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Objectives: To investigate the effect of emotion regulation skills-focused (ERSF) interventions to reduce pain intensity and improve psychological outcomes for people with chronic pain and to narratively report on safety and intervention compliance. Methods: Six databases and four registries were searched for randomized controlled trials (RCTs) up to 29 April 2022. Risk of bias was evaluated using the Cochrane RoB 2.0 tool, and certainty of evidence was assessed according to the Grading, Assessment, Development and Evaluation (GRADE). Meta-analyses for eight studies (902 participants) assessed pain intensity (primary outcome), emotion regulation, affect, symptoms of depression and anxiety, and pain interference (secondary outcomes), at two time points when available, post-intervention (closest to intervention end) and follow-up (the first measurement after the post-intervention assessment). Results: Compared to TAU, pain intensity improved post-intervention (weighted mean difference [WMD] = −10.86; 95% confidence interval [CI] [−17.55, −2.56]) and at follow-up (WMD = −11.38; 95% CI [−13.55, −9.21]). Emotion regulation improved post-intervention (standard mean difference [SMD] = 0.57; 95% CI [0.14, 1.01]), and depressive symptoms improved at follow-up (SMD = −0.45; 95% CI [−0.66, −0.24]). Compared to active comparators, anxiety symptoms improved favouring the comparator post-intervention (SMD = 0.10; 95% CI [0.03, 0.18]), and compared to CBT, pain interference improved post-intervention (SMD = −0.37; 95% CI [−0.69, −0.04]). Certainty of evidence ranged from very low to moderate. Significance: The findings provide evidence that ERSF interventions reduce pain intensity for people with chronic pain compared to usual treatment. These interventions are at least as beneficial to reduce pain intensity as the current gold standard psychological intervention, CBT. However, the limited number of studies and certainty of evidence mean further high-quality RCTs are warranted. Ad

Objectives: To examine relationships between visual function (ie, contrast sensitivity, visual field, color vision, and motion perception) and cognitive impairment, including any definition of “cognitive impairment,” mild cognitive impairment, or dementia. Design: Systematic review and meta-analyses. Setting and participants: Any settings; participants with (cases) or without (controls) cognitive impairment. Methods: We searched 4 databases (to January 2024) and included published studies that compared visual function between cases and controls. Standardized mean differences (SMD) with 95% CIs were calculated where data were available. Data were sufficient for meta-analyses when cases were people with dementia. The Joanna Briggs Institute checklists were used for quality assessment. Results: Fifty-one studies/69 reports were included. Cross-sectional evidence shows that people with dementia had worse contrast sensitivity function and color vision than controls: measured by contrast sensitivity (log units) on letter charts, SMD –1.22 (95% CI –1.98, −0.47), or at varied spatial frequencies, −0.92 (−1.28, −0.57); and by pseudoisochromatic plates, −1.04 (−1.59, −0.49); color arrangement, −1.30 (−2.31, −0.29); or matching tests, −0.51 (−0.78, −0.24). They also performed more poorly on tests of motion perception, −1.20 (−1.73, −0.67), and visual field: mean deviation, −0.87 (−1.29, −0.46), and pattern standard deviation, −0.69 (−1.24, −0.15). Results were similar when cases were limited to participants with clinically diagnosed Alzheimer disease. Sources of bias included lack of clarity on study populations or settings and definitions of cognitive impairment. The 2 included longitudinal studies with follow-ups of approximately 10 years were of good quality but reported inconsistent results. Conclusions and implications: In the lack of longitudinal data, cross-sectional studies indicate that individuals with cognitive impairment have poorer visual function than those with

Study design: Randomised controlled trial. Objectives: The objective is to describe an electroencephalography (EEG) neurofeedback intervention that will be provided in a randomised controlled trial for people with neuropathic pain following spinal cord injury (SCI): the StoPain Trial. In this trial, participants in the treatment group will implement an EEG neurofeedback system as an analgesic intervention at home, while participants in the control group will continue with the treatments available to them in the community. Setting: University-based study in Sydney, Australia. Methods/results: This manuscript describes the rationale and components of the EEG neurofeedback intervention designed for individuals with SCI neuropathic pain and intended for home-based implementation. Our report is based on the criteria of the Template for Intervention Description and Replication (TIDieR) checklist, and includes why the efficacy of EEG neurofeedback will be investigated, what will be provided, who will administer it, and how, where, when, and how much the EEG neurofeedback intervention will be administered. Conclusions: This manuscript provides a detailed description of a complex intervention used in a randomised controlled trial. This description will facilitate the subsequent interpretation of the trial results and allow for the replication of the intervention in clinical practice and future trials. Sponsorship: Australian Government Medical Research Future Fund (2020 Rare Cancers Rare Diseases and Unmet Needs Scheme: 2006020).

STUDY DESIGN: Randomised controlled trial. OBJECTIVES: The objective is to describe an electroencephalography (EEG) neurofeedback intervention that will be provided in a randomised controlled trial for people with neuropathic pain following spinal cord injury (SCI): the StoPain Trial. In this trial, participants in the treatment group will implement an EEG neurofeedback system as an analgesic intervention at home, while participants in the control group will continue with the treatments available to them in the community. SETTING: University-based study in Sydney, Australia. METHODS/RESULTS: This manuscript describes the rationale and components of the EEG neurofeedback intervention designed for individuals with SCI neuropathic pain and intended for home-based implementation. Our report is based on the criteria of the Template for Intervention Description and Replication (TIDieR) checklist, and includes why the efficacy of EEG neurofeedback will be investigated, what will be provided, who will administer it, and how, where, when, and how much the EEG neurofeedback intervention will be administered. CONCLUSIONS: This manuscript provides a detailed description of a complex intervention used in a randomised controlled trial. This description will facilitate the subsequent interpretation of the trial results and allow for the replication of the intervention in clinical practice and future trials. SPONSORSHIP: Australian Government Medical Research Future Fund (2020 Rare Cancers Rare Diseases and Unmet Needs Scheme: 2006020).

