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This research aims to analyze the role of Sharia economics in alleviating poverty in Indonesia. The method used in this research is a literature study, which is a series of activities relating to methods of collecting library data, reading and taking notes, as well as processing reading material into research data. Some of the literature that researchers use as data sources are books, journal articles, and news related to the role of Sharia economics in reducing poverty rates in Indonesia. One of the goals of religion is to encourage people from being left behind economically. Poverty is a serious problem that must be sought immediately for a solution because it can have very dangerous impacts, including disturbing one's faith or it could even disappear and that person becomes a disbeliever. The potential for ziswaf in Indonesia is extraordinary. If it can be managed professionally, it will have an impact on the welfare of society. How to overcome poverty can be done with various steps and strategies. The thing that must be done from the start to overcome the poverty that grips our society is to create an economic order that allows for a fair distribution system to be born, encouraging the birth of concern from people who have (aghniya') toward the needy, poor, dhu'afa' and mustard 'affine. One form of concern for Aghniya' is his willingness to pay zakat and issue sadaqah. [ABSTRACT FROM AUTHOR]

Place naming of a local area is closely related to geographic elements. Local names can also be used as a cultural identity and natural condition of a region. Nglanggeran Ancient Volcano (AV) area has local name elements highly influenced by those conditions. It is an interesting study material where a three-dimensional (3D) model can contribute as a medium to collect the place naming traces (toponym) in this area through participatory mapping. Participatory mapping, usually using 2D models in the form of sketches or maps, will be a separate story in interactive 3D media usage resulting from the photogrammetry process. It is expected that the media be able to show the geographical appearance of the Nglanggeran A. V area more clearly, such as geomorphology, geology, vegetation, hydrology; and even local culture, and contribute unexplored information* The results of participatory mapping in toponym collection are expected to be a medium that organizes the place naming of a local area into a complete database, along with the origin of the place names. It is also proposed that the results of participatory mapping through the 3D model be a means of preserving local culture in the Nglanggeran A.V area, making it more familiar to local residents and tourists. [ABSTRACT FROM AUTHOR]

The problem of finite-time robust adaptive stabilisation for a class of input saturated time-delay nonlinear systems with unknown parameters is studied in this article. Firstly, by applying the orthogonal decomposition method, a finite-time adaptive stabilisation controller is designed for the system. Then the finite-time robust adaptive stabilisation result for the system with external disturbance is obtained. Finally, a practical system example is presented to demonstrate the effectiveness of the proposed control scheme. [ABSTRACT FROM AUTHOR]

The RAS is a hormonal system playing a pivotal role in the control of blood pressure and electrolyte homeostasis, the alteration of which is associated with different pathologies, including acute respiratory distress syndrome (ARDS). As such, it is not surprising that a number of studies have attempted to elucidate the role and balance of the renin–angiotensin system (RAS) in COVID-19. In this review article, we will describe the evidence collected regarding the two main enzymes of the RAS (i.e., ACE and ACE2) and their principal molecular products (i.e., AngII and Ang1-7) in SARS-CoV-2 infection, with the overarching goal of drawing conclusions on their possible role as clinical markers in association with disease severity, progression, and outcome. Moreover, we will bring into the picture new experimental data regarding the systemic activity of ACE and ACE2 as well as the concentration of AngII and Ang1-7 in a cohort of 47 COVID-19 patients hospitalized at the IRCCS Sacro Cuore-Don Calabria Hospital (Negrar, Italy) between March and April 2020. Finally, we will discuss the possibility of considering this systemic pathway as a clinical marker for COVID-19. [ABSTRACT FROM AUTHOR]

