Search Results (251)

Considering the characteristics of urban planning that are becoming increasingly demanding, and the trend that urban zones should meet users’ needs based on the principle of everything in one place, this paper evaluates the potentials of urban zones in Novi Sad. An expert analysis defined 25 criteria related to urban, traffic, architectural, environmental and sociological aspects to assess the current potentials of urban zones in a sustainable manner. Based on these criteria, 10 urban zones were evaluated using a multi-structure fuzzy MCDM model, including: the Fuzzy FUCOM, IMF SWARA and Fuzzy OPARA methods, and the Fuzzy Heronian Mean and Fuzzy Bonferroni operators. Fuzzy FUCOM was applied to determine the importance of the main groups of criteria, while IMF SWARA was used to determine the importance of sub-criteria, with the final weights obtained using the Fuzzy Heronian Mean operator. The Fuzzy OPARA method was implemented to determine the rankings of urban zones based on the potentials they offer. This model represents an innovation, as it is being presented for the first time in the literature. The final values of the urban zones show that Liman and the Center are the two urban zones with the greatest potential, which was confirmed through extensive verification analysis. Such modeling can provide support in the sense that the management of the city can obtain information about the shortcomings and potentials of the location, which allows for the definition of a more specific planning and development policy, based on the previously verified state. Full article

The sugarcane cultivation has used heavy machinery on a large scale, which causes soil compaction. The minimum tillage has been used to reduce the traffic of machines on the crop, but there is a lack of appropriate tools for the implementation of this technique, especially in sugarcane areas. The University of Campinas—UNICAMP developed a conservation soil tillage tool called “Rotary paraplow”, the idea was to join the concepts of a vertical milling cutter with the paraplow, which is a tool for subsoiling without inversion of soil. The rotary paraplow is a conservationist tillage because it mobilizes only the planting line with little disturbance of the soil surface and does the tillage with the straw in the area. These conditions make this study pioneering in nature, by proposing an equipment developed to address these issues as an innovation in the agricultural machinery market. We sought to evaluate soil tillage using rotary paraplow and compare it with conventional tillage, regarding soil physical properties and yield. The experiment was conducted in an Oxisol in the city of Jaguariuna, Brazil. The comparison was made between the soil physical properties: soil bulk density, porosity, macroporosity, microporosity and penetration resistance. At the end, a biometric evaluation of the crop was carried out in both areas. The soil properties showed few statistically significant variations, and the production showed no statistical difference. The rotary paraplow proved to be an applicable tool in the cultivation of sugarcane and has the advantage of being an invention adapted to Brazilian soils, bringing a new form of minimal tillage to areas of sugarcane with less tilling on the soil surface, in addition to reducing machine traffic. Full article

This paper aims to solve the problem of erosion sediment that negatively affects the quality of fallowed soil through the development of a new type of agricultural machinery. The transported erosion sediment will be quantified locally to evaluate the danger of these negative effects on the fallowed soil and on the functionality of the grass cover. Subsequently, a new type of machinery will be proposed for the remediation of eroded sediment and conservation of the fallowed soil. In various fallow research areas with different management methods (such as biobelts, grassed valleys, and grassed waterways), agricultural land affected by eroded sediment was examined, and appropriate machinery was designed to rehabilitate the stands after erosion events. By identifying the physical and mechanical properties of the soil, as well as the eroded and deposited sediment/colluvium, the shape, material, attachment method, and assembly of the working tool for the relevant mobile energy device were designed. The developed tool, based on a plow–carry system using a tractor, features flexible tools that separate the eroded sediment from the fallow land surface, transfer it over a short distance, and accumulate it in a designated area to facilitate subsequent removal with minimal damage to the herbaceous vegetation. The calculated erosion event was 196.9 m³ (179.0 m³ ha⁻¹), corresponding to 295 tons (268.5 t ha⁻¹) deposited from the area of 90 ha. Afterward, the proposed machinery was evaluated for the cost of the removal of the eroded sediment. Based on experience from the field, we calculated that 174 m³ per engine hour results in EUR 0.22 m⁻³. From the performed experiment, it is evident that the proposed machinery offers a suitable solution for eroded sediment removal locally, which prevents further erosion and subsequent sediment deposition in water bodies where the costs for sediment removal are higher. Moreover, we have proven the potential negative impact of invasive plant species because their seeds were stored in the sediment. Finally, it is credible to state that the proposed agricultural machinery offers an effective solution for the eroded sediment relocation, which subsequently can be used for other purposes and monetized. This results in an increase in the profitability of the erosion sediment removal process, which is already in place at the source before further transportation to aquatic systems where the costs for removal are significantly higher. Full article

