Miracle-dz/UniFAR-FTUs huggingface dataset:
Development of a spherical motor with a 3-DOF sensing system

The thesis proposes a new design of a spherical wheel motor (SWM) with three layers of permanent magnets (PMs) located both inside and outside of double layers of electrical magnets (EMs), so as to fully utilize the magnetic field generated by the EMs and enhance the inclination torque density. The inclination torque is generally weak due to the limited number and space for the PMs and EMs involved in inclination compared to spinning. Finite element modeling (FEM) has been utilized for simulation, and important design parameters are optimized to maximize the inclination torque. The FEM modeling has been verified by experiments with discrepancy of less than 10% achieved. The optimized design can generate strong inclination torque which can support heavy loadings. Iron stator cores are applied in the new design to improve magnetic torque, and the magnetic field distribution (MFD) and magnetic torque become nonlinear due to magnetic saturation characteristics of the iron cores. Based on the analysis of the MFD of the whole SWM applying different current inputs, the thesis has constructed a dynamic model for SWMs with current inputs within the working range. A multi-DOF non-contact sensing system based on magnetic sensors and neural networks (NNs) is proposed. NNs are applied to approximate the function between orientations and MFD. The proposed sensing system is simulated and verified by experimental investigations. The sensing error to working range ratio is about 1.4%, which verifies its feasibility. With the proposed SWM design with enhanced torque capability, dynamic model and sensing system, the present findings provide strong basis to realize an integrated system of an SWM and take it a step closer to industrial applications.
Exploration of Black Boxes of Supervised Machine Learning Models: A Demonstration on Development of Predictive Heart Risk Score

Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated.
Truck and Unmanned Vehicle Routing Problem with Time Windows: A Satellite Synchronization Perspective

We consider an important feature of satellite synchronization in the practical scenario of using unmanned vehicles (UVs) carried by trucks for “last-meter” delivery and introduce the truck and UV routing problem with time windows (TUVRP-TW) for optimizing the routes of a homogeneous fleet of truck-UV combinations. A UV that has been dispatched from its truck must be picked up by the same truck or must return by itself to the depot. Customers with time windows are classified into two types: truck-UV customers (TUCs) and UV customers (UCs). The TUCs where trucks dispatch or pick up the carried UVs are regarded as satellites. Fleet coordination and satellite synchronization are essential for modelling the TUVRP-TW. We classify satellite synchronization into inner-satellite synchronization and intersatellite synchronization. The inner-satellite synchronization generally considered in the literature focuses on synchronization operations at the same satellite. Intersatellite synchronization, which focuses on synchronization operations at various satellites, allows UVs to not return to the dispatched locations, if necessary. In the mixed-integer linear programming model of the TUVRP-TW, both binary variables for identifying the appointed satellites and continuous variables for time continuity constraints are introduced to ensure the interaction between truck routes and UV routes. A hybrid algorithm based on a greedy randomized adaptive search procedure (GRASP) and a variable neighborhood search (VNS) is provided. Based on generated instances and benchmark instances, computational experiments are conducted to evaluate the performance of the intersatellite synchronization, the performance of the developed formulation, and the applicability of the hybrid algorithm.
Designing of a Novel Neural Network Model for Classification of Music Genre

Music genre classification is an important task that entails classifying music genres based on aural data. Music genre classification is widely used in the field of music information retrieval. Data preparation, feature extraction, and classification are the three primary processes in the proposed system. New neural network is used to classify music genres. To categorize songs into respective music genres, the proposed system leverages feature values from spectrograms created from slices of songs as input to a proposed system architecture. Extensive tests on the GTZAN dataset demonstrate the efficacy of the proposed approach in comparison to existing methods. The proposed system architecture is also tested on Indian rhythms. This paper consists of the comparison of proposed system architecture with existing algorithms.
Information Structuring and Model Reuse for Virtual Prototyping

Virtual Prototyping is a promising approach for testing innovative ideas or assessing man-machine interactions in early stages of machine design. But due to the high cost level of present approaches, virtual prototyping does not exploit its full potential. This paper presents a multi-level approach for systematic reuse of existing simulation models in different business cases. The approach bases on theoretical reflections on conceptual modeling and on a requirements analysis among manufacturers of mobile construction machines. The resulting method is evaluated by a prototypic implementation.
ALAMNI: Adaptive LookAside Memory Based Near-Memory Inference Engine for Eliminating Multiplications in Real-Time

