Impact differences among the landing phases of a drop vertical jump in soccer players

The aim of this study was to examine the differences of landing phase biomechanics between the players who had anterior cruciate ligament (ACL) reconstruction and healthy participants during single leg drop vertical jump. In this study, 11 soccer players who had anterior cruciate ligament reconstruction (aged 23.0±3.6 years, height 177±5.0 cm, weight 83.8±11.7 kg) and 9 healthy soccer players( aged 22.2±2.4 years, height 178±3.0 cm, weight 74.3±6.1 kg) participated voluntarily. During the data collection phase three high speed cameras synchronized to each other and force plate were used. Visual analysis programme and MATLAB were used to calculate kinetic and kinematic variables. Landing techniques of the subjects' were examined by flexion angle of knee, ground reaction force and moment parameters. The statistical analyses of the measured results were performed by t-test and Pearson Correlation analysis. According to the results, it was determined that peak vertical ground reaction force exhibited significant phase differences (p=0.00, and p=0.00, respectively) between the groups. Obtained results can be explained with "quadriceps avoidance" motion pattern which is characterized by decreased quadriceps activity and lower external knee flexion moment in an effort to control anterior translation of the tibia in subjects with ACL reconstruction. A better understanding of the different phases during single-leg landings can shed a light on mechanism of non-contact anterior crucaite ligament injuries therefore future researches should assess how phase differences affect drop vertical jump performance. © 2018 Montenegrin Sports Academy. All rights reserved

UnSplit: Data-Oblivious Model Inversion, Model Stealing, and Label Inference Attacks Against Split Learning

Training deep neural networks often forces users to work in a distributed or outsourced setting, accompanied with privacy concerns. Split learning aims to address this concern by distributing the model among a client and a server. The scheme supposedly provides privacy, since the server cannot see the clients' models and inputs. We show that this is not true via two novel attacks. (1) We show that an honest-but-curious split learning server, equipped only with the knowledge of the client neural network architecture, can recover the input samples and obtain a functionally similar model to the client model, without being detected. (2) We show that if the client keeps hidden only the output layer of the model to "protect" the private labels, the honest-but-curious server can infer the labels with perfect accuracy. We test our attacks using various benchmark datasets and against proposed privacy-enhancing extensions to split learning. Our results show that plaintext split learning can pose serious risks, ranging from data (input) privacy to intellectual property (model parameters), and provide no more than a false sense of security.Comment: Proceedings of the 21st Workshop on Privacy in the Electronic Society (WPES '22), November 7, 2022, Los Angeles, CA, US

Rationale, component description and pilot evaluation of a physical health promotion measure for people with mental disorders across Europe

Introduction: The HELPS project aimed at developing a toolkit for the promotion of physical health in people with mental disorders to reduce the substantial excess morbidity and mortality in the target group. Methods: The HELPS toolkit was developed by means of national and international literature reviews, Delphi rounds with mental health experts and focus groups with mental health experts and patients/ residents in 14 European countries. The toolkit was translated into the languages of all participating countries, and usability of toolkit modules was tested. Results: The toolkit consists of several modules addressing diverse somatic health problems, lifestyle, environment issues, patient goals and motivation for health-promotion measures. It aims at empowering people with mental illness and staff to identify physical health risks in their specific contexts and to select the most appropriate modules from a range of health promotion tools. Discussion: The HELPS project used an integrative approach to the development of simple tools for the target population and is available online in 14 European languages. Preliminary evidence suggests that the toolkit can be used in routine care settings and should be put to test in controlled trials to reveal its potential impact

Understanding the Blockchain Technology Adoption from Procurement Professionals’ Perspective - An Analysis of the Technology Acceptance Model Using Intuitionistic Fuzzy Cognitive Maps

International Conference on Intelligent and Fuzzy Systems, INFUS 2020 -- 21 July 2020 through 23 July 2020 -- -- 242349Nowadays, the trend towards new technologies has promoted fierce competition in supply chains. Blockchain technology attracts widespread attention in supply chain processes via its potential benefits. Efficiently managed supply chain processes provide operational and organizational advantages, and procurement is one of the critical processes to gain such advantages. With its multi-participant nature, procurement process deserves particular attention in terms of its potential to be transformed by blockchain technology. When evaluating a new technology, variables affecting behavior to use technology should be analyzed carefully. In literature, additional research is needed to obtain a broader understanding of blockchain technology acceptance. This study aims to analyze procurement professionals’ adoption of the blockchain technology with the Technology Acceptance Model (TAM) and Intuitionistic Fuzzy Cognitive Maps (IFCM). The IFCM was used as it has the capacity to model the professionals’ hesitation, and it also copes with incomplete or even conflicting information. Our results indicate that the influences between variables are in line with most of the other studies. However, the findings further strengthens that the procurement professionals give Intention, Job Relevance and Output Quality more importance. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerland AG

