Practical Benchmark Research Articles

An evaluation of the current mentorship/preceptorship practices for newly qualified radiographers in Northern Ireland

IntroductionMentorship/Preceptorship (M/P) has been utilised within the nursing profession since the early 1980's. Successful, structured M/P programmes can be hugely beneficial to Northern Ireland (NI) Trusts who recruit regularly and often rely on the fluidity of staff movement regionally. In the absence of standardised tools to accurately and universally measure the competency of newly qualified Radiographers (NQR) as they evolve, establishing the benchmark for effective practice within Radiology departments in NI is difficult and highly subjective at best. This study aimed to evaluate the current M/P strategies within NI as perceived by NQR and Radiology Managers (RM). MethodsA mix of both qualitative and quantitative data was obtained using questionnaires through a scoping exercise. Opinions were sought from a target audience of NQR, who began full-time employment following graduation in 2018, and RM involved in the delivery of current M/P programmes within the NI Trusts. ResultsResponses were gained from all five NI trusts to achieve a representative regional sample, with final opinions of RM (n = 8, 54%) and NQR (n = 30, 67%) received. With the exception of one response, all NQRs confirmed receiving some form of M/P support within their inaugural post. ConclusionSignificant variations were apparent in both the structure and delivery of Trust M/P induction programmes, calling into question the comparable competency of NQRs regionally. Implications for practiceThe disparity in approach towards M/P programmes across NI, and subsequent lack of comparability of NQR competence, endorses the development of a more robust and universal method for the regional assessment of NQRs, such as that of the ‘Flying Start NHS®’ programme utilised by National Health Service (NHS) Scotland, in combination with knowledge and skills framework (KSF) practices in supervision.

Fault Reconstruction of Islanded Nonlinear DC Microgrids: An LPV-Based Sliding Mode Observer Approach

This article investigates the problem of fault detection and reconstruction in direct current microgrids (dc MGs) with nonlinear loads. First, a state-space representation of the dc MG with nonlinear constant power loads (CPLs) is obtained. The faults in components, actuators, and sensors in a dc MG system are modeled by using additive terms in the state-space and measurement equations. A novel robust nonlinear detector is then proposed to estimate and reconstruct the faults. The proposed detector deploys a sliding mode technique and a polytopic linear parameter varying (LPV) approach. The proposed approach utilizes the robustness and simplicity offered by the sliding mode and polytopic LPV approaches to provide a simple, but effective, detector. By employing the Lyapunov stability theory, sufficient detector design conditions are derived in terms of linear matrix inequalities (LMIs) which are numerically solved by convex optimization techniques. The proposed approach is experimentally tested on a practical dc MG benchmark connected to a CPL. Furthermore, the results are compared with other state-of-the-art methods.

Towards fighting COVID-19: can servant leadership behaviour enhance commitment of medical knowledge-workers

PurposeThis article examines the potential of increasing commitment of medical knowledge-workers (medical-KWs) in hospitals, particularly in handling deadly pandemics like COVID-19, through servant leadership behaviour. The authors hold that medical-KWs like doctors and nurses form the core team of knowledge-workers (KWs) at the forefront of fighting COVID-19 through seeking possible vaccines, treating patients and promoting behaviours that curtail its spread. Thus research directed towards enhancing their continued commitment is both timely and valuable.Design/methodology/approachThe study uses an explanatory cross-sectional survey design.FindingsResults reveal that servant leadership behaviour significantly explains changes in commitment of medical-KWs. Results further establish that perceived fairness – a key psychological factor – significantly explains how servant leadership enhances the commitment of medical-KWs.Research limitations/implicationsData used were sourced from medical-KWs in selected public hospitals only. Thus results may differ among medical-KWs in private hospitals, yet they have also championed the fight against COVID-19. Never the less these results provide a direction of thought to guide practice and other related studies on a wider-scale.Practical implicationsIn their quest to eradicate COVID-19 and its negative effects on social-economic development, nations have to actively promote servant leadership behaviour in the hospitals (by establishing quality relationships, credibility and efficient processes for delivering the shared goal) as mechanisms for sustaining the continued commitment of medical-KWs towards fighting the pandemic.Originality/valueResults portray events from an economy that has registered successes in combating pandemics like Ebola and currently COVID-19 and thus offer a plausible benchmark for practice.

