Miracle-dz/UniFAR-FTUs huggingface dataset:
Assessing experimental apparatus for sheet metal electromagnetic forming process analysis

ABSTRACT Aiming to contribute to the development of electromagnetic forming (EMF) technology, this study proposes the design and development of an affordable EMF apparatus intended to be used in laboratory facilities. While commercial solutions for EMF machines are equipped with high voltage pulse capacitors and solid-state switches, this paper focuses on affordable components, such as low-voltage electrolytic capacitors, high-current switches, and the tool coil. The apparatus is evaluated through simulations, using an in-house mathematical model, and experiments considering the characterization and assessment of the pulse unit by means of the current frequency, the magnetic flux density, and the distribution of the pressure. The achieved simulated results for the discharge current, considering coupled and uncoupled metal sheet conditions, coincide with the measurements. The proposed main discharge switch effectively withstands both high discharge current peak and rise rate. In addition, a comparative evaluation of the discharge current oscillation frequency is presented, resulting in differences in terms of significant frequency and Fourier analysis of up to +32% and −15%, respectively, when compared to the formula of series RLC circuit. The proposed apparatus is useful for performing experiments, which can assist in analyzing process parameters and, consequently, aid to validating physical hypotheses and mathematical models.
Lessons Learned and Challenges Encountered in Retail Sales Forecast

Retail sales forecast is a special area of forecasting. Its unique characteristics call for unique data models and treatment, and unique forecasting processes. In this paper, we will address lessons learned and challenges encountered in retail sales forecast from a practical and technical perspective. In particular, starting with the data models of retail sales data, we proceed to address issues existing in estimating and processing each component in the data model. We will discuss how to estimate the multi-seasonal cycles in retail sales data, and the limitations of the existing methodologies. In addition, we will talk about the distinction between business events and forecast events, the methodologies used in event detection and event effect estimation, and the difficulties in compound event detection and effect estimation. For each of the issues and challenges, we will present our solution strategy. Some of the solution strategies can be generalized and could be helpful in solving similar forecast problems in different areas.
Optimal knee osteoarthritis diagnosis using hybrid deep belief network based on Salp swarm optimization method

Osteoarthritis (OA) damages the articular cartilage of the knee and is a severe degenerative joint condition. Currently, OA diagnosis is carried out by symptom analysis and progressive evaluation of the radiographs, although the method is subjective. This work presents a novel computer aided diagnostic system to diagnose the severity of the disease in its early stages. The proposed method comprises of stages that include image pre‐processing, extraction of features based on discrete wavelet decomposition, histogram, GLCM and texture features, and classification by stacked up‐RBM Deep Belief Networks (Hybrid DBN) finally. The performance of HDBN is improved by optimizing the hyper parameters with the Salp Swarm Optimization Algorithm (SSA). Experiments conducted on the Kaggle database consisting of 7503 images demonstrated an overall accuracy of 99.45% with 100% sensitivity and specificity. The impact on isolated and combined feature contributions is also analyzed using 10‐fold cross validation (CV). The robustness of the algorithm is tested on degraded images of salt‐pepper, Gaussian, and Poisson noise, which proved the effectiveness of the method. Comparison of the method proposed with similar techniques is done by employing different optimization algorithms and classifiers.
Review article: A comprehensive review of energy management strategies for hybrid electric vehicles

Abstract. In order to prevent the aggravation of global environmental problems, all industries are facing the challenge of green development. In the automotive field, the development of “new-energy vehicles” (plug-in electric vehicles) is particularly necessary. Hybrid electric vehicles (HEVs) have been proven to be an efficient way of solving environmental and energy problems. As the core of HEVs, the energy management strategy (EMS) plays an important role in fuel economy, power performance, and drivability. However, considering the randomness of actual driving conditions, there are great challenges involved in the establishment of an EMS. Therefore, it is critical to develop an efficient and adaptable EMS. This paper presents a systematic review of EMSs for HEVs. First, different issues that can affect the performance of EMSs are summarized. Second, recent studies on EMSs for HEVs are reviewed. Third, the advantages and disadvantages of different categories of EMSs are compared in detail. Finally, promising EMS research topics for future study are put forward.
Geostatistics of the Borden Aquifer: High‐Resolution Characterization Using Direct Groundwater Velocity Measurements

