bluuebunny/crossref_metadata_2025 huggingface dataset:
Characteristics of R2019 Processing of MODIS Sea Surface Temperature at High Latitudes
Authors: Jia Chong , Minnett Peter J. , Szczodrak Malgorzata
Published: November 2024

Satellite remote sensing is the best way to derive sea surface skin temperature (SSTskin) in the Arctic. However, as surface temperature retrieval algorithms in the infrared (IR) part of the electromagnetic spectrum are designed to compensate for atmospheric effects mainly due to water vapor, MODIS SSTskin retrievals have larger uncertainties at high latitudes where the atmosphere is very dry and cold, which is an extreme in the distribution of global conditions. MODIS R2019 SSTskin fields are currently derived using latitudinally and monthly dependent algorithm coefficients, including an additional band above 60°N to better represent the effects of Arctic atmospheres. However, the R2019 processing of MODIS SSTskin still has some unrevealed error characteristics. This study uses 21 years (2002–2022) of collocated, simultaneous satellite brightness temperature (BT) data from Aqua MODIS and in situ buoy-measured subsurface temperature data from iQuam for validation. Unlike elsewhere over the oceans, the 11 μm and 12 μm BT differences are poorly related to the column water vapor at high latitudes, resulting in poor atmospheric water vapor correction. Anomalous BT difference signals are identified, caused by the temperature and humidity inversions in the lower troposphere, which are especially significant during the summer. Although the existence of negative BT differences is physically reasonable, this makes the retrieval algorithm lose its effectiveness. Moreover, the statistics of the MODIS SSTskin data when compared with the iQuam buoy temperature data show large differences (in terms of mean and standard deviation) for the matchups at the Northern Atlantic and Pacific sides of the Arctic due to the disparity of in situ measurements and distinct surface and vertical atmospheric conditions. Therefore, it is necessary to further improve the retrieval algorithms to obtain more accurate MODIS SSTskin data to study surface ocean processes and climate change in the Arctic.
Calibration of Dual-Polarised Antennas for Air-Coupled Ground Penetrating Radar Applications
Authors: Forster Samuel J. I. , Peyton Anthony J. , Podd Frank J. W.
Published: November 2024

Radar polarimetry is a technique that can be used to enhance target detection, identification and classification; however, the quality of these measurements can be significantly influenced by the characteristics of the radar antenna. For an accurate and reliable system, the calibration of the antenna is vitally important to mitigate these effects. This study presents a methodology to calibrate Ultra-Wideband (UWB) dual-polarised antennas in the near-field using a thin elongated metallic cylinder as the calibration object. The calibration process involves measuring the scattering matrix of the metallic cylinder as it is rotated, in this case producing 100 distinct scattering matrices from which the calibration parameters are derived, facilitating a robust and stable solution. The calibration procedure was tested and validated using a Vector Network Analyser (VNA) and two quad-ridged antennas, which presented different performance levels. The calibration methodology demonstrated notable improvements, aligning the performance of both functioning and under-performing antennas to equivalent specifications. Mid-band validation measurements indicated minimal co-polar channel imbalance (<0.3 dB), low phase error (<0.8°) and improved cross-polar isolation (≈48 dB).
Precise Geoid Determination in the Eastern Swiss Alps Using Geodetic Astronomy and GNSS/Leveling Methods
Authors: Albayrak Müge , Marti Urs , Willi Daniel , Guillaume Sébastien , Hardy Ryan A.
Published: November 2024

Astrogeodetic deflections of the vertical (DoVs) are close indicators of the slope of the geoid. Thus, DoVs observed along horizontal profiles may be integrated to create geoid undulation profiles. In this study, we collected DoV data in the Eastern Swiss Alps using a Swiss Digital Zenith Camera, the COmpact DIgital Astrometric Camera (CODIAC), and two total station-based QDaedalus systems. In the mountainous terrain of the Eastern Swiss Alps, the geoid profile was established at 15 benchmarks over a two-week period in June 2021. The elevation along the profile ranges from 1185 to 1800 m, with benchmark spacing ranging from 0.55 km to 2.10 km. The DoV, gravity, GNSS, and levelling measurements were conducted on these 15 benchmarks. The collected gravity data were primarily used for corrections of the DoV-based geoid profiles, accounting for variations in station height and the geoid-quasigeoid separation. The GNSS/levelling and DoV data were both used to compute geoid heights. These geoid heights are compared with the Swiss Geoid Model 2004 (CHGeo2004) and two global gravity field models (EGM2008 and XGM2019e). Our study demonstrates that absolute geoid heights derived from GNSS/leveling data achieve centimeter-level accuracy, underscoring the precision of this method. Comparisons with CHGeo2004 predictions reveal a strong correlation, closely aligning with both GNSS/leveling and DoV-derived results. Additionally, the differential geoid height analysis highlights localized variations in the geoid surface, further validating the robustness of CHGeo2004 in capturing fine-scale geoid heights. These findings confirm the reliability of both absolute and differential geoid height calculations for precise geoid modeling in complex mountainous terrains.
A Novel Diagnostic Feature for a Wind Turbine Imbalance Under Variable Speed Conditions
Authors: Askari Amir R. , Gelman Len , King Russell , Hickey Daryl , Ball Andrew D.
Published: November 2024

