Miracle-dz/UniFAR-FTUs huggingface dataset:
Artiﬁcial bee colony programming for symbolic regression

Artiﬁcial bee colony algorithm simulating the intelligent foraging behavior of honey bee swarms is one of the most popular swarm based optimization algorithms. It has been introduced in 2005 and applied in several ﬁelds to solve different problems up to date. In this paper, an artiﬁcial bee colony algorithm, called as Artiﬁcial Bee Colony Programming (ABCP), is described for the ﬁrst time as a new method on symbolic regression which is a very important practical problem. Symbolic regression is a process of obtaining a mathematical model using given ﬁnite sampling of values of independent variables and associated values of dependent variables. In this work, a set of symbolic regression benchmark problems are solved using artiﬁcial bee colony programming and then its performance is compared with the very well-known method evolving computer programs, genetic programming. The simulation results indicate that the proposed method is very feasible and robust on the considered test problems of symbolic regression.
Decoding TROP2 in breast cancer: significance, clinical implications, and therapeutic advancements

Breast cancer is a heterogeneous disease characterized by distinct molecular subtypes, varied prognoses, and differential treatment responses. Understanding the molecular landscape and identifying therapeutic targets, such as trophoblast cell-surface antigen 2 (TROP2), is vital. TROP2 is notably overexpressed in breast cancer, playing a significant role in tumor growth, invasion, metastasis, and treatment resistance. While significant progress has been made in targeting TROP2 in breast cancer, several challenges and knowledge gaps remain. These challenges include the heterogeneity of TROP2 expression within breast cancer subtypes, resistance to its targeted therapies, potential off-target effects, limited therapeutic agents, and identifying optimal combination treatments. Integrating findings from clinical trials into clinical practice further complicates the landscape. This review article delves deep into TROP2 in breast cancer, highlighting its expression patterns, clinical implications, and therapeutic advancements. By understanding the role of TROP2, we can pave the way for personalized treatments, and transform the landscape of breast cancer care.
Aero structural and Electromagnetic Design Optimisation of Maritime Patrol Aircraft Radome Using Direct Search Algorithms

Airborne surveillance systems such as Maritime Patrol Aircraft are deployed by armed forces to collect surveillance information on airborne and sea surface enemy targets. Airborne Electronically Scanned Array Radar is an electromagnetic sensor integrated on this aircraft. The antenna of this radar is installed generally in belly of a turboprop aircraft. An electro-magnetically transparent cover, called radome, protects this antenna to protect itfrom various environmental effects, like rain, dust, etc. Installation of the radome results in additional drag, weightand electromagnetic signal loss. The Pareto optimality involving three design disciplines of structure, erodynamicsand electromagnetics is attempted with direct search optimisation algorithm NSGA II.
Scattering of trapped P and S waves in the hydrated subducting crust of the Philippine Sea plate at shallow depths beneath the Kanto region, Japan

Shunsuke Takemura,1,∗ Kazuo Yoshimoto2 and Takashi Tonegawa3 1Earthquake Research Institute, University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-0032, Japan. E-mail: shunsuke@bosai.go. jp 2Department of Materials System Science, Graduate School of Nanobioscience, Yokohama City University, 22-2 Seto, Kanazawa-ku, Yokohama 236-0027, Japan 3Research and Development Center for Earthquake and Tsunami, Japan Agency for Marine-Earth Science and Technology, 3173-25 Showa-machi, Kanazawa-ku, Yokohama 236-0001, Japan
Evaluating the amount of carbonic greenhouse gasses (GHGes) emission from rice paddies.

