Work Points Research Articles

Book review. Venipuncture for the Application of Blood Concentrates. Shahram Ghanaati, MD, DMD, PhD, Chicago: Quintessence Publishing Co Inc.

Book review. A small handbook on blood sampling for dentists and oral surgeons in the dental practice has been published. The sole author of this guide is Prof. Shahram Ghanaati, DDS, MD, PhD, Senior Consultant and Deputy Director of the Department of Oral and Maxillofacial Plastic Surgery at Frankfurt University Hospital, Germany. Although the advantages of platelet-rich fibrin (PRF) and other blood concentrates in dental implantology are well known, dentists and dental assistants are not used to taking blood samples. The author presents a guideline for collecting and preparing blood samples to ensure a high level of patient safety in these cases and a certain degree of professionalism in the dental profession. In his foreword, the author rightly points out that only regular practice can bring theory to life. The soft-cover book is extremely compact at 64 pages. The author focuses on the individual work steps and points out possible application errors using 88 photos, illustrations, and generally very easy-to-understand language. The result is an inexpensive clinical guide describing the necessary equipment, anatomy, and the correct sequence of steps for successful venipuncture. It should be included in any oral surgery practice that wants to work with blood concentrates. Of course, anyone looking for references or further information in this small book will not find what they want in the instructions. A list of possible application areas for blood concentrates must also be made. However, the reader is provided with efficient step-by-step instructions. Thus, the booklet elegantly helps close the knowledge gap among dentists regarding successful blood collection.

The paradox of predictability provides a bridge between micro- and macroevolution.

The relationship between the evolutionary dynamics observed in contemporary populations (microevolution) and evolution on timescales of millions of years (macroevolution) has been a topic of considerable debate. Historically, this debate centers on inconsistencies between microevolutionary processes and macroevolutionary patterns. Here, we characterize a striking exception: emerging evidence indicates that standing variation in contemporary populations and macroevolutionary rates of phenotypic divergence is often positively correlated. This apparent consistency between micro- and macroevolution is paradoxical because it contradicts our previous understanding of phenotypic evolution and is so far unexplained. Here, we explore the prospects for bridging evolutionary timescales through an examination of this "paradox of predictability." We begin by explaining why the divergence-variance correlation is a paradox, followed by data analysis to show that the correlation is a general phenomenon across a broad range of temporal scales, from a few generations to tens of millions of years. Then we review complementary approaches from quantitative genetics, comparative morphology, evo-devo, and paleontology to argue that they can help to address the paradox from the shared vantage point of recent work on evolvability. In conclusion, we recommend a methodological orientation that combines different kinds of short-term and long-term data using multiple analytical frameworks in an interdisciplinary research program. Such a program will increase our general understanding of how evolution works within and across timescales.

Optimizing Photocatalytic Dye Degradation: A Machine Learning and Metaheuristic Approach for Predicting Methylene Blue in Contaminated Water

Dye contamination in water sources has severe environmental and public health risks; therefore, it needs effective monitoring and remediation strategies. The aim of the study is to use machine learning techniques to develop predictive models that may be used to evaluate methylene blue dye degradation capacity in contaminated water. Ten different machine learning models, including AdaBoost, Bagging, CatBoost, Decision Tree, Extra Trees, Gradient Boosting, HistGradientBoosting, LightGBM, Random Forest, and XGBoost, were evaluated using CuWO₄@TiO₂ as a photocatalyst. R², MSE, RMSE, MAE, and MedAE were used to assess the performance of the models. Among all models, HistGradientBoosting had a very well-balanced performance. It reached a very high R² of 0.9998 on the training set and 0.9915 on the test set, coupled with low error metrics, showcasing its strong generalization capability. However, Gradient Boosting and CatBoost exhibited impressive predictive performance, while AdaBoost and Decision Tree models suffered from overfitting. The maximum prediction obtained in the case of the integral approach for MB dye degradation is 98.99%. Experimental validation indicated that the effectiveness under optimized conditions reached 98.5%. In the case of initial MB concentration at 10 mg/L, a dosage of CuWO4@TiO2 photocatalyst at 200.33 mg/L, light intensity at 150 mW/cm², contact time at 88.6 minutes at room temperature, and near-neutral pH 7.0. The final model metrics included an R² score of 0.9915, MedAE of 1.171, MSE of 5.634, MAE of 1.735, and RMSE of 2.374. This work points out the possibility of taking complete advantage of advanced machine learning algorithms along with metaheuristics optimization in improving photocatalytic processes, hence opening a bright avenue for real applications in water treatment.

