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Second language learners generally struggle with comprehending and identifying main ideas while reading academic texts. Moreover, students are expected to write summaries of texts as a part of their curriculum to facilitate their reading comprehension. Whereas summary writing can be considered a sound post-reading strategy that facilitates comprehension, it is a skill that needs to be mastered. The present action research introduces concept mapping, a fundamental cognitive learning strategy, to facilitate tertiary-level students' reading comprehension and summary writing skills. The sample consisted of 22 undergraduate students, male and female, from the Faculty of Social Sciences in a public sector university in Karachi. Two four-week action cycles engaged students in reading and summary writing tasks using a direct method with model summary samples and a concept mapping tool. Students’ reflective notes and written summaries were used as data collection tools. Thematic data analysis revealed that concept mapping proved to be an effective tool in summary writing and facilitated students in identifying and organizing main ideas. Findings and ethical considerations are discussed.

Radiation therapy (RT) plays a crucial role in the treatment of head and neck cancers (HNCs). This paper emphasizes the importance of effective communication and collaboration between radiation oncologists and dental specialists in the HNC care pathway. It also provides an overview of the role of RT in HNC treatment and illustrates the interdisciplinary collaboration between these teams to optimize patient care, expedite treatment, and prevent post-treatment oral complications. The methods utilized include a thorough analysis of existing research articles, case reports, and clinical guidelines, with terms such as dental management, oral oncology, head and neck cancer, and radiotherapy included for this review. The findings underscore the significance of the early involvement of dental specialists in the treatment planning phase to assess and prepare patients for RT, including strategies such as prophylactic tooth extraction to mitigate potential oral complications. Furthermore, post-treatment oral health follow-up and management by dental specialists are crucial in minimizing the incidence and severity of RT-induced oral sequelae. In conclusion, these proactive measures help minimize dental and oral complications before, during, and after treatment.

Jianmin Gong,1,2,* Bo Shi,1,3,* Ping Yang,1 Adeel Khan,4 Tao Xiong,2 Zhiyang Li1 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210000, People’s Republic of China; 2College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China; 3Department of Clinical Laboratory, Nanjing Jiangning Hospital of Chinese Medicine (CM), Nanjing, 211100, People’s Republic of China; 4Department of Biotechnology, University of Science and Technology Bannu, Bannu, 28100, Islamic Republic of Pakistan*These authors contributed equally to this workCorrespondence: Tao Xiong, College of Life Science, Yangtze University, Jingzhou, 434025, People’s Republic of China, Tel +8613872387410, Email xiongtao@yangtzeu.edu.cn Zhiyang Li, Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, Nanjing, 210008, People’s Republic of China, Tel +8618951703765, Email lizhiyang@nju.edu.cnBackground: Hypertrophic cardiomyopathy (HCM) is a dominantly inherited disease associated with sudden immune cell associations that remain unclear. The aim of this study was to comprehensively screen candidate markers associated with HCM and immune cells and explore potential pathogenic pathways.Methods: First, download the GSE32453 dataset to identify differentially expressed genes (DEGs) and perform Gene Ontology and pathway enrichment analysis using DAVID and GSEA. Next, construct protein-protein interaction (PPI) networks using String and Cytoscape to identify hub genes. Afterward, use CIBERSORT to determine the proportion of immune cells attributed to key genes in HCM and conduct ROC analysis based on the external dataset GSE36961 to evaluate their diagnostic value. Finally, validate the expression of key genes in the hypertrophic cardiomyocyte model through qRT-PCR using data fr

Chemotherapy treatment against pancreatic ductal adenocarcinoma (PDAC) is thwarted by tumoral activation of multiple therapy resistance pathways. The growth hormone (GH)–GH receptor (GHR) pair is a covert driver of multimodal therapy resistance in cancer and is overexpressed in PDAC tumors, yet the therapeutic potential of targeting the same has not been explored. Here, we report that GHR expression is a negative prognostic factor in patients with PDAC. Combinations of gemcitabine with different GHR antagonists (GHRAs) markedly improve therapeutic outcomes in nude mice xenografts. Employing cultured cells, mouse xenografts, and analyses of the human PDAC transcriptome, we identified that attenuation of the multidrug transporter and epithelial-to-mesenchymal transition programs in the tumors underlie the observed augmentation of chemotherapy efficacy by GHRAs. Moreover, in human PDAC patients, GHR expression strongly correlates with a gene signature of tumor promotion and immune evasion, which corroborate with that in syngeneic tumors in wild-type vs. GH transgenic mice. Overall, we found that GH action in PDAC promoted a therapy-refractory gene signature in vivo, which can be effectively attenuated by GHR antagonism. Our results collectively present a proof of concept toward considering GHR antagonists to improve chemotherapeutic outcomes in the highly chemoresistant PDAC.

