A supply chain is a mechanism designed to transfer goods from suppliers to customers. This system consists of buyers, producers, workers, information, resources, modes of transportation, and more. The control of the flow of raw materials and completed goods inventory from the point of production until they reach the final customer is known as supply chain management. The manufacturer is the initial link in the chain. You must select a supplier that can produce your product in a secure, economical, and timely way, albeit this will vary depending on the kinds of goods you offer. Demand planning enables you to foresee variations in demand and guarantee that orders are placed at the appropriate time to prevent inventory runs and money being locked up in excess inventory. This may be managed with the use of an inventory control system. The transportation of your product(s) or raw materials must be discussed with your manufacturer in advance. If you have many warehouses, you must make sure that the appropriate amount of merchandise gets to each location and that the freight shipments have the necessary paperwork. At a fulfillment center, merchandise must be properly stored when it is received. For precise and speedy recovery, each SKU requires a separate, special storage place. The process of fulfilling orders placed online is the last link in the supply chain. Picking the products from the order, putting them in a box or poly mailer, and sending the package to the consumer are all necessary steps. Fast shipping and fulfillment might provide your company a competitive edge over rivals. The ultimate reason for the authors to write the paper is to manage the shipment content from ecommerce using the supply chain management practices and Tree structures such as decision tree.

Matrix factorization-based recommender system is in effect an angle preserving dimensionality reduction technique. Since the frequency of items follows power-law distribution, most vectors in the original dimension of user feature vectors and item feature vectors lie on the same hyperplane. However, it is very difficult to reconstruct the embeddings in the original dimension analytically, so we reformulate the original angle preserving dimensionality reduction problem into a distance preserving dimensionality reduction problem. We show that the geometric shape of input data of recommender system in its original higher dimension are distributed on co-centric circles with interesting properties, and design a paraboloid-based matrix factorization named ParaMat to solve the recommendation problem. In the experiment section, we compare our algorithm with 8 other algorithms and prove our new method is the most fair algorithm compared with modern day recommender systems such as ZeroMat and DotMat Hybrid.

Self-supervised learning on point clouds has gained a lot of attention recently, since it addresses the label-efficiency and domain-gap problems on point cloud tasks. In this paper, we propose a novel self-supervised framework to learn informative features from partial point clouds. We leverage partial point clouds scanned by LiDAR that contain both content and pose attributes, and we show that disentangling such two factors from partial point clouds enhances feature learning. To this end, our framework consists of three main parts: 1) a completion network to capture holistic semantics of point clouds; 2) a pose regression network to understand the viewing angle where partial data is scanned from; 3) a partial reconstruction network to encourage the model to learn content and pose features. To demonstrate the robustness of the learnt feature representations, we conduct several downstream tasks including classification, part segmentation, and registration, with comparisons against state-of-the-art methods. Our method not only outperforms existing self-supervised methods, but also shows a better generalizability across synthetic and real-world datasets.

Industrial work environments are hazardous. Manufacturing facilities contain moving parts equipment, hazardous tools, and ergonomic risks. Falls, moving cars, and large materials are frequent occurrences at construction sites. Forklift traffic, lifting concerns, and even slip and fall dangers are common in warehouses. Even if accidents do occur, there are still things you can do to prevent them. In order to prevent illness and injury in the workplace, employees’ training is essential. According to research, most workplace changes and improvements require practical, small-group training for the safety of the workers that are working in that industry. In all industries, industrial safety and health should be given top priority by all firms. Accidents have typically been attributed to dangerous conduct, hazardous physical working circumstances, or malfunctioning technical systems. Industrial safety is a branch of safety science that attempts to provide businesses with a risk-free, hygienic workplace. The main aim of the paper is to predict if the industry measures are safer for the workers or not with the help of hybrid ensemble techniques.

