Aim/Purpose: Although cassava is one of the crops that can be grown during the dry season in Northeastern Thailand, most farmers in the region do not know whether the crop can grow in their specific areas because the available agriculture planning guideline provides only a generic list of dry-season crops that can be grown in the whole region. The purpose of this research is to develop a predictive model that can be used to predict suitable areas for growing cassava in Northeastern Thailand during the dry season. Background: This paper develops a decision support system that can be used by farmers to assist them determine if cassava can be successfully grown in their specific areas. Methodology: This study uses satellite imagery and data on land characteristics to develop a machine learning model for predicting suitable areas for growing cassava in Thailand’s Nakhon-Phanom province. Contribution: This research contributes to the body of knowledge by developing a novel model for predicting suitable areas for growing cassava. Findings: This study identified elevation and Ferric Acrisols (Af) soil as the two most important features for predicting the best-suited areas for growing cassava in Nakhon-Phanom province, Thailand. The two-class boosted decision tree algorithm performs best when compared with other algorithms. The model achieved an accuracy of .886, and .746 F1-score. Recommendations for Practitioners: Farmers and agricultural extension agents will use the decision support system developed in this study to identify specific areas that are suitable for growing cassava in Nakhon-Phanom province, Thailand Recommendation for Researchers: To improve the predictive accuracy of the model developed in this study, more land and crop characteristics data should be incorporated during model development. The ground truth data for areas growing cassava should also be collected for a longer period to provide a more accurate sample of the areas that are suitable for cassava growing. Impact on Society: The use of machine learning for the development of new farming systems will enable farmers to produce more food throughout the year to feed the world’s growing population. Future Research: Further studies should be carried out to map other suitable areas for growing dry-season crops and to develop decision support systems for those crops.

Aim/Purpose: In the United States, the Centers for Disease Control and Prevention (CDC) tracks the disease activity using data collected from medical practice's on a weekly basis. Collection of data by CDC from medical practices on a weekly basis leads to a lag time of approximately 2 weeks before any viable action can be planned. The 2-week delay problem was addressed in the study by creating machine learning models to predict flu outbreak. Background: The 2-week delay problem was addressed in the study by correlation of the flu trends identified from Twitter data and official flu data from the Centers for Disease Control and Prevention (CDC) in combination with creating a machine learning model using both data sources to predict flu outbreak. Methodology: A quantitative correlational study was performed using a quasi-experimental design. Flu trends from the CDC portal and tweets with mention of flu and influenza from the state of Georgia were used over a period of 22 weeks from December 29, 2019 to May 30, 2020 for this study. Contribution: This research contributed to the body of knowledge by using a simple bag-of-word method for sentiment analysis followed by the combination of CDC and Twitter data to generate a flu prediction model with higher accuracy than using CDC data only. Findings: The study found that (a) there is no correlation between official flu data from CDC and tweets with mention of flu and (b) there is an improvement in the performance of a flu forecasting model based on a machine learning algorithm using both official flu data from CDC and tweets with mention of flu. Recommendations for Practitioners: In this study, it was found that there was no correlation between the official flu data from the CDC and the count of tweets with mention of flu, which is why tweets alone should be used with caution to predict a flu out-break. Based on the findings of this study, social media data can be used as an additional variable to improve the accuracy of flu prediction models. It is also found that fourth order polynomial and support vector regression models offered the best accuracy of flu prediction models. Recommendations for Researchers: Open-source data, such as Twitter feed, can be mined for useful intelligence benefiting society. Machine learning-based prediction models can be improved by adding open-source data to the primary data set. Impact on Society: Key implication of this study for practitioners in the field were to use social media postings to identify neighborhoods and geographic locations affected by seasonal outbreak, such as influenza, which would help reduce the spread of the disease and ultimately lead to containment. Based on the findings of this study, social media data will help health authorities in detecting seasonal outbreaks earlier than just using official CDC channels of disease and illness reporting from physicians and labs thus, empowering health officials to plan their responses swiftly and allocate their resources optimally for the most affected areas. Future Research: A future researcher could use more complex deep learning algorithms, such as Artificial Neural Networks and Recurrent Neural Networks, to evaluate the accuracy of flu outbreak prediction models as compared to the regression models used in this study. A future researcher could apply other sentiment analysis techniques, such as natural language processing and deep learning techniques, to identify context-sensitive emotion, concept extraction, and sarcasm detection for the identification of self-reporting flu tweets. A future researcher could expand the scope by continuously collecting tweets on a public cloud and applying big data applications, such as Hadoop and MapReduce, to perform predictions using several months of historical data or even years for a larger geographical area.