Ethics, professionalism and legal education in Malaysian undergraduate dentistry: A reflection on the current requirements

The undergraduate dental degree in Malaysia is regulated by the Malaysian Dental Council (MDC) (Standard of Undergraduate Competencies), Code of Professional Conduct of the Malaysian Dental Council, the Malaysian Qualification Framework 2.0 and the Malaysian Quality Agency (MQA) quality assurance documents. These primary documents emphasize the importance of professional ethics and law in dentistry as will be further detailed and discussed later. This study reflects on the need to integrate ethics, professionalism and law with sufficient coverage in terms of content and emphasis in the dental undergraduate curriculum. Literature and benchmarking practices are reviewed for a deeper reflection of the current needs in a professional dental ethics and law education course. The present study is primarily focused on regulatory requirements, legal cases and comparative literature to serve as a reference on how professional ethics and law courses may be conducted. As an incidental measure, a reflection as to how the course of study of professional ethics is delivered in a local university is considered in a selected local university’s undergraduate dental curriculum. The findings indicate that ethics professionalism and dental law is not given sufficient emphasis as it is included as one of the minor components within another overcrowded subject and the coverage of what is taught is limited in context. This paper is an invitation to all dental schools to review and reflect whether, given the emphasis on professional ethics, legal and regulatory compliance in the regulatory documents, it is adequately reflected in their dental curriculum. In this regard, some benchmarking references are done to suggest what such a course should cover, for consideration of the dental schools.

Equitable Allocation of COVID-19 Vaccines: An Analysis of the Initial Allocation Plans of CDC's Jurisdictions with Implications for Disparate Impact Monitoring

Major global and national vaccine allocation guidelines urge planners to allocate vaccines in ways that recognize, and ideally reduce, existing societal inequities within countries. However, allocation plans of the US will be determined individually by each of the CDC’s 64 jurisdictions (states, the District of Columbia, five cities, and territories). We analyzed whether jurisdictions have incorporated novel approaches to reduce inequity, based on plans published by the CDC in early November 2020 (63 summaries [98% of all jurisdictions] and 47 full guidance documents [73% of all, including all 50 states]). Eighteen states adopted a novel proposal to use a disadvantage index to allocate vaccines more equitably, for five types of equity goals: 1) to prioritize disadvantaged groups directly, 2) to define priority groups in phased systems, 3) to plan tailored outreach and communication, 4) to plan the location of dispensing sites and 5) to monitor uptake. Yet just over a third of all states, and only half of the 16 states with the largest shares of disadvantaged populations—where reducing inequity would be most urgent—pursue such goals. While allocation frameworks are still evolving, the plans we analyzed mark important historical and practical benchmarks, and could become firm policy when COVID-19 vaccines are authorized and delivered. Vaccine roll-out poses unprecedented logistical and practical challenges. To minimize the risk that ethics and social justice falls by the wayside in the busy months to come, planners at the federal, state and local levels should carefully consider on what grounds they decline to adopt equity measures that other planners deem important and feasible for defining priority populations, designing allocation quotas, and just as critical, enabling, and monitoring, uptake.