Abstract This study used satellite imagery datasets to extract various morphometric parameters in a geospatial environment to prioritize problematic areas in the Rarhu watershed of Ranchi district, Jharkhand, India. Two decision-making methods, the analytical hierarchy process (AHP) and VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), were integrated to prioritize different sub-watersheds. The Rarhu watershed has an area of 630 km2 with an elevation ranging from 210 to 824 m. The NASA Digital Elevation Model (NASADEM) was used to extract drainage networks which were verified from Survey of India (SOI) toposheets. To prioritize 21 sub-watersheds using a multi-criteria decision making (MCDM) method, 11 morphometric parameters were selected from linear, areal, and relief parameters. The VIKOR method prioritized sub-watersheds using AHP criteria weights, which are classified into four priority levels ranging from very high to low. In addition, performing sensitivity analysis validated the robustness of the decision-making model. As per the analysis, Rarhu watershed was found to have an elongated shape and a highest 6th order stream with a dendritic pattern of streams. It is estimated that watershed degradation is around 36.17% in the study area, with very high priority needs for soil and water conservation measures. Using the results of the study, policymakers, watershed planners, watershed development programme, and soil and water conservation programme projects can identify vulnerable sub-watersheds that require urgent adaptation of soil and water management control measures.

Test-time adaptation approaches have recently emerged as a practical solution for handling domain shift without access to the source domain data. In this paper, we propose and explore a new multi-modal extension of test-time adaptation for 3D semantic segmentation. We find that, directly applying existing methods usually results in performance instability at test time, because multi-modal input is not considered jointly. To design a framework that can take full advantage of multi-modality, where each modality provides regularized self-supervisory signals to other modalities, we propose two complementary modules within and across the modalities. First, Intra-modal Pseudo-label Generation (Intra-PG) is introduced to obtain reliable pseudo labels within each modality by aggregating information from two models that are both pre-trained on source data but updated with target data at different paces. Second, Inter-modal Pseudo-label Refinement (Inter-PR) adaptively selects more reliable pseudo labels from different modalities based on a proposed consistency scheme. Experiments demonstrate that our regularized pseudo labels produce stable self-learning signals in numerous multi-modal test-time adaptation scenarios for 3D semantic segmentation. Visit our project website at https://www.nec-labs.com/~mas/MM-TTA

Humans have the ability to accumulate knowledge of new tasks in varying conditions, but deep neural networks of-ten suffer from catastrophic forgetting of previously learned knowledge after learning a new task. Many recent methods focus on preventing catastrophic forgetting under the assumption of train and test data following similar distributions. In this work, we consider a more realistic scenario of continual learning under domain shifts where the model must generalize its inference to an unseen domain. To this end, we encourage learning semantically meaningful features by equipping the classifier with class similarity metrics as learning parameters which are obtained through Mahalanobis similarity computations. Learning of the backbone representation along with these extra parameters is done seamlessly in an end-to-end manner. In addition, we propose an approach based on the exponential moving average of the parameters for better knowledge distillation. We demonstrate that, to a great extent, existing continual learning algorithms fail to handle the forgetting issue under multiple distributions, while our proposed approach learns new tasks under domain shift with accuracy boosts up to 10% on challenging datasets such as DomainNet and OfficeHome.

Unmanned systems and autonomous capabilities provide an avenue for improving enroute care in situations where medical evacuation assets are either not available or unable to reach the patient. To accomplish this, we conducted research to explore how intelligent algorithms can be used to supplement the current capabilities by improving the diagnosis, intervention, and monitoring of combat trauma care. Variability in the sensing technology available, environmental factors influencing the reliability of measurements, and lack of time and skills to synthesize this information can affect how data impacts treatment. The potential lack of knowledge and expertise of the on-the-site caregiver directly impacts whether important symptoms are seen, good medical decisions are made, and interventions are correctly executed. SoarTech has created the Intelligent System Architecture for Autonomous Care (ISAAC), a software system to support medical diagnosis, intervention, and monitoring. Our ISAAC Diagnosis algorithms provide support for physiological, medical imaging, and laboratory test results with an automated pipeline for processing raw data, identifying features, and mapping this information to symptoms. This information is reasoned over by an intelligent cognitive system that uses an expert understanding of symptom-condition mappings along with a logical inference reasoner to formulate hypotheses, recommend additional tests and construct diagnoses. Our ISAAC Intervention support provides knowledge and insight to instruct novice users to complete procedures from basic tunicate application to chest tube insertion. The combination of the diagnostic and intervention capabilities of ISAAC have to potential to provide life-saving support in austere combat environments.