Aim/Purpose: The aim of this study was to analyze various performance metrics and approaches to their classification. The main goal of the study was to develop a new typology that will help to advance knowledge of metrics and facilitate their use in machine learning regression algorithms Background: Performance metrics (error measures) are vital components of the evaluation frameworks in various fields. A performance metric can be defined as a logical and mathematical construct designed to measure how close are the actual results from what has been expected or predicted. A vast variety of performance metrics have been described in academic literature. The most commonly mentioned metrics in research studies are Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), etc. Knowledge about metrics properties needs to be systematized to simplify the design and use of the metrics. Methodology: A qualitative study was conducted to achieve the objectives of identifying related peer-reviewed research studies, literature reviews, critical thinking and inductive reasoning. Contribution: The main contribution of this paper is in ordering knowledge of performance metrics and enhancing understanding of their structure and properties by proposing a new typology, generic primary metrics mathematical formula and a visualization chart Findings: Based on the analysis of the structure of numerous performance metrics, we proposed a framework of metrics which includes four (4) categories: primary metrics, extended metrics, composite metrics, and hybrid sets of metrics. The paper identified three (3) key components (dimensions) that determine the structure and properties of primary metrics: method of determining point distance, method of normalization, method of aggregation of point distances over a data set. For each component, implementation options have been identified. The suggested new typology has been shown to cover a total of over 40 commonly used primary metrics Recommendations for Practitioners: Presented findings can be used to facilitate teaching performance metrics to university students and expedite metrics selection and implementation processes for practitioners Recommendation for Researchers: By using the proposed typology, researchers can streamline development of new metrics with predetermined properties Impact on Society: The outcomes of this study could be used for improving evaluation results in machine learning regression, forecasting and prognostics with direct or indirect positive impacts on innovation and productivity in a societal sense Future Research: Future research is needed to examine the properties of the extended metrics, composite metrics, and hybrid sets of metrics. Empirical study of the metrics is needed using R Studio or Azure Machine Learning Studio, to find associations between the properties of primary metrics and their “numerical” behavior in a wide spectrum of data characteristics and business or research requirements

Aim/Purpose: To predict the change-proneness of software from the continuous evolution using machine learning methods. To identify when software changes become statistically significant and how metrics change. Background: Software evolution is the most time-consuming activity after a software release. Understanding evolution patterns aids in understanding post-release software activities. Many methodologies have been proposed to comprehend software evolution and growth. As a result, change prediction is critical for future software maintenance. Methodology: I propose using machine learning methods to predict change-prone classes. Classes that are expected to change in future releases were defined as change-prone. The previous release was only considered by the researchers to define change-proneness. In this study, I use the evolution of software to redefine change-proneness. Many snapshots of software were studied to determine when changes became statistically significant, and snapshots were taken biweekly. The research was validated by looking at the evolution of five large open-source systems. Contribution: In this study, I use the evolution of software to redefine change-proneness. The research was validated by looking at the evolution of five large open-source systems. Findings: Software metrics can measure the significance of evolution in software. In addition, metric values change within different periods and the significance of change should be considered for each metric separately. For five classifiers, change-proneness prediction models were trained on one snapshot and tested on the next. In most snapshots, the prediction performance was excellent. For example, for Eclipse, the F-measure values were between 80 and 94. For other systems, the F-measure values were higher than 75 for most snapshots. Recommendations for Practitioners: Software change happens frequently in the evolution of software; however, the significance of change happens over a considerable length of time and this time should be considered when evaluating the quality of software. Recommendation for Researchers: Researchers should consider the significance of change when studying software evolution. Software changes should be taken from different perspectives besides the size or length of the code. Impact on Society: Software quality management is affected by the continuous evolution of projects. Knowing the appropriate time for software maintenance reduces the costs and impacts of software changes. Future Research: Studying the significance of software evolution for software refactoring helps improve the internal quality of software code.