Soil and terrestrial contamination with microplastics and nanoplastics has been discussed extensively, while tire wear particles (TWPs) have been largely overlooked. We investigated the root-surface interactions and growth response of mung bean (Vigna radiata L.) plants exposed to tire wear particles (TWPs) (0.05, 0.1, and 0.25% w/w) and nickel sulfate (50 and 100 mg kg-1 NiSO4) alone and in co-exposure scenarios for the full life cycle (105 days) under soil conditions. The results show that TWPs adhered to the root surface and reduced the water and nutrient uptake by the plant, particularly at higher concentrations of TWPs (0.25% w/w), without any observed organic contaminant accumulation in the root tissue. TWPs alone at 0.01, 0.1, and 0.25% (w/w) decreased mung bean yield by 11, 28, and 52%, respectively. Co-exposure to TWPs at 0.01, 0.1 and 0.25% w/w with 100 mg kg-1 NiSO4 decreased yield by 73, 79 and 88%, respectively. However, co-exposure to TWPs at 0.01 and 0.1% w/w with 50 mg kg-1 NiSO4 enhanced the yield by 32% and 7%, respectively. These changes in yield and nutritional aspects appear to be linked to Ni's regulatory influence on mineral homeostasis. Moreover, exposure to NiSO4 at 100 mg kg-1 increased Ni uptake in the root, shoot, and grain by 9, 26, and 20-fold, respectively as compared to the unamended control; this corresponded to increased antioxidant enzyme activity (10-127%) as compared to the control. TWPs caused blockages, significantly reducing plant yield and altering nutrient dynamics, highlighting emerging risks to plant health. Tire wear particles and nickel have detrimental effects on plant health by causing blockage and altering nutrient hemotasis, ultimately reducing plant yield.

We examine the most realistic class of models among the 33 candidates in the complete landscape of supersymmetric Pati-Salam models arising from intersecting D6-branes on a $\mathbb{T}^6/(\mathbb{Z}_2 \times \mathbb{Z}_2)$ orientifold in type IIA string theory. The class is characterized by twelve Higgs from the $\mathcal{N}=2$ sector that precisely account for the Standard Model fermion masses and mixings without Wilson fluxes. Notably, there is a natural prediction of Dirac neutrino-masses with normal ordering $(50.4,~10.5,~6.1)$~meV consistent with both experimental constraints and swampland bounds., Comment: 5 pages + appendix

We revisit the construction of the $\mathcal{N}=1$ supersymmetric trinification models with gauge group $\mathrm{U}(3)_C\times \mathrm{U}(3)_L \times \mathrm{U}(3)_R$ in Type IIA string theory on $\mathbf{T^6/(\mathbb{Z}_2\times \mathbb{Z}_2)}$ orientifold with intersecting D6-branes. The non-trivial K-theory conditions and tadpole cancellation conditions severely restrict the number of allowed models even for the case of rectangular two-tori. Using a supervised search algorithm, we find a few four-family models where the highest wrapping number is 2. For these models, we present the complete particle spectra and the gauge coupling relations at the string-scale., Comment: 18 pages, accepted in CTP. arXiv admin note: substantial text overlap with arXiv:2212.09644, arXiv:2303.02864

Speech, voice and communication changes are common in Parkinson's disease. HiCommunication is a novel group intervention for speech and communication in Parkinson's disease based on principles driving neuroplasticity. In a randomized controlled trial, 95 participants with Parkinson's disease were allocated to HiCommunication or an active control intervention. Acoustic analysis was performed pre-, post- and six months after intervention. Intention-to-treat analyses with missing values imputed in linear multilevel models and complimentary per-protocol analyses were performed. The proportion of participants with a clinically relevant increase in the primary outcome measure of voice sound level was calculated. Resting-state functional MRI was performed pre- and post-intervention. Spectral dynamic causal modelling and the parametric empirical Bayes methods were applied to resting-state functional MRI data to describe effective connectivity changes in a speech-motor-related network of brain regions. From pre- to post-intervention, there were significant group-by-time interaction effects for the measures voice sound level in text reading (unstandardized b = 2.3, P = 0.003), voice sound level in monologue (unstandardized b = 2.1, P = 0.009), Acoustic Voice Quality Index (unstandardized b = -0.5, P = 0.016) and Harmonics-to-Noise Ratio (unstandardized b = 1.3, P = 0.014) post-intervention. For 59% of the participants, the increase in voice sound level after HiCommunication was clinically relevant. There were no sustained effects at the six-month follow-up. In the effective connectivity analysis, there was a significant decrease in inhibitory self-connectivity in the left supplementary motor area and increased connectivity from the right supplementary motor area to the left paracentral gyrus after HiCommunication compared to after the active control intervention. In conclusion, the HiCommunication intervention showed promising effects on voice sound level and voice quality in

