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Prior to, and since the passage of the Corporate Manslaughter and Corporate Homicide Act 2008 (CMCHA) in the United Kingdom (UK), prosecutions for serious workplace health and safety offences have been mostly underwhelming. Australia has now followed the UK in legislating for industrial manslaughter within the various workplace health and safety laws across many of its states and territories. The aim of this paper is to explore the industrial manslaughter legislation across Australia and discusses the lessons which may be learned from the UK. An analysis of the CMCHA was undertaken to determine whether it has lived up to expectations and if there may be lessons to be learned for Australia as it embarks on its post-harmonization journey. A review of publications found that the CMCHA had fallen short of expectations within its first decade of existence in the UK in both the number of successful prosecutions and in the severity of the punishments handed down. The purported deficiencies in the prosecutorial success of the United Kingdom's CMCHA provides an opportunity for Australia to heed and avoid these deficiencies.

Climate change is one of the most critical challenges facing Australia and the global community today. Data from the Australian Bureau of Meteorology (BoM) indicates that Australia has been experiencing rising temperatures, particularly since the late 20th century. The frequency, duration, and intensity of heatwaves are projected to continue increasing . Since national records began in 1910, Australia has warmed by an average of 1.47°C (±0.24°C), with the highest official temperature recorded at 50.7 degrees Celsius in Onslow, Western Australia (WA), on January 13, 2022. Furthermore, a recent unprecedented high temperature of +41.6°C was recorded during winter on August 26, 2024, in Yampi Sound, WA. Among all natural disasters in Australia, heatwave (HW) represents a leading silent killer and pose a significant public health threat. However, innovative methods for assessing vulnerability for HW-related health services remain limited.

Introduction: Latvia faces a challenging shortage of available kidney donors, leading to a significant mismatch between demand for kidney transplantation and supply. Although older adult donors require a thorough pre-donation workup to rule out significant medical comorbidities, it offers hope for potential kidney transplantation candidates. Case Presentation: This case study presents the unique scenario of an 87-year-old living kidney donor, where individualized decision-making resulted in outstanding outcomes for both the donor and recipient. Conclusions: The initial assessment for donation, which involves renal scintigraphy, serves as a preventive measure. In cases where one of the kidneys exhibits insufficient function, this approach avoids the necessity for further costly tests, thus preserving resources in the healthcare budget. The decision concerning an older donor should undergo thorough discussion by a multidisciplinary team to minimize perioperative and long-term risks. Nonetheless, a thoughtful approach to elderly donors offers a valuable opportunity to expand the living donor pool in the context of the organ shortage problem.

Developing and managing complex IoT–Edge–Cloud Continuum (IECC) systems are challenging due to the system complexity and diversity. Internet of Things (IoT), Edge, and Cloud components combined with artificial intelligence (AI) in data processing systems must ensure strong security and privacy for data sources. The approach of the IECC Data Management Framework (DMF) introduces a novel combination of multiple easy-to-configure plugin environments using data visualization features. These contributions collectively address the critical challenges inherent in heterogeneous environments such as scalability, data privacy, and configuration management by standardizing data flow configurations and increasing stakeholder trust in sensitive applications, particularly in critical infrastructure monitoring.

The goal of the IoT–Edge–Cloud Continuum approach is to distribute computation and data loads across multiple types of devices taking advantage of the different strengths of each, such as proximity to the data source, data access, or computing power, while mitigating potential weaknesses. Most current machine learning operations are currently concentrated on remote high-performance computing devices, such as the cloud, which leads to challenges related to latency, privacy, and other inefficiencies. Distributed learning approaches can address these issues by enabling the distribution of machine learning operations throughout the IoT–Edge–Cloud Continuum by incorporating Edge and even IoT layers into machine learning operations more directly. Approaches like transfer learning could help to transfer the knowledge from more performant IoT–Edge–Cloud Continuum layers to more resource-constrained devices, e.g., IoT. The implementation of these methods in machine learning operations, including the related data handling security and privacy approaches, is challenging and actively being researched. In this article the distributed learning and transfer learning domains are researched, focusing on security, robustness, and privacy aspects, and their potential usage in the IoT–Edge–Cloud Continuum, including research on tools to use for implementing these methods. To achieve this, we have reviewed 145 sources and described the relevant methods as well as their relevant attack vectors and provided suggestions on mitigation.

To minimise the occurrence of injuries, illnesses and deaths due to work-related causes, it is important to have effective workplace health safety legislation that is known and used. The introduction of more stringent workplace health and safety legislation across Australia has brought greater responsibility, and harsher penalties, for managers. The importance of the role those in management play in influencing and shaping a culture of safety is well researched, but little has been done to determine whether those in management are ready to assume that role. This study aimed to identify what has informed Western Australian construction industry managers who are working within the mining sector and ultimately shaped their approach to occupational health and safety. NVivo software was used to analyse the data by the creation of codes and subcodes to identify themes and subthemes. Analysis of two focus groups’ participants’ responses identified that many managers had insufficient work health and safety education to understand their obligations and that other challenges include insufficient preparedness of managers, particularly newly promoted supervisors and other management staff, rapid promotion, and the bureaucracy of modern workplace health and safety. The findings from this study can assist organisations to better prepare managers to fulfil their workplace health and safety obligations and reduce some of the post-harmonisation challenges.