Global temperature change is one of the most important issues of GHGes emission. A scientific consensus is forming that human activities including modern agriculture by increasing the emission of GHGes may play a key role in elevating the global temperatures. This study was conducted to evaluate the amount of three important carbonic GHGes emission from rice paddies. Carbonic GHGes emission for 3 kinds of rice cultivation in one field were measured at Khuzestan province in Iran. The 3 kinds of cultivation were: wetbed-seeding (a), dry-bed-seeding (b) and transplanting (c). The experiment was performed with 4 sampling times at 4 different growth stages of rice cultivation and three repeats in a completely randomized design. We used a chamber method and gas chromatograph technique to measure the emission of CH4, CO2 and CO from rice paddies. Results showed that the highest emitted gas was CO2 and its highest emission happened at (a) and at the rice cultivation stage. On the whole CH4 emission was greatest for (a) and for all kinds of cultivation was high at the tillering and ripening stages and low at the rice cultivation and shooting stages.
Self-Conﬁguration Management towards Fix-Distributed Byzantine Sensors for Clustering Schemes in Wireless Sensor Networks

: To avoid overloading a network, it is critical to continuously monitor the natural environment and disseminate data streams in synchronization. Based on self-maintaining technology, this study presents a technique called self-conﬁguration management (SCM). The purpose is to ensure consistency in the performance, functionality, and physical attributes of a wireless sensor network (WSN) over its lifetime. During device communication, the SCM approach delivers an operational software package for the radio board of system problematic nodes. We offered two techniques to help cluster heads manage autonomous conﬁguration. First, we created a separate capability to determine which defective devices require the operating system (OS) replica. The software package was then delivered from the head node to the network’s malfunctioning device via communication roles. Second, we built an autonomous capability to automatically install software packages and arrange the time. The simulations revealed that the suggested technique was quick in transfers and used less energy. It also provided better coverage of system fault peaks than competitors. We used the proposed SCM approach to distribute homogenous sensor networks, and it increased system fault tolerance to 93.2%.
A review of best practices, standards, and approaches for transportation safety data and driver state prediction

This systematic review documents current best practices, standards, and approaches for transportation safety data analytics. While standards exist for defining measures, there are few available standards or guides for processing driving and driver data. Standards are crucial for ensuring repeatability and appropriate cost-benefit decisions. The review identified 36 relevant studies describing behavioral and physiological measures. Most studies do not comprehensively report data processing steps. Of the studies that did report data processing steps, few analyzed the impact of decisions made during data processing on algorithm performance. Most studies were conducted in a controlled simulator environment and may not generalize to naturalistic settings. The findings show that driver behavior and physiological data show efficacy for detecting fatigue, distraction, stress, and driver errors. The results of these studies may necessitate additional data processing standards and future work should focus on measuring the effects of data decisions on model performance.
Samskaping av tjenester – fire måter kunder skaper verdi på

Å involvere kunder i samskaping (co-creation på engelsk) kan gi store fordeler for bedrifter. Men med ulike definisjoner og begrep på samskaping er det utfordrende for bedrifter å danne seg et helhetlig bilde av samskaping og av hvordan de kan gjøre samskaping til en del av sin forretningsmodell. I denne artikkelen presenterer jeg en typologi som deler samskaping inn i fire grupper, illustrerer med praktiske eksempler, gir konkrete råd om hvordan bedriften kan gå frem for å involvere kunder i de fire formene for samskaping, og drøfter konsekvenser av samskapingen. Typologien er basert på to grunnleggende dimensjoner som går igjen i alle begrep for samskaping: Hvem leder samskapingsprosessen (kundeledet versus bedriftsledet samskaping), og hvem skaper de involverte kundene verdi for (bruksverdi eller bytteverdi fokusert samskaping).
Reef Reservoir Identification by Wavelet Decomposition and Reconstruction: A Case Study from Yuanba Gas Field in China

