Restrictive Constraints Research Articles

Sustainable corporate entrepreneurship as a catalyst for sustainability – a systematic literature review

Sustainability has emerged as a critical priority for both business strategy and academic research, driving a shift beyond traditional responsibility frameworks toward embedding sustainability within entrepreneurial paradigms. While start-ups and social entrepreneurs have effectively integrated sustainability into their business models, large corporation face significant challanges in balancing financial goals with the growing demand for sustainability. This study considers the potential of corporate entrepreneurship to foster sustainability within established companies. Sustainable corporate entrepreneurship (SCE) allows companies to leverage innovation for sustainability as a value-creating opportunity rather than a restrictive constraint. This study addresses a critical gap in the literature – the lack of a comprehensive framework for SCE – through a meticulous, systematic literature review of 95 recent publications. Our proposed SCE framework identifies three key dimensions: focus, approach and evaluation. We provide insights to deepen understanding and guide future research in this evolving field. Specifically, we introduce a model that conceptualizes how SCE can be developed as dynamic capability for generating value through sustainability-focused innovation. This model speficies capabilities on an individual and organizational level, as well as contextual enablers and hurdles.

A cognitive approach to better understand foraging strategies of the adult domestic hen

Foraging is known to be one of the most important activities in the behavioral budget of chickens. However, how these animals adapt different foraging strategies to diverse environmental variations is currently poorly understood. To gain further insight into this matter, in the present study, hens were submitted to the sloped-tubes task. In this task, the experimenter can manipulate the information that enables the hens to find a food reward (visible or not), placed in one of two hollow tubes. First, 12 hens were tested under free-choice conditions (no penalty for exhaustive searching in both tubes). Under these conditions, the hens adopted a non-random, side-biased strategy when the food location was not directly visible. Then, we divided the hens in two cohorts of equal size to study deeper the hens’ foraging strategy when faced (1) with a different container, or (2) with a restrictive environmental constraint under forced-choice conditions (no food reward if the unbaited tube is visited first). This latter constraint increased the risk of the hen not receiving food. A change in the containers didn’t modify the search behavior of the hens. However, in forced-choice conditions when the location of the food was not directly visible, four out of six hens learned to choose by exclusion. We conclude that hens can selectively adapt their foraging strategy to the point of adopting an exclusion performance, depending on available information and environmental constraints (high or low risk).

Trajectory tracking control based on genetic algorithm and proportional integral derivative controller for two-wheel mobile robot

This paper uses the genetic algorithm (GA) to optimize the proportional integral derivative (PID) controller parameters to present the motion control design for a two-wheeled mobile robot autonomous system. The GA algorithm determines a collision-free travel curve for a robot with a tangential velocity restriction constraint. A trajectory-tracking controller based on the PID control structure is developed to monitor the calculated route curves for the mobile robot. Simulation results show the effectiveness of the GA-PID controller compared to the PID controller. The GA-PID controller demonstrates improved performance in trajectory tracking and collision avoidance, making it suitable for controlling the motion of two-wheeled mobile robots. The GA's optimization process allows for better tuning of the PID controller parameters, resulting in more efficient and accurate robot motion control. The results suggest that the proposed GA-PID controller is a promising approach for enhancing mobile robots' autonomous navigation capabilities.

Identifying stationary microbial interaction networks based on irregularly spaced longitudinal 16S rRNA gene sequencing data

IntroductionThe microbial interactions within the human microbiome are complex, and few methods are available to identify these interactions within a longitudinal microbial abundance framework. Existing methods typically impose restrictive constraints, such as requiring long sequences and equal spacing, on the data format which in many cases are violated.MethodsTo identify microbial interaction networks (MINs) with general longitudinal data settings, we propose a stationary Gaussian graphical model (SGGM) based on 16S rRNA gene sequencing data. In the SGGM, data can be arbitrarily spaced, and there are no restrictions on the length of data sequences from a single subject. Based on the SGGM, EM -type algorithms are devised to compute the L1-penalized maximum likelihood estimate of MINs. The algorithms employ the classical graphical LASSO algorithm as the building block and can be implemented efficiently. ResultsExtensive simulation studies show that the proposed algorithms can significantly outperform the conventional algorithms if the correlations among the longitudinal data are reasonably high. When the assumptions in the SGGM areviolated, e.g., zero inflation or data from heterogeneous microbial communities, the proposed algorithms still demonstrate robustness and perform better than the other existing algorithms. The algorithms are applied to a 16S rRNA gene sequencing data set from patients with cystic fibrosis. The results demonstrate strong evidence of an association between the MINs and the phylogenetic tree, indicating that the genetically related taxa tend to have more/stronger interactions. These results strengthen the existing findings in literature. DiscussionThe proposed algorithms can potentially be used to explore the network structure in genome, metabolome etc. as well.