Dependency between the conventional imbalance diagnostic feature and the shaft rotational speed makes imbalance diagnosis challenging for variable-speed machines. This paper focuses on an investigation of this dependency and on a proposal for a novel imbalance diagnostic feature and a novel simplified version for this feature, which are independent of shaft rotational speed. An equivalent mass–spring–damper system is investigated to find a closed-form expression describing this dependency. By normalizing the conventional imbalance diagnostic feature by the obtained dependency, a diagnostic feature is proposed. By conducting comprehensive experimental trials with a wind turbine with a permissible imbalance, it is justified that the proposed simplified version of imbalance diagnostic feature is speed-invariant.
Underwater Wireless Communications
Authors: Esmaiel Hamada , Sun Haixin
Published: November 2024

Effective underwater wireless communications (UWCs) are essential for a variety of military and civil applications, such as submarine communication and discovery of new natural resources in the underwater environment [...]
Quantification of Size-Binned Particulate Matter in Electronic Cigarette Aerosols Using Multi-Spectral Optical Sensing and Machine Learning
Authors: Jiang Hao , Kolaczyk Keith
Published: November 2024

To monitor health risks associated with vaping, we introduce a multi-spectral optical sensor powered by machine learning for real-time characterization of electronic cigarette aerosols. The sensor can accurately measure the mass of particulate matter (PM) in specific particle size channels, providing essential information for estimating lung deposition of vaping aerosols. For the sensor’s input, wavelength-specific optical attenuation signals are acquired for three separate wavelengths in the ultraviolet, red, and near-infrared range, and the inhalation pressure is collected from a pressure sensor. The sensor’s outputs are PM mass in three size bins, specified as 100–300 nm, 300–600 nm, and 600–1000 nm. Reference measurements of electronic cigarette aerosols, obtained using a custom vaping machine and a scanning mobility particle sizer, provided the ground truth for size-binned PM mass. A lightweight two-layer feedforward neural network was trained using datasets acquired from a wide range of puffing conditions. The performance of the neural network was tested using unseen data collected using new combinations of puffing conditions. The model-predicted values matched closely with the ground truth, and the accuracy reached 81–87% for PM mass in three size bins. Given the sensor’s straightforward optical configuration and the direct collection of signals from undiluted vaping aerosols, the achieved accuracy is notably significant and sufficiently reliable for point-of-interest sensing of vaping aerosols. To the best of our knowledge, this work represents the first instance where machine learning has been applied to directly characterize high-concentration undiluted electronic cigarette aerosols. Our sensor holds great promise in tracking electronic cigarette users’ puff topography with quantification of size-binned PM mass, to support long-term personalized health and wellness.
Lower Limb Motion Recognition Based on sEMG and CNN-TL Fusion Model
Authors: Zhou Zhiwei , Tao Qing , Su Na , Liu Jingxuan , Chen Qingzheng , Li Bowen
Published: November 2024

To enhance the classification accuracy of lower limb movements, a fusion recognition model integrating a surface electromyography (sEMG)-based convolutional neural network, transformer encoder, and long short-term memory network (CNN-Transformer-LSTM, CNN-TL) was proposed in this study. By combining these advanced techniques, significant improvements in movement classification were achieved. Firstly, sEMG data were collected from 20 subjects as they performed four distinct gait movements: walking upstairs, walking downstairs, walking on a level surface, and squatting. Subsequently, the gathered sEMG data underwent preprocessing, with features extracted from both the time domain and frequency domain. These features were then used as inputs for the machine learning recognition model. Finally, based on the preprocessed sEMG data, the CNN-TL lower limb action recognition model was constructed. The performance of CNN-TL was then compared with that of the CNN, LSTM, and SVM models. The results demonstrated that the accuracy of the CNN-TL model in lower limb action recognition was 3.76%, 5.92%, and 14.92% higher than that of the CNN-LSTM, CNN, and SVM models, respectively, thereby proving its superior classification performance. An effective scheme for improving lower limb motor function in rehabilitation and assistance devices was thus provided.
Edge Computing for AI-Based Brain MRI Applications: A Critical Evaluation of Real-Time Classification and Segmentation
Authors: Memon Khuhed , Yahya Norashikin , Yusoff Mohd Zuki , Remli Rabani , Mustapha Aida-Widure Mustapha Mohd , Hashim Hilwati , Ali Syed Saad Azhar , Siddiqui Shahabuddin
Published: November 2024

Medical imaging plays a pivotal role in diagnostic medicine with technologies like Magnetic Resonance Imagining (MRI), Computed Tomography (CT), Positron Emission Tomography (PET), and ultrasound scans being widely used to assist radiologists and medical experts in reaching concrete diagnosis. Given the recent massive uplift in the storage and processing capabilities of computers, and the publicly available big data, Artificial Intelligence (AI) has also started contributing to improving diagnostic radiology. Edge computing devices and handheld gadgets can serve as useful tools to process medical data in remote areas with limited network and computational resources. In this research, the capabilities of multiple platforms are evaluated for the real-time deployment of diagnostic tools. MRI classification and segmentation applications developed in previous studies are used for testing the performance using different hardware and software configurations. Cost–benefit analysis is carried out using a workstation with a NVIDIA Graphics Processing Unit (GPU), Jetson Xavier NX, Raspberry Pi 4B, and Android phone, using MATLAB, Python, and Android Studio. The mean computational times for the classification app on the PC, Jetson Xavier NX, and Raspberry Pi are 1.2074, 3.7627, and 3.4747 s, respectively. On the low-cost Android phone, this time is observed to be 0.1068 s using the Dynamic Range Quantized TFLite version of the baseline model, with slight degradation in accuracy. For the segmentation app, the times are 1.8241, 5.2641, 6.2162, and 3.2023 s, respectively, when using JPEG inputs. The Jetson Xavier NX and Android phone stand out as the best platforms due to their compact size, fast inference times, and affordability.
Improving Factuality by Contrastive Decoding with Factual and Hallucination Prompts
Authors: Lv Bojie , Feng Ao , Xie Chenlong
Published: November 2024

