A Blackbox Model Is All You Need to Breach Privacy: Smart Grid Forecasting Models as a Use Case

This paper investigates the potential privacy risks associated with forecasting models, with specific emphasis on their application in the context of smart grids. While machine learning and deep learning algorithms offer valuable utility, concerns arise regarding their exposure of sensitive information. Previous studies have focused on classification models, overlooking risks associated with forecasting models. Deep learning based forecasting models, such as Long Short Term Memory (LSTM), play a crucial role in several applications including optimizing smart grid systems but also introduce privacy risks. Our study analyzes the ability of forecasting models to leak global properties and privacy threats in smart grid systems. We demonstrate that a black box access to an LSTM model can reveal a significant amount of information equivalent to having access to the data itself (with the difference being as low as 1% in Area Under the ROC Curve). This highlights the importance of protecting forecasting models at the same level as the data

Treatment Of Dewatering Construction Water Using An Integrated Forward Osmosis System

Forward osmosis (FO) has gained substantial research attention in recent years as a new emerging water treatment technology with low energy consumption. In this study, forward osmosis has been used to treat dewatering construction water (DCW). The impact of flow rates of feed solution (FS) and draw solution (DS), the placement of a spacer on the support layer of the FO membrane and the pretreatment of the feed solution on the performance of the forward osmosis process were investigated. It was found that a feed solution and draw solution flow rate of 2.9 LPM gave the highest membrane flux with an initial value of 0.055 L/m2.min compared to 0.048 L/m2.min, 0.048 L/m2.min and 0.044 L/m2.min at the flow rates of 2.2 LPM, 1.5 LPM and 0.8 LPM, respectively. The highest recovery rate of 24% was obtained at a flow rate of 2.2 LPM compared to a recovery rate of 16%, 21% and 15% for flow rates of 2.9 LPM, 1.5 LPM and 0.8 LPM, respectively. The influence of pretreating DCW on the performance of the FO process was also investigated. Pretreatment by primary settling and multimedia filtration were carried out. Results showed that the recovery rate of the FO process increased the most after pretreatment by multimedia filtration with a recovery rate of 30% compared to 22% and 15% for pretreatment by settling and without treatment, respectively. Furthermore, it was found that when the membrane’s active layer was facing the draw solution in (DS-AL) operation mode, a better membrane flux was achieved when compared to the membrane’s active layer facing the feed solution (FS-AL)

Oxidative stress contributes to cobalt oxide nanoparticles-induced cytotoxicity and DNA damage in human hepatocarcinoma cells.

BackgroundCobalt oxide nanoparticles (Co(3)O(4)NPs) are increasingly recognized for their utility in biological applications, magnetic resonance imaging, and drug delivery. However, little is known about the toxicity of Co(3)O(4)NPs in human cells.MethodsWe investigated the possible mechanisms of genotoxicity induced by Co(3)O(4)NPs in human hepatocarcinoma (HepG2) cells. Cell viability, reactive oxygen species (ROS), glutathione, thiobarbituric acid reactive substance, apoptosis, and DNA damage were assessed in HepG2 cells after Co(3)O(4)NPs and Co(2+) exposure.ResultsCo(3)O(4)NPs elicited a significant (P < 0.01) reduction in glutathione with a concomitant increase in lipid hydroperoxide, ROS generation, superoxide dismutase, and catalase activity after 24- and 48-hour exposure. Co(3)O(4)NPs had a mild cytotoxic effect in HepG2 cells; however, it induced ROS and oxidative stress, leading to DNA damage, a probable mechanism of genotoxicity. The comet assay showed a statistically significant (P < 0.01) dose- and time-related increase in DNA damage for Co(3)O(4)NPs, whereas Co(2+) induced less change than Co(3)O(4)NPs but significantly more than control.ConclusionOur results demonstrated that Co(3)O(4)NPs induced cytotoxicity and genotoxicity in HepG2 cells through ROS and oxidative stress

