Efficient Handling Research Articles

From Godown to Warehouse Management: Transformation at eKart, An Illustrative Case Study

The growth in Ecommerce has transformed warehouse and distribution centers to mega-fulfilment centers. The focus is increasingly on innovative storage solutions, better tracking mechanisms coupled with technology and training. Companies who do not realize this have little chance of surviving (Rachid Fergati, MD Indian subcontinent, UPS). The purpose of this case is to highlight the process, people, and operations improvements undertaken by an Indian e-commerce giant with the help of the research team at the CII School of Logistics for enhanced efficiency and customer service. The researchers spent 50 days onsite (at the company’s warehouse located in Binola) observing the processes, identifying issues, providing solutions, and tracking and recording improvements as per the action research methodology (Reason, 2001). Thus, two-fold objectives were achieved: (a) improved cost efficiency and material handling processes in the warehouse and (b) knowledge building in the strategic operations management area. The case is a representative illustration of how the Indian logistics industry is moving from fragmented, unorganized, and inefficient systems to a more organized, structured sector in an ultra-competitive global space.

Retrospective analysis of 217 fatal intoxication autopsy cases from 2009 to 2021: temporal trends in fatal intoxication at Tongji center for medicolegal expertise, Hubei, China.

This retrospective analysis of fatal intoxication case autopsies was performed at Tongji Center for Medicolegal Expertise in Hubei (TCMEH) from 2009 to 2021 to obtain up-to-date information on intoxication cases. The objective was to describe important data about evolving patterns in intoxication occurrences, enhance public safety policies, and assist forensic examiners and police in more efficient handling of such cases. Analyses based on sex, age, topical exposure routes, toxic agents, and mode of death were performed using 217 records of intoxication cases collected from TCMEH as a sample, and the results were compared with reports previously published (from 1999 to 2008) from this institution. Deaths from intoxications occurred at a higher rate in males than in females and were most common among individuals aged 30-39 years. The most frequent method of exposure was oral ingestion. The causative agents of deadly intoxications have changed when compared to the data from the previous 10 years. For instance, deaths from amphetamine overdoses are becoming more prevalent gradually, whereas deaths due to carbon monoxide and rodenticide intoxication have declined dramatically. In 72 cases, pesticides continued to be the most frequent intoxication cause. A total of 60.4% of the deaths were accidental exposure. Men died from accidents at a higher rate than women, although women were more likely to commit suicide. Particular focus is needed on the use of succinylcholine, cyanide, and paraquat in homicides.

Dearomatization‐Rearomatization Reaction of Metal‐Polarized Aza‐ortho‐Quinone Methides

AbstractMetal‐polarized aza‐ortho‐quinone methides (aza‐o‐QMs) are a unique and efficient handle for azaheterocycle synthesis. Despite great achievements, the potential of these reactive intermediates has not yet been fully exploited, especially the new reaction modes. Herein, we disclosed an unprecedented dearomatization process of metal‐polarized aza‐o‐QMs, affording transient dearomatized spiroaziridine intermediates. Based on this serendipity, we accomplished three sequential dearomatization‐rearomatization reactions of benzimidazolines with aza‐sulfur ylides, enabling the divergent synthesis of bis‐nitrogen heterocycles with high efficiency and flexibility. Moreover, experimental and theoretical studies were performed to explain the proposed mechanisms and observed selectivity. Further cellular evaluation of the dibenzodiazepine products identified a hit compound for new antitumor drugs.

Dearomatization-Rearomatization Reaction of Metal-Polarized Aza-ortho-Quinone Methides.

Metal-polarized aza-ortho-quinone methides (aza-o-QMs) are a unique and efficient handle for azaheterocycle synthesis. Despite great achievements, the potential of these reactive intermediates has not yet been fully exploited, especially the new reaction modes. Herein, we disclosed an unprecedented dearomatization process of metal-polarized aza-o-QMs, affording transient dearomatized spiroaziridine intermediates. Based on this serendipity, we accomplished three sequential dearomatization-rearomatization reactions of benzimidazolines with aza-sulfur ylides, enabling the divergent synthesis of bis-nitrogen heterocycles with high efficiency and flexibility. Moreover, experimental and theoretical studies were performed to explain the proposed mechanisms and observed selectivity. Further cellular evaluation of the dibenzodiazepine products identified a hit compound for new antitumor drugs.

