This study introduces an innovative approach to tackle multi-objective linear programming (MOLP) problems amidst uncertainty, employing interval-valued fuzzy numbers. The method is tailored to resolve ride-hailing matching challenges encompassing uncertain travel times. Findings reveal that managing uncertainty parameters within interval-valued fuzzy MOLP is achieved through strategic reformulations, focusing on constraint coefficients, resulting in streamlined linear programming formulations conducive to solution simplicity. The efficacy of the proposed model in efficiently handling ride-hailing matching quandaries is demonstrated. Moreover, this study delves into the prospective applications of the developed method, including its potential for generalization to address non-linear programming (NLP) issues pertinent to the ride-hailing domain. This research advances decision-making processes under uncertainty and paves the way for broader applications beyond ride-hailing. Full article

The firefighting protective suits (FPSs) of firefighters at fire scenes affect their health and safety. However, the association between firefighters’ health awareness of occupational exposure risks and the FPS use, washing and management remains unclear. Therefore, this study aimed to evaluate the association between firefighters’ health awareness of occupational exposure risks and their recognition, behaviors regarding the use, washing and management of FPSs. This study design is a cross-sectional study and used a web-based survey of the Seoul Metropolitan Government’s electronic survey system. The survey was conducted on metropolitan firefighters performing shift work in charge of fire and rescue work for 21 days from 1 to 22 April 2019, with 1097 (40.3%) respondents. Characteristics of FPS use, washing and management and the association between thoughts and behaviors thereof and health awareness of occupational exposure risks were evaluated. Data of 1097 firefighters were analyzed using the SAS 9.4 statistical package, chi-square test and logistic regression analysis. Firefighters’ fire scene awareness rate of possible carcinogens was 94.4%. There was an association between public health thinking of occupational exposure risks and the correct use of an FPS for one’s own safety (AOR 1.97. 95% CI 1.02–3.80). However, no association was shown between correct FPS use (AOR 1.49, 95% CI 0.48–4.59), washing (AOR 2.50, 95% CI 0.93–6.68) and management (AOR 1.38, 95% CI 0.75–2.50) behaviors. This study analyzed the relationship between the use, washing and management of personal protective equipment called firefighting clothing and firefighters perceived occupational exposure risks. This study found an association between the health awareness of occupational exposure risks and recognition of the correct use of FPSs at fire scenes but not between using, washing and managing behaviors of FPSs. This study is the first to analyze the relationship between firefighting clothing and occupational health awareness level. The results confirm that future interventions are required to help firefighters practice desirable behaviors toward FPSs and provided evidenced data for preventing occupational diseases among firefighters. Therefore, this study can be used to develop a firefighter occupational health curriculum and establish health and safety plans from mid- to long-term perspectives for firefighters’ safety against occupational exposure risks. Full article

With the increasing demand for energy and the depletion of traditional resources, the development of alternative energy sources has become a critical issue. Shale gas, as an abundant and widely distributed resource, has great potential as a substitute for conventional natural gas. However, due to the low permeability of shale-gas reservoirs, efficient extraction poses significant challenges. The application of hydraulic fracturing technology has been proven to effectively enhance rock permeability, but the influence of environmental factors on its efficiency remains unclear. In this study, we investigate the impact of gas fracturing on shale-gas extraction efficiency under varying environmental conditions using numerical simulations. Our simulations provide a comprehensive analysis of the physical changes that occur during the fracturing process, allowing us to evaluate the effects of gas fracturing on rock mechanics and permeability. We find that gas fracturing can effectively induce internal fractures within the rock, and the magnitude of tensile stress decreases gradually during the process. The boundary pressure of the rock mass is an important factor affecting the effectiveness of gas fracturing, as it exhibits an inverse relationship with the gas content present within the rock specimen. Furthermore, the VL constant demonstrates a direct correlation with gas content, while the permeability and PL constant exhibit an inverse relationship with it. Our simulation results provide insights into the optimization of gas fracturing technology under different geological parameter conditions, offering significant guidance for its practical applications. Full article

