Decision Level Research Articles

From Single Orders to Batches: A Sensitivity Analysis of Warehouse Picking Efficiency

Currently, companies are called to meet variable market demand whilst having to comply with tighter delivery times, also due to the growing spread of e-commerce systems in the last decade. As never before, it is therefore mandatory to increase the efficiency within distribution centers to minimize operating costs and increase environmental and economical sustainability. The picking process is the most expensive task in a warehouse, both for the required resources and time for completing all the operations, which is typically carried out manually. Several policies can be identified, such as discrete or batch picking. Many studies tend to optimize both policies, treating them distinctly and integrating them with other factors including, for instance, the logic of product allocation. This article stands on a higher decision-making level: starting from a database obtained with a simulative approach that contains the average distances covered by pickers in different warehouse configurations, the aim is to provide an analysis of which factors have the greatest impact in preferring a discrete order picking policy over the batch one. The factors in question are shape factor, input–output point, routing and storage location assignment policies. Results can be useful for industrial practitioners in defining the most efficient managerial strategies.

Factors Influencing the Interest of Prospective Students in Choosing the D3 Accounting Program at Politeknik Negeri Banjarmasin

Prospective students who wish to enter higher education must first choose the study program they desire, as this program will provide them with the knowledge and skills needed for their future. Selecting a study program may seem easy for prospective students, given the wide range of options available at a university. However, they also need to consider other factors, both internal and external, when making their choice. This study aims to identify the factors that influence the interest of prospective students in choosing the D3 Accounting Program at Politeknik Negeri Banjarmasin and to provide information regarding the profile of respondents based on student status, gender, and the level of decision-making in selecting the D3 Accounting Program at Politeknik Negeri Banjarmasin. The data analysis techniques used in this study include descriptive analysis, instrument testing, classical assumption testing, and hypothesis testing to analyze the effect of two or more independent variables on the dependent variable using regression equations for two or more variables. The t-test is used to examine the significance of each independent variable's constant, determining whether each variable has a partial (individual) effect on the dependent variable, while the F-test is used to assess the simultaneous impact of the independent variables on the dependent variable, determining whether the influence is significant or not. The results of the hypothesis testing using the F-test and t-test indicate that all alternative hypotheses (Ha) are accepted, and null hypotheses (Ho) are rejected. Thus, the findings reveal that social factors, job opportunities, accreditation, and education costs collectively influence the interest of prospective students in choosing the D3 Accounting Program at Poliban

The Role of Nudges and Behavioral Interventions in Financial Decision Making

Financial decision-making, both at individual and institutional levels, plays a critical role in determining economic outcomes. Traditional economic theory posits that individuals are rational agents who make decisions based on complete information and a consistent set of preferences. However, behavioral economics has challenged this notion, highlighting cognitive biases, heuristics, and social influences that can drive suboptimal decisions. Nudges and behavioral interventions have emerged as tools to guide individuals toward better financial choices without restricting their freedom of choice. The purpose of this research is to present a descriptive investigation of behavioral interventions and nudges in financial decision-making. The main cognitive biases influencing financial behavior are first described, and then several nudge kinds and their applicability in diverse financial contexts— retirement savings, debt management, spending control, and investment decisions—are explored. Along with reviewing empirical data on nudges' efficacy, the paper addresses the ethical issues surrounding their application and pinpoints the variables that affect their effectiveness. This study aims to educate consumers, financial institutions, and policymakers about the potential advantages and constraints of behavioral interventions by providing a thorough overview of how nudges might impact financial decision-making. Keywords: Nudges, Behavioral Intervention, Financial Decision-Making, Cognitive Biases, Financial Literacy.

Pendampingan Penyusunan Kajian Lingkungan Hidup Strategis sebagai Dasar dalam Pembangunan Berkelanjutan di Provinsi Kalimantan Tengah

The sustainable development goals (SDGs) are agreed-upon common goals that must be met by all nations by 2030. Efforts to meet the SDG targets are a national development priority that necessitates the coordination of planning policies at the national and regional levels. In Central Kalimantan, the provincial and district/city governments are currently preparing various Strategic Environmental Assessment documents (SEA). The goal of this activity is to increase the working group's and experts' understanding of the principles of sustainable development that have become the basis and integrated into the development of a region, so that they can provide recommendations for environmental considerations at the strategic decision-making level, particularly after the 2024 joint national and provincial election. It has been carried out to reduce the obstacles that have previously been a problem by providing technical guidance on various tools for preparing and validating SEA. Following the training, it was discovered that the understanding and mastery of district/city working groups and university experts could improve compared to before the assistance series.

