Reduction In Rate Research Articles

The role of alcohol control policies in the reversal of alcohol consumption levels and resulting attributable harms in China

Yearly adult per capita consumption of alcohol in China between 2016 and 2019 decreased by 2.4 L of pure alcohol, or 33%. According to the World Health Organization, this decrease in consumption was accompanied by reductions in alcohol-attributable mortality of 23% between 2015 and 2019. This paper examines the contribution of alcohol control policies in China to these public health gains. A systematic search of the literature was conducted on alcohol control policies and their effectiveness in China as part of a larger search of all countries in WHO Western Pacific Region. In addition to articles on empirical evidence on the impact of such alcohol control policies, we also searched for reviews. The plausibility of changes of traditional alcohol control policies (taxation increases, availability restrictions, restriction on advertisement and marketing, drink-driving laws, screening and brief interventions) in explaining reductions of consumption levels and attributable mortality rates was explored. There was some progress in the successful implementation of strict drink-driving policies, which could explain reductions in traffic injuries, including fatalities. Other traditional alcohol control policies seem to have played a minimal role in reducing alcohol consumption and attributable harms during the time period 2016–2019. However, an anti-corruption campaign was extensive enough to have substantially contributed to these reductions. The campaign prohibited the consumption of alcoholic beverages in everyday life of government officials and thus contributed to a de-normalization of alcohol. While this anti-corruption campaign was the only policy to potentially explain marked decreases in levels of alcohol consumption and attributable mortality, more detailed research is required to determine exactly how the campaign achieved these decreases.

Non-Equilibrium Long-Wave Infrared HgCdTe Photodiodes: How the Exclusion and Extraction Junctions Work Separately.

The cooling requirement for long-wave infrared detectors still creates significant limitations to their functionality. The phenomenon of minority-carrier exclusion and extraction in narrow-gap semiconductors has been intensively studied for over three decades and used to increase the operating temperatures of devices. Decreasing free carrier concentrations below equilibrium values by a stationary non-equilibrium depletion of the device absorber leads to a suppression of Auger generation. In this paper, we focus on analyzing exclusion and extraction effects separately, based on experimental and theoretical results for a HgCdTe photodiode. To carry out an experiment, the n+-P+-π-N+ heterostructure was grown by metal organic chemical vapor deposition on CdTe-buffered GaAs substrate. In order to separate the extraction and exclusive junctions, three different devices were evaluated: (1) a detector etched through the entire n+-P+-π-N+ heterostructure, (2) a detector made of the P+-π photoconductive junction and (3) a detector made of the π-N+ photodiode junction. For each device, the dark current density-voltage characteristics were measured at a high-temperature range, from 195 K to 300 K. Next, the carrier concentration distribution across the entire heterostructure and individual junctions was calculated using the APSYS simulation program. It was shown that when the n+-P+-π-N+ photodiode is reverse biased, the electron concentration in the π absorber drops below its thermal equilibrium value, due to the exclusion effect at the P+-π junction and the extraction effect at the π-N+ junction. To maintain the charge neutrality, the hole concentration is also reduced below the equilibrium value and reaches the absorber doping level (NA), leading to the Auger generation rate's reduction by a factor of 2ni/NA, where ni is the intrinsic carrier concentration. Our experiment conducted for three separate detectors showed that the exclusion P+-π photoconductive junction has the most significant effect on the Auger suppression-the majority of the hole concentration drops to the doping level not only at the P+-π interface but also deep inside the π absorber.

A Neurosurgical Readmissions Reduction Program in an Academic Hospital Leveraging Machine Learning, Workflow Analysis, and Simulation.

