#investigate AI usage in Workspace

[lmsintmedia]

How to Identify and Investigate ChatGPT Activity in Google Workspace (2025 Guide)

Why This Matters AI is reshaping the digital workplace faster than ever. ChatGPT, one of the most powerful AI tools, is now widely used by employees, students, and freelancers across Google Workspace tools like Docs, Gmail, Sheets, and more. While it offers productivity boosts, it also raises serious concerns for: Data privacy and compliance Unauthorized use of third-party AI tools Internal…

[regalokitchens95]

Top Updates in Modular Kitchen Layout in 2025

Source of info: https://www.linkedin.com/pulse/top-updates-modular-kitchen-layout-2025-regalo-kitchens-pvt-ltd-2zgoc/

Introduction

By combining practicality, style, and focus performance, the modular kitchen has become known as a key component for modern houses. Flexible kitchen designs are changing as a result of new trends and technology that both interior designers and homeowners are taking up as 2025 approaches. These upgrades, which range from creative storage options to eco-friendly materials, demonstrate a dedication to performance and style. This is an in-depth investigation of the most popular changes to functional kitchen designs in 2025.

1. Smart Kitchens: The Rise of Technology

Seamless Integration of Smart Appliances

In 2025, technology will be the main focus of the flexible modular kitchen. AI is now included into smart washing machines, freezers, and ovens to make cleaning and cooking easier. Touchless operation and voice-activated gadgets provide a simple and clean experience.

IoT in Modular Kitchen

Interconnected appliances have gained attention thanks to the rise of the Internet of Things (IoT). Homeowners can remotely manage inventory, keep an eye on appliance performance, and control lights with mobile apps.

2. Sustainable Materials for Eco-Friendly Kitchens

Recycled and Renewable Materials

In 2025, sustainability will be a top concern. These days, bamboo, repaired materials, and recycled wood are used to create customized kitchen cabinets, worktops, and flooring. These choices offer longevity and an authentic appeal in addition to reducing their negative effects on the environment.

Low-VOC Finishes

Low-VOC (volatile organic compounds) paints and materials are being used by manufacturers in modular kitchen structures to guarantee better indoor air quality. These environmentally friendly procedures provide an attractive appearance while lowering dangerous pollutants.

3. Maximized Storage with Space-Saving Solutions

Pull-Out Pantries and Corner Cabinets

Simplicity in storage is given top priority in the 2025 adjustable kitchen design. Rotating corner cabinets and pull-out pantries provide quicker access to goods kept in limited spaces. Small kitchens are perfect for these upgrades.

Vertical Storage Systems

Vertical storage options, which focus on making the best use of every available area, are already common in modular kitchen designs. There is enough storage without taking up too much space on the kitchen floor thanks to tall shelves and hanging racks.

4. Bold Color Schemes and Finishes

Trending Colors for 2025

Bolder colors like emerald green, deep blue, and terracotta are replacing softer colors like beige and ivory. These colors give kitchen styles character and coziness.

Matte and Textured Finishes

Matte and textured surfaces are taking the place of glossy finishes because they are simpler to maintain and give an improved look. Homeowners love the tactile feeling created by stone countertops and textured cabinets.

5. Multi-Functional Modular Kitchen Islands

Workspaces Reimagined

Meal preparation is no longer the exclusive use for kitchen islands. By 2025, they will serve as centers for storage, workstations, and dining tables. Some even come with built-in cooktops and sinks, providing busy homes with a one-stop shop.

Portable and Flexible Designs

To meet changing demands, modular kitchen islands now come with wheels or adjustable heights. This ability to adapt makes it easy to customize the arrangement for cooking, entertaining, or daily usage.

6. Energy Efficiency in Kitchen Design

LED Lighting Solutions

Energy-saving LED lighting is a popular trend in kitchen remodeling. Under-cabinet lighting, pendant chandeliers, and hidden ceiling lights provide enough brightness while saving energy.

Energy-Star Appliances

Energy-saving appliances are a necessity in today's customizable kitchens. These products, ranging from freezers to induction cooktops, assist to cut energy expenses and environmental effect.

