Approach Efficacy Research Articles

Determination of essential features for predicting start-up success: an empirical approach using machine learning

ABSTRACT Various significant features influence the success of a startup in an entrepreneurial ecosystem. However, identification of such features has not been performed coherently. This study aims to bridge the gap by determining the essential features impacting a startup's success. In the first part, a machine learning-based approach is applied empirically to determine critical features from ten datasets. Then the obtained features were validated using popular feature selection techniques based upon Recursive Feature Elimination (RFE), SelectKBest, and XGBoost to assure the final significant features. Furthermore, mutual information is used between the features obtained from the proposed approach and the ground truth to measure the approach's efficacy, which is calculated to be 90%. The significant features acquired through this approach imply how the entrepreneurs prioritise the determining features in startup success and maximise the socio-economic outputs. This study provides an essential contribution to the entrepreneurial finance literature by empirically exploring the features influencing a startup's success based on its financial performance in different stages of its lifecycle. Startups can apply the implications of this study to reduce uncertainty and enhance performance. It also helps founders, investors, and researchers understand a startup's performance and plan future steps accordingly.

Centralized optimal management of a smart distribution system considering the importance of load reduction based on prioritizing smart home appliances

AbstractThe distribution system's economic operation is significantly impacted by the management of distributed generation (DG) resources, energy storage (ES), and controllable loads. The paper employs a smart distribution system that incorporates dispatchable and non‐dispatchable DG resources, as well as battery storage, in addition to the demand response (DR) scheme. New modelling was performed in hourly steps to achieve the optimal unit commitment. In smart homes, appliances are prioritized and classified into four types: adjustable, interruptible, shiftable, and uncontrollable loads. Load reduction in smart homes is also considered based on load prioritization and customer participation in the DR scheme to achieve the proposed scheme's objectives. The proposed method considers costs associated with microturbines (MTs), including manufacturing, start‐up, shutdown, and pollution. Additionally, planning is conducted to purchase or sell electricity to the upstream network. The simulation is run on an IEEE 33‐bus system to demonstrate the proposed method's effectiveness. The system is assumed to be capable of operation in both island and grid‐connected modes. The results demonstrate the proposed approach's efficacy in load reduction, operation cost, and execution time.

Towards Safe Exploration for Autonomous Vehicles using Dual Model Predictive Control

Collision-free control and planning for autonomous vehicles in an only partially known environment is challenging. Often this problem is tackled passively - without active exploration, i.e., planning and control are performed based on the information obtained during operation without explicitly considering that reducing the environment uncertainty by active exploration can improve the overall performance, e.g., reduce the time needed to achieve the goal. We propose a moving horizon planning approach combined with a dual-mode predictive control formulation that actively explores the environment to obtain improved obstacle information. Given a path by the moving horizon planner, the dual-mode predictive tracking controller balances two objectives: fast and safe goal achievement, taking into account potential improvements due to active exploration - learning - of the environment. Adding an excitation term to the objective function incentivizes the system to explore the environment to enhance the overall objective and reduce uncertainty. The exploration-exploitation formulation enhances the overall performance while ensuring obstacle avoidance in combination with the moving horizon planning strategy. An example of an autonomous ground vehicle operating in a cluttered, only partially known environment demonstrates the approach's efficacy.

Success of Immediate Manual Detorsion of Testicular Torsion

Evidence shows that patients having testicular torsion usually present to the emergency department to apply immediate interventions. Immediate manual detorsion has been validated among different investigations as a productive technique for the initial management of testicular torsion. In the present literature review, we have elaborated on the success of manual detorsion for patients with torsion according to findings from previous relevant studies. Our findings indicate the favorable events that can be obtained after using manual detorsion. However, some studies reported that the success rate for the modality is not very high. In addition, it has been reported to reduce the time of ischemia as a temporary measure until surgery is prepared. Evidence also shows that the approach's efficacy is superior to that of surgical exploration and should be used since the time of diagnosis to prevent further deterioration of the affected case and enhance perfusion. However, the current evidence is based on a limited number of investigations that validated the efficacy of manual detorsion among a small population. Accordingly, we encourage further studies to be conducted in this context further to validate the current evidence with more adequate sample sizes.