In 1986, a seminal data set from the Canadian Forces Base (C.F.B.) Borden aquifer, Ontario, Canada, was published, illustrating, in unprecedented detail, the spatial distribution of hydraulic conductivity (K). Among many contributions attached to that data set was a geostatistical examination of field‐based data for comparison with theoretical predictions of macro‐dispersivity. However, that work treated K as a static parameter and the sole source of flow variability. Here, point velocity probes (PVPs) are used to extend the earlier work by collecting a novel high‐resolution data set of groundwater velocity (v) measurements in the C.F. B. Borden aquifer. Velocity is a dynamic parameter of fundamental importance, closely tied to solute dispersion. Over 400 velocity measurements were collected along a transect perpendicular to flow, analyzed geostatistically, and compared with the analysis of the B‐B′ cross‐section of K reported by Sudicky (1986), https://doi.org/10.1029/wr022i013p02069. The PVP measurements exhibited geostatistics similar to those previously estimated by Sudicky (1986), https://doi.org/10.1029/wr022i013p02069. This finding suggests, as implicitly assumed in the previous work, the distribution of v is primarily controlled by K. PVPs also provided a novel, high‐resolution data set of groundwater flow directions. Since the ultimate objective of aquifer characterization includes the definition of velocity fields, this work not only extends the 1986 work, but also demonstrates a viable alternative for characterizing flow patterns in aquifers—with the advantage that direct v measurements reflect variability in porosity and hydraulic gradient, as well as K. This could ultimately be advantageous at sites with greater heterogeneity or other dynamic hydrogeological variabilities more pronounced than those at the Borden site.
VTGemini: Universal iOS Application for Guided Emergency Response and Notification for the Virginia Tech Community

The ubiquitous use of mobile devices and smartphones in the United States presents an interesting opportunity for application developers with respect to emergency management. Software engineers from the federal government to individuals have recognized the unique prospect of utilizing always-connected devices to assist in emergency notification, preparedness, and response. The federal government has instituted and ratified multiple acts and mandates with respect to mobile communications during a crisis such as the Commercial Mobile Alert System. Likewise, individual organizations and developers have created mobile applications that access weather alerts from the National Weather Service. Many of these applications utilize push notification architectures to notify users and stakeholders about impeding emergency situations. While most of these applications are geared towards a national audience, there are a few that are highly granular with a focus on the local community. This thesis presents a universal iOS application running on all iOS mobile devices: iPhone, iPad, iPad Mini, and iPod Touch for the Virginia Tech community. The application is highly granular with respect to emergency response guidance and notification by providing clear, concise, and supportive information to citizens during a crisis. Additionally, the application provides another medium of delivery for the Office of Emergency Management at Virginia Tech to potentially mitigate the extent of collateral damage and secondary incidents while saving lives.
Virtual simulation verification of clothing structure rationality based on clothing comfort pressure threshold

In order to solve the problem that it is difficult to accurately judge the rationality of clothing virtual simulation design, a clothing virtual simulation structure design method based on clothing comfort pressure threshold is proposed. By systematically combing the relevant clothing structure and location pressure threshold affecting clothing comfort, young female students who meet the size 160 / 84A standard are selected as the experimental objects; the data of key characteristic parts of the experimental human body are obtained by using the vertical white light three-dimensional human body scanning system; the construction of digital human model of CLO virtual simulation design system is completed; the slim basic women’s pants are used as the experimental samples; and 40 count plain 100% cotton fabric is used as the experimental fabric to complete the virtual fitting experiment of the experimental sample clothing. The location pressure data of the key structure of the experimental sample clothing are collected. The experimental pressure data with the clothing comfort pressure threshold data are compared and analyzedby using Excel software. The virtual simulation verification, optimization and correction of the rationality of the clothing structure are completed. The experimental results show that this method can quickly and accurately judge the rationality of virtual garment structure design, and effectively improve the accuracy of clothing structure virtual simulation design.
Relay Hybrid Precoding in UAV-Assisted Wideband Millimeter-Wave Massive MIMO System

Millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) offers a promising technique to fulfil the high data demand and connectivity of the Internet-of-Things (IoT) and 5G communications because it owns valuable and unknown spectrum resources. Using massive antennas with recently introduced drone-enabled aerial computing platforms, named unmanned aerial vehicles (UAVs), can cast high energy consumption if fully-digital precoding is employed at the UAVs. Using hybrid precoding at a UAV can reduce hardware complexity and energy consumption but is challenging with a need for joint optimization of three precoding matrices at the UAV (sixth-order polynomial objective function). In this paper, we propose to decompose the original UAV hybrid precoding challenge into three subproblems and develop a coordinated descent optimization (CDO) algorithm to solve the three problems recursively. In addition, the convergence and complexity of this new technique are analyzed. Numerical studies indicate the improved effectiveness of the proposed solution over existing solutions.
Supplementary Materials for PolarStream