Light propagation facilitates digital information encryption by utilizing Epsilon Negative (ENG) metamaterials as a medium. Achieving the desired encryption hinges on synchronizing two pivotal features: the phase difference and the epsilon shifting of the metamaterials. The proposed metamaterial is intricately designed to represent digital bit 1 (states 0 and 1), contingent upon the arrangement of material multilayers within the metamaterial device. Specifically, state 1 is fashioned through a combination of Graphene and Gold, while state 0 is solely represented by Graphene on a metamaterial structure. The developed structure exhibits right hand metamaterial (epsilon of [Formula: see text]7.20) for state 0 and plasma metamaterial (epsilon [Formula: see text]6.94) for state 1 at 8.6 THz. Notably, the phase difference between 0 and 1 states is around [Formula: see text]. Primarily leveraging the synchronized combination phase and epsilon as the key design parameters for the metamaterial array, the beamforming pattern enables beam steering within the angular range of [Formula: see text] to [Formula: see text]. Secondly, employing a sophisticated material addition strategy involving Gallium Arsenide (GaAs) systematically manipulates the sidelobe in specific directions, thereby providing a mechanism to modulate the sidelobe and concurrently augment spatial resolution. After preparing the metamaterial coded (MC) lens, the source, lens and receiver strategically encrypt the scanning image, employing techniques including Doppler compression and 2D discrete Fourier transform. The proposed MC lens offers applicability like image encryption, security scanning, conveyor systems, hyperlenses and modern cryptography.

The current age is witnessing an unprecedented dependence on data originating from humans through the devices that comprise the Internet of Things. The data collected by these devices are used for many purposes, including predictive maintenance, smart analytics, preventive healthcare, disaster protection, and increased operational efficiency and performance. However, most applications and systems that rely on user data to achieve their business objectives fail to comply with privacy regulations and expose users to numerous privacy threats. Such privacy breaches raise concerns about the legitimacy of the data being processed. Hence, this paper reviews some notable techniques for transparently, securely, and privately separating and sharing personally identifiable and non-personally identifiable information in various domains. One of the key findings of this study is that, despite various advantages, none of the existing techniques or data sharing applications preserve data/user privacy throughout the data life cycle. Another significant issue is the lack of transparency for data subjects during the collection, storage, and processing of private data. In addition, as privacy is unique to every user, there cannot be a single autonomous solution to identify and secure personally identifiable information for users of a particular application, system, or people living in different states/countries. Therefore, this research suggests a way forward to prevent the leakage of personally identifiable information at various stages of the data life cycle in compliance with some of the common privacy regulations around the world. The proposed approach aims to empower data owners to select, share, monitor, and control access to their data. In addition, the data owner is a stakeholder and a party to all data sharing contracts related to his personal data. The proposed solution has broad security and privacy controls that can be tailored to the privacy needs of specific applications.

Meeting the diverse quality-of-service (QoS) requirements in ultra-dense Internet of Things (IoT) networks operating under varying network loads is challenging. Moreover, latency-critical IoT applications cannot afford excessive control signaling overheads caused by centralized access control methods. A distributed network access approach can potentially address this problem. In this regard, multi-agent multi-armed bandit (MAB) learning is a promising tool for designing distributed network access protocols. This paper proposes a multi-agent MAB learning-based grant-free access mechanism for ultra-dense networks, where multiple base stations (BSs) serve massive delay-sensitive and delay-tolerant IoT devices. Delay-sensitive devices are prioritized to choose the BSs with larger numbers of channels in a probabilistic manner. The proposed mechanism enables the devices to improve their BS selection over time to accommodate the maximum number of devices that can meet a prescribed latency-reliability criterion. Simulation results show that the proposed MAB learning-based network access mechanism outperforms the random BS selection strategy in which end devices do not employ any learning scheme to adapt to the network dynamics.

Capacitive coupling wireless power transfer (CCWPT) is one of the pervasive methods to transfer power in the reactive near-field zone. In this article, a flexible design methodology based on binary particle swarm optimization (BPSO) algorithm is proposed for a pixelated microstrip structure. The pixel configuration of each parallel plate (43 × 43 pixels) determines the frequency response of the system (S-parameters) and by changing this configuration, we can achieve the dedicated operating frequency (resonance frequency) and its related |S21| value. Due to the large number of pixels, iterative optimization algorithm (BPSO) is the solution for designing a CCWPT system. However, the output of each iteration should be simulated in electromagnetic (EM) simulators (e.g., computer simulation technology (CST), high-frequency structure simulator (HFSS), etc.); hence, the whole optimization process is time-consuming. This article develops a rapid, agile, and efficient method for designing two parallel pixelated microstrip plates of a CCWPT system based on deep neural networks. In the proposed method, CST-based BPSO algorithm is replaced with an AI-based method using residual network-18. Advantages of the AI-based iterative method are automatic design process, more efficient, less time-consuming, less computational resource-consuming, and less background EM knowledge requirements compared to the conventional techniques. Finally, the prototype of the proposed simulated structure is fabricated and measured. The simulation and measurement results validate the design procedure accuracy, using AI-based BPSO algorithm. The mean absolute error of prediction for the main resonance frequency and related |S21| are 110 MHz and 0.18 dB, respectively, and according to the simulation results, the whole design process is 3629 times faster than the CST-based BPSO, algorithm.