Modified Tumor Diagnosis by Classification and Use of Canonical Correlation and Support Vector Machines Methods

The main objective of this research is to investigate techniques for classifying tumor grades based on image processing. The algorithms to classify tumors are introduced, and their performance for the experimental results are investigated. In the proposed algorithm, first, the scan images of the lung are pre-processed, and then the histogram, texture, and geometric features are extracted. These features are then used in the Support Vector Machines (SVM) and Canonical Correlation Analysis (CCA) classifiers to diagnose tumors and classify benign and malignant types. These combined techniques in understanding medical images for researchers are an essential tool to increase the accuracy of diagnosis. In this paper, simulated and real medical images are used. The results obtained from the proposed methods in this paper were compared with the previous findings to approve the proposed approach's efficacy and reliability in diagnosing and classifying tumors. In addition to high accuracy in diagnosis, this method is also a low-cost and low-risk method. Due to its very high sensitivity and having the desired values of two criteria of precision and specificity, and the low number of features used for classification, the developed method was proposed as an efficient and appropriate method for tumor classification.

Opening the Conversation: The Development of a Faculty-Facing Module on Teaching Students With Dyslexia

Introduction: Accessibility training frequently relies upon checklist-based approaches to convey important standards and guidance. Such approaches limit scalability and acknowledgement of the user experience. About the case: A certificate program for faculty and graduate teaching assistants who are instructors of record at one university provided an opportunity to develop a learning module about dyslexia. Situating the case: Dyslexic students frequently require alterations to both teaching approaches and document design; however, studies show that some faculty do not see or feel a need to offer any accommodations for this dis/ability. Research indicates that train-the-trainer approaches to accessibility training offer improved scalability and efficacy when it comes to engaging and acknowledging the needs of dis/abled communities. Amplifying voices from the dis/abled community in training also personalizes the need to make content accessible. Approach: This article covers the module's creation and implementation via the iterative Analysis, Design, Development, Implementation, and Evaluation (ADDIE) method. In doing so, it provides a framework for creating similar modules that industry practitioners and faculty can implement within their own organizations. Results/discussion: The COVID-19 pandemic led to fewer participants in the module than anticipated; however, preliminary feedback suggests the approach's efficacy. Conclusion: A train-the-trainer approach that integrates testimonials from the dis/abled community offers a user-focused means of disseminating accessibility guidelines that organizations should consider if looking for rapid scalability for new accessibility initiatives.

Neoadjuvant intraarterial cytoreductive chemotherapy for lacrimal gland adenoid cystic carcinoma: A 33-year single-institution experience.

e18051 Background: Lacrimal gland adenoid cystic carcinoma (LGACC) has historically been associated with a poor prognosis even in localized disease, with the survival of 56% at 5 years and 49% at 10 years. In 1988, our group treated the first patient with neoadjuvant intra-arterial chemotherapy (IACC) followed by adjuvant chemoradiation. Since then, we use this protocol as the standard approach for localized LGACC. Herein, we aim to analyze the clinical characteristics and outcomes of LGACC patients at a single academic institution treated with the protocol to provide information on this approach's efficacy. Methods: We prospectively assessed all patients with pathologically confirmed LGACC treated at Bascom Palmer, Sylvester Comprehensive Cancer Center, or Jackson Memorial Hospital between 1988 and 2021. We calculated overall survival (OS) using the Kaplan-Meier and Cox proportional-hazards modeling methods with Log-Rank Test to estimate the 95% confidence interval. Results: 42 LGACC patients were identified, and 37 (88%) underwent IACC, with 2 (5%) being metastatic at the time of the protocol. In our cohort, the median follow-up was 10 years, 21 (56%) were men, and the median age at diagnosis was 42 (range 20-72). The average tumor size was 3cm (range 0.8-7.6). The dominant histological pattern was cribriform (n = 23, 54%), followed by basaloid (n = 6, 14%) and solid (n = 6, 14%). 16 patients underwent whole genome sequencing, and the most common mutations found were NOTCH1 (n = 7, 41%), ATM (n = 4, 25%), BPTF (n = 4, 25%), FGFR 2 (n = 3, 18%), Frem3 (n = 3, 18%), and NOTCH2 (n = 3, 18%), with additional biomarker data forthcoming. The 5, and 10-year survival rates were 85% (95% CI 73–97) and 71% (95% CI 53-89), respectively, giving and overall mean survival of 23 years (95% CI 19–28). The risk of death was higher with a tumor size larger than 3 cm and with bone invasion (HR: 6.5 and 7.5 respectively; P < 0.05). Conclusions: Despite the historically described poor prognosis, patients with LGACC treated with IACC have an excellent prognosis. Future research on tumor molecular characteristics might identify susceptibility to targeted therapies such as NOTCH or FGFR inhibitors that could be incorporated into the IACC protocol, further extending its benefits.

