Miracle-dz/UniFAR-FTUs huggingface dataset:
Design Sprint versus Design Thinking: A comparative analysis

Context: Design Sprint (DS) is a new design methodology for designing new products that resembles Design Thinking (DT) in designing solutions that diverge and focus on the problem and solution fields, to be then tested in prototypes. Yet it differs from DT by incorporating Lean Startup concepts such as agility and elimination of costly or low-value activities. Although it gained increased interest in the organizational world, it is still unexplored in the literature, without an analysis of best practices, and how this process may be more appropriate than traditional DT processes. Objective: In spite of the improvements proposed by the design community to the original process, this study aims to perform a comparative analysis of the DS process, as prescribed in the book that originated the methodology, against traditional DT processes, considering its phases and techniques , and what the advantages and disadvantages of each methodology are. Method: The research used the methodological approach of multiple case study by evaluating three DS processes in a company that executes processes with both methodologies. In addition to observing the processes and documenting the decisions made, six interviews were conducted with company partners and experts from the area, who participated in the processes. Results: The article describes the execution of DS processes in detail and then summarizes the advantages of these processes against DT processes, such as: low experimentation barrier, alignment with high management for continuing the work, orientation to incremental innovations. Nevertheless, the coexistence of the two methodologies is proposed, such that DS can be more efficient when used as an initial stage of requirements of new products, while DT proves to be more useful as a methodology for generating new ideas. Conclusion: The research on theoretical and practical contributions by investigating a topic not yet explored in the literature, positioning positive and negative aspects of DS in relation to other more established design methodology (DT), and suggesting how these methodologies can be applied more efficiently to creating new products and services.
A Pneumatic/Cable-Driven Hybrid Linear Actuator With Combined Structure of Origami Chambers and Deployable Mechanism

Pneumatic actuators have been widely used in robotics due to their inherent compliance and relatively high strength. In this letter, the authors report on the design, analysis and experimental validation of a novel pneumatic/cable-driven hybrid linear actuator whose structure is a combination of origami chambers and passive deployable mechanism. Under the joint actuation of the cable and compressed air, the proposed actuator can generate bidirectional motion; meanwhile, both thrust and tensile force can be produced. The combined structure of the rigid deployable mechanism and the axially soft origami chambers possesses high radial stiffness and an extension ratio up to 200% without radial expansion. The position can be controlled at whole motion range through a simple strategy and the actuation pressure can be as low as 2 kPa at no load. The kinematic as well as the quasi-static model is developed to accurately predict the behavior of the actuator and design the control strategy. A prototype is built based on a new fabrication method, on which the validation experiments are conducted. The experimental results prove the effectiveness of the model and show that the prototype possesses acceptable positioning accuracy, even when an external load is exerted on its moving plate.
One‐Step‐Printed, Highly Sensitive, Textile‐Based, Tunable Performance Strain Sensors for Human Motion Detection

Wearable strain sensors have attracted a lot of interests due to their unlimited prospects in applications such as human motion detections, electronic skin, and human–computer interactions. However, the complex manufacturing methods are generally used to fabricate the strain sensors with wide sensing ranges and high sensitivity. Herein, a high‐performance textile‐based strain sensor (TSS) is fabricated by one‐step screen‐printing method via transferring the silver nanowires ink onto a stretchable textile. Through designing the screen plate with various patterns, the optimized TSSs with different sensing ranges and sensitivity can be achieved simultaneously. Among them, a straight line‐structural TSS (with a width of 2 mm) exhibits a wide workable strain range of 120%, high gauge factor of 216, and superior durability (more than 2000 stretching/releasing cycles). As flexible wearable devices, these TSSs are used to monitor human joint motion, prefiguring the various applications in human health detection, soft robots, intelligent devices, etc.
Flow redirection based MPLS label algorithm in SDN

Aiming at the problem of congestion of flow table caused by limited performance of data plane TCAM in Software-Defined Networking (SDN), an MPLS labeling algorithm based on flow redirection is proposed. The algorithm utilizes the source node switch flow redirection and the path switch to decentralize the MPLS label, occupies a part of the data link bandwidth to alleviate the problem that the TCAM update flow table rate is too slow, and can increase the system switch load. Compared to the existing method, the link load is reduced by up to 60% and the flow entry insertion delay of nearly 90% is increased, and the system capacity is increased by up to 200%.
Factors Affecting Water Drainage Long-Time Series in the Salinized Low-Lying Coastal Area of Ravenna (Italy)