Far-field wireless power transfer (WPT), based on radio frequency (RF) waves, came into the picture to fulfill the power need of large Internet-of-Things (IoT) networks, the backbone of the 5G and beyond era. However, RF communication signals carry both information as well as energy. Therefore, recently, simultaneous wireless information and power transfer (SWIPT) has attracted much attention in order to wirelessly charge these IoT devices. In this article, we propose a novel $N$ -tone multitone phase shift keying (PSK) modulation scheme, taking advantage of the nonlinearity of integrated receiver rectifier architecture. The main advantage of the proposed modulation scheme is the reduction in ripple voltage, introduced by the symbol transmission through phases. Achievable power conversion efficiency (PCE) and bit-error-rate (BER) at the output are considered to measure the efficacy of the proposed modulation scheme. Simulation results are verified by the measurements over the designed rectifier circuitry. The effect of symbol phase range, modulation order, and the number of tones are analyzed. In the future, this transmission scheme can be utilized to satisfy the data and power requirements of low-power IoT sensor networks.

In this article, a multimode smart nonlinear circuit (MSNC) for wireless communications (transmitting (Tx) and receiving (Rx) modes) as well as energy harvesting (EH) and power saving is presented. The proposed MSNC is designed at 680 MHz and has three ports, which are connected to an antenna, T/R (transceiver), and power-saving modules. According to the input-output power level, the proposed MSNC has three modes of operations: Rx, power saving, and Tx, for low ( < !!-25 dBm), mid (>!!-25 and < 0 dBm), and high (>5 dBm) power ranges, respectively. In the power-saving mode, when the received power is greater than the sensitivity of the Rx module, the excess power is directed to the EH load (power storage), while the Rx direction is still in place. The fact that the proposed MSNC can manage the received power level smartly and without any external control, distinguishes the proposed MSNC from other EH circuits. The proposed MSNC operates within a power range from-50 to +15 dBm, demonstrates an efficiency of more than 60% in the power-saving mode, and has acceptable matching over a large frequency range. The design procedure of the proposed MSNC along with the theoretical, simulation, and measurement results are presented in this article. Good agreement between theory, simulation, and measurement results confirms the accuracy of the design procedure.

Background: Emotion dysregulation is key to the development and maintenance of chronic pain, feeding into a cycle of worsening pain and disability. Dialectical behavioral therapy (DBT), an evidence-based treatment for complex transdiagnostic conditions presenting with high emotion dysregulation, may be beneficial to manage and mitigate the emotional and sensory aspects of chronic pain. Increasingly, DBT skills training as a key component of standard DBT is being delivered as a stand-alone intervention without concurrent therapy to help develop skills for effective emotion regulation. A previous repeated-measure single-case trial investigating a novel technologically driven DBT skills training, internet-delivered DBT skills training for chronic pain (iDBT-Pain), revealed promising findings to improve both emotion dysregulation and pain intensity. Objective: This randomized controlled trial aims to examine the efficacy of iDBT-Pain in comparison with treatment as usual to reduce emotion dysregulation (primary outcome) for individuals with chronic pain after 9 weeks and at the 21-week follow-up. The secondary outcomes include pain intensity, pain interference, anxiety symptoms, depressive symptoms, perceived stress, posttraumatic stress, harm avoidance, social cognition, sleep quality, life satisfaction, and well-being. The trial also examines the acceptability of the iDBT-Pain intervention for future development and testing. Methods: A total of 48 people with chronic pain will be randomly assigned to 1 of 2 conditions: treatment and treatment as usual. Participants in the treatment condition will receive iDBT-Pain, consisting of 6 live web-based group sessions led by a DBT skills trainer and supervised by a registered psychologist and the iDBT-Pain app. Participants in the treatment-as-usual condition will not receive iDBT-Pain but will still access their usual medication and health interventions. We predict that iDBT-Pain will improve the primary outcome of emotion d

Objective: We aim to quantify the co-existence of age-related macular degeneration (AMD), glaucoma, or diabetic retinopathy (DR) and cognitive impairment or dementia. Method: MEDLINE, EMBASE, PsycINFO and CINAHL were searched (to June 2020). Observational studies reporting incidence or prevalence of AMD, glaucoma, or DR in people with cognitive impairment or dementia, and of cognitive impairment or dementia among people with AMD, glaucoma, or DR were included. Results: Fifty-six studies (57 reports) were included but marked by heterogeneities in the diagnostic criteria or definitions of the diseases, study design, and case mix. Few studies reported on the incidence. Evidence was sparse but consistent in individuals with mild cognitive impairment where 7.7% glaucoma prevalence was observed. Prevalence of AMD and DR among people with cognitive impairment ranged from 3.9% to 9.4% and from 11.4% to 70.1%, respectively. Prevalence of AMD and glaucoma among people with dementia ranged from 1.4 to 53% and from 0.2% to 25.9%, respectively. Prevalence of DR among people with dementia was 11%. Prevalence of cognitive impairment in people with AMD, glaucoma, and DR ranged from 8.4% to 52.4%, 12.3% to 90.2%, and 3.9% to 77.8%, respectively, and prevalence of dementia in people with AMD, glaucoma and DR ranged from 9.9% to 62.6%, 2.5% to 3.3% and was 12.5%, respectively. Conclusions: Frequency of comorbid eye disease and cognitive impairment or dementia varied considerably. While more population-based estimations of the co-existence are needed, interdisciplinary collaboration might be helpful in the management of these conditions to meet healthcare needs of an ageing population. Trial registration: PROSPERO registration: CRD42020189484.

