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Found 6278 publications
Passive Occupant Safety Solutions for Non-Conventional Seating Positions
Publication Name: Future Transportation
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
In a fully autonomous vehicle, the driver becomes a passenger, free to adopt different seating positions. This change challenges traditional passive safety systems—such as seatbelts, airbags and seat design—that are optimised for a forward-facing position. As autonomous vehicles are integrated into mixed traffic with conventional cars, solutions need to address these challenges. In this intermediate stage, fully autonomous cars will need a system that, in the event of an accident, can rotate the seats to the most ideal position tested by the manufacturer. This could be a number of positions where the seat, airbags and seatbelts are optimised, taking into account the expected direction of impact. It is important that the rotation is not too radical, as this would increase the risk of injury. In addition, the seat dimensions need to be increased to improve energy absorption in the event of a collision, thereby reducing the impact forces on the occupants and improving overall safety. To improve passive protection, airbags will continue to be used in the future, but in completely new positions, sizes and shapes. This research aims to identify potential passive occupant safety solutions for seat positions that have been rotated in fully autonomous vehicles. The finite element simulation model on which the results in this article are based was developed in an earlier phase of the research. The current research combines two previously conducted research directions, using the modified seat and the developed airbag concept. This research’s main outcome is a system that effectively protects occupants in rotated seat positions. It maintains all evaluated injury criteria below their threshold limits and ensures controlled occupant kinematics.
Open Access: Yes
Global burden of amphetamine, cannabis, cocaine and opioid use in 204 countries, 1990–2023: a Global Burden of Disease Study
Luís Manuel Lopes Rodrigues Silva
Premalatha K. ShettyS
Ireneous N. Soyiri
Aminu Shittu
Ker Kan Tan
Siavash Vaziri
Farrukh Sobia
Seyed Afshin Shorofi
Y. Waheed
Alexander C. Tsai
Aristidis Tsatsakis
Abdul Rohim Tualeka
Fathiah Zakham
Lukasz Szarpak
Ujjawal Sharma
Mircea Tampa
Nuwan Darshana Wickramasinghe
Manoj Sharma
Marco Torrado
Surjit Singh
Baljinder Singh
Emmanuel Edwar Siddig
Dehui Yin
Seyyed Mohammad Tabatabaei
Isidora S. Vujcic
Evangelia Eirini Tsermpini
Yonas Getaye Tefera
Ali Sheidaei
Mohamad Hani Temsah
Pavanchand H. Shetty
Sunil Shrestha
Matiwos Soboka
Marcos Roberto Tovani-Palone
Masood Ali Shaikh
Vetriselvan Subramaniyan
Samuel Joseph Tromans
Aurora Zanghì
Hadiza Yusuf
Minale Tareke
Mehran Shams-Beyranvand
Tenaw Yimer Tiruye
Inga Dora Sigfusdottir
Bin Zhu
Zwanden Sule Yahaya
Muhammad Suleman
Nguyen Tran Minh Duc
Tommi Juhani Vasankari
Renjulal Yesodharan
Paramdeep Singh
Kavumpurathu Raman Thankappan
Masood Ali Shaikh
Min Seo Kim
João Pedro Silva
Naohiro Yonemoto
Chuanhua Yu
Chandrashekhar T. Sreeramareddy
Harmanjit Singh
Atta Ullah
Jiseung Kang
Shu Wang
Haijun Zhang
Aniefiok John Udoakang
Payam Tabaee Damavandi
Saeed Ullah
Alireza Shakeri
Anna Aleksandrovna Skryabina
Reem Temsah
Masayuki Teramoto
Amin Sharifan
Georgios Ioannis Verras
Valentin Yurievich Skryabin
Mohammed G.M. Zeariya
Joe Varghese
Soroush Soraneh
Angga Wilandika
Mandaras Tariku Walde
Anthony Zhong
Jef Van den Eynde
Vishal Sharma
Sunder Sham
Hyeon Jin Kim
Vinay Suresh
Thang Huu Tran
Manish Vinayak
Asokan Govindaraj Vaithinathan
Joan B. Soriano
Jingya Zhang
Sa’ed H. Zyoud
Magdalena Zielińska
Asokan Govindaraj Vaithinathan
Paul Yip
Chandan Kumar Swain
Munkhtuya Tumurkhuu
Dan J. Stein
Shu Wang
Javad Sharifi Rad
Pavanchand H. Shetty
Sa’ed H. Zyoud
Marco Torrado
Roman Shrestha
Premalatha K. ShettyS
