Critical Input Research Articles

Novel feature selection based ANN for optimal solar panels tilt angles prediction in micro grid

Predicting PV system electricity output is necessary for daily operational management and annual power system planning when integrating solar collector-based photovoltaic (PV) stations into micro grids. Tilting the panels at the ideal angle to maximize solar energy capture is necessary to maximize PV station production. This optimal tilt angle (OTA) must be predicted as it is a nonlinear function of the total solar radiation, diffuse solar radiation, and direct solar radiation. This research explores the use of feature selection-based artificial neural networks (ANN) with various machine learning algorithms to predict the OTA for PV systems at specific locations, aiming to maximize PV output in micro grids. The study identifies global solar radiation, diffuse solar radiation, clarity index, and global solar radiation on inclined surfaces as the most critical inputs for predicting OTA, while extraterrestrial radiation is deemed the least significant. Implementing the appropriate input variables significantly enhanced prediction accuracy from 38.59 % to 90.72 %. Among the neural networks evaluated, the Elman neural network demonstrated the greatest improvement.

Linking global terrestrial and ocean biogeochemistry with process-based, coupled freshwater algae–nutrient–solid dynamics in LM3-FANSY v1.0

Abstract. Estimating global river solids, nitrogen (N), and phosphorus (P), in both quantity and composition, is necessary for understanding the development and persistence of many harmful algal blooms, hypoxic events, and other water quality issues in inland and coastal waters. This requires a comprehensive freshwater model that can resolve intertwined algae, solid, and nutrient dynamics, yet previous global watershed models have limited mechanistic resolution of instream biogeochemical processes. Here we develop the global, spatially explicit, and process-based Freshwater Algae, Nutrient, and Solid cycling and Yields (FANSY) model and incorporate it within the Land Model (LM3). The resulting model, LM3-FANSY v1.0, is intended as a baseline for eventual linking of global terrestrial and ocean biogeochemistry in next-generation Earth system models to project global changes that may challenge empirical approaches. LM3-FANSY explicitly resolves interactions between algae, N, P, and solid dynamics in rivers and lakes at 1° spatial and 30 min temporal resolution. Simulated suspended solids (SS), N, and P in multiple forms (particulate or dissolved, organic or inorganic) agree well with measurement-based yield (kg km−2 yr−1), load (kt yr−1), and concentration (mg L−1) estimates across a globally distributed set of large rivers, with an accuracy comparable to other global nutrient and SS models. Furthermore, simulated global river loads of SS, N, and P in different forms to the coastal ocean are consistent with published ranges, though regional biases are apparent. River N loads are estimated to contain approximately equal contributions by dissolved inorganic N (41 %) and dissolved organic N (39 %), with a lesser contribution by particulate organic N (20 %). For river P load estimates, particulate P, which includes both organic and sorbed inorganic forms, is the most abundant form (64 %), followed by dissolved inorganic and organic P (25 % and 11 %). Time series analysis of river solid and nutrient loads in large US rivers for the period ∼ 1963–2000 demonstrates that simulated SS and N loads in different N forms covary with variations of measurement-based loads. LM3-FANSY, however, has less capability to capture interannual variability of P loads, likely due to the lack of terrestrial P dynamics in LM3. Analyses of the model results and sensitivity to components, parameters, and inputs suggest that fluxes from terrestrial litter and soils, wastewater, and weathering are the most critical inputs to the fidelity of simulated river nutrient loads for observation-based estimates. Sensitivity analyses further demonstrate a critical role of algal dynamics in controlling the ratios of inorganic and organic nutrient forms in freshwaters. While the simulations are able to capture significant cross-watershed contrasts at a global scale, disagreement for individual rivers can be substantial. This limitation is shared by other global river models and could be ameliorated through further refinements in nutrient sources, freshwater model dynamics, and observations. Current targets for future LM3-FANSY development include the additions of terrestrial P dynamics, freshwater carbon, alkalinity, enhanced sediment dynamics, and anthropogenic hydraulic controls.