In an era where buildings are increasingly becoming multifaceted entities, the paradigm of smart buildings has witnessed significant evolution. This advancement integrates sophisticated communication technologies, the Internet of Things (IoT), artificial intelligence (AI), and data analytics. Intending to design an effective smart building monitoring system, this research paper explores and compares various solutions for measuring building parameters by identifying a broad spectrum of review articles considering building occupant behavior, sensor deployment, and implementation complexity. The objective of our paper is to compile diverse information on various sensors used for monitoring building conditions and provide a comprehensive overview of data structuring and processing, all within a single article. Additionally, this paper addresses the challenges of combining data from decentralized systems and the need for managerial tools to optimize user experiences. The findings contribute to the advancement of smart building management, offering valuable insights for improving building performance and user experience as well as evaluating future research directions in this field. This review is designed to serve as an introduction for anyone venturing into the field of building monitoring.

Patients who have Lyme neuroborreliosis (LNB) might experience lingering symptoms that persist despite antibiotic drug therapy. We tested whether those symptoms are caused by maladaptive immune responses by measuring 20 immune mediators in serum and cerebrospinal fluid (CSF) in 79 LNB patients followed for 1 year. At study entry, most mediators were highly concentrated in CSF, the site of the infection. Those responses resolved with antibiotic therapy, and associations between CSF cytokines and signs and symptoms of LNB were no longer observed. In contrast, subjective symptoms that persisted after use of antibiotics were associated with increased levels of serum interferon-α (IFN-α), which were already observed at study entry, and remained increased at each subsequent timepoint. Highest IFN-α levels corresponded with severe disease. Although the infection serves as the initial trigger, sequelae after antibiotic therapy are associated with unremitting systemic IFN-α levels, consistent with the pathogenic role of this cytokine in interferonopathies in other conditions.

Background The Internet of Things, similar to wireless sensor networks, has been integrated into the daily life of almost everyone. These wearable, stationary, or mobile devices are in multiple locations, collecting data or monitoring and executing certain tasks. Some can monitor environmental values and interact with the environment, while others are used for data collection, entertainment, or even lifesaving. To achieve the wireless part of the system, the majority of sensor nodes are designed to be battery-powered. While battery power has become increasingly ubiquitous, it tends to increase the global carbon footprint of electronic devices. This issue can be mitigated by employing some form of energy harvesting so that batteries can be refilled and the gadget lasts longer, but this does not alter the reality that batteries are still used and eventually discarded. Methods In this paper, the authors emphasize the significance of power consumption in battery-powered devices. To be able to monitor devices’ power consumption, one of the measurable parameters is current. When users know the exact current consumption, they can decrease it by polishing the program or tweaking the duty cycle, making radio transmit fewer data or less frequently, thus decreasing overall power draw. Results In order to simplify current consumption monitoring, the authors have developed a testbed facility that provides real-time current consumption measurements, which may be used to enhance the duty cycle and battery life of the aforementioned devices. Conclusions While minimizing total current consumption is a great way to extend the battery life and, thus, the carbon footprint, the primary culprit in the Internet of Things is radio communications. This transmission is the primary source of current consumption. By determining the exact amount of current drawn during transmission and adjusting it, users can significantly extend battery life.

Abstract In 2020, the COVID-19 pandemic impacted global life and transitioned economies and societal perceptions of life as we knew it. Professional and social life mostly ground to a nadir during the first lockdown in Europe in March. As a consequence, measures aimed at preventing the spread of the virus were established in medical facilities also and elective plastic surgery procedures were temporarily suspended in our clinic and others. A majority of the population, including those potentially contemplating plastic surgery procedures, spent most of their time at home with ample time available to research information about surgical procedures and other topics online. This investigation analyzes the relevance of plastic surgery during the pandemic on the basis of online search behavior patterns. Online traffic data from the online platform http://www.mooci.org were extracted using Google Analytics over a period of 6 months. The parameters analyzed were: pageviews, session duration, and bounce rate. Additionally, differentiation by areas of interest has been obtained. The data were compared and analyzed before and after the beginning of the first hard lockdown in Austria, Germany, and Switzerland. There were no significant differences in regard to pageviews and session duration when comparing time points before and after the beginning of the hard lockdown. The bounce rate exhibited a significant decrease after the beginning of the lockdown, implying a more conscious search for information and greater absorption and retention. There was no difference that could conclusively be attributed to the pandemic in terms of specific areas of interest researched. Society’s demand for information about plastic-surgical procedures continues to be steadily prevalent—despite, or even in particular, during a global pandemic. Providing reliable and readily available information about plastic surgery procedures is an important component of a functioning doctor–patient relationship and informed consent. This information may reflect society’s increased interest in plastic surgery during the pandemic, or be simply reflective of more spare time at hand to allow for such research. Further studies should investigate the relevance of elective procedures over the entire course of the pandemic.

The availability of data is an important aspect of any research as it determines the likelihood of the study’s commencement, completion, and success. The Internet of Things and Wireless Sensor Networks technologies have been attracting a huge amount of researchers for more than two decades, without having a consolidated or unified source that identifies and describes available Internet of Things and Wireless Sensor Network testbed facilities. In this paper, a dataset including 41 distinct testbed facilities is described. These testbed facilities are classified according to their key features such as Device Under Test (DUT) type, mobility, access level, facility count, connection/interaction interfaces, and other criteria. The systematic review process resulting in the gathered data set consisted of three filtering phases applied to relevant articles published between the years 2011 and 2021 as obtained from the Web of Science and SCOPUS databases.

