Miracle-dz/UniFAR-FTUs huggingface dataset:
Whale Optimization-Based Distributed Generation Placement in Distribution System for Loss Minimization

Small-scale electric power plants, known as distributed generation (DG) resources, can impart electricity to homes, businesses, or industrial units in a distribution system. Due to the rising demand for electricity, the usage of DG in a distribution system has increased dramatically. DG is a viable solution to the conventional energy issue since it may provide several advantages to the distribution feeder’s efficiency. It is commonly used in distribution networks because it boosts voltage profile and reduces power loss. The main goal of this study is to determine the ideal location and capacity for distributed generation (DG) within a distribution system, while accounting for various load models. The forward-backward technique is used to solve the system’s power flow. The whale optimization algorithm (WOA), an advanced metaheuristic optimization based on natural humpback whale hunting techniques, is used to find the DG position and size. Moreover, the algorithm introduced in this study is evaluated on the IEEE-33 bus system using Matlab 2022a to confirm its performance through multiple scenarios while considering four different load models. The results demonstrate that the technique presented in this study is exceptionally effective in determining the optimal placement and capacity of distributed generation within a distribution system
Overview and Prospect of the Aging Mechanism of Press-pack IGBTs

At present, the maintenance of flexible DC converter valves suffers from the problems of frequent temporary maintenance, insufficient or excessive maintenance. In particular, there is a lack of relevant research on the aging mechanism of IGBTs to support it. This article provides a comprehensive review of the research on the aging mechanism of press-pack IGBTs. The aging mechanism is divided into fretting wear aging, gate oxide aging, boundary warping aging, short-circuit aging and other aging, The corresponding aging mechanisms, experimental tests, etc. are carried out. Finally, the research on the aging of IGBTs for flexible DC converter valve maintenance is prospected, and the future looks promising.
Enhancing Handwritten Character Recognition with XGBoost: A Machine Learning Approach

Recognizing handwritten text poses a significant challenge due to the distinct characteristics inherent in each individual’s handwriting. Consequently, recognition systems must exhibit adaptability to identify similar or dissimilar characters with varying traits. Particularly challenging is the recognition of characters in ancient documents, where the presence of numerous types of noise introduces complexity. When digitizing these historical records, diverse noise patterns emerge, significantly impacting recognition systems. Hence, the essential elements of a successful recognition system encompass the conversion of historical documents into digital format, the application of suitable preprocessing methods, and the utilisation of a resilient classifier. This study presents a thorough system for recognising handwritten characters in English and Telugu, which utilises a deep convolutional neural network. The proposed system achieves an impressive accuracy of 95.20% with minimal model loss, underscoring its efficiency and effectiveness in handling the complexities of handwritten character recognition, especially in the context of ancient documents.
Enabling Fairness in Distributed PV Curtailment for Voltage Regulation: A Comparative Analysis

The growing adoption of photovoltaic systems in power distribution networks has yielded numerous advantages, but it has also introduced challenges, particularly in managing overvoltage issues. Uncoordinated photovoltaic integration can lead to voltage rise beyond acceptable levels. Curtailing active power is an effective method for addressing overvoltage issues in power distribution systems. However, it is essential to distribute the curtailment equitably among the resources to maintain fairness and achieve a well-balanced utilization of renewable energy. This paper presents a comparative study of different fairness schemes for active power curtailment in photovoltaic integration systems. Three different curtailment methods are selected for demonstration: i) proportional, ii) egalitarian, and iii) financial. The study involves evaluating the curtailment schemes based on their ability to distribute the curtailed power among the photovoltaic systems in a justifiable manner, focusing on their formulation and distributed solution. A detailed performance comparison is carried out to highlight the cost of catering for fairness schemes in photovoltaic curtailment using relevant metrics, where simulations are carried out using a modified IEEE 123-bus test case.
Color Crop Image Compression Technique using Singular Vector Sparse Reconstruction