The low-lying coastal area of Ravenna (North-eastern Italy), like the majority of delta and coastal zones around the world, is affected by groundwater salinization due to natural processes (such as low topography, natural land subsidence, seawater encroachment along estuaries, etc.) and anthropogenic activities (i.e., increased anthropogenic subsidence rate, sea level rise, geofluids extraction, and drainage). Among all factors causing aquifer salinization, water drainage plays an important role in lowering the hydraulic head and favouring saltwater seepage in the Ravenna coastal aquifer. A network of drainage canals and water pumping stations first allowed for the reclamation of the low-lying territory and today are fundamental to keep land and infrastructures dry and maintain effective soil depth for agriculture practices. The aim of this work is to identify and assess factors affecting water drainage long-time series (1971–2017) of the most important mechanical drainage basin in this low-lying coastal area. Statistical analyses of drainage, climate, and land use change datasets help constrain the relative weight of each single factor potentially causing an increase of water drainage through time. The results show that, among these factors, subsidence rates and seepage processes are the most significant. The data trends also indicate that the climate, especially in terms of precipitation amount and extreme events, played no important role during the studied time interval. The process of infiltration soil capacity loss due to urbanization and consequent soil sealing probably has a small secondary effect. Moreover, an increase in pumping through time will exacerbate aquifer salinization and compromise freshwater availability in the coastal area.
Financial Distress, Bankruptcy Analysis and Implications For Stock Prices of Consumer Goods Companies in Indonesia

Penelitian ini bertujuan untuk menganalisis financial distress dan kebangkrutan melalui metode Altman Z-Score serta pengaruhnya terhadap harga saham pada perusahaan barang konsumsi yang terdaftar di Bursa Efek Indonesia untuk periode 2012 hingga 2017. Pengambilan sampel ditentukan dengan teknik purposive sampling sedangkan pengolahan data dilakukan melalui perangkat lunak Eviews dan Microsoft Excel. Hasil penelitian menunjukkan bahwa kebangkrutan hanya terjadi di Indofarma Tbk (INAF) pada tahun 2017. Secara bersamaan, Z-score memiliki pengaruh yang signifikan terhadap harga saham dan hasil regresi data panel mengkonfirmasi bahwa Pendapatan Sebelum Bunga dan Pajak (EBIT) terhadap Total Aset memiliki pengaruh signifikan dengan kontribusi 38,25% terhadap harga saham. Keywords : Financial Distress, Harga Saham, Altman Z-Score
Preliminary study of thrombogenicity induced by the nanoparticle surface coating of intracranial stents

Endovascular treatment of intracranial aneurysms with intracranial stents was proven to be clinically safe and effective, but is still associated with a risk of thromboembolic complications. Stent thrombosis could be a sever complication associated with specific stent surface coatings and designs. Standardized in vitro tests for investigation of thrombogenicity induced by different nanomaterials were used as the basic method in carrying out the present study. Therefore, the aim of this study was to evaluate the thrombogenicity of three different nanomaterials (ZnO, TiO2 si Fe3O4) possible used as surface coating for intracranial stents. This study is based on a procedure for in vitro analyses of plasma coagulation time. To measure the plasma coagulation time, platelet-poor plasma from human whole blood was in vitro exposed to nanoparticles and analysed in prothrombin (PT) and activated partial thromboplastin (APTT).
Quality Improvement through Intensive Care Registries: The Value of Big Data in Clinical Decision Making

Background: Quality of Intensive care has got more attention in case of the high cost of healthcare and the potential for harm. Poor-quality care causes high cost and quality improvement initiatives in the ICU lead to an improvement in outcomes as well as a decrease in costs. One of the crucial tools that allow physicians and nurses to monitor change in a quality improvement effort is the development of an electronic database for data collection and reporting. The objective of Intensive Care Registries is to create a high-quality registry of patients through a collaboration of academic health centers performing uniform data collection with the purpose of improving the quality and accuracy of healthcare decisions and provide a data-driven clinical decision support system for critical care medicine. Methods: This article reviews real-world data sources in healthcare and considers registry as the main tool to address health services and outcomes research questions in critical care, and briefly describes objective, inputs and outputs of intensive care registries. As it can be comprehended from library research, the combination of patient clinical care data, quality parameters, and ICU operating costs, integrated into an electronic database, provides a valuable tool for quality improvement and overall efficiency of offered care. Results: Using Big Data effectively within ICUs for supporting clinical decision making can lead to predict numerous diseases and help to discover new patterns in healthcare. The ability to process multiple high-speed clinical data streams from multiple centers could dramatically improve both healthcare efficiency and patient outcomes. Conclusion: To gain this goal, developing reliable and standardized health analytics platforms as well as quality improvement processes that translate analytical results into new clinical guidelines, is recommended.
Film or Film Brand? UK Consumers’ Engagement with Films as Brands