How to Crystallize Glass with a Femtosecond Laser

The crystallization of glass through conventional thermal annealing in a furnace is a well-understood process. However, crystallization by femtosecond (fs) laser brings another dimension to this process. The pulsed nature of the irradiation necessitates a reevaluation of the parameters for optimal crystallization and an understanding of the particularities of using fs laser. This includes adjusting the laser pulse energy, the repetition rate, and the writing speed to either initiate nucleation or achieve substantial crystal growth. Therefore, a key challenge of this work is to establish reliable calculations for understanding the link between the size of the crystallized region and an ongoing transition (e.g., solid-to-solid, liquid-to-solid), while accounting for the aforementioned laser parameters. In this context, and based on previous work, a temperature distribution (in space and time) is simulated to model the thermal treatment at any point in the glass. By setting the condition that the temperatures are between the glass transition and melting temperature, the simulated crystallized region size can be compared with experimental observations. For that purpose, knowledge of the beam width at the focal point and of the absorbed beam energy fraction are critical inputs that were extracted from experiments found in the literature. After that, the management of the crystallization process and the width of the crystallization line can be achieved according to pulse energy, e.g., crystallite size, and also the effect of the scanning speed can be understood. Among the main conclusions to highlight, we disclose the laser conditions that determine the extent of the crystallized area and deduce that it is never of interest to increase the pulse energy too much as opposed to the repetition rate for the uniform crystallized line.

Investigating Laser Ablation Process Parameters for the Fabrication of Customized Microneedle Arrays for Therapeutic Applications.

Microneedles are an innovation in the field of medicine that have the potential to revolutionize drug delivery, diagnostics, and cosmetic treatments. This innovation provides a minimally invasive means to deliver drugs, vaccines, and other therapeutic substances into the skin. This research investigates the design and manufacture of customized microneedle arrays using laser ablation. Laser ablation was performed using an ytterbium laser on a polymethyl methacrylate (PMMA) substrate to create a mold for casting polydimethylsiloxane (PDMS) microneedles. An experimental design was conducted to evaluate the effect of process parameters including laser pulse power, pulse width, pulse repetition, interval between pulses, and laser profile on the desired geometry of the microneedles. The analysis of variance (ANOVA) model showed that lasing interval, laser power, and pulse width had the highest influence on the output metrics (diameter and height) of the microneedle. The microneedle dimensions showed an increase with higher pulse width and vice versa with an increase in pulse interval. A response surface model indicated that the laser pulse width and interval (independent variables) significantly affect the response diameter and height (dependent variable). A predictive model was generated to predict the microneedle topology and aspect ratio varying from 0.8 to 1.5 based on the variation in critical input process parameters. This research lays the foundation for the design and fabrication of customized microneedles based on variations in specific input parameters for therapeutic applications in dermal sensors, drug delivery, and vaccine delivery.

Reevaluating the linkage between trade openness and economic growth in Nigeria

This study reevaluated the linkage between trade openness and economic growth in Nigeria with data for the period of 1981 to 2022. The study employed Descriptive Statistics, Unit Root Test, Cointegration analysis, Regression Analysis (ARDL), Error Correction Model as the methods of analysis. The findings from the study showed that trade openness and external reserve impact positively and significantly on economic growth while exchange rate was found to be negative and significant. Also, findings from the error correction model showed that trade openness has a positive and insignificant impact on economic growth, external balance also has a positive significant impact on economic growth. Exchange rate on the other hand displays a negative insignificant impact on economic growth. The error correction term indicated that about 79 percent deviation in the previous year is corrected in the current year. The study therefore concluded that trade openness is a fundamental for economic growth in Nigeria and that external reserve crucial to funding of critical import necessary for domestic production. Furthermore, exchange rate of the naira showed that the economy will benefit more if the appropriate rate is prevalent in the market as this will make export to be attractive. A long run relationship was found among the variables used in the study. Based on the findings from the study we recommended that the government should engage more in international trade especially for key production inputs to boost domestic production. The government should also ensure that they maintain a healthy external reserve and ensure it is channel into the import of critical inputs. The true value of the exchange rate of the naira in relation to major currencies of the world should be targeted in order to make our exports attractive.

