Miracle-dz/UniFAR-FTUs huggingface dataset:
Civic Crowdfunding for Social Work Research: Opportunities, Challenges and Strategies

Civic crowdfunding for research purposes has been an emerging trend in scientific fields over the past few years. This article presents findings from a mixed methods analysis of 152 social science research projects that campaigned for crowdfunding. A total of US$583,074 was raised through the support of 6,663 backers amongst the 150 successfully funded projects. This study offers specific lessons for social work researchers engaging in crowdfunding campaigns. The data show that projects supported by endorsers and initiated by faculties were able to solicit more backers and funds. Another key finding is that the campaigns that had videos for promotional purposes were the most successful, but video length does not affect backers’ consideration. The article presents the potential ethical challenges for social work researchers in this crowdfunding arena. In what may first appear to be a democratic and emancipatory space, decisions are actually made about what topics are worthy of financing by people who have access to the online platforms and the disposable income to back the project. Nevertheless, these platforms offer a route to research funding for academics, practitioners and service user groups in a context where funding from research councils and foundations is increasingly limited and competitive.
An investigation of conceptual understanding ability K-11 student of linear motion

Identification of conceptual understanding is an important first step in order to learning 21st skill. Understanding concepts is the fundamental ability of students to achieve high order thinking skills. Thus, the student has ability to better acquire a knowledge when learning of high order thinking. The purpose of this study is determining the conceptual understanding ability of the grade 11th in high school. This study has been implemented through a single case study research. Participant in this study are 36 students from a senior high school, consists of 22 female and 14 male students. The instrument used in this study is a test of the ability to understand concepts with multiple choice questions as many as 15 questions. The low ability to understand students’ concepts in each indicator with the percentage for indicators exemplifies (72.22%), describes the conclusions (55.56%), interprets (41.67), compares (47.22%), explains (57.64%) determines (72.22%) and classify (61.11%). The result from the study, the student in grade 11st are low ability of concept understanding ability.
Machine Learning Methods for Industrial Protocol Security Analysis: Issues, Taxonomy, and Directions

Machine learning has been widely studied in the security analysis of Industrial Control Systems (ICSs). However, in industrial scenarios, the amount of data as well as the speed of data generation are very different from standard machine learning data sets. Using these heterogeneous data and finding meaningful insights for practical security applications in ICSs is a big challenge. In addition, ICSs have been built for quite a long time. Security has not been seriously taken into account when ICSs were built. Security assessment or attack prevention cannot always be done in real time, as an ICS requires to be online all the time, especially when it comes to systems that affect critical infrastructure. In this work, we are motivated to a provide a clear and comprehensive survey of the state-of-the-art work that employs machine learning in security applications in ICSs, including vulnerability analysis, vulnerability detection and exploitation, anomaly detection and security assessment. Based on our in-depth survey, we highlight the issues of industrial protocol analysis with machine learning methods, provide the security applications with machine learning in ICSs and indicate the future directions.
“SOFT” PERSPECTIVE OF THE BUSINESS PROCESS ORIENTATION

: The purpose of this paper is to represent practical approach on the empirically evaluated business process orientation (BPO) of the Slovenian power supply business. Within the empirical investigation, the level of BPO maturity was measured in the 19 organizations of the power supply business. The survey was focused on the top, middle and lower managers. As a measuring instrument, a questionnaire for the extended concept of process orientation with nine elements was used. The results of the BPO measurement shows that, despite this long-standing preoccupation with processes, certified management system and the computerization of operations, process maturity is not very high. Results suggested the opportunities for improvement, particularly for better use and exploit of information technology. Presented research is the first one which considers the BPO maturity in the Slovenian power supply business and therefore contributes to understanding of the ‘soft or intangible factors’ which have impact on the introduction, implementation and maintenance of Business process management (BPM). As a result, it is found that contemporary literature acknowledges the importance of business process (BP), BPM and BPO maturity of the organization. Therefore, scope of used terminology comprises; BP is included by BPM, which is further embraced by BPO. This research makes significant contributions to the literature and above all to scholars and practitioners who work professionally in this field and will find useful guid ance for a better understanding of applying BPO and suitable maturity models in different industries.
Elbow Patients’ Data Collection and Analysis: An Examination of Electromyography Healing Patterns