The CNN algorithm seeks high performance and energy efficiency in real-time inference. The costly off-chip memory accesses put additional burdens on CNN's execution. Towards avoiding off-chip accesses, we propose ALAMNI, a novel near-memory architecture that expedites the CNNs in the logic layer of the Hybrid Memory Cube. We exploit intra- and inter-vault parallelism to accelerate the highly parallel CNN operations. The proposed ALAMNI replaces costly multiplications of CNNs with lookaside memory (LAM) based searches. The proposed ALAMNI policy is effective on unseen data as it discards the data pre-profiling overhead by an adaptive LAM update policy. The ALAMNI controller keeps the most frequent triplets of weight (W), activation (A), and multiplication result (M), <inline-formula><tex-math notation="LaTeX">$\langle W, A, M \rangle$</tex-math><alternatives><mml:math><mml:mrow><mml:mo>〈</mml:mo><mml:mi>W</mml:mi><mml:mo>,</mml:mo><mml:mi>A</mml:mi><mml:mo>,</mml:mo><mml:mi>M</mml:mi><mml:mo>〉</mml:mo></mml:mrow></mml:math><inline-graphic xlink:href="das-ieq1-3174591.gif"/></alternatives></inline-formula>, in the LAM to eliminate redundant computations. As an optimization, we incorporate a bitmasking concept to raise the hit rate of LAMs and further amortize computations. We also present a study on the relation between the amount of bitmasking and the loss of classification accuracy of the popular ConvNets. We keep the bitmasking as a reconfigurable feature of the ALAMNI units to achieve desired classification accuracy. Experimental results show substantial improvement in the system's performance and energy efficiency compared to the baseline and state-of-the-art.
Conversational Spaces in Teams: A cross-cultural Study of American and Spanish MBA Teams

In today’s organizational environment, managers and leaders are beginning to find themselves not only working with teams but also that these teams are becoming more cross-cultural, even multicultural – increasing the need for global leaders and managers. In this study, we used the Conversational Space Inventory (CoSI) (Lingham, 2004) that both measures and maps out a team’s context (one involving human interaction and communication) based on their Real experiences and the Ideal experiences they would like to have along four major aspects: Divergent, Convergent, Status, and Recursive Spaces. We also provide a brief overview of the Theory of Conversational Learning (from which the CoSI was developed) and its relation to cross cultural research. The CoSI was administered to MBA students in similar programs in two countries – America (n=176) and Spain (n=152). The mapping from the data shows one distinct feature: Only the teams Real Divergent Spaces were different. Ideal
Comparison of therapeutic effects between patients treated with the facemask and the BAMP method - a meta-analysis

This study compares two methods of treating Class III malocclusions with maxillary hypoplasia: protraction using a facemask and Bollard plates (Bone Anchorage Maxillary Protraction, BAMP). Aim. The aim of the article was to compare clinical effects between patients treated with the facemask and the BAMP method based on the analysis of changes in cephalometric parameters before and after the treatment. Material and methods. A meta-analysis of articles available in electronic databases was performed. Articles that contained values of SNA, SNB and ANB angles and values of the WITS parameter, overjet and overbite from the
Multi‐objective optimization of thermoeconomic and component size of supercritical carbon dioxide recompression cycle based on small‐scale lead‐cooled fast reactor

When the supercritical CO2 power cycle is employed in confined spaces, such as nuclear‐powered ships and spacecraft, its size should be given priority. To estimate the component size, the one‐dimensional heat exchanger model developed in this study is used in a recompression supercritical CO2 cycle integrated on a small‐scale lead‐cooled fast reactor. A parameter analysis was performed to study the influence of several key parameters on the levelized cost of electricity, thermal efficiency, and system size. Moreover, four types of multi‐objective optimizations were conducted to provide optimization schemes for different scenarios. Results indicated that the increased turbine inlet temperature improved the thermoeconomic but augmented the system volume. The size of the low‐temperature recuperator was observably enlarged near the optimal flow split ratio, thereby increasing the system volume. Pareto optimal solution of bi‐objective optimization based on the levelized cost of electricity and system size reached the lowest system volume of 3.71 m3. Additionally, the best trade‐off result of three‐objective optimization was a thermal efficiency of 42.14%, a levelized cost of electricity of 56.30 $∙MWh−1, and a system volume of 4.43 m3. Meanwhile, maximal and minimal pressure drops of CO2 appeared in the cooler and intermediate heat exchanger, respectively.
Effects of Social Media Self-Efficacy on Informational Use, Loneliness, and Self-Esteem of Older Adults