Rational Linker Design to Accelerate Excretion and Reduce Background Uptake of Peptidomimetic PSMA-Targeting Hybrid Molecules.

The evolution of peptidomimetic hybrid molecules for preoperative imaging and guided surgery targeting the prostate-specific membrane antigen (PSMA) significantly progressed over the past few years, and some approaches are currently being evaluated for further clinical translation. However, accumulation in nonmalignant tissue such as kidney, bladder, spleen, or liver might limit tumor-to-background contrast for precise lesion delineation, particularly in a surgical setting. To overcome these limitations, a rational linker design aims at the development of a second generation of PSMA-11-based hybrid molecules with an enhanced pharmacokinetic profile and improved imaging contrast. Methods: A selection of rationally designed linkers was introduced to the PSMA-targeting hybrid molecule Glu-urea-Lys-HBED-CC-IRDye800CW, resulting in a second-generation peptidomimetic hybrid molecule library. The biologic properties were investigated in cell-based assays. In a preclinical proof-of-concept study with the radionuclide 68Ga, the impact of the modifications was evaluated by determination of specific tumor uptake, pharmacokinetics, and fluorescence imaging in tumor-bearing mice. Results: The modified hybrid molecules carrying various selected linkers revealed high PSMA-specific binding affinity and effective internalization. The highest tumor-to-background contrast of all modifications investigated was identified for the introduction of a histidine- (H) and glutamic acid (E)-containing linker ((HE)3-linker) between the PSMA-binding motif and the chelator. In comparison to the parental core structure, uptake in nonmalignant tissue was significantly reduced to a minimum, as exemplified by an 11-fold reduced spleen uptake from 38.12 ± 14.62 percentage injected dose (%ID)/g to 3.47 ± 1.39 %ID/g (1 h after injection). The specific tumor uptake of this compound (7.59 ± 0.95 %ID/g, 1 h after injection) was detected to be significantly higher than that of the parental tracer PSMA-11. These findings confirmed by PET and fluorescence imaging are accompanied by an enhanced pharmacokinetic profile with accelerated background clearance at early time points after injection. Conclusion: The novel generation of PSMA-targeting hybrid molecules reveals fast elimination, reduced background organ enrichment, and high PSMA-specific tumor uptake meeting the key demands for potent tracers in nuclear medicine and fluorescence-guided surgery. The approach's efficacy in improving the pharmacokinetic profile highlights the strengths of rational linker design as a powerful tool in strategic hybrid-molecule development.

Heterogeneous responses to price: Evidence from residential water consumers

Public utilities may respond to demand or supply fluctuations by adjusting prices to ration quantity. This approach's efficacy and distributional impacts depend on households' heterogeneous price sensitivity, which we estimate in a market for residential water usage. Our household-level panel data features a large change in marginal water prices and a novel measure of local hydrological stress. Contrary to prior research, we find that heavy-usage households are more price sensitive than other households, and price elasticity is largely invariant to household wealth. These findings suggest that price-based rationing can be an effective tool to reduce water usage.

Scalable Decision-Theoretic Planning in Open and Typed Multiagent Systems

In open agent systems, the set of agents that are cooperating or competing changes over time and in ways that are nontrivial to predict. For example, if collaborative robots were tasked with fighting wildfires, they may run out of suppressants and be temporarily unavailable to assist their peers. We consider the problem of planning in these contexts with the additional challenges that the agents are unable to communicate with each other and that there are many of them. Because an agent's optimal action depends on the actions of others, each agent must not only predict the actions of its peers, but, before that, reason whether they are even present to perform an action. Addressing openness thus requires agents to model each other's presence, which becomes computationally intractable with high numbers of agents. We present a novel, principled, and scalable method in this context that enables an agent to reason about others' presence in its shared environment and their actions. Our method extrapolates models of a few peers to the overall behavior of the many-agent system, and combines it with a generalization of Monte Carlo tree search to perform individual agent reasoning in many-agent open environments. Theoretical analyses establish the number of agents to model in order to achieve acceptable worst case bounds on extrapolation error, as well as regret bounds on the agent's utility from modeling only some neighbors. Simulations of multiagent wildfire suppression problems demonstrate our approach's efficacy compared with alternative baselines.