Musculoskeletal conditions are the most common cause of severe long-term pain and physical disability, accounting for the highest disability costs of about $17 billion yearly. To provide better rehabilitation tactics, the knowledge gap between injuries and their healing mechanisms needs to be addressed. The use of electromyography (EMG) is very popular in detecting neuromuscular diseases or nerve lesions; however, there is limited knowledge available for quantifying healing patterns of EMG in orthopedic patients who have injured their joints, muscles, or bones. In order to quantify the progress of orthopedic patients and assess their neuromuscular health and muscle synergy patterns, EMG signals were collected from 16 healthy individuals and 15 injured patients as they underwent rehabilitation. Subjects performed a series of standard motions such as flexion–extension of elbow and pronation–supination of the arm. Different metrics were used to process and analyze the EMG data collected using MATLAB. The metrics were as follows: root mean square, average rectified signal, mean spike amplitude, zero crossings, median power frequency, and mean power frequency. A normal range across the muscle groups has been identified and to which the patient population was compared. This comparison showed statistically significant differences in the magnitudes of muscle recruitment and activation between the two groups. Furthermore, a comparison within the patient population at the beginning of their therapy versus at the end of the therapy was conducted. Statistical differences arose in this second analysis, further proving that patients’ signals tend to change and showing trends closer to those of the healthy population. The time domain metrics showed the greatest significant differences between the groups, specifically the root mean square and average rectified signal. This analysis was successful in showing a general trend of increased mean in the patient population compared to healthy individuals. The frequency domain metrics did not show statistical significance. The work presented successfully used several EMG metrics in order to distinguish an injured person from a healthy person and to determine if an injured patient is healing. Additionally, a database of EMG signals to be fed into the control system of the mechatronics
Influence of Kinematics on the Calculation of Hip Joint Reaction Forces in Patients with Symptomatic Leg Length Inequality following Total Hip Replacement

Up to 10% of patients following Total Hip Replacement (THR) are symptomatic for a Leg Length Inequality (LLI), commonly being up to 20mm longer on the operated side. With 100,000 patients undergoing THR in 2015, 8.7% of all errors in the NHS being attributed to an LLI and malpractice claims being frequent, understanding why certain patients are symptomatic whilst others remain asymptomatic is of great importance. Anthropometric and demographic measurements together with gait analysis results were compared between a group of 26 symptomatic LLI patients following THR, 14 asymptomatic THR patients and 38 healthy individuals using Plug-in-Gait. Statistically significant results were found for height, with LLI patients generally being 6% shorter than their THR counterparts. Gait analysis results using Visual3D and AnyBody found LLI patients demonstrated reductions in peak joint forces, ground reaction forces, moments and knee flexion relative to the THR and healthy group. This was linked to LLI patients walking 20% and 59% slower than their THR and healthy counterparts respectively. Wear analysis found that LLI patients had 9% greater sliding distances than THR patients per stride together with more unidirectional motion paths. A thorough critique of Plug-in-Gait found the clinical results were generally reliable. Further sensitivity analyses however highlighted the weaknesses of the model if used improperly, with a 45mm error in lateral thigh marker positioning leading to a 10% change in hip flexion angle. The choice of hip joint centre regression equation, errors in joint width measurement and the use of CAST over PiG were also found to have a profound effect on kinematic results. It was concluded that LLI patients were symptomatic due to a combination of a greater LLI magnitude to height ratio, leading to greater pelvic obliquity in smaller individuals, and weakened muscles/soft tissues at the hip causing an asymmetric gait.
Performance analysis and hardware implementation of a nearly optimal buffer management scheme for high‐performance shared‐memory switches

Burst traffic is a common traffic pattern in modern IP networks, and it may lead to the unfairness problem and seriously degrade the performance of switches and routers. From the perspective of switching mechanism, the majority of commercial switches adopt the on‐chip shared‐memory switching architecture, and high‐speed packet buffer with efficient queue management is required to deal with the unfairness and congestion problem. In this paper, the performance of a shared‐private buffer management scheme is analyzed in detail. In the proposed scheme, the total memory space is split into shared area and private area. Each output port has a private memory area that cannot be used by other ports. The shared area is completely shared among all output ports. A theoretical queuing model of the proposed scheme is formulated, and closed‐form formulas for multiple performance parameters are derived. Through the numerical studies, we demonstrate that a nearly optimal buffer partition policy can be obtained by setting an equally small amount of private area for each queue. This work is validated by simulations as well as hardware experiments. Software simulations show that the proposed scheme performs better than existing methods, and packet dropping caused by burst traffic can be significantly reduced. Besides, a prototype of the buffer management module is implemented and evaluated in field programmable gate array platform. The evaluation shows that the proposed scheme can ensure the efficiency and fairness while keeping a high throughput in real workload.
Study of damage identification for bridges based on deep belief network