Vibration sensors are integral to a multitude of engineering applications, yet the development of low-cost, easily assembled devices remains a formidable challenge. This study presents a highly sensitive flexible vibration sensor, based on the piezoresistive effect, tailored for the detection of high-dynamic-range vibrations and accelerations. The sensor’s design incorporates a polylactic acid (PLA) housing with cavities and spherical recesses, a polydimethylsiloxane (PDMS) membrane, and electrodes that are positioned above. Employing femtosecond laser ablation and template transfer techniques, a parallel groove array is created within the flexible polymer sensing layer. This includes conductive pathways, and integrates stainless-steel balls as oscillators to further amplify the sensor’s sensitivity. The sensor’s performance is evaluated over a frequency range of 50 Hz to 400 Hz for vibrations and from 1 g to 5 g for accelerations, exhibiting a linear correlation coefficient of 0.92 between the sensor’s voltage output and acceleration. It demonstrates stable and accurate responses to vibration signals from devices such as drills and mobile phone ringtones, as well as robust responsiveness to omnidirectional and long-distance vibrations. The sensor’s simplicity in microstructure fabrication, ease of assembly, and low cost render it highly promising for applications in engineering machinery with rotating or vibrating components. Full article

This article offers a concise overview of the best practices for safety in offshore oil and gas operations, focusing on the risks associated with various types of equipment, particularly on the risk of fire. It identifies specific machinery and systems that could pose hazards, assesses their potential impact on safety, and explores conditions that may lead to accidents. Some of the largest accidents were analyzed for their associations with fire hazards and specific equipment. Two primary regulatory approaches to offshore safety are examined: the prescriptive approach in the United States (US) and the goal-oriented approach in Europe. The prescriptive approach mandates strict compliance with specific regulations, while in the goal-oriented approach a failure to adhere to recognized best practices can result in legal accountability for negligence, especially concerning human life and environmental protection. This article also reviews achievements in safety through the efforts of regulatory authorities, industry collaborations, technical standards, and risk assessments, with particular attention given to the status of Mobile Offshore Drilling Units (MODUs). Contrary to common belief, the most frequent types of accidents are not those involving a fire/explosion caused by the failure of the Blowout Preventer (BOP) after a well problem has already started. Following analysis, it can be concluded that the most frequent type of accident typically occurs without fire and is due to material fatigue. This can result in the collapse of the facility, capsizing of the platform, and loss of buoyancy of mobile units, particularly in bad weather or during towing operations. It cannot be concluded that accidents can be more efficiently prevented under a specific type of safety regime, whether prescriptive or goal-oriented. Full article

Adverse climatic conditions, particularly excessive water and frost, necessitate the design of thick protective sub-ballast layers when dealing with frost-susceptible subgrade surfaces, especially when using standard natural materials (crushed aggregate or gravel–sand). Given the current preference for conserving natural construction materials and promoting sustainable development in the dimensioning of sub-ballast layers, it is advisable to incorporate various thermal insulation, composite, or suitable recycled materials in their design. Therefore, the paper analyses the impact of incorporating different thermal insulation materials (including extruded polystyrene, Liapor, Liapor concrete, and composite foam concrete) into sub-ballast layers. As part of the experimental research, these modified sub-ballast layers were constructed on a real scale in the outdoor environment of the University of Žilina (UNIZA) campus. They were subsequently compared in terms of their thermal resistance to climatic loads. The research results demonstrate that extruded polystyrene provides the optimal thermal insulation effect in modified sub-ballast layers, which was subsequently used in the numerical modelling of railway track structure freezing under different climatic loads. Full article

