSM-SVR method enhanced the efficiency of the ANN, SVR, MARS, M5Tree, and RSM methods by RMSE margins ranging from 0.1% to 5.6%, 2.8% to 7.3%, 1.0% to 8.3%, 0.1% to 28%, and 2.0% to 5.9%, respectively.

Milk can be contaminated with organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), polybrominated diphenyl ethers (PBDEs), and polycyclic aromatic hydrocarbons (PAHs), which are known as endocrine disruptors (EDCs). However, the relationship between the lactation period of cows and the residues of EDCs is unknown. The aim of this study was to determine the relationship between the lactation period in cows and EDCs residues such as OCPs, PCBs, PBDEs, and PAHs. Milk collected from cows during each lactation period was analyzed in terms of fat content and EDC residues. One or more contaminants were detected in almost all (98%) of the milk taken in three lactation periods. Three PCBs and 11 PAHs were found together in 1st lactation period, and one more PAH was added to them in other periods. However, the sample rate exceeded the maximum residue limit of 48%, 10%, and 16% in the first, second and third lactation periods, respectively. Also, it was seen that the riskiest period was the first lactation period, followed by the third and second periods. The same ranking is valid for the fat content in milk. Thus, it was concluded that the excretion of lipophilic EDCs in milk is related to the fat content in milk, and the higher the fat content in milk, the more lipophilic EDCs are excreted in milk.

Abstract Accurate prediction of reference evapotranspiration (ETo) is crucial for many water-related fields, including crop modelling, hydrologic simulations, irrigation scheduling and sustainable water management. This study compares the performance of different soft computing models such as artificial neural network (ANN), wavelet-coupled ANN (WANN), adaptive neuro-fuzzy inference systems (ANFIS) and multiple nonlinear regression (MNLR) for predicting ETo. The Gamma test technique was adopted to select the suitable input combination of meteorological variables. The performance of the models was quantitatively and qualitatively evaluated using several statistical criteria. The study showed that the ANN-10 model performed superior to the ANFIS-06, WANN-11 and MNLR models. The proposed ANN-10 model was more appropriate and efficient than the ANFIS-06, WANN-11 and MNLR models for predicting daily ETo. Solar radiation was found to be the most sensitive input variable. In contrast, actual vapour pressure was the least sensitive parameter based on sensitivity analysis.

Abstract Currently, the Water Quality Index (WQI) model becomes a widely used tool to evaluate surface water quality for agriculture, domestic and industrial. WQI is one of the simplest mathematical tools that can assist water operator in decision making in assessing the quality of water and it is widely used in the last years. The water quality analysis and prediction is conducted for Johor River Basin incorporating the upstream to downstream water quality monitoring station data of the river. In this research, the numerical method is first used to calculate the WQI and identify the classes for validating the prediction results. Then, two ensemble and optimized machine learning models including gradient boosting regression (GB) and random forest regression (RF) are employed to predict the WQI. The study area selected is the Johor River basin located in Johor, Peninsular Malaysia. The initial phase of this study involves analyzing all available data on parameters concerning the river, aiming to gain a comprehensive understanding of the overall water quality within the river basin. Through temporal analysis, it was determined that Mg, E. coli, SS, and DS emerge as critical factors affecting water quality in this river basin. Then, in terms of WQI calculation, feature importance method is used to identify the most important parameters that can be used to predict the WQI. Finally, an ensemble-based machine learning model is designed to predict the WQI using three parameters. Two ensemble ML approaches are chosen to predict the WQI in the study area and achieved a R 2 of 0.86 for RF-based regression and 0.85 for GB-based ML technique. Finally, this research proves that using only the biochemical oxygen demand (BOD), the chemical oxygen demand (COD) and percentage of dissolved oxygen (DO%), the WQI can be predicted accurately and almost 96 times out of 100 sample, the water class can be predicted using GB ensembled ML algorithm. Moving forward, stakeholders may opt to integrate this research into their analyses, potentially yielding economic reliability and time savings.

