Patients often need the use of their arms to assist with functional activities, but after bone disruption, pushing is frequently limited to less than 4.5 kg. No method exists to measure arm weight bearing objectively in clinical settings. This project aimed to design, construct, and test a walker for patients who need to limit arm force to prevent excessive bone stress during post-fracture (iatrogenic or traumatic) ossification. First, a qualitative study was conducted to obtain critiques of a Clinical Force Measuring (CFM) walker prototype from rehabilitation professionals. Key statements and phrases were coded that allowed “themes” to emerge from transcribed interviews, which guided device revisions. Next, a second CFM Walker prototype was designed based on the qualitative data and device criteria/constraints and finally tested. The result was fabrication of a new lightweight, streamlined, and cost-effective prototype walker with a simple visual display and auditory cue with upper limit alarms. Key features included attachments for medical equipment and thin film force-sensing resistors integrated into the walker handles that progressively activated 3 LEDs and a buzzer when arm force exceeded programmed thresholds. The innovative CFM Walker will help patients with restricted arm weight bearing, especially elderly adults, recover safer and faster in the future.

The COVID-19 pandemic has put lifestyles in question, changed daily routines and limited citizen freedoms that seemed inalienable before. A human activity that was greatly affected since the beginning of the health crisis is mobility. Focusing on mobility, we aim to discuss the transformational impact that the pandemic brought on this specific urban domain, especially with regards to the promotion of the smart growth agenda and the acceleration towards the smart city paradigm. We collect 60 initial policy responses related to urban mobility from 86 cities around the world and analyse them based on the challenge they aim to address, the exact principles of smart growth and sustainable mobility that they encapsulate and the level of ICT penetration. Our findings suggest that emerging strategies, although mainly temporary, are transformational, in line with the principles of smart growth. As a result the pandemic becomes an opportunity for shifting towards more sustainable urban planning and mobility practices. However, most policy responses adopted during the first months of the pandemic fail to leverage advancements made in the field of smart cities, and to adopt off-the-shelf solutions such as in monitoring, alerting and operations management.

The ongoing COVID-19 global pandemic is affecting every facet of human lives (e.g., public health, education, economy, transportation, and the environment). This novel pandemic and citywide implemented lockdown measures are affecting virus transmission, people’s travel patterns, and air quality. Many studies have been conducted to predict the COVID-19 diffusion, assess the impacts of the pandemic on human mobility and air quality, and assess the impacts of lockdown measures on viral spread with a range of Machine Learning (ML) techniques. This review study aims to analyze results from past research to understand the interactions among the COVID-19 pandemic, lockdown measures, human mobility, and air quality. The critical review of prior studies indicates that urban form, people's socioeconomic and physical conditions, social cohesion, and social distancing measures significantly affect human mobility and COVID-19 transmission. During the COVID-19 pandemic, many people are inclined to use private transportation for necessary travel purposes to mitigate coronavirus-related health problems. This review study also noticed that COVID-19 related lockdown measures significantly improve air quality by reducing the concentration of air pollutants, which in turn improves the COVID-19 situation by reducing respiratory-related sickness and deaths of the people. It is argued that ML is a powerful, effective, and robust analytic paradigm to handle complex and wicked problems such as a global pandemic. This study also discusses policy implications, which will be helpful for policymakers to take prompt actions to moderate the severity of the pandemic and improve urban environments by adopting data-driven analytic methods.

In everyday life, the birth of a number of blind people takes place and millions of people are already available. Visually impaired people struggle to walk safely and in- dependently because they have trouble detecting obstacles. In this work, we suggest a novel approach to detecting obstacles utilizing an Arduino UNO board and an ul- trasonic sensor (HC-SR04). The main goal of this research is to increase the mobility and security of blind people in a variety of settings. The suggested system makes use of the HC-SR04 ultrasonic sensor’s capability to precisely measure distance and identify nearby obstructions. As the main processor component, the Arduino UNO board processes algorithms for real-time obstacle detection while also collecting data from the sensor. The device is intended to give the visually impaired user immedi- ate aural input while informing them of the location and proximity of obstacles. To Extensive testing is done in controlled and real-world settings to assess the system’s performance and dependability. In the studies, the effectiveness of obstacle detec- tion, the system’s response time, and its practicality and usefulness for blind people are all measured. The findings show that the suggested obstacle detection method provides very accurate obstacle identification at a safe distance. Users who are blind or visually handicapped may successfully understand their surroundings thanks to the aural feedback system, which helps them to navigate by making judgments that are well-informed. Additionally, the system responds quickly, guaranteeing that the user receives real-time obstacle updates to increase safety and confidence while mov- ing. The study’s conclusions demonstrate the possibility of using the Arduino UNO board with the HC-SR04 ultrasonic sensor as a practical and affordable approach for detecting obstacles.

Objective The aim of this study was to examine the impact of a modern lifestyle on college female students' health, specifically focusing on their hip and knee joint mobility. The objective of this study was to assess the influence of hydraulic resistance training with the self-myofascial release technique on improving joint mobility in female college students. The training also aimed to compare the effectiveness of hydraulic resistance exercise with and without the self-myofascial release technique on hip and knee joint mobility.Methods The study used a randomized controlled trial design to recruit 30 female college students aged 18-25 through campus advertisements and announcements. Medical conditions or injuries that might have affected participation was screened. Participants were indiscriminately allocated to Group A (Hydraulic Resistance Training with Self-Myofascial Technique) or Group B (Hydraulic Resistance Training without Self-Myofascial Technique). The baseline assessments included joint range of motion (ROM) measurements using a goniometer. Certified trainers supervised the training sessions, and the data were analyzed using SD and mean using SPSS. ResultsThis study aimed to evaluate the impact of hydraulic resistance training with self-myofascial release on hip and knee joint mobility in female college students. The results showed that both groups showed improved hip and knee flexion with and without the self-myofascial release technique. However, the Group A using the self-myofascial release technique (Group A) showed a more significant enhancement, as indicated by the smaller p value compared to Group B. It has been suggested that the self-myofascial release technique may improve hip and knee joint mobility. ConclusionThis study concluded that Hydraulic Resistance Training with Self-Myofascial Release (HSMR) could improve hip and knee joint mobility among female college students. These findings suggest that integrating the self-myofascial release technique into training regimens can improve joint mobility. However, further research with larger sample sizes and extended intervention periods was needed to understand the long-term effects and sustainability of these improvements.