In recent times, applications of crop image analysis is continuously increasing in agriculture fields for crop health monitoring, disease identification, and prevention. Crop images are generally captured by wireless sensor devices using High-resolution cameras, which causes difficulties in the transmission and storage of image data. In this regard, this paper presented singular vector sparse reconstruction (SVSR) method proposed by Xu et al. The main concept of this technique is based on the selection of coefficients and discarding the singular vector obtained from singular value decomposition (SVD) to improve the compression ratio. It is efficient in reconstructing a low-rank singular vector from very few observed entries. In this way, a low-rank approximation of the image data matrix is achieved by SVD, followed by sparse sampling of singular vectors up to a desired level. The reconstruction is performed by using the Makima interpolation (MI) on the sparse samples selected singular vector. This technique’s performance has been examined with MI, on color crop image data, in terms of following parameters peak signal-to-noise ratio (PSNR), structural similarity index (SSIM) and compression ratio (CR) as well as visual appearance. According to experimental results, MI interpolation with the SVSR technique achieved 90-98% of compression with good quality of the compressed image.
Blind Delegation with Certified Deletion

Can we outsource computation on encrypted sensitive data, while ensuring that the data is certifiably, information-theoretically deleted by the server after computation? This question was first posed by Broadbent and Islam (TCC 2020), and partial answers were recently provided by Poremba (arXiv 2022) and Bartusek and Khurana (arXiv 2022). We present the first fully-secure solution to this problem of blind delegation with certified deletion . In more detail, we obtain maliciously-secure blind delegation for classical circuits, assuming post-quantum hardness of the learning with errors (LWE) problem. Our construction carefully combines classical fully-homomorphic encryption and succinct non-interactive arguments for P with quantum subspace coset states . Building on these techniques, we also describe a generic compiler that adds a publicly-verifiable certified deletion property to a variety of cryptosystems, including blind delegation. Our publicly-verifiable schemes assume the existence of (post-quantum) indistinguishability obfuscation.
A Torque Regulated Induction Motor Drive With A Tiny DC Link Using Active Front-End Control

Variable Frequency Drives are commonly used in industries to control an induction machine for air compressors, fans, pumps, or other applications that demand speed or torque regulation. The topology of the drive comprises cascaded power converter systems with bulky DC link storage. The commonly used aluminium electrolytic DC link capacitors are the second most failed electrical component in a drive. This work proposes a suitable pulse width modulation technique in the input power converter to replace the troublesome electrolytic capacitors with more reliable and tiny film capacitors. A 1.5 kW active frontend converter cascaded with a tiny DC link and a field-oriented controlled induction motor is proposed for a torque regulation application. A dynamic current control using hysteresis PWM is implemented in the front-end converter, reducing the DC link capacitor to just 16$\mu$ F. The front-end converter control strategy is verified in both Software in Loop and Processor in Loop simulation using Texas Instruments C2000 LaunchpadXL F28069M Microcontroller.
A Floating Capacitor Cubic-Cell Boost Converter for Photovoltaic application

With the rising demand for renewable energy (e.g., photovoltaic, fuel cell, wind, etc.) based systems in today’s world, the demand for a highly boost-up, efficient, compactsized, and cost-effective DC-DC converter is also increasing. This paper investigates the design and implementation of an improved single-switched Floating Capacitor Cubic-Cell Boost Converter (FCCBC) for Photovoltaic applications. As the power generated by a Photovoltaic (PV) source is unregulated and nonlinear in nature, to mitigate these drawbacks a DC-DC converter is often employed with the PV source in various PV applications. In this paper, an FCCBC topology is proposed to accomplish a high voltage gain with lower input and output ripple using fewer circuit elements. This paper explains the steady-state analysis and the operating principles of the proposed converter. The proposed converter is designed in LTspice XVII software and implemented in a 120W, 10kHz laboratory experimental prototype to investigate the converter analysis and design parameters. The results show a voltage gain of up to eight times the input voltage in a 50% duty cycle with a very small amount of input inductor ripple current and output capacitor ripple voltage. Both the simulated and experimental analyses confirm the attainability of the proposed converter.
Gene-cassette for adaptation of Lactococcus lactis to a plant environment