Dendritic mesoporous silica nanoparticles own three‐dimensional center‐radial channels and hierarchical pores, which endows themselves with super‐high specific surface area, extremely large pore volumes, especially accessible internal spaces, and so forth. Dissimilar guest species (such as organic groups or metal nanoparticles) could be readily decorated onto the interfaces of the channels and pores, realizing the functionalization of dendritic mesoporous silica nanoparticles for targeted applications. As adsorbents and catalysts, dendritic mesoporous silica nanoparticles‐based materials have experienced nonignorable development in CO2 capture and catalytic conversion. This comprehensive review provides a critical survey on this pregnant subject, summarizing the designed construction of novel dendritic mesoporous silica nanoparticles‐based materials, the involved chemical reactions (such as CO2 methanation, dry reforming of CH4), the value‐added chemicals from CO2 (such as cyclic carbonates, 2‐oxazolidinones, quinazoline‐2,4(1H,3H)‐diones), and so on. The adsorptive and catalytic performances have been compared with traditional silica mesoporous materials (such as SBA‐15 or MCM‐41), and the corresponding reaction mechanisms have been thoroughly revealed. It is sincerely expected that the in‐depth discussion could give materials scientists certain inspiration to design brand‐new dendritic mesoporous silica nanoparticles‐based materials with superior capabilities towards CO2 capture, utilization, and storage. [ABSTRACT FROM AUTHOR]

Emotional research in film and animation has long been implemented in the West. This is because Elements of emotion are essential in an animated film to generate an affective impression and emotions in the audience. Malaysia's animation industry has increased recently, and Malaysian animators have produced many animation products. However, previous studies stated that the current situation of local animated films needed a more potent storytelling technique. There is a West scholar who argues that good storytelling can evoke the emotions of the audience. Applying emotions in animation through computer technology is complicated compared to live-action films that can control emotions through the actors' acting techniques. Thus, this study identified the elements of emotion in developing the case study of storytelling Agent Ali in the movie. The mixed method was used to identify aspects of emotion in Agent Ali the Movie, such as Freytag's Pyramid model and Hume AI. As a result, this study found that aspects of emotion exist in the story development process in Agent Ali the Movie, such as happiness, sadness, and anger in every three acts of structure (exposition, conflict, and resolution). The existence of emotion has proved that animated films in Malaysia need to be focused to overcome the weak storytelling technique. At the end of the discussion, this study also found that Artificial intelligence (AI) technology like Hume AI could speed up the animation production process, especially in identifying the facial expressions and body language of characters in the process of storytelling development. [ABSTRACT FROM AUTHOR]

IoT is a very fast-growing technology. IoT can be defined as interconnected small smart devices linked over the Internet to communicate with each other to perform meaningful action. There are major concerns regarding the security of data being produced from millions of devices in the IoT system. Different security concerns in various IoT system tiers have been covered in this study. IoT security concerns can be reduced by using Blockchain Technology, which is a decentralized distributed ledger with several Blockchain potentials, including persistence, transparency, verifiability, encryption, and operationaly strong The paper reviews whether they make a good fit along with certain challenges of Blockchain that should be examined while integrating it with IoT for resolving various security issues. [ABSTRACT FROM AUTHOR]

The study analyzed the relationship of leadership to knowledge management, leadership to innovation capability, psychological security climate to knowledge management, psychological security climate to innovation capability, self-efficacy to knowledge management, self-efficacy to innovation capability, knowledge management to innovation capability. The unit of analysis was vocational teachers and principals of vocational schools. The number of teachers from the respondents education unit is 3,056 teachers. By using the Slovin formula, the minimum number of sample sizes is 354 people and the sampling technique used was simple random sampling. The data analysis technique in this study uses partial least square (PLS). The results of data processing show that leadership has a significant effect on knowledge management, leadership has a significant effect on innovation capability, psychological security climate has a significant effect on knowledge management, psychological security climate has a significant effect on innovation capability, self-efficacy has a significant effect on knowledge management, self-efficacy has a significant effect on innovation capability, knowledge management has a significant effect on innovation capability. This research contributes to improving the quality of curriculum, teachers, and graduates of vocational schools/polytechnics in the 21st century. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Babylon Center for Humanities Studies is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Utilizing oil palm trunk (OPT) residue as a co-fuel in coal-fired plants offers an environmentally favorable solution to agricultural waste disposal and fossil fuel combustion impacts. This study investigates the potential of hydrochar from OPT (HTC-OPT) as a coal-like solid fuel for co-combustion. The combustion characteristics of coal, HTC-OPT, and their blends (10–50% HTC-OPT) were analyzed via thermogravimetric analysis with heating rates ranging from 10 to 30°C/min. The Flynn-Wall-Ozawa (FWO) and Kissinger-Akahira-Sunose (KAS) models were employed to explore co-combustion interactions and kinetic parameters. Results reveal enhanced combustion efficiency with HTC-OPT addition to coal. Higher HTC-OPT ratios correlate with increased maximum combustion rates, ignition and comprehensive combustibility indices, and decreased ignition and burnout temperatures. Additionally, blended fuels exhibit a proportionally synergistic co-combustion interaction. Both KAS and FWO models indicate similar trends in activation energies, with the lowest values (49.94 kJ/mol for KAS and 60.90 kJ/mol for FWO) at a 10% HTC-OPT ratio. [ABSTRACT FROM AUTHOR]