Aim/Purpose: Previous research has generally concentrated on identifying the variables that most significantly influence customer churn or has used customer segmentation to identify a subset of potential consumers, excluding its effects on forecast accuracy. Consequently, there are two primary research goals in this work. The initial goal was to examine the impact of customer segmentation on the accuracy of customer churn prediction in the banking sector using machine learning models. The second objective is to experiment, contrast, and assess which machine learning approaches are most effective in predicting customer churn. Background: This paper reviews the theoretical basis of customer churn, and customer segmentation, and suggests using supervised machine-learning techniques for customer attrition prediction. Methodology: In this study, we use different machine learning models such as k-means clustering to segment customers, k-nearest neighbors, logistic regression, decision tree, random forest, and support vector machine to apply to the dataset to predict customer churn. Contribution: The results demonstrate that the dataset performs well with the random forest model, with an accuracy of about 97%, and that, following customer segmentation, the mean accuracy of each model performed well, with logistic regression having the lowest accuracy (87.27%) and random forest having the best (97.25%). Findings: Customer segmentation does not have much impact on the precision of predictions. It is dependent on the dataset and the models we choose. Recommendations for Practitioners: The practitioners can apply the proposed solutions to build a predictive system or apply them in other fields such as education, tourism, marketing, and human resources. Recommendation for Researchers: The research paradigm is also applicable in other areas such as artificial intelligence, machine learning, and churn prediction. Impact on Society: Customer churn will cause the value flowing from customers to enterprises to decrease. If customer churn continues to occur, the enterprise will gradually lose its competitive advantage. Future Research: Build a real-time or near real-time application to provide close information to make good decisions. Furthermore, handle the imbalanced data using new techniques.

Aim/Purpose: While existing literature has extensively explored factors influencing the success of big data projects and proposed big data maturity models, no study has harnessed machine learning to predict project success and identify the critical features contributing significantly to that success. The purpose of this paper is to offer fresh insights into the realm of big data projects by leveraging machine-learning algorithms. Background: Previously, we introduced the Global Big Data Maturity Model (GBDMM), which encompassed various domains inspired by the success factors of big data projects. In this paper, we transformed these maturity domains into a survey and collected feedback from 90 big data experts across the Middle East, Gulf, Africa, and Turkey regions regarding their own projects. This approach aims to gather firsthand insights from practitioners and experts in the field. Methodology: To analyze the feedback obtained from the survey, we applied several algorithms suitable for small datasets and categorical features. Our approach included cross-validation and feature selection techniques to mitigate overfitting and enhance model performance. Notably, the best-performing algorithms in our study were the Decision Tree (achieving an F1 score of 67%) and the Cat Boost classifier (also achieving an F1 score of 67%). Contribution: This research makes a significant contribution to the field of big data projects. By utilizing machine-learning techniques, we predict the success or failure of such projects and identify the key features that significantly contribute to their success. This provides companies with a valuable model for predicting their own big data project outcomes. Findings: Our analysis revealed that the domains of strategy and data have the most influential impact on the success of big data projects. Therefore, companies should prioritize these domains when undertaking such projects. Furthermore, we now have an initial model capable of predicting project success or failure, which can be invaluable for companies. Recommendations for Practitioners: Based on our findings, we recommend that practitioners concentrate on developing robust strategies and prioritize data management to enhance the outcomes of their big data projects. Additionally, practitioners can leverage machine-learning techniques to predict the success rate of these projects. Recommendation for Researchers: For further research in this field, we suggest exploring additional algorithms and techniques and refining existing models to enhance the accuracy and reliability of predicting the success of big data projects. Researchers may also investigate further into the interplay between strategy, data, and the success of such projects. Impact on Society: By improving the success rate of big data projects, our findings enable organizations to create more efficient and impactful data-driven solutions across various sectors. This, in turn, facilitates informed decision-making, effective resource allocation, improved operational efficiency, and overall performance enhancement. Future Research: In the future, gathering additional feedback from a broader range of big data experts will be valuable and help refine the prediction algorithm. Conducting longitudinal studies to analyze the long-term success and outcomes of Big Data projects would be beneficial. Furthermore, exploring the applicability of our model across different regions and industries will provide further insights into the field.