Predicting 30-day hospital readmissions is crucial for improving patient outcomes, optimizing resource allocation, and achieving financial savings. Existing studies reporting the development of machine learning (ML) models predictive of neurosurgical readmissions do not report factors related to clinical implementation. Train individual predictive models with good performance (area under the receiver operating characteristic curve or AUROC > 0.8), identify potential interventions through semi-structured interviews, and demonstrate estimated clinical and financial impact of these models. Electronic health records were utilized with five ML methodologies: gradient boosting, decision tree, random forest, ridge logistic regression, and linear support vector machine. Variables of interest were determined by domain experts and literature. The dataset was split divided 80% for training and validation and 20% for testing randomly. Clinical workflow analysis was conducted using semi-structured interviews to identify possible intervention points. Calibrated agent-based models (ABMs), based on a previous study with interventions, were applied to simulate reductions of the 30-day readmission rate and financial costs. The dataset covered 12,334 neurosurgical intensive care unit (NSICU) admissions (11,029 patients); 1,903 spine surgery admissions (1,641 patients), and 2,208 traumatic brain injury (TBI) admissions (2,185 patients), with readmission rate of 13.13, 13.93, and 23.73%, respectively. The random forest model for NSICU achieved best performance with an AUROC score of 0.89, capturing potential patients effectively. Six interventions were identified through 12 semi-structured interviews targeting preoperative, inpatient stay, discharge phases, and follow-up phases. Calibrated ABMs simulated median readmission reduction rates and resulted in 13.13 to 10.12% (NSICU), 13.90 to 10.98% (spine surgery), and 23.64 to 21.20% (TBI). Approximately $1,300,614.28 in saving resulted from potential interventions. This study reports the successful development and simulation of an ML-based approach for predicting and reducing 30-day hospital readmissions in neurosurgery. The intervention shows feasibility in improving patient outcomes and reducing financial losses.

Motivation Program to Reduce Occupational Accidents in a Manufacturing Company

the purpose of this research study was to determine whether implementing a motivation program could lead to a reduction in the rate of occupational accidents in the bakery plant of a manufacturing company based in Lima. The study followed a quasi-experimental approach and used a pretest/posttest design with a single group and a sample of 117 workers. The data collection methods included statistical data on work-related accidents, direct observations in the workplace, recording and monitoring of interactions, and a motivation questionnaire. The research results revealed that implementing the IPLEFOR motivation program—which spanned 21 months—led to a 100% reduction in the accident rate. Consequently, it led to a significant improvement in the occupational health and safety system.

Eye-LRCN: A Long-Term Recurrent Convolutional Network for Eye Blink Completeness Detection.

Computer vision syndrome causes vision problems and discomfort mainly due to dry eye. Several studies show that dry eye in computer users is caused by a reduction in the blink rate and an increase in the prevalence of incomplete blinks. In this context, this article introduces Eye-LRCN, a new eye blink detection method that also evaluates the completeness of the blink. The method is based on a long-term recurrent convolutional network (LRCN), which combines a convolutional neural network (CNN) for feature extraction with a bidirectional recurrent neural network that performs sequence learning and classifies the blinks. A Siamese architecture is used during CNN training to overcome the high-class imbalance present in blink detection and the limited amount of data available to train blink detection models. The method was evaluated on three different tasks: blink detection, blink completeness detection, and eye state detection. We report superior performance to the state-of-the-art methods in blink detection and blink completeness detection, and remarkable results in eye state detection.

Downlink Scheduler for Delay Guaranteed Services Using Deep Reinforcement Learning

In this paper, we propose a novel scheduling scheme to guarantee per-packet delay in single-hop wireless networks for delay-critical applications. We consider several classes of packets with different delay requirements, where high-class packets yield high utility after successful transmission. Considering the correla-tionship of delays among competing packets, we apply a delay-laxity concept and introduce a new output gain function for scheduling decisions. Particularly, the selection of a packet takes into account not only its output gain but also the delay-laxity of other packets. In this context, we formulate a multi-objective optimization problem aiming to minimize the average queue length while maximizing the average output gain under the constraint of guaranteeing per-packet delay. However, due to the uncertainty in the environment (e.g., time-varying channel conditions and random packet arrivals), it is difficult and often impractical to solve this problem using traditional optimization techniques. We develop a deep reinforcement learning (DRL)-based framework to solve it. Specifically, we decompose the original optimization problem into a set of scalar optimization subproblems and model each of them as a partially observable Markov Decision Process (POMDP). We then resort to a Double Deep Q Network (DDQN)-based algorithm to learn an optimal scheduling policy for each subproblem, which can overcome the large-scale state space and reduce Q-value overestimation. Simulation results show that our proposed DDQN-based algorithm outperforms the conventional Q-learning algorithm in terms of reward and learning speed. In addition, our proposed scheduling scheme can achieve significant reductions in average delay and delay outage drop rate compared to other benchmark schemes.

