Miracle-dz/UniFAR-FTUs huggingface dataset:
Multilevel control of an anthropomorphic prosthetic hand for grasp and slip prevention

The success of grasping and manipulation tasks of commercial prosthetic hands is mainly related to amputee visual feedback since they are not provided either with tactile sensors or with sophisticated control. As a consequence, slippage and object falls often occur. This article wants to address the specific issue of enhancing grasping and manipulation capabilities of existing prosthetic hands, by changing the control strategy. For this purpose, it proposes a multilevel control based on two distinct levels consisting of (1) a policy search learning algorithm combined with central pattern generators in the higher level and (2) a parallel force/position control managing slippage events in the lower level. The control has been tested on an anthropomorphic robotic hand with prosthetic features (the IH2 hand) equipped with force sensors. Bi-digital and tri-digital grasping tasks with and without slip information have been carried out. The KUKA-LWR has been employed to perturb the grasp stability inducing controlled slip events. The acquired data demonstrate that the proposed control has the potential to adapt to changes in the environment and guarantees grasp stability, by avoiding object fall thanks to prompt slippage event detection.
Residents’ image perception of urban greenways based on improved pixel method

The main body of a city is the urban residents. In the process of rapid development of a city, paying attention to urban residents and meeting their demands for higher quality urban life is the key to the healthy development of it. This study adopted the improved pixel method to evaluate residents' image perception of greenways. Through the analysis of the three types of greenway photos, the natural greenway photo scene is dominated by green plants, while the photo scenes of built greenways includes buildings, roads, service facilities and other scenes. In general, the improved pixel method can quantitatively analyze residents’ subjective perceptions in a relatively simple way.
Diagnosis and Management of Atypical Chronic Myeloid Leukemia with a t(2;13)(q33;q12) Translocation

Atypical chronic myeloid leukemia (aCML) is a rare myeloproliferative disorder that shares clinical features with chronic myeloid leukemia but lacks the classic t(9;22) BCR-ABL1 translocation and features prominent dysgranulopoiesis and granulocytic dysplasia. Challenges of this diagnosis include clinical and biologic heterogeneity, the high risk of transformation to acute myeloid leukemia, and the lack of standard treatment options. Allogeneic hematopoietic stem cell transplant is likely the preferred treatment, but this can be limited by patient psychosocial support, age, concomitant medical conditions, and availability of an appropriate donor. We report the case of a 61-year-old male with no significant past medical history diagnosed with aCML with a rare t(2;13)(q33;q12). He presented with weight loss, night sweats, splenomegaly, hyperleukocytosis, a leukoerythroblastic differential with a predominant neutrophilia, anemia, and thrombocytopenia. Subsequent peripheral blood and bone marrow studies lead to the diagnosis of aCML. He was recommended to undergo an allogeneic stem cell transplant evaluation and declined. He was initially treated with hydroxyurea and imatinib to which he responded for approximately three years. After clinical progression, he was treated with sorafenib, a multiprotein kinase inhibitor more commonly used in the treatment of hepatocellular and renal cell carcinoma due to its off target FLT3 inhibition. The patient achieved complete hematologic response which has been sustained for 7 years with tolerable side effects.
Perspective The ethics of rodent control

Because western societies generally see animals as objects of moral concern, demands have been made on the way they are treated, e.g. during animal experimentation. In the case of rodent pests, however, inhumane control methods are often applied. This inconsistency in the human-animal relationship requires clarification. This paper analyses the criteria that must be met when judging the use of animals during experiments, and investigates whether these can be applied in rodent control. This is important, because, until now, animal welfare has been less of an issue in pest control: effectiveness, hygiene and cost efficiency have been leading principles. Two options are available to solve the inconsistency: the first is to abandon the criteria used in animal experimentation; the second is to apply these criteria to both animal experimentation and rodent control. This latter option implies that rodent control methods should not lead to intense pain or discomfort, and any discomfort should have a short duration and should allow escaped rodents to lead a natural life. Adherence to this option will, however, require a shift in the design of rodent control methods: effectiveness will no longer be the leading principle. It will have to share its position with animal welfare and humaneness.  2008 Society of Chemical Industry
Extraction, Characterization, and Comparison of Properties of Cassava Bagasse and Black Seed Fibers

ABSTRACT The current study presents a nontoxic, and low-cost preparation of black seeds and cassava bagasse fibers. It also aims at investigating the physical, thermal, and morphological characteristics and chemical composition of these fibers. The current study’s findings revealed significant differences in the properties in terms of chemical, morphological, physical, and thermal. The findings also showed that black seed fiber had a higher ash concentration (4.5%) compared to the cassava bagasse fiber of 0.4%. A density of 1.45 g/cm3 was observed for cassava bagasse fiber, while the black seed fiber exhibited a lower density of 1.22 g/cm3. From TGA results, only 21% of cassava bagasse and 38% of black seed fiber weight were observed for the highest degradation rates for both samples (200°C–500°C). In summary, this study suggests cassava bagasse and black seed fibers as promising feedstocks for biopolymers synthesis with an eco-friendly and cost-effective approach that may support the advancement of the biopolymer industry.
The Relationship Between Age And Intelligence Of Pregnant Mothers