Gravitational waves in a cyclic Universe: resilience through cycles and vacuum state

We present a generalised calculation for the spectrum of primordial tensor perturbations in a cyclic Universe, making no assumptions about the vacuum state of the theory and accounting for the contribution of tensor modes produced in the dark energy phase of the previous cycle. We show that these modes have minimal impact on the spectrum observed in the current cycle, except for corrections on scales as large as the comoving Hubble radius today. These corrections are due to sub-horizon modes produced towards the end of the dark energy phase, persisting into the ekpyrotic phase of the next cycle as additional quanta. In relation to the vacuum state, we argue that non-Bunch-Davies quanta can easily overwhelm the energy density driving the dark energy phase, potentially compromising the model. Therefore, avoiding backreaction effects sets restrictive constraints on deviations away from the Bunch-Davies vacuum during this phase, limiting the overall freedom to consider alternative vacua in the cyclic Universe.

Benchmarking of industrial wastewater treatment processes using a complex probabilistic hesitant fuzzy soft Schweizer–Sklar prioritized-based framework

The objective of this research work is to effectively handle the intricate and uncertain nature of decision-making in the context of industrial wastewater treatment. The prioritization of wastewater treatment is of utmost importance in order to save the environment, promote human health, ensure adherence to legal regulations, conserve resources, and foster overall sustainability. The demand for efficient and sustainable wastewater treatment processes is increasing globally, but selecting appropriate treatment technologies for industrial effluents is challenging due to its complex nature. Our study aims to provide a systematic framework for evaluating and selecting treatments based on efficacy, affordability, and environmental impact. Aggregation operators are one of the fundamental ideas in the framework of information fusion for addressing real-life problems. Numerous scholars have made significant contributions to the development of aggregation operators specifically designed for multi-parameter decision-making (MPDM) whenever circumstances are characterized by uncertainty. Regrettably, the current operators can only be employed within strict limits and constraints. Therefore, this research formulates novel prioritized aggregation operators that alleviate the restrictive constraints associated with the current operators. After this, we present a new methodology that combines the Schweizer–Sklar prioritized aggregation operators with a complex probabilistic hesitant fuzzy soft framework. For this purpose, we develop the averaging aggregation operators and geometric aggregation operators. Following that, we proceed to examine the theorems and properties associated with them by providing rigorous proofs. Then, we develop a novel decision-making technique with a numerical example based on the wastewater treatment process. Through this novel technique, the best process is the activated sludge process. We perform the validity tests to evaluate the feasibility and effectiveness of MPDM techniques.

Robust sliding-mode observer for unbounded state nonlinear systems

Designing a full-state observer for nonlinear systems has always been accompanied by challenges and restrictive constraints. Mainly, applying a state observer in nonlinear systems with non-minimum phase characteristics is more challenging when the limiting constraints are not satisfied due to diverging internal dynamics. In this paper, a robust sliding-mode observer approach has been successfully employed to estimate the states of nonlinear systems with unbounded and diverging dynamics. The design principles of this observer are based on applying a classifying algorithm in single-input single-output and multiple-input multiple-output nonlinear systems. It is noteworthy that this observer is highly robust against disturbance, uncertainty and measurement noise, and its conditions are less conservative compared to previous nonlinear sliding-mode observers. One novel feature of the proposed observer is that while the system's state gets unbounded and diverged in fault-occurring scenarios or critical circumstances, this observer retains accuracy. The efficiency of the proposed observer is verified in the simulation results for two nonlinear industrial systems, including a hydro-turbine power generation plant and a continuous stirred tank reactor.

