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In predictive modelling for high‐stake decision‐making, predictors must be not only accurate but also reliable. Conformal prediction (CP) is a promising approach for obtaining the coverage of prediction results with fewer theoretical assumptions. To obtain the prediction set by so‐called full‐CP, we need to refit the predictor for all possible values of prediction results, which is only possible for simple predictors. For complex predictors such as random forests (RFs) or neural networks (NNs), split‐CP is often employed where the data is split into two parts: one part for fitting and another for computing the prediction set. Unfortunately, because of the reduced sample size, split‐CP is inferior to full‐CP both in fitting as well as prediction set computation. In this paper, we develop a full‐CP of sparse high‐order interaction model (SHIM), which is sufficiently flexible as it can take into account high‐order interactions among variables. We resolve the computational challenge for full‐CP of SHIM by introducing a novel approach called homotopy mining. Through numerical experiments, we demonstrate that SHIM is as accurate as complex predictors such as RF and NN and enjoys the superior statistical power of full‐CP. [ABSTRACT FROM AUTHOR]

Image registration is a widely used tool for matching two images of the same scene with one another. In the literature, several image registration techniques are available to register rigid‐body and non‐rigid‐body transformations. One such important transformation is zooming. There are very few feature‐based methods that address this particular problem. These methods fail miserably when there are only a limited number of point features available in the image. This paper proposes a feature‐based approach that works with a feature that is readily available in almost all images, for registering two images of the same image object where one is a zoomed‐in version of the other. In the proposed method, we first detect the possible edge points which we consider as features in both the reference and the zoomed image. Then, we map these features of the reference and the zoomed image with one another and find the relationship between them using a mathematical model. Finally, we use the relationship to register the zoomed‐in image. This method outperforms some of the state‐of‐the‐art methods in many occasions. Several numerical examples and some statistical properties justify that this method works well in many applications. [ABSTRACT FROM AUTHOR]

Modelling multivariate time series of counts in a parsimonious way is a popular topic. In this paper, we consider an integer‐valued network autoregressive model with a non‐random neighbourhood structure, which uses negative binomial distribution as the conditional marginal distribution and the softplus function as the link function. The new model generalizes existing ones in the literature and has a great flexibility in modelling. Stationary conditions in cases of fixed dimension and increasing dimension are given. Parameters are estimated by maximizing the quasi‐likelihood function, and related asymptotic properties of the estimators are established. A simulation study is conducted to assess performances of the estimators, and a real data example is analysed to show superior performances of the proposed model compared with existing ones. [ABSTRACT FROM AUTHOR]

Data science consulting and collaboration units (DSUs) are core infrastructure for research at universities. Activities span data management, study design, data analysis, data visualization, predictive modelling, preparing reports, manuscript writing and advising on statistical methods and may include an experiential or teaching component. Partnerships are needed for a thriving DSU as an active part of the larger university network. Guidance for identifying, developing and managing successful partnerships for DSUs can be summarized in six rules: (1) align with institutional strategic plans, (2) cultivate partnerships that fit your mission, (3) ensure sustainability and prepare for growth, (4) define clear expectations in a partnership agreement, (5) communicate and (6) expect the unexpected. While these rules are not exhaustive, they are derived from experiences in a diverse set of DSUs, which vary by administrative home, mission, staffing and funding model. As examples in this paper illustrate, these rules can be adapted to different organizational models for DSUs. Clear expectations in partnership agreements are essential for high quality and consistent collaborations and address core activities, duration, staffing, cost and evaluation. A DSU is an organizational asset that should involve thoughtful investment if the institution is to gain real value. [ABSTRACT FROM AUTHOR]

The COVID‐19 pandemic led to unprecedented changes in all levels of society, including the statistical consulting field. This paper focuses on the experiences of graduate student consultants and clients at our statistical consulting center (SCC) that operates all year independent of semesters. During the lockdown period, work continued without interruption and was conducted remotely, but there was a temporary reduction in utilization. Advice on statistical methods, help with data analysis and educational offerings are the main appeals to utilize SCC services. We describe our mentoring approach for graduate student research assistants (RAs) and how pandemic changes affected RAs and clients. Based on experiences during the pandemic, we offer practical suggestions for SCCs' approaches to research support, work characteristics and collaborations to improve the experiences of graduate students, both as consultants and clients. Most collaboration meetings are now virtual by request from clients. Telecommuting supports flexible personal schedules and needs. Online educational offerings provide easier access for participants and more opportunities for a wider range of topics and presenters. However, mentoring sessions for RAs are best conducted in‐person, and every effort should be made to encourage in‐person interactions and collaborations between staff members to advance the effectiveness of post‐pandemic SCCs. [ABSTRACT FROM AUTHOR]

Deep learning has achieved unprecedented success in recent years. This approach essentially uses the composition of nonlinear functions to model the complex relationship between input features and output labels. However, a comprehensive theoretical understanding of why the hierarchical layered structure can exhibit superior expressive power is still lacking. In this paper, we provide an explanation for this phenomenon by measuring the approximation efficiency of neural networks with respect to discontinuous target functions. We focus on deep neural networks with rectified linear unit (ReLU) activation functions. We find that to achieve the same degree of approximation accuracy, the number of neurons required by a single‐hidden‐layer (SHL) network is exponentially greater than that required by a multi‐hidden‐layer (MHL) network. In practice, discontinuous points tend to contain highly valuable information (i.e., edges in image classification). We argue that this may be a very important reason accounting for the impressive performance of deep neural networks. We validate our theory in extensive experiments. [ABSTRACT FROM AUTHOR]

In recent years, multi‐response linear models have gained significant popularity in various statistical applications. However, the design aspects of multi‐response linear models with group‐wise considerations have received limited attention in the literature. This paper aims to thoroughly investigate D$$ D $$‐optimal designs for such models. Specifically, we focus on scenarios involving two groups, where the proportions of observations for each group can be arbitrarily selected or not. Two equivalence theorems are presented to elaborate the characterization of D$$ D $$‐optimal designs. Additionally, we delve into the admissibility of approximate designs and establish necessary conditions for a design to be deemed admissible. Several illustrative examples are addressed to demonstrate the application of the derived theoretical results. [ABSTRACT FROM AUTHOR]