Assessing the impact of a multidimensional approach and an 8-component bundle in reducing incidences of ventilator-associated pneumonia across 35 countries in Latin America, Asia, the Middle East, and Eastern Europe

BackgroundVentilator associated pneumonia (VAP) occurring in the intensive care unit (ICU) are common, costly, and potentially lethal. MethodsWe implemented a multidimensional approach and an 8-component bundle in 374 ICUs across 35 low and middle-income countries (LMICs) from Latin-America, Asia, Eastern-Europe, and the Middle-East, to reduce VAP rates in ICUs. The VAP rate per 1000 mechanical ventilator (MV)-days was measured at baseline and during intervention at the 2nd month, 3rd month, 4–15 month, 16–27 month, and 28–39 month periods. Results174,987 patients, during 1,201,592 patient-days, used 463,592 MV-days. VAP per 1000 MV-days rates decreased from 28.46 at baseline to 17.58 at the 2nd month (RR = 0.61; 95% CI = 0.58–0.65; P < 0.001); 13.97 at the 3rd month (RR = 0.49; 95% CI = 0.46–0.52; P < 0.001); 14.44 at the 4–15 month (RR = 0.51; 95% CI = 0.48–0.53; P < 0.001); 11.40 at the 16–27 month (RR = 0.41; 95% CI = 0.38–0.42; P < 0.001), and to 9.68 at the 28–39 month (RR = 0.34; 95% CI = 0.32–0.36; P < 0.001). The multilevel Poisson regression model showed a continuous significant decrease in incidence rate ratios, reaching 0.39 (p < 0.0001) during the 28th to 39th months after implementation of the intervention. ConclusionsThis intervention resulted in a significant VAP rate reduction by 66% that was maintained throughout the 39-month period.

Photobiomodulation for Blood Pressure and Heart Rate Reduction in Mastectomized Women on Hormone Blockers: A Randomized Controlled Trial.

Objective: To assess the impact of intravascular laser irradiation of blood (ILIB) on the primitive carotid artery (PCA) hemodynamic variables, specifically blood pressure (BP) and heart rate (HR), in mastectomized patients undergoing hormone blocker treatments. Materials and methods: This study is a controlled, experimental, and randomized clinical trial. Patients were allocated into two groups: the experimental group (G1)-patients who received ILIB therapy using a 660 nm laser targeted at the PCA, and the control group (G2)-patients who did not receive ILIB therapy. Clinical research was conducted weekly, with measurements of systolic blood pressure (SBP), diastolic blood pressure (DBP), and HR. The Mann-Whitney U test for independent samples was used, with a significance level of α = 0.05. Results: Systemic photobiomodulation on the PCA did not demonstrate a statistically significant difference in relation to SBP and DBP. However, for HR, the p-value was <0.05, indicating a significant difference between G1 and G2. The initial mean p > decreased from 142.3 to 116.4 mmHg in G1, and from 130.4 to 119.8 mmHg in G2. The DBP varied from 78.8 to 72.8 mmHg in G1, and from 79.1 to 74.2 mmHg in G2. A statistically significant difference was observed in HR, decreasing from 81.3 to 62.06 bpm in G1, and changing minimally from 74.1 to 75.1 bpm in G2. A considerable reduction was present in the timing of application. Conclusions: ILIB therapy applied to the PCA induces a reduction in BP and, more notably, HR in mastectomized women using the tamoxifen or aromatase inhibitors.

Influence of soil moisture conditions on severity of Botryosphaeria branch dieback caused by Lasiodiplodia iraniensis and impact of plant growth in macadamia

