Search results for: mental health detection

Authors: M. Walmsley, S. Elmatarri, S. Mannion

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Introduction: Self-inflicted injury is a recognised cause of major trauma in adults and is an independent indicator of a reduced functional outcome compared to non-intentional major trauma. There is little literature available on the inpatient mental health (MH) management of this vulnerable group. A retrospective review was conducted of inpatient MH management of major trauma patients admitted to a UK regional Major Trauma Centre (MTC). Their outcomes were compared to all major trauma patients. This group of patients required multiple MH interventions whilst on the Major Trauma Ward (MTW) and a had worse functional outcome compared to non-intentional trauma. Method: The national TARN (Trauma Audit and Research Network) database was used to identify patients admitted to a regional MTC over a 2-year period from June 2018 to July 2020. Patients with an ISS (Injury Severity Score) of greater than 15 with a mechanism of either self-harm or high-risk behavior were included for further analysis. Inpatient medical notes were reviewed for MH interventions on the MTW. Further outcomes, including mortality, length of stay (LOS) and Glasgow Outcome Score (GOS) were compared with all major trauma patients for the same time period. Results: A total of 60 patients were identified in the time period and of those, 27 spent time on the MTW. A total of 23 (85%) had a prior MH diagnosis, with 11 (41%) under the care of secondary MH services. Adequate inpatient records for review were available for 24 patients. During their inpatient stay, 8 (33%) were reviewed on the ward by the inpatient MH team. There were 10 interventions required for 6 (25%) patients on the MTW including, sections under the Mental Health Act, transfer to specialist MH facility, pharmacological sedation and security being called to the MTW. When compared to all major trauma patients, those admitted due to self-harm or high-risk behavior had a statistically significantly higher ISS (31.43 vs 24.22, p=0.0001) and LOS (23.51d vs 16.06d, p=0.002). Functional outcomes using the GOS were reduced in this group of patients, GOS 5 (low disability) (51.66% vs. 61.01%) and they additionally had a higher level of mortality, GOS 1 (15.00% vs 11.67%). Discussion: Intentional self-harm is a recognised cause of major trauma in adults and this patient group sustains more severe injuries, requiring a longer hospital stay with worse outcomes compared to all major trauma patients. Inpatient MH interventions are required for a significant proportion of these patients and therefore, there needs to be a close relationship with MH services. There is limited available evidence for how this patient group is best managed as an inpatient to aid their recovery and further work is needed on how outcomes in this vulnerable group can be improved.

Keywords: adult major trauma, attempted suicide, self-inflicted major trauma, inpatient management

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Authors: Bo Liu, Chi-Man Pun

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A novel technique for digital forgery detection by signal noise inconsistency is proposed in this paper. The forged area spliced from the other picture contains some features which may be inconsistent with the rest part of the image. Noise pattern and the level is a possible factor to reveal such inconsistency. To detect such noise discrepancies, the test picture is initially segmented into small pieces. The noise pattern and level of each segment are then estimated by using various filters. The noise features constructed in this step are utilized in energy-based graph cut to expose forged area in the final step. Experimental results show that our method provides a good illustration of regions with noise inconsistency in various scenarios.

Keywords: forgery detection, splicing forgery, noise estimation, noise

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Authors: Feng Yin

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Clouds are inevitable contaminants in optical satellite imagery, and prevent the satellite imaging systems from acquiring clear view of the earth surface. The presence of clouds in satellite imagery bring negative influences for remote sensing land resources investigation. As a consequence, detecting the locations of clouds in satellite imagery is an essential preprocessing step, and further remove the existing clouds is crucial for the application of imagery. In this paper, a multi-temporal based satellite imagery cloud detection and removal method is proposed, which will be used for large-scale land resource investigation. The proposed method is mainly composed of four steps. First, cloud masks are generated for cloud contaminated images by single temporal cloud detection based on multiple spectral features. Then, a cloud-free reference image of target areas is synthesized by weighted averaging time-series images in which cloud pixels are ignored. Thirdly, the refined cloud detection results are acquired by multi-temporal analysis based on the reference image. Finally, detected clouds are removed via multi-temporal linear regression. The results of a case application in Hubei province indicate that the proposed multi-temporal cloud detection and removal method is effective and promising for large-scale land resource investigation.

Keywords: cloud detection, cloud remove, multi-temporal imagery, land resources investigation

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Authors: Neeru Deep, Kimberly McAlister

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Mental health issues have been increasing worldwide for many decades, but the COVID-19 pandemic has brought mental health issues into the spotlight. Within higher education, promoting the well-being of students has dramatically increased in focus. The Northwestern State University of Louisiana opened the Center for Positivity, Well-being, and Hope using the action research process of reflecting, planning, acting, and observing. The study’s purpose is two-fold: First, it highlights how to create a collaborative team to reflect, plan, and act to develop a well-being culture in higher education institutions. Second, it investigates the efficacy of the center through Seligman’s lenses. The researchers shared their experience in the first three phases of the action research process and then applied an identical concurrent mixed methods design. A purposive sample evaluated the efficacy of the center through Seligman’s lenses. The researcher administered PERMA-Profiler Measure, the PERMA-Profiler Measure overview, the CoPWH Evaluation I, and the CoPWH Evaluation II questionnaires to collect qualitative and quantitative data. The thematic analysis for qualitative and descriptive statistics for quantitative data concluded that the center creates a well-being culture and promotes well-being in college students. In conclusion, this action research shares the successful implementation of the cyclic process of research in promoting a well-being culture in higher education with the implications for promoting a well-being culture in various educational settings, workplaces, and communities.