Coded Computing for Low-Latency Federated Learning Over Wireless Edge Networks

Federated learning enables training a global model from data located at the client nodes, without data sharing and moving client data to a centralized server. Performance of federated learning in a multi-access edge computing (MEC) network suffers from slow convergence due to heterogeneity and stochastic fluctuations in compute power and communication link qualities across clients. We propose a novel coded computing framework, CodedFedL, that injects structured coding redundancy into federated learning for mitigating stragglers and speeding up the training procedure. CodedFedL enables coded computing for non-linear federated learning by efficiently exploiting distributed kernel embedding via random Fourier features that transforms the training task into computationally favourable distributed linear regression. Furthermore, clients generate local parity datasets by coding over their local datasets, while the server combines them to obtain the global parity dataset. Gradient from the global parity dataset compensates for straggling gradients during training, and thereby speeds up convergence. For minimizing the epoch deadline time at the MEC server, we provide a tractable approach for finding the amount of coding redundancy and the number of local data points that a client processes during training, by exploiting the statistical properties of compute as well as communication delays. We also characterize the leakage in data privacy when clients share their local parity datasets with the server. We analyze the convergence rate and iteration complexity of CodedFedL under simplifying assumptions, by treating CodedFedL as a stochastic gradient descent algorithm. Furthermore, we conduct numerical experiments using practical network parameters and benchmark datasets, where CodedFedL speeds up the overall training time by up to $15\times$ in comparison to the benchmark schemes.

Rapid benchmarking: the case of a multinational dairy company

PurposeThe purpose of the study is to investigate and document a new approach to best practice benchmarking called rapid benchmarking. Rapid benchmarking is defined by the authors as an approach to dramatically shorten the typical length of time to conduct a successful best practice benchmarking project.Design/methodology/approachThe methodology involved a case study exploration of a multinational dairy company's best practice benchmarking approach using structured interviews and data collection to examine the speed and results achieved through its benchmarking approach and whether it was justified in naming it as rapid benchmarking. A comparison of the speed of the dairy company's approach was undertaken against 24 other organisations that had utilised the same benchmarking methodology (TRADE Best Practice Benchmarking). In addition, a literature review was undertaken to search for other cases of rapid benchmarking and compare rapid benchmarking with other rapid improvement approaches.FindingsThe findings revealed that the approach used by the dairy company was unique, with best practices being identified and action plans signed off for deployment within a five-day period (far quicker than the average time of 211 days reported by other organisations). Key success factors for rapid benchmarking were found to be allocating five dedicated days for the benchmarking team to spend on the project, identifying the right team members for the project, obtaining sponsorship support for the project and providing intensive facilitation support through a benchmarking facilitator.Research limitations/implicationsOnly one company was found to use a rapid benchmarking approach; therefore, the findings are from one case study. The depth of analysis presented was restricted due to commercial sensitivity.Practical implicationsThe rapid benchmarking approach is likely to be of great interest to practitioners, providing them with a new way of finding solutions and best practices to address challenges that need to be solved quickly or with minimal expense. For organisations that have been using benchmarking for many years, the research will enable them to re-evaluate their own benchmarking approach and consider if rapid benchmarking could be used for some projects, particularly for internal benchmarking where it is easier to apply.Originality/valueThis research is the first to identify and document a rapid benchmarking approach and the first to provide a detailed analysis of the length of time it takes to undertake best practice benchmarking projects (and each stage of a benchmarking project).

The model of evidence-based benchmarking: a more robust approach to benchmarking

PurposeThis conceptual paper aims to propose the evidence-based benchmarking model that bridges standard benchmarking practices with evidence-based management (EBMgt) principles and lessens tensions between two opposite views of benchmarking as a useful management tool vs a management hype and fashion.Design/methodology/approachThis conceptual paper is based on the critical reasoning, analysis and integration of so far largely separated research fields of benchmarking and EBMgt. The author employs a method of conceptual model building to identify connections between standard benchmarking model and EBMgt practices and to explain how a sequence of benchmarking events supplemented by EBMgt principles might lead to more reliable managerial decision-making.FindingsThe author argues that although there are no common benchmarking procedures, it is possible to identify a standard benchmarking model that resonates in most contemporary benchmarking procedures and consists of four main phases: plan, do, check and act (PDCA). The author integrated this standard model with EBMgt practices of searching for evidence in four sources of information and a six-step critical thinking process to put forward the model of evidence-based benchmarking.Originality/valueThe proposed model is a novel, comprehensive framework that puts together so far incompatible practices of benchmarking and EBMgt. The model clears up existing conceptual confusions around “casual” benchmarking and advances contemporary understanding of benchmarking practices. The model of evidence-based benchmarking might act as a practical, heuristic tool improving the quality of the managerial decisions and thus positively influencing the bottom line of business performance.