Hyperbaric inactivation at 150–250 MPa of Alicyclobacillus acidoterrestris spores at room temperature and effect of innovative technologies pre-treatments

This study evaluated hyperbaric inactivation (HI) at 150, 200 and 250 MPa, up to 24 h (at ambient temperatures,18–23 °C) to inactivate Alicyclobacillus acidoterrestris (ACB) spores in apple juice. The effects of pre-treatments on spores by thermal pasteurization (90 °C, 30 s), high pressure processing (600 MPa, 3 min, HPP), pulsed electric field (30 kV, 80 μs, 1400 Hz, PEF), and ultrasound (67 W, 20 kHz, 5 min, US) prior to HI was evaluated. The results were fitted to non-linear inactivation kinetic models, including Biphasic, Log-logistic, and Weibull. Without a previous pre-treatment, ACB spores were reduced ≈4.56 log units by HI after 24 h, regardless of the pressure level (initial load around 6 log CFU/mL). The pre-treatments did not affect ACB spores during HI (≈4–5 log units' inactivation after 24 h under pressure), except for PEF that resulted in a lower inactivation (≈3.5 log units after 24 h under pressure). Phase contrast microscopy revealed that the spores were unable to form a vegetative cell during HI. HI at 5 °C resulted in a lower inactivation level, with 3.86, 2.54 and 1.77 log units of inactivation, respectively, at 150, 200 and 250 MPa after 96 h. Industrial relevanceThe results of this work point to the possibility of using HI, before or after conventional thermal pasteurization and other nonthermal technologies (HPP, PEF, and US) to process apple juice, to inactivate ACB spores at room temperature and also under refrigeration. This methodology should be further studied in the context of ACB spores' high thermal stability and its industrial relevance.

Fluid and thermal effect on temperature-dependent power increase of electric vehicle's permanent magnet synchronous motor using compound water cooling method

Taking advantage of compound housing water jacket (HWJ) and hollow-shaft water jacket (SWJ) cooling based on the temperature-dependent power of permanent magnet synchronous motor (PMSM) is a solution for enhanced electric vehicle (EV) performance. The fluid and temperature field of a 40 kW PMSM at three typical continuous working points were studied, covering low to high speeds of EVs. The influence of different coolant flowrates on power of motor was obtained by multi-physics field coupling analysis method. The impact of current control modes was also investigated. 3D computational fluid dynamics (CFD) conjugate heat transfer calculation combined with 3D lumped parameter thermal network (LPTN) was adopted to calculate the flow and temperature. Temperature-dependent material properties were taken into consideration in electromagnetic finite element analysis (FEA). The models were modified and validated by experiments. Once compounding SWJ on the basis of a strong HWJ cooling, the PM temperature can continue to decrease over 20 degC. The insensitive characteristic of PM temperature towards SWJ flow rate was observed. Under constant current control mode, 3.8 %, 6 % and 4 % PMSM power enhancement by compound cooling were proved at three typical working points. Under current open-loop, 7 %, 16 %, and 10 % increases with compound cooling were confirmed.