In the main paper we present global panoptic fusion, i.e., considering detected object bounding boxes from all sectors at the same sweep when assigning instance ids. Here we also present stateful fusion: considering detected boxes from previous and current sectors. Additionally we show the results when each fusion method is combined with stateful NMS [1], i.e. instead of doing NMS within current sector, doing NMS considering boxes in current sector and previous sectors. We show the results of our PolarStream with bidirectional padding. As shown in Tab.1, with stateful fusion the panoptic quality for streaming drops significantly especially for 16 and 32 sectors. Stateful fusion is not reasonable for streaming because points from multiple sectors may belong to the same object, while the model can not see detected boxes from the following sectors with stateful fusion. We also find adding stateful-NMS improves panoptic quality because stateful-NMS suppress false positives of current sector by detected boxes from previous sectors, while pantoptic quality is very sensitive to false positives. When combining stateful NMS and global fusion the pantopic quality of streaming models is almost the same or even better than the full-sweep method (68.7).
Optimizing Mixed-Asset Portfolios Involving REITs

Real Estate Investment Trusts (REITs) is a popular investment choice as it allows investors to hold shares in real estate rather than investing large sums of money to purchase real estate by themselves. Previous work studied the effectiveness of multi-asset portfolios that include REITs via an efficient frontier analysis. However, the advantages of including (both domestic and international) REITs in multi-asset portfolios, as well as analyzing all the possible combinations of asset classes, has not been investigated before. In this paper, we fill in this gap by performing a thorough investigation across 456 different portfolios to demonstrate the added value of including REITs in mixed-asset portfolios in terms of different important financial metrics. To this end, we use a genetic algorithm approach to maximize the Sharpe ratio of the portfolios. Our results show that optimization via a genetic algorithm outperforms the results obtained from a global minimum variance portfolio. More importantly, our results also show that there can be significant improvements in average returns, risk and Sharpe ratio when including REITs.
A rich get richer effect governs intracellular condensate size distributions

Phase separation of biomolecules into condensates has emerged as a ubiquitous mechanism for intracellular organization and impacts many intracellular processes, including reaction pathways through clustering of enzymes and their intermediates. Precise and rapid spatiotemporal control of reactions by condensates requires tuning of their sizes. However, the physical processes that govern the distribution of condensate sizes remain unclear. Here, we utilize a combination of synthetic and native condensates to probe the underlying physical mechanisms determining condensate size. We find that both native nuclear speckles and FUS condensates formed with the synthetic Corelet system obey an exponential size distribution, which can be recapitulated in Monte Carlo simulations of fast nucleation followed by coalescence. By contrast, pathological aggregation of cytoplasmic Huntingtin polyQ protein exhibits a power-law size distribution, with an exponent of −1.41 ± 0.02. These distinct behaviors reflect the relative importance of nucleation and coalescence kinetics: introducing continuous condensate nucleation into the Monte Carlo coarsening simulations gives rise to polyQ-like power-law behavior. We demonstrate that the emergence of power-law distributions under continuous nucleation reflects a “rich get richer” effect, whose extent may play a general role in the determination of condensate size distributions.
Local and Regional Anesthesia versus Conventional Analgesia for Preventing (PPP) in Adults and Children

Anesthesia is a technique for reducing or eliminating pain, in medical and dental practice, local anesthetics have a long history of efficacy and safety. Persistent postoperative pain (PPP) is caused by tissue damage so local anesthetics used to block bundles or roots of nerves in the central nervous system is one technique to prevent persistent postoperative pain (PPP). Aim of this study aimed to synthesize outcome data across systematic review and metaanalysis for comparing local and regional anesthesia versus conventional analgesia to prevent persistent postoperative pain in adults and children undergoing surgery. Methods: Online databases Medline, Pub Med, and Google Scholar of Randomized controlled trials published between 2014 and 2021. The PRISMA checklist was utilized to guide this review. Results: 8 RCTs studies in the review; (2) RCTs favored epidural anesthesia for thoracotomy suggesting the odds of having PPP three to 18 months following an epidural for thoracotomy, (1) RCTs favored regional anesthesia after caesarean section, (5) RCTs also favored the infusion of intravenous local anesthetics for the prevention of PPP three to six months after breast cancer surgery. The regional anesthesia may moderate reduce the risk of developing PPP after thoracotomy and caesarean section. There is low evidence that regional anesthesia may reduce the risk of developing PPP after breast cancer surgery. Conclusions: This meta-analysis found that localized anesthesia has potential benefits, such as reduced post-anesthesia care unit utilization, nausea, and postoperative pain
A Comparison of Data-Driven and Template-Based Approaches to Natural Language Generation