Recreating Observed Convection‐Generated Gravity Waves From Weather Radar Observations via a Neural Network and a Dynamical Atmospheric Model

AbstractConvection‐generated gravity waves (CGWs) transport momentum and energy, and this momentum is a dominant driver of global features of Earth's atmosphere's general circulation (e.g., the quasi‐biennial oscillation, the pole‐to‐pole mesospheric circulation). As CGWs are not generally resolved by global weather and climate models, their effects on the circulation need to be parameterized. However, quality observations of GWs are spatiotemporally sparse, limiting understanding and preventing constraints on parameterizations. Convection‐permitting or ‐resolving simulations do generate CGWs, but validation is not possible as these simulations cannot reproduce the CGW‐forcing convection at correct times, locations, and intensities. Here, realistic convective diabatic heating, learned from full‐physics convection‐permitting Weather Research and Forecasting simulations, is predicted from weather radar observations using neural networks and a previously developed look‐up table. These heating rates are then used to force an idealized GW‐resolving dynamical model. Simulated CGWs forced in this way closely resembled those observed by the Atmospheric InfraRed Sounder in the upper stratosphere. CGW drag in these validated simulations extends 100s of kilometers away from the convective sources, highlighting errors in current gravity wave drag parameterizations due to the use of the ubiquitous single‐column approximation. Such validatable simulations have significant potential to be used to further basic understanding of CGWs, improve their parameterizations physically, and provide more restrictive constraints on tuning with confidence.

Potassium attenuates drought damage by regulating sucrose metabolism and gene expression in sesame leaf

Drought has been considered the most restrictive environmental constraint on agricultural production worldwide. Photosynthetic carbohydrate metabolism is a critical biochemical process connected with crop production and quality traits. A pot experiment was carried out under four potassium (K) rates (0, 0.75, 1.5 and 2.25 g pot−1 of K, respectively) and two water regimes to investigate the role of K in activating defense mechanisms on sucrose metabolism against drought damage in sesame. The soil moisture contents are 75 ± 5% (well-watered, WW) and 45 ± 5% (drought stress, DS) of field capacity respectively. The results showed that DS plants without K application have lower activities of ribulose-1,5-bisphosphate carboxylase (Rubisco), sucrose phosphate synthase (SPS), soluble acid invertase (SAI), and chlorophyll content and higher activity of sucrose synthase (SuSy), which resulted in declined synthesis and distribution of photosynthetic products to reproductive organs. Under drought, there was a significant positive correlation between leaf sucrose metabolizing enzymes and sucrose content. Plants subjected to drought stress increased the concentrations of soluble sugar and sucrose to produce osmo-protectants and energy sources for plants acclimating to stress but decreased starch content. Conversely, K application enhanced the carbohydrate metabolism, biomass accumulation and partitioning, thereby contributing to higher seed oil and protein yield (28.8%–43.4% and 27.5%–40.7%) as compared to K-deficiency plants. The positive impacts of K application enhanced as increasing K rates, and it was more pronounced in drought conditions. Furthermore, K application upregulated the gene expression of SiMYB57, SiMYB155, SiMYB176 and SiMYB192 while downregulated SiMYB108 and SiMYB171 in drought conditions, which may help to alleviate drought susceptibility. Conclusively, our study illustrated that the enhanced photo-assimilation and translocation process caused by the changes in sucrose metabolism activities under K application as well as regulation of MYB gene expression contributes towards drought resistance of sesame.

Optimization of Tunnel Construction Schedule Considering Soft Logic

Tunnel construction projects are a classic type of repetitive project, and hold a crucial position in the construction industry. The linear scheduling method (LSM) has been in the spotlight in scheduling optimization for repetitive construction projects since it was first proposed. However, the possibility of changing the construction sequence during application is very often overlooked, which is not in line with actual engineering practices. Incorporating soft logic into LSM and considering the characteristics of tunnel construction, we propose an optimization method for the tunnel construction schedule to shorten the duration by opening up an additional working surface. A mixed-integer programming (MIP) model is developed, which considers various constraints, such as construction sequence and work continuity. The exact algorithm and genetic algorithm (GA) are designed via Python, and the validity is verified through practical cases. A comparative analysis was conducted between the two algorithms and their outcomes demonstrates that the method can satisfy the restrictive constraints of tunnel construction projects. In addition, improvement strategies for tunnel construction management are recommended.