We introduce an innovative theoretical framework tailored for the analysis of Pair Distribution Function (PDF) data derived from Small-Angle X-ray Scattering (SAXS) measurements of core-shell micelles. The new approach involves the exploitation of the first derivative of the PDF and the derivation of analytical equations to solve the core-shell micelle structure under the hypothesis of a spheroidal shape. These analytical equations enable us to determine the micelle’s aggregation number, degree of ellipticity, and contrast in electron density between the core-shell and shell-buffer regions after having determined the whole micelle size and its shell size from the analysis of the first derivative of the PDF. We have formulated an overdetermined system of analytical equations based on the unknowns that characterize the micelle structure. This allows us to establish a Figure of Merit, which is utilized to identify the most reliable solution within the system of equations. Full article

SAR imagery plays a crucial role in geological and environmental monitoring, particularly in highland mountainous regions. However, inherent geometric distortions in SAR images often undermine the precision of remote sensing analyses. Accurately identifying and classifying these distortions is key to analyzing their origins and enhancing the quality and accuracy of monitoring efforts. While the layover and shadow map (LSM) approach is commonly utilized to identify distortions, it falls short in classifying subtle ones. This study introduces a novel LSM ground-range slope (LG) method, tailored for the refined identification of minor distortions to augment the LSM approach. We implemented the LG method on Sentinel-1 SAR imagery from the tri-junction area where the Xiaojiang, Pudu, and Jinsha rivers converge at the Yunnan-Sichuan border. By comparing effective monitoring-point densities, we evaluated and validated traditional methods—LSM, R-Index, and P-NG—against the LG method. The LG method demonstrates superior performance in discriminating subtle distortions within complex terrains through its secondary classification process, which allows for precise and comprehensive recognition of geometric distortions. Furthermore, our research examines the impact of varying slope parameters during the classification process on the accuracy of distortion identification. This study addresses significant gaps in recognizing geometric distortions and lays a foundation for more precise SAR imagery analysis in complex geographic settings. Full article

Outbreaks caused by foot-and-mouth disease (FMD) A/ASIA/G-VII lineage viruses have often occurred in Middle Eastern and Southeast Asian countries since 2015. Because A/ASIA/G-VII lineage viruses are reported to have distinct antigenic relatedness with available commercial FMD vaccine strains, it is necessary to investigate whether inoculation with vaccines used in Korea could confer cross-protection against A/ASIA/G-VII lineage viruses. In the present study, we conducted two vaccination challenge trials to evaluate the efficacy of three commercial FMD vaccines (O/Manisa + O/3039 + A/Iraq, O/Campos + A/Cruzeiro + A/2001, and O/Primorsky + A/Zabaikalsky) against heterologous challenge with ASIA/G-VII lineage viruses (A/TUR/13/2017 or A/BHU/3/2017 strains) in pigs. In each trial, clinical signs, viremia, and salivary shedding of virus were measured for 7 days after challenge. In summary, the O/Campos + A/Cruzeiro + A/2001 vaccine provided full protection against two A/ASIA/G-VII lineage viruses in vaccinated pigs, where significant protection was observed. Although unprotected animals were observed in groups vaccinated with O/Manisa + O/3039 + A/Iraq or O/Primorsky + A/Zabaikalsky vaccines, the clinical scores and viral RNA levels in the sera and oral swabs of vaccinated animals were significantly lower than those of unvaccinated controls. Full article

Future airspace is expected to become more congested with additional in-service cargo and commercial flights. Pilots will face additional burdens in such an environment, given the increasing number of factors that they must simultaneously consider while completing their work activities. Therefore, care and attention must be paid to the mental workload (MWL) experienced by operating pilots. If left unaddressed, a state of mental overload could affect the pilot’s ability to complete his or her work activities in a safe and correct manner. This study examines the impact of two different cockpit display interfaces (CDIs), the Steam Gauge panel and the G1000 Glass panel, on novice pilots’ MWL and situational awareness (SA) in a flight simulator-based setting. A combination of objective (EEG and HRV) and subjective (NASA-TLX) assessments is used to assess novice pilots’ cognitive states during this study. Our results indicate that the gauge design of the CDI affects novice pilots’ SA and MWL, with the G1000 Glass panel being more effective in reducing the MWL and improving SA compared with the Steam Gauge panel. The results of this study have implications for the design of future flight deck interfaces and the training of future pilots. Full article