Summary: Testing for equivalence, rather than testing for a difference, is an important component of some scientific studies. While the focus of the existing literature is on comparing two groups for equivalence, real‐world applications arise regularly that require testing across more than two groups. This paper reviews the existing approaches for testing across multiple groups and proposes a novel framework for multigroup equivalence testing under a Bayesian paradigm. This approach allows for a more scientifically meaningful definition of the equivalence margin and a more powerful test than the few existing alternatives. This approach also allows a new definition of equivalence based on future differences. [ABSTRACT FROM AUTHOR]

A key aspect where extreme values methods differ from standard statistical models is through having asymptotic theory to provide a theoretical justification for the nature of the models used for extrapolation. In multivariate extremes, many different asymptotic theories have been proposed, partly as a consequence of the lack of ordering property with vector random variables. One class of multivariate models, based on conditional limit theory as one variable becomes extreme has received wide practical usage. The underpinning value of this approach has been supported by further theoretical characterisations of the limiting relationships. However, the paper "Conditional extreme value models: fallacies and pitfalls" by Holger Drees and Anja Janßen provides a number of counterexamples to these results. This paper studies these counterexamples in a conditional extremes framework which involves marginal standardisation to a common exponentially decaying tailed marginal distribution. Our calculations show that some of the issues identified can be addressed in this way. [ABSTRACT FROM AUTHOR]

This paper introduces graph‐based mutually exciting processes (GB‐MEP) to model event times in network point processes, focusing on an application to docked bike‐sharing systems. GB‐MEP incorporates known relationships between nodes in a graph within the intensity function of a node‐based multivariate Hawkes process. This approach reduces the number of parameters to a quantity proportional to the number of nodes in the network, resulting in significant advantages for computational scalability when compared with traditional methods. The model is applied on event data observed on the Santander Cycles network in central London, demonstrating that exploiting network‐wide information related to geographical location of the stations is beneficial to improve the performance of node‐based models for applications in bike‐sharing systems. The proposed GB‐MEP framework is more generally applicable to any network point process where a distance function between nodes is available, demonstrating wider applicability. [ABSTRACT FROM AUTHOR]

This paper introduces a novel approach to reducing statistical errors in generative models, with a specific focus on generative adversarial networks (GANs). Inspired by the error analysis of GANs, we find that statistical errors mainly arise from random sampling, leading to significant uncertainties in GANs. To address this issue, we propose a selective sampling mechanism called space‐filling sampling. Our method aims to increase the sampling probability in areas with insufficient data, thereby improving the learning performance of the generator. Theoretical analysis confirms the effectiveness of our approach in reducing statistical errors and accelerating convergence in GANs. This research represents a pioneering effort in targeting the reduction of statistical errors in GANs, and it demonstrates the potential for enhancing the training of other generative models. [ABSTRACT FROM AUTHOR]

The deterministic non‐autonomous multispecies Holling type II model and its stochastic version with a simple type of noise have been proposed to infer multispecies community structure. However, these models fail to account for complex types of noises, which may render the model overly simplistic. In this paper, a non‐autonomous multispecies Holling type II model with a complex type of noise has been proposed. We establish sufficient conditions for various mathematical properties of the solutions, including existence and uniqueness, stochastic permanence and extinction. Additionally, numerical simulation studies are provided to illustrate our theoretical findings. [ABSTRACT FROM AUTHOR]

Summary: This paper studies the universally optimal designs for estimating total effects under crossover models with partial interactions. We provide necessary and sufficient conditions for a symmetric design to be universally optimal, based on which algorithms can be used to derive optimal symmetric designs under any form of the within‐block covariance matrix. To cope with the computational complexity of algorithms when the experimental scale is too large, we provide the analytical form of optimal designs under the type‐H covariance matrix. We find that for a fixed number of treatments, say t$$ t $$, the number of distinct treatments appearing in the support sequences increases with the increase of the number of periods, k$$ k $$, until k≥t2$$ k\ge {t}^2 $$, in which case all t$$ t $$ treatments appear. The optimal design can be constructed from up to two representative sequences, within which each treatment appears in consecutive periods with equal or almost equal numbers of replications. [ABSTRACT FROM AUTHOR]

In the prize‐collecting geometric enclosure problem (PCGEP), a set S$S$ of points in the plane is given, each with an associated benefit. The goal is to find a simple polygon P$\mathcal {P}$ with vertices in S$S$ that maximizes the sum of the benefits of the points of S$S$ enclosed by P$\mathcal {P}$ minus the perimeter of P$\mathcal {P}$ multiplied by a given nonnegative cost. The PCGEP is NP‐complete and has applications to land surveying for exploration or preservation of natural resources. In this paper, we develop the first heuristic, called PCGEP‐GR, for the PCGEP and revisit a previously proposed integer linear programming (ILP) model to solve it to optimality. We conducted a comprehensive experimental study of that heuristic and an exact algorithm based on the ILP model. We show that a new set of constraints, together with the previous set, is necessary to guarantee the correctness of the ILP model and introduce preprocessing strategies that allow us to prove optimality 40% faster on average. The proposed heuristic is able to reach the optimum in 32% of our benchmark instances and, for those with unknown optima, PCGEP‐GR was found better than or at least as good solutions as the ones obtained by the cplex ILP solver in 54% of the cases. Notwithstanding these positive results, the design of effective heuristics for the PCGEP proved to be very challenging, which also led us to obtain a result that provides the theoretical foundation for future advances in the study of this problem. [ABSTRACT FROM AUTHOR]