The organic reef is a special type of carbonate reservoir which always dominates the spatial distribution, reserves and accumulations of natural gas. However, it is difficult to determine the organic reef’s internal structure and gas reservoirs due to numerous adverse factors such as the low resolution of seismic data, depth of burial, strong anisotropy, irregular spatial distribution and complex internal structure. A case study of wavelet decomposition and reconstruction technology applied to elucidate the features of organic reef reservoirs in the Changxing formation from Yuanba gas field shows that the seismic record reconstructed by high frequency signal can adequately describe the internal properties of organic reef reservoirs. Furthermore, the root mean square amplitude ratio of both low and high frequency data obtained from the reconstructed seismic data clearly show spatial distribution of gas and water in reef reservoirs.
Long-Term Assessment of Thin-Strut BioMime Coronary Stent System in Real-World Population at Single-Center: A Retrospective Observational Study

Background The short-term clinical outcomes of first-generation thicker-strut durable polymer-based drug-eluting stents (DES) have been widely examined. However, there is a scarcity on qualitative research on the long-term usage of DES that evaluated the thinner strut biodegradable stents for coronary artery disease. Hence, we sought to investigate the long-term safety and performance of thinner strut biodegradable polymer-based BioMime sirolimus-eluting coronary stent system in real-world patients with symptomatic ischemic heart disease. Methods This was a retrospective, observational, single-center, post-marketing clinical follow-up study. The primary endpoints were the incidence of major adverse cardiac events (MACE), defined as a composite of cardiac death, myocardial infarction (MI) attributed to target vessel revascularization (TVR), and target lesion revascularization (TLR) at 1-, 2-, 3- and 4-year follow-ups. The secondary endpoints were cardiac death, MI, TLR, TVR, device and procedural success rates, and stent thrombosis (ST). Results In all, 1,188 consecutive patients were enrolled, and 1,333 (1,257 de novo and 76 in-stent restenotic lesions) out of 1,565 lesions were treated with the study device. The mean age of patients was 53.26 ± 10.31 years and 86.2% were male. The quantitative coronary angiographic derived mean lesion length and diameter were 29.62 ± 9.62 mm and 3.01 ± 0.29 mm, respectively. The average length and diameter of the study device implanted were 30.89 ± 6.31 mm and 3.17 ± 0.25 mm, respectively. The cumulative incidence of MACE at 1-, 2-, 3-, and 4 years was 0.61%, 1.47%, 2.08%, and 3.40%, respectively, and cumulative deaths due to cardiac causes were 0.61%, 1.13%, 1.22%, and 1.83%, respectively. There were no cases of TLR or TVR at 1-year follow-up. The cumulative rate of TLR at 2-, 3-, and 4 years was 0.35%, 0.87%, and 1.57%, respectively, while that of TVR was 0.61%, 1.47%, and 2.35%, respectively. Three (0.3%) incidences of probable ST occurred during the 6-month follow-up; no new cases were reported further. In subgroup analysis, MACEs were comparable across the long- and short-length stent groups through 4-year follow-up. Conclusions This long-term study demonstrates the safety and performance of the ultra-thin BioMime sirolimus-eluting stent with satisfactory clinical outcomes in patients with symptomatic ischemic heart disease in real-world scenario.
Synthesis and Characterization of PANI/ZnFe2O4 nRs with Different Doping Concentrations for Potential Applications in Various Fields

The wide-ranging potential of polyaniline (PANI) composites in energy storage, electrochemical, sensing, and electromagnetic shielding applications emphasizes researchers to improve its properties. Here, the doping of ZnFe2O4 nRs by 1, 3, and 5 wt. % within polyaniline has been done. Then, we characterize the doped material using techniques such as scanning electron microscopy (SEM), X-ray diffraction (XRD), thermal gravimetric analysis (TGA), and Fourier-transform infrared spectroscopy (FTIR) to verify the successful incorporation of polyaniline onto the nRs. TGA showed that doping of PANI with ZnFe2O4 nRs enhanced the interfacial interactions between the two components. This provided a more stable matrix structure and enhanced the thermal stability of the composite. The transmission of light has been increased by about 18% due to the increase in crystallinity accompanied by ZnFe2O4 doping. As the ZnFe2O4 nRs doping rose, our PANI samples’ optical band gap values slightly decreased by about 10%. In addition, it has been found that the optical characteristics such as refractive index, extension coefficient, surface, and volume energy loss function essentially showed ZnFe2O4 doping dependency. The nonlinear constants of the doped samples have increased due to the new charge carriers and altered the electronic and optical properties of the composite material. Our obtained results show that PANI@ ZnFe2O4 nRs have potential applications such as optical sensors, electrochemical, optoelectronics, and photocatalysis.
Spatial variability of methane, nitrous oxide, and carbon dioxide emissions from drained grasslands.