Detergents are amphiphilic molecules that serve as enabling steps for today's world applications. The increasing diversity of the detergentome is key to applications enabled by detergent science. Regardless of the application, the optimal design of detergents is determined empirically, which leads to failed preparations, and raising costs. To facilitate project planning, here we review synthesis strategies that drive the diversification of the detergentome. Synthesis strategies relevant for industrial and academic applications include linear, modular, combinatorial, bio‐based, and metric‐assisted detergent synthesis. Scopes and limitations of individual synthesis strategies in context with industrial product development and academic research are discussed. Furthermore, when designing detergents, the selection of molecular building blocks, i. e. head, linker, tail, is as important as the employed synthesis strategy. To facilitate the design of safe‐to‐use and tailor‐made detergents, we provide an overview of established head, linker, and tail groups and highlight selected scopes and limitations for applications. It becomes apparent that most recent contributions to the increasing chemical diversity of detergent building blocks originate from the development of detergents for membrane protein studies. The overview of synthesis strategies and molecular blocks will bring us closer to the ability to predictably design and synthesize optimal detergents for challenging future applications. [ABSTRACT FROM AUTHOR]

Internet of Things (IoT) is the amalgamation of hardware, like sensors and trackers, which monitor several parameters of the environment or physical objects, and software that processes all the data gathered by hardware. Globally, the IoT market is anticipated to reach 53.8 billion USD by 2025. This enhancing demand is due to its innate ability to automate, which drives several industries to adopt IoT. In addition, minimum memory cost, processing, and storage with an increase in Big Data (BD), cloud, and conjunction of industrial networks and the internet are the added factors for the increase in IoT development. Due to this significance, IoT has applications in numerous areas like medical management, farming, wearable technology, smart energy meters, smart cities, etc. The applications are not limited to the examples mentioned above. Considering this, existing studies have considered different applications and attempted to execute them. As different applications have been focused on by these studies, the present review intends to provide a compilation of potential applications of IoT as considered by conventional research between 2018 and 2022. The study also intends to explore the advantages and disadvantages of different IoT applications (deliberated by conventional studies) through tabular analysis. Further, this review emphasizes IoT’s major key challenges and countermeasures to resolve its security issues. Finally, the study affords recommendations that will assist all IoT experts in bringing IoT products with enhanced security into the market. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Arabian Sciences Heritage is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The experiment was carried out in the unheated greenhouse of the Department of Horticulture and Landscape, College of Agriculture, TikritUniversity, for the season 2020-2021, to study the effect of spraying seaweed extract with the addition of humic acid on the growth of strawberry seedlings. The experiment included 18 treatments, which consisted of spraying the plants with three concentrations of seaweed extract 0, 1 and 2 g.L-1 and two concentrations of humic acid 0 and ml.L-1. The Randomised complete block design (RCBD) was used as a factorial experiment with three blocks, and the averages were compared according to the LSD test at the probability level of 0.05. The results of the experiment showed that the addition of humic acid led to a significant increase in the concentration in ml.L-1 in each of the average leaves number, area per leaf, plant leaf area, percentage of dry matter and leaf content of Phaeophytinpigment A. The interactions between spraying the plants with seaweed extract and adding humic acid significantly affected the leaf area and leaf content of Phaeophytinpigment A. [ABSTRACT FROM AUTHOR]