Keywords: action research, mixed methods research design, Seligman, well-being.

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Authors: Konstantinos Perifanos, Eirini Florou, Dionysis Goutsos

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This paper presents and benchmarks a number of end-to-end Deep Learning based models for metaphor detection in Greek. We combine Convolutional Neural Networks and Recurrent Neural Networks with representation learning to bear on the metaphor detection problem for the Greek language. The models presented achieve exceptional accuracy scores, significantly improving the previous state-of-the-art results, which had already achieved accuracy 0.82. Furthermore, no special preprocessing, feature engineering or linguistic knowledge is used in this work. The methods presented achieve accuracy of 0.92 and F-score 0.92 with Convolutional Neural Networks (CNNs) and bidirectional Long Short Term Memory networks (LSTMs). Comparable results of 0.91 accuracy and 0.91 F-score are also achieved with bidirectional Gated Recurrent Units (GRUs) and Convolutional Recurrent Neural Nets (CRNNs). The models are trained and evaluated only on the basis of training tuples, the related sentences and their labels. The outcome is a state-of-the-art collection of metaphor detection models, trained on limited labelled resources, which can be extended to other languages and similar tasks.

Keywords: metaphor detection, deep learning, representation learning, embeddings

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Authors: Marcela Jelicova, Anna Lierova, Zuzana Sinkorova, Radovan Metelka

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At present, electrochemical methods are used in various research fields, especially for analysis of biological molecules. The fact offers the possibility of using the detection of oxidative damage induced indirectly by γ rays in DNA in biodosimentry. The main goal of our study is to optimize the detection of 8-hydroxyguanine by differential pulse voltammetry. The level of this stable and specific indicator of DNA damage could be determined in DNA isolated from peripheral blood lymphocytes, plasma or urine of irradiated individuals. Screen-printed carbon electrodes modified with carboxy-functionalized multi-walled carbon nanotubes were utilized for highly sensitive electrochemical detection of 8-hydroxyguanine. Electrochemical oxidation of 8-hydroxoguanine monitored by differential pulse voltammetry was found pH-dependent and the most intensive signal was recorded at pH 7. After recalculating the current density, several times higher sensitivity was attained in comparison with already published results, which were obtained using screen-printed carbon electrodes with unmodified carbon ink. Subsequently, the modified electrochemical technique was used for the detection of 8-hydroxoguanine in calf thymus DNA samples irradiated by 60Co gamma source in the dose range from 0.5 to 20 Gy using by various types of sample pretreatment and measurement conditions. This method could serve for fast retrospective quantification of absorbed dose in cases of accidental exposure to ionizing radiation and may play an important role in biodosimetry.

Keywords: biodosimetry, electrochemical detection, voltametry, 8-hydroxyguanine

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Authors: S. B. Kumbalavati, J. D. Mallapur, K. Y. Bendigeri

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A mobile Ad-hoc network (MANET) is a multihop wireless network where nodes communicate each other without any pre-deployed infrastructure. There is no central administrating unit. Hence, MANET is generally prone to many of the attacks. These attacks may alter, release or deny data. These attacks are nothing but intrusions. Intrusion is a set of actions that attempts to compromise integrity, confidentiality and availability of resources. A major issue in the design and operation of ad-hoc network is sharing the common spectrum or common channel bandwidth among all the nodes. We are performing intrusion detection using game theory approach. Game theory is a mathematical tool for analysing problems of competition and negotiation among the players in any field like marketing, e-commerce and networking. In this paper mathematical model is developed using game theory approach and intruders are detected and removed. Bandwidth utilization is estimated and comparison is made between bandwidth utilization with intrusion detection technique and without intrusion detection technique. Percentage of intruders and efficiency of the network is analysed.

Keywords: ad-hoc network, IDS, game theory, sensor networks

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Authors: Kevin William Taylor

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This work draws upon research in the fields of games development and mental health treatments to assess the influence that interactive entertainment has on the populous, and the potential of technology to affect areas of psychiatric assessment and treatment. It will use studies to establish the evolving direction of interactive media in the development of ‘digital self-identity,’ and how this can be incorporated into treatment to the benefit of psychiatry. It will determine that this approach will require collaborative production between developers and psychiatrists in order to ensure precise goals are met, improving the success of serious gaming for psychiatric assessment and treatment. Analysis documents the reach of video games across a growing global community of gamers, highlighting cases of the positives and negatives of video game usage. The games industry is largely oblivious to the psychological negatives, with psychiatrists encountering new conditions such as gaming addiction, which is now recognized by the World Health Organization. With an increasing amount of gamers worldwide, and an additional time per day invested in online gaming and character development, the concept of virtual identity as a means of expressing the id needs further study to ensure successful treatment. In conclusion, the assessment and treatment of game-related conditions are currently reactionary, and while some mental health professionals have begun utilizing interactive technologies to assist with the assessment and treatment of conditions, this study will determine how the success of these products can be enhanced. This will include collaboration between software developers and psychiatrists, allowing new avenues of skill-sharing in interactive design and development. Outlining how to innovate approaches to engagement will reap greater rewards in future interactive products developed for psychiatric assessment and treatment.