Data-drivenandtemplate-basedsystemsaretwopopularapproaches todevelopingNaturalLanguageGeneration(NLG)systems.There arevariousproblemsassociatedwiththetextsproducedbyboth thesemethods,whichraisesthequestionofwhetheroneapproach yieldsbetterresultsthantheother.Toanswerthisquestionadata-drivenaswellasatemplate-basedsystemwasdeveloped.The data-drivensystemconsistedofseparatesentenceplannerandlin-guisticrealisationmodules.Thesesystemsthengeneratedeight utterancesforthesamesetofinputMeaningRepresentations(MRs). TheseinputMRscamefromtheWikipediaPersondataset.The utterancesgeneratedbythesystemswerethenevaluatedby91 humanjudges.Thehumanjudgeswerepresentedwithpairsof textsgeneratedusingdifferentmethodsandaskedtorateeachtext basedonit’sclarityandfluency.Thejudgeswerethenaskedto choosewhichtextwasmorenatural.Thetextsinthequestionnaire includedreferencetextscorrespondingtotheinputMRtoactasa baseline. Thetemplate-basedsystemwasfoundtoproducetextsthatwere significantlymorefluentthanboththedata-drivenandreference texts.Thetemplate-basedsystemalsoproducedtextswithhigher clarityratingsthanthedata-drivensystemandtextofsimilarclarity tothereferencetexts.Despitethis,thereferencetextswerestill selectedasbeingmorenaturalthanthoseproducedbythetemplate-basedsystem.Whilethetextproducedbythedata-drivensystems werejudgedtobelessnaturalthanboththetemplate-basedsystem andreferencetexts.Thisismostlikelyduetothesemanticerrors whichmadeitobviousthatahumandidnotwritethesetexts.Thus weconcludethatthetemplate-basedapproachmayprovideamore promisingapproachtoconstrictingaNLGsystemforthedataset thanthedata-drivensystem.
EFFECT OF FERTILIZATION AND GA3 APPLICATION ON GARLIC (ALLIUM SATIVUM) GROWTH, YIELD AND ITS COMPONENTS

Two field experiments were conducted at the private farm in El-Mataryia region, Dakahlia Governorate during 2004/2005 and 2005/2006 seasons to study the effect of Gibberellic acid (GA3), chemical and organic fertilization on vegetative growth, yield and its components, of garlic crop. The split plot design with three replications was used. The main obtained results could be summarized as follows: GA3 application at 10 ppm caused significant increases on plant height, number of leaves/plant, plant leaf area, fresh weight of plant, dry weight of plant, total chlorophyll, neck diameter, bulb diameter, total yield of garlic (t/fed), bulb diameter, bulb weight, number of cloves/bulb and clove weight in both seasons as well as bulbing ratio in the second season only. All studied characters of this study were significantly increased by using 100 % chemical fertilizers (CF) and 50 % CF + 50 % FYM treatments in both seasons. Also, there were not significant differences between using 100 % CF and 50 % CF + 50 % FYM treatments in both seasons. The interaction between GA3 application and fertilization treatments had a significant effect on total yield of garlic, bulb diameter in both seasons. The highest values of these characters were resulted from using 100 % CF with GA3 application and 50 % CF + 50 % FYM treatments with application of GA3 without significant differences between them in both seasons.
Synergistic Effects of Spraying some Growth Regulators and Antioxidants on Growth and Fruiting of Tommy Atkins Mango Trees Growing in Sandy Soil

In order to study the effect of spraying two growth regulators (Gibberellic acid “GA3” and Jasmonic acid “JA” each one at two concentration 15 ppm & 30 ppm) or/and two antioxidants (folic and citric acids each one at 500 ppm) on vegetative growth, fruit set and yield as well as fruit quality characteristics of mango trees cv. Tommy Atkins, grown in sandy soil. A field experiment was conducted during successive seasons of 2019 and 2020 at a private orchard located at Al-Wakf district, Qena Governorate. The obtained results confirmed that spraying Tommy Atkins mango trees with GA3 and JA each one at 15 or 30 ppm as well as folic and citric acids each one at 500 ppm significantly enhanced trees vegetative growth. Fruit set%, fruit retention%, yield (kg)/tree and its component as well as fruit quality properties (i.e. TSS%, total sugars %, vitamin C contents, total fiber% and total acidity %) were significantly improved compared to untreated trees. However the best vegetative growth traits i.e. shoot length, number of leaves/shoot and leaf area, leaves pigments, fruit set %, fruit retention % and yield weight (kg/tree) were obtained from the trees received GA3 at 30 ppm combined with citric acid at 500 ppm, followed by those received JA at 30 ppm combined with citric acid at 500 ppm. While, spraying Tommy Atkins mango trees with JA at 30 ppm and citric acid at 500 ppm showed more effectiveness on fruit quality, followed by GA3 combined with citric acid at 500 ppm.