Evaluating ADO.NET Technology for Efficient Database Management: Concepts, Models, and Comparative Analysis

The implementation of financial report management applications based on Android information technology in mosques has become a significant innovation in the application of Shariah economic law. This research investigates the effectiveness of applying this information technology in managing mosque financial reports. An experimental research method was used involving the development of a specific Android application to record and track financial transactions in accordance with Shariah principles. The results indicate a significant improvement in the accuracy, speed, and reliability of financial reporting after the implementation of this application. Furthermore, the application enhances transparency and accountability in mosque financial management, thereby reinforcing the application of Shariah economic law in the context of mosque finances. Despite some challenges in adopting this technology, such as a lack of technological understanding, the benefits obtained are considerably greater. The implications of this study suggest that the use of information technology, such as the Android application, can enhance efficiency and compliance with Shariah economic principles in mosque financial management. The application of financial report management applications based on Android information technology in mosque financial management specifically focuses on implementing Shariah economic principles. This research explores the effectiveness of Android information technology in aiding the recording, reporting, and management of mosque finances in accordance with Shariah principles. The use of information technology in mosque financial management has significant implications for enhancing compliance with Shariah economic principles. This study highlights that the application of Android software in the context of mosque finances can make a significant contribution to Shariah-compliant fund management, with the potential to improve transparency, compliance, and efficiency in mosque financial management.

Introduction: Colon cancer is the fourth most common cancer in the United States and the third leading cause of cancer-related death. High throughput genomic sequencing has led to a number of significant advances in tumor biology and in identifying novel signaling molecules such as long non-coding RNA (lncRNA). The aim of this study was to identify differentially expressed lncRNA in colon cancer from a large RNA-sequencing (RNA-seq) data set. Methods: The raw RNA-seq files of 398 patients with colon cancer were downloaded from The Cancer Genome Atlas (TCGA). Sequencing files were aligned using STAR (Spliced Transcripts Alignment to a Reference) to the most recent genome annotation. Using a subset of patients with paired colon cancer and normal colon epithelium RNA-seq data (n=40), an exploratory binomial regression model was used to calculate differential RNA expression. The most differentially expressed lncRNA were identified from the exploratory analysis and verified by comparing the larger colon cancer RNA-seq data set (n=358) with the normal colon epithelium RNA-seq data set (n=40). Results: 33,514 genes were identified from the comparative analysis, and using differential expression cut off values of > +1.5 or < -1.5 log fold change and a false discovery rate of <0.05; 543 were upregulated and 1822 were downregulated. Within this dysregulated group, 60 lncRNAs were identified. Using the larger data set (n=358 vs n=40), 41/60 lncRNA remained differentially expressed, of which 15 were downregulated and 26 were upregulated. Twenty-four of these lncRNAs have not previously been described in colon cancer, 12 of which are upregulated and 12 of which are downregulated (Table 1). Conclusions: This analysis of RNA-seq data from TCGA has identified dysregulated lncRNAs which have not previously been described in human colon cancer. These lncRNAs may have significant roles in colon cancer tumor signaling. Table 1.Differential expression of lncRNA not described in colon cancerlncRNALog Fold ChangeP-valueDescription in other cancersKRT16P14.1041.36E-21Increased in lung squamous cell carcinomaBOK-AS13.7784.98E-09Increased in oral squamous cell carcinomaCELP3.1151.30E-09Not availableCLDN10-AS12.3944.61E-12Not availablePOU6F2-AS12.2678.25E-09Not availableSLC7A11-AS12.1931.60E-13Decreased in gastric adenocarcinomaCSAG42.1083.59E-10Not availableLUCAT12.0224.21E-11Increased in Ovarian cancer, renal clear cell carcinoma, head and neck squamous cell carcinoma, Non-small cell cancer, glioma, osteosarcoma, esophageal squamous cell carcinomaVPS9D1-AS11.9795.27E-21Decreased in gastric adenocarcinoma, Increased in non-small cell lung cancerLEF1-AS11.8464.48E-13Increased in glioblastomaCASC81.6485.64E-06Single nucleotide polymorphisms in colorectal adenocarcinomaLINC004911.5903.11E-04Not availableLINC00648-1.5021.02E-15Not availableLINC00163-1.6751.03E-22Not availableSATB2-AS1-1.8074.18E-09Increased in osteosarcomaLINC00702-2.1424.81E-18Not availableTRHDE-AS1-2.2623.44E-24Not availableLINC00908-2.3082.76E-103Not availableLINC00483-2.4012.87E-39Increased in gastric adenocarcinomaLINC00461-2.4401.42E-41Increased in glioma, multiple myelomaFENDRR-2.6872.12E-58Decreased in breast cancer, prostate cancer, osteosarcoma, gastric adenocarcinoma,ARHGEF26-AS1-2.7148.55E-32Not availableADAMTS9-AS2-2.9041.78E-74Increased in lung cancer salivary adenoid cystic carcinoma, Decreased in glioma, gastric adenocarcinomaMT1JP-4.4601.08E-114Decreased in retinoblastoma, Decreased in gastric adenocarcinoma Citation Format: Stephen J. O'Brien, Theodore Kalbfleisch, Sudhir Srivastava, Shesh Rai, Susan Galandiuk. Differential expression of long non-coding RNA in colon adenocarcinoma RNA-sequence data set [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1817.