This paper discusses various strategies for probabilistic analysis, with a focus on typical engineering applications. The emphasis is on sampling methods and sensitivity analysis. A new sampling method, Latinized particle sampling, is introduced and compared to existing sampling methods. While it can increase the quality of surrogate models, an optimized Latin hypercube sampling is mostly preferable as it shows slightly better results. In sensitivity analysis, the difficulty lies in correlated input variables, which are typical in engineering applications. First, the Sobol indices and the Shapley values are explained using an intuitive example. Then, the modified coefficient of importance is introduced as a new sensitivity measure, which can be used to reliably identify input variables without functional influence. Finally, these results are applied to a turbomachinery test case. In this case, the flow field of a compressor row is investigated, where the blades are subjected to geometric variability. The profile parameters used to describe the geometric variability are correlated. It is shown that the variability of the maximum camber and the thickness of the leading edge have a decisive influence on the variability of the isentropic efficiency. Full article

Despite the partial disclosure of driving scenario knowledge graphs, they still fail to meet the comprehensive needs of intelligent connected vehicles for driving knowledge. Current issues include the high complexity of pattern layer construction, insufficient accuracy of information extraction and fusion, and limited performance of knowledge reasoning models. To address these challenges, a hybrid knowledge graph method was adopted in the construction of a driving scenario knowledge graph (DSKG). Firstly, core concepts in the field were systematically sorted and classified, laying the foundation for the construction of a multi-level classified knowledge graph top-level ontology. Subsequently, by deeply exploring and analyzing the Traffic Genome data, 34 entities and 51 relations were extracted and integrated with the ontology layer, achieving the expansion and updating of the knowledge graph. Then, in terms of knowledge reasoning models, an analysis of the training results of the TransE, Complex, Distmult, and Rotate models in the entity linking prediction task of DSKG revealed that the Distmult model performed the best in metrics such as hit rate, making it more suitable for inference in DSKG. Finally, a standardized and widely applicable driving scenario knowledge graph was proposed. The DSKG and related materials have been publicly released for use by industry and academia. Full article

The $X_{CO}$ factor is defined as $X_{CO}=N\left(H_2\right)/W_{12CO}$. It is useful for estimating cloud mass. However, there is only limited research on how the $X_{CO}$ factor varies within a single cloud. Employing ${}^{12}CO(J=1-0)$ and ${}^{13}CO(J=1-0)$ spectral data, we computed an $X_{CO}$ factor of 3.6 $\times {10}^{20}{\mathrm{cm}}^{-2}$ (K km s${}^{-1}{)}^{-1}$ for luminous gas of the N55 region. Our analysis revealed a V-shaped correlation between the $X_{CO}$ factor and $H_2$ column densities, while the relationship with excitation temperature exhibited obscurity. This suggests that the CO-to-$H_2$ conversion is not consistent on small scale (∼1 pc). Additionally, we found that star formation activity has little influence on the variability in the $X_{CO}$ factor. Full article