Multi-consistency for semi-supervised medical image segmentation via diffusion models

Medical image segmentation presents a formidable challenge, compounded by the scarcity of annotated data in numerous datasets. Semi-supervised methods offer viable solutions to mitigate that, while the image generation capability of diffusion models has shown potential in capturing semantically meaningful information. This paper introduces Multi-Consistency for semi-supervised medical image Segmentation via Diffusion Models (MCSD). We propose a Diffusion-based Feature-guided Module (DFM) that extracts features from pre-trained diffusion models and uses multi-scale features to guide multi-consistency segmentation networks. Additionally, we introduce the Dual-branch Image Consistency (DIC) strategy, which performs multi-consistency learning by generating two independent strong augmented images and optimizing the network by encouraging consistency between strong and weak inputs at multiple levels of features and images. Our method outperforms current approaches, demonstrating its effectiveness in semi-supervised medical image segmentation through experimental results for various labeled data ratios. Furthermore, this work points out the potentiality of diffusion models in semi-supervised medical image segmentation and offers suggestions for improving their use in medical imaging tasks. The code is available at https://github.com/yunzhuC/MCSD.

Research into Energy-Saving Control Strategies of a Bulldozer Driven by a Torque Converter Based on the Minimum Fuel Consumption Rate of the Whole Machine

In order to address the issue of poor fuel efficiency in hydraulic bulldozers during operation, this paper proposes a speed–load control strategy aimed at minimizing the overall fuel consumption rate of the machine. First, input–output models of the engine, hydraulic torque converter and the entire vehicle were established. Then, based on the bulldozer’s output power and fuel consumption rate, a performance metric for the overall fuel consumption rate was proposed to reflect the machine’s working efficiency. The characteristics of the overall fuel consumption rate were analyzed under different load, speed, and gear conditions. Next, a work point optimization control strategy was proposed for the whole machine energy-saving mode and constant power mode, aiming to minimize the overall fuel consumption rate while using the load, speed, and gear as control variables. To verify the feasibility of the work point optimization control strategy, simulations were conducted for both the energy-saving and constant power modes. The simulation results showed that in the whole machine energy-saving mode, this control strategy resulted in the lowest fuel consumption compared to constant speed control strategies at 1500, 1700, and 2000 rpm, with significant reductions in most cases. In the constant power mode, a comparison between four optimal operating points and the whole machine energy-saving mode revealed that while the former had slightly higher fuel consumption, it ensured a stable power output. Finally, experimental testing demonstrated that the proposed control strategy reduced the overall fuel consumption rate by 12.5% compared to the constant speed mode, verifying the effectiveness of the strategy. The study concludes that the energy-saving control strategy, through the coordinated switching between the two modes in complex operating conditions, not only ensures a stable power output for the bulldozer but also significantly improves fuel efficiency, providing an important reference for optimizing the efficiency of transmission systems in construction machines.

Static Versus Dynamic Accessibility. Accessibility Management as a Tool to Enable Its Permanence over Time.

An accessibility monitoring or audit of an environment provides information at a given point in time (static view of accessibility). But accessibility, to be really effective, must be maintained over time. Therefore, it must be considered throughout all phases of the design process (different phases of the project) and construction (different phases of the work). But it is also essential that, once built, it is possible to maintain or even increase the level of accessibility initially achieved. There are many cases in which, despite a good accessible project, during the life of the building the accessibility conditions worsen substantially because the need for a system that guarantees the permanence of accessibility over time is not contemplated. This requires the adoption of a Universal Accessibility Management System (UAMS) which, as is done with other disciplines such as Quality or Sustainability, allows those responsible for the management of this environment to implement, prevent or correct the accessibility actions that correspond in each case. For the development of a UAMS, the first thing to be defined is the scope to be considered and the scope. Furthermore, from the point of view of use, accessibility can be considered from the visitor's point of view (customer service; customer's perspective) or from the worker's point of view (workstation; employee's perspective). In this article we will take as a reference two methodologies that have been widely used on numerous occasions, to analyse the strengths and weaknesses of each of them and their parts from a deep theoretical (processes and indicators) and practical (disruptors) knowledge. The ultimate aim of this work is to identify the aspects to be considered in the definition of a UAMS, in order to be able to define and specify the keys to approach an accessibility audit from a dynamic perspective, that is to say. one that really guarantees the sustainability of the accessibility of that place over time.

