This study focuses on traffic condition analysis, especially in under developing country India and makes more visible of traffic flow by using traffic flow theory in order to understand real traffic condition. India is one of rapid economic growing countries and large market with second largest population 1.3 billion in 2018. On the other hand, there are social issues such as environment air pollution and global warming by traffic CO2 emission of transportation. This kind of condition is not only in India, but in other South East Asia and Africa in future. From recent more than one-month traffic observation in a typical major city Ahmedabad in Gujarat state, which has about 8 million population and industrialized city. In terms of traffic data collection, 14 CCTV cameras are used in the city. Based on multiple traffic cameras monitoring, author found the unique traffic flow characteristics and compares traffic flow theory. In this study, it is described what is key parameters to show real traffic congestion condition and how these congestion occurs.

While numerous studies have explored the correlation between the built environment and ride-hailing demand, few have assessed their nonlinear interplay. Utilizing ride-hailing order data and multi-source built environment data from Nanjing, China, this paper uses the machine learning method, eXtreme Gradient Boosting (XGBoost), combined with SHapley Additive exPlanations (SHAP) and Partial Dependence Plots (PDP) to investigate the impact of built environment factors on ride-hailing travel demand, including their non-linear and threshold effects. The findings reveal that dining facilities have the most significant impact, with a contribution rate of 30.75%, on predicting ride-hailing travel demand. Additionally, financial, corporate, and medical facilities also exert considerable influence. The built environment factors need to reach a certain threshold or within a certain range to maximize the impact of ride-hailing travel demand. population density, land use mix and distance to the subway station collectively influence ride-hailing demand. The results are helpful for TNCs to allocate network ride-hailing resources reasonably and effectively.

Over the past couple of decades, Global positioning system (GPS) technology has been utilized to collect large-scale data from travel surveys. As the precise spatiotemporal characteristics of travel could be provided by GPS devices, the issues of traditional travel survey, such as misreporting and non-response, could be addressed. Considering the defects of dedicated GPS devices (e.g., need much money to buy devices, forget to take devices to collect data, limit the simple size because of the number of devices, etc.), and the phenomenon that the smartphone is becoming one of necessities of life, there is a great chance for the smartphone to replace dedicated GPS devices. Although, several general reviews have been done about smartphone-based GPS travel survey in the literature review section in some articles, a systematic review from smartphone-based GPS data collection to travel mode detection has none. The included studies were searched from six databases. The purpose of this review is to critically assess the current literature on the existing methodologies of travel mode detection based on GPS raw data collected by smartphones. Meanwhile, according to the systematic comparison among different methods from data-preprocessing to travel mode detection, this paper could carefully provide the Strengths and Weaknesses of existing methods. Furthermore, it is the crucial step to develop the methodologies and applications of GPS raw data collected by smartphones.

Urban rail transit hub platform is the most important area for passenger flow distribution. In order to calculate passenger flow volume in platform and evaluate platform service level during rush hours, this paper presents a method for modeling and simulation of passenger flow distribution in platform. Passenger flow distribution model (PFDM) is proposed based on the basic analysis and the superposition principle of passenger flow. Simulation design for PFDM is proposed by Anylogic, which contains simulation process and simulation model. Experiment results show that PFDM and simulation design are effective and accordant with the reality scenario, and the simulation precision is comparatively ideal. This research could provide a beneficial reference for train scheduling and operation management under the viewpoint of traffic safety and service level.

In response to growing industrial demands, necessitated by larger ships and increased cargo volumes, many port authorities worldwide are undertaking a significant expansion of their infrastructure to increase their capacity. Significant expansion raises challenges such as potential congestion and environmental concerns, especially for port located in urban area as the case study explored in this paper. To address these issues, the study employs a combination of scenario analysis developed in collaboration with the port authority and logistic system simulation. This approach assesses the capacity of existing access gates to handle the increased trucking traffic and optimizes the unloading process of construction trucks at a new terminal. Key findings suggest that while the current gate capacities are adequate, optimizing unloading times at the new terminal is critical to prevent road congestion in urban area before the gate and reduce negative impacts such as sound and atmospheric pollution. The study advocates for an appointment system during peak hours to manage traffic flow effectively and underscores the importance of collaboration between the city and port authorities in managing truck traffic to mitigate congestion on the city side. Nonetheless, the study recognizes certain constraints, including the incomplete evaluation of the influence of intermodal transport on truck traffic. Furthermore, the study emphasizes the need for future research to consider the correlation between vehicle numbers and service levels, aiming to effectively adjust the time in the system.

Self-organized criticality (SOC) is a celebrated paradigm from the 90’s for understanding dynamical systems naturally driven to its critical point, where the power-law dynamics taking place make predictions practically impossible, such as in stock prices, earthquakes, pandemics and many other problems in science related to phase transitions. Shortly thereafter, it was realized that traffic flow might be in the SOC category, implying that conventional traffic management strategies seeking to maximize the local flows can become detrimental. This paper shows that the Kinematic Wave model with triangular fundamental diagram, and many other related traffic models, indeed exhibit SOC, thanks in part to the fractal nature of traffic exposed here on the one hand, and our need to get to our destinations as soon as possible, on the other hand. Important implications for congestion management of traffic near the critical region are discussed, such as: (i) Jam sizes obey a power-law distribution with exponent 1/2, implying that both its mean and variance become ill-defined and therefore impossible to estimate. (ii) Traffic in the critical region is chaotic in the sense that predictions becomes extremely sensitive to initial conditions. (iii) However, aggregate measures of performance such as delays and average speeds are not heavy tailed, and can be characterized exactly by different scalings of the Airy distribution, (iv) Traffic state time-space “heat maps” are self-affine fractals where the basic unit is a triangle, in the shape of the fundamental diagram, containing 3 traffic states: voids, capacity and jams. This fractal nature of traffic flow calls for analysis methods currently not used in our field.

Hands-on type training of Integrated Computational Materials Engineering (ICME) is characterized by assisted application and combination of multiple simulation software tools and data. In this paper, we present recent experiences in establishing a cloud-based infrastructure to enable remote use of dedicated commercial and open access simulation tools during an interactive on-line training event. In the first part, we summarize the hardware and software requirements and illustrate how these have been met using cloud hardware services, a simulation platform environment, a suitable communication channel, common workspaces and more. The second part of the article focuses (i) on the requirements for suitable on-line hands-on training material and (ii) on details of some of the approaches taken. Eventually, the practical experiences made during three consecutive on-line training courses held in September 2020 with 35 nominal participants each, are discussed in detail.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Pipeline conflicts arising from data dependencies are a common issue encountered in the hardware implementation of layered QC-LDPC decoders. This paper proposes an efficient layered decoding architecture to reduce pipeline conflicts without introducing stall cycles, by reordering the processing order offline and using the patch method based on variable-to-check message. The experimental results on the LDPC code of the IEEE802.16 standard in the OFDM-PON system demonstrate that the proposed architecture has a sensitivity improvement of 0.125 dBm and 0.375 dBm, respectively, compared with our previous work and the method described in the other work.

The analogy between the theory of phase transitions in simple fluids and vehicular traffic flow has long been suspected, promising a new level of understanding of urban congestion by standing on one of the firmer foundations in physics. The obstacle has been the interpretation of the thermal energy of the gas-particle system, which remains unknown. This paper proposes the flow of cars through the network as a viable interpretation, where the fundamental diagram for traffic flow would be analogous to the coexistence curve in gas-liquid phase transitions. Thanks to the power-law form of the coexistence curve, it was possible to formalize that the resulting network traffic model belongs to the Kardar-Parisi-Zhang universality class. The scaling relationships arising in this universality class are found to be consistent with West's scaling theory for cities. It is shown that congestion costs (delays + fuel consumption) scale superlinearly with city population, possibly and worryingly more so than predicted by West's theory. Implications for sustainability and resiliency are discussed.

As traffic congestion gets worse year by year in metropolitan areas, cities search for solutions to improve their traffic performance and reduce their environmental impacts. This paper focuses on parking pricing and congestion pricing and their short-term effects not only on traffic congestion but also on the potential revenue for a city. We develop an easy to implement multimodal macroscopic traffic and parking search model for a central area based on aggregated data at the network level. Our methodology allows us to analyze how introducing parking pricing inside a network, or a congestion toll combined with a park and ride (P+R) scheme can affect the drivers’ decision between entering the network by car (private vehicle) or using P+R instead. This decision directly influences the number of drivers using P+R, and this impacts, in turn, the traffic performance. Based on such analysis, a city can get valuable insights to evaluate whether congestion pricing is a necessity or if the traffic improvements resulting from implementing parking pricing strategies are sufficient when combined with P+R facilities. A search algorithm is used to find the best trade-off between the parking fees and the congestion toll. Any additional revenue collected through these schemes can then be used to improve public transport or the P+R facilities themselves. With minor data collection efforts and little computational costs compared to most existing parking and congestion pricing models, we illustrate our proposed framework in a case study of an area with a high parking demand for public parking spaces within the city of Zurich, Switzerland. Results show that parking pricing combined with P+R is indeed a viable option compared to congestion pricing for improving traffic performance, even if parking pricing schemes do not target all the drivers.

Mechanism of traffic congestion generation is more than complicated, due to complex geometric road design and complicated driving behavior at urban expressway in China. We employ Cell transmission model (CTM) to simulate traffic flow spatiotemporal evolution process along the expressway, and reveal the characteristics of traffic congestion occurrence and propagation. Here we apply the variable-length-cell CTM to adapt the complicated road geometry and configuration, and propose the merge section CTM considering drivers' mandatory lane-changing and other unreasonable behavior at on-ramp merge section, and propose the diverge section CTM considering queue length end extending expressway mainline to generate dynamic bottleneck at diverge section. In the new improved CTM model, we introduce merge ratio and diverge ratio to describe the effect of driver behavior at merge and diverge section. We conduct simulation on the real urban expressway in China, results show that merge section and diverge section are the original location of expressway traffic congestion generation, on/off-ramp traffic flow has great effect on expressway mainline operation. When on-ramp traffic volume increases by 40%, merge section delay increases by 35%. And when off-ramp capacity increases by 100 veh/hr, diverge section delay decreases about by 10%, which proves the strong interaction between expressway and adjacent road networks . Our results provide the underlying insights of traffic congestion mechanism in urban expressway in China, which can be used to better understand and manage this issue.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

Background: By 2025, road traffic injuries are projected to rank third in the global burden of disease, posing a significant challenge that affects health, social well-being, and economic aspects. According to data from the Romanian Police National Statistics Center, there has been an average of 342 traffic accidents per year involving pediatric patients over the past 10 years. Materials and Methods: A retrospective research study was conducted, encompassing 358 cases of road traffic accidents identified for the study. Data collection was conducted at "St. Mary’s" Emergency Hospital for Children in Iasi, Romania, from January 2015 to December 2022. To facilitate statistical analyses, participants were categorized into three specific age groups, correlating between the anatomical region affected and different types of accidents was examined, and the classification of injury types was determined based on the characteristics of the harm inflicted. Results: An oscillating trend is observed from 2015 to 2020, with its lowest value recorded in 2017 at around 6.8% and its peak in 2019 at 20.1%. Notably, post-pandemic (Covid-19), the cases underwent a substantial decline of approximately 50%. At least 78.7% of those who did not undergo orthopedic reduction required surgery, whereas among those who underwent orthopedic reduction, only 23.4% needed surgery. Regarding the frequency of complications 7,9% of the total cases experienced complications. Conclusion: The increased frequency of pediatric traffic accidents is attributed to the extensive services provided by "St. Mary’s" Emergency Hospital for Children, Iasi, to a significant number of patients from both rural and urban areas across the seven counties of the Moldova, Romania.