The Need for a Leadership Competency Framework for Physical Therapists: A Perspective in Action

Background and Purpose. “Leadership at all levels” has been identified as one of the 12 critical issues facing organizations around the world. The American Physical Therapy Association Vision calls physical therapists “to lead” efforts to transform society. Leadership competencies essential for practice recently have been identified. These competencies illustrate how physical therapists independent of professional role and position can support achievement of the Quadruple Aim. The profession has taken steps to prepare physical therapists with clinical competencies to practice as movement system experts; however, like many health professions, education has struggled to keep pace with professional formation and leadership skills that shape role identity. More emphasis needs to be placed on developing skills needed to lead, advocate, and influence change. The purpose of this position article was to expand conversation on leadership and propose an evidence-based framework, which can be used to build competence throughout entry-level curricula. This framework can bring the profession one step closer to adopting leadership as an essential domain of competency and providing the missing link to bridge the gap in achievement of the Vision. Position and Rationale. Leadership should be recognized as an obligation of all physical therapists and developing leadership an obligation of education, as it is critical to our evolving role identity. Adoption of a leadership competency framework informed by competencies will help assure graduates exhibit leadership skills to challenge current practices and advocate for transformation of the health care system. The team explored established leadership frameworks to evaluate “fit” of the 57 leadership competencies. Although frameworks offered valuable perspectives, none sufficiently “fit” the evidence to support widespread adoption. This led us to design a Leadership Competency Framework for Physical Therapists (LCF-PT) to categorize, enhance utility, and clarify interpretation of these competencies. Discussion and Conclusion. The LCF-PT organizes 57 competencies into 3 tiers and 11 thematic clusters that can further advance our role as movement system experts and build a more robust role identity consistent with a doctoral profession. Without a framework, DPT education programs will likely remain fragmented, unintentional, and inconsistent in the development of leadership competencies in Doctor of Physical Therapy graduates. Future research needs to validate the LCF-PT by stakeholders to confirm the framework is sufficiently robust to guide leadership development at all levels. This framework also can support eventual benchmarking of best practices in education in the future. To move forward as practitioners of choice and to ensure our voices are heard, all physical therapists must acknowledge that “to lead” at all levels is critical to role identity, achievement of the Quadruple Aim and the Vision to transform society by improving the human experience. This clarity will move us closer to “dreaming the not-so-impossible dream,” challenge current practices, implement evidence, and advocate for transformation of the health care system.

Benchmarking the Capability of Symbolic Execution Tools with Logic Bombs

Symbolic execution has become an indispensable technique for software testing and program analysis. However, since several symbolic execution tools are presently available off-the-shelf, there is a need for a practical benchmarking approach. This paper introduces a fresh approach that can help benchmark symbolic execution tools in a fine-grained and efficient manner. The approach evaluates the performance of such tools against known challenges faced by general symbolic execution techniques, e.g., floating-point numbers and symbolic memories. We first survey related papers and systematize the challenges of symbolic execution. We extract 12 distinct challenges from the literature and categorize them into two categories: symbolic-reasoning challenges and path-explosion challenges . Next, we develop a dataset of logic bombs and a framework for benchmarking symbolic execution tools automatically. For each challenge, our dataset contains several logic bombs, each addressing a specific challenging problem. Triggering one or more logic bombs confirms that the symbolic execution tool in question is able to handle the corresponding problem. Real-world experiments with three popular symbolic execution tools, namely, KLEE, angr, and Triton have shown that our approach can reveal the capabilities and limitations of the tools in handling specific issues accurately and efficiently. The benchmarking process generally takes only a few dozens of minutes to evaluate a tool. We have released our dataset on GitHub as open source, with an aim to better facilitate the community to conduct future work on benchmarking symbolic execution tools.