Early detection of brain tumors is critical for effective treatment planning. Identifying tumors in their nascent stages can significantly enhance the chances of patient survival. While there are various types of brain tumors, each with unique characteristics and treatment protocols, tumors are often minuscule during their initial stages, making manual diagnosis challenging, time-consuming, and potentially ambiguous. Current techniques predominantly used in hospitals involve manual detection via MRI scans, which can be costly, error-prone, and time-intensive. An automated system for detecting brain tumors could be pivotal in identifying the disease in its earliest phases. This research applies several data augmentation techniques to enhance the dataset for diagnosis, including rotations of 90 and 180 degrees and inverting along vertical and horizontal axes. The CIELAB color space is employed for tumor image selection and ROI determination. Several deep learning models, such as DarkNet-53 and AlexNet, are applied to extract features from the fully connected layers, following the feature selection using entropy-coded Particle Swarm Optimization (PSO). The selected features are further processed through multiple SVM kernels for classification. This study furthers medical imaging with its automated approach to brain tumor detection, significantly minimizing the time and cost of a manual diagnosis. Our method heightens the possibilities of an earlier tumor identification, creating an avenue for more successful treatment planning and better overall patient outcomes. [ABSTRACT FROM AUTHOR]

Background: The SARS-CoV-2 virus as well as the COVID-19 mRNA vaccines cause an increased production of proinflammatory cytokines. Aim: We investigated the relationship between ACE2, CTSL, AngII, TNFα and the serum levels of IL-6, IL-10, IL-33, IL-28A, CD40L, total IgM, IgG, IgA and absolute count of T- and B-lymphocytes in COVID-19 patients, vaccinees and healthy individuals. Methods: We measured the serum levels ACE2, AngII, CTSL, TNFα and humoral biomarkers (CD40L, IL-28A, IL-10, IL-33) by the ELISA method. Immunophenotyping of lymphocyte subpopulations was performed by flow cytometry. Total serum immunoglobulins were analyzed by the turbidimetry method. Results: The results established an increase in the total serum levels for ACE2, CTSL, AngII and TNFα by severely ill patients and vaccinated persons. The correlation analysis described a positive relationship between ACE2 and proinflammatory cytokines IL-33 (r = 0.539) and CD40L (r = 0.520), a positive relationship between AngII and CD40L (r = 0.504), as well as between AngII and IL-33 (r = 0.416), and a positive relationship between CTSL, total IgA (r = 0.437) and IL-28A (r = 0.592). Correlation analysis confirmed only two of the positive relationships between TNFα and IL-28A (r = 0.491) and CD40L (r = 0.458). Conclusions: In summary, the findings presented in this study unveil a complex web of interactions within the immune system in response to SARS-CoV-2 infection and vaccination. [ABSTRACT FROM AUTHOR]

Purpose: Diabetes mellitus (DM) is a global health concern linked to various complications, including cardiovascular disease (CVD). However, long-term follow-up studies on the risk of DM and CVD using different blood glucose assessment methods in the general Korean population are lacking. This study aimed to assess the predictive abilities of fasting plasma glucose (FPG), 2-h oral glucose tolerance test (OGTT), and glycosylated hemoglobin (HbA1c) for new-onset DM and high CVD risk in a middle-aged and older Korean population., Methods: This study used data from the Korean Genome and Epidemiology Study, a population-based prospective cohort. Blood sugar measures (FPG, OGTT, and HbA1c) were examined. The primary endpoint was the development of new-onset DM, and CVD risk was evaluated using the Framingham risk score. The predictive abilities for new-onset DM based on glycemic values were evaluated using Harrell's Concordance index and 95% confidence intervals., Results: Among the 10,030 participants, data of 6813 participants without DM at baseline were analyzed. The study revealed that OGTT outperformed FPG and HbA1c in predicting new-onset DM. The combination of FPG and HbA1c did not significantly enhance predictions for DM compared with OGTT alone. OGTT also outperformed FPG and HbA1c in predicting high CVD risk, and this difference remained significant even after adjusting for additional confounders., Conclusion: OGTT has superior predictive capabilities in identifying new-onset DM and high CVD risk in the Korean population. This suggests that relying solely on individual blood sugar measures may be insufficient for assessing DM and CVD risks., (© 2024. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)