In recent years, many cities throughout the world are facing the impact of last-mile logistics and the resulting rise in urban traffic and pollution: effects on the environment have been curbed by these dynamics. Urban traffic has a considerable impact in terms of noise emissions, road safety and air pollution: hence, both public and private parties shall undertake innovative solutions for reducing the negative effects of last-mile logistics and improving their operational effectiveness. This study aims to provide a systematic literature review on environmentally sustainable last-mile logistics. The main contributions of this paper are as follows: (1) exploring and analysing the existing literature on last-mile logistics, with a focus on various types of sustainable measures implemented by companies, governments (mainly local) and supra-national institutions; (2) clustering existing studies into five categories (i.e., agents’ preference and choices, shared logistics, stakeholders, studies on vehicles, and policy and decisions) clarifying the implications of the public economics analysis; (3) identifying the gaps and limitations of the existing literature and proposing potential issues for future research insights. The review of the literature reveals that the majority of recent studies have been based on engineering and urban planning approaches. Costs and benefits of last-mile logistics are researched with a public economics focus, gathering details from the different papers, starting from the business studies to the technology-oriented ones. This study aims at detecting the different topics and policies discussed in the literature and suggests how to incorporate them in creating new measures and policies or revamping current ones. Although the initial corpus was 583, only 108 papers were ultimately chosen for the review, since public economics studies were not thoroughly explored: following the results of this work, the topic has significant scope for further study.

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

Background High Mobility Group Box-1 (HMGB-1) is implicated in the pathogenesis of thrombosis and cancer. In the present study, we aimed to evaluate its potential role as a diagnostic biomarker of portal vein thrombosis (PVT) among patients with hepatocellular carcinoma (HCC). Methods The study population included N=100 prospectively recruited patients with a novel diagnosis of HCC. We compared circulating HMGB-1 levels between 34 healthy controls, HCC patients with PVT (N=22), and HCC patients without PVT (N=78). Results HCC patients without PVT showed significantly higher median HMGB-1 serum levels (8.2 [5.5-13.1] ng/ml) than those observed in the case of HCC with PVT (5.5 [4.4-8.5] ng/ml; p=0.012) and in healthy controls (4.1 [3.1-8.2] ng/ml; p<0.001). Among HCC patients, at univariate analysis, the presence of PVT was associated with higher median age (p=0.036), larger major cancer node diameter (p<0.001), and lower HMGB-1 serum level (p=0.012). At multivariate analysis, the presence of PVT maintained a positive association with major node diameter p=0.001) and an inverse association with serum HMGB-1 levels (p=0.003). Conclusions These findings indicate that serum HMGB-1 bears an inverse association with PVT complicating HCC at the time of diagnosis and suggest that serum HMGB-1 depletion may mark PVT development in cirrhotic patients with HCC.

Existence of hybrid electric vehicles nowadays is one of the emerging technologies worldwide. Different countries are using this technology on their transportation, and nowadays, a lot of other countries are gradually adapting on using this kind of innovation in their transport system. Philippines is among these countries which aims to have a sustainable mobility by embracing the use of hybrid - electric vehicles but still lots of consumers are lacking awareness on this technology. Lack of information on the social, environmental, and economic potential benefits are among the main issues of Philippines consumers that really affect its uptake. The goal of this study was to evaluate the consumer’s perception and purchase intention on the transition of hybrid - electric vehicles for sustainable mobility and transportation here in the Philippines. It aimed to assess consumer awareness with hybrid - electric vehicles and its potential benefits to human beings in terms of social, environmental, and economic aspects. The study also gauged the impact of this perception on consumer’s purchase intention on hybrid - electric vehicles. These objectives were attained using the Descriptive Statistics and Correlation Analysis. Using these methods, the results revealed that majority of the consumers believed on the positive benefits of transitioning to hybrid - electric vehicles in terms of social, environmental, and economic aspects. This study also showed that these benefits have high impact on the consumer’s intention of buying or having a hybrid – electric vehicle. Electric vehicle is one of the most effective measure of decarbonizing the transport system. Furthermore, government and automotive businesses must strengthen their drive in spreading awareness of hybrid – electric vehicle’s benefits to consumers to further support country’s effort for development.

Background: In a Beveridgean decentralized healthcare system, like the Italian one, where regions are responsible for their own health planning and financing, the analysis of patients’ migration appears very interesting as it has economic and social implications. The study aims to analyze both patients’ migration for hospital rehabilitation and if the beds endowment is a driver for these flows; Methods: from 2011 to 2019, admissions data were collected from the Hospital Dis-charge Cards database of the Italian Ministry of Health, population data from Italian National Institute of Statistics and data of beds endowment from the Italian Ministry of Health website. To evaluate patients’ migration, we used Gandy’s Nomogram, while to assess if beds endowments are mobility drivers, we have created two matrices, one with attraction indexes (AI) and one with escape indexes (EI). The beds endowment, for each Italian region, were correlated with AI and EI. Spearman’s test was carried out through STATA software; Results: Gandy’s Nomogram showed that only some northern regions had good hospital planning for rehabilitation. A statistically significant correlation between beds endowment and AI was found for four regions, while with EI for eight regions. Conclusions: Only some northern regions appear able to satisfy the care needs of their residents, with an attraction minus escapes positive epidemiological balance. The beds endowment seems to be a driver of Patients’ migration, mainly for escapes. Certainly, the search for mobility drivers needs further investigation given the events in Molise and Basilicata.

Most vulnerable individuals are particularly affected by the COVID-19 pandemic. This study takes place in a large city in France. The aim of this study is to describe the mobility of the homeless population at the begin-ning of the health crisis and to analyze its impact in terms of COVID-19 prevalence. From June to August 2020 and September to December 2020, 1272 homeless people were invited to be tested for SARS-CoV-2 antibodies and virus in and completed questionnaires. Our data show that homeless populations are sociologically dif-ferent depending on where they live. We show that people living on the street were most likely to be relocated to emergency shelters than other inhabitants. Some neighborhoods are points of attraction for homeless peo-ple in the city while others emptied during the health crisis, which had consequences for virus circulation. People with a greater number of different dwellings reported became more infected. This first study of the mo-bility and epidemiology of homeless people in time of pandemic provides unique information about mobility mapping, sociological factors of this mobility, mobility at different scales and epidemiological consequences. We suggest that homeless policies need to be radically transformed since actual model exposes people to infection in emergency.