The areas around the Ching-Shuei River saw numerous landslides (2004–2017) after the Jiji earthquake, profoundly harming the watershed’s geological environment. The 33 catchment areas in the Ching-Shuei River watershed and five typhoon and rainstorm events, with a total of 165 occurrences and non-occurrences, were analyzed, and the training and validation were categorized into 70% training and 30% validation. A landslide disaster is deemed, for the purposes of this research, to have taken place if SPOT satellite images taken before and after an incident show a Normalized Difference Vegetation Index difference larger than 0.25, a slope of less than 30 degrees, and a number of connected grids greater than 10. The analysis was carried out using the instability index method analysis with Rogers regression analysis and artificial neural network. The accuracy rates of neural network, logit regression, and instability index analyses were, respectively, 93.3%, 80.6%, and 70.9%. The neural network’s area under the curve was 0.933, indicating excellent discrimination ability; that of the logit regression analysis was 0.794, which is considered good; and that of the instability index analysis was 0.635, or fair. This suggests that any of the three models are suitable for the danger assessment of large post-earthquake debris flows. The results of this study also provide a reference and evidence for specific sites’ potential susceptibility to debris flows. Full article

This paper presents the results of research conducted in the field of the technology of surface hardening of castings from unalloyed and low-alloy nodular cast iron using the laser remelting method. The range of studies included macro- and microhardness measurements using Rockwell and Vickers methods as well as metallographic microscopic examinations using a scanning electron microscope. Moreover, abrasive wear resistance tests were performed using the pin-on-disk method in the friction pair of nodular cast iron—SiC abrasive paper and the reciprocating method in the friction pair of nodular cast iron—unalloyed steel. Analysis of the test results shows that the casting surface layer remelting by laser for unalloyed nodular cast iron results in a greater improvement in its resistance to abrasive wear in the metal–mineral system, as compared to low-alloy cast iron. Additionally, carrying out the laser hardening treatment of the surface layer made of the tested grades of nodular cast iron is justified only if the tribological system of the cooperating working parts and allowable dimensional changes during their operation are known. Full article

Spondylodiscitis is defined by infectious conditions involving the vertebral column. The incidence of the disease has constantly increased over the last decades. Imaging plays a key role in each phase of the disease. Indeed, radiological tools are fundamental in (i) the initial diagnostic recognition of spondylodiscitis, (ii) the differentiation against inflammatory, degenerative, or calcific etiologies, (iii) the disease staging, as well as (iv) to provide clues to orient towards the microorganisms involved. This latter aim can be achieved with a mini-invasive procedure (e.g., CT-guided biopsy) or can be non-invasively supposed by the analysis of the CT, positron emission tomography (PET) CT, or MRI features displayed. Hence, this comprehensive review aims to summarize all the multimodality imaging features of spondylodiscitis. This, with the goal of serving as a reference for Physicians (infectious disease specialists, spine surgeons, radiologists) involved in the care of these patients. Nonetheless, this review article may offer starting points for future research articles. Full article

This study focuses on the development of a body for an electric racecar, utilizing CAD software for the design. A simplified full-vehicle geometric model was constructed. Based on fundamental theories of computational fluid dynamics and using CAE software platforms, the shear stress transport (SST) k-ω physical model was chosen to establish a three-dimensional computational model of the racecar’s external flow field. Simulations were conducted to analyze the pressure, airflow streamlines, and velocity distribution around the body and its surrounding flow field, elucidating the impact of body shape structure on aerodynamic characteristics. Finally, a manufacturing process for the body was designed, and a prototype was produced and integrated into the complete vehicle for road testing. The results indicate that the designed electric racecar body maintained consistent airflow over its surface, meeting the basic requirements of aerodynamics. Full article

Electric vehicle (EV) adoption grows steadily on a global scale, yet there is no consistent experience for EV drivers to charge their vehicles, which hinders the important EV mass market adoption. The Open Charge Point Protocol (OCPP) is the solution to this challenge, as it provides standardization and open communication between EV infrastructure components. The interplay of the OCPP with open cross-functional communication standards boosters driver experience on the one hand, while the charging station itself is integrated into a renewable energy ecosystem. This paper presents a deep dive into the combination of the OCPP with the OpenADR protocol, the Open Smart Charging Protocol (OSCP), the ISO 15118, and eRoaming protocols to explore possibilities and limitations. Furthermore, we suggest LoRa communication as an alternative to IP-based communication for deep-in building applications. Hence, this paper reveals the next important steps towards a successful EV mass market transition powered by user-friendliness and green energy. Full article

