A new guide has been launched to help tackle the growing threat of domestic abusers using technology, like smart doorbells and mobiles, against older people.

Developed by Aberystwyth University researchers, Dyfed-Powys Police, and Parental Education Growth Support (PEGS), the resource for staff on the frontline highlights the growing threat of the use of new technology to abuse over 60-year-olds.

Evidence shows that perpetrators are increasingly using digital technology to threaten, stalk, or harass—1 in 3 women have experienced abuse on social media or other online platforms at some point in their lives.

Meanwhile, evidence gathered by PEGS shows that 14% of the older parents they supported had experienced digital abuse from children or grandchildren.

The new guide includes advice on practical ways to prevent technology abuse, such as how to prevent stalking via mobile phone tracking, secure access to bank accounts and lock smart devices.

The new toolkit draws on police data, victim insights, and real-life case studies to show how perpetrators misuse everyday technologies to monitor, control, exploit and intimidate older victims.

One case involves 67-year-old Keith, a terminally ill man, whose adult son spent over £20,000 of Keith’s money without permission using his smartphones and online shopping accounts.

Another case highlights Katherine, who was stalked, harassed and coercively controlled by her partner using a fake Facebook profile, a mobile tracking app and a Ring doorbell camera, alongside hundreds of abusive calls and messages.

Based at Aberystwyth University’s Center for Age, Gender and Social Justice, the Dewis Choice initiative supports older victims of domestic abuse. It combines direct service delivery with ground-breaking research and is the first long-term study exploring decision-making in later life.

Rebecca Zerk, Co-lead of the Dewis Choice project at Aberystwyth University said, “As more services shift online, increasing numbers of older people rely on family members or caregivers to manage digital tasks on their behalf. Without direct control over their online accounts, older adults can be at heightened risk of exploitation by the people closest to them. This dependency can leave older people at increased risk of financial abuse, fraud, identity theft, and coercion.

“Additionally, limited exposure to technology, lower confidence in navigating digital tools, and unfamiliarity with online risks can expose older adults to risks such as scams, fraud, and technology-facilitated abuse.

“Frontline staff and practitioners and the criminal justice system are increasingly aware of the role of technology in facilitating domestic abuse. However, the nature and impact of technology-facilitated abuse on older victims remains underexplored. This guide aims to address this gap and brings together knowledge, insights, and data from our partners. We really hope this guide can help protect older people from this risk of harm.”

Recent Dyfed-Powys Police data shows that between May 2024 and April 2025, over 950 domestic abuse-related crimes involved older victims. More than 10% of these cases were linked to technology such as mobile phones, social media and online banking.

Dyfed-Powys Police serves Carmarthenshire, Ceredigion, Pembrokeshire and Powys and is the largest geographical policing area in England and Wales. Covering more than 515,000 people, almost half of the total resident population is aged 45 and over, and 22% are aged over 65.

Allan Rush from Dyfed Powys Police added, “Technology-enabled abuse poses a growing threat to older people who may be less familiar with digital tools, making them vulnerable to manipulation. From online scams and phishing attacks to remote surveillance and financial exploitation, perpetrators can use smartphones, social media, and smart home devices to control, deceive, or isolate older individuals. The digital divide often leaves older people without the skills or support to recognize or report abuse.

“One challenge for police and frontline professionals is keeping up to date with this rapidly evolving technology and how its misuse could lead to the abuse of those in need of safeguarding.

“This guide will be a valuable resource for practitioners who may not be tech specialists by equipping them with the knowledge and tools to identify, respond to, and prevent digital forms of abuse.”

Michelle John, Director of PEGS added, “At PEGS, we see first-hand how technology can be used to extend patterns of abuse, including cases where parents and grandparents are exploited by those closest to them. The rise in technology-facilitated abuse against older people is a growing concern, and this guide provides frontline professionals with the tools and knowledge to respond effectively. By sharing evidence and practical strategies, we can ensure practitioners are better equipped to recognize digital abuse and protect victims from further harm.”

Researchers from EPFL, AMD, and the University of Novi Sad have uncovered a long-standing inefficiency in the algorithm that programs millions of reconfigurable chips used worldwide, a discovery that could reshape how future generations of these are designed and programmed.

Many industries, including telecoms, automotive, aerospace and particle physics rely on a special breed of chip called the Field-Programmable Gate Array (FPGA). Unlike traditional chips, FPGAs can be reconfigured almost endlessly, making them invaluable in fast-moving fields where designing a custom chip would take years and cost a fortune. But this flexibility comes with a catch: FPGA efficiency depends heavily on the software used to program them.

Since the late 1990s, an algorithm known as PathFinder has been the backbone of FPGA routing. Its job: connecting thousands of tiny circuit components without creating overlaps.

For decades, it worked so well that it became the standard. However, as circuits grew larger, engineers began encountering frustrating slowdowns and occasional outright failures. Designs that should have worked were often labeled “unroutable.”

