Miracle-dz/UniFAR-FTUs huggingface dataset:
Koopman Operator–Based Knowledge-Guided Reinforcement Learning for Safe Human–Robot Interaction

We developed a novel framework for deep reinforcement learning (DRL) algorithms in task constrained path generation problems of robotic manipulators leveraging human demonstrated trajectories. The main contribution of this article is to design a reward function that can be used with generic reinforcement learning algorithms by utilizing the Koopman operator theory to build a human intent model from the human demonstrated trajectories. In order to ensure that the developed reward function produces the correct reward, the demonstrated trajectories are further used to create a trust domain within which the Koopman operator–based human intent prediction is considered. Otherwise, the proposed algorithm asks for human feedback to receive rewards. The designed reward function is incorporated inside the deep Q-learning (DQN) framework, which results in a modified DQN algorithm. The effectiveness of the proposed learning algorithm is demonstrated using a simulated robotic arm to learn the paths for constrained end-effector motion and considering the safety of the human in the surroundings of the robot.
Subthreshold SRAM Design for Energy Efficient Applications in Nanometric CMOS Technologies

Embedded SRAM circuits are vital components in a modern system on chip (SOC) that can occupy up to 90% of the total area. Therefore, SRAM circuits heavily affect SOC performance, reliability, and yield. In addition, most of the SRAM bitcells are in standby mode and significantly contribute to the total leakage current and leakage power consumption. The aggressive demand in portable devices and billions of connected sensor networks requires long battery life. Therefore, careful design of SRAM circuits with minimal power consumption is in high demand. Reducing the power consumption is mainly achieved by reducing the power supply voltage in the idle mode. However, simply reducing the supply voltage imposes practical limitations on SRAM circuits such as reduced static noise margin, poor write margin, reduced number of cells per bitline, and reduced bitline sensing margin that might cause read/write failures. In addition, the SRAM bitcell has contradictory requirements for read stability and writability. Improving the read stability can cause difficulties in a write operation or vice versa. In this thesis, various techniques for designing subthreshold energy-efficient SRAM circuits are proposed. The proposed techniques include improvement in read margin and write margin, speed improvement, energy consumption reduction, new bitcell architecture and utilizing programmable wordline boosting. A programmable wordline boosting technique is exploited on a conventional 6T SRAM bitcell to improve the operational speed. In addition, wordline boosting can reduce the supply voltage while maintaining the operational frequency. The reduction of the supply voltage allows the memory macro to operate with reduced power consumption. To verify the design, a 16-kb SRAM was fabricated using the TSMC 65 nm CMOS technology. Measurement results show that the maximum operational frequency increases up to 33.3% when wordline boosting is applied. Besides, the supply voltage can be reduced while maintaining the same frequency. This allows reducing the energy consumption to be reduced by 22.2%. The minimum energy consumption achieved is 0.536 fJ/b at 400 mV. Moreover, to improve the read margin, a 6T bitcell SRAM with a PMOS access transistor is proposed. Utilizing a PMOS access transistor results in lower zero level degradation, and hence higher read stability. In addition, the access transistor connected to the internal node holding VDD acts as a stabilizer and counterbalances the effect of zero level degradation. In order to improve the writability, wordline boosting is exploited. Wordline boosting also helps to compensate for the lower speed of the PMOS access transistor compared to a NMOS transistor. To verify our design, a 2kb SRAM is fabricated in the TSMC 65 nm CMOS technology. Measurement results show that the maximum operating frequency of the test chip is at 3.34 MHz at 290 mV. The minimum energy consumption is measured as 1.1 fJ/b at 400 mV.
Whole lesion histogram analysis of apparent diffusion coefficient predicts therapy response in locally advanced rectal cancer