7. Open Shelving for a Modern Aesthetic

Mixing Open and Closed Storage

Open shelving is gaining popularity because of the way it can display fine plates and cups and kitchen supplies. Modular kitchen plans increasingly combine open shelves and closed cabinets to create a balanced appearance.

Minimalist Display

With simple design at the top of their lists, homeowners want to display a few carefully selected pieces. This method maintains the kitchen appearing organized and free of mess.

8. Sustainable Lighting Techniques

Natural Light Optimization

Designers are focused on improving natural light in kitchens with modular design through the use of bigger windows and reflecting materials. This not only reduces the need for artificial lighting, but also makes the area brighter and more welcoming.

Solar-Powered Lighting

For environmentally friendly homes, solar-powered lights are a game changer. These fixtures support environmentally friendly practices while being stylish and affordable.

9. Customized Modular Kitchen Designs

Tailored to Individual Needs

In 2025, modular kitchen concepts will be focused around personalization. From material selection to storage design, homeowners can now construct kitchens that are customized to their individual interests and lifestyles.

Multi-Zone Kitchens

Kitchens are becoming multi-zone places with different cooking, cleaning, and eating sections. This categorized strategy improves functionality while reducing confusion, especially for busy families.

10. Compact and Urban-Friendly Layouts

Solutions for Small Spaces

As the population grows, modular kitchen layouts are being customized to fit small residences. Foldable tables, wall-mounted storage, and compact cabinets make even small kitchens useful and fashionable.

Efficient Use of Space

In 2025, kitchens with modular design will prioritize performance. Pull-down racks, folding worktops, and combined sitting arrangement maximize space while maintaining comfort.

11. Incorporating Nature: Biophilic Design

Bringing the Outdoors In

Biophilic design is a popular trend in modular cooking areas, with components such as indoor plants, natural wood, and stone finishes used. This design concept improves mental health and offers a relaxing environment.

Herb Gardens

Mini herb gardens built into modular kitchen arrangements are both functional and appealing to the eye. They offer healthy alternatives with a hint of plant life, making them perfect for modern houses.

12. Advanced Ventilation Systems

Quiet and Efficient Chimneys

Chimneys in 2025 are intended to be quieter and more energy-effective. They connect perfectly with the modular cooking style while providing enough ventilation.

Air Purification

Some modern kitchens now include built-in air purifiers to remove smells and pollutants, guaranteeing a clean and healthy cooking environment.

13. Touch of Luxury: Premium Materials and Finishes

Quartz and Granite Countertops

Quartz and granite remain popular countertop materials because of their durability and luxurious look. These materials are available in a range of colors and designs to suit different tastes.

Metallic Accents

Gold, bronze, and copper decorations give a luxurious touch to modular kitchen designs. From knobs to light fixtures, these details improve the entire design.

14. Sustainability Meets Style with Water-Saving Fixtures

Eco-Friendly Faucets

Water-saving taps and dishwashers will be necessary in flexible kitchens by 2025. These fixtures use less water while maintaining high performance.

Smart Water Systems

In order to save costs and improve environmentally friendly practices, some customized kitchens come with smart water systems that track usage and identify leaks.

15. Focus on Safety and Ergonomics

Child-Friendly Features

Modern kitchen designs that put safety first are becoming more and more popular among families with kids. A safe atmosphere is guaranteed with smooth edges, non-slip floors, and secured cabinets.

Ergonomic Designs

Because pull-down shelves and adjustable counters allow people of all heights, kitchens with modular design are more accessible and easier to use.

Conclusion

The modular kitchen of 2025 displays creativity and imagination. Homeowners can improve their kitchens more effectively and beautifully by embracing the latest innovations, environmentally friendly practices, and individualized designs. Regalo Kitchens, a modular interior designer company, remains ahead of trends by providing solutions that fulfill a wide range of demands and tastes. Whether you're remodeling or constructing a new kitchen, the 2025 improvements will make sure that your modular cooking area is at the top of luxury and affordability.