Cloud Computing is a promising and emerging technology that is rapidly being adopted by many IT companies due to a number of benefits that it provides, such as large storage space, low investment cost, virtualization, resource sharing, etc. Users are able to store a vast amount of data and information in the cloud and access it from anywhere, anytime on a pay-per-use basis. Since many users are able to share the data and the resources stored in the cloud, there arises a need to provide access to the data to only those users who are authorized to access it. This can be done through access control schemes which allow the authenticated and authorized users to access the data and deny access to unauthorized users. In this paper, a comprehensive review of all the existing access control schemes has been discussed along with analysis. Keywords: role-based access control, attribute-based access control, attribute-based encryption

Atta capiguara is a grass-cutting ant species frequently found in Cerrado biome. However, little is known about the giant nest architecture of this ant. In this study, we investigated the architecture of three A. capiguara nests from the fragment of cerrado in Botucatu, São Paulo, Brazil. Casts were made of the nests by filling them with cement to permit better visualization of internal structures such as chambers and tunnels. After excavation, the depth and dimensions (length, width, and height) of the chambers were measured. The results showed the typical shape of Atta capiguara nests consisting of mounds of loose soil with unique features resembling a conic section. The fungus chambers were found outside the apparent main part of the nest and were spaced apart and distributed laterally at ground level. The waste chambers were located beneath the largest mound of loose soil. Both the fungus and waste chambers exhibited a sectoral distribution. Our study contributes to a better understanding of the so far unknown nest architecture of the grass-cutting ant A. capiguara.

In the investment casting process, the pattern made of wax is obtained in a die for further formation of shell mold. This work investigates commercial pattern waxes fluidity with a newly developed injection fluidity test. A good correlation between wax fluidity and its viscosity is observed, which is different from metallic alloys, where the solidification behavior is more critical. The difference in investigated filled waxes fluidity can be associated with the type and amount of filler.

Terms related to gradients of scalar fields are introduced as scalar products into the formulation of entropy. A Lagrange density is then formulated by adding constraints based on known conservation laws. Applying the Lagrange formalism to the resulting Lagrange density leads to the Poisson equation of gravitation and also includes terms being related to curvature of space. The formalism further leads to terms possibly explaining nonlinear extensions known from modified Newtonian dynamics approaches. The article concludes with a short discussion of the presented methodology and provides an outlook on other phenomena, which might be tackled using this new approach.

Gold nanoparticles (AuNPs) are promising candidates in various biomedical applications such as sensors, imaging, and cancer therapy. Understanding the influence of AuNPs on lipid membranes is important to assure their safety in the biological environment and to improve their scope in nanomedicine. In this regard, the present study aims to analyze the effects of different concentrations (0.5, 1, and 2 w %) of dodecanethiol functionalized hydrophobic AuNPs on the structure and fluidity of zwitterionic 1-stearoyl-2-oleoyl-sn-glycerol-3-phosphocholine (SOPC) lipid bilayer membranes using Fourier transform infrared (FTIR) spectroscopy and fluorescent spectroscopy. The size of AuNPs was found to be 2.2 ± 1.1 nm using transmission electron microscopy. FTIR results have shown that the AuNPs induced a slight shift in methylene stretching bands, while the band positions of carbonyl and phosphate group stretching were unaffected. Temperature-dependent fluorescent anisotropy measurements have shown that the incorporation of AuNPs up to 2 w % did not affect the lipid order in membranes. Overall, these results indicate that the hydrophobic AuNPs in the studied concentration did not cause any significant alterations in the structure and membrane fluidity, which suggests the suitability of these particles to form liposome-AuNP hybrids for diverse biomedical applications including drug delivery and therapy.

Diabetic foot ulcers (DFUs) significantly affect the lives of patients and increase the risk of hospital stay and amputation. We suggest a remote-monitoring platform for better DFU care. This system uses digital health metrics (scaled from 0 to 10, where higher scores indicate a greater risk of slow healing) to provide a comprehensive overview through a visual interface. The platform features smart offloading devices that capture behavioral metrics such as offloading adherence, daily steps, and cadence. Coupled with remotely measurable frailty and phenotypic metrics, it offers an in-depth patient profile. Additional demographic data, wound characteristics, and clinical parameters, such as cognitive function, were integrated, contributing to a comprehensive risk factor profile. We evaluated the feasibility of this platform with 124 DFU patients over 12 weeks; 39% experienced unfavorable outcomes such as dropout, adverse events, or non-healing. Digital biomarkers were benchmarked (0-10), categorized into low, medium, and high risk for unfavorable outcomes, and visually represented using color-coded radar plots. The initial results of case reports illustrate the value of this holistic visualization to pinpoint the underlying risk factors for unfavorable outcomes, including a high number of steps, poor adherence, and cognitive impairment. Although future studies are needed to validate the effectiveness of this visualization in personalizing care and improving wound outcomes, early results in identifying risk factors for unfavorable outcomes are promising.

The speed-density or flow-density relationship is a fundamental aspect of traffic flow theory. Single-regime models often face challenges in consistently fitting empirical data under diverse traffic conditions. This paper addresses the inaccuracies of single-regime models, attributing them not only to their functional forms but also to data dispersion and sample selection bias. To mitigate these issues, the data is segmented into day and night periods, leading to the creation of separate models for each. Calibration results, conducted on data from two freeways, Tehran-Karaj and Tehran-Qom, reveal distinct challenges, with the former exhibiting high dispersion and the latter showing low dispersion. Surprisingly, single-regime models demonstrate notable effectiveness when applied to distinct day and night data. Further analysis, categorizing data into day and night, results in reduced errors (20-60% improvement in R-Square), emphasizing the potential for enhanced accuracy by considering distinct regimes. Overall, the study underscores the significance of regime-specific considerations for accurate traffic flow modeling. Upon thorough evaluation, the study highlights the superiority of the neural network Multi-layer Perceptron (MLP) over traditional models. The research discusses the implications of incorporating day and night parameters into the models, emphasizing their potential for accuracy enhancement.

Candida spondylodiscitis (CS) is a rare disease, for which therapeutic option are limited to fluconazole or echinocandins and the treatment should be administered up to 12 months. In case of reduced azole susceptibility, there is a scarcity of therapeutic options with adequate bone penetration and limited toxicity. We report the first case of successful use of rezafungin for a spondylodiscitis due to Candida parapsilosis with reduced susceptibility to azoles. The patient, affected by paraplegia and short bowel syndrome, was diagnosed with CS with culture on vertebral biopsy guided by a 18-FDG-PET/CT scan. He received rezafungin 200 mg weekly for 26 weeks, after 10 weeks of previous antifungal treatment. He had a full clinical, radiologic and biochemical response to the therapy with rezafungin, with no adverse effects. Rezafungin is a promising therapy for Candida osteomyelitis, especially when first line therapies are ineffective or contraindicated.

Congestion not only represents a cardinal sign of heart failure but is also now recognized as the primary cause for hospital admissions, rehospitalizations, and mortality among patients with acute heart failure. Congestion can manifest through various heart failure phenotypes in acute settings: volume overload, volume redistribution, or both. Recognizing the congestion phenotype is paramount, as it implies different therapeutic strategies for decongestion. Complete decongestion is hardly achieved among patients with acute heart failure, as more than half still have residual congestion at discharge. Residual congestion is one of the strongest predictors of future cardiovascular events and poor outcomes. Through this review, we try to provide a better understanding of the congestion phenomenon among patients with acute heart failure by highlighting insights into the pathophysiology mechanisms behind congestion and new diagnostic and management tools to achieve and maintain efficient decongestion.

As more end devices are getting connected, the Internet will become more congested. A variety of congestion control techniques have been developed either on transport or network layers. Active Queue Management (AQM) is a paradigm that aims at mitigating the congestion on the network layer by active buffer control to avoid overflow. However, finding the right parameters for an AQM scheme is challenging, due to the complexity and dynamics of the networks. On the other hand, the Explicit Congestion Notification (ECN) mechanism is a solution that makes visible incipient congestion on the network layer to the transport layer. In this work, we propose to exploit the ECN information to improve AQM algorithms by applying Machine Learning techniques. Our intelligent method uses an artificial neural network to predict congestion and an AQM parameter tuner based on reinforcement learning. The evaluation results show that our solution can enhance the performance of deployed AQM, using the existing TCP congestion control mechanisms.

Vehicular traffic in urban areas continues to be a constant challenge today, due to the continuous population growth and the increase of vehicles in circulation. Vehicular congestion, as a recurrent problem, generates a negative impact on urban mobility and citizens’ quality of life. It is hypothesized that a dynamic clustering method of vehicle trajectory data can provide an accurate and up-to-date representation of real-time traffic behavior. To evaluate this hypothesis, data were collected from three different cities: San Francisco, Rome and Guayaquil. A dynamic clustering algorithm was applied to identify traffic congestion patterns and an indicator was applied to identify and evaluate the congestion conditions of the areas. The results show a high precision and recall in congestion classification compared to a method based on static cells.

Background and Objectives: Cancer as the second leading cause of death in the United States poses a huge healthcare burden. Barriers to access to advanced therapies influence the outcome of cancer treatment. In this study, we examined whether insurance types affect the quality of cancer clinical care regarding access to an important monoclonal antibody, ramucirumab. Materials and Methods: Data for 13,340 cancer patients with Purchased or Medicaid insurance from the All of Us Database were collected for this study. The Chi-square test of proportions was employed to determine the significance of patient cohort characteristics and ramucirumab usage between Purchased and Medicaid insurance groups. The independent t-test was utilized to assess the influences of other determinants on insurance types. Results: Cancer patients who are African American, with lower socioeconomic status, or with lower educational attainment are more likely to be insured by Medicaid. Analysis of survey questions demonstrated the relationship between income and education level with insurance type as Medicaid cancer patients were less likely to receive primary care and specialist physician access and more likely to request lower cost medications. In addition, those with Medicaid insurance were identified to have poorer access to expensive therapeutics like ramucirumab compared to those with Purchased insurance.  Conclusions: The inequities of the US healthcare system are observed for cancer patient care; access to physicians and therapeutics are highly varied and dependent on insurance types. Socioeconomic status determines insurance type, which unfortunately generates a significant impact on cancer treatment and disease outcome.

In the context of sustainable development, integrating the work aspect into health promotion systems is essential, ensuring that employees maintain a healthy balance between their professional and personal lives, which respects and amplifies the potential of every individual. Lately, there has been a noticeable shift of medical professionals from the public to the private sector. This trend paints a bleak picture, highlighting the vulnerabilities of a system teetering on the brink of failure, with significant impacts on patient care. Among the myriad reasons behind this shift, burnout stands out prominently. Medical professionals face increasing pressures due to understaffing, limited organizational support, and insufficient resources, leading to feelings of cynicism, exhaustion, and detachment from their roles. Utilizing the Maslach et al. questionnaire, we assessed the risk of burnout among physicians in public hospitals to identify and underscore preventive measures and improvements, correlating our findings with key aspects of organizational empowerment. Our research indicates that enhancing interpersonal relationships and resources—both within teams and between the organization and its physicians—is crucial for the revival of the healthcare system. This approach aims at improving the delivery of healthcare services and offering appropriate recognition to healthcare professionals, aligning with the principles of sustainable employment.

Background: Symptomatic Pelvic Congestion Syndrome (PCS) is often underdiagnosed and undertreated. We investigate a symptoms-based screening algorithm and treatment algorithm focused on position and location of pain for each type of PCS. Methods: This study is a retrospective, single-institution, multi-site review of 343 female patients who presented with pelvic pain. Results: Following a symptoms-based algorithm, 302 patients fulfilled criteria for inclusion; 299 patients had positional symptoms with a positive predictive value (PPV) of 97.99%, sensitivity of 98.99% and Odds Ratio (OR) of 48.83 (p=0.0003) while duplex ultrasonography (DUS) had a PPV of 98.84%, sensitivity of 86.49%, and OR of 6.4 (p=0.0396) confirmed by contrast venography. Patients with Type I PCS were treated with embolization and 87.5% reported total relief of anterior symptoms. Patients with Type II PCS were treated with embolization and/or vascular stenting based on their associated lesion, May-Thurner syndrome or Nutcracker syndrome, and 88.54% reported ≥50% symptom relief and 59.03% reported ≥80% symptom relief. Full treatment resulted in a mean “overall satisfaction” score of 96.07%. Conclusions: Algorithmic approaches are essential for PCS evaluation, diagnosis, and treatment. Patients with positional pelvic symptoms consistent with PCS should undergo diagnostic venography. Distinguishing between the types of PCS and using symptoms as a guide may be beneficial over a “one-size-fits-all” approach.