Abstract Social media is convenient for older adults to obtain and share information (i.e., informational use). However, a major barrier to using social media for older adults is their relatively low social media self-efficacy. The effects of this on informational use and mental well-being have not been well studied. Therefore, this study surveyed 276 older Chinese adults aged 60–90 and constructed a structural equation model. We found that higher social media self-efficacy was strongly and directly associated with more informational use, less loneliness, and higher self-esteem. It also positively affected happiness, mediated by loneliness and self-esteem. Informational use decreased loneliness but did not significantly affect self-esteem. We explained these results by the moderation effects of age and social media self-efficacy. This study confirmed the urgency of increasing older adults’ social media self-efficacy for their mental well-being and successful aging. We also outlined design implications for increasing social media self-efficacy.
The first Chinese representative of the Cenozoic hawker dragonfly Oligaeschna (Odonata: Aeshnidae) from the Eocene of North Tibet

ABSTRACT Oligaeschna sinica sp. nov., the oldest recorded and first Chinese representative of the Cenozoic aeshnid genus Oligaeschna, is described from the Bartonian of Tibet. This fossil is also among the oldest known representatives of the family Aeshnidae sensu stricto. Its presence in the Niubao Formation supports a temperate, cool to warm climate for the area during this period. urn:lsid:zoobank.org:pub:9BAD44C0-135D-40AF-B3DD-1B7D695720A8 urn:lsid:zoobank.org:act:6D7E18CF-1A22-4B37-B816-BAD0890C39F9
Design of the Power Online Monitoring System Based on LabVIEW

In this paper, for the production system of Handan steel plant, it designs a power line monitoring system using LabVIEW as a platform, its functions includes: data collection, data storage and analysis, trend analysis, fault diagnosis. In the design, it is full use of the LabVIEW graphical programming quick, convenient advantages, combining DQmax powerful features and DataSocket, to complete remote monitoring and fault diagnosis of production system in steel plant. It is a great significance in steel product that to accelerate the speed of access the production information and improve the utilization rate of energy, and optimize the process of production. Copyright © 2013 IFSA.
Adaptive Power Shifting for Power-Constrained Heterogeneous Systems

The number and heterogeneity of compute devices, even within a single compute node, has been steadily on the rise. Since all systems must operate under a power cap, the number of discrete devices that can run simultaneously at their highest frequency is limited by the globally-imposed power cap. Current systems incorporate a centralized power management unit that statically controls the distribution of power among the devices within the node. However, such static distribution policies are unaware of the dynamic utilization profile across the devices, which leads to power allocations that end up degrading system throughput performance. The problem is particularly acute in the presence of heterogeneity since type-specific performance-boost capabilities cannot be leveraged via utilization-agnostic static power allocations. This paper proposes Adaptive Power Shifting for multi-accelerator heterogeneous systems (APS), a technique that leverages system utilization information to dynamically allocate and re-distribute power budgets across multiple discrete devices. Democratizing the power allocation based on dynamic needs results in dramatic speedup over a need-agnostic static allocation. We use APS in a real OpenPOWER compute node with 2 CPUs and 4 GPUs to demonstrate the value of on-demand, equitable power allocations. Overall, the proposed solution increases performance with respect to two state-of-the-art techniques by up to 14.9% and 13.8%.
The TLR7/8 agonist R848 optimizes host and tumor immunity to improve therapeutic efficacy in murine lung cancer

Treatment with the Toll-like receptor 7 (TLR7) agonist, resiquimod (R848), is effective in various types of cancer, such as breast, pancreatic and colorectal cancer. The reported antitumor effect of R848 in lung cancer is considered to be achieved by targeting macrophages. In the present study, it was demonstrated that TLR7 expression on various immune cell types initially rises, then declines in the late stage of lung cancer. Intraperitoneal injection of R848 resulted in a reduction in tumor burden and prolonged survival in both subcutaneous and metastatic lung cancer models in C57BL/6 mice. Initial treatment with R848 at an early stage was found to be the optimal choice. Systemic injection of R848 promoted the activation of innate and adaptive immune responses. Systemic administration of R848 upregulated TLR7 expression in dendritic cells (DCs) and enhanced the activation of DCs and natural killer (NK) cells. Moreover, this treatment also resulted in increased production of T helper cell-associated cytokines in serum, including IFN-γ, TNF-α and IL-2. In addition, continuous treatment with R848 increased the proportion of DCs, NK and CD8+ T cells, and reduced that of Foxp3+ regulatory T cells in the tumor microenvironment. These findings supported the use of R848 treatment for lung cancer via TLR7 targeting and provided insight into the underlying therapeutic mechanism.
Framework for Live Digital Forensics Using Data Mining. Engineering