Many films are produced annually, but only a small number of films reach the state of being considered and identified by consumers as film brands. Film-brand identification is difficult to achieve, but it leads to engagement behaviours (e.g. repetitive viewing, positive word-of-mouth and purchase intention of relevant merchandise/franchise). To help filmmakers better develop films as brands and benefit from their brand status, this paper takes a consumer-centric approach to investigate how and why films are identified and engaged by consumers as brands. Using an abductive mode of reasoning, a consumer film-brand engagement framework was developed through qualitative data collected from 35 semistructured interviews and then validated using survey data with 1030 participants. This consumer film-brand engagement framework shows that film identity coherency drives film-brand identification through the mediation effects of popularity, sequels and emotional bonding, whilst marketing effort, iconic status, franchising/merchandising activities and timelessness are highlighted as key moderators, resulting in positive brand engagement behaviour. The paper sheds new light on film-branding literature by theoretically explaining and empirically showing a sequential and consolidated process, which consumers go through to identify and engage with films as brands, leading to several managerial and marketing implications for film-makers.
Engineer Energy Dissipation in 3D Graphene Nanolattice Via Reversible Snap-Through Instability

Carbon micro/nanolattice materials, defined as three-dimensional (3D) architected metamaterials made of micro/nanoscale carbon constituents, have demonstrated exceptional mechanical properties, including ultrahigh specific strength, stiffness, and extensive deformability through experiments and simulations. The ductility of these carbon micro/nanolattices is also important for robust performance. In this work, we present a novel design of using reversible snap-through instability to engineer energy dissipation in 3D graphene nanolattices. Inspired by the shell structure of flexible straws, we construct a type of graphene counterpart via topological design and demonstrate its associated snap-through instability through molecular dynamics (MD) simulations. One-dimensional (1D) straw-like carbon nanotube (SCNT) and 3D graphene nanolattices are constructed from a unit cell. These graphene nanolattices possess multiple stable states and are elastically reconfigurable. A theoretical model of the 1D bi-stable element chain is adopted to understand the collective deformation behavior of the nanolattice. Reversible pseudoplastic behavior with a finite hysteresis loop is predicted and further validated via MD. Enhanced by these novel energy dissipation mechanisms, the 3D graphene nanolattice shows good tolerance of crack-like flaws and is predicted to approach a specific energy dissipation of 233 kJ/kg in a loading cycle with no permanent damage (one order higher than the energy absorbed by carbon steel at failure, 16 kJ/kg). This study provides a novel mechanism for 3D carbon nanolattice to dissipate energy with no accumulative damage and improve resistance to fracture, broadening the promising application of 3D carbon in energy absorption and programmable materials.
Transformer neural network for protein-specific de novo drug generation as a machine translation problem

Drug discovery for a protein target is a very laborious, long and costly process. Machine learning approaches and, in particular, deep generative networks can substantially reduce development time and costs. However, the majority of methods imply prior knowledge of protein binders, their physicochemical characteristics or the three-dimensional structure of the protein. The method proposed in this work generates novel molecules with predicted ability to bind a target protein by relying on its amino acid sequence only. We consider target-specific de novo drug design as a translational problem between the amino acid “language” and simplified molecular input line entry system representation of the molecule. To tackle this problem, we apply Transformer neural network architecture, a state-of-the-art approach in sequence transduction tasks. Transformer is based on a self-attention technique, which allows the capture of long-range dependencies between items in sequence. The model generates realistic diverse compounds with structural novelty. The computed physicochemical properties and common metrics used in drug discovery fall within the plausible drug-like range of values.
Increasing Accountability Using Data Journalism: Challenges for the Pakistani Journalists

ABSTRACT A distinct era of data-driven reporting is on rise – a period when software, algorithms, programming, and data-processing techniques are being used by media organizations, especially in many developed countries of the world. Amid the increased practice of data journalism, it is imperative to analyse how journalists can use it for holding the power holders accountable, particularly in authoritarian and competitive authoritarian regimes where democratic norms are often abused and access to information is constrained. Drawing on the new institutionalism theory, thus this study aims to analyse the ability of Pakistan’s journalists to practice data journalism and to explore challenges that they face in the practice of data-driven reporting. To address this aim, this study uses the qualitative methods of document review and in-depth interviews (face-to-face). This study uses thematic analysis to offer insights into the gathered data.
Modeling Quality of IoT Experience in Autonomous Vehicles