U-turns and left turns are sometimes forbidden even though it increases travel distances. The greater travel distances are sometimes outweighed by the improved movement through intersections due to there being fewer conflicting lanes of traffic. One can, further, forbid straight-throughs. Restricting a sufficient number of turns can make intersections free from crossing lanes of traffic (``zero traffic conflict,'' ``ZTC"), though there may still be merging lanes of traffic. It's possible to make \begin{it}all\end{it} intersections in a road \begin{it}network\end{it} ZTC. However, keeping all destinations accessible and travel distances moderate requires careful selection of allowed driving directions and turning directions. We demonstrate through numerical microscopic and macroscopic simulations that there are road networks and ranges of traffic loads for which, in comparison with conventional schemes, ZTC road network can carry approximately $50$\% more vehicular traffic without incurring gridlock.

Data4HumanRights training materials have been developed as open-source and tailored to limited resource settings where community data collectors often live and work. An overview of how the training material was developed together with community data collectors in Nigeria and Kenya is provided. The paper gives insights into the fundamental principles (i.e., inclusiveness, adaptive, limited resources, gender- and incentive-sensitive) and the structure of the open-access training materials. This resulted in 28 modules designed to be delivered in a face-to-face format in less than one day by a local trainer. To maximize adaptivity, the training modules can be mixed and matched (e.g., as individual modules or a learning path of several modules around a specific training need). The individual modules cover a range of methods and tools that are useful to human rights work, for example, documenting evictions, performing rapid needs assessments after acute crises, community profiling, and monitoring community development indicators. The training materials contain instructions for the facilitator(s) of the training and all necessary training materials to conduct the training. To maximize inclusivity, the training covers basic to advanced topics. Most training modules address basic training topics that can be optimally followed with a mobile phone (to avoid using a computer and printing handouts). The training was developed and implemented with local community groups in Nigeria and Kenya. The material is free to use, adapt, and translate into different contexts and languages.

Addressing sustainable mobility in urban areas has become a priority in today’s society, given the growing population and increasing vehicular flow in these areas. Intelligent Transportation Systems have emerged as innovative and effective technological solutions to address these challenges. Research in this area has become crucial, as it contributes not only to improve mobility in urban areas, but also to positively impact the quality of life of its inhabitants. To address this, a dynamic clustering methodology for vehicular trajectory data is proposed which can provide an accurate representation of the traffic state. Data was collected for the city of San Francisco, a dynamic clustering algorithm was applied and then an indicator was applied to identify areas with traffic congestion. Several experiments were also conducted with different parameterizations of the forgetting factor of the clustering algorithm. The results showed in terms of precision that the dynamic clustering methodology achieved high match rates compared to the congestion indicator applied to static cells.

Traffic congestion is a persistent and challenging problem in urban areas, leading to increased travel times, fuel consumption, and environmental pollution. Signalized intersections play a pivotal role in regulating traffic flow, and their efficiency has a direct impact on the overall traffic performance of a city. This study investigates the effect of traffic signal in managing traffic volume and reducing congestion and delays at signalized intersections through a comprehensive analysis of existing research, data collection, and simulations.The research begins by analyzing the traffic characteristics by an installed LiDAR sensor at E Cold Spring Ln – Hillen Rd intersection in Baltimore City, MD. When the signal at this intersection stopped working for some hours during a working day in September 2023, the LiDAR recorded vehicle and pedestrian counts, vehicle-vehicle and vehicle-pedestrian conflicts, and jaywalking events conflicts. The research aims to assess the impact of traffic signal failures on traffic flow, congestion, safety (V2V and V2P conflicts), and the frequency of jaywalking events before, during, and after improper performance of the traffic signal. Furthermore, this study explores the factors influencing traffic signal performance, including traffic demand, geometric layout, pedestrian interactions, and the integration of emerging technologies. The analysis results highlighted the importance of signal control systems existence at this intersection that can adjust signal timing in response to changing the real-time traffic conditions.Reduced congestion, minimized delays, and enhanced traffic flow are observed outcomes, contributing to a more sustainable and efficient urban transportation system. However, it is crucial to consider the trade-offs and challenges associated with traffic signal optimization, such as the potential for increased travel times for certain modes of transportation and the need for ongoing maintenance and updates. In conclusion, this study underscores the pivotal role of traffic signals in managing traffic volume and reducing congestion and delays at signalized intersections. Through evidence-based analysis and innovative signal control strategies, urban planners and transportation authorities can work towards creating more efficient, sustainable, and less congested transportation networks. The insights derived from this research can inform policy decisions and guide the development of future traffic management solutions, ultimately leading to improved quality of life in urban areas.

Access to medicines is one of the essential problems in Public Health of low- and middle-income countries (LMICs). The World Health Organization (WHO) defines access to medicines as the possibility of "having continuously accessible and affordable medicines in public or private health facilities that are within a kilometer of the place of residence." Access to medicines, as defined by the WHO, is not fully guaranteed in many LMICs and even in many regions of high-resource countries. The WHO identifies several factors as determinants of limitations in the access to medicines: rational selection, affordable prices, sustainable financing, and reliable health services. The action on these factors makes it possible to improve universal access to medicines with consequent improvement in Public Health. Adequate access to antibiotics and vaccination will avoid a large part of the deaths caused by infectious diseases in the LMICs. However, the emergence of resistance and the difficulties in vaccination campaigns due to socio-political or cultural problems make it challenging to fight many easily treatable infectious diseases. The use and abuse of antibiotics are inevitably associated with the appearance of resistances that make them ineffective. Thus, whereas limited access to antibiotics raises mortality rates from infectious diseases, generalized open access to them ends up eliminating their clinical value. Moreover, the contraction of research in this field for many years has reduced the success in discovering new drugs. Additionally, local market regulations, inadequate selection, inaccessible prices, especially for those of second and third-generation, inefficient health systems, and difficulties of administration and control of prescription compliance, especially in the case of combined therapies, are additional obstacles to universal access to antibiotics. In order to simultaneously improve access to antibiotics and keep resistances under control, it is necessary to develop training and education activities at different social levels (from patients to various Health Care Providers) to complement the national or supranational strategic plans.

The growing integration of renewable energy sources (RES) into the power system causes congestion to occur more frequently. In order to reduce congestion in the short term and to make the utilization of the power system more efficient in the long term, power flow control (PFC) in the transmission system has been proposed. However, exemplary studies show that congestion will increase also in the distribution system if the transmission system is expanded. For this reason, the potential of PFC to reduce congestion in a model of a real 110 kV distribution system is investigated. Several Unified Power Flow Controller (UPFC) devices are optimized in terms of their number and placement in the power system, their size, control parameters, and costs, by using a Parallel Tempering approach as well as a greedy algorithm. Two optimization variants are considered, one reducing the number of degrees of freedom by integrating system knowledge while the other is not. We find that near a critical grid state and disregarding costs, PFC can reduce congestion significantly (99.13 %). When costs of the UPFCs are taken into account, PFC can reduce congestion by 73.2 %. A basic economic analysis of the costs reveals that the usage of UPFCs is profitable. Furthermore, it is found that the reduction of the solution space of the optimization problem leads to better results faster and that, contrary to expectations, the optimization problem is simple to solve. The developed methods allow not only for the determination of the optimal use of UPFCs to minimize congestion, but also to estimate their profitability.

Traffic congestion is one of the foremost problems confronted by the urban and suburban tenants of today. Traffic congestion increases vehicle emissions and degrades air quality. Urban planners and policy makers have consequently been always investigating choices to alleviate traffic congestion and to enhance air quality. Teleworking is one option that has received significant consideration and has been studied in the recent past. The aim of the study is to explore the relationship between teleworking, air quality and traffic in Switzerland. The analysis relies on panel individual and household level data over the period 2002-2013. We examine five main air pollutants; the sulphur dioxide (SO₂), the ground-level ozone (O₃) the nitrogen dioxide (NO₂), the carbon monoxide (CO) and the particulate matter less than 10 microns (PM₁₀). Based on the fixed effects estimates, teleworking reduces traffic volume by 1.9 per cent. Furthermore, the reduction observed on air pollution is higher for NO₂, CO and PM₁₀ ranging between 3.3-3.7 per cent, followed by O₃ at 2.3 per cent and SO₂ at 2.1 per cent. According to instrumental variable (IV) approach and the two stage least squares (2SLS) method, the effect is higher ranging between 2.6-4.1 per cent. The respective reduction on traffic becomes 2.7 per cent. Overall, the main concluding remark of the study is that teleworking can be a promising tool for urban planning and development, focusing at the traffic volume reduction, and the air quality improvement. Additional policy implications of teleworking and its beneficial effects for the society are further discussed.

In recent years supply chains have evolved into huge ecosystems, demanding trust, provenance, and data privacy. Since blockchain technology (BCT) allows for the development of a distributed environment, it is ideal for supply chain management (SCM) applications. However, concerns regarding data privacy have impeded the development of blockchains. Despite the fact that some blockchains can restrict participants to read and write data, the blockchain’s transparency makes protecting sensitive data challenging. To solve the data privacy challenge, this paper proposes a framework, AccessChain, that is an SCM access control framework based on an attribute-based access control (ABAC) model that restricts access to competing parties while allowing for network scalability. This proposed AccessChain model has two types of ledgers in the system: local and global. Local ledgers are used to store business contracts between stakeholders and the ABAC model management, whereas the global ledger is used to record transaction data. AccessChain can enable decentralized, fine-grained and dynamic access control management in SCM when combined with the ABAC model and BCT. This paper’s experimental results illustrate that high throughput can be achieved in a large-scale request environment while maintaining data privacy and sustaining a scalable network.

Exposure to traffic-related air pollution is linked with acute alterations in blood pressure (BP). We examined the cumulative short-term effect of black carbon (BC) exposure on systolic (SBP) and diastolic (DBP) BP and assessed effect modification by participant characteristics. SBP and DBP were repeatedly measured on 152 traffic enforcers. Using a linear mixed-effects model with random intercepts, quadratic (QCDL) and cubic (CCDL) constrained distributed lag models were fitted to estimate the cumulative effect of BC concentration on SBP and DBP during the 10-hours (daily exposure) and 7-days (weekly exposure) before the BP measurement. Ambient BC was related to increased BP with QCDL models. An interquartile range change in BC cumulative during the 7-days before the BP measurement was associated with increased BP [1.2% change in mean SBP, 95% confidence interval (CI), 0.1 to 2.3; and 0.5% change in mean DBP, 95% CI, –0.8 to 1.7]. Moreover, the association between the 10-hours cumulative BC exposure and SBP was stronger for females (4.0% change, 95% CI: 2.1–5.9) versus males, and for obese (2.9% change, 95% CI: 1.0–4.8) vs. non-obese traffic enforcers. Short-term cumulative exposure to ambient traffic-related BC could bring about cardiovascular diseases through mechanisms involving increased BP.

Minimally invasive approaches to the aortic valve have been described since 1993, with great hopes that they would become universal and facilitate day-case cardiac surgery. The literature has shown that these procedures can be undertaken with equivalent mortality, similar operative time, comparative cost and some benefits in hospital length of stay. The competing efforts of the transcatheter aortic valve implantation for these same outcomes have provided an excellent range of treatment options for patients from the Heart Team. We describe the current state of the art, including technical considerations, caveats and complications of minimal access aortic surgery and predict future directions in this space.