With the rapid advancements in information and communication technology in the world, crimes committed are becoming technically intensive. When crimes committed use digital devices, forensic examiners have to adopt practical frameworks and methods to recover data for analysis which can pose as evidence. This concept explains emerging cyber crimes, forensic analysis steps in the storage media, hidden data analysis in the file system, network forensic methods, Memory Forensic Modules and cyber crime data mining. This paper introduces the K-Means and apriori algorithm for finding the cyber attack and the counting of the attacks during the system working time. For this purpose, system uses the tools of Win cap, jpcap and wmic. This tool combines the technique of digital forensic investigation and crime data mining. Thus this tool provides the defence and reduces the vulnerability.
Investigation of effects of negotiations of sociomathematical norms on mathematical process skills

Abstract This study aimed to contribute to the literature by focusing on the effects of the negotiations of sociomathematical norms perceived by students on mathematical process skills. Problem-based mathematical activities were implemented in the seventh grade from a public secondary school in Turkey over a ten-week period in the second semester. The data obtained from students’ worksheets, video recordings, and interviews were analyzed descriptively and were presented depending on their nature. The results indicated that the negotiations of sociomathematical norms perceived by students contribute to the development of some mathematical process skills (communication skills, reasoning skills, verification skills, and creativity skills) while limiting the development of others (connection skills). Therefore, it can be said that it is important to design and implement mathematics learning and teaching taking into account the effects of sociomathematical norms perceived by students on mathematical process skills.
Closed-loop planning and control of steerable medical needles

Steerable needles have the potential to increase the effectiveness of needle-based clinical procedures such as biopsy, drug delivery, and radioactive seed implantation for cancer treatment. These needles can trace curved paths when inserted into tissue, thereby increasing maneuverability and targeting accuracy while reaching previously inaccessible targets that are behind sensitive or impenetrable anatomical regions. Guiding these flexible needles along an intended path requires continuously inserting and twisting the needle at its base, which is not intuitive for a human operator. In addition, the needle often deviates from its intended trajectory due to factors such as tissue deformation, needle-tissue interaction, noisy actuation and sensing, modeling errors, and involuntary patient motions. These challenges can be addressed with the assistance of robotic systems that automatically compensate for these perturbations by performing motion planning and feedback control of the needle in a closed-loop fashion under sensory feedback. We present two approaches for efficient closed-loop guidance of steerable needles to targets within clinically acceptable accuracy while safely avoiding sensitive or impenetrable anatomical structures. The first approach uses a fast motion planning algorithm that unifies planning and control by continuously replanning, enabling correction for perturbations as they occur. We evaluate our method using a needle steering system in phantom and ex vivo animal tissues. The second approach integrates motion planning and feedback control of steerable needles in highly deformable environments. We demonstrate that this approach significantly improves the probability of success compared to prior approaches that either consider uncertainty or deformations but not both simultaneously. We also propose a data-driven method to estimate parameters of stochastic models of steerable needle motion. These models can be used to create realistic medical simulators for clinicians wanting to train for steerable needle procedures and to improve the effectiveness of existing planning and control methods. This dissertation advances the state of the art in planning and control of steerable needles and is an important step towards realizing needle steering in clinical practice. The methods developed in this dissertation also generalize to important applications beyond medical needle steering, such as manipulating deformable objects and control of mobile robots.
SEMI-AUTOMATED SEARCH FOR MAXIMUM Keff OF MULTI-CONSTITUENT SYSTEMS

New maximum keff search options have been developed for SMORES – a pr ototypic analysis sequence recently developed for incorporation in the SCALE-5 code package. SMORES provides for an intelligent, semi-automatic search for the maximum keff of a given amount of specified fissile materials in combination with specified moderating/reflecting materials. SMORES will also search for the minimum critical mass. The new options developed are set to handle multi fuel and multi moderator/reflector constituents. The newly developed algorithms are illustrated for three types of problems, all involving search for the maximum possible keff. In two of the problems the mass of each of the two fissile materials is fixed. In another problem the total volume fraction of the two fissile materials is fixed. SMORES can be useful for determining margins of subcriticalit y and for other criticality safety applications.
Does road investment produce accessibility gains at a constant rate

This note comments on the general practise to use public road capital as a proxy for the service of the road system, as also employed in Shirley and Winston (2004). It shows that the required assumptions are violated in the Swiss case and in all likelihood in most other spatial contexts as well. Die Literatur zur Schatzung der Produktivitatswirkung des Strassennetz verwendet den Kapitalstock im Strassennetz als Mass fur die Dienstleistungen, die das Strassennetz produziert. Diese Notiz zeigt mit Beispielen aus der Schweiz, dass diese Verwendung bei der Betrachtung langerer Zeitraume und grosser Raumeinheiten nicht korrekt ist.