Flexibility Requirements Research Articles

Adding Redundancy to LoRaWAN for Emergency Communications at the Factory Floor

Wireless communications may be the key solution to satisfy mobility, flexibility, and scalability requirements of the Internet of Things. Particularly, the low power wide area networks, operating in unlicensed bands, are promising because they permit both private and public backends. Unfortunately, their target applications are situations where sporadic transmissions are tolerated. They could not fit scenarios in which high reliability and short round trip time are required. In this article, the LoRaWAN solution is considered for managing emergency and alarm communications. Reliability and timeliness limits have been overcome by jointly using a message replication scheme with variable payload length together with low-cost repeaters. Experiments have been carried out in both fixed and low-speed mobility scenarios (with an interfering traffic of 95 message/h, generated by regular LoRaWAN colocated network). Results, in the former case, show that the success probability of confirmed messages is 100%, the 99.6% of the available data are delivered to the backend, and the average delay decreases by 150 ms. When mobility is considered (e.g., for an automatic guided vehicle use case) results confirm significant improvements for both the transaction success ratio and the data extraction rate that significantly increase up to 95% and 87%, respectively. On the contrary, the overall average delay is not always reduced.

A new three-term conjugate gradient method for training neural networks with global convergence

Conjugate gradient methods (CG) constitute excellent neural network training methods that are simplicity, flexibility, numerical efficiency, and low memory requirements. In this paper, we introduce a new three-term conjugate gradient method, for solving optimization problems and it has been tested on artificial neural networks (ANN) for training a feed-forward neural network. The new method satisfied the descent condition and sufficient descent condition. Global convergence of the new (NTTCG) method has been tested. The results of numerical experiences on some wellknown test function shown that our new modified method is very effective, by relying on the number of functions evaluation and number of iterations, also included the numerical results for training feed-forward neural networks with other well-known method in this field.

Multi-time Scale Coordinated Optimization of New Energy High Permeability Power System Considering Flexibility Requirements

Multi-time Scale Coordinated Optimization of New Energy High Permeability Power System Considering Flexibility Requirements

Applying Activity System-Based Process Model in Augmented Reality-Based Learning for Natural Science Course in Elementary School

Since learning environments have the support of innovational technology, blended learning environments could integrate the physical learning methods with multimedia learning materials, which combines the use of technology with the traditional teaching methods and offers a potential for teachers and students to meet the requirements of learning flexibility and innovation. This study aims to redesign a user-centered Activity System-Based Process Model (ASPM) and integrate the Augmented Reality (AR) into blended learning to implement the Augmented Reality-Based Blended Learning (ARBL) system. There were 57 participants who learned with the ARBL while the control group was made up of 52 students who learned with the Traditional Blended Learning (TBL) approach. Analytical results indicate that the learning outcomes and learning attitude of the ARBL group were better than those of the TBL group. The activity system-based process model could provide a helpful structure in the ARBL to guide course designers, teachers, and researchers for designing the AR learning activity.

A slotted random request scheme for connectionless data transmission in bluetooth low energy 5.0

Bluetooth low energy (BLE) is a personal area network (PAN) technology that has been widely implemented in various Internet of Things (IoT) services. BLE defines a connectionless communication method based on request–response to more effectively adapt to services with high flexibility requirements. This communication method can be divided into three steps: the neighbor discovery process (NDP), scan request, and scan response. First, the NDP enables a device known as the scanner to discover another device known as the advertiser nearby. In the second step, the scanner sends a scan request to the advertiser which, after receiving the scan request, sends a data packet in response. Unfortunately, this method is flawed in that the simultaneous discovery of an advertiser by multiple scanners would cause the request packets they send to collide with high probability. In fact, these collisions may always occur when scanners continue to discover common advertisers. To solve this problem, various schemes have been proposed to reduce the probability of a collision. However, these schemes are only suitable for application scenarios with fewer scanners; when more scanners are present, their latency and energy consumption increase rapidly. This prompted us to propose a scheme to improve the performance of the connectionless communication method. In addition, convex optimization was used in the proposed model to minimize the transmission delay and energy consumption. The final simulation results prove that the proposed scheme effectively reduces the delay and the energy consumption when many scanners are involved.