DAIRY FARMERS' VIEWS ON OVERCOMING BARRIERS TO ADOPTING CLIMATE CHANGE ADAPTATION STRATEGIES

This study investigates the challenges inhibiting dairy farmers in South Gujarat from adopting climate change adaptation strategies and presents their perspectives on overcoming these constraints. Data were collected from 280 dairy farmers who were selected through multistage random sampling method in seven districts of South Gujarat. Survey findings revealed predominant issues: Communication participation, social engagement, and key farm-related factors significantly correlated with dairy farmers' adaptation strategies, while certain demographic and economic factors showed significant relationships at different probability levels. Non-significant correlations were found for age, gender, education, and other variables, suggesting nuanced influences that may require further exploration.86.77% cited inadequate technical guidance for livestock practices as the primary barrier, followed by 75.00% experiencing the unavailability of critical adaptation inputs. Financial constraints (67.85%) and limited access to timely weather forecasts (63.57%) were also significant hurdles. Reasons for non-adoption varied with 79.28% attributing it to insufficient resources and 72.50% believing climate change was exaggerated. Conversely, 51.78% sought innovative dairy farming to engage rural youth. The suggestions provided by farmers highlighted urgent needs: establishing low-cost weather forecasting systems (87.85%) and regular technical guidance visits (82.14%). Financial aid (72.50%) and awareness about livestock insurance schemes (60.71%) were stressed, alongside training in climate mitigation practices (59.28%).These findings underscore the multifaceted challenges faced by dairy farmers and offer a clear direction for policymakers and stakeholders. Implementing the suggested solutions could empower the farming community in adopting resilient climate adaptation strategies in South Gujarat reason.

INFORM-AF II-Phase 1 pilot randomised controlled trial evaluating the effect of an atrial fibrillation digital education programme: study protocol.

To evaluate the effect of a novel, co-designed, digital AF educational program, 'INFORM-AF', to reduce re-hospitalisation of people with AF. The secondary aims are to examine the effect of the intervention on: (a) reducing cardiovascular-related hospitalisation, (b) increasing medication adherence, AF-related knowledge, and Atrial fibrillation (AF)-related quality of life and (c) determining the cost-effectiveness of the intervention. AF is an increasingly prevalent cardiac arrythmia that involves complex clinical management. Comprehensive education is essential for successful self-management of AF and is associated with positive health-related outcomes. There has been an increase in technology-based education for AF. However, its effects on hospitalisation, medication adherence and patient-reported outcomes are unclear. A prospective, randomised (1:1), open-label, blinded-endpoint, multicentre clinical trial. Eligible participants are aged 18 years or above, diagnosed with AF, and own a smartphone. The study will be conducted at two metropolitan hospitals. In the intervention group, participants will receive the AF educational program delivered via Qstream®. In the control group, participants will receive the Stroke Foundation 'Living with AF' booklet. The primary outcome is re-hospitalisation within 12 months from an indexed presentation or hospital admission. This clinical trial is part of a developing program of work that will examine mHealth educational-behavioural interventions on cardiovascular outcomes. Findings from this pilot study will inform the development of a digital educational framework for patients living with AF. There remain many gaps in providing high-quality patient education for patients with AF. This trial will test a new theory-driven, smartphone-based education program on important clinical outcomes, including rehospitalisation. This study evaluates a novel, co-designed, digital AF educational program, 'INFORM-AF', to reduce the re-hospitalisation of people with AF. Study results are expected to be reported in 2025. Findings are expected to inform practice recommendations for AF patient education that may be included in future clinical practice guideline recommendations. SPIRIT Checklist. JL is a consumer co-researcher on the project and provided critical input into intervention design, and feedback and input across the study duration.

Impact of COVID-19 on market efficiency

PurposeCOVID-19 and its accompanying lockdowns were arguably the most traumatic events of our times. This paper investigates the impact of COVID-19 on market efficiency.Design/methodology/approachI analyze all publicly traded U.S. equities for 2014–September 2021, using intraday data from TAQ, TRACE, I/B/E/S and Capital IQ and daily data from CRSP, Thomson Reuters, Compustat, CRSP-Compustat Merged Database and FRED, using a controlled contrast between absolute abnormal returns for relevant halfhours versus absolute abnormal returns in control halfhours, measured by the negative of the coefficient of the fixed effect of the interaction between the indicator variable, and as the case may be, ticker and/or time period of interest, in the regression of halfhour-level absolute abnormal returns on tickers, months and interactions.FindingsUsing two separate objective, systematic, independent and ordinal per se measures of market efficiency based upon market reactions separately to key developments and earnings announcements, I find that U.S. equities markets were statistically and economically significantly less efficient during the first two-three months of the COVID-19 lockdowns.Practical implicationsEfficient capital markets provide substantial social benefits and are a sine qua non for the democratization of markets and the protection of investors, and constitute a critical mission of regulatory bodies such as the U.S. Securities and Exchange Commission (SEC) and the U.S. Financial Industry Regulatory Authority (FINRA).Social implicationsThe impact on market efficiency provides one critical input into the social cost-benefit analysis of public health policy and that of government interventions in general.Originality/valueThere has been no previous work done on the systematic and objective characterization of the impact of COVID-19 and associated lockdowns on market efficiency.