Aim/Purpose: This paper introduces a Social Machine for collaborative sensemaking that the developers have configured to the requirements and challenges of transdisciplinary literature reviews. Background: Social Machines represent a promising model for unifying machines and social processes for a wide range of purposes. A development team led by the author is creating a Social Machine for activities that require users to combine pieces of information from multiple online sources and file types for various purposes. Methodology: The development team has applied emergent design processes, usability testing, and formative evaluation in the execution of the product road map. Contribution: A major challenge of the digital information age is how to tap into large volumes of online information and the collective intelligence of diverse groups to generate new knowledge, solve difficult problems, and drive innovation. A Transdisciplinary Social Machine (TDSM) enables new forms of interactions between humans, machines, and online content that have the potential to (a) improve outcomes of sensemaking activities that involve large collections of online documents and diverse groups and (b) make machines more capable of assisting humans in their sensemaking efforts. Findings: Preliminary findings suggest that TDSM promotes learning and the generation of new knowledge. Recommendations for Practitioners: TDSM has the potential to improve outcomes of literature reviews and similar activities that require distilling information from diverse online sources. Recommendation for Researchers: TDSM is an instrument for investigating sensemaking, an environment for studying various forms of human and machine interactions, and a subject for further evaluation. Impact on Society: In complex areas such as sustainability and healthcare research, TDSM has the potential to make decision-making more transparent and evidence-based, facilitate the production of new knowledge, and promote innovation. In education, TDSM has the potential to prepare students for the 21st century information economy. Future Research: Research is required to measure the effects of TDSM on cross-disciplinary communication, human and machine learning, and the outcomes of transdisciplinary research projects. The developers are planning a multiple case study using design-based research methodology to investigate these topics.

Aim/Purpose: The amount of information published has increased dramatically due to the information explosion. The issue of managing information as it expands at this rate lies in the development of information extraction technology that can turn unstructured data into organized data that is understandable and controllable by computers Background: The primary goal of named entity recognition (NER) is to extract named entities from amorphous materials and place them in pre-defined semantic classes. Methodology: In our work, we analyze various machine learning algorithms and implement K-NN which has been widely used in machine learning and remains one of the most popular methods to classify data. Contribution: To the researchers’ best knowledge, no published study has presented Named entity recognition for the Kikuyu language using a machine learning algorithm. This research will fill this gap by recognizing entities in the Kikuyu language. Findings: An evaluation was done by testing precision, recall, and F-measure. The experiment results demonstrate that using K-NN is effective in classification performance. Recommendation for Researchers: With enough training data, researchers could perform an experiment and check the learning curve with accuracy that compares to state of art NER. Future Research: Future studies may be done using unsupervised and semi-supervised learning algorithms for other resource-scarce languages.

Product innovation has become a crucial part of the sustainability of the coconut agro-industry in Indonesia, covering upstream and downstream sides. To overcome this challenge, it is necessary to create several model stages using a hybrid method that combines machine learning based on multiple criteria decision making and mass balance analysis. The research case study was conducted in Tembilahan district, Riau province, Indonesia, one of the primary coconut producers in Indonesia. The analysis results showed that potential products for domestic customers included coconut milk, coconut cooking oil, coconut chips, coconut jelly, coconut sugar, and virgin coconut oil. Furthermore, considering the experts, the most potential product to be developed was coconut sugar with a weight of 0.26. Prediction of coconut sugar demand reached 13,996,607 tons/year, requiring coconut sap as a raw material up to 97,976,249.

Carlo Lipizzi’s Societal impacts of artificial intelligence and machine learning offers a critical and comprehensive analysis of artificial intelligence (AI) and machine learning’s effects on society. This book provides a balanced perspective, cutting through the

The wethepeople "Chaos Machine 22" BMX Frame - 22 Inch is the signature BMX frame from the Australian BMX Trails legend Tyson Jones-Peni and comes in a 22" inch version for 22" inch wheels. The wethepeople "Chaos Machine 22" BMX Frame - 22 Inch features a long and stable geometry and mounts for a disc brake (and regular U-brake).

• Wheel Size: 20"
• Material: 100% 4130 Japanese CrMo, top tube and down tube gussets, butted tubes, integrated headset, integrated seatclamp, offset thickness dent resistant chainstays and mid bb for grind resistance
• Geometry:
  Top Tube Length (TT): 22.25"
  Chainstay Length (CS): 14" (35.56cm) - 14.75" (37.47cm)
  Head Tube Angle (HA): 74.25°
  Seat Tube Angle (SA): 71°
  Bottom Bracket Height: 12.25"
  Standover Height (SO): 9.75" (24.77cm)
• Seat Clamp: integrated
• Seat Post Diameter: 25.4mm
• Bottom Bracket: Mid BB
• Dropouts: Investment cast, 6mm thick, 14mm slots, CNC machined, 4130 CrMo
• Chain Tensioners: integrated
• Brake Type: U-Brake & Disc Brake
• Brakemounts: without
• Brakemounts included with delivery: No
• Gyro compatible: No
• Features: Tyson Jones-Peni Signature, long trail geometry, disc mount for 120mm - 160mm rotors, 127mm high head tube, strut tube with larger radius, stiffer & stronger back end, larger weld on the head tube, wider dropout for wide 2.4" tires
• Model Year: 2024

A judge in a rural Virginia city has ordered two officials there to certify the results of the election after they filed a lawsuit last month threatening not to certify unless they could hand-count the ballots.