Keywords: virtual reality, virtual identity, interactivity, psychiatry

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Authors: Sehreen Moorat, Hiba, Maham Mahnoor, Faryal Soomro

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Leakage of gases in a system makes infrastructures and users vulnerable; it can occur due to its environmental conditions or old groundwork. In hospitals and industries, it is very important to detect any small level of gas leakage because of their sensitivity. In this research, a portable detection system for the small leakage of gases has been developed, gas sensor (MQ-2) is used to find leakage when it’s at its initial phase. The sensor and transmitting module senses the change in level of gas by using a sensing circuit. When a concentration of gas reach at a specified threshold level, it will activate an alarm and send the alarming situation notification to receiver through GSM module. The proposed system works well in hospitals, home, and industries.

Keywords: gases, detection, Arduino, MQ-2, alarm

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Authors: Puspa Maharani, Hendy Muagiri Margono, Izzatul Fithriyah

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Contraception is a medium used to prevent conception, aimed at couples who do not want pregnancy to occur. Unintended pregnancies have a prevalence of 48% per year of the total pregnancies that occur throughout the world. Schizophrenia sufferers have a high probability of being involved in unwanted sexual relations, but are not supported by adequate knowledge and use of contraception, so they are vulnerable to experiencing unwanted pregnancies. Unwanted pregnancy can pose significant health risks for patients with schizophrenia. There are many types of contraception that can be discussed and considered for patients with schizophrenia in order to improve the quality and well- being of their lives. Choosing the right contraceptive for patients with schizophrenia requires consideration of its use by taking into account the many factors that influence it.

Keywords: schizophrenia, contraception, pregnancy, mental health

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Authors: Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

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Surface robots in modern underground mine rescue operations suffer from several limitations in enabling a prompt self-rescue. Therefore, the possibility of designing and deploying in-mine robots to expedite miner self-rescue can have a transformative impact on miner safety. These in-mine robots for miner self-rescue can be envisioned to carry out diverse tasks such as object detection, autonomous navigation, and payload delivery. Specifically, this paper investigates the challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time. A total of 125 thermal images were collected in the Missouri S&T Experimental Mine with the help of student volunteers using the FLIR TG 297 infrared camera, which were pre-processed into training and validation datasets with 100 and 25 images, respectively. Three state-of-the-art, pre-trained real-time object detection models, namely YOLOv5, YOLO-FIRI, and YOLOv8, were considered and re-trained using transfer learning techniques on the training dataset. On the validation dataset, the re-trained YOLOv8 outperforms the re-trained versions of both YOLOv5, and YOLO-FIRI.

Keywords: miner self-rescue, object detection, underground mine, YOLO

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Authors: Zahra Soltani Shal

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Introduction: The COVID-19 pandemic marks an extraordinary global public health crisis unseen in the last century, with its rapid spread worldwide and associated mortality burden. Healthcare resilience during a pandemic is crucial not only for continuous and safe patients care but also for control of any outbreak. Aim: The present study was conducted to discover the organizational variables effective in increasing resilience and continuing the work of nurses in critical and stressful pandemic conditions. Method: The study population is nurses working in hospitals for patients with coronavirus. Sampling was done purposefully and information was collected from 15 nurses through In-depth semi-structured interviews. The interview was conducted to analyze the data using the framework analysis method consisting of five steps and is classified in the table. Results: According to the findings through semi-structural interviews, among organizational variables, organizational commitment (Affective commitment, continuous commitment, normative commitment) has played a prominent role in nurses' resilience. Discussion: despite the non-withdrawal of nurses and their resilience, due to the negative quality of their working life, the mentioned variable has affected their level of performance and ability and leads to fatigue and physical and mental exhaustion. Implications for practice: By equipping hospitals and improving the facilities of nurses, their organizational commitment can be increased and lead to their resilience in critical situations. Supervisors and senior officials at the hospitals should be responsible for nurses' health and safety. A clear and codified program in critical situations and comprehensive management is effective in improving the quality of the work-life of nurses. Creating an empathetic and interactive environment can help promote nurses' mental health.

Keywords: organizational commitment, quality of work life, nurses resilience, pandemic, coronavirus

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Authors: Amani Abdallah, Isam Shahrour

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The quality of drinking water is a major concern of public health. The control of this quality is generally performed in the laboratory, which requires a long time. This type of control is not adapted for accidental pollution from sudden events, which can have serious consequences on population health. Therefore, it is of major interest to develop real-time innovative solutions for the detection of accidental contamination in drinking water systems This paper presents researches conducted within the SunRise Demonstrator for ‘Smart and Sustainable Cities’ with a particular focus on the supervision of the water quality. This work aims at (i) implementing a smart water system in a large water network (Campus of the University Lille1) including innovative equipment for real-time detection of abnormal events, such as those related to the contamination of drinking water and (ii) develop a numerical modeling of the contamination diffusion in the water distribution system. The first step included verification of the water quality sensors and their effectiveness on a network prototype of 50m length. This part included the evaluation of the efficiency of these sensors in the detection both bacterial and chemical contamination events in drinking water distribution systems. An on-line optical sensor integral with a laboratory-scale distribution system (LDS) was shown to respond rapidly to changes in refractive index induced by injected loads of chemical (cadmium, mercury) and biological contaminations (Escherichia coli). All injected substances were detected by the sensor; the magnitude of the response depends on the type of contaminant introduced and it is proportional to the injected substance concentration.