This paper presents a novel ultrasonic-assisted electrodeposition process of Mn-doped NiCo₂O₄ onto a commercial nickel foam in a neutral electroplating bath (pH = 7.0) under an ultrasonic power of 1.2 V and 100 W. Different sample properties were studied based on their crystallinity through X-ray diffraction (XRD), morphology was studied through scanning electron microscopy (SEM), and photodegradation was studied through ultraviolet–visible (UV–Vis) spectrophotometry. Based on the XRD results, the dominant crystallite phase obtained was shown to be a pure single NiCo₂O₄ phase. The optical properties of the photocatalytic film showed a range of energy band gaps between 1.72 and 1.73 eV from the absorption spectrum. The surface hydroxyl groups on the catalytic surface of the Mn-doped NiCo₂O₄ thin films showed significant improvements in removing methyl red via photodegradation, achieving 88% degradation in 60 min, which was approximately 1.6 times higher than that of pure NiCo₂O₄ thin films. The maximum hydrogen rate of the composite films under 100 mW/cm² illumination was 38 μmol/cm² with a +3.5 V external potential. The electrochemical performance test also showed a high capacity retention rate (96% after 5000 charge–discharge cycles), high capacity (260 Fg⁻¹), and low intrinsic resistance (0.8 Ω). This work concludes that the Mn-doped NiCo₂O₄ hybrid with oxygen-poor conditions (oxygen vacancies) is a promising composite electrode candidate for methyl red removal, hydrogen evolution, and high-performance hybrid supercapacitor applications. Full article

Queen triggerfish Balistes vetula is an important reef-associated species for commercial fisheries in the U.S. Caribbean. It exhibits a relatively unique reproductive strategy as a nesting benthic spawner, investing substantial energy in territorial defense, building and maintaining nests, and caring for fertilized eggs during the reproductive season. Prior to this study, no comprehensive life history information existed in the literature for queen triggerfish. This study provides the first comprehensive documentation of age, growth, size/age at sexual maturity, reproductive seasonality, and reproductive output for a Balistes species in the Caribbean. From 2013 to 2023, we collected 2190 fish samples from fisheries-dependent and -independent sources from the waters of Puerto Rico and the U.S. Virgin Islands. Fish ranged from 67 to 477 mm fork length (FL). We documented that queen triggerfish is sexually dimorphic with males attaining larger mean sizes-at-age compared to females and the species is characterized by a moderately young age at median sexual maturity (A₅₀ = 3.3 y). The maximum age for our U.S. Caribbean samples was 23 y based on increment counts from otoliths. Spawning season encompassed the months of December to August in the region, and female spawning frequency ranged from an estimated 2 to 84 times per year; female spawning frequency increased with increasing size and age of fish. We documented that commercial fishers in the U.S. Caribbean mainly target “plate-size” individuals, defined in our study as 235–405 mm FL, which appears to act as a self-imposed slot size range limit and results in the fishery not removing individuals in the smallest and largest size groups at high rates. The percentage of immature fish from fisheries-dependent sources was close to 0 (0.8%). Commercial fishing for queen triggerfish in the region currently appears to be sustainable, but monitoring of the population should continue. Full article

The position of the fetal head during engagement and progression in the birth canal is the primary cause of dystocic labor and arrest of progression, often due to malposition and malrotation. The authors performed an investigation on pregnant women in labor, who all underwent vaginal digital examination by obstetricians and midwives as well as intrapartum ultrasonography to collect four “geometric parameters”, measured in all the women. All parameters were measured using artificial intelligence and machine learning algorithms, called AIDA (artificial intelligence dystocia algorithm), which incorporates a human-in-the-loop approach, that is, to use AI (artificial intelligence) algorithms that prioritize the physician’s decision and explainable artificial intelligence (XAI). The AIDA was structured into five classes. After a number of “geometric parameters” were collected, the data obtained from the AIDA analysis were entered into a red, yellow, or green zone, linked to the analysis of the progress of labor. Using the AIDA analysis, we were able to identify five reference classes for patients in labor, each of which had a certain sort of birth outcome. A 100% cesarean birth prediction was made in two of these five classes. The use of artificial intelligence, through the evaluation of certain obstetric parameters in specific decision-making algorithms, allows physicians to systematically understand how the results of the algorithms can be explained. This approach can be useful in evaluating the progress of labor and predicting the labor outcome, including spontaneous, whether operative VD (vaginal delivery) should be attempted, or if ICD (intrapartum cesarean delivery) is preferable or necessary. Full article

