Miracle-dz/UniFAR-FTUs huggingface dataset:
Research on Semantic Segmentation of Fish-Eye Images for Autonomous Driving

Abstract: Fisheye cameras, valued for their wide field of view, play a crucial role in perceiving the surrounding environment of vehicles. However, there is a lack of specific research addressing the processing of significant distortion features in segmenting fish-eye images. Additionally, fish-eye images for autonomous driving face the challenge of few datasets, potentially causing over fitting and hindering the model's generalization ability. Based on the semantic segmentation task, a method for transforming normal images into fish-eye images is proposed, which expands the fish-eye image dataset. By employing the Transformer network and the Across Feature Map Attention, the segmentation performance is further improved, achieving a 55.6% mIOU on Woodscape. Additionally, leveraging the concept of knowledge distillation, the network ensures a strong generalization based on dual-domain learning without compromising performance on Woodscape (54% mIOU).
Antidiabetic Activity of Ajwain Oil in Different In Vitro Models

Context: Ajwain oil is an essential oil with thymol as its major constituent which is known for many pharmacological activities.Aims: To evaluate the anti-hyperglycaemic potential of ajwain oil using different in vitro models.Methods and Material: In vitro α-amylase and α-glucosidase inhibitory effect of ajwain oil was carried out by the method of Bernfeld and Shibano et al. 1997 with minor modifications respectively. Cytotoxicity of the ajwain oil was assessed using MTT assay. Glucose uptake potential was assessed in differentiated L6 myotubes using fluorescent tagged 6-NBDG.Results: Ajwain oil showed very good α – amylase inhibitory activity. A maximum inhibition of 88.55 ±0.43 % was achieved at a concentration of 4µL/ml by ajwain oil which was comparable to that of standard acarbose, 90.96 ± 1.81%. The IC50 of the extract was found to be 0. 47µL/ml and for acarbose 0. 69µL/ml. The maximum in vitro α – glucosidase inhibitory activity was found to be 89 ± 0.72 % and 91.67 ± 1.09% at 4µL/ml for ajwain oil and acarbose. The IC50 of the extract and acarbose were found to be 0. 37µL/ml and 0. 41µL/ml respectively. Ajwain oil has enhanced glucose uptake in L6 myotubes in a dose dependent manner.Conclusion: The anti-hyperglycaemic activity of the ajwain oil strongly support its ability to decrease sugar level hence it may be further validated for its use as an antidiabetic agent.
Exergy-Based Optimization of a CO2 Polygeneration System: A Multi-Case Study

A polygeneration system for power, heat, and refrigeration has been evaluated and optimized using exergy-based methods. CO2 is the working fluid. The study considered two environmental conditions for the potential implementation of the polygeneration system: cold (Casecold) and hot (Casehot). Aspen HYSYS® was used to perform steady-state simulations, Python was used for the automation of the process, and the connection of Aspen HYSYS® with Python was successfully applied for single-objective and multi-objective optimizations. A wide range of decision variables was implemented. The minimization of the average cost of a product per unit of exergy was the goal of single-objective optimization and was included in the multi-objective optimization in addition to the maximization of the overall exergy efficiency. Single-objective and multi-objective optimization were applied. Both optimization algorithms result in the necessity to increase the pinch temperature in the heat exchanger (ΔTpinch,HE), maintain the pinch temperature in the gas cooler (ΔTpinch,GC), and augment this value for the evaporator (ΔTpinch,EVAP). Notably, higher isentropic efficiency for turbomachinery correlates with improved optimization outcomes. These findings contribute to the applicability and performance of the polygeneration system, offering potential advancements in sustainable energy solutions.
Enhancing pedestrian perceived safety through walking environment modification considering traffic and walking infrastructure

Urban policies have recently been formulated, following the increasing interest in pedestrian-friendly cities, people-centered safety, and accessibility. Despite the research efforts on physical walking safety, safety evaluations centered on pedestrian perception have been under-reported. Investigating the factors affecting pedestrian subjective safety perception is critical to promoting walking intention because pedestrians forgo walking if they feel unsafe. This study explored the relationship between various walking environmental factors and pedestrians’ psychological perception of safety by surveying 99 pedestrians’ perceptions at nine study sites and conducting a field investigation. Because of the multifaceted nature of pedestrian perception, mediation effect analyses were also conducted to understand the relationship between walking environment factors and perceived safety in depth, considering the role of the perception of traffic characteristics and walking infrastructure. This study found that walking environmental factors closely related to physical safety (e.g., traffic safety facilities and crosswalks) may not greatly contribute to perceived safety and demonstrated that maintaining infrastructure quality is essential for enhancing perceived safety, considering the mediating effect of the perception of infrastructure on perceived safety. The results imply that to improve the walking environment, it is necessary to consider both the physical safety and the perceived safety of pedestrians. This requires comprehensive planning for enhancing traffic safety facilities as well as ensuring user comfort and pleasure through quality infrastructure. This study can provide a basis for enhancing pedestrian-centered safety and promoting residents’ walking intention for public health while increasing their perceptions of safety.
A novel approach to fake news classification using LSTM-based deep learning models