This paper introduces the three‐dimensional case picking problem (3D‐CPP) and proposes a multistart biased‐randomized algorithm (BRA) to solve it. The 3D‐CPP combines two important topics in modern warehouse logistics: the pallet loading problem and the routing of pickers in manual warehouses. The proposed optimization procedure aims at minimizing the overall distance traveled by the pickers, and is achieved by combining a routing problem (i.e., the order in which picking positions are visited) with a loading problem (i.e., the way in which cases are placed onto the pallet). We also consider additional constraints regarding the weight, vertical support, and strength of the cases. In order to solve this problem, we first propose a constructive heuristic which combines routing and packing procedures. This initial heuristic is then extended into a multistart BRA by employing a skewed probability distribution to introduce a certain degree of randomness during the solution‐construction process. A series of computational experiments allow us to assess the quality of the proposed approach, through a comparison with other algorithms as well as using real‐life data provided by an industrial partner. [ABSTRACT FROM AUTHOR]

Waste collection is an important logistic operation that is often inefficient due to the high uncertainty associated with bin fill levels, resulting either in routes that visit empty bins or in bins overflowing due to lack of routes. To reduce such uncertainty, sensors installed in the bins can provide real‐time information on waste levels. However, this is not enough, and the management of this information needs to be combined with dynamic optimization approaches to effectively design smart collection routes. This paper investigates this challenge and uses real‐time information on waste levels, treated through machine learning techniques, to feed a dynamic reverse inventory routing optimization model. The resulting data‐driven optimization approach allows the definition of a medium‐term collection route plan that can be updated daily as new information becomes available. To demonstrate the applicability of such an approach, a real‐world case is solved, and the results show significant improvements in operational efficiency and service levels. [ABSTRACT FROM AUTHOR]

This paper deals with the problem of finding the optimal configuration of a network of land parcels connecting and protecting core areas through corridors and buffer areas within budget. We focus on polynomial‐sized integer linear programming formulations, building on previous research and borrowing some ideas from other authors. We establish the relationships between the proposed and past models in linear programming relaxation terms and test them on synthetic instances with up to 400 land parcels. [ABSTRACT FROM AUTHOR]

The optimization of Multidimensional Knapsacks with Family‐Split Penalties has been introduced in the literature as a variant of the more classical Multidimensional Knapsack and Multi‐Knapsack problems. This problem deals with a set of items partitioned in families, and when a single item is picked to maximize the utility, then all items in its family must be picked. Items from the same family can be assigned to different knapsacks, and in this situation split penalties are paid. This problem arises in real applications in various fields. This paper proposes a new exact and fast algorithm based on a specific Combinatorial Benders Cuts scheme. An extensive experimental campaign computationally shows the validity of the proposed method and its superior performance compared to both commercial solvers and state‐of‐the‐art approaches. The paper also addresses algorithmic flexibility and scalability issues, investigates challenging cases, and analyzes the impact of problem parameters on the algorithm behavior. Moreover, it shows the applicability of the proposed approach to a wider class of realistic problems, including fixed costs related to each knapsack utilization. Finally, further possible research directions are considered. [ABSTRACT FROM AUTHOR]

This paper studies a pickup and delivery routing problem for fourth‐party logistics providers. The problem aims to schedule routes of vehicles to pick up orders from suppliers and deliver them to factory locations considering multiple time windows at suppliers and factory locations, a non‐conventional cost structure, and certain factory dock constraints. We formulate the problem as a mathematical model and develop an efficient algorithm based on the adaptive large neighborhood search to solve the problem. The algorithm incorporates several heuristics to efficiently explore the search space for optimal solutions. The algorithm is refined through extensive statistical experiments to optimize the performances of the heuristics and to tune the parameters of the algorithm. The mathematical model and algorithm are evaluated on several problem instances based on a real case study in Europe. The numerical results demonstrate that the solution algorithm consistently obtains near‐optimal solutions to real‐sized problem instances. [ABSTRACT FROM AUTHOR]

This research addresses a flexible vehicle scheduling problem considering precedence constraints (FVS‐P problem), which is prevalent in many scenic areas worldwide. Each group of tourists in the FVS‐P problem comprises a set of visit requests that must be served by shuttle vehicles in a predefined order. A three‐indexed integer linear programming model is introduced. Furthermore, an index‐reduction strategy is proposed to strengthen the model. A math‐heuristic route‐segment generation algorithm embedded in a mathematical model is designed to solve the FVS‐P problem, particularly in the case of large‐sized instances. Experiments on different sizes of near‐practical instances validate the mathematical models and the algorithm. The proposed math‐heuristic can provide better solutions for large‐sized instances in a much shorter time than the models. The trade‐off between tourists' feelings and the cost of the area is investigated to provide further insights for managers. [ABSTRACT FROM AUTHOR]

As the prefabrication construction method plays an increasingly important role in the construction of cross‐sea bridges, coordinating the schedule between off‐site prefabrication and on‐site assembly becomes crucial and challenging. This paper studies a just‐in‐time single‐machine scheduling problem with due windows and release dates, which originates from the production and supply process of large segments of precast steel box girders in bridge construction. To solve this problem, we equivalently decompose it into the independent and same type of subproblems and formulate a mixed‐integer linear mathematical programming model. Furthermore, we propose a branch‐and‐bound algorithm with the initialization of a novel linear early and tardy dispatching rule with due windows to solve the problem exactly. The dispatching rule, based on the local dominance criteria with due windows, is designed and used to improve the upper bound. Moreover, a lower bound is obtained by further decomposing and relaxing the subproblems. Finally, the parameter of the dispatching rule is calibrated, and the best search strategy of the branch‐and‐bound algorithm is determined. The comprehensive computational experiments carried out show the efficiency and effectiveness of the proposed branch‐and‐bound algorithm. [ABSTRACT FROM AUTHOR]

Multi‐criteria methods and systems have been developed over many years to support decision‐making with a process of eliciting preferences. The emphasis has been on those that incorporate compensatory rationality, the characteristics of which make the process more complex. Neuroscience has offered the opportunity to capture aspects of cognition, generating insights. This paper investigates the preference elicitation process with Flexible and Interactive Tradeoff (FITradeoff), using electroencephalography and eye‐tracking applied to a sample of undergraduate and graduate students. While FITradeoff offers greater flexibility when evaluating recent advances in the area, the experiment carried out incorporates paradigms present in other methods developed for compensatory rationality. The results indicate the effect the type of predominant criterion has on cognitive effort. Moreover, a non‐linear relationship between the number of criteria and questions was found, which suggests that decision‐makers use a strategy of minimizing cognitive effort. Such findings help to direct efforts to enhance systems and methods with compensatory rationality. [ABSTRACT FROM AUTHOR]