A modified decontamination and storage method for sputum from patients with tuberculosis

Background: Collecting and storing large number of sputum samples with a view to culturing these in the future requires an efficient initial handling method. We devised a modified sputum digestion and decontamination method that maximised storage capacity and Mycobacterium tuberculosis (M.tb) recovery from culture while minimising laboratory workload and risk of contamination. Methods: We collected smear microscopy positive sputum samples from patients with pulmonary tuberculosis (TB). The sputum samples were split and processed using both the standard N-Acetyl-L-cysteine and sodium hydroxide (NALC-NaOH) method and our modified method before freezing and later culturing in BD BACTEC 960 Mycobacterium Growth Indicator Tubes (MGIT) system. We assessed the Time to Positivity (TPP) and Growth Unit (GU) data. Results: We selected 22 sputum samples to compare two digestion and decontamination methods. The samples that underwent the modified method had longer TTP (p < 0.05) but similar GU in comparison to standard method. Overall, 1/22 samples failed to grow in MGIT after being processed by the modified method. We then applied the modified method to 348 sputum samples with Rifampicin resistance detected by GeneXpert MTB/RIF assay, which were frozen for between 1-25 months. The overall MGIT positive, negative, and contamination rate was 90.5%, 7.8%, and 1.7%, respectively. There was no significant difference in MGIT result when samples were grouped by duration of storage or positive smear grade. Conclusions: Our modified method yielded acceptable M.tb recovery rate and low contamination risk while allowing us to collect and store thousands of sputum samples over a long period of time for future tests.

Long-Term Culture Performance of a Polyelectrolyte Complex Microcapsule Platform for Hyaline Cartilage Repair.

Articular cartilage (AC) tissue repair and regeneration remains an ongoing challenge. One component of the challenge is the limited ability to scale an engineered cartilage graft to clinically relevant sizes while maintaining uniform properties. In this paper, we report on the evaluation of our polyelectrolyte complex microcapsule (PECM) platform technology as a technique for generating cartilage-like spherical modules. Bone marrow-derived mesenchymal stem cells (bMSCs) or primary articular chondrocytes were encapsulated within PECMs composed of methacrylated hyaluronan, collagen I, and chitosan. The formation of cartilage-like tissue in the PECMs over a 90-day culture was characterized. The results showed that chondrocytes exhibited superior growth and matrix deposition compared to either chondrogenically-induced bMSCs or a mixed PECM culture containing both chondrocytes and bMSCs. The chondrocyte-generated matrix filled the PECM and produced substantial increases in capsule compressive strength. The PECM system thus appears to support intracapsular cartilage tissue formation and the capsule approach promotes efficient culture and handling of these micro tissues. Since previous studies have proven the feasibility of fusing such capsules into large tissue constructs, the results suggest that encapsulating primary chondrocytes in PECM modules may be a viable route toward achieving a functional articular cartilage graft.

Prediction of the Form of a Hardened Metal Workpiece during the Straightening Process

In industry, metal workpieces are often heat-treated to improve their mechanical properties, which leads to unwanted deformations and changes in their geometry. Due to their high hardness (60 HRC or more), conventional bending and rolling straightening approaches are not effective, as a failure of the material occurs. The aim of the research was to develop a predictive model that predicts the change in the form of a hardened workpiece as a function of the arbitrary set of strikes that deform the surface plastically. A large-scale laboratory experiment was carried out in which a database of 3063 samples was prepared, based on the controlled application of plastic deformations on the surface of the workpiece and high-resolution capture of the workpiece geometry. The different types of input data, describing, on the one hand, the performed plastic surface deformations on the workpieces, and on the other hand the point cloud of the workpiece geometry, were combined appropriately into a form that is a suitable input for a U-Net convolutional neural network. The U-Net model’s performance was investigated using three statistical indicators. These indicators were: relative absolute error (RAE), root mean squared error (RMSE), and relative squared error (RSE). The results showed that the model had excellent prediction performance, with the mean values of RMSE less than 0.013, RAE less than 0.05, and RSE less than 0.004 on test data. Based on the results, we concluded that the proposed model could be a useful tool for designing an optimal straightening strategy for high-hardness metal workpieces. Our results will open the doors to implementing digital sustainability techniques, since more efficient handling will result in fewer subsequent heat treatments and shorter handling times. An important goal of digital sustainability is to reduce electricity consumption in production, which this approach will certainly do.