Minimal access cardiac surgery appears to be the future. It is increasingly desired by cardiologists and demanded by patients who perceive superiority. Minimal access coronary artery revascularisation has been increasingly adopted throughout the world. Here we review the history of minimal access coronary revascularization and see that it is almost as old as the history of cardiac surgery. Modern minimal access coronary revascularization takes a variety of forms – namely minimal access direct coronary artery bypass grafting (MIDCAB), hybrid coronary revascularisation (HCR) and totally Endoscopic Coronary Artery Bypass Grafting (TECAB). It is noteworthy that there is significant variation in nomenclature and approaches for minimal access coronary surgery and this truly presents a challenge to comparing the different methods. However, these approaches are increasing in frequency and proponents demonstrate clear advantages for their patients. The challenge that remains, as for all areas of surgery, is demonstrating superiority of these techniques over tried and tested open techniques which is very difficult. There is a paucity of randomized controlled trials to help answer this question, and the future of minimal access coronary revascularisation to some extent is dependent on such trials. Thankfully some are underway and the results eagerly anticipated.

Accurate estimation of vehicle emissions is essential and can be helpful in the decision-making process. Thanks to calculation methods, it is possible to accurately estimate the emissions that result from driving a car and to determine their impact on, for example, the health of pedestrians who are on the pavement near an arterial road. Recent years have seen an increase in the use and importance of emission models, as well as vehicle traffic simulation models. Increasingly, emission models are being combined with traffic simulation models, as it is not practical to actually measure the on-road emissions of an entire fleet of vehicles over a given stretch of road. This paper provides an overview of the selected work on the topic of emission modelling and vehicle traffic simulation models. The models are distinguished according to their respective scales of accuracy, i.e., micro, meso, and macro. Selected work combining emission and traffic simulation models is also presented, as well as development trends in the field. In particular, the paper also highlights the fact of proper calibration of vehicle traffic simulation models in order to obtain the most reliable vehicle emissions results from computational models. The review of the works carried out may be helpful for future projects concerning the use of simulation tools for transport and environmental decision-makers in the area of arterial roads.

The protection and regulation of international trade of Australia’s native species is legislated by its Environment Protection and Biodiversity Conservation Act of 1999. In response to overwhelming evidence that the Act is ineffective at protecting biodiversity, the Australian Government is currently reforming its environmental laws. One problematic component of the Act is in cases where threatened species interact with multiple regulatory and management frameworks, such as commercially fished marine species that are traded as seafood. Here, we analyzed international seafood trade data and found that Australia exports four threatened fish species listed under the Act and additional species acknowledged as globally threatened. In light of our analysis, we have three recommendations for Australia’s new environmental legislation: 1) apply the same precautions to commercially harvested species as to other threatened species; 2) review the status of all threatened species annually; and 3) assess species listed on global conservation conventions. These recommendations will help ensure that Australia’s marine biodiversity is protected, despite its commercial value.

Over the past four decades, China has experienced a nutritional transition and has developed the largest population of internet users. In this study, we evaluated the impacts of internet access on the nutritional intake in Chinese rural residents. An IV-Probit-based propensity score matching method was used to determine the impact of internet access on nutritional intake. The data were collected from 10042 rural households in six Chinese provinces. The results reveal that rural residents with internet access have significantly higher energy, protein, and fat intake than those without. Chinese rural residents with internet access consumed 1.35% (28.62 kcal), 5.02% (2.61 g), and 4.33% (3.30 g) more energy, protein, and fat, respectively. There was heterogeneity as regards the intake of energy, protein, and fat among those in different income groups. Moreover, non-staple food consumption is the main channel through which internet access affects nutritional intake. The results demonstrate that the local population should use the internet to improve their nutritional status. Further studies are required to investigate the impact of internet use on food consumed away from home and micronutrients intake.

We present a set of software packages that provide uniform access to diverse biological vocabulary resources that are instrumental for current biocuration efforts and tools. The Unified Biological Dictionaries (UniBioDicts or UBDs) provide a single query-interface for accessing the online API services of leading biological data providers. Given a search string, UBDs return a list of matching term, identifier and metadata units from databases (e.g. UniProt), controlled vocabularies (e.g. PSI-MI), and ontologies (e.g. GO, via BioPortal). This can be coupled to for instance the ‘vsm-autocomplete’ module: an input field (user-interface component) that offers autocomplete lookup for these dictionaries. UBDs create a unified gateway for accessing life science concepts, helping curators find annotation terms across resources (based on descriptive metadata and unambiguous identifiers), and data users search and retrieve the right query terms.

The growing congestion in delivery rooms and maternity wards in Israeli hospitals is the underlying issue of the current study. The study sought to examine various alternatives for an effective public policy aimed at reducing congestion. The choice of Israel as a case study derives from its conspicuous imbalance between supply and demand in the maternity, resulting from the high demand for prenatal and birthing services on one hand and the low supply, both in absolute terms and relative to OECD countries, of medical resources such as physicians, nurses, and hospital beds. The research findings present fertility and birth data for Israel in the last decade, which are leading to increased congestion and crowding in delivery rooms and maternity wards at local hospitals. At the same time, two possible alternatives are proposed for designing and implementing public policy capable of contributing to the regulation of this congestion. One alternative is the option of home birth and the second is promoting the establishment of private birthing centers outside the hospitals. The research conclusions indicate that policy makers in Israel must anticipate the increasing congestion in hospital delivery rooms and maternity wards and expedite efforts at designing alternative solutions before the emergence of a crisis situation that will make it essential to identify immediate solutions. The study recommends exploring and advancing the adoption of the two alternatives simultaneously as part of public policy, in the assumption that the increasing demand for pregnancy and childbirth services in Israeli society will persist and therefore it is necessary to act to increase the supply of childbirth facilities.

Routing a person through a traffic network presents a tension between selecting a fixed route that is easy to navigate and selecting an aggressively adaptive route that minimizes the expected travel time. We propose to create non-aggressive adaptive routes in the middle-ground seeking the best of both these extremes. Specifically, these routes still adapt to changing traffic conditions, however we limit the number of adjustments made in the route. This improves the user experience, by providing a continuum of options between saving travel time and minimizing navigation. We design strategies to model single and multiple route adjustments, and investigate enumerative techniques to solve these models. To alleviate the intractability with handling real-life traffic data, we develop efficient algorithms with easily computable lower and upper bounds. We finally present computational experiments highlighting the benefits of limited adaptability in terms of reducing the expected travel time.

Manual traffic surveillance can be a daunting task as Traffic Management Centers operate a myriad of cameras installed over a network. Injecting some level of automation could help lighten the workload of human operators performing manual surveillance and facilitate making proactive decisions which would reduce the impact of incidents and recurring congestion on roadways. This article presents a novel approach to automatically monitor real time traffic footage using deep convolutional neural networks and a stand-alone graphical user interface. The authors describe the results of research received in the process of developing models that serve as an integrated framework for an artificial intelligence enabled traffic monitoring system. The proposed system deploys several state-of-the-art deep learning algorithms to automate different traffic monitoring needs. Taking advantage of a large database of annotated video surveillance data, deep learning-based models are trained to detect queues, track stationary vehicles, and tabulate vehicle counts. A pixel-level segmentation approach is applied to detect traffic queues and predict severity. Real-time object detection algorithms coupled with different tracking systems are deployed to automatically detect stranded vehicles as well as perform vehicular counts. At each stages of development, interesting experimental results are presented to demonstrate the effectiveness of the proposed system. Overall, the results demonstrate that the proposed framework performs satisfactorily under varied conditions without being immensely impacted by environmental hazards such as blurry camera views, low illumination, rain, or snow.

The WTO is here to stay. Institutions are never perfect. The way the WTO, as an institution, runs its business may not be perfect either. Arab countries are attempting to broaden their engagement in the multilateral trading system in a manner that has many implications. This engagement includes accession to the WTO, participation in WTO dispute resolution mechanism, and representations at the WTO.

As urban populations and transport demand expand, Pakistani metropolitan centers face traffic congestion and environmental challenges. This research aims to assess carpooling (CP) to alleviate traffic congestion, fuel demand, and pollution. This study was conducted in Islamabad, a fast-growing city with a high percentage of personal cars in Pakistan, to assess the travelers’ perceptions, and public acceptance of CP and determine the effects on traffic congestion, fuel consumption, and CO2 emissions. The investigation of travelers' perceptions towards CP was conducted through a stated preference questionnaire survey. A total of 700 responses from one million population of Islamabad were collected as per Sloven's Formula. SPSS and Smart PLS software were used for data analysis. Various factors influencing peoples’ travel perceptions, mode choice, and tendency to carpool, were categorized to evaluate the relationship between travelers’ perception and the tendency to carpool in the presence of mode choice as a mediating variable. Confirmatory factor analysis was conducted in SMART PLS to refine the number of scale items for each variable. One-way ANOVA, step-wise and mediated multiple regression analysis was conducted in SPSS to check the direct and mediating relationship between different variables. Moreover, the probability of reducing traffic congestion and assessing fuel reduction resulting from CP was also analyzed. The findings imply that travelers’ mode choice acts as a mediator between the relationship between travelers’ perception and tendency to carpool. Furthermore, results predicted that CP might reduce 0.25 million cars on the road, about 33.6% of the total private vehicles. Thereby, saving up to 546,810 barrels per year, about 3.42% of Pakistan's total petrol consumption. The findings of this study could be helpful for transport planners, environmentalists, and policymakers to implement carpool systems. Thus, reducing traffic congestion and harmful CO2 emissions and maintaining a sustainable environment.

The urban transportation network design problem involving exclusive bus lanes (XBLs) has been widely discussed and analyzed in recent years. An improved and more flexible transit lane management strategy—intermittent bus lanes (IBLs)—prove to be potentially more efficient and car-friendly than XBLs. The common benefit of XBLs and IBLs arises from the fact that they separate the bus and car traffic and hence can eliminate the impacts of slowly moving buses on the car traffic. This paper proposes a cell transmission model for separate car and bus traffic (CTM-SCB) in a network with some dedicated roadway segments reserved for buses. By encapsulating the CTM-SCB model, an XBL-based network design problem and an IBL-based network design problem are then formulated and solved, respectively, where the former model statically sets bus lanes while the later one allows a dynamic allocation of bus lanes. A synthetic freeway-arterial network and a real-world urban street network (where the latter was extracted from the Harbin South New Industrial City) are used as test networks for evaluating the proposed models and methods. The numerical results show that both XBLs and IBLs enjoy significant operational efficiency benefits compared to the situation of no protected bus lanes. However, we believe that the expected improvement from XBLs to IBLs need further tests and validations.

Accurate detection and state analysis of traffic flows are essential for effectively reconstructing traffic flows and reducing the risk of severe injury and fatality. For this reason, several studies on resolving traffic problems have proposed the use of crowdsourcing, in which drivers provide real-time traffic information using mobile devices, to monitor traffic conditions. Using data collected via crowdsourcing for traffic event detection has advantages in terms of improved accuracy and reduced time cost in collecting relevant data. In this paper, we propose a technique that employs crowdsourcing to collect traffic-related data and uses these data to detect events that influence traffic. The proposed technique uses various machine-learning methods to more accurately identify events and find accurate location information. Therefore, it is able to resolve problems typically encountered with conventionally provided location information, such as broadly defined locations or inaccurate location information. The proposed technique has advantages in terms of reducing time and cost while increasing accuracy. Its validity and effectiveness were also demonstrated through various performance evaluations.