Today’s research on Quality of Experience (QoE) mainly addresses multimedia services. With the introduction of the Internet of Things (IoT), there is a need for new ways of evaluating the QoE. Emerging IoT services, such as autonomous vehicles (AVs), are more complex and involve additional quality requirements, such as those related to machine-to-machine communication that enables self-driving. In fully autonomous cases, it is the intelligent machines operating the vehicles. Thus, it is not clear how intelligent machines will impact end-user QoE, but also how end users can alter and affect a self-driving vehicle. This article argues for a paradigm shift in the QoE area to cover the relationship between humans and intelligent machines. We introduce the term Quality of IoT-experience (QoIoT) within the context of AV, where the quality evaluation, besides end users, considers quantifying the perspectives of intelligent machines with objective metrics. Hence, we propose a novel architecture that considers Quality of Data (QoD), Quality of Network (QoN), and Quality of Context (QoC) to determine the overall QoIoT in the context of AVs. Finally, we present a case study to illustrate the use of QoIoT.
Implementing tasks in young learners’ language classrooms: A collaborative teacher education initiative through task evaluation

Despite empirical evidence in support of the effectiveness of using tasks in young learners’ classrooms, task implementation has been repeatedly reported as a thorny problem. An essential but under-researched issue is how in-service teacher education programme can be conducted to facilitate teachers’ implementing tasks in their classrooms. This practitioner research article reports on a researcher and teacher educator’s action research study in which she worked with Lucille (pseudonym), a novice English language teacher, to design and implement two repeated task-based language teaching (TBLT) lessons for Grade 2 students at a Chinese primary school. During a six-week teacher education program consisting of two cycles of TBLT lesson planning, implementation, evaluation, and reflection, the teacher educator provided continuous support to guide and scaffold Lucille’s reflective endeavours at crafting TBLT practices in her classrooms. The teacher educator also conducted student-based, response-based, learning-based, and community-based task evaluations to facilitate the teacher’s reflective practices. The study illustrates how an in-service teacher education program, fuelled by on-going professional support and empirical evaluation, facilitated a practitioner’s task implementation in young learners’ foreign language classrooms.
Historical and Future Spatial and Temporal Changes in Land Use and Land Cover in the Little Ruaha River Catchment, Tanzania

Increased anthropogenic activities in the Little Ruaha River Catchment have modulated the catchment condition, nevertheless, the future changes as a result of increased anthropogenic activities are unknown. Understanding the future changes is vitally important for the design of appropriate strategies towards sustainable management of the catchment resources. This study applied Remote Sensing and GIS techniques (Jensen & Lulla, 1987) to assess the historical long-term changes in land use and land cover using Landsat satellite images of 1990, 2005 and 2015, and modelled the future change in land use and land cover up to 2040 using the stochastic CA-Markov chain (Almeida et al., 2005). The historical land use and land cover change detection results indicate that between 1990 and 2005 the area under forest changed from 39,872 ha to 22,957 ha, woodland changed from 109,692 ha to 72,809 ha, wetland decreased from 19,157 ha to 11,785 ha, the cultivated land increased from 106,782 ha to 109,047 ha, likewise, the built-up area increased from 9408 ha to 11,674 ha. Results between 2005 and 2015 show the substantial changes where the forest decline from 22,957 ha to 15,950 ha, woodland decreased from 72,809 ha to 58,554 ha and the wetland changed from 11,785 ha to 5622 ha. Cultivated land and built up area increased from 109,047 ha and 11,674 ha to 143,468 ha and 13,765 ha respectively. Generally, the study has revealed the substantial decline in forest, woodland and wetland by 23,922 ha, 51,138 ha and 13,535 ha respectively, and an increase of cultivated land and built up area by 36,668 ha and 4357 ha respectively in 15 years, between 1990 and 2015. The predicted future land use and cover for the next 15 years (2040) showed an overall increase in cultivated land, built up area, grassland and bushland to 24.82%, 2.24%, 25.18% and 20.41% respectively, and a decrease in forest, woodland and wetland in the order of 1.87%, 7.87% and 0.03% respectively. The study concludes that, there have been significant changes in land use and cover in the catchment which likely to impend the sustainability of the catchment productivity, hence recommends the holistic system thinking and analysis approach in management and utilization of catchment resources.
BCI for a brain state control in a dual-task paradigm

The brain resource is limited and it needs to be distributed among the different concurrent tasks. When the subject accomplishes the resource-demanding main task requiring sustained attention, the additional task leads to the reduction of resources allocated for the main task accomplishing. This additional task can be either important or caused by the distraction. Anyway, it causes a performance decrease in the main task. In this paper, we propose a brain-computer interface (BCI) to control the cognitive performance of the visual task accomplishing in the presence of the additional (mental arithmetic) task. We demonstrate how the additional task affects the performance of the main task accomplishing.
Estimation of error variance via ridge regression

SummaryWe propose a novel estimator of error variance and establish its asymptotic properties based on ridge regression and random matrix theory. The proposed estimator is valid under both low- and high-dimensional models, and performs well not only in nonsparse cases, but also in sparse ones. The finite-sample performance of the proposed method is assessed through an intensive numerical study, which indicates that the method is promising compared with its competitors in many interesting scenarios.