[qubemagazine]

New Post has been published on Qube Magazine

New Post has been published on https://www.qubeonline.co.uk/artificial-intelligence-in-the-field-of-building-automation/

Artificial Intelligence in the field of  Building Automation

Professor Michael Krödel, CEO, Institute of Building Technology, Ottobrunn, Germany and Professor for Building Automation and Technology, University of Applied Sciences at Rosenheim and Graham Martin Chairman & CEO, EnOcean Alliance

The term “AI – Artificial Intelligence“ is increasingly associated with buildings and building automation. The question is: what is it, where do its tangible benefits lie in this field, and how does the building infrastructure need to be adapted to realise those benefits?

Today’s building automation systems in the main operate ‘statically’ in response to fixed time programs or simple control parameters. Room temperature control is based on a preset temperature that is the same throughout the day. Lighting is operated manually, with switches, or on the basis of simple presence switches.  None of this is truly ‘intelligent.’ The new dimension that AI can add into the building automation environment is to use autonomous analysis of the data as a basis for optimised operation. Thus the heating and cooling dynamic of rooms, weather forecasts, predicted room occupancy during the course of the day can all be factored into the operation of the heating. Similarly, cleaning schedules can be based not only on the current actual values in terms of the intensity of use of kitchens, canteens and toilets and other areas, but can be based on predictions drawn from an analysis of usage patterns in the previous days and weeks. This kind of forward looking building management can be applied in almost every area of building services, leading to increased energy efficiency, reduced operating costs, improved space utilisation and other advantages.

All this – and much more – is possible when data on building system status and conditions is intelligently evaluated. This requires intensive processing of large amounts of data, with many variables to be considered. Artificial Intelligence (AI) offers many new, tailor-made solutions which are eminently suited to efficient building management.

“Building Automation”, “Smart Building” and “Cognitive Building”

Initially, „Building Automation“ was comparatively „un-intelligent“. Systems were programmed to follow a set of simple rules, allowing for quick system start-up and subsequent ease of maintenance.

The „Smart Building“ typically builds on this classic building automation with flexible IT-based management systems . These offer unrestricted programming using modern IT languages and tools, easy integration with other IT systems such as workspace/room reservation systems or data banks, and data visualisation for facility managers and for „ordinary“ users.

The growing assimilation of sensor-generated data into the IT-based management level opens the way for more advanced data processing solutions to come into play – such as AI tools. This is the pre-condition for the implementation of any prognosis-based form of building management. The sophisticated  processing of sensor-generated data makes the Smart Building into a „Cognitive Building“.

AI-learning process

The first step in any Artificial Intelligence process is system learning. This can take three forms.

–          Unsupervised Learning

–          Supervised Learning

–          Reinforcement Learning

„Unsupervised Learning“ is used when large quantities of data must be processed and categorised. This grouping enables the recognition of deviations from norms and interdependencies. For example, sensor data from identical circulation pumps can be grouped. If data from one pump or group of pumps deviates from the norm,  there may be a defect and a human engineer can be sent to investigate.

„Supervised Learning“ often makes use of neural networks. They consist of entry and exit nodes as well as further nodes in the intermediate layers. Mathematically weighted relationships exist between the diverse nodes (neurons). In order to optimise these relationships, the neural network is subjected to a training phase with known input and output patterns. In the field of building automation, for example, a neural network can ‚learn‘ the current consumption profiles of different appliances and which appliances are active when. This information can be used to avoid ‚spikes‘ in building energy consumption, by shutting down some appliances and extending the operation time of others.

Another form of Artificial Intelligence is represented by processes that autonomously determine which actions are appropriate in a given situation. They emulate human behaviour whereby different solutions are tried in order to determine the best way forwards in a hitherto unknown situation, and conclusions drawn retrospectively. The learning task becomes more challenging when feedback is given much later and hinges upon events in the relatively distant past. This is true in a human context, and equally true in computer environments. The best-known example in this category is „Reinforcement Learning“. Consider the issue of determining the optimal start and stop times of heating to achieve a comfortable temperature when the building opens. At the simplest level, the learning algorithm receives the value from the room temperature sensor and can act on the actuator on the radiator. By a process of trial and error, the algorithm can determine the necessary lead time. However, this simple example ignores the fact that, for instance, the speed of heating also depends on the outside temperature, so the reading from an exterior temperature sensor needs to be considered. Instead of providing a pre-set target temperature, the algorithm may be be given evaluations (good / OK / cold) during the day and must learn in response to this feedback. In addition, the algorithm can be provided with an addditional rating every month based on the overall energy cost: encouraging efficient behaviour and discouraging inefficient responses.  A ‚stable‘ response that balances comfort and efficiency can be established, but exploration should continue to accomodate changes in behaviour and the environment.