Anterolateral approach to the upper cervical spine: Case report and operative technique

Transcervical approaches to the upper cervical spine are challenging because several upper anterior neurovascular structures need to be displaced to provide access. Although various techniques have been described, the anterolateral approach is one of the safest and most effective methods available to access the anterior C2-C3 disc space. Despite the approach's efficacy, however, it can cause postoperative complications because of, at least partly, the inter-surgeon differences in the methods by which the larynx and hypopharynx are displaced medially. We present a case report of a patient treated with a modified anterolateral approach to C2-C3. The approach provided excellent visualization while protecting vital structures. The patient recovered without any postoperative dysphagia or other surgical complications. The anterolateral approach to C2-C3 described herein safely protects the contents of the submandibular triangle while providing a wide exposure for direct access to the C2-C3 disc space.

High-throughput transcriptome sequencing identifies candidate genetic modifiers of vulnerability to fetal alcohol spectrum disorders.

Fetal alcohol spectrum disorders (FASD) is a leading cause of neurodevelopmental disability. Genetic factors can modify vulnerability to FASD, but these elements are poorly characterized. We performed high-throughput transcriptional profiling to identify gene candidates that could potentially modify vulnerability to ethanol's (EtOH's) neurotoxicity. We interrogated a unique genetic resource, neuroprogenitor cells from 2 closely related Gallus gallus lines having well-characterized robust or attenuated EtOH responses with respect to intracellular calcium mobilization and CaMKII/β-catenin-dependent apoptosis. Samples were not exposed to EtOH prior to analysis. We identified 363 differentially expressed genes in neuroprogenitors from these 2 lines. Kyoto Encyclopedia of Genes and Genomes analysis revealed several gene clusters having significantly differential enrichment in gene expression. The largest and most significant cluster comprised ribosomal proteins (38 genes, p = 1.85 × 10(-47) ). Other significantly enriched gene clusters included metabolism (25 genes, p = 0.0098), oxidative phosphorylation (18 genes, p = 1.10 × 10(-11) ), spliceosome (13 genes, p = 7.02 × 10(-8) ), and protein processing in the endoplasmic reticulum (9 genes, p = 0.0011). Inspection of gene ontogeny (GO) terms identified 24 genes involved in the calcium/β-catenin signals that mediate EtOH's neurotoxicity in this model, including β-catenin itself and both calmodulin isoforms. Four of the identified pathways with altered transcript abundance mediate the flow of cellular information from RNA to protein. Importantly, ribosome biogenesis also senses nucleolar stress and regulates p53-mediated apoptosis in neural crest. Human ribosomopathies produce craniofacial malformations and 11 known ribosomopathy genes were differentially expressed in this model of neural crest apoptosis. Rapid changes in ribosome expression are consistently observed in EtOH-treated mouse embryo neural folds, a model that is developmentally similar to ours. The recurring identification of ribosome biogenesis suggests it is a candidate modifier of EtOH vulnerability. These results highlight this approach's efficacy to formulate new, mechanistic hypotheses regarding EtOH's developmental damage.

Structural modeling of Ge6.25As32.5Se61.25 using a combination of reverse Monte Carlo and Ab initio molecular dynamics.

Ternary glass structures are notoriously difficult to model accurately, and yet prevalent in several modern endeavors. Here, a novel combination of Reverse Monte Carlo (RMC) modeling and ab initio molecular dynamics (MD) is presented, rendering these complicated structures computationally tractable. A case study (Ge6.25As32.5Se61.25 glass) illustrates the effects of ab initio MD quench rates and equilibration temperatures, and the combined approach's efficacy over standard RMC or random insertion methods. Submelting point MD quenches achieve the most stable, realistic models, agreeing with both experimental and fully ab initio results. The simple approach of RMC followed by ab initio geometry optimization provides similar quality to the RMC-MD combination, for far fewer resources.

Kr-collision shift of the RbD1transition: The isoclinic point and precision optical spectroscopy

Measuring the energy dependence of optical-transition collision shifts has proven extremely difficult, in part because of Doppler broadening and the manifold of overlapping hyperfine components that must be disentangled in the spectra. Here, we demonstrate an approach to these measurements based on spectroscopic isoclinic points. To illustrate the approach's efficacy, we investigated the Kr collision shift of the Rb ${D}_{1}$ transition at 795 nm. For the expected Rb-Kr van der Waals interaction, the collision shift should scale like ${(T/{T}_{o})}^{\ensuremath{\kappa}}$, where ${T}_{\mathrm{o}}$ is a reference temperature and \ensuremath{\kappa}${}_{\mathrm{theo}}$ = 0.31. Exemplifying the difficulty of \ensuremath{\kappa} determinations, previous alkali-metal--noble-gas experimental measurements of \ensuremath{\kappa} have varied widely, sometimes in striking disagreement with theory (i.e., factor of 2 larger). In the present work, we not only demonstrate a measurement precision better than 10${}^{\ensuremath{-}10}$/\ifmmode^\circ\else\textdegree\fi{}C, but with our technique we validate the theoretical scaling constant, finding ${\ensuremath{\kappa}}_{\mathrm{expt}}$ = 0.36 \ifmmode\pm\else\textpm\fi{} 0.06.