Large language models have demonstrated impressive capabilities in many domains. But they sometimes generate irrelevant or nonsensical text, or produce outputs that deviate from the provided input, an occurrence commonly referred to as hallucination. To mitigate this issue, we introduce a novel decoding method that incorporates both factual and hallucination prompts (DFHP). It applies contrastive decoding to highlight the disparity in output probabilities between factual prompts and hallucination prompts. Experiments on both multiple-choice and text generation tasks show that our approach significantly improves factual accuracy of large language models without additional training. On the TruthfulQA dataset, the DFHP method significantly improves factual accuracy of the LLaMA model, with an average improvement of 6.4% for the 7B, 13B, 30B, and 65B versions. Its high accuracy in factuality makes it an ideal choice for high reliability tasks like medical diagnosis and legal cases.
Experimental and Numerical Studies of the Temperature Field in a Dielectrophoretic Cell Separation Device Subject to Joule Heating
Authors: Seki Yoshinori , Tada Shigeru
Published: November 2024

Technologies for rapid and high-throughput separation of rare cells from large populations of other types of cells have recently attracted much attention in the field of bioengineering. Among the various cell separation technologies proposed in the past, dielectrophoresis has shown particular promise because of its preciseness of manipulation and noninvasiveness to cells. However, one drawback of dielectrophoresis devices is that their application of high voltage generates Joule heat that exposes the cells within the device to high temperatures. To further explore this problem, this study investigated the temperature field in a previously developed cell separation device in detail. The temperature rise at the bottom of the microfluidic channel in the device was measured using a micro-LIF method. Moreover, the thermofluidic behavior of the cell separation device was numerically investigated by adopting a heat generation model that takes the electric-field-dependent heat generation term into account in the energy equation. Under the operating conditions of the previously developed cell separation device, the experimentally obtained temperature rise in the device was approximately 20 °C, and the numerical simulation results generally agreed well. Next, parametric calculations were performed with changes in the flow rate of the cell sample solution and the solution conductivity, and a temperature increase of more than 40 °C was predicted. The results demonstrated that an increase in temperature within the cell separation device may have a significant impact on the physiological functions of the cells, depending on the operating conditions of the device.
Nationwide Surveillance and Cumulative Risk Assessment of Pesticide Residues in Egyptian Vegetables: Results from 2018 to 2021
Authors: Malhat Farag , Saber Ayman , Saber El-Sayed , Shokr Shokr Abel Salam , Abdel-Megeed Mohammed
Published: November 2024

Pesticide residues in food are a global concern due to their potential impacts on human health and the environment. This study investigates pesticide residues in vegetables commonly consumed in Egypt, utilizing advanced analytical techniques such as Liquid Chromatography–Tandem Mass Spectrometry (LC-MS/MS) and Gas Chromatography–Tandem Mass Spectrometry (GC-MS/MS). A total of 4200 vegetable samples, including cucumber, squash, eggplant, okra, peas, onion, green onion, parsley, lettuce, and cantaloupe, were collected from 20 markets across different Egyptian governorates between 2018 and 2021. The analysis revealed that 42% of the samples contained pesticide residues, with only 13% of the analyzed samples exceeding the maximum residue limits (MRLs) established by Codex/EU-MRL standards. Despite this, the estimated daily intake (EDI) of the detected pesticides did not surpass acceptable daily intake (ADI) limits, suggesting no immediate chronic health risks to consumers. These findings highlight Egypt’s ongoing commitment to food safety, while also underscoring the importance of continuous monitoring and the promotion of sustainable agricultural practices to ensure the long-term safety and quality of the food supply.
Libertarian Populism? Making Sense of Javier Milei’s Political Discourse
Authors: Heinisch Reinhard , Gracia Oscar , Laguna-Tapia Andrés , Muriel Claudia
Published: November 2024

This study seeks to understand the political discourse of Javier Milei and to determine which concept of populism best captures his approach. Although perceived by many as a populist, Milei is unusual in that he sees himself as a liberal libertarian and defender of the West against collectivist policies. To this end, this study analyzes selected speeches by Milei from three different periods during and after the 2024 presidential election campaign and applies a deductive coding scheme designed to identify ideational populism, populist discursive framing, populism as strategy, and populism as crisis performance. The analysis confirms that Milei is at best a partial populist, as he fails to define the core populist concept of “the people”. It concludes that the concept of crisis performance emerges as the most apt theoretical framework to classify Milei’s type of populism. By rhetorically transforming the crisis not only into an existential economic issue but also into a moral tale of corruption and failure at the highest levels, he can appeal for radical change and offer himself as the national political savior. Milei’s discourse also illustrates that, unlike ideological populism or discursive populist framing, in the performative turn, the victims of the crisis, the people, often remain a vague signifier defined by their suffering at the hands of elites.
Situational and Dispositional Achievement Goals and Measures of Sport Performance: A Systematic Review with a Meta-Analysis
Authors: Lochbaum Marc , Sisneros Cassandra
Published: November 2024