The Factors Affecting the Small and Medium Enterprises Management in Shanghai

Administrative aspects are recognized as crucial components for the efficient functioning of the entire enterprise, especially in influencing daily operations. Each administrative link influences regular operations in different ways. At the level of decision-making, the scientific implementation of command and control is highly urgent. The optimum performance of corporate departments relies heavily on the implementation of effective decision-making processes. Although prominent leaders and principals hold significant guiding power over each department, this authority is often misunderstood. The power of leadership should not be overexerted or left unused. Instead, it should be strategically employed to foster and enhance the efficiency and productivity of each department. This study investigates the lasting impact of administrative factors on the efficiency of enterprise management. By promoting efficient administrative practices and well-informed decision-making, leaders can ensure that each department operates at its highest potential. This approach not only streamlines the enterprise's daily operations but also contributes to its long-term success and sustainability. Overall, the study highlights the intricate and essential relationship between administrative factors and enterprise management efficiency.

European Academy of Neurology: First 10 years.

The European Academy of Neurology (EAN) was founded in May 2014 as the scientific society of all neurologists and all national neurological societies in Europe. The creation of EAN was based on the conscious decision of two predecessor societies (the European Federation of Neurological Societies and the European Neurological Society) to have a unique society for this continent with its 53 countries according to the World Health Organization and with the EU as the most important continental political decision level. In this report, the important milestones and the motivations of the decision-makers during the first 10 years are described. The development of the annual congress, the broad educational agenda, and the scientific groundwork including the European guidelines for the practise of neurology are described. Collaboration with and work for the national neurological societies is an important task for EAN. The political representation of neurology in Brussels and collaboration with the scientific societies of neighbouring medical disciplines and patient organizations are other major tasks on the agenda of the organization. EAN's goal is to reduce the burden of neurological diseases and to be the "home of neurology" in Europe for physicians, patients, and society. EAN communicates and interacts with its members, patients, partners, politicians, and the public through different channels. EAN is the owner of the scientifically independent European Journal of Neurology. EAN is based in Vienna, where its head office is located, but is also strongly represented in Brussels.

Intuitionistic Linguistic EDAS Method with New Score Function: Case Study on Evaluating Universities’ Innovation and Entrepreneurship Education

Intuitionistic linguistic numbers (ILNs) describe expert evaluation information by representing semantic assessment values and reflecting the confidence level and hesitation of decision-makers. ILNs are widely used to handle uncertain and incomplete information. The Evaluation Based on Distance from Average Solution (EDAS) method selects the optimal solution based on the distance of each alternative from the average solution, making it suitable for multi-attribute decision-making with conflicting attributes. This study proposes a new scoring function for ILNs and develops an evaluation method combining ILNs with EDAS (IL-EDAS). Experts’ evaluations of each alternative’s indices are expressed using ILNs, and the EDAS method ranks the alternatives to select the optimal solution. We apply this method to assess innovation and entrepreneurship education capabilities in universities, and compare the results with those from other methods to verify their applicability and practicality.

NKDFF-CNN: A convolutional neural network with narrow kernel and dual-view feature fusion for multitype gesture recognition based on sEMG