Cap‐and‐trade regulation is increasingly adopted by countries and governments to curb carbon emissions. This regulation can be implemented based on two rules, that is, the traditional cap allocation rule and the linear cap allocation rule. Considering the impact of online channel structure on the manufacturer's production and low‐carbon investment strategy, this paper aims to explore the economic and environmental performances of the two rules. The results show that the linear rule encourages the government to allocate less cap to each unit product as the emission abatement level increases, preventing the manufacturer from gaining a windfall profit from the free carbon credits. Besides, the government could use the traditional cap allocation rule to better control the total carbon emissions but use the linear cap allocation rule to obtain more social welfare as well as economic benefit. No matter which cap allocation rule is implemented, the manufacturer always performs better in reseller mode, and the e‐tailer might perform better in reseller (marketplace) mode given a high (low) low‐carbon investment efficiency. This study enriches the literature on the interplay between the cap allocation rule and selling mode and provides insights into decision‐making for governments and firms. [ABSTRACT FROM AUTHOR]

For plenty of existing maintenance policies in literature, it is always assumed that the spare unit for replacement is available at any time if needed, while this perquisite may be unrealistic or even impossible. In this paper, we investigate an ordering and replacement modeling framework for a deteriorating repairable system with two failure types, involving a repairable failure (Type I failure) and a nonrepairable failure (Type II failure). A regular order and an expedited order are both incorporated into the spare unit ordering policy while a preventive replacement and a corrective replacement are both considered into the replacement policy, of which the ordering and replacement policy is scheduled in regard to the number of repairable failures. The expected profit per unit of time over an infinite time span is maximized to seek the optimal solutions in terms of the renewal reward theorem with a recursive algorithm procedure. An illustrative example is designed to validate the applicability and effectiveness of the developed ordering and replacement policy. In addition, the sensitivity of the optimal ordering‐replacement policy is studied and the effect of some key characteristics on the optimal decisions is discussed. [ABSTRACT FROM AUTHOR]

This paper aims to help financial institutions identify credit risk and reduce default losses more effectively by improving the accuracy and efficiency of the credit evaluation process using two methods. The paper first mines indicators that comprehensively describe the supply chain aspects of credit risk to obtain a precise credit risk portrait of the enterprise. The study then innovatively designs a random forest‐weighted naive Bayes (RF‐WNB) model that offers good interpretability and generalization qualities. The model forms an effective two‐stage process of enterprise credit risk evaluation that selects key features, quantifies the importance of each, and then evaluates credit risk. By applying empirical analysis to 1363 listed enterprises and performing one typical case analysis, we verified the effectiveness of the indicators and the two‐stage RF‐WNB model in evaluating enterprise credit risk. [ABSTRACT FROM AUTHOR]

The article discusses the 2030 Agenda for Sustainable Development and the 17 sustainable development goals (SDGs) approved by the United Nations in 2015. It highlights the importance of Multiple Criteria Decision Making (MCDM) in modeling complex decision-making problems related to the SDGs. The article announces a special issue of the International Transactions in Operational Research (ITOR) dedicated to the latest developments and applications of MCDM to the SDGs. Researchers and practitioners from around the world are invited to contribute original research papers on topics such as MCDM methodologies, applications of MCDM to SDGs, decision support systems, uncertainty in MCDM, and artificial intelligence and machine learning in MCDM. The deadline for submissions is February 28, 2025. [Extracted from the article]

Because of the significant advantages of bone adhesives compared with traditional mechanical fixation, many scholars have studied polyurethane bone adhesives. However, polyurethane will release a lot of heat in the reaction process, producing high temperature. The high temperature does not only easily cause a human discomfort but also will destroy the tissue structure of human. In this paper, a new polyurethane composite based on phase transition materials and hydroxyapatite is prepared to reduce released heat for application in bone binder. The mechanical properties, adhesive strength, and released heat‐induced temperature of polyurethane composite are investigated in detail. It exhibits good viscoelastic performance with a large elastic deformation (>30%) and good adhesive strength (>0.2 MPa). Furthermore, the released heat of polyurethane composite is effectively reduced, in which the temperature drastically decreases from 110.4 to 41.8°C. The work does not only confirm formation of high‐performance bone binder but also provides a new method to reduce released heat of polyurethane for various applications. [ABSTRACT FROM AUTHOR]

The problem of strength loss in electrically conductive composites made with dispersed fillers in the polymer binder is solved by spraying an aerosol of graphite on a textile material with a 3D weave of threads, or on knitwear of fibers with various structure and chemical composition. The loose microstructure of cotton fibers and the developed surface of elastic filaments made of synthetic polymers retain colloidal graphite particles in a layered composite during cyclic deformation. The electrical conductivity of knitwear with graphite changes during stretching due to the reversible displacement of graphite particles, first during straightening and then during stretching of elastic threads. The 130 ± 5 strain sensitivity GF and 12,000 ± 95 кPa−1 stress sensitivity QF of the composites presented in this paper exceed the corresponding parameters of known electrically conductive composites. The combination of a significant change in electrical conductivity with the elasticity of the developed knitwear‐based composite is important for the use in load cells, smart clothes, medical devices, and robotics. [ABSTRACT FROM AUTHOR]

The blending of different elastomers can greatly broaden the application range of rubber materials. However, due to the poor compatibility between polar elastomers and non‐polar elastomers, severe phase separation occurs during the blending process, which severely degrades their processing performance and mechanical properties. In this paper, we prepared SBR‐P/XNBR blends by emulsion blending polar modified styrene‐butadiene rubber (SBR‐P) latex with carboxylated nitrile rubber (XNBR) latex. The effects of the blending ratios on the vulcanization behavior, mechanical strength, dynamic mechanical properties, and oil resistance of the rubber compounds were studied. Experimental results showed that the mechanical strength and oil resistance of the modified SBR‐P/XNBR blends are significantly higher than the unmodified SBR and XNBR blends. The dynamic mechanical analysis curve results of the rubber compounds showed that SBR‐P has good compatibility with XNBR. With the increasing addition of XNBR, the oil resistance of the rubber compounds improves, and the solvent adsorption is reduced by over 60%.This study proposes a solution for utilizing non‐polar rubbers, which are typically not oil‐resistant, in oily environments. The proposed solution enables the preparation of composites with excellent mechanical properties in such environments. [ABSTRACT FROM AUTHOR]