Flexible and efficient handling of nanopore sequencing signal data with slow5tools

Nanopore sequencing is being rapidly adopted in genomics. We recently developed SLOW5, a new file format with advantages for storage and analysis of raw signal data from nanopore experiments. Here we introduce slow5tools, an intuitive toolkit for handling nanopore data in SLOW5 format. Slow5tools enables lossless data conversion and a range of tools for interacting with SLOW5 files. Slow5tools uses multi-threading, multi-processing, and other engineering strategies to achieve fast data conversion and manipulation, including live FAST5-to-SLOW5 conversion during sequencing. We provide examples and benchmarking experiments to illustrate slow5tools usage, and describe the engineering principles underpinning its performance.

Do Preprocessing and Class Imbalance Matter to the Deep Image Classifiers for COVID-19 Detection? An Explainable Analysis

In a world withstanding the waves of a raging pandemic, respiratory disease detection from chest radiological images using machine-learning approaches has never been more important for a widely accessible and prompt initial diagnosis. A standard machine-learning disease detection workflow that takes an image as input and provides a diagnosis in return usually consists of four key components, namely input preprocessor, data irregularities (like class imbalance, missing and absent features, etc.) handler, classifier, and a decision explainer for better clarity. In this study, we investigate the impact of the three primary components of the disease-detection workflow leaving only the deep image classifier. We specifically aim to validate if the deep classifiers may significantly benefit from additional preprocessing and efficient handling of data irregularities in a disease-diagnosis workflow. To elaborate, we explore the applicability of seven traditional and deep preprocessing techniques along with four class imbalance handling approaches for a deep classifier, such as ResNet-50, in the task of respiratory disease detection from chest radiological images. While deep classifiers are more capable than their traditional counterparts, explaining their decision process is a significant challenge. Therefore, we also employ three gradient visualization algorithms to explain the decision of a deep classifier to understand how well each of them can highlight the key visual features of the different respiratory diseases.

Grid-Stamping on a Polygon Model for Implementing Arbitrary-Shaped Boundary Conditions in a Moving Particle Semi-Implicit Method

This study proposes a new wall boundary condition for the grid-stamping on a polygon (G-StoP) model, which enables a simpler and more efficient handling of boundary surfaces of arbitrarily complex-shaped bodies represented using polygons (or meshes). For example, computer-aided design surface data can be used to analyze flow using a particle-based fluid-solver moving particle semi-implicit method. For coupling simulations of fluid–multibody dynamics, the Pusan-National-University-modified MPS method is improved, and the coupling analysis is performed using RecurDyn, a commercial software package for multibody (or flexible multibody) dynamics. To confirm the applicability of the developed G-StoP model, hydrostatic pressure simulations are conducted in a rectangular tank at various corner angles. Then, the hydrostatic pressure results are compared with previously proposed polygonal wall boundary model results and theoretical solutions. That is, in the case with a corner angle of 30°, it was confirmed that the relative error to the experiment of the polygon model was 11.3%, while that of the G-StoP model was 1.3%. This demonstrates that the proposed G-StoP model is exceptional for numerical stability and robustness even when it is difficult to secure information on neighboring particles as the corner angle of the object becomes small. In addition, the G-StoP model was applied to dam breaking, subaerial landslide tsunami, and wine sloshing problems, and its accuracy and applicability were tested through comparison with experimental and other simulation results. As a result, it was shown that the present simulation results were much closer to the experiments than other simulations.

Reinforcement learning-based allocation of fog nodes for cloud-based smart grid

Real-time monitoring in smart grids requires efficient handling of massive amount of data. Fog cloud nodes can be strategically located within the smart grid to: pull readings from smart meters, implement local processing and control, and make all data available to the smart grid control center with minimum overall latency. Unlike existing studies in literature, we propose a novel Fog node allocation strategy that is tightly coupled with the power grid structure, and hence, accounts for the spatial distribution of data traffic sources (e.g., smart meters) within the power grid. Furthermore, the allocation strategy considers the diverse latency requirements of fixed scheduling and event driven data services within the power grid. The proposed allocation strategy first implements an unsupervised machine learning approach to determine initial number and locations of Fog nodes that can serve the data traffic with minimum overall latency. Then, a reinforcement-based mechanism is applied to minimize the required number of Fog nodes, and hence capital cost, through efficient mapping between Fog nodes and smart meters while still complying with the latency requirements. Our simulation studies demonstrate that a 50% reduction in required number of Fog nodes can be achieved while minimizing overall latency when the proposed allocation strategy is adopted.