ObjectiveWe tested Goal Management Training (GMT), which has been recommended as an executive training protocol that may improve the deficits in the complex tasks inherent in life role participation experienced by those with chronic mild traumatic brain injury and post-traumatic stress disease (mTBI/PTSD). We assessed, not only cognitive function, but also life role participation (quality of life).MethodsWe enrolled and treated 14 individuals and administered 10 GMT sessions in-person and provided the use of the Veterans Task Manager (VTM), a Smartphone App, which was designed to serve as a “practice-buddy” device to ensure translation of in-person learning to independent home and community practice of complex tasks. Pre-/post-treatment primary measure was the NIH Examiner, Unstructured Task. Secondary measures were as follows: Tower of London time to complete (cTOL), Community Reintegration of Service Members (CRIS) three subdomains [Extent of Participation; Limitations; Satisfaction of Life Role Participation (Satisfaction)]. We analyzed pre-post-treatment, t-test models to explore change, and generated descriptive statistics to inspect given individual patterns of change across measures.ResultsThere was statistically significant improvement for the NIH EXAMINER Unstructured Task (p

This qualitative study aimed to identify mental health hazards in the offshore oil and gas industry, as well as the role of the personality types of the Five Factor Model (FFM) in coping with these stressors. A focus group with 8 participants and a pilot study with 5 participants were conducted. Results showed that several stressors are currently present for Australian offshore oil and gas employees, in particular COVID-19 and the resulting negative effects on rosters, working hours, job security and time spent away from home. Other stressors revealed by participants were lack of space, working in a high-risk environment, stigma, helicopter travel and pressure to keep up with production. Poor safety behaviours were associated with neuroticism, extraversion and openness, while risk avoidance appear to be associated with agreeableness and conscientiousness. Tolerance to shift work was positively related to extraversion, yet negatively associated to neuroticism. Furthermore, neuroticism showed a negative association with help-seeking and productivity, as well as higher levels of concern relating to COVID-19 and job uncertainty. As personality traits are enduring throughout life, it is vital that employees are managed effectively through workplace interventions so that they are able to cope effectively, particularly during stressful events.

Hybrid heat pump (HHP) represents a heating system (HS) that simultaneously utilises two or more energy sources. The key novelty is the integration of an optimised control strategy, effectively maximising the efficiency of both the heat pump and gas boiler segments. In this study, a HHP system that consists of an air-source heat pump (ASHP) and a condensing-type gas boiler (GB) for a two-story single-family house was calculated and validated. The results from three different calculation methods were obtained: the heat pump manufacturer’s calculator, the simulator programme, and the calculations based on real measured data. Two operation modes were tested: economic mode, where the cheapest energy resource becomes the priority, and ecological mode, where the system operates using the energy resource with lowest CO2 emissions. When comparing consumed energy, there was a 15.36% variance between the manufacturer’s program and the actual measured data, while for produced energy, the difference amounted to 25.81%. It was found that the balance point temperature is −7 °C, where the heat pump unit of the HHP system can cover necessary heat losses until that point, while the gas boiler operates at lower temperatures. Changes in electricity price can significantly influence the point of the system switch, but the possibility to cover heat loads of the heat pump unit always has to be taken into account.

In this paper, we address the most pressing challenges faced by the manufacturing sector, particularly the manufacturing of small and medium-sized enterprises (SMEs), where the transition towards high-mix low-volume production and the availability of cost-effective solutions are crucial. To overcome these challenges, this paper presents 14 innovative solutions that can be utilized to support the introduction of agile manufacturing processes in SMEs. These solutions encompass a wide range of key technologies, including reconfigurable fixtures, low-cost automation for printed circuit board (PCB) assembly, computer-vision-based control, wireless sensor networks (WSNs) simulations, predictive maintenance based on Internet of Things (IoT), virtualization for operator training, intuitive robot programming using virtual reality (VR), autonomous trajectory generation, programming by demonstration for force-based tasks, on-line task allocation in human–robot collaboration (HRC), projector-based graphical user interface (GUI) for HRC, human safety in collaborative work cells, and integration of automated ground vehicles for intralogistics. All of these solutions were designed with the purpose of increasing agility in the manufacturing sector. They are designed to enable flexible and modular manufacturing systems that are easy to integrate and use while remaining cost-effective for SMEs. As such, they have a high potential to be implemented in the manufacturing industry. They can be used as standalone modules or combined to solve a more complicated task, and contribute to enhancing the agility, efficiency, and competitiveness of manufacturing companies. With their application tested in industrially relevant environments, the proposed solutions strive to ensure practical implementation and real-world impact. While this paper presents these solutions and gives an overview of their methodologies and evaluations, it does not go into their details. It provides summaries of comprehensive and multifaceted solutions to tackle the evolving needs and demands of the manufacturing sector, empowering SMEs to thrive in a dynamic and competitive market landscape.