This paper (i) explores the internal structure of two quantum mechanics datasets (QM7b, QM9), composed of several thousands of organic molecules and described in terms of electronic properties, and (ii) further explores an inverse design approach to molecular design consisting of using machine learning methods to approximate the atomic composition of molecules, using QM9 data. Understanding the structure and characteristics of this kind of data is important when predicting the atomic composition from physical‐chemical properties in inverse molecular designs. Intrinsic dimension analysis, clustering, and outlier detection methods were used in the study. They revealed that for both datasets the intrinsic dimensionality is several times smaller than the descriptive dimensions. The QM7b data is composed of well‐defined clusters related to atomic composition. The QM9 data consists of an outer region predominantly composed of outliers, and an inner, core region that concentrates clustered inliner objects. A significant relationship exists between the number of atoms in the molecule and its outlier/inliner nature. The spatial structure exhibits a relationship with molecular weight. Despite the structural differences between the two datasets, the predictability of variables of interest for inverse molecular design is high. This is exemplified by models estimating the number of atoms of the molecule from both the original properties and from lower dimensional embedding spaces. In the generative approach the input is given by a set of desired properties of the molecule and the output is an approximation of the atomic composition in terms of its constituent chemical elements. This could serve as the starting region for further search in the huge space determined by the set of possible chemical compounds. The quantum mechanic's dataset QM9 is used in the study, composed of 133,885 small organic molecules and 19 electronic properties. Different multi‐target regression approaches were considered for predicting the atomic composition from the properties, including feature engineering techniques in an auto‐machine learning framework. High‐quality models were found that predict the atomic composition of the molecules from their electronic properties, as well as from a subset of only 52.6% size. Feature selection worked better than feature generation. The results validate the generative approach to inverse molecular design. [ABSTRACT FROM AUTHOR]

Through this paper, the authors propose using the quantum cluster equilibrium (QCE) theory to reinvestigate ammonia clusters in the liquid phase. The ammonia clusters from size monomer to hexadecamer were considered to simulate the liquid ammonia in this approach. The clusterset used to model the liquid ammonia is an ensemble of different structures of ammonia clusters. After studious research of the representative configurations of ammonia clusters through the cluster research program ABCluster, the configurations have been optimized at the MN15/6‐31++G(d,p) level of theory. These optimizations lead to geometries and frequencies as inputs for the Peacemaker code. The QCE study of this molecular system permits us to get the liquid phase populations in a temperature range of 190–260 K, covering the temperatures from the melting point to the boiling point. The results show that the population of liquid ammonia comprises mainly the ammonia hexadecamer followed by pentadecamer, tetradecamer, and tridecamer. We noted that the small‐sized ammonia clusters do not contribute to the population of liquid ammonia. In addition, the thermodynamic properties, such as heat of vaporization, heat capacity, entropy, enthalpy, and free energies, obtained by the QCE theory have been compared to the experiment given some relatively good agreements in the gas phase and show considerable discrepancies in liquid phase except the density. Finally, based on the predicted population, we calculated the infrared spectrum of liquid ammonia at 215 K temperature. It comes out that the calculated infrared spectrum qualitatively agrees with the experiment. [ABSTRACT FROM AUTHOR]