– The generally accepted opinion is that the natural niche for lactococci are plants. Several genes reminiscent of the environmental adaptation of these bacteria to the plant habitat were found as a result of our work on the pullulanase coding region in the Lactococcus lactis IBB500 strain. All genes were located within an 11-kb DNA fragment of a 35-kb plasmid. Analysis of the nucleotide sequence of the 11-kb DNA fragment showed three regions: (i) a middle region – encoding the potential pullulanase operon (ii); a pullulanase upstream region – encoding two cold shock proteins; and (iii) a pullulanase downstream region – encoding two cadmium resistance proteins. Analysis of the phylogenetic relationships of these regions through inspection of their G+C content and codon preference, as well as homology of their protein products and constructions of phylogenetic trees strongly supported the hypothesis of their acquisition through a horizontal gene transfer. Focusing further research on the middle region we have found that the pullulanase gene, belonging to the putative pul operon, was preceded by a long, non-translated region in which five putative promoter sequences were identified. Moreover, a DNA sequence similar to the highly conserved cre box involved in glucose repression was found in front of one of them. The hypothesis of glucose repression was further tested using transcriptional fusion between the pul and luxAB -reporter genes, located on the parental plasmid in the L. lactis IBB500 strain.
Short Term Electricity Price Forecasting by Optimized LSTM Model of Deep Learning with Genetic Algorithm

The complexity of competitive markets, which are characterised by high volatility and frequent price spikes, makes forecasting power prices a challenging task. This study used various statistical methods to forecast day-ahead prices, however, statistical methods don’t provide good results for non-linear and high-volatility data. Thus to deal with the volatility in electricity prices, advanced deep-learning techniques have been used where LSTM model provides significantly better results. Furthermore, hyperparameter optimization is done using Genetic algorithm optimization which optimizes the hyperparameters of the LSTM model and enhances its performance. Forecasting performance is here evaluated by three well-known error metrics: Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Root Mean Square Error (RMSE).
Exploring sector-speci ﬁ c sustainability indicators: a content analysis of sustainability reports in the logistics sector

Purpose – Businesses produce corporate sustainability information in support of the decision-making of their stakeholders through sustainability reporting. However, the use of such information has been limited because of the broadness of sustainability indicators used in sustainability reports. This study aims to identify sector-speci ﬁ c sustainability indicators and prioritiesbased onthe materialissuesofthe logistics sector. Design/methodology/approach – The authors conducted an exploratory study using 64 sustainability reports from the logistics sector. Qualitative content analysis was performed using Leximancer software to identifykeythemes andmaterialconcepts ofsustainabilityreports. Findings – The results showed that the most important indicators of the logistics sector are economic performance and energy, yet sustainability reports appear to focus more on reporting social sustainability information. Of the several sustainability measures, environmental and social factors dominated the reporting (8 economic, 62 environmental and 58 social). This discrepancy can also imply inconsistencies in sustainability reporting. Practical implications – Identifying sector-speci ﬁ c indicators enables assessing the impact of sustainability issues on value creation and performance comparison among similar organizations. This is also bene ﬁ cial in ensuringconsistencyofsustainabilityreporting,whichisaprerequisiteforpolicymakinginsustainablelogistics. Originality/value – Prior studies emphasized that no sector-speci ﬁ c sustainability indicators were established intheliteratureandstandardizedindicatorsareneededtoensurethecomparabilityofresults.Thisstudyaddresses thisgapbyidentifyingsector-speci ﬁ csustainabilityindicatorsbasedonthematerialissuesofthelogisticssector.
The Importance of Big Data Analytics in the Field of Cyber Security