BACKGROUND Whole-tumor apparent diffusion coefficient (ADC) histogram analysis is relevant to predicting the neoadjuvant chemoradiation therapy (nCRT) response in patients with locally advanced rectal cancer (LARC). AIM To evaluate the performance of ADC histogram-derived parameters for predicting the outcomes of patients with LARC. METHODS This is a single-center, retrospective study, which included 48 patients with LARC. All patients underwent a pre-treatment magnetic resonance imaging (MRI) scan for primary tumor staging and a second restaging MRI for response evaluation. The sample was distributed as follows: 18 responder patients (R) and 30 non-responders (non-R). Eight parameters derived from the whole-lesion histogram analysis (ADCmean, skewness, kurtosis, and ADC10th, 25th, 50th, 75th, 90th percentiles), as well as the ADCmean from the hot spot region of interest (ROI), were calculated for each patient before and after treatment. Then all data were compared between R and non-R using the Mann-Whitney U test. Two measures of diagnostic accuracy were applied: the receiver operating characteristic curve and the diagnostic odds ratio (DOR). We also reported intra- and interobserver variability by calculating the intraclass correlation coefficient (ICC). RESULTS Post-nCRT kurtosis, as well as post-nCRT skewness, were significantly lower in R than in non-R (both P < 0.001, respectively). We also found that, after treatment, R had a larger loss of both kurtosis and skewness than non-R (∆%kurtosis and ∆skewness, P < 0.001). Other parameters that demonstrated changes between groups were post-nCRT ADC10th, ∆%ADC10th, ∆%ADCmean, and ROI ∆%ADCmean. However, the best diagnostic performance was achieved by ∆%kurtosis at a threshold of 11.85% (Area under the receiver operating characteristic curve [AUC] = 0.991, DOR = 376), followed by post-nCRT kurtosis = 0.78 × 10-3 mm2/s (AUC = 0.985, DOR = 375.3), ∆skewness = 0.16 (AUC = 0.885, DOR = 192.2) and post-nCRT skewness = 1.59 × 10-3 mm2/s (AUC = 0.815, DOR = 168.6). Finally, intraclass correlation coefficient analysis showed excellent intraobserver and interobserver agreement, ensuring the implementation of histogram analysis into routine clinical practice. CONCLUSION Whole-tumor ADC histogram parameters, particularly kurtosis and skewness, are relevant biomarkers for predicting the nCRT response in LARC. Both parameters appear to be more reliable than ADCmean from one-slice ROI.
Managing supplier selection problem with integrated fuzzy AHP and fuzzy VIKOR: A manufacturing company case

The ability of businesses to gain a strong place in the sector in which they operate is directly related to the effective management of each member of the supply chain. In the globalizing world with increasing competitive conditions, businesses have to manage their supply chains efficiently to be successful in the market. Companies should improve their strategies by rapidly adapting to the new circumstances formed by various crises such as wars, climate change, or pandemics. The high performance of a business is greatly correlated with the performance of its suppliers. Therefore, supplier selection by evaluating supplier performance is a Multi-Criteria Decision-Making (MCDM) problem that simultaneously includes many qualitative and quantitative factors. In this article, the performances of the suppliers of a Turkish small-medium enterprise (SME) operating in the manufacturing sector during COVID-19 are evaluated using an integrated fuzzy MCDM technique. Despite its importance, supplier selection is less considered for SMEs. The purpose of including fuzzy logic in the study is to ensure that linguistic expressions are converted into fuzzy numbers to overcome uncertainty and subjectivity. Four supplier alternatives are evaluated by four main criteria, and in total by eleven sub-criteria determined by expert opinions. The weights of the performance criteria are obtained by the Fuzzy Analytic Hierarchy Process (FAHP), and the performance ranking of the suppliers is determined by the fuzzy VIKOR. The absence of a manufacturing sector-based study on SMEs operating in Turkey during the COVID-19 period constitutes the main motivation
Sugar Palm Lignocellulosic Fiber Reinforced Polymer Composite: a Review