Turbulence correlation between moving trains and anemometer towers: Theoretical analysis, field measurements and simulation

A precise command of railway operations according to the measured instantaneous wind speed on an anemometer tower along a railway line is the development trend, whose challenges lie in the unknown transfer relation(s) between wind speed fluctuations on a moving train and an anemometer tower, i.e. the turbulence correlation between them. To address this issue, in the current work, the cross-correlation functions of wind speed fluctuations at the moving train and anemometer tower are derived, and an empirical formula of the coherence functions is obtained. The turbulence correlation is inversely related to the separation distance from the anemometer tower to the line, and there is little correlation when this distance is longer than double the longitudinal turbulence length scale. Field measurements of wind characteristics were carried out on an anemometer tower and a moving vehicle, and the turbulence correlation and its expression were validated. Three methods are proposed and compared to evaluate the instantaneous wind speed at the anemometer tower and moving train with this correlation. The methods based on the cross-spectral density and coherence function can accurately simulate the correlation, and the latter performance is slightly better (its simulation of the frequency domain correlation is 52.9% better than the former); the method based on solely independent and identically distributed random phases cannot fully simulate the correlation. From this, the effects of the correlation on train operations are studied and analysed in detail. Our analysis shows that neglecting this correlation leads to conservative estimates: wind speed differences between the anemometer tower and the moving train are at least 18.1% greater, and the safety and economic assessments of train operations in crosswinds are underestimated by at least 32.0%. Considering the correlation can reduce the (excess) safety risk/margin and is an inevitable development of adapting to the detailed assessment of the crosswind stability of vehicles. The quantitative description and simulation of the correlation presented in this work point to the critical importance of wind speed monitoring systems for the detailed crosswind assessment, and provide a theoretical basis for further research work on the crosswind stability of vehicles under true/realistic turbulent flow wind.

Fast radio bursts as standard candles for cosmology

Recently, fast radio bursts (FRBs) have become a thriving field in astronomy and cosmology. Due to their extragalactic and cosmological origin, they are useful to study the cosmic expansion and the intergalactic medium (IGM). In the literature, the dispersion measure (DM) of FRB has been considered extensively. It could be used as an indirect proxy of the luminosity distance dL of FRB. The observed DM contains the contributions from the Milky Way (MW), the MW halo, IGM, and the host galaxy. Unfortunately, IGM and the host galaxy of FRB are poorly known to date, and hence the large uncertainties of DMIGM and DMhost in DM plague the FRB cosmology. Could we avoid DM in studying cosmology? Could we instead consider the luminosity distance dL directly in the FRB cosmology? We are interested to find a way out for this problem in the present work. From the lessons of calibrating type Ia supernovae (SNIa) or long gamma-ray bursts (GRBs) as standard candles, we consider a universal subclassification scheme for FRBs, and there are some empirical relations for them. In the present work, we propose to calibrate type Ib FRBs as standard candles by using a tight empirical relation without DM. The calibrated type Ib FRBs at high redshifts can be used like SNIa to constrain the cosmological models. We also test the key factors affecting the calibration and the cosmological constraints. Notice that the results are speculative, since they are based on simulations for the future (instead of now). We stress that the point in the present work is just to propose a viable method and a workable pipeline using type Ib FRBs as standard candles for cosmology in the future.