Deep learning algorithms have been widely applied to gesture recognition based on multi-channel surface electromyography (sEMG). However, the limitations in feature extraction capabilities of existing algorithms have restricted the performance of multitype gesture recognition. To address this challenge, we propose a novel sEMG-based gesture recognition algorithm, namely, Narrow Kernel and Dual-view Feature Fusion Convolutional Neural Network (NKDFF-CNN). Firstly, to overcome the issue of traditional square kernel convolution operation, which loses channel independence features, we employ the narrow kernel convolution in the model to learn time-related features in each independent channel of sEMG, resulting in obtaining representative correlation information between specific muscles and gestures. Then, the dual-view structure is used to capture both shallow and deep features, which are fused at the decision level. Thus, the multi-dimensional feature information is extracted. The NKDFF-CNN is further extended to ACCNKDFF-CNN by introducing acceleration signals for multimodal feature integration. Experimental validation on the NinaPro DB2 dataset demonstrates the superior classification performance of NKDFF-CNN, achieving 88.03 % accuracy for 49 hand gestures, outperforming other state-of-the-art MSFF-net. In addition, the ACCNKDFF-CNN model with multimodal feature information significantly improved the accuracy to 95.25 %. We also validated the proposed NKDFF-CNN on NinaPro DB3 with the disabled subjects and the NinaPro DB4 with healthy subjects. The results showcased that the NKDFF-CNN achieved advanced accuracies of 70.58 % and 85.91 % for the multitype hand gestures classification, respectively, showing the high generalization ability of the proposed model. As a consequence, the proposed NKDFF-CNN method achieved superior recognition performance in both accuracy and generality compared to other advanced models. Thus, it provides a reliable algorithm for research in fields such as rehabilitative medicine.

Integrating storage allocation with manual order picking and replenishment operations in a distribution centre

ABSTRACT This paper introduces a mathematical programming formulation and a simulation-based heuristic for the allocation of storage positions to products picked by human operators on man-aboard vehicles traveling through the warehouse of a wholesale company. In this problem, the tactical level of the assignment decisions affects the operational level of the picking process. We propose a simulation-optimisation framework that integrates the two. Our formulation of the storage location assignment problem also handles the constraint according to which a picking position should be paired with a (vertical) replenishment position for a given item. To solve realistic instances, we design an iterated local search (ILS) metaheuristic with an embedded discrete-event simulator (DES) that evaluates the most promising moves at each iteration. The DES allows reproducing the handling operations performed by multiple order pickers under uncertainty, mutual interferences and congestion-related phenomena. Overall, the flexible simulation-optimisation (SO) framework evaluates the operational times and daily productivity of the order picking organisation. Numerical results are presented for real data, under an S-shape picking policy with a skip-and-go rule to deal with lacking items. Under a proper tuning of the ILS parameters, the SO framework allows to achieve a nearly 17% improvement in warehouse productivity.

Exploring Influencing Factors of Interactive Teaching Decision-Making Level Among Novice Junior High School Music Teachers

This study investigates the factors influencing interactive teaching decision-making level among novice music teachers in junior high schools, a fundamental skill that spans the entire music teaching process. Focusing on classroom instruction and student learning, the study addresses two primary questions: (1) What factors affect the interactive teaching decision-making of novice music teachers? (2) How do these factors vary by gender, educational background, and teaching experience? A questionnaire survey of novice music teachers in Boshan, China, was conducted, and the collected data were analyzed using SPSSAU software. The findings indicate that student response is the most influential factor, while evaluation is the least. Comparative analysis shows that novice teachers with 2-3 years of teaching experience rank higher in classroom resilience and teaching skills, whereas those with 0-2 years score higher in classroom management. Nonparametric statistics further illuminate differences in interactive decision-making by teaching experience. The study suggests that novice music teachers should prioritize student responses, enhance classroom resilience and teaching skills, and emphasize classroom evaluation and reflection. These insights aim to improve decision-making processes and support the professional development of novice music teachers.

Glioma Identification Based on Digital Multimodal Spectra Integrated With Deep Learning Feature Fusion Using a Miniature Raman Spectrometer.