Shape transformation on curved surfaces using a phase-field model

Shape transformation on evolving curved surfaces is essential for its diverse applications across various scientific disciplines and facilitates the deeper understanding of natural phenomena, the development of new materials, and engineering design optimization. In this study, we develop a phase-field model and its numerical methods for shape transformation on curved surfaces. A modified surface Allen–Cahn (AC) equation with a fidelity term is proposed to simulate shape transformation on curved surfaces. To numerically solve the modified surface AC equation on curved surfaces, we propose a fully explicit scheme and an unconditionally stable method. The proposed stable approach is not only simple and efficient to implement numerically but is also unconditionally stable and eliminates the restrictive temporal time step size constraints. Through numerical experiments using the proposed approach, we demonstrate that shape transformation on evolving curved surfaces can be implemented on both simple and complex curved surfaces.

Pythagorean fuzzy Aczel Alsina Hamy mean aggregation operators and its applications to multi-attribute decision-making process

Multiple-attribute group decision-making (MAGDM) technique is often used to make decisions when several optimal options are under consideration. It can be difficult to select a reasonable optimal option for the decision maker under consideration of insufficient information. The theory of Hamy mean (HM) operators are used to express correlation among different input arguments and provide a smooth approximation during the decision-making process. Recently, Aczel Alsina aggregating expressions gained a lot of attention from numerous mathematicians under different fuzzy circumstances. This article aims to illustrate the notion of a Pythagorean fuzzy (PyF) set (PyFS) with some restricted constraints, such as a sum of the square of truth membership value and falsity membership value. We developed a series of new approaches under consideration of the HM tools, including PyF Aczel Alsina Hamy mean (PyFAAHM), and PyF Aczel Alsina weighted Hamy mean (PyFAAWHM) operators. Further, we also extend the theory of Dual Hamy mean (DHM) operators and derived a series of new methodologies such as PyF Aczel Alsina Dual Hamy mean (PyFAADHM) and PyF Aczel Alsina weighted Dual Hamy mean (PyFAAWDHM) operators. To demonstrate the flexibility of our derived approaches, we illustrate an application of a multinational company considering the MAGDM technique. An experimental case study is also illustrated to evaluate a reasonable option from a group of options. We see the advantages and compatibility of our findings by comparing the results of existing approaches with the results of currently discussed methodologies.

Effects of heterogeneous data sets and time-lag measurement techniques on cosmological parameter constraints from Mg ii and C iv reverberation-mapped quasar data

ABSTRACT Previously, we demonstrated that Mg ii and C iv reverberation-mapped quasars (RM QSOs) are standardizable and that the cosmological parameters inferred using the broad-line region radius–luminosity (R–L) relation are consistent with those determined from better-established cosmological probes. With more data expected from ongoing and future spectroscopic and photometric surveys, it is imperative to examine how new QSO data sets of varied quality, with their own specific luminosity and time-delay distributions, can be best used to determine more restrictive cosmological parameter constraints. In this study, we test the effect of adding 25 OzDES Mg ii RM QSOs as well as 25 lower quality SDSS RM C iv QSOs, which increases the previous sample of RM QSOs by $\sim 36{{\ \rm per\ cent}}$. Although cosmological parameter constraints become tighter for some cosmological models after adding these new QSOs, the new combined data sets have increased differences between R–L parameter values obtained in different cosmological models and thus a lower standardizability for the larger Mg ii + C iv compilation. Different time-delay methodologies, particularly the ICCF and CREAM methods used for inferring time delays of SDSS RM QSOs, slightly affect cosmological and R–L relation parameter values, however, the effect is negligible for (smaller) compilations of robust time-delay detections. Our analysis indicates that increasing the sample size is not sufficient for tightening cosmological constraints and a quality cut is necessary to obtain a standardizable RM QSO sample.