A computer-based feedback model for design/build organizations

2nd International Conference on Structural and Construction Engineering -- SEP 23-26, 2003 -- ROME, ITALYBuilding production process is a multi-disciplinary and multi-phased activity. Various factors increased the complexity of building production process and consequently the number of participants taking place in the process. Co-ordination and integration of the production process became more important in the fragmented structure of construction industry. Obviously the lack of communication between designers and construction team causes quality problems. Insufficient communication between design and construction phases causes design failures regenerated in various projects. Design failures can be determined in both construction and occupation stages. Feedback of systematically recorded information of design failures, which were identified in construction stage would help to create and maintain the firm's memory and can be used as a medium to increase the quality of the design process as well as the design itself. In this paper, the conceptual and practical parts of a computer-based model developed for design/build organizations that aims to organize design failure information identified in construction stage are presented.Univ Rome La Sapienza,MIUR,Societa Stretto Messina SpA,HILTI Italia Sp

SplitGuard: Detecting and Mitigating Training-Hijacking Attacks in Split Learning

Distributed deep learning frameworks, such as split learning, have recently been proposed to enable a group of participants to collaboratively train a deep neural network without sharing their raw data. Split learning in particular achieves this goal by dividing a neural network between a client and a server so that the client computes the initial set of layers, and the server computes the rest. However, this method introduces a unique attack vector for a malicious server attempting to steal the client\u27s private data: the server can direct the client model towards learning a task of its choice. With a concrete example already proposed, such training-hijacking attacks present a significant risk for the data privacy of split learning clients. In this paper, we propose SplitGuard, a method by which a split learning client can detect whether it is being targeted by a training-hijacking attack or not. We experimentally evaluate its effectiveness, and discuss in detail various points related to its use. We conclude that SplitGuard can effectively detect training-hijacking attacks while minimizing the amount of information recovered by the adversaries

A novel TOPSIS–CBR goal programming approach to sustainable healthcare treatment

Cancer is one of the most common diseases worldwide and its treatment is a complex and time-consuming process. Specifically, prostate cancer as the most common cancer among male population has received the attentions of many researchers. Oncologists and medical physicists usually rely on their past experience and expertise to prescribe the dose plan for cancer treatment. The main objective of dose planning process is to deliver high dose to the cancerous cells and simultaneously minimize the side effects of the treatment. In this article, a novel TOPSIS case based reasoning goal-programming approach has been proposed to optimize the dose plan for prostate cancer treatment. Firstly, a hybrid retrieval process TOPSIS–CBR [technique for order preference by similarity to ideal solution (TOPSIS) and case based reasoning (CBR)] is used to capture the expertise and experience of oncologists. Thereafter, the dose plans of retrieved cases are adjusted using goal-programming mathematical model. This approach will not only help oncologists to make a better trade-off between different conflicting decision making criteria but will also deliver a high dose to the cancerous cells with minimal and necessary effect on surrounding organs at risk. The efficacy of proposed method is tested on a real data set collected from Nottingham City Hospital using leave-one-out strategy. In most of the cases treatment plans generated by the proposed method is coherent with the dose plan prescribed by an experienced oncologist or even better. Developed decision support system can assist both new and experienced oncologists in the treatment planning process

Temporal variations of the fractal properties of seismicity in the western part of the north Anatolian fault zone: possible artifacts due to improvements in station coverage

International audienceSeismically-active fault zones are complex natural systems exhibiting scale-invariant or fractal correlation between earthquakes in space and time, and a power-law scaling of fault length or earthquake source dimension consistent with the exponent b of the Gutenberg-Richter frequency-magnitude relation. The fractal dimension of seismicity is a measure of the degree of both the heterogeneity of the process (whether fixed or self-generated) and the clustering of seismic activity. Temporal variations of the b-value and the two-point fractal (correlation) dimension Dc have been related to the preparation process for natural earthquakes and rock fracture in the laboratory These statistical scaling properties of seismicity may therefore have the potential at least to be sensitive short- term predictors of major earthquakes. The North Anatolian Fault Zone (NAFZ) is a seismicallyactive dextral strike slip fault zone which forms the northern boundary of the westward moving Anatolian plate. It is splayed into three branches at about 31oE and continues westward toward the northern Aegean sea. In this study, we investigate the temporal variation of Dc and the Gutenberg-Richter b-value for seismicity in the western part of the NAFZ (including the northern Aegean sea) for earthquakes of Ms > 4.5 occurring in the period between 1900 and 1992. b ranges from 0.6-1.6 and Dc from 0.6 to 1.4. The b-value is found to be weakly negatively correlated with Dc (r=-0.56). However the (log of) event rate N is positively correlated with b, with a similar degree of statistical significance (r=0.42), and negatively correlated with Dc (r=-0.48). Since N increases dramatically with improved station coverage since 1970, the observed negative correlation between b and Dc is therefore more likely to be due to this effect than any underlying physical process in this case. We present this as an example of how man-made artefacts of recording can have similar statistical effects to underlying processes