Background: Pregnancy is a natural condition that is expected by a woman. During pregnancy, a woman may experience psychological changes, such as anxiety, that can affect the condition of the fetus she is carrying. Pregnant women in Results: The results obtained p value = 0.025, because p <0.05, it meant that Ho was rejected, therefore there was a correlation between age and anxiety of pregnant woman. Conclusion: There is a correlation between age and anxiety in pregnant women, so that the age of pregnant women can affect the occurrence of anxiety experienced by pregnant women. Suggestion Pregnant women are expected to be able to manage their emotions, so that when pregnant they do not experience anxiety, but feel safe and comfortable. For further research, research will be conducted on screening the psychological state of pregnant women.
Separation of underwater acoustic signals based on C-RNN network

The sound signal can be transmitted over a long distance in the water environment, but there is often interference from other signal sources in the real environment, which will seriously reduce the sensitivity and recognizability of the underwater acoustic signal. At this time, it is necessary to use underwater acoustic signal separation technology to separate mixed underwater acoustic signals. Due to the time sequence of the audio signal, the feature extraction ability of the separation model for the input sound signal largely determines the performance of the model. We propose a C-RNN network model that combines convolutional and recurrent neural network to achieve the improvement of separation performance. The advantages and disadvantages of separation based on time domain and frequency domain are compared, and a hybrid coding module is proposed to achieve a new state-of-the-art for underwater acoustic signal separation.
Dissection of the Genetic Basis of Yield Traits in Line per se and Testcross Populations and Identification of Candidate Genes for Hybrid Performance in Maize

Dissecting the genetic basis of yield traits in hybrid populations and identifying the candidate genes are important for molecular crop breeding. In this study, a BC1F3:4 population, the line per se (LPS) population, was constructed by using elite inbred lines Zheng58 and PH4CV as the parental lines. The population was genotyped with 55,000 SNPs and testcrossed to Chang7-2 and PH6WC (two testers) to construct two testcross (TC) populations. The three populations were evaluated for hundred kernel weight (HKW) and yield per plant (YPP) in multiple environments. Marker–trait association analysis (MTA) identified 24 to 151 significant SNPs in the three populations. Comparison of the significant SNPs identified common and specific quantitative trait locus/loci (QTL) in the LPS and TC populations. Genetic feature analysis of these significant SNPs proved that these SNPs were associated with the tested traits and could be used to predict trait performance of both LPS and TC populations. RNA-seq analysis was performed using maize hybrid varieties and their parental lines, and differentially expressed genes (DEGs) between hybrid varieties and parental lines were identified. Comparison of the chromosome positions of DEGs with those of significant SNPs detected in the TC population identified potential candidate genes that might be related to hybrid performance. Combining RNA-seq analysis and MTA results identified candidate genes for hybrid performance, providing information that could be useful for maize hybrid breeding.
Deep Learning-Based Long-Horizon MPC: Robust, High Performing, and Computationally Efficient Control for PMSM Drives

This article presents a computationally efficient and high performing approximate long-horizon model predictive control (MPC) for permanent magnet synchronous motors (PMSMs). Two continuous control set MPC (CCS-MPC) formulations are considered: the classical current tracking delta MPC (Del-MPC) and the torque tracking economic MPC (EMPC). To achieve offset-free torque tracking under model uncertainties and in all regions of operation, a disturbance observer and a dq-current reference generator are used. To enable real-time implementation of the long-horizon CCS-MPC, the development of a real-time capable solver is not required, since MPC approximation based on deep neural networks (DNNs) is considered and utilized for controller’s evaluation at run time. The approximation is done by training the DNN to learn the MPC functionality based on offline-generated training data and in an open-loop manner. The robust and offset-free tracking performance of the proposed DNN-based approximate long-horizon Del-MPC and EMPC has been validated through simulation and real-time implementation at test bench and compared to the state-of-the-art field oriented control (FOC) using internal model controller with field-weakening (FW) part and to the exact short-horizon MPC based on the fast gradient method (FGM-MPC). Results show that the long-horizon MPC can achieve significantly faster torque transient responses in comparison with the short-horizon FGM-MPC and the conventional FOC, especially in FW region.
AC 2011-467: ASSESSMENT OF FIRST YEAR EXPERIENCES AT SJSU