To solve the problem of poor anti-noise ability faced by traditional pattern recognition methods in damage identification field, a bridge damage identification method based on deep belief network was proposed. Taken the modal curvature difference as the damage index, three restricted Boltzmann machines were constructed for pre-training. Then, the Softmax classifier and neural network were used to identify the damage location and degree under the environmental cases of no noise, weak noise, and strong noise, respectively. Subsequently, the influence of incomplete measurement modal data on the method was studied. Finally, damage identification based on deep belief network was implemented to a continuous beam bridge and compared with that of the back propagation neural network. The results showed that the proposed method could be highly effective not only on damage location but also on degree identification. Compared with back propagation neural network, deep belief network method may possess better identification ability and stronger anti-noise ability. It also demonstrates good identification effect under the condition of incomplete measurement modal data.
Study of the Utilisation of Almond Residues for Low-Cost Panels

A large amount of research is being carried out to increase the use of renewable and eco-friendly resources like plant fibres for manufacturing new products in order to reduce energy consumption and improve their environmental impact. The almond is a deciduous tree that is native to Mediterranean areas, although nowadays the United States is the world’s main almond producer. The almond fruit has three distinct parts: the inner core or flesh, the hard middle part or shell, and the outer covering of the shell, called the hull (exocarp and mesocarp). This work analyses the use of almond residues for producing eco-friendly particleboards. The ground hull of the almond has been used as a raw material, obtaining 4 different particle sizes. Eight type of board has been manufactured without using any kind of adhesive. The particle size influences some physical and mechanical properties. With particle sizes <0.25 mm it is possible to achieve greater strength in terms of modulus of rupture (MOR): 14.01 N/mm2, modulus of elasticity (MOE): 2295.32 N/mm2 and internal bonding strength (IB): 0.57 N/mm2. This study shows that it is technically possible to manufacture boards with this material without using adhesives.
A robust EM clustering approach: ROBEM

Abstract Cluster analysis is defined as a group of multivariate statistical methods that are used to classify identical, or similar units. As is the case with all other classical statistical methods, classical clustering analysis gives misleading results when there is an outlier in the multivariate data set. To solve this problem many approaches have been proposed. This study focuses on developing a new approach, aiming to make the expectation maximization (EM) clustering algorithm resistant to outliers. We proposed a new robust hybrid clustering algorithm called robust EM (ROBEM) to reach our aim. This algorithm combines the EM clustering algorithm with robust principal component analysis (ROBPCA) algorithm. Spatial EM algorithm was proposed as a robust EM algorithm in the literature, but our simulation results and sample data applications showed that the ROBEM algorithm was more successful than the spatial EM algorithm in terms of outlier detection rate and faulty classification rate. Moreover, the proposed algorithm ROBEM provides similar results to the other well known robust clustering algorithms, such as TCLUST and Trimmed k-Means.
High Sex Ratios and Household Portfolio Choice in China

This work examines how high sex ratios (more men than women) affect household portfolio choice. Using data from a nationally representative Chinese household finance survey, we find that a one standard deviation increase in the sex ratio would raise the stock market participation rate by 2.9 percentage points, or 52.2 percent, for families with a son relative to families with a daughter. Our estimates imply that rising sex ratios explain around 10 percent of the significant growth in China’s stock market size in recent decades.
The Three-Legged Stool: Synthesizing and Extending Our Understanding of the Career Advancement Facilitators of Persons With Disabilities in Leadership Positions

I examine the career advancement facilitators of organizational stakeholders who may be identified as simultaneously “core” and “fringe” in this article, via the insights of 21 leaders with disabilities. To navigate barriers and advance their careers, these leaders benefited from three categories of facilitators, including career self-management strategies, social networks, and organizational and societal factors. Facilitators are synthesized with a metaphor, the three-legged stool, which depicts three foundational pillars that underlie the leaders’ success. Focusing on an understudied element of the social networks pillar, I examine how leaders’ external networks (family, friends, acquaintances, and role models) facilitated their career advancement. Findings point to the role of strong and weak ties in developing leaders’ career self-management strategies as well as their access to core stakeholder positions. Last, contributions, implications, and limitations of this article are discussed.
Evidence for Immediate Enhancement of Hippocampal Memory Encoding by Network-Targeted Theta-Burst Stimulation during Concurrent fMRI