Perspectives of care providers on obstacles to healthcare access for people with disabilities in Ethiopia: a qualitative investigation

BackgroundDespite having similar healthcare needs to non-disabled individuals, people with disabilities in Ethiopia face significant challenges accessing healthcare services due to existing barriers. This study aimed to explore these barriers from the perspectives of care providers in primary healthcare facilities.MethodsQualitative research was conducted with care providers in Dire Dawa to identify barriers to healthcare access for people with disabilities. Thematic analysis was used to categorize data into themes.ResultsEight healthcare providers from four service departments participated in eight in-depth interviews and two focus groups. Six themes emerged: physical barriers, financial difficulties, non-accommodating diagnostic facilities, care providers’ attitudes and capacity, communication barriers, and lack of access to health information. Non-confidential and non-dignified care was also identified as a barrier.ConclusionThe study highlights important barriers that prevent people with disabilities from accessing primary healthcare services. Addressing these barriers is crucial for achieving disability-inclusive healthcare services. This research can serve as a starting point for further work in this area to promote equitable healthcare for people with disabilities.

FUZZY LOGIC-BASED POWER MANAGEMENT IN GRID-CONNECTED SOLAR ENERGY SYSTEMS

With the ever-increasing application of photovoltaic systems on the power grid, energy flow management and grid stability have become a key factor, especially under varying conditions like variable cloud cover and fluctuating load demands. The paper looks forward to proposing a fuzzy logic approach to the management of energy regarding a solar energy system connected to a grid, aiming at obtaining an optimised power distribution among PV generation, the grid, and battery storage. The proposed system will be driven by five input parameters, PV value, grid availability, load demand, battery state of charge, and tariff rate, to regulate the energy flow activity within different scenarios. The simulation results developed in the MATLAB Simulink environment proved the effectiveness of the applied controller in maintaining energy stability and fulfilling the requirements of a load within seven case studies for a 24-hour duration. The work points out the potential of fuzzy logic, besides reliability, in enhancing the efficiency of various renewable energy systems integrated with conventional energy supply grids.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/soc/0799/a.php" alt="Hit counter" /></p>

MACHINE-LEARNING TECHNIQUES IN MULTIPLE SCLEROSIS PREDICTION USING EEG

The diagnosis and quantification of Multiple Sclerosis (MS) have typically depended on skilled doctors recognizing visual patterns, such as Magnetic Resonance Imaging (MRI) and Electroencephalography (EEG), which resulted in a costly, time-consuming and non-reproducible process. The application of Machine Learning (ML) in MS diagnosis has been getting a lot of attention in the last few years due to the volume of scientific data, the heterogeneity of disease courses, and the variety of diagnostic methods. The EEG has the capability to detect important changes in the brain’s inherent electrical activity, which are influenced by changes in the neural network connections associated with inflammatory demyelinating and neural damage characteristic of the MS. Utilizing multimodal machine learning over the clinically available data may be a contemporary strategy with amazing potential to facilitate early diagnosis. Considering recent EEG investigations as well as the accessibility to their datasets would be the initial steps in utilizing the ML in MS diagnosis. This paper provides a systematic review of the latest techniques for MS diagnostic based on PRISMA guidelines with the prospect of ML application in their investigations. The goal is to find if EEG could be considered a robust and accurate technique for MS diagnosis. In accordance with PRISMA guidelines, we consider 111 papers. In our review, 404 people are considered including 209 with MS and 195 healthy controls. As a result, we generated an updated investigation looking at the ML approaches as well as utilizing EEG as an accurate, but less often used method to help with early diagnosis of MS. We summarize, analyze, discuss, and synthesize the recently published works, current trends, and open research issues. The review also points out knowledge gaps about the necessity of validating results and addressing constraints such as limited sample number. Our investigation proves that the precision of the supervised strategies such as [Formula: see text]NN and SVM is typically higher than that of the unsupervised strategies. On the other hand, by utilizing various techniques such as splitting EEG signal sub-bands, signal windowing, and identifying effective features from the data analysis approaches, we have been able to achieve significant classification accuracy higher than 99%. According to the high degree of accuracy of the results, this approach is becoming the focal point of the research works. The high diagnostic accuracy of the proposed machine learning techniques on the EEG signal analysis shows its potential capacity to become a more widespread procedure as the MS diagnostic techniques. To develop the reliability of these methods, gathering, and analyzing more EEG signals from MS patients and creating more suitable EEG protocols are essential in future studies. In the same way, for the dynamic and online analyses of the EEG signals, in-depth studies are needed to determine more effective EEG protocols and machine-learning techniques.