RNAi Therapeutics: The Teenage Years

RNAi Therapeutics: The Teenage Years

Bäcklund transformations for a matrix second Painlevé equation

Higher-order Painlevé equations are a topic of much current interest. Here we are interested in deriving auto-Bäcklund transformations for one particular kind of higher-order Painlevé equation, namely, a matrix Painlevé equation. The extension of a recently derived approach to deal with the matrix second Painlevé equation considered here represents a further demonstration of that approach's efficacy.

Topic taxonomy adaptation for group profiling

A topic taxonomy is an effective representation that describes salient features of virtual groups or online communities. A topic taxonomy consists of topic nodes. Each internal node is defined by its vertical path (i.e., ancestor and child nodes) and its horizonal list of attributes (or terms). In a text-dominant environment, a topic taxonomy can be used to flexibly describe a group's interests with varying granularity. However, the stagnant nature of a taxonomy may fail to timely capture the dynamic change of a group's interest. This article addresses the problem of how to adapt a topic taxonomy to the accumulated data that reflects the change of a group's interest to achieve dynamic group profiling. We first discuss the issues related to topic taxonomy. We next formulate taxonomy adaptation as an optimization problem to find the taxonomy that best fits the data. We then present a viable algorithm that can efficiently accomplish taxonomy adaptation. We conduct extensive experiments to evaluate our approach's efficacy for group profiling, compare the approach with some alternatives, and study its performance for dynamic group profiling. While pointing out various applications of taxonomy adaption, we suggest some future work that can take advantage of burgeoning Web 2.0 services for online targeted marketing, counterterrorism in connecting dots, and community tracking.

ONTRACK: Dynamically adapting music playback to support navigation

Listening to music on personal, digital devices whilst mobile is an enjoyable, everyday activity. We explore a scheme for exploiting this practice to immerse listeners in navigation cues. Our prototype, ONTRACK, continuously adapts audio, modifying the spatial balance and volume to lead listeners to their target destination. First we report on an initial lab-based evaluation that demonstrated the approach's efficacy: users were able to complete tasks within a reasonable time and their subjective feedback was positive. Encouraged by these results we constructed a handheld prototype. Here, we discuss this implementation and the results of field-trials. These indicate that even with a low-fidelity realisation of the concept, users can quite effectively navigate complicated routes.

Complementary identification of multiple flux distributions and multiple metabolic pathways

Cell robustness and complexity have been recognized as unique features of biological systems. Such robustness and complexity of metabolic-reaction systems can be explored by discovering, or identifying, the multiple flux distributions (MFD) and redundant pathways that lead to a given external state; however, this is exceedingly cumbersome to accomplish. It is, therefore, highly desirable to establish an effective computational method for their identification, which, in turn, gives rise to a novel insight into the cellular function. An effective approach is proposed for complementarily identifying MFD in metabolic flux analysis and multiple metabolic pathways (MMP) in structural pathway analysis. This approach judiciously integrates flux balance analysis (FBA) based on linear programming and the graph-theoretic method for determining reaction pathways. A single metabolic pathway, with the concomitant flux distribution and the overall reaction manifesting itself as the desired phenotype under some environmental conditions, is determined by FBA from the initial candidate sequence of metabolic reactions. Subsequently, the graph-theoretic method recovers all feasible MMP and the corresponding MFD. The approach's efficacy is demonstrated by applying it to the in silico Escherichia coli model under various culture conditions. The resultant MMP and MFD attaining a unique external state reveal the surprising adaptability and robustness of the intricate cellular network as a key to cell survival against environmental or genetic changes. These results indicate that the proposed approach would be useful in facilitating drug discovery.

Using dynamic programming for solving variational problems in vision

Dynamic programming is discussed as an approach to solving variational problems in vision. Dynamic programming ensures global optimality of the solution, is numerically stable, and allows for hard constraints to be enforced on the behavior of the solution within a natural and straightforward structure. As a specific example of the approach's efficacy, applying dynamic programming to the energy-minimizing active contours is described. The optimization problem is set up as a discrete multistage decision process and is solved by a time-delayed discrete dynamic programming algorithm. A parallel procedure for decreasing computational costs is discussed. >