Objective To evaluate both humoral and cellular immune responses to the COVID-19 messenger RNA (mRNA; BNT162b2) vaccine in patients with childhood-onset SLE (cSLE) compared with healthy controls and patient controls (kidney transplant (KTx) recipients).Methods This single-centre, cross-sectional and case–control study included 16 patients with cSLE, 19 healthy controls and 19 KTx recipients. We assessed SARS-CoV-2-specific humoral (anti-SARS-CoV-2 IgG, neutralising antibody (nAb)) and cellular (interferon gamma release assay (IGRA)) immune responses at least 1 month after administration of two doses of the mRNA vaccine.Results Humoral immune response rates (anti-SARS-CoV-2 IgG and nAb seropositivity) in patients with cSLE were comparable to healthy controls (100% vs 100% and 100% vs 95%, respectively) but significantly higher than in KTx recipients (74% and 42%, p

Abstract The objective of this work was to compare the performances of logistic regression and machine learning algorithms to predict infection caused by Fasciola hepatica in cattle. A dataset on 30,151 bovines from Uruguay was used. Logistic regression (LR) and the algorithms k-nearest neighbor (KNN), classification and regression trees (CART), and random forest (RF) were compared. The interquartile range (IQR) and z-score were used to improve the classification and compared to each another. Sex, age, carcass conformation score, fat score, productive purpose, and carcass weight were used as independent variables for all algorithms. Infection by F. hepática was used as a binary dependent variable. The accuracies of LR, KNN, CART, and RF were 0.61, 0.57, 0.57, and 0.58, respectively. The variable importance of LR showed that adult cattle tended to be infected by F. hepatica. All models showed low accuracy, but LR successfully distinguished variables related to F. hepatica. Both the IQR and z-score show similar results in improving the classification metrics for the used dataset. In the dataset, data related to climate or factors such as body weight can improve the reliability of the model in future studies.

The study’s goal is to investigate the moderating effect of the workplace spirituality of employee on the relationship between their organization’s justice perception and job satisfaction. The study included a sample of 360 employees from two Turkish airline companies. The findings show a relationship between organizational justice and job satisfaction. The study also finds that workplace spirituality moderates the effect of organizational justice on job satisfaction. Employees with high workplace spirituality are more satisfied than those with low workplace spirituality. The study is important, especially in examining the moderator role of workplace spirituality between these two variables, and fills a gap in the literature. Empirical data of Turkiye’s two largest airline companies, which aim to become and remain competitive in the aviation industry, were shared. This research-based approach provides guidelines for this industry on the effects of workplace spirituality on job satisfaction and organizational justice.

Summary: Human genetic studies of critical COVID-19 pneumonia have revealed the essential role of type I interferon-dependent innate immunity to SARS-CoV-2 infection. Conversely, an association between the HLA-B∗15:01 allele and asymptomatic SARS-CoV-2 infection in unvaccinated individuals was recently reported, suggesting a contribution of pre-existing T cell-dependent adaptive immunity. We report a lack of association of classical HLA alleles, including HLA-B∗15:01, with pre-omicron asymptomatic SARS-CoV-2 infection in unvaccinated participants in a prospective population-based study in the United States (191 asymptomatic vs. 945 symptomatic COVID-19 cases). Moreover, we found no such association in the international COVID Human Genetic Effort cohort (206 asymptomatic vs. 574 mild or moderate COVID-19 cases and 1,625 severe or critical COVID-19 cases). Finally, in the Human Challenge Characterisation study, the three HLA-B∗15:01 individuals infected with SARS-CoV-2 developed symptoms. As with other acute primary infections studied, no classical HLA alleles favoring an asymptomatic course of SARS-CoV-2 infection were identified.

In this era of rapidly advancing technology, artificial intelligence (AI) has emerged as a transformative force, even being called the Fourth Industrial Revolution, along with gene editing and robotics. While it has undoubtedly become an increasingly important part of our daily lives, it must be recognized that it is not an additional tool, but rather a complex concept that poses a variety of challenges. AI, with considerable potential, has found its place in both medical care and clinical research. Within the vast field of pediatrics, it stands out as a particularly promising advancement. As pediatricians, we are indeed witnessing the impactful integration of AI-based applications into our daily clinical practice and research efforts. These tools are being used for simple to more complex tasks such as diagnosing clinically challenging conditions, predicting disease outcomes, creating treatment plans, educating both patients and healthcare professionals, and generating accurate medical records or scientific papers. In conclusion, the multifaceted applications of AI in pediatrics will increase efficiency and improve the quality of healthcare and research. However, there are certain risks and threats accompanying this advancement including the biases that may contribute to health disparities and, inaccuracies. Therefore, it is crucial to recognize and address the technical, ethical, and legal challenges as well as explore the benefits in both clinical and research fields.