The purposes of this systematic review (PROSPERO ID: CRD42024510614, no funding source) were to quantify relationships between situational and dispositional dichotomous achievement goals and sport performance and explore potential relationship moderators. Published studies that reported at least one situational or dispositional achievement goal and a performance score were included. Studies without performance scores or based in a non-sport context were excluded. Information sources consisted of studies found in relevant published meta-analyses and EBSCOhost databases (finalized September 2024). The following statistics were conducted to assess the risk of bias: class-fail-safe n, Orwin’s fail-safe n, and funnel plots with trim and fill estimates. The summary statistics were r and d. Thirty studies from 1994 to 2024 met all inclusion criteria with 8708 participants from Europe, Asia, North America, and Oceania. The majority of samples were non-elite male youths and adolescents. The random-effects relationships (r) between task climate, 0.20 [0.14, 0.25], task orientation, 0.17 [0.12, 0.23], ego orientation, 0.09 [0.03, 0.16], and sport performance were small and significantly different (p < 0.05) from zero, while the ego motivational climate relationship was not, −0.00 [−0.48, 0.05]. The random-effects standard differences in means (d) for both the task orientation, 0.08 [0.02, 0.14], and ego orientation, 0.11 [−0.05, 0.26] were minimal in meaningfulness. Mixed-effects moderator analyses resulted in the following significant (p < 0.05) sub-group differences: subjective compared to objective performance measures (task orientation), elite compared to non-elite samples (task climate), and athlete-completed compared to coach-completed performance measures and performance records (task orientation). Finding only 30 studies meeting the inclusion criteria, which limited sub-group samples for moderation analyses, was the main limitation. Despite this limitation, AGT provides athletes and practitioners performance enhancement strategies. However, caution is warranted regarding relationship expectations given the small mean effect size values and the true prediction interval ranging from negative to positive, perhaps as a result of the heterogeneous samples and performance measures. A clear line of future research, considering the reviewed studies, with elite athletes is needed to verify the performance benefits of the task climate and ego orientation as well as the use of the ego goal orientation in selection decisions.
Intelligent Fault Diagnosis Method Based on Neural Network Compression for Rolling Bearings
Authors: Wang Xinren , Hu Dongming , Fan Xueqi , Liu Huiyi , Yang Chenbin
Published: November 2024

Rolling bearings are often exposed to high speeds and pressures, leading to the symmetry in their rotating structure being disrupted, which can lead to serious failures. Intelligent rolling bearing fault diagnosis is a critical part of ensuring operation of machinery, and it has been facilitated by the growing popularity of convolutional neural networks (CNNs). The outstanding performance of fault diagnosis CNNs results from complex and redundant network structures and parameters, resulting in huge storage and computational requirements, which makes it challenging to implement these models in resource-limited industrial devices. This study aims to address this problem by proposing a comprehensive compression method for CNNs that is applied to intelligent fault diagnosis. It involves several different compression methods, including tensor train decomposition, parameter quantization, and knowledge distillation for deep network compression. This results in a significant decrease in redundancy and speeding up the training of CNN models. Firstly, tensor train decomposition is applied to reduce redundant connections in both convolutional and fully connected layers. The next step is to perform parameter quantization to minimize the bits needed for parameter representation and storage. Finally, knowledge distillation is used to restore accuracy to the compressed model. The effectiveness of the proposed approach is confirmed by an experiment and ablation study with different models on several datasets. The results show that it can significantly reduce redundant information and floating-point operations with little degradation in accuracy. Notably, on the CWRU dataset, with about 60% parameter reduction, there is no degradation in our model’s accuracy. The proposed approach is a new attempt at the intelligent fault diagnosis of rolling bearings in industrial equipment.
Path Tracking Control for Four-Wheel Independent Steering and Driving Vehicles Based on Improved Deep Reinforcement Learning
Authors: Hua Xia , Zhang Tengteng , Cheng Xiangle , Ning Xiaobin
Published: November 2024

We propose a compound control framework to improve the path tracking accuracy of a four-wheel independent steering and driving (4WISD) vehicle in complex environments. The framework consists of a deep reinforcement learning (DRL)-based auxiliary controller and a dual-layer controller. Samples in the 4WISD vehicle control framework have the issues of skewness and sparsity, which makes it difficult for the DRL to converge. We propose a group intelligent experience replay (GER) mechanism that non-dominantly sorts the samples in the experience buffer, which facilitates within-group and between-group collaboration to achieve a balance between exploration and exploitation. To address the generalization problem in the complex nonlinear dynamics of 4WISD vehicles, we propose an actor-critic architecture based on the method of two-stream information bottleneck (TIB). The TIB method is used to remove redundant information and extract high-dimensional features from the samples, thereby reducing generalization errors. To alleviate the overfitting of DRL to known data caused by IB, the reverse information bottleneck (RIB) alters the optimization objective of IB, preserving the discriminative features that are highly correlated with actions and improving the generalization ability of DRL. The proposed method significantly improves the convergence and generalization capabilities of DRL, while effectively enhancing the path tracking accuracy of 4WISD vehicles in high-speed, large-curvature, and complex environments.
A Framework for Upgrading Contaminated Urban Land and Soil by Nature-Based Solutions: Demonstration with a Swedish Case
Authors: Chowdhury Shaswati , Volchko Yevheniya , Norrman Jenny
Published: November 2024