In the more than 70 years since Markowitz introduced mean-variance optimization for portfolio construction, academics and practitioners have documented numerous weaknesses in the approach. In this paper, we propose two easily understandable improvements to mean-variance optimization in the context of multi-asset class portfolios, each of which provides less extreme and more stable portfolio weights. The first method sacrifices a small amount of expected optimality for reduced weight concentration, while the second method randomly resamples the available assets. Additionally, we develop a process for testing the performance of portfolio construction approaches on simulated data assuming variable degrees of forecasting skill. Finally, we show that the improved methods achieve better out-of-sample risk-adjusted returns than standard mean-variance optimization for realistic investor skill levels. Full article

(1) The main goals of general anesthesia include pain management and a safe anesthetic protocol for smooth recovery. In this retrospective study, we compared two anesthetic protocols for general anesthesia with isoflurane during emergency laparotomy: sedation with xylazine and the intraoperative infusion of lidocaine (X group) versus medetomidine as a preoperative sedation and intraoperative infusion (M group). (2) The medical records of horses who underwent emergency laparotomies between 2016 and 2023 were reviewed. According to the anesthetic protocol, patients were allocated to the X or M groups. Data about the horse, signalment, history, and anesthetic variables were analyzed. (3) Group X had a significantly higher heart rate (HR), lower respiratory rate (RR) and mean and diastolic arterial pressure (MAP/DAP). A progressive increase in HR and RR was observed in both groups. Group X underwent a decrease in RR and an increase in DAP. In Group M, a decrease in MAP and DAP was observed. Group M exhibited a longer recovery time with similar recovery scores. Both protocols provided safe anesthesia for emergency laparotomy, with minor cardiovascular and respiratory depression. Minor respiratory depression was detected when xylazine was used, while recovery was longer with medetomidine. Full article

With a view to maintaining the reputation of wine-producing regions among consumers, minimising economic losses caused by wine fraud, and achieving the purpose of data-driven terroir classification, the use of an absorbance–transmission and fluorescence excitation–emission matrix (A-TEEM) technique has shown great potential based on the molecular fingerprinting of a sample. The effects of changes in wine composition due to ageing and the stability of A-TEEM models over time had not been addressed, however, and the classification of wine blends required investigation. Thus, A-TEEM data were combined with an extreme gradient boosting discriminant analysis (XGBDA) algorithm to build classification models based on a range of Shiraz research wines (n = 217) from five Barossa Valley sub-regions over four vintages that had aged in bottle for several years. This spectral fingerprinting and machine learning approach revealed a 100% class prediction accuracy based on cross-validation (CV) model results for vintage year and 98.8% for unknown sample prediction accuracy when splitting the wine samples into training and test sets to obtain the classification models. The modelling and prediction of sub-regional production area showed a class CV prediction accuracy of 99.5% and an unknown sample prediction accuracy of 93.8% when modelling with the split dataset. Inputting a sub-set of the current A-TEEM data into the models generated previously for these Barossa sub-region wines yielded a 100% accurate prediction of vintage year for 2018–2020 wines, 92% accuracy for sub-region for 2018 wines, and 91% accuracy for sub-region using 2021 wine spectral data that were not included in the original modelling. Satisfactory results were also obtained from the modelling and prediction of blended samples for the vintages and sub-regions, which is of significance when considering the practice of wine blending. Full article