Background: Students' initial experiences at university often shape their attendance and attainment. For some students, university is a positive experience, whereas others seemingly struggle and have more negative emotions. Unearthing distinctions in first-year students' emotionality may be valuable in understanding their academic engagement and performance., Aims: The study's aim was to identify distinct profiles of students based on their achievement emotions and explore whether these profiles differed in university attendance and attainment. At least three emotional profiles were hypothesized (positive; moderate; negative) with a positive profile expected to display the highest attendance and attainment., Sample: Participants were 294 first-year undergraduate students from a university in the United Kingdom (M age  = 19.33 years; 127 men; 162 women)., Methods: Students completed self-report measures of eight achievement emotions for general learning. Attendance and attainment data were collected from official records. Hierarchical cluster analysis was performed to profile students on their achievement emotions. Analysis of covariance explored profile differences in attendance and attainment., Results: Four emotional profiles emerged: positive; moderate; negative; mixed-valence (both positive and negative activating emotions). The positive and mixed-valence groups displayed equally high attendance compared to the moderate and negative groups. The positive group obtained higher academic attainment than the mixed-valence and negative emotion groups, but not the moderate group., Conclusions: The findings highlight the diverse emotional experiences of first-year university students and the existence of co-occurring positive and negative activation emotions. This evidence may be of practical worth to educators in understanding variability in students' emotions, attendance and attainment., (© 2024 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Background: When second-career teachers (SCTs) learn to teach, they need to cope with the tension-evoking moments they encounter. Little is known about the coping strategies SCTs use to manage tensions., Aim: The purpose of this study was to investigate the coping strategies SCTs use during the first 1.5 years of teacher training. The SCTs' own perceptions about the usefulness, evolution and specificity of these coping strategies were also studied., Sample: Twenty-four SCTs in the alternative teacher training programme (ATTP) at the University of Amsterdam participated in this study. These SCTs aspired to become teachers of mathematics, physics, economics, computer science or chemistry., Method: For each participant, written logbook fragments and interviews were analysed in Atlas-ti., Results: The SCTs used in decreasing order: intrapersonal approaching (e.g., resolving problems autonomously), interpersonal approaching (e.g., consulting others) and intrapersonal avoiding strategies (e.g., ignoring an undesired situation). Interpersonal avoiding strategies were not mentioned at all. While the SCTs reported approaching strategies (inter- and intrapersonal) as being useful for their development, we also found disadvantages to intrapersonal approaching strategies and advantageous uses of avoiding strategies. The SCTs noted several inter- and intrapersonal approaching coping strategies that they considered typical for SCTs., Conclusion: Intrapersonal approaching coping strategies can be a risk because of the invisibility of these strategies. Interpersonal strategies should be stimulated because SCTs benefit from the social network in school. Avoiding strategies may serve a purpose for managing the complexity of the teaching profession., (© 2023 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Background: Real-world problems are important in math instruction, but they do not necessarily trigger students' task motivation. Personalizing real-world problems by (1) matching problems to students' shared living environment (context personalization) and (2) asking students to pose their own problems (active personalization) might be two interventions to increase students' task motivation., Aim: In the current study, we investigated the effects of context personalization and active personalization on students' self-efficacy expectations, intrinsic value, attainment value, utility value, and cost., Sample: The participants were 28 fifth- and sixth-grade students who voluntarily took part in a six-month afterschool program in which they posed problems with the aim of creating a math walk in their hometown., Method: Using a within-subjects design, at the end of the afterschool program, the students rated their self-efficacy expectations and task values for four self-developed problems associated with their hometown, four peer-developed problems associated with their hometown, and four instructor-provided problems associated with unfamiliar locations., Results: Students reported higher self-efficacy expectations, intrinsic value, attainment value, and utility value for active-personalized than non-personalized problems. To a lesser extent, context personalization promoted intrinsic value and attainment value. No effect was found for cost., Conclusions: Active personalization (i.e. asking students to pose their own real-world problems) is suited to enhance students' task motivation, specifically their self-efficacy expectations, intrinsic value, attainment value, and utility value. Context personalization still boosts students' intrinsic value and attainment value. Implementation in classroom instruction is discussed., (© 2024 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Background: Previous evidence has suggested a strong association between school exclusion and health outcomes. However, as health risks are themselves related to the risk of experiencing a school exclusion, it has been challenging to determine the extent to which school exclusion impacts later health outcomes, as opposed to reflecting a marker for pre-existing risks., Aim: The aim of the current study was to address this challenge in estimating the medium-to-long-term impact of school exclusion of health and well-being outcomes., Methods: To this end, we used an inverse propensity weighting approach in the Next Steps data set (N = 6534, from wave 1, 2014, to wave 8, 2015)., Results: We found that after weighting for propensity of treatment scores estimated based on a wide range of factors, including previous health indicators, there was a significant effect of school exclusion on a wide range of health and well-being outcomes., Discussion: These results provide some of the most robust evidence to date that school exclusion harms long-term health outcomes., Conclusion: The findings suggest that policies should aim to reduce exclusion and ensure access to preventative health support for those who experience a school exclusion., (© 2023 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Background: Math anxiety (MA) is a worldwide appearing academic anxiety that can affect student mental health and deter students from math and science-related career choices., Method: Using the Arabic version of the Modified-Abbreviated Math Anxiety Scale (m-AMAS), the prevalence of MA was investigated in a very large sample of students (N = 10093) from grades 7 to 12 in Qatar., Results: The results showed a better fit to the original two-factor model of the m-AMAS (learning MA and Evaluation MA) than to a single-factor solution. This two-factor model was also confirmed in each grade. Notably, the distribution of MA scores was right-skewed, especially for learning MA. Using the inter-quartiles ranges, norms for MA were provided: A score of ≤16 indicates low MA whereas a score of ≥30 identifies high MA. Previous studies conducted in Western countries defined high math-anxious students as those who score above the 90th percentile corresponding to a score of 30 on the m-AMAS. Using this cut-off criterion, the current study found that one-fifth of students in Qatar were highly math-anxious, with a higher proportion of females than males. We also calculated the percentage of participants selecting each response category for each questionnaire item. Results showed that attending a long math class was the context that elicited the highest levels of learning MA. In contrast, having an unexpected math test was the situation that triggered the highest levels of evaluation MA., Conclusion: The prevalence of MA might vary across different cultures., (© 2024 British Psychological Society.)

Background: Taking notes during learning has benefits both during class (through writing things down to encode information) and after class (by using written notes as external storage for revision). Comparisons of note-taking methods (i.e., using paper or a computer) have mainly shown that paper leads to better learning. However, previous studies have mostly been conducted in laboratory contexts., Aims: The current study investigates university students' perceptions of the efficacity of their own preferred note-taking method, along with the strategies they employ., Sample: Data were collected from 108 university students., Methods: Students answered a questionnaire about their note-taking strategies during initial note-taking (in class) and revision (after class)., Results: The results show that students who take notes on paper do not consider their method to be more effective, but they report engaging in more reformulation and less multitasking. Students who take notes on a computer are more likely to reformat their notes, and thus to reformulate at a later stage. For all students, review sheets are mostly done on paper., Conclusions: These results suggest that although students are not necessarily aware of the benefits of reformulation associated with handwriting on paper during initial note-taking, when revising, they tend to choose handwriting and benefit from reformulation when aiming for deeper processing. Therefore, revision activities remain mainly paper-based., (© 2024 British Psychological Society.)