Accuracy and reproducibility of somatic point mutation calling in clinical-type targeted sequencing data

BackgroundTreating cancer depends in part on identifying the mutations driving each patient’s disease. Many clinical laboratories are adopting high-throughput sequencing for assaying patients’ tumours, applying targeted panels to formalin-fixed paraffin-embedded tumour tissues to detect clinically-relevant mutations. While there have been some benchmarking and best practices studies of this scenario, much variant calling work focuses on whole-genome or whole-exome studies, with fresh or fresh-frozen tissue. Thus, definitive guidance on best choices for sequencing platforms, sequencing strategies, and variant calling for clinical variant detection is still being developed.MethodsBecause ground truth for clinical specimens is rarely known, we used the well-characterized Coriell cell lines GM12878 and GM12877 to generate data. We prepared samples to mimic as closely as possible clinical biopsies, including formalin fixation and paraffin embedding. We evaluated two well-known targeted sequencing panels, Illumina’s TruSight 170 hybrid-capture panel and the amplification-based Oncomine Focus panel. Sequencing was performed on an Illumina NextSeq500 and an Ion Torrent PGM respectively. We performed multiple replicates of each assay, to test reproducibility. Finally, we applied four different freely-available somatic single-nucleotide variant (SNV) callers to the data, along with the vendor-recommended callers for each sequencing platform.ResultsWe did not observe major differences in variant calling success within the regions that each panel covers, but there were substantial differences between callers. All had high sensitivity for true SNVs, but numerous and non-overlapping false positives. Overriding certain default parameters to make them consistent between callers substantially reduced discrepancies, but still resulted in high false positive rates. Intersecting results from multiple replicates or from different variant callers eliminated most false positives, while maintaining sensitivity.ConclusionsReproducibility and accuracy of targeted clinical sequencing results depend less on sequencing platform and panel than on variability between replicates and downstream bioinformatics. Differences in variant callers’ default parameters are a greater influence on algorithm disagreement than other differences between the algorithms. Contrary to typical clinical practice, we recommend employing multiple variant calling pipelines and/or analyzing replicate samples, as this greatly decreases false positive calls.

Introduction of ABCEP as an automatic programming method

Automatic programming is a branch of artificial intelligence that presents each solution as a mathematical formula based on heuristic mechanisms. In this study, artificial bee colony expression programming (ABCEP) is presented, which is combined simultaneously with expression programming. By using expression sharing to generate new solutions, the proposed method can minimize certain deficiencies of artificial bee colony programming, such as weak convergence and high locality. A total number of 15 real-world regression benchmark functions was used to evaluate the performance of the proposed model. For comparison purposes, successful run percentage, mean best cost, convergence performance, and run time of ABCEP were compared to those of other tested automatic programming algorithms, including artificial bee colony programming, gene expression programming, genetic programming, and quick artificial bee colony programming. A Wilcoxon signed-rank test was also done to compare the behavior of the algorithms. Additionally, the accuracy of all algorithms was then evaluated using three real-world practical benchmarks. The results indicate that the predictions generated by ABCEP are better than those obtained by other control algorithms based on successful runs, mean fitness values, and convergence rate.

Regularized Evolutionary Multitask Optimization: Learning to Intertask Transfer in Aligned Subspace

This article proposes a novel and computationally efficient explicit intertask information transfer strategy between optimization tasks by aligning the subspaces. In evolutionary multitasking, the tasks might have biases embedded in function landscapes and decision spaces, which often causes the threat of predominantly negative transfer. However, the complementary information among different tasks can give an enhanced performance of solving complicated problems when properly harnessed. In this article, we distill this insight by introducing an intertask knowledge transfer strategy implemented in the low-dimension subspaces via a learnable alignment matrix. Specifically, to unveil the significant features of the function landscapes, the task-specific low-dimension subspaces is established based on the distribution information of subpopulations possessed by tasks, respectively. Next, the alignment matrix between pairwise subspaces is learned by minimizing the discrepancies of the subspaces. Given the aligned subspaces by applying the alignment matrix to subspaces' base vectors, the individuals from different tasks are then projected into aligned subspaces and reproduce therein. Moreover, since this method only considers the leading eigenvectors, it turns out to be intrinsically regularized and noise-insensitive. Comprehensive experiments are conducted on the synthetic and practical benchmark problems so as to assess the efficacy of the proposed method. According to the experimental results, the proposed method exhibits a superior performance compared with existing evolutionary multitask optimization algorithms.