The digital society is an outcome of the Internet which has nearly made everything connected and accessible no matter where or when. Nevertheless, despite the fact that conventional IP networks are complicated and very hard to manage, they are still widely adopted. The already established policies make the network configuration/reconfiguration a complex process that reacts to errors, load, and modifications. The prevailing networks are vertically integrated which makes things more and more complicated: Data planes and control are strapped together. Software-defined networking is a model that is meant to solve this issue by splitting the vertical integration and detaching the network’s control logic from the implicit routers and switches; this could be achieved by reinforcing centralization of network control and making the network programmable. In this work, we worked to implement MPLS networks with SDN, to enhance the traffic engineering over the network, and to minimize the network delay and latency, with minimum cost using three of the different SDN networks. The experiment results showed the advantage of the proposed approach for reducing the network delay, comparing with previous studies. Where the average of network delay in our approach reaches to 3.01 milliseconds.

Access and Benefit Sharing (ABS) is a framework which refers to a relatively recent type of legal requirements for access to and/or use of “genetic resources”. They are based on diverse national and regional laws and regulations, which mostly result from the implementation of the United Nations’ Convention on Biological Diversity (CBD) and its supplementary agreement, the Nagoya Protocol. Their ambition is to achieve fair and equitable sharing of benefits arising out of the use of genetic resources as an incentive to conserve and sustainably use them. This paper describes the experiences, practical constraints and complexities encountered by users of genetic resources when dealing with ABS legislation, with a focus on users from the private sector. We provide insights on how ABS laws have fundamentally changed the way of working with genetic resources in the hope that it inspires re-thinking of the ABS framework, to better support the overall objectives of the CBD.

This paper details the evolution of access network sharing models from legacy DSL to the most recent fibre-based technology and the main challenges faced from a technical and business perspectives. We first give an overview of existing access sharing models, that span physical local loop unbundling and virtual unbundled local access. We then describe different types of optical access technologies and highlight how they support network sharing. Next, we examine how the concept of SDN and network virtualization has been pivotal in enabling the idea of “true multi-tenancy”, through the use of programmability, flexible architecture and resource isolation. We give examples of recent developments of cloud central office and OLT virtualization. Finally, we provide an insight into the role that novel business models, such as blockchain and smart contract technology, could play in 5G networks. We discuss how these might evolve, to provide flexibility and dynamic operations that are needed in the data and control planes.

This review paper presents a flashback on the evolution of the disease throughout the centuries, describes in summary pathophysiologic mechanisms, discusses briefly the mechanism of action of the diuretics, presents their role in decongesting heart failure patients and unfolds the data behind ultrafiltration in the management of acutely or chronically decompensated heart failure (ADHF), focusing on all the available data and advancements in this field. Acutely decompensated heart failure (ADHF) presents a critical clinical condition characterized by worsening symptoms and signs of heart failure, necessitating prompt intervention to alleviate congestion and improve cardiac function. Diuretics have traditionally been the mainstay for managing fluid overload in ADHF. Mounting evidence suggests that due to numerous causes, such as coexisting renal failure or chronic use of the loop diuretics, an increasing rate of diuretic resistance is noticed and needs to be addressed. There has been a series of trials that combined diuretics of different categories, without the expected results. Emerging evidence suggests that ultrafiltration may offer an alternative or adjunctive approach.

_{Superconducting foams of YBa$_2$Cu$_3$O$_y$ (YBCO) are proposed as trapped field magnets or supermagnets. The foams with an open-porous structure are light-weight, mechanically strong and can be prepared in large sample sizes. The trapped field distributions were measured using a scanning Hall probe on various sides of an YBCO foam sample after field-cooling in a magnetic field of 0.5 T produced by a square Nd-Fe-B permanent magnet. The maximum trapped field (TF) measured is about 400 G (77 K) at the bottom of the sample. Several details of the TF distribution, the current flow and possible applicatons of such superconducting foam samples in space applications, e.g., as active elements in flux-pinning docking interfaces (FPDI) or as portable strong magnets to collect debris in space, are outlined.}

Much of the debate on Plan S seems to concentrate on how to make toll access journals open access, taking for granted that existing open access journals are Plan S compliant. We suspected this was not so, and set out to explore this using DOAJ's journal metadata. We conclude that an overwhelmingly large majority of open access journals are not Plan S compliant, and that it is small HSS publishers not charging APCs that are least compliant and will face major challenges with becoming compliant. Plan S need to give special considerations to smaller publishers and/or non-APC-based journals.

Preprints is a multidisciplinary preprint platform that makes scientific manuscripts from all fields of research immediately available at www.preprints.org. Preprints is a free (not-for-profit) open access service supported by MDPI in Basel, Switzerland.

Depressive disorder (DD) is a psychiatric disorder whose molecular basis is not fully understood. It is assumed that reduced consumption of fish and omega-3 fatty acids (FA) is associated with DD. Other lipids like total cholesterol (TCH), LDL- and HDL-cholesterols (LDL-CH, HDL-CH) also play a role in depression. This study aimed to investigate the relationship between depressive disorder symptoms and lipid profile, LDL- and HDL-cholesterol subfractions, Paraoxonase 1 (PON1) activities and erythrocyte membrane fluidity in 58 depressive children and adolescents, as well as the effect of omega-3 FA on the monitored parameters. Depressive symptoms were assessed by the Children's Depression Inventory (CDI), lipid profile by standard biochemical procedures, LDL- and HDL-subfractions by the Lipoprint system. Basic biochemical parameters including lipid profile were compared with levels in 20 healthy children and were in the physiological range. We are the first to report that omega-3 FAs increase after 12 weeks of supplementation large HDL subfractions (anti-atherogenic) and decrease small HDL subfractions (pro-atherogenic) in depressed children. We found a negative correlation between CDI score and HDL-CH and large HDL subfraction, but not LDL-CH subfractions. CDI score was not associated with erythrocyte membrane fluidity. Our results suggest that HDL-CH and its subfractions, but not LDL-CH may play a role in the pathophysiology of depressive disorder.

Nowadays, the identification and characterization of grapevine cultivars resilient to climate and water stress while preserving quality traits is crucial for the wine industry. Therefore, the objective of this work was to characterize according to their aromatic potential 9 white and 6 red minority cultivars recently recovered from Castilla-La Mancha region (Spain), subjected at two different water deficit regimes: rainfed, with subsistence irrigation, and irrigated. For this, the varietal aromatic potential index (IPAv) and the detailed aromatic composition were analyzed by HS-SBSE-GC/MS, in extracts of two different ph. For IPAv values, red varieties did not show a clear trend with respect to irrigation. However, in white minority varieties, higher values were obtained under irrigation conditions. Thus, a clear differentiation of the minority varieties in comparison to the references was observed, primarily attributed to the content of esters and acids, in both white and red varieties. A notable contrast was observed at different pH, indicating a greater extractability of certain compounds like linalool, under more acidic conditions. This suggests that some recovered minority cultivars could be promising for cultivation in semi-arid regions with limited water, contributing to the wine sector sustainability in the future.

While the role of safe riding behavior as a safety contributor for cyclists has been increasingly studied in recent years, there have been few studies analysing cycling behavior in relation to crash-related outcomes in Baltic countries. Indeed, to the best of our knowledge, this is the first time this issue has been addressed in the case of Latvia. Aim: The objective of this study was to assess the relationships among self-repotted cyclists’ behavior, traffic safety literacy, and their cycling crash involvement rates. Method: A total of 299 cyclists aged M=32.8 from across Latvia (42% females) participated in an online survey, which comprised questions regarding respondents’ demographics, frequency of riding, and the number of crashes in the past five years. The Cycling Behavior Questionnaire (CBQ) and the Cyclist Risk Perception and Regulation Scale (RPRS) were applied to assess cyclists' behavior patterns and traffic safety literacy. Results: According to the findings, it can be inferred that cyclists frequently engage in riding errors and traffic violations while cycling. Those who exhibit more antisocial behavior patterns are also more likely to be involved in road crashes. Conversely, cyclists with greater positive behavior rates more often also tend to possess better knowledge of traffic rules and exhibit a heightened risk perception, indicating a greater awareness of road traffic safety. Conclusion: All in sum, this study underscores key age differences, with older individuals significantly less involved in riding crashes, exhibiting fewer driving errors and a higher level of risk perception, which serves as a crucial factor in road safety. At the practical level, these results stress the need to address both traffic safety literacy and protective cycling factors of cyclists, in a manner to improve overall road safety and promote active transport modes in Latvia.

It was recently shown that urban networks might exhibit two different critical points, one for the percolation of congested links through the network, and another one for the flow of vehicles through the network. It was observed that the percolation critical point happens after the flow critical point, where network throughput is maximized. This result is important because if a causal relationship exists between these two processes, it can be leveraged for improving control methods to avoid network collapse. This paper presents the results of numerical experiments to understand the possible causal relationship between the percolation and flow processes by focusing on the relative timing between the two critical points. The goal is to understand the impacts of important factors such as: (i) urban network type: long- and short-block networks, (ii) signal timing control type: fixed, random, responsive, and (iii) route choice: turning proportions at intersections. We find that the threshold density $k^*_c$ to determine if a link is congested has the main impact on the timing of the percolation transition, where lower values trigger earlier transitions. This implies that $k^*_c$ can be set such that the percolation transition happens before the flow critical point, opening the door for improved congestion management strategies. If $k^*_c$ is set to the average network critical density, maximum network throughput happen simultaneously with the percolation transition. Surprisingly, it appears that these results, and more generally, the relationship between the percolation and flow processes is independent of (i) and (ii) above.

Wireless Sensor Networks (WSNs) are gaining traction in the realm of network communication, renowned for their adaptability, configuration, and flexibility. The forthcoming network traffic within WSNs can be forecasted through temporal sequence models. In this correspondence, We present a method (TSENet) that can accurately predict the traffic in the cellular network. TSENet is composed of transformers and self-attention network. We have designed a temporal transformer module specifically for extracting temporal features. This module accomplishes this by modeling the traffic flow within each grid of the communication network at both near-term and periodical intervals. Simultaneously, we amalgamate the spacial features of each grid with information from its correlated grids, generating spacial predictions within the spacial transformer. Furthermore, we employ self-attention aggregation to capture dependencies between external factor features and celluar data features. Empirical assessments performed on a genuine cellular traffic dataset offer compelling evidence substantiating the efficacy of TSENet.

This research looks into the evolving dynamics of ransomware, shifting from conventional encryption-based attacks to sophisticated data exfiltration strategies. Employing the Bidirectional Encoder Representations from Transformers (BERT) model, the study analyzes network traffic patterns to detect ransomware activities, offering new insights into their covert operations. The findings emphasize the need for advanced AI tools in cybersecurity, highlighting the significance of adapting and innovating defense strategies to counter the changing landscape of ransomware threats. The study contributes to a deeper understanding of ransomware evolution and underscores the importance of integrating AI in cybersecurity practices.

The exponential growth of digital media content has introduced new challenges in managing and classifying internet traffic. Digital media traffic is composed of various applications such as video, audio, social media, and search, and its data structure is complex, incorporating a vast array of features. The classification of traffic data is a crucial aspect of internet traffic management and network security, and it forms the basis for several scenarios, including content distribution, advertising recommendations, and data analysis. Traditional classification methods rely mainly on deep packet inspection and port-based techniques, which have become increasingly ineffective due to the rapid evolution of network traffic. To address this issue, this study proposes a machine learning-based traffic classification method aimed at enhancing the accuracy and efficiency of digital media traffic classification to meet the current needs of traffic management and network security. The paper also analyzes and evaluates the classification effect and prediction capability of various algorithms under different training set sizes to validate the feasibility and effectiveness of the proposed method. The result demonstrates that the neural network algorithm has superior classification and prediction capabilities compared to the decision tree and support vector machine algorithms. Furthermore, our proposed method achieves the highest accuracy of 96.88% with a large training sample of 40,000 data streams, proving its superiority in handling high-dimensional data and complex datasets. The research results are significant for the development of digital media traffic classification and prediction methods and are expected to be applied in practical scenarios.