The increasingly used approach of combining different simulation software in testing of automated driving systems (ADS) increases the need for potential and convenient software designs. Recently developed co-simulation platforms (CSP) provide the possibility to cover the high demand on testing kilometers for ADS by combining vehicle simulation software (VSS) with traffic flow simulation software (TFSS) environments. The emphasis on the demand of testing kilometers is not enough to choose a suitable CSP. The complexity level of the used vehicle, object, sensors and environment models is essential for valid and representative simulation results. Choosing a suitable CSP raises the question of how the test procedures should be defined and constructed and what the relevant test scenarios are. Parameters of the ADS, the environments, objects, sensors in VSS as well as traffic parameters in TFSS can be used to define and generate test scenarios. In order to generate a large number of scenarios in a systematic and automated way, suitable and appropriate software designs are required. In this paper we present a software design for CSP based on the Model-View-Controller (MVC) design pattern and implementation of a complex CSP for virtual testing of ADS. Based on this design, an implementation of a CSP is presented using the VSS from IPG Automotive called CarMaker and the TFSS from PTV Group called Vissim. The results have shown that the presented CSP design and the implementation of the co-simulation can be used to generate relevant scenarios for testing of ADS.

Amorphous solid dispersions are considered a promising formulation strategy for the oral delivery of poorly soluble drugs. The limiting factor for the applicability of this approach is the physical (in)stability of the amorphous phase in solid samples. Minimizing the risk of reduced shelf life for a new drug by establishing a suitable excipient/polymer-type from first principles would be desirable to accelerate formulation development. Here we perform Molecular Dynamics simulations to determine properties of blends of eight different polymer-small molecule drug combinations for which stability data is available from a consistent set of literature data. We calculate thermodynamic factors (mixing energies) as well as mobilities (diffusion rates and roto-vibrational fluctuations). We find that either of the two factors, mobility and energetics, can determine the relative stability of the amorphous form for a given drug. Which factor is rate limiting depends on physico-chemical properties of the drug and the excipients/polymers. The methods outlined here can be readily employed for an in-silico pre-screening of different excipients for a given drug to establish a qualitative ranking of the expected relative stabilities, thereby accelerating and streamlining formulation development.

Micro-Raman spectroscopy was carried out to evaluate the localized residual stresses in commercial Gallium Nitride-based devices, specifically AlGaN/GaN High Electron Mobility Transistors (HEMTs) in a novel packaging design provided by STMicroelectronics S.r.l. The packaging plays a key role in protecting the device core against the external environment, thus minimizing damages caused by mechanical shocks, exposure to light, and contact with chemicals, conjointly achieving an efficient heat dissipation rate. Even though the packaging is a required step for the proper functioning of ready-to-use electronic devices, its application typically may introduce mechanical stress to AlGaN/GaN HEMTs, which can result in various reliability issues. In this paper, we investigate the impact of packaging on residual stress by analyzing the frequency shift of the E2 Raman peak along GaN layers and at the GaN/Si interface. An extensive evaluation was conducted using both a packaged device and a wafer-level device. The correlation between Raman frequency shifts of the E2 mode was accurately quantified, revealing a stress mitigation of approximately 0.1 GPa. This reduction is ascribed to the compressive stress introduced by the packaging, which partially offsets the intrinsic tensile stress of the wafer-level device. The proposed methodology could, in principle, be implemented to improve the development of a packaging.

In recent years, the widespread introduction of bike-sharing systems in China has had a profound impact on the daily lives of Chinese citizens and the development of the urban transport system. This article attempts to analyse the impact of this phenomenon on sustainability. The gradual improvement of related monitoring measures has facilitated the maturation of the bike-sharing industry from the initial stage of uncontrolled growth to the current stage of standardised management. By tracing the global development of bike-sharing systems with a special focus on China, this study sheds light on the platformisation of bicycles and its multiple impacts on technical, environmental, cultural, economic and social sustainability. Furthermore, the study provides a comprehensive analysis of the transformation of bicycles in China and highlights the diverse impacts of platform-based bike sharing on various facets of Chinese society. The development of different bike-sharing systems in China is a unique and crucial case to interpret the current situation of bike-sharing and imagine future scenarios. In contrast to the prevailing and uniform approach derived from the experiences of Northern European countries, the massive and widespread experimentation with different bike-sharing schemes in China reveals not only potentials and aspects of sustainability, innovation and urban and social regeneration, but also some hidden shadows similar to those in small-scale contexts such as Northern Europe. Furthermore, the study emphasises the crucial role of sustainable development principles in addressing urban challenges specific to China.

The research methods were used to investigate the following questions: Does a mobility training intervention change tissue stiffness, joint range of motion, and compensate for possible imbalances? Do the interventions influence the pressure pain threshold and the overall well-being?

Urban mobility and sustainable transportation are fundamental for the European Union's goal of achieving climate neutrality by 2050. The EU encourages national governments to prioritize zero-emission urban transport systems that emphasize safety, accessibility, and inclusiveness. Promoting walking plays a fundamental role in sustainable urban mobility, offering advantages such as emission reduction, better air quality, and enhanced public health. Recent research underscores the importance of creating appealing and safe pedestrian environments to encourage walking. These efforts align with the United Nations' Agenda 2030 sustainability goals, particularly Objective 11, which aims to build inclusive, safe, and sustainable cities and communities. This paper explores the factors influencing pedestrians' willingness to walk and categorizes them into four main groups: Physical Characteristics, Comfort, Safety, and Attractiveness. Significantly, the study reveals that the importance of these factors varies based on demographics, mainly the age of the users. Understanding these factors and their relative significance for pedestrian satisfaction is crucial for shaping effective policies and urban planning strategies aimed at promoting sustainable mobility. By prioritizing pedestrian satisfaction and addressing the specific needs and preferences of diverse groups, cities can create more walkable and environmentally friendly urban environments. These findings offer valuable insights for policymakers and urban planners working toward EU climate-neutral objectives and enhancing the well-being of citizens.