The rapid dissemination of information has been accompanied by the proliferation of fake news, posing significant challenges in discerning authentic news from fabricated narratives. This study addresses the urgent need for effective fake news detection mechanisms. The spread of fake news on digital platforms has necessitated the development of sophisticated tools for accurate detection and classification. Deep learning models, particularly Bi-LSTM and attention-based Bi-LSTM architectures, have shown promise in tackling this issue. This research utilized Bi-LSTM and attention-based Bi-LSTM models, integrating an attention mechanism to assess the significance of different parts of the input data. The models were trained on an 80% subset of the data and tested on the remaining 20%, employing comprehensive evaluation metrics including Recall, Precision, F1-Score, Accuracy, and Loss. Comparative analysis with existing models revealed the superior efficacy of the proposed architectures. The attention-based Bi-LSTM model demonstrated remarkable proficiency, outperforming other models in terms of accuracy (97.66%) and other key metrics. The study highlighted the potential of integrating advanced deep learning techniques in fake news detection. The proposed models set new standards in the field, offering effective tools for combating misinformation. Limitations such as data dependency, potential for overfitting, and language and context specificity were acknowledged. The research underscores the importance of leveraging cutting-edge deep learning methodologies, particularly attention mechanisms, in fake news identification. The innovative models presented pave the way for more robust solutions to counter misinformation, thereby preserving the veracity of digital information. Future research should focus on enhancing data diversity, model efficiency, and applicability across various languages and contexts.
A Geometric Auto-Calibration Method for Multiview UAV-Borne FMCW SAR Images

In recent years, the geometric calibration for synthetic aperture radar (SAR) has been developing toward automation and lack of ground control points (GCPs). In this case, the calibration equations become ill-conditioned, making the results unstable. The slant range error of unmanned aerial vehicle (UAV)-borne SAR, whose transmitting signal is a frequency modulation continuous wave, is usually spatially variable, which makes this problem more complicated. In this letter, a geometric auto-calibration method for UAV-borne SAR is proposed. First, a robust auto-calibration model is proposed to calibrate the space-varying slant range error. Second, a weighting strategy based on the quality of tie points (TPs) is proposed to further improve the calibration accuracy of this method. Finally, an iterative method based on correcting characteristic values is utilized to find the least squares solution robustly. Simulation results confirm about 70% performance enhancement of the proposed method over the traditional approach. In real-data experiments, the auto-calibration yielded a positioning accuracy improvement to 11.5 cm.
Comparative Study of Metainspired Resonators for Copper Sensing in Water Bodies

This work intends to observe the simulated response, based on experimental data, of three proposed sensors aiming to identify different concentrations of copper in water samples, as the MUT. Initially, solutions of distilled water, with 99.6% purity metallic copper powder, were prepared at concentrations of 0 mg/L, 8 mg/L and 10 mg/L. With the difference of resonance frequencies presented by each one of solutions on reflection coefficient, there was defined the frequency shifts analysis. The sensors were considered more sensitive according to the values of frequencies shifts. There were designed three types of sensors: a CSRR with a FR4 substrate, two SCRC - based with FR4 substrate and a CSRR with a PLA substrate. The last one presented almost 400 MHz by varying the concentration from 0 mg/L to 10 mg/L.
Comparative Genome Analysis of Polar Mesorhizobium sp. PAMC28654 to Gain Insight into Tolerance to Salinity and Trace Element Stress

In this study, Mesorhizobium sp. PAMC28654 was isolated from a soil sample collected from the polar region of Uganda. Whole-genome sequencing and comparative genomics were performed to better understand the genomic features necessary for Mesorhizobium sp. PAMC28654 to survive and thrive in extreme conditions and stresses. Additionally, diverse sequence analysis tools were employed for genomic investigation. The results of the analysis were then validated using wet-lab experiments. Genome analysis showed trace elements’ resistant proteins (CopC, CopD, CzcD, and Acr3), exopolysaccharide (EPS)-producing proteins (ExoF and ExoQ), and nitrogen metabolic proteins (NarG, NarH, and NarI). The strain was positive for nitrate reduction. It was tolerant to 100 mM NaCl at 15 °C and 25 °C temperatures and resistant to multiple trace elements (up to 1 mM CuSO4·5H2O, 2 mM CoCl2·6H2O, 1 mM ZnSO4·7H2O, 0.05 mM Cd(NO3)2·4H2O, and 100 mM Na2HAsO4·7H2O at 15 °C and 0.25 mM CuSO4·5H2O, 2 mM CoCl2·6H2O, 0.5 mM ZnSO4·7H2O, 0.01 mM Cd(NO3)2·4H2O, and 100 mM Na2HAsO4·7H2O at 25 °C). This research contributes to our understanding of bacteria’s ability to survive abiotic stresses. The isolated strain can be a potential candidate for implementation for environmental and agricultural purposes.
“This is fine”: the impact of blowouts on subsequent game performance in the National Hockey League (NHL)