Numerical investigation of the behavior of combination connections with pretensioned high-strength bolts and longitudinal fillet welds

Numerical analysis is conducted to investigate the behavior of double shear combination connections made with pretensioned high-strength bolts and longitudinal fillet welds. The finite element (FE) models were validated using the results of an experimental investigation and utilized to develop a better understanding of the influence of critical input parameters on the connection behavior. Slip-dependent surface frictional and weld ductile fracture models were incorporated into the numerical models to simulate the nonlinear behavior of the connections. The numerical simulations confirm that at low slip displacements, the force carried by the combination connection can be estimated as the summation of the friction resistance introduced by the pretensioned bolts and the shear force carried by the fillet welds. The results also indicate that the behavior and capacity of the combination connection are not sensitive with respect to the weld location compared to the center of gravity of the bolts as long as no eccentricity is introduced within the force transfer mechanism. The paper also investigates the stress distribution within the connection plates and studies the effect of fillet weld size on the behavior of combination connections.

Extended HYDRUS-1D freezing module emphasizes thermal conductivity schemes for simulation of soil hydrothermal dynamics

Soil thermal conductivity (λ) is required to investigate coupled heat and water transport in disciplines such as agriculture, hydrology and engineering. Parameterization schemes or models of λ are also the critical input parameter for various numerical simulation programs like the widely used HYDRUS, one of the most commonly used models for mimicking water, heat, and solute transport. However, λ has not received enough attention in HYDRUS, and it remains unclear how different λ schemes affect the simulated soil water and thermal regimes. Thus, we programmed 24 λ schemes (including two built-in schemes) used in mainstream land surface, hydrological, and soil–vegetation–atmosphere transfer models into HYDRUS-1D (freezing module) to assess the effects of different λ schemes on soil temperature and water content simulations under freezing–thawing. The results showed that the 24 λ schemes performed differently in the simulation of soil temperature within 1 m below ground, with eight λ schemes, i.e., de Vries 1963 scheme/DV1963 (R=0.99), Camillo and Schmugge 1981/CS1981 scheme (R=0.98), Desborough and Pitman 1998/DP1998 scheme (R=0.97), Cass et al. 1984/CS1984 scheme (R=0.96), Shmakin 1998/SA1998 scheme (R=0.95), Dharssi et al. 2009/DI2009 scheme (R=0.95), Becker et al. 1992/BB1992 scheme (R=0.95), Hubrechts 1998/HL1998 scheme (R=0.94), performing superiorly to the built-in λ schemes. This study highlights the importance of choosing appropriate λ schemes in soil water and heat simulations.

An ultrasound-assisted resin transfer molding to improve the impregnation and dual-scale flow for carbon fiber reinforced resin composites

The imbalance of the dual-scale flow, i.e., microscopic intra-bundles and macroscopic inter-bundles flow, always generates voids in resin transfer molding (RTM) of composites, substantially weakening the mechanical performance. An ultrasound-assisted strategy to improve the impregnation and to balance dual-scale flow is promising to suppress the voids, while, it has still not been explored. Here, an ultrasound-assisted RTM for carbon fiber reinforced resin composites, realized by a self-developed device, is originally proposed. The influences of ultrasound vibration on the carbon fibers, resins, wettability, and impregnation process are systematically explored. Furthermore, the dual-scale flow, affected by the ultrasound vibration, is optimized. It is identified that the ultrasound vibration effectively reduces the viscosity of the resin and increases the roughness and surface activity of the fibers, hence significantly weakening the delayed impregnation in the intra-bundles, and speeding up the impregnation velocity in the inter-bundles. The reduction of the viscosity and the contact angle synergistically lowers the modified capillary number Ca*, hence balancing the dual-scale flow, while the excessively high impregnation velocity in the inter-bundles detrimentally enlarges Ca*, leading to the imbalance of the dual-scale flow. Accordingly, a critical ultrasound energy input (4 min of ultrasound vibration at the power range of 400–600 W) is figured out to achieve an optimal Ca*, providing a promising approach to suppress the flow-induced voids in composites manufacturing by RTM.