This paper aims at providing a multisource data analysis, including direct data collection, focussed on daily average distances covered with motorised mobility. Its results can be used as a basis for policies involving a shift towards new propulsions, electric motors or hybrid electric vehicles (HEV) for road vehicles. A number of variables influence the propensity of drivers to acquire or use electric traction, even the option of plug-in hybrid electric vehicles (PHEV). This paper addresses one of such variable: the compliancy of electric traction regarding both hybrid plug-in solutions and full-electric vehicles, in addition to the autonomy of batteries (range), with the daily travels by road vehicles, mainly by automobiles. We want to understand whether the constraints leading towards a greater independence from crude oil rather than constraints concerning emissions, mainly in urban contexts, might be compliant with the habitual daily trips of drivers. We also want to understand if these daily trips have varied much during recent years and the consequences they may have on operational costs of plug-in automobiles. We are well aware that the average distances do not represent the actual daily runs of vehicles; yet similar distributions of daily distances for different case studies indicate that a high percentage of trips respond to certain features. After introducing a general overview of road-motorised mobility in Italy, the paper compares data from other studies to provide an indication of average daily driving distances. This reveals how different recent analyses converge on a limited range of average road distances covered daily by Italians, which is compliant with ranges allowed by electric batteries, provided that their low energy density in comparison with that of oil-derived fuels do not imply a significant increase in vehicle mass. Subsequently, average distances in some EU Countries are taken from the literature, and the results are also compared with U.S. data. The study extends the analysis of trends on the use of automobiles and road-vehicles to the international context by also addressing average daily distances covered for freight transport in some EU Countries, thereby providing a further basis for comparison and for understanding whether the daily motorised mobility can be considered as a stable phenomenon. Finally, an analysis is provided of the economic operational advantages from using plug-in vehicles. The main aim of this paper is thereafter to investigate the average daily motorised mobility of single vehicles – so not an aggregated motorised mobility as collected by some statistics – by using private motorised vehicles in Italy, with related trends; thereafter, to compare these data with those obtained from other countries, making use of both existing research studies and directly collected data; the final aim is to understand both the compliance of daily activities based on the use of automobiles with the autonomy of batteries (range) and to calculate some economic outcomes.

The development of dispersed urbanism in Spain ran parallel to the real estate boom and consolidated a new model of city sprawl based on the expansion of suburban areas. This process, which started in the mid 1980s, came to a halt with the onset of the economic crisis in 2007. With it, construction stopped, mobility fell and urban growth came to a standstill. The purpose of this article is to carry out an analysis of the recent evolution and chronology of the expansion of dispersed urbanism in the Barcelona Metropolitan Region (BMR) in order to gain an insight into some of the explanatory factors of such expansion and to deal with the future prospects of middle-term development of dispersed urbanism in the BMR and in Spain. To do this, we examine the trends in the housing market, in residential mobility and we take stock of the impact of business cycles. The conclusion is that dispersed areas retain their appeal in the stages of creation and expansion of households. For this reason, an effective economic recovery and a renewed rise in the price of housing in denser cities may contribute to an upturn in the popularity of the dispersed residential model, which nowadays could be considered to be in a ‘lethargic’ stage, waiting for certain factors to coincide and re-activate its expansion.

During the last decades the quantitative description of electrical and magnetotransport properties of solid-state materials has been a remarkable challenge in materials science. Recently, the discovery of a novel class of materials, the topological semimetals, has led to a growing interest for the full understanding of their magnetotransport properties. In this review, the strong interplay among topology, band structure and carrier mobility in recently discovered high carriers mobility topological semimetals is discussed and their effect on their magnetotransport properties is outlined. The huge magnetoresistance effect, especially in the Hall transverse configuration, and a new version of a three-dimensional quantum Hall effect observed in high-mobility Weyl and Dirac semimetals are reviewed. The design of novel quantum sensors based on solid-state semimetals is also encouraged.

(1) Background: The mediolateral patella displacement is of interest for diagnostics and clinically relevant research questions. Apart from manual testing, no standardized method is currently available. Proper quantification of patella mobility is necessary to understand pathologies at the patellofemoral joint better; (2) Methods: Patella mobility was assessed in 25 healthy individuals using a Patellostabilometer, a new prototype instrument for quantification of the mediolateral patella displacement. The participants underwent measurements of the mediolateral displacement three times using the Patellostabilometer. A maximal force of 10N was applied for patella movement. Additionally, leg length, circumference of the knee, upper- and lower leg was measured. (3) Results: Lateral patella displacement of 18.27±3.76 mm (range 15.85-20.64mm, interquartile range (IQR) of 4.79) was measured. The medial patella displacement showed 24.47±6.59mm (range 19.29-29.76mm, IQR of 10.47). The test-retest measurement error was 2.32±1.76mm (IQR of 2.38mm), with five outliers. There was greater test-retest variability between the measurements of the medial displacement comparing to the lateral one (4) Conclusions: The test-retest variability reached 7% of the patella displacement. Other parameters provided no significant correlations. Based on the natural patellofemoral mobility a precise and clinically relevant quantification of patella mobility is allowed.

Personal Mobility Vehicle (PMV) which has an inward tilting angle turns with the lateral force due to large camber angle, so it is necessary to consider the lateral movement of the tire vertical load axis during turning. Although the steering torque mechanism are very different from those of automobiles, there are not many examples which studied the steering torque mechanism of PMV. In this paper, based on the effects of six components force acting on the tires, a method for setting the steering axis specifications is derived, including the geometrical minimization of steering moment disturbance due to the vertical load reaction force during turning. Although, automobile tires have a significant ground camber angle when traveling on rutted roads, they do not have it on slant roads because the vehicle body tilts along the road surface. On the other hand, in PMV, the vehicle body always keeps upright when traveling both on slanted roads and on rutted roads. Therefore, the tires have ground camber angles on both types of road surface. We study the straight running ability under such road surface disturbances based on the geometrical minimization of steering moment disturbance due to the vertical load reaction force during turning. This straight running ability can be a remarkable strong point of PMV with an inward tilt mechanism.