Background Depressive symptoms are often comorbid with chronic pain. These conditions share aberrant emotion processing and regulation, as well as having common brain networks. However, the relationship between depressive symptoms and chronic pain and the effects on emotional brain function are unclear. Aims The present study aimed to disentangle the effects of chronic pain and depressive symptoms on functional connectivity between regions implicated in both these conditions. Method Twenty-six individuals with chronic pain (referred to as the pain group) and 32 healthy controls underwent resting-state functional magnetic resonance imaging and completed the Beck Depression Inventory. Main effects of group, depressive symptoms (total severity score) and their interaction on the functional connectivity of three seed regions (the left and right amygdalae and the medial prefrontal cortex; mPFC) with the rest of the brain were evaluated. In cases of significant interaction, moderation analyses were conducted. Results The group × depressive symptoms interaction was significantly associated with changes in connectivity between the right amygdala and the mPFC (family-wise error-corrected P-threshold (pFWEc = 0.008). In the moderation analysis, the pain group showed weaker connectivity between these regions at lower levels of depressive symptoms (P = 0.020), and stronger connectivity at higher levels of depressive symptoms (P = 0.003), compared with the healthy controls. In addition, the strength of connectivity decreased in the healthy controls (P = 0.005) and increased in the pain group (P = 0.014) as the severity of depressive symptoms increased. Conclusions Depressive symptoms moderate the impact of chronic pain on emotional brain function, with potential implications for the choice of treatment for chronic pain.

In Australia, digital agriculture is considered immature and its adoption ad hoc, despite a relatively advanced technology innovation sector. In this review, we focus on the technical, governance and social factors of digital adoption that have created a disconnect between technology development and the end user community (farmers and their advisors). Using examples that reflect both successes and barriers in Australian agriculture, we first explore the current enabling technologies and processes, and then we highlight some of the key socio-technical factors that explain why digital agriculture is immature and ad hoc. Pronounced issues include fragmentation of the innovation system (and digital tools), and a lack of enabling legislation and policy to support technology deployment. To overcome such issues and increase adoption, clear value propositions for change are necessary. These value propositions are influenced by the perceptions and aspirations of individuals, the delivery of digitally-enabled processes and the supporting legislative, policy and educational structures, better use/conversion of data generated through technology applications to knowledge for supporting decision making, and the suitability of the technology. Agronomists and early adopter farmers will play a significant role in closing the technology-end user gap, and will need support and training from technology service providers, government bodies and peer-networks. Ultimately, practice change will only be achieved through mutual understanding, ownership and trust. This will occur when farmers and their advisors are an integral part of the entire digital innovation system.

Slant polarized Multi Input Multi Output MIMO antennas are able to improve the performance of mobile communication systems in terms of channel capacity Specifically the implementation of MIMO configurations for automotive applications requires the consideration of high gain wideband low profile and affordable antennas in the communication link In this work design simulation and measurement of a new dual band slant polarized MIMO antenna with HPBW Half Power Beam Width of around 900 are presented Then four replicas of the proposed antenna set are placed at four different poles North South West and East to cover 3600 around the vehicle in an omni directional pattern In a real world scenario the proper antenna set is selected to communicate with the intended user Each slant MIMO antenna set consists of two inclined 450 low band LB 700 to 900 MHz and two inclined high band HB 1 7 to 2 7 GHz log periodic antennas The measured gain of LB and HB antennas are 7 dBi and 8 dBi respectively Strong correlation between simulation and measurement results confirms the accuracy of the design and simulation procedures of the proposed antenna system obtained using a genetic optimization algorithm The fabricated antenna was also measured in the field for industrial applications

This article presents a multiservice pixelated stacked antenna for dual-band application at 5.2 and 5.8-GHz bands. An efficient method for designing multilayer or stacked antennas for multistandard Internet of Things (IoT) applications is proposed in this article. The proposed antenna topology is based on two different pixel shapes and offers essential aspects, such as flexibilities in the design of low-profile single-band or multiband antennas for wireless systems. The antenna design consists of two pixelated layers of radiating patch, consisting of different shaped pixels. The V-shaped binary particle swarm optimization (VBPSO) algorithm has been implemented for the optimization of pixel positions. Only by considering the predefined regions, the antenna can be designed and optimized for various design goals. In the design procedure, triangular-shaped pixels are used to form the principal radiating patch, while square-shaped pixels are implemented on the stacked parasitic patch. Measurement and simulation results are in good agreement which proves the accuracy of the design and simulation procedure. The antenna operates at 5.16-5.21 GHz and 5.79-5.86 GHz. With a compact design, the proposed antenna achieves 4.7 and 4.4-dBi gain at 5.2 and 5.8 GHz, respectively. The proposed antenna design methodology offers great potentials to be aligned with specific design requirements of wireless low-profile IoT devices.