Bilateral inverted and impacted maxillary third molars: a case report

Bilateral inverted third molar impaction is an extremely rare condition. We reported the case of a 50-year-old female patient with bilateral inverted and impacted maxillary third molars. Both were asymptomatic and pathology free clinically and radiographically. Surgical extraction of these inverted third molars with inaccessible positions requires an aggressive bone removal on the tuberosity bilaterally. Moreover, it contains a high risk of displacement of the inverted third molar into the maxillary sinus. Conservative management was the choice, with the patient’s agreement, and the inverted third molars were left in sit

Audio-Based Drone Detection and Identification Using Deep Learning Techniques with Dataset Enhancement through Generative Adversarial Networks

Drones are becoming increasingly popular not only for recreational purposes but in day-to-day applications in engineering, medicine, logistics, security and others. In addition to their useful applications, an alarming concern in regard to the physical infrastructure security, safety and privacy has arisen due to the potential of their use in malicious activities. To address this problem, we propose a novel solution that automates the drone detection and identification processes using a drone’s acoustic features with different deep learning algorithms. However, the lack of acoustic drone datasets hinders the ability to implement an effective solution. In this paper, we aim to fill this gap by introducing a hybrid drone acoustic dataset composed of recorded drone audio clips and artificially generated drone audio samples using a state-of-the-art deep learning technique known as the Generative Adversarial Network. Furthermore, we examine the effectiveness of using drone audio with different deep learning algorithms, namely, the Convolutional Neural Network, the Recurrent Neural Network and the Convolutional Recurrent Neural Network in drone detection and identification. Moreover, we investigate the impact of our proposed hybrid dataset in drone detection. Our findings prove the advantage of using deep learning techniques for drone detection and identification while confirming our hypothesis on the benefits of using the Generative Adversarial Networks to generate real-like drone audio clips with an aim of enhancing the detection of new and unfamiliar drones

Factors Associated with Choice of Career in Family Medicine Among Junior Doctors in Oman

Objectives: The number of family physicians in Oman is far below that recommended by the World Health Organization. This study aimed to determine factors influencing junior doctors’ choice of a career in family medicine. Methods: This cross-sectional study was conducted between March and June 2018 and targeted applicants to Oman Medical Specialty Board residency programmes during the 2018–2019 academic year. Applicants were grouped according to their choice of either family medicine (n = 64) or other specialities (n = 81). A self-administered questionnaire was utilised to compare the applicants’ sociodemographic characteristics, factors influencing their choice of career and their Myers-Briggs Type Indicator® (MBTI) personality traits. Results: A total of 52 family medicine and 43 other residency applicants participated in the study (response rates: 81.3% and 53.1%, respectively). Most family medicine applicants were female (86.5%), married (65.4%) and resided in rural areas (73.1%); moreover, 19.2% were ≥30 years of age. Overall, emphasis on continuity of care, opportunity to deal with a variety of medical problems, the ability to use a wide range of skills and knowledge, early exposure to the discipline, opportunity to teach and perform research and the influence of family or friends were important factors in determining choice of a career in family medicine. Moreover, the MBTI analysis revealed that family medicine applicants were commonly extroverted-sensing-thinking-judging personality types. Conclusion: Knowledge of the factors influencing career choice among junior doctors may be useful in determining future admission policies in order to increase the number of family physicians in Oman.Keywords: Career Choice; Internship and Residency; Medical Specialty; Family Practice; Family Physicians; Myers-Briggs Type Indicator; Oman

Dental and Anaesthetic Challenges in a Patient with Dystrophic Epidermolysis Bullosa

Epidermolysis bullosa is a group of rare genetic disorders characterised by skin and mucous membrane fragility and systemic manifestations of variable severity. We report a case of dystrophic epidermolysis bullosa in an 18-year-old male patient who presented to the Department of Oral Health at Sultan Qaboos University Hospital, Muscat, Oman, in 2015 with recurrent dental pain and infections. Due to the poor dental status of the patient and anticipated operative difficulties due to microstomia and limited mouth opening, the patient underwent full dental clearance under general anaesthesia. This article discusses the dental and anaesthetic challenges encountered during the management of this patient and provides a brief literature review

Can creative circles improve reading comprehension and creative thinking of Saudi third-grade middle school EFL learners?