San Jose State University (SJSU), located in San Jose, California, is the oldest campus in the California State University (CSU) system. SJSU produces an exceptionally rewarding learning environment for students, especially for those learning to become engineers. With nearly 5,000 students, the Charles W. Davidson College of Engineering (CoE) is the largest provider of engineers to Silicon Valley companies and is ranked 12th among engineering programs for master’s-level institutions in 2009 U.S. News & World Report's listing of "America’s Best Colleges. " The campus is classified as a Carnegie “Community Engagement campus. ” To increase student retention in engineering programs, CoE offers several first-year experience (FYE) programs including residential and non-residential student learning communities, advising and support centers, and introductory engineering courses. These programs strive to increase student retention rates through three strategies: immersion, community, and hands-on learning. This paper will discuss the College’s efforts to increase retention and graduation rates of its incoming freshmen students.
A positioning algorithm for fusion of image point and line features

In order to solve the problem that the ORB-SLAM2 algorithm will cause positioning failure in a scene with a relatively single texture, the line features in the environment are extracted and matched. The point and line feature constraints are used to ensure the positioning accuracy. Firstly, the acquired image was processed by gradient density filtering. Then the feature points in the image were extracted by the ORB algorithm, the linear features in the image were extracted by the LSD algorithm and the linear features in the image were described by the LBD algorithm. In the above process, a certain strategy was used to select the keyframes. Next the point and line reprojection error term was constructed and the weighting strategy was used to weight the reprojection error. Finally, the nonlinear optimization method was used to obtain accurate pose information. The experimental results on the EuRoC dataset prove that the accuracy of the positioning algorithm fusing point and line features is higher than that of the positioning algorithm using only point features.
High-Sensitivity Pressure Sensors Based on a Low Elastic Modulus Adhesive

With the rapid development of intelligent applications, the demand for high-sensitivity pressure sensor is increasing. However, the simple and efficient preparation of an industrial high-sensitivity sensor is still a challenge. In this study, adhesives with different elastic moduli are used to bond pressure-sensitive elements of double-sided sensitive grids to prepare a highly sensitive and fatigue-resistant pressure sensor. It was observed that the low elastic modulus adhesive effectively produced tensile and compressive strains on both sides of the sensitive grids to induce greater strain transfer efficiency in the pressure sensor, thus improving its sensitivity. The sensitivity of the sensor was simulated by finite element analysis to verify that the low elastic modulus adhesive could enhance the sensitivity of the sensor up to 12%. The preparation of high-precision and fatigue-resistant pressure sensors based on low elastic modulus, double-sided sensitive grids makes their application more flexible and convenient, which is urgently needed in the miniaturization and integration electronics field.
PREDICTION OF TYPE 2 DIABETES MELLITUS USING FEATURE SELECTION-BASED MACHINE LEARNING ALGORITHMS

The aim of this study is to develop and evaluate a machine learning model for the early diagnosis of type 2 diabetes to allow for treatments to be applied in the early stages of the disease. Material and methods. A proposed hybrid machine learning model was developed and applied to the Early-stage diabetes risk prediction dataset from the UCI database. The prediction success of the proposed model was compared with other machine learning models. Pearson’s correlation and SelectKBest feature selection methods were employed to examine the relationships between the dataset input parameters and the results. Results. Of the 520 patients included in the dataset, 320 were diagnosed with diabetes and 328 (63.08%) were males. The most commonly observed diabetes diagnosis criterion was obesity (n=482, 83.08%). While the strongest feature detected with Pearson’s correlation was polyuria, the strongest feature detected with SelectKBest was polydipsia. With Pearson’s feature extraction, the most successful machine learning method was the proposed hybrid method, with an accuracy of 97.28%. Using SelectKBest feature selection, the same model was able to predict type 2 diabetes with accuracy of 95.16%. Conclusions. Early detection of type 2 diabetes will allow for a prompter and more effective treatment of the patient. Thus, use of the proposed model may help to improve the quality of patient care and lower the number of deaths caused by this disease.
Layered Media Parameter Inversion Based on Common Middle Point Model and Pattern Search Method

Ground-penetrating radar (GPR) is an effective detection tool for multilayered structures such as road detection and underground exploration. However, traditional inversion algorithm in GPR cannot achieve layered media parameter inversion accurately due to neglect of multiple reflections and complicated refraction effect. To address this issue, an inversion algorithm for layered media parameters based on common middle point (CMP) model and pattern search (PS) method is proposed in this letter. The proposed method combines the wave propagation equation and Snell’s law to build the cost functions in CMP model, and takes the layered media parameters as the optimization variables. Both numerical and experiment results indicate that inversion efficiency and accuracy can be improved. The novel inversion algorithm saves at least a quarter of time to control relative errors within 1% and 6% in simulation and laboratory experiment, respectively. The field test in asphalt highway shows its potential benefits in the field of multilayered structure.
Nitrogen-Doped Zinc Oxide for Photo-Driven Molecular Hydrogen Production