The weather phenomenon of frost poses great threats to agriculture. As recent frost prediction methods are based on on-site historical data and sensors, extra development and deployment time are required for data collection in any new site. The aim of this article is to eliminate the dependency on on-site historical data and sensors for frost prediction methods. In this article, a frost prediction method based on spatial interpolation is proposed. The models use climate data from existing weather stations, digital elevation models surveys, and normalized difference vegetation index data to estimate a target site's next hour minimum temperature. The proposed method utilizes ensemble learning to increase the model accuracy. Climate datasets are obtained from 75 weather stations across New South Wales and Australian Capital Territory areas of Australia. The results show that the proposed method reached a detection rate up to 92.55%.

Discrete fractional Fourier transforms (DFrFTs) are universal mathematical tools in signal processing, communications, and microwave sensing. Despite the excessive applications of DFrFT, the implementation of corresponding fractional orders in the baseband signal often leads to bulky, power-hungry, and high-latency systems. In this article, we present a passive metamaterial coupled lines network (MCLN) that performs the analog DFrFT in real-time at microwave frequencies. The proposed MCLN consists of M parallel microstrip transmission lines (TLs) in which adjacent TLs are loaded with interdigital capacitors to enhance the coupling level. We show that with the proper design of the coupling coefficients between adjacent channels, the MCLN can perform an M-point DFrFT of an arbitrary fractional order that can be designed through the length of the network. In the context of real-time signal processing for the realization of DFrFT, we design, model, simulate, and implement a 16×16 MCLN and experimentally demonstrate the performance of the proposed structure. The proposed innovative approach is versatile and is capable to be used in various applications where DFrFT is an essential tool. The proposed design scheme based on MCLN is scalable across the frequency spectrum and can be applied to the millimeter and submillimeter wave systems.

In this article, a compact multiservice antenna (MSA) is presented for sensing and communication using a reconfigurable complementary spiral resonator. A three turns complementary spiral resonator (3-CSR) is inserted in the ground plane of a modified patch antenna to create a miniaturized structure. Two positive-intrinsic-negative (p-i-n) diodes (D1 and D2) are also integrated with the 3-CSR to achieve frequency reconfiguration. The proposed structure operates in three different modes, i.e., dual-band joint communication and sensing antenna (JCASA), dual-band antenna, and single-band antenna. The required mode can be selected by changing the state of the p-i-n diodes. In mode-1, the first band (0.95-0.97 GHz) of the antenna is dedicated to sensing by using frequency-domain reflectometry (FDR), while the second band (1.53-1.56 GHz) is allocated to communication. The sensing ability of the proposed structure is utilized to measure soil moisture using FDR. Based on the frequency shift, the permittivity of the soil is observed to measure soil moisture. In mode-2 and mode-3, the structure operates as a standard dual- and single-band antenna, respectively, with a maximum gain of 1.5 dBi at 1.55 GHz. The proposed planar structure, with its simple geometry and a high sensitivity of 1.7%, is a suitable candidate for precision farming. The proposed structure is versatile and capable of being utilized as a single- or dual-band antenna and also measuring the permittivity of materials within the range of 1-20. Hence, it is adaptable to a range of applications.

Introduction Chronic pain, defined as pain persisting longer than 3 months, is more than an unpleasant sensory experience. Persistent negative emotions and emotional comorbidities, such as depression and anxiety, plague people with chronic pain leading to worsening pain intensity and increasing disability. While cognitive-behavioural therapy (CBT) is the gold standard psychological treatment, recent evidence highlights that CBT lacks efficacy for the physical and emotional aspects of chronic pain. Increasingly, researchers are investigating emotion-centric psychological therapies. While treatment modalities vary, these interventions frequently target understanding emotions, and train individuals for an emotionally adaptive response. The aim of this systematic review and meta-analysis is to quantify the efficacy of emotion-centric interventions for the physical and emotional characteristics of chronic pain. Methods/analysis Electronic databases (EMBASE, PubMed, PsychINFO, Cochrane Central Register of Controlled Trials, CINAHL and Web of Science) will be systematically searched from inception to 28 April 2022 for randomised controlled trials. Studies that compare an emotion-centric intervention with another form of treatment or placebo/control for adults (≥18 years old) with chronic pain will be included. All treatment modes (eg, online or in-person), any duration and group-based or individual treatments will be included. Studies that do not investigate at least one emotion-centric treatment will be excluded. The primary outcome is pain intensity. Secondary outcomes include emotion dysregulation, depression, anxiety, affect, safety and intervention compliance. A quantitative synthesis using a random effects meta-analysis will be adopted. Risk of bias will be evaluated using Cochrane Risk of Bias V.2.0 with the certainty of evidence assessed according to Recommendation, Assessment, Development and Evaluation. Data permitting, subgroup analysis will be conducted for int

In the original article, there was an error in the Author Contributions. It has been updated to align with the guidelines of the International Committee of Medical Journal Editors. A correction has been made to the Author Contributions.