Alfiya Shamsutdinova
Rafael Tabarés-Seisdedos
Jovana Todorovic
Mahabalesh Shetty
Jasvinder A. Singh
Yuan Pang Wang
Publication Name: Nature Medicine
Publication Date: 2026-02-01
Volume: 32
Issue: 2
Page Range: 527-544
Description:
Drug use disorders (DUDs) are emerging global public health challenges. Here we investigated the global and regional estimates of the prevalence and burden of DUDs, including amphetamine, cannabis, cocaine and opioid use disorders, from 1990 to 2023 for 204 countries and territories by using the Global Burden of Disease Study 2023. Overall, trends in global age-standardized disability-adjusted life-years of DUDs increased from 169.3 (95% uncertainty interval (95% UI), 134.4–203.9) per 100,000 people in 1990 to 212.0 (95% UI, 179.2–245.6) in 2023. In 2023, both prevalence and burden of DUDs were higher in high-income countries, particularly in the USA. The most prevalent DUDs in 2023 were cannabis use disorder (age-standardized prevalence, 270.8 (95% UI, 201.7–350.0) per 100,000 people) and opioid use disorder (205.9 (95% UI, 178.7–235.0)). Particularly, opioid use disorder showed a nearly twofold increase in prevalence and burden between 1990 and 2023. In 2023, compared with countries where cannabis use was illegal, countries permitting both recreational and medical cannabis use had higher prevalence rates for all types of DUDs. Proactive and effective policies are essential to mitigate the increasing global burden of DUDs.
Open Access: Yes
Parents’ first aid knowledge and educational expectations based on a study conducted in Győr-Moson-Sopron county
Publication Name: Orvosi Hetilap
Publication Date: 2026-02-01
Volume: 167
Issue: 7
Page Range: 265-273
Description:
Introduction: Childhood injuries are among the leading causes of mortality worldwide and in Hungary. The quality of first aid provided by laypeople has a fundamental impact on survival rates. Objective: To assess parents’ knowledge of first aid, to explore their need for further practical training, and to determine whom they consider the most reliable source of such knowledge. Method: During our quantitative research conducted in Győr-Moson-Sopron county, we used a self-designed, online, anonymous questionnaire (n = 545) and performed descriptive statistical analyses. Associations were examined using the chi-square test and binary logistic regression (p<0.05). Results: The majority of parents (94.3%) possess basic, primarily theoretical first aid knowledge; however, this knowledge is often incomplete or outdated. The greatest deficiencies were in the practical application of cardiopulmonary resuscitation and in the airway obstruction caused by foreign bodies. The majority of respondents (93.4%) would be willing to learn from paramedics (84.9%), health visitor (60%), registered nurses (57.6%), physicians (56.4%). Based on the association analyses, first aid experience gained in real-life emergency situations was significantly associated with self-reported willingness to intervene (p = 0.012) as well as with a more favorable self-assessment of first aid competence (p<0.001). According to the results of the binary logistic regression, having an official, examination-based first aid qualification was an independent predictor of having provided first aid in a real-life emergency situation; among respondents without such qualification, the odds of providing first aid were reduced by approximately half (OR = 0.516; p = 0.001; 95% CI: 0.345–0.774). Conclusion: The goal is to clarify the knowledge of parents and provide training in practical skills from professionals. Both formal first aid training and practical experience play a decisive role in shaping the willingness to intervene in real-life emergency situations as well as self-confidence. These findings support the need for structured, practice-oriented first aid education among parents. Orv Hetil. 2026; 167(7): 265–273.