With the advancement of forestry modernization, the research and development of forestry vehicles provide solid technical support for the efficiency and sustainability of forest operations. This study aims to reduce the mass of the forest-use tri-axle unmanned vehicle frame through structural optimization design, improve its static and dynamic characteristics, and enhance vehicle mobility and environmental adaptability while maintaining or enhancing its structural strength and stability. Initially, the finite element model of the vehicle frame was established using the finite element software Hypermesh (2022), and its static and dynamic characteristics were analyzed using OptiStruct (2022) software. The accuracy of the finite element calculations was verified through experiments. Subsequently, a sensitivity analysis method was employed to screen the design variables of the thin-walled beam structure of the forest-use tri-axle unmanned vehicle. Response surface models were created using least squares regression (LSR) and radial basis function network (RBF). Considering indicators such as frame mass, modal frequency, and maximum bending and torsional stresses, the multi-objective genetic algorithm (MOGA) was applied to achieve a multi-objective lightweight design of the vehicle frame. This comprehensive optimization method is rarely reported in forestry vehicle design. By employing the proposed optimization approach, a weight reduction of 10.1 kg (a 7.44% reduction) was achieved for the vehicle frame without compromising its original static and dynamic performance. This significant lightweighting result demonstrates considerable practical application potential in the field of forestry vehicle lightweight design. It responds to the demand for efficient and environmentally friendly forestry machinery under forestry modernization and holds important implications for reducing energy consumption and operational costs. Full article

Energy conservation and emission reduction is a common concern in various industries. The construction process of electric wheel loaders has the advantages of being zero-emission and having a high energy efficiency, and has been widely recognized by the industry. The frequent shift in wheel loader working processes poses a serious challenge to the operator. Automatic shift is an effective way to improve the operator’s comfort and safety. The driving intention is an important input judgment condition to achieve efficient automatic shift. However, the current methods of vehicle driving intention recognition mainly focus on passenger cars. The working condition of the wheel loader is significantly different from that of the passenger car, with a high shifting frequency and severe load fluctuation. The driving intention recognition method of passenger cars is difficult to transplant directly. In this paper, aiming at the characteristics of wheel loader working conditions, a fuzzy recognition method based on fuzzy control is applied to driving intention recognition for electric wheel loaders. The throttle, throttle change rate and braking signals are used as inputs for recognizing the driving intention at the current moment of the whole machine. Five types of driving intentions, namely, rapid acceleration, normal acceleration, acceleration maintenance, deceleration and braking, are defined and recognized. In order to verify the effectiveness of the proposed method, simulation and experimental research are carried out. The results show that the proposed driving intention recognition method can effectively identify the driver’s intention and provide effective shift signal input for the wheel loader. Full article

Rotating machinery is widely used across various industries, making its reliable operation crucial for industrial production. However, in real-world settings, intelligent fault diagnosis faces challenges due to imbalanced fault data and the complexity of neural network models. These challenges are particularly pronounced when defining decision boundaries accurately and managing limited computational resources in real-time machine monitoring. To address these issues, this study presents KDE-ADASYN-based MobileNet with SENet (KAMS), a lightweight convolutional neural network designed for fault diagnosis in rotating machinery. KAMS effectively handles data imbalances commonly found in industrial applications and is optimized for real-time monitoring. The model employs the Kernel Density Estimation Adaptive Synthetic Sampling (KDE-ADASYN) algorithm for oversampling to balance the data, applies fast Fourier transform (FFT) to convert time-domain signals into frequency-domain signals, and utilizes a 1D-MobileNet network enhanced with a Squeeze-and-Excitation (SE) block for feature extraction and fault diagnosis. Experimental results across datasets with varying imbalance ratios demonstrate that KAMS achieves excellent performance, maintaining nearly 90% accuracy even on highly imbalanced datasets. Comparative experiments further demonstrate that KAMS not only delivers exceptional diagnostic performance but also significantly reduces network parameters and computational resource requirements. Full article