Background: Electroencephalographic (EEG) neurofeedback has been utilized to regulate abnormal brain activity associated with chronic pain. Methods: In this systematic review, we synthesized the evidence from randomized controlled trials (RCTs) to evaluate the effect of EEG neurofeedback on chronic pain using random effects meta-analyses. Additionally, we performed a narrative review to explore the results of non-randomized studies. The quality of included studies was assessed using Cochrane risk of bias tools, and the GRADE system was used to rate the certainty of evidence. Results: Ten RCTs and 13 non-randomized studies were included. The primary meta-analysis on nine eligible RCTs indicated that although there is low confidence, EEG neurofeedback may have a clinically meaningful effect on pain intensity in short-term. Removing the studies with high risk of bias from the primary meta-analysis resulted in moderate confidence that there remained a clinically meaningful effect on pain intensity. We could not draw any conclusion from the findings of non-randomized studies, as they were mostly non-comparative trials or explorative case series. However, the extracted data indicated that the neurofeedback protocols in both RCTs and non-randomized studies mainly involved the conventional EEG neurofeedback approach, which targeted reinforcing either alpha or sensorimotor rhythms and suppressing theta and/or beta bands on one brain region at a time. A posthoc analysis of RCTs utilizing the conventional approach resulted in a clinically meaningful effect estimate for pain intensity. Conclusion: Although there is promising evidence on the analgesic effect of EEG neurofeedback, further studies with larger sample sizes and higher quality of evidence are required.

Emotion dysregulation frequently co-occurs with chronic pain, which in turn leads to heightened emotional and physical suffering. This cycle of association has prompted a recommendation for psychological treatment of chronic pain to target mechanisms for emotion regulation. The current trial addressed this need by investigating a new internet-delivered treatment incorporating emotional skills training from dialectical behavioral therapy (DBT). Using a single-case experimental design that is suited to heterogeneous populations and can demonstrate efficacy with a small sample, three participants with chronic pain were recruited. Participants received four weeks of online DBT skills training (iDBT-Pain intervention) which incorporated one-on-one sessions over Zoom and a web app. Results revealed compelling evidence for the intervention on the primary outcome of emotion dysregulation and were promising for the secondary outcome of pain intensity. Improvement was also identified on pre-and post-measures of depression, coping behaviors, sleep problems, wellbeing, and harm avoidance, indicating that the intervention may positively influence other factors related to chronic pain. Overall, the trial provides preliminary efficacy for the intervention to improve chronic pain. However, we recommend further investigation of the iDBT-Pain intervention, either in single case trials, which when conducted with scientific rigor may be aggregated to derive nomothetic conclusions, or in a group-comparison trial to compare with usual modes of treatment. Perspective: This trial advances understanding of emotion-focused treatment for chronic pain and provides evidence for a viable new technological treatment. Importantly, as an internet-delivered approach, the iDBT-Pain intervention is accessible to those with restricted mobility and remote communities where there are often limited psychological services for people with chronic pain.

[This corrects the article DOI: 10.3389/fneur.2019.01110.].

This paper presents an efficient dual-band rectifier for radiofrequency energy harvesting (RFEH) applications at FM and GSM bands. The single-port rectifier circuit, which comprises a 3-port network, optimized T-matching circuits and voltage doubler, is designed, simulated and fabricated to obtain a high RF-to-DC power conversion efficiency (PCE). Measurement results show PCE of26% and 22% at -20dBm, and also 58% and 51% at -10dBm with a maximum amount of 69% and 65% at -2.5dBm and -5dBm, with single tone at 95 and 925 MHz, respectively. Besides, the fractional bandwidth of 21% at FM and 11% at GSM band is achieved. The measurement and simulation results are in good agreement. Consequently, the proposed rectifier can be a potential candidate for ambient RF energy harvesting and wireless power transfer (WPT). It should be noted that a 3-port network as a duplexer is designed to be integrated with single-port antennas which cover both FM and GSM bands as a low-cost solution. Moreover, based on simulation results, PCE has small variations when the load resistor varies from 10 to 18 k$\Omega$. Therefore, this rectifier can be utilized for any desired resistance within the range, such as sensors and IoT devices.

Soil moisture monitoring and irrigation scheduling are essential parameters in farming efficiency. Internet-of-Things (IoT) technology is a promising solution for automating irrigation procedures and improving farming efficiency by removing human faults. In this article, a new method is introduced to measure soil moisture level along with providing energy to run a low-power transmitter as an alarm signal. A combination of a metamaterial perfect absorber (MPA) and two rectifiers that are designed at different frequencies specifies 5% and 25% soil moisture levels. The sensor monitors the soil moisture continuously without consuming energy. Once the soil moisture becomes 5% of the first rectifier starts working and provides 65 $\text{uW}$ dc output, while the second rectifier is off. Increasing the soil moisture to 25%, the second rectifier creates 100 uW dc output when the first rectifier is off. The designed structure is fabricated on RO4003 in a $4$ $\times$ $4$ array. The measurement results are provided by performing a set of different experiments. Initially, the MPA’s absorption characteristics are validated facing different polarization and incident angles. Then, the sensing capability is proven by burying the proposed sensor under sand and measuring the dc outputs of rectifiers. A strong correlation between simulation and measurement results validates the design procedure.

This paper presents the development of a dual-band antenna for novel RF Energy Harvesting and Wireless Power Transfer systems operating on the fifth-generation (5G) cellular networks frequencies. A logarithmic spiral is selected to provide circular polarization in 5G bands. A conformal balun is designed to ensure a symmetric excitation of the spirals' arms. A ring reflector is proposed to ascertain the stable gain (>5dBic) and axial ratio (<3db) in the 20 and 40 GHz frequency bands after the spiral and the balun were integrated. A ring reflector for realised gain improvement is implemented. The internal radius, external radius and thickness of the ring were optimised to provide additional gain over the desired frequencies without noticeable deterioration of the axial ratio.