The offshore oil and gas working environment is an inherently dangerous one, with risks posed to physical safety on a daily basis. One neglected field of research is the added psychosocial stressors present in this environment. This research examined the experiences of offshore oil and gas workers through one-on-one online interviews which were recorded and transcribed. Transcripts were analyzed through the qualitative software NVivo, which generated themes and patterns for the responses given to questions that were developed through a focus group. The results of the analysis showed that multiple psychosocial stressors are present in this population, such as fear of speaking up, unsatisfactory company-provided facilities, work–life interference, work status, micromanaging, gender harassment and bullying. In addition, interviews identified that production and time pressures, along with fatigue, can influence accidents and mistakes. Climate factors also cause discomfort. However, these are managed according to best practices by organizations. Due to the timing of the study, COVID-19 was a significant stressor for some, but not all, employees. In conclusion, offshore oil and gas workers face multiple stressors in a dangerous environment that may lead to devastating consequences.

As the popularity and complexity of WSN devices and IoT systems are increasing, the testing facilities should keep up. Yet, there is no comprehensive overview of the landscape of the testbed facilities conducted in a systematic manner. In this article, we provide a systematic review of the availability and usage of testbed facilities published in scientific literature between 2011 and 2021, including 359 articles about testbeds and identifying 32 testbed facilities. The results of the review revealed what testbed facilities are available and identified several challenges and limitations in the use of the testbed facilities, including a lack of supportive materials and limited focus on debugging capabilities. The main contribution of this article is the description of how different metrics impact the uasge of testbed facilities, the review also highlights the importance of continued research and development in this field to ensure that testbed facilities continue to meet the changing needs of the ever-evolving IoT and WSN domains.

With long-term changes in temperature and weather patterns, ecologically adaptable fruit varieties are becoming increasingly important in agriculture. For selection of candidate cultivars in fruit breeding or for yield predictions, fruit set characteristics at different growth stages need to be described and evaluated, which is largely done visually. This is a time-consuming and labor-intensive process that also requires sufficient expert knowledge. The annotated dataset for Japanese quince - QuinceSet - consists of images of Japanese quince (Chaenomeles japonica) fruits taken at two phenological developmental stages and annotated for detection and phenotyping. First, after flowering, when the second fruit fall is over and the fruits have reached 30-50% of their final size, and second, at the ripening stage of quince, just before the fruits are yielded. Both stages of quince images classified as unripe and ripe were annotated using ground truth ROI and presented in YOLO format. The dataset contains 1515 high-resolution RGB .jpg images with the same number of annotated .txt files. Images in the dataset were manually annotated using LabelImg software. A total of 17,171 annotations were provided by the experts. The images were acquired on site at the Institute of Horticulture in Dobele, Latvia. Homogenization of the images was performed under different weather conditions, at different times of the day, and from different capturing angles. The dataset contains both fully visible quinces and quinces partially obscured by leaves. Care was also taken to ensure that the foreground, which contains the leaves has adequate brightness with minimal shadows, while the background is darker. The presented dataset will allow to increase the efficiency of the breeding process and yield estimation, to identify and phenotype quinces more reliably, and may also be useful for breeding other crops.

Background: The relationship between risk factors and likelihood of occupational injury has been studied. However, what has been published has only provided a limited explanation of why some of the employees working in the same environment as other employees suffered a single-injury event, while other employees experienced multiple-injury events. This article reports on an investigation of whether artisanal and small-scale miners in Migori County of Kenya are susceptible to a single-injury or multiple-injury incidences, and if so, what underpinning parameters explain the differences between the single incident injured and the multiple incident injured group. Mine management commitment to safety in artisanal and small-scale mining (ASM) operations is also considered. Materials and methods: The research objectives were achieved by surveying 162 uninjured and 74 injured miners. A structured, closed-end questionnaire was administered to participants after the stratification of the study population and systematic selection of the representative samples. Results: The results showed that most injured miners suffer a single-injury incident rather than experiencing multiple-injury events, and laceration (28.40%) was the common injury suffered by the miners. The analysis showed that the risk factors for the single incident injured group were not similar to those in the multiple incident injured group. The research also found mine workers have low opinion about mine management/owners commitment to safety. Conclusion: The study concluded that mine management and miners need to be educated and sensitized on the dangers of this operation. Provision of safety gears and positive safety culture must be a top priority for management. Keywords: Incidence, Logistic model, Miners, Occupational injuries, Risk factors

Background: Few “interventions” around suicide and stigma have reached into psychiatric institutions. Lived Lives is a science-arts approach to addressing suicide and stigma, informed by a psychobiographical and visual arts autopsy. The resulting artworks and mediated exhibition ( Lived Lives), has facilitated dialogue, response and public action around stigma-reduction, consistent with a community intervention. Recent evidence from Lived Lives moved us to consider how it may situate within a psychiatric hospital. Methods: Lived Lives manifested in St. Patrick’s University Hospital (Ireland’s oldest and largest psychiatric hospital) in November 2017. A mixed-methods approach was used to evaluate the exhibition as a potential intervention to address stigma around suicide, with quantitative and qualitative data collected via written questionnaire and oral data collected via video documentation. Bereavement support was available. A Clinician and an artist also provided independent evaluation. Results: 86 participants engaged with the exhibition, with 68 completing questionnaire data. Audiences included service users, policy makers, health professionals, senior hospital administrators and members of the public. 62% of participants who completed questionnaires were suicide-bereaved; 46% had experienced a mental health difficulty, and 35% had been suicidal in the past. 91% thought Lived Lives could be of benefit in the aftermath of a suicide death. Half of participants thought Lived Lives could help reduce suicidal feelings, whereas 88% thought it could benefit those with Mental Health difficulties. The emotional response was of a visceral nature, including fear, anger, sadness, disgust and anxiety. Conclusions: Lived Lives sits comfortably in discomfort, unafraid to call out the home-truths about stigma and its pervasive and pernicious impact, and with restoring identity at its core. Lived Lives can operate within a psychiatric hospital, as well as in community. The challenge is to move it forward for greater exposure and impacts in at-risk communities.