As ideias de desenvolvimento local são controversas, pouco consensuadas e, às vezes, até antagônicas entre si, tanto no Brasil quanto no debate internacional. Diante dessa polêmica, este artigo discute a gestão do desenvolvimento local a partir de instrumentos selecionados por meio de uma análise exploratória não apenas conceitual e teórica, mas sobretudo prática. Do ponto de vista metodológico, foi realizada uma análise comparada de experiências regionalmente distribuídas, levando-se em conta suas inserções em redes sociais e seus resultados econômicos na região em que está inserida (Sul, Sudeste e Nordeste). Percebe-se que refletir sobre o desenvolvimento local desdobra-se em duas frentes: uma de reprodução da lógica capitalista em escala localizada (tradicional) e outra de tentativas contra-hegemônicas (solidária). Assim, algumas questões centrais foram identificadas para orientar uma reflexão sobre a intervenção dos atores da sociedade civil e dos governos na formulação de propostas e iniciativas de desenvolvimento econômico local.

The spreading of the globalisation process is actually modifying the traditional spatial pattern including tourist field organization. The traditional territorial structure of the tourist region is being remodelled at local level in new systemic, self-creating, self-regulating and self-promoting entities in a constant connection with a supra-regional reality. In Italy strictly tourist areas are being replaced by new territorial entities which can have the structure of tourist local systems, productive microcosms based on local relational networks involving municipalities, enterprises, tourist associations and other local stakeholders of the territorial system. So the spatial dimension of tourism is now influenced by local organizational models no more based on administrative units or traditional tourist regions but rather on spaces created by relationships among local actors. Such a self-structured development of local tourist systems is being observed in Sicily too, under the dynamic pressures expressed by the world market of vacations. Public and private organizations are joining forces to promote territorial development in recognition of their organizing abilities and of the sustainable progress perspectives of their spatial aggregation.

Technological advancement has exposed companies to various risks. With the adoption of technological infrastructures in many companies, various processes have been rendered vulnerable to different forms of threats. Evidence from the current empirical studies on organizational management indicates effective risk assessment is a crucial aspect in any organization. Global technological advancement has equally redefined risk assessment and management strategies. Global leading technology companies such as Apple Inc., Amazon Inc., and Google are leveraging modern technology to unlock hidden data to aid in risk assessment and management. As evidenced in this research report, the use of big data has revolutionized risk assessment in many companies across the world. Big data technology has enabled organizations to collect, store, and assess huge information to aid in risk assessment and management. The pro-cesses of risk identification, assessment, mitigation, monitoring, and reporting have been redefined due to the adoption of big data analytical technology. As a result, this research reviews the specific roles played by big data in organizational risks management. The research carries out a comparative case study analysis among three companies that utilize big data in risk management. Specifically, the roles of big data technology in risk management at Apple Inc., Amazon Inc., and at Google are identified. Results from this comparative analysis are used in formulating recommendations for various organizations that desire to adopt big data analytical technology in risk management.

Big Data computing provides a promising new opportunity for scientific discoveries and innovations. However, it also poses a significant challenge to the high-end computing community. An effective I/O solution is urgently required to support big data applications run on high-end computing systems. In this study, we propose a new approach namely DDiHA, Data Deduplication in Hybrid Architecture, to improve the write performance for write-intensive big data applications. The DDiHA approach utilizes data deduplications to reduce the size of data volumes before they are transfered and written to the storage. A hybrid architecture is introduced to facilitate data deduplications. Both theoretical study and prototyping verification were conducted to evaluate the DDiHA approach. The initial results have shown that, given the same compute resources, the DDiHA system outperformed the conventional architecture, even though it introduces additional computation workload from data deduplications. The DDiHA approach reduces the data size transferred across the network and improves the I/O system performance. It has a promising potential for write-intensive big data applications.