In this paper, a new method is proposed to detect traffic regulations at intersections using GPS traces. The knowledge of traffic rules of regulated locations can help various location-based applications in the context of Smart Cities, such as the accurate estimation of travel time and fuel consumption from a starting point to a destination. Traffic regulations as map features, however, are surprisingly still largely absent from maps, although they do affect traffic flow which in turn affects vehicle idling time at intersections, fuel consumption, CO_2 emissions and arrival time. In addition, mapping them using surveying equipment is costly and any update process has severe time constraints. This fact is precisely the motivation for this study. Therefore, its objective is to propose an automatic, fast, scalable and inexpensive way to identify the type of intersection control (e.g. traffic lights, stop signs). A new method based on summarizing the collective behavior of vehicles crossing intersections is proposed. A modification of a well-known clustering algorithm for detecting stopping and decelerating events is presented. These detected events are then used to categorize vehicle crossing of intersections into four possible traffic categories (p1: free flow, p2: deceleration without stopping events, p3: only one stopping event, p4: more than one stopping event). The percentages of crossings of each class per junction arm, together with other speed/stop/deceleration features, extracted from trajectories, are then used as features to classify the junction arms according to their traffic control type dynamic model). The classification results of the dynamic model are compared with those of the static model, where the classification features are extracted from OpenStreetMap. Finally, a hybrid model is also tested, where a combination of dynamic and static features is used, which outperforms the other two models. For each of the three models, two variants of the feature vector are tested: one where only features associated with a single junction arm are used (one-arm model) and another where features also from neighbouring junction arms of the same junction are used to classify an arm (all-arm model). The methodology was tested on three datasets and the results show that all-arm models perform better than single-arm models with an accuracy of 94\% to 97\%.

Due to the rapid growth of the Internet of Things (IoT), securing communications between a huge number of devices has become very difficult. Access controls and authentication secure the connection between IoT devices and protect sensitive data from unauthorized users or malicious attackers. In this paper, we provide a comprehensive review to emphasize the critical role of authentication and access control in securing IoT environments. This paper explores different types of access controls, like Attribute-Based Access Control and Role-Based Access Control. Furthermore, it demonstrates different authentication techniques, like passwords and biometrics. Based on the previous mechanisms, we discuss different IoT security challenges regarding authentication and access control mechanisms and highlight future directions. In addition, this paper investigates the integration between access control and authentication to safeguard IoT communications.

River monitoring has the potential to grow substantially with present-day available affordable and locally sourced hardware. Yet river monitoring networks are still under pressure due to lack of resources, difficulties with maintenance, and rapidly changing conditions which in part may be due to climate change. We advocate that to turn around this trend, monitoring stations must rely on local people, work with locally available devices, work without contact with water and operate through openly available knowledge. River observations with camera videos have this potential. IP cameras, drones or smartphones are widely available as observation platforms. The scientific methods are well established in literature. Yet current attempts to establish scalable open-source software solutions that can be operated anywhere are lacking. In fact, currently available software is either research-oriented, more aimed at incidental observations, is restricted to single-use licenses, entirely proprietary, or restricted to operations through a third-party Software as a Service. To overcome this obstacle, we present OpenRiverCam, a free and open-source software ecosystem for river observations that can fulfill a wide variety of use cases and business cases through a well-documented and simple to use Application Programming Interface, service workflows, cloud scalability and interoperability, and options to extend to several applications within the hydrological, hydrodynamic, environmental and geospatial domains. We demonstrate its current technical abilities through three different case studies that all originate from a user perspective. We discuss the future developments to meet further requirements, which include documentation, widely available training materials and embedding in curricula, and further hardware and software developments.

Facing unprecedented global declines in the extent and integrity of ecosystems, the 15^th UN Biodiversity Conference (COP-15) in Kunming, China, presents an opportunity for transformative change. However, a lack of consensus on two key issues – resource mobilization and Access and Benefit Sharing (ABS) associated with Digital Sequence Information (DSI) – risks stalling negotiations for an ambitious ‘Post-2020 Global Biodiversity Framework’ as the next 10-year strategic plan under the Convention on Biological Diversity. We highlight systemic misconceptions concerning the financing of biodiversity and the burden this places on the ABS system. In the context of DSI, we caution that conflating ABS with resource mobilization risks disrupting modern science policy built on open access, with potentially severe ramifications for scientific research and innovation. To resolve these tensions, we call for a recalibration of discussions on ABS in order to maximize the value delivered by biodiversity for all of society, including indigenous peoples and local communities.

This paper describes some of the challenges that need to be addressed in order to develop collaborative spectrum sharing systems. The importance of these challenges stems from the assumption that rules for spectrum sharing can change after the deployment of radio networks and the whole system must be able to adapt to them. To address such a requirement, we used a policy-based approach in which transmissions are controlled by a policy interpreter system, and the policies can be modified during system operation. Our primary goal was to develop a prototype of such a system. In this paper, we outline the implementation of policy interpretation, automatic generation of transmission opportunities in case a request for transmission is denied by the policy reasoner, and the generation of rendezvous channels for the synchronization of otherwise asynchronously running software defined radios.

Abstract：Commuting of residents in big city often brings tidal traffic pressure or congestions. Understanding the causes behind this phenomenon is of great significance for urban space optimization. Various spatial big data make possible the fine description of urban residents travel behaviors, and bring new approaches to related studies. The present study focuses on two aspects: one is to obtain relatively accurate features of commuting behaviors by using mobile phone data, and the other is to simulate commuting behaviors of residents through the agent-based model and inducing backward the causes of congestion. Taking the Baishazhou area of Wuhan, a local area of a mega city in China, as a case study, travel behaviors of commuters are simulated: the spatial context of the model is set up using the existing urban road network and by dividing the area into travel units; then using the mobile phone call detail records (CDR) of a month, statistics of residents' travel during the four time slots in working day mornings are acquired and then used to generated the OD matrix of travels at different time slots; and then the data are imported into the model for simulation. By the preset rules of congestion, the agent-based model can effectively simulate the traffic conditions of each traffic intersection, and can also induce backward the causes of traffic congestion using the simulation results and the OD matrix. Finally, the model is used for the evaluation of road network optimization, which shows evident effects of the optimizing measures adopted in relieving congestion, and thus also proves the value of this method in urban studies.

Studying and understanding the behavior of people and vehicles on public roads can be of utmost importance for supporting the activities of many institutional stakeholders. It may allow automated supervision of the ongoing situation in a given place, with warnings or alarms raised in case of anomalies. It may be used to plan their interventions on road and town organization. It may provide them with advanced support to decision-making. The number of involved entities and places to manage makes it infeasible to manually handle all the traffic-related tasks. Moreover, the complexity of the tasks to be carried out requires the adoption of advanced approaches. Many AI solutions are nowadays mature to support these requirements. In some cases, the motivations and objectives of traffic management require the AI outcomes to be understandable, interpretable and explainable. In this paper, we propose TrAnSIT (TRaffic ANalysis Supervision and Interpretation Tool), an AI-based framework that combines several modules, each aimed at tackling a specific traffic-related task, so as to cover a wide landscape of traffic-related issues, from overall urban or suburban traffic management to surveying specific road segments that fall under the scope of one camera. Most of these modules are based on AI techniques that support a human-level understanding of the outcomes.

(1) Background: The 2002 Budapest Open Access Initiative recommended on self-archiving of scientific articles in open repositories as the “green road” to open access. Twenty years later, only one part of the researchers deposits their publications in open repositories; moreover, one part of the repositories’ content is not based on self-archived deposits but on mediated nonfaculty contributions. The purpose of the paper is to provide more empirical evidence on this situation and to assess the impact on the future of the green road. (2) Methods: We analyzed the contributions on the French national HAL repository from more than 1,000 laboratories affiliated to the ten most important French research universities, with a focus on 2020, representing 14,023 contributor accounts and 166,939 deposits. (3) Results: We identified seven different types of contributor accounts, including deposits from nonfaculty staff and import flows from other platforms. Mediated nonfaculty contribution accounts for at least 48% of the deposits. We also identified difference between institutions and disciplines. (4) Conclusions: Our empirical results reveal a transformation of open repositories from self-archiving and direct scientific communication towards research information management. Repositories like HAL are somewhere in the middle of the process. The paper describes data quality as the main issue and major challenge of this transformation.

The worldwide interconnected objects, called Internet of Things (IoTs), have been increasingly growing in the last several years. Different social media platforms and devices are continuously generating data about individuals and facilitate the technological and the social convergence of their Internet-based data and services with globalized users. These social and device-related IoTs create rooms for data breaches as such platforms provide ability to collect private and sensitive data. We assert that data breaches are fundamentally failures of access control - most users are too busy or technically ill-equipped to understand access control policy expressions and decisions. We argue that this is symptomatic of globalised societies structured by the conditions of algorithmic modernity; an era in which our data is increasingly interdependent on, and enmeshed with, ever more complex systems and processes that are vulnerable to attack. Ethically managing data breaches is now too complex for current access control systems, such as Role-Based Access Control (RBAC) and Context-Aware Access Control (CAAC). These systems do not provide an explicit mechanism to engage in decision making processes, about who should have access to what data and when, that are involved in data breaches. We argue that a policy ontology will contribute towards the development of Ethical CAAC better suited to attributing accountability for data breaches in the context of algorithmic modernity. We interrogate our proposed Ethical CAAC as a theoretical construct with implications for future policy ontology models and data breach countermeasures. An experimental study on the performance of the proposed framework is carried out with respect to a more generic CAAC framework.

The increasing growth in road users who are unfamiliar with traffic rules and regulations provides a serious safety problem for Pakistani traffic conditions. This study has two main objectives. First, it seeks to validate the reliability and applicability of the Dula Dangerous Driving Index (DDDI) in Pakistan. Second, it intends to investigate the effects of DDDI, sociodemographic characteristics, seat belt use, and driving training on the occurrence of traffic accidents. A total of 623 Pakistani individuals completed a questionnaire as part of this study. The questionnaire consists of items related to dangerous driving behaviors, sociodemographic characteristics, seat belt use, driving instruction, and accident involvement. The DDDI's accuracy was evaluated by comparing it to self-reported traffic incidents and seat belt compliance. To discover predictors within the DDDI dimensions, generalized linear models were used. A binary logistic regression analysis was also performed to evaluate the factors that influence traffic accidents. DDDI dimensions, demographic characteristics, seat belt usage, and driving training were all considered in this study. The use of seat belts while driving reduced the likelihood of an accident by 25%. On the other hand, aggressive driving and risky driving increased the chances of getting involved in traffic accidents by 36%% and 20%. Furthermore, the chances of being involved in a traffic accident decreased by 21% as the drivers age increased. The results also illustrate that Pakistani drivers are more aggressive and risk taker compared to Chinese drivers who are more disciplined. This study suggests that lack of comprehensive and standardized driving training and education programs, in consistent enforcement of traffic laws, the quality of road infrastructure and lack of public awareness campaigns can be attributed to adverse road safety situations in country.