To move towards a circular economy and to meet the upcoming EU soil health legislation, both contaminated land and contaminated soil should be acknowledged as fragile and valuable resources to be restored and recycled to provide essential ecosystem services to humans. Underused, often contaminated land can be returned to beneficial use as urban greenspace (UGS) with the help of gentle remediation options (GROs). GROs are plant-, fungi-, bacteria-, and soil amendments-based risk management mechanisms that can also simultaneously improve soil functions and the provision of ecosystem services. This study (i) presents a framework including tools and methods for exploring opportunities for transforming brownfields to UGS using GROs to deal with soil contamination, (ii) demonstrates its application for a case study site in Gothenburg, Sweden, and (iii) presents stakeholders’ views on the suggested framework. This framework can support the transition of brownfields to UGS while recycling both land and soil and increasing the market value of the site and its surroundings. Stakeholders found the suggested framework useful but identified some challenges for its practical implementation, mainly associated with financial aspects and the existing practice and level of knowledge. Stakeholders also identified the need for additional practical tools to (a) make predictions about the time required for GROs to reach acceptable risk levels, (b) monetize non-market benefits such as ecosystem services for communicating benefits to decision-makers, and (c) provide support for plant and soil amendment selection for various GROs and contaminants.
Bovine Gammaherpesvirus 6 Tropism in the Natural Host
Authors: Fabian Rosalie , Rosato Giuliana , Stewart James P. , Kipar Anja
Published: November 2024

Bovine gammaherpesvirus 6 (BoHV-6) is endemic in cattle in Europe, with a high prevalence. There is evidence that the virus is a commensal and not associated with disease processes. For other gammaherpesviruses, it is known that they have a rather specific target cell spectrum, generally including B cells and, at least in the early phase of infection, the epithelium of the respiratory tract. In a previous study we detected BoHV-6 by quantitative PCR for the gB gene sequence of BoHV-6 in lung, bronchial lymph nodes, spleen and tongue with variable loads, suggesting cells in these tissues as target cells. In the present study, formalin-fixed, paraffin embedded samples of the same tissues from 10 cattle, with high overall BoHV-6 copy numbers, were examined by RNA in situ hybridization for BoHV-6 ORF73. This revealed extremely limited viral ORF73 transcription. A signal was only detected in individual lymphocytes within lymphatic follicles in bronchial lymph nodes, and within very rare alveolar epithelial cells and interstitial cells in the lungs, without any evidence of pathological changes in the tissues. No signal was detected in the spleen or in the oral mucosa of the tongue. The results are consistent with previous findings with other gammaherpesviruses, murine herpesvirus-68, ovine herpesvirus-2 and/or Epstein–Barr virus. They provide further evidence that BoHV-6 is without any consequence to the host and can indeed represent a commensal in cattle.
Exposure to Pollutants and Vaccines’ Effectiveness: A Systematic Review
Authors: Protano Carmela , Valeriani Federica , Vitale Katia , Del Prete Jole , Liguori Fabrizio , Liguori Giorgio , Gallè Francesca
Published: November 2024

Background: Many human activities release harmful substances, contaminating the air, water, and soil. Since exposure to environmental pollutants is currently unavoidable, it is important to verify how these compounds may influence individual immune responses to vaccines. Methods: This review was conducted in accordance with the PRISMA statement. The protocol was registered on the PROSPERO platform with the following ID: CRD42024582592. We evaluated all observational, semi-experimental, and experimental studies written in both Italian and English that reported possible effects of exposure to environmental pollutants on the production of vaccine-induced antibodies. Results: Forty-two studies were included. The effects of pollutants were examined mainly in terms of antibody production in relation to mumps, measles and rubella, diphtheria and tetanus, hepatitis A and B, Haemophilus influenzae type B, influenza, tuberculosis, pertussis, Japanese encephalitis, poliomyelitis, and COVID-19 vaccines. Perfluorinated compounds were the most studied pollutants. Conclusions: Correlations between exposure to pollutants and reductions in antibody production were found in quite all the selected studies, suggesting that pollution control policies could contribute to increase the efficacy of vaccination campaigns. However, the heterogeneity of the examined studies did not allow us to perform a meta-analysis, and the literature on each type of vaccine or pollutant is still too limited to generate robust evidence. In order to confirm the findings of the present systematic review, and in the perspective of establishing possible exposure limit values for each type of pollutant, further research in this field is required.
Vehicle Lane Changing Game Model Based on Improved SVM Algorithm
Authors: Wang Jian , Wang Hongxiang , Fei Mingzhe , Zhou Gang
Published: November 2024

In order to improve the autonomous lane-changing performance of unmanned vehicles, this paper aims to solve the problem of inaccurate decision classification in traditional support vector machine (SVM) algorithms applied to the lane-changing decision-making stage of intelligent driving vehicles. By using game theory-related theories and combining the improved support vector machine (SSA-SVM) method, a vehicle autonomous lane-changing strategy based on game theory is established. The optimized SVM method has certain advantages for vehicle lane-changing decision-making with a small sample size in actual production processes. The lane-changing decision judgment accuracy rate of the SSA-SVM algorithm model can reach 93.6% compared with the SVM algorithm model without algorithm optimization; the SSA-SVM algorithm model has obvious advantages in decision performance and running speed. Therefore, the proposed new algorithm can effectively solve the problem of the objective consideration of the payoff function in conventional decision game theory.
المنافسة في قطاع البنوك الكويتية
Authors: المطيري عبدالله خالد , العمر حسين علي
Published: November 2024

تهدف هذه الدراسة إلى محاولة التعرف على درجة المنافسة في قطاع البنوك التجارية بدولة الكويت، وذلك باستخدام مؤشر "هرفندال - هيرشمن" للتركز ونموذج "بانزار". وقد تم لتحقيق هذا الهدف استخدام بيانات سنوية لسبعة بنوك تجارية تغطي الفترة من عام 1993 إلى عام 2005، وذلك لتقدير نموذج ذي ستة متغيرات. وتشير النتائج إلى أن قطاع البنوك التجارية يعاني من درجة تركز عالية، ويعمل في ظل ظروف المنافسة الاحتكارية، وهي نتائج تتفق مع تلك التي توصلت لها الدراسات حول الدول الأخرى.
فجوة التوقعات في مراجعة الحسابات العامة: حالة المملكة العربية السعودية
Authors: القرني أحمد عبدالقادر
Published: November 2024