Australian macadamias experience outbreaks of Botryosphaeria branch dieback. Extreme environmental conditions are thought to be responsible for the episodic outbreaks of diseases caused by Botryosphaeriaceae species in horticultural ecosystems. However, studies on the interactive effects of biotic and abiotic stresses in macadamia are unknown. Knowledge of management practices that contribute to the severity of the disease would assist in decisions for Botryosphaeria branch dieback control in commercial macadamia orchards. Using an in planta stem wound inoculation assay with Lasiodiplodia iraniensis, we investigated the association of soil moisture conditions as a factor of plant stress with the severity of Botryosphaeriaceae infection in macadamia. Sap flow rate and soil water potential were monitored in 72 macadamia potted cv. HAES 344 plants (18-month-old and 12-month-old grown in pine bark-sand mixture) that were kept under three watering treatments, based on the amount of water used per day (Wd) at the start of the trial, at Wd, ½ of Wd and ¼ of Wd for 14 weeks. A significant differential effect of the watering regimes was observed on disease severity, revealing a strong (R2 > 0.87) negative linear relationship between disease severity and sap flow rates. Necrotic lesions were significantly (P < 0.001) longer in the lowest and highest watering regimes. The average sap flow rate of 0.4 kg h−1 was recorded in the plants under moderately watered conditions, ½Wd regime, whereas 50% and 75% reductions in sap flow rates were recorded at the lowest and highest watering treatments, respectively. Our results indicate that maintaining optimal soil water potential between −50 and −80 kPa through irrigation practices may reduce the risk of Botryosphaeria branch dieback outbreaks in macadamia orchards.

Abstract 2888: Sensitization of radiation resistant colon cancer - The oxygenated microbubble hydrogel approach

Abstract Background: Hypoxia, a state of reduced oxygen that develops within the tumor due to its irregular vasculature, could result in treatment resistance. Mounting evidence suggests that reoxygenation of the tumor could improve outcomes of conventional cancer therapies. Our approach combats hypoxia using oxygenated lipid microbubbles (OMB) loaded in a temperature sensitive gel. The OMB would carry the oxygen while the gel would sustain its release into the tumor microenvironment. Objectives: To formulate an oxygen loaded MB dispersed in a temperature sensitive poloxamer hydrogel for intratumoral administration. Methods: OMB were generated by first preparing a liposomal mixture DSPC:DSPE-PEG2000, which was dispersed in a thermosensitive poloxamer hydrogel (P407:P188). This was pressurized with oxygen and vortexed in a gas-tight vial. Characterizations included OMB size distribution, injectability, thermosensitivity, oxygen loading and release, and the impact of the formulation on efficacy of radiotherapy against a colon cancer cell line (HCT116) was evaluated. Results: DSPC:DSPE-PEG2000 liposomes at a molar ratio of 94:6 dispersed in P407:P188 (21:6.5 %w/w) generated OMB with a size distribution in the acceptable range of 0.8-8 µm. Formulations were within the established limits of injectability (F&amp;lt;38N) at room temperature, and gelled near physiological temperatures enabling a sustained release of oxygen.. The formulation co-treatment enhanced radiotherapy significantly as evidenced by the remarkable reductions of clonogenic survival rates in both hypoxic and normoxic HCT116 cells (reduction by 78% vs 68% compared to just their respective radiated controls; p&amp;lt;0.0001 for both cases). Conclusion: Reoxygenation with a newly developed OMB hydrogel formulation effectively sensitized HCT116 to radiotherapy in vitro. Studies are underway to explore the importance of reoxygenation rate and extent for optimal tumor sensitization. Citation Format: Ashok David Jose, Celine Hui-Ning Chong, Jagdish Jaiswal, Zimei Wu, Sachin Sunil Thakur. Sensitization of radiation resistant colon cancer - The oxygenated microbubble hydrogel approach [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2888.

Hybridization in meta-heuristic techniques for green layout design in the tyre manufacturing industry: An optimal analysis

To stop or lessen factory pollution, many academics in the field of study have produced a vast array of strategies to achieve optimization goals. But, it is unable to follow a uniform solution for all production facilities. In this research work, green layout design (GLD) is proposed to minimize total material handling cost (TMHC) and CO2 emissions. The green facility layout refers to the facility layout with minimum CO2 emissions and to eliminate unnecessary energy dissipation from material handling equipments &amp; production machineries, as well as plant re-layout in-term on material handling cost. In this study context, a Lagrangian relaxation model has been used at the allocation level. It computes a suboptimal solution to the dual problem in order to obtain a feasible solution. This enhanced algorithm method iteratively tries to minimize the gap between the dual and feasible solutions on minimal of TMHC and CO2 emission rate. It has been experimentally simulated using the MATLAB (Matrix Laboratory) application and upgraded adaptive salp swarm optimization (ASSO), dragon fly optimization (DFO) and Hybrid (combination) of ASSO-DFO evolutionary optimization techniques. The primary focus of this implementation is to control material handling between departments or cells, production flow between departments, energy consumption (electrical and gas consumption for production), and manpower utilization within and across departments. This research has chosen an existing and acceptable designs from targeted tyre manufacturing company in South India. The hybrid ASSO-DFO implementation TMHC differs from ABC by 60%, DFO by 65%, 58% with simulated annealing (SA), and ASSO by 48%. Emission rate decreased by 26% ABC, 21% DFO, and 24% SA &amp; 18% with ASSO. An average of 50% decrease in TMHC and 25% reduction of CO2 emission rate in GLD have been achieved.