Blowouts in sports involve large margins of victory or loss between teams and have long been perceived as influencing subsequent performances by athletes, coaches, fans, and other stakeholders. Under the backdrop of the hot hand ph enomenon, the current study explores the impact of blowouts on subsequent game performance in the National Hockey League (NHL). Specifically, we examine the potential carryover of a “hot (or cold) hand” on the subsequent game following a large win or loss. In our study, we defined blowouts as outlying goal differentials for regular season games (i.e., a difference of approximately 6 goals between teams in a single game based on 3 standard deviations from the mean goal differential during the sampled period). Using this criterion, data from 285 games over the 2005–06 to 2018–19 NHL regular seasons were gathered for analysis. We performed a series of multiple regressions using blowout goal differential as the main predictor, adjusting for location of the subsequent game, number of time zones from the home base city, whether the subsequent game was a back-to-back, and winning percentages of the team and opponent. Our results revealed no significant over or under performance by teams that either won through a blowout or those that lost by a blowout. Our findings are consistent with previous work in other and similar sports contexts. Practical applications and future directions for research are discussed.
IoT security with blockchain: A review

The relationship between the Internet of Things (IoT) and Blockchain has emerged to address the challenges of security, data integrity and transparency of IoT devices. Blockchain, with its distributed and decentralized nature, ensures reliable data transmission between IoT devices and security by preventing data manipulation. It also enhances the security and transparency of IoT applications by supporting automated and trusted transactions through smart contracts. In this context, Blockchain plays a critical role in making the IoT ecosystem more reliable and effective. In this study, we review studies that emphasize the importance of Blockchain technology for IoT security and propose various solutions. The proposed solutions include scalable frameworks for secure transactions in dynamic applications, Hyperledger Sawtooth-based frameworks that support secure logging of industrial activities, specialized access control mechanisms such as Trust-Based Access Control Mechanism (TABI), and IoT Cop monitoring framework that offers advanced security features. In addition, blockchain-based access control protocols, authentication systems and security models are aimed at enhancing IoT security. While most of the studies in the literature focus on the current challenges of IoT and Blockchain integration, they also reveal future research opportunities. These studies are briefly mentioned and the necessary ones are expressed in a table. In this way, it is aimed to create the necessary infrastructure for those who will work in this field.
Exploring relationship satisfaction in mothers of children with disabilities: the predictive role of interparental conflicts and moderating role of dyadic coping

Background Parenting responsibilities, while a source of happiness, often lead to considerable stress for parents of children with disabilities. While most parents try to cope with these challenges together, some level of interparental conflict is almost inevitable. Frequently assuming primary responsibility for children with disabilities, mothers can be substantially impacted by interparental conflict with their partners and the resulting stress. However, implementing dyadic coping strategies in managing stressful situations serves as a buffer against adverse effects, while also enhancing relationship satisfaction. Objective This study aimed to explore the relationship between interparental conflicts, dyadic coping, and relationship satisfaction among 232 mothers of children with disabilities in Croatia who were in an intimate relationship and shared a household with their partners, regardless of marital status. Methods We analysed a non-probabilistic sample of women in Croatia who reported being in an intimate relationship, sharing a household with a partner (whether married or simply cohabitating) and being a mother to a child with a diagnosed developmental disability. Participants completed questionnaires online in March of 2021 (sociodemographic variables, the Parent Problem Checklist, the Dyadic Coping Inventory and the Marriage Quality Index). Results Results of hierarchical multiple regression show that mothers’ perceptions of how they help their partner cope with stress do not predict how satisfied they are in a relationship; instead, relationship satisfaction depends on their perceptions of how their partner helps them and how they cope with stress as a team. Furthermore, moderation analysis revealed the effect of interparental conflicts were entirely mitigated by high supportive and low negative dyadic coping of the partner. When mothers felt adequately supported by their partner, the negative impact of stress caused by interparental conflicts did not spill over into their relationship. Conclusion Our results suggest that by recognizing the importance of psychological support and couples therapy in strengthening dyadic coping, government and non-profit initiatives can effectively empower parents of children with disabilities, fostering healthier and more resilient family dynamics that promotes the well-being of parents and their children.
Studying lack of awareness of cognitive decline in neurodegenerative diseases requires measures of both anosognosia and denial