The motor gear system (MGS) is recognized for its potential in enhancing transmission efficiency and optimizing space utilization. However, the system is subjected to challenges, notably the occurrence of abnormal vibrations. These issues stem from the dynamic interaction between the motor and gears, the presence of nonlinear factors in gear system, and the impact of gear faults, all of which contribute to complex vibration patterns. Traditional dynamic models have been found to be inadequate in effectively addressing the complexities associated with electromechanical coupling problems in MGS. To address these limitations, a comprehensive analysis approach is proposed in this paper, which is grounded in the development of an electromechanical coupling model. This method involves establishing a coupled dynamic model of the motor and gear system, integrating numerical simulations, and experimental validations to thoroughly analyze the vibration characteristics of the system. Through this multifaceted methodology, a detailed analysis of the system’s vibration characteristics is conducted. The results indicate that internal excitations from tooth root cracks not only directly affect dynamic characteristics of the gear transmission system (GTS) but also indirectly influence dynamic behavior of the motor, which offers valuable insights into modeling integrated MGS and provides significant solutions for fault diagnosis within these systems. Full article

Recent technological advancements and evolving regulatory frameworks are catalysing the integration of renewable energy sources in construction equipment, with the objective of significantly reducing greenhouse gas emissions. The electrification of non-road mobile machinery (NRMM), particularly self-propelled Rough-Terrain Variable Reach Trucks (RTVRT) equipped with telescopic booms, presents notable stability challenges. The transition from diesel to electric propulsion systems alters, among other factors, the centre of gravity and the inertial matrix, necessitating precise load capacity determinations through detailed load charts to ensure operational safety. This paper introduces a virtual model constructed through multiphysics modelling utilising the bond graph methodology, incorporating both scalar and vector bonds to facilitate detailed interconnections between mechanical and hydraulic domains. The model encompasses critical components, including the chassis, rear axle, telescopic boom, attachment fork, and wheels, each requiring a comprehensive three-dimensional treatment to accurately resolve spatial dynamics. An illustrative case study, supported by empirical data, demonstrates the model’s capabilities, particularly in calculating ground wheel reaction forces and analysing the hydraulic self-levelling behaviour of the attachment fork. Notably, discrepancies within a 10% range are deemed acceptable, reflecting the inherent variability of field operating conditions. Experimental analyses validate the BG-3D simulation model of the telehandler implemented in 20-SIM establishing it as an effective tool for estimating stability limits with satisfactory precision and for predicting dynamic behaviour across diverse operating conditions. Additionally, the paper discusses prospective enhancements to the model, such as the integration of the virtual vehicle model with a variable inclination platform in future research phases, aimed at evaluating both longitudinal and lateral stability in accordance with ISO 22915 standards, promoting operator safety. Full article

The objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency. Full article

Infection by retroviruses and the mobilization of transposable elements cause DNA damage that can be catastrophic for a cell. If the cell survives, the mutations generated by retrotransposition may confer a selective advantage, although, more commonly, the effect of new integrants is neutral or detrimental. If retrotransposition occurs in gametes or in the early embryo, it introduces genetic modifications that can be transmitted to the progeny and may become fixed in the germline of that species. PIWI-interacting RNAs (piRNAs) are single-stranded, 21–35 nucleotide RNAs generated by the PIWI clade of Argonaute proteins that maintain the integrity of the animal germline by silencing transposons. The sequence specific manner by which piRNAs and germline-encoded PIWI proteins repress transposons is reminiscent of CRISPR, which retains memory for invading pathogen sequences. piRNAs are processed preferentially from the unspliced transcripts of piRNA clusters. Via complementary base pairing, mature antisense piRNAs guide the PIWI clade of Argonaute proteins to transposon RNAs for degradation. Moreover, these piRNA-loaded PIWI proteins are imported into the nucleus to modulate the co-transcriptional repression of transposons by initiating histone and DNA methylation. How retroviruses that invade germ cells are first recognized as foreign by the piRNA machinery, as well as how endogenous piRNA clusters targeting the sequences of invasive genetic elements are acquired, is not known. Currently, koalas (Phascolarctos cinereus) are going through an epidemic due to the horizontal and vertical transmission of the KoRV-A gammaretrovirus. This provides an unprecedented opportunity to study how an exogenous retrovirus becomes fixed in the genome of its host, and how piRNAs targeting this retrovirus are generated in germ cells of the infected animal. Initial experiments have shown that the unspliced transcript from KoRV-A proviruses in koala testes, but not the spliced KoRV-A transcript, is directly processed into sense-strand piRNAs. The cleavage of unspliced sense-strand transcripts is thought to serve as an initial innate defense until antisense piRNAs are generated and an adaptive KoRV-A-specific genome immune response is established. Further research is expected to determine how the piRNA machinery recognizes a new foreign genetic invader, how it distinguishes between spliced and unspliced transcripts, and how a mature genome immune response is established, with both sense and antisense piRNAs and the methylation of histones and DNA at the provirus promoter. Full article

Decarbonising transport is a crucial element of the UK’s strategy to achieve net-zero carbon emissions by 2050, as the transport sector is currently the largest contributor to the UK’s greenhouse gas emissions. Rural communities face distinct challenges in this effort due to their reliance on internal combustion engines (ICEs) across vehicles and machinery essential for daily life, including farming equipment and private transport. While the upcoming ban on new petrol and diesel vehicles paves the way for the adoption of Electric Vehicles (EVs), this solution may not fully address the unique needs of rural areas where infrastructure limitations and specific mobility requirements pose significant barriers. In this context, synthetic fuels, produced using renewable energy sources, offer a potential alternative. These fuels can be used directly in existing internal combustion engines without requiring major modifications and have the added benefit of reducing overall greenhouse gas emissions by capturing CO₂ during production. This entry explores the potential advantages of adopting synthetic fuels, particularly in rural areas, and examines how community-based buying cooperatives could support their wider use through bulk purchasing, cost reduction, and community empowerment. Full article

The pursuit of studying the quadratic minimum spanning tree (QMST) problem has captivated numerous academics because of its distinctive characteristic of taking into account the cost of interaction between pairs of edges. A QMST refers to the minimum spanning tree, which is a graph that is both acyclic and minimally connected. It also includes the interaction cost between a pair of edges in the minimum spanning tree. These interaction costs can occur between any pair of edges, whether they are adjacent or non-adjacent. In the QMST problem, our objective is to minimize both the cost of the edges and the cost of interactions. This eventually classifies the task as NP-hard. The interaction costs, sometimes referred to as quadratic costs, inherently exhibit a contradictory relationship with linear edge costs when solving a multi-objective problem that aims to minimize both linear and quadratic costs simultaneously. This study addresses the bi-objective adjacent only quadratic minimum spanning tree problem (AQMSTP) by incorporating the uncertain nature of the linear and quadratic costs associated with the problem. The focus is on the interaction costs between adjacent edges. Consequently, we have introduced a multi-objective problem called the uncertain adjacent only quadratic minimum spanning tree problem (mUAQMSTP) and formulated it using the uncertain chance-constrained programming technique. Afterwards, two MOEAs—non-dominated sorting genetic algorithm II (NSGAII) and duplicate elimination non-dominated sorting evolutionary algorithm (DENSEA)—and the traditional multi-objective solution approach, the global criterion method, are employed to solve the deterministic transformation of the model. Finally, we provide a suitable numerical illustration to substantiate our suggested framework. Full article