Metal‐Free Iodine‐Catalyzed Oxidative Dehydrogenation of N‐Heterocycles

AbstractA general and straightforward iodine‐catalyzed oxidative dehydrogenation system of saturated five and six‐membered N‐heterocycles for the synthesis of N‐heteroarenes is investigated. In this protocol, a series of indole, (iso)quinoline, β‐carboline analogues could be achieved respectively under metal‐free, additive‐free and external‐oxidant‐free conditions in moderate to good yields with broad functional groups compatibility. Preliminary mechanistic investigations support that DMSO plays dual roles as the solvent and the oxidant source in the transformation. The current synthetic strategies can be utilized as the efficient handle to elaborate natural products.

SDAFA: Secure Data Aggregation in Fog-Assisted Smart Grid Environment

The tremendous growth of about 8 billion devices connected to each other in various domains of Internet of Things (IoT)-based applications have attracted researchers from both industry and academia. IoT is a network of several devices connected with each other to provide sensing capabilities, particularly in smart grid (SG) environment. Various challenges such as the efficient handling of massive IoT data can be addressed with advances in fog computing. The secure data aggregation challenge is one such issue in IoT-based smart grid systems, which include millions of smart meters. Typical SG-based data aggregation approaches have high computation and communication costs, however, many efforts have been made to overcome these limitations while leveraging fog computing but no satisfactory results have been obtained. Moreover, existing solutions also suffer from high storage requirements. The traditional data aggregation schemes such as GCEDA (Grouping of Clusters for Efficient Data Aggregation) and SPPDA (Secure Privacy-Preserving Data Aggregation) also suffer from a few shortcomings. SPPDA follows a mixed aggregation architecture that includes trees and clusters which can lead to some performance complexities and is not energy-efficient, whereas GCEDA does not support heterogeneity. To overcome these problems, this research provides a fog-assisted strategy for secure and efficient data aggregation in smart grid. The concept of smart grid is implemented in fog environment, which was not the case in previous schemes. We used communication between smart meters (SMs) and fog nodes (FNs) to transmit confidential data in compressed form towards FN. The FN further aggregates the received data which can then be updated in cloud repositories later. We presented two algorithms—data aggregation and data extraction at FN and cloud, respectively, to achieve secure communication. The performance of the proposed strategy has been evaluated against existing data aggregation techniques GCEDA and SPPDA for various performance parameters such as storage, communication cost and transmission cost. The proposed scheme overcomes the limitation of heterogeneity and mixed aggregation which was faced in GCEDA and SPPDA and the results revealed outstanding performance in comparison with both, so the proposed solution can be used in a smart grid environment for efficient and secure data transmission.

Elucidating dynamic anaerobe metabolism with HRMAS 13C NMR and genome-scale modeling

Anaerobic microbial metabolism drives critical functions within global ecosystems, host–microbiota interactions, and industrial applications, yet remains ill-defined. Here we advance a versatile approach to elaborate cellular metabolism in obligate anaerobes using the pathogen Clostridioides difficile, an amino acid and carbohydrate-fermenting Clostridia. High-resolution magic angle spinning nuclear magnetic resonance (NMR) spectroscopy of C. difficile, grown with fermentable 13C substrates, informed dynamic flux balance analysis (dFBA) of the pathogen’s genome-scale metabolism. Analyses identified dynamic recruitment of oxidative and supporting reductive pathways, with integration of high-flux amino acid and glycolytic metabolism at alanine’s biosynthesis to support efficient energy generation, nitrogen handling and biomass generation. Model predictions informed an approach leveraging the sensitivity of 13C NMR spectroscopy to simultaneously track cellular carbon and nitrogen flow from [U-13C]glucose and [15N]leucine, confirming the formation of [13C,15N]alanine. Findings identify metabolic strategies used by C. difficile to support its rapid colonization and expansion in gut ecosystems.