The Biodiversity Data Journal (BDJ) and the associated Pensoft Writing Tool (PWT), launched on 16th of September 2013, offer several innovations - some of them unique - at every stage of the publishing process. The workflow allows for authoring, peer-review and dissemination to take place within the same online, collaborative platform.

Open access to content and data is quickly becoming the prevailing model in academic publishing, resulting in part from changes to policies of governments and funding agencies and in part from scientist's desire to get their work more widely read and used. Open access benefits scientists with greater dissemination and citation of their work, and provides society as a whole access to the latest research.

To publish effectively in open access, it is not sufficient simply to provide PDF files online. It is crucial to put them under a reuse-friendly license and to implement technologies that allow machine-readable content and data to be harvested by computers that can collate small scattered data into a big pool. Analyses and modelling of community-owned big data are the only way to confront environmental challenges to society, such as climate change, ecosystems destruction, biodiversity loss and others.

Manuscripts are not submitted to BDJ in the usual way, as word processor files, but are written in the online, collaborative Pensoft Writing Tool (PWT), that provides a set of pre-defined, but flexible article templates. Authors may work on a manuscript and invite external contributors, such as mentors, potential reviewers, linguistic and copy editors, and colleagues, who may read and comment on the text before submission. When a manuscript is completed, it is submitted to the journal with a simple click of a button. The tool also allows automated import of manuscripts from data management platforms, such as Scratchpads.

"This is the first workflow ever to support the full life cycle of a manuscript, from initial drafting through submission, community peer-review, publication and dissemination within a single, online, collaborative platform. By publishing papers in all branches of biodiversity science, including novel article types, such as data papers and software descriptions, BDJ becomes a gateway for either large or small data into the emerging world of "big data", said Prof. Lyubomir Penev, managing director and founder of Pensoft Publishers.

BDJ shortens the distance between "narrative (text)" and "data" publishing. Many data types, such as species occurrences, checklists, measurements and others, are converted into text from spreadsheets into a human-readable format. Conversely text from an article can be downloaded as structured data or harvested by computers for further use.

A novel community-based peer-review provides the opportunity for a large number of specialists in the field to review a manuscript. Authors may also opt for an entirely public peer-review process. Reviewers may opt to be anonymous or to disclose their names. Editors no longer need to check different reviewers' and author's versions of a manuscript because all versions can be consolidated into a single online document, again at the click of a button.

"The Biodiversity Data Journal is not just a journal, not even a data journal in the conventional sense. It is a completely novel workflow and infrastructure to mobilise, review, publish, store, disseminate, make interoperable, collate and re-use data through the act of scholarly publishing!" concluded Dr Vincent Smith from the Natural History Museum in London, the journal's Editor-in-Chief.

The platform has been designed by Pensoft Publishers and was funded in part by the European Union's Seventh Framework Program (FP7) project ViBRANT.

Smith V, Georgiev T, Stoev P, Biserkov J, Miller J, Livermore L, Baker E, Mietchen D, Couvreur T, Mueller G, Dikow T, Helgen K, Frank J, Agosti D, Roberts D, Penev L (2013) Beyond dead trees: integrating the scientific process in the Biodiversity Data Journal. Biodiversity Data Journal 1: e995. DOI: 10.3897/BDJ.1.e995

Deep learning technology has enabled a machine to compose chorales in the style of Johann Sebastian Bach, according to researchers. Gaetan Hadjeres and Francois Pachet ... Read more

The post Machine capable of composing music in style of Bach appeared first on CMUSE.

The couple spoke about experiencing a miscarriage more than a year ago. They announced their engagement in 2022.

The post Megan Fox is expecting a baby with Machine Gun Kelly appeared first on Boston.com.

The TAB Wrapper Tornado line of orbital stretch-wrapping packaging machines secures a load to its pallet as a single unit for improved protection, safety, stability and confidence in handling and transportation.

Vertical mills are essential cutting tools in machine shops, used to remove material from the surface of metal workpieces.

PROTECTOR Shields defend machine operators from flying debris, lubricants, coolants and swarf. They’re constructed from 14-gauge powder-coated steel and thick 3/16-inch shock-proof, scratch-resistant polycarbonate.

Arlington, VA — The “collective effort” of mine industry workers and stakeholders in 2022 contributed to a 21.6% decrease in worker deaths over the previous year, Mine Safety and Health Administration head Chris Williamson said during a recent conference call.