Open Access: Yes
Complex Test Scenarios for Functional Validation Prior to Type Approval
Publication Name: Future Transportation
Publication Date: 2026-02-01
Volume: 6
Issue: 1
Page Range: Unknown
Description:
The continuous tightening of European regulatory requirements, particularly under the General Safety Regulation (GSR), has considerably increased the scope and cost of proving ground testing required for the validation of Advanced Driver Assistance Systems (ADASs) and Automated Driving Systems (ADSs). This study presents a methodology for constructing complex proving ground test scenarios aimed at supporting early-stage functional validation and cost-efficient preparation for type approval. The method is based on the systematic analysis of proving ground–relevant ADAS regulations and the classification of test case variations according to sensing, actuation, and execution complexity. By filtering and combining representative test cases, minimum and maximum complexity scenarios were developed and evaluated on the ZalaZONE proving ground in Hungary. The results demonstrate that the proposed approach can substantially reduce test duration, facility occupancy, and overall validation costs, while maintaining the representativeness and credibility of results. Beyond cost savings, the methodology offers a scalable and practical framework for physical validation, supporting manufacturers in achieving regulatory compliance with reduced time and expenditure.
Open Access: Yes
Assessment of soil erosion patterns in Maharloo watershed using remote sensing techniques and early warning signals
Publication Name: Journal of Arid Environments
Publication Date: 2026-02-01
Volume: 232
Issue: Unknown
Page Range: Unknown
Description:
This study assessed the soil erosion dynamics in Iran's Maharloo watershed using remote sensing indices (Normalized Difference Vegetation Index (NDVI), Normalized Difference Salinity Index (NDSI), and Topsoil Grain Size Index (TGSI)) and machine learning models (RF, SVM, and BRT). Landsat 8 satellite images (2005–2024) were processed via the Google Earth Engine, with field validation ensuring accuracy. Among the indices, TGSI (R2 = 0.86), NDSI (R2 = 0.89), and NDVI (R2 = 0.87) showed the strongest correlations with ground data (Rain, Soil and Vegetation). The RF outperformed the other models (AUC = 0.89), identifying the central and western regions as warning erosion zones. Breakpoint analysis revealed abrupt changes in NDVI and NDSI (2013), while early warning signals (autocorrelation, variance, and skewness) indicated an escalating erosion warning, particularly near wetlands and rainfed fields. Spatial trends highlighted significant NDVI declines (Kendall's τ = 0.69) in wetland peripheries and NDSI increased (τ = 0.52) in northern farmlands. These findings underscore the efficacy of integrating machine learning and remote sensing for erosion monitoring, providing actionable insights for land management and conservation strategies.
Open Access: Yes
Comparative Assessment of Machine Learning Approaches for Early Lung Cancer Diagnosis
Publication Name: Emerging Science Journal
Publication Date: 2026-02-01
Volume: 10
Issue: 1
Page Range: 20-54
Description:
Lung cancer, a leading cause of cancer-related mortality worldwide, often escapes early detection due to the absence of distinct symptoms in its initial stages. This work investigates how Machine Learning (ML) might improve early diagnosis by analyzing Electronic Health Records (EHR) data. Multiple ML models were developed and evaluated on a synthetic dataset created to replicate real-world patient characteristics, allowing controlled experimentation while safeguarding privacy. Model performance was tuned using both conventional optimization methods and nature-inspired approaches, with the aim of balancing predictive accuracy and computational efficiency. In our synthetic dataset experiments, ensemble learners optimized with metaheuristic techniques reached accuracy levels approaching 99 percent while maintaining computational efficiency and generally outperformed simpler baselines. The contribution of this work lies in exploring the integration of GFO and WOA for feature selection and hyperparameter tuning of XGBoost, together with a soft-voting ensemble. This approach provides an experimental pathway for enhancing predictive performance under computational constraints. However, as the dataset is synthetic, the conclusion remains experimental; validation against clinical records will be essential before translation into practice.
Open Access: Yes
From scroll to sale: Predicting of customer engagement in impulse buying cycle on TikTok through the S-O-R model
Publication Name: Acta Psychologica
Publication Date: 2026-02-01
Volume: 262
Issue: Unknown
Page Range: Unknown
Description:
The popularity of TikTok is evident on a global scale, leading to its adoption by numerous companies as a promotional platform. The Stimulus - Organization - Reaction model has been validated in its capacity to describe the purchase process. The present study investigates the Hungarian manifestations of the factors of this model to develop a forecasting method that will allow for prediction of the success of TikTok impulse purchase marketing strategies. The prediction method under analysis is based on factor and regression analyses and validation. The exploratory part utilizes a questionnaire survey of 283 Hungarian university students. The validity of the results is measured on a different university sample of 104 active users of TikTok and users of alternative social media platforms. The findings indicate that the factors comprising the impulse purchase cycle involvement include the urge to buy impulsively, utilitarian value, perceived similarity, and co-promotion. The validation process revealed that the rates of agreement with the predictions among TikTok users exceeded 90 %, while the rate among users of other social media platforms reached over 80 %. The principal strength of this study lies in its quantification and transformation of questionnaire data into a pivotal key performance indicator suitable for companies.
