Search Results (68)

This study examines variations in personal exposure to PM_2.5 and NO₂ while commuting by bicycle, vehicle, and walking during heating and non-heating seasons in Gliwice, an industrial city in Upper Silesia, Poland. Understanding these variations is crucial for assessing health risks and developing effective mitigation strategies. Personal exposure was measured using low-cost sensors, while stationary measurements provided comparative background concentrations. The results indicate statistically significant seasonal differences in pollutant concentrations. NO₂ levels were higher during the heating season (mean: 30.84 µg/m³, median: 25.60 µg/m³) than in the non-heating season (mean: 22.61 µg/m³, median: 20.37 µg/m³; p = 0.025). In contrast, PM_2.5 concentrations were higher in the non-heating season (mean: 12.1 µg/m³) compared to the heating season (mean: 9.5 µg/m³; p = 0.032). Inhaled doses instead of concentrations evaluated the exposure of participants. The inhaled doses of NO₂ and PM_2.5 per km were significantly higher for walking (mean: 141.3 and 30.7 µg/km for the male participant; 77.9 and 31.6 µg/km for the female participant) than for bicycle and walking (p < 0.05). These findings underscore the impact of transport mode and seasonality on air pollution exposure, highlighting the necessity for targeted mitigation strategies to reduce commuters’ exposure to traffic-related pollutants. Full article

Bike-sharing initiatives play a crucial role in sustainable urban transportation, addressing vehicular congestion, air quality issues, and sedentary lifestyles. However, the connection between bike-sharing facilities and the advantages perceived by users remains insufficiently explored particular in post-industrial regions, such as Silesia, Poland. This study develops a multidimensional framework linking infrastructure elements—such as station density, bicycle accessibility, maintenance standards, and technological integration—to perceived benefits. Using a mixed-methods approach, a survey conducted in key Silesian cities combines quantitative analysis (descriptive statistics, factor analysis, and regression modelling) with qualitative insights from user feedback. The results indicate that the most valuable benefits are health improvements (e.g., improved physical fitness and mobility) and environmental sustainability. However, infrastructural deficiencies—disjointed bike path systems, uneven station placements, and irregular maintenance—substantially hinder system efficiency and accessibility. Inadequate bike maintenance adversely affects efficiency, safety, and sustainability, highlighting the necessity for predictive upkeep and optimised services. This research underscores innovation as a crucial factor for enhancing systems, promoting seamless integration across multiple modes, diversification of fleets (including e-bikes and cargo bikes), and the use of sophisticated digital solutions like real-time tracking, contactless payment systems, and IoT-based monitoring. Furthermore, the transformation of post-industrial areas into cycling-supportive environments presents strategic opportunities for sustainable regional revitalisation. These findings extend beyond the context of Silesia, offering actionable insights for policymakers, urban mobility planners, and Smart City stakeholders worldwide, aiming to foster inclusive, efficient, and technology-enabled bike-sharing systems. Full article

Planning in mass-customization supply and manufacturing processes is a complex process that requires continuous planning and optimization to minimize time and cost across a wide variety of choices in large production volumes. While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. However, their full potential has yet to be explored. This article proposes a machine learning-based approach to address this challenge, aiming to optimize both the supply and manufacturing planning phases as a practical solution for industry planning or optimization problems. The proposed approach examines supervised machine learning and deep learning techniques for manufacturing time and cost planning in various scenarios of a large-scale real-life pilot study in the bicycle manufacturing domain. This experimentation included K-Nearest Neighbors with regression and Random Forest from the machine learning family, as well as Neural Networks and Ensembles as deep learning approaches. Additionally, Reinforcement Learning was used in scenarios where real-world data or historical experiences were unavailable. The training performance of the pilot study was evaluated using cross-validation along with two statistical analysis methods: the t-test and the Wilcoxon test. These performance evaluation efforts revealed that machine learning techniques outperform deep learning methods and the reinforcement learning approach, with K-NN combined with regression yielding the best results. The proposed approach was validated by industry experts in bicycle manufacturing. It demonstrated up to a 37% reduction in both time and cost for orders compared to traditional expert estimates. Full article