Background: Gestational diabetes mellitus (GDM) has a strong genetic predisposition. Integrating metabolomics with Mendelian randomisation (MR) analysis offers a potent method to uncover the metabolic factors causally linked to GDM pathogenesis., Objectives: This study aims to identify specific metabolites and metabolic pathways causally associated with GDM susceptibility through a comprehensive MR analysis. Additionally, it seeks to explore the potential of these identified metabolites as circulating biomarkers for early GDM detection and risk assessment. Furthermore, it aims to evaluate the implicated metabolic pathways as potential therapeutic targets for preventive or interventional strategies against GDM., Methods: A two-sample MR study was conducted using summary statistics from a metabolite genome-wide association study (GWAS) of 8299 individuals and a GDM GWAS comprising 13,039 cases and 197,831 controls. Rigorous criteria were applied to select robust genetic instruments for 850 metabolites., Results: MR analysis revealed 47 metabolites exhibiting putative causal associations with GDM risk. Among these, five metabolites demonstrated statistically significant associations after multiple-testing correction: Beta-citrylglutamate, Isobutyrylcarnitine (c4), 1,2-dilinoleoyl-GPC (18:2/18:2), Alliin and Cis-3,4-methyleneheptanoylcarnitine. Importantly, all these metabolites exhibited protective effects against GDM development. Additionally, metabolic pathway enrichment analysis implicated the methionine metabolism and spermidine and spermine biosynthesis pathways in the pathogenesis of GDM., Conclusion: This comprehensive MR study has robustly identified specific metabolites and metabolic pathways with causal links to GDM susceptibility. These findings provide novel insights into the metabolic underpinnings of GDM aetiology and offer promising translational implications. The identified metabolites could serve as potential circulating biomarkers for early detection and risk stratification, while the implicated metabolic pathways may represent therapeutic targets for preventive or interventional strategies against GDM., (© 2024 John Wiley & Sons Ltd.)

Purpose: This paper is to offer insights for designing research utilizing Olink technology to identify biomarkers and potential therapeutic targets for disease treatment., Experimental Design: We discusses the application of Olink technology in oncology, cardiovascular, respiratory and immune-related diseases, and Outlines the advantages and limitations of Olink technology., Results: Olink technology simplifies the search for therapeutic targets, advances proteomics research, reveals the pathogenesis of diseases, and ultimately helps patients develop precision treatments., Conclusions: Although proteomics technology has been rapidly developed in recent years, each method has its own disadvantages, so in the future research, more methods should be selected for combined application to verify each other., (© 2024 Wiley‐VCH GmbH.)

The catalytic performance of supported metals is greatly influenced by the interaction with the support material. The role of the support becomes even more important when dealing with metal nanoparticles and high reaction temperatures. Herein, we show that interfacial sites between two metal oxides, MgAlOx and ZrO2 can bestow high stability as well as enhanced reactivity to nickel (Ni) nanoparticles. We use the MgAlOx as thin oxides on an underlying bulk ZrO2. We demonstrate the effect of the metal‐support interactions (MSI) in different support locations on the performance in dry reforming of methane (DRM). We find that the rate of DRM catalysis produces a concave‐down trend with respect to Ni loading with a maximum at ~0.8–1.1 % wt Ni. Measuring the Ni2p3/2 binding energy (BE), we find a similar concave‐down trend whereas for the Mg2p BE we find a concave‐up trend with respect to the Ni loading both with the maximum and minimum centered at 0.8–1.1 % wt Ni, respectively. These trends were correlated with the stability of Ni calculated by DFT. Overall, our results suggest that heterointerfacial sites can be used to tailor moderate MSI, which can be used in the design of DRM catalyst with significantly increased activity and high stability. [ABSTRACT FROM AUTHOR]

Copyright of IUG Journal of Islamic Studies is the property of Islamic University of Gaza and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Brazilian Journal of Education, Technology & Society (BRAJETS) / Cadernos de Educação Tecnologia e Sociedade (CETS) is the property of Brazilian Journal of Education, Technology & Society - BRAJETS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In this paper, a dc–dc boost converter with continuous input is investigated. In addition to its input current, the converter offers the advantage of using a reduced number of electronic components. The objective is to design an appropriate controller for the nonlinear model taking into account the system nonlinearities, the uncertainty of the resistance load, the constrained fuel cell current, and the saturation of the control input signal (duty ratio).The point is that the converter presents a no minimum phase feature, and the duty ratio must constrain between 0 and 1. Then, a saturated nonlinear controller is elaborated. Detailed analysis and simulation show that the proposed controller meets all control objectives, namely: tight dc-bus voltage regulation and asymptotic stability of the closed-loop system. Experimental results are also given, which show the effectiveness of the controller. [ABSTRACT FROM AUTHOR]