Type I interferon (IFN) has been identified in patients with Lyme disease, and its abundant expression in joint tissues of C3H mice precedes development of Lyme arthritis. Forward genetics using C3H mice with severe Lyme arthritis and C57BL/6 (B6) mice with mild Lyme arthritis identified the Borrelia burgdorferi arthritis-associated locus 1 (Bbaa1) on chromosome 4 (Chr4) as a regulator of B. burgdorferi-induced IFNβ expression and Lyme arthritis severity. B6 mice introgressed with the C3H allele for Bbaa1 (B6.C3-Bbaa1 mice) displayed increased severity of arthritis, which is initiated by myeloid lineage cells in joints. Using advanced congenic lines, the physical size of the Bbaa1 interval has been reduced to 2 Mbp, allowing for identification of potential genetic regulators. Small interfering RNA (siRNA)-mediated silencing identified Cdkn2a as the gene responsible for Bbaa1 allele-regulated induction of IFNβ and IFN-stimulated genes (ISGs) in bone marrow-derived macrophages (BMDMs). The Cdkn2a-encoded p19 alternative reading frame (p19ARF) protein regulates IFNβ induction in BMDMs as shown by siRNA silencing and overexpression of ARF. In vivo studies demonstrated that p19ARF contributes to joint-specific induction of IFNβ and arthritis severity in B. burgdorferi-infected mice. p19ARF regulates B. burgdorferi-induced IFNβ in BMDMs by stabilizing the tumor suppressor p53 and sequestering the transcriptional repressor BCL6. Our findings link p19ARF regulation of p53 and BCL6 to the severity of IFNβ-induced Lyme arthritis in vivo and indicate potential novel roles for p19ARF, p53, and BCL6 in Lyme disease and other IFN hyperproduction syndromes.

Global warming has contributed to more frequent and severe extreme weather events, which has led to increased research on the health impacts of extreme heat. However, research on heatwaves, air quality, and their spatial impact on health service demand is limited. This study used machine learning (ML) approaches to obtain the optimised model to predict health service demand associated with those risk factors for an all-age model and compared it with young children (0–4 years) model in Perth. Ten years’ data (2006–2015) on emergency department attendances (EDA), socioeconomic status (SES), heatwaves, landscape fires, and gaseous and particulate air pollutants were collected. ML approaches, including decision tree, random forest (RF), and geographical random forest (GRF) models, were used to compare and select the best model for predicting EDA and identify important risk factors. Five-hundred cross validations were performed using the testing data, and a construct validation was performed by comparing actual and predicted EDA data. The results showed that the RF model outperformed other models, and SES, air quality, and heatwaves were among the important risk factors to predict EDA. The GRF model was fitted well to the data (R ^2 = 0.975) and further showed that heatwaves had significant geographic variations and a joint effect with PM _2.5 in the southern suburbs of the study area for young children. The RF and GRF models have satisfactory performance in predicting the impact of heatwaves, air quality, and SES on EDA. Heatwaves and air quality have great spatial heterogeneity. Spatial interactions between heatwaves, SES, and air quality measures were the most important predictive risk factors of EDA for young children in the Perth southern suburbs. Future studies are warranted to confirm the findings from this study on a wider scale.

Abstract Background There is a successful outcome after surgical management of spinal arachnoid diverticula (SAD) in up to 82% of cases. Hypothesis/Objectives We hypothesized that Pugs have favorable short‐term and poor long‐term prognosis after surgical treatment of thoracolumbar SAD. The aim of the present investigation was to describe clinical findings, short‐ and long‐term outcomes, and follow‐up magnetic resonance imaging (MRI) findings in Pugs with thoracolumbar SAD. Animals Twenty‐five client owned Pugs with 12‐month follow‐up information after surgical treatment of thoracolumbar SAD. Methods Multicenter retrospective case series. All medical records were searched for Pugs diagnosed with SAD. Data regarding signalment, history, surgical procedure, outcome, histopathology, and follow‐up MRI results were extracted. Results Mean age at presentation was 7.32 (range 2‐11) years, 80% were males. Short‐term outcome was available in 25 dogs, and improvement was confirmed in 80% of dogs. Long‐term outcome was available in 21 dogs, and deterioration was confirmed in 86% of cases, with late‐onset recurrence of clinical signs after initial postsurgical improvement affecting 85% of Pugs. A moderate correlation (r = 0.50) was found between duration of clinical signs and outcome. In 8 dogs with deteriorating clinical signs, follow‐up MRI revealed regrowth of the SAD in 2 cases, new SAD formation in 2 cases, and intramedullary T2W hyperintensity/syringomyelia in 6 cases. Conclusions and Clinical Importance This study suggests that Pugs with thoracolumbar SAD do not have a favorable long‐term prognosis after surgical treatment for reasons yet to be determined.

The research community has been working on sensor networks for more that seven decades and altogether more than a million research articles on sensor networks have been published, with this number growing every day. In this article, we try to provide a thorough and complete systematic review on the sensor network field resulting from representative subset of more than 3000 articles which include actual sensor network deployments. After a thorough analysis of these data, we provide a definitive overview of the trends in sensor network deployment described in published research articles.