The pharmaceutical industry is undergoing fast change, and one key factor influencing organizational success and salesperson performance is the leadership style of field managers. This study assesses how ethical leadership style affects central salespersons’ outcomes like positive emotions, work engagement, and creative performance. This study focused on the theoretical and conceptual literature on focal variables. Seven hypotheses were developed to explain how ethical leadership style drives salesperson outcomes in Pakistan’s pharmaceutical industry. A sample of 350 pharmaceutical medical representatives from 30 national and multinational firms was used to evaluate the relationships between the selected variables using SPSS-25.00 and AMOS-22.0. Pharmaceutical companies can benefit from the study's findings in various ways like establishing ethical leadership standards among field managers to enhance creative performance. Investing in programs that foster ethical leadership skills can result in more engaged salespeople with higher creative performance. Limitations and future directions are also discussed.

Developing critical thinking is a fundamental goal of effective education, which aims to cultivate informed citizens and a skilled workforce, ultimately contributing to a wiser nation. This study evaluates the effectiveness of the STEM (science, technology, engineering, and mathematics) approach in enhancing critical thinking skills among elementary school students. Utilizing an adapted Watson-Glaser critical thinking skills appraisal, this research examines a representative sample of eighth-grade public sector school students, acquired through simple random sampling. A 2x2 factorial experimental design, grounded in the Solomon Four-Group approach, was employed to rigorously evaluate the intervention, involving 184 students divided into control and experimental groups through random assignment. The experimental groups received STEM-based instruction, while the control groups continued with traditional teaching methods. Standardized critical thinking assessments were administered as pretests, posttests, and retention posttests. Quantitative analysis revealed significant improvements in critical thinking skills among students in the experimental groups. From the pretest (M = 45.32, SD = 4.15) to the posttest (M = 63.47, SD = 5.03), experimental groups exhibited marked increases in the critical thinking scores, and these gains were sustained in the retention posttest (M = 61.89, SD = 4.72). In contrast, there were hardly any changes in the control groups, with the pretest (M = 4.87, SD = 4.23) and posttest (M = 46.12, SD = 4.33). When compared to traditional approaches, the STEM approach, as evidence by the highly significant interaction effect F(1, 180) = 36.53, P <0.05) with the effect size (Cohen’s d – 1.52). Independent analysis of the main effects of the pretest and treatment was F(1, 180) = 40.28, P <0.05) and F(1, 180) = 576.21, P <0.05) respectively.

Digital Twins (DTs) developments are still in the pilot stages of deployment in supply chain management (SCM), and their full integration with real-time synchronization and autonomous decision-making poses many challenges. This paper aims to identify these common challenges and provide a conceptual framework for establishing a Digital Twin (DT) system to improve supply chain management performance. The paper presents a systematic literature review of 129 research papers on DT applications for SCM improvement. The selected papers were reviewed and classified into three categories: manufacturing and production, supply chain, and logistics. The development of digital technologies such as the Internet of Things (IoT), Radio Frequency Identification (RFID) devices, cloud computing, cyber-physical systems (CPSs), cybersecurity (CS), and simulation modeling has increased the opportunities to explore the creation of supply chain DTs. However, there are limitations and various challenges due to the complexity of most systems. The results indicate that DT for SCM should include external links (i.e. suppliers, distributors) and internal links (i.e. procurement, production, logistics) to deal with any disruption through data-driven modeling with real-time synchronization. Based on the review findings, this study proposes a three-layered conceptual framework to improve supply chain management performance. The proposed framework provides future directions for DT research in SCM. It provides a holistic and integrated approach to DT implementation, the common DT technologies, and data analytics techniques for improved supply chain performance.

Purpose: Remote working has brought forward many challenges for employees as the phenomenon is still new for most employees across the globe. Some of these challenges may be addressed by the recent adoption of digital technologies by organizations. In this vein, our study explores the impact of digital platform capability on the creativity of employees through the mediating mechanism of explicit and tacit knowledge sharing. Design/methodology/approach: The data were gathered from higher education institutes (HEIs) in a developing country, Pakistan which recently saw a major disruption during the Covid-19 pandemic. The proposed hypotheses were tested through Structural Equational Modeling (SEM) and the results confirmed our hypotheses. Findings: The findings confirmed that the digital platform capabilities impact both tacit and explicit knowledge sharing among these remote employees. Likewise, the results also supported the mediating role of both explicit and tacit knowledge sharing on the creativity of these remote workers. Originality/value: Our results are significant as they confirm the impact of digitalization on remote workers’ creativity predisposition. We thus advance the academic debate on the problems of knowledge sharing in remote working. We prove that digital capabilities outweigh the challenges created due to new forms of work driven by the pandemic. It further highlights the important areas to focus on while planning human resource policies in the new normal.