Novel concept of tailorable magnetic field and electron pressure distribution in a magnetic nozzle for effective space propulsion

Magnetic nozzle appears to be a practical solution for prolonged space missions. For efficient handling of the spaceship, an in-flight solution to customize the thrust from the magnetic nozzle is essential. Here a new concept of three-thick coils system is proposed for tailoring the magnetic field in-flight in accordance with electron pressure distribution. The role of peak position of the pressure and its axial gradient is also uncovered for realizing higher thrust. About three-fold increase in thrust is observed when the electron temperature is raised to ∼2.5 times of its original value at the exit plane. The set-up is optimized for its best performance and efficient use in the electric space propulsion sector with thrust approaching 5 mN. In particular, this can contribute to the attitude control or the precision pointing of the spacecraft, the technology for removal of space debris and manipulating the ion momentum flux lost to a wall or unsteady laser produced plasma flow in a magnetic nozzle.

Construction of Z-scheme MnCo2O4/Sn3O4 heterostructured photoanodes with enhanced photoelectrocatalytic degradation of reactive brilliant blue KN-R

Rapid and efficient handling of the printing and dyeing wastewater benefits environmental conservation. Photoelectrocatalysis (PEC) technology is a novel method used to mineralize large molecules of non-degradable organic dye molecules in water completely into small molecules in recent years. By generating intermediate reactive species (e.g., superoxide radicals and hydroxyl radicals) during the processing, the products of PEC technology are cleaner and pollution-free, making it a desirable environment-friendly approach. In this work, a composite semiconductor material with Z-Scheme heterostructure was constructed as a photoanode to promote the generation of intermediate reactive radicals for the efficient degradation of organic dyestuff wastewater. A series of composite materials with Sn3O4 nanosheets decorated spinel-type MnCo2O4 nanorods were synthesized through a simple two-step hydrothermal process on the titanium metal substrate. The degradation reaction cell was assembled with these Ti/MnCo2O4-Sn3O4 photoanodes, and the PEC approach was used to decolorize the reactive brilliant blue KN-R. The synthesized photoelectrode exhibited a large electroactive region, low charge transfer resistance, high hydroxyl radical generation efficiency, and excellent PEC activity. The optimal composite photoanode presented 86.4 % reactive brilliant blue KN-R removal in 2 h and practical stability in 5 circulation tests. The mechanism during the PEC process was also analyzed and discussed on the basis of radical trapping experiments. In this work, MnCo2O4-Sn3O4 materials possessing Z-scheme heterojunction were constructed, providing insight into the techniques for catalytic purification of organic wastewater.

Automatic transformation of irreducible representations for efficient contraction of tensors with cyclic group symmetry

Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel’s MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer.

Codebase release 1.3 for SymTensor

Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel’s MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer.

Topology optimization of locomoting soft bodies using material point method

Topology optimization methods have widely been used in various industries, owing to their potential for providing promising design candidates for mechanical devices. However, their applications are usually limited to the objects which do not move significantly due to the difficulty in computationally efficient handling of the contact and interactions among multiple structures or with boundaries by conventionally used simulation techniques. In the present study, we propose a topology optimization method for moving objects incorporating the material point method, which is often used to simulate the motion of objects in the field of computer graphics. Several numerical experiments demonstrate the effectiveness and the utility of the proposed method.

Analysis of Technical Errors Made by Motorcycle Riders

This article analyses road traffic accidents caused by motorcycle riders' technical errors. It presents an accident table that links the nature of the accidents to the causes of the accident. The connection provides a comprehensive overview of the typical motorcycle accidents that can be extended to events that have occurred. With this in mind, a conclusion can be drawn in the sense that an accident could have been avoided by a proper reaction of the motorcycle rider, and it would also make suggestions for developing a methodology for motorcycling education. Currently, motorcycling on the domestic road network is a hobby rather than a means of everyday travel, but the 10% participation rate in accidents certainly justifies the need to investigate this mode of transport. Today's state-of-the-art and technically well-equipped high-performance motorcycles require efficient vehicle handling and road use preparation. Therefore, drivers need to be able to respond appropriately to the challenges of our accelerated traffic.