Assessing flexibility for integrating renewable energies into carbon neutral multi-regional systems: The case of the Chilean power system

Reducing emissions from power systems requires enhancing the penetration of non-conventional renewable energy sources (NCRE) in the generation mix. However, such penetration requires high levels of operational flexibility in order to ensure an adequate balance between generation and demand. Concentrating solar power plants with thermal storage (CSP-TES) and battery energy storage systems (BESS) have shown to possess technical characteristics compatible with such high flexibility requirements. However, due to the high capital costs of these technologies, decision-makers must seek for cost-effective configurations and operation modes. This study presents the development of a methodological framework for designing the long-term transition of a multi-regional energy system towards a low carbon emission system. The sought system is characterized by a high penetration of NCRE, and the use of CSP-TES, BESS and electricity transmission settings for providing effective levels of operational flexibility. For this, the transformation of the Chilean electricity system between the years 2018–2050 is studied, using a tailored modification of the well-known OSeMOSYS optimization tool for energy systems planning. The main results indicate that by 2050, and considering a baseline scenario defined for 2016, for most of the scenarios studied the renewable electricity generation would be at least a 90 % and CO2 emissions would be 75 % lower. Furthermore, it is shown that providing operational flexibility to the system requires a mixed generation from hydroelectric reservoirs, CSP-TES plants, BESS, pumped-storage hydropower and natural gas generators. The obtained results allow planning the capacity and operation of CSP and BESS plants, which are adapted to the future flexibility requirements of the Chilean electric power system. Incentive policies like stimuli to growth BESS, would favor primarily the photovoltaic growth of the system at the expense of CSP-TES capacity, while CSP-TES growth incentives would maintain photovoltaic generation levels, but would decrease Wind and natural gas generation.

Flexible Aluminum-Air Battery Based on Ionic Liquid-Gel Polymer Electrolyte.

There is an urgent demand to develop high-performance flexible batteries for a wide range of contemporary emerging fields, including flexible electronics, wearable sensors, and implantable medical devices. However, the inherent safety and stability issues of traditional organic liquid-based electrolytes make their application in flexible batteries unsatisfactory. Therefore, exploring gel electrolytes with superior ionic conductivity and safety is considered to be the key to the development of flexible batteries. In this paper, two types of high-quality ionic liquid-based gel polymer electrolyte membranes (PVDF-ILs) are created by a conventional solution-casting method, which are further integrated into flexible aluminum-air batteries to guide the interface and process research, and the related discharge properties of two ionic liquid-based electrolyte membrane (PVDF-[C4mpyr]Cl, PVDF-[BMIM]Cl) in different bending states are discussed. The results show that PVDF-ILs have a rich pore structure and interwoven skeleton network, leading to relatively high ionic conductivity (2.97 × 10-3 S cm-1). Moreover, two types of batteries can meet the needs of flexibility, although there is a slight loss of power density under various bending conditions. In general, a PVDF-[C4mpyr]Cl-based flexible aluminum-air battery is suitable for the working conditions of high power and low bending angle, while the PVDF-[BMIM]Cl-based flexible aluminum-air battery is favored for microwatt low-power devices with high flexibility requirements.

Precision and Flexible Bending Control Strategy Based on Analytical Models and Data Models

Forming of various customized bending parts, small batches, as well as numerous types of materials is a new challenges for Industry 4.0, the current control strategies can not meet the precision and flexibility requirement, expected control strategy of bending processes need to not only resist unknown interferences of process condition and models, but also produce various new parts automatically and efficiently. In this paper, a precision and flexible bending control strategy based on analytical models and data models is proposed to build adaptive bending systems. New analytical prediction models for loading and unloading are established and suitable for various materials, a sequential identification strategy is proposed to search nominal properties using the four sub-optimization models. A data-based feedback model is established to prevent over-bending and eliminate online deviation. Above models are merged into a precision and flexible control strategy. The system firstly uses sub-optimization models to search the nominal point which is near to target point, secondly the system further uses feedback model to eliminate residual error between the nominal point and target point. Compared with four kinds sheet metals, the allowable ranges for variables are determined for a good convergence. The target bending angles were set to 20°, 40°, and 60°. Forty parts were tracked for each kind material, the adaptive bending system converged after one iteration, and exhibited better performances.