Background/Aims: Gastrointestinal stromal tumors (GISTs), the most common mesenchymal tumors in the gastrointestinal tract, are increasingly treated with minimally invasive surgeries. Developed techniques include laparoscopic, endoscopic, and hybrid methods for gastric GIST resection. Our study, focusing on single-incision laparoscopic intragastric resection for gastric GISTs, aims to evaluate its safety, efficacy, and long-term outcomes. Materials and Methods: In a retrospective study of GIST surgery involving 14 patients who underwent single-incision laparoscopic intragastric resections, we analyzed and compared their preoperative demographics, American Society of Anesthesiologists (ASA) scores, tumor size, neoadjuvant treatment, operation duration, hospital stay, mitotic and Ki-67 indexes, and histological features with those of patients who underwent open and laparoscopic wedge resections, to assess the impact on both survival and disease-free survival. Results: Average operation time was 93.07 minutes (range 81-120 minutes). Average blood loss: 67 ± 20 mL (range 40-110 mL). Postoperative hospital stay averaged 6.79 days (range 4-16 days). Strong correlations were observed between preoperative and pathological tumor sizes (P = .001, P < .001). Survival analysis indicated a significant association with ASA scores (P = .031), but not with mitotic index, Ki-67, or tumor size. Average survival was 80.57 months, with no recurrence or metastasis during follow-up. Conclusion: Based on our experience, the single-incision laparoscopic intragastric resection method emerges as a highly efficient, timesaving, and gentle oncological procedure, providing a safe and minimally invasive alternative resulting in shorter hospital stays and excellent long-term outcomes with minimal recurrence. For more definitive conclusions, larger, multicenter, and prospective studies are recommended.

Objective: With numerous informative videos for patients and surgeons, Youtube™ is the most popular video sharing website worldwide. The aim of this study was to evaluate the quality and reliability of videos related to the “circumcision” procedure on Youtube™. Materials and Methods: The keyword “circumcision” was searched on the Youtube™ search engine, and those in English were included according to the inclusion criteria sorting by relevance. Basic data (such as number of views and upload sources) of these videos were recorded. Videos were assessed by four independent urologists using the global quality scale (GQS) for quality and the validated 5-question Modified DISCERN scoring for reliability. Results: A search for “circumcision” on Youtube™ search engine identified 800 videos, of which 155 videos were included in the study. Most videos were uploaded by individual users/patients (42.6%). The mean GQS score of the videos was low (2.30±0.99) and the mean modified DISCERN score was also low (1.71±1.10). The GQS and modified DISCERN scores of videos uploaded by physicians/professional organizations/universities were significantly higher than those of other uploaders (p0.05). Conclusion: Most of the videos related to circumcision surgery on Youtube™ platform were uploaded by patients, and the upload rate of healthcare professionals was significantly low. The uploaded videos are of poor quality, and healthcare professionals should produce better quality, reliable, and up-to-date content.

Objective: To compare the femoral artery and other ultrasonographic Doppler measurements between fetuses with late-onset fetal growth restriction and uncomplicated fetuses. Material and method: This prospective cohort study was conducted with 168 patients, including 83 pregnancies presenting with late-onset fetal growth restriction and 85 uncomplicated control pregnancies at similar gestational weeks. The study group was further divided into two according to the neonatal intensive care unit requirements. Demographic characteristics, obstetric characteristics, femoral artery and other ultrasonographic Doppler measurements, and pregnancy outcomes were compared between the groups. Results: Uterine artery pulsatility index, umbilical artery systolic/diastolic ratio, and peak systolic velocity of the middle cerebral artery were similar between the study and control groups. However, while the middle cerebral artery pulsatility index was significantly lower in the study group, the femoral artery pulsatility index value was significantly higher (p