Abstract. The increasing depletion of petroleum resources, as well as increased awareness of global environmental problems linked with the usage of petroleum-based plastics, are the key driving factors for the widespread acceptance of natural fibres and biopolymers composites. Sugar palm fibre (Arenga pinnata Wurmb. Merr) is one of Malaysia's most abundant and renewable fibres. The purpose of this paper is to explore the development of a sugar palm lignocellulosic fibre reinforced polymer composite. SPF is mostly composed of cellulose (43.88 %), which results in good mechanical properties. According to the review of literature, no comprehensive review article on sugar palm lignocellulosic fibre reinforced polymer composite has been published. The current investigation is focused on the mechanical, thermal, and morphological aspects of SPFs and polymers. The research also demonstrates the potential of SPF polymer hybrid composites for industrial applications such as automotive, household goods, packaging, bioenergy, and others.
Communication and Control of an Assembly, Disassembly and Repair Flexible Manufacturing Technology on a Mechatronics Line Assisted by an Autonomous Robotic System

This paper aims to describe modeling and control in what concerns advanced manufacturing technology running on a flexible assembly, disassembly and repair on a mechatronic line (A/D/RML) assisted by an Autonomous Robotic System (ARS), two robotic manipulators (RM) and visual servoing system (VSS). The A/D/RML consists of a six workstations (WS) mechatronics line (ML) connected to a flexible cell (FC) equipped with a 6-DOF ABB industrial robotic manipulator (IRM) and an ARS used for manipulation and transport. A hybrid communication and control based on programmable logic controller (PLC) architecture is used, which consists of two interconnected systems that feature both distributed and centralized topology, with specific tasks for all the manufacturing stages. Profinet communication link is used to interconnect and control FC and A/D/RML. The paper also discusses how to synchronize data between different field equipment used in the industry and the control systems. Synchronization signals between the master PLC and ARS is performed by means of Modbus TCP protocol and OPC UA. The structure of the ARS consists of a wheeled mobile robot (WMR) with two driving wheels and one free wheel (2DW/1FW) equipped with a 7-DOF RM. Trajectory tracking sliding-mode control (TTSMC) is used to control WMR. The end effector of the ARS RM is equipped with a mobile eye-in-hand VSS technology for the precise positioning of RM to pick and place the workparts in the desired location. Technology operates synchronously with signals from sensors and from the VSS HD camera. If the workpiece does not pass the quality test, the process handles it by transporting back from the end storage unit to the flexible cell where it will be considered for reprocessing, repair or disassembling with the recovery of the dismantled parts. The recovered or replaced components are taken over by the ARS from disassembling location and transported back to the dedicated storage warehouses to be reused in the further assembly processes.
Carbon Quantum Dots Based Chemosensor Array for Monitoring Multiple Metal Ions

The simultaneous identification of multiple metal ions in water has attracted enormous research interest in the past few decades. We herein describe a novel method for multiple metal ion detection using a carbon quantum dots (CQDs)-based chemosensor array and the CQDs are functionalized with different amino acids (glutamine, histidine, arginine, lysine and proline), which act as sensing elements in the sensor array. Eleven metal ions are successfully identified by the designed chemosensor array, with 100% classification accuracy. Importantly, the proposed method allowed the quantitative prediction of the concentration of individual metal ions in the mixture with the aid of a support vector machine (SVM). The sensor array also enables the qualitative detection of unknown metal ions under the interference of tap water and local river water. Thus, the strategy provides a novel high-throughput approach for the identification of various analytes in complex systems.
Analysis of Student Sentiment Dynamics to Evaluate Teachers Performance in Online Course using Machine Learning