Asymptotic analysis of single-slip crystal plasticity in the limit of vanishing thickness and rigid elasticity

Abstract We perform via Γ-convergence a 2d-1d dimension reduction analysis of a single-slip elastoplastic body in large deformations. Rigid plastic and elastoplastic regimes are considered. In particular, we show that limit deformations can essentially freely bend even if subjected to the most restrictive constraints corresponding to the elastically rigid single-slip regime. The primary challenge arises in the upper bound where the differential constraints render any bending without incurring an additional energy cost particularly difficult. We overcome this obstacle with suitable non-smooth constructions and prove that a Lavrentiev phenomenon occurs if we artificially restrict our model to smooth deformations. This issue is absent if the differential constraints are appropriately softened.

Power-Based Safety Constraint for Redundant Robotic Manipulators

A robot making or losing contact with its environment will experience a sudden change in its dynamics. This may cause instability, possibly causing the robot to harm itself and its environment. To prevent this while not placing overly restrictive constraints on the energy generated by the controller, we place a time-varying constraint on the system's power. The power limit varies with a heuristic measure of a desired task trajectory's stability, which is based on the largest Lyapunov exponent. When the trajectory is deemed unstable, the controller is forced to dissipate energy, while it is allowed to generate energy when the trajectory is stable. The constraint is included in a strict task-priority framework, allowing a redundant robotic platform to perform several tasks simultaneously while ensuring that the performance of the higher-priority tasks is not affected by the lower-priority tasks. The presented method is validated by simulation of an articulated intervention autonomous underwater vehicle (AIAUV).

New method to optimize protection of the organs at risk allows to regain coverage and homogeneity in the Planning Target Volume

The construction of auxiliary structures within the Planning Target Volume (PTV) is proposed as a method to recover coverage and homogeneity, and to optimize the protection of the organs at risk (OARs) in radiotherapy treatment plans. To this purpose, it was performed the Volumetric Modulated Arc Therapy (VMAT) treatment planning of the “mock prostate” test. Three plans were optimized in the EclipseTM treatment planning system. In plan 1 (P1), it was defined an initial objective function only for the PTV. Then, the dose received by the OARs was restricted, guaranteeing the PTV level of coverage. Plan 2 (P2) used the same dose-volume objectives for the PTV as in P1, but more restrictive dose constraints were set for the OARs. From the dose distribution obtained in P2, it was built auxiliary subvolumes contained within the PTV to regain coverage and homogeneity in plan 3 (P3). To this end, it was compared the coverage (D95%), the Homogeneity Index (HI) and dose-volume histogram for the PTV, and OARs dose sparing for rectum and Bladder. P1 and P3 resulted in similar PTV coverage and HI values, however, the OARs received a lower dose in P3. Despite in P2 it was achieved a higher OARs protection, the PTV coverage and HI were considerably reduced. In this sense P3 allowed to reach the best balance between coverage, HI, and OARs protection. As a consequence of these results, it has been shown that the introduction of auxiliary structures as target subvolumes constitute a powerful and easy to implement tool in the treatment planning optimization process and can be employed in any pathology that requires VMAT.

Integrasi Aplikasi Pengelolaan Keuangan untuk Mendukung Sistem Penyusunan Laporan Keuangan Pemerintah Daerah Terpadu Provinsi XYZ

Province XYZ Government has several application systems to support financial management and reporting system like budgeting and treasury system, asset management system and accounting system. All systems are working separately which may cause data to exist only in one of those systems when another system is needed for those data. They need integration among those systems to have more beneficiaries from the systems. This research used Enterprise Application Integration (EAI) to confront the problem. We used data level integration to integrate the asset management system and accounting system and application interface level integration using APIs to integrate the budgeting and treasury system and accounting system. Our experiment results that data-level integration is simpler to develop than application interface-level integration. Application interface level integration needed more prerequisite requirements and is more difficult to develop but more flexible and secure to connect and integrate into another system. Our performance test shows that integration using APIs needs more resources and probably gives a problem when the text data that is interchanged is too big. When all systems are owned by one institution that does not need restricted security constraints, data level integration gave more benefit for institution system integration.