Paradoxical Luxury in the Sint Maarten’s Property Market

This article outlines the concept of ‘paradoxical luxury’ derived through an ethnographic exploration of Sint Maarten’s high-end real estate market. Classic works analyzed luxury goods as markers of status and their consumption playing a role in reinforcing social stratification. More recent work points to the allure of luxury consumption as predicated upon references to heritage, tradition, durability, and quality as selling points while also serving older functions of display and stratification. The luxury concept has also been influenced by more recent concerns among consumers about issues such as sustainability. This study, however, reveals a luxury real estate market through which the ability to display status remains unclear as well as lacking key defining elements of luxury such as heritage and quality. Further, the cycle of repeated destruction of these properties due to seasonal hurricanes present a form of luxury that challenges the traditional views of the concept. As such, the marketed exclusivity and perceived value of properties are directly confronted by their vulnerability to natural disasters, sub-standard building quality and inferior local infrastructure, as well as lax regulatory framework. The concept of paradoxical luxury underscores the contradictory nature of luxury in this setting.

A new plant-based drug delivery platform based on alkyl polyglucosides and β-sitosterol nanovesicles for topical delivery

The finding of new vesicular systems is a challenging process that depends on different factors such as the components used, the interactions between them or the dispersant media. Our objective was to develop a new vesicular delivery system formed by self-assembly of β-Sitosterol (Sit), Lauryl Glucoside (LGL) and Lauryl Glucose Carboxylate (LGC) molecules, all plant-based, biodegradable and biocompatible components. Nanovesicles (NVs) with different molar ratios of Sit, LGL and LGC were prepared using a single step method named DELOS, and characterized by dynamic light scattering, cryo-electron microscopy and small-angle X-ray scattering. Antioxidant compound α-tocopherol (TCP) was integrated in the NVs showing their potential to nanoformulate hydrophobic payloads. Finally, in vitro biocompatibility assays with reconstructed human epidermis and ex vivo skin retention studies using multiphoton microscopy and NVs labelled with Nile Red (NR) were carried out. As a result of this work, a new platform of NVs has been obtained by the self-assembly of Sit, LGL and LGC, obtaining vesicular systems with tunable physicochemical properties in terms of size (130 – 220 nm), surface charge ((-70) – (-40) mV) and lamellarity (unilamellar and multilamellar vesicles), when the carbon chain of the alkyl polyglucoside was >12. The vesicles could efficiently integrate TCP, proving their potential role as delivery systems and maintaining its antioxidant activity after loading. Finally, they also showed biocompatibility with the skin and improved the permeability of the poorly water-soluble molecule NR in terms of time and depth through the epidermis. Overall, the results of this work point to the successful development of an attractive platform based on stable and homogeneous nanovesicles composed of plant-derived ingredients for topical delivery.

Enhanced Interfaces for High-Temperature Purposes—Practical Methodology and Characterization

The current study is focused on devising treated diatomite interfaces with the robustness and boiling water resistance necessary for high-temperature purposes. This work describes the synthesis methodology of the diatomite-based coatings, which followed the production of a composite formulation composed by treated diatomite powder dispersed in an epoxy resin matrix. After its preparation, the suspension was applied via the dip-coating technique over AISI-304 stainless-steel foils, which, after being air dried, underwent a post-curing treatment. Also, the interfaces were characterized by diverse techniques such as scanning electron microscopy and optical tensiometry. Apart from this, their thermophysical properties like thermal conductivity were also determined. Further, the physical and chemical durability of the interfaces was also evaluated via the elaboration of robustness tests including abrasion resistance, adhesion strength, solid impact resistance, and solvent resistance. The results showed satisfactory resistant interfaces, and with a wettability characterized by contact angles superior to 150°. Also, the interfaces confirmed improved durability when immersed in boiling water at 1 atm, since their wetting characteristics and durability remained nearly unaltered after 762 h of testing. Additionally, the synthesized interfaces possessed self-cleaning ability and chemical and thermal shock aging resistance. Generally, the fundamental outcomes of this work point out the suitability of the produced diatomite-based interfaces to be explored in high-temperature applications like flow boiling, pool boiling, and condensation. In terms of practicality, the method of preparation of the interfaces was a relatively easy and rapid approach to obtaining enhanced wettability and resilient interfaces, and with the required adaptations like the ratios between the raw materials, its suitability for large-scale applications makes this an appealing option.