Now, with colleagues from the University of Novi Sad and the technology company AMD, researchers from the Parallel Systems Architecture Laboratory (PARSA) in the School of Computer and Communication Sciences have come one step closer to untangling the inner workings of this classic algorithm.

In their paper, which received the Best Paper Award at the 33rd IEEE International Symposium on Field-Programmable Custom Computing Machines, they revealed why these failures happen and how PathFinder’s limits can be overcome.

Cracks in the algorithm

“In fact, it’s not surprising that PathFinder sometimes fails,” explained Shashwat Shrivastava, Ph.D. student with PARSA and first author of the paper.

“Very early on, researchers showed that the problem behind FPGA routing is extremely hard. Later, the creators of the original algorithm, together with a few collaborators, found cases where PathFinder would never succeed—but they noted such cases wouldn’t appear in practice.”

For decades, it seemed they were correct—PathFinder worked surprisingly well.

“PathFinder worked so well, in fact, that when it failed, people rarely questioned the algorithm. Instead of venturing inside to see what was going on, they tweaked its parameters, modified circuits, or switched to larger FPGAs,” added Stefan Nikolić, an EPFL alumnus and now a professor at the University of Novi Sad.

“Part of the reason for this is that it is rather difficult to understand what PathFinder is actually doing on examples of practical importance. Modern circuits are so large that their signals form veritable on-chip jungles.”

Enter the forest

“So, we really needed to look at the individual trees in that jungle,” continued Shrivastava, “and I really mean trees. Each signal—a connection that carries information between circuit components—must reach multiple destinations without overlapping other signals. FPGA routing is essentially about building one tree for each signal on the chip.”

While working on another project that relied on PathFinder, the team kept seeing results that defied intuition. At first, they blamed external factors, not the algorithm itself. Eventually, they realized they needed controlled examples: small, tricky cases where a solution definitely existed, and in which PathFinder should succeed.

“We needed real, practical examples, and lots of them, to understand what was really going on,” Shrivastava explains. “So, we built a framework to automatically extract small, hard problems from real circuits. Watching how PathFinder struggled with these helped us uncover issues that had remained hidden for a very long time.”

Power in partnership

“This breakthrough would have been much harder without industry support,” said Mirjana Stojilović, Shrivastava’s Ph.D. advisor. “From the start, we collaborated with Chirag Ravishankar and Dinesh Gaitonde from AMD. They helped us model FPGAs as close as possible to commercial devices, ensuring our findings had real-world impact.”

Once the framework was ready, things moved quickly. The team found that PathFinder often built routing trees larger than necessary, increasing the risk of overlaps. The problem came from the order in which it created and added new branches to the trees.

“In retrospect, this is intuitive, but somehow it went largely unnoticed for many years,” Shrivastava said. “Our first solution was simple: try different orders and pick the one that results in the smallest tree. Experimentally, it worked surprisingly well.”

The team is now exploring more scalable solutions. “I am especially proud that Summer@EPFL interns have been contributing significantly. One of them, Sun Tanaka, is also a co-author of the paper,” added Stojilović.

“Our discovery could reshape how millions of FPGAs are programmed and influence the design of future generations of these reconfigurable chips.”

Despite increasing awareness, AI shopping assistants have yet to achieve widespread adoption in the US. While 43 per cent of Americans are aware of these tools, only 14 per cent have used them. Usage is highest among Gen Z (24 per cent) and parents of children under 18 (21 per cent), whereas just 7 per cent of baby boomers have tried them, according to a new YouGov study.

For current users, key benefits include getting answers to product questions (44 per cent), finding specific items (41 per cent), and locating the best deals (34 per cent). For non-users who are open to trying AI, the most appealing features are price comparisons (67 per cent), evaluating similar products (56 per cent), and accessing product information (55 per cent).

However, trust is a major barrier. Forty-one per cent of Americans say they do not trust AI shopping assistants at all, and only 13 per cent mostly or completely trust them—compared to 53 per cent who trust personal recommendations. Privacy concerns, a preference for human assistance, and fears of upselling further fuel scepticism.

AI shopping assistants remain underused in the US despite 43 per cent awareness, with only 14 per cent having tried them.
Gen Z and parents show the highest adoption.
Users seek answers, deals, and product info, but trust remains low due to privacy concerns and scepticism.
Interest is strongest in clothing, electronics, and groceries.
Wider adoption hinges on building trust and proving real value.

When it comes to shopping categories, consumers are most open to using AI for clothing and accessories (20 per cent), consumer electronics (21 per cent), groceries and household essentials (19 per cent), and travel planning (18 per cent). Interest is much lower for purchases involving finances, vehicles, or pet care.

Retailers such as Walmart and Amazon are already deploying AI tools like Sparky and Rufus to improve customer experience. Yet, the study highlights that broader adoption depends on demonstrating genuine value, safeguarding data, and rebuilding trust—especially among more cautious consumers.

Fibre2Fashion News Desk (SG)