Production scheduling is a critical task in the management of manufacturing systems. It is difficult to derive an optimal schedule due to the problem complexity. Computationally expensive and time-consuming solutions have created major issues for companies trying to respect their customers’ demands. Simple dispatching rules have typically been applied in manufacturing practice and serve as a good scheduling option, especially for small and midsize enterprises (SMEs). However, in recent years, the progress in smart systems enabled by artificial intelligence (AI) and machine learning (ML) solutions has revolutionized the scheduling approach. Under different production circumstances, one dispatch rule may perform better than others, and expert knowledge is required to determine which rule to choose. The objective of this work is to design and implement a framework for the modeling and deployment of a deep reinforcement learning (DRL) agent to support short-term production scheduling. The DRL agent selects a dispatching rule to assign jobs to manufacturing resources. The model is trained, tested and evaluated using a discrete event simulation (DES) model that simulates a pilot case from the bicycle production industry. The DRL agent can learn the best dispatching policy, resulting in schedules with the best possible production makespan. Full article

Dockless bike-sharing mobility brings considerable benefits to building low-carbon transportation. However, the operators often rush to seize the market and regulate the services without a good knowledge of this new mobility option, which results in unreasonable layout and management of shared bicycles. Therefore, it is meaningful to explore the relationship between the built environment and bike-sharing ridership. This study proposes a novel framework integrated with the extreme gradient boosting tree model to evaluate the impacts and threshold effects of the built environment on the origin–destination bike-sharing ridership. The results show that most built environment features have strong nonlinear effects on the bike-sharing ridership. The bus density, the industrial ratio, the local population density, and the subway density are the key explanatory variables impacting the bike-sharing ridership. The threshold effects of the built environment are explored based on partial dependence plots, which could improve the bike-sharing system and provide policy implications for green travel and sustainable transportation. Full article

α-Pinene is the bicyclic, unsaturated terpene hydrocarbon present in many plants. Due to its beneficial chemical properties, this compound is of great interest and has found numerous applications as a raw material in many chemical industries as well as in medicine and cosmetics. The aim of this study was to evaluate the antioxidant activities of ethanolic extracts obtained from plants containing α-pinene and to test the properties of cosmetic emulsions prepared with these extracts. The raw plant materials consisted of fresh parts of Pinus sylvestris L., such as cones, needles, and branches, as well as dried unground and ground pinecones; dried and fresh Rosmarinus officinalis leaves; dried Levisticum officinale leaves; and dried Salvia officinalis L. leaves. The plant materials were individually extracted with 40% (v/v), 70% (v/v), and 96% (v/v) ethanol using ultrasound-assisted extraction (UAE) for 15, 30, or 60 min. This method is a green extraction technique, frequently applied to isolate active substances from plants. For the selected plant materials, Soxhlet extraction with 96% (v/v) ethanol was also performed. The qualitative and quantitative analyses of the components in the selected extracts were performed with gas chromatography coupled with mass spectrometry (GC-MS). The antioxidant activities of the extracts were evaluated with the DPPH and ABTS methods. The extracts of three plant materials with the highest antioxidant activities—dried Rosmarinus officinalis leaves, dried Salvia officinalis L. leaves, and dried and ground Pinus sylvestris L. cones—were selected to be incorporated in cosmetic emulsions containing glyceryl monostearate and Olivem 1000 as emulsifiers. The stabilities and antioxidant activities of the emulsions were evaluated. Moreover, the antimicrobial properties of the emulsions using microbiological tests were also determined. The findings suggest that the prepared emulsions are stable cosmetic products with a high antioxidant potential. Full article

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

The emerging digitalization in today’s industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system’s decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection. Full article