The title of this Special Issue is: “Underlying Mechanisms and Neurorehabilitation of Gait after Stroke” [...]

Background/Problem. Given the treatment-resistant gait deficits after stroke and known elevated energy cost of gait after stroke, it is important to study the patterns of mechanical energies of the lower limb segments. There is a dearth of information regarding mechanical energies specifically for the thigh and shank across the gait cycle. Therefore, the purpose of the current work was to characterize the following: (1) relative patterns of oscillation kinetic energy (KE) and potential energy (PE) within lower limb segments and across lower limb segments in healthy adults during the swing phase at chosen and slow gait speeds; (2) KE and PE swing phase patterns and values for stroke survivors versus healthy adults walking at slow speed; and (3) KE and PE patterns during the swing phase for two different compensatory gait strategies after stroke,. Methods. This was a gait characterization study, a two-group, parallel-cohort study of fourteen stroke survivors with gait deficits, walking at KE and PE of the limb segments (thigh and shank) across the gait cycle. Results. In healthy adults, we identified key energy conservation mechanisms inherent in the interactions of KE and PE, both within the thigh and shank segments and across those limb segments, partially explaining the low cost of energy of the normal adult chosen speed gait pattern, and the underlying mechanism affording the known minimal set of activated muscles during walking, especially during the early swing phase. In contrast, KE was effectively absent for both healthy adults at imposed slow walking speed and stroke survivors at their very slow chosen speed, eliminating the normal conservation of energy between KE and PE within the thigh and across the thigh and shank. Moreover, and in comparison to healthy adult slow speed, stroke survivors exhibited greater abnormalities in mechanical energies patterns, reflected in either a compensatory stepping strategy (over-flexing the hip) or circumducting strategy (stiff-legged gait, with knee extended throughout the swing phase). Conclusions and contribution to the field. Taken together, these findings support targeted training to restore normal balance control and normal activation and de-activation coordination of hip, knee, and ankle muscles, respectively (agonist/antagonist at each joint), so as to eliminate the known post-stroke abnormal co-contractions; this motor training is critical in order to release the limb to swing normally in response to mechanical energies and afford the use of conservation of KE and PE energies within the thigh and across thigh and shank.

With discoveries of brain and spinal cord mechanisms that control gait, and disrupt gait coordination after disease or injury, and that respond to motor training for those with neurological disease or injury, there is greater ability to construct more efficacious gait coordination training paradigms. Therefore, it is critical in these contemporary times, to use the most precise, sensitive, homogeneous (i.e., domain-specific), and comprehensive measures available to assess gait coordination, dyscoordination, and changes in response to treatment. Gait coordination is defined as the simultaneous performance of the spatial and temporal components of gait. While kinematic gait measures are considered the gold standard, the equipment and analysis cost and time preclude their use in most clinics. At the same time, observational gait coordination scales can be considered. Two independent groups identified the Gait Assessment and Intervention Tool (G.A.I.T.) as the most suitable scale for both research and clinical practice, compared to other observational gait scales, since it has been proven to be valid, reliable, sensitive to change, homogeneous, and comprehensive. The G.A.I.T. has shown strong reliability, validity, and sensitive precision for those with stroke or multiple sclerosis (MS). The G.A.I.T. has been translated into four languages (English, Spanish, Taiwanese, and Portuguese (translation is complete, but not yet published)), and is in use in at least 10 countries. As a contribution to the field, and in view of the evidence for continued usefulness and international use for the G.A.I.T. measure, we have provided this update, as well as an open access copy of the measure for use in clinical practice and research, as well as directions for administering the G.A.I.T.

The central nervous system (CNS) control of human gait is complex, including descending cortical control, affective ascending neural pathways, interhemispheric communication, whole brain networks of functional connectivity, and neural interactions between the brain and spinal cord. Many important studies were conducted in the past, which administered gait training using externally targeted methods such as treadmill, weight support, over-ground gait coordination training, functional electrical stimulation, bracing, and walking aids. Though the phenomenon of CNS activity-dependent plasticity has served as a basis for more recently developed gait training methods, neurorehabilitation gait training has yet to be precisely focused and quantified according to the CNS source of gait control. Therefore, we offer the following hypotheses to the field: Hypothesis 1. Gait neurorehabilitation after stroke will move forward in important ways if research studies include brain structural and functional characteristics as measures of response to treatment. Hypothesis 2. Individuals with persistent gait dyscoordination after stroke will achieve greater recovery in response to interventions that incorporate the current and emerging knowledge of CNS function by directly engaging CNS plasticity and pairing it with peripherally directed, plasticity-based motor learning interventions. These hypotheses are justified by the increase in the study of neural control of motor function, with emerging research beginning to elucidate neural factors that drive recovery. Some are developing new measures of brain function. A number of groups have developed and are sharing sophisticated, curated databases containing brain images and brain signal data, as well as other types of measures and signal processing methods for data analysis. It will be to the great advantage of stroke survivors if the results of the current state-of-the-art and emerging neural function research can be applied to the development of new gait training interventions.

The field of neurorehabilitation has moved considerably beyond a narrow use of gait speed [...]