Strategic narratives are meaning-making devices implemented by political actors and constitute a discourse weapon for great power competition. In the context of the COVID-19 pandemic, discourses of fear, conspiracy theories, exclusive nationalism and populism are spreading rapidly on social media platforms. The “other” narrative is committed to deconstructing the other party’s leadership legitimacy and advancing narrative coercion and emotion. mobilization. During the first half of the epidemic, the Trump administration’s rhetoric and hostile narrative caused Sino-US trust to spiral downward. In the postepidemic era, the new Biden administration came to power and provided a window of opportunity to break the narrative inertia. As a typical establishment president, although Biden will still emphasize competition with China, his narrative style is more rational and balanced. In response to the Biden administration's narrative orientation of relative restraint, maintaining "strategic patience" and limited multilateralism, China can adopt a more diversified narrative response strategy and "walk slowly" to reshape the narrative space between China and the United States.  &nbsp

Academic-related demands that exceed students’ adaptive capabilities are collectively known as academic stress. High levels of academic stress are associated with an increased likelihood of depression, insomnia, substance addiction, self-harm, suicidal ideation, and subsequently, quitting education. Globally, academic stress is now a common phenomenon due to COVID-19-induced changes in the education system. Knowledge of the magnitude of academic stress and its factors can enable early recognition, intervention, and alleviation of the problem. The objective of this study was to assess the magnitude of perceived academic stress and identify the main stressors through a cross-sectional survey using the Manipal Inventory of Academic Stress scale. The study participants involved 2152 Grade 11 and 12 Commerce students enrolled in 34 pre-university colleges in Poonch AJK. A stratified cluster sampling method was used in the study. Statistical methods, namely descriptive statistics, multiple linear regression analysis, two-sample independent t-test, and one-way ANOVA tests, were used in the study. The study observed that one in every four pre-university students experienced high levels of perceived academic stress. Parent expectations, academic queries from neighbors and relatives, and lack of time for revision were identified as the top three stressors. Gender, grade, and mother’s education were associated with academic stress. Interventions at the individual, family, institutional, and community levels are the need of the hour to safeguard adolescents from negative experiences that might deprive them of wellness in their future.

The depletion of finite fossil fuel reserves and the severe environmental degradation resulting from human activities have compelled the expeditious development and application of sustainable waste to energy technologies. To encapsulate energy and environment in sustainability paradigm, bio waste based energy production is need to be forged in organic bio refinery setup. According to world bioenergy association, biomass can cover 50 % of the primary energy demand of the world. Therefore, the present study focuses on reforming the energy mix for a clean energy generation, where, sample composition of cotton stalk was acidified in dilute (5% wt.) hydrochloric acid (HCL) for analyzing material burnout patterns in biomass conversion systems utilized in organic bio refinery sector. Advanced thermochemical burning technique, which includes pyrolysis and combustion was applied at four different leaching times from 0 to 180 min under nitrogen environment from 0 degrees C to 500 degrees C and air from 500 degrees C to 900 degrees C, respectively. Different analyses including proximate, ultimate, gross calorific value (GCV), thermos-gravimetric, kinetic, XRD, FTIR, SEM-EDS were used for analyzing the degradation of demineralized cotton stalk at different treatment rates. Proximate study demonstrated that cotton stalk leaching for 180 min has efficiently infused HCL, leading in a significant increase in fixed carbon and higher heating value of 20.23 % and 12.48%, respectively, as well as a reduction in carbon footprint of around 54.80%. The findings of proximate was validated by GCV analysis and CHNS analysis as value of carbon and hydrogen has shown increasing behavior with the time delay in demineralization Thermo-gravimetric and derivative thermo-gravimetric data analyses shows an increasing trend of conversion efficiency, with the maximum increase of 98 % reported for sample 3H. TT.DEM. XRD characterization has reported 23 degrees to 25 degrees angle for all the observed p

Siyun Cheng,1,2,&ast; Zhijie Tu,2,&ast; Shuai Zheng,3 Adeel Khan,4 Ping Yang,1 Han Shen,1 Bing Gu2,3 1Department of Clinical Laboratory, Nanjing Drum Tower Hospital, the Affiliated Hospital of Nanjing University Medical School, Nanjing, People’s Republic of China; 2Medical Technology School of Xuzhou Medical University, Xuzhou, People’s Republic of China; 3Department of Clinical Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, People’s Republic of China; 4Department of Biotechnology, University of Science and Technology, Bannu, KP, Pakistan&ast;These authors contributed equally to this workCorrespondence: Han Shen, Department of Clinical Laboratory, Nanjing Drum Tower Hospital, Nanjing, People’s Republic of China, Tel +8602583304616, Fax +02568183912, Email shenhan@njglyy.com Bing Gu, Department of Clinical Laboratory Medicine, Guangdong Provincial People’s Hospital, Guangzhou, People’s Republic of China, Tel +8602083827812, Fax +02083875881, Email gubing@gdph.org.cnIntroduction: Ultrasensitive bacterial detection methods are crucial to ensuring accurate diagnosis and effective clinical monitoring, given the significant threat bacterial infections pose to human health. The aim of this study is to develop a biosensor with capabilities for broad-spectrum bacterial detection, rapid processing, and cost-effectiveness.Methods: A magnetically-assisted SERS biosensor was designed, employing wheat germ agglutinin (WGA) for broad-spectrum recognition and antibodies for specific capture. Gold nanostars (AuNSs) were sequentially modified with the Raman reporter molecules and WGA, creating a versatile SERS tag with high affinity for a diverse range of bacteria. Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa) antibody-modified Fe3O4 magnetic gold nanoparticles (MGNPs) served as the capture probes. Target bacteria were captured by MGNP