Ph. D. ThesisReading is an abundant source of creativity and one of the main ways for foreign language learners (EFL) to acquire information. Likewise, creativity is an essential life skill which is highly related to EFL development. Yet, studies have shown that EFL learners lack basic reading skills and many face comprehension difficulties. Nor is creativity fully established and appreciated in the context of EFL. This study explored perceptions of supervisors’, teachers' and learners' on reading, collaboration and creativity. It investigated the effects of incorporating Creative Circles (C.C.) approach on Saudi EFL learners' reading comprehension and creative thinking. A mixed method approach was adopted in this quasi-experimental study. Eight EFL supervisors, 45 EFL teachers and 90 EFL learners from three natural classes in one middle-school participated in the study. Prior to the intervention, surveys and interviews were conducted to find out the extent to which reading skills and creativity are promoted in reading classes and to explore participants’ perceptions on collaborative reading and creativity. The three classes were taught by the same teacher with one being an experiment class (C.C. class) and the other two as comparison classes. During the three-month long intervention, learners in the experiment class were introduced to the Creative Circles approach, while the other two classes approached reading lessons as they normally did without any changes or modifications. All the participants were tested for their reading comprehension and creativity prior to and after the completion of the intervention. In addition to quantitative data, learners in the experiment class and the teacher were asked to keep journals to describe their learning/teaching experience about the C.C. approach. The quantitative data was then analysed using t-test, ANOVA and correlation analysis, whereas the qualitative data was analysed thematically. The findings reveal an insufficient understanding and lack in promoting of reading skills, collaboration and creative thinking among Saudi EFL supervisors, teachers and students. Comparisons of pre-and post-tests results show that incorporating C.C. approach in teaching reading could improve students’ reading comprehension and creative thinking domains (with the exception of originality). the C.C. approach also appears to have a positive impact on students’ attitudes towards reading and collaboration. The correlation analysis did not show a significant relationship between reading and creativity. Drawing from the findings of this study, suggestions and pedagogical implications for reading instruction and fostering creativity in the Saudi EFL classroom and the wider EFL context are discusse

Machine Learning Applications in Studying Mental Health Among Immigrants and Racial and Ethnic Minorities: A Systematic Review

Background: The use of machine learning (ML) in mental health (MH) research is increasing, especially as new, more complex data types become available to analyze. By systematically examining the published literature, this review aims to uncover potential gaps in the current use of ML to study MH in vulnerable populations of immigrants, refugees, migrants, and racial and ethnic minorities. Methods: In this systematic review, we queried Google Scholar for ML-related terms, MH-related terms, and a population of a focus search term strung together with Boolean operators. Backward reference searching was also conducted. Included peer-reviewed studies reported using a method or application of ML in an MH context and focused on the populations of interest. We did not have date cutoffs. Publications were excluded if they were narrative or did not exclusively focus on a minority population from the respective country. Data including study context, the focus of mental healthcare, sample, data type, type of ML algorithm used, and algorithm performance was extracted from each. Results: Our search strategies resulted in 67,410 listed articles from Google Scholar. Ultimately, 12 were included. All the articles were published within the last 6 years, and half of them studied populations within the US. Most reviewed studies used supervised learning to explain or predict MH outcomes. Some publications used up to 16 models to determine the best predictive power. Almost half of the included publications did not discuss their cross-validation method. Conclusions: The included studies provide proof-of-concept for the potential use of ML algorithms to address MH concerns in these special populations, few as they may be. Our systematic review finds that the clinical application of these models for classifying and predicting MH disorders is still under development

Rapidly progressive periodontal disease associated with human immunodeficiency virus

Severe periodontal inflammation with generalized dental plaque accumulation, spontaneous and severe gingival bleeding, fungal infection, and interdental papillae necrosis are presented in a patient infected with human immunodeficiency virus (HIV). Bite-wing radiographs revealed a generalized horizontal alveolar bone loss of 7-8 millimetres in both arches. Erythematous patches were noted on the gingival mucosa in both jaws. DNA testing was performed to indentify the periodontopathogens. The patient had no signs or symptoms of acquired immunodeficiency syndrome. This case-report presents the massive periodontal destruction that occurred in a patient infected with HIV. Therefore, it is highly recommended that patients infected with HIV should be regularly monitored to aid in early detection and to provide proper management of periodontal inflammatory conditions to minimize its destruction