The cause(s) of lack of awareness of cognitive decline in neurodegenerative diseases can be multifactorial. Yet neurologically oriented research on anosognosia of cognitive decline almost exclusively assumes that the underlying disturbance of neuro-networks that support various cognitive functions accounts for the reduced self-awareness. Cultural and psychosocial factors, including the person’s emotional state, however, can contribute to the underreporting or avoidance of admitting to cognitive impairments in neurodegenerative diseases. Research on the causes of lack of awareness of cognitive decline in neurodegenerative disorders needs to include these variables. We briefly present two case examples of underreporting or “unawareness” of memory difficulties in persons with mild cognitive impairment (MCI) (or minor neurocognitive disorder). One presented with classic anosognosia for memory impairment, while the other initially reported no memory impairment but later admitted to “denying” her memory difficulties secondary to anxiety. Based on these patients’ clinical presentations and available research, we suggest three potential screening items that may help identify probable denial of memory impairments when studying anosognosia in MCI.
Application of hybrid fuzzy interval-based machine learning models on financial time series — A case study of Taiwan biotech index during the epidemic period

In recent years, the use of machine learning to predict stock market indices has emerged as a vital concern in the FinTech domain. However, the inherent nature of point estimation in traditional supervised machine learning models leads to an almost negligible probability of achieving perfect predictions, significantly constraining the applicability of machine learning prediction models. This study employs 4 machine learning models, namely BPN, LSTM, RF, and ELM, to establish predictive models for the Taiwan biotech index during the COVID-19 period. Additionally, it integrates the Gaussian membership function MF from fuzzy theory to develop 4 hybrid fuzzy interval-based machine learning models, evaluating their predictive accuracy through empirical analysis and comparing them with conventional point estimation models. The empirical data is sourced from the financial time series of the “M1722 Listed Biotechnology and Medical Care Index” compiled by the Taiwan Economic Journal during the outbreak of the COVID-19 pandemic, aiming to understand the effectiveness of machine learning models in the face of significant disruptive factors like the pandemic. The findings demonstrate that despite the influence of COVID-19, machine learning remains effective. LSTM performs the best among the models, both in traditional mode and after fuzzy interval enhancement, followed by the ELM and RF models. The predictive results of these three models reach a certain level of accuracy and all outperform the BPN model. Fuzzy-LSTM effectively predicts at a 68% confidence level, while Fuzzy-ELM and Fuzzy-RF yield better results at a 95% confidence level. Fuzzy-BPN exhibits the lowest predictive accuracy. Overall, the fuzzy interval-based LSTM excels in time series prediction, suggesting its potential application in forecasting time series data in financial markets to enhance the efficacy of investment analysis for investors.
CPU Usage Estimation for 5G UE by mmWave Carrier Aggregation

5G networks bring a major advance in bandwidth extension through millimeter wave (mmWave). Previously impractical services such as enhanced mobile broadband (eMBB), ultra-reliable latency communications (URLCC), and machine type communications (mMTC) can be applied with QoS (Quality of Service) and QoE (Quality of Experience) for users connected to the network. Under mmWave, higher data rates are achieved with the multiple-input multiple-output (MIMO) and carrier aggregation (CA), meeting several demands of the user. However, a significant increase in user equipment (UE) capabilities is required over very high data rates, reflecting extra CPU processing demands and battery consumption, which can reach critical levels faster with potential disconnection. Considering the CPU processing growth in UE over such a situation, this work presents an estimation model to obtain processing growth curves according to the network features on mm Wave. As a result, the obtained CPU monitoring functions can predict the longer battery life for UE data throughput demands.
The psychological and physical impacts of spending time in forests: a case study of two forests in Ireland.

The overall aim of this study was to investigate the psychological and physical health/wellbeing effects of spending time in forests, and the role that the species composition of the forests plays in these effects. The effects were assessed by a simple questionnaire survey to 179 visitors to two adjacent forests; a conifer plantation forest and a broadleaf plantation forest. The immediate psychological effects from spending time in forests were also assessed by a mood test, and the longer-term psychological and physical health impacts of forest use were explored by comparing the results of the survey of forest visitors with that of a national survey. The results indicated that the main psychological well-being benefits experienced by forest users were “mental relaxation” and “enjoyment and fun”. Significant improvements in mood immediately after a walk in the forest were exhibited by the respondents, but the species composition of the forests was not shown to influence the improvements. It was also revealed that forest visitors engaged in greater levels of physical activity than the general population, but there was no correlation found between forest users’ physical activity level and frequency of forest use. It was recommended that further research should aim to make use of large-scale surveys of a random sample of the population including visitors and non-visitors to forests.