Keywords: distribution system, drinking water, refraction index, sensor, real-time

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Authors: Michele L. Tilstra, Tiffany J. Peets

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Interprofessional collaboration in behavioral healthcare education is increasingly recognized for its value in training students to address diverse client needs. While interprofessional education (IPE) is well-documented in occupational therapy education to address physical health, limited research exists on collaboration with counselors to address mental health concerns and the psychosocial needs of individuals receiving care. Counseling education literature primarily examines the collaboration of counseling students with psychiatrists, psychologists, social workers, and marriage and family therapists. This pretest/posttest survey research study explored changes in attitudes toward interprofessional teams among 56 Master of Occupational Therapy (MOT) (n = 42) and Counseling and Human Development (CHD) (n = 14) students participating in the Counselors and Occupational Therapists Professionally Engaged in the Community (COPE) program. The COPE program was designed to strengthen the behavioral health workforce in high-need and high-demand areas. Students accepted into the COPE program were divided into small MOT/CHD groups to complete multiple interprofessional multicultural learning modules using videos, case studies, and online discussion board posts. The online modules encouraged reflection on various behavioral healthcare roles, benefits of team-based care, cultural humility, current mental health challenges, personal biases, power imbalances, and advocacy for underserved populations. Using the Student Perceptions of Interprofessional Clinical Education- Revision 2 (SPICE-R2) scale, students completed pretest and posttest surveys using a 5-point Likert scale (Strongly Agree = 5 to Strongly Disagree = 1) to evaluate their attitudes toward interprofessional teamwork and collaboration. The SPICE-R2 measured three different factors: interprofessional teamwork and team-based practice (Team), roles/responsibilities for collaborative practice (Roles), and patient outcomes from collaborative practice (Outcomes). The mean total scores for all students improved from 4.25 (pretest) to 4.43 (posttest), Team from 4.66 to 4.58, Roles from 3.88 to 4.30, and Outcomes from 4.08 to 4.36. A paired t-test analysis for the total mean scores resulted in a t-statistic of 2.54, which exceeded both one-tail and two-tail critical values, indicating statistical significance (p = .001). When the factors of the SPICE-R2 were analyzed separately, only the Roles (t Stat=4.08, p =.0001) and Outcomes (t Stat=3.13, p = .002) were statistically significant. The item ‘I understand the roles of other health professionals’ showed the most improvement from a mean score for all students of 3.76 (pretest) to 4.46 (posttest). The significant improvement in students' attitudes toward interprofessional teams suggests that the unique integration of OT and CHD students in the COPE program effectively develops a better understanding of the collaborative roles necessary for holistic client care. These results support the importance of IPE through structured, engaging interprofessional experiences. These experiences are essential for enhancing students' readiness for collaborative practice and align with accreditation standards requiring interprofessional education in OT and CHD programs to prepare practitioners for team-based care. The findings contribute to the growing body of evidence supporting the integration of IPE in behavioral healthcare curricula to improve holistic client care and encourage students to engage in collaborative practice across healthcare settings.

Keywords: behavioral healthcare, counseling education, interprofessional education, mental health education, occupational therapy education

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Authors: Sulaiman Al Amro

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There are currently considerable challenges concerning data security and privacy, particularly in relation to modern technologies. This includes the virtual world known as the Metaverse, which consists of a virtual space that integrates various technologies and is therefore susceptible to cyber threats such as malware, phishing, and identity theft. This has led recent studies to propose the development of Metaverse forensic frameworks and the integration of advanced technologies, including machine learning for intrusion detection and security. In this context, the application of first-order logic offers a formal and systematic approach to defining the conditions of cyberattacks, thereby contributing to the development of effective detection mechanisms. In addition, formalizing the rules and patterns of cyber threats has the potential to enhance the overall security posture of the Metaverse and, thus, the integrity and safety of this virtual environment. The current paper focuses on the primary actions employed by avatars for potential attacks, including Interval Temporal Logic (ITL) and behavior-based detection to detect an avatar’s abnormal activities within the Metaverse. The research established that the proposed framework attained an accuracy of 92.307%, resulting in the experimental results demonstrating the efficacy of ITL, including its superior performance in addressing the threats posed by avatars within the Metaverse domain.

Keywords: security, privacy, metaverse, cyberattacks, detection, first-order logic

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Authors: Deepanjali Gurav, Kun Qian

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In-vitro metabolic diagnosis relies on designed materials-based analytical platforms for detection of selected metabolites in biological samples, which has a key role in disease detection and therapeutic evaluation in clinics. However, the basic challenge deals with developing a simple approach for metabolic analysis in bio-samples with high sample complexity and low molecular abundance. In this work, we report a designer plasmonic nanoshells based platform for direct detection of small metabolites in clinical samples for in-vitro metabolic diagnostics. We first synthesized a series of plasmonic core-shell particles with tunable nanoshell structures. The optimized plasmonic nanoshells as new matrices allowed fast, multiplex, sensitive, and selective LDI MS (Laser desorption/ionization mass spectrometry) detection of small metabolites in 0.5 μL of bio-fluids without enrichment or purification. Furthermore, coupling with isotopic quantification of selected metabolites, we demonstrated the use of these plasmonic nanoshells for disease detection and therapeutic evaluation in clinics. For disease detection, we identified patients with postoperative brain infection through glucose quantitation and daily monitoring by cerebrospinal fluid (CSF) analysis. For therapeutic evaluation, we investigated drug distribution in blood and CSF systems and validated the function and permeability of blood-brain/CSF-barriers, during therapeutic treatment of patients with cerebral edema for pharmacokinetic study. Our work sheds light on the design of materials for high-performance metabolic analysis and precision diagnostics in real cases.