تهدف الدراسة إلى توثيق فجوة التوقعات في بيئة مراجعة الحسابات العامة في المملكة العربية السعودية. ولتحقيق هذا الهدف تم استطلاع وتحليل آراء مراجعي الحسابات العامة ومديري الإدارات المالية في الجهات التي تخضع حساباتها للمراجعة العامة، وكذلك آراء المستفيدين أو المستخدمين لتقارير المراجعة العامة حول أربعة متغيرات رئيسة: (1) مسئوليات وواجبات مراجع الحسابات العامة، (2) تقرير المراجعة، (3) الاستقلال، و (4) أدلة الإثبات. وقد تم صياغة أربعة فروض للدراسة في ضوء هذه المتغيرات واختبارها باستخدام الاختبارات الوصفية، واختبار التحليل الإحصائي اللامعلمي كروسكال - واليس (KW) وكذلك اختبار مان- ويتني (MW). ومن استنتاجات هذه الدراسة ثبوت قيام أدلة ميدانية على وجود فجوة للتوقعات في مراجعة الحسابات العامة في جوانب تتعلق بمسئوليات مراجع الحسابات العامة وواجباته واستقلاله، كما تتعلق كذلك بأدلة الإثبات. وقد خلصت الدراسة إلى عدد من التوصيات تم تبويبها بحسب عينة الدراسة.
قياس كفاءة التمويل الحكومي الموجه نحو تنمية المشروعات الصغيرة في الأردن
Authors: النسور إياد عبدالفتاح
Published: November 2024

هدفت الدراسة إلى تقدير دالة الإنتاج للمشروعات الصغيرة الممولة من المؤسسات الحكومية في الأردن وهي: صندوق التنمية والتشغيل، ومؤسسة الإقراض الزراعي، وصندوق الحرفيين، كما هدفت إلى قياس الكفاءة الاقتصادية والاجتماعية ومؤشرات الإنتاجية، إضافة إلى قياس مرونة الإحلال بين عناصر الإنتاج في تلك المشروعات، وكذلك قياس أثر التغيرات النسبية في أسعار عناصر الإنتاج على تكلفة فرصة العمل ومستويات العمالة فيها. استخدمت الدراسة مجموعة من النماذج الاقتصادية القياسية لاختبار فرضيات الدراسة أهمها: دالة الإنتاج كوب دوغلاس ومعيار فارل للكفاءة، وتوصلت إلى مجموعة من النتائج أهمها: أن المشروعات الصغيرة الممولة من صناديق التنمية والتشغيل وقروض الحرفيين تتسم بكثافة في عنصر العمل، بينما تتسم المشروعات الممولة من مؤسسة الإقراض الزراعي بكثافة عنصر رأس المال. كما وجد أن المشروعات الصغيرة الممولة من مؤسسة الإقراض الزراعي تتمتع بكفاءة اجتماعية تفوق تلك الممولة من المصادر الحكومية الأخرى، ووجد أن المشروعات الصغيرة الممولة من صندوق قروض الحرفيين حققت المعدل الأعلى لإنتاجية عناصر الإنتاج الكلية، مقارنة بالمشروعات الممولة من المصدرين الآخرين، إضافة إلى أن تكلفة فرصة العمل ومستويات العمالة في المشروعات الصغيرة الممولة من مؤسسة الإقراض الزراعي، تتأثر بصورة أكثر من المشروعات الممولة من المصادر الأخرى وذلك بسبب تغير أسعار عناصر الإنتاج.أخيراً، اقترحت مجموعة من التوصيات أهمها: العمل على إنشاء الهيئة العليا لتنمية المشروعات الصغيرة في الأردن، ودمج مؤسسات التمويل الحكومية في مؤسسة واحدة.
The Influence of Artificial Intelligence Tools on Student Performance in e-Learning Environments: Case Study
Authors: Eltahir Mohd Elmagzoub , Mohd Elmagzoub Babiker Frdose
Published: November 2024

This study investigated the impact of AI-powered personalized learning tools on the academic performance and perceptions of pre-service student teachers enrolled in the Educational Technology course, a compulsory component of the Professional Postgraduate Diploma in Teaching program at Ajman University. A quasi-experimental design was employed with 55 students in the experimental group and 55 in the control group. The experimental group utilized AI-powered tools within the Moodle platform, while the control group received traditional instruction. Pre- and post-tests, along with a rubric-based assessment and a questionnaire, were administered to assess performance, knowledge retention, critical thinking, motivation, and engagement. Statistical analysis revealed significant differences between the groups, favoring the experimental group in terms of academic performance and knowledge retention. Additionally, students in the experimental group demonstrated higher levels of critical thinking, motivation, and engagement. These findings underscore the transformative potential of AI-powered personalized learning tools in teacher education. By integrating AI into educational practices, institutions can revolutionize e-learning by providing personalized instruction, intelligent tutoring, automated grading, and adaptive learning experiences. This can enhance student engagement, improve learning outcomes, and reduce the workload of educators. Ultimately, AI empowers institutions to create more effective and inclusive learning environments that cater to the diverse needs of students and prepare them for the future.
Stages and main tasks of the century-long control theory and system identification development. Part VII. Multirate sampling control systems
Authors: Romanenko Viktor , Gubarev Vyacheslav
Published: November 2024