Unified deep learning models for enhanced lung cancer prediction with ResNet-50-101 and EfficientNet-B3 using DICOM images.

Significant advancements in machine learning algorithms have the potential to aid in the early detection and prevention of cancer, a devastating disease. However, traditional research methods face obstacles, and the amount of cancer-related information is rapidly expanding. The authors have developed a helpful support system using three distinct deep-learning models, ResNet-50, EfficientNet-B3, and ResNet-101, along with transfer learning, to predict lung cancer, thereby contributing to health and reducing the mortality rate associated with this condition. This offer aims to address the issue effectively. Using a dataset of 1,000 DICOM lung cancer images from the LIDC-IDRI repository, each image is classified into four different categories. Although deep learning is still making progress in its ability to analyze and understand cancer data, this research marks a significant step forward in the fight against cancer, promoting better health outcomes and potentially lowering the mortality rate. The Fusion Model, like all other models, achieved 100% precision in classifying Squamous Cells. The Fusion Model and ResNet-50 achieved a precision of 90%, closely followed by EfficientNet-B3 and ResNet-101 with slightly lower precision. To prevent overfitting and improve data collection and planning, the authors implemented a data extension strategy. The relationship between acquiring knowledge and reaching specific scores was also connected to advancing and addressing the issue of imprecise accuracy, ultimately contributing to advancements in health and a reduction in the mortality rate associated with lung cancer.

How to make full use of dental pulp stem cells: an optimized cell culture method based on explant technology.

Dental pulp stem cells from humans possess self-renewal and versatile differentiation abilities. These cells, known as DPSC, are promising for tissue engineering due to their outstanding biological characteristics and ease of access without significant donor site trauma. Existing methods for isolating DPSC mainly include enzyme digestion and explant techniques. Compared with the enzymatic digestion technique, the outgrowth method is less prone to cell damage and loss during the operation, which is essential for DPSC with fewer tissue sources. In order to maximize the amount of stem cells harvested while reducing the cost of DPSC culture, the feasibility of the optimized explant technique was evaluated in this experiment. Cell morphology, minimum cell emergence time, the total amount of cells harvested, cell survival, and proliferative and differentiation capacity of DPSC obtained with different numbers of explant attachments (A1-A5) were evaluated. There was a reduction in the survival rate of the cells in groups A2-A5, and the amount of harvested DPSC decreased in A3-A5 groups, but the DPSC harvested in groups A1-A4 had similar proliferative and differentiation abilities. However, starting from group A5, the survival rate, proliferation and differentiation ability of DPSC decreased significantly, and the adipogenic trend of the cells became more apparent, indicating that the cells had begun to enter the senescence state. The results of our study demonstrated that the DPSC obtained by the optimized explant method up to 4 times had reliable biological properties and is available for tissue engineering.

Atomic-scale simulation of ultrasonic vibration-assisted polishing process for graphene/GaN-layered composites

In the realm of semiconductor materials, the integration of graphene (Gr) and gallium nitride (GaN) has emerged as a promising avenue for enhancing device performance. In this study, a Gr/GaN layered nanocomposite was constructed using molecular dynamics simulation and the LAMMPS code. Diamond abrasive was pressed into the composite at a speed of 50 m/s at 300 K, followed by ultrasonic vibration-assisted polishing on the (0001) plane along the [01‾10] direction. By rigorously exploring various factors such as number of Gr layers, vibration amplitudes, vibration frequencies and grinding speeds, the study elucidates the intricate mechanisms governing material removal and subsurface damage. Key findings reveal optimal processing conditions, including three layers of Gr, an amplitude of 5 Å, a vibration frequency of 80 GHz and a grinding speed of 100 m/s, which result in enhanced material removal rates and improved surface quality. Additionally, the insertion of Gr layers within the GaN matrix demonstrates a remarkable reduction in friction and wear rates, highlighting the potential for enhancing device reliability and performance. This comprehensive investigation not only advances our understanding of ultrasonic vibration-assisted grinding processes but also underscores the transformative impact of Gr/GaN composites in semiconductor device fabrication.