Open Access: Yes
Data-Driven Planning for Casualty Evacuation and Treatment in Sustainable Humanitarian Logistics
Publication Name: Algorithms
Publication Date: 2026-02-01
Volume: 19
Issue: 2
Page Range: Unknown
Description:
After large-scale disasters, swift and robust humanitarian logistics are crucial to provide timely assistance to injured people and displaced individuals. This study proposes a bi-objective optimization model for humanitarian logistics network design to simultaneously consider the facility location-allocation decisions, along with the transportation operation issues under uncertainty. The framework addresses the needs of both severely and mildly injured casualties and homeless populations. A hybrid robust optimization approach is accordingly developed that incorporates scenario-based, box-type, and polyhedral uncertainty representations to handle the uncertainty of factors such as casualty volume, travel times, facility failures, and demands for resources. More recently, machine learning methods have been applied to classify casualties and displaced individuals with respect to their geographic distribution and severity, further improving demand estimates and operational efficacy. This study seeks to develop a data-driven and robust optimization framework for designing humanitarian logistics networks under uncertainty, enabling decision-makers and emergency planners to gain insights into enhancing casualty evacuation, medical treatment, and shelter allocation in disaster response operations. The case of the Kermanshah earthquake in Iran is used for assessing the applicability of the model. The computational experiments and comparative analyses conducted show that the developed model exhibits high efficiency and robustness. The results are useful for guiding disaster preparedness and strategic decisions in humanitarian logistics. Besides operational performance, the model optimizes sustainability in the area of emergency response based on cost efficiency and social fairness, as underlined by SDGs 3 and 11.
Open Access: Yes
DOI: 10.3390/a19020104
Interactive Effects of Tillage, Nitrogen Fertilisation, and Herbicide Management: Impacts on Soil CO2 Emissions and Agroecosystem Dynamics in a Maize Production
Publication Name: Soil Systems
Publication Date: 2026-02-01
Volume: 10
Issue: 2
Page Range: Unknown
Description:
Agriculture must balance productivity with greenhouse gas emissions, biodiversity, and resource concerns. This study examined how tillage (conventional, CT; minimum, MT), nitrogen fertilisation (0–221 kg N ha−1), and herbicide rates (0–100%) interactively affected soil CO2 emissions, vegetation vigour, and weed diversity in maize production during 2022. A factorial experiment was conducted on a 1 ha with 40 plots monitored soil temperature, moisture, penetration resistance, normalised difference vegetation index (NDVI), weed diversity (Simpson’s Index), and CO2 emissions (closed-chamber method). Minimum tillage increased soil water retention (9.3 ± 6.5% vs. 5.4 ± 4.3%), soil temperature (28.0 ± 1.5), and compaction (0.6 ± 0.3 vs. 0.1 ± 0.0 MPa), while enhancing weed diversity (0.53–0.80 vs. 0.38–0.67). MT produced higher CO2 emissions than CT, especially at 147 kg N ha−1 (49.9 ± 15.7 vs. 29.1 ± 11.6 μmol m−2 s−1), peaking under MT-147 kg N ha−1-H75 (79.4 ± 1.2 μmol m−2 s−1). NDVI responses varied between tillage systems; under CT, vegetation vigour peaked at 75% herbicide application, while under MT vegetation was more responsive to nitrogen and more sensitive to herbicide, highlighting nitrogen × herbicide interaction trade-offs. Overall, MT enhanced water conservation and weed diversity but increased short-term CO2 emissions. This study reports first-year, site-specific results from an ongoing multi-year field experiment; therefore, the findings were interpreted as short-term, season-specific responses. This highlights the need for site-specific, climate-smart management that integrates emissions, soil health, biodiversity, and productivity.
Open Access: Yes