Resting state functional magnetic resonance imaging (rsfMRI) provides researchers and clinicians with a powerful tool to examine functional connectivity across large-scale brain networks, with ever-increasing applications to the study of neurological disorders, such as traumatic brain injury (TBI). While rsfMRI holds unparalleled promise in systems neurosciences, its acquisition and analytical methodology across research groups is variable, resulting in a literature that is challenging to integrate and interpret. The focus of this narrative review is to address the primary methodological issues including investigator decision points in the application of rsfMRI to study the consequences of TBI. As part of the ENIGMA Brain Injury working group, we have collaborated to identify a minimum set of recommendations that are designed to produce results that are reliable, harmonizable, and reproducible for the TBI imaging research community. Part one of this review provides the results of a literature search of current rsfMRI studies of TBI, highlighting key design considerations and data processing pipelines. Part two outlines seven data acquisition, processing, and analysis recommendations with the goal of maximizing study reliability and between-site comparability, while preserving investigator autonomy. Part three summarizes new directions and opportunities for future rsfMRI studies in TBI patients. The goal is to galvanize the TBI community to gain consensus for a set of rigorous and reproducible methods, and to increase analytical transparency and data sharing to address the reproducibility crisis in the field.

In today technological world we can easily accessed on to the desired information through Intelligent Personal Assistants. These Intelligent Personal Assistants performs concierge-type tasks or provides input based on voice inputs or commands. We found still other issues related to the intelligent Personal Assistants published during our study 1) IPA security needs to be done in order to enhance the system intelligence. 2) Voice recognition sometimes fails to recognize voice. 3) IPA intelligence will be challenged if learning and suggestions failed. 4) Set standards between personal and professional tasks. 5) IPA has to go through intense knowledge that’s helps react when user is not available to perform task in his absence. 6) Improvement in IPAs in terms of the context awareness IPAs. 7) IPA intense knowledge that’s helps react when user is not available to perform task. In this paper we will explore the mentioned usability issues of Intelligent Personal Assistants with their suggested solutions. Our focus is only to provide the concept and idea and maybe there is need to explore each of the issue in detail in future aspects.

This study investigates the Levels of transformational leadership among secondary school principals in Muzaffarabad, by a focus on its essential role in shaping educational environments. Transformational leadership, characterized by its emphasis on inspiring and motivating followers, encouragement innovation, and driving progressive change, holds significant importance in the realm of educational institutions. The research employs a quantitative methodology, involving 90 secondary school principals, to investigate into the comprehension and implementation of transformational leadership. Descriptive statistics indication a moderate level of transformational leadership displayed by principals across various dimensions. The findings add valuable understandings to the continuing discourse on educational leadership, involvement associations for the development of impactful platforms and knowledgeable policies. This research highlights the requirement for nuanced leadership approaches to promote educational consequences in Muzaffarabad, underscoring the transformative prospective of effective leadership in the energetic landscape of secondary education.

BACKGROUND: While the exploration of serotonergic psychedelics as psychiatric medicines deepens, so does the pressure to better understand how these compounds act on the brain. METHODS: We used a double-blind, placebo-controlled, crossover design and administered lysergic acid diethylamide (LSD), 3,4-methylenedioxymethamphetamine (MDMA), and d-amphetamine in 25 healthy participants. By using spectral dynamic causal modeling, we mapped substance-induced changes in effective connectivity between the thalamus and different cortex types (unimodal vs. transmodal) derived from a previous study with resting-state functional magnetic resonance imaging data. Due to the distinct pharmacological modes of action of the 3 substances, we were able to investigate specific effects mainly driven by different neurotransmitter systems on thalamocortical and corticothalamic interactions. RESULTS: Compared with placebo, all 3 substances increased the effective connectivity from the thalamus to specific unimodal cortices, whereas the influence of these cortices on the thalamus was reduced. These results indicate increased bottom-up and decreased top-down information flow between the thalamus and some unimodal cortices. However, for the amphetamines, we found the opposite effects when examining the effective connectivity with transmodal cortices, including parts of the salience network. Intriguingly, LSD increased the effective connectivity from the thalamus to both unimodal and transmodal cortices, indicating a breach in the hierarchical organization of ongoing brain activity. CONCLUSIONS: The results advance our knowledge about the action of psychedelics on the brain and refine current models aiming to explain the underlying neurobiological processes.