ObjectiveIn stroke survivors, a treatment-resistant problem is inability to volitionally differentiate upper limb wrist extension versus flexion. When one intends to extend the wrist, the opposite occurs, wrist flexion, rendering the limb non-functional. Conventional therapeutic approaches have had limited success in achieving functional recovery of patients with chronic and severe upper extremity impairments. Functional magnetic resonance imaging (fMRI) neurofeedback is an emerging strategy that has shown potential for stroke rehabilitation. There is a lack of information regarding unique blood-oxygenation-level dependent (BOLD) cortical activations uniquely controlling execution of wrist extension versus uniquely controlling wrist flexion. Therefore, a first step in providing accurate neural feedback and training to the stroke survivor is to determine the feasibility of classifying (or differentiating) brain activity uniquely associated with wrist extension from that of wrist flexion, first in healthy adults.ApproachWe studied brain signal of 10 healthy adults, who performed wrist extension and wrist flexion during fMRI data acquisition. We selected four types of analyses to study the feasibility of differentiating brain signal driving wrist extension versus wrist flexion, as follows: 1) general linear model (GLM) analysis; 2) support vector machine (SVM) classification; 3) 'Winner Take All'; and 4) Relative Dominance.ResultsWith these four methods and our data, we found that few voxels were uniquely active during either wrist extension or wrist flexion. SVM resulted in only minimal classification accuracies. There was no significant difference in activation magnitude between wrist extension versus flexion; however, clusters of voxels showed extension signal > flexion signal and other clusters vice versa. Spatial patterns of activation differed among subjects.SignificanceWe encountered a number of obstacles to obtaining clear group results in healthy adults. These obstacles included the following: high variability across healthy adults in all measures studied; close proximity of uniquely active voxels to voxels that were common to both the extension and flexion movements; in general, higher magnitude of signal for the voxels common to both movements versus the magnitude of any given uniquely active voxel for one type of movement. Our results indicate that greater precision in imaging will be required to develop a truly effective method for differentiating wrist extension versus wrist flexion from fMRI data.

ObjectivePilot testing of real time functional magnetic resonance imaging (rt-fMRI) and real time functional near infrared spectroscopy (rt-fNIRS) as brain computer interface (BCI) neural feedback systems combined with motor learning for motor recovery in chronic severely impaired stroke survivors.ApproachWe enrolled a four-case series and administered three sequential rt-fMRI and ten rt-fNIRS neural feedback sessions interleaved with motor learning sessions. Measures were: Arm Motor Assessment Tool, functional domain (AMAT-F; 13 complex functional tasks), Fugl-Meyer arm coordination scale (FM); active wrist extension range of motion (ROM); volume of activation (fMRI); and fNIRS HbO concentration. Performance during neural feedback was assessed, in part, using percent successful brain modulations during rt-fNIRS.Main resultsPre-/post-treatment mean clinically significant improvement in AMAT-F (.49 ± 0.22) and FM (10.0 ± 3.3); active wrist ROM improvement ranged from 20° to 50°. Baseline to follow-up change in brain signal was as follows: fMRI volume of activation was reduced in almost all ROIs for three subjects, and for one subject there was an increase or no change; fNIRS HbO was within normal range, except for one subject who increased beyond normal at post-treatment. During rt-fNIRS neural feedback training, there was successful brain signal modulation (42%-78%).SignificanceSeverely impaired stroke survivors successfully engaged in spatially focused BCI systems, rt-fMRI and rt-fNIRS, to clinically significantly improve motor function. At the least, equivalency in motor recovery was demonstrated with prior long-duration motor learning studies (without neural feedback), indicating that no loss of motor improvement resulted from substituting neural feedback sessions for motor learning sessions. Given that the current neural feedback protocol did not prevent the motor improvements observed in other long duration studies, even in the presence of fewer sessions of motor learning in the current work, the results support further study of neural feedback and its potential for recovery of motor function in stroke survivors. In future work, expanding the sophistication of either or both rt-fMRI and rt-fNIRS could hold the potential for further reducing the number of hours of training needed and/or the degree of recovery. ClinicalTrials.gov ID: NCT02856035.

Weed management technologies that can identify weeds and distinguish them from crops are in need of artificial intelligence solutions based on a computer vision approach, to enable the development of precisely targeted and autonomous robotic weed management systems. A prerequisite of such systems is to create robust and reliable object detection that can unambiguously distinguish weed from food crops. One of the essential steps towards precision agriculture is using annotated images to train convolutional neural networks to distinguish weed from food crops, which can be later followed using mechanical weed removal or selected spraying of herbicides. In this data paper, we propose an open-access dataset with manually annotated images for weed detection. The dataset is composed of 1118 images in which 6 food crops and 8 weed species are identified, altogether 7853 annotations were made in total. Three RGB digital cameras were used for image capturing: Intel RealSense D435, Canon EOS 800D, and Sony W800. The images were taken on food crops and weeds grown in controlled environment and field conditions at different growth stages

Performing a good-quality indoor localization of a mobile target is a challenging task, which can be affected by many factors such as radio wave behavior, the nature of the experimental environment, and available infrastructure. (1) Background: An indoor localization experiment using an Internet of Things (IoT) wireless sensor network (WSN) testbed is performed, in order to study the influence of transmission power level on the quality of received signals, and consequently, the estimated target positional coordinates. (2) Methods: A received signal strength indicator (RSSI) range-based localization system using a geometrical constrained weighted least squares (WLS) estimator multilateration technique is selected to validate the influence of the transmission power level on the performance of the localization algorithm. (3) Results: Fair localization quality was obtained at the highest transmission power level instead of the proposed transmission power level. (4) Conclusion: Additional factors are discussed to fully represent the required operational transmission power for a better localization quality, along with suggested improvements of the infrastructure configuration as a future work.