The article provides a review and generalization of the main approaches and methods of designing discrete controllers with multirate sampling for multidimensional and multi-connected control objects described by models in the state space, which have the property of functioning in two time scales. At the same time, the initial model of the object is decomposed into slow- and fast-acting subsystems based on the assumption of a quasi-steady state of rapidly changing processes in the control object. In the introduction, an analysis of the well-known methods of synthesis of composite multirate discrete regulators based on linear-quadratic optimality criteria is carried out. The method of different multirate sampling of singularly perturbed continuous systems, which functioning in a stochastic environment and two time scales under the action of disturbances of the white noise type, is considered. Method for the synthesis of a composite multirate state regulator for multidimensional objects with a delay under the action of slow-acting disturbances with an average value other than zero has been developed. On the basis of this method, discrete slow- and fast-acting models are formed with a delay and different multirate sampling in the state spaces and measurement variables, which made it possible to compensate for slow-acting disturbances. At the same time, the measurability of slow- and fast-acting subsystems with different multirate sampling does not depend on the value of the delay. This article solves the problem of stabilization and coordination at a given level of non-measurable slow- and fast-changing components of the vector of initial measured coordinates in systems that implicitly operate in two time scales.
Forecasting techniques based on the use of time series models for forecasting expenditures on social protection and social security
Authors: Zarudnyi Oleksii , Koval Roman
Published: November 2024

In the context of a military conflict, it is growing the number of people who are in difficult life circumstances and need state assistance. Therefore, the issue of forecasting spending on social protection and social security is becoming increasingly relevant. The aim of the paper is to consider modern methods of analyzing and forecasting time series and propose their application to determine the need for spending on social protection and social security. The article provides a brief overview of regression models, their advantages and features of use for analyzing and predicting nonlinear and linear processes described by time series of statistical indicators used in the population social protection system. The paper offers a combination of predictions that can be made based on locally linear and nonlinear models, such as threshold autoregression (TAR) and exponential autoregression (ExAR), which have known quality characteristics. This approach primarily concerns time series that contain data on structural changes in the analyzed processes. Using combined predictions, it is possible to estimate the contribution of each component for both different and selected time points using variable weights. To build forecasts, depending on the scenario of distribution of social protection and social security expenditures, it is proposed to use Bayes networks. Their advantages are flexibility, high quality of results, the possibility of structural and parametric optimization and adaptation to new data and conditions, support for quality assessment by appropriate sets of statistical criteria, which makes it possible to analyze thoroughly intermediate and final results. It helps to increase the adequacy of models and the high quality of the final result of their application.
DIVERSIDAD Y ESTRUCTURA GENÉTICA EN HÍBRIDOS INTRAESPECÍFICOS DE CRUZA SIMPLE DE VAINILLA Y ACCESIONES PARENTALES
Authors: Hernández-Leal Enrique , Solano-Campos Frank , Azofeifa-Bolaños José B , Paniagua-Vásquez Amelia , Hernández-Bautista Aurelio , López-Morales Fernando , Pascual-Ramírez Fermín , Reyes-López Delfino
Published: November 2024

El conocimiento de la diversidad genética es de gran importancia en el diseño y conducción de un programa de mejoramiento genético, ya que de esto depende su éxito; en este sentido, el objetivo del presente estudio fue estimar la diversidad genética de 21 híbridos intraespecíficos de cruza simple y sus parentales en Vanilla planifolia con datos moleculares obtenidos de la amplificación por PCR de cuatro marcadores inter secuencias simples repetidas (ISSR) a partir de ADN genómico aislado de hojas. Se estimaron los parámetros de contenido de información polimórfica (CIP), índice de marcador (IM) y poder de resolución (PR). Para evaluar la diferenciación genética dentro y entre poblaciones se realizó un análisis de varianza molecular (AMOVA) y un análisis de componentes principales. Para identificar los genotipos con mayor similitud se realizó un análisis de conglomerados con las distancias genéticas de Jaccard y para conocer la estructura genética se realizó un análisis de agrupamiento bayesiano. El marcador ISSR-T06 fue el que presentó mayor CIP (0.31), mientras que el ISSR-C10 tuvo el IM (2.55) y PR (17.08) más altos. El AMOVA reveló el 97 % de varianza genética dentro de poblaciones y el 3 % entre poblaciones (P ≤ 0.01), estos resultados indican que se deben retener más individuos por población para la conservación de la especie. Las pruebas de GAP y Elbow y el análisis de agrupamiento bayesiano diferenciaron dos grupos, en tanto que el análisis de distancias genéticas de Jaccard detectó bajos niveles de asociación entre los genotipos, sugiriendo la existencia de un solo grupo. El primer grupo estuvo formado por accesiones parentales, las cuales presentaron pocas diferencias alélicas, mientras que el segundo se integró por los híbridos, que presentaron mayor porcentaje de loci polimórficos; así mismo, reveló un incremento del 3 % de la diversidad entre poblaciones. La hibridación entre individuos de diferente origen es una herramienta factible para incrementar la diversidad entre poblaciones.
Paradigma Değişimi Bağlamında Tüketimin Medikalleşmesi (Batı Akdeniz Bölgesi Örneklemi)
Authors: Aydin Amine , Özkul Metin
Published: November 2024