Introduction: Paving the way towards inclusive education, it is essential to aim for positive social outcomes for all students, including cultivating a positive self-concept and fostering acceptance and friendships with peers. Although self-concept, acceptance and friendships are interrelated, research focussing on the relationship between these constructs remains limited., Method: This study examined the self-concept, acceptance and friendships of two groups of typically developing students in secondary education (n = 401) and two groups of students in special secondary education with either an intellectual disability (ID) (n = 58) or social, emotional and behavioural difficulties (SEBD) (n = 68)., Results: Lower self-concept scores were found for students with ID on some dimensions, whereas typically developing students reported lower acceptance and friendship scores. Multilevel analyses indicated that acceptance is a predictor for several different dimensions of self-concept in the different groups of students, but friendship is less likely to predict self-concept scores., Discussion: The results of the study emphasize the importance of promoting peer acceptance amongst all students in inclusive school settings in order to realize the intended positive social outcomes of inclusive education., (© 2024 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Background: Socio-emotional skills are critical to life outcomes such as achievement, well-being and job success. However, existing research has mostly focused on the consequences of socio-emotional skills, with less attention devoted to the role of school climate in the deployment of these skills., Aims: This study investigated the role of school climate in socio-emotional skills. More specifically, we investigated whether cooperative or competitive school climates are associated with students' socio-emotional skills., Sample: Our study utilized data from the OECD Survey on Social and Emotional Skills, collected from 10 cities across nine countries. Participants were 60,985 students, including 31,187 10-year-olds (49.70% females) and 29,798 15-year-olds (51.6% females)., Methods: We conducted multilevel structural equation modelling to test whether cooperative and competitive climates were associated with socio-emotional skills. These skills include five broad domain skills and 15 more specific skills: task performance (self-control, responsibility and persistence), emotion regulation (stress resistance, emotional control and optimism), collaboration (empathy, trust and cooperation), open-mindedness (tolerance, curiosity and creativity) and engaging with others (sociability, assertiveness and energy)., Results: Our findings indicated a positive relationship between a cooperative climate and socio-emotional skills. In contrast, the relationship between a competitive climate and socio-emotional skills was primarily negative., Conclusion: This study highlights the contrasting roles of cooperative and competitive climates in students' socio-emotional skills., (© 2024 The Authors. British Journal of Educational Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Background: Students' music self-concept and music emotions are becoming prominent topics within the area of music education., Aims, Samples and Methods: The majority of previous research on self-concept and music emotions has examined the two constructs independently and focused on gender differences in externalizing behaviours in music learning, but has neglected the internal interactions between individual music self-concept and music emotions. Network analysis is a promising method for visually examining music self-concept and music emotions as part of a network of interactions to identify core features and interrelationships among nodes in the network. In this study, 515 students majoring in music from a Chinese university were recruited., Results: The results showed that high music self-concept and boredom were the common features at the core of the network for both men and women college students. The boredom exhibited by women differed from that of men in that men's boredom was directed at the entire music course, while boredom in women manifested as daydreaming and boredom with learning materials., Conclusions: This study is the first to explore gender differences in the music self-concept and music emotions from a holistic perspective. The findings could help music teachers gain insight into the complex system of music self-concept and music emotions. Music teachers could capture the respective features of men and women to design individualized teaching strategies., (© 2024 British Psychological Society.)

Objective: Psychological formulation is a key competency for clinical psychologists. However, there is a lack of consensus regarding the key components and processes of formulation that are hypothesized to contribute to poor reliability of formulations. The aim of this study was to develop consensus on the essential components of a formulation to inform training for clinical psychologists and best practice guidelines., Methods: A Delphi methodology was used. Items were generated from the literature and discussed and refined with a panel of experts (n = 10). In round one, 110 clinical psychologists in the United Kingdom rated the importance of components of formulation via an online questionnaire. Criteria for consensus were applied and statements were rerated in round two if consensus was not achieved., Results: Consensus was achieved on 30 items, with 18 statements regarding components of a formulation and 12 statements regarding formulation process. Items that clinicians agreed upon emphasized the importance of integrating sociocultural, biological, strengths and personal meaning alongside well-established theoretical frameworks. Consensus was not reached on 20 items, including whether a formulation should be parsimonious or adhere to a model., Conclusion: Our findings provide mixed evidence regarding consensus on the key components of formulation. There was an agreement that formulation should be client-led and incorporate strengths and sociocultural factors. Further research should explore client perspectives on the key components of formulation and how these compare to the clinicians' perspectives., (© 2024 The Authors. British Journal of Clinical Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Family history (FH) of autism and ADHD is not often considered during the recruitment process of developmental studies, despite high recurrence rates. We looked at the rate of autism or ADHD amongst family members of young children (9 to 46 months) in three UK-based samples (N = 1055) recruited using different methods. The rate of FH-autism or FH-ADHD was 3%-9% for diagnosed cases. The rate was highest in the sample recruited through an online participant pool, which also consisted of the most socio-economically diverse families. Lower parental education and family income were associated with higher rates of FH-ADHD and lower parental education with increased FH-autism. Thus, recruitment strategies have a meaningful impact on neurodiversity and the conclusions and generalizations that can be drawn. Specifically, recruitment using crowdsourcing websites could create a sample that is more representative of the wider population, compared to those recruited through university-related volunteer databases and social media., (© 2023 The Authors. British Journal of Developmental Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

Current methods used to investigate emotional inference and conflict resolution knowledge are limited in their suitability for use with children with language disorders due to a reliance on language processing. This is problematic, as nearly 8% of the population are estimated to have developmental language disorder (DLD). In this paper, we present 'Zoti's Social Toolkit', a set of animated scenarios that can be used to assess emotion inferencing and conflict resolution knowledge. All animated scenarios contain interpersonal situations centred around a gender-neutral alien named Zoti. Four studies investigated the face and construct validity of the stimuli. The final stimulus set can be used with children, who may or may not have language difficulties and is openly available for use in research., (© 2024 The Authors. British Journal of Developmental Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

In the previous psychoeducational literature, many theorists have argued that creativity and originality require the ability to make predictions and assumptions regarding other individuals' ideas. Thus, it has been widely hypothesized that social cognition and theory of mind (ToM) might be a fundamental component or even a prerequisite of creativity. Despite their common grounds, the empirical evidence examining the potential link between ToM and creative thinking throughout their development seems to be indirect, limited, and fragmented. In this respect, this scoping review aimed to collect and synthesize the existing knowledge about the relationship between ToM and creativity at different ages to identify significant literature gaps and generate updated research questions that might guide future research. The search process led to the inclusion, analysis, and collation of 6 relevant studies only, indicating that this research topic has been poorly investigated. Results demonstrated that ToM and creativity are strongly correlated even after partialling out significant mediators, such as age and intelligence. Such a strong association should be further investigated and explained., (© 2024 The Authors. British Journal of Developmental Psychology published by John Wiley & Sons Ltd on behalf of British Psychological Society.)