Objective: It has been reported in many studies that Growth Differentiation Factor-15 (GDF-15) has an important role in physiological or pathological processes. As there is no study in the current scientific literature examining the status of GDF-15 in infertility and its treatment outcomes, we aimed to investigate this. Material and methods: According to their ovarian reserve characteristics, 88 infertile women were divided into three groups: normal ovarian reserve (NOR), diminished ovarian reserve (DOR), and polycystic ovary syndrome (PCOS). Estradiol (E2), follicle-stimulating hormone (FSH), luteinizing hormone (LH), anti-mullerian hormone (AMH), and GDF-15 levels were measured in their serum. The antagonist protocol patients' total oocyte, meiosis II (MII) oocytes, embryo count, and clinical pregnancy rates were documented. Results: In terms of serum GDF-15 concentrations, there was no statistically significant difference among the mean values of the three study groups. The mean FSH level at baseline was substantially higher in the DOR group compared to the PCOS group. The median serum AMH levels of all three groups were found to be statistically different. The antagonist protocol patients' total oocytes, meiosis II (MII) oocytes, embryo count, and clinical pregnancy rates were documented. Conclusion: In the present study, a significant and strong correlation between serum GDF-15 level and consequent embryo number was detected. Thereby, serum GDF-15 level may be considered to be a biomarker for predicting IVF clinical outcomes.

The intricate calculation of bed sediment load (BSL), which is influenced by hydraulic, hydrological, and sedimentary factors, is vital for informed decision-making in water resource management. Machine learning models, which are gaining popularity due to their accessibility and ability to reveal complex relationships, play a significant role in tackling these challenges. The efficacy of gene expression programming (GEP) models, support vector machines (SVMs), multi-layer perceptron (MLP), and multivariate adaptive regression splines (MARS) has been assessed through measured data of number 540 obtained from six rivers, namely Oak Creek, Nahal Yatir, Sagehen Creek, Elbow River, Jacoby River, and Goodwin Creek from 1954 to 1992. The assessment of model performance has been conducted utilizing root mean square error (RMSE), R2, Nash–Sutcliffe coefficient (NSE), and developed discrepancy ratio (DDR) as indices. Following data normalization within the range of 0–1, the data models underwent training and testing processes with a partition ratio of 80% for training and 20% for testing. Four dimensionless parameters, denoted as Fr = U/√gy, U/U*, Se, and ω = τU/γs√gyDs3, were employed as inputs in the models. The outcomes indicate that they exhibit superior performance compared to other methods, as evidenced by the following metrics in predicting BSL during the test stage: RMSE = 1.4088, NSE = 0.73054, R2 = 0.8729, and maximum QDDR(max) = 1.9564. HIGHLIGHTS The present work proposes BSL prediction using field data from six rivers.; Four machine learning models including SVM, GEP, MLP and MARS are employed to forecast BSL.; A variety of inputs is implemented for each MLMs to find optimum output.; Statistical indices are used to opt for superior MLMs for BSL prediction.;

Abstract This study investigates the viability of a strong algorithm (PSOGSA) merging particle swarm optimization (PSO) and gravity search algorithm (GSA) in tuning adaptive neuro-fuzzy system (ANFIS) parameters for modeling dimensionless experimental discharge of combined weir–orifices. The results are compared with the standard ANFIS and two hybrid models ANFIS tuned with PSO and GSA. The models are assessed by applying several dimensionless input parameters, consisting h/D (the ratio of upstream water depth to channel diameter), W/D (the ratio of orifice opening height to channel diameter), H/D (the ratio of plate height to channel diameter) and using comparison indices such as root-mean-square error and mean absolute error. The outcomes reveal that the new ANFIS-PSOGSA method provides superior accuracy in modeling dimensionless experimental discharge over the ANFIS-PSO, ANFIS-GSA and standard ANFIS method. Among the input parameters, the h/D was found to be the most effective input on modeling dimensionless experimental discharge while involving the H/D parameter deteriorated the models’ performances. The relative root-mean-square error differences between ANFIS-PSOGSA and ANFIS are found as 50% and 68.29% for pipe A and B, respectively. By implementing the ANFIS-PSOGSA, the accuracy of ANFIS-PSO and ANFIS-GSA is also improved in modeling dimensionless experimental discharge by 45.71% and 29.63% in pipe A and by 63.89% and 45.83% in pipe B with respect to root-mean-square error.