This paper proposes joint mobile node participation and routing protocol for multi-hop device-to-device (MD2D) networking in intelligent transportation systems, called fuzzy-based participation and routing protocol for MD2D (FPRM). Our proposed protocol is designed to operate over future open-radio access networks (O-RANs). We introduce a sub-layer at the network layer that can determine nodes with the highest participation probability in routing using a fuzzy logic system, thus building a framework to create more stable routes. To ensure the participating nodes are capable of handling the data traffic, two constraints are proposed, mobility and coverage constraints. The former enables the creation of sustainable communication links, and the latter enforces the communication service to the entire MD2D network. Simulation results show that our approach can increase the network lifetime, decrease the end-to-end (E2E) delay, and increase the packet delivery ratio (PDR) compared to the existing proactive routing protocol. Our protocol outperforms the benchmarked MD2D protocols and other investigated ad hoc protocols.

Future 6G wireless communication systems are expected to feature intelligence and automation. Knowledge-defined networking (KDN) is an evolutionary step toward autonomous and self-driving networks. The building blocks of the KDN paradigm in achieving self-driving networks are software-defined networking (SDN), packet-level network telemetry, and machine learning (ML). The KDN paradigm intends to integrate intelligence to manage and control networks automatically. In this study, we first introduce the disadvantages of current network technologies. Then, the KDN and associated technologies are explored with three possible KDN architectures for heterogeneous wireless networks. Furthermore, a thorough investigation of recent survey studies on different wireless network applications was conducted. The aim is to identify and review suitable ML-based studies for KDN-based wireless cellular networks. These applications are categorized as resource management, network management, mobility management, and localization. Resource management applications can be further classified as spectrum allocation, power management, quality-of-service (QoS), base station (BS) switching, cache, and backhaul management. Within network management configurations, routing strategies, clustering, user/BS association, traffic classification, and data aggregation were investigated. Applications in mobility management include user mobility prediction and handover management. To improve the accuracy of positioning in indoor environments, localization techniques were discussed. We classify existing research into the respective KDN architecture and identify how the knowledge obtained will enhance future networks; as a result, researchers can extend their work to empower intelligence and self-organization in the network using the KDN paradigm. Finally, the requirements, motivations, applications, challenges, and open issues are presented.

Controlling water salinity plays a key role in farming efficiency. Current sensors are mostly expensive and need regular maintenance. In addition, they require electrical connections or extra power supply that leads to difficult and costly implementation in remote-sensing scenarios. In this article, an accurate and low-profile sensor is developed using a metamaterial perfect absorber (MPA) structure. The proposed sensor works based on the level and frequency of the absorbed signals. Hence, there is no need for electrical connections, which enables remote-sensing applications. Square-shaped channels have been created in a regular FR-4 substrate to facilitate sensing of water salinity levels. A 7 × 7 array with a total size of 140 mm × 160 mm has been fabricated that shows a resolution of 10 MHz per percentage of water salinity. The absorption frequency shifts from f=3.12 to 3.59 GHz for salinity level from 0% to 50%. A strong correlation between measurement and simulation results validates the design procedure.

Absorbing ambient electromagnetic (EM) signals is required in some applications such as energy harvesting and sensing. Metamaterial perfect absorbers (MPAs) are a promising candidate to absorb the EM signal with near-unity efficiency. However, designing a structure with multi absorption bands is still challenging among researchers due to the requiring multi-layer and multi-resonators structures. Herein, a multi-band MPA based on SIW cavity structure is introduced. Taking advantage of various resonance modes of the cavity at higher order, a low-profile, highly efficient, and easy to implement MPA is achieved. In addition, the proposed structure is completely polarization angle insensitive, which is crucial to have a highly efficient EM waves absorber.

Mission-critical Internet-of-Things (IoT) applications require communication interfaces that provide ultra-reliability and low latency. Acquiring knowledge regarding the number of active devices and their latency-reliability requirements becomes essential to optimize resource allocation in heterogeneous networks. Due to the inherent heavy computation overheads, the conventional centralized decision-making approaches result in large latency. The distributed computing and device-level prediction of network parameters can play a significant role in designing mission-critical IoT applications operating in dynamic environments. This paper considers the medium access control (MAC) layer of heterogeneous networks employing a framed-ALOHA-based restricted transmission strategy to enhance reliability. We present a statistical learning-based device-level network exploration mechanism in which end-devices use their transmission history to predict different network parameters. The IoT devices share the learned parameters with the base station (BS) to identify different groups presented in the network. The simulation results show that the mean square error (MSE) in predicting different network parameters can be reduced by increasing the history window size. In this regard, the optimal size of the history window under the given accuracy constraints is also determined. We demonstrate that the proposed device-level network load prediction mechanism is more robust as compared to the BS-centered approach.

This article presents a time domain transmissometry soil moisture sensor (TDT-SMS) using a dispersive phase shifter (DPS), consisting of an interdigital capacitor that is loaded with a stacked four-turn complementary spiral resonator (S4-CSR). Soil moisture measurement technique of the proposed sensor is based on the complex permittivity sensing property of a DPS in time domain. Soil relative permittivity which varies with its moisture content is measured by burying the DPS under a soil mass and changing its phase difference while excited with a 114-MHz sine wave (single tone). DPS output phase and magnitude are compared with the reference signal and measured with a phase/loss detector. The proposed sensor exhibits accuracy better than ±1.2% at the highest volumetric water content (VWC = 30%) for sandy-type soil. Precise design guide is developed and simulations are performed to achieve a highly sensitive sensor. The measurement results validate the accuracy of theoretical analysis and design procedure. Owning the advantages of low profile, low power consumption, and high sensitivity makes the proposed TDT-SMS a good candidate for precision farming and internet of things (IoT) systems.