Keywords: plasmonic nanoparticles, metabolites, fingerprinting, mass spectrometry, in-vitro diagnostics

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Authors: Bill D. Geis

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Suicide wrongful death forensic cases are the fastest rising tort in mental health law. It is estimated that suicide-related cases have accounted for 15% of U.S. malpractice claims since 2006. Most suicide-related personal injury claims fall into the legal category of “wrongful death.” Though mental health experts may be called on to address a range of forensic questions in wrongful death cases, the central consultation that most experts provide is about the negligence element—specifically, the issue of whether the clinician met the clinical standard of care in assessing, treating, and managing the deceased person’s mental health care. Standards of care, varying from U.S. state to state, are broad and address what a reasonable clinician might do in a similar circumstance. This fact leaves the issue of the suicide standard of care, in each case, up to forensic experts to put forth a reasoned estimate of what the standard of care should have been in the specific case under litigation. Because the general state guidelines for standard of care are broad, forensic experts are readily retained to provide scientific and clinical opinions about whether or not a clinician met the standard of care in their suicide assessment, treatment, and management of the case. In the past and in much of current practice, the assessment of suicide has centered on the elicitation of verbalized suicide ideation. Research in recent years, however, has indicated that the majority of persons who end their lives do not say they are suicidal at their last medical or psychiatric contact. Near-term risk assessment—that goes beyond verbalized suicide ideation—is needed. Our previous research employed structural equation modeling to predict lethal suicide risk--eight negative thought patterns (feeling like a burden on others, hopelessness, self-hatred, etc.) mediated by nine transdiagnostic clinical factors (mental torment, insomnia, substance abuse, PTSD intrusions, etc.) were combined to predict acute lethal suicide risk. This structural equation model, the Lethal Suicide Risk Pattern (LSRP), Acute model, had excellent goodness-of-fit [χ2(df) = 94.25(47)***, CFI = .98, RMSEA = .05, .90CI = .03-.06, p(RMSEA = .05) = .63. AIC = 340.25, ***p < .001.]. A further SEQ analysis was completed for this paper, adding a measure of Acute Suicide Ideation to the previous SEQ. Acceptable prediction model fit was no longer achieved [χ2(df) = 3.571, CFI > .953, RMSEA = .075, .90% CI = .065-.085, AIC = 529.550].This finding suggests that, in this additional study, immediate verbalized suicide ideation information was unhelpful in the assessment of lethal risk. The LSRP and other dynamic, near-term risk models (such as the Acute Suicide Affective Disorder Model and the Suicide Crisis Syndrome Model)—going beyond elicited suicide ideation—need to be incorporated into current clinical suicide assessment training. Without this training, the standard of care for suicide assessment is out of sync with current research—an emerging dilemma for the forensic evaluation of suicide wrongful death cases.

Keywords: forensic evaluation, standard of care, suicide, suicide assessment, wrongful death

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Authors: Hyeon-Kyo Lim, Tong-Il Jang, Yong-Hee Lee

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For an operator in a nuclear power plant, human error is one of the most dreaded factors that may result in unexpected accidents. The possibility of human errors may be low, but the risk of them would be unimaginably enormous. Thus, for accident prevention, it is quite indispensable to analyze the influence of any factors which may raise the possibility of human errors. During the past decades, not a few research results showed that performance of human operators may vary over time due to lots of factors. Among them, stress is known to be an indirect factor that may cause human errors and result in mental illness. Until now, not a few assessment tools have been developed to assess stress level of human workers. However, it still is questionable to utilize them for human performance anticipation which is related with human error possibility, because they were mainly developed from the viewpoint of mental health rather than industrial safety. Stress level of a person may go up or down with work time. In that sense, if they would be applicable in the safety aspect, they should be able to assess the variation resulted from work time at least. Therefore, this study aimed to compare their applicability for safety purpose. More than 10 kinds of work stress tools were analyzed with reference to assessment items, assessment and analysis methods, and follow-up measures which are known to close related factors with work stress. The results showed that most tools mainly focused their weights on some common organizational factors such as demands, supports, and relationships, in sequence. Their weights were broadly similar. However, they failed to recommend practical solutions. Instead, they merely advised to set up overall counterplans in PDCA cycle or risk management activities which would be far from practical human error prevention. Thus, it was concluded that application of stress assessment tools mainly developed for mental health seemed to be impractical for safety purpose with respect to human performance anticipation, and that development of a new assessment tools would be inevitable if anyone wants to assess stress level in the aspect of human performance variation and accident prevention. As a consequence, as practical counterplans, this study proposed a new scheme for assessment of work stress level of a human operator that may vary over work time which is closely related with the possibility of human errors.

Keywords: human error, human performance, work stress, assessment tool, time-variant, accident prevention

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Authors: Maha Jazouli

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Suicide is one of the most important causes of death in the prison environment, both in Canada and internationally. Rates of attempts of suicide and self-harm have been on the rise in recent years, with hangings being the most frequent method resorted to. The objective of this article is to propose a method to automatically detect in real time suicidal behaviors. We present a gesture recognition system that consists of three modules: model-based movement tracking, feature extraction, and gesture recognition using machine learning algorithms (MLA). Our proposed system gives us satisfactory results. This smart video surveillance system can help assist staff responsible for the safety and health of inmates by alerting them when suicidal behavior is detected, which helps reduce mortality rates and save lives.