In this study, we investigated the balsamic contents, total phenolic contents, flavoneflavonol contents, and flavanones-dihydroflavonols contents, and antimicrobial effects of seven geopropolis samples produced by four neotropical stingless bee species: Venezuelan Melipona favosa, and Bolivian M. grandis, Scaptotrigona depilis, S. polysticta. The balsamic content value was found highest in the sample of S. polysticta (sample 7) collected from Bolivia, highest total phenolic content in M. favosa geopropolis (sample 1) from Venezuela, highest flavone-flavonol content in M. favosa geopropolis (sample 2) collected from Venezuela and highest flavones-dihydroflavonols content in S. polystica (sample 7) from Bolivia. As a result, except for balsamic content values, other investigated values were lower compare to the previous researches about different stingless bee species geopropolis. This is proved that climatic conditions, bee species and collecting area affected the chemical content of geopropolis significantly. The antimicrobial findings indicated that the examined geopropolis extracts displayed different degrees of inhibition against the growth of Gram-positive bacteria and fungi, which correlated with their phenolic contents. Nevertheless, these extracts did not demonstrate a comprehensive inhibitory effect on Gram-negative bacteria. Standardized geopropolis samples, rich in phenolic content, can complement antibiotics naturally for preventing and treating infections from Gram-positive bacteria and Candida albicans. However, further studies are still needed regarding the clinical applications of geopropolis in various infections.

Objective: Mercury poisoning is a condition with multiple-organ dysfunction that has effects on the central nervous system, gastrointestinal system, cardiovascular system, skin, lungs, and kidneys. It can be fatal or may result in sequelae such as neurological disturbances, if treated late or left untreated. The endocrinological effects of mercury exposure are not well-known. We aimed to evaluate patients with mercury poisoning. Materials and Methods: A total of 6 cases of mercury poisoning from 3 families were included in the study. Clinical, laboratory, and follow-up data were recorded. Results: Thyroid dysfunction was presented as high thyroid hormones and normal thyrotropin level (unsuppressed) in 5 cases (83.3%). On the other hand, pheochromocytoma-like syndrome was detected in 5 cases (83.3%) with hypertension. The 4 cases were the first to use methimazole for mercury poisoning due to tachycardia and hypertension despite antihypertensive treatment due to catecholamine excess and thyroid dysfunction. Hyponatremia was detected in 3 cases (50%). Conclusion: Mercury poisoning is difficult to diagnose because it is rare and presents with nonspecific physical and laboratory findings. Early diagnosis and providing appropriate treatment are essential in order to prevent sequelae. Mercury poisoning should be considered in patients with unexplained hypertension and tachycardia suggesting the involvement of thyroid hormones and catecholamines

Abstract Background Juvenile idiopathic arthritis (JIA) is a prevalent childhood chronic arthritis, often persisting into adulthood. Effective transitional care becomes crucial as these patients transition from pediatric to adult healthcare systems. Despite the concept of transitional care being recognized, its real-world implementation remains inadequately explored. This study aims to evaluate the thoughts and practices of healthcare providers regarding transitional care for JIA patients. Methods A cross-sectional survey was conducted among pediatric and adult rheumatologists in Turkey. Based on the American Academy of Pediatrics’ six core elements of transitional care, the survey included 86 questions. The respondents’ demographic data, attitudes towards transitional care, and practical implementation were assessed. Results The survey included 48 rheumatologists, with 43.7% having a transition clinic. The main barriers to establishing transition programs were the absence of adult rheumatologists, lack of time, and financial constraints. Only 23.8% had a multidisciplinary team for transition care. Participants agreed on the importance of coordination and cooperation between pediatric and adult healthcare services. The timing of the transition process varied, with no consensus on when to initiate or complete it. Participants advocated for validated questionnaires adapted to local conditions to assess transition readiness. Conclusions The study sheds light on the challenges and perspectives surrounding transitional care for JIA patients in Turkey. Despite recognized needs and intentions, practical implementation remains limited due to various barriers. Cultural factors and resource constraints affect the transition process. While acknowledging the existing shortcomings, the research serves as a ground for further efforts to improve transitional care and ensure better outcomes for JIA patients transitioning into adulthood.