Wide bandgap semiconductor nanomembranes as a long-term biointerface for flexible, implanted neuromodulator

Electrical neuron stimulation holds promise for treating chronic neurological disorders, including spinal cord injury, epilepsy, and Parkinson's disease. The implementation of ultrathin, flexible electrodes that can offer noninvasive attachment to soft neural tissues is a breakthrough for timely, continuous, programable, and spatial stimulations. With strict flexibility requirements in neural implanted stimulations, the use of conventional thick and bulky packages is no longer applicable, posing major technical issues such as short device lifetime and long-term stability. We introduce herein a concept of long-lived flexible neural electrodes using silicon carbide (SiC) nanomembranes as a faradic interface and thermal oxide thin films as an electrical barrier layer. The SiC nanomembranes were developed using a chemical vapor deposition (CVD) process at the wafer level, and thermal oxide was grown using a high-quality wet oxidation technique. The proposed material developments are highly scalable and compatible with MEMS technologies, facilitating the mass production of long-lived implanted bioelectrodes. Our experimental results showed excellent stability of the SiC/silicon dioxide (SiO2) bioelectronic system that can potentially last for several decades with well-maintained electronic properties in biofluid environments. We demonstrated the capability of the proposed material system for peripheral nerve stimulation in an animal model, showing muscle contraction responses comparable to those of a standard non-implanted nerve stimulation device. The design concept, scalable fabrication approach, and multimodal functionalities of SiC/SiO2 flexible electronics offer an exciting possibility for fundamental neuroscience studies, as well as for neural stimulation-based therapies.

QoS-Aware Fog Node Placement for Intensive IoT Applications in SDN-Fog Scenarios

The advent of the Internet of Things (IoT) paradigm to intensive domains, such as industry, is a key enabler for the automation of critical, real-world processes. The strict Quality-of-Service (QoS) requirements of these domains make low-latency computing paradigms, such as fog computing, very attractive for meeting these requirements. Moreover, the requirements of scalability and flexibility in the underlying network communications motivate the use of software-defined networking (SDN) in the infrastructure. To enable these fog-SDN environments, fog nodes (FNs) that have both computing and SDN capabilities can be deployed, thus easing the deployment of fog in SDN networks. However, the exact placement of these FNs is key to the latency of the hosts that make use of them, and thus, must be carefully assessed to meet the stringent QoS requirements of critical, time-strict IoT applications. This article focuses on this FN placement problem by formalizing it and solving it through both optimal and approximated methods, including comparisons with state-of-the-art benchmarks. In particular, we analyze the performance of each of these methods in terms of latency and execution time in both SDN Internet topologies and Industrial IoT infrastructures. Our proposed heuristic provides placements with near-optimal latencies, with smaller optimality gaps than the benchmark, and computes them in tractable times.

From Control to Mutual Gains: A Systematic Review and Repositioning of Flexible Working

Flexible working has been increasingly used by employees as a work practice for juggling career pressures alongside other aspects of life. The COVID-19 pandemic catapulted flexible work to normative work status, temporarily if not permanently. As employees and employers navigate complex social expectations and environments, the mutual gains stemming from flexible work are increasingly valuable to individual careers, organizations, and society. Despite its rising value and usage, minimal research explores flexible working holistically to understand its purpose and significance. Our systematic review addresses this gap by investigating the multiple meanings of flexible working presented by scholars in order to understand its nature as well as exploring how the practice of flexible working has evolved. A review of the scope, prevalence, and context of prior research reveals the need for greater clarity in conceptualizing flexible work and identifies the changing nature of key drivers that influence the requirement for flexibility. The review finds evidence of two paradigmatic shifts. First, previous studies generally explore flexible work in relation to a paradigm of power whereby organizations control employees. Second, the review reveals the emergence of a neo-ideal worker, an employee that achieves agreed outcomes but is not expected to do so within the confines of the office environment. By raising the level of investigation and exploring flexible working at the phenomenon level this review exposes a pivot away from the construct of power towards mutual gain. These shifts in thinking regarding flexible working have significant implications for employee careers and management practice.

PENGARUH PENDIDIKAN NON FORMAL (TPA) TERHADAP PEMAHAMAN MATERI TENTANG AGAMA ISLAM

Non-formal education is a systematic activity outside the school system that is useful for improving students' knowledge or understanding and achieving their learning goals. Non-formal education itself has the following characteristics: short-term education, more flexible registration requirements, a place to learn outside the classroom and inside the classroom, conditions that are not strict and are not limited to students and specific curricula. This study discusses the effect of non-formal education (TPA) on students' understanding of material about Islam in SD Negeri 83 Palembang. The formulation of the problem in this study are (1) What is the ability to understand Islam among students in SD Negeri 83 Palembang who do not attend non-formal education. (2) What is the ability to understand Islam among students in SD Negeri 83 Palembang who do not attend formal education. (3) What is the influence of non-formal education on the ability of students to understand Islamic religious education material.The variable in this study is non-formal education (x) and the dependent variable is the understanding of the material (y).To obtain the result of this study, the methods of written tests, documentation and questionnaires were used. Based on the obtained research results, non-formal education (TPA) affects the understanding of the material about Islam, because the test results of the experimental class are greater than the control class. The difference can be seen from the results of calculating the analysis using the T-test formula through the SPSS application, which shows the resulting significance is less than 0.05.