Are current shipbuilding cost estimation methods ready for a sustainable future? A literature review of cost estimation methods and challenges

In the fiercely competitive shipbuilding industry, precise cost estimates must be considered as they serve as a critical input for determining market prices effectively and ensuring a small profit for the shipyard. In Western Europe, where most projects are Engineering-To-Order (ETO), cost estimations are extra challenging, due to lack of similarity between projects. On top of this, cost estimations are becoming increasingly difficult in a market confronted by mounting challenges related to safety regulations, cost-effectiveness, and the pressing need to address energy conservation and environmental protection. New technologies introduce changes in nearly all aspects of shipbuilding design and construction. This article conducts a literature review, to present the state-of-the-art methods for estimating man-hours, explicitly focusing on man-hours for shipyard production, excluding overhead costs and challenges the suitability of existing systems for ETO and especially for the changes caused to ships by the energy transition. An indication is given of the practicality of each method as outlined in the literature. A solution direction, incorporating the construction process, is proposed to improve cost estimations for ETO projects in pre-contract phase.

Exploring temporal dependencies among country-level logistics performance indicators

PurposeThe Logistics Performance Index (LPI), published by the World Bank, is a key measure of national-level logistics performance. It comprises six indicators: customs, infrastructure, international shipments, service quality, timeliness, and tracking and tracing. The objective of this study is to explore temporal dependencies among the six LPI indicators while operationalizing the World Bank’s LPI framework in terms of mapping the input indicators (customs, infrastructure, and service quality) to the outcome indicators (international shipments representing cost, timeliness, and tracking and tracing representing reliability).Design/methodology/approachA Bayesian Belief Network (BBN)-based methodology was adopted to effectively map temporal dependencies among variables in a probabilistic network setting. Using forward and backward propagation features of BBN inferencing, critical variables were also identified. A BBN model was developed using the World Bank’s LPI datasets for 2010, 2012, 2014, 2016, 2018, and 2023, covering the six LPI indicators for 118 countries.FindingsThe prediction accuracy of the model is 88.1%. Strong dependencies are found across the six LPI indicators over time. The forward propagation analysis of the model reveals that “logistics competence and quality” is the most critical input indicator that can influence all three outcome indicators over time. The backward propagation analysis indicates that “customs” is the most critical indicator for improving the performance on the “international shipments” indicator, whereas “logistics competence and quality” can significantly improve the performance on the “timeliness” and “tracking and tracing” indicators. The sensitivity analysis of the model reveals that “logistics competence and quality” and “infrastructure” are the key indicators that can influence the results across the three outcome indicators. These findings provide useful insights to researchers regarding the importance of exploring the temporal modeling of dependencies among the LPI indicators. Moreover, policymakers can use these findings to help their countries target specific input indicators to improve country-level logistics performance.Originality/valueThis paper contributes to the literature on logistics management by exploring the temporal dependencies among the six LPI indicators for 118 countries over the last 14 years. Moreover, this paper proposes and operationalizes a data-driven BBN modeling approach in this unique context.

Modeling imaged welding process dynamic behaviors using Generative Adversarial Network (GAN) for a new foundation to monitor weld penetration using deep learning

This paper aims to model the dynamic behaviors of welding process that can be observed by humans and monitored by imaging sensors. This is fundamental in deciding if such dynamic behaviors can be used as raw data, that contains sufficient relevant information, to monitor what occurs underneath the workpiece determining the weld integrity. Challenges arise as featurization of these observations proves difficult, and the governing laws for the underlying process remain largely unknown. Leveraging the ability of Generative Adversarial Networks (GANs) to generate intricate phenomena from simple inputs by automatically approximating unknown underlying laws, we propose using a GAN to predict the dynamic behaviors of welding processes. In this framework, dynamic behaviors serve as the output, while the trained GAN functions as the model for the underlying process, generating the observed behaviors from the inputs. The success of predictions depends not only on the GAN's capability to approximate the process but also on the sufficiency of the inputs provided. With this consideration, we model an exceptionally complex process that correlates weld joint penetration with observed phenomena to determine if the underlying process involves critical inputs beyond penetration alone. Our findings reveal the necessity of welding currents for accurate predictions by the GAN model. This implies that the combined information from human-observed/imaged phenomena and welding currents provides a more comprehensive basis for monitoring weld penetration, contributing to the advancement of robotizing welding processes with the necessary intelligence.