Direct implicit and explicit energy-conserving particle-in-cell methods for modeling of capacitively coupled plasma devices

Achieving large-scale kinetic modeling is a crucial task for the development and optimization of modern plasma devices. With the trend of decreasing pressure in applications, such as plasma etching, kinetic simulations are necessary to self-consistently capture the particle dynamics. The standard, explicit, electrostatic, momentum-conserving particle-in-cell method suffers from restrictive stability constraints on spatial cell size and temporal time step, requiring resolution of the electron Debye length and electron plasma period, respectively. This results in a very high computational cost, making the technique prohibitive for large volume device modeling. We investigate the direct implicit algorithm and the explicit energy conserving algorithm as alternatives to the standard approach, both of which can reduce computational cost with a minimal (or controllable) impact on results. These algorithms are implemented into the well-tested EDIPIC-2D and LTP-PIC codes, and their performance is evaluated via 2D capacitively coupled plasma discharge simulations. The investigation reveals that both approaches enable the utilization of cell sizes larger than the Debye length, resulting in a reduced runtime, while incurring only minor inaccuracies in plasma parameters. The direct implicit method also allows for time steps larger than the electron plasma period; however, care must be taken to avoid numerical heating or cooling. It is demonstrated that by appropriately adjusting the ratio of cell size to time step, it is possible to mitigate this effect to an acceptable level.

Emerging Two-Dimensional Organic Semiconductor-Incorporated Perovskites─A Fascinating Family of Hybrid Electronic Materials.

Halide perovskites have attracted a great amount of attention owing to their unique materials chemistry, excellent electronic properties, and low-cost manufacturing. Two-dimensional (2D) halide perovskites, originating from three-dimensional (3D) perovskite structures, are structurally more diverse and therefore create functional possibilities beyond 3D perovskites. The much less restrictive size constraints on the organic component of these hybrid materials particularly provide an exciting platform for designing unprecedented materials and functionalities at the molecular level. In this Perspective, we discuss the concept and recent development of a sub-class of 2D perovskites, namely, organic semiconductor-incorporated perovskites (OSiPs). OSiPs combine the electronic functionality of organic semiconductors with the soft and dynamic halide perovskite lattice, offering opportunities for tailoring the energy landscape, lattice and carrier dynamics, and electron/ion transport properties for various fundamental studies, as well as device applications. Specifically, we summarize recent advances in the design, synthesis, and structural analysis of OSiPs with various organic conjugated moieties as well as the application of OSiPs in photovoltaics, light-emitting devices, and transistors. Lastly, challenges and further opportunities for OSiPs in molecular design, integration of novel functionality, film quality, and stability issues are addressed.

Silicon alleviates drought damage by increasing antioxidant and photosynthetic performance in cowpea

AbstractWater deficits have been considered the most restrictive environmental constraint on agricultural production worldwide. The current study aimed to investigate the role of silicon nutrition (Si) in activating defence mechanisms against drought damage in cowpea cultivars. The experiments were carried out in a randomized block design in a 2 × 2 × 4 factorial scheme, corresponding to two cowpea cultivars (BRS Novaera and BRS Tumucumaque), two water regimes (control well‐irrigated and water deficit) and four Si levels (0, 1.0, 2.0 and 4.0 mM). Plant growth and physiological and biochemical indicators were evaluated 28 days after drought imposition. Drought significantly reduced the photosynthetic pigments (Chl b and Chl total), gas exchanges (net photosynthesis, transpiration and stomatal conductance) and, consequently, all growth parameters of cowpea plants compared with well‐irrigated plants. However, Si at 2.0 mM activated critical responses in the BRS Novaera cultivar under drought, almost recovering plant performance and increasing drought tolerance. The beneficial Si‐induced effects were closely related to increased accumulation of Si, carbohydrates and free amino acids that likely promoted osmoregulation and were associated with an improved antioxidant system composed of proline and the activity of SOD, CAT and APX. These metabolic alterations were sufficient to enable enhanced net photosynthesis and plant growth. In conclusion, Si counteracts the deleterious effects of water deficit by efficiently inducing antioxidant defence and photosynthetic performance in Novaera plants. Si nutrition may constitute a potential strategy to cultivate cowpea plants in water‐scarce areas from arid and semiarid regions.