The miniature fiber Raman spectroscopy detection technology can reflect the properties of biomolecules through spectral characteristics and has the advantages of noninvasiveness, real-time, safety, label-free operation, and potential for early cancer diagnosis. This technology holds promise for developing portable, low-cost, intraoperative tumor detection instruments. Glioma is one of the most common malignant tumors of the central nervous system with rapid growth and a short disease course. However, the considerable heterogeneity of the glioma sample leads to substantial intraclass variance in collected spectra, coupled with the miniature Raman spectrometer's low signal-to-noise ratio. These factors diminish the accuracy of the brain glioma recognition model. To address this issue, a glioma identification method based on digital multimodal spectra integrated with deep learning features fusion (DMS-DLFF) using the miniature Raman spectrometer is proposed. Different from existing multimodal tumor detection methods employing multiple spectral instruments, DMS-DLFF enhances tumor identification accuracy without increasing hardware costs. The method mathematically decomposes the original spectra to Raman and fluorescence spectra, so as to augment the biospectral information. Then, the deep learning method is used to extract the feature information of the two kinds of spectra, respectively, and the digital multimodal spectral fusion is realized at the feature level. Moreover, a two-layer pattern recognition model is constructed based on the ensemble strategy, amalgamating the strengths of diverse classifiers. Meanwhile, the bagging strategy is introduced to improve support vector machine algorithms, one of the basic classifiers. Compared with traditional methodologies, DMS-DLFF operates at both the feature level and decision level, employing high-information-density feature vectors to train ensemble classification models for increasing overall recognition accuracy. This study collected 260 Raman spectra of glioma and 151 Raman spectra of normal brain tissue. The accuracy, sensitivity, and specificity were 91.9%, 96.7%, and 80.8%, respectively. The proposed method outperforms traditional algorithms in brain glioma detection, which helps doctors formulate precise surgical plans and thereby improve patient prognosis.

Multimodal Biometric System Fusion Using Fingerprint and Iris with Convolutional Neural Network

Biometric sensing technology became everyday life frequent component as a result of world requirement for info security and safety legislation. A strong and efficient individual authentication has appeared because of new developments in multimodal biometrics. Multimodal biometrics integrates different biological traits in trying for creating considerable effect on identification performance. Latent fingerprint biometrics refer to effective human identification system for criminals given the accessible crime evidence shreds. Although, biometric trait restrictions like intra-class variation, sensed data noise, lack of individuality caused low matching score that possesses a negative effect on recognition and investigation process. This paper uses two unimodal biometrics—the fingerprint and the iris—applied as multi-biometrics to show that using these biometrics can produce excellent results with high accuracy. Every biometric result is weighted for involvement in the final decision, and the decision level is utilized for fusion. For every biometric result integration effect, a neural network is used. The datasets' experimental findings have demonstrated a notable biometric system identification capability. The accuracy performance of the suggested approach is 100, the FAR is 0.1, and the EER is 0.1. To demonstrate the efficacy of the suggested system, the suggested method is contrasted with a few other approaches currently in use.

Raising the voice of specialist stoma care nurses: a call for a national strategy in stoma care

Specialist stoma care nurses (SSCNs) are highly educated professionals who play a critical role in the care of people living with a stoma. However, they experience some unique challenges. Currently in the UK, stoma care has no national standards, targets or best practice pathway, resulting in significant variation and an undervalued service. As members of an evidence-based profession, SSCNs need to demonstrate their value. Specialist nurses in cancer care and inflammatory bowel disease are supported by nationally, with pathways, targets and standards of care, as well as career and education frameworks. This system could be used for SSCNs. A national approach is needed to develop an evidence-based best-practice pathway, commissioned and implemented across the UK for all stoma patients and with SSCNs at its heart. SSCNs need to demonstrate their value through data and evidence. Working with Getting it Right First Time, a taskforce aims to review the evidence and establish minimum standards of care for all stoma patients. SSCNs should be co-ordinators and drivers of change at the highest level of national innovation, policy and decision-making. The work of SSCNs should be is recognised and valued because what they do matters.

Enhancing Facial Expression Recognition through Light Field Cameras.

In this paper, we study facial expression recognition (FER) using three modalities obtained from a light field camera: sub-aperture (SA), depth map, and all-in-focus (AiF) images. Our objective is to construct a more comprehensive and effective FER system by investigating multimodal fusion strategies. For this purpose, we employ EfficientNetV2-S, pre-trained on AffectNet, as our primary convolutional neural network. This model, combined with a BiGRU, is used to process SA images. We evaluate various fusion techniques at both decision and feature levels to assess their effectiveness in enhancing FER accuracy. Our findings show that the model using SA images surpasses state-of-the-art performance, achieving 88.13% ± 7.42% accuracy under the subject-specific evaluation protocol and 91.88% ± 3.25% under the subject-independent evaluation protocol. These results highlight our model's potential in enhancing FER accuracy and robustness, outperforming existing methods. Furthermore, our multimodal fusion approach, integrating SA, AiF, and depth images, demonstrates substantial improvements over unimodal models. The decision-level fusion strategy, particularly using average weights, proved most effective, achieving 90.13% ± 4.95% accuracy under the subject-specific evaluation protocol and 93.33% ± 4.92% under the subject-independent evaluation protocol. This approach leverages the complementary strengths of each modality, resulting in a more comprehensive and accurate FER system.