Internet is a changing technology and it is impossible to anticipate the changes that are occurring constantly. Every change leads to more dangerous threats that had not been previously imagined, reason for this condition is expeditious expansion in internet innovations. This elaboration of the cyberspace has involved with it an aggressive raise in the type and frequency of cyber-attacks. So there is a need of cyber security solutions to block these attacks. However, the generation of Big Data over computer networks is expeditiously rendering these traditional solutions are outdated. To overcome this problem, corporate researchers are now focusing on Security Analytics, i.e., the application of Big Data Analytics approach to cyber security. The security in big data analytics has the ability to congregate huge amounts of data to be analyzed visualized and drawn insights that can make it possible to predict and avoid cyber-attacks. This paper will presents a inclusive survey on the state of the art of security analytics i e its representation, the technology, the trends and its tools. It hence achieves to satisfy the reader of the forthcoming application of analytics as an unparalleled cyber security solution in the near future.
Why Survey-Based Subjective Expectations are Meaningful and Important

For decades, households' subjective expectations elicited via surveys have been considered meaningless because they often differ substantially from the forecasts of professionals and ex-post realizations. In sharp contrast, the literature we review shows household characteristics and the ways in which households collect and process economic information help us understand previously-considered puzzling facts about their subjective expectations. In turn, subjective expectations contribute to explain heterogeneous consumption, saving, investment, and debt choices as well as different reactions by similar households to the same monetary and fiscal policy measures. Matching microdata on households' characteristics with the price signals the same households observe, their subjective expectations, and their real-world economic decisions is crucial to establishing these facts. Our growing understanding of households' subjective expectations inspires several theoretical and empirical research directions and begets the design of innovative and more effective policy instruments.
A Hierarchical Probe Evaluation Method for Teaching Effect of University Engineering Courses Based on the Keypoints of Knowledge

It has been five years since China's higher education entered the stage of universal education. With the increase of the gross enrollment rate of college students, the differences of students who choose courses jointly have increased significantly. The traditional average evaluation method of all-aspect framework tends to weaken the evaluation, supervision and help of course teaching. In this paper, a probe deep detection method is proposed. The key knowledge points of the course are selected, and the position of each student's learning effect is evaluated by nine levels. The selection principle of key knowledge points and the implementation method of probe evaluation are put forward. Taking the Mechanics of Materials and Pumps and Compressors of two university engineering courses as examples, the construction idea of hierarchy criterion is demonstrated.
Monopolistic Competition and E¢ ciency under Firm Heterogeneity and Non-Additive Preferences (cid:3)

We consider the single-sector version of the Melitz-Ottaviano model of monop-olistic competition with heterogeneous (cid:133)rms. We characterize the (cid:133)rst best and market allocations. We (cid:133)nd that the market provides the (cid:133)rst best level of entry but too little selection; hence, the market provides too many varieties and allocates too little (much) production to low (high) cost realizations.
StyLitGAN: Image-based Relighting via Latent Control

We describe a novel method, StyLitGAN, for relighting and resurfacing images in the absence of labeled data. StyL-itGAN generates images with realistic lighting effects, including cast shadows, soft shadows, inter-reflections, and glossy effects, without the need for paired or CGI data. StyLit-GAN uses an intrinsic image method to decompose an image, followed by a search of the latent space of a pretrained Style-GAN to identify a set of directions. By prompting the model to fix one component (e.g., albedo) and vary another (e.g., shading), we generate relighted images by adding the identified directions to the latent style codes. Quantitative metrics of change in albedo and lighting diversity allow us to choose effective directions using a forward selection process. Qualitative
Digital Marketing Impact on Tourism Destination in Malaysia: The Role of Social Media Reviews