Keywords: suicide detection, Kinect azure, RGB-D camera, SVM, machine learning, gesture recognition

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Authors: M. F. Abreu, A. I. Aguiar, R. Gaio, R. Duarte

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Background: Prison establishments constitute high-risk environments for the transmission and spread of tuberculosis (TB), given their epidemiological context and the difficulty of implementing preventive and control measures. Guidelines for control and prevention of tuberculosis in prisons have been described as incomplete and heterogeneous internationally, due to several identified obstacles, for example scarcity of human resources and funding of prisoner health services. In Portugal, a protocol was created in 2014 with the aim to define and standardize procedures of detection and prevention of tuberculosis within prisons. Objective: The main objective of this study was to identify and describe barriers to tuberculosis detection in prisons of Porto and Lisbon districts in Portugal. Methods: A cross-sectional study was conducted from 2ⁿᵈ January 2018 till 30ᵗʰ June 2018. Semi-structured questionnaires were applied to health care professionals working in the prisons of the districts of Porto (n=6) and Lisbon (n=8). As inclusion criteria we considered having work experience in the area of tuberculosis (either in diagnosis, treatment, or follow up). The questionnaires were self-administered, in paper format. Descriptive analyses of the questionnaire variables were made using frequencies and median. Afterwards, a hierarchical agglomerative clusters analysis was performed. After obtaining the clusters, the chi-square test was applied to study the association between the variables collected and the clusters. The level of significance considered was 0.05. Results: From the total of 186 health professionals, 139 met the criteria of inclusion and 82 health professionals were interviewed (62,2% of participation). Most were female, nurses, with a median age of 34 years, with term employment contract. From the cluster analysis, two groups were identified with different characteristics and behaviors for the procedures of this protocol. Statistically significant results were found in: elements of cluster 1 (78% of the total participants) work in prisons for a longer time (p=0.003), 45,3% work > 4 years while 50% of the elements of cluster 2 work for less than a year, and more frequently answered they know and apply the procedures of the protocol (p=0.000). Both clusters answered frequently the need of having theoretical-practical training for TB (p=0.000), especially in the areas of diagnosis, treatment and prevention and that there is scarcity of funding to prisoner health services (p=0.000). Regarding procedures for TB screening (periodic and contact screening) and procedures for transferring a prisoner with this disease, cluster 1 also answered more frequently to perform them (p=0.000). They also referred that the material/equipment for TB screening is accessible and available (p=0.000). From this clusters we identified as barriers scarcity of human resources, the need to theoretical-practical training for tuberculosis, inexperience in working in health services prisons and limited knowledge of protocol procedures. Conclusions: The barriers found in this study are the same described internationally. This protocol is mostly being applied in portuguese prisons. The study also showed the need to invest in human and material resources. This investigation bridged gaps in knowledge that could help prison health services optimize the care provided for early detection and adherence of prisoners to treatment of tuberculosis.

Keywords: barriers, health care professionals, prisons, protocol, tuberculosis

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Authors: Mubarak Suliman, Mohamed Hassan

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Detection of downed conductors occurring with high fault resistance (reaching kilo-ohms) has always been a challenge, especially in countries like Saudi Arabia, on which earth resistivity is very high in general (reaching more than 1000 Ω-meter). The new approaches for the detection of resistive and high impedance faults are based on the analysis of the fault current waveform. These methods are still under research and development, and they are currently lacking security and dependability. The other approach is communication-based solutions which depends on voltage measurement at the end of overhead line branches and communicate the measured signals to substation feeder relay or a central control center. However, such a detection method is costly and depends on the availability of communication medium and infrastructure. The main objective of this research is to utilize the available standard protection schemes to increase the probability of detection of downed conductors occurring with a low magnitude of fault currents and at the same time avoiding unwanted tripping in healthy conditions and feeders. By specifying the operating region of the faulty feeder, use of tripping curve for discrimination between faulty and healthy feeders, and with proper selection of core balance current transformer (CBCT) and voltage transformers with fewer measurement errors, it is possible to set the pick-up of sensitive earth fault current to minimum values of few amps (i.e., Pick-up Settings = 3 A or 4 A, …) for the detection of earth faults with fault resistance more than (1 - 2 kΩ) for 13.8kV overhead network and more than (3-4) kΩ fault resistance in 33kV overhead network. By implementation of the outcomes of this study, the probability of detection of downed conductors is increased by the utilization of existing schemes (i.e., Directional Sensitive Earth Fault Protection).

Keywords: sensitive earth fault, zero sequence current, grounded system, resistive fault detection, healthy feeder

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Authors: Neha Ahirwar

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In the contemporary digital era, the rise of credit card fraud poses a significant threat to both financial institutions and consumers. As fraudulent activities become more sophisticated, there is an escalating demand for robust and effective fraud detection mechanisms. Advanced machine learning algorithms have become crucial tools in addressing this challenge. This paper conducts a thorough examination of the design and evaluation of a credit card fraud detection system, utilizing four prominent machine learning algorithms: random forest, logistic regression, decision tree, and XGBoost. The surge in digital transactions has opened avenues for fraudsters to exploit vulnerabilities within payment systems. Consequently, there is an urgent need for proactive and adaptable fraud detection systems. This study addresses this imperative by exploring the efficacy of machine learning algorithms in identifying fraudulent credit card transactions. The selection of random forest, logistic regression, decision tree, and XGBoost for scrutiny in this study is based on their documented effectiveness in diverse domains, particularly in credit card fraud detection. These algorithms are renowned for their capability to model intricate patterns and provide accurate predictions. Each algorithm is implemented and evaluated for its performance in a controlled environment, utilizing a diverse dataset comprising both genuine and fraudulent credit card transactions.