Traffic shaping is a critical task in software-defined -IoT networks (SDN-IoTs) to efficiently manage network resources and ensure Quality of Service (QoS) for end-users. However, traditional traffic shaping approaches based on queuing theory or static policies may not be effective due to the dynamic and unpredictable nature of network traffic. In this paper, we propose a novel approach that leverages Graph Neural Networks (GNNs) and Multi-arm Bandit algorithms to dynamically optimize traffic shaping policies based on real-time network traffic patterns. Specifically, our approach uses a GNN model to learn and predict network traffic patterns and a Multi-arm Bandit algorithm to optimize traffic shaping policies based on these predictions. We evaluate the proposed approach on three different datasets, including a simulated corporate network (KDD Cup 1999), a collection of network traffic traces (CAIDA), and a simulated network environment with both normal and malicious traffic (NSL-KDD). The results demonstrate that our approach outperforms other state-of-the-art traffic shaping methods, achieving higher throughput, lower packet loss, and lower delay, while effectively detecting anomalous traffic patterns. The proposed approach offers a promising solution to traffic shaping in SDNs, enabling efficient resource management and QoS assurance

Small and medium-sized enterprises (SMEs) help and support the country in financial and business aspects and employs in the country, overcomes unemployment, and creates job opportunities in different sectors of the country. The study aims to know the entrepreneurial-specific factors like age, gender, education, experience, and skills used as predictors for access to financing of small firms in Khyber Pakhtunkhwa. The questionnaire is used for data collection, and about 204 questionnaires were distributed, of which 192 returned and analyzed through binary logistic regression. The study results reveal that age and level of education have been found significant while gender, experience and skills have insignificant with the access to finance in Khyber Pakhtunkhwa, Pakistan.

The environmental and social sustainability of seafood is jeopardized by rampant illegal, unreported, and unregulated fishing. Regulations implemented by fishing countries and Regional Fisheries Management Organizations are insufficient to combat illegal fishing; complementary efforts from the consumer end of the supply chain are essential. Despite the growing reliance on imported seafood globally, only four market states have implemented legislation to regulate the legality of seafood products from other countries. We provide an overview of the existing import regulations to address the widespread confusion about the structure and scope of these measures. We propose eight key design criteria for more effective seafood import policies, emphasizing the need for a centralized, fully electronic catch documentation and import monitoring system with automated fraud checks and a broad scope of species covered. The Australian government is currently developing its new seafood import policy and has the opportunity to design a world-leading system that encourages multilateral cooperation to help combat illegal fishing. Through the implementation of import regulations that address key flaws in existing frameworks, market states like Australia can foster a more cohesive and effective front against illegally sourced seafood, improving the sustainability of global fisheries.

Quite a number of new metered parking systems have been introduced in recent years by the local authorities of various spa towns in Poland in connection with home zone conversion projects. The traffic signs posted in these locations were limited to the beginning and end of the demarcated parking area. Traffic circle (TC) is an example of traffic calming measures (TCM) used in home zones to slow down the traffic (case study – home zone in a small spa village). This article presents the results of a study investigating the speed reductions obtained with a home zone and traffic circle used as traffic calming measures. The indispensable speed surveys were carried out in relation to this study in two periods: in summer when the streets are crowded with tourists and in September with little pedestrian traffic. Two research hypotheses were formulated as part of the speed data analysis to verify the slowing effect of traffic circle and the relevance of traffic circles design parameters and location, road function and the surrounding streetscape. For each hypothesis statistical analyses were carried out using two nonparametric tests: two-sample Kolmogorov–Smirnov test and median test. The third research hypothesis formulated in this study was related to sustainable development factors related to fuel consumption and traffic-related air pollution including carbon dioxide, carbon monoxide, nitrogen oxide and hydrocarbons. This hypothesis was verified by estimating the amount of air pollution in the home zone under analysis in three different situations (scenarios): in summer with the travel speed reduced by pedestrian traffic to ca. 8–10 km/h and in September with a small number of pedestrians and 20–25 km/h resulting speed between traffic circles, reduced at traffic circle, and in a theoretical 30 km/h zone with 25–30 km/h assumed speed between traffic circles, dropping at traffic circle. These analyses confirmed the appropriateness of traffic circle as a home zone traffic calming measure, as long as its design is based on a detailed analysis of the relevant factors, including location, road function and the surrounding streetscape.

This research work deals with traffic accidents that are very concurrent in several countries, which in turn contribute to the mortality rate from 19 to 25 years of age and up. In the research work, immediate notifications for traffic accidents were carried out through a mobile application (app) where it will have an emergency button which will help the injured a lot, since the nearest help centers such as the police, fire brigades and the nearest hospitals, so that they can provide support to the people who suffered the accident. In the project, the implementation of Scrum Methodology was used since it served us an amount for the development of the prototypes of the App, and in turn with their roles it did not serve much help for the project, however, passers-by are of vital importance already that they will be the ones to make said report when observing a traffic accident and thus notify the nearest aid centers. As data collection for the creation of the software functions, a survey was carried out with more than 30 people, the functions applied in the application were programmed with the Dart language and generated under the flutter tool in order to be able to distribute it on different platforms such as Play Store and AppStore. The results obtained were the most anticipated since the application fulfills its function, thus reducing deaths and improving the speed of attention to traffic accidents and thus saving a life that is in danger.

Centralized/Cloud Radio Access Networks (C-RAN) are deployed in converged fixed-mobile networks to exploit the flexibility coming from joint application of Network Function Virtualization (NFV) and Software Defined Networking (SDN). In this context, optical links connecting C-RAN nodes, possibly based on the Ethernet standards, may carry traffic with different requirements in terms of latency and throughput. This paper considers the problem of traffic aggregation on C-RAN optical Ethernet links with latency control for fronthaul traffic and throughput capability for backhaul traffic. Integrated hybrid nework technique is applied to show how time transparency can be enforced for Ethernet encapsulated Common Public Radio Interface (CPRI) traffic while allowing statistical multiplexing of backhaul traffic. Simulation results show the effectiveness of segmentation of backhaul traffic to allow exploitation of the available bandwidth even with high capacity CPRI options.

This paper develops a two-layer optimization approach that provides energy-optimal control for vehicles and traffic signal controllers. The optimizer in the first layer computes the traffic signal timings to minimize the total energy consumption levels of approaching vehicles from upstream traffic. The traffic signal optimization can be easily implemented in real-time signal controllers, and it overcomes the issues in the traditional Webster’s method of overestimating the cycle length when the traffic volume-to-capacity ratio exceeds 50 percent. The second layer optimizer is the vehicle speed controller, which calculates the optimal vehicle brake and throttle levels to minimize the energy consumption of individual vehicles. The A-star dynamic programming is used to solve the formulated optimization problem in the second layer to expedite the computation speed so that the optimal vehicle trajectories can be computed in real-time and can be easily implemented in simulation software for testing. The proposed integrated controller is first tested on an isolated signalized intersection, and then an arterial network with multiple intersections to investigate the performance of the proposed controller under various traffic demand levels. The test results demonstrate that the proposed integrated controller can greatly improve energy efficiency with fuel savings up to 17.7%, at the same time enhancing traffic mobility by up to 47.18% reduction in traffic delay and up to 24.84% reduction in vehicle stops.

Recently, inter-vehicle communication, which helps to avoid collision accidents (by driving safety support system) and facilitate self-driving (by dissemination of road and traffic information), has attracted much attention. In this paper, in order to efficiently collect road / traffic information in the request / response manner, first a basic method, Content-centric network (CCN) for Vehicular network (CV), is proposed, which applies CCN cache function to inter-vehicle communication. Content naming and routing, which take vehicle mobility into account, are investigated. On this basis, the CV method is extended (named as ECV) to avoid the cache miss problem caused by vehicle movement, and is further enhanced (named as ECV+) to more efficiently exploit cache buffer in vehicles, caching content according to a probability decided by channel usage rate. Extensive evaluations on network simulator Scenargie, with realistic open street map, confirm that the CV method and its extensions (ECV, ECV+) effectively reduce the average number of hops of data packets (by up to 47%, 63%, 83% respectively) and greatly improve the content acquisition success rate (by up to 356%, 444%, 689%, respectively), compared to the method without cache mechanism.

This paper aims to investigate carbon monoxide (CO) concentrations on roadways of Karachi, potential blood levels carboxy-hemoglobin (COHb) in Karachi. Geographical information system (GIS) was used for spatial analysis of diseases potentiality while an interpolation technique has been applied for surface generation with town boundaries and later evaluates risk areas.The higher concentration of carbon monoxide in the ambient is mainly due to automobile emissions. The City center and CBD areas are more perilous.

Sharing confidential information is a critical concern in today’s world. Secret sharing schemes facilitate the sharing of secrets in a way that ensures only authorized participants (shareholders) can access the secret using their allocated shares. Hierarchical secret sharing schemes (HSSSs) build upon Shamir’s scheme by organizing participants into different levels based on priority. Within HSSS, participants at each level can reconstruct the secret if a specified number, denoted as the threshold value (t), or more of them are present. Each level has a predetermined threshold value. If the number of participants falls below the threshold at any level, higher-level participants must be involved in reconstructing the secret at lower levels. Our paper proposes schemes that implement hierarchical access structures and enable the sharing of multiple secrets. Additionally, our proposed scheme includes share verification. We have analyzed potential attacks and demonstrated the scheme’s resistance against them. Through security analysis and comparison with existing schemes, we highlight the novelty and superiority of our proposed approach, contributing to advancements in secure information sharing practices.

Several empirical studies indicate that the lack of financing is one of the main barriers that affects the economic growth of small and medium enterprises (SMEs). The main objective of this investigation was to determine to what extent the economic sector; enterprise size; the characteristics inherent to the enterprise; legal status; the variables linked to the performance of the enterprise; and the attributes of the owner influence the access to bank financing of SMEs in Mexico. Using a discrete response probit regression model, the impact of enterprises characteristics on the probability of obtaining a bank loan was determined. The data collected is from the Enterprise Surveys of Mexico, carried out by the World Bank. The sample of 1,480 enterprises is representative by enterprise size, by economic sector, and by region. The research has a quantitative approach with a correlational scope and a non-experimental and transectional design. One of the main results highlights that the determinants with the greatest influence on access to bank financing are: age, the small size, foreign participation, and the manufacturing sector. These results are consistent with other empirical studies, as well as with the pecking order theory and the financial life cycle theory.

Nowadays, cloud computing is one of the important and rapidly growing paradigms that extend its capabilities and applications in various areas of life. The cloud computing system challenges many security issues, such as scalability, integrity, confidentiality, and unauthorized access, etc. An illegitimate intruder may gain access to the sensitive cloud computing system and use the data for inappropriate purposes that may lead to losses in business or system damage. This paper proposes a hybrid unauthorized data handling (HUDH) scheme for Big data in cloud computing. The HUDU aims to restrict illegitimate users from accessing the cloud and data security provision. The proposed HUDH consists of three steps: data encryption, data access, and intrusion detection. HUDH involves three algorithms; Advanced Encryption Standards (AES) for encryption, Attribute-Based Access Control (ABAC) for data access control, and Hybrid Intrusion Detection (HID) for unauthorized access detection. The proposed scheme is implemented using Python and Java language. Testing results demonstrate that the HUDH can delegate computation overhead to powerful cloud servers. User confidentiality, access privilege, and user secret key accountability can be attained with more than 97% high accuracy.

The world is growing very rapidly concerning technology. In the next-generation Internet, the existing architecture requires to be upgraded from Host-Centric Networking paradigm to Information-centric networking architecture. The unique aspect of information-centric networking is in-network cashing. Due to the system augmentation and In-network cashing technique, this novel system needs extremely high content security to ensure system integrity and maintenance. 5G network may be supported by the Information-Centric Network due to its high data transmission rate. In order to handle the serious security issues such as attack on confidentiality, authentication and integrity of the content, a Digital Signature based Access Control Mechanism in Information-Centric Network (DSAC) scheme is proposed to enhance security of ICN. Briefly, this new scheme uses Digital Signature, hash function, Trusted Third Party (TTP) and Proxy TTP. The client request for content, after receiving a request, the content provider generates and encrypts content with the digital signature and random value ‘k’ hash function and send it to TTP. After the signing process, the TTP sends the encryption hash key to Proxy TTP. In this proposed scheme authentication, confidentiality, the integrity aspects of the content security are improved.