BCIs enable direct brain-to-device communication by decoding brain signals, with applications in assistive technologies, neurorehabilitation, and human-computer interaction. This study explores deep learning models, including CNNs, RNNs, and hybrid architectures, for BCI signal decoding. A synthetic EEG dataset was used to benchmark these models against traditional classifiers like SVM and LDA, focusing on accuracy, latency, and generalization. Preprocessing techniques such as noise reduction, signal normalization, and feature extraction were applied to enhance signal quality. Results showed CNNs and hybrid CNN-RNN models outperformed traditional classifiers with greater accuracy and robustness. Deep learning effectively addressed challenges like low signal-to-noise ratio, subject variability, and class imbalance. The study also demonstrated practical applications in assistive device control and cognitive monitoring, emphasizing deep learning's potential to advance BCI technology while underscoring the need for validation on larger datasets.

During COVID-19 emergency the majority of health structures in Europe saturated or nearly saturated their availabilities already in the first weeks of the epidemic period especially in some regions of Italy and Spain. The aim of this study is to analyse the efficiency in the management of hospital beds before the COVID-19 outbreak at regional level in France, Germany, Italy and Spain. This analysis can indicate a reference point for future analysis on resource management in emergency periods and help hospital managers, emergency planners as well as policy makers to put in place a rapid and effective response to an emergency situation. The results of this study clearly underline that France and Germany could rely on the robust structural components of the hospital system, compared to Italy and Spain. Presumably, this might have had an impact on the efficacy in the management of the COVID-19 diffusion. In particular, the high availability of beds in the majority of the France regions paired with the low occupancy rate and high turnover interval led these regions to have a high number of available beds. Consider also that this country generally manages complex cases. A similar structural component is present in the German regions where the number of available beds is significantly higher than in the other countries. The impact of the COVID-19 was completely different in Italy and Spain that had to deal with a relevant large number of patients relying on a reduced number of both hospital beds and professionals. A further critical factor compared to France and Germany concerns the dissimilar distribution of cases across regions. Even if in these countries the hospital beds were efficiently managed, the concentration of hospitalized patients and the scarcity of beds have put pressure on the hospital systems.

More advanced and complex applications in social networks, gaming, entertainment, medical research, and GIS, to name a few, drive the requirements to process large data sets, commonly called BigData. As mentioned in an IBM report [1], BigData is characterized not only by volume (terabytes and petabytes) but also by velocity (speed requirements) and variety (types of data sets). The manipulation of such large data sets requirement massively parallel software using a large number of servers.

Fine-grained supply voltage management is highly favorable for high system power efficiency of a system-on-a-chip (SoC) or multicore microprocessors. Fully integrated low-dropout regulators (LDOs) can provide a compact and cost-effective solution to supply the multiple divided and adaptive voltage domains. Meanwhile, they enable a fast dynamic voltage and frequency scaling (DVFS) for digital systems. A conventional analog LDO (A-LDO) may not fit well in such high-current applications, due to its relatively low-frequency pole at the gate of the power transistor, and the performance degradation in a low-input voltage case. Instead, digital LDO (D-LDO), switching LDO (S-LDO), and hybrid architectures are more suitable for the digital loads. This chapter first discusses the classic LDO control methods and power stage selection considerations. Then, we detail the design techniques of the analog-assisted digital LDO, hybrid controlled LDO, and the ampere-level switching LDO, in an advanced nanoscale CMOS (complementary metal-oxide semiconductor) process.KeywordsCMOSDynamic voltage and frequency scaling (DVFS)Power managementFine-grainedLinear regulatorLow-dropout regulator (LDO)Digital LDOSwitching LDO

Radar Sounders (RSs) are sensors operating in the nadir-looking geometry (with HF or VHF bands) by transmitting modulated electromagnetic (EM) pulses and receiving the backscattering response from different subsurface targets. Recently, convolutional neural network (CNN) architectures were established for characterizing RS signals under the semantic segmentation framework. In this paper, we design a Fast Fourier Transform (FFT) based CNN-Transformer encoder to effectively capture the long-range contexts in the radargram. In our hybrid architecture, CNN models the high-dimensional local spatial contexts, and the Transformer establishes the global spatial contexts between the local spatial ones. To overcome Transformer complex self-attention layers by reducing learnable parameters; - we replace the self-attention mechanism of the Transformer with unparameterized FFT modules as depicted in FNet architecture for Natural Language Processing (NLP). The experimental results on the MCoRDS dataset indicate the capability of the CNN-Transformer encoder along with the unparameterized FFT modules to characterize the radargram with limited accuracy cost and by reducing the time consumption. A comparative analysis is carried out with the state-of-the-art Transformer-based architecture.