Emissions of CH4, N2O, and CO2 from soils are the result of a number of biological and physical processes, each influenced by several environmental and management factors exhibiting spatial variability. This study aimed to assess the spatial variability and spatial dependence of CH4, N2O, and CO2 emissions and their underlying soil processes and properties from grasslands on drained peat soil (Terric Histosol). Emissions and possible controlling factors were measured at a field location in Sweden. Measurements were done on two adjacent sites on peat soil on two successive days for each site. Spatial variability was analyzed with trend analysis and variograms. Both sites consumed small amounts of atmospheric CH4, i.e., 0.03 and 0.05 mg CH4 m-2 d-1, and emitted N2O and CO2, i.e., 5 to 19 mg N2O m-2 d-1 and 4 to 6 g CO2 m-62 d-1. Spatial variability of emissions was high, with coefficients of variation of 50 to 1400EEmissions either showed a spatial trend or were spatially dependent with ranges of spatial dependence of 50 to >200 m. However, spatial dependence of emissions showed differences between sites and short-term temporal variability. Variograms of emissions and soil processes, which are partly biological in nature and have a high degree of inherent variability, should be interpreted with care. Emissions of CH4, N2O, and CO2 from soils are the result of a number of biological and physical processes, each influenced by several environmental and management factors exhibiting spatial variability. This study aimed to assess the spatial variability and spatial dependence of CH4, N2O, and CO2 emissions and their underlying soil processes and properties from grasslands on drained peat soil (Terric Histosol). Emissions and possible controlling factors were measured at a field location in Sweden. Measurements were done on two adjacent sites on peat soil on two successive days for each site. Spatial variability was analyzed with trend analysis and variograms. Both sites consumed small amounts of atmospheric CH4, i.e., 0.03 and 0.05 mg CH4 m-2 d-1, and emitted N2O and CO2, i.e., 5 to 19 mg N2O m-2 d-1 and 4 to 6 g CO2 m-2 d-1. Spatial variability of emissions was high, with coefficients of variation of 50 to 1400EEmissions either showed a spatial trend or were spatially dependent with ranges of spatial dependence of 50 to >200 m. However, spatial dependence of emissions showed differences between sites and short-term temporal variability. Variograms of emissions and soil processes, which are partly biological in nature and have a high degree of inherent variability, should be interpreted with care.
Síntese e customização de terrenos a partir de modelos digitais de elevação

This paper presents an approach for terrain synthesis from digital elevation models (DEM). The development in terrain visualization techniques, supported by rapid growth of computational power, has increased the demand for terrain models with greater realism. Computer games, flight simulators, special effects for the film industry are examples of applications that make use of three-dimensional models generated from the terrain synthesis. However, the difficulty in determining the position of differentiated entities in the terrain such as hills, valleys and rivers, and the difficulty in generating synthetic terrain inspired in known regions, are some of the imposed limitations by the methods used in existing commercial applications. Using texture synthesis techniques, the proposed solution creates a new DEM from a specification of the main features provided by user. Therefore, visually pleasing terrain can be created from letters, words or other symbols expressed as user sketch.
Using Proteomics Data to Identify Personalized Treatments in Multiple Myeloma: A Machine Learning Approach