Differential terrace configurations in the Upper Yangtze River: Evaluating distinct intensifies of external perturbation and their impact on river behaviors

The interplay of tectonics, climate and topography shapes the river dynamics that give rise to terrace formation in mountainous regions. Unraveling the distinct influences of these factors on fluvial processes is a complex challenge due to their intricate interactions. By combining field observations with optically stimulated luminescence (OSL) dating, we have discerned and examined the sedimentary and geomorphological attributes of terraces, as well as the distinctive event sediments linked to recurrent mass movement across four varied reaches of the Upper Yangtze River. Each reach exhibits distinct tectonic settings, such as subsidence depressions, uplift gorges, or relict planation topography, which have imparted varied responses of river processes since the penultimate glacial period. Evidence from all study reaches indicates the presence of two cycles of fluvial aggradation-incision, yet the differentiated tectonic activities, precipitation patterns, and topographic reliefs have elicited divergent river behaviors across different reaches. In the subsidence depression, a single set of terraces has been formed, whereas in the most upstream region, characterized by relict planation topography, two superimposed sets of terraces have developed. In contrast, two separate sets of terraces have emerged in the uplifted gorge since the post-penultimate glacial period. The striking differences in terrace configurations across the valley reaches suggest that the magnitude of environment perturbations has exceeded critical thresholds of the different equilibrium conditions. One transition in equilibrium conditions relates to the minimum riverbed sediment thickness required to supply abrasive agents necessary for riverbed armoring. Another transition triggers a shift from fluvial-dominated processes to those dominated by extreme events, with critical sediment inputs modulating this transition, particularly within the gorge setting.

A Multiplex Rupture Sequence Under Complex Fault Network Due To Preceding Earthquake Swarms During the 2024 Mw 7.5 Noto Peninsula, Japan, Earthquake

AbstractA devastating earthquake with moment magnitude 7.5 occurred in the Noto Peninsula in central Japan on 1 January 2024. We estimate the rupture evolution of this earthquake from teleseismic P‐wave data using the potency‐density tensor inversion method, which provides information on the spatiotemporal slip distribution including fault orientations. The results show a long and quiet initial rupture phase that overlaps with regions of preceding earthquake swarms and associated aseismic deformation. The following three major rupture episodes evolve on segmented, differently oriented faults bounded by the initial rupture region. The irregular initial rupture process followed by the multi‐scale rupture growth is considered to be controlled by the preceding seismic and aseismic processes and the geometric complexity of the fault system. Such a discrete rupture scenario, including the triggering of an isolated fault rupture, adds critical inputs on the assessment of strong ground motion and associated damages for future earthquakes.

Comprehensive Multidisciplinary Electric Vehicle Modeling: Investigating the Effect of Vehicle Design on Energy Consumption and Efficiency

In this study, an electric vehicle (EV) dynamic model is devised that amalgamates mechanical design aspects—such as aerodynamic effects, tire friction, and vehicle frontal area—with crucial components of the electrical infrastructure, including the electric motor, power converters, and battery systems. Verification of the model is executed through a comprehensive multidisciplinary analysis utilizing CATIA, ANSYS Electromagnetics, ANSYS Fluent, and MATLAB–Simulink tools, which are applied to evaluate two alternative lightweight EV prototypes. The process involves initial computations of critical inputs for the dynamic model, including aerodynamic lift (C1), drag coefficients (Cd), and frontal area (Af). Subsequent stages entail the detailed design and analysis of a 2 kW brushless permanent magnet electric motor in ANSYS Electromagnetics to map efficiency contours across various speed–torque values. Integration of these parameters into a MATLAB–Simulink dynamic model, connected with motor drive inverter and battery models, allows for simulation-based energy consumption analysis under race track slope profiles. Remarkably, the findings underscore the considerable impact of neglected parameters on energy consumption, often exceeding fifty percent of the total. Consequently, an energy-efficient EV prototype is manufactured and rigorously tested under specified drive conditions, affirming the validation of the comprehensive multidisciplinary EV dynamic model.