Bu çalışmanın üzerine eğildiği konu; modernleşme sürecinin günümüze geldiği aşamaya kadar birey algılarına yönelik paradigma değişiminin; tüketim toplumunda, sağlık/hastalık/güzellik çerçevesinde toplumsal yaşam üzerindeki görünümlerini ortaya koymaktır. Konuyla ilgili değişen bakış açısının pratikteki işleyişini ortaya koymak bu çalışmanın temel amaçlarından birini oluşturmaktadır. Dolayısıyla düşünsel ya da teorik düzeyde ifade edilen sağlık/hastalık ve güzellikle ilgili anlayış değişiminin pratik yaşamda nasıl deneyimlenmekte olduğu tespit edilmeye ve bu tespitlerden yola çıkılarak, paradigma değişimi bağlamında hasta/sağlıklı/güzel bedenin tüketimle ilişkisi açıklanmaya çalışılmaktadır. Sağlık/hastalık ve güzellikle ilgili değişen düşünce biçiminin pratikteki işleyişini betimlemek için saha çalışmasından elde edilen verilerden yararlanılmıştır. Saha çalışması Antalya, Isparta ve Burdur illerini kapsayan Batı Akdeniz bölgesinde yapılmıştır. Araştırma sonucunda bireylerin; sağlık ve fiziksel görünümleriyle ilgili tüketim pratiklerine ruhen, zihnen ve bedenen iyi olma halini salık veren bütünsel sağlık anlayışıyla yöneldiği, bu tüketim pratiklerine yönelik eyleme geçerken hem bireysel hem de kurumsal araçları kullanarak inşa ettiği tüketim ağından yararlandığı, sağlık ve fiziksel görünümüyle ilgilenirken tercih hakkına sahip sorumlu birey olmanın yanı sıra kurumsal yapı ve uzmanlık sistemlerinin de etkisinde kaldığı görülmüştür. Tüketim toplumunda tercihlerin bolluğu, bireylerin sağlık ve fiziksel görünümleriyle ilgili olarak birden fazla tüketim pratiklerine yönelmesini sağlamıştır.
Enhancing Credit Card Fraud Detection Using Synthetic Minority Over-Sampling Technique (SMOTE) and Deep Neural Networks: A Comprehensive Analysis
Authors: Mousa Abdulazeez , Özyurt Fatih , Avcı Engin
Published: November 2024

Detecting credit card fraud iѕ particularly challenɡinɡ due to the ѕiɡnificant claѕѕ imbalance in tranѕaction data, where leɡitimate tranѕactionѕ far outnumber fraudulent oneѕ. Thiѕ reѕearch examineѕ the effectiveneѕѕ of inteɡratinɡ the Ѕynthetic Minority Over-Ѕamplinɡ Technique (ЅMOTE) with a Deep Neural Network (DNN) to improve the identification of fraudulent tranѕactionѕ. Uѕinɡ a publicly acceѕѕible dataѕet from Kaɡɡle, which includeѕ 284,807 credit card tranѕactionѕ with only 492 beinɡ fraudulent, ЅMOTE waѕ applied to balance the dataѕet, providinɡ equal repreѕentation of both claѕѕeѕ. The DNN was ѕubѕequently trained on thiѕ balanced dataѕet. The model'ѕ architecture compriѕed an input layer, ѕeveral hidden layerѕ with dropout reɡularization to mitiɡate overfittinɡ, and an output layer for binary claѕѕification. Evaluation metricѕ ѕuch aѕ accuracy, preciѕion, recall, and F1-ѕcore were uѕed to aѕѕeѕѕ the model, which achieved an overall accuracy of 97.55%. Ѕiɡnificantly, the preciѕion and recall for both fraudulent and non-fraudulent tranѕactionѕ were hiɡh, demonѕtratinɡ the model'ѕ robuѕtneѕѕ and effectiveneѕѕ in practical applicationѕ. The ѕtudy'ѕ reѕultѕ indicate that the inteɡration of ЅMOTE ɡreatly enhanceѕ the DNN'ѕ capability to detect fraud, effectively addreѕѕinɡ the claѕѕ imbalance iѕѕue. The hiɡh performance metricѕ hiɡhliɡht the potential of thiѕ approach for implementation in real-time fraud detection ѕyѕtemѕ within financial inѕtitutionѕ, providinɡ a dependable and efficient ѕolution to reduce financial loѕѕeѕ and build cuѕtomer trust.
C-band Hexagon Slotted Textile Antenna Design with Jeans Substrate for Satellite Communication Applications
Authors: Islam Md. Riazul , Muhammad Touhidul Islam Abu Zafor
Published: November 2024

This paper presents the design of a hexagonal slotted circular flexible textile microstrip patch antenna that is used for C-band satellite communication applications. In the proposed antenna design, a textile material blue Jeans fabric with relative permittivity 1.67, loss tangent of 0.01 and the thickness of 1 mm is used as the substrate with an overall dimension of 40×38 mm2. The textile material is used as it is washable, wearable, flexible and very economical. Self-adhesive Copper tape is used to make the patch and ground of the antenna that is fed through a microstrip inset feed line. The proposed antenna contains a hexagonal-shaped slot on the patch and a partial ground configuration for getting better bandwidth and gain. The antenna has been designed and simulated using CST microwave Studio software. The simulated antenna performance parameters like return loss, bandwidth, VSWR, gain, efficiency, radiation pattern and surface current are presented. The antenna is resonating at 4.28 GHz and operating in the frequency range of 2.91 -5.2 GHz, with reflection coefficient of -24.42 dB, and a bandwidth of 2.29 GHz. The peak gain of the antenna is 3.42 dBi and radiation efficiency of 87.3% at 4.2 GHz. The proposed antenna covers C-band and with its compact size, wearability and wide bandwidth capabilities, the antenna proves to be a suitable candidate for C-band satellite communication applications.