Multimedia materials represent a promising approach for the Geoheritage promotion. Despite Geology is normally associated with natural environments, new tendencies are projected towards a better knowledge of the “geological reason” for the location and the development of urban settlements. The urban environment is, in fact, a perfect “laboratory” for opening the scientific topics to a broad audience. In this paper the experience of a geological exhibition organized in the town of Perugia city (Umbria, central Italy) is illustrated, highlighting the SECRET (SEe and CREaTe) for an effective dissemination activity. Panels, interactive tools, laboratories and trekking tours outside the museum represent the main activities, which counted more than eight thousand visitors in a few months. Moreover, the exhibition was the starting point for ongoing projects on geotourism in the city with important consequences in terms of visibility and financial return.

With the development of IoT technology, various information resources, such as social resources and physical resources, are deeply integrated for different comprehensive applications. Social networking, car networking, medical services, video surveillance and other forms of IoT information Service model gradually change people's daily life. Facing the vast amounts of IoT information data, IoT search technology is used to quickly find accurate information to meet real-time search needs of users. However, IoT search requires to use a large number of user privacy Information, such as personal health information, location information, social relations information, to provide personalized services. User privacy information will meet security problems if an effective access control mechanism is missing during the IoT search process. Access control mechanism can effectively monitor the access activities of resources, and ensure authorized users to access information resources under legitimate conditions. This survey examines the growing literature on access control for IoT search. Problems and challenges of access control mechanism are analyzed to facilitate the adoption of access control solutions in real-life settings. This paper aims to provides theoretical, methodological and technical guidance for IoT search access control mechanism in large-scale dynamic heterogeneous environment. Based on the literature study, we also analyzed future development direction of access control in the age of IoT.

Gyroscopes are fascinating instruments with a history of about 200 years. When J.G.F. Bohnenberger presented his machine to his students in 1810 at the University of Tuebingen, Germany, nobody could have foreseen that this fascinating development would be used for complex orientation and positioning. At the University of Stuttgart, Germany, a collection of 160 exhibits is available and in transition for a sustainable future. Here, the systems are digitized in 2D, 2.5D and 3D and are made available for a world-wide community using OpenAccess platforms. The technologies being used are Computed Tomography, Computer Vision, Endoscopy and Photogrammetry. The workflows for combining voxel representations and colored point clouds are described, to create Digital Twins of the tangible assets. Advantages and disadvantages are discussed und work for near future is outlined in this new and challenging field of Tech Heritage digitization.

High dimensional statistical analysis for the yield of large-scale circuits is quite difficult due to expensive simulations, especially for the memory circuits with high sigma requirement (e.g., SRAM). In this paper, we developed an efficient sparse additive model to substitute simulations. To fit high sigma region accurately, the modeling center is moved to near failure boundary searched by scaling the shape of sampling function. To solve the model efficiently, the process variables are grouped by standard cells so that the model can be solved by our developed blockwise greedy algorithm. The experiments on the 28nm memory circuits validate that our method achieves high accuracy and efficiency compared with other state-of-art works.

A new intervention model for promoting healthy ageing grounded on integrated value-based care was developed and tested in the city of Valencia (Spain). Its implementation raised relevant barriers for older adults in their access to health, health promotion, and health self-management linked with their health and digital literacy. This new intervention model included several aspects. On the one hand, researchers together with older adults and their informal caregivers participating in the study, designed personalised care plans, based on older adults’ specific needs, to be implemented with the support of a digital solution. On the other hand, researchers and health and social professionals implemented a series of six workshops in different locations of the city to encourage the sense of community among participants, reinforcing their trust in the new care model and increasing their adherence. Social activities were at the core of the workshops to understand older people’s interaction with the health and social services provided in the neighbourhood. Qualitative and quantitative methods were combined to extract information from older participants on how to engage them as active actors of their own health and understanding their values and preferences. Qualitative results show that after a post-pandemic situation, they were more concerned about social isolation and desired face-to-face contact with their professional care team; however, feelings of loneliness and/or sadness were not considered amongst the reasons to visit health professionals. Some of the conclusions reveal that the use of technology as a supportive tool is well received but with a stress on its role as “supportive”, and not replacing the close contact with healthcare professionals. Professionals recognised the benefits of this new approach but required more time and incentives to dedicate the effort needed.

This research article aims to examine the factors that influence behavioral intentions using the traffic accident claim system in Indonesia. This research uses the Theory of Planned Behavior, the Model of Technology Acceptance, and the Integrated Theory of Technology Acceptance and Use, as well as using the Structural Equation Modeling-Partial Least Squares. The questionnaire was distributed to 580 respondents from three institutions – police, hospitals, and state-owned insurance companies – using quota sampling techniques. The results showed that TPB, TAM, and UTAUT had a significant influence in encouraging behavioral intentions using the traffic accident claim system. Further, the study found that 21st-century digital skills have a moderate role in the impact of acceptance models, planned behavior, and technology use on behavioral intent. Although the study was conducted in Indonesia and is limited to three institutions, it provides strategic guidance for policymakers to improve the use of traffic accident claim systems and reduce Road Traffic Accidents. Further research is recommended to explore from different angles and focus on service providers/operators of traffic accident claim systems to refine the findings of this study. This study contributes to the literature by investigating the factors that influence behavioral intentions using traffic accident claim systems and has a high degree of accuracy of answers from the point of view of traffic safety officers.

The International Maritime Organization (IMO) is enforcing increasingly stringent regulations on ship carbon emissions, The International Maritime Organization (IMO) has been implementing progressively strict regulations on ship carbon emissions, leading to the adoption of the virtual arrival (VA) method by many vessels to reduce their carbon footprint. However, the effectiveness of the traditional VA method often varies in busy ports with complex traffic organization scenarios. To address this, our study presents a novel, comprehensive model that integrates vessel scheduling with the VA approach. This model is designed to achieve a dual objective: reducing carbon emissions through Virtual Arrival and simultaneously minimizing vessel waiting times. In addition to these goals, it incorporates essential aspects of safety, efficiency, and fairness in port management, utilizing the NSGA-2 algorithm to find optimal solutions. This model has been tested and validated through a case study at Ningbo-Zhoushan port, employing its dataset. The results demonstrate that our innovative model and algorithm significantly outperform traditional scheduling methods, such as First-Come-First-Serve (FCFS) and Virtual-Arrival Last-Serve (VALS), particularly in terms of operational efficiency and reduction in vessel carbon emissions.

The predominance of traffic lights in urban settings often induces fluctuations in traffic patterns and energy utilization among vehicles. To counteract the adverse effects of traffic lights on the energy efficiency of electric vehicles (EVs), a Multi-Intersections-Based Eco-Approach and Departure strategy (M-EAD) is proposed. This strategy aims to enhance vehicle energy efficiency, traffic flow, and battery longevity, all while upholding satisfactory driving comfort. The M-EAD strategy unfolds in two distinct stages: the optimization of an eco-friendly green signal window and the refinement of speed trajectories. The initial stage tackles the optimization of traffic light green signal windows, underpinned by the minimization of travel delays via solving the shortest path problem. In the subsequent stage, a receding horizon framework takes center stage, leveraging an iterative dynamic programming algorithm to tackle the speed optimization challenge. The objective here is to curtail energy consumption and reduce battery wear by finding an optimal speed trajectory. Furthermore, the real-world efficacy of this approach is substantiated through on-road vehicle tests, attesting to its viability in actual road scenarios.

In this paper, a complete tool for real-time detection of atypical energy behaviors of airplanes is presented. The methodology extends in real time an existing offline process using Dubins trajectories as a predictor of the remaining distance to the runway threshold. Two major contributions are presented in this paper. First, a real-time measure of the aircraft energy behaviour is defined, indicating whether the aircraft is in good condition to intercept the extended runway centreline from its current position. Secondly, a 2D trajectory suggestion is given, allowing safe management of the approach path according to atypical criteria of historical data. Finally, this document proposes a comprehensive tool for air traffic controllers, which is a major step forward in understanding, becoming aware of and resolving critical situations that could lead to accidents.

The aim of this study was to describe the trend of traffic accident mortality and social inequalities in Ecuador for the period 2011-2020, distributed by year, gender, age group, geographic location, and type of accident, alongside social disparities. An ecological approach was used to evaluate traffic accident mortality data. The average traffic accident mortality rate in the country during this period was 19.1 per 100,000 inhabitants, with rates being significantly higher in males (31.3) than in females (7.2). The highest mortality rate was recorded in 2011, predominantly impacting the 60 and older age group, the Santo Domingo de los Tsáchilas province, and the Amazon region. The study also noted a declining trend in these rates over the years, with an Annual Percentage Variation of -1.2%. A notable finding was the decrease in traffic accident mortality inequality based on per capita income in 2019, compared to 2011, which rose by 247.7%, whereas the inequality based on illiteracy rates decreased by 18.9% from 2014 to 2019. These findings highlight the significant decrease in traffic accident mortality rates across Ecuador, though marked disparities remain evident across different genders, age groups, and provinces.

Adaptive Cruise Control (ACC) is useful in the most dangerous maneuvers such as braking and acceleration. This study assesses how ACC modifies traffic flows by analysing the differences be-tween manual and autonomous driving in connected and autonomous vehicles (CAVs). Using a platoon of 80 vehicles, tested in pairs on the road, it was possible to define the speed trends during braking and acceleration and the reaction times to the driving maneuvers (PRT, TH, TCC) with a kinematic data detector. The interactions between the CAV and the driver, have been studied in-novatively, i.e through gaze analysis. Situations of potential danger, characterized by the braking of the vehicle that precedes the car with the driver equipped with the eye tracker tool, have been recreated, considering the influence of the driver’s ACC experience. Results statistically confirmed that with the ACC switched on the reaction times are greater than manual driving (2.4/3.8 sec); this can lead to a reduction in road safety, further motivated by the rapid decrease in speed. The interpolation between automated and human data, finally, has allowed the detection of some criticalities of the system that are fundamental in order to reach the second level of automation.

Current adaptive cruise control (ACC) systems adopt fixed desired time headway, which leads to an abrupt speed reduction after being cut-in by a lane changer in front or when changing lanes too close to the new leader. In contrast, human drivers behave differently and feature a variable spacing within 20 or 30 seconds right after a cut-in or lane change. Motivated by the smooth transition found in driver relaxation, the paper aims to incorporate relaxation into ACC systems. Based on the open-source ACC platform, Openpilot, Comma.ai, the paper proposes a feasible relaxation model compatible with current factory ACCs, which has also been tested using a market car with stock ACC hardware. The study further investigates the impact of relaxation ACC on traffic operation. Numerical simulation suggests that incorporating relaxation into ACC can help: i) reduce the magnitude of speed perturbations in both cut-in vehicles and followers; ii) stabilize the lane-changing traffic by reducing the speed variance and prevent the lateral propagation of congestion, and iii) increase the average vehicle speed and capacity in merging traffic.

As construction costs continue to rise and adequate amounts of funding continues to be a challenge, the allocation of resources is of critical importance when it comes to the maintenance and rehabilitation (M&R) of highway infrastructure. A Life Cycle Assessment (LCA) methodology is presented here that integrates realistic traffic conditions in the operational phase to compare M&R scenarios over the analysis period of a 26-km stretch of Interstate-495. Pavement International Roughness Index (IRI) were determined using AASHTO’s PavementME System. Meanwhile, vehicle fuel consumption and emission factors were calculated using a combination of Google Maps®, the U.S. EPA’s Motor Vehicle Emission Simulator, the SHRP2 Naturalistic Driving Study, and MassDOT’s Transportation Data Management System. The evaluation of pavement performance with realistic traffic conditions, varying M&R strategies and material characteristics was quantified in terms of Life Cycle Cost (LCC), Global Warming Potential (GWP) and Cumulative Energy Demand (CED) for both agencies and users. The inclusion of realistic traffic conditions into the use phase of the LCA resulted in a 6.4% increase in CED and GWP when compared to baseline conditions simulated for a week long operation duration. Results from this study show that optimization of M&R type, material selection and timing may lead to a 2.72% decrease in operations cost and 47.6% decrease in construction/maintenance costs.