Keywords: efficient credit card fraud detection, random forest, logistic regression, XGBoost, decision tree

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Authors: Waroton Paisuwan, Mongkol Sukwattanasinitt, Mamoru Tobisu, Anawat Ajavakom

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Novel dihydroquinoline derivatives (DHP and DHP-OH) were synthesized in one pot via a tandem trimerization-cyclization of methylpropiolate. DHP and DHP-OH possess strong blue fluorescence with high quantum efficiencies over 0.70 in aqueous media. DHP-OH displays a remarkable fluorescence quenching selectively to the presence of Au3+ through the oxidation of dihydropyridine to pyridinium ion as confirmed by NMR and HRMS. DHP-OH was used to demonstrate the quantitative analysis of Au3+ in water samples with the limit of detection of 33 ppb and excellent recovery (>95%). This fluorescent probe was also applied for the determination of Au3+ residue in the gold nanoparticle solution and a paper-based sensing strip for the on-site detection of Au3+.

Keywords: Gold(III) ion detection, Fluorescent sensor, Fluorescence quenching, Dihydropyridine, Gold nanoparticles (AuNPs)

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Authors: Sehrish Ather

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Introduction: To improve organizational achievements through the production of new knowledge, health policy and system research is the basic requirement. An aging population is always the source of the increased burden of chronic diseases, disabilities, mental illnesses, and other co-morbidities; therefore the provision of quality health care services to every group of the population should be achieved by making strong policy and system research for the betterment of health care system. Unfortunately, the whole world is lacking policies and system research for providing health care to their elderly population. Materials and Methods: A literature review of published studies on aging diseases was done, ranging from the year 2011-2018. Geriatric, population, health policy, system, and research were the key terms used for the search. Databases searched were Google Scholar, PubMed, Science Direct, Ovid, and Research Gate. Grey literature was searched from various websites, including IHME, Library of the University of Lahore, World Health Organization (Ageing and Life Course), and Personal communication with Neuro-physicians. After careful reviewing published and un-published information, it was decided to carry on with commentary. Results and discussion: Most of the published studies have highlighted the need to advocate the funders of health policy and stakeholders of healthcare system research, and it was detected as a major issue, research on policy and healthcare system to provide health care to 'geriatric population' was found as highly neglected area. Conclusion: It is concluded that physicians are more involved with the policy and system research regarding any type of diseases, but scientists and researchers of basic and social science are less likely to be involved in methods used for health policy and system research due to lack of funding and resources. Therefore ageing diseases should be considered as a priority, and comprehensive policy and system research should be initiated for diseases of the geriatric population.

Keywords: geriatric population, health care system, health policy, system research

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Authors: Nermeen Saad Elbeltagy, Hoda Ghareeb, Hesham Adel Elsheshtawy, Nadim Hamed, Amany Ibrahim Mostafa, Sara Hazem Hassan

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Introduction: Depression is one of the most common mental health problems occurring in women during their child bearing years. Perinatal depression refers to major and minor depressive episodes that occur either during pregnancy or aer delivery. Although perinatal depression is common in developing countries, it is under-recognized in low and middle income countries making a substantial contribution to maternal and infant morbidity and mortality. About 12.5 - 42% of pregnant women and, 12 - 50% of post natal mothers in low and middle income countries such as Ethiopia had depression AIM OF THE WORK: To study prevalence, associated factors and impact of maternal perinatal depression in Alexandria. Patients and method: This study was conducted on 300 mothers at the postnatal ward in ElShatby Maternity Hospital from April 2022 unl October 2022. Females with past history of depression before pregnancy or females who receive medications inducing depression were excluded. The participants were asked to complete the questionnaire that includes the Edinburgh Postnatal Depression Scale (EPDS) as a screening test to obtain information concerning the current frame of mind at antepartum, partum and postpartum periods Results: The prevalence of perinatal depression was 22.3%. It was found that there is a significant negave moderate correlation between socioeconomic status and perinatal depression(r=-0.42). The present study revealed that about two thirds (60.7%) of postpartum women had low socioeconomic level. Also, less than one fourth (20%) of parents had high education and only one fourth (25.3%) of postpartum women were working. There was a statically significance difference between the number of previous abortions and perinatal depression (p=0.04). There was a significant moderate correlation between the amount of blood lost during delivery and an increased risk of developing postpartum depression. The prevalence of perinatal depression was high in cases of female neonates more than male ones. Conclusion: the prevalence of perinatal depression among the studied women was 22.3% of studied group. The significant factors identified in this study can be targeted to reduce the occurrence of perinatal depression among pregnant women in Alexandria through appropriate health interventions which includes perinatal depression screening, counseling, and the provision of support for pregnant women during antenatal care as well as lifestyle modification.

Keywords: mental health, depression in pregnancy, mental disorders, psychology in pregnancy

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Authors: Tris Kee

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This research investigates social sustainability factors in transitional housing projects and their impact on fostering healthy living environments that promote physical activity and social interaction for residents. Social sustainability is integral to individual health and well-being, as emphasized by Goal 11 of the 2030 Agenda for Sustainable Development, which highlights the importance of safe, affordable, and accessible transport systems, green spaces, and public spaces catering to vulnerable populations' needs. Communal spaces in urban environments are essential for fostering social sustainability, as they serve as settings for physical activities and social interactions among diverse socio-economic groups. Factors such as neighborhood social atmosphere, historical context, social disparity, and mobility can influence the relationship between existing and transitional communities. Mental health effects can be measured through housing segregation, mobility and accessibility, and housing tenure. A significant research gap exists in understanding the living environment of transitional housing in Hong Kong and the social sustainability factors affecting residents' mental and physical health. To address this gap, our study employs a mixed-methods approach combining survey questionnaires and interviews to gather both quantitative and qualitative data. This methodology will provide comprehensive insights into residents' experiences and perceptions. Our research's main contribution is identifying key social sustainability factors in transitional housing and their impact on residents' well-being, informing policy-making and the creation of inclusive, healthy living environments. By addressing this research gap, we aim to provide valuable insights for future housing projects, ultimately promoting the development of socially sustainable transitional communities.