Angiotensin-converting enzyme 2 (ACE2) is not only the viral receptor for the novel coronavirus severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) but is also classically known as a key carboxypeptidase, which through multiple interacting partners plays vital physiological roles in the heart, kidney, lung, and gastrointestinal tract. An accumulating body of evidence has implicated the dysregulation of ACE2 abundance and activity in the pathophysiology of multiple disease states. ACE2 has recently regained attention due to its evolving role in driving the susceptibility and disease severity of coronavirus disease 2019 (COVID-19). This narrative review outlines the current knowledge of the structure and tissue distribution of ACE2, its role in mediating SARS-CoV-2 cellular entry, its interacting partners, and functions. It also highlights how SARS-CoV-2-mediated dysregulation of membrane-bound and circulating soluble ACE2 during infection plays an important role in the pathogenesis of COVID-19. We explore contemporary evidence for the dysregulation of ACE2 in populations that have emerged as most vulnerable to COVID-19 morbidity and mortality, including the elderly, men, and pregnant women, and draw attention to ACE2 dynamics and discrepancies across the mRNA, protein (membrane-bound and circulating), and activity levels. This review highlights the need for improved understanding of the basic biology of ACE2 in populations vulnerable to COVID-19 to best ensure their clinical management and the appropriate prescription of targeted therapeutics. [ABSTRACT FROM AUTHOR]

The gasification of biomass with supercritical water, also known as SCWG, is a sustainable method of hydrogen production. The process produces a mixture of hydrogen, carbon oxides, and hydrocarbons. Upgrading this mixture through steam or dry reforming of hydrocarbons to create synthesis gas and then extra hydrogen is a viable way to increase hydrogen production from biomass. This literature review discusses combining these two processes and recent experimental work on catalytic SCWG of biomass and its model compounds and steam/dry reforming of produced hydrocarbons. It focuses on catalysts used in these processes and their key criteria, such as activity, selectivity towards hydrogen and methane, and ability to inhibit carbon formation and deposition. A new criterion is proposed to evaluate catalyst performance in biomass SCWG and the need for further upgrading via reforming, based on the ratio of hydrogen bound in hydrocarbons to total hydrogen produced during SCWG. The review concludes that most catalysts used in biomass SCWG trap a large proportion of hydrogen in hydrocarbons, necessitating further processing of the product stream. [ABSTRACT FROM AUTHOR]

Copyright of International Journal of Sports Science & Arts - Arabic is the property of Helwan University, Faculty of Physical Education for Girls and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of IUG Journal of Sharia & Law Studies is the property of Islamic University of Gaza and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In any EFL/ESL teaching context worldwide, it is likely that teachers are receiving regular observation visits, evaluation and feedback on their teaching. Indeed, administrators in EFL/ESL teaching contexts heavily invest resources in teacher observation. However, there is rarely an investment made in the observation of supervisors or in their professional learning. Too frequently, the return on the investment in supervisory staff is either unexamined, or under-valued. Leaders in EFL/ESL teaching settings want to optimise learning outcomes for their English learner students and understand that developing effective teachers is their priority. What is overlooked in this equation is how the investment in supervisor learning translates into teacher, and then student learning gains. Investments in supervisor learning can translate into student learning because the more effective supervisors are at observing and providing feedback, the more the process can power teacher learning, which in turn impacts learners. In this conceptual and practice-based report, we draw on expertise as current institutional leaders and as researchers of teacher supervision to share a framework for an intensive, longitudinal professional learning series based on current understandings of how adults learn and become better enabled to advance their teaching skills through effective supervision. In this article, we write for leaders and managers who "supervise the supervisors" and are in the position to design professional learning for those who supervise/coach EFL/ESL teachers. We lay out a foundation for powerful supervision by making expectations observable, differentiating supervisory approaches and re-envisioning the observation cycle. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Al-Anbar University for Islamic Sciences is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Knee osteoarthritis (OA) is a common disease that impairs knee function and causes pain. Currently, studies on the detection of knee OA mainly focus on X-ray images, but X-ray images are insensitive to the changes in knee OA in the early stage. Since magnetic resonance (MR) imaging can observe the early features of knee OA, the knee OA detection algorithm based on MR image is innovatively proposed to judge whether knee OA is suffered. Firstly, the knee MR images are preprocessed before training, including a region of interest clipping, slice selection, and data augmentation. Then the data set was divided by patient-level and the knee OA was classified by the deep transfer learning method based on the DenseNet201 model. The method divides the training process into two stages. The first stage freezes all the base layers and only trains the weights of the embedding neural networks. The second stage unfreezes part of the base layers and trains the unfrozen base layers and the weights of the embedding neural network. In this step, we design a block-by-block fine-tuning strategy for training based on the dense blocks, which improves detection accuracy. We have conducted training experiments with different depth modules, and the experimental results show that gradually adding more dense blocks in the fine-tuning can make the model obtain better detection performance than only training the embedded neural network layer. We achieve an accuracy of 0.921, a sensitivity of 0.960, a precision of 0.885, a specificity of 0.891, an F1-Score of 0.912, and an MCC of 0.836. The comparative experimental results on the OAI-ZIB dataset show that the proposed method outperforms the other detection methods with the accuracy of 92.1%. [ABSTRACT FROM AUTHOR]