Efficient recursive Adams–Bashforth methods in molecular dynamics simulations of N-body systems interacting through pairwise potentials

ABSTRACT A recursive multistep Adams–Bashforth method applied to the Molecular Dynamics simulations of N-body systems interacting through pairwise force fields is introduced and analysed. Equations of motion are obtained using a set of Cartesian coordinates solved by means of an Adams–Bashforth numerical integration scheme of order s, which requires the iterative computation of function time derivatives. The proposed algorithm has been implemented using a programming approach that makes it possible to re-use a source code resulting in small codes, easy to maintain. Practical examples and benchmarks that illustrate the performance of these implementations are included. The study of its performance gives clues to evaluate its efficiency and precision. Numerical tests for a N-particle system are made on the equilibrium configuration of liquid argon near its triple point at 86.5 K and 0.021 Å. In most cases, the algorithms here presented outperform those implemented traditionally as the Gear corrector-predictor or the Verlet family, leading to important savings in terms of total computation times and significantly increasing the numerical precision obtained with standard algorithms.

Improvement of the Key Performance Indicators (KPIs) and Evaluation of Challenges

To benchmark the performance of the Bus System (BS), Performance Measurement Systems (PMSs) should be used. In that regard, this contribution reviews the advancement of the standard PMSs used globally: Key Performance Indicators (KPIs). This research will further identify the BS that has a record of effective performance throughout their operations. With reference from the experiences in benchmarking, we have presented a literature review of KPIs and evaluated inputs to confirm that KPIs are a standardized approach used to compare and identify the performances of best practices for BS participation. The practical experience with the systems haspotentially identified various critical issues in collecting comparable and consistent data. Dealing with these issues, giving the comparable information and undertaking essential study to comprehend and point out the basis for effective performance is considered a process which provides lessons for considerable benchmarking practices. This paper further evaluates the rules of the benchmarking processes, the PMSs of the BS, the overall challenges faced when collecting data andlastly, the findings of the benchmarking practice.

Wireless-Powered Edge Computing With Cooperative UAV: Task, Time Scheduling and Trajectory Design

A wireless-powered mobile edge computing (MEC) architecture with the cooperation between an access point (AP) and an unmanned aerial vehicle (UAV) is studied in this article. The AP, powered by the grid, is integrated with a high-performance processing server to help compute the user equipment’s (UEs’) offloaded tasks while also performing high-power laser-like energy charging for the UAV. The UAV serves as (1) an information relay to help the UEs offload/download their computation tasks/results, (2) an energy relay to broadcast energy from the AP to the UEs, as well as (3) an MEC server to help the UEs compute their tasks. We aim at maximizing the weighted sum completed task-input bits (WSCTB) of UEs under the task and time allocation, information-causality, energy-causality, and the UAV’s trajectory constraints, by jointly optimizing the task and time allocation as well as the UAV’s energy transmit power and trajectory. The formulated WSCTB maximization problem is non-convex, and we propose a three-step block coordinate descending algorithm to address three sub-problems iteratively for obtaining a proper solution. Simulation results show that the UAV’s trajectories highly depend on the AP’s location and the UEs’ weight values. In addition, significant performance improvement is achieved by the proposed algorithm compared to some practical benchmarks.