The optimal decision of service provider considering extra waiting area value-added services – pooled or dedicated?

Service providers often provide chargeable extra waiting area value-added services (EWS) to enhance customers’ waiting experiences, e.g., in VIP and business lounges at airports, train stations, luxury brand stores, etc., to improve profitability. The impact of EWS on customers’ value perception is subject to their heterogeneous sensitivities and also depends on the reference effect due to EWS’ observable status. This paper investigates the optimal level of EWS and price decisions for a service provider facing heterogeneous customers, namely the EWS-preferring (E-type) customers and ordinary (O-type) customers, considering the observable status (observable vs. unobservable) of EWS using pooled or dedicated capacities in operations through M/M/1 queue models. The optimal price and level are solved and the optimal profit is calculated under each scenario. It is found that customers’ heterogeneous sensitivities play an important role in the price and intensity decisions. Offering observable EWS to leverage the reference effect in customers’ value perception can lead to a higher profit. Furthermore, using pooled capacity in operations with EWS is more profitable when service costs are higher. These results offer significant managerial implications and provide practical guidelines for service providers regarding the intensity of EWS, service price, and whether EWS should be observable to customers through pooled or dedicated capacities.

EGCN++: A New Fusion Strategy for Ensemble Learning in Skeleton-Based Rehabilitation Exercise Assessment.

Skeleton-based exercise assessment focuses on evaluating the correctness or quality of an exercise performed by a subject. Skeleton data provide two groups of features (i.e., position and orientation), which existing methods have not fully harnessed. We previously proposed an ensemble-based graph convolutional network (EGCN) that considers both position and orientation features to construct a model-based approach. Integrating these types of features achieved better performance than available methods. However, EGCN lacked a fusion strategy across the data, feature, decision, and model levels. In this paper, we present an advanced framework, EGCN++, for rehabilitation exercise assessment. Based on EGCN, a new fusion strategy called MLE-PO is proposed for EGCN++; this technique considers fusion at the data and model levels. We conduct extensive cross-validation experiments and investigate the consistency between machine and human evaluations on three datasets: UI-PRMD, KIMORE, and EHE. Results demonstrate that MLE-PO outperforms other EGCN ensemble strategies and representative baselines. Furthermore, the MLE-PO's model evaluation scores are more quantitatively consistent with clinical evaluations than other ensemble strategies.

Heparin-Calibrated Anti-Factor Xa Assay for the Measurement of Direct Anticoagulants such as Apixaban, Rivaroxaban, and Edoxaban.

The first purpose of this study was to determine whether a measurement of the level of direct oral anticoagulants (DOACs) was possible with heparin-calibrated chromogenic anti-factor Xa activity (AXA). The second purpose of this study was to evaluate whether the antidote treatment decision level (30 or 50 ng/mL of DOAC) can be determined by unfractionated heparin (UHF)/low molecular weight heparin (LMWH)-calibrated AXA. AXA was measured by using two reagents and dedicated analyzers (Sysmex CS-5100 analyzer and STA R Max3). Four types of calibrators were used: 1) Stago DOAC (rivaroxaban, edoxaban, and apixaban)-specific calibrator, 2) Stago LMWH calibrator, 3) Sysmex UHF calibrator, and 4) Sysmex LMWH calibrator. Regression analysis was used between assays. Receiver operating characteristic (ROC) curves were performed, and the concordance rate was calculated. The correlation coefficients were in the range of 0.75 - 0.91 for rivaroxaban and 0.81 - 0.94 for apixaban. The correlation coefficient between edoxaban-calibrated AXA and Sysmex LMWH/Sysmex UHF calibrator-calibrated AXA was low (r = 0.47). Overall correlation between DOAC-calibrated AXA and Stago LMWH-calibrated AXA was linear, at only low concentration in all three DOACs. The concordance rate (89.3 - 100%) is good for de-termining the antidote management level by UFH/LMWH-calibrated AXA, compared with those of DOAC-calibrated AXA in rivaroxaban and apixaban. The concordance rate ranged from 63% to 67% between Sysmex UFH/ LMWH-calibrated AXA and edoxaban-calibrated AXA. The findings of our study suggest limitations in calculating accurate concentrations, when using UFH/LMWH-calibrated AXA to measure DOAC. This study demonstrates that UFH/LMWH-calibrated AXA may be useful in determining the presence of DOACs at the cutoff level for the antidote treatment in rivarovaban and apixaban. However, in edoxaban, UFH/LMWH-calibrated AXA could not accurately measure the presence of DOACs at the cutoff for antidote treatment.