In this paper, we present a complete, flexible and safe convex-optimization-based method to solve speed planning problems over a fixed path for autonomous driving in both static and dynamic environments. Our contributions are five fold. First, we summarize the most common constraints raised in various autonomous driving scenarios as the requirements for speed planner developments and metrics to measure the capacity of existing speed planners roughly for autonomous driving. Second, we introduce a more general, flexible and complete speed planning mathematical model including all the summarized constraints compared to the state-of-the-art speed planners, which addresses limitations of existing methods and is able to provide smooth, safety-guaranteed, dynamic-feasible, and time-efficient speed profiles. Third, we emphasize comfort while guaranteeing fundamental motion safety without sacrificing the mobility of cars by treating the comfort box constraint as a semi-hard constraint in optimization via slack variables and penalty functions, which distinguishes our method from existing ones. Fourth, we demonstrate that our problem preserves convexity with the added constraints, thus global optimality of solutions is guaranteed. Fifth, we showcase how our formulation can be used in various autonomous driving scenarios by providing several challenging case studies in both static and dynamic environments. A range of numerical experiments and challenging realistic speed planning case studies have depicted that the proposed method outperforms existing speed planners for autonomous driving in terms of constraint type covered, optimality, safety, mobility and flexibility.

By 2020, over 100 countries expanded electric and plug-in hybrid electric vehicle (EV/PHEV) technologies, with global sales surpassing 7 million units. Governments are adopting cleaner vehicle technologies due to proven environmental and health implications of internal combustion engine vehicles (ICEVs), evidenced by the recent COP26 meeting. This article proposes an agent-based model of vehicle activity as a tool for quantifying energy consumption by simulating a fleet of EV/PHEVs within an urban street network at various spatio-temporal resolutions. Driver behaviour plays a significant role in fuel consumption, thus, simulating various levels of individual behaviour enhancing heterogeneity should provide more accurate results of potential energy demand in cities. The study found that 1) energy consumption is lowest when speed limit adherence increases (low variance in behaviour) and is highest when acceleration/deceleration patterns vary (high variance in behaviour) and 2) on average, for tested vehicles, EV/PHEVs were £116.33 cheaper to run than ICEVs across all experiment conditions. The difference in the average fuel costs (electricity and petrol) shrinks at the vehicle level as driver behaviour is less varied (more homogeneous). This research should allow policymakers to quantify the demand for energy and subsequent fuel costs in cities.

Transparent conducting oxides (TCO) with high electrical conductivity and at the same time high transparency in the visible spectrum are an important class of materials widely used in many devices requiring a transparent contact such as light-emitting diodes, solar cells and display screens. Since the improvement of electrical conductivity usually leads to degradation of optical transparency, a fine-tuning sample preparation process and a better understanding of the correlation between structural and transport properties is necessary for optimizing the properties of TCO for use in such devices. Here we report a structural and magnetotransport study of tin oxide (SnO₂), a well-known and commonly used TCO, prepared by a simple and relatively cheap Atmospheric Pressure Chemical Vapour Deposition (APCVD) method in the form of thin films deposited on soda-lime glass substrates. The thin films were deposited at two different temperatures (which were previously found to be close to optimum for our setup), 590 °C and 610 °C, and with (doped) or without (undoped) the addition of fluorine dopants. Scanning Electron Microscopy (SEM) and Grazing Incidence X-ray Diffraction (GIXRD) revealed the presence of inhomogeneity in the samples, on a bigger scale in form of grains (80–200 nm), and on a smaller scale in form of crystallites (10–25 nm). Charge carrier density and mobility extracted from DC resistivity and Hall effect measurements were in the ranges 1–3 × 10²⁰ cm⁻³ and 10–20 cm²/Vs, which are typical values for SnO₂ films, and show a negligible temperature dependence from room temperature down to -269 °C. Such behaviour is ascribed to grain boundary scattering, with the interior of the grains degenerately doped (i.e., the Fermi level is situated well above the conduction band minimum) and with negligible electrostatic barriers at the grain boundaries (due to high dopant concentration). The observed difference for factor 2 in mobility among the thin-film SnO₂ samples most likely arises due to the difference in the preferred orientation of crystallites (texture coefficient).

The transport sector in Germany causes one-quarter of energy-related greenhouse gas emissions. One potential solution to reduce these emissions is the use of battery electric vehicles. Although a number of life cycle assessments have been conducted for these vehicles, the influence of a transport system wide transition has not been researched sufficiently. Therefore, we developed a method which combines life cycle assessment with an agent-based transport simulation and synthetic electric, diesel and gasoline powered vehicle models. We use the transport simulation to obtain the number of vehicles, their lifetime mileage and road-specific consumption. Subsequently we analyze the product systems’ vehicle production, use phase and End-of-Life. The results are scaled depending on the covered distance, the vehicle weight and the consumption for the whole life cycle. The results indicate that the sole transition of drive trains is insufficient to significantly lower the greenhouse gas emissions. However, sensitivity analyses demonstrate that there is a considerable potential to reduce greenhouse gas emissions with higher shares of renewable energies, a different vehicle distribution and a higher lifetime mileage. The method facilitates the assessment of the ecological impacts of the complete car based transportation in urban agglomerations and is able to analyze different transport sectors.

The use of unmanned aerial aircrafts (UAVs) is governed by strict regulatory frameworks that prioritize safety. To guarantee safety, it is necessary to acquire and maintain situational awareness (SA) throughout the operation. Existing Canadian regulations require pilots to operate their aircrafts in visual line-of-sight. Therefore, the task of acquiring and maintaining SA primary falls to the pilots. However, the development of aerial transport is entering a new era with the adoption of a highly dynamic and complex system known as advanced air mobility (AAM), which involves UAV operating autonomously beyond visual line-of-sight. SA must therefore be acquired and maintained primarily by each UAV through specific technologies and procedures. In this paper, we review these technologies and procedures in order to decompose the SA of the UAV in the AAM. We then use the system modeling language to provide a high-level structural and behavioral representation of the AAM as a system having UAV as its main entity. In a case study, we analyze one of the flagships UAVs of our industrial partner. Results show that this UAV doesn’t have all the technologies and methodologies necessary to achieve all the identified SA goals identified for the safety of the AAM. This work is a theoretical framework intended to contribute to the realization of the AAM, and we also expect to impact the future design and utilization of UAVs.