Suhail Hasan Serbaya,1,* Adeel Ahmed Khan,2,* Saud Hasan Surbaya,3,* Safar Majhood Alzahrani4,* 1Department of Industrial Engineering, Faculty of Engineering, King Abdulaziz University, Jeddah, Kingdom of Saudi Arabia; 2Saudi Board Program of Preventive Medicine, Makkah Healthcare Cluster, Makkah, Kingdom of Saudi Arabia; 3Inter-Professional Training Director Administration, Makkah Healthcare Cluster, Makkah, Kingdom of Saudi Arabia; 4Inter-professional Training Administration, Makkah Healthcare Cluster, Makkah, Kingdom of Saudi Arabia*These authors contributed equally to this workCorrespondence: Adeel Ahmed Khan, Saudi Board Program of Preventive Medicine, Makkah Healthcare Cluster, Makkah, Kingdom of Saudi Arabia, Tel +966-582265945, Fax +966-122366014, Email adeel_ahmed_khan@hotmail.comPurpose: The objective of our study was to assess awareness, attitudes, and practices regarding artificial intelligence (AI) among healthcare workers in private polyclinics in Jeddah, Saudi Arabia.Methods: We conducted cross-sectional study among healthcare workers in private clinics in Jeddah. Data was collected using a structured, validated questionnaire in Arabic and English on awareness, attitudes, and behaviors regarding AI. Cronbach’s alpha for the questionnaire ranged from 0.6 to 0.8. Descriptive and bivariate analysis was done to assess the scores and their association of various sociodemographic variables with awareness, attitudes, and behaviors regarding AI. Multiple linear regression was performed to predict the scores of awareness, attitudes, and behaviors based on the sociodemographic variables.Results: We recruited 361 participants for this study. Approximately, 62% of the healthcare workers were female. The majority (36%) of healthcare workers were nurses, while 25% were physicians. The median awareness, attitude, and behavioral scores were 5/6 (IQR 3– 6), 5/8 (IQR 4– 7), and 0/3 (IQR 0), respectively. Approximately three-fourths (74%) o

Which systems/organisms are conscious? New tests for consciousness (‘C-tests’) are urgently needed. There is persisting uncertainty about when consciousness arises in human development, when it is lost due to neurological disorders and brain injury, and how it is distributed in nonhuman species. This need is amplified by recent and rapid developments in artificial intelligence (AI), neural organoids, and xenobot technology. Although a number of C-tests have been proposed in recent years, most are of limited use, and currently we have no C-tests for many of the populations for which they are most critical. Here, we identify challenges facing any attempt to develop C-tests, propose a multidimensional classification of such tests, and identify strategies that might be used to validate them., SCOPUS: re.j, info:eu-repo/semantics/published

Given the rapid advancement of artificial intelligence, understanding the foundations of intelligent behaviour is increasingly important. Active inference, regarded as a general theory of behaviour, offers a principled approach to probing the basis of sophistication in planning and decision-making. In this paper, we examine two decision-making schemes in active inference based on 'planning' and 'learning from experience'. Furthermore, we also introduce a mixed model that navigates the data-complexity trade-off between these strategies, leveraging the strengths of both to facilitate balanced decision-making. We evaluate our proposed model in a challenging grid-world scenario that requires adaptability from the agent. Additionally, our model provides the opportunity to analyze the evolution of various parameters, offering valuable insights and contributing to an explainable framework for intelligent decision-making., Comment: 13 pages, 8 figures

Since the advent of Deep Learning (DL), Speech Enhancement (SE) models have performed well under a variety of noise conditions. However, such systems may still introduce sonic artefacts, sound unnatural, and restrict the ability for a user to hear ambient sound which may be of importance. Hearing Aid (HA) users may wish to customise their SE systems to suit their personal preferences and day-to-day lifestyle. In this paper, we introduce a preference learning based SE (PLSE) model for future multi-modal HAs that can contextually exploit audio information to improve listening comfort, based upon the preferences of the user. The proposed system estimates the Signal-to-noise ratio (SNR) as a basic objective speech quality measure which quantifies the relative amount of background noise present in speech, and directly correlates to the intelligibility of the signal. Additionally, to provide contextual information we predict the acoustic scene in which the user is situated. These tasks are achieved via a multi-task DL model, which surpasses the performance of inferring the acoustic scene or SNR separately, by jointly leveraging a shared encoded feature space. These environmental inferences are exploited in a preference elicitation framework, which linearly learns a set of predictive functions to determine the target SNR of an AV (Audio-Visual) SE system. By greatly reducing noise in challenging listening conditions, and by novelly scaling the output of the SE model, we are able to provide HA users with contextually individualised SE. Preliminary results suggest an improvement over the non-individualised baseline model in some participants., Comment: This has been submitted to the Trends in Hearing journal