The hippocampus supports episodic memory via interaction with a distributed brain network. Previous experiments using network-targeted noninvasive brain stimulation have identified episodic memory enhancements and modulation of activity within the hippocampal network. However, mechanistic insights were limited because these effects were measured long after stimulation and therefore could have reflected various neuroplastic aftereffects with extended time courses. In this experiment with human subjects of both sexes, we tested for immediate stimulation impact on encoding-related activity of the hippocampus and immediately adjacent medial-temporal cortex by delivering theta-burst transcranial magnetic stimulation (TBS) concurrent with fMRI, as an immediate impact of stimulation would suggest an influence on neural activity. We reasoned that TBS would be particularly effective for influencing the hippocampus because rhythmic neural activity in the theta band is associated with hippocampal memory processing. First, we demonstrated that it is possible to obtain robust fMRI correlates of task-related activity during concurrent TBS. We then identified immediate effects of TBS on encoding of visual scenes. Brief volleys of TBS targeting the hippocampal network increased activity of the targeted (left) hippocampus during scene encoding and increased subsequent recollection. Stimulation did not influence activity during an intermixed numerical task with no memory demand. Control conditions using beta band and out-of-network stimulation also did not influence hippocampal activity or recollection. TBS targeting the hippocampal network therefore immediately impacted hippocampal memory processing. This suggests direct, beneficial influence of stimulation on hippocampal neural activity related to memory and supports the role of theta-band activity in human episodic memory. SIGNIFICANCE STATEMENT Can noninvasive stimulation directly impact function of indirect, deep-brain targets, such as the hippocampus? We tested this by targeting an accessible region of the hippocampal network via transcranial magnetic stimulation during concurrent fMRI. We reasoned that theta-burst stimulation would be particularly effective for impacting hippocampal function, as this stimulation rhythm should resonate with the endogenous theta-nested-gamma activity prominent in hippocampus. Indeed, theta-burst stimulation targeting the hippocampal network immediately impacted hippocampal activity during encoding, improving memory formation as indicated by enhanced later recollection. Rhythm- and location-control stimulation conditions had no such effects. These findings suggest a direct influence of noninvasive stimulation on hippocampal neural activity and highlight that the theta-burst rhythm is relatively privileged in its ability to influence hippocampal memory function.
Cantilevered Piezoelectric Micro Generator Design Issues and Application to the Mining Locomotive

This paper will present a complete discussion in recent design strategies for harvesting vibration energy using piezoelectric cantilever transducers. The interest in this primary energy source is due to its presence in non-negligible quantities in most of the engines used in the industrial process. Previous work has shown that it is possible to harvest significant amounts of energy capable of supplying a wireless sensor (WS) node. However, in most research, only one step of the energy conversion and utilization chain is studied. Starting from the definition of the different design issues for a piezoelectric micro generator (PMG), the leading optimization solutions will be reviewed in this paper. Based on the findings, the quantification of the data transmission range of wireless sensor nodes powered by a PMG is proposed to support the objectives envisioned by Industry 4.0. The vibration characteristics taken from mining locomotives that have not yet been treated previously are used to illustrate the improvement of the various optimization solutions. Through our objectives, this work offers a comprehensive discussion on the use of vibrational energy by wireless sensors, bringing together the fields of mechanics, electrical, electronics, and wireless communications. The theoretical basis for each design stage is provided through the design equations. Based on actual measurements of ambient vibration, it is demonstrated, considering an optimal design of the PMG, that a WS could transmit data beyond 1 km for physical phenomena to be controlled every 7 min.
Real-Time Emotion Recognition System to Monitor Student’s Mood in a Classroom

Depression is a commonly unattended health problem, unbounded by age border, and greatly affecting student’s performance in their study. To prevent it, the writer built a real-time facial emotion recognition system, so the teacher can monitor students’ mood through class activity. The system should be reliable enough when running on mid-end computer specification. The student will be given questionnaire to measure their stress. The questionnaire result will be used to analyze whether the use of the system able to reduce student’s stress or not. The result from the questionnaire shows that the use of the system able to detect student’s mood early so the teacher may minimize student’s stress.
One method of generating synthetic data to assess the upper limit of machine learning algorithms performance

Abstract Based on statistics from the World Nuclear Association, Kazakhstan has the highest uranium production in the world. Most of the uranium in the country is mined via in-situ leaching and the accurate classification of lithologic composition using electric logging data is economically crucial for this type of mining. In general, this classification is done manually, which is both inefficient and erroneous. Information technology tools, such as predictive analytics with Supervised Machine Learning (SML) algorithms and Artificial Neural Networks (ANN) models, are nowadays widely used to automate geophysical processes, but little is known about their application for uranium mines. Previous experiments showed an ANN accuracy of about 60% in the task of lithological interpretation of logging data. To determine the upper limit of the accuracy of machine learning algorithms in such task and for indirect assessment of the experts’ influence, a digital borehole model was developed. This made it possible to generate a complete set of data avoiding subjective expert assessments. Using these data, the work of various ML algorithms, both simple (kNN) and deep learning models (LSTM), was evaluated.