Patients recovering from bone disruption due to trauma or surgery need to limit movement to minimize shear force, thereby protecting callus formation and osteogenesis. Patients often use their arms to assist with functional activities, but pushing is frequently limited to <10 lb (4.5 kg). With only verbal instructions, patients’ ability to accurately limit weight-bearing (WB) force is poor. A therapeutic intervention to improve patient adherence with upper extremity (UE) WB guidelines during functional mobility using an instrumented walker could be beneficial. Therefore, the purpose of this article is to describe a feedback training protocol to improve the ability to modulate weight-bearing force in older adults and then provide an overview of the efficacy of this protocol and subsequent development of a Clinical Force Measuring Walker. An instrumented walker was used to measure UE WB during functional mobility in older healthy subjects (n = 30) before, during, and after (immediately and 2 hours) a visual and auditory concurrent feedback training session. During feedback training, force was significantly reduced with all 3 sessions as compared to baseline. When using the front wheeled walker, UE WB force during the second and third feedback training trials went down compared to the first trial. During the third feedback training trial, force was greater than the two previous trials while transferring sit-to-stand and stand-to-sit. After completion of practice with feedback, UE WB force was significantly reduced and remained so 2 hours later. These findings suggest that feedback training is effective for helping patients to modulate UE WB. Use of an instrumented walker and feedback training would be beneficial in clinical practice, especially with older patients. A more intensive feedback training with additional trials and or simultaneous visual and auditory cues during whole-practice may be needed to get UE WB below a 10 lb threshold.

We applied a generalized SEIR epidemiological model to the recent SARS-CoV-2 outbreak in the world, with a focus on Italy and its Lombardia, Piemonte, and Veneto regions. We focus on the application of a stochastic approach in fitting the model numerous parameters using a Particle Swarm Optimization (PSO) solver, to improve the reliability of predictions in the medium term (30 days). We analyze the official data and the predicted evolution of the epidemic in the Italian regions, and we compare the results with data and predictions of Spain and South Korea. We link the model equations to the changes in people’s mobility, with reference to Google’s COVID-19 Community Mobility Reports. We discuss the effectiveness of policies taken by different regions and countries and how they have an impact on past and future infection scenarios.

A novel concept of ion separation Anisotropic Diffusion Mobility Analyser (ADMA) with Individual Ion Counter (IIC) was shown by numerical modelling to be able to separate ions with high resolution and ultra-high sensitivity. An ADMA-IIC device has been developed and tested at sheath flow rates below 40 l/min with a number of chemicals relevant to airport security and military applications. The resolving power of the ADMA was observed to be in the range of 40 to 80. These data were close to the resolution obtained in numerical modelling predictions. It is greater than or comparable with the RP of currently available on-the-market devices for airport security and other applications. It was also confirmed that the ADMA technology enables very fast detection of analytes, at least 100 times faster than ion mobility spectrometers. It was shown that Brownian diffusion can be controlled and even overpowered by generating highly anisotropic diffusion in the ADMA device. The ultimate sensitivity of single-ion detection below the ppq range has been demonstrated.

Recently, smart mobility intelligent traffic services have become a critical task in Intelligent Transportation Systems (ITS). This involves not only the use of advanced sensors and controllers but also the ability to respond to real-time traffic situations at intersections, alleviate congestion, and generate policies to prevent traffic jams. DRL (Deep Reinforcement Learning) provides a natural framework for processing tasks. In DRL, each intersection can control itself and coordinate with neighbors to achieve optimal network-wide policies. However, comparing approaches remains a challenging task due to the existence of numerous possible configurations. This research performs a critical comparison of various traffic controllers found in the literature. It demonstrates that using a nonlinear approximator for coordination mechanisms and enhancing observability at each intersection are key performance drivers.

This paper investigates urban microclimate in relation to the typical major streets and mobility layout in Turin, i.e. boulevards with lateral minor roads, called “controviali”, an almost unique urban mobility layout. The aim is to examine the possibility of intervention in relation to the type and scope of mobility for microclimate mitigation and climate adaptation purposes. To this, this paper evaluates to which extent urban morphology and mobility features contribute to determining microclimatic conditions, to finally identify site-specific prioritised strategies to cope with its mitigation. This research is structured in two phases. The first phase is dedicated to urban-scale analysis of the streets with “controviali”, resulting in the creation of a database containing their properties and typology, to finally derive six clusters of boulevards. The second phase involves the selection of one representative case study per cluster and simulation of their microclimate conditions, performed by the software ENVI-met. The results are discussed in relation to the parameters proven in the prior literature review to have a major impact on the microclimate. The assessment of the results indicated which elements affect the climatic conditions the most, allowing for the proposal of different prioritised mitigation strategies in the conclusion.

Electrical properties of the thin films of poly(arylene ether ketone) copolymers (co-PAEKs) with the fraction of phthalide-containing units of 3, 5 and 50 mol% in the main chain were investigated by using radiation induced conductivity (RIC) measurements. Transient current signals and current-voltage (I-V) characteristics were obtained under exposing 20÷25 thick films of the co-PAEKs to monoenergetic electron pulses of the energy ranged from 3 to 50 keV in the electric field ranged from 5 to 40 V/μm. The Rose-Fowler-Vaisberg semi-empirical model based on a multiple trapping formalism was used for an analysis of the RIC data and the parameters of the highly dispersive charge carrier transport were evaluated. The analysis revealed that charge carriers moved in isolation from each other and the applied electric fields were below the threshold field triggering the switching effect (a reversible high-to-low resistivity transition) in the co-PAEK films. It was also found that the co-PAEK films due to the super-linear I-V characteristics are highly resistant to electrostatic discharges arising from the effects of ionizing radiation. This property is important for the development of protective coatings for electronic devices.

To enable the deployment of battery-electric vehicles (BEV) as passenger cars in the private transport sector, a suitable charging infrastructure is crucial. In this paper a methodology for efficient spatial distribution of charging infrastructure is evaluated by investigating a scenario with a market penetration of BEVs of 100 percent (around 1.3 million vehicles). It aims towards the development of various charging infrastructure scenarios - including public and private charging - which are suitable to cover the charging demand. Therefore, these scenarios are investigated in detail with focus on number of public charging points, their spatial distribution, the available charging power and the necessary capital costs. For the creation of those charging infrastructure scenarios a placement model is developed. It uses the data of a MATSim (Multi-Agent Transport Simulation) traffic simulation of the metropolitan area of Berlin to evaluate and optimize different distributions of charging infrastructure. The model uses a genetic algorithm and the principle of multi-objective optimization. The capital cost of the charging points and the mean detour car drivers must cover additionally are used as optimization criteria. Using these criteria should lead to cost efficient infrastructure solutions which provide high usability at the same time. The main advantage of the method selected is that multiple optimal solutions with different characteristics can be found and suitable solutions can be selected by using other criteria subsequently. The optimized charging infrastructure solutions show capital costs between 624 and 2950 million euro. Users must cover an additionally mean detour of 254m to 590m per charging process to reach an available charging point. According to the results a suitable ratio between charging points and vehicles is between 11:1 and 5:1. A share of fast charging infrastructure (&gt;50kW) of less than ten percent seems to be sufficient, if it is situated at main traffic routes and highly frequented places.