The aim of this review is to present the potential application of camphor—a bicyclic monoterpene ketone—in the prevention of skin infections. Skin diseases represent a heterogeneous group of disorders characterized by prolonged symptoms that significantly diminish the quality of life. They affect the dermis, the epidermis, and even subcutaneous tissue. They very often have a bacterial or fungal background. Therapy for dermatological skin disorders is difficult and long-term. Therefore, it is important to find a compound, preferably of natural origin, that (i) prevents the initiation of this infection and (ii) supports the skin’s repair process. Based on its documented anti-inflammatory, antibacterial, antifungal, anti-acne, anesthetic, strengthening, and warming properties, camphor can be used as a preventative measure in dermatological infectious diseases and as a component in medical and cosmetic products. This work discusses the structure and physicochemical properties of camphor, its occurrence, and methods of obtaining it from natural sources as well as through chemical synthesis. The use of camphor in industrial preparations is also presented. Additionally, after a detailed review of the literature, the metabolism of camphor, its interactions with other medicinal substances, and its antimicrobial properties against bacteria and fungi involved in skin diseases are discussed with regard to their resistance. Full article

The aim of this research is to address the challenge of transforming car-oriented industrial parks into pedestrian- and bicycle-friendly environments. Through the implementation of a multi-criteria decision-making (MCDM) approach, the study aims to evaluate alternative pathway connections and assess their potential impact on bicycle and pedestrian traffic volumes. By enhancing the connectivity of the cycling pathway network, the research seeks to demonstrate the potential for substantial increases in cycling and walking within industrial zones. This research leverages a multi-criteria decision-making framework, specifically the ARAS-G method, and integrates geographic information system analysis alongside Python scripting to project future bicycle usage and assess alternative pathway connections. The study underscores the potential for substantial increases in cycling and walking by augmenting the connectivity of the cycling pathway network. The findings hold practical significance for urban planners and industrial zone developers, advocating a holistic approach to sustainable transportation. The research contributes a comprehensive set of criteria encompassing connectivity, safety, accessibility, efficiency, integration within the urban fabric, and cost-effectiveness to evaluate sustainability and prioritize actions and measures for reestablishing industrial zones as bicycle-friendly spaces. Full article

This article provides a detailed review of the types of magnesium wheels available in the industry and the current methods of the wheels’ production. The past several years have seen a significant development of magnesium-based lightweight alloys employed as a structural material for modern light vehicles. Magnesium alloys are characterized by their low density while maintaining good mechanical properties. The use of these alloys in the industry enables vehicles’ weight reduction while increasing their technical parameters. The first part of the article presents the unique properties of magnesium alloys that determine the application of this material for lightweight vehicle wheels. The advantages of using magnesium wheels over aluminum wheels are also presented. Next, a classification of the types of magnesium wheels was made in regard to their construction, applications, and manufacturing methods. At present, magnesium wheels by construction can be classified according to their geometry as single parts or assembled parts. In reference to geometry, wheels can have different shapes: classic, multi-spoke, with holes, or with frames. Depending on the geometry used, magnesium wheels can have different parameters, such as their mounting hole spacing, wheel diameters, or rim width. Considering the applications in various industries, main distinctions can be made between magnesium wheels for automobiles, motorcycles, bicycles, and wheelchairs. Magnesium wheels can also be categorized in regards to the manufacturing methods: casting, machining, forging, and hybrid manufacturing. The second part of the article focuses on the analysis of magnesium alloy wheel-manufacturing technologies used in the industry and developed by research centers. This article discusses these manufacturing technologies in detail and indicates prospective directions for further development. Full article

Monoterpene thiols are one of the classes of natural flavors that impart the smell of citrus fruits, grape must and wine, black currants, and guava and are used as flavoring agents in the food and perfume industries. Synthetic monoterpene thiols have found an application in asymmetric synthesis as chiral auxiliaries, derivatizing agents, and ligands for metal complex catalysis and organocatalysts. Since monoterpenes and monoterpenoids are a renewable source, there are emerging trends to use monoterpene thiols as monomers for producing new types of green polymers. Monoterpene thioderivatives are also known to possess antioxidant, anticoagulant, antifungal, and antibacterial activity. The current review covers methods for the synthesis of acyclic, mono-, and bicyclic monoterpene thiols, as well as some investigations related to their usage for the preparation of the compounds with antimicrobial properties. Full article