With the rapid augmentation of the Internet and communication technology, interest in online learning continues to rapidly broaden the horizons of academic institutions. One of the most important components of the e-learning system is the Online Learning Management System (OLMS). The Feedback module of OLMS includes the ability to gather possible teacher feedback which is considered as an important component of education and a valuable contributor to improving the nature of instruction. Student feedback is usually rich and varied. They also provide a complete insight of how their professors motivate and educate their students. The student feedback allows the instructor to experience and comprehend the significance of teaching. The feedback module integrated with Artificial Intelligence (AI), especially Machine learning (ML) enables deep analysis of large amounts of data, discover patterns and the performance of the teachers. In this progression it is observed that sentiment analysis may effectively capture student sentiments, potentially reducing both obscurity and feedback gaps. This work investigates that the incorporation of like Machine Learning algorithms and NLP for sentiment analysis, with the OLMS data may assist in improving teaching efficiency.
Design and Development of a Dexterous Bilateral Robotic Microinjection System Based on Haptic Feedback

In this paper, a dexterous bilateral robotic system with haptic feedback is designed and developed for microinjection of biological samples. For the first time, the robotic system has been developed for microinjection of both zebrafish larvae and embryos. The master device is constructed by a seven-degree-of-freedom (7-DOF) haptic interface. As the slave device, a 6-DOF microinjection robot is developed by integrating a 3-DOF micropositioning stage and a 3-DOF micromanipulator. One uniqueness of the proposed robotic system is that the injection targets can be placed directly in the Petri dish without prior pose adjustment. The master haptic device not only enables remote control of the microinjection robot by a human operator, but also provides an intuitive haptics feedback to the operator for executing the microinjection operation with a high success rate. The haptic interface is deployed as a viscous damping to enhance the smoothness and stability of its movement. Moreover, a hybrid bilateral control architecture is developed for the system to achieve ideal performance of position tracking and haptic telepresence. A prototype system is developed for performance test. The experimental results of individual and continuous microinjection of zebrafish larvae demonstrate fine position/force tracking capability and flexibility. Note to Practitioners—Microinjection is a universal operation in biological study for delivering foreign materials into biological samples. The existing manual and automated microinjections have limited flexibility. Meanwhile, the stand-alone visual information cannot provide enough telepresence to the operator, which results in low success rate and poor repeatability. This paper proposes a new haptics-based dexterous bilateral robotic system dedicated to microinjection task of both zebrafish larvae and embryos. Both the master haptic interface and slave microinjection robot exhibit multi-DOF motion, which enables a dexterous micromanipulation with high success rate for the human operator. The feasibility of the reported robotic system has been verified by performing microinjection operation of different targets.
Unpacking digital options thinking for innovation renewal: a clinical inquiry into car connectivity

ABSTRACT Options thinking is a powerful approach for managing uncertainty and change in digital environments. It is recognised as a structured process for identifying, developing, and realising options into novel products and services. At Volvo Cars, we have learned it can also become a powerful instrument for innovation renewal, although it can be difficult to apply because it challenges existing firm practices. We elaborate this tension by presenting digital options strategizing as a process of applying options thinking to negotiate capability gaps and configure innovation resources. Our clinical study reveals that this facilitates innovation renewal through emergent processes, practice-oriented design, and opportunity-driven management.
Automatic information extraction from childhood cancer pathology reports

Abstract Objectives The International Classification of Childhood Cancer (ICCC) facilitates the effective classification of a heterogeneous group of cancers in the important pediatric population. However, there has been no development of machine learning models for the ICCC classification. We developed deep learning-based information extraction models from cancer pathology reports based on the ICD-O-3 coding standard. In this article, we describe extending the models to perform ICCC classification. Materials and Methods We developed 2 models, ICD-O-3 classification and ICCC recoding (Model 1) and direct ICCC classification (Model 2), and 4 scenarios subject to the training sample size. We evaluated these models with a corpus consisting of 29 206 reports with age at diagnosis between 0 and 19 from 6 state cancer registries. Results Our findings suggest that the direct ICCC classification (Model 2) is substantially better than reusing the ICD-O-3 classification model (Model 1). Applying the uncertainty quantification mechanism to assess the confidence of the algorithm in assigning a code demonstrated that the model achieved a micro-F1 score of 0.987 while abstaining (not sufficiently confident to assign a code) on only 14.8% of ambiguous pathology reports. Conclusions Our experimental results suggest that the machine learning-based automatic information extraction from childhood cancer pathology reports in the ICCC is a reliable means of supplementing human annotators at state cancer registries by reading and abstracting the majority of the childhood cancer pathology reports accurately and reliably.
Role of internal resources on the competitive advantage building in a knowledge-intensive organisation in an emerging market