Gait deficits are often persistent after stroke, and current rehabilitation methods do not restore normal gait for everyone. Targeted methods of focused gait therapy that meet the individual needs of each stroke survivor are needed. Our objective was to develop and test a combination protocol of simultaneous brain stimulation and focused stance phase training for people with chronic stroke (>6 months). We combined Transcranial Direct Current Stimulation (tDCS) with targeted stance phase therapy using Virtual Reality (VR)-assisted treadmill training and overground practice. The training was guided by motor learning principles. Five users (>6 months post-stroke with stance phase gait deficits) completed 10 treatment sessions. Each session began with 30 min of VR-assisted treadmill training designed to apply motor learning (ML)-based stance phase targeted practice. During the first 15 min of the treadmill training, bihemispheric tDCS was simultaneously delivered. Immediately after, users completed 30 min of overground (ML)-based gait training. The outcomes included the feasibility of protocol administration, gait speed, Timed Up and Go (TUG), Functional Gait Assessment (FGA), paretic limb stance phase control capability, and the Fugl–Meyer for lower extremity coordination (FMLE). The changes in the outcome measures (except the assessments of stance phase control capability) were calculated as the difference from baseline. Statistically and clinically significant improvements were observed after 10 treatment sessions in gait speed (0.25 ± 0.11 m/s) and FGA (4.55 ± 3.08 points). Statistically significant improvements were observed in TUG (2.36 ± 3.81 s) and FMLE (4.08 ± 1.82 points). A 10-session intervention combining tDCS and ML-based task-specific gait rehabilitation was feasible and produced clinically meaningful improvements in lower limb function in people with chronic gait deficits after stroke. Because only five users tested the new protocol, the results cannot be generalized to the whole population. As a contribution to the field, we developed and tested a protocol combining brain stimulation and ML-based stance phase training for individuals with chronic stance phase deficits after stroke. The protocol was feasible to administer; statistically and/or clinically significant improvements in gait function across an array of gait performance measures were observed with this relatively short treatment protocol.

Background/Problem: Standard neurorehabilitation and gait training has not proved effective in restoring normal gait coordination for many stroke survivors. Rather, persistent gait dyscoordination occurs, with associated poor function, and progressively deteriorating quality of life. One difficulty is the array of symptoms exhibited by stroke survivors with gait deficits. Some researchers have addressed lower limb weakness following stroke with exercises designed to strengthen muscles, with the expectation of improving gait. However, gait dyscoordination in many stroke survivors appears to result from more than straightforward muscle weakness. Purpose: Thus, the purpose of this case study is to report results of long-duration gait coordination training in an individual with initial good strength, but poor gait swing phase hip/knee and ankle coordination. Methods: Mr. X was enrolled at >6 months after a left hemisphere ischemic stroke. Gait deficits included a ‘stiff-legged gait’ characterized by the absence of hip and knee flexion during right mid-swing, despite the fact that he showed good initial strength in right lower limb quadriceps, hamstrings, and ankle dorsiflexors. Treatment was provided 4 times/week for 1.5 h, for 12 weeks. The combined treatment included the following: motor learning exercises designed for coordination training of the lower limb; functional electrical stimulation (FES) assisted practice; weight-supported coordination practice; and over-ground and treadmill walking. The FES was used as an adjunct to enhance muscle response during motor learning and prior to volitional recovery of motor control. Weight-supported treadmill training was administered to titrate weight and pressure applied at the joints and to the plantar foot surface during stance phase and pre-swing phase of the involved limb. Later in the protocol, treadmill training was administered to improve speed of movement during the gait cycle. Response to treatment was assessed through an array of impairment, functional mobility, and life role participation measures. Results: At post-treatment, Mr. X exhibited some recovery of hip, knee, and ankle coordination during swing phase according to kinematic measures, and the stiff-legged gait was resolved. Muscle strength measures remained essentially constant throughout the study. The modified Ashworth scale showed improved knee extensor tone from baseline of 1 to normal (0) at post-treatment. Gait coordination overall improved by 12 points according to the Gait Assessment and Intervention Tool, Six Minute Walk Test improved by 532′, and the Stroke Impact Scale improved by 12 points, including changes in daily activities; mobility; and meaningful activities. Discussion: Through the combined use of motor learning exercises, FES, weight-support, and treadmill training, coordination of the right lower limb improved sufficiently to exhibit a more normal swing phase, reducing the probability of falls, and subsequent downwardly spiraling dysfunction. The recovery of lower limb coordination during swing phase illustrates what is possible when strength is sufficient and when coordination training is targeted in a carefully titrated, highly incrementalized manner. Conclusions/Contribution to the Field: This case study contributes to the literature in several ways: (1) illustrates combined interventions for gait training and response to treatment; (2) provides supporting case evidence of relationships among knee flexion coordination, swing phase coordination, functional mobility, and quality of life; (3) illustrates that strength is necessary, but not sufficient to restore coordinated gait swing phase after stroke in some stroke survivors; and (4) provides details regarding coordination training and progression of gait training treatment for stroke survivors.