Theoretical study of point defects on transport properties in metallic interconnections

During the line width reduction, electron scattering caused by various defects in metal interconnects increases dramatically, which causes leakage or short circuit problems in the device, reducing device performance and reliability. Point defects are one of the important factors. Here, using density functional theory and non-equilibrium Green's function methods, we systematically study the effects of point defects on the transport properties of metals Al, Cu, Ag, Ir, Rh, and Ru, namely vacancy defects and interstitial doping of C atom. The results show that the conductivity of all systems decreases compared to perfect systems, because defects cause unnecessary electron scattering. Since the orbital hybridization of the C atom with the Al, Cu and Ag atoms is stronger than that metals Ir, Rh and Ru, the doping of C atom significantly reduces the conductivity of metals Al, Cu and Ag compared to vacancy defects. In contrast, vacancy defects have a greater impact than doping on the transport properties of metals Ir, Rh and Ru, which is mainly attributed to the larger charge transfer of the host atoms around the vacancies caused by lattice distortion. In addition, metal Rh exhibits excellent conductivity in all systems. Therefore, in order to optimize the transport properties of interconnect metals, our work points out that the doping of impurity atoms should be avoided for metals Al, Cu and Ag, while the presence of vacancy defects should be avoided for metals Ir, Rh and Ru, and Rh may be an excellent candidate material for future metal interconnects.

Visual Attention and Cardiac Response to Faces in Relation to Emotion Recognition

Abstract: Rapid and accurate emotion recognition is a crucial skill for social interactions, and visual attention to informative social cues in the environment can facilitate emotion recognition. Studies have found that emotion recognition is also associated with underlying cardiac autonomic responses. The current study examined (1) visual attention and cardiac response (change in heart rate) to emotionally-expressive faces and houses (as a nonsocial control image), and (2) associations between these responses and emotion recognition on the Reading the Mind in the Eyes Test (RMET) in college students. Results showed increased overall attention, but shorter first fixation durations, to faces as compared to houses. Across faces, attention was greater to eyes than mouth, but the magnitude of this difference was dependent on emotion. Analysis of cardiac responses revealed greater heart rate deceleration to happy and fearful faces as compared to neutral faces and houses. Better emotion recognition accuracy on the RMET was related to greater attention to faces, and eyes specifically, as well as greater heart rate deceleration to faces relative to houses. No relations between visual attention and cardiac response were found. The current work points to variations in visual attention and cardiac responses while viewing emotional faces that can be markers of emotion processing ability.

Degree of Impact of Applying Environmental Cost Accounting on Tax Revenues from Oil Companies Operating in Iraq

Protecting the environment and its natural resources is an issue of great importance at the local and global levels. The phenomenon of environmental pollution resulting from the work of oil companies is one of the dangerous phenomena facing the environment in Iraq in general, and the environment of Kirkuk Governorate in particular, as the latter is considered one of the governorates rich in mineral resources, especially oil, as it contains six oil fields, and it contains the largest oil field in Iraq. Estimates indicate that it produces (40%) of the total Iraqi oil, so it suffers from a significant increase in the proportions of toxic and environmentally polluting gases resulting from the work of these companies in it. The work of oil companies from exploration, extraction and refining oil will naturally result in the emission of many gases that are highly toxic, dangerous and harmful to human health. The most important of these gases are carbon monoxide, sulfur dioxide, nitrogen dioxide, hydrocarbon compounds, hydrogen sulfide and other gases that pollute the environment, which has resulted in the spread and increase in the number of patients with respiratory diseases in a clear and increasing manner in the governorate. This increase in environmental costs falls on society as a whole, so it was necessary to identify and measure environmental costs from the society’s point of view and work to re-charge them to oil companies by applying the environmental tax.