This paper presents Mechanistic Neural Networks, a neural network design for machine learning applications in the sciences. It incorporates a new Mechanistic Block in standard architectures to explicitly learn governing differential equations as representations, revealing the underlying dynamics of data and enhancing interpretability and efficiency in data modeling. Central to our approach is a novel Relaxed Linear Programming Solver (NeuRLP) inspired by a technique that reduces solving linear ODEs to solving linear programs. This integrates well with neural networks and surpasses the limitations of traditional ODE solvers enabling scalable GPU parallel processing. Overall, Mechanistic Neural Networks demonstrate their versatility for scientific machine learning applications, adeptly managing tasks from equation discovery to dynamic systems modeling. We prove their comprehensive capabilities in analyzing and interpreting complex scientific data across various applications, showing significant performance against specialized state-of-the-art methods.

The internet of things (IoT) based wireless sensor networks (WSNs) face an energy shortage challenge that could be overcome by the novel wireless power transfer (WPT) technology. The combination of WSNs and WPT is known as wireless rechargeable sensor networks (WRSNs), with the charging efficiency and charging scheduling being the primary concerns. Therefore, this paper proposes a probabilistic on-demand charging scheduling for integrated sensing and communication (ISAC)-assisted WRSNs with multiple mobile charging vehicles (MCVs) that addresses three parts. First, it considers the four attributes with their probability distributions to balance the charging load on each MCV. The distributions are residual energy of charging node, distance from MCV to charging node, degree of charging node, and charging node betweenness centrality. Second, it considers the efficient charging factor strategy to partially charge network nodes. Finally, it employs the ISAC concept to efficiently utilize the wireless resources to reduce the traveling cost of each MCV and to avoid the charging conflicts between them. The simulation results show that the proposed protocol outperforms cutting-edge protocols in terms of energy usage efficiency, charging delay, survival rate, and travel distance., Comment: Accepted for publication at the IEEE Global Communications Conference (GLOBECOM) 2023

This paper presents BioNeRF, a biologically plausible architecture that models scenes in a 3D representation and synthesizes new views through radiance fields. Since NeRF relies on the network weights to store the scene's 3-dimensional representation, BioNeRF implements a cognitive-inspired mechanism that fuses inputs from multiple sources into a memory-like structure, improving the storing capacity and extracting more intrinsic and correlated information. BioNeRF also mimics a behavior observed in pyramidal cells concerning contextual information, in which the memory is provided as the context and combined with the inputs of two subsequent neural models, one responsible for producing the volumetric densities and the other the colors used to render the scene. Experimental results show that BioNeRF outperforms state-of-the-art results concerning a quality measure that encodes human perception in two datasets: real-world images and synthetic data.

We consider channel coding for discrete memoryless channels (DMCs) with a novel cost constraint that constrains both the mean and the variance of the cost of the codewords. We show that the maximum (asymptotically) achievable rate under the new cost formulation is equal to the capacity-cost function; in particular, the strong converse holds. We further characterize the optimal second-order coding rate of these cost-constrained codes; in particular, the optimal second-order coding rate is finite. We then show that the second-order coding performance is strictly improved with feedback using a new variation of timid/bold coding, significantly broadening the applicability of timid/bold coding schemes from unconstrained compound-dispersion channels to all cost-constrained channels. Equivalent results on the minimum average probability of error are also given.

How do biological systems and machine learning algorithms compare in the number of samples required to show significant improvements in completing a task? We compared the learning efficiency of in vitro biological neural networks to the state-of-the-art deep reinforcement learning (RL) algorithms in a simplified simulation of the game `Pong'. Using DishBrain, a system that embodies in vitro neural networks with in silico computation using a high-density multi-electrode array, we contrasted the learning rate and the performance of these biological systems against time-matched learning from three state-of-the-art deep RL algorithms (i.e., DQN, A2C, and PPO) in the same game environment. This allowed a meaningful comparison between biological neural systems and deep RL. We find that when samples are limited to a real-world time course, even these very simple biological cultures outperformed deep RL algorithms across various game performance characteristics, implying a higher sample efficiency. Ultimately, even when tested across multiple types of information input to assess the impact of higher dimensional data input, biological neurons showcased faster learning than all deep reinforcement learning agents., Comment: 13 Pages, 6 Figures - 38 Supplementary Pages, 6 Supplementary Figures, 4 Supplementary Tables