Vietnam's Long-run Growth: Connecting the Dots through Climate Damage Spillovers

We propose to examine how climate damage may transform Vietnam's long-run growth rate. Because of cross-country linkages forged by bilateral trade, there are two channels through which international damage spillovers may occur. First, the dynamics of partners' growth determine future trends in Vietnam's volume of exports. Second, since the domestic impact of climate change may be heterogeneous across countries, there will be a differentiated impact on export and import market shares. Both terms play a critical role in changing trade patterns that are likely to shift Vietnam's external constraint. This demand-side view of growth based on the balance-of-payments constraint is a powerful predictor of inter-country growth differences. Our study show that the consequences of climate change could equate to a 2.5% reduction in Vietnam's growth rate over the period 2020-2060. Our decomposition exercise by effect and by partner area shows that international damage spillovers result from very different individual behaviours.

Effects of the prone position on gas exchange and ventilatory mechanics and their correlations with mechanical power in burn patients with ARDS

Background Prone position has many documented benefits on severe ARDS patients especially on mortality. The benefits in ARDS secondary to severe burns have not been fully documented. Aim To quantify the effects of prone positioning on gas exchange, ventilatory mechanics and their correlations with mechanical power in burn subjects with ARDS. Methods Cross-sectional observational analytical study that took place between January 2023 and October 2023 in Burns ICU in Brazil on subjects with moderate to severe ARDS ventilated with the volume controlled mode. Data were collected in the first prone positioning lasting 24 hours in the first 30 minutes after changing position and 30 minutes before returning to the supine position. The parameters of the components of mechanical ventilation and mechanical power calculated by the Gatinoni’s formula (respiratory rate, tidal volume, driving pressure, PEEP, peak and plateau pressures) were collected to evaluate ventilatory mechanics, and the values of the FiO2, PaO2, PaO2/FiO2 ratio, SpO2, EtCO2, PaCO2, PaCO2 - EtCO2 gradient to assess gas exchange. Mean, minimum and maximum values, 1st and 3rd quartiles, median and standard deviation are calculated. To compare the results obtained at the two evaluation moments, the student’s t-test for dependent samples and non-parametric Wilcoxon tests were considered. To evaluate the association between the variation between the two moments of each variable, and the variation in mechanical power, the Pearson correlation coefficient was calculated. The normality of the variables was assessed using the Jarque-Béra test. P values &lt;0.05 indicated statistical significance. Results Except for EtCO2 (P 0.939) and PaCO2 (P 0.391) all other variables presented statistical significance in relation to their variations with reduction in FiO2 (P &lt;0.001), reduction in PaCO2 - EtCO2 gradient (P 0.011), and increases in PaO2 (P 0.008), PaO2/FiO2 (P &lt;0.001), SpO2 (P 0.004). In the analysis of variables, reduction in respiratory rate (P 0.142), VT (P 0.385), peak pressure (P 0.085), plateau pressure (P 0.009), PEEP (P 0.032), driving pressure (P 0.083), elastance (P 0.180), mechanical power (P &lt; 0.001) with increase static compliance (P 0.414) and resistance pressure (P 0.443). Among the ventilatory mechanics variables, only the reductions in plateau pressure, PEEP, and mechanical power showed statistical significance. Conclusion The prone position in burns induced ARDS improved oxygenation and reduced arterial partial pressure to end tidal CO2 gradient, furthermore, reducing plateau pressures and PEEP, which in turn reduced mechanical power. Keywords: mechanical power, burns, ARDS, prone position

Determination of the Effects of Different EMS Doses Applied to Seeds of Chickpea and Lentil Varieties on Some Seedling Characteristics