Wireless cellular networks have rapidly evolved to be software-defined in nature. This has created opportunities to improve their performance. One such opportunity is through enabling programming and integration of multi-hop device-to-device (MD2D) at the edge. However, efficient integration of MD2D at the edge requires a highly adaptable and scalable routing protocol, where its development is underpinned through understanding of which type of current routing characteristics and architectures are suitable over dynamic networking conditions. To develop such understanding, we conducted a detailed analysis and performance study on three routing protocols, namely virtual ad-hoc routing protocol-source based (VARP-S) Abolhasan et al. (2018), SDN-based multi-hop device-to-device routing protocol (SMDRP) Abdollahi et al. (2019) and hybrid SDN architecture for wireless distributed networks (HSAW) Abolhasan et al. (2015). Our investigations illustrate that VARP-S and SMDRP perform best in terms of energy consumption and cellular routing overhead. However, HSAW shows better performance in terms of end-to-end (E2E) delay and packet loss over lower network and traffic densities.

The number of sensors deployed in the world is expected to explode in the near future. At this moment, nearly 30 billion Internet of Things (IoT) devices are connected, and the number is expected to double in the next four years. While not all of these are battery powered, as technology becomes smaller and mobility becomes more important to consumers, soon a larger portion will be. This forecast predicts that the number of machine-To-machine devices will have the largest increase, representing nearly 50% of all devices in 2023 [1], [2]. These devices are typically small and therefore ideal candidates to be wirelessly powered. Typical examples are health-care monitoring, smart homes, and industrial applications.

Frost events incur substantial economic losses to farmers. These events could induce damage to plants and crops by damaging the cells. In this article, a recurrent neural network-based method, automating the frost prediction process, is proposed. The recurrent neural network-based models leveraged in this article include the standard recurrent neural network, long short-term memory, and gated recurrent unit. The proposed method aims to increase the prediction frequency from once per 12–24 h for the next day or night events to minute-wise predictions for the next hour events. To achieve this goal, datasets from NSW and ACT of Australia are obtained. The experiments are designed considering the scene of deploying the model to the Internet of Things systems. Factors such as model processing speed, long-term error and data availability are reviewed. After model construction, there are three experiments. The first experiment tests the errors between different model types. The second and third experiments test the effect of sequence length on error and performance for recurrent neural network-based models. All tests introduce artificial neural network models as the baseline. Also, all tests for model error are conducted in two rounds with testing datasets from the current year (2016) and next year (2017). As a result, recurrent neural network-based models are more suitable for short-term deployment with a smaller sequence length. In contrast, artificial neural network models demonstrate a lower error over the long term with faster processing time. With the results presented, the limitations of the proposed method are discussed.

A low-profile dual-band pixelated defected ground antenna has been proposed at 3.5 GHz and 5.8 GHz bands. This work presents a flexible design guide for achieving single-band and dual-band antenna using pixelated defected ground (PDG). The unique pixelated defected ground has been designed using the binary particle swarm optimization (BPSO) algorithm. Computer Simulation Technology Microwave Studio incorporated with Matlab has been utilized in the antenna design process. The PDG configuration provides freedom of exploration to achieve the desired antenna performance. Compact antenna design can be achieved by making the best use of designated design space on the defected ground (DG) plane. Further, a V-shaped transfer function based on BPSO with fast convergence allows us to efficiently implement the PDG technique. In the design procedure, pixelization is applied to a small rectangular region of the ground plane. The square pixels on the designated defected ground area of the antenna have been formed using a binary bit string, consisting of 512 bits taken during each iteration of the algorithm. The PDG method is concerned with the shape of the DG and does not rely on the geometrical dimension analysis used in traditional defected ground antennas. Initially, three single band antennas have been designed at 3.5 GHz, 5.2 GHz and 5.8 GHz using PDG technique. Finally, same PDG area has been used to design a dual-band antenna at 3.5 GHz and 5.8 GHz. The proposed antenna exhibits almost omnidirectional radiation performance with nearly 90% efficiency. It also shows dual radiation pattern property with similar patterns having different polarizations at each operational band. The antenna is fabricated on a ROGERS RO4003 substrate with 1.52 mm thickness. Reflection coefficient and radiation patterns are measured to validate its performance. The simulated and measured results of the antenna are closely correlated. The proposed antenna is suitable for different applications in I

Radio frequency energy harvesting (RFEH) and wireless power transmission (WPT) are two emerging alternative energy technologies that have the potential to offer wireless energy delivery in the future. One of the key components of RFEH or WPT system is the receiving antenna. The receiving antenna's performance has a considerable impact on the power delivery capability of an RFEH or WPT system. This paper provides a well-rounded review of recent advancements of receiving antennas for RFEH and WPT. Antennas discussed in this paper are categorized as low-profile antennas, multi-band antennas, circularly polarized antennas, and array antennas. A number of contemporary antennas from each category are presented, compared, and discussed with particular emphasis on design approach and performance. Current design and fabrication challenges, future development, open research issues of the antennas and visions for RFEH and WPT are also discussed in this review.