“From the moment they start to operate the machine,” one safeguarding expert says, “you look at the design and you think, ‘Well, could someone potentially get hurt?’”

Comprehensive checklist based on ANSI B11.TR1-1993 includes considerations for machine operation, installation and maintenance.

Washington — As part of a settlement agreement, OSHA has issued a proposed rule that would grant exemptions to its Cranes and Derricks in Construction Standard for work on or along railroad tracks.

Washington — The Federal Railroad Administration has finalized amendments to a pair of regulations related to roadway worker safety and on-track roadway maintenance machines and hi-rail vehicles.

Washington — Concerned by the deaths of two workers struck by roadway maintenance machines in separate instances within the past two years, the Federal Railroad Administration has issued a safety advisory.

Chicago — A new Local Emphasis Program from OSHA is aimed at protecting workers in Wisconsin food manufacturing establishments from machine and amputation hazards.

A certified field technician was killed after he climbed into a mechanical vertical storage unit to make repairs.

A 39-year-old hay press operator died when he was struck by the machine’s steel guillotine blade.

A worker at a manufacturing facility was fatally injured while operating a 200-ton mechanical press.

The Quick Quote mobile app allows users to quickly and easily request a custom machine guarding quote from their smartphone, eliminating the need to speak with a salesperson.

These state-of-the-art machine safety guards feature built-in Visorguard LEDs to provide super-bright illumination in the work area.

The Proton is an advanced solenoid interlock switch designed for use on machines for which hazardous conditions persist even after the machine has been switched off. Its heavy-duty solenoid can withstand up to 3000N hold force and energize under a lateral load.

What would the requirement be if a lengthy lockout procedure was needed to shut down complex equipment with numerous energy sources, but we are only working in one area of the machine with a limited number of energy sources?

In the event of a power interruption, the Sensing-Saf-Start automatically disconnects power to a machine so that when power is restored, the machine won’t restart. Only after the reset button has been pressed by the machine operator can the machine be restarted.

Woodworking machines – with their moving parts and sharp blades – can be extremely dangerous if not used correctly. Amputations, blindness and lacerations are common injuries related to working with these machines.

Used in the plastics industry, thermoplastic injection molding machines “produce molded plastic parts by converting plastic pellets into molten material, injecting the molten plastic into a mold and cooling the plastic material,” OSHA says. Industries that use these machines include toy, medical device and beverage container manufacturers.

Washington — Spurred by two separate fatal incidents this year, the Federal Railroad Administration is re-emphasizing the importance of rules and procedures to protect workers who operate or work near roadway maintenance machines.

Iowa City, IA — A NIOSH-funded study of farm machinery found that the machine operators experienced whole-body vibration at levels that reached the European Union’s “action level” for exposure limit within two hours of operation on nearly 30 percent of the equipment tested.

Arlington, VA — Alarmed by a series of 10 fatal incidents related to miners operating machinery, the Mine Safety and Health Administration has issued a safety alert.

The Machine Safeguarding Annual Verification Audit service verifies safeguarded equipment is being used as designed. The primary evaluation criteria are visual inspection and function testing of safeguarding, controls, disconnects, motor starters and properly applied mechanical power transmission apparatus covers.

This personal protective equipment vending machine can hold 810-1,080 different products. A multilingual touchscreen display features product descriptions and pictures to help ensure workers select the proper PPE.

Every step of the production process and the supply chain is under scrutiny. Filling equipment is not exempt.

Vertical form fill seal (VFFS) machines can mean different things to different people. The criteria used in evaluating, and ultimately determining, which bagging machine to buy is likely different if you are a small startup manufacturer versus operating for a global Fortune 500 brand.

The eight-lane machine provides auger filling of 40 grams of powder at 75 cycles per minute.

Stiles Machinery will showcase surface solutions, such as sanding, veneering, and finishing at the International Woodworking Fair (IWF) in booth 4835 in Hall B, located at the Georgia World Congress Center in Atlanta, Georgia, from August 6 – 9, 2024.

While the vocabulary doesn’t have to be large, each machine should know about stops and starts upstream and downstream.

As packaging has become significantly more complex and consumers more demanding, the need has only grown for strict hygienic procedures to ensure food safety, from the factory to the retail shelf to the table.

The beer and beverage maker turned to PET Technologies to improve productivity, reduce downtimes and increase brand awareness.

Oxipital AI’s 3D vision and AI offerings aim to be more convenient and effective through a different method of “training” its products.