Female-age-dependent changes in the lipid fingerprint of the mammalian oocytes.

Can oocyte functionality be assessed by observing changes in their intracytoplasmic lipid droplets (LDs) profiles? Lipid profile changes can reliably be detected in human oocytes; lipid changes are linked with maternal age and impaired developmental competence in a mouse model. In all cellular components, lipid damage is the earliest manifestation of oxidative stress (OS), which leads to a cascade of negative consequences for organelles and DNA. Lipid damage is marked by the accumulation of LDs. We hypothesized that impaired oocyte functionality resulting from aging and associated OS could be assessed by changes in LDs profile, hereafter called lipid fingerprint (LF). To investigate if it is possible to detect differences in oocyte LF, we subjected human GV-stage oocytes to spectroscopic examinations. For this, a total of 48 oocytes derived from 26 young healthy women (under 33 years of age) with no history of infertility, enrolled in an oocyte donation program, were analyzed. Furthermore, 30 GV human oocytes from 12 women were analyzed by transmission electron microscopy (TEM). To evaluate the effect of oocytes' lipid profile changes on embryo development, a total of 52 C57BL/6 wild-type mice and 125 Gnpat+/- mice were also used. Human oocytes were assessed by label-free cell imaging via coherent anti-Stokes Raman spectroscopy (CARS). Further confirmation of LF changes was conducted using spontaneous Raman followed by Fourier transform infrared (FTIR) spectroscopies and TEM. Additionally, to evaluate whether LF changes are associated with developmental competence, mouse oocytes and blastocysts were evaluated using TEM and the lipid dyes BODIPY and Nile Red. Mouse embryonic exosomes were evaluated using flow cytometry, FTIR and FT-Raman spectroscopies. Here we demonstrated progressive changes in the LF of oocytes associated with the woman's age consisting of increased LDs size, area, and number. LF variations in oocytes were detectable also within individual donors. This finding makes LF assessment a promising tool to grade oocytes of the same patient, based on their quality. We next demonstrated age-associated changes in oocytes reflected by lipid peroxidation and composition changes; the accumulation of carotenoids; and alterations of structural properties of lipid bilayers. Finally, using a mouse model, we showed that LF changes in oocytes are negatively associated with the secretion of embryonic exosomes prior to implantation. Deficient exosome secretion disrupts communication between the embryo and the uterus and thus may explain recurrent implantation failures in advanced-age patients. Due to differences in lipid content between different species' oocytes, the developmental impact of lipid oxidation and consequent LF changes may differ across mammalian oocytes. Our findings open the possibility to develop an innovative tool for oocyte assessment and highlight likely functional connections between oocyte LDs and embryonic exosome secretion. By recognizing the role of oocyte LF in shaping the embryo's ability to implant, our original work points to future directions of research relevant to developmental biology and reproductive medicine. This research was funded by National Science Centre of Poland, Grants: 2021/41/B/NZ3/03507 and 2019/35/B/NZ4/03547 (to G.E.P.); 2022/44/C/NZ4/00076 (to M.F.H.) and 2019/35/N/NZ3/03213 (to Ł.G.). M.F.H. is a National Agency for Academic Exchange (NAWA) fellow (GA ULM/2019/1/00097/U/00001). K.F. is a Diamond Grant fellow (Ministry of Education and Science GA 0175/DIA/2019/28). The open-access publication of this article was funded by the Priority Research Area BioS under the program "Excellence Initiative - Research University" at the Jagiellonian University in Krakow. The authors declare no competing interest. N/A.