The burgeoning field of Urban Air Mobility (UAM) promises transformative solutions to urban transportation challenges, with the potential to revolutionize cargo logistics in metropolitan areas. This research investigates the role of tiltrotor technology as a pivotal enabler of seamless UAM integration for efficient cargo transportation within urban environments. The study delves into the existing challenges within UAM, such as infrastructure limitations and the need for adaptable aerial vehicles that can navigate confined spaces while maintaining payload capacity. Tiltrotor Unmanned Aerial Vehicles (UAVs) are examined as a solution to these challenges due to their unique capabilities, combining vertical takeoff and landing (VTOL) with efficient forward flight. This research evaluates the potential of tiltrotor UAVs for urban cargo transportation, focusing on their operational efficiency, safety, and economic viability. Furthermore, the study explores the potential impact of tiltrotor technology on urban cargo logistics, considering factors such as reduced congestion, shorter delivery times, and environmentally sustainable operations. Real-world case studies and practical implementation strategies are discussed, shedding light on the importance of integration of tiltrotor UAVs into existing cargo transportation networks.The findings of this research underscore the transformative potential of tiltrotor technology in enhancing UAM for cargo transportation within urban areas. The study contributes valuable insights to the ongoing discourse on UAM integration and lays the groundwork for future developments in autonomous aerial cargo delivery systems.

Over a billion people around the world are disabled, among them, 253 million are visually impaired or blind, and this number is greatly increasing due to ageing, chronic diseases, poor environment, and health. Despite many proposals, the current devices and systems lack maturity and do not completely fulfill user requirements and satisfaction. Increased research activity in this field is required to encourage the development, commercialization, and widespread acceptance of low-cost and affordable assistive technologies for visual impairment and other disabilities. This paper proposes a novel approach using a LiDAR with a servo motor and an ultrasonic sensor to collect data and predict objects using deep learning for environment perception and navigation. We adopted this approach in a pair of smart glasses, called LidSonic V2.0, to enable the identification of obstacles for the visually impaired. The LidSonic system consists of an Arduino Uno edge computing device integrated into the smart glasses and a smartphone app that transmits data via Bluetooth. Arduino gathers data, operates the sensors on smart glasses, detects obstacles using simple data processing, and provides buzzer feedback to visually impaired users. The smartphone application collects data from Arduino, detects and classifies items in the spatial environment, and gives spoken feedback to the user on the detected objects. In comparison to image processing-based glasses, LidSonic uses far less processing time and energy to classify obstacles using simple LiDAR data, according to several integer measurements. We comprehensively describe the proposed system's hardware and software design, construct their prototype implementations, and test them in real-world environments. Using the open platforms, WEKA and TensorFlow, the entire LidSonic system is built with affordable off-the-shelf sensors and a microcontroller board costing less than $80. Essentially, we provide designs of an inexpensive, miniature, green device that can be built into, or mounted on, any pair of glasses or even a wheelchair to help the visually impaired. Our approach affords faster inference and decision-making using relatively low energy with smaller data sizes as well as faster communications for the edge, fog, and cloud computing.

The construction of different roads, such as freeways, highways, major roads or minor roads must be accompanied by constant monitoring and evaluation of service delivery. Pavements are generally assessed by engineers in terms of the smoothness, surface condition, structural condition and surface safety. Pavement assessment is often conducted using the qualitative indices such as international roughness index (IRI), pavement condition index (PCI), structural condition index (SCI) and skid resistance value (SRV), which are used for smoothness assessment, surface condition assessment, structural condition assessment, and surface safety assessment, respectively. In this paper, Tehran-Qom Freeway in Iran has been selected as the case study and its smoothness and pavement surface conditions are assessed. At 2-km intervals, a 100-meter sample unit is selected in the slow-speed lane (totally, 118 sample units). In these sample units, the PCI is calculated after a visual inspection of the pavement and the recording of distresses. Then, in each sample unit, the average IRI is computed. The purpose of this study is to provide a method for estimating PCI based on IRI. The proposed theory was developed by Random Forest (RF), and Random Forest optimized by Genetic Algorithm (RF-GA) methods and these methods were validated using correlation coefficient (CC), scattered index (SI), and Willmott’s index of agreement (WI) criteria. The proposed method reduces costs, saves time and eliminates the safety risks.

Prediction models in mobility and transportation maintenance systems have been dramatically improved through using machine learning methods. This paper proposes novel machine learning models for an intelligent road inspection. The traditional road inspection systems based on the pavement condition index (PCI) are often associated with the critical safety, energy and cost issues. Alternatively, the proposed models utilize surface deflection data from falling weight deflectometer (FWD) tests to predict the PCI. Machine learning methods are the single multi-layer perceptron (MLP) and radial basis function (RBF) neural networks as well their hybrids, i.e., Levenberg-Marquardt (MLP-LM), scaled conjugate gradient (MLP-SCG), imperialist competitive (RBF-ICA), and genetic algorithms (RBF-GA). Furthermore, the committee machine intelligent systems (CMIS) method was adopted to combine the results and improve the accuracy of the modeling. The results of the analysis have been verified through using four criteria of average percent relative error (APRE), average absolute percent relative error (AAPRE), root mean square error (RMSE), and standard error (SD). The CMIS model outperforms other models with the promising results of APRE=2.3303, AAPRE=11.6768, RMSE=12.0056, and SD=0.0210.