In this study, it was aimed to determine the effects of 11 different doses of Ethyl Methane Sulphonate (EMS) applied to the seed to create variation in the M1 generation of chickpea (Gökçe) and lentil (Şakar) genotypes during germination and seedling development periods and to determine the lethal dose that caused a 50% reduction in plant emergence rate. The research was conducted under the greenhouse and laboratory conditions of the Faculty of Agriculture of Dicle University in 2019/2020. The study was conducted according to a randomized block design with three replicates. For the M1 generation, seeds of each genotype in the elite stage were treated with EMS solution at 0 (control), 10, 20, 30, 40, 40, 50, 60, 60, 70, 70, 80, 90, and 100 mM (1000 seeds for each dose) and then sown in the greenhouse. A total of 132 tubes were sown with 30 seeds for each dose, and the effective EMS dose was determined for each genotype based on the traits examined in the developing seedlings. In the study, it was concluded that 11 different EMS doses applied to the seeds of chickpea and lentil varieties had negative effects on seedling development in the M1 generation, and increasing EMS doses from the control caused a decrease in all traits examined. With increasing EMS doses, plant emergence was observed in both chickpea and lentil up to 60 mM dose, while no germination was observed at 60 mM dose, and the dose rate varied according to species and varieties. The dose that caused a 50% decrease in the plant emergence rate in Gökçe chickpea and Şakar lentil varieties was determined as the LD50 dose. Accordingly, it was determined that the LD50 dose was 30 mM for Gökçe chickpea variety and 60 mM for Şakar lentil variety.

Heterogeneous paths to stability

Abstract In this paper, we show that incentives to convert temporary contracts into permanent ones might be less effective when the employment protection legislation associated with temporary contracts is lax. Drawing upon rich administrative data and using a difference-in-differences methodology, we estimate that workers at their first work experience hired on more flexible contracts undergo a reduction in the conversion rate to permanent employment of 6.3 percentage points after the implementation of the incentives (and of 3.2 percentage points over a year), compared to peers hired on more rigid contracts. This reduced conversion rate, which results in a 17% wage penalty even 2 years into their professional journey, points to a significant negative impact of flexible temporary jobs on the future prospects of young workers.

Management of inorganic elements by overwintering physiology of cold hardy larvae of European corn borer (Ostrinia nubilalis, Hbn.).

The European corn borer (Ostrinia nubilalis, Hbn.), enters diapause, a strategy characterized by arrest of development and reproduction, reduction of metabolic rate and the emergence of increased resistance to challenging seasonal conditions as low sub-zero winter temperatures. The aim of this study was to investigate the potential role of inorganic elements in the ecophysiology of O. nubilalis, analysing their content in the whole body, hemolymph and fat body, both metabolically active, non-diapausing and overwintering diapausing larvae by ICP-OES spectrometer following the US EPA method 200.7:2001. O nubilalis as many phytophagous lepidopteran species maintain a very low extracellular sodium concentration and has potassium as dominant cation in hemolymph of their larvae. Changes in hemolymph and the whole body sodium content occur already at the onset of diapause (when the mean environmental temperatures are still high above 0ºC) and remain stable during the time course of diapause when larvae of this species cope with sub-zero temperatures, it seems that sodium content regulation is rather a part of diapausing program than the direct effect of exposure to low temperatures. Compared to non-diapausing O. nubilalis larvae, potassium levels are much higher in the whole body and fat body of diapausing larvae and substantially increase approaching the end of diapause. The concentration of Ca, Mg, P and S differed in the whole body, hemolymph and fat body between non-diapausing and diapausing larvae without a unique trend during diapause, except an increase in their contents at the end of diapause.

Implementation of IFPTML Computational Models in Drug Discovery Against Flaviviridae Family.

The Flaviviridae family consists of single-stranded positive-sense RNA viruses, which contains the genera Flavivirus, Hepacivirus, Pegivirus, and Pestivirus. Currently, there is an outbreak of viral diseases caused by this family affecting millions of people worldwide, leading to significant morbidity and mortality rates. Advances in computational chemistry have greatly facilitated the discovery of novel drugs and treatments for diseases associated with this family. Chemoinformatic techniques, such as the perturbation theory machine learning method, have played a crucial role in developing new approaches based on ML models that can effectively aid drug discovery. The IFPTML models have shown its capability to handle, classify, and process large data sets with high specificity. The results obtained from different models indicates that this methodology is proficient in processing the data, resulting in a reduction of the false positive rate by 4.25%, along with an accuracy of 83% and reliability of 92%. These values suggest that the model can serve as a computational tool in assisting drug discovery efforts and the development of new treatments against Flaviviridae family diseases.