In remote sensing, we are interested in modeling various modalities for some geographic location. Several works have focused on learning the relationship between a location and type of landscape, habitability, audio, textual descriptions, etc. Recently, a common way to approach these problems is to train a deep-learning model that uses satellite images to infer some unique characteristics of the location. In this work, we present a deep-learning model, GeoBind, that can infer about multiple modalities, specifically text, image, and audio, from satellite imagery of a location. To do this, we use satellite images as the binding element and contrastively align all other modalities to the satellite image data. Our training results in a joint embedding space with multiple types of data: satellite image, ground-level image, audio, and text. Furthermore, our approach does not require a single complex dataset that contains all the modalities mentioned above. Rather it only requires multiple satellite-image paired data. While we only align three modalities in this paper, we present a general framework that can be used to create an embedding space with any number of modalities by using satellite images as the binding element. Our results show that, unlike traditional unimodal models, GeoBind is versatile and can reason about multiple modalities for a given satellite image input., Comment: 2024 IEEE International Geoscience and Remote Sensing Symposium

Depth estimation from 2D images is a common computer vision task that has applications in many fields including autonomous vehicles, scene understanding and robotics. The accuracy of a supervised depth estimation method mainly relies on the chosen loss function, the model architecture, quality of data and performance metrics. In this study, we propose a simplified and adaptable approach to improve depth estimation accuracy using transfer learning and an optimized loss function. The optimized loss function is a combination of weighted losses to which enhance robustness and generalization: Mean Absolute Error (MAE), Edge Loss and Structural Similarity Index (SSIM). We use a grid search and a random search method to find optimized weights for the losses, which leads to an improved model. We explore multiple encoder-decoder-based models including DenseNet121, DenseNet169, DenseNet201, and EfficientNet for the supervised depth estimation model on NYU Depth Dataset v2. We observe that the EfficientNet model, pre-trained on ImageNet for classification when used as an encoder, with a simple upsampling decoder, gives the best results in terms of RSME, REL and log10: 0.386, 0.113 and 0.049, respectively. We also perform a qualitative analysis which illustrates that our model produces depth maps that closely resemble ground truth, even in cases where the ground truth is flawed. The results indicate significant improvements in accuracy and robustness, with EfficientNet being the most successful architecture.

As IoT-based wireless sensor networks (WSNs) become more prevalent, the issue of energy shortages becomes more pressing. One potential solution is the use of wireless power transfer (WPT) technology, which is the key to building a new shape of wireless rechargeable sensor networks (WRSNs). However, efficient charging and scheduling are critical for WRSNs to function properly. Motivated by the fact that probabilistic techniques can help enhance the effectiveness of charging scheduling for WRSNs, this article addresses the aforementioned issue and proposes a novel ISAC-assisted WRSN protocol. In particular, our proposed protocol considers several factors to balance the charging load on each mobile charging vehicle (MCV), uses an efficient charging factor strategy to partially charge network devices, and employs the ISAC concept to reduce the traveling cost of each MCV and prevent charging conflicts. Simulation results demonstrate that this protocol outperforms other classic, cutting-edge protocols in multiple areas., Comment: Accepted for publication in the Special Issue Q1'2024, "Integrating Sensing and Communication for Ubiquitous Internet of Things," IEEE Internet of Things Magazine

Tuberculosis stands as a prominent cause of mortality in developing countries. The treatment of tuberculosis involves a complex procedure requiring the administration of a panel of at least four antimicrobial drugs for the duration of six months. The occurrence of treatment failure after the completion of a standard treatment course presents a serious medical problem. The purpose of this study was to evaluate antimicrobial drug resistant features of Mycobacterium tuberculosis associated with treatment failure. Additionally, it aimed to evaluate the effectiveness of second line drugs such as amikacin, linezolid, moxifloxacin, and the efflux pump inhibitor verapamil against M. tuberculosis isolates associated with treatment failure. We monitored 1200 tuberculosis patients who visited TB centres in Lahore and found that 64 of them were not cured after six months of treatment. Among the M. tuberculosis isolates recovered from the sputum of these 64 patients, 46 (71.9%) isolates were simultaneously resistant to rifampicin and isoniazid (MDR), and 30 (46.9%) isolates were resistant to pyrazinamide, Resistance to amikacin was detected in 17 (26,5%) isolates whereas resistance to moxifloxacin and linezolid was detected in 1 (1.5%) and 2 (3.1%) isolates respectively. Among MDR isolates, the additional resistance to pyrazinamide, amikacin, and linezolid was detected in 15(23.4%), 4(2.6%) and 1(1.56%) isolates respectively. One isolate simultaneously resistant to rifampicin, isoniazid, amikacin, pyrazinamide, and linezolid was also identified. In our investigations, the most frequently mutated amino acid in the treatment failure group was Serine 315 in katG. Three novel mutations were detected at codons 99, 149 and 154 in pncA which were associated with pyrazinamide resistance. The effect of verapamil on the minimum inhibitory concentration of isoniazid and rifampicin was observed in drug susceptible isolates but not in drug resistant isolates. Rifampicin and isoniazid enhance