Due to its thermal stability, conductivity, high exciton binding energy and high electron mobility, zinc oxide is one of the most studied semiconductors in the field of photocatalysis. However, the wide bandgap requires the use of UV photons to harness its potential. A convenient way to appease such a limitation is the doping of the lattice with foreign atoms which, in turn, introduce localized states (defects) within the bandgap. Such localized states make the material optically active in the visible range and reduce the energy required to initiate photo-driven charge separation events. In this work, we employed a green synthetic procedure to achieve a high level of doping and have demonstrated how the thermal treatment during synthesis is crucial to select specific the microscopic (molecular) nature of the defect and, ultimately, the type of chemistry (reduction versus oxidation) that the material is able to perform. We found that low-temperature treatments produce material with higher efficiency in the water photosplitting reaction. This constitutes a further step in the establishment of N-doped ZnO as a photocatalyst for artificial photosynthesis.
Near-optimal scheduling and decision-making models for reactive and proactive fault tolerance mechanisms

As High Performance Computing (HPC) systems increase in size to fulfill computational power demand, the chance of failure occurrences dramatically increases, resulting in potentially large amounts of lost computing time. Fault Tolerance (FT) mechanisms aim to mitigate the impact of failure occurrences to the running applications. However, the overhead of FT mechanisms increases proportionally to the HPC systems' size. Therefore, challenges arise in handling the expensive overhead of FT mechanisms while minimizing the large amount of lost computing time due to failure occurrences. In this dissertation, a near-optimal scheduling model is built to determine when to invoke a hybrid checkpoint mechanism, by means of stochastic processes and calculus of variations. The obtained schedule minimizes the waste time caused by checkpoint mechanism and failure occurrences. Generally, the checkpoint/restart mechanisms periodically save application states and load the saved state, upon failure occurrences. Furthermore, to handle various FT mechanisms, an adaptive decision-making model has been developed to determine the best FT strategy to invoke at each decision point. The best mechanism at each decision point is selected among considered FT mechanisms to globally minimize the total waste time for an application execution by means of a dynamic programming approach. In addition, the model is adaptive to deal with changes in failure rate over time.
Heuristics for scalable compound exposure analysis: a foundation for a comprehensive security risk assessment

Network and data protection requires continuous improvements in response to changing technologies and threats. Security risk assessments embody quantitative and qualitative processes that systematically review risks, threats, and concerns and evaluate countermeasures commensurate with risk. Information assurance hinges on an administrator's knowledge and understanding of threats posed to their network. This knowledge must account for the consequences and implications of threat and vulnerability interactions that yield compound, coordinated threats. Attack graphs provide an automated means of realizing these compound threats. However, techniques employed to construct attack graphs are plagued with a combinatorial search space, making their generation impractical. This research presents a framework for modeling complex networks that reduces the complexity associated with attack graph construction. The framework provides a comprehensive, extensible, and scalable solution for organizing security relevant information about enterprise network capabilities and establishes a basis for improved network analysis.
PURDUE IMPROVED COWPEA STORAGE (PICS) SUPPLY CHAIN STUDY

The Purdue Improved Cowpea Storage (PICS) project was launched in 2007 with a grant from the Bill and Melinda Gates Foundation. The project was designed to help farmers access low-cost and chemical-free cowpea storage technology designed to store their cowpeas at harvest affordably, with minimal loss, in order to take advantage of seasonal price variability. One of the specific objectives was to develop supply chains for triple-layer plastic bags to make the technology available to farmers and provide opportunities to local businesses. To achieve the supply chain objective, the project has been pioneering investments in the development of factory-to-farm distribution systems across West and Central Africa. While notable progress has been made, project interventions have evolved over time, and experiences across the PICS countries have been uneven. This study provides an overview of the supply chain experience by country and some of the key lessons learned including: 1) PICS bags are well adapted to the storage needs of smallholder farmers, but not as well accepted by large scale grain traders; 2) the PICS incremental rollout strategy over five years allowed for learning in the early years to be incorporated in subsequent countries; 3) risk sharing strategies are needed for manufacturers and distributors of PICS bags; 4) agro-dealers are some of the best PICS vendors, but cell phone vendors and other entrepreneurs can be effective PICS retailers; 5) public sector institutions like national extension services are not well suited to sales of PICS bags, but individual public sector employees can be good vendors as a supplementary activity; 6) credit is not a key constraint for most PICS vendors, but low profitability and risk are; 7) first year reference price programs have substantial negative effects on market development in subsequent years; 8) crop forecasts are key to timely availability of PICS bags; 9) trademarking the PICS logo was a useful step, but it is not a substitute for patent protection; and 10) PICS project business consultants played a key role in market development which should be gradually shifted to the private sector.