This paper describes a machine learning (ML) decision support system to provide a list of chemotherapeutics that individual multiple myeloma (MM) patients are sensitive/resistant to, based on their proteomic profile. The methodology used in this study involved understanding the parameter space and selecting the dominant features (proteomics data), identifying patterns of proteomic profiles and their association to the recommended treatments, and defining the decision support system of personalized treatment as a classification problem. During the data analysis, we compared several ML algorithms, such as linear regression, Random Forest, and support vector machines, to classify patients as sensitive/resistant to therapeutics. A further analysis examined data-balancing techniques that emerged due to the small cohort size. The results suggest that utilizing proteomics data is a promising approach for identifying effective treatment options for patients with MM (reaching on average an accuracy of 81%). Although this pilot study was limited by the small patient cohort (39 patients), which restricted the training and validation of the explored ML solutions to identify complex associations between proteins, it holds great promise for developing personalized anti-MM treatments using ML approaches.
Modality Attention for Prediction-Based Robot Motion Generation: Improving Interpretability and Robustness of Using Multi-Modality

We developed a modality attention motion generation model on the basis of multi-modality prediction. This model provides interpretability about modality usage and demonstrates robustness against disturbances. We used a hierarchical model consisting of low-level recurrent neural networks (RNNs) for processing each modality individually and a high-level RNN that integrates the multi-modality. This integration is achieved by efficiently gating multi-modality and inputting it to the high-level RNN. We verified the interpretability and robustness of the task of inserting a furniture part, which consists of the “approach” phase to bring the wooden dowel closer to the hole and the “insertion” phase. While the proposed model achieves the same task success rate as the conventional model, it clarifies that it refers to vision during “approach” and force during “insertion,” providing interpretability regarding modality use. Furthermore, in contrast to the non-modality attention model, whose task success rate drops significantly under disturbance, the proposed model enhances robustness against disturbances to modalities it does not direct attention during the task, resulting in a consistently high success rate ($\simeq\! 90\%$).
Cost-effective conservation programs: The role of economics

As global demand for food and fuel grows, the potential tradeoff between agricultural production and environmental quality has renewed urgency. While grain and soybean prices have retreated from previous peaks, demand for food and fuel remains strong and prices continue at levels well above their 2004 to 2006 averages. These market signals are encouraging the use of additional land, water, and other inputs in crop production. If pursued without caution, however, increasingly intensive agricultural production can damage land, deplete water resources, and degrade the environment. Environmental protection often requires government intervention. Agricultural producers generally do not receive a financial benefit from using production methods that could protect or improve the environment downstream or downwind. Unless these methods are also the most profitable way to farm, producers have no tangible incentive to adopt them. Because nonpoint source pollution processes such as nutrient runoff cannot be directly observed, farmers and ranchers may not even realize the environmental effect of their actions. Federal agricultural conservation policy seeks to provide incentives for environmental protection where markets have failed to do so. Agricultural conservation policy is implemented largely through voluntary programs that offer economic incentives for the adoption and use of environmentally sound practices. Between…
Design and Development of an Augmented Reality Interface for Space Exploration

The Holographic Augmented Reality Visualization Interface for Exploration (HARVIE) was developed for the 2022 NASA SUITS (Spacesuit User Interface for Students) challenge. HARVIE assists astronauts with elevated demands of the lunar surface through navigation, terrain sensing, and an optimal display of suit status elements (e.g., oxygen, battery, and heart rate). Considering environmental constraints, the system architecture promotes efficient cross modal communication between the mission control center, other astronauts, and the user interface. Currently, the system utilizes a hands-free modality such as speech recognition. Throughout the design process, we conducted heuristic evaluations on a low-fidelity prototype. Then, we implemented HARVIE into a high-fidelity prototype on the HoloLens 2 and utilized the Rapid Iterative Testing & Evaluation (RITE) method for human-in-the-loop testing. Lastly, we evaluated our final design at NASA Johnson Space Center. Our interface serves as a novel approach to enhance how astronauts navigate on missions using augmented reality.