Carbon dioxide (CO2) utilization and conversion, as one of the main parts of carbon capture, utilization, and storage (CCUS), is not only considered an important way to mitigate global warming but also an attractive industrial route to produce valuable fuels and chemical feedstocks. Catalytic dry reforming of methane (DRM) is a promising technology for carbon dioxide utilization and conversion as it can produce syngas, carbon monoxide (CO), and hydrogen (H2) for widespread industrial production processes. In most studies of the DRM reaction, a relatively high operational temperature (i.e., >700°C) has been applied since the reactivity limitation of widely used Ni‐based catalysts at low temperatures and the extremely endothermic property of the DRM reaction. However, high cost and high requirement of thermal stability for catalysts have become a severe problem impeding the further commercialization of DRM technology. Decreasing the operational temperature (i.e., <700°C) is considered a promising way for further application of the DRM route to convert CO2 and produce syngas. However, traditional Ni‐based catalysts suffered from unsatisfied reactivity and severe coke formation, leading to quick deactivation at low temperatures. Developing a catalyst with excellent catalytic activity, coke resistance, and improved thermal stability is necessary for low‐temperature DRM reactions. In recent years, with significant development in materials, catalyst design, and computational simulation, some synthesized catalysts have achieved considerable improvement in catalytic performance in low‐temperature DRM. Hence, a review of recent development on low‐temperature DRM catalysts is provided here to further guide and profoundly understand catalyst design for low‐temperature DRM. [ABSTRACT FROM AUTHOR]

Secure and reliable exchange of information between devices is crucial for any network in the current digital world. This information is maintained on storage devices, routing devices, and communication over the cloud. Cryptographic techniques are used to ensure the secure transmission of data, ensuring the user's privacy by storing and transmitting data in a particular format. Using encryption, only the intended user possessing the key can access the information. During data or essential transmission, the channel should be secured by using robust encryption techniques. Homomorphic Encryption (HE) techniques have been used in the past for this purpose. However, one of the flaws of the conventional HE is seen either in its slow transmission or fast key decryption. Thus, this paper proposes an optimized Homomorphic Encryption Chinese Remainder Theorem with a Rivest-Shamir-Adleman (HE-CRT-RSA) algorithm to overcome this challenge. The proposed Technique, HE-CRT-RSA, utilizes multiple keys for efficient communication and security. In addition, the performance of the HE-CRT-RSA algorithm was evaluated in comparison with the classical RSA algorithm. The result of the proposed algorithm shows performance improvement with reduced decryption time. It is observed that the proposed HE-CRT-RSA is 3–4% faster than the classical Rivest-Shamir-Adleman (RSA). The result also suggests that HE-CRT-RSA effectively enhances security issues of the cloud and helps to decrease the involvement of intruders or any third party during communication or inside the data/server centers. [ABSTRACT FROM AUTHOR]