Over the past few years, there has been a significant emphasis on improving the capabilities of Wireless Sensor Networks (WSNs) by making advancements in communication protocols, energy efficiency, optimal deployment, data analytics, and integration with emerging technologies, such as the Internet of Things and artificial intelligence. The deployment of WSN nodes can greatly enhance the effectiveness, scalability, and capability of different systems, resulting in cost reductions, enhanced performance, and improved safety in which the deployment of WSN involves determining the best positioning of sensor nodes to attain maximum coverage and connectivity while minimizing the number of nodes needed. WSNs often face challenges in deploying nodes effectively and Genetic Algorithms (GAs) offer a valuable approach for tackling this problem due to their ability to efficiently search large and complex solution spaces, such as those of complex network design, taking into account various constraints and objectives, which are common characteristics of real-world WSN deployment scenarios. The objective of this study is to use a new method, called the vibrational genetic algorithm, which can be used to optimize the placement of sensor nodes more efficiently. Apart from the other research, it is preferred to use heterogeneous sensor nodes to increase the coverage rate in an irregularly shaped area. The results of the experiments demonstrate that the proposed model offers an effective solution for achieving maximum coverage in application theaters that are more realistic and complex.

In today’s digital landscape, the increasing prevalence of internet-connected devices, including smartphones, personal computers, and IoT devices, has enabled users to perform a wide range of daily activities such as shopping, banking, and communication in the online world. However, cybercriminals are capitalizing on the Internet’s anonymity and the ease of conducting cyberattacks. Phishing attacks have become a popular method for acquiring sensitive user information, including passwords, bank account details, social security numbers and more, often through social engineering and messaging tools. To protect users from such threats, it is essential to establish sophisticated phishing detection systems on computing devices. Many of these systems leverage machine learning techniques for accurate classification. In recent years, deep learning algorithms have gained prominence, especially when dealing with large datasets. This study presents the development of a phishing detection system based on deep learning, employing five different algorithms: artificial neural networks, convolutional neural networks, recurrent neural networks, bidirectional recurrent neural networks, and attention networks. The system primarily focuses on the fast classification of web pages using URLs. To assess the system’s performance, a relatively extensive dataset of labeled URLs, comprising approximately five million records, was collected and shared. The experimental results indicate that convolutional neural networks achieved the highest performance, boasting a detection accuracy of 98.74% for phishing attacks. This research underscores the effectiveness of deep learning algorithms, particularly in enhancing cybersecurity in the face of evolving cyber threats.

Araştırmada, öğretmen adaylarının fizik kaygılarını yordayan değişkenlerin C5.0 karar ağacı algoritması ile incelenmesi amaçlanmıştır. İlişkisel araştırma niteliğinde olan bu çalışmada, veriler “Fizik Kaygı Ölçeği” ile 186 öğretmen adayından toplanmıştır. Araştırmadan elde edilen verilerin analizinde ilk aşamada İki Aşamalı Kümeleme Analizi, ikinci aşamada C5.0 Karar Ağacı Algoritması kullanılmıştır. Araştırmada öğretmen adaylarının çoğunun fizik dersi/sınav kaygılarının yüksek, matematik kaygılarının orta, fizik bilgisi eksikliği ve fizik laboratuvar kaygılarının düşük ve orta düzeyde olduğu ortaya çıkmıştır. Fizik dersi/sınav ve fizik bilgisi eksikliği kaygısı boyutu üzerinde etki düzeyi en yüksek olan değişkenin öğretmen adaylarının genel akademik not ortalamaları olduğu tespit edilmiştir. Bununla birlikte matematik kaygısı boyutu üzerinde öğretmen adaylarının yaşları ve fizik laboratuvarı kaygısı boyutu üzerinde ise öğretmen adaylarının cinsiyetleri olduğu tespit edilmiştir.