Innovation Strategy After IPO: How AI Analytics Spurs Innovation After IPO

We examine the role of AI analytics in facilitating innovation in firms that have gone through IPO. Using patent data on over 1,000 publicly traded firms, we find that firms acquiring AI analytics capability post-IPO experience less of a decline in innovation quality compared with similar firms that have not acquired that capability. This effect is greater when only machine learning capabilities are considered. Moreover, we find this sustained rate of innovation is driven principally by the continued development of innovations that combine existing technologies into new ones—a form of innovation that is especially well supported by analytics. By examining three main mechanisms that hampered post-IPO innovation, we find that AI analytics can ameliorate the pressure to meet short-term financial goals and disclosure requirements. However, it has limited effect in addressing managerial incentives. For firms with long product cycles, the disclosure effect is reduced to a greater extent than it is for those with short cycles. Overall, our results show the importance of examining technology as a critical input factor in innovation. We show that the increased deployment of AI analytics may reduce some of the innovative penalties suffered by IPOs and that investors and managers can potentially mitigate post-IPO reductions in innovative output by directing capital acquired in the IPO process to the acquisition of AI analytics capabilities. This paper was accepted by D. J. Wu, information systems. Funding: The authors appreciate the generous financial support from Wharton Dean’s Research Fund and Mack Institute for Innovation Management. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2022.01559 .

Test Input Prioritization for 3D Point Clouds

3D point cloud applications have become increasingly prevalent in diverse domains, showcasing their efficacy in various software systems. However, testing such applications presents unique challenges due to the high-dimensional nature of 3D point cloud data and the vast number of possible test cases. Test input prioritization has emerged as a promising approach to enhance testing efficiency by prioritizing potentially misclassified test cases during the early stages of the testing process. Consequently, this enables the early labeling of critical inputs, leading to a reduction in the overall labeling cost. However, applying existing prioritization methods to 3D point cloud data is constrained by several factors: (1) inadequate consideration of crucial spatial information, and (2) susceptibility to noises inherent in 3D point cloud data. In this article, we propose PCPrior , the first test prioritization approach specifically designed for 3D point cloud test cases. The fundamental concept behind PCPrior is that test inputs closer to the decision boundary of the model are more likely to be predicted incorrectly. To capture the spatial relationship between a point cloud test and the decision boundary, we propose transforming each test (a point cloud) into a low-dimensional feature vector, toward indirectly revealing the underlying proximity between a test and the decision boundary. To achieve this, we carefully design a group of feature generation strategies, and for each test input, we generate four distinct types of features, namely spatial features, mutation features, prediction features, and uncertainty features. Through a concatenation of the four feature types, PCPrior assembles a final feature vector for each test. Subsequently, a ranking model is employed to estimate the probability of misclassification for each test based on its feature vector. Finally, PCPrior ranks all tests based on their misclassification probabilities. We conducted an extensive study based on 165 subjects to evaluate the performance of PCPrior, encompassing both natural and noisy datasets. The results demonstrate that PCPrior outperforms all of the compared test prioritization approaches, with an average improvement of 10.99% to 66.94% on natural datasets and 16.62% to 53% on noisy datasets.

Income Generation Opportunities through Secondary Agriculture towards Sustainability in Angul District of Odisha: A Case Study

Aims: The aim of the study was for attempting various Income Generation interventions to address poverty, unemployment and lack of economic opportunities which could increase participants’ ability towards secured livelihoods. Secondary agriculture is essentially a way of business development that aids in providing farming households in rural areas with income and employment. Place and Duration of the Study: The study was conducted in Krishi Vigyan Kendra Angul, Odisha between 2016-17 to 2021-22. Methodology: PRA survey of the village followed by group discussions and other approaches were conducted to identify the constraints and accordingly the process planning was performed for strengthening their livelihood. By the technological interventions from Krishi Vigyan Kendra, Angul the famers took trainings for grasping ideas & scientific knowledge about the enterprises. Periodically some critical inputs under various programme were also provided from the KVK. Results: As a result, year after year the farmers practicing bee-keeping could able to earn in an average of Rs. 5,14,100/- from 180 bee boxes by selling pure honey & bee-colonies. Besides this, another group of farmers with improved colour poultry breeds under backyard condition with an average yield of 1.62 kg could earn an average net return of Rs. 14,742/-. Conclusion: Given that the world's population is quickly growing, it is imperative for global agriculture to turn to secondary agriculture (honey bee and poultry farming) in order to provide opportunities not only for improving livelihood but also for nutritional security.