Autonomous aircraft are the key enablers of future urban services, such as postal and transportation systems. Digital Twins (DT) are promising cutting-edge technologies that can transform the future transport ecosystem into an autonomous and resilient system. However, since DT is a data-driven solution based on AI, proper data management is essential in implementing DT as a Service (DTaaS). One of the challenges in DT development is the availability of real-life data, particularly for training algorithms and verifying the functionality of DT. The current article focuses on data augmentation through synthetic data generation. This approach can facilitate the development of DT in case the developers do not have enough data to train the Machine Learning (ML) algorithm. The current twinning approach provides a prospective ideal state of the engine used for proactive monitoring of the engine’s health as an anomaly detection service. In line with the track of Unmanned Aircraft Vehicles (UAV) for Urban Air Mobility in Smart Cities applications, this paper focuses specifically on the common Hybrid turbo-shaft in drones/helicopters. However, there is a significant gap in real-life similar synthetic data generation in the UAV domain literature. Therefore, Rolling Linear Regression and Kalman Filter algorithms were implemented on noise-added data, which simulate the data measured from the engine in a real-life operational lifecycle. For both thermal and hybrid models, the corresponding DT model has shown high efficiency in noise filtration and a certain amount of predictions with a lower error rate on all engine parameters, except the engine torque.

Design and advancement of the durable urban train infrastructures are of utmost importance for reliable mobility in the smart cities of the future. Given the importance of urban train lines, tunnels, and subway stations, these structures should be meticulously analyzed. In this research, two-dimensional modeling and analysis of the soil-structure mass of the Alan Dasht station of Mashhad Urban Train are studied. The two-dimensional modeling was conducted using Hashash’s method and displacement interaction. After calculating the free-field resonance and side distortion of the soil mass, this resonance was entered into PLAXIS finite element program, and finally, stress and displacement contours together with the bending moment, shear force and axial force curves of the structure were obtained.

The reliable online analysis of volatile compounds in exhaled breath remains a challenge as a plethora of molecules occur in different concentration ranges (i.e. ppt to %), and need to be detected against an extremely complex background matrix. While this complexity is commonly addressed by hyphenating a specific analytical technique with appropriate preconcentration and/or preseparation strategies prior to detection, we herein propose the combination of three analytical tools based on truly orthogonal measurement principles as an alternative solution: field-asymmetric ion mobility spectrometry (FAIMS), Fourier-transform infrared (FTIR) spectroscopy-based sensors utilizing substrate-integrated hollow waveguides (iHWG), and luminescence sensing (LS). These three tools have been integrated into a single compact analytical platform suitable for online exhaled breath analysis. The analytical performance of this prototype system was tested via artificial breath samples containing nitrogen (N2), oxygen (O2), carbon dioxide (CO2) and acetone as a model volatile organic compound (VOC) commonly present and detected in breath. Functionality of the combined system was demonstrated by detecting these analytes in their respectively breath-relevant concentration range and mutually independent of each other generating orthogonal yet correlated analytical signals. Finally, adaptation of the system towards the analysis of real breath samples during future studies is discussed.

This work targets the development of a mobility control system for high-speed series hybrid electric tracked vehicles, which operate with independent traction motors for each track. The scope of the research encompasses the modeling of the series hybrid powertrain specific to military tracked vehicles, as well as an in-depth analysis of their dynamic behavior. Subsequently, the study conducts a critical review of the mobility control approaches sourced from literature, identifying key techniques relevant to high-inertia vehicular applications. Building on the foundational models, the study puts forward a robust closed-loop mobility control system aimed at ensuring precise and stable off-road vehicle operations. The system's resilience and adaptability to a variety of driving conditions are emphasized, with a particular focus on handling maneuvers such as steering and pivoting, which are challenging operations for tracked vehicle agility. The performance of the proposed mobility control system is tested through a series of simulations, covering a spectrum of operational scenarios. These tests are carried out in both offline simulation settings, which permit meticulous fine-tuning of system parameters, and real-time environments that replicate actual field conditions. The simulation results demonstrate the system's capacity to improve vehicular response and highlight its potential impact on the future designs of mobility control systems for the heavy-duty vehicle sector, particularly in defense applications.

During the past two decades of e-commerce growth, the concept of a business model has become increasingly popular. More recently, the research on this realm has grown rapidly, with diverse research activity covering a wide range of application areas. Considering the sustainable development goals, the innovative business models have brought a competitive advantage to improve the sustainability performance of organizations. The concept of the sustainable business model describes the rationale of how an organization creates, delivers, and captures value, in economic, social, cultural, or other contexts, in a sustainable way. The process of sustainable business model construction forms an innovative part of a business strategy. Different industries and businesses have utilized sustainable business models’ concept to satisfy their economic, environmental, and social goals simultaneously. However, the success, popularity, and progress of sustainable business models in different application domains are not clear. To explore this issue, this research provides a comprehensive review of sustainable business models literature in various application areas. Notable sustainable business models are identified and further classified in fourteen unique categories, and in every category, the progress -either failure or success- has been reviewed, and the research gaps are discussed. Taxonomy of the applications includes innovation, management and marketing, entrepreneurship, energy, fashion, healthcare, agri-food, supply chain management, circular economy, developing countries, engineering, construction and real estate, mobility and transportation, and hospitality. The key contribution of this study is that it provides an insight into the state of the art of sustainable business models in various application areas and future research directions. This paper concludes that popularity and the success rate of sustainable business models in all application domains have been increased along with the increasing use of advanced technologies.

Tea (Camellia sinensis) is one of the most popular beverages worldwide. Many types of tea prod-ucts continuously emerge in an endless stream; so, the classification or verification of tea be-comes more difficult. Aroma is a vital indicator of tea quality. The present study deals with the identification of aroma compounds in 18 different kinds of tea belonging to three typical tea va-rieties, including green tea, oolong tea, and black tea, using GC-IMS and GC×GC-O-MS. More-over, the clustering of all 18 tea samples and the in-depth correlation analysis between sensory evaluation and instrumental data were performed using the PCA and OPLS-DA. The results re-vealed that in all 18 kinds of tea, a total of 85 aroma compounds were detected by GC-IMS, whereas 318 were detected by GC×GC-O-MS. The PCA result revealed that green tea, oolong tea, and black tea could be clearly separated based on their peak areas. The OPLS-DA result showed that a total of 49 aroma compounds with VIP value > 1.0 could be considered as the potential in-dicators to quickly classify or verify tea types. This study not only compared the aroma differ-ences across different types of teas, but also provided ideas for the rapid monitoring of tea qual-ity and variety.