Rare earth element (REE) permanent magnets (NdFeB) are a critical element in a vast and growing number of industrial applications. In consumer electronics, a broad category encompassing computer, CD, and DVD hard drives, in addition to the ubiquitous cell phones, the nominal NdFeB magnet content may be small, but the global market share for this sector accounts for almost 30% of NdFeB demand, due to a large and continually increasing consumer base. It is estimated that wind turbines that primarily employ permanent magnets will add roughly 110 GW annually of on- and off-shore capability over the next few years. Electric vehicles (EVs) and E-bicycles (EBs) equipped with permanent magnet motors comprise the transportation contribution. Permanent magnet motors have garnered nearly 100% of the market share among EV manufacturers worldwide. Industrial, professional service, and personal robots, most using permanent magnets, are also included in the projected global need for rare earths, particularly Nd and Dy. The sector projects significant growth of approximately 10% across robotic categories. In this paper, we calculate the future demand for Nd and Dy through 2050 across these sectors using a compounded annual growth rate coupled with magnet weight and rare earth content. Uncertainties in the estimates, such as the true global production of Nd, a range of end-product scales and/or unit types in each sector, varied magnet compositions, and the variety of uses within a sector, are all considered. Full article

People are gradually coming around to the idea of living a low-carbon lifestyle and using green transportation, and given the severe urban traffic congestion, electric bicycle commuting has taken over as the preferred mode of short-distance transportation for many. Since batteries are used to power electric bicycles, there are no greenhouse gas emissions while they are in use, which is more in line with the requirement for sustainable development around the world. The public has been increasingly concerned about the safety issues brought on by electric bicycles as a result of the industry’s quick development and the rapid increase in the number of electric bicycles worldwide. The unsafe operation of the elevator and the safety of the building have been seriously compromised by the unauthorized admission of electric bicycles into the elevator. To meet the need for fast detection and identification of electric bicycles in elevators, we designed a modified YOLOv5-based identification approach in this study. We propose the use of the EIoU loss function to address the occlusion problem in electric bicycle recognition. By considering the interaction ratio and overlap loss of the target frames, we are able to enhance localization accuracy and reduce the missed detection rate of occluded targets. Additionally, we introduce the CBAM attention mechanism in both the backbone and head of YOLOv5 to improve the expressive power of feature maps. This allows the model to prioritize important regions of the target object, leading to improved detection accuracy. Furthermore, we utilize the CARAFE operator during upsampling instead of the nearest operator in the original model. This enables our model to recover details and side information more accurately, resulting in finer sampling results. The experimental results demonstrate that our improved model achieves an mAP of 86.35 percent, a recall of 81.8 percent, and an accuracy of 88.0 percent. When compared to the original model under the same conditions, our improved YOLOv5 model shows an average detection accuracy increase of 3.49 percent, a recall increase of 5.6 percent, and an accuracy increase of 3.5 percent. Tests in application scenarios demonstrate that after putting the model on the hardware platform Jeston TX2 NX, stable and effective identification of electric bicycles can be accomplished. Full article

The auto industry is accelerating, and self-driving cars are becoming a reality. However, the acceptance of such cars will depend on their social and environmental integration into a road traffic ecosystem comprising vehicles, motorcycles, bicycles, and pedestrians. One of the most vulnerable groups within the road ecosystem is pedestrians. Assistive technology focuses on ensuring functional independence for people with disabilities. However, little effort has been devoted to exploring possible interaction mechanisms between pedestrians with disabilities and self-driving cars. This paper analyzes how self-driving cars and disabled pedestrians should interact in a traffic ecosystem supported by wearable devices for pedestrians to feel safer and more comfortable. We define the concept of an Assistive Self-driving Car (ASC). We describe a set of procedures to identify people with disabilities using an IEEE 802.11p-based device and a group of messages to express the intentions of disabled pedestrians to self-driving cars. This interaction provides disabled pedestrians with increased safety and confidence in performing tasks such as crossing the street. Finally, we discuss strategies for alerting disabled pedestrians to potential hazards within the road ecosystem. Full article