Community Policing Philosophy

A sociological understanding of crime can adequately respond to criminal challenges through practical engagement with society. Community policing is a proactive strategy that uses problem-solving methods and strategic partnerships to solve problems related to public safety, including crime and social disturbance. The core of this policing system is a flexible organizational structure and decentralized power, in which the officer decides how best to preserve security and order in line with local needs. Decentralized administration at both the command and decision-making levels has been used as an approach to promote the effectiveness of community policy. Consequently, police officers could independently interact with communities, identifying problems and developing feasible solutions. However, this idea has variations, particularly in the debate on how community policy can construct a social system to create better strategies against criminal acts. It is essential to analyze the sociological perspective to understand the main objective of the community-oriented policing strategy. Mainly, a theory that focuses on reducing crime and crime fear enhances the conditions of life for law enforcement personnel and members of society. Hence, this article analyses each strategy component using pertinent and specific sociological theories and criminological concepts, highlighting the significant contribution of sociologists in shaping the community policing strategy. Therefore, based on sociological perspectives, this article argues that community policy can create an inclusive social system within social actors’ collaboration. Keywords: community-oriented, crime, neighborhood, sociological perspective, partnerships

The Job Value Perceived by College Students and Analysis of the Impact on Career Decision Level: Focusing on the Mediating Effect of Job Image and the Moderating Effect of Social Support

The Job Value Perceived by College Students and Analysis of the Impact on Career Decision Level: Focusing on the Mediating Effect of Job Image and the Moderating Effect of Social Support

CNN-ELMNet: fault diagnosis of induction motor bearing based on cross-modal vector fusion

Abstract As the primary driving equipment in industrial, accurate fault diagnosis and condition monitoring of induction motor is crucial for ensuring operational safety. This paper focuses on the bearing faults of induction motors, which have a substantial impact on both the mechanical and electromagnetic systems of the motors. However, in diagnostic tasks, we are faced with the challenges of multi-source, multi-modal data, significant influence from environmental noise, and minimal differentiation between fault data. This paper proposed a novel cross-modal vector fusion fault diagnosis and classification model (CNN-ELMNet), which includes a cross-modal vector fusion network (VF) based on D-S evidence theory, feature extraction layer (FE) and classification layer (CL). Specifically, the VF prioritizes the integration of diagnostic results from individual vibration signals or stator current signals within convolutional neural networks with the features of the input implicit vectors as decision-making evidence, followed by weighted vector fusion through D-S evidence theory at the decision level. The FE focuses on retaining the convolutional, pooling, and fully connected layers of the convolutional network and freezing the final fully connected layer, thus preserving training parameters and fully utilizing the network’s powerful FE capabilities. The CL includes an Extreme Learning Machine optimized for random hyperparameters using the snow ablation optimizer (SAO) algorithm, which offers rapid convergence and high classification recognition rates. The CNN-ELMNet model combines a convolutional network with an extreme learning machine optimized by the SAO algorithm, which not only preserves the model’s FE capability but also enhances the convergence speed and classification recognition rate of the model. Experimental results on real datasets demonstrate that the proposed model exhibits strong stability, generalization, and high accuracy in fault diagnosis, achieving accuracy rate of 99.29% and 98.75%. This provides a more feasible solution for the bearing fault diagnosis of induction motors and holds promising prospects for practical applications.