A Research Framework for Sustainable Digital Innovation: Case Studies of Japanese Firms

Today’s competitive and highly volatile environment calls for a new kind of flexibility and adaptability. Limited studies are available that examine how firms achieve both speed and creativity requirements in this digital era. In view of the rare empirical studies on real-world cases that apply rigorous research methods for sustainable digital innovation (SDI), this research investigates the key strategic requirements of organizational agility and flexibility for SDI. The research framework defines four types of innovators. This study used the benchmark tool to assess the status of their innovation effectiveness. This research framework is useful for firms to classify, assess, and evaluate their innovation type. The study’s findings also suggest the road map for future strategic goals. This theoretical framework illustrates the causal relationship between Japanese-style digital innovation and the firms’ sustainable competitive advantage. This model might be extended to other firms in different contexts (e.g., Korea, India, USA, Brazil, and a host of other countries). The theoretical and practical implications are discussed for future research.

Model Learning With Backlash Compensation for a Tendon-Driven Surgical Robot

Robots for minimally invasive surgery are becoming more and more complex, due to miniaturization and flexibility requirements. The vast majority of surgical robots are tendon-driven and this, along with the complex design, causes high nonlinearities in the system which are difficult to model analytically. In this work we analyse how incorporating a backlash model and compensation can improve model learning and control. We combine a backlash compensation technique and a Feedforward Artificial Neural Network (ANN) with differential relationships to learn the kinematics at position and velocity level of highly articulated tendon-driven robots. Experimental results show that the proposed backlash compensation is effective in reducing nonlinearities in the system, that compensating for backlash improves model learning and control, and that our proposed ANN outperforms traditional ANN in terms of path tracking accuracy.

FLEXIBILITY IN PRIMARY HEALTH CARE DESIGN IN MARGINAL AREAS

This research focuses on the analysis of Primary Health Care facilities located in rural areas. The health facility for primary care is the minimum unit of a complex system since it can be extended and implemented with other services and functions in the future. The most helpful method to develop the basic health unit in a small hospital will mainly depend on the nature of the expertise area, the living conditions of the community and its particular needs, and the prevalence of specific diseases and socio-cultural conditions. This study adopted a mixed-methods focused on analyzing the literature review of the principles of flexibility and evaluating the design of ten international primary care facilities. The factors that influence the design of the architectural space have been analyzed under three macro requirements: expansibility, versatility, reversibility. These macro aspects have been investigated in three criteria relating to form, space, and structure. The intersection of each case study with the three requirements of expansibility, versatility, and reversibility highlighted the flexibility requirements met. The expansibility requirement was not achieved, as the buildings analyzed were designed as finished units with no possibility of extension in the future. Therefore, it is necessary to pay more attention to the architecture of the primary health care facilities during the planning and design phase to accommodate changing needs. Only an integrated approach can make steady progress on social and health care conditions improving community’s needs in marginal areas.

Two-Stage Cooperative Intelligent Home Energy Management System for Optimal Scheduling

Intelligent home energy management system (IHEMS) manages various home appliances depending on user preferences in order to save energy cost and assure user satisfaction. To realize cooperation between the distribution system operator (DSO) and end-user, a two-stage scheduling optimization model is developed based on the flexibility quantification, with the user privacy in demand-side management in a specific cluster taken into account. It also aids to reduce the network operation cost, and the distribution system's uncertainty caused by wind power is represented using chance constraints. The user uploads the IHEMS-calculated offered flexibility to DSO via cluster; DSO optimizes the optimal flexibility to minimize the operation cost; then, clusters distribute the flexibility requirement based on the flexibility index; finally, the user updates the optimal schedule. The numerical results using MATLAB are provided to show the effectiveness of the proposed approach.

Flexible thermoelectric device with excellent durability towards self-powered light intensity detection

Thermoelectric technology can generate thermoelectric electromotive force under the condition of temperature difference, which have unlimited prospects in the emerging fields of sensing, wearable electronic devices, self-powered arbitrary doors, electronic skin. These application scenarios also put forward higher requirements for thermoelectric devices, excellent flexibility and self-supporting characteristics. Herein, a novel flexible self-supported thermoelectric device based on carbon nanotubes (CNTs) was designed and constructed with simple laminated construction of electrical series and thermal parallel. CNTs/PEDOT:PSS and polyethylenimine (PEI)-doped CNTs/polyvinylidene fluoride (PVDF) flexible self-supported films were used as p- and n-type films respectively. The obtained flexible self-supported thermoelectric device can generate a voltage of 3.31 mV under temperature difference of 35 K. The excellent light absorption and photothermal conversion of CNTs, together with the thermoelectric characteristics of CNTs and PEDOT:PSS, enables its promising applications in self-powered light intensity detection. Furthermore, its excellent flexibility and self-supporting characteristics give it outstanding durability.