The authors of this response article express gratitude for the comments made by Martin and colleagues on their previous paper. They argue that their review articles provide evidence supporting the idea that women were capable of hunting in the Paleolithic era. They disagree with Martin and colleagues' assertion that reproductive behavioral ecology work among modern foragers should preclude reconstructions of past gender roles. The authors also address criticisms regarding testable hypotheses, estrogen levels, the value of mothering and gathering activities, and the feasibility of a strict sexual division of labor in small forager groups. They invite Martin and colleagues to collaborate on further research to explore the reasons behind the absence of evidence for a sexual division of labor until relatively recently. [Extracted from the article]

In this paper, we obtain the optimal instability threshold of the Couette flow for Navier–Stokes equations with small viscosity ν>0$\nu >0$, when the perturbations are in the critical spaces Hx1Ly2$H^1_xL_y^2$. More precisely, we introduce a new dynamical approach to prove the instability for some perturbation of size ν12−δ0$\nu ^{\frac{1}{2}-\delta _0}$ with any small δ0>0$\delta _0>0$, which implies that ν12$\nu ^{\frac{1}{2}}$ is the sharp stability threshold. In our method, we prove a transient exponential growth without referring to eigenvalue or pseudo‐spectrum. As an application, for the linearized Euler equations around shear flows that are near the Couette flow, we provide a new tool to prove the existence of growing modes for the corresponding Rayleigh operator and give a precise location of the eigenvalues. [ABSTRACT FROM AUTHOR]

Film therapy, the therapeutic use of movies in psychological therapies, is a growing area of interest to therapists and researchers. This paper discusses the benefits of film therapy and presents a method, the MOVIE model of film therapy, a widely applicable, trauma‐informed framework for individual or group therapy, counsellor education and self‐help. Film or cinema therapy is a highly accessible and relatable method for many diverse groups. The benefits identified include emotional processing, greater ease of dialogue, increased empathy, interpersonal learning, new perspectives and learning skills. Following the model's reflective steps, clients mindfully observe and voice their emotional and psychological responses to movie‐watching; identify how the imagery, characters, themes and story symbolise personal experience; and consider any new possibilities that emerged for their own lives. The model is informed by a practice‐focussed review of literature and draws on methods from mindfulness and experiential and narrative therapies. The steps are widely applicable in a range of settings and can be adapted across counselling modalities. Further research to consider the outcomes of using the model, alongside qualitative research on clients' experiences of working this way with film, is recommended. [ABSTRACT FROM AUTHOR]

Aim: Routine outcome monitoring (ROM), including the use of feedback, has become a much vaunted method in psychological therapies but is little used in university/college counselling and mental health services, perhaps because its adoption raises questions for many practitioners and service leaders. There is a need for both clinical‐ and research‐based statements to clarify the reasoning and rationale for ROM. This paper aims to present and respond to common challenges of and reservations about using ROM in student counselling and/or mental health services. Method: The article poses 15 questions and issues about the adoption of ROM drawn from the literature on this topic and further refined by practitioner‐ and researcher‐members of a consortium comprising service leads, practitioners, and researchers working in the field of student counselling in the UK. The questions address nine themes: (1) the purpose and yield of ROM; (2) the burden of measurement; (3) the impact on clients and process of therapy; (4) consistency with therapeutic theory; (5) client groups and settings; (6) concern from practitioners; (7) equality, diversity, and inclusion; (8) implementation; and (9) relationship with the paradigm of practice‐based evidence. Findings: Responses to each of the 15 questions are provided from a methodological, evidence‐based, and clinical perspective. Conclusions: The responses provide practitioners with the necessary information to enable them to make informed decisions as to the value, or otherwise, of adopting ROM, including feedback, in the delivery of counselling interventions, and generating evidence created from clinical practice. [ABSTRACT FROM AUTHOR]

Background: The use of artificial intelligence (AI) is increasing in many areas of healthcare, including mental healthcare. The automated nature of such technologies has the potential to be developed to work with large numbers of people. This paper examines the way that student therapists experience using an interactive text‐based machine client as a training tool. Methodology: Chatbot technology has been used to develop an emotionally responsive interactive client (ERIC). This introduces individuals to concepts of person‐centred therapy (empathy, congruence and unconditional positive regard) by using a series of pre‐programmed scenarios. Twenty‐eight student therapists evaluated ERIC's potential as a learning tool. Individuals were recruited from one university from a postgraduate and an undergraduate counselling programme. Findings: Feedback was generally positive, with all reporting that they enjoyed engaging with ERIC as a learning method. ERIC helped individuals consider their understanding of counselling skills in a non‐judgemental environment. Participants felt the scenarios were realistic and engaging, with many reporting that they felt they were engaging with a real client/person due to ERIC's ability to express emotions. Discussion: ERIC is at the proof‐of‐concept phase. From the feedback presented here, it is evident that it can be a useful learning tool. Further development of ERIC with feedback from a larger sample is, however, required. ERIC is currently a text‐based client, and further development would like to see the intervention be voice‐activated to enhance the experience. ERIC can be further enhanced and adapted to be a useful learning platform for student therapists, as well as for students in other (healthcare‐related) disciplines, whereby a client or patient is required. [ABSTRACT FROM AUTHOR]

Objective: The aim of this research was to understand counsellors' and therapists' perceptions and experiences of working online during the COVID‐19 pandemic. Method: Five hundred and ninety clinicians, mostly UK‐based, responded to an online qualitative survey, which allowed data to be gathered from a broader range of participants than is typical for qualitative interviews or focus group studies, and provided a wide‐angle lens. The survey generated over 130,000 words, on which a five‐staged framework analysis was conducted. Seven superordinate themes were identified. Results: In this paper, three of these superordinate themes specifically addressing online therapeutic practice with individual adult clients are presented. Therapists' accounts addressed the diverse ways in which the online space changed how they thought about the therapy relationship and their interactions with clients. Respondents described instances where online therapy had been beneficial for facilitating and cultivating the therapeutic process and relationship as well as how online therapy could have a potentially disruptive impact on therapeutic practice. Implications: For practitioners who continue to deliver therapy solely online and/or offer hybrid services, this study identifies the specific knowledge and skills required for effective and safe online therapeutic work. [ABSTRACT FROM AUTHOR]