Keywords: social sustainablity, affordable housing, transitional housing, high density housing

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Authors: Trupti Chavan, Ramachandra Guda, Kameshwar Rao

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The lack of data is a pain problem in medical image analysis using a convolutional neural network (CNN). This work uses various spatial transformation techniques to address the medical image augmentation issue for knee detection and localization using an enhanced single shot detector (SSD) network. The spatial transforms like a negative, histogram equalization, power law, sharpening, averaging, gaussian blurring, etc. help to generate more samples, serve as pre-processing methods, and highlight the features of interest. The experimentation is done on the OpenKnee dataset which is a collection of knee images from the openly available online sources. The CNN called enhanced single shot detector (SSD) is utilized for the detection and localization of the knee joint from a given X-ray image. It is an enhanced version of the famous SSD network and is modified in such a way that it will reduce the number of prediction boxes at the output side. It consists of a classification network (VGGNET) and an auxiliary detection network. The performance is measured in mean average precision (mAP), and 99.96% mAP is achieved using the proposed enhanced SSD with spatial transformations. It is also seen that the localization boundary is comparatively more refined and closer to the ground truth in spatial augmentation and gives better detection and localization of knee joints.

Keywords: data augmentation, enhanced SSD, knee detection and localization, medical image analysis, openKnee, Spatial transformations

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Authors: Naser Safdarian, Nader Jafarnia Dabanloo

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In this paper we used four features i.e. Q-wave integral, QRS complex integral, T-wave integral and total integral as extracted feature from normal and patient ECG signals to detection and localization of myocardial infarction (MI) in left ventricle of heart. In our research we focused on detection and localization of MI in standard ECG. We use the Q-wave integral and T-wave integral because this feature is important impression in detection of MI. We used some pattern recognition method such as Artificial Neural Network (ANN) to detect and localize the MI. Because these methods have good accuracy for classification of normal and abnormal signals. We used one type of Radial Basis Function (RBF) that called Probabilistic Neural Network (PNN) because of its nonlinearity property, and used other classifier such as k-Nearest Neighbors (KNN), Multilayer Perceptron (MLP) and Naive Bayes Classification. We used PhysioNet database as our training and test data. We reached over 80% for accuracy in test data for localization and over 95% for detection of MI. Main advantages of our method are simplicity and its good accuracy. Also we can improve accuracy of classification by adding more features in this method. A simple method based on using only four features which extracted from standard ECG is presented which has good accuracy in MI localization.

Keywords: ECG signal processing, myocardial infarction, features extraction, pattern recognition

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Authors: Md Fazle Alam, Amaj Ahmed Laskar, Hina Younus

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Melamine toxicity which causes renal failure and death of humans and animals have recently attracted worldwide attention. Developing an easy, fast and sensitive method for the routine melamine detection is the need of the hour. Herein, we have developed a rapid, sensitive, one step and selective colorimetric method for the detection of melamine in milk samples based upon in-situ formation of silver nanoparticles (AgNPs) via tannic acid at room temperature. These AgNPs thus formed were characterized by UV-VIS spectrophotometer, transmission electron microscope (TEM), zetasizer and dynamic light scattering (DLS). Under optimal conditions, melamine could be selectively detected within the concentration range of 0.05-1.4 µM with a limit of detection (LOD) of 10.1 nM, which is lower than the strictest melamine safety requirement of 1 ppm. This assay does not utilize organic cosolvents, enzymatic reactions, light sensitive dye molecules and sophisticated instrumentation, thereby overcoming some of the limitations of conventional methods.

Keywords: milk adulteration, melamine, silver nanoparticles, tannic acid

Procedia PDF Downloads 252

Authors: Sireen Irsheid

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Despite concerted efforts to improve education attainment with progress in recent years, student achievement and attainment remain among the most significant challenges for school districts across the United States. Many scholars have argued that students who do not complete high school do not drop out of school voluntarily but are ‘pushed out’ of schools through multiple mechanisms related to structural and socioeconomic barriers, behavioral health challenges, pedagogical practices, and administrative procedures. Extant literature has shown that living in historically disadvantaged neighborhoods or attending under-resourced schools exacerbates student-level risk factors for grade retention and school pushout. Most efforts to respond to the school pushout phenomenon have focused on individual characteristics of students, with relatively little attention to addressing these multiple system-level characteristics related to perpetuating inequities. This study is built on a growing body of social justice-oriented research concerned with the systemic influences that shape the experiences and mental health challenges of young people. Specifically, this study examined how young people who have been experiencing education inequities make meaning and navigate the structural factors related to neighborhood and school disinvestment and access to resources and supports, and their risk for school pushout. Furthermore, schools as political, cultural, and ideologically reproductive spaces often serve as sites of resistance and can support students who are impacted by educational inequity. Study findings provide education, neighborhood, school psychology, social work practice, and policy considerations.

Keywords: education policy, mental health, school prison nexus, school pushout, structural trauma

Procedia PDF Downloads 66