Malaysia boasts a plethora of picturesque and unspoiled locations that serve as ideal settings for recreational pursuits, while also offering a rich tapestry of cultural diversity that entices visitors to explore the country. Tourists from around the world are captivated by the diverse cultural activities and iconic landmarks that embody the rich heritage and ancient civilizations of Malaysia. Tourism is a multifaceted phenomenon encompassing social, cultural, and economic dimensions, involving the displacement of individuals to foreign countries or destinations beyond their customary surroundings, driven by personal or business/professional motivations. This adaptability is crucial to guarantee customer happiness, safety, and overall enjoyment. Consequently, travellers have increasingly relied upon websites as primary sources for acquiring pertinent information, while also utilizing social media platforms for various purposes. Therefore, this study explored the three main important facets of social media reviews including quality, quantity, and credibility reviews. A total of 140 responses were recorded through a self-administered questionnaire survey. Findings found a significant effect of the reviews on travellers’ purchase decision. The findings of this research will provide the tourism industry with a deeper comprehension of the influence exerted by social media reviews on their business operations and its importance in influencing travellers’ decisions .
Dark energy as a remnant of inﬂation and electroweak symmetry breaking

: It is shown that dark energy can be obtained from the interplay of the Higgs boson and the inﬂaton. A key element is the realization that electroweak symmetry breaking can trigger a second phase of rolling of the inﬂaton, which, when provided with the appropriate couplings between the ﬁelds, can be suﬃciently slow to source accelerated expansion in the late time Universe. The observed dark energy density is obtained without ﬁne-tuning of parameters or initial conditions due to an intricate conspiracy of numbers related to inﬂation, gravity and electroweak physics.
International Journal of Research Publication and Reviews

Face detection and recognition are critical technologies used in a variety of applications, including security systems, access control, surveillance, and social media. YOLO (You Only Look Once) is a class of object identification models that employ deep learning methods to recognise things in real-time video and photos. Unlike standard object detection systems, which employ numerous steps to detect objects, YOLO uses a single neural network to forecast the bounding boxes and class probabilities for each identified object in the input picture or video frame. This makes it incredibly quick and efficient, allowing it to handle video streams at real-time speeds on a conventional GPU. In the course of extensive research conducted in various domains, researchers found that YOLO is not commonly utilised for face recognition tasks. Instead, YOLO can be used to discover faces in an image or video frame, and then a different face recognition algorithm can be used to recognise the found faces. This study presents the existing face detection and recognition systems built using YOLO.
Testing the clinical value of multifocal electroretinography and microperimetry and the effects of intravitreal therapy with ranibizumab on macular function in the course of wet age-related macular degeneration: a 1-year prospective study

Purpose To investigate the clinical value of multifocal electroretinography (mfERG) and microperimetry and the effects of intravitreal therapy with ranibizumab (Lucentis®) on macular function in the course of neovascular age-related macular degeneration (nAMD). Materials and methods We conducted a prospective single-arm interventional cohort study with 20 nAMD patients older than 50 years. Examinations were scheduled monthly for 1 year during intravitreal therapy with ranibizumab. The examinations included mfERG, microperimetry, spectral domain optical coherence tomography, and best-corrected visual acuity using ETDRS score. Results During the 12-month observation period, a significant positive linear correlation between the logarithm of minimum angle of resolution (logMAR) and scotoma area (r=0.28, 95% confidence interval [CI] 0.21–0.35), between logMAR and fovea thickness in optical coherence tomography (r=0.11, 95% CI 0.04–0.2), and a significant negative correlation between logMAR and mfERG (−0.37, 95% CI −0.43 to −0.31) were observed. A significant ranibizumab effect on logMAR was found (P=0.0065). From a total of 25 relapses, 14 were able to be predicted correctly by mfERG P1 decrease in the preceding month. However, there was no statistically significant relation between prediction and observed relapses (Fisher’s exact test, P=0.6726). Conclusion Our results indicate a possible role of mfERG and microperimetry in the monitoring of macular function and prediction of recurrence during intravitreal pharmacotherapy in wet AMD.