In the rapid economic development today, accelerating coal mining to ensure energy supply is an important guarantee for the healthy and stable development of the economy. Compared with underground mining, open-pit coal mine is of relatively low technical difficulty and mining cost. Through high-efficient, environment-friendly and energy-saving green mining of large complete sets of equipment, open-pit coal resources can be fully utilized. In order to improve the efficiency of open-pit coal mining, this paper studies the application of information technology in the management and maintenance of mining equipment in large open-pit mines. This paper builds an intelligent control model based on the study on strengthening the intellectualization of the complete set of semi-continuous mining equipment of 10,000-ton open-pit coal mine, so as to improve the operation efficiency and safety of the semi-continuous mining equipment of 10,000-ton open-pit coal mine through automatic detection, monitoring, protection and control of the mining equipment. The results show that the equipment management efficiency is improved by 42.12% after using the intelligent control model. This is conducive to improving the management efficiency and maintenance of mining equipment.

Traditional geothermal detection methods, such as extensive ground-based surveys and drillings, are often costly, time-consuming, and environmentally intrusive. To address these challenges, this study presents a novel hybrid fuzzy multi-criteria decision-making model to evaluate and prioritize non-invasive, cost-effective remote sensing (RS) techniques. This model uses T-spherical dual-hesitant fuzzy set to manage the inherent ambiguities in the evaluation of multiple criteria. The logarithmic percentage change-driven objective weighting technique assigns the relative importance of criteria, and the multiple triangle scenarios-II methodology helps in comprehensive evaluation and ranking. By incorporating expert judgment and addressing inherent uncertainties, this model provides a systematic framework for optimizing RS technique selection. Findings indicate that thermal infrared imaging, with a significance score of 0.7187, holds transformative potential for geothermal energy development. Sensitivity and comparative analyses further confirm the robustness of this approach. This research offers a valuable resource for energy developers and policymakers aiming to leverage RS technologies for efficient geothermal resource management and development.

Vector polygon overlay is a complex type of a geo-spatial analysis algorithm used in geographical information system, which requires increasingly higher computational efficiency given the rapid growth of spatial data sets. Parallel computing has provided an effective approach to improve the computational efficiency and expand the processing scales of polygon overlay algorithms. Polygon overlay analysis algorithms that are based on traditional vector polygon clipping algorithms will experience significant efficiency decreases when handling overlap among polygons that have quantities of vertices. The discretization processing method-based rasterization-based polygon clipping algorithm (RaPC) algorithm exhibits higher efficiency for addressing clipping issues among such polygons. In this paper, we designed and implemented two types of parallel polygon overlay analysis algorithms to determine the differences in efficiency between the RaPC algorithm and the Vatti algorithm under the open multi-processing and message passing interface parallel computing environments. The results show that parallel polygon overlay analysis methods based on the RaPC algorithm present advantages for processing overlap among large data sets. The parallel algorithm based on the Vatti algorithm presents higher efficiency when handling overlap among small spatial data sets. The discretization process between two overlapped geometries of the RaPC algorithm provided a potential approach to solve the polygon overlay problem using a fine-grained parallelization method.

The mechanisms of achieving high phase efficiency of a meta-surface are researched and analyzed systematically. For a reflective element, an electrically small size, a complete phase-shift range of more than 360°, and also a smooth phase response play an essential role in determining the high phase efficiency. Based on the design principle, an excellent element is proposed consisting of an isotropic three-turn square-ring resonator (ITSR) on a single-layer slab. The characterizations of the ITSR element are investigated in depth through theoretical calculation, comparative analysis, and electromagnetic (EM) simulation. A focusing meta-lens, with a parabolic phase distribution, is well optimized using 14 × 14 elements. Good focusing effect is demonstrated within a wide bandwidth of more than 1 GHz. Exciting the meta-lens with a waveguide feed antenna at the focus, a high-efficiency reflectarray antenna is designed, which shows a series of advances, such as high aperture efficiency of better than 70%, 1-dB gain bandwidth wider than 13.07%, and also competitive radiation gain. The proposed strategy opens an avenue to new types of high-efficiency RMSs and reflectarray antennas with enhanced radiation characteristics.