It can be seen that these three approaches are complementary. The learning method should be chosen depending on the task in hand – each has its merits.

Concrete applications

Many diverse AI-based applications are available in the field of building automation. They can be broadly categorised as follows:

Optimised facility management: needs-based control of heating plants, circulating pumps, lighting etc. (as opposed to control on the basis of simple parameters or by timer).

Optimised utilisation of spaces and infrastructure:  capacity analysis and forecasting, e.g. for

meeting rooms, canteens, pantries, transit areas, toilets and parking spaces as well as the provision of information in the short term (for building occupants) and in the long term (for facility managers, e.g. in form of advice on building restructuring).

Load management: forward-looking operation of electrical systems in order to avoid (costly) peak loads.

Precautionary maintenance and optimised servicing: analysis of failure probability, timely maintenance and consequential avoidance of technical failures.

Employee-oriented value added services: mobile devices can – for instance – be used to generate space utilisation forecasts, view canteen usage intensity, request parking space availability and preferred workspace location or select individual meals.

Compensation of skilled-staff shortages: making effective use of facility maintenance staff in managing the building’s technical systems.

Focus on meaningful sensor data: generate as much data as possible from as few sensors as possible – reducing redundancy, cutting investment and operating costs.

Demands upon system architecture

An AI platform is indispensable for the introduction of intelligent learning processes such as those described above. This can be either cloud-based or server-based. Cloud-based server farms offer more processing power, and cloud-based AI frameworks offer a broader range of features, so this currently represents common practice.

The AI platform is built on a Smart Building infrastructure, and all technical systems should ideally be  connected to a BMS (Building Management System). The BMS must be able to govern the building facility and room automation systems.

Demands on building infrastructure

The AI platform requires a rich set of data from a variety of sensors around the building to operate effectively. Conventional smart building systems use a sensor network to determine the status of a  building now.‘ Cognitive“ buildings store and analyse historical sensor data to make predictions for the future. For this reason, such buildings are even more critically dependent on the data inputs they receive for their success. Cognitive buildings need to be instrumented throughout with IoT sensor devices that make the algorithms fully aware of every aspect of their operation: environment, occupants, energy requirements, service needs, security, and safety. The richer the data, the more complete and intelligent the response of the AI. Wiring sufficient sensors into an established building is hugely expensive – and even if it were done would create an inflexible architecture that couldn’t be adapted as new applications emerge and learning progresses. The only effective solution is battery- and maintenance- free energy harvesting sensors that can be fitted in a moment and moved at will. Energy harvesting wireless devices utilize the tiniest amounts of energy from their environment. Kinetic motion, pressure, light, differences in temperature are converted into energy which, in combination with ultra-low power wireless technology, creates maintenance-free sensor solutions for use in smart buildings and the IoT.

The EnOcean Alliance eco-system offers more than 5,000 multi-vendor interoperable energy-harvesting sensors enabling data collection for multiple applications, such as room or desk/chair occupancy, temperature and air quality, energy usage and restroom usage.  In addition to the traditional option of collecting and analysing the data via the BMS (Building Management System), this can now be done by using the existing Wi-Fi network with the building.  By securely interfacing those IoT devices with new and existing Aruba Wi-Fi 5 and Wi-Fi 6 Access Points via a plug-in 800/900MHz radio, building control and business applications can become hyperaware of their operating environments.  This information can be used to better model building behavior, to optimize human activity monitoring, organizational redesign, augmented reality, human productivity, and occupant health and safety.

Conclusions

AI-based processes enable a broad range of applications in the field of building automation. The concrete benefits anticipated from AI-based solutions should be clearly defined before implementation, since this plays a determining role in the choice of learning process and its modelling, as well as in the choice of AI platform and the type, number and location of the energy harvesting sensors needed to supply the data inputs.

www.igt-institut.de

www.enocean-alliance.org

  Artificial Intelligence in the field of  Building Automation