Benchmarking the Nutrition-Related Policies and Commitments of Major Food Companies in Australia, 2018

The food industry has an important role to play in efforts to improve population diets. This study aimed to benchmark the comprehensiveness, specificity and transparency of nutrition-related policies and commitments of major food companies in Australia. In 2018, we applied the Business Impact Assessment on Obesity and Population Level Nutrition (BIA-Obesity) tool and process to quantitatively assess company policies across six domains. Thirty-four companies operating in Australia were assessed, including the largest packaged food and non-alcoholic beverage manufacturers (n = 19), supermarkets (n = 4) and quick-service restaurants (n = 11). Publicly available company information was collected, supplemented by information gathered through engagement with company representatives. Sixteen out of 34 companies (47%) engaged with data collection processes. Company scores ranged from 3/100 to 71/100 (median: 40.5/100), with substantial variation by sector, company and domain. This study demonstrated that, while some food companies had made commitments to address population nutrition and obesity-related issues, the overall response from the food industry fell short of global benchmarks of good practice. Future studies should assess both company policies and practices. In the absence of stronger industry action, government regulations, such as mandatory front-of-pack nutrition labelling and restrictions on unhealthy food marketing, are urgently needed.

Protecting the Public? An Analysis of Professional Regulation—Comparing Outcomes in Fitness to Practice Proceedings for Social Workers, Nurses and Doctors

AbstractThe regulation of professional activity in the Health and Social Care sector in the UK is carried out by a number of statutory bodies that hold legal mandates to manage the risks of professional malpractice. The prime method used to perform this duty, and thereby protect the public, is the construction of a register of the suitability qualified—and creation of appropriate professional standards to establish a benchmark for practice. When registrant’s performance or conduct is felt not to meet these standards, they are placed within a fitness to practice process administered by the regulatory body. This article examines the publicly available data on fitness to practice cases from UK regulatory bodies relating to the professions of social workers, nurses, midwives and doctors. Examining nearly 1,000 cases, the authors run a statistical analysis of the data to establish whether any differences are found amongst and between these professional groupings. We find there are several areas where significant differences arise, namely gender, attendance and representation. Most of these regulatory bodies are, in turn, regulated in the UK by the Professional Standards Authority (PSA), and the article concludes by suggesting ways forward for the PSA in addressing or further examining apparent inequalities. The analysis is placed within a wide range of literature, with an emphasis on the international transferability of the approach to professional regulation.

Benchmarking to reduce restrictive practices in forensic mental health services: a Delphi study

Forensic mental health services (FMHS) have higher rates, duration and frequency of restrictive practices (seclusion, physical restraint and mechanical restraint). Data generated by services can be used to set targets (benchmarks) with like services to reduce or eliminate restrictive practices. The aim of this study was to develop restrictive practice benchmarks for Australian and New Zealand FMHS. A participatory action research methodology was used by conducting a Delphi study. FMHS experts provided de-identified service-specific restrictive practice data, which was used to stimulate reflection towards the development of restrictive practice benchmarks. Consensus decision-making was reached on benchmarks for the rate, duration and frequency of seclusion and physical restraint. Mechanical restraint was found to be a rare event, requiring event specific review in each service. Benchmarking with FMHS may assist in working to reduce restrictive practices and encourage the scrutiny of service practice. Establishment of a FMHS network might assist to embed the proposed benchmarks.

AIRBORNE AND MOBILE LIDAR, WHICH SENSORS FOR WHICH APPLICATION?

Abstract. The UAV mapping industry expanded tremendously during the last five years. Thanks to miniaturization, automation and advertising, this technology may give a wrong impression that mapping of certain quality is as simple as clicking few buttons on a PC. Moreover, with a large and continuously increasing offer of hardware and software, the identification of the right tools is not easy, especially when aiming at certain standard. In this respect, the mapping with LiDAR is more delicate than with a camera due to a lower level of redundancy within the process of orientation/georeferencing and somewhat higher threshold on the size/weight per performance ratio within these sensors. This fact motivated us to present a practical benchmark evaluating a popular small LiDAR sensor in realistic conditions for intrinsic parameters such as noise or capacity to penetrate canopy, as well as the “low-weight” inertial technology in terms of geometrical influences on the resulting point cloud. The practical limitations are indeed considerably lower than those specified by the manufacturers or tested in laboratory conditions. These should be considered together with other “mapping-productivity” factors that are summarized in the last part of this study.