Transparent Quasi-Random Structures for Multimodal Light Trapping in Ultrathin Solar Cells with Broad Engineering Tolerance.

Waveguide modes are well-known to be a valuable light-trapping resource for absorption enhancement in solar cells. However, their scarcity in the thinnest device stacks compromises the multiresonant performance required to reach the highest efficiencies in ultrathin devices. We demonstrate that enriching the modal structure on such reduced length-scales is possible by integrating transparent semiconductor/dielectric scattering structures to the device architecture as opposed to more widely studied metallic textures. This phenomenon allows transparent quasi-random structures to emerge as strong light-trapping candidates for ultrathin solar cells, given that their broad scattering profiles are well-suited to exploit the increased number of waveguide modes for multiresonant absorption enhancement. A thorough study of the design space of quasi-random textures comprising more than 1500 designs confirms the superiority of transparent structures over a metallic embodiment, identifies broad and flexible design requirements to achieve optimal performances, and demonstrates photon harvesting capabilities leading to 20% efficiency with an 80 nm GaAs absorber. Our light-trapping strategy can be applied to a wide range of material systems and device architectures, is compatible with scalable low-cost fabrication techniques, and can assist current trends to reach the highest efficiencies in ever-thinner photovoltaics.

POSS hybrid poly(ionic liquid) ionogel solid electrolyte for flexible lithium batteries

Traditional lithium-ion batteries based on liquid electrolytes cannot adapt to the increasing and various highly flexible requirements. In this work, a confinement-driven strategy has been developed to confine the ionic liquids in a polyhedral oligomeric silsesquioxanes (POSS) and poly(ethylene glycol) diacrylate (PEGDA) hybrid cross-linked poly(ionic liquid)s ionogel (POSS-CPIL-n), which endows this novel electrolyte superior flame retardancy and dramatically flexibility. The POSS-CPIL-n ionogel renders exceptional flexibility (bend, fold and twist) and desired mechanical properties (2.5 MPa). Advantageous for high dissociation of lithium salt, high ionic conductivity (2.5 mS cm−1 at 25 °C) and considerable lithium ion mobility (0.51) are provided. Thanks to the flexibility of ionogel electrolyte together with excellent thermal and electrochemical stability, the LiFePO4||Li cell based on POSS-CPIL-n exhibits significant rate performance and cycling stability, the capacity of the cell can reach to 100 mAh g−1 at 4 C and maintain a substantial discharge capacity of 160.5 mAh g−1 after 200 cycles at 0.2C. Furthermore, the soft-package assembled with POSS-CPIL-n can successfully charge a cell phone and continue to supply power stably even when the battery is exposed to the air, which indicates that POSS-CPIL-n has a promising potential application in high safety flexible electronic devices.

Application Research of Bridge Damage Detection Based on the Improved Lightweight Convolutional Neural Network Model

To ensure the safety and rational use of bridge traffic lines, the existing bridge structural damage detection models are not perfect for feature extraction and have difficulty meeting the practicability of detection equipment. Based on the YOLO (You Only Look Once) algorithm, this paper proposes a lightweight target detection algorithm with enhanced feature extraction of bridge structural damage. The BIFPN (Bidirectional Feature Pyramid Network) network structure is used for multi-scale feature fusion, which enhances the ability to extract damage features of bridge structures, and uses EFL (Equalized Focal Loss) to optimize the sample imbalance processing mechanism, which improves the accuracy of bridge structure damage target detection. The evaluation test of the model has been carried out in the constructed BDD (Bridge Damage Dataset) dataset. Compared with the YOLOv3-tiny, YOLOv5S, and B-YOLOv5S models, the mAP@.5 of the BE-YOLOv5S model increased by 45.1%, 2%, and 1.6% respectively. The analysis and comparison of the experimental results prove that the BE-YOLOv5S network model proposed in this paper has a better performance and a more reliable performance in the detection of bridge structural damage. It can meet the needs of bridge structure damage detection engineering with high requirements for real-time and flexibility.