Purpose This study aims to analyse the way the internal resources and their attributes contribute to the competitive advantages in an intensive organisation in knowledge of the pharmaceutical industry in an emerging market. Design/methodology/approach This is a qualitative case study focused where the NVivo software was used for information analysis and thematic analysis. Findings The outcomes showed that from the VRIO framework (value, rarity, imitability and organisation), the plant and equipment and the technical knowledge of its workers are the resources that, due to their attributes, especially the rare, those that grant an advantage competitive position compared to other companies in its sector. Those findings highlight that the resource-based view (RBV) is a good approximation to explain the construction of competitive advantage (CA) and, in addition, the relevance of rare attribute in pharmaceutical companies was confirmed. Practical implications The study points out empirical evidence on the relevancy of RBV, from the VRIO framework and the competitive profile matrix (CPM) for the analysis of the management of organisations from the emerging market (economy) perspective. The study also provides competitive advantage analysis tools with which managers can identify strategic resources for their companies. Originality/value The VRIO framework and CPM were integrated in the study to analyse the role of internal resources and their attributes in achieving CAs. This integration is the first time that it has been carried out in companies in the context of an emerging market.
Is rural household debt sustainable in a financially included region? Evidence from three districts of Kerala, India

ABSTRACT This paper explores whether institutional change brought about by financial inclusion results in sustainable debt management by households. We analyze household indebtedness and its various dimensions using primary data collected from 600 households across 3 districts of rural Kerala in India. We find that more than half of the sample households are indebted. Using flow and stock analysis, we assess the repayment capacity of households. While the flow analysis based on interest and income comparison shows that debt is sustainable, the stock analysis indicates an alarming debt situation considering the illiquid nature of land assets. Both agricultural and non-agricultural households appeared to be caught in a debt trap. Our econometric analyses show that socio-economic factors like education and age of the household head, main source of household income and household asset value without land, are significant determinants of household level indebtedness.
Estimating Lake Water Volume With Regression and Machine Learning Methods

The volume of a lake is a crucial component in understanding environmental and hydrologic processes. The State of Minnesota (USA) has tens of thousands of lakes, but only a small fraction has readily available bathymetric information. In this paper we develop and test methods for predicting water volume in the lake-rich region of Central Minnesota. We used three different published regression models for predicting lake volume using available data. The first model utilized lake surface area as the sole independent variable. The second model utilized lake surface area but also included an additional independent variable, the average change in land surface area in a designated buffer area surrounding a lake. The third model also utilized lake surface area but assumed the land surface to be a self-affine surface, thus allowing the surface area-lake volume relationship to be governed by a scale defined by the Hurst coefficient. These models all utilized bathymetric data available for 816 lakes across the region of study. The models explained over 80% of the variation in lake volumes. The sum difference between the total predicted lake volume and known volumes were <2%. We applied these models to predicting lake volumes using available independent variables for over 40,000 lakes within the study region. The total lake volumes for the methods ranged from 1,180,000- and 1,200,000-hectare meters. We also investigated machine learning models for estimating the individual lake volumes and found they achieved comparable and slightly better predictive performance than from the three regression analysis methods. A 15-year time series of satellite data for the study region was used to develop a time series of lake surface areas and those were used, with the first regression model, to calculate individual lake volumes and temporal variation in the total lake volume of the study region. The time series of lake volumes quantified the effect on water volume of a dry period that occurred from 2011 to 2012. These models are important both for estimating lake volume, but also provide critical information for scaling up different ecosystem processes that are sensitive to lake bathymetry.